Jiahui Liu^1,2, Lang Li^1,2,*, Di Li^1,2, Yu Ou^1,2

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4641-4657, 2024, DOI:10.32604/cmc.2024.051613

Abstract Correlation power analysis (CPA) combined with genetic algorithms (GA) now achieves greater attack efficiency and can recover all subkeys simultaneously. However, two issues in GA-based CPA still need to be addressed: key degeneration and slow evolution within populations. These challenges significantly hinder key recovery efforts. This paper proposes a screening correlation power analysis framework combined with a genetic algorithm, named SFGA-CPA, to address these issues. SFGA-CPA introduces three operations designed to exploit CPA characteristics: propagative operation, constrained crossover, and constrained mutation. Firstly, the propagative operation accelerates population evolution by maximizing the number of correct bytes… More >

Nasser Sweilam¹, Seham Al-Mekhlafi^2,*, Aya Ahmed³, Ahoud Alsheri⁴, Emad Abo-Eldahab³

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1619-1645, 2024, DOI:10.32604/cmes.2024.047896

Abstract In this paper, two crossover hybrid variable-order derivatives of the cancer model are developed. Grünwald-Letnikov approximation is used to approximate the hybrid fractional and variable-order fractional operators. The existence, uniqueness, and stability of the proposed model are discussed. Adams Bashfourth’s fifth-step method with a hybrid variable-order fractional operator is developed to study the proposed models. Comparative studies with generalized fifth-order Runge-Kutta method are given. Numerical examples and comparative studies to verify the applicability of the used methods and to demonstrate the simplicity of these approximations are presented. We have showcased the efficiency of the proposed More >

Zakir Hussain Ahmed^1,*, Habibollah Haron², Abdullah Al-Tameem³

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2399-2425, 2024, DOI:10.32604/cmc.2024.049704

Abstract Genetic algorithms (GAs) are very good metaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems. A simple GA begins with a set of solutions represented by a population of chromosomes and then uses the idea of survival of the fittest in the selection process to select some fitter chromosomes. It uses a crossover operator to create better offspring chromosomes and thus, converges the population. Also, it uses a mutation operator to explore the unexplored areas by the crossover operator, and thus, diversifies the GA search space. A combination of crossover and mutation operators… More >

Liang Zeng^1,2, Junyang Shi¹, Yanyan Li¹, Shanshan Wang^1,2,*, Weigang Li³

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 375-392, 2024, DOI:10.32604/cmc.2023.045803

Abstract The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems. It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives. The Non-dominated Sorting Genetic Algorithm III (NSGA-III) is an effective approach for solving the multi-objective job shop scheduling problem. Nevertheless, it has some limitations in solving scheduling problems, including inadequate global search capability, susceptibility to premature convergence, and challenges in balancing convergence and diversity. To enhance its performance, this paper introduces a strengthened dominance relation NSGA-III… More >

Mustufa Haider Abidi^*, Hisham Alkhalefah, Mohamed K. Aboudaif

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 977-997, 2024, DOI:10.32604/cmes.2023.044169

Abstract The healthcare data requires accurate disease detection analysis, real-time monitoring, and advancements to ensure proper treatment for patients. Consequently, Machine Learning methods are widely utilized in Smart Healthcare Systems (SHS) to extract valuable features from heterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities. These methods are employed across different domains that are susceptible to adversarial attacks, necessitating careful consideration. Hence, this paper proposes a crossover-based Multilayer Perceptron (CMLP) model. The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on the medical… More >

Lisang Liu¹, Chengyang Ke^1,*, He Lin²

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2443-2461, 2023, DOI:10.32604/cmc.2023.042582

Abstract Aiming at the problems of inaccurate edge segmentation, the hole phenomenon of segmenting large-scale targets, and the slow segmentation speed of printed circuit boards (PCB) in the image segmentation process, a PCB image segmentation model Mobile-Deep based on DeepLabv3+ semantic segmentation framework is proposed. Firstly, the DeepLabv3+ feature extraction network is replaced by the lightweight model MobileNetv2, which effectively reduces the number of model parameters; secondly, for the problem of positive and negative sample imbalance, a new loss function is composed of Focal Loss combined with Dice Loss to solve the category imbalance and improve… More >

Hassan Rabiei¹, Masoud Kaveh², Mohammad Reza Mosavi¹, Diego Martín^3,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4831-4845, 2023, DOI:10.32604/cmc.2023.034981

Abstract With the expanding use of the Internet of Things (IoT) devices and the connection of humans and devices to the Internet, the need to provide security in this field is constantly growing. The conventional cryptographic solutions need the IoT device to store secret keys in its non-volatile memory (NVM) leading the system to be vulnerable to physical attacks. In addition, they are not appropriate for IoT applications due to their complex calculations. Thus, physically unclonable functions (PUFs) have been introduced to simultaneously address these issues. PUFs are lightweight and easy-to-access hardware security primitives which employ… More >

Zakir Hussain Ahmed^1,*, Naif Al-Otaibi¹, Abdullah Al-Tameem², Abdul Khader Jilani Saudagar²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1575-1605, 2023, DOI:10.32604/cmc.2023.031325

Abstract We study the capacitated vehicle routing problem (CVRP) which is a well-known NP-hard combinatorial optimization problem (COP). The aim of the problem is to serve different customers by a convoy of vehicles starting from a depot so that sum of the routing costs under their capacity constraints is minimized. Since the problem is very complicated, solving the problem using exact methods is almost impossible. So, one has to go for the heuristic/metaheuristic methods and genetic algorithm (GA) is broadly applied metaheuristic method to obtain near optimal solution to such COPs. So, this paper studies GAs… More >

Mariem Ayedi^1,2,*, Walaa H. ElAshmawi^3,4, Esraa Eldesouky^1,3

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2963-2980, 2022, DOI:10.32604/cmc.2022.025741

Abstract Resource management in Underground Wireless Sensor Networks (UWSNs) is one of the pillars to extend the network lifetime. An intriguing design goal for such networks is to achieve balanced energy and spectral resource utilization. This paper focuses on optimizing the resource efficiency in UWSNs where underground relay nodes amplify and forward sensed data, received from the buried source nodes through a lossy soil medium, to the aboveground base station. A new algorithm called the Hybrid Chaotic Salp Swarm and Crossover (HCSSC) algorithm is proposed to obtain the optimal source and relay transmission powers to maximize… More >

Yousef Jaradat^*, Mohammad Masoud, Ismael Jannoud, Dema Zeidan

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 791-808, 2022, DOI:10.32604/iasc.2022.024218

Abstract Optimal node clustering in wireless sensor networks (WSNs) is a major issue in reducing energy consumption and extending network node life time and reliability measures. Many techniques for optimizing the node clustering process in WSN have been proposed in the literature. The metaheuristic algorithms are a subset of these techniques. Genetic algorithm (GA) is an evolutionary metaheuristic technique utilized to improve the network reliability and extending the network life time by optimizing the clustering process in the network. The GA dynamic clustering (GA-DC) algorithm is proposed in this paper to extend the network reliability and… More >