Hanifah Rahmi Fajrin^1,2, Se Dong Min^1,3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072609 - 12 January 2026

Abstract Accurate segmentation of breast cancer in mammogram images plays a critical role in early diagnosis and treatment planning. As research in this domain continues to expand, various segmentation techniques have been proposed across classical image processing, machine learning (ML), deep learning (DL), and hybrid/ensemble models. This study conducts a systematic literature review using the PRISMA methodology, analyzing 57 selected articles to explore how these methods have evolved and been applied. The review highlights the strengths and limitations of each approach, identifies commonly used public datasets, and observes emerging trends in model integration and clinical relevance. More >

Atheer Aleran¹, Hanan Almukhalfi¹, Ayman Noor¹, Reyadh Alluhaibi², Abdulrahman Hafez³, Talal H. Noor^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070741 - 12 January 2026

Abstract Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs. Conventional maintenance methods, such as reactive maintenance (i.e., run to failure) or time-based preventive maintenance (i.e., scheduled servicing), prove ineffective for complex systems with many Internet of Things (IoT) devices and sensors because they fall short in detecting faults at early stages when it is most crucial. This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory (LSTM) Networks and Convolutional Neural Networks (CNNs). The framework… More >

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 >

Saurabh Singh^1,2, Sudip Pandey^3,*, Ankush Kumar Jain¹

Revue Internationale de Géomatique, Vol.34, pp. 899-914, 2025, DOI:10.32604/rig.2025.071156 - 09 December 2025

Abstract Urban expansion in semi-arid regions poses critical challenges for sustainable land management, ecological resilience, and heritage conservation. Jaipur, India—a United Nations Educational, Scientific and Cultural Organization (UNESCO) World Heritage City located in a semi-arid environment—faces rapid urbanization that threatens agricultural productivity, fragile ecosystems, and cultural assets. This study quantifies past and projects future land use/land cover (LULC) dynamics in Jaipur to support evidence-based planning. Using the Dynamic World dataset, we generated annual 10-m LULC maps from 2016 to 2025 within the municipal boundary. Temporal change detection was conducted through empirical transition probability analysis, and future… More >

Mudasir Ali¹, Muhammad Faheem Mushtaq², Urooj Akram², Nagwan Abdel Samee^3,*, Mona M. Jamjoom⁴, Imran Ashraf^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1863-1901, 2025, DOI:10.32604/cmes.2025.070389 - 26 November 2025

Abstract Differentiating between regular and abnormal noises in machine-generated sounds is a crucial but difficult problem. For accurate audio signal classification, suitable and efficient techniques are needed, particularly machine learning approaches for automated classification. Due to the dynamic and diverse representative characteristics of audio data, the probability of achieving high classification accuracy is relatively low and requires further research efforts. This study proposes an ensemble model based on the LeNet and hierarchical attention mechanism (HAM) models with MFCC features to enhance the models’ capacity to handle bias. Additionally, CNNs, bidirectional LSTM (BiLSTM), CRNN, LSTM, capsule network 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 >

Bibhuprasad Sahu¹, Amrutanshu Panigrahi², Abhilash Pati², Ashis Kumar Pati³, Janmejaya Mishra⁴, Naim Ahmad^5,*, Salman Arafath Mohammed⁶, Saurav Mallik^7,8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1029-1054, 2025, DOI:10.32604/cmes.2025.069618 - 30 October 2025

Abstract Metaheuristic optimization methods are iterative search processes that aim to efficiently solve complex optimization problems. These basically find the solution space very efficiently, often without utilizing the gradient information, and are inspired by the bio-inspired and socially motivated heuristics. Metaheuristic optimization algorithms are increasingly applied to complex feature selection problems in high-dimensional medical datasets. Among these, Teaching-Learning-Based optimization (TLBO) has proven effective for continuous design tasks by balancing exploration and exploitation phases. However, its binary version (BTLBO) suffers from limited exploitation ability, often converging prematurely or getting trapped in local optima, particularly when applied to… More >

Nameer Baht^1,*, Enrique Domínguez^1,2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4441-4455, 2025, DOI:10.32604/cmc.2025.069528 - 23 October 2025

Abstract Coffee is an important agricultural commodity, and its production is threatened by various diseases. It is also a source of concern for coffee-exporting countries, which is causing them to rethink their strategies for the future. Maintaining crop production requires early diagnosis. Notably, Coffee Leaf Miner (CLM) Machine learning (ML) offers promising tools for automated disease detection. Early detection of CLM is crucial for minimising yield losses. However, this study explores the effectiveness of using Convolutional Neural Networks (CNNs) with transfer learning algorithms ResNet50, DenseNet121, MobileNet, Inception, and hybrid VGG19 for classifying coffee leaf images as… More >

Shige Wang¹, Yalong Liang², Lian Huang³, Pei Li^4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2729-2748, 2025, DOI:10.32604/cmc.2025.068351 - 23 September 2025

Abstract This study introduces a hybrid Cuckoo Search-Deep Neural Network (CS-DNN) model for uncertainty quantification and composition optimization of Na_1/2Bi_1/2TiO₃ (NBT)-based dielectric energy storage ceramics. Addressing the limitations of traditional ferroelectric materials—such as hysteresis loss and low breakdown strength under high electric fields—we fabricate (1 − x)NBBT8-xBMT solid solutions via chemical modification and systematically investigate their temperature stability and composition-dependent energy storage performance through XRD, SEM, and electrical characterization. The key innovation lies in integrating the CS metaheuristic algorithm with a DNN, overcoming local minima in training and establishing a robust composition-property prediction framework. Our model accurately… More >