Hotspots Detection in Spatial Analysis via the Extended Gustafson-Kessel Algorithm by Ferdinando Di Martino, Salvatore Sessa

“…The data consist of georeferenced patterns corresponding to positions of Taliban’s attacks against civilians and soldiers in Afghanistan that happened during the period 2004–2010. …”
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Effect of spontaneous activity on stimulus detection in a simple neuronal model by Marie Levakova

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Selfish Node Detection in Delay Tolerant Networks Using Fuzzy Logic by Fahimeh Rashidjafari, Nahideh Derakhshanfard, Behrouz Shahrokhzadeh, Ali Ghaffari

“…Due to frequent disruptions in communication links, nodes in DTNs rely on a store, carry, and forward pattern to transmit messages. This forwarding and carrying of messages is achieved through cooperation among relay nodes. …”
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RADNet: Adaptive Spatial-Dilation Learning for Efficient Road Crack Detection by Kehao Du, Yifan Dai

“…Our approach introduces three key innovations: an adaptive spatial learning module (ASDown) for efficient feature extraction, a multi-scale dilation strategy (C2f-MSD) for comprehensive crack pattern capture, and a lightweight detection enhancement mechanism. …”
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LBP-Based Edge Detection Method for Depth Images With Low Resolutions by Xinyu Wang, Jie Cao, Qun Hao, Kaiyu Zhang, Zihan Wang, Saad Rizvi

“…The proposed method modifies the local binary pattern and selects an adaptive threshold to obtain a rough edge map. …”
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A New Local Optimal Spline Wavelet for Image Edge Detection by Dujuan Zhou, Zizhao Yuan, Zhanchuan Cai, Defu Zhu, Xiaojing Shen

“…Wavelet-based edge detection methods have evolved significantly over the years, contributing to advances in image processing, computer vision, and pattern recognition. …”
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Design of a Heterogeneous-Based Network Intrusion Detection System and Compiler by Zhigui Lin, Xiaofeng Zhang, Qi Liu, Jun Cui

“…We construct an FPGA + CPU heterogeneous detection system featuring a five-tuple segmented matching architecture, which integrates hash bitmap and shift-or algorithms to achieve fast-pattern matching. …”
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Detection and diagnosis of air compressor faults using weightless neural networks by Anubhab Bhattacharyya, Naveen Venkatesh Sridharan, Aravinth Sivakumar, Sugumaran Vaithiyanathan

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High genetic diversity detected in olives beyond the boundaries of the Mediterranean Sea. by Mehdi Hosseini-Mazinani, Roberto Mariotti, Bahareh Torkzaban, Massoma Sheikh-Hassani, Saeedeh Ataei, Nicolò G M Cultrera, Saverio Pandolfi, Luciana Baldoni

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Structural Fault Detection and Diagnosis for Combine Harvesters: A Critical Review by Haiyang Wang, Liyun Lao, Honglei Zhang, Zhong Tang, Pengfei Qian, Qi He

“…Therefore, developing accurate and timely Fault Detection and Diagnosis (FDD) techniques is crucial for ensuring food security. …”
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HTAD: a human-in-the-loop framework for supervised chromatin domain detection by Wei Shen, Ping Zhang, Yiwei Jiang, Hailin Tao, Zhike Zi, Li Li

“…Performance assessments using public curation and synthetic datasets demonstrate HTAD’s superiority over other state-of-the-art methods and reveal highly hierarchical TAD structures, offering a human-in-the-loop solution for detecting complex genomic patterns.…”
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Transformer-Guided Serial Knowledge Distillation for High-Precision Anomaly Detection by Danyang Wang, Bingyan Wang

“…Notably, it shows particular effectiveness in industrial inspection scenarios, where anomalous patterns are subtle and training data are strictly limited to normal samples.…”
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Detection of Rare Antimicrobial Resistance Profiles by Active and Passive Surveillance Approaches. by Alison E Mather, Richard Reeve, Dominic J Mellor, Louise Matthews, Richard J Reid-Smith, Lucie Dutil, Daniel T Haydon, Stuart W J Reid

“…Surveillance data of phenotypic AMR of Canadian poultry Salmonella Heidelberg and swine Salmonella Typhimurium var. 5- were used to contrast active (representative isolates derived from healthy animals) and passive (diagnostic isolates) surveillance and assess their suitability for detecting emerging resistance patterns. Although in both datasets the prevalences of resistance to individual antimicrobials were not significantly different between the two surveillance systems, analysis of the diversity of entire resistance phenotypes demonstrated that passive surveillance of diagnostic isolates detected more unique phenotypes. …”
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Unsupervised Learning With Hybrid Models for Detecting Electricity Theft in Smart Grids by Ali Jaber Almalki

“…The hybrid models work well in identifying anomalous consumption patterns that point to theft without requiring large amounts of labeled data. …”
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Anomaly Detection in Data Streams Using Machine Learning and Deep Learning by Muhammad Amin Daneshwar

“…The goal of this anomaly identification in the data streams is to find patterns that deviate substantially from the way things usually work. …”
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Objective Assessment of Cancer Biomarkers Using Semi-Rare Event Detection by Jeroen A. W. M. van der Laak, Albertus G. Siebers, Sabine A. A. P. Aalders, Johanna M. M. Grefte, Peter C. M. de Wilde, Johan Bulten

“…Objective and reproducible assessment of cancer biomarkers may be performed using rare event detection systems. Because many biomarkers are not true ‘rare events’, in this study a semi-rare event detection system was developed. …”
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Detection, attribution and projection of changes in the extreme temperature range in the Northern Hemisphere by Xiao-Fan Feng, Cheng Qian

“…We found that anthropogenic forcing could be detected and separated from natural forcing, and greenhouse gas forcing could be detected and separated from anthropogenic aerosol forcing, in the spatiotemporal pattern of the NH and in the regional average of the high latitudes. …”
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Iterative Assessment of Edge Criticality: Efficiency Enhancement or Hidden Insufficiency Detection by Vasily Lubashevskiy, Hamza Ejjbiri, Ihor Lubashevsky

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Breast cancer survival prediction using an automated mitosis detection pipeline by Nikolas Stathonikos, Marc Aubreville, Sjoerd deVries, Frauke Wilm, Christof A Bertram, Mitko Veta, Paul J vanDiest

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Attention dual transformer with adaptive temporal convolutional for diabetic retinopathy detection by Mishmala Sushith, Ajanthaa Lakkshmanan, M. Saravanan, S. Castro

“…Abstract An Attention Dual Transformer with Adaptive Temporal Convolutional (ADT-ATC) model is proposed in this research work for enhanced detection of Diabetic Retinopathy (DR) from retinal fundus images. …”
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