Multi-Modal Dynamic Fusion for Defect Detection in Electronic Products: A Novel Approach Based on Energy and Deep Learning by Yulin Liu, Yang Gao

“…Conventional defect detection approaches, which typically depend on a single modality, often fall short in both efficiency and reliability. …”
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A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction by Md. Alamin Talukder, Majdi Khalid, Nasrin Sultana

“…This hybrid approach addresses class imbalance and high-dimensionality challenges, providing scalable and robust intrusion detection. Complexity analysis reveals that the proposed model reduces training and prediction times, making it suitable for real-time applications.…”
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Comparative analysis of data-driven models on detection and classification of electrical faults in transmission systems: Explainability, applicability and industrial implications by Chibueze D. Ukwuoma, Dongsheng Cai, Chiagoziem C. Ukwuoma, Chinedu I. Otuka, Qi Huang

“…Most data-driven fault detection methods often face challenges in accuracy, adaptability, and real-time implementation, particularly in complex transmission networks. …”
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Non-Destructive Detection of Fillet Fish Quality Using MQ135 Gas Sensor and Neutrosophic Logic-Enhanced System by M. Y. Shams, M. R. Darwesh, Roheet Bhatnagar, N. S. A. Al-Sattary, A. A. Salama, M. S. Ghoname

“…This integration enhances decision-making by accurately modeling complex real-world uncertainties. These methods, combining electronic nose technology, artificial intelligence, and neutrosophic inference, provide a robust, non-destructive, and cost-effective approach to detecting spoilage in fillet fish. …”
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Golden Chip-Free Hardware Trojan Detection Using Attention-Based Non-Local Convolution With Simple Recurrent Unit by Rama Devi Maddineni, Deepak Ch

“…The emergence of machine learning and deep learning models has enhanced the feasibility of hardware Trojan detection, as these models can learn complex patterns and representations from extensive datasets. …”
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A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8 by Ye Zheng, Jun Yan, Junxia Meng, Ming Liang

“…Because of their low cost and ease of deployment, side-scan sonars is one of the most widely used underwater survey instruments. However, the complexity of the marine environment and the difficulty in target acquisition limit the detection accuracy of side-scan sonars. …”
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Abnormality detection of sliding surface and exploration suitable sensor data for condition monitoring by calculating contribution using machine learning by Ryo NAKASHIMA, Tomomi HONDA, Tomohiko KON

“…As machines become more sophisticated and manufacturing processes more complex in the manufacturing industry, there is a need to build simple systems that enable condition monitoring without specialized knowledge. …”
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Proposal for an Intelligent Methodology to Manage Energy in Buildings and Detect Anomalies as a Compass Towards Zero Energy Buildings (ZEB) by Irati Prol-Godoy, Roberto Santana, Francisco-Javier Rey-Martinez, Ana Picallo-Perez

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Real-Time Smoke Detection in Surveillance Videos Using an Enhanced RT-DETR Framework with Triplet Attention and HS-FPN by Lanyan Yang, Yuanhang Cheng, Fang Xu, Boning Li, Xiaoxu Li

“…This study addresses the urgent need for an efficient and accurate smoke detection system to enhance safety measures in fire monitoring, industrial safety, and urban surveillance. …”
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Adaptive Congestion Detection and Traffic Control in Software-Defined Networks via Data-Driven Multi-Agent Reinforcement Learning by Kaoutar Boussaoud, Abdeslam En-Nouaary, Meryeme Ayache

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Morphological Analysis and Subtype Detection of Acute Myeloid Leukemia in High-Resolution Blood Smears Using ConvNeXT by Mubarak Taiwo Mustapha, Dilber Uzun Ozsahin

“…(1) Background: Acute Myeloid Leukemia (AML) is a complex hematologic malignancy where accurate subtype classification is crucial for targeted treatment and improved patient outcomes. …”
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Double-Condensing Attention Condenser: Leveraging Attention in Deep Learning to Detect Skin Cancer from Skin Lesion Images by Chi-en Amy Tai, Elizabeth Janes, Chris Czarnecki, Alexander Wong

“…A recent movement for TinyML applications is integrating Double-Condensing Attention Condensers (DC-AC) into a self-attention neural network backbone architecture to allow for faster and more efficient computation. This paper explores leveraging an efficient self-attention structure to detect skin cancer in skin lesion images and introduces a deep neural network design with DC-AC customized for skin cancer detection from skin lesion images. …”
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Detection of low-altitude infrared small targets for UAVs using a density-based artificial bee colony algorithm by Haixia Wang, Hailong Wang, Fen Han

“…This study enriches the application framework of density-driven intelligent optimization strategies in the field of infrared small target detection. Moreover, it provides theoretical support and technical path for low-altitude unmanned aerial vehicles to achieve efficient and stable infrared target detection in tasks such as security patrols, disaster search and rescue, and border monitoring.…”
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A Comprehensive Approach to Intrusion Detection in IoT Environments Using Hybrid Feature Selection and Multi-Stage Classification Techniques by G. Logeswari, J. Deepika Roselind, K. Tamilarasi, V. Nivethitha

“…The rapid expansion of Internet of Things (IoT) devices has led to an increasingly complex threat landscape, challenging traditional Intrusion Detection Systems (IDS) to effectively handle the vast and diverse data generated by IoT networks. …”
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Nanoarchitectonics with cetrimonium bromide on metal nanoparticles for linker-free detection of toxic metal ions and catalytic degradation of 4-nitrophenol by Akash Kumar, Raja Gopal Rayavarapu

“…This restricts its utility in heavy metal detection and 4-NP degradation, requiring additional surface modifications using linker molecules, thereby increasing process complexity and cost. …”
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QD-MLL-Based Single-Sideband Superchannel Generation Scheme With Kramers–Kronig Direct Detection Receivers by Mustafa Al-Qadi, Govind Vedala, Maurice O'Sullivan, Chongjin Xie, Rongqing Hui

“…For their capability of electronic dispersion compensation, transmission systems based on direct detection of single-sideband (SSB) signals are attractive candidates as energy-efficient and cost-effective alternative solutions to intradyne digital coherent systems for interdata center and metro applications. …”
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RFCS-YOLO: Target Detection Algorithm in Adverse Weather Conditions via Receptive Field Enhancement and Cross-Scale Fusion by Gang Liu, Yingzheng Huang, Shuguang Yan, Enxiang Hou

“…It addresses challenges like complex backgrounds and problems of missing and mis-detecting traffic targets in bad weather. …”
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YOLO-SW: A Real-Time Weed Detection Model for Soybean Fields Using Swin Transformer and RT-DETR by Yizhou Shuai, Jingsha Shi, Yi Li, Shaohao Zhou, Lihua Zhang, Jiong Mu

“…The research stands out for its novel integration of three key advancements: the Swin Transformer backbone, which leverages local window self-attention to achieve linear O(N) computational complexity for efficient global context capture; the CARAFE dynamic upsampling operator, which enhances small target localization through context-aware kernel generation; and the RTDETR encoder, which enables end-to-end detection via IoU-aware query selection, eliminating the need for complex post-processing. …”
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Neurovision: A deep learning driven web application for brain tumour detection using weight-aware decision approach by Thota Rishik Sai Santhosh, Sachi Nandan Mohanty, Nihar Ranjan Pradhan, Tauseef Khan, Morched Derbali

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Regional classification and logistic regression modeling for surface freeze/thaw detection on the Qinghai-Tibet Plateau using CYGNSS data by Jiaxing He, Nanshan Zheng, Rui Ding, Xuexi Liu, Zhiyong Ma

“…To overcome these limitations, this study proposes an innovative approach that integrates the application of GNSS-R data with regional classification and logistic regression modeling, establishing an efficient framework for detecting surface F/T states. …”
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