Sarfaraz Abdul Sattar Natha^1,*, Mohammad Siraj^2,*, Majid Altamimi², Adamali Shah², Maqsood Mahmud³

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4179-4201, 2025, DOI:10.32604/cmes.2025.072529 - 23 December 2025

Abstract A brain tumor is a disease in which abnormal cells form a tumor in the brain. They are rare and can take many forms, making them difficult to treat, and the survival rate of affected patients is low. Magnetic resonance imaging (MRI) is a crucial tool for diagnosing and localizing brain tumors. However, the manual interpretation of MRI images is tedious and prone to error. As artificial intelligence advances rapidly, DL techniques are increasingly used in medical imaging to accurately detect and diagnose brain tumors. In this study, we introduce a deep convolutional neural network… More >

Amit Prakash Singh¹, Kajal Kaul^1,*, Anuradha Chug¹, Ravinder Kumar², Veerubommu Shanmugam²

Journal on Artificial Intelligence, Vol.7, pp. 451-468, 2025, DOI:10.32604/jai.2025.070773 - 06 November 2025

Abstract In agricultural farms in India where the staple diet for most of the households is potato, plant leaf diseases, namely Potato Early Blight (PEB) and Potato Late Blight (PLB), are quite common. The class label Plant Healthy (PH) is also used. If these diseases are not identified early, they can cause massive crop loss and thereby incur huge economic losses to the farmers in the agricultural domain and can impact the gross domestic product of the nation. This paper presents a hybrid approach for potato plant disease severity estimation and classification of diseased and healthy… More >

Subrata Kumer Paul^1,2, Abu Saleh Musa Miah^3,4, Rakhi Rani Paul^1,2, Md. Ekramul Hamid², Jungpil Shin^4,*, Md Abdur Rahim⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2513-2530, 2025, DOI:10.32604/cmc.2025.063563 - 03 July 2025

Abstract The Internet of Things (IoT) and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients. Recognizing Medical-Related Human Activities (MRHA) is pivotal for healthcare systems, particularly for identifying actions critical to patient well-being. However, challenges such as high computational demands, low accuracy, and limited adaptability persist in Human Motion Recognition (HMR). While some studies have integrated HMR with IoT for real-time healthcare applications, limited research has focused on recognizing MRHA as essential for effective patient monitoring. This study proposes a novel HMR method tailored for MRHA detection, leveraging multi-stage deep… More >

Yu-Xiang Zhao^*, Yi Lee

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 791-810, 2025, DOI:10.32604/cmc.2025.065048 - 09 June 2025

Abstract Bird species classification is not only a challenging topic in artificial intelligence but also a domain closely related to environmental protection and ecological research. Additionally, performing edge computing on low-level devices using small neural networks can be an important research direction. In this paper, we use the EfficientNetV2B0 model for bird species classification, applying transfer learning on a dataset of 525 bird species. We also employ the BiRefNet model to remove backgrounds from images in the training set. The generated background-removed images are mixed with the original training set as a form of data augmentation.… More >

Zhigao Zeng¹, Jun Liu¹, Bing Zheng², Shengqiu Yi¹, Xinpan Yuan¹, Qiang Liu^1,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 513-536, 2025, DOI:10.32604/cmc.2025.063257 - 09 June 2025

Abstract To solve the problems of complex lesion region morphology, blurred edges, and limited hardware resources for deploying the recognition model in pneumonia image recognition, an improved EfficientNetV2 pneumonia recognition model based on multiscale attention is proposed. First, the number of main module stacks of the model is reduced to avoid overfitting, while the dilated convolution is introduced in the first convolutional layer to expand the receptive field of the model; second, a redesigned improved mobile inverted bottleneck convolution (IMBConv) module is proposed, in which GSConv is introduced to enhance the model’s attention to inter-channel information,… More >

Wafaa H. Alwan^1,*, Sabah M. Alturfi²

Computer Systems Science and Engineering, Vol.49, pp. 419-434, 2025, DOI:10.32604/csse.2025.064195 - 30 April 2025

Abstract Plant diseases pose a significant challenge to global agricultural productivity, necessitating efficient and precise diagnostic systems for early intervention and mitigation. In this study, we propose a novel hybrid framework that integrates EfficientNet-B8, Vision Transformer (ViT), and Knowledge Graph Fusion (KGF) to enhance plant disease classification across 38 distinct disease categories. The proposed framework leverages deep learning and semantic enrichment to improve classification accuracy and interpretability. EfficientNet-B8, a convolutional neural network (CNN) with optimized depth and width scaling, captures fine-grained spatial details in high-resolution plant images, aiding in the detection of subtle disease symptoms. In… More >

Panru Liang¹, Guojiang Xin^1,*, Xiaolei Yi², Hao Liang³, Changsong Ding¹

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2481-2504, 2025, DOI:10.32604/cmc.2025.060961 - 16 April 2025

Abstract Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases, facilitating treatment evaluations, and designing surgical plans. Due to the pancreas’s tiny size, significant variability in shape and location, and low contrast with surrounding tissues, achieving high segmentation accuracy remains challenging. To improve segmentation precision, we propose a novel network utilizing EfficientNetV2 and multi-branch structures for automatically segmenting the pancreas from CT images. Firstly, an EfficientNetV2 encoder is employed to extract complex and multi-level features, enhancing the model’s ability to capture the pancreas’s intricate morphology. Then, a residual multi-branch dilated attention… More >

Manjit Singh, Sunil Kumar Singla^*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3003-3029, 2025, DOI:10.32604/cmes.2025.060863 - 03 March 2025

Abstract Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security. The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data. Considering the concerns of existing methods, in this work, a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism. Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a… More > Graphic Abstract

Youhe Wang, Yi Sun^*, Yujie Li, Chuanqi Zhou

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1233-1254, 2025, DOI:10.32604/cmc.2024.057710 - 03 January 2025

Abstract With the development of anti-virus technology, malicious documents have gradually become the main pathway of Advanced Persistent Threat (APT) attacks, therefore, the development of effective malicious document classifiers has become particularly urgent. Currently, detection methods based on document structure and behavioral features encounter challenges in feature engineering, these methods not only have limited accuracy, but also consume large resources, and usually can only detect documents in specific formats, which lacks versatility and adaptability. To address such problems, this paper proposes a novel malicious document detection method-visualizing documents as GGE images (Grayscale, Grayscale matrix, Entropy). The… More >

Parthasarathi Manivannan¹, Palaniyappan Sathyaprakash¹, Vaithiyashankar Jayakumar², Jayakumar Chandrasekaran³, Bragadeesh Srinivasan Ananthanarayanan⁴, Md Shohel Sayeed^5,*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4327-4347, 2024, DOI:10.32604/cmc.2024.055628 - 19 December 2024

Abstract Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational effectiveness. However, accurately classifying diverse and complex weather conditions remains a significant challenge. While advanced techniques such as Vision Transformers have been developed, they face key limitations, including high computational costs and limited generalization across varying weather conditions. These challenges present a critical research gap, particularly in applications where scalable and efficient solutions are needed to handle weather phenomena’ intricate and dynamic nature in real-time. To address this gap, we propose a Multi-level Knowledge Distillation (MLKD) framework, which leverages… More >