Evaluating Deep Learning Networks Versus Hybrid Network for Smart Monitoring of Hydropower Plants by Fatemeh Hajimohammadali, Emanuele Crisostomi, Mauro Tucci, Nunzia Fontana

“…With modern hydropower plants equipped with sensors that capture extensive data, machine learning algorithms utilizing these data to detect and predict anomalies have gained research attention. …”
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A Machine-Learning-Based Analysis of Resting State Electroencephalogram Signals to Identify Latent Schizotypal and Bipolar Development in Healthy University Students by Flórián Gubics, Ádám Nagy, József Dombi, Antónia Pálfi, Zoltán Szabó, Zsolt János Viharos, Anh Tuan Hoang, Vilmos Bilicki, István Szendi

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Research on airport baggage anomaly retention detection technology based on machine vision, edge computing, and blockchain by Yuzhou Chen, Gang Mao, Xue Yang, Mingqian Du, Hongqing Song

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Rapid differentiation of patients with lung cancers from benign lung nodule based on dried serum Fourier-transform infrared spectroscopy combined with machine learning algorithms by Huanyu Li, Lixue Dai, Shaomei Guo, Hongluan Wang, Lei Lei, Jie Yu, Xiaoyun Li, Jun Wang

“…Five machine learning models, linear discriminant analysis (LDA), support vector machine (SVM), random forest, multilayer perceptron (MLP), and LightGBM, were optimized using FTIR spectral data (1800–900 cm−1 band). …”
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Deep learning-based analysis of 12-lead electrocardiograms in school-age children: a proof of concept study by Shuhei Toba, Yoshihide Mitani, Yusuke Sugitani, Yusuke Sugitani, Hiroyuki Ohashi, Hirofumi Sawada, Mami Takeoka, Naoki Tsuboya, Kazunobu Ohya, Noriko Yodoya, Takato Yamasaki, Yuki Nakayama, Hisato Ito, Masahiro Hirayama, Motoshi Takao

“…The specificity of the deep learning-based model for detecting abnormal electrocardiograms was not significantly different from that of the conventional algorithm. …”
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TLDDM: An Enhanced Tea Leaf Pest and Disease Detection Model Based on YOLOv8 by Jun Song, Youcheng Zhang, Shuo Lin, Huijie Han, Xinjian Yu

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Multi-Channel Fusion Decision-Making Online Detection Network for Surface Defects in Automotive Pipelines Based on Transfer Learning VGG16 Network by Jian Song, Yingzhong Tian, Xiang Wan

“…In order to improve the detection efficiency and reduce the amount of data transmission and processing, an improved ROI detection algorithm for surface defects is proposed. …”
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A stacked ensemble model for traffic conflict prediction using emerging sensor data by Bowen Cai, Léah Camarcat, Nicolette Formosa, Mohammed Quddus

“…Employing machine learning approaches to handle the extensive and disaggregated data, a novel stacked ensemble learning model is proposed. …”
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Future of Alzheimer's detection: Advancing diagnostic accuracy through the integration of qEEG and artificial intelligence by Sahar Rezaei, Farzan Asadirad, Alireza Motamedi, Mohammadsadegh Kamran, Farzaneh Parsa, Haniyeh Samimi, Parna Ghannadikhosh, Mahdi Zahmatyar, Seyed Ali Hosseinzadeh, Hossein Arabi

“…Through systematic analysis of 11 key studies across multiple international databases, we evaluated various AI architectures, including machine learning algorithms and deep learning networks, applied to qEEG data for AD detection. …”
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From Simulation to Field Validation: A Digital Twin-Driven Sim2real Transfer Approach for Strawberry Fruit Detection and Sizing by Omeed Mirbod, Daeun Choi, John K. Schueller

“…This study presents a photorealistic digital twin of a commercial-scale strawberry farm, coupled with a simulated ground vehicle, to address these constraints by generating high-fidelity synthetic RGB and LiDAR data. These data enable the rapid development and evaluation of a deep learning-based machine vision pipeline for fruit detection and sizing without continuously relying on real-field access. …”
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MS3OSD: A Novel Deep Learning Approach for Oil Spills Detection Using Optical Satellite Multisensor Spatial-Spectral Fusion Images by Kai Du, Yi Ma, Zhongwei Li, Rongjie Liu, Zongchen Jiang, Junfang Yang

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A Semi-Supervised Machine Learning Approach Using K-Means Algorithm to Prevent Burst Header Packet Flooding Attack in Optical Burst Switching Network by Patwary et al.

“…In this study, we propose a semi-supervised machine learning approach using k-means algorithm, to detect malicious nodes in an OBS network. …”
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Machine learning-based pipeline for automated intracerebral hemorrhage and drain detection, quantification, and classification in non-enhanced CT images (NeuroDrAIn). by Samer Elsheikh, Ahmed Elbaz, Alexander Rau, Theo Demerath, Elias Kellner, Ralf Watzlawick, Urs Würtemberger, Horst Urbach, Marco Reisert

“…<h4>Conclusion</h4>We developed and statistically validated an automated pipeline for evaluating computed tomography scans after minimally invasive surgery for intracerebral hemorrhage. The algorithm reliably detects drains, quantifies drain coverage by the hemorrhage, and uses machine learning to detect malpositioned drains. …”
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The optimization path of agricultural industry structure and intelligent transformation by deep learning by Xingchen Pan, Jinyu Chen

“…Abstract This study addresses key challenges in optimizing agricultural industry structures and facilitating intelligent transformation through the application of deep learning algorithms and advanced optimization techniques. …”
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Weed-crop dataset in precision agriculture: Resource for AI-based robotic weed control systemsMendeley Data by Arjun Upadhyay, Sunil G. C, Maria Villamil Mahecha, Joseph Mettler, Kirk Howatt, William Aderholdt, Michael Ostlie, Xin Sun

“…Recent advancement in robotic technologies and advanced deep learning (DL) models is shaping the future of robotic weed control systems. …”
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System analysis and information processing to solve the problem of detecting breakdowns of computer information storage by N. M. Kodatsky, E. A. Revyakina, A. R. Gazizov

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Anomaly Detection Method for Hydropower Units Based on KSQDC-ADEAD Under Complex Operating Conditions by Tongqiang Yi, Xiaowu Zhao, Yongjie Shi, Xiangnan Jing, Wenyang Lei, Jiang Guo, Yang Meng, Zhengyu Zhang

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Classification of SERS spectra for agrochemical detection using a neural network with engineered features by Mateo Frausto-Avila, Monserrat Ochoa-Elias, Jose Pablo Manriquez-Amavizca, María del Carmen González-López, Gonzalo Ramírez-García, Mario Alan Quiroz-Juárez

“…Compared to other machine-learning algorithms, our approach offers reduced computational complexity while maintaining or exceeding the accuracy of more complex models. …”
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High-resolution surface soil moisture retrieval: A hybrid machine learning framework integrating change detection and downscaling for precision water management by Zihao Wang, Qi Gao, Michele Crosetto, Maria Jose Escorihuela

“…This study presents an innovative high-resolution surface soil moisture (SSM) retrieval framework combining machine learning (ML), change detection and downscaling (CD-DS) methods. …”
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Radiomics for Machine Learning—A Multi-Class System for the Automatic Detection of COVID-19 and Community-Acquired Pneumonia from Computed Tomography Images by Vasileia Paschaloudi, Dimitris Fotopoulos, Ioanna Chouvarda

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