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

“…Analytical and graphical methods were used to predict structural changes in morbidity, mortality, and factors affecting it. …”
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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

“…For generating the abnormalities in the data, the Fast-Gradient Sign Method (FGSM) is used, through which adversarial inputs are obtained, and these adversarial inputs are fed to the proposed LSTM autoencoder to obtain the anomalous data. …”
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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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Non-destructive detection of internal quality of apple based on CT image by HUANG Taotao, SUN Teng, ZHANG Jingping

“…We intend to make CT non-destructive detection method much more useful in the prediction of the apple quality.Firstly, the window/level number of CT image should be unified at an appropriate level, before building the model between CT numbers and gray level values. …”
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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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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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Recurrent neural networks for anomaly detection in magnet power supplies of particle accelerators by Ihar Lobach, Michael Borland

“…This research illustrates how time-series forecasting employing recurrent neural networks (RNNs) can be used for anomaly detection in particle accelerators—complex machines that accelerate elementary particles to high speeds for various scientific and industrial applications. …”
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Molecular detection of Anaplasma phagocytophilum in field-collected Haemaphysalis larvae in the Republic of Korea by KyuSung Ahn, Badriah Alkathiri, Seung-Hun Lee, Haeseung Lee, Dongmi Kwak, Yun Sang Cho, Hyang-Sim Lee, SoYoun Youn, Mi-Sun Yoo, Jaemyung Kim, SungShik Shin

“…Methods From March to October 2021 and again from March to October 2022, we collected a total of 36,912 unfed, questing ticks of Haemaphysalis spp. from 149 sites in South Korea. …”
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Research on Road Crack Detection Based on RGB-LPC-GPR Data Fusion by Z. Wang, D. Qiu, R. Wu, R. Wu, Y. Shi, W. Niu

“…Moreover, a trend prediction model integrating ConvLSTM and a spatiotemporal attention mechanism achieved an MAE of 8.7% in a six-month damage trend prediction experiment, reducing prediction error by 34% compared to existing methods, underscoring the model's effectiveness in forecasting damage progression.The experimental results demonstrate that the proposed framework exhibits strong adaptability and stability across diverse road damage detection tasks, particularly excelling in the joint detection of cracks and underground voids with high accuracy. …”
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Detection of Pear Quality Using Hyperspectral Imaging Technology and Machine Learning Analysis by Zishen Zhang, Hong Cheng, Meiyu Chen, Lixin Zhang, Yudou Cheng, Wenjuan Geng, Junfeng Guan

“…Spectral data within the 398~1004 nm wavelength range were analyzed to compare the predictive performance of the Least Squares Support Vector Machine (LS-SVM) models on various quality parameters, using different preprocessing methods and the selected feature wavelengths. The results indicated that the combination of Fast Detrend-Standard Normal Variate (FD-SNV) preprocessing and Competitive Adaptive Reweighted Sampling (CARS)-selected feature wavelengths yielded the best improvement in model predictive ability for forecasting key quality parameters such as firmness, soluble solids content (SSC), pH, color, and maturity degree. …”
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