Global air quality index prediction using integrated spatial observation data and geographics machine learning by Tania Septi Anggraini, Hitoshi Irie, Anjar Dimara Sakti, Ketut Wikantika

“…The GML considers geographical characteristics in the analysis by calculating the optimal bandwidth area in its algorithm. The study employs nine scenarios to identify which parameters significantly contribute to the model and determine the best parameter combinations. …”
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Machine learning-based prediction of pervaporation permeation using physicochemical properties of permeant-membrane and process conditions by Muhammad Mujiburohman, Marwen Elkamel, Farzad Hourfar, Ali Elkamel

“…Given many experimental data of PV, a predictive model on PV permeation based on their physicochemical properties and process conditions can be constructed. …”
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A hybrid ensemble framework with particle swarm optimization for network anomaly detection by Narinder Verma, Neerendra Kumar, Gourav Kumar, Kuljeet Singh

“…We evaluate multiple machine learning models, including Decision Trees (DT), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Random Forests (RF), and an ensemble of these models for both binary and multi-class classification tasks. …”
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AI and IoT-powered edge device optimized for crop pest and disease detection by Jean Pierre Nyakuri, Celestin Nkundineza, Omar Gatera, Kizito Nkurikiyeyezu, Gervais Mwitende

“…This study presents the development of a portable smart IoT device that integrates a lightweight convolutional neural network (CNN), called Tiny-LiteNet, optimized for edge applications with built-in support of model explainability. …”
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Optimization of the Naive Bayes Algorithm with SMOTETomek Combination for Imbalance Class Fraud Detection by Arief Tri Arsanto, Arif Faizin, Moch lutfi, Zulfatun Nikmatus Saadah

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Using Machine Learning Approaches on Dynamic Patient-Reported Outcomes to Cluster Cancer Treatment-Related Symptoms by Nora Asper, Hans Friedrich Witschel, Louise von Stockar, Emanuele Laurenzi, Hans Christian Kolberg, Marcus Vetter, Sven Roth, Gerd Kullak-Ublick, Andreas Trojan

“…The machine learning model was not able to recognize any of the patients with prostate and blood–lymph cancer, likely as these cancer types were barely represented in the dataset. …”
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Comparative RSM analysis of CO2 laser and abrasive waterjet machining on Armox 500 T armored steel by M. M. Salem, Tamer Ismail, Y. M. Elattar, A. M. El-Wardany, Ahmed Salama

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MACHINE LEARNING USING MULTIPLE LOGISTIC REGRESSION FOR ANTIMICROBIAL AND HEMOLYTIC PEPTIDES PREDICTION AND THEIR IDENTIFICATION IN LARGE PROTEINS by Krenev I.A.

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Machine and Deep Learning-Based Seizure Prediction: A Scoping Review on the Use of Temporal and Spectral Features by Yousif A. Saadoon, Mohamad Khalil, Dalia Battikh

“…This review provides a comprehensive overview of seizure prediction models that integrate temporal and spectral features as inputs or enhanced representations for ML and DL models. …”
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Design and Experiment of an Inter-Row Weeding Machine Applied in Soybean and Corn Strip Compound Planting (SCSCP) by Zihao Tang, Xiaobo Xi, Baofeng Zhang, Yangjie Shi, Yajuan Wang, Ruihong Zhang

“…This paper details the machine’s principles by analyzing the geometric relationship and mechanical model between the corresponding profiling quantities, which determine the relevant parameters for adaptive contouring to ensure stable operation on undulating ground. …”
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Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru by Lucia Enriquez, Kevin Ortega, Dennis Ccopi, Claudia Rios, Julio Urquizo, Solanch Patricio, Lidiana Alejandro, Manuel Oliva-Cruz, Elgar Barboza, Samuel Pizarro

“…Machine learning algorithms, including classification and regression trees (CART) and random forest (RF), modeled the soil parameters (N-ppm, P-ppm, K-ppm, OM%, and EC-mS/m). …”
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Optimization of particle filter tracking algorithm based on weakly supervised attribute learning by Hui Zhang, Dawang Shen

“…This method combines weakly supervised learning with energy function optimization to raise the efficiency of image feature annotation in object detection models. …”
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Evaluating ground vibration attenuation through leca‐filled trenches: A support vector machine approach by Mohsen Naghizadeh Rokni, Omid Tavasoli, Reza Esmaeilabadi, Amirpouya Saraf

“…Additionally, a support vector machine algorithm was developed to forecast the performance of single‐wall trenches, showing a high correlation with numerical model outcomes. …”
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Machine Learning Strategy to Achieve Maximum Energy Harvesting and Monitoring Method for Solar Photovoltaic Panel Applications by Bibhu Prasad Ganthia, Sudheer Hanumanthakari, Hemachandra Gudimindla, Harishchander Anandaram, M. Siva Ramkumar, Monalisa Mohanty, S. Raja Gopal, Atul Sarojwal, Kibrom Menasbo Hadish

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Computational machine learning estimation of digitoxin solubility in supercritical solvent at different temperatures utilizing ensemble methods by Hadil Faris Alotaibi, Waqed H. Hassan, Ahmed Kateb Jumaah Al-Nussairi, Narinderjit Singh Sawaran Singh, Ahmed Salah Al-Shati, M. M. Rekha, Subhashree Ray, Aashna Sinha, Gunjan Garg

“…Temperature and pressure are the input parameters, while solvent density and digitoxin solubility are the output parameters. Several machine learning models along with optimizer were used for correlation of the dataset. …”
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A hybrid deep learning-based approach for optimal genotype by environment selection by Zahra Khalilzadeh, Motahareh Kashanian, Saeed Khaki, Lizhi Wang

“…By applying the Generalized Ensemble Method (GEM), we combined the CNN-based models and optimized their weights to improve overall predictive performance. …”
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Machine learning supported ground beef freshness monitoring based on near‐infrared and paper chromogenic array by Yihang Feng, Yi Wang, Burcu Beykal, Zhenlei Xiao, Yangchao Luo

“…Nippy, an open‐source Python module, was used for automated NIR spectra preprocessing. The optimal preprocessing pipeline was found by 10‐fold cross‐validation in machine learning model development. …”
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Support Vector Machine – Recursive Feature Elimination for Feature Selection on Multi-omics Lung Cancer Data by Nuraina Syaza Azman, Azurah A Samah, Ji Tong Lin, Hairudin Abdul Majid, Zuraini Ali Shah, Nies Hui Wen, Chan Weng Howe

“…The results show that all 5 VAE models can obtain an accuracy and AUC score of 1.000, while only 2 out of 5 SDAE models (FS 1000 & 4000) can do so. 3 out of 5 SDAE models have an AUC score of 0.500, indicating zero capability in separating the binary class labels. …”
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Optimizing mRNA Vaccine Degradation Prediction via Penalized Dropout Approaches by Hwai Ing Soon, Azian Azamimi Abdullah, Hiromitsu Nishizaki, Latifah Munirah Kamarudin

“…A novel tetramer-label encoding approach (4-mer-lbA) was proposed, integrating biological relevance with data-driven analysis to enhance predictive accuracy. To further optimize model performance, two advanced hyperparameter optimization (HPO) techniques—Dropout-Enhanced Technique (DEet) and Hyperparameter Optimization Algorithm Penalizer (HOPeR)—are proposed to mitigate overfitting, address inefficiencies in conventional HPO algorithms (HPOAs), and accelerate model convergence. …”
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Aerodynamic analysis and ANN-based optimization of NACA airfoils for enhanced UAV performance by Sanan H. Khan, Mohd Danish, Md. Ayaz, Afsar Husain, Shamma Saeed, Shamma Abdulla, Shama Shaheen, Alia Saeed, Ahmed Thaher

“…Overall, this study highlighted the potential of combining computational techniques and machine- learning models to optimize UAV airfoil designs. …”
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