Misbah Anwer^1,*, Ghufran Ahmed¹, Maha Abdelhaq², Raed Alsaqour³, Shahid Hussain⁴, Adnan Akhunzada^5,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.068673 - 10 November 2025

Abstract The exponential growth of the Internet of Things (IoT) has introduced significant security challenges, with zero-day attacks emerging as one of the most critical and challenging threats. Traditional Machine Learning (ML) and Deep Learning (DL) techniques have demonstrated promising early detection capabilities. However, their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints, high computational costs, and the costly time-intensive process of data labeling. To address these challenges, this study proposes a Federated Learning (FL) framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in… More >

Md Sabir Hossain¹, Md Mahfuzur Rahman^1,2,*, Mufti Mahmud^1,3

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1087-1116, 2025, DOI:10.32604/cmes.2025.068779 - 30 October 2025

Abstract This article presents a human fall detection system that addresses two critical challenges: privacy preservation and detection accuracy. We propose a comprehensive framework that integrates state-of-the-art machine learning models, multimodal data fusion, federated learning (FL), and Karush-Kuhn-Tucker (KKT)-based resource optimization. The system fuses data from wearable sensors and cameras using Gramian Angular Field (GAF) encoding to capture rich spatial-temporal features. To protect sensitive data, we adopt a privacy-preserving FL setup, where model training occurs locally on client devices without transferring raw data. A custom convolutional neural network (CNN) is designed to extract robust features from More > Graphic Abstract

Si-Yu Gao¹, Siu-Man Ng^2,*

International Journal of Mental Health Promotion, Vol.27, No.10, pp. 1471-1482, 2025, DOI:10.32604/ijmhp.2025.068702 - 31 October 2025

Abstract Background: Existing Chinese stigma scales focus on the perceptions of people with mental illness (PMI) without assessing the general public’s attitudes toward integrating PMI into the community. Developing a valid and reliable Chinese instrument measuring the attitude domain will be helpful to future research in this area. The current study aimed to validate a shortened Chinese version of the Community Attitudes towards the Mentally Ill Scale (C-CAMI-SF). Methods: Four hundred participants who are (1) Chinese; (2) aged 18 years and above; and (3) able to complete the Chinese questionnaire in a self-reported manner participated in… More >

Hanyong Lee^#, Ye-Chan Park^#, Jaesung Lee^*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4977-4993, 2025, DOI:10.32604/cmc.2025.068156 - 23 October 2025

Abstract Restoring texts corrupted by visually perturbed homoglyph characters presents significant challenges to conventional Natural Language Processing (NLP) systems, primarily due to ambiguities arising from characters that appear visually similar yet differ semantically. Traditional text restoration methods struggle with these homoglyph perturbations due to limitations such as a lack of contextual understanding and difficulty in handling cases where one character maps to multiple candidates. To address these issues, we propose an Optical Character Recognition (OCR)-assisted masked Bidirectional Encoder Representations from Transformers (BERT) model specifically designed for homoglyph-perturbed text restoration. Our method integrates OCR preprocessing with a… More >

Ayman Noor^1,2, Hanan Almukhalfi^1,2, Arthur Souza^2,3, Talal H. Noor^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3085-3111, 2025, DOI:10.32604/cmes.2025.068393 - 30 September 2025

Abstract People with visual impairments face substantial navigation difficulties in residential and unfamiliar indoor spaces. Neither canes nor verbal navigation systems possess adequate features to deliver real-time spatial awareness to users. This research work represents a feasibility study for the wearable IoT-based indoor object detection assistant system architecture that employs a real-time indoor object detection approach to help visually impaired users recognize indoor objects. The system architecture includes four main layers: Wearable Internet of Things (IoT), Network, Cloud, and Indoor Object Detection Layers. The wearable hardware prototype is assembled using a Raspberry Pi 4, while the… More >

Panyu Liu¹, Tongqing Zhou^1,*, Guofeng Lu², Huaizhe Zhou³, Zhiping Cai¹

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2913-2934, 2025, DOI:10.32604/cmc.2025.069175 - 23 September 2025

Abstract The isolation of healthcare data among worldwide hospitals and institutes forms barriers for fully realizing the data-hungry artificial intelligence (AI) models promises in renewing medical services. To overcome this, privacy-preserving distributed learning frameworks, represented by swarm learning and federated learning, have been investigated recently with the sensitive healthcare data retaining in its local premises. However, existing frameworks use a one-size-fits-all mode that tunes one model for all healthcare situations, which could hardly fit the usually diverse disease prediction in practice. This work introduces the idea of ensemble learning into privacy-preserving distributed learning and presents the More >

Saad Sadiq¹, Kashif Sultan¹, Muhammad Sheraz², Teong Chee Chuah^2,*, Muhammad Usman Hashmi³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3945-3963, 2025, DOI:10.32604/cmc.2025.067538 - 23 September 2025

Abstract Vehicle recognition plays a vital role in intelligent transportation systems, law enforcement, access control, and security operations—domains that are becoming increasingly dynamic and complex. Despite advancements, most existing solutions remain siloed, addressing individual tasks such as vehicle make and model recognition (VMMR), automatic number plate recognition (ANPR), and color classification separately. This fragmented approach limits real-world efficiency, leading to slower processing, reduced accuracy, and increased operational costs, particularly in traffic monitoring and surveillance scenarios. To address these limitations, we present a unified framework that consolidates all three recognition tasks into a single, lightweight system. The More >

Li Ji^1,2, Jiawei Mao^1,3, Ruixiang Li^1,*, Jiaqi Xu^1,*

Journal of Renewable Materials, Vol.13, No.9, pp. 1683-1706, 2025, DOI:10.32604/jrm.2025.02025-0043 - 22 September 2025

Abstract Primary diamines play an important role in the chemical industry, where they are widely used as raw materials for the manufacture of pharmaceuticals and polymers. Currently, primary diamines are mainly derived from petroleum, while harsh or toxic conditions are often needed. Biomass is abundant and renewable , which serves as a promising alternative raw material to produce primary diamines. This review primarily focuses on the synthesis of 2,5-bis(aminomethyl)furan (BAMF), a bio-based diamine with potential as a biomonomer for polyamides and polyureas. Specifically, this review emphasizes the synthesis of BAMF from three biomass-derived alcohols and aldehydes,… More > Graphic Abstract

Meriem Bounib¹, Aicha Bouhezza^2,3,*, Abdelkrim Khelifa⁴, Mohamed Teggar⁵, Hasan Köten⁶, Aissa Atia⁷, Yassine Cherif ⁸

Frontiers in Heat and Mass Transfer, Vol.23, No.4, pp. 1323-1350, 2025, DOI:10.32604/fhmt.2025.065254 - 29 August 2025

Abstract This study investigates laminar convection in three regimes (forced convection, mixed convection, and natural convection) of a bi-nanofluid (Cu-Al₂O₃-water)/mono-nanofluid (Al₂O₃-water) inside a square enclosure of sliding vertical walls which are kept at cold temperature and moving up, down, or in opposite directions. The enclosure bottom is heated partially by a central heat source of various sizes while the horizontal walls are considered adiabatic. The thermal conductivity and dynamic viscosity are dependent on temperature and nanoparticle size. The conservation equations are implemented in the solver ANSYS R2 (2020). The numerical predictions are successfully validated by comparison with… More >

Younes Lahraoui¹, Jihane Jebrane², Youssef Amal¹, Saiida Lazaar¹, Cheng-Chi Lee^3,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 615-641, 2025, DOI:10.32604/cmes.2025.067778 - 31 July 2025

Abstract Due to their resource constraints, Internet of Things (IoT) devices require authentication mechanisms that are both secure and efficient. Elliptic curve cryptography (ECC) meets these needs by providing strong security with shorter key lengths, which significantly reduces the computational overhead required for authentication algorithms. This paper introduces a novel ECC-based IoT authentication system utilizing our previously proposed efficient mapping and reverse mapping operations on elliptic curves over prime fields. By reducing reliance on costly point multiplication, the proposed algorithm significantly improves execution time, storage requirements, and communication cost across varying security levels. The proposed authentication… More >