Ameer Hamza, Robertas Damaševičius^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-41, 2026, DOI:10.32604/cmc.2025.069721 - 10 November 2025

Abstract This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities, focusing on recent trends from 2022 to 2025. The primary objective is to evaluate methodological advancements, model performance, dataset usage, and existing challenges in developing clinically robust AI systems. We included peer-reviewed journal articles and high-impact conference papers published between 2022 and 2025, written in English, that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification. Excluded were non-open-access publications, books, and non-English articles. A structured search was… More >

Mohamed Ezz¹, Meshrif Alruily^1,*, Ayman Mohamed Mostafa^2,*, Alaa S. Alaerjan¹, Bader Aldughayfiq², Hisham Allahem², Abdulaziz Shehab²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-28, 2026, DOI:10.32604/cmc.2025.063189 - 10 November 2025

Abstract Automated essay scoring (AES) systems have gained significant importance in educational settings, offering a scalable, efficient, and objective method for evaluating student essays. However, developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology, diglossia, and the scarcity of annotated datasets. This paper presents a hybrid approach to Arabic AES by combining text-based, vector-based, and embedding-based similarity measures to improve essay scoring accuracy while minimizing the training data required. Using a large Arabic essay dataset categorized into thematic groups, the study conducted four experiments to evaluate the impact of feature selection,… More >

Haoyun Fan¹, Soon Poh Yap^1,*, Shengkang Zhang¹, Ahmed El-Shafie^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 17-65, 2025, DOI:10.32604/cmes.2025.070880 - 30 October 2025

Abstract Data-driven research on recycled aggregate concrete (RAC) has long faced the challenge of lacking a unified testing standard dataset, hindering accurate model evaluation and trust in predictive outcomes. This paper reviews critical parameters influencing mechanical properties in 35 RAC studies, compiles four datasets encompassing these parameters, and compiles the performance and key findings of 77 published data-driven models. Baseline capability tests are conducted on the nine most used models. The paper also outlines advanced methodological frameworks for future RAC research, examining the principles and challenges of physics-informed neural networks (PINNs) and generative adversarial networks (GANs), More >

Mohammed Alshahrani¹, Mohammed Al-Jabbar^1,*, Ebrahim Mohammed Senan^2,3, Fatima Ali Amer jid Almahri⁴, Sultan Ahmed Almalki¹, Eman A. Alshari^3,5

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3639-3675, 2025, DOI:10.32604/cmes.2025.064668 - 30 June 2025

Abstract Multiple Sclerosis (MS) poses significant health risks. Patients may face neurodegeneration, mobility issues, cognitive decline, and a reduced quality of life. Manual diagnosis by neurologists is prone to limitations, making AI-based classification crucial for early detection. Therefore, automated classification using Artificial Intelligence (AI) techniques has a crucial role in addressing the limitations of manual classification and preventing the development of MS to advanced stages. This study developed hybrid systems integrating XGBoost (eXtreme Gradient Boosting) with multi-CNN (Convolutional Neural Networks) features based on Ant Colony Optimization (ACO) and Maximum Entropy Score-based Selection (MESbS) algorithms for early… More >

Haydar Abdulameer Marhoon^1,2,*, Rafid Sagban^3,4, Atheer Y. Oudah^1,5, Saadaldeen Rashid Ahmed^6,7

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4181-4218, 2025, DOI:10.32604/cmc.2025.058822 - 06 March 2025

Abstract In order to address the critical security challenges inherent to Wireless Sensor Networks (WSNs), this paper presents a groundbreaking barrier-based machine learning technique. Vital applications like military operations, healthcare monitoring, and environmental surveillance increasingly deploy WSNs, recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational integrity. The proposed method innovatively partitions the network into logical segments or virtual barriers, allowing for targeted monitoring and data collection that aligns with specific traffic patterns. This approach not only improves the diversit. There are more types of data in the training set,… More >

Huajiang Ruan^1,2, Panjun Sun^1,2, Yuanyuan Dong^1,2, Hamid Tahaei¹, Zhaoxi Fang^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1645-1680, 2025, DOI:10.32604/cmc.2024.059746 - 17 February 2025

Abstract Traditional Global Positioning System (GPS) technology, with its high power consumption and limited performance in obstructed environments, is unsuitable for many Internet of Things (IoT) applications. This paper explores LoRa as an alternative localization technology, leveraging its low power consumption, robust indoor penetration, and extensive coverage area, which render it highly suitable for diverse IoT settings. We comprehensively review several LoRa-based localization techniques, including time of arrival (ToA), time difference of arrival (TDoA), round trip time (RTT), received signal strength indicator (RSSI), and fingerprinting methods. Through this review, we evaluate the strengths and limitations of More >

Moein Tosan¹, Mohammad Reza Gharib^2,*, Nasrin Fathollahzadeh Attar³, Ali Maroosi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1109-1154, 2025, DOI:10.32604/cmes.2025.058595 - 27 January 2025

Abstract Accurate estimation of evapotranspiration (ET) is crucial for efficient water resource management, particularly in the face of climate change and increasing water scarcity. This study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers, selected according to PRISMA guidelines, to evaluate the performance of Hybrid Artificial Neural Networks (HANNs) in ET estimation. The findings demonstrate that HANNs, particularly those combining Multilayer Perceptrons (MLPs), Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs), are highly effective in capturing the complex nonlinear relationships and temporal dependencies characteristic of hydrological processes. These… More >

Mohammad Aldossary^1,*, Hatem A. Alharbi², Nasir Ayub³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4603-4620, 2024, DOI:10.32604/cmc.2024.050862 - 20 June 2024

Abstract Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure, thereby revolutionizing computer processes. However, the rising energy consumption in cloud centers poses a significant challenge, especially with the escalating energy costs. This paper tackles this issue by introducing efficient solutions for data placement and node management, with a clear emphasis on the crucial role of the Internet of Things (IoT) throughout the research process. The IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around… More >

Weisi Chen^1,*, Walayat Hussain^2,*, Francesco Cauteruccio³, Xu Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 187-224, 2024, DOI:10.32604/cmes.2023.031388 - 30 December 2023

Abstract Financial time series prediction, whether for classification or regression, has been a heated research topic over the last decade. While traditional machine learning algorithms have experienced mediocre results, deep learning has largely contributed to the elevation of the prediction performance. Currently, the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking, making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better, what techniques and components are involved, and how the model can be designed and implemented. This review article provides an… More > Graphic Abstract

Baydaa Abdul Kareem^1,2, Salah L. Zubaidi^2,3, Nadhir Al-Ansari^4,*, Yousif Raad Muhsen^2,5

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 1-41, 2024, DOI:10.32604/cmes.2023.027954 - 22 September 2023

Abstract Forecasting river flow is crucial for optimal planning, management, and sustainability using freshwater resources. Many machine learning (ML) approaches have been enhanced to improve streamflow prediction. Hybrid techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone approaches. Current researchers have also emphasised using hybrid models to improve forecast accuracy. Accordingly, this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years, summarising data preprocessing, univariate machine learning modelling strategy, advantages and disadvantages of standalone ML… More > Graphic Abstract