Isha Sood^*, Varsha Sharma

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2791-2818, 2024, DOI:10.32604/cmc.2024.055463 - 18 November 2024

Abstract Ransomware has emerged as a critical cybersecurity threat, characterized by its ability to encrypt user data or lock devices, demanding ransom for their release. Traditional ransomware detection methods face limitations due to their assumption of similar data distributions between training and testing phases, rendering them less effective against evolving ransomware families. This paper introduces TLERAD (Transfer Learning for Enhanced Ransomware Attack Detection), a novel approach that leverages unsupervised transfer learning and co-clustering techniques to bridge the gap between source and target domains, enabling robust detection of both known and unknown ransomware variants. The proposed method More >

Aniqua Nusrat Zereen¹, Abir Das², Jia Uddin^3,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3463-3484, 2024, DOI:10.32604/cmc.2024.054886 - 12 September 2024

Abstract Machine fault diagnostics are essential for industrial operations, and advancements in machine learning have significantly advanced these systems by providing accurate predictions and expedited solutions. Machine learning models, especially those utilizing complex algorithms like deep learning, have demonstrated major potential in extracting important information from large operational datasets. Despite their efficiency, machine learning models face challenges, making Explainable AI (XAI) crucial for improving their understandability and fine-tuning. The importance of feature contribution and selection using XAI in the diagnosis of machine faults is examined in this study. The technique is applied to evaluate different machine-learning More >

Ahmad Alzu’bi^1,*, Amjad Albashayreh², Abdelrahman Abuarqoub³, Mai A. M. Alfawair⁴

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3785-3802, 2024, DOI:10.32604/cmc.2024.052599 - 12 September 2024

Abstract In the era of the Internet of Things (IoT), the proliferation of connected devices has raised security concerns, increasing the risk of intrusions into diverse systems. Despite the convenience and efficiency offered by IoT technology, the growing number of IoT devices escalates the likelihood of attacks, emphasizing the need for robust security tools to automatically detect and explain threats. This paper introduces a deep learning methodology for detecting and classifying distributed denial of service (DDoS) attacks, addressing a significant security concern within IoT environments. An effective procedure of deep transfer learning is applied to utilize More >

Sannasi Chakravarthy¹, Bharanidharan Nagarajan², Surbhi Bhatia Khan^3,7,*, Vinoth Kumar Venkatesan², Mahesh Thyluru Ramakrishna⁴, Ahlam Al Musharraf⁵, Khursheed Aurungzeb⁶

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 5029-5045, 2024, DOI:10.32604/cmc.2024.052531 - 12 September 2024

Abstract Breast cancer is a type of cancer responsible for higher mortality rates among women. The cruelty of breast cancer always requires a promising approach for its earlier detection. In light of this, the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast tumors. In addition, the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input mammogram. Accordingly, the work proposed an EfficientNet-B0 having a Spatial Attention Layer with More >

Amit Singhal¹, Krishna Kant Agrawal², Angeles Quezada³, Adrian Rodriguez Aguiñaga⁴, Samantha Jiménez⁴, Satya Prakash Yadav^5,,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 401-441, 2024, DOI:10.32604/cmes.2024.051363 - 20 August 2024

Abstract The use of Explainable Artificial Intelligence (XAI) models becomes increasingly important for making decisions in smart healthcare environments. It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms. These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence. Nevertheless, the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images. This research presents an advanced investigation of XAI models to classify cancer images. It describes the different levels of explainability… More >

Saad Akbar^1,2, Humera Azam¹, Sulaiman Sulmi Almutairi^3,*, Omar Alqahtani⁴, Habib Shah⁴, Aliya Aleryani⁴

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1075-1104, 2024, DOI:10.32604/cmc.2024.050913 - 18 July 2024

Abstract The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic tools. In this article, a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19, pneumonia, and normal conditions in chest X-ray images (CXIs) is proposed coupled with Explainable Artificial Intelligence (XAI). Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3, VGG16, and VGG19 that excel in the task of feature extraction. The methodology is further enhanced by the inclusion of the t-SNE (t-Distributed… More >

Md. Atiqur Rahman¹, Mustavi Ibne Masum¹, Khan Md Hasib², M. F. Mridha^3,*, Sultan Alfarhood⁴, Mejdl Safran^4,*, Dunren Che⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2425-2448, 2024, DOI:10.32604/cmes.2024.050760 - 08 July 2024

Abstract Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality. This study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging (MRI). It focuses on distinguishing between Low-Grade Gliomas (LGG) and High-Grade Gliomas (HGG). LGGs are benign and typically manageable with surgical resection, while HGGs are malignant and more aggressive. The research introduces an innovative custom convolutional neural network (CNN) model, Glioma-CNN. GliomaCNN stands out as a lightweight CNN model compared to its predecessors. The research utilized the BraTS 2020 More >

Peng Xiao¹, Qi Zhong², Jingxue Chen¹, Dongyuan Wu¹, Zhen Qin¹, Erqiang Zhou^1,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4703-4724, 2024, DOI:10.32604/cmc.2024.049791 - 20 June 2024

Abstract In the intelligent medical diagnosis area, Artificial Intelligence (AI)’s trustworthiness, reliability, and interpretability are critical, especially in cancer diagnosis. Traditional neural networks, while excellent at processing natural images, often lack interpretability and adaptability when processing high-resolution digital pathological images. This limitation is particularly evident in pathological diagnosis, which is the gold standard of cancer diagnosis and relies on a pathologist’s careful examination and analysis of digital pathological slides to identify the features and progression of the disease. Therefore, the integration of interpretable AI into smart medical diagnosis is not only an inevitable technological trend but… More >

Sujithra Sankar^1,*, S. Sathyalakshmi²

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3111-3138, 2024, DOI:10.32604/cmc.2024.048408 - 15 May 2024

Abstract In the era of advanced machine learning techniques, the development of accurate predictive models for complex medical conditions, such as thyroid cancer, has shown remarkable progress. Accurate predictive models for thyroid cancer enhance early detection, improve resource allocation, and reduce overtreatment. However, the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency. This paper proposes a novel association-rule based feature-integrated machine learning model which shows better classification and prediction accuracy than present state-of-the-art models. Our study also focuses on the application of SHapley Additive exPlanations (SHAP) values as… More >

Moshe Dayan Sirapangi¹, S. Gopikrishnan^1,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2229-2251, 2024, DOI:10.32604/cmc.2024.047438 - 15 May 2024

Abstract Medical Internet of Things (IoT) devices are becoming more and more common in healthcare. This has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of multimodal data to find potential health risks early and help individuals in a personalized way. Existing methods, while useful, have limitations in predictive accuracy, delay, personalization, and user interpretability, requiring a more comprehensive and efficient approach to harness modern medical IoT devices. MAIPFE is a multimodal approach integrating pre-emptive analysis, personalized feature selection, and explainable AI for real-time health… More >