Yiming Tang^1,2, Jianwei Gao^1,*, Yifan Huang¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1063-1088, 2024, DOI:10.32604/cmc.2024.049341

Abstract The α-universal triple I (α-UTI) method is a recognized scheme in the field of fuzzy reasoning, which was proposed by our research group previously. The robustness of fuzzy reasoning determines the quality of reasoning algorithms to a large extent, which is quantified by calculating the disparity between the output of fuzzy reasoning with interference and the output without interference. Therefore, in this study, the interval robustness (embodied as the interval stability) of the α-UTI method is explored in the interval-valued fuzzy environment. To begin with, the stability of the α-UTI method is explored for the case of an individual rule,… More >

Khaled Soliman^1,*, Mohamed Sobh², Ayman M. Bahaa-Eldin²

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1419-1439, 2024, DOI:10.32604/cmc.2024.048883

Abstract The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leads to wide losses for various organizations. These dangers have proven that signature-based approaches are insufficient to prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious Executable Detection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE) files in hosts using Windows operating systems through collecting PE headers and applying machine learning mechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031 benign files and 179,071 malware samples from diverse sources to ensure the efficiency… More >

Chunbin Qin^*, Xiaotian Ran

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1319-1334, 2024, DOI:10.32604/cmc.2024.048850

Abstract Traditional feature-based image stitching techniques often encounter obstacles when dealing with images lacking unique attributes or suffering from quality degradation. The scarcity of annotated datasets in real-life scenes severely undermines the reliability of supervised learning methods in image stitching. Furthermore, existing deep learning architectures designed for image stitching are often too bulky to be deployed on mobile and peripheral computing devices. To address these challenges, this study proposes a novel unsupervised image stitching method based on the YOLOv8 (You Only Look Once version 8) framework that introduces deep homography networks and attention mechanisms. The methodology is partitioned into three distinct… More >

Meng Huang, Honglei Wei^*, Xianyi Zhai

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 531-547, 2024, DOI:10.32604/cmc.2024.048510

Abstract In pursuit of cost-effective manufacturing, enterprises are increasingly adopting the practice of utilizing recycled semiconductor chips. To ensure consistent chip orientation during packaging, a circular marker on the front side is employed for pin alignment following successful functional testing. However, recycled chips often exhibit substantial surface wear, and the identification of the relatively small marker proves challenging. Moreover, the complexity of generic target detection algorithms hampers seamless deployment. Addressing these issues, this paper introduces a lightweight YOLOv8s-based network tailored for detecting markings on recycled chips, termed Van-YOLOv8. Initially, to alleviate the influence of diminutive, low-resolution markings on the precision of… More >

Jun Wang^1,2, Linxi Zhang^1,2, Hao Zhang¹, Funan Peng^1,*, Mohammed A. El-Meligy³, Mohamed Sharaf³, Qiang Fu¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1281-1299, 2024, DOI:10.32604/cmc.2024.048495

Abstract The existing algorithms for solving multi-objective optimization problems fall into three main categories: Decomposition-based, dominance-based, and indicator-based. Traditional multi-objective optimization problems mainly focus on objectives, treating decision variables as a total variable to solve the problem without considering the critical role of decision variables in objective optimization. As seen, a variety of decision variable grouping algorithms have been proposed. However, these algorithms are relatively broad for the changes of most decision variables in the evolution process and are time-consuming in the process of finding the Pareto frontier. To solve these problems, a multi-objective optimization algorithm for grouping decision variables based… More >

Hongle Li¹, SeongKi Kim^2,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 473-492, 2024, DOI:10.32604/cmc.2024.048402

Abstract Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetables and determines the stop and the start according to the demands. This study explores the optimization of dynamic vehicle scheduling and real-time route planning in urban public transportation systems, with a focus on bus services. It addresses the limitations of current shared mobility routing algorithms, which are primarily designed for simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. The research introduces an route planning algorithm designed to dynamically accommodate passenger travel needs and enable real-time route modifications.… More >

Maria Gul¹, Imran Ali Khan¹, Gohar Zaman², Atta Rahman^3,*, Jamaluddin Mir², Sardar Asad Ali Biabani^4,5, May Issa Aldossary⁶, Mustafa Youldash⁷, Ashraf Saadeldeen⁸, Maqsood Mahmud⁹, Asiya Abdus Salam⁶, Dania Alkhulaifi³, Abdullah AlTurkey³

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1631-1657, 2024, DOI:10.32604/cmc.2024.048289

Abstract Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, many researchers proposed game theoretic models to avoid and remove obstacles for crowd evacuation. Game theoretical models aim to study and analyze the strategic behaviors of individuals within a crowd and their interactions during the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. These models consider a group of individuals as homogeneous objects with the same goals, involve complex mathematical formulation, and cannot model real-world scenarios such as panic, environmental information, crowds that move dynamically, etc. The proposed work presents… More >

Ankita Gangwar¹, Vijaypal Singh Dhaka¹, Geeta Rani^2,*, Shrey Khandelwal¹, Ester Zumpano^3,4, Eugenio Vocaturo^3,4

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 117-142, 2024, DOI:10.32604/cmc.2024.048119

Abstract A consumption of 46.9 million tons of processed tomatoes was reported in 2022 which is merely 20% of the total consumption. An increase of 3.3% in consumption is predicted from 2024 to 2032. Tomatoes are also rich in iron, potassium, antioxidant lycopene, vitamins A, C and K which are important for preventing cancer, and maintaining blood pressure and glucose levels. Thus, tomatoes are globally important due to their widespread usage and nutritional value. To face the high demand for tomatoes, it is mandatory to investigate the causes of crop loss and minimize them. Diseases are one of the major causes… More >

Ameni Ellouze¹, Nesrine Kadri², Alaa Alaerjan^3,*, Mohamed Ksantini¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 351-372, 2024, DOI:10.32604/cmc.2024.048061

Abstract Recognizing human activity (HAR) from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases. Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not. Typically, smartphones and their associated sensing devices operate in distributed and unstable environments. Therefore, collecting their data and extracting useful information is a significant challenge. In this context, the aim of this paper is twofold: The first is to analyze human behavior based on the recognition of physical activities. Using the results of physical activity detection… More >

Xue Zhao, Shaojun Tao, Hongying Tang, Jiang Wang^*, Baoqing Li^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1335-1351, 2024, DOI:10.32604/cmc.2024.047996

Abstract In recent years, target tracking has been considered one of the most important applications of wireless sensor network (WSN). Optimizing target tracking performance and prolonging network lifetime are two equally critical objectives in this scenario. The existing mechanisms still have weaknesses in balancing the two demands. The proposed heuristic multi-node collaborative scheduling mechanism (HMNCS) comprises cluster head (CH) election, pre-selection, and task set selection mechanisms, where the latter two kinds of selections form a two-layer selection mechanism. The CH election innovatively introduces the movement trend of the target and establishes a scoring mechanism to determine the optimal CH, which can… More >