A GNN-Based False Data Detection Scheme for Smart Grids by Junhong Qiu, Xinxin Zhang, Tao Wang, Huiying Hou, Siyuan Wang, Tiejun Yang

“…Based on spatial temporal graph neural networks (STGNNs), anomaly detection technology has been presented to detect anomaly data in smart grids with good performance. …”
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An optimize canny algorithm with traditional machine learning for edge detection enhancement by Russel Lafta, Zainab Sultani

“…Edge detection still represents a major challenge in image processing and computer vision because of the complexity and variability of real-world images. …”
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An Automated Method for Detection and Enumeration of Olive Trees Through Remote Sensing by Muhammad Waleed, Tai-Won Um, Aftab Khan, Zubair Ahmad

“…To address this problem, we propose an automatic scheme for the detection and enumeration of olive trees. The proposed technique comprises of multi-step image processing techniques applied over a single band of imagery. …”
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Leveraging glycosylation for early detection and therapeutic target discovery in pancreatic cancer by Tomasz Pienkowski, Katarzyna Wawrzak-Pienkowska, Anna Tankiewicz-Kwedlo, Michal Ciborowski, Krzysztof Kurek, Dariusz Pawlak

“…Furthermore, therapeutic strategies targeting glycosylation processes, including sialylation, and fucosylation, have shown potential in curbing PC metastasis and enhancing immune response. …”
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Integrated Communication and RIS-Aided Track-Before-Detect Radar Sensing by Georgios Mylonopoulos, Luca Venturino, Emanuele Grossi, Stefano Buzzi, Ciro D'Elia

“…Our results show that, by increasing the number of scans processed by the radar detector (and therefore its implementation complexity), we can reduce the amount of power dedicated to the radar function while maintaining the same sensing performance (measured in terms of probability of target detection and root mean square error in the estimation of target position); this excess power can be reused to increase the user sum-rate.…”
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Obstacle Detection and Warning System for Visually Impaired Using IoT Sensors by Sunnia Ikram, Imran Sarwar Bajwa, Amna Ikram, Isabel de la Torre Diez, Carlos Eduardo Uc Rios, Angel Kuc Castilla

“…Ensuring safe and independent mobility for visually impaired individuals requires efficient obstacle detection systems. This study introduces an innovative smart knee glove, integrating machine learning technologies for real-time obstacle detection and alerting. …”
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Detection Prospects of Electromagnetic Signatures from OGLE-2011-BLG-0462 by Shigeo S. Kimura, Lena Murchikova, Kailash C. Sahu

“…We examine prospects for detecting electromagnetic signatures from an accretion flow surrounding the IsoBH. …”
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Anomaly Detection in Elderly Health Monitoring via IoT for Timely Interventions by Cosmina-Mihaela Rosca, Adrian Stancu

“…The collected data for average heart rate (AR), minimum (mR), maximum (MR), number of steps (S), deep sleep time (DST), and superficial sleep time (SST) is processed in real-time through a health anomaly detection algorithm (HADA), based on the dimensionality reduction method using PCA. …”
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Automatic Ladle Tracking with Object Detection and OCR in Steel Melting Shops by Kabil Murugan, Mahinas Senthilmurugan, Venbha V. Senthilkumar, Harshita Velusamy, Karthiga Sekar, Vasanthan Buvanesan, Manikandan Venugopal

“…The first method utilizes a YOLOv8 framework for detecting the two classes from the input images, such as the ladles and their unique numbers. …”
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The use of large language models in detecting Chinese ultrasound report errors by Yuqi Yan, Kai Wang, Bojian Feng, Jincao Yao, Tian Jiang, Zhiyan Jin, Yin Zheng, Yahan Zhou, Chen Chen, Lin Sui, Xiayi Chen, Yanhong Du, Jie Yang, Qianmeng Pan, Lingyan Zhou, Vicky Yang Wang, Ping Liang, Dong Xu

“…In few-shot, Claude 3.5 Sonnet outperformed senior and resident radiologists, while GPT-4o excelled in spelling error detection. LLMs processed reports faster than the quickest radiologist (Claude 3.5 Sonnet: 13.2 s, GPT-4o: 15.0 s, radiologist: 42.0 s per report). …”
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Research on Intelligent Detection and Segmentation of Rock Joints Based on Deep Learning by Lei Peng, Haibo Wang, Chun Zhou, Feng Hu, Xiaoyang Tian, Zhu Hongtai

“…Additionally, to tackle the challenge of low detection accuracy in existing image processing methods, particularly for complex tunnel joint surfaces in dark environments, the paper introduces a path aggregation network (PANet) to enhance the fusion capability of feature information in Mask R-CNN, thereby improving the accuracy of the intelligent detection method. …”
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Automated Visual Inspection for Precise Defect Detection and Classification in CBN Inserts by Li Zeng, Feng Wan, Baiyun Zhang, Xu Zhu

“…This paper proposes an automated detection and classification system using machine vision to scrutinize these surface defects. …”
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Exploring explanation deficits in subclinical mastitis detection with explainable boosting machines by Changhong Jin, John Upton, Brian Mac Namee

“…Given the difficulty in detecting subclinical mastitis and the growing availability of sensor data during milking, the application of machine learning techniques may offer a solution to more effectively and efficiently detect the problematic condition. …”
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Enhancing surface detection: A comprehensive analysis of various YOLO models by G. Deepti Raj, B. Prabadevi

“…However, implementing accurate and automated object detection algorithms mitigates these challenges, allowing errors or defects to be detected before processing. …”
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Obstacle detection and avoidance system based on layered costmaps for robot tractors by Ricardo Ospina, Kota Itakura

“…A static layer map was created using a simple image processing technique, so it can be easily integrated with the layered costmaps. …”
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Research on Lightweight Small Object Detection Algorithm Based on Context Representation by Li Qiang, Cui Jianghui

“…The convolutional self-attention model is employed into traditional feature pyramid network (FPN) structures to reduce information loss during up-sampling and down-sampling processes, and a differential detection head is used to detect targets of different sizes. …”
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Optimising energy distribution and detecting vulnerabilities in networks using artificial intelligence by D. Koshkin, O. Sadovoy, A. Rudenko, V. Sokolik

“…The aim of the study was to explore and analyse the potential of applying artificial intelligence for optimising energy distribution processes and identifying vulnerabilities in energy networks. …”
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MRD: A Linear-Complexity Encoder for Real-Time Vehicle Detection by Kaijie Li, Xiaoci Huang

“…To address these limitations, this study introduces Mamba RT-DETR (MRD), an optimized architecture featuring three principal innovations: (1) We devise an efficient vehicle detection Mamba (EVDMamba) network that strategically integrates a linear-complexity state space model (SSM) to substantially mitigate computational overhead while preserving feature extraction efficacy. (2) To counteract the constrained receptive fields and suboptimal spatial localization associated with conventional SSM sequence modeling, we implement a multi-branch collaborative learning framework that synergistically optimizes channel dimension processing, thereby augmenting the model’s capacity to capture critical spatial dependencies. (3) Comprehensive evaluations on the BDD100K benchmark demonstrate that MRD architecture achieves a 3.1% enhancement in mean average precision (mAP) relative to state-of-the-art RT-DETR variants, while concurrently reducing parameter count by 55.7%—a dual optimization of accuracy and efficiency.…”
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An Obstacle Detection and Distance Measurement Method for Sloped Roads Based on VIDAR by Guoxin Jiang, Yi Xu, Xiaotong Gong, Shanshang Gao, Xiaoqing Sang, Ruoyu Zhu, Liming Wang, Yuqiong Wang

“…First, the road images are collected and processed. Then, the road distance and slope information provided by a digital map is input into the VIDAR to detect and eliminate false obstacles (i.e., those for which no height can be calculated). …”
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Detection of Suicidal Ideation on Twitter using Machine Learning & Ensemble Approaches by Syed Tanzeel Rabani, Qamar Rayees Khan, Akib Mohi UD Din Khanday

“…The Tweets are then pre-processed and annotated manually. Finally, various machine learning and ensemble methods are used to automatically distinguish Suicidal and Non-Suicidal tweets. …”
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