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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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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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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Prognostic tools or clinical predictions: Which are better in palliative care? by P Stone, V Vickerstaff, A Kalpakidou, C Todd, J Griffiths, V Keeley, K Spencer, P Buckle, D Finlay, R Z Omar

“…PPI was significantly (p<0.001) worse than CPS at predicting which patients would survive for 3 or 6 weeks. …”
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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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Efficient Segmentation Using Attention-Fusion Modules With Dense Predictions by Serdar Erisen

“…Dense predictions are deployed to augment the efficacy of dense-attention-boosting modules and dense-attention-fusion networks, enabling the extraction of global context feature maps in the encoder. …”
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Integration of geospatial techniques and machine learning in land parcel prediction by Nekkanti Haripavan, Subhashish Dey, Chimakurthi Harika Mani Chandana

“…The integration of geospatial techniques and machine learning algorithms has revolutionized our ability to analyze and predict changes in land parcels. …”
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Credit card default prediction using ML and DL techniques by Fazal Wahab, Imran Khan, Sneha Sabada

“…Subsequently, various techniques are employed to preprocess the unprocessed data and visually present the outcomes through the use of exploratory data analysis (EDA). Furthermore, the algorithms are hypertuned to evaluate the enhancement in prediction. …”
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Predicting Court Judgment in Criminal Cases by Text Mining Techniques by Mohammad Farhadishad, Mohammad Kazemifard, Zahra Rezaei

“…Due to high pressures and stress, it may be difficult for them to carefully examine documents and evidence, which leads to more subjective judgments. Legal judgment prediction with artificial intelligence algorithms can benefit judicial bodies, legal experts, and litigants as well as judges. …”
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Prediction-Based Tip-Over Prevention for Planetary Exploration Rovers by Siddhant Shete, Raúl Domínguez, Ravisankar Selvaraju, Mariela De Lucas Álvarez, Frank Kirchner

“…By leveraging deep learning algorithms, the system evaluates predictions and true measurements in real time to identify potential tip-overs. …”
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Predicting the Tensile Strength of Plant Leaves Based on GA-SVM by Wei Chang, Meihong Liu, Yayu Huang, Junjie Lei, Kai Wu

“…A comparative analysis with other predictive algorithms demonstrates that the GA-SVM model achieves the lowest prediction error and highest accuracy, with mean absolute error and root mean squared error values of 0.0774 and 0.0745, respectively. …”
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