Xin Ma^1,2, Jin Lei^3,4,*, Chenying Pei⁴, Chunming Wu⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-17, 2026, DOI:10.32604/cmc.2025.069353 - 09 December 2025

Abstract This study proposes a lightweight apple detection method employing cascaded knowledge distillation (KD) to address the critical challenges of excessive parameters and high deployment costs in existing models. We introduce a Lightweight Feature Pyramid Network (LFPN) integrated with Lightweight Downsampling Convolutions (LDConv) to substantially reduce model complexity without compromising accuracy. A Lightweight Multi-channel Attention (LMCA) mechanism is incorporated between the backbone and neck networks to effectively suppress complex background interference in orchard environments. Furthermore, model size is compressed via Group_Slim channel pruning combined with a cascaded distillation strategy. Experimental results demonstrate that the proposed model More >

Fatima Khan¹, Amna Khan¹, Tariq Ali², Tariq Shahzad³, Tehseen Mazhar^4,*, Sunawar Khan⁵, Muhammad Adnan Khan^6,*, Habib Hamam^7,8,9,10

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-27, 2026, DOI:10.32604/cmc.2025.068228 - 09 December 2025

Abstract The rise in noise and air pollution poses severe risks to human health and the environment. Industrial and vehicular emissions release harmful pollutants such as CO₂, SO₂, CO, CH₄, and noise, leading to significant environmental degradation. Monitoring and analyzing pollutant concentrations in real-time is crucial for mitigating these risks. However, existing systems often lack the capacity to monitor both indoor and outdoor environments effectively.This study presents a low-cost, IoT-based pollution detection system that integrates gas sensors (MQ-135 and MQ-4), a noise sensor (LM393), and a humidity sensor (DHT-22), all connected to a Node MCU (ESP8266) microcontroller. The… More >

Yeasul Kim¹, Chaeeun Won¹, Hwankuk Kim^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-28, 2026, DOI:10.32604/cmc.2025.069608 - 10 November 2025

Abstract With the increasing emphasis on personal information protection, encryption through security protocols has emerged as a critical requirement in data transmission and reception processes. Nevertheless, IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices, spanning a range of devices from non-encrypted ones to fully encrypted ones. Given the limited visibility into payloads in this context, this study investigates AI-based attack detection methods that leverage encrypted traffic metadata, eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices. Using the UNSW-NB15 and CICIoT-2023 dataset, encrypted and… More >

Kemahyanto Exaudi^1,2, Deris Stiawan^3,*, Bhakti Yudho Suprapto¹, Hanif Fakhrurroja⁴, Mohd. Yazid Idris⁵, Tami A. Alghamdi⁶, Rahmat Budiarto⁶

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-24, 2026, DOI:10.32604/cmc.2025.069377 - 10 November 2025

Abstract Sudden wildfires cause significant global ecological damage. While satellite imagery has advanced early fire detection and mitigation, image-based systems face limitations including high false alarm rates, visual obstructions, and substantial computational demands, especially in complex forest terrains. To address these challenges, this study proposes a novel forest fire detection model utilizing audio classification and machine learning. We developed an audio-based pipeline using real-world environmental sound recordings. Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network (CNN), enabling the capture of distinctive fire acoustic signatures (e.g., crackling, roaring) that are minimally impacted by… More >

John Hordines¹, Shirley Ge², Dima Raskolnikov¹, Alexander C. Small¹, Kara L. Watts^1,*

Canadian Journal of Urology, Vol.32, No.6, pp. 551-560, 2025, DOI:10.32604/cju.2025.065988 - 30 December 2025

Abstract Background: The healthcare industry contributes nearly 5% of worldwide carbon emissions. In an effort to mitigate this impact, urology practices can take steps to reduce their carbon footprints. We conducted a systematic review which aimed to summarise the current literature on the environmental impact of urologic-related care. Methods: A systematic literature review evaluating the impact of urologic procedures, telehealth and conferences/interviews was conducted on PubMed and Cochrane databases using a Boolean search strategy and the following search terms: urology, planetary health, environmental impact, carbon emissions, carbon footprint, and waste. Full-text articles published in English were… More >

Nader Karmous¹, Wadii Jlassi¹, Mohamed Ould-Elhassen Aoueileyine¹, Imen Filali^2,*, Ridha Bouallegue¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4363-4393, 2025, DOI:10.32604/cmes.2025.074178 - 23 December 2025

Abstract This paper introduces a robust Distributed Denial-of-Service attack detection framework tailored for Software-Defined Networking based Internet of Things environments, built upon a novel, synthetic multi-vector dataset generated in a Mininet-Ryu testbed using real-time flow-based labeling. The proposed model is based on the XGBoost algorithm, optimized with Principal Component Analysis for dimensionality reduction, utilizing lightweight flow-level features extracted from OpenFlow statistics to classify attacks across critical IoT protocols including TCP, UDP, HTTP, MQTT, and CoAP. The model employs lightweight flow-level features extracted from OpenFlow statistics to ensure low computational overhead and fast processing. Performance was rigorously… More >

Rıdvan Yayla, Hakan Üçgün^*, Onur Ali Korkmaz

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4055-4087, 2025, DOI:10.32604/cmes.2025.072703 - 23 December 2025

Abstract Recent advancements in autonomous vehicle technologies are transforming intelligent transportation systems. Artificial intelligence enables real-time sensing, decision-making, and control on embedded platforms with improved efficiency. This study presents the design and implementation of an autonomous radio-controlled (RC) vehicle prototype capable of lane line detection, obstacle avoidance, and navigation through dynamic path planning. The system integrates image processing and ultrasonic sensing, utilizing Raspberry Pi for vision-based tasks and Arduino Nano for real-time control. Lane line detection is achieved through conventional image processing techniques, providing the basis for local path generation, while traffic sign classification employs a… More > Graphic Abstract

Daskshnamoorthy Manivannan^*

Journal on Internet of Things, Vol.7, pp. 71-97, 2025, DOI:10.32604/jiot.2025.072809 - 24 December 2025

Abstract Attribute-Based Encryption (ABE) secures data by tying decryption rights to user attributes instead of identities, enabling fine-grained access control. However, many ABE schemes are unsuitable for Internet of Things (IoT) due to limited device resources. This paper critically surveys ABE schemes developed specifically for IoT over the past decade, examining their evolution, strengths, limitations, and access control capabilities. It provides insights into their security, effectiveness, and real-world applicability, highlights the current state of ABE in securing IoT data and access, and discusses remaining challenges and open issues. More >

S. Saleem^a, U. Parveen^a, H. AL-Ghamdi^b,*, M. Yaseen^a, I. Sajjad^a, Nasarullah^a

Chalcogenide Letters, Vol.22, No.3, pp. 223-237, 2025, DOI:10.15251/CL.2025.223.223

Abstract Present research reveals the doping effect on physical properties of Sr_1-xFe_xS by employing ab-initio calculations. The negative formation energy and optimization outcomes exhibit the stability of the Sr_1-xFe_xS alloys with ferromagnetic phase. Spin dependent band structure (BS) and density of states (DOS) interpret that Sr_1-xFe_xS revealed half metallic ferromagnetic (HMF) nature at 6.25% and 12.5% of Fe doping while metallic character is revealed at 25% concentration of dopant. Spin-up state of Sr_0.9375Fe_0.0625S and Sr_0.8750Fe_0.1250S depicts semiconductive behavior with bandgap value of 2.01/2.33 eV, correspondingly, while metallic in spin-down channel. The magnetism in the system is mainly originated because… More >

K. Wang^a,b,*, G. L. Lian^c, Y. F. Qiao^d

Chalcogenide Letters, Vol.22, No.10, pp. 889-904, 2025, DOI:10.15251/CL.2025.2210.889

Abstract The remediation of acid mine drainage (AMD), characterized by its high concentrations of toxic metal ions and low pH, presents a significant environmental challenge. In this study, exfoliated two-dimensional MoS_₂ nanosheets were prepared using a liquid-phase ultrasonication method and evaluated for their efficiency in removing Cd^²⁺, Cu^²⁺, and Pb^²⁺ from aqueous solutions. Detailed structural and morphological analyses confirmed that the exfoliation process significantly enhanced surface area, pore volume, and exposure of reactive sulfur sites. Through isotherm and kinetic modeling analyses, the adsorption behavior was found to align with the Langmuir model and pseudo-second-order kinetic equation, which implies More >