Leveraging LLMs for optimised feature selection and embedding in structured data: A case study on graduate employment classification by Radiah Haque, Hui-Ngo Goh, Choo-Yee Ting, Albert Quek, M.D. Rakibul Hasan

“…Feature selection methods, including Boruta and Extra Tree Classifier (ETC) are employed to identify the optimal feature set, guided by a sliding window algorithm for automatic feature selection. …”
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Topology identification and parameters estimation of LV distribution networks using open GIS data by Dong Liu, Juan S. Giraldo, Peter Palensky, Pedro P. Vergara

“…The proposed approach exploits the fact that underground cables usually follow the street pattern, thus relying on open street map (OSM) and smart meter (SM) data. Three stages compose the proposed approach: In the first stage, a hierarchical minimum spanning tree algorithm is proposed to generate the initial topology with an accurate number of sub-branches from the pre-processed OSM data and peak demand. …”
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Impact of climate change over distribution and potential range of chestnut in the Iberian Peninsula by Pedro Álvarez-Álvarez, Adrián Aviñoa-Arias, Adrián Aviñoa-Arias, Emilio Díaz-Varela, José Vicente López-Bao, José Carlos Pérez-Girón

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The Utilization of Naive Bayes and C.45 in Predicting The Timeliness of Students’ Graduation by Agung Wibowo, Danny Manongga, Hindriyanto Dwi Purnomo

“…In the Desicion Tree calculation, the highest gain values are obtained in the IPK3, IPS1 and IPK2 attributes. …”
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Evaluation of a Machine Learning Model Based on Laboratory Parameters for the Prediction of Influenza A and B in Chongqing, China: Multicenter Model Development and Validation Stud... by Weiwei Hu, Yulong Liu, Jian Dong, Xuelian Peng, Chunyan Yang, Honglin Wang, Yong Chen, Shan Shi, Jin Li

“…ResultsIn the internal testing cohort, 7 models (K-nearest neighbors, naïve Bayes, decision tree, random forest, extreme gradient boosting, gradient-boosting decision tree, and CatBoost) were evaluated. …”
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Performance Evaluation of Robotic Harvester with Integrated Real-Time Perception and Path Planning for Dwarf Hedge-Planted Apple Orchard by Tantan Jin, Xiongzhe Han, Pingan Wang, Yang Lyu, Eunha Chang, Haetnim Jeong, Lirong Xiang

“…For motion planning, a Dynamic Temperature Simplified Transition Adaptive Cost Bidirectional Transition-Based Rapidly Exploring Random Tree (DSA-BiTRRT) algorithm was developed, demonstrating significant improvements in the path planning performance. …”
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Climatic suitability and potential distribution of earleaf acacia and its candidate biological control agent Trichilogaster sp by Sara S. MacDonald, Emily J. Le Falchier, Lindsay P. Campbell, Ryan Zonneveld, Carey R. Minteer

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Machine learning analysis of survival outcomes in breast cancer patients treated with chemotherapy, hormone therapy, surgery, and radiotherapy by Eyachew Misganew Tegaw, Betelhem Bizuneh Asfaw

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Unveiling shadows: A data-driven insight on depression among Bangladeshi university students by Sanjib Kumar Sen, Md. Shifatul Ahsan Apurba, Anika Priodorshinee Mrittika, Md. Tawhid Anwar, A.B.M. Alim Al Islam, Jannatun Noor

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Integrative analysis of RNA-Seq data and machine learning approaches to identify Biomarkers for Rhizoctonia solani resistance in sugar beet by Bahman Panahi, Mahdi Hassani, Nahid Hosseinzaeh Gharajeh

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Developing an efficient explainable artificial intelligence approach for accurate reverse osmosis desalination plant performance prediction: application of SHAP analysis by Meysam Alizamir, Mo Wang, Rana Muhammad Adnan Ikram, Sungwon Kim, Kaywan Othman Ahmed, Salim Heddam

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Cheating Detection in Online Exams Using Deep Learning and Machine Learning by Bahaddin Erdem, Murat Karabatak

“…For regression and classification, deep neural network (DNN) from deep learning algorithms and support vector machine (SVM), decision trees (DTs), k-nearest neighbor (KNN), random forest (RF), logistic regression (LR), and extreme gradient boosting (XGBoost) algorithms from machine learning algorithms were used. …”
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Analisa Sentimen Financial Technology Peer To Peer Lending Pada Aplikasi Koinworks by Rousyati Rousyati, Windu Gata, Dany Pratmanto, Nia Kusuma Wardhani

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Hybrid Weighted Random Forests Method for Prediction & Classification of Online Buying Customers by Umesh Kumar Lilhore, Sarita Simaiya, Devendra Prasad, Deepak Kumar Verma

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Energy Demand Forecasting Scenarios for Buildings Using Six AI Models by Khaled M. Salem, Francisco J. Rey-Martínez, A. O. Elgharib, Javier M. Rey-Hernández

“…This research addresses a significant gap in energy demand forecasting across three building types by comparing six machine learning algorithms: Artificial Neural Networks, Random Forest, XGBoost, Radial Basis Function Network, Autoencoder, and Decision Trees. …”
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Machine learning frameworks to accurately predict coke reactivity index by Ayat Hussein Adhab, Morug Salih Mahdi, Krunal Vaghela, Anupam Yadav, Jayaprakash B, Mayank Kundlas, Ankur Srivastava, Jayant Jagtap, Aseel Salah Mansoor, Usama Kadem Radi, Nasr Saadoun Abd, Samim Sherzod

“…To minimize overfitting in each algorithm, K-fold cross-validation methodology is employed during the training phase. …”
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Big Data Analytics in IoT, social media, NLP, and information security: trends, challenges, and applications by Kamal Taha

“…The taxonomy and experiments collectively demonstrate the need for context-aware algorithm selection, particularly for real-time and scalable Big Data applications. …”
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Machine learning-driven insights into phase prediction for high entropy alloys by Reliance Jain, Sandeep Jain, Sheetal Kumar Dewangan, Lokesh Kumar Boriwal, Sumanta Samal

“…Herein, a method of designing substitutional high entropy alloys with optimization of input features and predict their phase formation, using different ML algorithms are proposed. The ML models such as multi layer precreptron MLP, Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), KNN, XGB nad SVM Classifier algorithm were used for the identifying the phase of HEAs. …”
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Radiomics-based machine learning for automated detection of Pneumothorax in CT scans. by Hanieh Alimiri Dehbaghi, Karim Khoshgard, Hamid Sharini, Samira Jafari Khairabadi, Farhad Naleini

“…The used machine learning algorithms are Gradient Tree Boosting (GBM), eXtreme Gradient Boosting (XGBoost), and Light GBM. …”
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Investigating the Use of Electrooculography Sensors to Detect Stress During Working Activities by Alessandra Papetti, Marianna Ciccarelli, Andrea Manni, Andrea Caroppo, Gabriele Rescio

“…Employing supervised machine learning (ML) algorithms—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbors (KNN)—the analysis revealed accuracy rates exceeding 80%, with RF leading at 85.8% and 82.4% for two classes and three classes, respectively. …”
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