Development of a UR5 Cobot Vision System with MLP Neural Network for Object Classification and Sorting by Szymon Kluziak, Piotr Kohut

“…Object classification was conducted using contour similarity with Hu moments, SIFT-based descriptors with FLANN matching, and MLP-based neural models trained on preprocessed images. Conducted performance evaluations encompassed accuracy metrics for used identification methods (MLP classifier, contour similarity, and feature descriptor matching) and the effectiveness of the vision system in controlling the cobot for sorting tasks. …”
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Adaptive variable channel heat dissipation control of ground control station under various work modes by Dexin Wang, Jiali Tao, Jin Lei, Xinyan Qin, Yanqi Wang, Jie Song, Tianming Feng, Yujie Zeng

“…This research introduces an Adaptive Variable Channel Control (AVCC) cooling approach using the Deep Reinforcement Learning Soft Actor-Critic (SAC) algorithm. The primary contributions of this paper include: (1) the design of a distributed cooling module featuring multiple cooling fans, which enables a variable channel cooling structure; (2) the development of a multi-module temperature control platform that simulates the heat generation conditions of each module under six work modes, providing a training environment for the cooling control algorithm; (3) the formulation of a model-free control method based on the SAC algorithm, AVCC, to optimize the cooling efficiency and endurance of the GCS. …”
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Leveraging Thermal Infrared Imaging for Pig Ear Detection Research: The TIRPigEar Dataset and Performances of Deep Learning Models by Weihong Ma, Xingmeng Wang, Simon X. Yang, Lepeng Song, Qifeng Li

“…To validate the dataset’s utility, it was evaluated across various object detection algorithms. …”
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Estimating the Significance of Computer Model Factors Based on a Simple Neural Network by Volodymyr Pepelyaev, Nataliia Oriekhova, Ihor Lukyanov

“…The purpose of the work is to develop an algorithm for determining insignificant factors in the presence of a set of training data, in which the number of data samples is relatively small and exceeds the number of factors by only 2-3 times. …”
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Machine learning for predicting medical outcomes associated with acute lithium poisoning by Omid Mehrpour, Varun Vohra, Samaneh Nakhaee, Seyed Ali Mohtarami, Farshad M. Shirazi

“…This study aimed to evaluate the effectiveness of the random forest algorithm in predicting medical outcomes related to acute lithium toxicity. …”
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Wavelet Multiresolution Analysis-Based Takagi–Sugeno–Kang Model, with a Projection Step and Surrogate Feature Selection for Spectral Wave Height Prediction by Panagiotis Korkidis, Anastasios Dounis

“…The novelty of the proposed model lies on its hybrid training approach, which combines least squares with AdaBound, a gradient-based algorithm derived from the deep learning literature. …”
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Inversion of SPAD Values of Pear Leaves at Different Growth Stages Based on Machine Learning and Sentinel-2 Remote Sensing Data by Ning Yan, Qu Xie, Yasen Qin, Qi Wang, Sumin Lv, Xuedong Zhang, Xu Li

“…Subsequently, four machine learning algorithms—K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and an Optimized Integrated Algorithm (OIA)—were employed to develop SPAD retrieval models, and the performance differences across various input combinations and models were systematically evaluated. …”
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Machine learning-assisted early detection of keratoconus: a comparative analysis of corneal topography and biomechanical data by Arkadiusz Syta, Arkadiusz Podkowiński, Tomasz Chorągiewicz, Robert Karpiński, Jakub Gęca, Dominika Wróbel-Dudzińska, Katarzyna E Jonak, Dariusz Głuchowski, Marcin Maciejewski, Robert Rejdak, Kamil Jonak

“…Various machine learning algorithms were trained and evaluated on datasets containing different parameters obtained using the Pentacam device. …”
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FL-DBENet: Double-branch encoder network based on segment anything model for farmland segmentation of large very-high-resolution optical remote sensing images by W. Feng, F. Guan, C. Sun, W. Xu, W. Xu

“…Although deep learning algorithms have been extensively applied to farmland extraction, their performance remains limited due to the scarcity of labeled farmland samples and restricted generalization capabilities. …”
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A hybrid framework for colorectal cancer detection and U-Net segmentation using polynetDWTCADx by Akella S Narasimha Raju, K Venkatesh, Makineedi Rajababu, Ranjith Kumar Gatla, Marwa M. Eid, Enas Ali, Nataliia Titova, Ahmed B. Abou Sharaf

“…We can also employ the semantic segmentation algorithms of the U-Net architecture to accurately identify and segment cancerous colorectal regions. …”
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Early Diagnosis of Alzheimer’s Disease Using Adaptive Neuro K-Means Clustering Technique by Karan Kumar, Shweta Agrawal, Isha Suwalka, Celestine Iwendi, Cresantus N. Biamba

“…Classification is performed using various algorithms, evaluated on sensitivity, accuracy, precision, and similarity metrics. …”
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Tree-Based Machine Learning Approach for Predicting the Impact Behavior of Carbon/Flax Bio-Hybrid Fiber-Reinforced Polymer Composite Laminates by Manzar Masud, Aamir Mubashar, Shahid Iqbal, Hassan Ejaz, Saad Abdul Raheem

“…Additionally, two tree-based machine learning (ML) algorithms were used: random forest (RF) and decision tree (DT). …”
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Development of a deep learning based approach for multi-material decomposition in spectral CT: a proof of principle in silico study by Jayasai R. Rajagopal, Saikiran Rapaka, Faraz Farhadi, Ehsan Abadi, W. Paul Segars, Tristan Nowak, Puneet Sharma, William F. Pritchard, Ashkan Malayeri, Elizabeth C. Jones, Ehsan Samei, Pooyan Sahbaee

“…Classification and quantification performance was evaluated across a range of patient size and dose parameters. …”
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Q-learning global path planning for UAV navigation with pondered priorities by Kevin B. de Carvalho, Hiago de O.B. Batista, Leonardo A. Fagundes-Junior, Iure Rosa L. de Oliveira, Alexandre S. Brandão

“…The user can freely tailor the system’s priorities by modifying each of their weights prior to training. Additionally, scalability tests reveal the algorithm’s swift convergence, achieving stability within just 35 s for larger environments spanning up to 40 × 40 units. …”
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Resilient VPP cost optimization in DER-driven microgrids for large distribution systems considering uncertainty during extreme events by T.D. Suresh, M. Thirumalai, R. Hemalatha, Mohit Bajaj, Vojtech Blazek, Lukas Prokop

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Machine Learning‐Enhanced Optimization for High‐Throughput Precision in Cellular Droplet Bioprinting by Jaemyung Shin, Ryan Kang, Kinam Hyun, Zhangkang Li, Hitendra Kumar, Kangsoo Kim, Simon S. Park, Keekyoung Kim

“…In this study, a high‐throughput cellular droplet bioprinter is designed, capable of printing over 50 cellular droplets simultaneously, producing the large dataset required for effective machine learning training. Among the five algorithms evaluated, the multilayer perceptron model demonstrates the highest prediction accuracy, while the decision tree model offers the fastest computation time. …”
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High sensitivity in spontaneous intracranial hemorrhage detection from emergency head CT scans using ensemble-learning approach by Juuso Takala, Heikki Peura, Riku Pirinen, Katri Väätäinen, Sergei Terjajev, Ziyuan Lin, Rahul Raj, Miikka Korja

“…Although the success of DL algorithms depends on multiple factors, including training data versatility and quality of annotations, using the proposed ensemble-learning approach and rule-based post-processing may help clinicians to develop highly accurate DL solutions for clinical imaging diagnostics.…”
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Integrating UAV-Based Multispectral Data and Transfer Learning for Soil Moisture Prediction in the Black Soil Region of Northeast China by Tong Zhou, Shoutian Ma, Tianyu Liu, Shuihong Yao, Shenglin Li, Yang Gao

“…This study evaluates the performance of three algorithms: Random Forest (RF), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) network. …”
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Volcano activity classification from synergy of EO data and machine learning: an application to Mount Etna volcano (Italy) by C. Petrucci, G. Romoli, A. Pignatelli, E. Trasatti, F. Zuccarello, F. Greco, M. Dozzo, G. Bilotta, F. Spina, G. Ganci

“…The study addresses challenges like temporal and spatial disparities and class imbalances through data preprocessing, ensuring a reliable dataset for training and validation. A k-fold cross-validation approach was used to evaluate model performance systematically. …”
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A Spatiotemporal Fusion Network for Remote Sensing Based on Global Context Attention Mechanism by Weisheng Li, Yusha Liu, Yidong Peng, Fengyan Wu

“…Spatial-temporal fusion algorithms commonly encounter difficulties in effectively striking a balance between the extraction of intricate spatial details and changes over time. …”
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