Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case Study by Maryam Abbasi, Marco V. Bernardo, Paulo Váz, José Silva, Pedro Martins

“…This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. …”
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Ensemble machine learning algorithm for cost-effective and timely detection of diabetes in Maiduguri, Borno State by Emmanuel Gbenga Dada, Aishatu Ibrahim Birma, Abdulkarim Abbas Gora

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LEVERAGING MACHINE LEARNING METHODS IN PREDICTING AND ANALYZING THE ASSOCIATION BETWEEN DIETARY INFLAMMATORY INDEX AND ALOPECIA by Mohammed Sarwat M Salih, Hawal Lateef Fateh, Soran Abdulkarim Pasha, Hassan M Tawfiq

“…Three machine learning models were developed: K-Nearest Neighbors (KNN) with dimensionality reduction to prevent overfitting, Logistic Regression with L2 regularization, and Random Forest enhanced through grid search for hyperparameter tuning. …”
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Combination of machine learning and Raman spectroscopy for prediction of drug release in targeted drug delivery formulations by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

“…The considered drug is 5-aminosalicylic acid for colonic drug delivery, and its release was estimated using Raman data as inputs along with other categorical parameters. The models, including Kernel Ridge Regression (KRR), Kernel-based Extreme Learning Machine (K-ELM), and Quantile Regression (QR) incorporate sophisticated approaches like the Sailfish Optimizer (SFO) for hyperparameter optimization and K-fold cross-validation to enhance predictive accuracy. …”
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Predicting postoperative complications after pneumonectomy using machine learning: a 10-year study by Yaxuan Wang, Shiyang Xie, Jiayun Liu, He Wang, Jiangang Yu, Wenya Li, Aika Guan, Shun Xu, Yong Cui, Wenfei Tan

“…All the net benefits of the five machine-learning models in the training and validation sets demonstrated excellent clinical applicability, and the calibration curves showed good agreement between the predicted and observed risks.Conclusion The combination of machine-learning models and nomograms may contribute to the early prediction and reduction in the incidence of PCNC.…”
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Improved Alzheimer Disease Diagnosis With a Machine Learning Approach and Neuroimaging: Case Study Development by Lilia Lazli

“…While not curable, earlier detection can help improve symptoms substantially. Machine learning (ML) models are popular and well suited for medical image processing tasks such as computer-aided diagnosis. …”
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Explainable Machine Learning and Predictive Statistics for Sustainable Photovoltaic Power Prediction on Areal Meteorological Variables by Sajjad Nematzadeh, Vedat Esen

“…This study proposes an explainable machine-learning framework that simultaneously ranks the most informative weather parameters and reveals their physical relevance to PV generation. …”
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A Portable Real-Time Electronic Nose for Evaluating Seafood Freshness Using Machine Learning by Muhammad Rafi Mahfuz Setyagraha, Hurul Aini Nurqamaradillah, Laksamana Mikhail Hermawan, Nyoman Raflly Pratama, Ledya Novamizanti, Dedy Rahman Wijaya

“…This study presents an electronic nose (e-nose) system designed to assess seafood freshness using gas sensors and machine learning (ML) algorithms. The system detects volatile organic compounds (VOCs) released during spoilage and employs hyperparameter-optimized ML models for both classification (fresh vs. not fresh) and regression (shelf-life prediction). …”
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A Modified Support Vector Machine Classifiers Using Stochastic Gradient Descent with Application to Leukemia Cancer Type Dataset by Ghadeer Jasim Mahdi

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CT Radiomics-based machine learning approach for the invasiveness of pulmonary ground-glass nodules prediction by Rui Chen, Hu Zhang, Xingwen Huang, Haitao Han, Jinbo Jian

“…Objective: To develop and validate a machine learning model based on CT radiomics to improve the ability to differentiate pathological subtypes of pulmonary ground-glass nodules (GGN). …”
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Versatile machine learning algorithms for FTIR spectroscopy: differentiating crosslinked and non-crosslinked gelatin samples by Juliana Rincón-López, Eliana Álvarez-Valdés, Daniela Velez-Arango, Estefanía Rojas Zuleta, Leidy Yuliana Vargas Soto, Liliana Lellesch, Victor Alonso García Londoño, Milton Rosero-Moreano, Gonzalo Taborda-Ocampo

“…For the FTIR results, machine learning classification models developed in the Python language were used to distinguish between cross-linked and non-cross-linked gelatin samples and two dimensionality reduction techniques (PCA, PLS) and four classification models (NCA-KNN, SVM, LDA, DT) were incorporated, all effectively classifying spectra across gelatin types in adjustment, training, test stages, and predictions, with higher precision observed for gelatins A, C, and D. …”
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Spatial analysis of air pollutant exposure and its association with metabolic diseases using machine learning by Jingjing Liu, Chang Liu, Zhangdaihong Liu, Yibin Zhou, Xiaoguang Li, Yang Yang

“…(iii) AP exposure is adjusted by demographic and lifestyle confounders to predict MDs using machine learning models (e.g., eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), LightGBM, and Multi-Layer Perceptron (MLP)). …”
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Experimental and machine learning based analysis of pervious concrete enhanced with fly ash and silica fume by Siva Shanmukha Anjaneya Babu Padavala, Siva Avudaiappan, Venkatesh Noolu

“…Machine learning (ML) models were also created in order to predict compressive strength based on mix composition and curing age using Orange Data Mining software version 3.36. …”
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A Device for the Rapid Detection of Benzodiazepines and Synthetic Cannabinoids via Fluorescence Spectroscopy and Machine Learning by A. Power, M. Gardner, C. Pudney

“…In the UK, the abuse of Benzodiazepines and Synthetic Cannabinoids is particularly prevalent, especially in healthcare and custodial settings, and there is currently no solution to quickly detect these substances for harm reduction. Methods: We are developing a portable and rapid device that utilizes Fluorescence Spectroscopy and Machine Learning to detect Benzodiazepines and Synthetic Cannabinoids in a variety of media, including saliva. …”
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Gait-based Parkinson’s disease diagnosis and severity classification using force sensors and machine learning by Navita, Pooja Mittal, Yogesh Kumar Sharma, Anjani Kumar Rai, Sarita Simaiya, Umesh Kumar Lilhore, Vimal Kumar

“…Abstract A dual-stage model for classifying Parkinson’s disease severity, through a detailed analysis of Gait signals using force sensors and machine learning approaches, is proposed in this study. …”
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Insights into ozone pollution control in urban areas by decoupling meteorological factors based on machine learning by Y. Qiu, X. Li, X. Li, W. Chai, Y. Liu, M. Song, X. Tian, Q. Zou, W. Lou, W. Zhang, J. Li, Y. Zhang

“…Primarily due to adverse changes in meteorological conditions, the effects of emission reduction are masked. In this study, we integrated a machine learning model, an observation-based model, and a positive matrix factorization model based on 4 years of continuous observation data from a typical urban site. …”
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Optimization and predictive performance of fly ash-based sustainable concrete using integrated multitask deep learning framework with interpretable machine learning techniques by Bhupesh P. Nandurkar, Jayant M. Raut, Pawan K. Hinge, Boskey V. Bahoria, Tejas R. Patil, Sachin Upadhye, Vikrant S. Vairagade, Sagar D. Shelare

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Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods by Bin Zhang, Shengsheng Huang, Chenxing Zhou, Jichong Zhu, Tianyou Chen, Sitan Feng, Chengqian Huang, Zequn Wang, Shaofeng Wu, Chong Liu, Xinli Zhan

“…Background Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. …”
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Electrical discharge machining: Recent advances and future trends in modeling, optimization, and sustainability by Muhamad Taufik Ulhakim, Sukarman, Khoirudin, Dodi Mulyadi, Hendri Susilo, Rohman, Muji Setiyo

“…Future studies should focus on the effects of AI-driven approaches on environmentally friendly EDM practices by prioritizing green dielectrics, energy-efficient machining, and waste reduction strategies. This review highlights the interconnected roles of modeling, optimization, and sustainability in advancing EDM and outlines key research directions to address the remaining challenges.…”
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Energy Management and Edge-Driven Trading in Fractal-Structured Microgrids: A Machine Learning Approach by Mostafa Pasandideh, Jason Kurz, Mark Apperley

“…This paper introduces a novel adaptive energy management framework that integrates streaming machine learning (SML) with a hierarchical fractal microgrid architecture to deliver precise real-time electricity demand forecasts for a residential community. …”
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