Yongjiang Zhao, Haoyi Zhong, Chang Cyoon Lim^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 449-471, 2024, DOI:10.32604/cmc.2024.048771

Abstract This paper examines the difficulties of managing distributed power systems, notably due to the increasing use of renewable energy sources, and focuses on voltage control challenges exacerbated by their variable nature in modern power grids. To tackle the unique challenges of voltage control in distributed renewable energy networks, researchers are increasingly turning towards multi-agent reinforcement learning (MARL). However, MARL raises safety concerns due to the unpredictability in agent actions during their exploration phase. This unpredictability can lead to unsafe control measures. To mitigate these safety concerns in MARL-based voltage control, our study introduces a novel approach: Safety-Constrained Multi-Agent Reinforcement Learning… More >

Aizaz Ali¹, Maqbool Khan^1,2, Khalil Khan³, Rehan Ullah Khan⁴, Abdulrahman Aloraini^4,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 713-733, 2024, DOI:10.32604/cmc.2024.048712

Abstract Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understanding public opinion and user sentiment across diverse languages. While numerous scholars conduct sentiment analysis in widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grappling with resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language, characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu, Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguistic features, presents an additional hurdle due… More >

Supeng Yu¹, Fen Huang^1,*, Chengcheng Fan^2,3,4,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 549-562, 2024, DOI:10.32604/cmc.2024.048608

Abstract Significant advancements have been achieved in road surface extraction based on high-resolution remote sensing image processing. Most current methods rely on fully supervised learning, which necessitates enormous human effort to label the image. Within this field, other research endeavors utilize weakly supervised methods. These approaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such as scribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised and edge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equipped with a distinct decoder module dedicated to road extraction tasks. One… 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 >

Ananthakrishnan Balasundaram^1,2, Ayesha Shaik^1,2,*, Japmann Kaur Banga², Aman Kumar Singh²

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 779-799, 2024, DOI:10.32604/cmc.2024.048362

Abstract Industrial activities, through the human-induced release of Green House Gas (GHG) emissions, have been identified as the primary cause of global warming. Accurate and quantitative monitoring of these emissions is essential for a comprehensive understanding of their impact on the Earth’s climate and for effectively enforcing emission regulations at a large scale. This work examines the feasibility of detecting and quantifying industrial smoke plumes using freely accessible geo-satellite imagery. The existing system has so many lagging factors such as limitations in accuracy, robustness, and efficiency and these factors hinder the effectiveness in supporting timely response to industrial fires. In this… More >

Tsu-Yang Wu^1,2, Haonan Li², Saru Kumari³, Chien-Ming Chen^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 19-46, 2024, DOI:10.32604/cmc.2024.048347

Abstract Hyperspectral image classification stands as a pivotal task within the field of remote sensing, yet achieving high-precision classification remains a significant challenge. In response to this challenge, a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm (AFLA-SCNN) is proposed. The Adaptive Fick’s Law Algorithm (AFLA) constitutes a novel metaheuristic algorithm introduced herein, encompassing three new strategies: Adaptive weight factor, Gaussian mutation, and probability update policy. With adaptive weight factor, the algorithm can adjust the weights according to the change in the number of iterations to improve the performance of the algorithm. Gaussian mutation helps the algorithm avoid… More >

Cheng Wan¹, Jiani Zhao¹, Xiangqian Hong², Weihua Yang^2,*, Shaochong Zhang^2,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 391-407, 2024, DOI:10.32604/cmc.2024.048307

Abstract Age-related macular degeneration (AMD) ranks third among the most common causes of blindness. As the most conventional and direct method for identifying AMD, color fundus photography has become prominent owing to its consistency, ease of use, and good quality in extensive clinical practice. In this study, a convolutional neural network (CSPDarknet53) was combined with a transformer to construct a new hybrid model, HCSP-Net. This hybrid model was employed to tri-classify color fundus photography into the normal macula (NM), dry macular degeneration (DMD), and wet macular degeneration (WMD) based on clinical classification manifestations, thus identifying and resolving AMD as early as… More >

Chunming Wu¹, Wukai Liu^2,*, Xin Ma³

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1441-1461, 2024, DOI:10.32604/cmc.2024.048136

Abstract A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase the visual impression of fused images by improving the quality of infrared and visible light picture fusion. The network comprises an encoder module, fusion layer, decoder module, and edge improvement module. The encoder module utilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformer to achieve deep-level co-extraction of local and global features from the original picture. An edge enhancement module (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy is introduced to enhance the… 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 >

Zezheng Meng, Zefeng Cai, Jie Feng^*, Hanjie Ma, Haixiang Zhang, Shaohua Li

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1481-1496, 2024, DOI:10.32604/cmc.2024.048002

Abstract Optical braille recognition methods typically employ existing target detection models or segmentation models for the direct detection and recognition of braille characters in original braille images. However, these methods need improvement in accuracy and generalizability, especially in densely dotted braille image environments. This paper presents a two-stage braille recognition framework. The first stage is a braille dot detection algorithm based on Gaussian diffusion, targeting Gaussian heatmaps generated by the convex dots in braille images. This is applied to the detection of convex dots in double-sided braille, achieving high accuracy in determining the central coordinates of the braille convex dots. The… More >