Explainable artificial intelligence with temporal convolutional networks for adverse weather condition detection in driverless vehicles by Samah Alzanin

“…Therefore, this paper proposes a Complex Data Analysis for Adverse Weather Detection in Autonomous Vehicles Using Explainable Artificial Intelligence (CDAAWD-AVXAI) approach. …”
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Energy-Efficient Islanding Detection Using CEEMDAN and Neural Network Integration in Photovoltaic Distribution System by Sulayman Kujabi, Emmanuel Asuming Frimpong, Francis Boafo Effah

“…Furthermore, feature permutation importance analysis highlighted the critical role of certain features in the model's performance.  …”
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Enhanced RT-DETR with Dynamic Cropping and Legendre Polynomial Decomposition Rockfall Detection on the Moon and Mars by Panpan Zang, Jinxin He, Yongbin Yang, Yu Li, Hanya Zhang

“…Our coordinated optimization strategy integrates dynamic cropping optimization with architectural innovations: Kolmogorov–Arnold Network based C3 module (KANC3) replaces RepC3 through Legendre polynomial decomposition to strengthen feature representation, while our dynamic cropping strategy significantly improves small-target detection in low-contrast grayscale imagery by mitigating background and target imbalance. …”
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High-Precision Defect Detection in Solar Cells Using YOLOv10 Deep Learning Model by Lotfi Aktouf, Yathin Shivanna, Mahmoud Dhimish

“…Detailed analysis of the model’s performance revealed exceptional precision and recall rates for most defect classes, notably achieving 100% accuracy in detecting black core, corner, fragment, scratch, and short circuit defects. …”
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Deep learning approach based on a patch residual for pediatric supracondylar subtle fracture detection by Qingming Ye, Zhilu Wang, Yi Lou, Yang Yang, Jue Hou, Zheng Liu, Weiguang Liu, Jiayu Li

“…However, their diagnosis can be particularly challenging due to the anatomical characteristics and imaging features of the pediatric skeleton. In recent years, convolutional neural networks (CNNs) have achieved notable success in medical image analysis, though their performance typically relies on large-scale, high-quality labeled datasets. …”
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Conformal On-Body Antenna System Integrated with Deep Learning for Non-Invasive Breast Cancer Detection by Marwa H. Sharaf, Manuel Arrebola, Khalid F. A. Hussein, Asmaa E. Farahat, Álvaro F. Vaquero

“…Breast cancer detection through non-invasive and accurate techniques remains a critical challenge in medical diagnostics. …”
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The MacqD deep-learning-based model for automatic detection of socially housed laboratory macaques by Genevieve Jiawei Moat, Maxime Gaudet-Trafit, Julian Paul, Jaume Bacardit, Suliann Ben Hamed, Colline Poirier

“…Abstract Despite advancements in video-based behaviour analysis and detection models for various species, existing methods are suboptimal to detect macaques in complex laboratory environments. …”
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Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection by Muhammad Shafiq, J. Kavitha, Dhruva R. Rinku, N. K. Senthil Kumar, Kamal Poon, Amar Y. Jaffar, V. Saravanan

“…It does most of the data analysis and decision-making. Feature Extraction (FE) is done through CAT-Swarm Optimization. …”
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Integrating the landscape scale supports SAR-based detection and assessment of the phenological development at the field level by Johannes Löw, Steven Hill, Insa Otte, Christoph Friedrich, Michael Thiel, Tobias Ullmann, Christopher Conrad

“…TSMs were generated through breakpoint analyses over different smoothing intensities for Sentinel-1 polarisation (PolSAR) and interferometric coherence (InSAR) features, capturing crop, orbit and sensor-specific responses. …”
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Boosting Cyberattack Detection Using Binary Metaheuristics With Deep Learning on Cyber-Physical System Environment by Alanoud Al Mazroa, Fahad R. Albogamy, Mohamad Khairi Ishak, Samih M. Mostafa

“…In addition, the binary grey wolf optimizer (BGWO) model is utilized to choose an optimal feature subset. Moreover, the Enhanced Elman Spike Neural Network (EESNN) model detects cyber-attacks. …”
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BCTDNet: Building Change-Type Detection Networks with the Segment Anything Model in Remote Sensing Images by Wei Zhang, Jinsong Li, Shuaipeng Wang, Jianhua Wan

“…Moreover, an interactive attention module bridges SAM with a Convolutional Neural Network, enabling seamless interaction between fine-grained structural information and deep semantic features. Furthermore, we develop a change-aware attribute decoder that integrates building semantics into the change detection process via an extraction decoding network. …”
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Automatic Detection of Landslide Surface Cracks from UAV Images Using Improved U-Network by Hao Xu, Li Wang, Bao Shu, Qin Zhang, Xinrui Li

“…Surface cracks are key indicators of landslide deformation, crucial for early landslide identification and deformation pattern analysis. However, due to the complex terrain and landslide extent, manual surveys or traditional digital image processing often face challenges with efficiency, precision, and interference susceptibility in detecting these cracks. …”
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A Transductive Zero-Shot Learning Framework for Ransomware Detection Using Malware Knowledge Graphs by Ping Wang, Hao-Cyuan Li, Hsiao-Chung Lin, Wen-Hui Lin, Nian-Zu Xie

“…As a result, these conventional approaches frequently fail to detect newly emerging malware variants in a timely manner. …”
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A Comparative Crash-Test of Manual and Semi-Automated Methods for Detecting Complex Submarine Morphologies by Vasiliki Lioupa, Panagiotis Karsiotis, Riccardo Arosio, Thomas Hasiotis, Andrew J. Wheeler

“…Multibeam echosounders provide ideal data for the semi-automated seabed feature extraction and accurate morphometric measurements. …”
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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

“…This framework provides a scalable and efficient solution for automated pavement crack analysis. It addresses critical challenges in accuracy, adaptability, and reliability under diverse operational conditions, marking significant progress in crack detection technology.…”
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PAB-Mamba-YOLO: VSSM assists in YOLO for aggressive behavior detection among weaned piglets by Xue Xia, Ning Zhang, Zhibin Guan, Xin Chai, Shixin Ma, Xiujuan Chai, Tan Sun

“…The mean average precision (mAP) of 0.985 reflected the model's overall effectiveness in detecting all classes of aggressive behaviors. The model achieved a detection speed FPS of 69 f/s, with model complexity measured by 7.2 G floating-point operations (GFLOPs) and parameters (Params) of 2.63 million. …”
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Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning by Muhammad Umair, Jawad Ahmad, Nada Alasbali, Oumaima Saidani, Muhammad Hanif, Aizaz Ahmad Khattak, Muhammad Shahbaz Khan

“…IntroductionMajor Depressive Disorder (MDD) remains a critical mental health concern, necessitating accurate detection. Traditional approaches to diagnosing MDD often rely on manual Electroencephalography (EEG) analysis to identify potential disorders. …”
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A Multi-Strategy Active Learning Framework for Enhanced Peripheral Blood Cell Image Detection by Yuheng Feng, Jiangtao He, Linjin Wang, Wuchen Yang, Sihan Deng, Lanlin Li, Xinwei Li

“…The process begins with entropy-based uncertainty selection to identify the most uncertain samples, followed by clustering analysis to capture diverse samples from the feature space, and concludes with density-based selection using the k-nearest neighbors algorithm to prioritize samples from high-density regions. …”
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YOLOv8n-SSDW: A Lightweight and Accurate Model for Barnyard Grass Detection in Fields by Yan Sun, Hanrui Guo, Xiaoan Chen, Mengqi Li, Bing Fang, Yingli Cao

“…Based on the baseline YOLOv8n model, a novel Separable Residual Coord Conv (SRCConv) was designed to replace the original convolution module, significantly reducing parameters while maintaining detection accuracy. The Spatio-Channel Enhanced Attention Module (SEAM) was introduced and optimized to improve sensitivity to barnyard grass edge features. …”
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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

“…The review further distinguishes between artificial intelligence-based smart approaches that directly process raw distribution networks signal data and those that rely on advanced feature extraction techniques to enhance functional performance. …”
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