Combination of Artificial Neural Network and Particle Swarm Intelligence Algorithm for Diagnosing Diabetes by Cillian Thompson, Oscar Higgins

“…Furthermore, in diabetic disease modelling, artificial neural networks have demonstrated outstanding accuracy compared to alternative methods such as machine learning, regression, artificial neural networks, and decision trees.…”
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Self-Organizing Mapping Neural Network Implementation Based on 3-D NAND Flash for Competitive Learning by Anyi Zhu, Lei Jin, Wen Zhou, Tianchun Ye, Zongliang Huo

“…Self-organizing Map (SOM) neural network is a prominent algorithm in unsupervised machine learning, which is widely used for data clustering, high-dimensional visualization, and feature extraction. …”
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The Adventure of Artificial Intelligence in Educational Research from the Past to the Present by Deniz Kaya

“…As a thematic change in the studies, there has been an evolution towards new technological developments such as deep learning, machine learning, ChatGPT, chatbots, learning analytics, blockchain, and generative AI. …”
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Analysis of Noise Pollution during Dussehra Festival in Bhubaneswar Smart City in India: A Study Using Machine Intelligence Models by Sourav Kumar Bhoi, Chittaranjan Mallick, Chitta Ranjan Mohanty, Ranjan Soumya Nayak

“…In this work, the noise pollution level of Bhubaneswar smart city during Dussehra 2020 is predicted using different supervised machine learning (ML) prediction models. The input parameters considered for this work are area or zones of Bhubaneswar city, time at which sound level recorded, equivalent continuous sound level (Leq in dBA), and noise level (high/low compared to the standard value). …”
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Pruning and optimization of optical neural network as a binary optical trigger by Bokun Zhao, Xuening Dong, Kaveh Rahbardar Mojaver, Brett H. Meyer, Odile Liboiron-Ladouceur

“…Optical neural networks implemented with Mach-Zehnder Interferometer (MZI) arrays are a promising solution to enable fast and energy-efficient machine learning inference, yet finding a practical application has proven challenging due to sensitivity to thermal noise and loss. …”
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Software Integration of Power System Measurement Devices with AI Capabilities by Victoria Arenas-Ramos, Federico Cuesta, Victor Pallares-Lopez, Isabel Santiago

“…Moreover, real-time Artificial Intelligence capabilities have been validated by implementing three Machine Learning classifiers (Neural Network, Decision Tree, and Random Forest) to distinguish between three different loads in real time.…”
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The Implementation of a Support Vector Regression Model Utilizing Meta-Heuristic Algorithms for Predicting Undrained Shear Strength by Rami Al-Qasimi, Firas Al-Hajri

“…To address these limitations, this study introduces innovative machine learning techniques, employing the Support Vector Regression (SVR) model to accurately predict USS. …”
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A data-driven state identification method for intelligent control of the joint station export system by Guangli Xu, Yifu Wang, Zhihao Zhou, Yifeng Lu, Liangxue Cai

“…Compared with the traditional hydraulic calculation modified (THCM) models and other machine learning algorithms, the PSO-GWO-BP model has significant advantages in prediction accuracy. …”
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On the enhancement of estimator efficiency of population variance through stratification, transformation, and formulation with application to COVID-19 data by Hameed Ali, Zafar Mahmood, T.H. AlAbdulaal

“…This work introduces a novel data-driven machine learning algorithm aiming stratification problem, formally based on subjective approach. …”
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Transcriptomics profiling of Parkinson’s disease progression subtypes reveals distinctive patterns of gene expression by Carlo Fabrizio, Andrea Termine, Carlo Caltagirone

“…Methods Whole blood RNA-Sequencing data underwent differential gene expression analysis, followed by multiple pathway analyses. A Machine Learning (ML) classifier, namely XGBoost, was trained using data from multiple modalities, including gene expression values. …”
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Incident duration prediction through integration of uncertainty and risk factor evaluation: A San Francisco incidents case study. by Amirreza Salehi, Ardavan Babaei, Majid Khedmati

“…Finally, we employ both traditional Machine Learning (ML) and Deep Learning (DL) models to perform classification and regression tasks. …”
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Cloud-Based Transaction Fraud Detection: An In-depth Analysis of ML Algorithms by Ali Alhchaimi

“…Objectives: This study assesses machine learning (ML) algorithms' ability to detect fraud in cloud environments, focusing on Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVM), and XGBoost (XGB). …”
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STA-HAR: A Spatiotemporal Attention-Based Framework for Human Activity Recognition by Md. Khaliluzzaman, Md. Furquan, Mohammod Sazid Zaman Khan, Md. Jiabul Hoque

“…This paper investigates the difficulties encountered in human activity recognition (HAR), precisely differentiating between various activities by extracting spatial and temporal features from sequential data. Traditional machine learning approaches necessitate manual feature extraction, hindering their effectiveness. …”
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Classifying Economic Areas for Urban Planning using Deep Learning and Satellite Imagery in East Africa by Davy Uwizera, Dr. Charles Ruranga, Prof. Patrick McSharry

“…By benchmarking performance against traditional machine learning models, results show that deep learning techniques yielded superior performance. …”
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Side information-driven image coding for hybrid machine–human vision by Zhongpeng Zhang, Ying Liu, Wen-Hsiao Peng

“…Abstract With the development of machine learning, advanced photography and image transmission systems, images are being processed more and more by machines, so image coding for machines (ICM) came into being. …”
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Bridging the Gap in the Adoption of Trustworthy AI in Indian Healthcare: Challenges and Opportunities by Sarat Kumar Chettri, Rup Kumar Deka, Manob Jyoti Saikia

“…It finds that the existing studies mostly used conventional machine learning (ML) algorithms and artificial neural networks (ANNs) for a variety of tasks, such as drug discovery, disease surveillance systems, early disease detection and diagnostic accuracy, and management of healthcare resources in India. …”
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Remaining Useful Life Prediction Techniques of Electric Valves for Nuclear Power Plants with Convolution Kernel and LSTM by Hang Wang, Min-jun Peng, Yong-kuo Liu, Shi-wen Liu, Ren-yi Xu, Hanan Saeed

“…Experiments show that the proposed method could predict RUL more accurately compared to other typical machine learning and deep learning methods. This will further enhance maintenance efficiency of any plant.…”
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Introducing Iterative Model Calibration (IMC) v1.0: a generalizable framework for numerical model calibration with a CAESAR-Lisflood case study by C. Banerjee, K. Nguyen, C. Fookes, G. Hancock, T. Coulthard

“…Moreover, the utility of machine-learning-based and data-driven approaches is curtailed by the requirement for the numerical model to be differentiable for optimization purposes, which challenges their generalizability across different models. …”
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Detection and identification technology of rotor unmanned aerial vehicles in 5G scene by Fengtong Xu, Tao Hong, Jingcheng Zhao, Tao Yang

“…In order to identify the “black flying” unmanned aerial vehicle, combined with the advantages of 5G millimeter wave radar and machine learning methods, the following methods are adopted in this article. …”
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Avances en el aprovechamiento de biopolímeros y productos peruanos by Erika Del Milagro Lozano-Flores

“…Asimismo, el análisis de palabras clave destaca la relevancia de técnicas como "machine learning", "deep learning" y "neural networks". …”
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