Feng Guo, Chen Yang^*, Dezhong Xia, Jingxiang Xu

Energy Engineering, Vol.122, No.5, pp. 1975-1999, 2025, DOI:10.32604/ee.2025.062035 - 25 April 2025

Abstract Short-term photovoltaic (PV) power forecasting plays a crucial role in enhancing the stability and reliability of power grid scheduling. To address the challenges posed by complex environmental variables and difficulties in modeling temporal features in PV power prediction, a short-term PV power forecasting method based on an improved CNN-LSTM and cascade learning strategy is proposed. First, Pearson correlation coefficients and mutual information are used to select representative features, reducing the impact of redundant features on model performance. Then, the CNN-LSTM network is designed to extract local features using CNN and learn temporal dependencies through LSTM,… More > Graphic Abstract

Yiming Tang^*, Yanqiu Chen

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1707-1726, 2025, DOI:10.32604/cmc.2025.062412 - 16 April 2025

Abstract Semantic segmentation of novel object categories with limited labeled data remains a challenging problem in computer vision. Few-shot segmentation methods aim to address this problem by recognizing objects from specific target classes with a few provided examples. Previous approaches for few-shot semantic segmentation typically represent target classes using class prototypes. These prototypes are matched with the features of the query set to get segmentation results. However, class prototypes are usually obtained by applying global average pooling on masked support images. Global pooling discards much structural information, which may reduce the accuracy of model predictions. To… More >

Jagadesh Balasubramani¹, Surendran Rajendran^1,*, Mohammad Zakariah², Abeer Alnuaim²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2793-2807, 2025, DOI:10.32604/cmc.2025.062266 - 16 April 2025

Abstract Autism spectrum disorder (ASD) is a multifaceted neurological developmental condition that manifests in several ways. Nearly all autistic children remain undiagnosed before the age of three. Developmental problems affecting face features are often associated with fundamental brain disorders. The facial evolution of newborns with ASD is quite different from that of typically developing children. Early recognition is very significant to aid families and parents in superstition and denial. Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD. Presently, artificial intelligence (AI) significantly contributes to the emerging computer-aided… More >

Kaiyang Han¹, Xiaoqiang Jia¹, Yangfei Lin², Tsutomu Yoshinaga², Yalong Li², Jiale Wu^2,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2971-2988, 2025, DOI:10.32604/cmc.2025.061563 - 16 April 2025

Abstract With the rapid development of artificial intelligence and the Internet of Things, along with the growing demand for privacy-preserving transmission, the need for efficient and secure communication systems has become increasingly urgent. Traditional communication methods transmit data at the bit level without considering its semantic significance, leading to redundant transmission overhead and reduced efficiency. Semantic communication addresses this issue by extracting and transmitting only the most meaningful semantic information, thereby improving bandwidth efficiency. However, despite reducing the volume of data, it remains vulnerable to privacy risks, as semantic features may still expose sensitive information. To… More >

Weitao Ha¹, Sheng Gang², Yahya D. Navaei³, Abubakar S. Gezawa⁴, Yaser A. Nanehkaran^2,5,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3025-3057, 2025, DOI:10.32604/cmc.2025.061343 - 16 April 2025

Abstract Music recommendation systems are essential due to the vast amount of music available on streaming platforms, which can overwhelm users trying to find new tracks that match their preferences. These systems analyze users’ emotional responses, listening habits, and personal preferences to provide personalized suggestions. A significant challenge they face is the “cold start” problem, where new users have no past interactions to guide recommendations. To improve user experience, these systems aim to effectively recommend music even to such users by considering their listening behavior and music popularity. This paper introduces a novel music recommendation system… More >

Min Yao^1,*, Guangjie Hu¹, Yaozu Zhang²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2751-2771, 2025, DOI:10.32604/cmc.2025.060860 - 16 April 2025

Abstract Semantic segmentation of remote sensing images is a critical research area in the field of remote sensing. Despite the success of Convolutional Neural Networks (CNNs), they often fail to capture inter-layer feature relationships and fully leverage contextual information, leading to the loss of important details. Additionally, due to significant intra-class variation and small inter-class differences in remote sensing images, CNNs may experience class confusion. To address these issues, we propose a novel Category-Guided Feature Collaborative Learning Network (CG-FCLNet), which enables fine-grained feature extraction and adaptive fusion. Specifically, we design a Feature Collaborative Learning Module (FCLM)… More >

Xuewen Mu^*, Bingcong Zhao

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2143-2159, 2025, DOI:10.32604/cmc.2025.060739 - 16 April 2025

Abstract When dealing with imbalanced datasets, the traditional support vector machine (SVM) tends to produce a classification hyperplane that is biased towards the majority class, which exhibits poor robustness. This paper proposes a high-performance classification algorithm specifically designed for imbalanced datasets. The proposed method first uses a biased second-order cone programming support vector machine (B-SOCP-SVM) to identify the support vectors (SVs) and non-support vectors (NSVs) in the imbalanced data. Then, it applies the synthetic minority over-sampling technique (SV-SMOTE) to oversample the support vectors of the minority class and uses the random under-sampling technique (NSV-RUS) multiple times More >

Iman S. Al-Mahdi¹, Saad M. Darwish^1,*, Magda M. Madbouly²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 875-909, 2025, DOI:10.32604/cmes.2025.061623 - 11 April 2025

Abstract Heart disease prediction is a critical issue in healthcare, where accurate early diagnosis can save lives and reduce healthcare costs. The problem is inherently complex due to the high dimensionality of medical data, irrelevant or redundant features, and the variability in risk factors such as age, lifestyle, and medical history. These challenges often lead to inefficient and less accurate models. Traditional prediction methodologies face limitations in effectively handling large feature sets and optimizing classification performance, which can result in overfitting poor generalization, and high computational cost. This work proposes a novel classification model for heart… More >

Huaruo Chen^1,2, Wanru Song¹, Jian Xie¹, Huadi Wang³, Feifei Zheng³, Ya Wen^4,*

International Journal of Mental Health Promotion, Vol.27, No.3, pp. 379-400, 2025, DOI:10.32604/ijmhp.2025.060370 - 31 March 2025

Abstract Objectives: Teachers are facing unprecedented new challenges leading them to face an increasing number of tasks that are not part of their job, as well as having to cope with the additional skills acquisition that comes with non-traditional forms of teaching and learning, and increased work pressure leading to an increase in the rate of teachers leaving the profession. Therefore, this study aims to explore the mechanism of the career calling on job burnout through career adaptability and work engagement. Methods: This study conducted a cross-sectional survey of 465 primary and secondary school teachers (PSST)… More >

Sumaiya Noor¹, Salman A. AlQahtani², Salman Khan^3,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1435-1450, 2025, DOI:10.32604/cmc.2025.061700 - 26 March 2025

Abstract The liver is a crucial gland and the second-largest organ in the human body and also essential in digestion, metabolism, detoxification, and immunity. Liver diseases result from factors such as viral infections, obesity, alcohol consumption, injuries, or genetic predispositions. Pose significant health risks and demand timely diagnosis and treatment to enhance survival rates. Traditionally, diagnosing liver diseases relied heavily on clinical expertise, often leading to subjective, challenging, and time-intensive processes. However, early detection is essential for effective intervention, and advancements in machine learning (ML) have demonstrated remarkable success in predicting various conditions, including Chronic Obstructive… More >