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• Building a Trust Model for Secure Data Sharing (TM-SDS) in Edge Computing Using HMAC Techniques
• K. Karthikeyan^*, P. Madhavan
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4183-4197, 2022, DOI:10.32604/cmc.2022.019802
• Abstract With the rapid growth of Internet of Things (IoT) based models, and the lack amount of data makes cloud computing resources insufficient. Hence, edge computing-based techniques are becoming more popular in present research domains that makes data storage, and processing effective at the network edges. There are several advanced features like parallel processing and data perception are available in edge computing. Still, there are some challenges in providing privacy and data security over networks. To solve the security issues in Edge Computing, Hash-based Message Authentication Code (HMAC) algorithm is used to provide solutions for preserving data from various attacks that… More
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• Adaptive Runtime Monitoring of Service Level Agreement Violations in Cloud Computing
• Sami Ullah Khan¹, Babar Nazir¹, Muhammad Hanif^2,*, Akhtar Ali³, Sardar Alam¹, Usman Habib⁴
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4199-4220, 2022, DOI:10.32604/cmc.2022.020852
• （This article belongs to this Special Issue: AI for Wearable Sensing--Smartphone / Smartwatch User Identification / Authentication)
• Abstract The cloud service level agreement (SLA) manage the relationship between service providers and consumers in cloud computing. SLA is an integral and critical part of modern era IT vendors and communication contracts. Due to low cost and flexibility more and more consumers delegate their tasks to cloud providers, the SLA emerges as a key aspect between the consumers and providers. Continuous monitoring of Quality of Service (QoS) attributes is required to implement SLAs because of the complex nature of cloud communication. Many other factors, such as user reliability, satisfaction, and penalty on violations are also taken into account. Currently, there… More
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• Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images
• José Escorcia-Gutierrez^1,*, Romany F. Mansour², Kelvin Beleño³, Javier Jiménez-Cabas⁴, Meglys Pérez¹, Natasha Madera¹, Kevin Velasquez¹
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4221-4235, 2022, DOI:10.32604/cmc.2022.022322
• Abstract Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated deep learning based breast cancer… More
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• SSABA: Search Step Adjustment Based Algorithm
• Fatemeh Ahmadi Zeidabadi¹, Ali Dehghani², Mohammad Dehghani³, Zeinab Montazeri⁴, Štěpán Hubálovský⁵, Pavel Trojovský^3,*, Gaurav Dhiman⁶
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4237-4256, 2022, DOI:10.32604/cmc.2022.023682
• （This article belongs to this Special Issue: AI-Aided Innovative Cryptographic Techniques for Futuristic Secure Computing Systems)
• Abstract Finding the suitable solution to optimization problems is a fundamental challenge in various sciences. Optimization algorithms are one of the effective stochastic methods in solving optimization problems. In this paper, a new stochastic optimization algorithm called Search Step Adjustment Based Algorithm (SSABA) is presented to provide quasi-optimal solutions to various optimization problems. In the initial iterations of the algorithm, the step index is set to the highest value for a comprehensive search of the search space. Then, with increasing repetitions in order to focus the search of the algorithm in achieving the optimal solution closer to the global optimal, the… More
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• SVM and KNN Based CNN Architectures for Plant Classification
• Sukanta Ghosh¹, Amar Singh¹, Kavita^2,*, N. Z. Jhanjhi³, Mehedi Masud⁴, Sultan Aljahdali⁴
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4257-4274, 2022, DOI:10.32604/cmc.2022.023414
• Abstract Automatic plant classification through plant leaf is a classical problem in Computer Vision. Plants classification is challenging due to the introduction of new species with a similar pattern and look-a-like. Many efforts are made to automate plant classification using plant leaf, plant flower, bark, or stem. After much effort, it has been proven that leaf is the most reliable source for plant classification. But it is challenging to identify a plant with the help of leaf structure because plant leaf shows similarity in morphological variations, like sizes, textures, shapes, and venation. Therefore, it is required to normalize all plant leaves… More
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• Adaptive Fuzzy Logic Controller for Harmonics Mitigation Using Particle Swarm Optimization
• Waleed Rafique¹, Ayesha Khan², Ahmad Almogren³, Jehangir Arshad¹, Adnan Yousaf⁴, Mujtaba Hussain Jaffery¹, Ateeq Ur Rehman⁵, Muhammad Shafiq^6,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4275-4293, 2022, DOI:10.32604/cmc.2022.023588
• Abstract An excessive use of non-linear devices in industry results in current harmonics that degrades the power quality with an unfavorable effect on power system performance. In this research, a novel control technique-based Hybrid-Active Power-Filter (HAPF) is implemented for reactive power compensation and harmonic current component for balanced load by improving the Power-Factor (PF) and Total–Hormonic Distortion (THD) and the performance of a system. This work proposed a soft-computing technique based on Particle Swarm-Optimization (PSO) and Adaptive Fuzzy technique to avoid the phase delays caused by conventional control methods. Moreover, the control algorithms are implemented for an instantaneous reactive and active… More
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• Binary Fruit Fly Swarm Algorithms for the Set Covering Problem
• Broderick Crawford^1,*, Ricardo Soto¹, Hanns de la Fuente Mella¹, Claudio Elortegui¹, Wenceslao Palma¹, Claudio Torres-Rojas¹, Claudia Vasconcellos-Gaete², Marcelo Becerra¹, Javier Peña¹, Sanjay Misra³
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4295-4318, 2022, DOI:10.32604/cmc.2022.023068
• （This article belongs to this Special Issue: Recent Advances in Metaheuristic Techniques and Their Real-World Applications)
• Abstract Currently, the industry is experiencing an exponential increase in dealing with binary-based combinatorial problems. In this sense, metaheuristics have been a common trend in the field in order to design approaches to solve them successfully. Thus, a well-known strategy consists in the use of algorithms based on discrete swarms transformed to perform in binary environments. Following the No Free Lunch theorem, we are interested in testing the performance of the Fruit Fly Algorithm, this is a bio-inspired metaheuristic for deducing global optimization in continuous spaces, based on the foraging behavior of the fruit fly, which usually has much better sensory… More
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• Unified FPGA Design for the HEVC Dequantization and Inverse Transform Modules
• Turki M. Alanazi, Ahmed Ben Atitallah^*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4319-4335, 2022, DOI:10.32604/cmc.2022.022988
• Abstract As the newest standard, the High Efficiency Video Coding (HEVC) is specially designed to minimize the bitrate for video data transfer and to support High Definition (HD) and ULTRA HD video resolutions at the cost of increasing computational complexity relative to earlier standards like the H.264. Therefore, real-time video decoding with HEVC decoder becomes a challenging task. However, the Dequantization and Inverse Transform (DE/IT) are one of the computationally intensive modules in the HEVC decoder which are used to reconstruct the residual block. Thus, in this paper, a unified hardware architecture is proposed to implement the HEVC DE/IT module for… More
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• An EFSM-Based Test Data Generation Approach in Model-Based Testing
• Muhammad Luqman Mohd-Shafie^1,*, Wan Mohd Nasir Wan Kadir¹, Muhammad Khatibsyarbini¹, Mohd Adham Isa¹, Israr Ghani¹, Husni Ruslai²
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4337-4354, 2022, DOI:10.32604/cmc.2022.023803
• Abstract Testing is an integral part of software development. Current fast-paced system developments have rendered traditional testing techniques obsolete. Therefore, automated testing techniques are needed to adapt to such system developments speed. Model-based testing (MBT) is a technique that uses system models to generate and execute test cases automatically. It was identified that the test data generation (TDG) in many existing model-based test case generation (MB-TCG) approaches were still manual. An automatic and effective TDG can further reduce testing cost while detecting more faults. This study proposes an automated TDG approach in MB-TCG using the extended finite state machine model (EFSM).… More
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• Parking Availability Prediction with Coarse-Grained Human Mobility Data
• Aurora Gonzalez-Vidal¹, Fernando Terroso-Sáenz^2,*, Antonio Skarmeta¹
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4355-4375, 2022, DOI:10.32604/cmc.2022.021492
• Abstract Nowadays, the anticipation of parking-space demand is an instrumental service in order to reduce traffic congestion levels in urban spaces. The purpose of our work is to study, design and develop a parking-availability predictor that extracts the knowledge from human mobility data, based on the anonymized human displacements of an urban area, and also from weather conditions. Most of the existing solutions for this prediction take as contextual data the current road-traffic state defined at very high temporal or spatial resolution. However, access to this type of fine-grained location data is usually quite limited due to several economic or privacy-related… More
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• Enhance Egocentric Grasp Recognition Based Flex Sensor Under Low Illumination
• Chana Chansri, Jakkree Srinonchat^*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4377-4389, 2022, DOI:10.32604/cmc.2022.024026
• Abstract Egocentric recognition is exciting computer vision research by acquiring images and video from the first-person overview. However, an image becomes noisy and dark under low illumination conditions, making subsequent hand detection tasks difficult. Thus, image enhancement is necessary to make buried detail more visible. This article addresses the challenge of egocentric hand grasp recognition in low light conditions by utilizing the flex sensor and image enhancement algorithm based on adaptive gamma correction with weighting distribution. Initially, a flex sensor is installed to the thumb for object manipulation. The thumb placement that holds in a different position on the object of… More
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• Efficient Data Compression of ECG Signal Based on Modified Discrete Cosine Transform
• Ashraf Mohamed Ali Hassan¹, Mohammed S. Alzaidi², Sherif S. M. Ghoneim^2,3,*, Waleed El Nahal⁴
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4391-4408, 2022, DOI:10.32604/cmc.2022.024044
• Abstract This paper introduced an efficient compression technique that uses the compressive sensing (CS) method to obtain and recover sparse electrocardiography (ECG) signals. The recovery of the signal can be achieved by using sampling rates lower than the Nyquist frequency. A novel analysis was proposed in this paper. To apply CS on ECG signal, the first step is to generate a sparse signal, which can be obtained using Modified Discrete Cosine Transform (MDCT) on the given ECG signal. This transformation is a promising key for other transformations used in this search domain and can be considered as the main contribution of… More
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• PSO Based Multi-Objective Approach for Controlling PID Controller
• Harsh Goud¹, Prakash Chandra Sharma², Kashif Nisar³, Ag. Asri Ag. Ibrahim^3,*, Muhammad Reazul Haque⁴, Narendra Singh Yadav², Pankaj Swarnkar⁵, Manoj Gupta⁶, Laxmi Chand⁶
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4409-4423, 2022, DOI:10.32604/cmc.2022.019217
• Abstract CSTR (Continuous stirred tank reactor) is employed in process control and chemical industries to improve response characteristics and system efficiency. It has a highly nonlinear characteristic that includes complexities in its control and design. Dynamic performance is compassionate to change in system parameters which need more effort for planning a significant controller for CSTR. The reactor temperature changes in either direction from the defined reference value. It is important to note that the intensity of chemical actions inside the CSTR is dependent on the various levels of temperature, and deviation from reference values may cause degradation of biomass quality. Design… More
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• Explainable Artificial Intelligence Solution for Online Retail
• Kumail Javaid¹, Ayesha Siddiqa², Syed Abbas Zilqurnain Naqvi², Allah Ditta³, Muhammad Ahsan², M. A. Khan⁴, Tariq Mahmood⁵, Muhammad Adnan Khan^6,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4425-4442, 2022, DOI:10.32604/cmc.2022.022984
• Abstract Artificial intelligence (AI) and machine learning (ML) help in making predictions and businesses to make key decisions that are beneficial for them. In the case of the online shopping business, it’s very important to find trends in the data and get knowledge of features that helps drive the success of the business. In this research, a dataset of 12,330 records of customers has been analyzed who visited an online shopping website over a period of one year. The main objective of this research is to find features that are relevant in terms of correctly predicting the purchasing decisions made by… More
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• Traffic Priority-Aware Medical Data Dissemination Scheme for IoT Based WBASN Healthcare Applications
• Muhammad Anwar¹, Farhan Masud², Rizwan Aslam Butt³, Sevia Mahdaliza Idrus^4,*, Mohammad Nazir Ahmad⁵, Mohd Yazid Bajuri⁶
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4443-4456, 2022, DOI:10.32604/cmc.2022.022826
• Abstract Wireless Body Area Sensor Network (WBASN) is an automated system for remote health monitoring of patients. WBASN under umbrella of Internet of Things (IoT) is comprised of small Biomedical Sensor Nodes (BSNs) that can communicate with each other without human involvement. These BSNs can be placed on human body or inside the skin of the patients to regularly monitor their vital signs. The BSNs generate critical data as it is related to patient's health. The data traffic can be classified as Sensitive Data (SD) and Non-sensitive Data (ND) packets based on the value of vital signs. These data packets have… More
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• Gauss Gradient and SURF Features for Landmine Detection from GPR Images
• Fatma M. El-Ghamry^1,2, Walid El-Shafai², Mahmouad I. Abdalla¹, Ghada M. El-Banby³, Abeer D. Algarni^4,*, Moawad I. Dessouky², Adel S. Elfishawy², Fathi E. Abd El-Samie^2,4, Naglaa F. Soliman^1,4
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4457-4487, 2022, DOI:10.32604/cmc.2022.022328
• Abstract Recently, ground-penetrating radar (GPR) has been extended as a well-known area to investigate the subsurface objects. However, its output has a low resolution, and it needs more processing for more interpretation. This paper presents two algorithms for landmine detection from GPR images. The first algorithm depends on a multi-scale technique. A Gaussian kernel with a particular scale is convolved with the image, and after that, two gradients are estimated; horizontal and vertical gradients. Then, histogram and cumulative histogram are estimated for the overall gradient image. The bin values on the cumulative histogram are used for discrimination between images with and… More
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• Machine Learning-Based Predictions on the Self-Heating Characteristics of Nanocomposites with Hybrid Fillers
• Taegeon Kil¹, D. I. Jang¹, H. N. Yoon¹, Beomjoo Yang^2,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4487-4502, 2022, DOI:10.32604/cmc.2022.020940
• Abstract A machine learning-based prediction of the self-heating characteristics and the negative temperature coefficient (NTC) effect detection of nanocomposites incorporating carbon nanotube (CNT) and carbon fiber (CF) is proposed. The CNT content was fixed at 4.0 wt.%, and CFs having three different lengths (0.1, 3 and 6 mm) at dosage of 1.0 wt.% were added to fabricate the specimens. The self-heating properties of the specimens were evaluated via self-heating tests. Based on the experiment results, two types of artificial neural network (ANN) models were constructed to predict the surface temperature and electrical resistance, and to detect a severe NTC effect. The… More
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• Atmospheric Convection Model Based Digital Confidentiality Scheme
• Noor Munir¹, Majid Khan^1,*, Mohammad Mazyad Hazzazi², Amer Aljaedi³, Sajjad Shaukat Jamal², Iqtadar Hussain⁴
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4503-4522, 2022, DOI:10.32604/cmc.2022.021077
• Abstract Nonlinear dynamics is a fascinating area that is intensely affecting a wide range of different disciplines of science and technology globally. The combination of different innovative topics of information security and high-speed computing has added new visions into the behavior of complex nonlinear dynamical systems which uncovered amazing results even in the least difficult nonlinear models. The generation of complex actions from a very simple dynamical method has a strong relation with information security. The protection of digital content is one of the inescapable concerns of the digitally advanced world. Today, information plays an important role in everyday life and… More
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• A Real-Time Oral Cavity Gesture Based Words Synthesizer Using Sensors
• Palli Padmini¹, C. Paramasivam¹, G. Jyothish Lal², Sadeen Alharbi^3,*, Kaustav Bhowmick⁴
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4523-4554, 2022, DOI:10.32604/cmc.2022.022857
• Abstract The present system experimentally demonstrates a synthesis of syllables and words from tongue manoeuvers in multiple languages, captured by four oral sensors only. For an experimental demonstration of the system used in the oral cavity, a prototype tooth model was used. Based on the principle developed in a previous publication by the author(s), the proposed system has been implemented using the oral cavity (tongue, teeth, and lips) features alone, without the glottis and the larynx. The positions of the sensors in the proposed system were optimized based on articulatory (oral cavity) gestures estimated by simulating the mechanism of human speech.… More
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• Use of Local Region Maps on Convolutional LSTM for Single-Image HDR Reconstruction
• Seungwook Oh, GyeongIk Shin, Hyunki Hong^*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4555-4572, 2022, DOI:10.32604/cmc.2022.022086
• （This article belongs to this Special Issue: Application of Machine-Learning in Computer Vision)
• Abstract Low dynamic range (LDR) images captured by consumer cameras have a limited luminance range. As the conventional method for generating high dynamic range (HDR) images involves merging multiple-exposure LDR images of the same scene (assuming a stationary scene), we introduce a learning-based model for single-image HDR reconstruction. An input LDR image is sequentially segmented into the local region maps based on the cumulative histogram of the input brightness distribution. Using the local region maps, SParam-Net estimates the parameters of an inverse tone mapping function to generate a pseudo-HDR image. We process the segmented region maps as the input sequences on… More
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• Transfer Learning-based Computer-aided Diagnosis System for Predicting Grades of Diabetic Retinopathy
• Qaisar Abbas^1,*, Mostafa E. A. Ibrahim^1,2, Abdul Rauf Baig¹
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4573-4590, 2022, DOI:10.32604/cmc.2022.023670
• （This article belongs to this Special Issue: Innovative Technology For Machine Intelligence)
• Abstract Diabetic retinopathy (DR) diagnosis through digital fundus images requires clinical experts to recognize the presence and importance of many intricate features. This task is very difficult for ophthalmologists and time-consuming. Therefore, many computer-aided diagnosis (CAD) systems were developed to automate this screening process of DR. In this paper, a CAD-DR system is proposed based on preprocessing and a pre-train transfer learning-based convolutional neural network (PCNN) to recognize the five stages of DR through retinal fundus images. To develop this CAD-DR system, a preprocessing step is performed in a perceptual-oriented color space to enhance the DR-related lesions and then a standard… More
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• SBOOSP for Massive Devices in 5G WSNs Using Conformable Chaotic Maps
• Chandrashekhar Meshram^1,*, Agbotiname Lucky Imoize^2,3, Sajjad Shaukat Jamal⁴, Amer Aljaedi⁵, Adel R. Alharbi⁵
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4591-4608, 2022, DOI:10.32604/cmc.2022.022642
• Abstract The commercialization of the fifth-generation (5G) wireless network has begun. Massive devices are being integrated into 5G-enabled wireless sensor networks (5G WSNs) to deliver a variety of valuable services to network users. However, there are rising fears that 5G WSNs will expose sensitive user data to new security vulnerabilities. For secure end-to-end communication, key agreement and user authentication have been proposed. However, when billions of massive devices are networked to collect and analyze complex user data, more stringent security approaches are required. Data integrity, non-repudiation, and authentication necessitate special-purpose subtree-based signature mechanisms that are pretty difficult to create in practice.… More
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• Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting
• Saba Awan¹, Zahid Mehmood^2,*, Hassan Nazeer Chaudhry³, Usman Tariq⁴, Amjad Rehman⁵, Tanzila Saba⁵, Muhammad Rashid⁶
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4609-4626, 2022, DOI:10.32604/cmc.2022.019404
• Abstract To determine the individual circumstances that account for a road traffic accident, it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels. Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model. In this article, we proposed a multi-model hybrid framework of the weighted majority voting (WMV) scheme with parallel structure, which is designed by integrating individually implemented multinomial logistic regression (MLR) and multilayer perceptron (MLP) classifiers using three different… More
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• Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels
• Marwa M. Eid^1,*, Fawaz Alassery², Abdelhameed Ibrahim³, Mohamed Saber⁴
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4627-4641, 2022, DOI:10.32604/cmc.2022.024043
• Abstract Digital signal processing of electroencephalography (EEG) data is now widely utilized in various applications, including motor imagery classification, seizure detection and prediction, emotion classification, mental task classification, drug impact identification and sleep state classification. With the increasing number of recorded EEG channels, it has become clear that effective channel selection algorithms are required for various applications. Guided Whale Optimization Method (Guided WOA), a suggested feature selection algorithm based on Stochastic Fractal Search (SFS) technique, evaluates the chosen subset of channels. This may be used to select the optimum EEG channels for use in Brain-Computer Interfaces (BCIs), the method for identifying… More
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• Skeleton Split Strategies for Spatial Temporal Graph Convolution Networks
• Motasem S. Alsawadi^*, Miguel Rio
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4643-4658, 2022, DOI:10.32604/cmc.2022.022783
• Abstract Action recognition has been recognized as an activity in which individuals’ behaviour can be observed. Assembling profiles of regular activities such as activities of daily living can support identifying trends in the data during critical events. A skeleton representation of the human body has been proven to be effective for this task. The skeletons are presented in graphs form-like. However, the topology of a graph is not structured like Euclidean-based data. Therefore, a new set of methods to perform the convolution operation upon the skeleton graph is proposed. Our proposal is based on the Spatial Temporal-Graph Convolutional Network (ST-GCN) framework.… More
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• Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition: The Possibilities of Next Generation 5G Data Networks
• Radek Martinek¹, Jan Baros¹, Rene Jaros¹, Lukas Danys^1,*, Jan Nedoma²
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4659-4676, 2022, DOI:10.32604/cmc.2022.019904
• Abstract This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction. Modern vehicles are nowadays increasingly supporting voice commands, which are one of the pillars of autonomous and SMART vehicles. Robust speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle background noise. This article presents the new concept of a hybrid system, which is implemented as a virtual instrument. The highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise reduction. The study… More
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• Drone-based AI/IoT Framework for Monitoring, Tracking and Fighting Pandemics
• Abdelhamied A. Ateya^1,2, Abeer D. Algarni¹, Andrey Koucheryavy³, Naglaa. F. Soliman^1,2,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4677-4699, 2022, DOI:10.32604/cmc.2022.021850
• Abstract Since World Health Organization (WHO) has declared the Coronavirus disease (COVID-19) a global pandemic, the world has changed. All life's fields and daily habits have moved to adapt to this new situation. According to WHO, the probability of such virus pandemics in the future is high, and recommends preparing for worse situations. To this end, this work provides a framework for monitoring, tracking, and fighting COVID-19 and future pandemics. The proposed framework deploys unmanned aerial vehicles (UAVs), e.g.; quadcopter and drone, integrated with artificial intelligence (AI) and Internet of Things (IoT) to monitor and fight COVID-19. It consists of two… More
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• Modified UNet Model for Brain Stroke Lesion Segmentation on Computed Tomography Images
• Batyrkhan Omarov^1,2,3, Azhar Tursynova^1,*, Octavian Postolache⁴, Khaled Gamry⁵, Aidar Batyrbekov⁵, Sapargali Aldeshov^6,7, Zhanar Azhibekova⁹, Marat Nurtas^5,8, Akbayan Aliyeva⁶, Kadrzhan Shiyapov^10,11
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4701-4717, 2022, DOI:10.32604/cmc.2022.020998
• Abstract The task of segmentation of brain regions affected by ischemic stroke is help to tackle important challenges of modern stroke imaging analysis. Unfortunately, at the moment, the models for solving this problem using machine learning methods are far from ideal. In this paper, we consider a modified 3D UNet architecture to improve the quality of stroke segmentation based on 3D computed tomography images. We use the ISLES 2018 (Ischemic Stroke Lesion Segmentation Challenge 2018) open dataset to train and test the proposed model. Interpretation of the obtained results, as well as the ideas for further experiments are included in the… More
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• Plant Identification Using Fitness-Based Position Update in Whale Optimization Algorithm
• Ayman Altameem¹, Sandeep Kumar², Ramesh Chandra Poonia³, Abdul Khader Jilani Saudagar^4,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4719-4736, 2022, DOI:10.32604/cmc.2022.022177
• （This article belongs to this Special Issue: Recent advancements in Environment Sustainability, AgriFood using applied artificial intelligence in Multimedia Systems)
• Abstract Since the beginning of time, humans have relied on plants for food, energy, and medicine. Plants are recognized by leaf, flower, or fruit and linked to their suitable cluster. Classification methods are used to extract and select traits that are helpful in identifying a plant. In plant leaf image categorization, each plant is assigned a label according to its classification. The purpose of classifying plant leaf images is to enable farmers to recognize plants, leading to the management of plants in several aspects. This study aims to present a modified whale optimization algorithm and categorizes plant leaf images into classes.… More
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• Graphene-Based RFID Tag Antenna for Vehicular Smart Border Passings
• Prach Asavanarakul, Amnoiy Ruengwaree^*, Suwat Sakulchat
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4737-4748, 2022, DOI:10.32604/cmc.2022.023743
• Abstract Globalization has opened practically every country in the globe to tourism and commerce today. In every region, the volume of vehicles traveling through border crossings has increased significantly. Smartcards and radio frequency identification (RFID) have been proposed as a new method of identifying and authenticating passengers, products, and vehicles. However, the usage of smartcards and RFID tag cards for vehicular border crossings continues to suffer security and flexibility challenges. Providing a vehicle's driver a smartcard or RFID tag card may result in theft, loss, counterfeit, imitation, or vehicle transmutation. RFID sticker tags would replace RFID tags as vehicle border passes… More
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• E-mail Spam Classification Using Grasshopper Optimization Algorithm and Neural Networks
• Sanaa A. A. Ghaleb^1,3,4, Mumtazimah Mohamad¹, Syed Abdullah Fadzli¹, Waheed A.H.M. Ghanem^2,3,4,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4749-4766, 2022, DOI:10.32604/cmc.2022.020472
• Abstract Spam has turned into a big predicament these days, due to the increase in the number of spam emails, as the recipient regularly receives piles of emails. Not only is spam wasting users’ time and bandwidth. In addition, it limits the storage space of the email box as well as the disk space. Thus, spam detection is a challenge for individuals and organizations alike. To advance spam email detection, this work proposes a new spam detection approach, using the grasshopper optimization algorithm (GOA) in training a multilayer perceptron (MLP) classifier for categorizing emails as ham and spam. Hence, MLP and… More
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• Mobile Devices Interface Adaptivity Using Ontologies
• Muhammad Waseem Iqbal¹, Muhammad Raza Naqvi², Muhammad Adnan Khan^3,4, Faheem Khan⁵, T. Whangbo^5,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4767-4784, 2022, DOI:10.32604/cmc.2022.023239
• Abstract Currently, many mobile devices provide various interaction styles and modes which create complexity in the usage of interfaces. The context offers the information base for the development of Adaptive user interface (AUI) frameworks to overcome the heterogeneity. For this purpose, the ontological modeling has been made for specific context and environment. This type of philosophy states to the relationship among elements (e.g., classes, relations, or capacities etc.) with understandable satisfied representation. The context mechanisms can be examined and understood by any machine or computational framework with these formal definitions expressed in Web ontology language (WOL)/Resource description frame work (RDF). The… More
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• A New Hybrid SARFIMA-ANN Model for Tourism Forecasting
• Tanzila Saba¹, Mirza Naveed Shahzad^2,*, Sonia Iqbal^2,3, Amjad Rehman¹, Ibrahim Abunadi¹
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4785-4801, 2022, DOI:10.32604/cmc.2022.022309
• Abstract Many countries developed and increased greenery in their country sights to attract international tourists. This planning is now significantly contributing to their economy. The next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment; it is only possible if an upcoming number of tourists’ arrivals are accurately predicted. But accurate prediction is not easy as empirical evidence shows that the tourists’ arrival data often contains linear, nonlinear, and seasonal patterns. The traditional model, like the seasonal autoregressive fractional integrated moving average (SARFIMA), handles seasonal trends with seasonality. In contrast, the artificial neural network… More
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• Milestones of Wireless Communication Networks and Technology Prospect of Next Generation (6G)
• Mohammed H. Alsharif¹, Md. Sanwar Hossain², Abu Jahid³, Muhammad Asghar Khan⁴, Bong Jun Choi^5,*, Samih M. Mostafa^6,7
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4803-4818, 2022, DOI:10.32604/cmc.2022.023500
• Abstract Since around 1980, a new generation of wireless technology has arisen approximately every 10 years. First-generation (1G) and second-generation (2G) began with voice and eventually introduced more and more data in third-generation (3G) and became highly popular in the fourth-generation (4G). To increase the data rate along with low latency and mass connectivity the fifth-generation (5G) networks are being installed from 2020. However, the 5G technology will not be able to fulfill the data demand at the end of this decade. Therefore, it is expected that 6G communication networks will rise, providing better services through the implementation of new enabling… More
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• Robust Watermarking of Screen-Photography Based on JND
• Siyu Gu¹, Jin Han^1,*, Xingming Sun^1,2, Yi Cao^1,3
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4819-4833, 2022, DOI:10.32604/cmc.2022.023955
• Abstract With the popularity of smartphones, it is often easy to maliciously leak important information by taking pictures of the phone. Robust watermarking that can resist screen photography can achieve the protection of information. Since the screen photo process can cause some irreversible distortion, the currently available screen photo watermarks do not consider the image content well and the visual quality is not very high. Therefore, this paper proposes a new screen-photography robust watermark. In terms of embedding region selection, the intensity-based Scale-invariant feature transform (SIFT) algorithm used for the construction of feature regions based on the density of feature points,… More
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• Hydrodynamics and Heat Transfer Analysis of Airflow in a Sinusoidally Curved Channel
• Abid. A. Memon¹, M. Asif Memon¹, Kaleemullah Bhatti¹, Thanin Sitthiwirattham^2,*, Nichaphat Patanarapeelert³
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4835-4853, 2022, DOI:10.32604/cmc.2022.023912
• Abstract For heat transfer enhancement in heat exchangers, different types of channels are often tested. The performance of heat exchangers can be made better by considering geometry composed of sinusoidally curved walls. This research studies the modeling and simulation of airflow through a units long sinusoidally curved wavy channel. For the purpose, two-dimensional Navier Stokes equations along with heat equations are under consideration. To simulate the fluid flow problem, the finite element-based software COMSOL Multiphysics is used. The parametric study for Reynolds number from to and the period of vibration P from to are observed. The surface plots, streamline patterns, contours,… More
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• Fuzzy Control Based Resource Scheduling in IoT Edge Computing
• Samah Alhazmi, Kailash Kumar^*, Soha Alhelaly
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4855-4870, 2022, DOI:10.32604/cmc.2022.024012
• Abstract Edge Computing is a new technology in Internet of Things (IoT) paradigm that allows sensitive data to be sent to disperse devices quickly and without delay. Edge is identical to Fog, except its positioning in the end devices is much nearer to end-users, making it process and respond to clients in less time. Further, it aids sensor networks, real-time streaming apps, and the IoT, all of which require high-speed and dependable internet access. For such an IoT system, Resource Scheduling Process (RSP) seems to be one of the most important tasks. This paper presents a RSP for Edge Computing (EC).… More
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• Efficient Computer Aided Diagnosis System for Hepatic Tumors Using Computed Tomography Scans
• Yasmeen Al-Saeed^1,2, Wael A. Gab-Allah¹, Hassan Soliman¹, Maysoon F. Abulkhair³, Wafaa M. Shalash⁴, Mohammed Elmogy^1,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4871-4894, 2022, DOI:10.32604/cmc.2022.023638
• Abstract One of the leading causes of mortality worldwide is liver cancer. The earlier the detection of hepatic tumors, the lower the mortality rate. This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors. Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range, intensity values overlap between the liver and neighboring organs, high noise from computed tomography scanner, and large variance in tumors shapes. The proposed method consists of three main stages; liver segmentation using Fast… More
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• Smart Bubble Sort: A Novel and Dynamic Variant of Bubble Sort Algorithm
• Mohammad Khalid Imam Rahmani^*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4895-4913, 2022, DOI:10.32604/cmc.2022.023837
• Abstract In the present era, a very huge volume of data is being stored in online and offline databases. Enterprise houses, research, medical as well as healthcare organizations, and academic institutions store data in databases and their subsequent retrievals are performed for further processing. Finding the required data from a given database within the minimum possible time is one of the key factors in achieving the best possible performance of any computer-based application. If the data is already sorted, finding or searching is comparatively faster. In real-life scenarios, the data collected from different sources may not be in sorted order. Sorting… More
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• Hybridization of CNN with LBP for Classification of Melanoma Images
• Saeed Iqbal^1,*, Adnan N. Qureshi¹, Ghulam Mustafa²
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4915-4939, 2022, DOI:10.32604/cmc.2022.023178
• Abstract Skin cancer (melanoma) is one of the most aggressive of the cancers and the prevalence has significantly increased due to increased exposure to ultraviolet radiation. Therefore, timely detection and management of the lesion is a critical consideration in order to improve lifestyle and reduce mortality. To this end, we have designed, implemented and analyzed a hybrid approach entailing convolutional neural networks (CNN) and local binary patterns (LBP). The experiments have been performed on publicly accessible datasets ISIC 2017, 2018 and 2019 (HAM10000) with data augmentation for in-distribution generalization. As a novel contribution, the CNN architecture is enhanced with an intelligible… More
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• 5G Smart Mobility Management Based Fuzzy Logic Controller Unit
• Chafaa Hamrouni^1,*, Slim Chaoui²
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4941-4953, 2022, DOI:10.32604/cmc.2022.023732
• Abstract In the paper, we propose a fuzzy logic controller system to be implemented for smart mobility management in the 5G wireless communication network. Mobility management is considered as a main issue for all-IP mobile networks future generation. As a network-based mobility management protocol, Internet Engineering Task Force developed the Proxy Mobile IPv6 (PMIPv6) in order to support the mobility of IP devices, and many other results were presented to reduce latency handover and the amount of PMIPv6 signaling, but it is not enough for the application needs in real-time. The present paper describes an approach based on the IEEE 802.21… More
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• Man Overboard Detection System Using IoT for Navigation Model
• Hüseyin Gürüler¹, Murat Altun¹, Faheem Khan², Taegkeun Whangbo^2,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4955-4969, 2022, DOI:10.32604/cmc.2022.023556
• （This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
• Abstract Security measures and contingency plans have been established in order to ensure human safety especially in the floating elements like ferry, ro-ro, catamaran, frigate, yacht that are the vehicles services for the purpose of logistic and passenger transport. In this paper, all processes in the event of Man overboard (MOB)are initiated for smart transportation. In MOB the falling person is totally dependent on the person who first saw the falling person. The main objective of this paper is to develop a solution to this significant problem. If a staff member or a passenger does not see the fall into the… More
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• Text Encryption Using Pell Sequence and Elliptic Curves with Provable Security
• Sumaira Azhar¹, Naveed Ahmed Azam^2,*, Umar Hayat¹
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4971-4988, 2022, DOI:10.32604/cmc.2022.023685
• Abstract The demand for data security schemes has increased with the significant advancement in the field of computation and communication networks. We propose a novel three-step text encryption scheme that has provable security against computation attacks such as key attack and statistical attack. The proposed scheme is based on the Pell sequence and elliptic curves, where at the first step the plain text is diffused to get a meaningless plain text by applying a cyclic shift on the symbol set. In the second step, we hide the elements of the diffused plain text from the attackers. For this purpose, we use… More
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• Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters
• El-Sayed M. El-kenawy^1,2, Abdelhameed Ibrahim^3,*, Seyedali Mirjalili^4,5, Yu-Dong Zhang⁶, Shaima Elnazer^7,8, Rokaia M. Zaki^9,10
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4989-5003, 2022, DOI:10.32604/cmc.2022.023884
• （This article belongs to this Special Issue: Artificial Intelligence and Machine Learning Algorithms in Real-World Applications and Theories)
• Abstract Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve performance. Metamaterial antennas can overcome the bandwidth constraint associated with tiny antennas. Machine learning is receiving a lot of interest in optimizing solutions in a variety of areas. Machine learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today's technology. The accuracy of the forecast is mostly determined by the model used. The purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial Antenna. Support Vector… More
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• Brain Tumor Detection and Segmentation Using RCNN
• Maham Khan¹, Syed Adnan Shah¹, Tenvir Ali², Quratulain², Aymen Khan², Gyu Sang Choi^3,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5005-5020, 2022, DOI:10.32604/cmc.2022.023007
• （This article belongs to this Special Issue: Machine Learning Applications in Medical, Finance, Education and Cyber Security)
• Abstract Brain tumors are considered as most fatal cancers. To reduce the risk of death, early identification of the disease is required. One of the best available methods to evaluate brain tumors is Magnetic resonance Images (MRI). Brain tumor detection and segmentation are tough as brain tumors may vary in size, shape, and location. That makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors’. So an automated brain tumor detection and segmentation is required. This work suggests a Region-based Convolution Neural Network (RCNN) approach for automated brain tumor identification and segmentation using MR images,… More
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• Perceptual Image Outpainting Assisted by Low-Level Feature Fusion and Multi-Patch Discriminator
• Xiaojie Li¹, Yongpeng Ren¹, Hongping Ren¹, Canghong Shi², Xian Zhang¹, Lutao Wang¹, Imran Mumtaz³, Xi Wu¹
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5021-5037, 2022, DOI:10.32604/cmc.2022.023071
• Abstract Recently, deep learning-based image outpainting has made greatly notable improvements in computer vision field. However, due to the lack of fully extracting image information, the existing methods often generate unnatural and blurry outpainting results in most cases. To solve this issue, we propose a perceptual image outpainting method, which effectively takes the advantage of low-level feature fusion and multi-patch discriminator. Specifically, we first fuse the texture information in the low-level feature map of encoder, and simultaneously incorporate these aggregated features reusability with semantic (or structural) information of deep feature map such that we could utilize more sophisticated texture information to… More
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• Security Threat and Vulnerability Assessment and Measurement in Secure Software Development
• Mamoona Humayun¹, NZ Jhanjhi^2,*, Maram Fahhad Almufareh¹, Muhammad Ibrahim Khalil³
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5039-5059, 2022, DOI:10.32604/cmc.2022.019289
• Abstract Security is critical to the success of software, particularly in today's fast-paced, technology-driven environment. It ensures that data, code, and services maintain their CIA (Confidentiality, Integrity, and Availability). This is only possible if security is taken into account at all stages of the SDLC (Software Development Life Cycle). Various approaches to software quality have been developed, such as CMMI (Capability maturity model integration). However, there exists no explicit solution for incorporating security into all phases of SDLC. One of the major causes of pervasive vulnerabilities is a failure to prioritize security. Even the most proactive companies use the “patch and… More
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• Energy-Efficient Scheduling for a Cognitive IoT-Based Early Warning System
• Saeed Ahmed^1,2, Noor Gul^1,3, Jahangir Khan⁴, Junsu Kim¹, Su Min Kim^1,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5061-5082, 2022, DOI:10.32604/cmc.2022.023639
• （This article belongs to this Special Issue: Artificial Intelligence Convergence Healthcare System Leveraging Blockchain Networks)
• Abstract Flash floods are deemed the most fatal and disastrous natural hazards globally due to their prompt onset that requires a short prime time for emergency response. Cognitive Internet of things (CIoT) technologies including inherent characteristics of cognitive radio (CR) are potential candidates to develop a monitoring and early warning system (MEWS) that helps in efficiently utilizing the short response time to save lives during flash floods. However, most CIoT devices are battery-limited and thus, it reduces the lifetime of the MEWS. To tackle these problems, we propose a CIoT-based MEWS to slash the fatalities of flash floods. To extend the… More
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• Fruits and Vegetables Freshness Categorization Using Deep Learning
• Labiba Gillani Fahad¹, Syed Fahad Tahir^2,*, Usama Rasheed¹, Hafsa Saqib¹, Mehdi Hassan², Hani Alquhayz³
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5083-5098, 2022, DOI:10.32604/cmc.2022.023357
• Abstract The nutritional value of perishable food items, such as fruits and vegetables, depends on their freshness levels. The existing approaches solve a binary class problem by classifying a known fruit\vegetable class into fresh or rotten only. We propose an automated fruits and vegetables categorization approach that first recognizes the class of object in an image and then categorizes that fruit or vegetable into one of the three categories: pure-fresh, medium-fresh, and rotten. We gathered a dataset comprising of 60K images of 11 fruits and vegetables, each is further divided into three categories of freshness, using hand-held cameras. The recognition and… More
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• Sum Rate Maximization-based Fair Power Allocation in Downlink NOMA Networks
• Mohammed Abd-Elnaby^*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5099-5116, 2022, DOI:10.32604/cmc.2022.022020
• （This article belongs to this Special Issue: Intelligent Computing Techniques for Communication Systems)
• Abstract Non-orthogonal multiple access (NOMA) has been seen as a promising technology for 5G communication. The performance optimization of NOMA systems depends on both power allocation (PA) and user pairing (UP). Most existing researches provide sub-optimal solutions with high computational complexity for PA problem and mainly focuses on maximizing the sum rate (capacity) without considering the fairness performance. Also, the joint optimization of PA and UP needs an exhaustive search. The main contribution of this paper is the proposing of a novel capacity maximization-based fair power allocation (CMFPA) with low-complexity in downlink NOMA. Extensive investigation and analysis of the joint impact… More
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• Deep Learning Based Intrusion Detection in Cloud Services for Resilience Management
• S. Sreenivasa Chakravarthi^1,*, R. Jagadeesh Kannan², V. Anantha Natarajan³, Xiao-Zhi Gao⁴
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5117-5133, 2022, DOI:10.32604/cmc.2022.022351
• Abstract In the global scenario one of the important goals for sustainable development in industrial field is innovate new technology, and invest in building infrastructure. All the developed and developing countries focus on building resilient infrastructure and promote sustainable developments by fostering innovation. At this juncture the cloud computing has become an important information and communication technologies model influencing sustainable development of the industries in the developing countries. As part of the innovations happening in the industrial sector, a new concept termed as ‘smart manufacturing’ has emerged, which employs the benefits of emerging technologies like internet of things and cloud computing.… More
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• Fruit Image Classification Using Deep Learning
• Harmandeep Singh Gill^1,*, Osamah Ibrahim Khalaf², Youseef Alotaibi³, Saleh Alghamdi⁴, Fawaz Alassery⁵
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5135-5150, 2022, DOI:10.32604/cmc.2022.022809
• Abstract Fruit classification is found to be one of the rising fields in computer and machine vision. Many deep learning-based procedures worked out so far to classify images may have some ill-posed issues. The performance of the classification scheme depends on the range of captured images, the volume of features, types of characters, choice of features from extracted features, and type of classifiers used. This paper aims to propose a novel deep learning approach consisting of Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) application to classify the fruit images. Classification accuracy depends on the extracted… More
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• Machine Learning Based Analysis of Real-Time Geographical of RS Spatio-Temporal Data
• Rami Sameer Ahmad Al Kloub^*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5151-5165, 2022, DOI:10.32604/cmc.2022.024309
• Abstract Flood disasters can be reliably monitored using remote sensing photos with great spatiotemporal resolution. However, satellite revisit periods and extreme weather limit the use of high spatial resolution images. As a result, this research provides a method for combining Landsat and MODIS pictures to produce high spatiotemporal imagery for flood disaster monitoring. Using the spatial and temporal adaptive reflectance fusion model (STARFM), the spatial and temporal reflectance unmixing model (STRUM), and three prominent algorithms of flexible spatiotemporal data fusion (FSDAF), Landsat fusion images are created by fusing MODIS and Landsat images. Then, to extract flood information, utilize a support vector… More
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• Data Hiding in AMBTC Images Using Selective XOR Hiding Scheme
• Yung-Yao Chen^1,*, Yu-Chen Hu², Ting-Kai Yang³, You-An Wang³
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5167-5182, 2022, DOI:10.32604/cmc.2022.023993
• （This article belongs to this Special Issue: AI-Aided Innovative Cryptographic Techniques for Futuristic Secure Computing Systems)
• Abstract Nowadays since the Internet is ubiquitous, the frequency of data transfer through the public network is increasing. Hiding secure data in these transmitted data has emerged broad security issue, such as authentication and copyright protection. On the other hand, considering the transmission efficiency issue, image transmission usually involves image compression in Internet-based applications. To address both issues, this paper presents a data hiding scheme for the image compression method called absolute moment block truncation coding (AMBTC). First, an image is divided into non-overlapping blocks through AMBTC compression, the blocks are classified four types, namely smooth, semi-smooth, semi-complex, and complex. The… More
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• Attention-Based Deep Learning Model for Early Detection of Parkinson's Disease
• Mohd Sadiq¹, Mohd Tauheed Khan^2,*, Sarfaraz Masood³
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5183-5200, 2022, DOI:10.32604/cmc.2022.020531
• （This article belongs to this Special Issue: Future Generation of Artificial Intelligence and Intelligent Internet of Things)
• Abstract Parkinson's disease (PD), classified under the category of a neurological syndrome, affects the brain of a person which leads to the motor and non-motor symptoms. Among motor symptoms, one of the major disabling symptom is Freezing of Gait (FoG) that affects the daily standard of living of PD patients. Available treatments target to improve the symptoms of PD. Detection of PD at the early stages is an arduous task due to being indistinguishable from a healthy individual. This work proposed a novel attention-based model for the detection of FoG events and PD, and measuring the intensity of PD on the… More
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• New Hybrid IoT LoRaWAN/IRC Sensors: SMART Water Metering System
• Vlastimil Slany¹, Petr Koudelka^1,*, Eva Krcalova¹, Jan Jobbagy², Lukas Danys³, Rene Jaros³, Zdenek Slanina³, Michal Prauzek³, Radek Martinek³
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5201-5217, 2022, DOI:10.32604/cmc.2022.021349
• Abstract The massive development of internet of things (IoT) technologies is gaining momentum across all areas of their possible deployment—spanning from Industry 4.0 to eHealth, smart city, agriculture or waste management. This ongoing development is further pushed forward by the gradual deployment of 5G networks. With 5G capable smart devices, it will be possible to transfer more data with shorter latency thereby resulting in exciting new use cases such as Massive IoT. Massive-IoT (low-power wide area network-LPWAN) enables improved network coverage, long device operational lifetime and a high density of connections. Despite all the advantages of massive-IoT technology, there are certain… More
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• Location Prediction for Improved Human Safety at Complex Environments
• S. G. Siddharth^1,*, G. M. Tamilselvan², C. Venkatesh³
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5219-5234, 2022, DOI:10.32604/cmc.2022.019252
• Abstract In underground operation, primary consideration is safety. In recent decades, for minimizing accident and for preventing major economic losses and casualties, wireless sensors are used by various large mineral countries through early warning. The Improved DV-Hop Localization Algorithm (IDVHLA) is used in existing works for doing this. However, accurate anchor node detection is impossible in existing works with the malicious nodes presence, where there won't be any accurate sharing of anchor node's location information. In case of emergency situation, faster communication is a highly complex one. A technique called Modified Distance Vector Hop based Multipath Routing Protocol (MDVHMRP) is introduced… More
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• Automated Facial Expression Recognition and Age Estimation Using Deep Learning
• Syeda Amna Rizwan¹, Yazeed Yasin Ghadi², Ahmad Jalal¹, Kibum Kim^3,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5235-5252, 2022, DOI:10.32604/cmc.2022.023328
• Abstract With the advancement of computer vision techniques in surveillance systems, the need for more proficient, intelligent, and sustainable facial expressions and age recognition is necessary. The main purpose of this study is to develop accurate facial expressions and an age recognition system that is capable of error-free recognition of human expression and age in both indoor and outdoor environments. The proposed system first takes an input image pre-process it and then detects faces in the entire image. After that landmarks localization helps in the formation of synthetic face mask prediction. A novel set of features are extracted and passed to… More
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• Digital Watermarking Scheme for Securing Textual Database Using Histogram Shifting Model
• Khalid A. El Drandaly¹, Walid Khedr¹, Islam S. Mohamed¹, Ayman Mohamed Mostafa^2,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5253-5270, 2022, DOI:10.32604/cmc.2022.023684
• （This article belongs to this Special Issue: Security and Privacy issues for various Emerging Technologies and Future Trends)
• Abstract Information security is one of the most important methods of protecting the confidentiality and privacy of internet users. The greater the volume of data, the more the need to increase the security methods for protecting data from intruders. This task can be challenging for researchers in terms of managing enormous data and maintaining their safety and effectiveness. Protection of digital content is a major issue in maintaining the privacy and secrecy of data. Toward this end, digital watermarking is based on the concept of information security through the insertion and detection of an embedded watermark in an efficient manner. Recent… More
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• Lightweight Direct Acyclic Graph Blockchain for Enhancing Resource-Constrained IoT Environment
• Salaheddine Kably^1,2,*, Mounir Arioua¹, Nabih Alaoui²
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5271-5291, 2022, DOI:10.32604/cmc.2022.020833
• （This article belongs to this Special Issue: Security, Privacy, and Trust in Industrial IoTs)
• Abstract Blockchain technology is regarded as the emergent security solution for many applications related to the Internet of Things (IoT). In concept, blockchain has a linear structure that grows with the number of transactions entered. This growth in size is the main obstacle to the blockchain, which makes it unsuitable for resource-constrained IoT environments. Moreover, conventional consensus algorithms such as PoW, PoS are very computationally heavy. This paper solves these problems by introducing a new lightweight blockchain structure and lightweight consensus algorithm. The Multi-Zone Direct Acyclic Graph (DAG) Blockchain (Multizone-DAG-Blockchain) framework is proposed for the fog-based IoT environment. In this context,… More
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• Examination of Pine Wilt Epidemic Model through Efficient Algorithm
• Ali Raza^1,*, Emad E. Mahmoud², A. M. Al-Bugami², Dumitru Baleanu^3,4, Muhammad Rafiq⁵, Muhammad Mohsin⁶, Muneerah Al Nuwairan⁷
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5293-5310, 2022, DOI:10.32604/cmc.2022.024535
• （This article belongs to this Special Issue: Bio-Inspired Computational Intelligence and Optimization Techniques for Real-World Engineering Applications)
• Abstract Pine wilt is a dramatic disease that kills infected trees within a few weeks to a few months. The cause is the pathogen Pinewood Nematode. Most plant-parasitic nematodes are attached to plant roots, but pinewood nematodes are found in the tops of trees. Nematodes kill the tree by feeding the cells around the resin ducts. The modeling of a pine wilt disease is based on six compartments, including three for plants (susceptible trees, exposed trees, and infected trees) and the other for the beetles (susceptible beetles, exposed beetles, and infected beetles). The deterministic modeling, along with subpopulations, is based on… More
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• Metaheuristic Based Data Gathering Scheme for Clustered UAVs in 6G Communication Network
• Ahmed S. Almasoud¹, Siwar Ben Haj Hassine², Nadhem NEMRI², Fahd N. Al-Wesabi^2,3, Manar Ahmed Hamza^4,*, Anwer Mustafa Hilal⁴, Abdelwahed Motwakel⁴, Mesfer Al Duhayyim⁵
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5311-5325, 2022, DOI:10.32604/cmc.2022.024500
• Abstract The sixth-generation (6G) wireless communication networks are anticipated in integrating aerial, terrestrial, and maritime communication into a robust system to accomplish trustworthy, quick, and low latency needs. It enables to achieve maximum throughput and delay for several applications. Besides, the evolution of 6G leads to the design of unmanned aerial vehicles (UAVs) in providing inexpensive and effective solutions in various application areas such as healthcare, environment monitoring, and so on. In the UAV network, effective data collection with restricted energy capacity poses a major issue to achieving high quality network communication. It can be addressed by the use of clustering… More
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• Contrast Correction Using Hybrid Statistical Enhancement on Weld Defect Images
• Wan Azani Mustafa^1,2,*, Haniza Yazid³, Ahmed Alkhayyat⁴, Mohd Aminudin Jamlos³, Hasliza A. Rahim³, Midhat Nabil Salimi⁵
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5327-5342, 2022, DOI:10.32604/cmc.2022.023492
• Abstract Luminosity and contrast variation problems are among the most challenging tasks in the image processing field, significantly improving image quality. Enhancement is implemented by adjusting the dark or bright intensity to improve the quality of the images and increase the segmentation performance. Recently, numerous methods had been proposed to normalise the luminosity and contrast variation. A new approach based on a direct technique using statistical data known as Hybrid Statistical Enhancement (HSE) is presented in this study. The HSE method uses the mean and standard deviation of a local and global neighbourhood and classified the pixel into three groups; the… More
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• Decision Support System for Diagnosis of Irregular Fovea
• Ghulam Ali Mallah¹, Jamil Ahmed¹, Muhammad Irshad Nazeer^2,*, Mazhar Ali Dootio³, Hidayatullah Shaikh¹, Aadil Jameel¹
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5343-5353, 2022, DOI:10.32604/cmc.2022.023581
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract Detection of abnormalities in human eye is one of the well-established research areas of Machine Learning. Deep Learning techniques are widely used for the diagnosis of Retinal Diseases (RD). Fovea is one of the significant parts of retina which would be prevented before the involvement of Perforated Blood Vessels (PBV). Retinopathy Images (RI) contains sufficient information to classify structural changes incurred upon PBV but Macular Features (MF) and Fovea Features (FF) are very difficult to detect because features of MF and FF could be found with Similar Color Movements (SCM) with minor variations. This paper presents novel method for the… More
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• Machine Learning-based Optimal Framework for Internet of Things Networks
• Moath Alsafasfeh^1,*, Zaid A. Arida², Omar A. Saraereh³
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5355-5380, 2022, DOI:10.32604/cmc.2022.024093
• Abstract Deep neural networks (DNN) are widely employed in a wide range of intelligent applications, including image and video recognition. However, due to the enormous amount of computations required by DNN. Therefore, performing DNN inference tasks locally is problematic for resource-constrained Internet of Things (IoT) devices. Existing cloud approaches are sensitive to problems like erratic communication delays and unreliable remote server performance. The utilization of IoT device collaboration to create distributed and scalable DNN task inference is a very promising strategy. The existing research, on the other hand, exclusively looks at the static split method in the scenario of homogeneous IoT… More
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• Machine Learning-based Stable P2P IPTV Overlay
• Muhammad Javid Iqbal^1,2, Ihsan Ullah^2,*, Muhammad Ali², Atiq Ahmed², Waheed Noor², Abdul Basit²
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5381-5397, 2022, DOI:10.32604/cmc.2022.024116
• Abstract Live video streaming is one of the newly emerged services over the Internet that has attracted immense interest of the service providers. Since Internet was not designed for such services during its inception, such a service poses some serious challenges including cost and scalability. Peer-to-Peer (P2P) Internet Protocol Television (IPTV) is an application-level distributed paradigm to offer live video contents. In terms of ease of deployment, it has emerged as a serious alternative to client server, Content Delivery Network (CDN) and IP multicast solutions. Nevertheless, P2P approach has struggled to provide the desired streaming quality due to a number of… More
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• An Efficient Machine Learning Based Precoding Algorithm for Millimeter-Wave Massive MIMO
• Waleed Shahjehan¹, Abid Ullah¹, Syed Waqar Shah¹, Ayman A. Aly², Bassem F. Felemban², Wonjong Noh^3,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5399-5411, 2022, DOI:10.32604/cmc.2022.022034
• Abstract Millimeter wave communication works in the 30–300 GHz frequency range, and can obtain a very high bandwidth, which greatly improves the transmission rate of the communication system and becomes one of the key technologies of fifth-generation (5G). The smaller wavelength of the millimeter wave makes it possible to assemble a large number of antennas in a small aperture. The resulting array gain can compensate for the path loss of the millimeter wave. Utilizing this feature, the millimeter wave massive multiple-input multiple-output (MIMO) system uses a large antenna array at the base station. It enables the transmission of multiple data streams,… More
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• A BPR-CNN Based Hand Motion Classifier Using Electric Field Sensors
• Hunmin Lee¹, Inseop Na², Kamoliddin Bultakov³, Youngchul Kim^3,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5413-5425, 2022, DOI:10.32604/cmc.2022.023172
• Abstract In this paper, we propose a BPR-CNN (Biometric Pattern Recognition-Convolution Neural Network) classifier for hand motion classification as well as a dynamic threshold algorithm for motion signal detection and extraction by EF (Electric Field) sensors. Currently, an EF sensor or EPS (Electric Potential Sensor) system is attracting attention as a next-generation motion sensing technology due to low computation and price, high sensitivity and recognition speed compared to other sensor systems. However, it remains as a challenging problem to accurately detect and locate the authentic motion signal frame automatically in real-time when sensing body-motions such as hand motion, due to the… More
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• Twisted Pair Cable Fault Diagnosis via Random Forest Machine Learning
• N. B. Ghazali¹, F. C. Seman^1,*, K. Isa¹, K. N. Ramli¹, Z. Z. Abidin¹, S. M. Mustam¹, M. A. Haek², A. N. Z. Abidin², A. Asrokin²
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5427-5440, 2022, DOI:10.32604/cmc.2022.023211
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract Applying the fault diagnosis techniques to twisted pair copper cable is beneficial to improve the stability and reliability of internet access in Digital Subscriber Line (DSL) Access Network System. The network performance depends on the occurrence of cable fault along the copper cable. Currently, most of the telecommunication providers monitor the network performance degradation hence troubleshoot the present of the fault by using commercial test gear on-site, which may be resolved using data analytics and machine learning algorithm. This paper presents a fault diagnosis method for twisted pair cable fault detection based on knowledge-based and data-driven machine learning methods. The… More
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• Mathematical Modelling of Quantum Kernel Method for Biomedical Data Analysis
• Mahmoud Ragab^1,2,3, Ehab Bahauden Ashary⁴, Maha Farouk S. Sabir⁵, Adel A. Bahaddad⁵, Romany F. Mansour^6,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5441-5457, 2022, DOI:10.32604/cmc.2022.024545
• Abstract This study presents a novel method to detect the medical application based on Quantum Computing (QC) and a few Machine Learning (ML) systems. QC has a primary advantage i.e., it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of seconds. So, this model is suggested for medical application only by recent researchers. A novel strategy i.e., Quantum Kernel Method (QKM) is proposed in this paper for data prediction. In this QKM process, Linear Tunicate Swarm Algorithm (LTSA), the optimization technique is used to calculate the loss function initially and is aimed… More
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• IEEE802.11 Access Point's Service Set Identifier (SSID) for Localization and Tracking
• Mohammad Z. Masoud^1,*, Yousef Jaradat¹, Mohammad Alia²
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5459-5476, 2022, DOI:10.32604/cmc.2022.023781
• Abstract IEEE802.11, known as WiFi has proliferated in the last decade. It can be found in smartphones, laptops, smart TVs and surveillance cameras. This popularity has revealed many issues in health, data privacy and security. In this work, a WiFi measurement study has been conducted in Amman, the capital city of Jordan. An Android App has been written to harvest WiFi information of the transmitted frames of any surrounding Access points (APs). More than 240,000 APs information has been harvested in this work. The harvested data have been analyzed to find statistics of WiFi devices in this city. Moreover, three power… More
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• Fusion Based Tongue Color Image Analysis Model for Biomedical Applications
• Esam A. AlQaralleh¹, Halah Nassif², Bassam A. Y. Alqaralleh^2,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5477-5490, 2022, DOI:10.32604/cmc.2022.024364
• Abstract Tongue diagnosis is a novel and non-invasive approach commonly employed to carry out the supplementary diagnosis over the globe. Recently, several deep learning (DL) based tongue color image analysis models have existed in the literature for the effective detection of diseases. This paper presents a fusion of handcrafted with deep features based tongue color image analysis (FHDF-TCIA) technique to biomedical applications. The proposed FDHF-TCIA technique aims to investigate the tongue images using fusion model, and thereby determines the existence of disease. Primarily, the FHDF-TCIA technique comprises Gaussian filtering based preprocessing to eradicate the noise. The proposed FHDF-TCIA model encompasses a… More
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• A New Handover Management Model for Two-Tier 5G Mobile Networks
• Mohammad Arifin Rahman Khan^1,*, Mohammed Golam Kaosar², Mohammad Shorfuzzaman³, Kire Jakimoski⁴
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5491-5509, 2022, DOI:10.32604/cmc.2022.024212
• （This article belongs to this Special Issue: Intelligent Computing Techniques for Communication Systems)
• Abstract There has been an exponential rise in mobile data traffic in recent times due to the increasing popularity of portable devices like tablets, smartphones, and laptops. The rapid rise in the use of these portable devices has put extreme stress on the network service providers while forcing telecommunication engineers to look for innovative solutions to meet the increased demand. One solution to the problem is the emergence of fifth-generation (5G) wireless communication, which can address the challenges by offering very broad wireless area capacity and potential cut-power consumption. The application of small cells is the fundamental mechanism for the 5G… More
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• Automatic Speaker Recognition Using Mel-Frequency Cepstral Coefficients Through Machine Learning
• Uğur Ayvaz¹, Hüseyin Gürüler², Faheem Khan³, Naveed Ahmed⁴, Taegkeun Whangbo^3,*, Abdusalomov Akmalbek Bobomirzaevich³
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5511-5521, 2022, DOI:10.32604/cmc.2022.023278
• （This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)
• Abstract Automatic speaker recognition (ASR) systems are the field of Human-machine interaction and scientists have been using feature extraction and feature matching methods to analyze and synthesize these signals. One of the most commonly used methods for feature extraction is Mel Frequency Cepstral Coefficients (MFCCs). Recent researches show that MFCCs are successful in processing the voice signal with high accuracies. MFCCs represents a sequence of voice signal-specific features. This experimental analysis is proposed to distinguish Turkish speakers by extracting the MFCCs from the speech recordings. Since the human perception of sound is not linear, after the filterbank step in the MFCC… More
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• COCP: Coupling Parameters Content Placement Strategy for In-Network Caching-Based Content-Centric Networking
• Salman Rashid¹, Shukor Abd Razak¹, Fuad A. Ghaleb^1,*, Faisal Saeed², Eman H. Alkhammash³
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5523-5543, 2022, DOI:10.32604/cmc.2022.020587
• Abstract On-path caching is the prominent module in Content-Centric Networking (CCN), equipped with the capability to handle the demands of future networks such as the Internet of Things (IoT) and vehicular networks. The main focus of the CCN caching module is data dissemination within the network. Most of the existing strategies of in-network caching in CCN store the content at the maximum number of routers along the downloading path. Consequently, content redundancy in the network increases significantly, whereas the cache hit ratio and network performance decrease due to the unnecessary utilization of limited cache storage. Moreover, content redundancy adversely affects the… More
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• Annealing Harmony Search Algorithm to Solve the Nurse Rostering Problem
• Mohammed Hadwan^1,2,3,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5545-5559, 2022, DOI:10.32604/cmc.2022.024512
• Abstract A real-life problem is the rostering of nurses at hospitals. It is a famous nondeterministic, polynomial time (NP) -hard combinatorial optimization problem. Handling the real-world nurse rostering problem (NRP) constraints in distributing workload equally between available nurses is still a difficult task to achieve. The international shortage of nurses, in addition to the spread of COVID-19, has made it more difficult to provide convenient rosters for nurses. Based on the literature, heuristic-based methods are the most commonly used methods to solve the NRP due to its computational complexity, especially for large rosters. Heuristic-based algorithms in general have problems striking the… More
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• LCF: A Deep Learning-Based Lightweight CSI Feedback Scheme for MIMO Networks
• Kyu-haeng Lee^*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5561-5580, 2022, DOI:10.32604/cmc.2022.024562
• Abstract Recently, as deep learning technologies have received much attention for their great potential in extracting the principal components of data, there have been many efforts to apply them to the Channel State Information (CSI) feedback overhead problem, which can significantly limit Multi-Input Multi-Output (MIMO) beamforming gains. Unfortunately, since most compression models can quickly become outdated due to channel variation, timely model updates are essential for reflecting the current channel conditions, resulting in frequent additional transmissions for model sharing between transceivers. In particular, the heavy network models employed by most previous studies to achieve high compression gains exacerbate the impact of… More
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• Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic
• Durr-e-Nayab¹, Ali Mustafa Qamar^2,*, Rehan Ullah Khan³, Waleed Albattah³, Khalil Khan⁴, Shabana Habib³, Muhammad Islam⁵
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5581-5601, 2022, DOI:10.32604/cmc.2022.022153
• （This article belongs to this Special Issue: Application of Artificial Intelligence, Internet of Things, and Learning Approach for Learning Process in COVID-19/Industrial Revolution 4.0)
• Abstract The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks. Video surveillance and crowd management using video analysis techniques have significantly impacted today's research, and numerous applications have been developed in this domain. This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis. Managing the Kaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic. The Umrah videos are analyzed, and a… More
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• Sentiment Analysis in Social Media for Competitive Environment Using Content Analysis
• Shahid Mehmood¹, Imran Ahmad¹, Muhammad Adnan Khan^1,2, Faheem Khan³, T. Whangbo^3,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5603-5618, 2022, DOI:10.32604/cmc.2022.023785
• Abstract Education sector has witnessed several changes in the recent past. These changes have forced private universities into fierce competition with each other to get more students enrolled. This competition has resulted in the adoption of marketing practices by private universities similar to commercial brands. To get competitive gain, universities must observe and examine the students’ feedback on their own social media sites along with the social media sites of their competitors. This study presents a novel framework which integrates numerous analytical approaches including statistical analysis, sentiment analysis, and text mining to accomplish a competitive analysis of social media sites of… More
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• Enhancing Task Assignment in Crowdsensing Systems Based on Sensing Intervals and Location
• Rasha Sleem¹, Nagham Mekky¹, Shaker El-Sappagh^2,3, Louai Alarabi^4,*, Noha A. Hikal¹, Mohammed Elmogy¹
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5619-5638, 2022, DOI:10.32604/cmc.2022.023716
• Abstract The popularity of mobile devices with sensors is captivating the attention of researchers to modern techniques, such as the internet of things (IoT) and mobile crowdsensing (MCS). The core concept behind MCS is to use the power of mobile sensors to accomplish a difficult task collaboratively, with each mobile user completing much simpler micro-tasks. This paper discusses the task assignment problem in mobile crowdsensing, which is dependent on sensing time and path planning with the constraints of participant travel distance budgets and sensing time intervals. The goal is to minimize aggregate sensing time for mobile users, which reduces energy consumption… More
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• Enhancing Parkinson's Disease Prediction Using Machine Learning and Feature Selection Methods
• Faisal Saeed^1,2,*, Mohammad Al-Sarem^1,3, Muhannad Al-Mohaimeed¹, Abdelhamid Emara^1,4, Wadii Boulila^1,5, Mohammed Alasli¹, Fahad Ghabban¹
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5639-5658, 2022, DOI:10.32604/cmc.2022.023124
• （This article belongs to this Special Issue: Computational Models for Pro-Smart Environments in Data Science Assisted IoT Systems)
• Abstract Several millions of people suffer from Parkinson's disease globally. Parkinson's affects about 1% of people over 60 and its symptoms increase with age. The voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners, but which could be analyzed using recorded speech signals. With the huge advancements of technology, the medical data has increased dramatically, and therefore, there is a need to apply data mining and machine learning methods to extract new knowledge from this data. Several classification methods were used to analyze medical data sets and diagnostic problems, such as Parkinson's Disease… More
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• Hybrid Whale Optimization Algorithm for Resource Optimization in Cloud E-Healthcare Applications
• Punit Gupta¹, Sanjit Bhagat², Dinesh Kumar Saini^1,*, Ashish Kumar², Mohammad Alahmadi³, Prakash Chandra Sharma¹
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5659-5676, 2022, DOI:10.32604/cmc.2022.023056
• （This article belongs to this Special Issue: Edge Computing and Machine Learning for Improving Healthcare Services)
• Abstract In the next generation of computing environment e-health care services depend on cloud services. The Cloud computing environment provides a real-time computing environment for e-health care applications. But these services generate a huge number of computational tasks, real-time computing and comes with a deadline, so conventional cloud optimization models cannot fulfil the task in the least time and within the deadline. To overcome this issue many resource optimization meta-heuristic models are been proposed but these models cannot find a global best solution to complete the task in the least time and manage utilization with the least simulation time. In order… More
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• FSpot: Fast and Efficient Video Encoding Workloads Over Amazon Spot Instances
• Anatoliy Zabrovskiy^1,3, Prateek Agrawal^1,2,*, Vladislav Kashansky¹, Roland Kersche⁴, Christian Timmerer^1,4, Radu Prodan¹
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5677-5697, 2022, DOI:10.32604/cmc.2022.023630
• Abstract HTTP Adaptive Streaming (HAS) of video content is becoming an undivided part of the Internet and accounts for most of today's network traffic. Video compression technology plays a vital role in efficiently utilizing network channels, but encoding videos into multiple representations with selected encoding parameters is a significant challenge. However, video encoding is a computationally intensive and time-consuming operation that requires high-performance resources provided by on-premise infrastructures or public clouds. In turn, the public clouds, such as Amazon elastic compute cloud (EC2), provide hundreds of computing instances optimized for different purposes and clients’ budgets. Thus, there is a need for… More
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• Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification
• Mesfer Al Duhayyim¹, Taiseer Abdalla Elfadil Eisa², Fahd N. Al-Wesabi^3,4, Abdelzahir Abdelmaboud⁵, Manar Ahmed Hamza^6,*, Abu Sarwar Zamani⁶, Mohammed Rizwanullah⁶, Radwa Marzouk^7,8
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5699-5715, 2022, DOI:10.32604/cmc.2022.024431
• Abstract The Smart City concept revolves around gathering real time data from citizen, personal vehicle, public transports, building, and other urban infrastructures like power grid and waste disposal system. The understandings obtained from the data can assist municipal authorities handle assets and services effectually. At the same time, the massive increase in environmental pollution and degradation leads to ecological imbalance is a hot research topic. Besides, the progressive development of smart cities over the globe requires the design of intelligent waste management systems to properly categorize the waste depending upon the nature of biodegradability. Few of the commonly available wastes are… More
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• Deep Learning-based Wireless Signal Classification in the IoT Environment
• Hyeji Roh, Sheungmin Oh, Hajun Song, Jinseo Han, Sangsoon Lim^*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5717-5732, 2022, DOI:10.32604/cmc.2022.024135
• Abstract With the development of the Internet of Things (IoT), diverse wireless devices are increasing rapidly. Those devices have different wireless interfaces that generate incompatible wireless signals. Each signal has its own physical characteristics with signal modulation and demodulation scheme. When there exist different wireless devices, they can suffer from severe Cross-Technology Interferences (CTI). To reduce the communication overhead due to the CTI in the real IoT environment, a central coordinator can be able to detect and identify wireless signals existing in the same communication areas. This paper investigates how to classify various radio signals using Convolutional Neural Networks (CNN), Long… More
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• Fake News Classification Using a Fuzzy Convolutional Recurrent Neural Network
• Dheeraj Kumar Dixit^*, Amit Bhagat, Dharmendra Dangi
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5733-5750, 2022, DOI:10.32604/cmc.2022.023628
• （This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
• Abstract In recent years, social media platforms have gained immense popularity. As a result, there has been a tremendous increase in content on social media platforms. This content can be related to an individual's sentiments, thoughts, stories, advertisements, and news, among many other content types. With the recent increase in online content, the importance of identifying fake and real news has increased. Although, there is a lot of work present to detect fake news, a study on Fuzzy CRNN was not explored into this direction. In this work, a system is designed to classify fake and real news using fuzzy logic.… More
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• Optimized Deep Learning Model for Colorectal Cancer Detection and Classification Model
• Mahmoud Ragab^1,2,3,*, Khalid Eljaaly⁴, Maha Farouk S. Sabir⁵, Ehab Bahaudien Ashary⁶, S. M. Abo-Dahab^7,8, E. M. Khalil^3,9
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5751-5764, 2022, DOI:10.32604/cmc.2022.024658
• Abstract The recent developments in biological and information technologies have resulted in the generation of massive quantities of data it speeds up the process of knowledge discovery from biological systems. Due to the advancements of medical imaging in healthcare decision making, significant attention has been paid by the computer vision and deep learning (DL) models. At the same time, the detection and classification of colorectal cancer (CC) become essential to reduce the severity of the disease at an earlier stage. The existing methods are commonly based on the combination of textual features to examine the classifier results or machine learning (ML)… More
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• Elite Opposition Based Metaheuristic Framework for Load Balancing in LTE Network
• M. R. Sivagar^1,*, N. Prabakaran²
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5765-5781, 2022, DOI:10.32604/cmc.2022.024273
• Abstract In present scenario of wireless communications, Long Term Evolution (LTE) based network technology is evolved and provides consistent data delivery with high speed and minimal delay through mobile devices. The traffic management and effective utilization of network resources are the key factors of LTE models. Moreover, there are some major issues in LTE that are to be considered are effective load scheduling and traffic management. Through LTE is a depraved technology, it is been suffering from these issues. On addressing that, this paper develops an Elite Opposition based Spider Monkey Optimization Framework for Efficient Load Balancing (SMO-ELB). In this model,… More
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• Detection of Osteoarthritis Based on EHO Thresholding
• R. Kanthavel^1,*, R. Dhaya², Kanagaraj Venusamy³
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5783-5798, 2022, DOI:10.32604/cmc.2022.023745
• Abstract Knee Osteoarthritis (OA) is a joint disease that is commonly observed in people around the world. Osteoarthritis commonly affects patients who are obese and those above the age of 60. A valid knee image was generated by Computed Tomography (CT). In this work, efficient segmentation of CT images using Elephant Herding Optimization (EHO) optimization is implemented. The initial stage employs, the CT image normalization and the normalized image is incited to image enhancement through histogram correlation. Consequently, the enhanced image is segmented by utilizing Niblack and Bernsen algorithm. The (EHO) optimized outcome is evaluated in two steps. The initial step… More
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• Automated Multi-Document Biomedical Text Summarization Using Deep Learning Model
• Ahmed S. Almasoud¹, Siwar Ben Haj Hassine², Fahd N. Al-Wesabi^2,3, Mohamed K. Nour⁴, Anwer Mustafa Hilal⁵, Mesfer Al Duhayyim⁶, Manar Ahmed Hamza^5,*, Abdelwahed Motwakel⁵
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5799-5815, 2022, DOI:10.32604/cmc.2022.024556
• Abstract Due to the advanced developments of the Internet and information technologies, a massive quantity of electronic data in the biomedical sector has been exponentially increased. To handle the huge amount of biomedical data, automated multi-document biomedical text summarization becomes an effective and robust approach of accessing the increased amount of technical and medical literature in the biomedical sector through the summarization of multiple source documents by retaining the significantly informative data. So, multi-document biomedical text summarization acts as a vital role to alleviate the issue of accessing precise and updated information. This paper presents a Deep Learning based Attention Long… More
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• Double-E-Triple-H-Shaped NRI-Metamaterial for Dual-Band Microwave Sensing Applications
• Shafayat Hossain¹, Md. Iquebal Hossain Patwary¹, Sikder Sunbeam Islam¹, Sultan Mahmud^1,2, Norbahiah Binti Misran², Ali F. Almutairi³, Mohammad Tariqul Islam^2,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5817-5836, 2022, DOI:10.32604/cmc.2022.022042
• Abstract This paper presents a new Double-E-Triple-H-Shaped NRI (negative refractive index) metamaterial (MM) for dual-band microwave sensing applications. Here, a horizontal H-shaped metal structure is enclosed by two face-to-face E-shaped metal structures. This double-E-H-shaped design is also encased by two vertical H-shaped structures along with some copper links. Thus, the Double-E-Triple-H-Shaped configuration is developed. Two popular substrate materials of Rogers RO 3010 and FR-4 were adopted for analyzing the characteristics of the unit cell. The proposed structure exhibits transmission resonance inside the S-band with NRI and ENG (Epsilon Negative) metamaterial properties, and inside the C-band with ENG and MNG (Mu Negative)… More
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• Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria
• El-Sayed M. El-Kenawy^1,2, Abdelhameed Ibrahim³, Nadjem Bailek^4,*, Kada Bouchouicha⁵, Muhammed A. Hassan⁶, Basharat Jamil⁷, Nadhir Al-Ansari⁸
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5837-5854, 2022, DOI:10.32604/cmc.2022.023257
• （This article belongs to this Special Issue: Artificial Intelligence and Machine Learning Algorithms in Real-World Applications and Theories)
• Abstract In order to achieve a highly accurate estimation of solar energy resource potential, a novel hybrid ensemble-learning approach, hybridizing Advanced Squirrel-Search Optimization Algorithm (ASSOA) and support vector regression, is utilized to estimate the hourly tilted solar irradiation for selected arid regions in Algeria. Long-term measured meteorological data, including mean-air temperature, relative humidity, wind speed, alongside global horizontal irradiation and extra-terrestrial horizontal irradiance, were obtained for the two cities of Tamanrasset-and-Adrar for two years. Five computational algorithms were considered and analyzed for the suitability of estimation. Further two new algorithms, namely Average Ensemble and Ensemble using support vector regression were developed… More
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• Quantum Particle Swarm Optimization Based Convolutional Neural Network for Handwritten Script Recognition
• Reya Sharma¹, Baijnath Kaushik¹, Naveen Kumar Gondhi¹, Muhammad Tahir^2,*, Mohammad Khalid Imam Rahmani²
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5855-5873, 2022, DOI:10.32604/cmc.2022.024232
• Abstract Even though several advances have been made in recent years, handwritten script recognition is still a challenging task in the pattern recognition domain. This field has gained much interest lately due to its diverse application potentials. Nowadays, different methods are available for automatic script recognition. Among most of the reported script recognition techniques, deep neural networks have achieved impressive results and outperformed the classical machine learning algorithms. However, the process of designing such networks right from scratch intuitively appears to incur a significant amount of trial and error, which renders them unfeasible. This approach often requires manual intervention with domain… More
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• Optimization Model in Manufacturing Scheduling for the Garment Industry
• Chia-Nan Wang¹, Yu-Chen Wei², Po-Yuk So^3,*, Viet Tinh Nguyen⁴, Phan Nguyen Ky Phuc⁵
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5875-5889, 2022, DOI:10.32604/cmc.2022.023880
• （This article belongs to this Special Issue: Big Data for Supply Chain Management in the Service and Manufacturing Sectors)
• Abstract The garment industry in Vietnam is one of the country's strongest industries in the world. However, the production process still encounters problems regarding scheduling that does not equate to an optimal process. The paper introduces a production scheduling solution that resolves the potential delays and lateness that hinders the production process using integer programming and order allocation with a make-to-order manufacturing viewpoint. A number of constraints were considered in the model and is applied to a real case study of a factory in order to view how the tardiness and lateness would be affected which resulted in optimizing the scheduling… More
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• Robust Authentication and Session Key Agreement Protocol for Satellite Communications
• Somayeh Soltani¹, Seyed Amin Hosseini Seno¹, Juli Rejito², Rahmat Budiarto^3,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5891-5910, 2022, DOI:10.32604/cmc.2022.023697
• （This article belongs to this Special Issue: Security and Privacy issues for various Emerging Technologies and Future Trends)
• Abstract Satellite networks are recognized as the most essential communication infrastructures in the world today, which complement land networks and provide valuable services for their users. Extensive coverage and service stability of these networks have increased their popularity. Since eavesdropping and active intrusion in satellite communications are much easier than in terrestrial networks, securing satellite communications is vital. So far, several protocols have been proposed for authentication and key exchange of satellite communications, but none of them fully meet the security requirements. In this paper, we examine one of these protocols and identify its security vulnerabilities. Moreover, we propose a robust… More
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• Emotion Based Signal Enhancement Through Multisensory Integration Using Machine Learning
• Muhammad Adnan Khan^1,2, Sagheer Abbas³, Ali Raza³, Faheem Khan⁴, T. Whangbo^4,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5911-5931, 2022, DOI:10.32604/cmc.2022.023557
• Abstract Progress in understanding multisensory integration in human have suggested researchers that the integration may result into the enhancement or depression of incoming signals. It is evident based on different psychological and behavioral experiments that stimuli coming from different perceptual modalities at the same time or from the same place, the signal having more strength under the influence of emotions effects the response accordingly. Current research in multisensory integration has not studied the effect of emotions despite its significance and natural influence in multisensory enhancement or depression. Therefore, there is a need to integrate the emotional state of the agent with… More
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• PNN-SVM Approach of Ti-Based Powder’s Properties Evaluation for Biomedical Implants Production
• Ivan Izonin^1,*, Roman Tkachenko¹, Michal Gregus², Zoia Duriagina^1,3, Nataliya Shakhovska¹
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5933-5947, 2022, DOI:10.32604/cmc.2022.022582
• Abstract The advent of additive technologies has provided a significant breakthrough in the production of medical implants. It has reduced costs, increased productivity and accuracy of the implant manufacturing process. However, there are problems associated with assessing defects in the microstructure, mechanical and technological properties of alloys, both during their production by powder metallurgy and in the process of 3D printing. Thus traditional research methods of alloys properties demand considerable human, material, and time resources. At the same time, artificial intelligence tools create opportunities for intelligent evaluation of the conformity for the microstructure, phase composition, and properties of titanium powder’s alloys.… More
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• DNA Sequence Analysis for Brain Disorder Using Deep Learning and Secure Storage
• Ala Saleh Alluhaidan^*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5949-5962, 2022, DOI:10.32604/cmc.2022.022028
• Abstract Analysis of brain disorder in the neuroimaging of Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT) needs to understand the functionalities of the brain and it has been performed using traditional methods. Deep learning algorithms have also been applied in genomics data processing. The brain disorder diseases of Alzheimer, Schizophrenia, and Parkinson are analyzed in this work. The main issue in the traditional algorithm is the improper detection of disorders in the neuroimaging data. This paper presents a deep learning algorithm for the classification of brain disorder using Deep Belief Network (DBN) and securely storing the… More
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• An Efficient HW/SW Design for Text Extraction from Complex Color Image
• Mohamed Amin Ben Atitallah^1,2,3,*, Rostom Kachouri², Ahmed Ben Atitallah⁴, Hassene Mnif¹
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5963-5977, 2022, DOI:10.32604/cmc.2022.024345
• Abstract In the context of constructing an embedded system to help visually impaired people to interpret text, in this paper, an efficient High-level synthesis (HLS) Hardware/Software (HW/SW) design for text extraction using the Gamma Correction Method (GCM) is proposed. Indeed, the GCM is a common method used to extract text from a complex color image and video. The purpose of this work is to study the complexity of the GCM method on Xilinx ZCU102 FPGA board and to propose a HW implementation as Intellectual Property (IP) block of the critical blocks in this method using HLS flow with taking account the… More
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• Design of Intelligent Alzheimer Disease Diagnosis Model on CIoT Environment
• Anwer Mustafa Hilal¹, Fahd N. Al-Wesabi^2,3, Mohamed Tahar Ben Othman⁴, Khaled Mohamad Almustafa⁵, Nadhem Nemri⁶, Mesfer Al Duhayyim⁷, Manar Ahmed Hamza^1,*, Abu Sarwar Zamani¹
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5979-5994, 2022, DOI:10.32604/cmc.2022.022686
• Abstract Presently, cognitive Internet of Things (CIoT) with cloud computing (CC) enabled intelligent healthcare models are developed, which enables communication with intelligent devices, sensor modules, and other stakeholders in the healthcare sector to avail effective decision making. On the other hand, Alzheimer disease (AD) is an advanced and degenerative illness which injures the brain cells, and its earlier detection is necessary for suitable interference by healthcare professional. In this aspect, this paper presents a new Oriented Features from Accelerated Segment Test (FAST) with Rotated Binary Robust Independent Elementary Features (BRIEF) Detector (ORB) with optimal artificial neural network (ORB-OANN) model for AD diagnosis… More
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• AMDnet: An Academic Misconduct Detection Method for Authors’ Behaviors
• Shihao Zhou¹, Ziyuan Xu^3,4, Jin Han^1,*, Xingming Sun^1,2, Yi Cao⁵
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5995-6009, 2022, DOI:10.32604/cmc.2022.023316
• Abstract In recent years, academic misconduct has been frequently exposed by the media, with serious impacts on the academic community. Current research on academic misconduct focuses mainly on detecting plagiarism in article content through the application of character-based and non-text element detection techniques over the entirety of a manuscript. For the most part, these techniques can only detect cases of textual plagiarism, which means that potential culprits can easily avoid discovery through clever editing and alterations of text content. In this paper, we propose an academic misconduct detection method based on scholars’ submission behaviors. The model can effectively capture the atypical… More
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• Sustainable-Security Assessment Through a Multi Perspective Benchmarking Framework
• Ahmed Saeed Alfakeeh¹, Abdulmohsen Almalawi², Fawaz Jaber Alsolami², Yoosef B. Abushark², Asif Irshad Khan^2,*, Adel Aboud S. Bahaddad¹, Md Mottahir Alam³, Alka Agrawal⁴, Rajeev Kumar⁵, Raees Ahmad Khan⁴
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6011-6037, 2022, DOI:10.32604/cmc.2022.024903
• （This article belongs to this Special Issue: Advances in Artificial Intelligence and Machine learning in Biomedical and Healthcare Informatics)
• Abstract The current cyber-attack environment has put even the most protected systems at risk as the hackers are now modifying technologies to exploit even the tiniest of weaknesses and infiltrate networks. In this situation, it's critical to design and construct software that is both secure and long-lasting. While security is the most well-defined aspect of health information software systems, it is equally significant to prioritise sustainability because any health information software system will be more effective if it provides both security and sustainability to the customers at the same time. In this league, it is crucial to determine those characteristics in… More
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• Reinforced CNN Forensic Discriminator to Detect Document Forgery by DCGAN
• Seo-young Lim, Jeongho Cho^*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6039-6051, 2022, DOI:10.32604/cmc.2022.024862
• Abstract Recently, the technology of digital image forgery based on a generative adversarial network (GAN) has considerably improved to the extent that it is difficult to distinguish it from the original image with the naked eye by compositing and editing a person's face or a specific part with the original image. Thus, much attention has been paid to digital image forgery as a social issue. Further, document forgery through GANs can completely change the meaning and context in a document, and it is difficult to identify whether the document is forged or not, which is dangerous. Nonetheless, few studies have been… More
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• An Optimized Convolution Neural Network Architecture for Paddy Disease Classification
• Muhammad Asif Saleem¹, Muhammad Aamir^{1,2, * ,*}, Rosziati Ibrahim¹, Norhalina Senan¹, Tahir Alyas³
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6053-6067, 2022, DOI:10.32604/cmc.2022.022215
• （This article belongs to this Special Issue: Application of Machine-Learning in Computer Vision)
• Abstract Plant disease classification based on digital pictures is challenging. Machine learning approaches and plant image categorization technologies such as deep learning have been utilized to recognize, identify, and diagnose plant diseases in the previous decade. Increasing the yield quantity and quality of rice forming is an important cause for the paddy production countries. However, some diseases that are blocking the improvement in paddy production are considered as an ominous threat. Convolution Neural Network (CNN) has shown a remarkable performance in solving the early detection of paddy leaf diseases based on its images in the fast-growing era of science and technology.… More
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• Low-Cost Flexible Graphite Monopole Patch Antenna for Wireless Communication Applications
• Suwat Sakulchat¹, Amnoiy Ruengwaree^1,*, Voranuch Thongpool², Watcharaphon Naktong³
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6069-6088, 2022, DOI:10.32604/cmc.2022.024050
• （This article belongs to this Special Issue: Advances in 5G Antenna Designs and Systems)
• Abstract This research investigates a monopole patch antenna for Wi-Fi applications at 2.45 and 5.2 GHz, and WiMax at 3.5 GHz. A low-cost and flexible graphite sheet with good conductivity, base on graphite conductive powder and glue is used to create a radiator patch and ground plane. Instead of commercially available conductive inks or graphite sheets, we use our self-produced graphite liquid to create the graphite sheet because it is easy to produce and inexpensive. The antenna structure is formed using a low-cost and easy hand-screen printing approach that involved placing graphite liquid on a bendable polyester substrate. This research focuses… More
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• A Hybrid Meta-Classifier of Fuzzy Clustering and Logistic Regression for Diabetes Prediction
• Altyeb Altaher Taha^*, Sharaf Jameel Malebary
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6089-6105, 2022, DOI:10.32604/cmc.2022.023848
• Abstract Diabetes is a chronic health condition that impairs the body's ability to convert food to energy, recognized by persistently high levels of blood glucose. Undiagnosed diabetes can cause many complications, including retinopathy, nephropathy, neuropathy, and other vascular disorders. Machine learning methods can be very useful for disease identification, prediction, and treatment. This paper proposes a new ensemble learning approach for type 2 diabetes prediction based on a hybrid meta-classifier of fuzzy clustering and logistic regression. The proposed approach consists of two levels. First, a base-learner comprising six machine learning algorithms is utilized for predicting diabetes. Second, a hybrid meta-learner that… More
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• Estimator-Based GPS Attitude and Angular Velocity Determination
• Dah-Jing Jwo^*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6107-6124, 2022, DOI:10.32604/cmc.2022.024722
• Abstract In this paper, the estimator-based Global Positioning System (GPS) attitude and angular velocity determination is presented. Outputs of the attitude estimator include the attitude angles and attitude rates or body angular velocities, depending on the design of estimator. Traditionally as a position, velocity and time sensor, the GPS also offers a free attitude-determination interferometer. GPS research and applications to the field of attitude determination using carrier phase or Doppler measurement has been extensively conducted. The raw attitude solution using the interferometry technique based on the least-squares approach is inherently noisy. The estimator such as the Kalman filter (KF) or extended… More
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• A Provably Secure and Efficient Remote Password Authentication Scheme Using Smart Cards
• Fairuz Shohaimay^1,2, Eddie Shahril Ismail^1,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6125-6145, 2022, DOI:10.32604/cmc.2022.022759
• Abstract Communication technology has advanced dramatically amid the 21^st century, increasing the security risk in safeguarding sensitive information. The remote password authentication (RPA) scheme is the simplest cryptosystem that serves as the first line of defence against unauthorised entity attacks. Although the literature contains numerous RPA schemes, to the best of the authors’ knowledge, only few schemes based on the integer factorisation problem (IFP) and the discrete logarithm problem (DLP) that provided a provision for session key agreement to ensure proper mutual authentication. Furthermore, none of the previous schemes provided formal security proof using the random oracle model. Therefore, this study… More
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• Optimal Resource Allocation in Fog Computing for Healthcare Applications
• Salman Khan^1,*, Ibrar Ali Shah¹, Nasser Tairan², Habib Shah², Muhammad Faisal Nadeem³
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6147-6163, 2022, DOI:10.32604/cmc.2022.023234
• （This article belongs to this Special Issue: Advancements in Lightweight AI for Constrained Internet of Things Devices for Smart Cities)
• Abstract In recent years, the significant growth in the Internet of Things (IoT) technology has brought a lot of attention to information and communication industry. Various IoT paradigms like the Internet of Vehicle Things (IoVT) and the Internet of Health Things (IoHT) create massive volumes of data every day which consume a lot of bandwidth and storage. However, to process such large volumes of data, the existing cloud computing platforms offer limited resources due to their distance from IoT devices. Consequently, cloud-computing systems produce intolerable latency problems for latency-sensitive real-time applications. Therefore, a new paradigm called fog computing makes use of… More
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• Accurate Location Estimation of Smart Dusts Using Machine Learning
• Shariq Bashir^1,*, Owais Ahmed Malik², Daphne Teck Ching Lai²
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6165-6181, 2022, DOI:10.32604/cmc.2022.024269
• （This article belongs to this Special Issue: Emergent Computer-Based Methods and Internet of Things Technologies for Physical Therapy, Dentistry, Medicine, and Engineering)
• Abstract Traditional wireless sensor networks (WSNs) are not suitable for rough terrains that are difficult or impossible to access by humans. Smart dust is a technology that works with the combination of many tiny sensors which is highly useful for obtaining remote sensing information from rough terrains. The tiny sensors are sprinkled in large numbers on rough terrains using airborne distribution through drones or aircraft without manually setting their locations. Although it is clear that a number of remote sensing applications can benefit from this technology, but the small size of smart dust fundamentally restricts the integration of advanced hardware on… More
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• Hybrid Energy Storage to Control and Optimize Electric Propulsion Systems
• Sikander Hans¹, Smarajit Ghosh¹, Suman Bhullar¹, Aman Kataria², Vinod Karar^2,*, Divya Agrawal²
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6183-6200, 2022, DOI:10.32604/cmc.2022.020768
• Abstract Today, ship development has concentrated on electrifying ships in commercial and military applications to improve efficiency, support high-power missile systems and reduce emissions. However, the electric propulsion of the shipboard system experiences torque fluctuation, thrust, and power due to the rotation of the propeller shaft and the motion of waves. In order to meet these challenges, a new solution is needed. This paper explores hybrid energy management systems using the battery and ultracapacitor to control and optimize the electric propulsion system. The battery type and ultracapacitor are ZEBRA and MAXWELL, respectively. The 3-, 4-and 5-blade propellers are considered to produce… More
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• Content Feature Extraction-based Hybrid Recommendation for Mobile Application Services
• Chao Ma^1,*, YinggangSun¹, Zhenguo Yang¹, Hai Huang¹, Dongyang Zhan^2,3, Jiaxing Qu⁴
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6201-6217, 2022, DOI:10.32604/cmc.2022.022717
• Abstract The number of mobile application services is showing an explosive growth trend, which makes it difficult for users to determine which ones are of interest. Especially, the new mobile application services are emerge continuously, most of them have not be rated when they need to be recommended to users. This is the typical problem of cold start in the field of collaborative filtering recommendation. This problem may makes it difficult for users to locate and acquire the services that they actually want, and the accuracy and novelty of service recommendations are also difficult to satisfy users. To solve this problem,… More
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• IoMT Enabled Melanoma Detection Using Improved Region Growing Lesion Boundary Extraction
• Tanzila Saba¹, Rabia Javed^2,3, Mohd Shafry Mohd Rahim², Amjad Rehman^1,*, Saeed Ali Bahaj⁴
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6219-6237, 2022, DOI:10.32604/cmc.2022.020865
• （This article belongs to this Special Issue: Pervasive Computing and Communication: Challenges, Technologies & Opportunities)
• Abstract The Internet of Medical Things (IoMT) and cloud-based healthcare applications, services are beneficial for better decision-making in recent years. Melanoma is a deadly cancer with a higher mortality rate than other skin cancer types such as basal cell, squamous cell, and Merkel cell. However, detection and treatment at an early stage can result in a higher chance of survival. The classical methods of detection are expensive and labor-intensive. Also, they rely on a trained practitioner's level, and the availability of the needed equipment is essential for the early detection of Melanoma. The current improvement in computer-aided systems is providing very… More
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• Exploration of IoT Nodes Communication Using LoRaWAN in Forest Environment
• Anshul Sharma¹, Divneet Singh Kapoor¹, Anand Nayyar^2,3,*, Basit Qureshi⁴, Kiran Jot Singh¹, Khushal Thakur¹
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6239-6256, 2022, DOI:10.32604/cmc.2022.024639
• Abstract The simultaneous advances in the Internet of Things (IoT), Artificial intelligence (AI) and Robotics is going to revolutionize our world in the near future. In recent years, LoRa (Long Range) wireless powered by LoRaWAN (LoRa Wide Area Network) protocol has attracted the attention of researchers for numerous applications in the IoT domain. LoRa is a low power, unlicensed Industrial, Scientific, and Medical (ISM) band-equipped wireless technology that utilizes a wide area network protocol, i.e., LoRaWAN, to incorporate itself into the network infrastructure. In this paper, we have evaluated the LoRaWAN communication protocol for the implementation of the IoT (Internet of Things)… More
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• Optimal Hybrid Feature Extraction with Deep Learning for COVID-19 Classifications
• Majdy M. Eltahir¹, Ibrahim Abunadi², Fahd N. Al-Wesabi^3,4, Anwer Mustafa Hilal⁵, Adil Yousif⁶, Abdelwahed Motwakel⁵, Mesfer Al Duhayyim⁷, Manar Ahmed Hamza^5,*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6257-6273, 2022, DOI:10.32604/cmc.2022.024312
• Abstract Novel coronavirus 2019 (COVID-19) has affected the people's health, their lifestyle and economical status across the globe. The application of advanced Artificial Intelligence (AI) methods in combination with radiological imaging is useful in accurate detection of the disease. It also assists the physicians to take care of remote villages too. The current research paper proposes a novel automated COVID-19 analysis method with the help of Optimal Hybrid Feature Extraction (OHFE) and Optimal Deep Neural Network (ODNN) called OHFE-ODNN from chest x-ray images. The objective of the presented technique is for performing binary and multi-class classification of COVID-19 analysis from chest… More
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• Human Faces Detection and Tracking for Crowd Management in Hajj and Umrah
• Riad Alharbey¹, Ameen Banjar¹, Yahia Said^2,3,*, Mohamed Atri⁴, Abdulrahman Alshdadi¹, Mohamed Abid⁵
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6275-6291, 2022, DOI:10.32604/cmc.2022.024272
• Abstract Hajj and Umrah are two main religious duties for Muslims. To help faithfuls to perform their religious duties comfortably in overcrowded areas, a crowd management system is a must to control the entering and exiting for each place. Since the number of people is very high, an intelligent crowd management system can be developed to reduce human effort and accelerate the management process. In this work, we propose a crowd management process based on detecting, tracking, and counting human faces using Artificial Intelligence techniques. Human detection and counting will be performed to calculate the number of existing visitors and face… More
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• A Secure Three-Party Authenticated Key Exchange Protocol for Social Networks
• Vivek Kumar Sinha¹, Divya Anand^1,*, Fahd S. Alharithi², Ahmed H. Almulihi²
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6293-6305, 2022, DOI:10.32604/cmc.2022.024877
• Abstract The 3PAKE (Three-Party Authenticated Key Exchange) protocol is a valuable cryptographic method that offers safe communication and permits two diverse parties to consent to a new safe meeting code using the trusted server. There have been explored numerous 3PAKE protocols earlier to create a protected meeting code between users employing the trusted server. However, existing modified 3PAKE protocols have numerous drawbacks and are incapable to provide desired secrecy against diverse attacks such as man-in-the-middle, brute-force attacks, and many others in social networks. In this article, the authors proposed an improved as well as safe 3PAKE protocol based on the hash… More
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• Coronavirus Detection Using Two Step-AS Clustering and Ensemble Neural Network Model
• Ahmed Hamza Osman^*
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6307-6331, 2022, DOI:10.32604/cmc.2022.024145
• Abstract This study presents a model of computer-aided intelligence capable of automatically detecting positive COVID-19 instances for use in regular medical applications. The proposed model is based on an Ensemble boosting Neural Network architecture and can automatically detect discriminatory features on chest X-ray images through Two Step-As clustering algorithm with rich filter families, abstraction and weight-sharing properties. In contrast to the generally used transformational learning approach, the proposed model was trained before and after clustering. The compilation procedure divides the datasets samples and categories into numerous sub-samples and subcategories and then assigns new group labels to each new group, with each… More
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• Rainfall Forecasting Using Machine Learning Algorithms for Localized Events
• Ganapathy Pattukandan Ganapathy¹, Kathiravan Srinivasan², Debajit Datta², Chuan-Yu Chang^3,4,*, Om Purohit⁵, Vladislav Zaalishvili⁶, Olga Burdzieva⁶
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6333-6350, 2022, DOI:10.32604/cmc.2022.023254
• Abstract A substantial amount of the Indian economy depends solely on agriculture. Rainfall, on the other hand, plays a significant role in agriculture–while an adequate amount of rainfall can be considered as a blessing, if the amount is inordinate or scant, it can ruin the entire hard work of the farmers. In this work, the rainfall dataset of the Vellore region, of Tamil Nadu, India, in the years 2021 and 2022 is forecasted using several machine learning algorithms. Feature engineering has been performed in this work in order to generate new features that remove all sorts of autocorrelation present in the… More
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• Echo Location Based Bat Algorithm for Energy Efficient WSN Routing
• Anwer Mustafa Hilal^1,*, Siwar Ben Haj Hassine², Jaber S. Alzahrani³, Masoud Alajmi⁴, Fahd N. Al-Wesabi^2,5, Mesfer Al Duhayyim⁶, Ishfaq Yaseen¹, Abdelwahed Motwakel¹
• CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6351-6364, 2022, DOI:10.32604/cmc.2022.024489
• Abstract Due to the wide range of applications, Wireless Sensor Networks (WSN) are increased in day to day life and becomes popular. WSN has marked its importance in both practical and research domains. Energy is the most significant resource, the important challenge in WSN is to extend its lifetime. The energy reduction is a key to extend the network's lifetime. Clustering of sensor nodes is one of the well-known and proved methods for achieving scalable and energy conserving WSN. In this paper, an energy efficient protocol is proposed using metaheuristic Echo location-based BAT algorithm (ECHO-BAT). ECHO-BAT works in two stages. First… More
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