Home / Journals / CMC / Vol.71, No.3, 2022
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  • Open AccessOpen Access

    ARTICLE

    Deep Learning Based Intrusion Detection in Cloud Services for Resilience Management

    S. Sreenivasa Chakravarthi1,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    Fruit Image Classification Using Deep Learning

    Harmandeep Singh Gill1,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    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 >

  • Open AccessOpen Access

    ARTICLE

    Data Hiding in AMBTC Images Using Selective XOR Hiding Scheme

    Yung-Yao Chen1,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    Attention-Based Deep Learning Model for Early Detection of Parkinson's Disease

    Mohd Sadiq1, Mohd Tauheed Khan2,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    New Hybrid IoT LoRaWAN/IRC Sensors: SMART Water Metering System

    Vlastimil Slany1, Petr Koudelka1,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    Location Prediction for Improved Human Safety at Complex Environments

    S. G. Siddharth1,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    Automated Facial Expression Recognition and Age Estimation Using Deep Learning

    Syeda Amna Rizwan1, Yazeed Yasin Ghadi2, Ahmad Jalal1, Kibum Kim3,*
    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 >

  • Open AccessOpen Access

    ARTICLE

    Digital Watermarking Scheme for Securing Textual Database Using Histogram Shifting Model

    Khalid A. El Drandaly1, Walid Khedr1, Islam S. Mohamed1, Ayman Mohamed Mostafa2,*
    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 >

  • Open AccessOpen Access

    ARTICLE

    Lightweight Direct Acyclic Graph Blockchain for Enhancing Resource-Constrained IoT Environment

    Salaheddine Kably1,2,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    Examination of Pine Wilt Epidemic Model through Efficient Algorithm

    Ali Raza1,*, Emad E. Mahmoud2, A. M. Al-Bugami2, Dumitru Baleanu3,4, 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 >

  • Open AccessOpen Access

    ARTICLE

    Metaheuristic Based Data Gathering Scheme for Clustered UAVs in 6G Communication Network

    Ahmed S. Almasoud1, Siwar Ben Haj Hassine2, Nadhem NEMRI2, Fahd N. Al-Wesabi2,3, Manar Ahmed Hamza4,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    Contrast Correction Using Hybrid Statistical Enhancement on Weld Defect Images

    Wan Azani Mustafa1,2,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    Decision Support System for Diagnosis of Irregular Fovea

    Ghulam Ali Mallah1, Jamil Ahmed1, Muhammad Irshad Nazeer2,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    Machine Learning-based Optimal Framework for Internet of Things Networks

    Moath Alsafasfeh1,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    Machine Learning-based Stable P2P IPTV Overlay

    Muhammad Javid Iqbal1,2, Ihsan Ullah2,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    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 Noh3,*
    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 >

  • Open AccessOpen Access

    ARTICLE

    A BPR-CNN Based Hand Motion Classifier Using Electric Field Sensors

    Hunmin Lee1, Inseop Na2, Kamoliddin Bultakov3, Youngchul Kim3,*
    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 >

  • Open AccessOpen Access

    ARTICLE

    Twisted Pair Cable Fault Diagnosis via Random Forest Machine Learning

    N. B. Ghazali1, F. C. Seman1,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    Mathematical Modelling of Quantum Kernel Method for Biomedical Data Analysis

    Mahmoud Ragab1,2,3, Ehab Bahauden Ashary4, Maha Farouk S. Sabir5, Adel A. Bahaddad5, Romany F. Mansour6,*
    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 >

  • Open AccessOpen Access

    ARTICLE

    IEEE802.11 Access Point's Service Set Identifier (SSID) for Localization and Tracking

    Mohammad Z. Masoud1,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    Fusion Based Tongue Color Image Analysis Model for Biomedical Applications

    Esam A. AlQaralleh1, Halah Nassif2, Bassam A. Y. Alqaralleh2,*
    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 >

  • Open AccessOpen Access

    ARTICLE

    A New Handover Management Model for Two-Tier 5G Mobile Networks

    Mohammad Arifin Rahman Khan1,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    Automatic Speaker Recognition Using Mel-Frequency Cepstral Coefficients Through Machine Learning

    Uğur Ayvaz1, Hüseyin Gürüler2, Faheem Khan3, Naveed Ahmed4, Taegkeun Whangbo3,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    COCP: Coupling Parameters Content Placement Strategy for In-Network Caching-Based Content-Centric Networking

    Salman Rashid1, Shukor Abd Razak1, Fuad A. Ghaleb1,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    Annealing Harmony Search Algorithm to Solve the Nurse Rostering Problem

    Mohammed Hadwan1,2,3,*
    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 >

  • Open AccessOpen Access

    ARTICLE

    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 >

  • Open AccessOpen Access

    ARTICLE

    Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

    Durr-e-Nayab1, Ali Mustafa Qamar2,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    Sentiment Analysis in Social Media for Competitive Environment Using Content Analysis

    Shahid Mehmood1, Imran Ahmad1, Muhammad Adnan Khan1,2, Faheem Khan3, T. Whangbo3,*
    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 >

  • Open AccessOpen Access

    ARTICLE

    Enhancing Task Assignment in Crowdsensing Systems Based on Sensing Intervals and Location

    Rasha Sleem1, Nagham Mekky1, Shaker El-Sappagh2,3, Louai Alarabi4,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    Enhancing Parkinson's Disease Prediction Using Machine Learning and Feature Selection Methods

    Faisal Saeed1,2,*, Mohammad Al-Sarem1,3, Muhannad Al-Mohaimeed1, Abdelhamid Emara1,4, Wadii Boulila1,5, 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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    ARTICLE

    Hybrid Whale Optimization Algorithm for Resource Optimization in Cloud E-Healthcare Applications

    Punit Gupta1, Sanjit Bhagat2, Dinesh Kumar Saini1,*, 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 >

  • Open AccessOpen Access

    ARTICLE

    FSpot: Fast and Efficient Video Encoding Workloads Over Amazon Spot Instances

    Anatoliy Zabrovskiy1,3, Prateek Agrawal1,2,*, Vladislav Kashansky1, Roland Kersche4, Christian Timmerer1,4, 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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    ARTICLE

    Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification

    Mesfer Al Duhayyim1, Taiseer Abdalla Elfadil Eisa2, Fahd N. Al-Wesabi3,4, Abdelzahir Abdelmaboud5, Manar Ahmed Hamza6,*, Abu Sarwar Zamani6, Mohammed Rizwanullah6, Radwa Marzouk7,8
    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 >

  • Open AccessOpen Access

    ARTICLE

    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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    ARTICLE

    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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    ARTICLE

    Optimized Deep Learning Model for Colorectal Cancer Detection and Classification Model

    Mahmoud Ragab1,2,3,*, Khalid Eljaaly4, Maha Farouk S. Sabir5, Ehab Bahaudien Ashary6, S. M. Abo-Dahab7,8, E. M. Khalil3,9
    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 >

  • Open AccessOpen Access

    ARTICLE

    Elite Opposition Based Metaheuristic Framework for Load Balancing in LTE Network

    M. R. Sivagar1,*, 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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    ARTICLE

    Detection of Osteoarthritis Based on EHO Thresholding

    R. Kanthavel1,*, 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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    ARTICLE

    Automated Multi-Document Biomedical Text Summarization Using Deep Learning Model

    Ahmed S. Almasoud1, Siwar Ben Haj Hassine2, Fahd N. Al-Wesabi2,3, Mohamed K. Nour4, Anwer Mustafa Hilal5, Mesfer Al Duhayyim6, Manar Ahmed Hamza5,*, 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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    ARTICLE

    Double-E-Triple-H-Shaped NRI-Metamaterial for Dual-Band Microwave Sensing Applications

    Shafayat Hossain1, Md. Iquebal Hossain Patwary1, Sikder Sunbeam Islam1, Sultan Mahmud1,2, Norbahiah Binti Misran2, Ali F. Almutairi3, Mohammad Tariqul Islam2,*
    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 >

  • Open AccessOpen Access

    ARTICLE

    Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria

    El-Sayed M. El-Kenawy1,2, Abdelhameed Ibrahim3, Nadjem Bailek4,*, 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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    ARTICLE

    Quantum Particle Swarm Optimization Based Convolutional Neural Network for Handwritten Script Recognition

    Reya Sharma1, Baijnath Kaushik1, Naveen Kumar Gondhi1, Muhammad Tahir2,*, 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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    ARTICLE

    Optimization Model in Manufacturing Scheduling for the Garment Industry

    Chia-Nan Wang1, Yu-Chen Wei2, Po-Yuk So3,*, 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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    ARTICLE

    Robust Authentication and Session Key Agreement Protocol for Satellite Communications

    Somayeh Soltani1, Seyed Amin Hosseini Seno1, Juli Rejito2, Rahmat Budiarto3,*
    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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    ARTICLE

    Emotion Based Signal Enhancement Through Multisensory Integration Using Machine Learning

    Muhammad Adnan Khan1,2, Sagheer Abbas3, Ali Raza3, Faheem Khan4, T. Whangbo4,*
    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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    ARTICLE

    PNN-SVM Approach of Ti-Based Powder’s Properties Evaluation for Biomedical Implants Production

    Ivan Izonin1,*, Roman Tkachenko1, Michal Gregus2, Zoia Duriagina1,3, 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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    ARTICLE

    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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    ARTICLE

    An Efficient HW/SW Design for Text Extraction from Complex Color Image

    Mohamed Amin Ben Atitallah1,2,3,*, 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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    ARTICLE

    Design of Intelligent Alzheimer Disease Diagnosis Model on CIoT Environment

    Anwer Mustafa Hilal1, Fahd N. Al-Wesabi2,3, Mohamed Tahar Ben Othman4, Khaled Mohamad Almustafa5, Nadhem Nemri6, Mesfer Al Duhayyim7, Manar Ahmed Hamza1,*, 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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