Igor Musikhin^*

Revue Internationale de Géomatique, Vol.34, pp. 939-957, 2025, DOI:10.32604/rig.2025.071168 - 09 December 2025

Abstract Urban environmental quality research is crucial, as cities become competitive centers concentrating human talent, industrial activity, and financial resources, contributing significantly to national economies. Municipal and government priorities include retaining residents, preventing skilled worker outflow, and meeting the evolving needs of urban populations. The study presents the development and application of a scenario-based spatial analysis tool for assessing urban environmental quality at a detailed spatial scale within the city of Novosibirsk. Using advanced geoinformatics, GIS techniques, and an expert knowledge base, the tool integrates diverse thematic data layers with user-defined scenarios to compute and visualize… More >

Anis Ben Ghorbal^1,*, Azedine Grine¹, Marwa M. Eid^2,3,*, El-Sayed M. El-Kenawy^4,5

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2001-2028, 2025, DOI:10.32604/cmes.2025.068212 - 31 August 2025

Abstract Preterm birth remains a leading cause of neonatal complications and highlights the need for early and accurate prediction techniques to improve both fetal and maternal health outcomes. This study introduces a hybrid approach integrating Long Short-Term Memory (LSTM) networks with the Hybrid Greylag Goose and Particle Swarm Optimization (GGPSO) algorithm to optimize preterm birth classification using Electrohysterogram signals. The dataset consists of 58 samples of 1000-second-long Electrohysterogram recordings, capturing key physiological features such as contraction patterns, entropy, and statistical variations. Statistical analysis and feature selection methods are applied to identify the most relevant predictors and More > Graphic Abstract

Abrar Hussain¹, Hafiz Aftab Anwar¹, Kifayat Ullah^1,2, Dragan Pamucar^3,*, Vladimir Simic^4,5,6

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1311-1337, 2025, DOI:10.32604/cmc.2025.066569 - 29 August 2025

Abstract For any country, the availability of electricity is crucial to the development of the national economy and society. As a result, decision-makers and policy-makers can improve the sustainability and security of the energy supply by implementing a variety of actions by using the evaluation of these factors as an early warning system. This research aims to provide a multi-criterion decision-making (MCDM) method for assessing the sustainability and security of the electrical supply. The weights of criteria, which indicate their relative relevance in the assessment of the sustainability and security of the energy supply, the MCDM… More >

Sameera V Mohd Sagheer^1,*, Meghana K H², P M Ameer³, Muneer Parayangat⁴, Mohamed Abbas⁴

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4259-4297, 2025, DOI:10.32604/cmc.2025.063726 - 30 July 2025

Abstract Integrating multiple medical imaging techniques, including Magnetic Resonance Imaging (MRI), Computed Tomography, Positron Emission Tomography (PET), and ultrasound, provides a comprehensive view of the patient health status. Each of these methods contributes unique diagnostic insights, enhancing the overall assessment of patient condition. Nevertheless, the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution, data collection methods, and noise levels. While traditional models like Convolutional Neural Networks (CNNs) excel in single-modality tasks, they struggle to handle multi-modal complexities, lacking the capacity to model global relationships. This research presents a novel approach for… More >

Sana Shahab¹, Ibtehal Alazman², Ashit Kumar Dutta³, Mohd Anjum⁴, Vladimir Simic^5,6,7,*, Željko Stević⁸, Nouf Abdulrahman Alqahtani²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2155-2183, 2025, DOI:10.32604/cmes.2025.064553 - 30 May 2025

Abstract With the increasing complexity of hotel selection, traditional decision-making models often struggle to account for uncertainty and interrelated criteria. Multi-criteria decision-making (MCDM) techniques, particularly those based on fuzzy logic, provide a robust framework for handling such challenges. This paper presents a novel approach to MCDM within the framework of Circular Intuitionistic Fuzzy Sets (C-IFS) by combining three distinct methodologies: Weighted Aggregated Sum Product Assessment (WASPAS), an Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN), and the CRITIC method (Criteria Importance Through Intercriteria Correlation). To address the dynamic nature of traveler preferences in hotel selection,… More >

Takanobu Hirosawa^*, Taro Shimizu

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 3919-3944, 2025, DOI:10.32604/cmc.2025.063803 - 19 May 2025

Abstract Artificial Intelligence (AI) is fundamentally transforming medical diagnostics, driving advancements that enhance accuracy, efficiency, and personalized patient care. This narrative review explores AI integration across various diagnostic domains, emphasizing its role in improving clinical decision-making. The evolution of medical diagnostics from traditional observational methods to sophisticated imaging, laboratory tests, and molecular diagnostics lays the foundation for understanding AI’s impact. Modern diagnostics are inherently complex, influenced by multifactorial disease presentations, patient variability, cognitive biases, and systemic factors like data overload and interdisciplinary collaboration. AI-enhanced clinical decision support systems utilize both knowledge-based and non-knowledge-based approaches, employing machine… More >

Ali Hasan Dakheel^1,*, Mohammed Raheem Mohammed¹, Zainab Ali Abd Alhuseen¹, Wassan Adnan Hashim^2,3

Intelligent Automation & Soft Computing, Vol.40, pp. 195-220, 2025, DOI:10.32604/iasc.2025.061622 - 28 February 2025

Abstract The prediction of pregnancy-related hazards must be accurate and timely to safeguard mother and fetal health. This study aims to enhance risk prediction in pregnancy with a novel deep learning model based on a Long Short-Term Memory (LSTM) generator, designed to capture temporal relationships in cardiotocography (CTG) data. This methodology integrates CTG signals with demographic characteristics and utilizes preprocessing techniques such as noise reduction, normalization, and segmentation to create high-quality input for the model. It uses convolutional layers to extract spatial information, followed by LSTM layers to model sequences for superior predictive performance. The overall More >

Soha Rawas¹, Cerine Tafran¹, Duaa AlSaeed², Nadia Al-Ghreimil^2,*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4575-4601, 2024, DOI:10.32604/cmc.2024.055307 - 19 December 2024

Abstract In the rapidly evolving landscape of healthcare, the integration of Artificial Intelligence (AI) and Natural Language Processing (NLP) holds immense promise for revolutionizing data analytics and decision-making processes. Current techniques for personalized medicine, disease diagnosis, treatment recommendations, and resource optimization in the Internet of Medical Things (IoMT) vary widely, including methods such as rule-based systems, machine learning algorithms, and data-driven approaches. However, many of these techniques face limitations in accuracy, scalability, and adaptability to complex clinical scenarios. This study investigates the synergistic potential of AI-driven optimization techniques and NLP applications in the context of the… More >

Israa Ibraheem Al Barazanchi^1,2,*, Wahidah Hashim¹, Reema Thabit¹, Mashary Nawwaf Alrasheedy^3,4, Abeer Aljohan⁵, Jongwoon Park⁶, Byoungchol Chang⁶

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4787-4832, 2024, DOI:10.32604/cmc.2024.055079 - 19 December 2024

Abstract This research aims to enhance Clinical Decision Support Systems (CDSS) within Wireless Body Area Networks (WBANs) by leveraging advanced machine learning techniques. Specifically, we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) layers and echo state cells. These models are tailored to improve diagnostic precision, particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal diseases. Traditional diagnostic methods and existing CDSS frameworks often fall short in managing complex, sequential medical data, struggling with long-term dependencies and data… More >

Bushra Kanwal¹, Rana Saud Shoukat², Saif Ur Rehman^2,*, Mahwish Kundi³, Tahani AlSaedi⁴, Abdulrahman Alahmadi⁴

Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 829-854, 2024, DOI:10.32604/iasc.2024.054645 - 31 October 2024

Abstract Education is the base of the survival and growth of any state, but due to resource scarcity, students, particularly at the university level, are forced into a difficult situation. Scholarships are the most significant financial aid mechanisms developed to overcome such obstacles and assist the students in continuing with their higher studies. In this study, the convoluted situation of scholarship eligibility criteria, including parental income, responsibilities, and academic achievements, is addressed. In an attempt to maximize the scholarship selection process, numerous machine learning algorithms, including Support Vector Machines, Neural Networks, K-Nearest Neighbors, and the C4.5… More >