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Jason Elroy Martis¹, Sannidhan Manjaya Shetty^2,*, Manas Ranjan Pradhan³, Usha Desai⁴, Biswaranjan Acharya^5,*
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 915-938, 2023, DOI:10.32604/cmc.2023.038847
（This article belongs to the Special Issue: Susceptibility to Adversarial Attacks and Defense in Deep Learning Systems)
Abstract In the past, sketches were a standard technique used for recognizing offenders and have remained a valuable tool for law enforcement and social security purposes. However, relying on eyewitness observations can lead to discrepancies in the depictions of the sketch, depending on the experience and skills of the sketch artist. With the emergence of modern technologies such as Generative Adversarial Networks (GANs), generating images using verbal and textual cues is now possible, resulting in more accurate sketch depictions. In this study, we propose an adversarial network that generates human facial sketches using such cues provided by an observer. Additionally, we… More >

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Zaid Abdulsalam Ibrahim^1,*, Muhammad Ilyas²
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 939-956, 2023, DOI:10.32604/cmc.2023.035798
Abstract Most research works nowadays deal with real-time Internet of Things (IoT) data. However, with exponential data volume increases, organizations need help storing such humongous amounts of IoT data in cloud storage systems. Moreover, such systems create security issues while efficiently using IoT and Cloud Computing technologies. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has the potential to make IoT data more secure and reliable in various cloud storage services. Cloud-assisted IoTs suffer from two privacy issues: access policies (public) and super polynomial decryption times (attributed mainly to complex access structures). We have developed a CP-ABE scheme in alignment with a Hidden Hierarchy Ciphertext-Policy… More >

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Faguo Wu^1,2,3,4,*, Bo Zhou², Jie Jiang⁵, Tianyu Lei¹, Jiale Song¹
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 957-973, 2023, DOI:10.32604/cmc.2023.038771
Abstract With the rapid increase in demand for data trustworthiness and data security, distributed data storage technology represented by blockchain has received unprecedented attention. These technologies have been suggested for various uses because of their remarkable ability to offer decentralization, high autonomy, full process traceability, and tamper resistance. Blockchain enables the exchange of information and value in an untrusted environment. There has been a significant increase in attention to the confidentiality and privacy preservation of blockchain technology. Ensuring data privacy is a critical concern in cryptography, and one of the most important protocols used to achieve this is the secret-sharing method.… More >

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Hadeel Alsolai¹, Mohammed Rizwanullah^2,*, Mashael Maashi³, Mahmoud Othman⁴, Amani A. Alneil², Amgad Atta Abdelmageed²
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 975-992, 2023, DOI:10.32604/cmc.2023.037936
Abstract Soil classification is one of the emanating topics and major concerns in many countries. As the population has been increasing at a rapid pace, the demand for food also increases dynamically. Common approaches used by agriculturalists are inadequate to satisfy the rising demand, and thus they have hindered soil cultivation. There comes a demand for computer-related soil classification methods to support agriculturalists. This study introduces a Gradient-Based Optimizer and Deep Learning (DL) for Automated Soil Classification (GBODL-ASC) technique. The presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision approaches. In the presented GBODL-ASC technique, three major… More >

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A. Manju¹, R. kaladevi², Shanmugasundaram Hariharan³, Shih-Yu Chen^4,5,*, Vinay Kukreja⁶, Pradip Kumar Sharma⁷, Fayez Alqahtani⁸, Amr Tolba⁹, Jin Wang¹⁰
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 993-1007, 2023, DOI:10.32604/cmc.2023.039567
Abstract The medical community has more concern on lung cancer analysis. Medical experts’ physical segmentation of lung cancers is time-consuming and needs to be automated. The research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning techniques. Computer-Aided Diagnostic (CAD) system aids in the diagnosis and shortens the time necessary to detect the tumor detected. The application of Deep Neural Networks (DNN) has also been exhibited as an excellent and effective method in classification and segmentation tasks. This research aims to separate lung cancers from images of Magnetic Resonance Imaging… More >

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Muhammad Rashid^1,*, Omar S. Sonbul¹, Muhammad Arif², Furqan Aziz Qureshi³, Saud. S. Alotaibi⁴, Mohammad H. Sinky¹
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1009-1025, 2023, DOI:10.32604/cmc.2023.039901
Abstract This work presents a flexible/unified hardware architecture of Elliptic-curve Cryptography (ECC) and PRESENT for cryptographic applications. The features of the proposed work are (i) computation of only the point multiplication operation of ECC over for a 163-bit key generation, (ii) execution of only the variant of an 80-bit PRESENT block cipher for data encryption & decryption and (iii) execution of point multiplication operation (ECC algorithm) along with the data encryption and decryption (PRESENT algorithm). To establish an area overhead for the flexible design, dedicated hardware architectures of ECC and PRESENT are implemented in the first step, and a sum of… More >

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Sharad Saxena¹, Shailendra Mishra^2,*, Mohammed Baljon^2,*, Shamiksha Mishra³, Sunil Kumar Sharma², Prakhar Goel¹, Shubham Gupta¹, Vinay Kishore¹
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1027-1045, 2023, DOI:10.32604/cmc.2023.039677
Abstract In recent years, the growth of female employees in the commercial market and industries has increased. As a result, some people think travelling to distant and isolated locations during odd hours generates new threats to women’s safety. The exponential increase in assaults and attacks on women, on the other hand, is posing a threat to women’s growth, development, and security. At the time of the attack, it appears the women were immobilized and needed immediate support. Only self-defense isn’t sufficient against abuse; a new technological solution is desired and can be used as quickly as hitting a switch or button.… More >

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Hong Zhang^1,2,*, Gang Yang¹, Hailiang Yu¹, Zan Zheng¹
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1047-1063, 2023, DOI:10.32604/cmc.2023.039274
Abstract To accurately predict traffic flow on the highways, this paper proposes a Convolutional Neural Network-Bi-directional Long Short-Term Memory-Attention Mechanism (CNN-BiLSTM-Attention) traffic flow prediction model based on Kalman-filtered data processing. Firstly, the original fluctuating data is processed by Kalman filtering, which can reduce the instability of short-term traffic flow prediction due to unexpected accidents. Then the local spatial features of the traffic data during different periods are extracted, dimensionality is reduced through a one-dimensional CNN, and the BiLSTM network is used to analyze the time series information. Finally, the Attention Mechanism assigns feature weights and performs Softmax regression. The experimental results… More >

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Syed Asghar Ali Shah¹, Tariqullah Jan¹, Syed Muslim Shah², Ruhul Amin Khalil^1,*, Ahmad Sawalmeh³, Muhammad Anan⁴
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1065-1084, 2023, DOI:10.32604/cmc.2023.039826
Abstract Fractional order algorithms have shown promising results in various signal processing applications due to their ability to improve performance without significantly increasing complexity. The goal of this work is to investigate the use of fractional order algorithm in the field of adaptive beamforming, with a focus on improving performance while keeping complexity lower. The effectiveness of the algorithm will be studied and evaluated in this context. In this paper, a fractional order least mean square (FLMS) algorithm is proposed for adaptive beamforming in wireless applications for effective utilization of resources. This algorithm aims to improve upon existing beamforming algorithms, which… More >

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Zunliang Chen^1,2, Chengxu Huang^1,2, Lucheng Duan^1,2, Baohua Tan^1,2,*
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1085-1102, 2023, DOI:10.32604/cmc.2023.039451
Abstract In response to the problem of the high cost and low efficiency of traditional water surface litter cleanup through manpower, a lightweight water surface litter detection algorithm based on improved YOLOv5s is proposed to provide core technical support for real-time water surface litter detection by water surface litter cleanup vessels. The method reduces network parameters by introducing the deep separable convolution GhostConv in the lightweight network GhostNet to substitute the ordinary convolution in the original YOLOv5s feature extraction and fusion network; introducing the C3Ghost module to substitute the C3 module in the original backbone and neck networks to further reduce… More >

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413

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Qurat-ul-Ain Arshad¹, Mudassar Raza¹, Wazir Zada Khan², Ayesha Siddiqa², Abdul Muiz², Muhammad Attique Khan^3,*, Usman Tariq⁴, Taerang Kim⁵, Jae-Hyuk Cha^5,*
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1103-1125, 2023, DOI:10.32604/cmc.2023.039752
（This article belongs to the Special Issue: Multimedia Encryption and Information Security)
Abstract Anomalous situations in surveillance videos or images that may result in security issues, such as disasters, accidents, crime, violence, or terrorism, can be identified through video anomaly detection. However, differentiating anomalous situations from normal can be challenging due to variations in human activity in complex environments such as train stations, busy sporting fields, airports, shopping areas, military bases, care centers, etc. Deep learning models’ learning capability is leveraged to identify abnormal situations with improved accuracy. This work proposes a deep learning architecture called Anomalous Situation Recognition Network (ASRNet) for deep feature extraction to improve the detection accuracy of various anomalous… More >

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Inès Hilali-Jaghdam^1,*, Azhari A Elhag², Anis Ben Ishak³, Bushra M. Elamin Elnaim⁴, Omer Eltag Mohammed Elhag⁵, Feda Muhammed Abuhaimed¹, S. Abdel-Khalek^2,6
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1127-1150, 2023, DOI:10.32604/cmc.2023.038915
Abstract In 2019, the novel coronavirus disease 2019 (COVID-19) ravaged the world. As of July 2021, there are about 192 million infected people worldwide and 4.1365 million deaths. At present, the new coronavirus is still spreading and circulating in many places around the world, especially since the emergence of Delta variant strains has increased the risk of the COVID-19 pandemic again. The symptoms of COVID-19 are diverse, and most patients have mild symptoms, with fever, dry cough, and fatigue as the main manifestations, and about 15.7% to 32.0% of patients will develop severe symptoms. Patients are screened in hospitals or primary… More >

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Jahanzaib Latif¹, Shanshan Tu^1,*, Chuangbai Xiao¹, Anas Bilal², Sadaqat Ur Rehman³, Zohaib Ahmad⁴
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1151-1172, 2023, DOI:10.32604/cmc.2023.040152
（This article belongs to the Special Issue: Recent Advances in Ophthalmic Diseases Diagnosis using AI)
Abstract Glaucoma is a progressive eye disease that can lead to blindness if left untreated. Early detection is crucial to prevent vision loss, but current manual scanning methods are expensive, time-consuming, and require specialized expertise. This study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine (EGWO-SVM) method. The proposed method involves preprocessing steps such as removing image noise using the adaptive median filter (AMF) and feature extraction using the previously processed speeded-up robust feature (SURF), histogram of oriented gradients (HOG), and Global features. The enhanced Grey Wolf Optimization (GWO) technique is then employed… More >

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Amjed Al Fahoum^*, Ala’a Zyout, Hiam Alquran, Isam Abu-Qasmieh
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1173-1193, 2023, DOI:10.32604/cmc.2023.038304
（This article belongs to the Special Issue: Intelligent Computational Models based on Machine Learning and Deep Learning for Diagnosis System)
Abstract Complex proteins are needed for many biological activities. Folding amino acid chains reveals their properties and functions. They support healthy tissue structure, physiology, and homeostasis. Precision medicine and treatments require quantitative protein identification and function. Despite technical advances and protein sequence data exploration, bioinformatics’ “basic structure” problem—the automatic deduction of a protein’s properties from its amino acid sequence—remains unsolved. Protein function inference from amino acid sequences is the main biological data challenge. This study analyzes whether raw sequencing can characterize biological facts. A massive corpus of protein sequences and the Globin-like superfamily’s related protein families generate a solid vector representation.… More >

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Yixin Tang^*
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1195-1212, 2023, DOI:10.32604/cmc.2023.039182
Abstract Visual illustration transformation from real-world to cartoon images is one of the famous and challenging tasks in computer vision. Image-to-image translation from real-world to cartoon domains poses issues such as a lack of paired training samples, lack of good image translation, low feature extraction from the previous domain images, and lack of high-quality image translation from the traditional generator algorithms. To solve the above-mentioned issues, paired independent model, high-quality dataset, Bayesian-based feature extractor, and an improved generator must be proposed. In this study, we propose a high-quality dataset to reduce the effect of paired training samples on the model’s performance.… More >

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Muhammad Rizwan^1,*, Sana Ul Haq^1,*, Noor Gul^1,2, Muhammad Asif¹, Syed Muslim Shah³, Tariqullah Jan⁴, Naveed Ahmad⁵
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1213-1247, 2023, DOI:10.32604/cmc.2023.038211
Abstract Appearance-based dynamic Hand Gesture Recognition (HGR) remains a prominent area of research in Human-Computer Interaction (HCI). Numerous environmental and computational constraints limit its real-time deployment. In addition, the performance of a model decreases as the subject’s distance from the camera increases. This study proposes a 3D separable Convolutional Neural Network (CNN), considering the model’s computational complexity and recognition accuracy. The 20BN-Jester dataset was used to train the model for six gesture classes. After achieving the best offline recognition accuracy of 94.39%, the model was deployed in real-time while considering the subject’s attention, the instant of performing a gesture, and the… More >

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Shuja Ali¹, Ahmad Jalal¹, Mohammed Hamad Alatiyyah², Khaled Alnowaiser³, Jeongmin Park^4,*
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1249-1265, 2023, DOI:10.32604/cmc.2023.038114
Abstract Unmanned aerial vehicles (UAVs) can be used to monitor traffic in a variety of settings, including security, traffic surveillance, and traffic control. Numerous academics have been drawn to this topic because of the challenges and the large variety of applications. This paper proposes a new and efficient vehicle detection and tracking system that is based on road extraction and identifying objects on it. It is inspired by existing detection systems that comprise stationary data collectors such as induction loops and stationary cameras that have a limited field of view and are not mobile. The goal of this study is to… More >

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M. Muzaffar Hameed^1,2, Rodina Ahmad^1,*, Laiha Mat Kiah¹, Ghulam Murtaza³, Noman Mazhar¹
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1267-1289, 2023, DOI:10.32604/cmc.2023.035063
Abstract Offline signature verification (OfSV) is essential in preventing the falsification of documents. Deep learning (DL) based OfSVs require a high number of signature images to attain acceptable performance. However, a limited number of signature samples are available to train these models in a real-world scenario. Several researchers have proposed models to augment new signature images by applying various transformations. Others, on the other hand, have used human neuromotor and cognitive-inspired augmentation models to address the demand for more signature samples. Hence, augmenting a sufficient number of signatures with variations is still a challenging task. This study proposed OffSig-SinGAN: a deep… More >

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Wan Norsyafizan W. Muhamad¹, Kaharudin Dimyati², Muhammad Awais Javed³, Suzi Seroja Sarnin^1,*, Divine Senanu Ametefe¹
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1291-1308, 2023, DOI:10.32604/cmc.2023.037214
（This article belongs to the Special Issue: Innovations in Pervasive Computing and Communication Technologies)
Abstract The tremendous advancement in distributed computing and Internet of Things (IoT) applications has resulted in the adoption of fog computing as today’s widely used framework complementing cloud computing. Thus, suitable and effective applications could be performed to satisfy the applications’ latency requirement. Resource allocation techniques are essential aspects of fog networks which prevent unbalanced load distribution. Effective resource management techniques can improve the quality of service metrics. Due to the limited and heterogeneous resources available within the fog infrastructure, the fog layer’s resources need to be optimised to efficiently manage and distribute them to different applications within the IoT network.… More >

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Puneet Kumar Mishra¹, Amrita Rai¹, Nitin Sharma², Kanika Sharma³, Nitin Mittal⁴, Mohd Anul Haq^5,*, Ilyas Khan⁶, ElSayed M. Tag El Din⁷
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1309-1320, 2023, DOI:10.32604/cmc.2023.033828
Abstract The fundamental advantages of carbon-based graphene material, such as its high tunnelling probability, symmetric band structure (linear dependence of the energy band on the wave direction), low effective mass, and characteristics of its 2D atomic layers, are the main focus of this research work. The impact of channel thickness, gate under-lap, asymmetric source/drain doping method, workfunction of gate contact, and High-K material on Graphene-based Tunnel Field Effect Transistor (TFET) is analyzed with 20 nm technology. Physical modelling and electrical characteristic performance have been simulated using the Atlas device simulator of SILVACO TCAD with user-defined material syntax for the newly included… More >

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