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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Detection and simulation of Kermanshah dust storm using HYSPLIT and WRF-chem models by Tooba Alizadeh, Majid Rezaei Banafsheh, Hashem Rostamzadeh, Gholamreza Goodarzi, Hedar Maleki, Hamzeh Alizadeh

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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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TBM Advanced Geological Prediction via Ellipsoidal Positioning Velocity Analysis by Zhen Gao, Xin Rong, Wei Wang, Bin Huang, Junqiang Liu

“…Traditional seismic wave-based tunnel advanced geological forecasting techniques are primarily designed for drill and blast method construction tunnels. …”
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Ground Based Cloud Recognition with an Anchor Free Method by LI Yichao, GUO Rui, ZHANG Shaodi, SHOU Zefeng, CHEN Jing, LIU Yifei

“…In order to solve the problems of cloud type recognition of the ground-based cloud such as complex target candidate box selection and slow detection speed, a recognition method of cloud types in ground-based cloud map based on anchor free is proposed.First, the paper takes Center Net as the basic architecture of cloud type recognition.Based on thermodynamic diagrams prediction, key point prediction, center point prediction and candidate box prediction, a anchor free ground-based cloud type detection process is constructed.And then, the main network, loss function and candidate box prediction method of cloud type recognition model are designed.Finally, take CenterNet Resdcn101 as the model, compared the algorithm recognition accuracy, candidate boxes predict confidence and identify speed with mainstream target recognition methods and cloud type recognition method The results showed that the cloud type recognition method of the paper has higher recognition accuracy and faster recognition speed.…”
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An IoT Framework for the Detection of Lung Cancer Using a Decision Support System by Ahamd Habboush, Bassam Elzaghmouri, Binod Kumar Pattanayak, Pravat Kumar Rautaray

“… Cancer remains an ongoing global health challenge, necessitating the progress of innovative techniques for early detection and risk assessment. In this study, a comprehensive method is presented for predicting lung cancer by utilizing a carefully curated dataset consisting of 1000 individuals from the Kaggle dataset. …”
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Time-Series Large Language Models: A Systematic Review of State-of-the-Art by Shamsu Abdullahi, Kamaluddeen Usman Danyaro, Abubakar Zakari, Izzatdin Abdul Aziz, Noor Amila Wan Abdullah Zawawi, Shamsuddeen Adamu

“…Key findings reveal advancements in architectures and novel tokenization strategies tailored for temporal data. Forecasting dominates the identified tasks with 79.66% of the selected studies, while classification and anomaly detection remain underexplored. …”
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Detection of Staphylococcus Enterotoxin sea and seb in Milk Samples by Duplex Droplet Digital PCR by Xiaoyue Wei, Wenzhou Wang, Tong Wang, Yuhua Yang, Yahui Guo, Lijin Long, Jiaming Fan, Fanliang Meng, Wentao Liu, Wanting Wang, Yakun Zhao, Jianling Chen, Fei Zhao, Jianzhong Zhang, Xiaomei Yan

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Adverse childhood experiences: terms, concepts, and study methods by Diana S. Shumskaia, Anna V. Trusova, Alexander O. Kibitov

“…We discuss the definitions and phenomenology of the ACE, the specificity and features of some of the methodological approaches to measuring ACE, and various approaches to using ACE to build predictive models.To summarize, research on ACE and its consequences is actively developing: earlier studies' data are being corrected and clarified, methods of detecting and grading ACE are being improved, and ways of forecasting the consequences of ACE in adulthood are being improved. …”
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RESEARCH STATUS OF SMART ROLLING BEARING MONITORING METHODS by CHEN JinHai, LI Wei, ZHANG WenYuan, WANG Heng

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Optimising Solar Power Plant Reliability Using Neural Networks for Fault Detection and Diagnosis by Mohammed Bouzidi, Abdelfatah Nasri, Omar Ouledali, Messaoud Hamouda

“…This study introduces an intelligent method to monitor grid-connected solar power stations, focussing on detecting problems in their energy output through the use of artificial neural networks (ANN). …”
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Automated detection and identification of white-backed planthoppers in paddy fields using image processing by Qing YAO, Guo-te CHEN, Zheng WANG, Chao ZHANG, Bao-jun YANG, Jian TANG

“…A new three-layer detection method was proposed to detect and identify white-backed planthoppers (WBPHs, Sogatella furcifera (Horváth)) and their developmental stages using image processing. …”
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The Detectability of Coronal Mass Ejections in the Low Corona Using Multislit Extreme-ultraviolet Spectroscopy by Lami Chan, Xianyong Bai, Hui Tian, Yufei Feng, Yu Xu, Tibor Török, Yuhang Gao, Tanmoy Samanta, Zheng Sun

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Detection of Low-Flying Target under the Sea Clutter Background Based on Volterra Filter by Hongyan Xing, Yan Yan

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A Transformer-based Approach for aAnomaly Detection in Wire eElectrical Discharge by Waleed Hammed, Ameer H. Al-Rubaye, Bashar S. Bashar, Merzah Kareem Imran, Mustafa Ghanim Rzooki, Ali Mohammed Hashesh

“…Our method is able to achieve 94.32 % and 94.16 % accuracy in Z 135 and Z 15 datasets, respectively. …”
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Soft detection model of corrosion leakage risk based on KNN and random forest algorithms by Yang YANG, Chengzhi LI, Xuan DU, Xiao YU, Shaohua DONG

“…Objective The integrity management of urban gas pipeline networks demands effective risk assessment methods. Corrosion leakage risk assessment necessitates the comprehensive integration of risk assessment factors with various detection operations. …”
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Integrated neural network framework for multi-object detection and recognition using UAV imagery by Mohammed Alshehri, Tingting Xue, Tingting Xue, Ghulam Mujtaba, Yahya AlQahtani, Nouf Abdullah Almujally, Ahmad Jalal, Ahmad Jalal, Hui Liu, Hui Liu, Hui Liu

“…YOLOv11 provides high precision and quick vehicle detection and Deep SORT allows reliable tracking without losing track of individual cars. …”
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