In search of prosociality in rodents: A scoping review. by Valérie Charron, Joey Talbot, Patrick R Labelle, Anne T M Konkle, Hélène Plamondon

“…An update using a semi-supervised machine learning approach (ASReview) was then conducted to collect studies from 2021-2023. …”
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Stability Assessment of Concrete Gravity Dams via Multifidelity Surrogate Models by Rodrigo José de Almeida Torres Filho, Rocio L. Segura, Patrick Paultre

“…In recent years, many researchers have studied the potential of machine learning techniques to reduce the computational burden of dam assessment. …”
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Artificial Intelligence as a Catalyst for Management System Adaptability, Agility and Resilience: Mapping the Research Agenda by Ion Popa, Simona Cătălina Ștefan, Andrei Josan, Corina-Elena Mircioiu, Nicoleta Căruceru

“…Likewise, its thematic and strategic evolution is characterized as a surprising one, managing to incorporate and relate concepts with a strong technical and IT character such as feature extraction, machine learning, reinforcement learning with concepts of a managerial nature as supporting customer-tailored interaction, employee skills development, company productivity, and innovation.…”
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Chaotic gradient based optimization with fuzzy temporal optimized CNN for heart failure prediction by G. Kajeeth Kumar, S. Muthurajkumar

“…Furthermore, a tenfold cross-validation process ensures a comprehensive evaluation and the proposed method outperforms different Machine Learning (ML) / Deep Learning (DL) classifiers. …”
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Evaluating the Performance of Large Language Models in Predicting Diagnostics for Spanish Clinical Cases in Cardiology by Julien Delaunay, Jordi Cusido

“…This study explores the potential of large language models (LLMs) in predicting medical diagnoses from Spanish-language clinical case descriptions, offering an alternative to traditional machine learning (ML) and deep learning (DL) techniques. …”
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A Rectal Cancer Surgery Dataset: Use of artificial intelligence to aid automation of error identification by Walaa Ghamrawi, Nathan Curtis, Jialang Xu, Matt Boal, Evangelos Mazomenos, Eddie Edwards, Danail Stoyanov, Nader Francis

“…This dataset can inform machine learning to assist progress toward a fully automated, objective assessment of surgical skills.…”
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Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification by C. Fernandez-Lozano, C. Canto, M. Gestal, J. M. Andrade-Garda, J. R. Rabuñal, J. Dorado, A. Pazos

“…Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected.…”
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Peptidomic analysis of whey protein hydrolysates and prediction of their antioxidant peptides by Jesus Morales García, Chibuike C. Udenigwe, Jorge Duitama, Andrés Fernando González Barrios

“…We also developed an ensemble machine learning predictor to classify peptides obtained from whey hydrolysis. …”
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Improving Diabetes Prediction by Selecting Optimal K and Distance Measures in KNN Classifier by Emad Majeed Hameed, Hardik Joshi

“…This paper discusses diabetes prediction by using the K-Nearest Neighbors (KNN) classifier, which is a widely used algorithm in machine learning. Most studies only dealt with investigating the optimal value of k in the KNN algorithm and did not address the best method to measure distance alone or together with the optimal value of k to improve the efficiency of diabetes prediction. …”
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A Multi-Index Generative Adversarial Network for Tool Wear Detection with Imbalanced Data by Guokai Zhang, Haoping Xiao, Jingwen Jiang, Qinyuan Liu, Yimo Liu, Liying Wang

“…Experimental results show that our method obtains 97% accuracy in tool wear detection based on imbalanced data without manual feature extraction, which outperforms traditional machine learning methods.…”
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Role of topological indices in predictive modeling and ranking of drugs treating eye disorders by Nazeran Idrees, Esha Noor, Saima Rashid, Fekadu Tesgera Agama

“…We can precisely forecast the behavior of chemicals by utilizing machine learning to analyze large amounts of data. This method may contribute to the discovery of new relevant drugs with desirable properties and helpful in comprehending the effects of chemicals on their efficacy.…”
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End-to-End Semantic Leaf Segmentation Framework for Plants Disease Classification by Khalil Khan, Rehan Ullah Khan, Waleed Albattah, Ali Mustafa Qamar

“…Plant disease detection and classification is a much-developed research area due to enormous development in machine learning (ML). Over the last ten years, computer vision researchers proposed different algorithms for plant disease identification using ML. …”
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Optimizing K-Means Algorithm Using the Purity Method for Clustering Oil Palm Producing Regions in North Aceh by Novia Hasdyna, Rozzi Kesuma Dinata, Balqis Yafis

“… The K-Means algorithm is a fundamental tool in machine learning, widely utilized for data clustering tasks. …”
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Data-Driven Approaches for Diagnosis of Incipient Faults in Cutting Arms of the Roadheader by Qiang Liu, Songyong Liu, Qianjin Dai, Xiao Yu, Daoxiang Teng, Ming Wei

“…In this study, four machine learning tools (the back-propagation neural network based on genetic algorithm optimization, the naive Bayes based on genetic algorithm optimization, the support vector machines based on particle swarm optimization, and the support vector machines based on dynamic cuckoo) are applied to address the challenge in the IFDI of cutting arms. …”
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Unsupervised self-organising map classification of Raman spectra from prostate cell lines uncovers substratified prostate cancer disease states by Daniel West, Susan Stepney, Y. Hancock

“…This finding shows the potential for unsupervised machine learning to discover distinct disease-state features for more accurate characterisation of highly heterogeneous prostate cancer. …”
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Online COVID-19 Infection Diagnoses via Chest X-Ray Images using Alexnet Deep Learning Model by Ali Heidari, Hamidreza Erfanian

“…As the datasets of Covid19 patient CXR images are increasing in number, it is possible to use machine learning techniques in order to assess CXR images automatically and even online. …”
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Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands by Yuan Feng, Jongwan Eun, Seunghee Kim, Yong-Rak Kim

“…Recently, physics-informed neural networks (PINNs), which incorporate partial differential equations (PDEs) to solve forward and inverse problems, have attracted increasing attention in machine learning research. In this study, we applied PINNs to tackle hydraulic and thermal transport coupling forward problems in silty sands. …”
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Vertical Force Monitoring of Racing Tires: A Novel Deep Neural Network-Based Estimation Method by Semih Öngir, Egemen Cumhur Kaleli, Mehmet Zeki Konyar, Hüseyin Metin Ertunç

“…This comparison emphasizes the significant benefits of incorporating advanced deep learning with traditional machine learning to provide a comprehensive and interpretable solution for complex estimation challenges in automotive engineering.…”
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Mitigating GNSS Multipath Effects Using XGBoost Integrated Classifier Based on Consistency Checks by Dengao Li, Xiaoli Ma, Jumin Zhao, Fanming Wu

“…Then, the remaining available measurements are used as the second-layer input, and the measurements are used as learning data using an integrated machine learning method, XGBoost, to efficiently detect and identify non-line-of-sight (NLOS), LOS, and other reflective multipath signals. …”
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A novel and highly efficient botnet detection algorithm based on network traffic analysis of smart systems by Li Duan, Jingxian Zhou, You Wu, Wenyao Xu

“…The traditional methods of detecting botnets commonly used machine learning algorithms, and it is difficult to detect and control botnets in a network because of unbalanced traffic data. …”
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