Umit Cigdem Turhal¹, Yasemin Onal^1,*, Kutalmis Turhal²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2307-2332, 2025, DOI:10.32604/cmes.2025.064269 - 30 May 2025

Abstract The reliability and efficiency of photovoltaic (PV) systems are essential for sustainable energy production, requiring accurate fault detection to minimize energy losses. This study proposes a hybrid model integrating Neighborhood Components Analysis (NCA) with a Convolutional Neural Network (CNN) to improve fault detection and diagnosis. Unlike Principal Component Analysis (PCA), which may compromise class relationships during feature extraction, NCA preserves these relationships, enhancing classification performance. The hybrid model combines NCA with CNN, a fundamental deep learning architecture, to enhance fault detection and diagnosis capabilities. The performance of the proposed NCA-CNN model was evaluated against other More > Graphic Abstract

Tallha Akram^1,*, Fahdah Almarshad¹, Anas Alsuhaibani¹, Syed Rameez Naqvi^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2333-2359, 2025, DOI:10.32604/cmes.2025.064079 - 30 May 2025

Abstract Melanoma is the deadliest form of skin cancer, with an increasing incidence over recent years. Over the past decade, researchers have recognized the potential of computer vision algorithms to aid in the early diagnosis of melanoma. As a result, a number of works have been dedicated to developing efficient machine learning models for its accurate classification; still, there remains a large window for improvement necessitating further research efforts. Limitations of the existing methods include lower accuracy and high computational complexity, which may be addressed by identifying and selecting the most discriminative features to improve classification… More >

Lord Amoah^1,2, Jinwei Wang^1,2,3,*, Bernard-Marie Onzo^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1635-1660, 2025, DOI:10.32604/cmes.2025.063992 - 30 May 2025

Abstract Medical image segmentation, i.e., labeling structures of interest in medical images, is crucial for disease diagnosis and treatment in radiology. In reversible data hiding in medical images (RDHMI), segmentation consists of only two regions: the focal and nonfocal regions. The focal region mainly contains information for diagnosis, while the nonfocal region serves as the monochrome background. The current traditional segmentation methods utilized in RDHMI are inaccurate for complex medical images, and manual segmentation is time-consuming, poorly reproducible, and operator-dependent. Implementing state-of-the-art deep learning (DL) models will facilitate key benefits, but the lack of domain-specific labels… More >

Mohammed Al-Jabbar¹, Mohammed Alshahrani^1,*, Ebrahim Mohammed Senan^2,3, Ibrahim Abunadi⁴, Sultan Ahmed Almalki¹, Eman A Alshari^3,5

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2429-2457, 2025, DOI:10.32604/cmes.2025.063938 - 30 May 2025

Abstract Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by tremors, rigidity, and decreased movement. PD poses risks to individuals’ lives and independence. Early detection of PD is essential because it allows timely intervention, which can slow disease progression and improve outcomes. Manual diagnosis of PD is problematic because it is difficult to capture the subtle patterns and changes that help diagnose PD. In addition, the subjectivity and lack of doctors compared to the number of patients constitute an obstacle to early diagnosis. Artificial intelligence (AI) techniques, especially deep and automated learning models, provide promising… More >

Wencheng Wang^1,2,*, Baoxin Yin^1,2, Lei Li^2,*, Lun Li¹, Hongtao Liu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1595-1616, 2025, DOI:10.32604/cmes.2025.063595 - 30 May 2025

Abstract In low-light environments, captured images often exhibit issues such as insufficient clarity and detail loss, which significantly degrade the accuracy of subsequent target recognition tasks. To tackle these challenges, this study presents a novel low-light image enhancement algorithm that leverages virtual hazy image generation through dehazing models based on statistical analysis. The proposed algorithm initiates the enhancement process by transforming the low-light image into a virtual hazy image, followed by image segmentation using a quadtree method. To improve the accuracy and robustness of atmospheric light estimation, the algorithm incorporates a genetic algorithm to optimize the… More >

Rajendran Thanikachalam¹, Anandhavalli Muniasamy², Ashwag Alasmari³, Rajendran Thavasimuthu^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1971-2000, 2025, DOI:10.32604/cmes.2025.063063 - 30 May 2025

Abstract Content-Based Image Retrieval (CBIR) and image mining are becoming more important study fields in computer vision due to their wide range of applications in healthcare, security, and various domains. The image retrieval system mainly relies on the efficiency and accuracy of the classification models. This research addresses the challenge of enhancing the image retrieval system by developing a novel approach, EfficientNet-Convolutional Neural Network (EffNet-CNN). The key objective of this research is to evaluate the proposed EffNet-CNN model’s performance in image classification, image mining, and CBIR. The novelty of the proposed EffNet-CNN model includes the integration… More >

Duy Dao¹, Hervé Kerivin², Philippe Lacomme^2,*, Bogdan Vulpescu³

Journal of Quantum Computing, Vol.7, pp. 39-54, 2025, DOI:10.32604/jqc.2025.064764 - 30 May 2025

Abstract Track finding is a complex optimization problem, originally introduced in particle physics for the reconstruction of the trajectories of particles. A track is typically composed of several consecutive segments, which together form a smooth curve without any bifurcations. In this paper, we investigate various modeling approaches to assess their effectiveness and impact when applied to track finding, using both quantum and classical methods. We present implementations of three classical models using CPLEX, two quantum models on actual D-Wave quantum computers, and one quantum model on a D-Wave simulator. The results show that, while CPLEX provides… More >

Muhammad Naeem^*, Abdul Majid

Journal on Artificial Intelligence, Vol.7, pp. 115-141, 2025, DOI:10.32604/jai.2025.062446 - 30 May 2025

Abstract Brain tumor identification is a challenging task in neuro-oncology. The brain’s complex anatomy makes it a crucial part of the central nervous system. Accurate tumor classification is crucial for clinical diagnosis and treatment planning. This research presents a significant advancement in the multi-classification of brain tumors. This paper proposed a novel architecture that integrates Enhanced ResNeXt 101_32×8d, a Convolutional Neural Network (CNN) with a multi-head self-attention (MHSA) mechanism. This combination harnesses the strengths of the feature extraction, feature representation by CNN, and long-range dependencies by MHSA. Magnetic Resonance Imaging (MRI) datasets were employed to check… More >

Zhebin Sun¹, Wei Wang¹, Mingxuan Du², Tao Liang¹, Yang Liu¹, Hailong Fan³, Cuiping Li², Xingxu Zhu², Junhui Li^2,*

Energy Engineering, Vol.122, No.6, pp. 2155-2175, 2025, DOI:10.32604/ee.2025.062218 - 29 May 2025

Abstract Responding to the stochasticity and uncertainty in the power height of distributed photovoltaic power generation. This paper presents a distributed photovoltaic ultra-short-term power forecasting method based on Variational Mode Decomposition (VMD) and Channel Attention Mechanism. First, Pearson’s correlation coefficient was utilized to filter out the meteorological factors that had a high impact on historical power. Second, the distributed PV power data were decomposed into a relatively smooth power series with different fluctuation patterns using variational modal decomposition (VMD). Finally, the reconstructed distributed PV power as well as other features are input into the combined CNN-SENet-BiLSTM… More >

Xiaobing Li^1,2, Yongsheng Liu¹, Limin Wei¹, Li Rao¹, Jingxin Mao^1,*, Xuemei Li^3,*

BIOCELL, Vol.49, No.5, pp. 813-832, 2025, DOI:10.32604/biocell.2025.062124 - 27 May 2025

Abstract Yeast-based models have become a powerful platform in pharmaceutical research, offering significant potential for producing complex drugs, vaccines, and therapeutic agents. While many current drugs were discovered before fully understanding their molecular mechanisms, yeast systems now provide valuable insights for drug discovery and personalized medicine. Recent advancements in genetic engineering, metabolic engineering, and synthetic biology have improved the efficiency and scalability of yeast-based production systems, enabling more sustainable and cost-effective manufacturing processes. This paper reviews the latest developments in yeast-based technologies, focusing on their use as model organisms to study disease mechanisms, identify drug targets,… More >