MHFS-FORMER: Multiple-Scale Hybrid Features Transformer for Lane Detection by Dongqi Yan, Tao Zhang

“…Furthermore, a significant drawback of most existing lane-detection algorithms lies in their reliance on complex post-processing and strong prior knowledge. …”
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Enhancing credit card fraud detection: highly imbalanced data case by Dalia Breskuvienė, Gintautas Dzemyda

“…Given the inherent imbalance in fraud detection data, feature selection must be done with an enhanced focus. …”
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Shifting of Research Trends in Fault Detection and Estimation of Location in Power System by Mallinath, Soham Dutta, Jayalakshimi N. S., Vinay Kumar Jadoun

“…Fault detection is a critical process in ensuring the reliability and safety of modern power systems. …”
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Detection of Malicious Clients in Federated Learning Using Graph Neural Network by Anee Sharma, Ningrinla Marchang

“…However, due to its distributed nature, this paradigm is susceptible to adversarial threats such as sign-flipping attacks, in which malicious clients reverse model parameter signs in order to poison the global aggregation process. This study introduces a detection framework that is graph-based and leverages Graph Attention Networks (GATs) to overcome these challenges. …”
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Detecting Obfuscated Malware Infections on Windows Using Ensemble Learning Techniques by Yadigar Imamverdiyev, Elshan Baghirov, John Chukwu Ikechukwu

“…The analysis revealed vital features significantly impacting malware detection, such as process services, active services, file handles, registry keys, and callback functions. …”
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Automated Seizure Detection through EEG Analysis and Deep Learning Technique by Srinivas Nowduri, M. Madhusudhana Subramanyam

“…This is because neurologists are burdened with analyzing electroencephalogram (EEG) data via visual inspection, and automating the process can reduce their workload. However, one of the challenges of automatic seizure detection using EEG analysis is extracting optimal features that can distinguish between different states of epilepsy. …”
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Multimodal fusion based few-shot network intrusion detection system by Congyuan Xu, Yong Zhan, Zhiqiang Wang, Jun Yang

“…Existing few-shot learning methods, while reducing reliance on large datasets, mostly handle single-modality data and fail to fully exploit complementary information across different modalities, limiting detection performance. To address this challenge, we introduce a multimodal fusion based few-shot network intrusion detection method that merges traffic feature graphs and network feature sets. …”
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Longitudinal tear detection system for belt conveyor based on deep learning by Zhiqiang YU, Xiangsheng PAN, Wei JIANG

“…In response to the common problems faced by belt conveyors in the field of longitudinal tear detection, such as high missed detection rate, frequent false alarms, and insufficient intelligence level, we develop an intelligent detection system for longitudinal tearing of strip surfaces that integrates machine vision and line laser technology. …”
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Radiographic detection of welding defects based on neural ordinary differential equations by Wenyong WU, Hongyu CHENG, Qican ZHANG

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PVLF: point-voxel local feature fusion for 3D detection by Haowei Zhao, Zhuolei Xiao

“…PVLF explores local spatial features to improve accuracy regarding tiny object detection. To address the potential complex computational issues in the convolution process, we have designed an innovative Adaptive Sparse Convolution (ASC) module that effectively eliminates redundant information in the feature layer. …”
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Application of Deep Learning in Food Safety Detection and Risk Early Warning by DING Haohan, WANG Long, HOU Haoke, XIE Zhenqi, HAN Yu, CUI Xiaohui

“…The application of deep learning in food safety detection and risk early warning is becoming more and more extensive, thus providing new opportunities for improving food safety, quality control and authenticity identification. …”
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An explainable transformer model for Alzheimer’s disease detection using retinal imaging by Saeed Jamshidiha, Alireza Rezaee, Farshid Hajati, Mojtaba Golzan, Raymond Chiong

“…These findings are compared to existing clinical studies on detecting AD using retinal biomarkers, allowing us to identify the most important features for AD detection in each imaging modality. …”
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Analysis and Detection of Four Typical Arm Current Measurement Faults in MMC by Qiaozheng Wen, Shuguang Song, Jiaxuan Lei, Qingxiao Du, Wenzhong Ma

“…The entire fault detection process takes less than 20 ms. Finally, the feasibility and effectiveness of the proposed method are validated through MATLAB/Simulink simulations and experimental results.…”
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Automatic eddy detection in the MIZ based on YOLO algorithm and SAR images by Nikita Sandalyuk, Eduard Khachatrian

“…Thus, we explored the feasibility of automating the eddy detection process by applying YOLOv8, a state-of-the-art computer vision model, to high-resolution synthetic aperture radar data, specifically targeting the dynamic region of the Fram Strait. …”
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RFID-embedded mattress for sleep disorder detection for athletes in sports psychology by Metin Pekgor, Aydolu Algin, Turhan Toros

“…A multi-layered mattress design integrates advanced RFID technology with machine learning algorithms—Gaussian process regression (GPR) and linear regression (LR)—to classify postures and detect movement anomalies. …”
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An Object Detection Algorithm for Orchard Vehicles Based on AGO-PointPillars by Pengyu Ren, Xuyun Qiu, Qi Gao, Yumin Song

“…With the continuous expansion of the orchard planting area, there is an urgent need for autonomous orchard vehicles that can reduce the labor intensity of fruit farmers and improve the efficiency of operations to assist operators in the process of orchard operations. An object detection system that can accurately identify potholes, trees, and other orchard objects is essential to achieve unmanned operation of the orchard vehicle. …”
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ANALYZING EEG SIGNALS FOR STRESS DETECTION USING RANDOM FOREST ALGORITHM by Fi Imanur Sifaunnufus Ms, Fitra Abdurrachman Bachtiar, Barlian Henryranu Prasetio

“…Detection of stress using EEG signals has gained much interest because of monitoring and early intervention. …”
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Physical-social attributes integrated Sybil detection for Tor bridge distribution by Xin SHI, Yunfei GUO, Yawen WANG, Xiaoli SUN, Hao LIANG

“…As one of the most widely utilized censorship circumvention systems, Tor faces serious Sybil attacks in bridge distribution.Censors with rich network and human resources usually deploy a large number of Sybils, which disguise themselves as normal nodes to obtain bridges information and block them.In the process, due to the different identities, purposes and intentions of Sybils and normal nodes, individual or group behavior differences occur in network activities, called as node behavior characteristics.To handle the Sybil attacks threat, a Sybil detection mechanism integrating physical-social attributes was proposed based on the analysis of node behavior characteristics.The physical-social attributes evaluation methods were designed.The credit value of nodes objectively reflecting the operation status of bridges on the nodes and the suspicion index of nodes reflecting the blocking status of bridges, were utilized to evaluate the physical attributes of nodes.The social attributes of nodes were evaluated by the social similarity, which described the static attribute labels of nodes and their social trust characterizing the dynamic interaction behaviors of nodes.Furthermore, integrating the physical-social attributes, the credibility of nodes were defined as the possibility of the current node being a Sybil, which was exploited as a guidance on inferring the true identifies of nodes, so as to achieve accurate detection on Sybils.The detection performance of the proposed mechanism based on the constructed Tor network operation status simulator and the Microblog PCU dataset were simulated.The results show that the proposed mechanism can effectively improve the true positive rate on Sybils, and decrease the false positive rate.It also has stronger resistance on the deceptive behavior of censors, and still performs well in the absence of node social attributes.…”
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An Earlier Predictive Rollover Index Designed for Bus Rollover Detection and Prevention by Shun Tian, Lang Wei, Chris Schwarz, WenCai Zhou, Yuan Jiao, YanQin Chen

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JDroid: Android malware detection using hybrid opcode feature vector by Recep Sinan Arslan

“…Experimental results show that the proposed approach has an accuracy value of 98.6% and an area under the curve (AUC) value of 99.6% in malware detection without being affected by the obfuscation process.…”
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