Grounding DINO and distillation-enhanced model for advanced traffic sign detection and classification in autonomous vehicles by Hoang Ngoc Tran, Nam Nhat Ngo Nguyen, Nhi Quynh Phan Le, Thu Anh Ngoc Le, Anh Duy Nguyen

“…The self-distilled network further reduces model size by tenfold compared to baseline models (e.g., MobileNetV2, VGG16, ResNet18), yet maintains competitive accuracy, providing a robust, resource-efficient solution for real-time deployment. Our results indicate that integrating semantic grounding with distillation-based compression not only enhances traffic sign detection performance but also delivers a scalable and efficient approach for complex traffic environments. …”
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State-of-the-Art Methodologies for Self-Fault Detection, Diagnosis and Evaluation (FDDE) in Residential Heat Pumps by Francesco Pelella, Adelso Flaviano Passarelli, Belén Llopis-Mengual, Luca Viscito, Emilio Navarro-Peris, Alfonso William Mauro

“…The complexity and added value of these tools grow as they progress from simple fault detection to quantitative fault evaluation, enabling more accurate and timely maintenance strategies. …”
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SDRFPT-Net: A Spectral Dual-Stream Recursive Fusion Network for Multispectral Object Detection by Peida Zhou, Xiaoyong Sun, Bei Sun, Runze Guo, Zhaoyang Dang, Shaojing Su

“…Multispectral object detection faces challenges in effectively integrating complementary information from different modalities in complex environmental conditions. …”
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Enhancing automated detection and classification of dementia in individuals with cognitive impairment using artificial intelligence techniques by Shoayee Dlaim Alotaibi, Abeer A. K. Alharbi

“…This paper explores the use of AI in the automated detection and classification of dementia, aiming to streamline early diagnosis and improve patient outcomes. …”
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Attomolar electrochemical direct and sandwich immunoassays for the ultrasensitive detection of tomato brown rugose fruit virus by Negin Rezaei, Ahmad Moshaii, Mohammad Reza Safarnejad, Reza H. Sajedi, Mahsa Rahmanipour, Masoud Shams-Bakhsh

“…Electrochemical characterization confirmed the successful immobilization of TBRFV-CP-IgG, its specific interaction with the recombinant coat protein of TBRFV (rp-CP-TBRFV), the subsequent binding of TBRFV-CP-IgGHRP as the detection antibody, and the formation of the complete sandwich complex. …”
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Multi-Object Recognition and Motion Detection Based on Flexible Pressure Sensor Array and Deep Learning by Hao Zhang, Yanan Tao, Kai Shi, Jiali Li, Jianjun Shi, Shaofeng Xu, Ying Guo

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A comparative analysis of fault detection and process diagnosis methods based on a signal processing paradigm by Dorel Aiordăchioaie

“…The results of the computer-based experiments show acceptable performance for all methods for the test case of bearings but the potential to detect more complex faults and change detection in the behavior of the machines, in general. …”
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A cost-effective protocol for detecting fluorescent microplastics in arable soils to study redistribution processes by Saunak Sinha Ray, David Zumr, Florian Wilken, Tomáš Dostál, Peter Fiener

“…It offers potential for enhancement through advanced imaging and machine learning, enabling more efficient and accessible detection in environmental research.…”
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FCDNet: A Lightweight Network for Real-Time Wildfire Core Detection in Drone Thermal Imaging by Linfeng Wang, Oualid Doukhi, Deok Jin Lee

“…The increasing number of wildfires damage nature and human life, making the early detection of wildfires in complex outdoor environments critical. …”
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Detection of Bipolar Disorder and Schizophrenia Employing Bayesian-Optimized Grad-CAM-Driven Deep Learning by Osman Tayfun Bişkin, Cemre Candemir, Mustafa Alper Selver

“…Building on its success in other domains, including autism spectrum disorder and Alzheimer’s disease, DL models have demonstrated the potential to detect subtle structural changes in BD and SCH. Recent advancements suggest that DL can outperform traditional statistical methods, offering higher classification accuracy and enabling the differentiation of complex psychiatric disorders. …”
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Enhancing Efficiency in Hybrid Solar–Wind–Battery Systems Using an Adaptive MPPT Controller Based on Shadow Motion Prediction by Abdorreza Alavi Gharahbagh, Vahid Hajihashemi, Nasrin Salehi, Mahyar Moradi, José J. M. Machado, João Manuel R. S. Tavares

“…This study focuses on enhancing the speed and efficiency of the maximum power point tracking (MPPT) system in a solar power plant. …”
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Intrusion detection system based on machine learning using least square support vector machine by Pratik Waghmode, Manideep Kanumuri, Hosam El-Ocla, Tanner Boyle

“…Unauthorized access or attack can be avoided in critical systems using a comprehensive approach via an effective intrusion detection system (IDS). Traditional intrusion detection techniques are no longer accurate and effective enough to handle the demands of the big data age. …”
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A Multi-Task Based Deep Learning Framework With Landmark Detection for MRI Couinaud Segmentation by Dong Miao, Ying Zhao, Xue Ren, Meng Dou, Yu Yao, Yiran Xu, Yingchao Cui, Ailian Liu

“…By correlating landmark detection with segmentation, we enhance surgical planning precision. …”
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Integrated pixel-level crack detection and quantification using an ensemble of advanced U-Net architectures by Rakshitha R, Srinath S, N Vinay Kumar, Rashmi S, Poornima B V

“…Automated pavement crack detection faces significant challenges due to the complex shapes of crack patterns, their similarity to non-crack textures, and varying environmental conditions such as lighting and noise. …”
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Smart Fault Detection, Classification, and Localization in Distribution Networks: AI-Driven Approaches and Emerging Technologies by Jianxian Wang, Hazlie Mokhlis, Nurulafiqah Nadzirah Mansor, Hazlee Azil Illias, Agileswari K. Ramasamy, Xingyu Wu, Siqi Wang

“…However, with nations worldwide actively pursuing carbon neutrality and emission peak goals, sustainable energy sources such as solar and wind are increasingly penetrating distribution networks, posing significant challenges to conventional fault detection, classification, and localization techniques due to bidirectional power flows, dynamic fault currents, and rising network complexity. …”
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U-ActionNet: Dual-Pathway Fourier Networks With Region-of-Interest Module for Efficient Action Recognition in UAV Surveillance by Abdul Monaf Chowdhury, Ahsan Imran, Md Mehedi Hasan, Riad Ahmed, Akm Azad, Salem A. Alyami

“…Leveraging a dual-path approach, we propose a novel framework U-ActionNet that integrates FFT-based substance separation and space-time self-attention techniques to improve the accuracy and efficiency of HAR tasks. Catering the server side the first pathway employs a modified C3D model with Fast Fourier Transform (FFT)-based object movement and attention detection mechanisms to effectively extract human actors from complex scenes and capture spatiotemporal dynamics in UAV footage. …”
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Integration of Carbon Nanotubes into Manganese Dioxide Nanorods for Enhanced Enzymeless Electrochemical Glucose Sensing with High Sensitivity and Selectivity by Khawtar Hasan Ahmed, Alonso Moreno Zuria, Mohamed Mohamedi

“…These attributes are essential for accurate glucose detection in complex biological samples. The integration of MnO₂ nanorods with CNTs in freestanding nanostructures opens up exciting opportunities for developing high-performance, robust electrochemical sensors for diverse applications.…”
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DynaOOD-Net: Dynamic Progressive Feature Fusion and Energy Balancing for Robust Out-of-Distribution Detection by Jiting Zhou, Zhihao Zhou, Pu Zhang

“…Experimental results on seven datasets demonstrate that DynaOOD-Net significantly improves the mAP for known categories and consistently outperforms existing state-of-the-art methods in unknown object detection metrics. These findings establish DynaOOD-Net as a robust and efficient solution to the challenges inherent in open-set object detection. …”
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New Methods for Waterfowl and Habitat Survey Using AI and Drone Imagery by Zhenduo Zhai, Zhiguang Liu, Yang Zhang, Andrew Zhao, Yi Shang

“…Existing methods for waterfowl monitoring face critical challenges such as low accuracy in identifying overlapping image regions and limited segmentation accuracy in complex habitats. To address these issues, this paper presents an end-to-end system and several new methods for efficiently and accurately identifying waterfowl populations in their natural habitats using AI and drone imagery. …”
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Enhancing Human Detection in Occlusion-Heavy Disaster Scenarios: A Visibility-Enhanced DINO (VE-DINO) Model with Reassembled Occlusion Dataset by Zi-An Zhao, Shidan Wang, Min-Xin Chen, Ye-Jiao Mao, Andy Chi-Ho Chan, Derek Ka-Hei Lai, Duo Wai-Chi Wong, James Chung-Wai Cheung

“…Natural disasters create complex environments where effective human detection is both critical and challenging, especially when individuals are partially occluded. …”
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