Zheng Zhang, Hengyang Wu^*, Na Wang

Journal on Artificial Intelligence, Vol.7, pp. 17-37, 2025, DOI:10.32604/jai.2025.059607 - 19 March 2025

Abstract This paper proposes a knowledge-enhanced disease diagnosis method based on a prompt learning framework. Addressing challenges such as the complexity of medical terminology, the difficulty of constructing medical knowledge graphs, and the scarcity of medical data, the method retrieves structured knowledge from clinical cases via external knowledge graphs. The method retrieves structured knowledge from external knowledge graphs related to clinical cases, encodes it, and injects it into the prompt templates to enhance the language model’s understanding and reasoning capabilities for the task. We conducted experiments on three public datasets: CHIP-CTC, IMCS-V2-NER, and KUAKE-QTR. The results More >

Hamayun Khan^1,*, Muhammad Atif Imtiaz², Hira Siddique³, Muhammad Tausif Afzal Rana⁴, Arshad Ali⁵, Muhammad Zeeshan Baig⁶, Saif ur Rehman⁷, Yazed Alsaawy⁵

Computer Systems Science and Engineering, Vol.49, pp. 317-331, 2025, DOI:10.32604/csse.2025.061118 - 19 March 2025

Abstract The migration of tasks aided by machine learning (ML) predictions IN (DPM) is a system-level design technique that is used to reduce energy by enhancing the overall performance of the processor. In this paper, we address the issue of system-level higher task dissipation during the execution of parallel workloads with common deadlines by introducing a machine learning-based framework that includes task migration using energy-efficient earliest deadline first scheduling (EA-EDF). ML-based EA-EDF enhances the overall throughput and optimizes the energy to avoid delay and performance degradation in a multiprocessor system. The proposed system model allocates processors… More >

Vatche Bahudian, John Valdovinos^*

Congenital Heart Disease, Vol.20, No.1, pp. 115-127, 2025, DOI:10.32604/chd.2025.063991 - 18 March 2025

Abstract Background: The population of Fontan patients, patients born with a single functioning ventricle, is growing. There is a growing need to develop algorithms for this population that can predict health outcomes. Artificial intelligence models predicting short-term and long-term health outcomes for patients with the Fontan circulation are needed. Generative adversarial networks (GANs) provide a solution for generating realistic and useful synthetic data that can be used to train such models. Methods: Despite their promise, GANs have not been widely adopted in the congenital heart disease research community due, in some part, to a lack of knowledge… More >

Abdul Ahad^1,2, Ira Puspitasari^1,3,*, Jiangbin Zheng², Shamsher Ullah⁴, Farhan Ullah⁵, Sheikh Tahir Bakhsh⁶, Ivan Miguel Pires^7,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5115-5134, 2025, DOI:10.32604/cmc.2025.062605 - 06 March 2025

Abstract This research explores the use of Fuzzy K-Nearest Neighbor (F-KNN) and Artificial Neural Networks (ANN) for predicting heart stroke incidents, focusing on the impact of feature selection methods, specifically Chi-Square and Best First Search (BFS). The study demonstrates that BFS significantly enhances the performance of both classifiers. With BFS preprocessing, the ANN model achieved an impressive accuracy of 97.5%, precision and recall of 97.5%, and an Receiver Operating Characteristics (ROC) area of 97.9%, outperforming the Chi-Square-based ANN, which recorded an accuracy of 91.4%. Similarly, the F-KNN model with BFS achieved an accuracy of 96.3%, precision More >

Huiyao Dong¹, Igor Kotenko^2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4263-4285, 2025, DOI:10.32604/cmc.2025.062439 - 06 March 2025

Abstract The growing complexity of cyber threats requires innovative machine learning techniques, and image-based malware classification opens up new possibilities. Meanwhile, existing research has largely overlooked the impact of noise and obfuscation techniques commonly employed by malware authors to evade detection, and there is a critical gap in using noise simulation as a means of replicating real-world malware obfuscation techniques and adopting denoising framework to counteract these challenges. This study introduces an image denoising technique based on a U-Net combined with a GAN framework to address noise interference and obfuscation challenges in image-based malware analysis. The… More >

Aziz Alotaibi^*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3741-3771, 2025, DOI:10.32604/cmc.2025.061998 - 06 March 2025

Abstract Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of data. Recently, both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/decisions. With the growth in popularity of deep learning and ensemble learning algorithms, they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big data. Ensemble deep learning has exhibited significant performance in enhancing learning generalization through… More >

Zhen-Yu Chen¹, Feng-Chi Liu², Xin Wang³, Cheng-Hsiung Lee¹, Ching-Sheng Lin^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4287-4300, 2025, DOI:10.32604/cmc.2025.061661 - 06 March 2025

Abstract In the domain of knowledge graph embedding, conventional approaches typically transform entities and relations into continuous vector spaces. However, parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations. In particular, resource-intensive embeddings often lead to increased computational costs, and may limit scalability and adaptability in practical environments, such as in low-resource settings or real-world applications. This paper explores an approach to knowledge graph representation learning that leverages small, reserved entities and relation sets for parameter-efficient embedding. We introduce a hierarchical attention network designed to refine More >

Ruhul Amin¹, Md. Jamil Khan¹, Tonway Deb Nath¹, Md. Shamim Reza², Jungpil Shin^3,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3907-3919, 2025, DOI:10.32604/cmc.2025.061645 - 06 March 2025

Abstract Heart disease includes a multiplicity of medical conditions that affect the structure, blood vessels, and general operation of the heart. Numerous researchers have made progress in correcting and predicting early heart disease, but more remains to be accomplished. The diagnostic accuracy of many current studies is inadequate due to the attempt to predict patients with heart disease using traditional approaches. By using data fusion from several regions of the country, we intend to increase the accuracy of heart disease prediction. A statistical approach that promotes insights triggered by feature interactions to reveal the intricate pattern… More >

Kassymbek Ozhikenov¹, Zhadyra Alimbayeva^1,*, Chingiz Alimbayev^1,2,*, Aiman Ozhikenova¹, Yeldos Altay¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5257-5284, 2025, DOI:10.32604/cmc.2025.061508 - 06 March 2025

Abstract Myocardial infarction (MI) is one of the leading causes of death globally among cardiovascular diseases, necessitating modern and accurate diagnostics for cardiac patient conditions. Among the available functional diagnostic methods, electrocardiography (ECG) is particularly well-known for its ability to detect MI. However, confirming its accuracy—particularly in identifying the localization of myocardial damage—often presents challenges in practice. This study, therefore, proposes a new approach based on machine learning models for the analysis of 12-lead ECG data to accurately identify the localization of MI. In particular, the learning vector quantization (LVQ) algorithm was applied, considering the contribution… More >

Tawfeeq Shawly¹, Ahmed A. Alsheikhy^2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4161-4179, 2025, DOI:10.32604/cmc.2025.061497 - 06 March 2025

Abstract A healthy brain is vital to every person since the brain controls every movement and emotion. Sometimes, some brain cells grow unexpectedly to be uncontrollable and cancerous. These cancerous cells are called brain tumors. For diagnosed patients, their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans. Nowadays, Physicians and radiologists rely on Magnetic Resonance Imaging (MRI) pictures for their clinical evaluations of brain tumors. These evaluations are time-consuming, expensive, and require expertise with high skills to provide an accurate diagnosis. Scholars and industrials have recently partnered to implement… More >