Home / Journals / CMC / Vol.73, No.2, 2022
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  • Open AccessOpen Access

    ARTICLE

    Blockchain-Based Light-Weighted Provable Data Possession for Low Performance Devices

    Yining Qi1,2,*, Zhen Yang3, Yubo Luo4, Yongfeng Huang1,2, Xing Li1,2
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2205-2221, 2022, DOI:10.32604/cmc.2022.027939
    Abstract Provable Data Possession (PDP) schemes have long been proposed to solve problem of how to check the integrity of data stored in cloud service without downloading. However, with the emerging of network consisting of low performance devices such as Internet of Things, we find that there are still two obstacles for applying PDP schemes. The first one is the heavy computation overhead in generating tags for data blocks, which is essential for setting up any PDP scheme. The other one is how to resist collusion attacks from third party auditors with any possible entities participating the auditing. In this paper,… More >

  • Open AccessOpen Access

    ARTICLE

    Root-Of-Trust for Continuous Integration and Continuous Deployment Pipeline in Cloud Computing

    Abdul Saboor1,*, Mohd Fadzil Hassan2, Rehan Akbar1, Erwin Susanto3, Syed Nasir Mehmood Shah4, Muhammad Aadil Siddiqui5, Saeed Ahmed Magsi5
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2223-2239, 2022, DOI:10.32604/cmc.2022.028382
    Abstract Cloud computing has gained significant use over the last decade due to its several benefits, including cost savings associated with setup, deployments, delivery, physical resource sharing across virtual machines, and availability of on-demand cloud services. However, in addition to usual threats in almost every computing environment, cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location paradigm. Furthermore, since there are a growing number of attacks directed at cloud environments (including dictionary attacks, replay code attacks, denial of service attacks, rootkit attacks, code injection attacks, etc.), customers require additional assurances… More >

  • Open AccessOpen Access

    ARTICLE

    Metaheuristic Optimization for Mobile Robot Navigation Based on Path Planning

    El-Sayed M. El-kenawy1,2, Zeeshan Shafi Khan3,*, Abdelhameed Ibrahim4, Bandar Abdullah Aloyaydi5, Hesham Arafat Ali2,4, Ali E. Takieldeen2
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2241-2255, 2022, DOI:10.32604/cmc.2022.026672
    Abstract Recently, the path planning problem may be considered one of the most interesting researched topics in autonomous robotics. That is why finding a safe path in a cluttered environment for a mobile robot is a significant requisite. A promising route planning for mobile robots on one side saves time and, on the other side, reduces the wear and tear on the robot, saving the capital investment. Numerous route planning methods for the mobile robot have been developed and applied. According to our best knowledge, no method offers an optimum solution among the existing methods. Particle Swarm Optimization (PSO), a numerical… More >

  • Open AccessOpen Access

    ARTICLE

    Automating Transfer Credit Assessment-A Natural Language Processing-Based Approach

    Dhivya Chandrasekaran*, Vijay Mago
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2257-2274, 2022, DOI:10.32604/cmc.2022.027236
    Abstract Student mobility or academic mobility involves students moving between institutions during their post-secondary education, and one of the challenging tasks in this process is to assess the transfer credits to be offered to the incoming student. In general, this process involves domain experts comparing the learning outcomes of the courses, to decide on offering transfer credits to the incoming students. This manual implementation is not only labor-intensive but also influenced by undue bias and administrative complexity. The proposed research article focuses on identifying a model that exploits the advancements in the field of Natural Language Processing (NLP) to effectively automate… More >

  • Open AccessOpen Access

    ARTICLE

    Optimal IoT Based Improved Deep Learning Model for Medical Image Classification

    Prasanalakshmi Balaji1,*, B. Sri Revathi2, Praveetha Gobinathan3, Shermin Shamsudheen3, Thavavel Vaiyapuri4
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2275-2291, 2022, DOI:10.32604/cmc.2022.028560
    Abstract Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis system. Despite deep learning has proved to be superior to previous approaches that depend on handcrafted features; it remains difficult to implement because of the high intra-class variance and inter-class similarity generated by the wide range of imaging modalities and clinical diseases. The Internet of Things (IoT) in healthcare systems is quickly becoming a viable alternative for delivering high-quality medical treatment in today’s e-healthcare systems. In recent years, the Internet of Things (IoT) has been identified as one of the most interesting research subjects in… More >

  • Open AccessOpen Access

    ARTICLE

    HDLIDP: A Hybrid Deep Learning Intrusion Detection and Prevention Framework

    Magdy M. Fadel1,*, Sally M. El-Ghamrawy2, Amr M. T. Ali-Eldin1, Mohammed K. Hassan3, Ali I. El-Desoky1
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2293-2312, 2022, DOI:10.32604/cmc.2022.028287
    Abstract Distributed denial-of-service (DDoS) attacks are designed to interrupt network services such as email servers and webpages in traditional computer networks. Furthermore, the enormous number of connected devices makes it difficult to operate such a network effectively. Software defined networks (SDN) are networks that are managed through a centralized control system, according to researchers. This controller is the brain of any SDN, composing the forwarding table of all data plane network switches. Despite the advantages of SDN controllers, DDoS attacks are easier to perpetrate than on traditional networks. Because the controller is a single point of failure, if it fails, the… More >

  • Open AccessOpen Access

    ARTICLE

    Cache Memory Design for Single Bit Architecture with Different Sense Amplifiers

    Reeya Agrawal1,*, Anjan Kumar1, Salman A. AlQahtani2, Mashael Maashi3, Osamah Ibrahim Khalaf4, Theyazn H. H. Aldhyani5
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2313-2331, 2022, DOI:10.32604/cmc.2022.029019
    Abstract Most modern microprocessors have one or two levels of on-chip caches to make things run faster, but this is not always the case. Most of the time, these caches are made of static random access memory cells. They take up a lot of space on the chip and use a lot of electricity. A lot of the time, low power is more important than several aspects. This is true for phones and tablets. Cache memory design for single bit architecture consists of six transistors static random access memory cell, a circuit of write driver, and sense amplifiers (such as voltage… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Integrated Learning Scheme for Predictive Diagnosis of Critical Care Patient

    Sarika R. Khope1, Susan Elias2,*
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2333-2350, 2022, DOI:10.32604/cmc.2022.029423
    Abstract Machine learning has proven to be one of the efficient solutions for analyzing complex data to perform identification and classification. With a large number of learning tools and techniques, the health section has significantly benefited from solving the diagnosis problems. This paper has reviewed some of the recent scientific implementations on learning-based schemes to find that existing studies of learning have mainly focused on predictive analysis with less emphasis on preprocessing and more inclination towards adopting sophisticated learning schemes that offer higher accuracy at the cost of the higher computational burden. Therefore, the proposed method addresses the concern mentioned above… More >

  • Open AccessOpen Access

    ARTICLE

    Threefold Optimized Forecasting of Electricity Consumption in Higher Education Institutions

    Majida Kazmi1,*, Hashim Raza Khan1,2, Lubaba2, Mohammad Hashir Bin Khalid2, Saad Ahmed Qazi1,2
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2351-2370, 2022, DOI:10.32604/cmc.2022.026265
    Abstract Energy management benefits both consumers and utility companies alike. Utility companies remain interested in identifying and reducing energy waste and theft, whereas consumers’ interest remain in lowering their energy expenses. A large supply-demand gap of over 6 GW exists in Pakistan as reported in 2018. Reducing this gap from the supply side is an expensive and complex task. However, efficient energy management and distribution on demand side has potential to reduce this gap economically. Electricity load forecasting models are increasingly used by energy managers in taking real-time tactical decisions to ensure efficient use of resources. Advancement in Machine-learning (ML) technology… More >

  • Open AccessOpen Access

    ARTICLE

    Transfer Learning for Chest X-rays Diagnosis Using Dipper Throated Algorithm

    Hussah Nasser AlEisa1, El-Sayed M. El-kenawy2,3, Amel Ali Alhussan1,*, Mohamed Saber4, Abdelaziz A. Abdelhamid5,6, Doaa Sami Khafaga1
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2371-2387, 2022, DOI:10.32604/cmc.2022.030447
    Abstract Most children and elderly people worldwide die from pneumonia, which is a contagious illness that causes lung ulcers. For diagnosing pneumonia from chest X-ray images, many deep learning models have been put forth. The goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia cases. By varying the deep learning approach, three pre-trained models, GoogLeNet, ResNet18, and DenseNet121, are employed in this research to extract the main features of pneumonia and normal cases. In addition, the binary dipper throated optimization (DTO) algorithm is utilized to select the most significant features, which are then… More >

  • Open AccessOpen Access

    ARTICLE

    Pedestrian Physical Education Training Over Visualization Tool

    Tamara al Shloul1, Israr Akhter2, Suliman A. Alsuhibany3, Yazeed Yasin Ghadi4, Ahmad Jalal2, Jeongmin Park5,*
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2389-2405, 2022, DOI:10.32604/cmc.2022.027007
    Abstract E-learning approaches are one of the most important learning platforms for the learner through electronic equipment. Such study techniques are useful for other groups of learners such as the crowd, pedestrian, sports, transports, communication, emergency services, management systems and education sectors. E-learning is still a challenging domain for researchers and developers to find new trends and advanced tools and methods. Many of them are currently working on this domain to fulfill the requirements of industry and the environment. In this paper, we proposed a method for pedestrian behavior mining of aerial data, using deep flow feature, graph mining technique, and… More >

  • Open AccessOpen Access

    ARTICLE

    Smart-City-based Data Fusion Algorithm for Internet of Things

    Jawad Khan1, Muhammad Amir Khan2, N. Z. Jhanjhi3,*, Mamoona Humayun4, Abdullah Alourani5
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2407-2421, 2022, DOI:10.32604/cmc.2022.026693
    Abstract Increasingly, Wireless Sensor Networks (WSNs) are contributing enormous amounts of data. Since the recent deployments of wireless sensor networks in Smart City infrastructures, significant volumes of data have been produced every day in several domains ranging from the environment to the healthcare system to transportation. Using wireless sensor nodes, a Smart City environment may now be shown for the benefit of residents. The Smart City delivers intelligent infrastructure and a stimulating environment to citizens of the Smart Society, including the elderly and others. Weak, Quality of Service (QoS) and poor data performance are common problems in WSNs, caused by the… More >

  • Open AccessOpen Access

    ARTICLE

    Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

    Sara A. Althubiti1, José Escorcia-Gutierrez2,3,*, Margarita Gamarra4, Roosvel Soto-Diaz5, Romany F. Mansour6, Fayadh Alenezi7
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2423-2439, 2022, DOI:10.32604/cmc.2022.028878
    Abstract Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things (IoT), sensor technologies, cloud computing, and others. Besides, the latest advances of Artificial Intelligence (AI) tools find helpful for decision-making in innovative healthcare to diagnose several diseases. Ovarian Cancer (OC) is a kind of cancer that affects women’s ovaries, and it is tedious to identify OC at the primary stages with a high mortality rate. The OC data produced by the Internet of Medical Things (IoMT) devices can be utilized to differentiate OC. In this aspect, this paper introduces a new quantum black… More >

  • Open AccessOpen Access

    ARTICLE

    Development of Voice Control Algorithm for Robotic Wheelchair Using NIN and LSTM Models

    Mohsen Bakouri1,2,*
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2441-2456, 2022, DOI:10.32604/cmc.2022.025106
    Abstract In this work, we developed and implemented a voice control algorithm to steer smart robotic wheelchairs (SRW) using the neural network technique. This technique used a network in network (NIN) and long short-term memory (LSTM) structure integrated with a built-in voice recognition algorithm. An Android Smartphone application was designed and configured with the proposed method. A Wi-Fi hotspot was used to connect the software and hardware components of the system in an offline mode. To operate and guide SRW, the design technique proposed employing five voice commands (yes, no, left, right, no, and stop) via the Raspberry Pi and DC… More >

  • Open AccessOpen Access

    ARTICLE

    A Compact Flexible Circularly Polarized Implantable Antenna for Biotelemetry Applications

    Sarosh Ahmad1,*, Shakir Ullah2, Adnan Ghaffar3, Daniel Segovia Vargas1
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2457-2472, 2022, DOI:10.32604/cmc.2022.025527
    Abstract With the help of in-body antennas, the wireless communication among the implantable medical devices (IMDs) and exterior monitoring equipment, the telemetry system has brought us many benefits. Thus, a very thin-profile circularly polarized (CP) in-body antenna, functioning in ISM band at 2.45 GHz, is proposed. A tapered coplanar waveguide (CPW) method is used to excite the antenna. The radiator contains a pentagonal shape with five horizontal slits inside to obtain a circular polarization behavior. A bendable Roger Duroid RT5880 material (εr = 2.2, tanδ = 0.0009) with a typical 0.25 mm-thickness is used as a substrate. The proposed antenna has… More >

  • Open AccessOpen Access

    ARTICLE

    Research on the Design Method of Equipment in Service Assessment Subjects

    Jianhua Luo, Yuhang Zhou*, Hua Li, Xi Chen, Xigong Xia
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2473-2484, 2022, DOI:10.32604/cmc.2022.029640
    Abstract Combined with equipment activities such as combat readiness, training, exercises and management, it is proposed that the design of equipment in-service assessment subjects should follow the principles of combination, stage and operability. Focusing on the design of equipment in-service assessment subjects, a design method for in-service assessment subjects based on the combination of trial and training mode is proposed. Based on the actual use of high-equipment use management and training and the established indicator system, the army’s bottom-level equipment activity subjects and bottom-level assessments are combined. The indicators are mapped and analyzed. Through multiple rounds of iterations, the mapping relationship… More >

  • Open AccessOpen Access

    ARTICLE

    Fuzzy Multi-criteria Decision Making for Decision Support in Port Capacity Upgrade

    Chia-Nan Wang, Tien–Lin Chao*
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2485-2494, 2022, DOI:10.32604/cmc.2022.026682
    Abstract In many port capacity upgrade projects, choosing a supplier of equipment is a complicated decision, project managers must consider many criteria to choose a supplier to ensure the project is completed on time, optimal in terms of benefit and cost. Therefore, selecting the equipment supplier in this project is a multi-criteria decision-making process. The multi-criteria decision-making (MCDM) model is applied in many fields to select the optimal solution, but there are very few studies using the MCDM model to support project managers in evaluating and selecting optimal solutions in port capacity upgrade project. In this research, the authors combine Fuzzy… More >

  • Open AccessOpen Access

    ARTICLE

    SAFT-VNDN: A Socially-Aware Forwarding Technique in Vehicular Named Data Networking

    Amel Boudelaa1, Zohra Abdelhafidi1, Nasreddine Lagraa1, Chaker Abdelaziz Kerrache1, Muhammad Bilal2, Daehan Kwak3,*, Mohamed Bachir Yagoubi1
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2495-2512, 2022, DOI:10.32604/cmc.2022.028619
    Abstract Vehicular Social Networks (VSNs) is the bridge of social networks and Vehicular Ad-Hoc Networks (VANETs). VSNs are promising as they allow the exchange of various types of contents in large-scale through Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication protocols. Vehicular Named Data Networking (VNDN) is an auspicious communication paradigm for the challenging VSN environment since it can optimize content dissemination by decoupling contents from their physical locations. However, content dissemination and caching represent crucial challenges in VSNs due to short link lifetime and intermittent connectivity caused by vehicles’ high mobility. Our aim with this paper is to improve content delivery and… More >

  • Open AccessOpen Access

    ARTICLE

    An Automated and Real-time Approach of Depression Detection from Facial Micro-expressions

    Ghulam Gilanie1, Mahmood ul Hassan2, Mutyyba Asghar1, Ali Mustafa Qamar3,*, Hafeez Ullah4, Rehan Ullah Khan5, Nida Aslam6, Irfan Ullah Khan6
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2513-2528, 2022, DOI:10.32604/cmc.2022.028229
    Abstract Depression is a mental psychological disorder that may cause a physical disorder or lead to death. It is highly impactful on the social-economical life of a person; therefore, its effective and timely detection is needful. Despite speech and gait, facial expressions have valuable clues to depression. This study proposes a depression detection system based on facial expression analysis. Facial features have been used for depression detection using Support Vector Machine (SVM) and Convolutional Neural Network (CNN). We extracted micro-expressions using Facial Action Coding System (FACS) as Action Units (AUs) correlated with the sad, disgust, and contempt features for depression detection.… More >

  • Open AccessOpen Access

    ARTICLE

    Multi-Level Feature Aggregation-Based Joint Keypoint Detection and Description

    Jun Li1, Xiang Li1, Yifei Wei1,*, Mei Song1, Xiaojun Wang2
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2529-2540, 2022, DOI:10.32604/cmc.2022.029542
    Abstract Image keypoint detection and description is a popular method to find pixel-level connections between images, which is a basic and critical step in many computer vision tasks. The existing methods are far from optimal in terms of keypoint positioning accuracy and generation of robust and discriminative descriptors. This paper proposes a new end-to-end self-supervised training deep learning network. The network uses a backbone feature encoder to extract multi-level feature maps, then performs joint image keypoint detection and description in a forward pass. On the one hand, in order to enhance the localization accuracy of keypoints and restore the local shape… More >

  • Open AccessOpen Access

    ARTICLE

    Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment

    K. Vijaya Kumar1, E. Laxmi Lydia2, Ashit Kumar Dutta3, Velmurugan Subbiah Parvathy4, Gobi Ramasamy5, Irina V. Pustokhina6,*, Denis A. Pustokhin7
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2541-2554, 2022, DOI:10.32604/cmc.2022.028570
    Abstract Recently, big data becomes evitable due to massive increase in the generation of data in real time application. Presently, object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation, augmented reality, surveillance, etc. This paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN (AIA-IFRCNN) model in big data environment. The AIA-IFRCNN model annotates the images by Discriminative Correlation Filter (DCF) with Channel and Spatial Reliability tracker (CSR), named DCF-CSRT model. The AIA-IFRCNN technique employs Faster RCNN for object detection and tracking,… More >

  • Open AccessOpen Access

    ARTICLE

    A Hybrid Duo-Deep Learning and Best Features Based Framework for Action Recognition

    Muhammad Naeem Akbar1,*, Farhan Riaz1, Ahmed Bilal Awan1, Muhammad Attique Khan2, Usman Tariq3, Saad Rehman2
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2555-2576, 2022, DOI:10.32604/cmc.2022.028696
    Abstract Human Action Recognition (HAR) is a current research topic in the field of computer vision that is based on an important application known as video surveillance. Researchers in computer vision have introduced various intelligent methods based on deep learning and machine learning, but they still face many challenges such as similarity in various actions and redundant features. We proposed a framework for accurate human action recognition (HAR) based on deep learning and an improved features optimization algorithm in this paper. From deep learning feature extraction to feature classification, the proposed framework includes several critical steps. Before training fine-tuned deep learning… More >

  • Open AccessOpen Access

    ARTICLE

    A Dynamic Reputation–based Consensus Mechanism for Blockchain

    Xiaofang Qiu1,2, Zhi Qin1,2,*, Wunan Wan1,2, Jinquan Zhang1,2, Jinliang Guo1,2, Shibin Zhang1,2, Jinyue Xia3
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2577-2589, 2022, DOI:10.32604/cmc.2022.028757
    Abstract In recent years, Blockchain is gaining prominence as a hot topic in academic research. However, the consensus mechanism of blockchain has been criticized in terms of energy consumption and performance. Although Proof-of-Authority (PoA) consensus mechanism, as a lightweight consensus mechanism, is more efficient than traditional Proof-of-Work (PoW) and Proof-of-Stake (PoS), it suffers from the problem of centralization. To this end, on account of analyzing the shortcomings of existing consensus mechanisms, this paper proposes a dynamic reputation-based consensus mechanism for blockchain. This scheme allows nodes with reputation value higher than a threshold apply to become a monitoring node, which can monitor… More >

  • Open AccessOpen Access

    ARTICLE

    A Unified Decision-Making Technique for Analysing Treatments in Pandemic Context

    Fawaz Alsolami1, Abdullah Saad Al-Malaise Alghamdi2, Asif Irshad Khan1,*, Yoosef B. Abushark1, Abdulmohsen Almalawi1, Farrukh Saleem2, Alka Agrawal3, Rajeev Kumar4, Raees Ahmad Khan3
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2591-2618, 2022, DOI:10.32604/cmc.2022.025703
    Abstract The COVID-19 pandemic has triggered a global humanitarian disaster that has never been seen before. Medical experts, on the other hand, are undecided on the most valuable treatments of therapy because people ill with this infection exhibit a wide range of illness indications at different phases of infection. Further, this project aims to undertake an experimental investigation to determine which treatments for COVID-19 disease is the most effective and preferable. The research analysis is based on vast data gathered from professionals and research journals, making this study a comprehensive reference. To solve this challenging task, the researchers used the HF… More >

  • Open AccessOpen Access

    ARTICLE

    Optimization Ensemble Weights Model for Wind Forecasting System

    Amel Ali Alhussan1, El-Sayed M. El-kenawy2,3, Hussah Nasser AlEisa1,*, M. El-SAID4,5, Sayed A. Ward6,7, Doaa Sami Khafaga1
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2619-2635, 2022, DOI:10.32604/cmc.2022.030445
    Abstract Effective technology for wind direction forecasting can be realized using the recent advances in machine learning. Consequently, the stability and safety of power systems are expected to be significantly improved. However, the unstable and unpredictable qualities of the wind predict the wind direction a challenging problem. This paper proposes a practical forecasting approach based on the weighted ensemble of machine learning models. This weighted ensemble is optimized using a whale optimization algorithm guided by particle swarm optimization (PSO-Guided WOA). The proposed optimized weighted ensemble predicts the wind direction given a set of input features. The conducted experiments employed the wind… More >

  • Open AccessOpen Access

    ARTICLE

    A Compact Rhombus Shaped Antenna with Extended Stubs for Ultra-Wideband Applications

    Syed Misbah un Noor1, Muhammad Amir Khan2, Shahid Khan3, NZ Jhanjhi4,*, Mamoona Humayun5, Hesham A. Alhumyan6
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2637-2650, 2022, DOI:10.32604/cmc.2022.026061
    Abstract Ultra-wideband (UWB) is highly preferred for short distance communication. As a result of this significance, this project targets the design of a compact UWB antennas. This paper describes a printed UWB rhombus-shaped antenna with a partial ground plane. To achieve wideband response, two stubs and a notch are incorporated at both sides of the rhombus design and ground plane respectively. To excite the antenna, a simple microstrip feed line is employed. The suggested antenna is built on a 1.6 mm thick FR4 substrate. The proposed design is very compact with overall electrical size of 0.18λ × 0.25λ (14 × 18… More >

  • Open AccessOpen Access

    ARTICLE

    A Scalable Double-Chain Storage Module for Blockchain

    Hui Han1,2, Wunan Wan1,2,*, Jinquan Zhang1,2, Zhi Qin1,2, Xiaofang Qiu1,2, Shibin Zhang1,2, Jinyue Xia3
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2651-2662, 2022, DOI:10.32604/cmc.2022.028607
    Abstract With the growing maturity of blockchain technology, its peer-to-peer model and fully duplicated data storage pattern enable blockchain to act as a distributed ledger in untrustworthy environments. Blockchain storage has also become a research hotspot in industry, finance, and academia due to its security, and its unique data storage management model is gradually becoming a key technology to play its value in various fields’ applications. However, with the increasing amount of data written into the blockchain, the blockchain system faces many problems in its actual implementation of the application, such as high storage space occupation, low data flexibility and availability,… More >

  • Open AccessOpen Access

    ARTICLE

    Data Reliability and Sensors Lifetime in Bridge Health Monitoring using LoRaWAN-Zigbee

    Awad Ali1,*, Reyazur Rashid Irshad1, Ahmed Abdu Alattaab1, Aamir Fatahayab2
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2663-2678, 2022, DOI:10.32604/cmc.2022.028503
    Abstract The Wireless Sensor Network (WSN) is regarded as the fastest expanding technological trend in recent years due its application in a variety of sectors. In the monitoring region, several sensor nodes with various sensing capabilities are installed to gather appropriate data and communicate it to the gateway. The proposed system of the heterogeneous WSN employing LoRaWAN-Zigbee based hybrid communication is explored in this research study. To communicate in a network, two Long–Range Wide Area Network (LoRaWAN) sensor clusters and two Zigbee sensor clusters are employed, together with two Zigbee and LoRaWAN converters. The suggested Golden eagle shepherd optimization (GESO) method… More >

  • Open AccessOpen Access

    ARTICLE

    Construction and Optimization of TRNG Based Substitution Boxes for Block Encryption Algorithms

    Muhammad Fahad Khan1,2,*, Khalid Saleem1, Mohammed Alotaibi3, Mohammad Mazyad Hazzazi4, Eid Rehman2, Aaqif Afzaal Abbasi2, Muhammad Asif Gondal5
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2679-2696, 2022, DOI:10.32604/cmc.2022.027655
    Abstract Internet of Things is an ecosystem of interconnected devices that are accessible through the internet. The recent research focuses on adding more smartness and intelligence to these edge devices. This makes them susceptible to various kinds of security threats. These edge devices rely on cryptographic techniques to encrypt the pre-processed data collected from the sensors deployed in the field. In this regard, block cipher has been one of the most reliable options through which data security is accomplished. The strength of block encryption algorithms against different attacks is dependent on its nonlinear primitive which is called Substitution Boxes. For the… More >

  • Open AccessOpen Access

    ARTICLE

    EEG Emotion Recognition Using an Attention Mechanism Based on an Optimized Hybrid Model

    Huiping Jiang1,*, Demeng Wu1, Xingqun Tang1, Zhongjie Li1, Wenbo Wu2
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2697-2712, 2022, DOI:10.32604/cmc.2022.027856
    Abstract Emotions serve various functions. The traditional emotion recognition methods are based primarily on readily accessible facial expressions, gestures, and voice signals. However, it is often challenging to ensure that these non-physical signals are valid and reliable in practical applications. Electroencephalogram (EEG) signals are more successful than other signal recognition methods in recognizing these characteristics in real-time since they are difficult to camouflage. Although EEG signals are commonly used in current emotional recognition research, the accuracy is low when using traditional methods. Therefore, this study presented an optimized hybrid pattern with an attention mechanism (FFT_CLA) for EEG emotional recognition. First, the… More >

  • Open AccessOpen Access

    ARTICLE

    A Hybrid Security Framework for Medical Image Communication

    Walid El-Shafai1,2, Hayam A. Abd El-Hameed3, Ashraf A. M. Khalaf3, Naglaa F. Soliman4, Amel A. Alhussan5,*, Fathi E. Abd El-Samie1
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2713-2730, 2022, DOI:10.32604/cmc.2022.028739
    Abstract Authentication of the digital image has much attention for the digital revolution. Digital image authentication can be verified with image watermarking and image encryption schemes. These schemes are widely used to protect images against forgery attacks, and they are useful for protecting copyright and rightful ownership. Depending on the desirable applications, several image encryption and watermarking schemes have been proposed to moderate this attention. This framework presents a new scheme that combines a Walsh Hadamard Transform (WHT)-based image watermarking scheme with an image encryption scheme based on Double Random Phase Encoding (DRPE). First, on the sender side, the secret medical… More >

  • Open AccessOpen Access

    ARTICLE

    Trustworthiness Evaluation for Permissioned Blockchain-Enabled Applications

    Shi-Cho Cha1, Chuang-Ming Shiung1, Wen-Wei Li1, Chun-Neng Peng1, Yi-Hsuan Hung1, Kuo-Hui Yeh2,3,*
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2731-2755, 2022, DOI:10.32604/cmc.2022.029161
    Abstract As permissioned blockchain becomes a common foundation of blockchain-based circumstances for current organizations, related stakeholders need a means to assess the trustworthiness of the applications involved within. It is extremely important to consider the potential impact brought by the Blockchain technology in terms of security and privacy. Therefore, this study proposes a rigorous security risk management framework for permissioned blockchain-enabled applications. The framework divides itself into different implementation domains, i.e., organization security, application security, consensus mechanism security, node management and network security, host security and perimeter security, and simultaneously provides guidelines to control the security risks of permissioned blockchain applications… More >

  • Open AccessOpen Access

    ARTICLE

    Aortic Dissection Diagnosis Based on Sequence Information and Deep Learning

    Haikuo Peng1, Yun Tan1,*, Hao Tang2, Ling Tan2, Xuyu Xiang1, Yongjun Wang2, Neal N. Xiong3
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2757-2771, 2022, DOI:10.32604/cmc.2022.029727
    Abstract Aortic dissection (AD) is one of the most serious diseases with high mortality, and its diagnosis mainly depends on computed tomography (CT) results. Most existing automatic diagnosis methods of AD are only suitable for AD recognition, which usually require preselection of CT images and cannot be further classified to different types. In this work, we constructed a dataset of 105 cases with a total of 49021 slices, including 31043 slices expert-level annotation and proposed a two-stage AD diagnosis structure based on sequence information and deep learning. The proposed region of interest (RoI) extraction algorithm based on sequence information (RESI) can… More >

  • Open AccessOpen Access

    ARTICLE

    An Improved Approach to the Performance of Remote Photoplethysmography

    Yi Sheng1, Wu Zeng1,*, Qiuyu Hu1, Weihua Ou2, Yuxuan Xie3, Jie Li1
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2773-2783, 2022, DOI:10.32604/cmc.2022.027985
    Abstract Heart rate is an important metric for determining physical and mental health. In recent years, remote photoplethysmography (rPPG) has been widely used in characterizing physiological signals in human subjects. Currently, research on non-contact detection of heart rate mainly focuses on the capture and separation of spectral signals from video imagery. However, this method is very sensitive to the movement of the test subject and light intensity variation, and this results in motion artifacts which presents challenges in extracting accurate physiological signals such as heart rate. In this paper, an improved method for rPPG signal preprocessing is proposed. Based on the… More >

  • Open AccessOpen Access

    ARTICLE

    Super Compact UWB Monopole Antenna for Small IoT Devices

    Ahmed Jamal Abdullah Al-Gburi1, Zahriladha Zakaria1,*, Merih Palandoken2, Imran Mohd Ibrahim1, A. A. Althuwayb3, Sarosh Ahmad4, Samir Salem Al-Bawri5
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2785-2799, 2022, DOI:10.32604/cmc.2022.028074
    Abstract This article introduces a novel, ultrawideband (UWB) planar monopole antenna printed on Roger RT/5880 substrate in a compact size for small Internet of Things (IoT) applications. The total electrical dimensions of the proposed compact UWB antenna are 0.19 λo × 0.215 λo × 0.0196 λo with the overall physical sizes of 15 mm × 17 mm × 1.548 mm at the lower resonance frequency of 3.8 GHz. The planar monopole antenna is fed through the linearly tapered microstrip line on a partially structured ground plane to achieve optimum impedance matching for UWB operation. The proposed compact UWB antenna has an… More >

  • Open AccessOpen Access

    ARTICLE

    An Optimal Method for High-Resolution Population Geo-Spatial Data

    Rami Sameer Ahmad Al Kloub*
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2801-2820, 2022, DOI:10.32604/cmc.2022.027847
    Abstract Mainland China has a poor distribution of meteorological stations. Existing models’ estimation accuracy for creating high-resolution surfaces of meteorological data is restricted for air temperature, and low for relative humidity and wind speed (few studies reported). This study compared the typical generalized additive model (GAM) and autoencoder-based residual neural network (hereafter, residual network for short) in terms of predicting three meteorological parameters, namely air temperature, relative humidity, and wind speed, using data from 824 monitoring stations across China’s mainland in 2015. The performance of the two models was assessed using a 10-fold cross-validation procedure. The air temperature models employ basic… More >

  • Open AccessOpen Access

    ARTICLE

    Convergence of Stereo Vision-Based Multimodal YOLOs for Faster Detection of Potholes

    Sungan Yoon, Jeongho Cho*
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2821-2834, 2022, DOI:10.32604/cmc.2022.027840
    Abstract Road potholes can cause serious social issues, such as unexpected damages to vehicles and traffic accidents. For efficient road management, technologies that quickly find potholes are required, and thus researches on such technologies have been conducted actively. The three-dimensional (3D) reconstruction method has relatively high accuracy and can be used in practice but it has limited application owing to its long data processing time and high sensor maintenance cost. The two-dimensional (2D) vision method has the advantage of inexpensive and easy application of sensor. Recently, although the 2D vision method using the convolutional neural network (CNN) has shown improved pothole… More >

  • Open AccessOpen Access

    ARTICLE

    A Secure Multiparty Quantum Homomorphic Encryption Scheme

    Jing-Wen Zhang1, Xiu-Bo Chen1,*, Gang Xu2,3, Heng-Ji Li4, Ya-Lan Wang5, Li-Hua Miao6, Yi-Xian Yang1
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2835-2848, 2022, DOI:10.32604/cmc.2022.029125
    Abstract The significant advantage of the quantum homomorphic encryption scheme is to ensure the perfect security of quantum private data. In this paper, a novel secure multiparty quantum homomorphic encryption scheme is proposed, which can complete arbitrary quantum computation on the private data of multiple clients without decryption by an almost dishonest server. Firstly, each client obtains a secure encryption key through the measurement device independent quantum key distribution protocol and encrypts the private data by using the encryption operator and key. Secondly, with the help of the almost dishonest server, the non-maximally entangled states are pre-shared between the client and… More >

  • Open AccessOpen Access

    ARTICLE

    Optimal Deep Transfer Learning Model for Histopathological Breast Cancer Classification

    Mahmoud Ragab1,2,3,*, Alaa F. Nahhas4
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2849-2864, 2022, DOI:10.32604/cmc.2022.028855
    Abstract Earlier recognition of breast cancer is crucial to decrease the severity and optimize the survival rate. One of the commonly utilized imaging modalities for breast cancer is histopathological images. Since manual inspection of histopathological images is a challenging task, automated tools using deep learning (DL) and artificial intelligence (AI) approaches need to be designed. The latest advances of DL models help in accomplishing maximum image classification performance in several application areas. In this view, this study develops a Deep Transfer Learning with Rider Optimization Algorithm for Histopathological Classification of Breast Cancer (DTLRO-HCBC) technique. The proposed DTLRO-HCBC technique aims to categorize… More >

  • Open AccessOpen Access

    ARTICLE

    Statistical Analysis with Dingo Optimizer Enabled Routing for Wireless Sensor Networks

    Abdulaziz S. Alghamdi1,*, Randa Alharbi2, Suliman A. Alsuhibany3, Sayed Abdel-Khalek4,5
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2865-2878, 2022, DOI:10.32604/cmc.2022.028088
    Abstract Security is a vital parameter to conserve energy in wireless sensor networks (WSN). Trust management in the WSN is a crucial process as trust is utilized when collaboration is important for accomplishing trustworthy data transmission. But the available routing techniques do not involve security in the design of routing techniques. This study develops a novel statistical analysis with dingo optimizer enabled reliable routing scheme (SADO-RRS) for WSN. The proposed SADO-RRS technique aims to detect the existence of attacks and optimal routes in WSN. In addition, the presented SADO-RRS technique derives a new statistics based linear discriminant analysis (LDA) for attack… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Metaheuristic Algorithm: The Team Competition and Cooperation Optimization Algorithm

    Tao Wu1, Xinyu Wu1, Jingjue Chen1, Xi Chen2,*, Amir Homayoon Ashrafzadeh3
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2879-2896, 2022, DOI:10.32604/cmc.2022.028942
    Abstract Metaheuristic algorithm is a generalization of heuristic algorithm that can be applied to almost all optimization problems. For optimization problems, metaheuristic algorithm is one of the methods to find its optimal solution or approximate solution under limited conditions. Most of the existing metaheuristic algorithms are designed for serial systems. Meanwhile, existing algorithms still have a lot of room for improvement in convergence speed, robustness, and performance. To address these issues, this paper proposes an easily parallelizable metaheuristic optimization algorithm called team competition and cooperation optimization (TCCO) inspired by the process of human team cooperation and competition. The proposed algorithm attempts… More >

  • Open AccessOpen Access

    ARTICLE

    Design and Implementation of a State-feedback Controller Using LQR Technique

    Aamir Shahzad1,*, Shadi Munshi2, Sufyan Azam2, Muhammad Nasir Khan3
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2897-2911, 2022, DOI:10.32604/cmc.2022.028441
    Abstract The main objective of this research is to design a state-feedback controller for the rotary inverted pendulum module utilizing the linear quadratic regulator (LQR) technique. The controller maintains the pendulum in the inverted (upright) position and is robust enough to reject external disturbance to maintain its stability. The research work involves three major contributions: mathematical modeling, simulation, and real-time implementation. To design a controller, mathematical modeling has been done by employing the Newton-Euler, Lagrange method. The resulting model was nonlinear so linearization was required, which has been done around a working point. For the estimation of the controller parameters, MATLAB… More >

  • Open AccessOpen Access

    ARTICLE

    Mordell Elliptic Curve Based Design of Nonlinear Component of Block Cipher

    Hafeez ur Rehman1,*, Tariq Shah1, Mohammad Mazyad Hazzazi2, Ali Alshehri3, Bassfar Zaid4
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2913-2930, 2022, DOI:10.32604/cmc.2022.028765
    Abstract Elliptic curves (ECs) are deemed one of the most solid structures against modern computational attacks because of their small key size and high security. In many well-known cryptosystems, the substitution box (S-box) is used as the only nonlinear portion of a security system. Recently, it has been shown that using dynamic S-boxes rather than static S-boxes increases the security of a cryptosystem. The conferred study also extends the practical application of ECs in designing the nonlinear components of block ciphers in symmetric key cryptography. In this study, instead of the Mordell elliptic curve (MEC) over the prime field, the Galois… More >

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    ARTICLE

    Criss-Cross Attentional Siamese Networks for Object Tracking

    Zhangdong Wang1, Jiaohua Qin1,*, Xuyu Xiang1, Yun Tan1, Neal N. Xiong2
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2931-2946, 2022, DOI:10.32604/cmc.2022.028896
    Abstract Visual object tracking is a hot topic in recent years. In the meanwhile, Siamese networks have attracted extensive attention in this field because of its balanced precision and speed. However, most of the Siamese network methods can only distinguish foreground from the non-semantic background. The fine-tuning and retraining of fully-convolutional Siamese networks for object tracking(SiamFC) can achieve higher precision under interferences, but the tracking accuracy is still not ideal, especially in the environment with more target interferences, dim light, and shadows. In this paper, we propose criss-cross attentional Siamese networks for object tracking (SiamCC). To solve the imbalance between foreground… More >

  • Open AccessOpen Access

    ARTICLE

    Development of Data Mining Models Based on Features Ranks Voting (FRV)

    Mofreh A. Hogo*
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2947-2966, 2022, DOI:10.32604/cmc.2022.027300
    Abstract Data size plays a significant role in the design and the performance of data mining models. A good feature selection algorithm reduces the problems of big data size and noise due to data redundancy. Features selection algorithms aim at selecting the best features and eliminating unnecessary ones, which in turn simplifies the structure of the data mining model as well as increases its performance. This paper introduces a robust features selection algorithm, named Features Ranking Voting Algorithm FRV. It merges the benefits of the different features selection algorithms to specify the features ranks in the dataset correctly and robustly; based… More >

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    ARTICLE

    Recognition of Urdu Handwritten Alphabet Using Convolutional Neural Network (CNN)

    Gulzar Ahmed1, Tahir Alyas2, Muhammad Waseem Iqbal3,*, Muhammad Usman Ashraf4, Ahmed Mohammed Alghamdi5, Adel A. Bahaddad6, Khalid Ali Almarhabi7
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2967-2984, 2022, DOI:10.32604/cmc.2022.029314
    Abstract Handwritten character recognition systems are used in every field of life nowadays, including shopping malls, banks, educational institutes, etc. Urdu is the national language of Pakistan, and it is the fourth spoken language in the world. However, it is still challenging to recognize Urdu handwritten characters owing to their cursive nature. Our paper presents a Convolutional Neural Networks (CNN) model to recognize Urdu handwritten alphabet recognition (UHAR) offline and online characters. Our research contributes an Urdu handwritten dataset (aka UHDS) to empower future works in this field. For offline systems, optical readers are used for extracting the alphabets, while diagonal-based… More >

  • Open AccessOpen Access

    ARTICLE

    NLP-Based Subject with Emotions Joint Analytics for Epidemic Articles

    Woo Hyun Park1, Isma Farah Siddiqui2, Dong Ryeol Shin1, Nawab Muhammad Faseeh Qureshi3,*
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2985-3001, 2022, DOI:10.32604/cmc.2022.028241
    Abstract For the last couple years, governments and health authorities worldwide have been focused on addressing the Covid-19 pandemic; for example, governments have implemented countermeasures, such as quarantining, pushing vaccine shots to minimize local spread, investigating and analyzing the virus’ characteristics, and conducting epidemiological investigations through patient management and tracers. Therefore, researchers worldwide require funding to achieve these goals. Furthermore, there is a need for documentation to investigate and trace disease characteristics. However, it is time consuming and resource intensive to work with documents comprising many types of unstructured data. Therefore, in this study, natural language processing technology is used to… More >

  • Open AccessOpen Access

    ARTICLE

    An Asset-Based Approach to Mitigate Zero-Day Ransomware Attacks

    Farag Azzedin*, Husam Suwad, Md Mahfuzur Rahman
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3003-3020, 2022, DOI:10.32604/cmc.2022.028646
    Abstract This article presents an asset-based security system where security practitioners build their systems based on information they own and not solicited by observing attackers’ behavior. Current security solutions rely on information coming from attackers. Examples are current monitoring and detection security solutions such as intrusion prevention/detection systems and firewalls. This article envisions creating an imbalance between attackers and defenders in favor of defenders. As such, we are proposing to flip the security game such that it will be led by defenders and not attackers. We are proposing a security system that does not observe the behavior of the attack. On… More >

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    ARTICLE

    Network Invulnerability Enhancement Algorithm Based on WSN Closeness Centrality

    Qian Sun1,2, Fengbo Yang1,2, Xiaoyi Wang2,3, Jing Li4,*, Jiping Xu1,2, Huiyan Zhang1,2, Li Wang1,2, Jiabin Yu1,2, Xiao Peng1,2, Ruichao Wang5
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3021-3038, 2022, DOI:10.32604/cmc.2022.029367
    Abstract Wireless Sensor Network (WSN) is an important part of the Internet of Things (IoT), which are used for information exchange and communication between smart objects. In practical applications, WSN lifecycle can be influenced by the unbalanced distribution of node centrality and excessive energy consumption, etc. In order to overcome these problems, a heterogeneous wireless sensor network model with small world characteristics is constructed to balance the centrality and enhance the invulnerability of the network. Also, a new WSN centrality measurement method and a new invulnerability measurement model are proposed based on the WSN data transmission characteristics. Simulation results show that… More >

  • Open AccessOpen Access

    ARTICLE

    Cuckoo Optimized Convolution Support Vector Machine for Big Health Data Processing

    Eatedal Alabdulkreem1, Jaber S. Alzahrani2, Majdy M. Eltahir3, Abdullah Mohamed4, Manar Ahmed Hamza5,*, Abdelwahed Motwakel5, Mohamed I. Eldesouki6, Mohammed Rizwanullah5
    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3039-3055, 2022, DOI:10.32604/cmc.2022.029835
    Abstract Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many features. Several cloud-based IoT health providers have been described in the literature previously. Furthermore, there are a number of issues related to time consumed and overall network performance when it comes to big data information. In the existing method, less performed optimization algorithms were used for optimizing the data. In the proposed method, the Chaotic Cuckoo Optimization algorithm was used for feature selection, and Convolutional Support Vector Machine (CSVM) was used. The research presents a method for analyzing healthcare… More >

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