The RMaP challenge of predicting RNA modifications by nanopore sequencing by Jannes Spangenberg, Stefan Mündnich, Anne Busch, Stefan Pastore, Anna Wierczeiko, Winfried Goettsch, Vincent Dietrich, Leszek P. Pryszcz, Sonia Cruciani, Eva Maria Novoa, Kandarp Joshi, Ranjan Perera, Salvatore Di Giorgio, Paola Arrubarrena, Irem Tellioglu, Chi-Lam Poon, Yuk Kei Wan, Jonathan Göke, Andreas Hildebrandt, Christoph Dieterich, Mark Helm, Manja Marz, Susanne Gerber, Nicolo Alagna

“…Results demonstrate that a low prediction error and a high prediction accuracy can be achieved on these modifications across different approaches and algorithms. …”
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Toward Intelligent Fading Channel Prediction: A Comprehensive Survey by Ramoni Adeogun

“…Through this survey, we aim to provide a foundation for future research in intelligent channel prediction, highlighting the need for more sophisticated and adaptive algorithms to cope with the increasing complexity of wireless communication systems.…”
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Research on Customer Churn Prediction Using Machine Learning Models by Jia Xiaolei

“…With the increasing availability of customer data and advancements in machine learning techniques, accurate churn prediction has become more feasible and impactful. This research compares and analyzes the advantages and disadvantages of three different machine learning algorithms applied to customer churn prediction: random forest, decision tree, and neural network. …”
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Use of Machine Learning to Predict California Bearing Ratio of Soils by Semachew Molla Kassa, Betelhem Zewdu Wubineh

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Predicting Financial Market Volatility with Modern Model and Traditional Model by R. G. Aldeki

“…This paper aims to predict and forecast volatility to develop a two-stage forecasting approach the volatility of the Amman Stock Exchange Index (ASE) effectively. …”
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Unsupervised Learning for Heart Disease Prediction: Clustering-Based Approach by Jetty Janani., Sk Sajida Sultana., Polepalle Ranga Bhavitha., Parusu Vishwitha.

“…This paper on the prediction of heart disease addresses the application of unsupervised machine learning algorithms, digs up the latent pattern of risk in the data of patients for early diagnosis, and intervenes. …”
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Machine learning modeling for predicting adherence to physical activity guideline by Ju-Pil Choe, Seungbak Lee, Minsoo Kang

“…Variables were categorized into demographic, anthropometric, and lifestyle categories. 18 prediction models were created by 6 ML algorithms and evaluated via accuracy, F1 score, and area under the curve (AUC). …”
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Predicting Absenteeism at Workplace Using Machine Learning and Network Analysis by Donggeun Kim, Jai Woo Lee

“…Absenteeism at work, possibly leading to productivity loss in business, is related to various psychological, social, and economic factors. Since predicting absenteeism is involved with complex associations of such factors, appropriately utilizing machine learning algorithms is required in the analysis. …”
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Student Dropout Prediction Using Random Forest and XGBoost Method by Lalu Ganda Rady Putra, Didik Dwi Prasetya, Mayadi Mayadi

“…Objective: This study aims to evaluate the effectiveness of the Random Forest and XGBoost algorithms in predicting student attrition based on demographic, socioeconomic, and academic performance factors. …”
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Predicting Days on Market to Optimize Real Estate Sales Strategy by Mauro Castelli, Maria Dobreva, Roberto Henriques, Leonardo Vanneschi

“…For this reason, building an accurate predictive model for the number of days a published listing will be online can be very helpful to accomplish the task of identifying fake listings. …”
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Application of CT Radiomics in Predicting Differentiation Level of Lung Adenocarcinoma by Shuai ZHANG, Peng HAN, Suya ZHANG, Dingli YE, Zhicheng HUANG

“…CT image features were extracted, and seven machine learning algorithms were used to construct prediction models to obtain the AUC, accuracy, specificity, and sensitivity. …”
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Application of Artificial Neural Network(s) in Predicting Formwork Labour Productivity by Sasan Golnaraghi, Zahra Zangenehmadar, Osama Moselhi, Sabah Alkass

“…Artificial Neural Network (ANN) techniques that use supervised learning algorithms have proved to be more useful than statistical regression techniques considering factors like modeling ease and prediction accuracy. …”
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An Introduction to B-Cell Epitope Mapping and In Silico Epitope Prediction by Lenka Potocnakova, Mangesh Bhide, Lucia Borszekova Pulzova

“…In the last decade, in-depth in silico analysis and categorization of the experimentally identified epitopes stimulated development of algorithms for epitope prediction. Recently, various in silico tools are employed in attempts to predict B-cell epitopes based on sequence and/or structural data. …”
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Zero-Shot Prediction of Conversational Derailment With Large Language Models by Kenya Nonaka, Mitsuo Yoshida

“…This study aims to evaluate the zero-shot prediction performance of conversational derailment using LLMs. …”
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Systems Biology of Human Microbiome for the Prediction of Personal Glycaemic Response by Nikhil Kirtipal, Youngchang Seo, Jangwon Son, Sunjae Lee

“…We explore how the gut microbiota affects glucose metabolism and insulin sensitivity by examining a variety of -omics data, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, and metagenomics. Machine learning algorithms and genome-scale modeling are now being applied to find microbiological biomarkers associated with diabetes risk, predicted disease progression, and guide customized therapy. …”
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Smart Grid Security: Proactive Prediction of Advanced Persistent Threats by Motahareh Dehghan, Erfan Khosravain

“…This paper proposes the use of Deep Reinforcement Learning to enhance cybersecurity in smart grids by leveraging the ProAPT model, which is specifically designed to predict and mitigate Advanced Persistent Threats. …”
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Recent advances in AI-based toxicity prediction for drug discovery by Hyundo Lee, Jisan Kim, Ji-Woon Kim, Yoonji Lee, Yoonji Lee

“…This review provides an in-depth examination of AI-driven toxicity prediction, emphasizing its transformative impact on drug discovery and its growing importance in improving safety assessments.…”
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Computational analysis and experimental validation of gene predictions in Toxoplasma gondii. by Joseph M Dybas, Carlos J Madrid-Aliste, Fa-Yun Che, Edward Nieves, Dmitry Rykunov, Ruth Hogue Angeletti, Louis M Weiss, Kami Kim, Andras Fiser

“…Commonly used gene prediction algorithms produce very disparate sets of protein sequences, with pairwise overlaps ranging from 1.4% to 12%. …”
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Cuproptosis genes in predicting the occurrence of allergic rhinitis and pharmacological treatment. by Ting Yi

“…Finally, AR signature genes were used as targets for drug prediction and molecular docking to identify candidate drugs that may affect SAR.…”
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An Improved Artificial Neural Network Model for Effective Diabetes Prediction by Muhammad Mazhar Bukhari, Bader Fahad Alkhamees, Saddam Hussain, Abdu Gumaei, Adel Assiri, Syed Sajid Ullah

“…Data analytics, machine intelligence, and other cognitive algorithms have been employed in predicting various types of diseases in health care. …”
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