Timestamped list-mode data from coincidence γ-ray spectrometry with HPGe detectors on air-filter samplesSwedish National Data Service (SND) by Alf Göök, Erik Andersson Sundén, Peter Andersson, Stefan Jarl-Holm, Peter Jansson, Catharina Söderström

“…The data set provides a testing ground for investigating the use of multi-fold coincidence spectrometry as a tool to lower the minimum detectable activity of anthropogenic radionuclides in air filter samples.Access to this data set allows researchers to explore and evaluate analysis methodologies for coincidence γ-ray spectrometry on real samples.…”
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Multimodal Fusion Anomaly Detection Model for Agricultural Wireless Sensors by Zhenggui Zhou

“…In order to detect anomalous data accurately for agricultural wireless sensors, in this article, we propose an anomaly detection model that combines multimodal fusion and error reconstruction. …”
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Research progress of abnormal user detection technology in social network by Qiang QU, Hongtao YU, Ruiyang HUANG

“…In social networks,the problem of anomalous users detection is one of the key problems in network security research.The anomalous users conduct false comments,cyberbullying or cyberattacks by creating multiple vests,which seriously threaten the information security of normal users and the credit system of social networks ,so a large number of researchers conducted in-depth study of the issue.The research results of the issue in recent years were reviewed and an overall structure was summarized.The data collection layer introduces the data acquisition methods and related data sets,and the feature presentation layer expounds attribute features,content features,network features,activity features and auxiliary features.The algorithm selection layer introduces supervised algorithms,unsupervised algorithms and graph algorithms.The result evaluation layer elaborates the method of data annotation method and index.Finally,the future research direction in this field was looked forward.…”
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Enhancing Security of Databases through Anomaly Detection in Structured Workloads by Charanjeet Dadiyala, Faijan Qureshi, Kritika Anil Bhattad, Sourabh Thakur, Nida Tabassum Sharif Sheikh, Kushagra Anil Kumar Singh

“…The present research utilized the Isolation Forest algorithm to detect outliers in high-dimensional data sets. The main contribution and novelty of this research lies in leveraging the Isolation Forest algorithm for structured database workloads to proactively identify and mitigate potential security threats. …”
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Enhancing Security of Databases through Anomaly Detection in Structured Workloads by Charanjeet Dadiyala, Faijan Qureshi, Kritika Anil Bhattad, Sourabh Thakur, Nida Tabassum Sharif Sheikh, Kushagra Anil Kumar Singh

“…The present research utilized the Isolation Forest algorithm to detect outliers in high-dimensional data sets. The main contribution and novelty of this research lies in leveraging the Isolation Forest algorithm for structured database workloads to proactively identify and mitigate potential security threats. …”
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An image and text-based fake news detection with transfer learning. by Esther Irawati Setiawan, Patrick Sutanto, Christian Nathaniel Purwanto, Joan Santoso, F X Ferdinandus, Nemuel Daniel Pah, Mauridhi Hery Purnomo

“…This research proposes a multimodal classification approach that combines text and images to improve fake news detection, particularly in low-resource settings where labeled data is scarce. …”
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Weed detection in cabbage fields using RGB and NIR images by Adam Hruška, Pavel Hamouz, Jakub Lev, Josef Pavlíček, Milan Kroulík, Kateřina Hamouzová, Pavlína Košnarová, Josef Holec, Pavel Kouřím

“…This article evaluates the effectiveness of integrating near-infrared (NIR) data with RGB imaging in enhancing weed detection and classification in real-time field settings using the YOLO deep learning model family. …”
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Unsupervised selective labeling for semi-supervised industrial defect detection by Jian Ge, Qin Qin, Shaojing Song, Jinhua Jiang, Zhiwei Shen

“…For example, it boosts Efficient Teacher by 5%, 6.6%, and 7.8% in mean Average Precision(mAP) on the Automotive Sealing Rings Defect Dataset, the Metallic Surface Defect Dataset, and the Printed Circuit Boards (PCB) Defect Dataset with 10% labeled data. Our work sets a new benchmark for SSL in industrial settings.…”
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Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection by Sabina Umirzakova, Shakhnoza Muksimova, Abrayeva Mahliyo Olimjon Qizi, Young Im Cho

“…Despite its lightweight design, RASST achieves superior performance on the DOTA-v1.5 benchmark, outperforming existing state-of-the-art methods in supervised and semi-supervised settings. The proposed framework demonstrates high scalability, precise orientation sensitivity, and effective utilization of unlabeled data, establishing a new benchmark for efficient oriented object detection in remote sensing imagery.…”
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Early detection of choroidal neovascularization in age related macular degeneration by Tural Galbinur

“…Adherence to follow-up in the routine clinic setting also facilitates early detection of CNV.…”
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LDoS attack detection method based on simple statistical features by Xueyuan DUAN, Yu FU, Kun WANG, Bin LI

“…Traditional low-rate denial of service (LDoS) attack detection methods were complex in feature extraction, high in computational cost, single in experimental data background settings, and outdated in attack scenarios, so it was difficult to meet the demand for LDoS attack detection in a real network environment.By studying the principle of LDoS attack and analyzing the features of LDoS attack traffic, a detection method of LDoS attack based on simple statistical features of network traffic was proposed.By using the simple statistical features of network traffic packets, the detection data sequence was constructed, the time correlation features of input samples were extracted by deep learning technology, and the LDoS attack judgment was made according to the difference between the reconstructed sequence and the original input sequence.Experimental results show that the proposed method can effectively detect the LDoS attack traffic in traffic and has strong adaptability to heterogeneous network traffic.…”
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Statistical Difference Representation-Based Transformer for Heterogeneous Change Detection by Xinhui Cao, Minggang Dong, Xingping Liu, Jiaming Gong, Hanhong Zheng

“…Extensive experiments have been carried out to fully investigate the influences of inner manual parameters and compare them with state-of-the-art methods in several public heterogeneous change detection data sets. The experimental results indicate that the proposed methods have shown competitive performance.…”
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The inconvenient truth of ground truth errors in automotive datasets and DNN-based detection by Pak Hung Chan, Boda Li, Gabriele Baris, Qasim Sadiq, Valentina Donzella

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Particle swarm optimization with YOLOv8 for improved detection performance of tomato plants by Sarah M. Ayyad, Nada M. Sallam, Samah A. Gamel, Zainab H. Ali

“…Abstract Identification and precise classification of plants are crucial in improving plant quality and economic viability, particularly in an industrial setting. In a faster-growing world, there is a growing demand for fully automated tomato detection and grading systems. …”
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A secure hybrid deep learning framework for brain tumor detection and classification by Sandeep Kumar Mathivanan, Saravanan Srinivasan, Manjula Sanjay Koti, Virendra Singh Kushwah, Rose Bindu Joseph, Mohd Asif Shah

“…The second dataset, D-II (BraTS), is the widely recognized Brain Tumor Segmentation Dataset, frequently used for brain tumor detection and segmentation. Lastly, D-III (Kaggle Data Repository) is a collection of brain tumor MRI images obtained from the Kaggle platform, further diversifying the data sources for performance evaluation. …”
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Pharmacological Properties of Synthetic Cannabinoids, Specifics of Their Detection and Forensic Examination by O. M. Serdega

“…The article analyzes both the therapeutic potential of synthetic cannabinoids and the adverse effects of their misuse in uncontrolled settings. It identifies key issues that must be addressed to resolve challenges in the forensic examination of objects containing these psychotropic substances and outlines the specific features of their detection and seizure. …”
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Unsupervised Anomaly Detection for Volcanic Deformation in InSAR Imagery by Robert Popescu, Nantheera Anantrasirichai, Juliet Biggs

“…Manual inspection could miss these anomalies, and an automatic system modeled with supervised learning requires suitably labeled data sets. To tackle these issues, this paper explores the use of unsupervised deep learning on InSAR images for the purpose of identifying volcanic deformation as anomalies. …”
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Public hesitancy for AI-based detection of neurodegenerative diseases in France by Ismaël Rafaï, Bérengère Davin-Casalena, Dimitri Dubois, Thierry Blayac, Bruno Ventelou

“…We interpret these figures as the shadow price that the public attaches to medical data privacy. Beyond monetization, our representative sample of the French population appears reluctant to adopt AI-powered screening, particularly when performed on large sets of personal data. …”
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DualAD: Dual adversarial network for image anomaly detection⋆ by Yonghao Wan, Aimin Feng

“…Experiments on three data sets CIFAR10, MNIST, and FashionMNIST demonstrate the validity of the authors’ work. …”
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A streamlined POCT solution for rapid infectious disease detection by Yaping Xie, Yan Yu, Shuaiwu Huang, Liangcheng Wan, Chang Wu, Chiwei Yin, Jianjun Li, Jiangang Ling, Lizhong Dai

“…Abstract Point-of-care testing (POCT) plays a crucial role in infectious disease screening due to its rapid detection and portability. However, current POCT systems face challenges such as lengthy sample preparation time, complex nucleic acid extraction, and limited real-time data transmission, extending patient waiting time. …”
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