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 >

Fiaz Majeed¹, Abdul Razzaq Ahmad Shakir¹, Maqbool Ahmad², Shahzada Khurram³, Muhammad Qaiser Saleem⁴, Muhammad Shafiq^5,*, Jin-Ghoo Choi⁵, Habib Hamam^6,7,8,9,10, Osama E. Sheta¹¹

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 765-781, 2024, DOI:10.32604/iasc.2023.034029 - 06 September 2024

Abstract Machine learning (ML) and data mining are used in various fields such as data analysis, prediction, image processing and especially in healthcare. Researchers in the past decade have focused on applying ML and data mining to generate conclusions from historical data in order to improve healthcare systems by making predictions about the results. Using ML algorithms, researchers have developed applications for decision support, analyzed clinical aspects, extracted informative information from historical data, predicted the outcomes and categorized diseases which help physicians make better decisions. It is observed that there is a huge difference between women… More >

Xiuyang Meng^1,2, Chunling Wang^1,2,*, Jingran Yang^1,2, Mairui Li^1,2, Yue Zhang^1,2, Luo Wang^1,2

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4259-4281, 2024, DOI:10.32604/cmc.2024.050325 - 20 June 2024

Abstract Suicide has become a critical concern, necessitating the development of effective preventative strategies. Social media platforms offer a valuable resource for identifying signs of suicidal ideation. Despite progress in detecting suicidal ideation on social media, accurately identifying individuals who express suicidal thoughts less openly or infrequently poses a significant challenge. To tackle this, we have developed a dataset focused on Chinese suicide narratives from Weibo’s Tree Hole feature and introduced an ensemble model named Text Convolutional Neural Network based on Social Network relationships (TCNN-SN). This model enhances predictive performance by leveraging social network relationship features More >

Aiman¹, Muhammad Arshad¹, Bilal Khan¹, Khalil Khan², Ali Mustafa Qamar^3,*, Rehan Ullah Khan⁴

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 301-317, 2024, DOI:10.32604/iasc.2024.045402 - 21 May 2024

Abstract This study explores the area of Author Profiling (AP) and its importance in several industries, including forensics, security, marketing, and education. A key component of AP is the extraction of useful information from text, with an emphasis on the writers’ ages and genders. To improve the accuracy of AP tasks, the study develops an ensemble model dubbed ABMRF that combines AdaBoostM1 (ABM1) and Random Forest (RF). The work uses an extensive technique that involves text message dataset pretreatment, model training, and assessment. To evaluate the effectiveness of several machine learning (ML) algorithms in classifying age… More >

Yasir Maqsood¹, Syed Muhammad Usman^1,*, Musaed Alhussein², Khursheed Aurangzeb^2,*, Shehzad Khalid³, Muhammad Zubair⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2795-2811, 2024, DOI:10.32604/cmc.2024.049410 - 15 May 2024

Abstract Wheat is a critical crop, extensively consumed worldwide, and its production enhancement is essential to meet escalating demand. The presence of diseases like stem rust, leaf rust, yellow rust, and tan spot significantly diminishes wheat yield, making the early and precise identification of these diseases vital for effective disease management. With advancements in deep learning algorithms, researchers have proposed many methods for the automated detection of disease pathogens; however, accurately detecting multiple disease pathogens simultaneously remains a challenge. This challenge arises due to the scarcity of RGB images for multiple diseases, class imbalance in existing… More >

M. L. Maruthi Prasad^*, R. Santhosh

Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 1-13, 2024, DOI:10.32604/iasc.2023.036628 - 29 March 2024

Abstract This research concentrates to model an efficient thyroid prediction approach, which is considered a baseline for significant problems faced by the women community. The major research problem is the lack of automated model to attain earlier prediction. Some existing model fails to give better prediction accuracy. Here, a novel clinical decision support system is framed to make the proper decision during a time of complexity. Multiple stages are followed in the proposed framework, which plays a substantial role in thyroid prediction. These steps include i) data acquisition, ii) outlier prediction, and iii) multi-stage weight-based ensemble More >

R. Sujatha, K. Nimala^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1669-1686, 2024, DOI:10.32604/cmc.2023.046963 - 27 February 2024

Abstract Sentence classification is the process of categorizing a sentence based on the context of the sentence. Sentence categorization requires more semantic highlights than other tasks, such as dependence parsing, which requires more syntactic elements. Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence, recognizing the progress and comparing impacts. An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus. The conversational sentences are classified into four categories: information, question, directive, and commission. These classification label sequences are for… More >