Streamlined Bearing Fault Detection Using Artificial Intelligence in Permanent Magnet Synchronous Motors by Javier de las Morenas, Lidia M. Belmonte, Rafael Morales

“…Permanent magnet synchronous motors (PMSMs) are widely used in industrial applications due to their high efficiency and reliability. However, bearing faults remain a critical issue, necessitating robust fault detection strategies. …”
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A lightweight remote sensing image detection model with feature aggregation diffusion network by Xiaohui Cheng, Xukun Wang, Yun Deng, Qiu Lu, Yanping Kang, Jian Tang, Yuanyuan Shi, Junyu Zhao

“…However, existing deep learning models often face challenges in balancing detection accuracy and computational efficiency, especially for small objects in complex scenes. …”
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A novel approach to skin disease segmentation using a visual selective state spatial model with integrated spatial constraints by Yu Bai, Hai Zhou, Hongjie Zhu, Shimin Wen, Binbin Hu, Haotian Li, Huazhang Wang, Daji Ergu, Fangyao Liu

“…To address limitations in existing approaches, we introduce a novel U-shaped segmentation architecture based on our Residual Space State Block. This efficient model, termed ‘SSR-UNet,’ leverages bidirectional scanning to capture both global and local features in image data, achieving strong performance with low computational complexity. …”
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Detection and Classification of Power Quality Disturbances Based on Improved Adaptive S-Transform and Random Forest by Dongdong Yang, Shixuan Lü, Junming Wei, Lijun Zheng, Yunguang Gao

“…The increasing penetration of renewable energy into power systems has intensified transient power quality (PQ) disturbances, demanding efficient detection and classification methods to enable timely operational decisions. …”
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Cutting-Edge Phishing Detection Using Novel Features and Hybrid Machine Learning Techniques by Jin Zhang

“…Classification Information Mining (DM) strategies are highly effective tools for identifying and detecting e-banking phishing websites. This study introduces a novel approach to address the challenges and complexities associated with predicting and identifying such websites. …”
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Deep learning-based ensemble stacking for enhanced intrusion detection in IoT-edge platforms by P. R. Chithra Rani, K. Baalaji

“…This research contributes a scalable, efficient, and resource-aware intrusion-detection approach to extending the resilience of IoT-edge systems in distributed computing environments.…”
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Spin-Wheel: A Fast and Secure Chaotic Encryption System with Data Integrity Detection by Luis D. Espino-Mandujano, Rogelio Hasimoto-Beltran

“…This approach exacerbates the complexity in the event of an attack, since the trajectories may come from the same or totally different maps in a non-sequential time order. …”
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Real-Time Interference Mitigation for Reliable Target Detection with FMCW Radar in Interference Environments by Youlong Weng, Ziang Zhang, Guangzhi Chen, Yaru Zhang, Jiabao Chen, Hongzhan Song

“…In this paper, we propose an efficient solution for real-time radar interference mitigation. …”
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A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion by Qi Wang, Guanghu Xu, Donglin Jing

“…Through multi-scale feature interaction and direction-aware optimization, MIHE-Net effectively addresses the challenges posed by scale gradation and direction diversity in remote sensing target detection, providing an efficient and feasible solution for high-precision remote sensing target detection.…”
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PlantNet: Scalable Convolutional Neural Network for Image-Based Plant Disease Detection by Sinha Anupa, Kumaraswamy Balasubramaniam

“…By leveraging deep learning techniques, PlantNet processes large-scale image datasets to detect disease symptoms with high precision. The model employs transfer learning, utilizing pre-trained networks on vast image repositories before fine-tuning on a specialized plant disease dataset, thereby enhancing feature extraction while minimizing computational complexity. …”
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Defect Detection of Gas Insulation Switch by Infrared Thermography Technology With an Improved Yolo Algorithm by Ma Tianci, Chen Xiangping, Li Bo, Bai Jie

“…However, conventional nondestructive detection methods like ultrasound and X-ray face the challenges in anti-interference capability, cost, and structural complexity. …”
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Sowing, Monitoring, Detecting: A Possible Solution to Improve the Visibility of Cropmarks in Cultivated Fields by Filippo Materazzi

“…This study explores the integration of UAS-based multispectral remote sensing and targeted agricultural practises to improve cropmark detection in buried archaeological contexts. The research focuses on the Vignale plateau, part of the pre-Roman city of Falerii (Viterbo, Italy), where traditional remote sensing methods face challenges due to complex environmental and archaeological conditions. …”
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Alpine Wetlands Information Extraction Using Optimized Multifeatures and Random Forest Algorithm by Dongchuan Wang, Fei Yang, Shijie Jia, Zhiheng Wang, Chunhua Dong, Mingwei Lang, Kai Ye, Haotian Liu, Tingrong Li

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Detection of Citrus Huanglongbing in Natural Field Conditions Using an Enhanced YOLO11 Framework by Liang Cao, Wei Xiao, Zeng Hu, Xiangli Li, Zhongzhen Wu

“…We constructed a multi-symptom HLB leaf dataset (MS-HLBD) containing 9219 annotated images across five classes: Healthy (1862), HLB blotchy mottling (2040), HLB Zinc deficiency (1988), HLB yellowing (1768), and Canker (1561), collected under diverse field conditions. To improve detection performance, the DCH-YOLO11 framework incorporates three novel modules: the C3k2 Dynamic Feature Fusion (C3k2_DFF) module, which enhances early and subtle lesion detection through dynamic feature fusion; the C2PSA Context Anchor Attention (C2PSA_CAA) module, which leverages context anchor attention to strengthen feature extraction in complex vein regions; and the High-efficiency Dynamic Feature Pyramid Network (HDFPN) module, which optimizes multi-scale feature interaction to boost detection accuracy across different object sizes. …”
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Pareto Front Transformation in the Decision-Making Process for Spectral and Energy Efficiency Trade-Off in Massive MIMO Systems by Eni Haxhiraj, Desar Shahu, Elson Agastra

“…It proposes the transformation of the group of non-dominated alternatives using the Box–Cox transformation with values of λ < 1 so that the graph with a complex shape is transformed into a concave graph. …”
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Semi-supervised anomaly detection from Chlorella vulgaris cultivations using hyperspectral imaging by Salli Pääkkönen, Ilkka Pölönen, Pauliina Salmi

“…More evolved anomaly detection methods are needed to ensure efficient quality control of microalgae cultivations. …”
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A novel feature extractor based on constrained cross network for detecting sleep state by Chenlei Tian, Fei Song

“…Most existing methods that utilize wrist-worn devices data for detection rely on heuristic algorithms or traditional machine learning, which suffer from low classification efficiency and insufficient accuracy. …”
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RGE-YOLO enables lightweight road packaging bag detection for enhanced driving safety by Dangfeng Pang, Zhiwei Guan, Tao Luo, Yanhao Liang, Ruzhen Dou

“…However, research on detecting road packaging bags remains limited, and existing object detection models face challenges in small object detection, computational efficiency, and embedded deployment. …”
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MHS-VIT: Mamba hybrid self-attention vision transformers for traffic image detection. by Xude Zhang, Weihua Ou, Xiaoping Wu, Changzhen Zhang

“…The self-attention mechanism of the transformer is good at capturing long-distance spatial dependencies in images, which is crucial for understanding complex traffic scenes. Experimental results showed that MHS-VIT exhibited excellent performances in traffic image detection tasks. …”
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EGRN-YOLO: An Enhanced Multi-View Remote Sensing Detection Algorithm for Onshore Wind Turbines Based on YOLOv7 by Renzheng Xue, Haiqiang Xu, Qianlong Wu

“…Wind turbines, as the core components of wind power generation systems, play a crucial role in determining the overall generation efficiency and operational safety. However, the challenges posed by complex backgrounds, significant variations in the scale of wind turbine targets, and arbitrary orientations in unmanned aerial vehicle (UAV) remote sensing images have significantly increased the difficulty of real-time wind turbine detection. …”
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