AI-driven epidemic intelligence: the future of outbreak detection and response by Jasleen Kaur, Jasleen Kaur, Zahid Ahmad Butt

“…Epidemic intelligence, the process of detecting, verifying, and analyzing public health threats to enable timely responses, traditionally relies heavily on manual reporting and structured data, often causing delays and coverage gaps. …”
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MLAD: A Multi-Task Learning Framework for Anomaly Detection by Kunqi Li, Zhiqin Tang, Shuming Liang, Zhidong Li, Bin Liang

“…Anomaly detection in multivariate time series is a critical task across a range of real-world domains, such as industrial automation and the internet of things. …”
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Theoretical Research Aspects of the Key COVID-19 Trends and Transformation of Indicators in the Healthcare Sphere by Iryna Didenko, Yuliia Kurovska, Henryk Dzwigol

“…The data forecast period covers the years 2021-2023. The accuracy of forecasts is assessed by the MAPE and RMSPE quality criteria. …”
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Transformer Online Monitoring Data Abnormal Value Detection and Cleaning by QIAN Yucheng, ZHEN Chao, JI Kun, ZHAO Changwei, FU Longming, ZHANG Yajing

“…Finally, the time series forecasting method is studied, the trend forecast is completed and the missing values and noise values are filled to maintain data integrity The algorithm is verified by the online monitoring data of a substation The results show that the method can complete abnormal detection and cleaning in time The accuracy rate after cleaning is 93.9%, and the completion rate can reach 98.6%, which has high use value…”
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Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence by Marco Zeppieri, Lorenzo Gardini, Carola Culiersi, Luigi Fontana, Mutali Musa, Fabiana D’Esposito, Pier Luigi Surico, Caterina Gagliano, Francesco Saverio Sorrentino

“…Through the fast and accurate analysis of massive amounts of imaging data, artificial intelligence (AI), in particular machine learning (ML) and deep learning (DL), has emerged as a promising method to improve the early detection and management of glaucoma. …”
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Early detection of risky spatio-temporal congestion in urban traffic by Shaobo Sui, Dan Xu, Mingyang Bai, Xiaoke Zhang, Zhaojun Mao, Daqing Li

“…In this article, we develop a detection method for risky congestion based on its spatio-temporal evolution feature, which can detect risky spatio-temporal congestion clusters (SCCs) when they are small. …”
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Fluorescence Angiography to Determine the Features of Active Retinopathy of Prematurity Course by I. G. Trifanenkova, A. V. Tereshhenko

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Experience of Various Types of Keratoplasty in Children by A. V. Tereshhenko, I. G. Trifanenkova, S. K. Demyanchenko, E. V. Erohina, M. S. Tereshhenkova

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Machine Learning Detection of Melting Layers From Radar Observations by Yan Xie, Fraser King, Claire Pettersen, Mark Flanner

“…Compared to a traditional detection method, the U‐Net model increases the Probability of Detection by 57% and improves the mean Dice‐Sørensen coefficient from 0.69 to 0.91. …”
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Optimizing Smart Grid Load Forecasting via a Hybrid Long Short-Term Memory-XGBoost Framework: Enhancing Accuracy, Robustness, and Energy Management by Falah Dakheel, Mesut Çevik

“…The results extend the literature on the development of fusion-based machine learning models for time series forecasting, and the future work of energy consumption analysis, anomaly detection, and resource allocation in SM grids.…”
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A distributed photovoltaic cluster power quantification model integrating ground-based cloud image segmentation technology and high-resolution weather forecasting technology by Wenxin Fan, Yu Han, Xincong Shi, Qiang Jin, Yicong Chen, Hua Qin

“…To address this, we propose a distributed photovoltaic cluster power prediction model that integrates ground-based cloud image segmentation with high-resolution weather forecasting technology. First, a fine-grained segmentation technique for cloud images is employed, with image preprocessing through homomorphic filtering to detect occluded cloud clusters. …”
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Vision-based detection algorithm for monitoring dynamic change of fire progression by Yongyoon Suh

“…This vision-based approach provides a more effective method for detecting and forecasting fire development, contributing to improved fire safety and emergency response strategies.…”
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Anomaly detection of adversarial cyber attacks on electric vehicle charging stations by Sagar Babu Mitikiri, Vedantham Lakshmi Srinivas, Mayukha Pal

“…This paper proposes an effective approach in detecting anomalies in the current magnitude of charging ports. …”
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A Review of Link Prediction Algorithms in Dynamic Networks by Mengdi Sun, Minghu Tang

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Detection of Banana Diseases Based on Landsat-8 Data and Machine Learning by Renata Retkute, Kathleen S. Crew, John E. Thomas, Christopher A. Gilligan

“…These findings support the potential of our method as a scalable early warning system for banana disease detection.…”
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STUDY, FORECAST AND CONTROLLED SEISMIC HAZARD REDUCTION IN THE IDENTIFIED SEGMENTS OF THE MAIN FAULTS BY CYCLIC INJECTION OF FLUID THROUGH DEEP MULTI-BRANCH DIRECTIONALLY INCLINED... by V. V. Ruzhich, A. G. Vakhromeev, S. A. Sverkunov, V. M. Ivanishin, R. H. Akchurin, E. A. Levina

“…The forecast emphasizes the detection of places for 1–11-year earthquake generation cycles.A comprehensive analysis of the collected information made it possible to substantiate the conclusion about an opportunity to prevent earthquake damage by using hydrodynamic damping of seismically hazardous fault segments. …”
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A survey of image object detection algorithm based on deep learning by Tingting ZHANG, Jianwu ZHANG, Chunsheng GUO, Huahua CHEN, Di ZHOU, Yansong WANG, Aihua XU

“…Image object detection is to find out the objects of interest in the image and determine their classifications and locations.It is a research hotspot in the field of computer vision.In recent years,due to the significant improvement in the accuracy of image classification with deep learning,image object detection models based on deep learning have gradually became mainstream.Firstly,the convolutional neural networks commonly used in image object detection were briefly introduced.Then,the existing classical image object detection models were reviewed from the perspective of candidate regions,regression and anchor-free methods.Finally,according to the detection results on the public dataset,the advantages and disadvantages of the models were analyzed,the problems in the image object detection research were summarized and the future development was forecasted.…”
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A Hybrid Deep Multistacking Integrated Model for Plant Disease Detection by Majdi Khalid, MD. Alamin Talukder

“…These results show that combining advanced ensemble methods with well-tuned models can make plant disease detection systems more generic and reliable. …”
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On the Method of Identification of Atypical Observations in Time Series by Maciej Oesterreich

“…The paper presents a method of detecting atypical observations in time series with or without seasonal fluctuations. …”
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Introduction of neural network technologies to optimise the control of the operating modes of a sucker-rod pump installation by O. Turchyn

“…Analysis of data from the unit’s sensors using neural networks helped to identify optimal operating modes that ensure maximum production with minimal energy consumption. A forecasting model has been developed that can detect potential equipment failures in advance, which reduces the risks of emergencies and maintenance costs. …”
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