Jagadesh Balasubramani¹, Surendran Rajendran^1,*, Mohammad Zakariah², Abeer Alnuaim²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2793-2807, 2025, DOI:10.32604/cmc.2025.062266 - 16 April 2025

Abstract Autism spectrum disorder (ASD) is a multifaceted neurological developmental condition that manifests in several ways. Nearly all autistic children remain undiagnosed before the age of three. Developmental problems affecting face features are often associated with fundamental brain disorders. The facial evolution of newborns with ASD is quite different from that of typically developing children. Early recognition is very significant to aid families and parents in superstition and denial. Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD. Presently, artificial intelligence (AI) significantly contributes to the emerging computer-aided… More >

Xuewen Mu^*, Bingcong Zhao

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2143-2159, 2025, DOI:10.32604/cmc.2025.060739 - 16 April 2025

Abstract When dealing with imbalanced datasets, the traditional support vector machine (SVM) tends to produce a classification hyperplane that is biased towards the majority class, which exhibits poor robustness. This paper proposes a high-performance classification algorithm specifically designed for imbalanced datasets. The proposed method first uses a biased second-order cone programming support vector machine (B-SOCP-SVM) to identify the support vectors (SVs) and non-support vectors (NSVs) in the imbalanced data. Then, it applies the synthetic minority over-sampling technique (SV-SMOTE) to oversample the support vectors of the minority class and uses the random under-sampling technique (NSV-RUS) multiple times More >

Iman S. Al-Mahdi¹, Saad M. Darwish^1,*, Magda M. Madbouly²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 875-909, 2025, DOI:10.32604/cmes.2025.061623 - 11 April 2025

Abstract Heart disease prediction is a critical issue in healthcare, where accurate early diagnosis can save lives and reduce healthcare costs. The problem is inherently complex due to the high dimensionality of medical data, irrelevant or redundant features, and the variability in risk factors such as age, lifestyle, and medical history. These challenges often lead to inefficient and less accurate models. Traditional prediction methodologies face limitations in effectively handling large feature sets and optimizing classification performance, which can result in overfitting poor generalization, and high computational cost. This work proposes a novel classification model for heart… More >

Sumaiya Noor¹, Salman A. AlQahtani², Salman Khan^3,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1435-1450, 2025, DOI:10.32604/cmc.2025.061700 - 26 March 2025

Abstract The liver is a crucial gland and the second-largest organ in the human body and also essential in digestion, metabolism, detoxification, and immunity. Liver diseases result from factors such as viral infections, obesity, alcohol consumption, injuries, or genetic predispositions. Pose significant health risks and demand timely diagnosis and treatment to enhance survival rates. Traditionally, diagnosing liver diseases relied heavily on clinical expertise, often leading to subjective, challenging, and time-intensive processes. However, early detection is essential for effective intervention, and advancements in machine learning (ML) have demonstrated remarkable success in predicting various conditions, including Chronic Obstructive… More >

Zhicheng Yin^1,2, Lang Li^1,2,*, Yu Ou^1,2

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1001-1022, 2025, DOI:10.32604/cmc.2025.060925 - 26 March 2025

Abstract The adoption of deep learning-based side-channel analysis (DL-SCA) is crucial for leak detection in secure products. Many previous studies have applied this method to break targets protected with countermeasures. Despite the increasing number of studies, the problem of model overfitting. Recent research mainly focuses on exploring hyperparameters and network architectures, while offering limited insights into the effects of external factors on side-channel attacks, such as the number and type of models. This paper proposes a Side-channel Analysis method based on a Stacking ensemble, called Stacking-SCA. In our method, multiple models are deeply integrated. Through 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 >

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 >

Tajmal Hussain, Jongwon Seok^*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 231-250, 2025, DOI:10.32604/cmes.2024.056621 - 17 December 2024

Abstract Smart manufacturing and Industry 4.0 are transforming traditional manufacturing processes by utilizing innovative technologies such as the artificial intelligence (AI) and internet of things (IoT) to enhance efficiency, reduce costs, and ensure product quality. In light of the recent advancement of Industry 4.0, identifying defects has become important for ensuring the quality of products during the manufacturing process. In this research, we present an ensemble methodology for accurately classifying hot rolled steel surface defects by combining the strengths of four pre-trained convolutional neural network (CNN) architectures: VGG16, VGG19, Xception, and Mobile-Net V2, compensating for their… More >

Duc-Dam Nguyen¹, Nguyen Viet Tiep^2,*, Quynh-Anh Thi Bui¹, Hiep Van Le¹, Indra Prakash³, Romulus Costache^4,5,6,7, Manish Pandey^8,9, Binh Thai Pham¹

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 467-500, 2025, DOI:10.32604/cmes.2024.056576 - 17 December 2024

Abstract This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand, India, using advanced ensemble models that combined Radial Basis Function Networks (RBFN) with three ensemble learning techniques: DAGGING (DG), MULTIBOOST (MB), and ADABOOST (AB). This combination resulted in three distinct ensemble models: DG-RBFN, MB-RBFN, and AB-RBFN. Additionally, a traditional weighted method, Information Value (IV), and a benchmark machine learning (ML) model, Multilayer Perceptron Neural Network (MLP), were employed for comparison and validation. The models were developed using ten landslide conditioning factors, which included slope, aspect, elevation, curvature, land cover, geomorphology,… More >

Sarfaraz Natha^1,2,*, Fareed A. Jokhio¹, Mehwish Laghari¹, Mohammad Siraj^3,*, Saif A. Alsaif³, Usman Ashraf⁴, Asghar Ali⁵

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3707-3729, 2024, DOI:10.32604/cmc.2024.057684 - 19 December 2024

Abstract Surveillance cameras have been widely used for monitoring in both private and public sectors as a security measure. Close Circuits Television (CCTV) Cameras are used to surveillance and monitor the normal and anomalous incidents. Real-world anomaly detection is a significant challenge due to its complex and diverse nature. It is difficult to manually analyze because vast amounts of video data have been generated through surveillance systems, and the need for automated techniques has been raised to enhance detection accuracy. This paper proposes a novel deep-stacked ensemble model integrated with a data augmentation approach called Stack… More >