Leveraging Machine and Deep Learning Algorithms for hERG Blocker Prediction by Syed Mohammad, Vaisali Chandrasekar, Omar Aboumarzouk, Ajay Vikram Singh, Sarada Prasad Dakua

“…The current study utilises state-of-the-art ML and DL models for predicting the hERG-blocking ability of chemical compounds using a dataset of 8337 molecules. …”
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PREDICTION OF TELECOM SERVICES CONSUMERS CHURN BY USING MACHINE LEARNING ALGORITHMS by Edin Osmanbegović, Anel Džinić, Mirza Suljić

“…One of those areas is the prediction and prevention of consumer churn. There are two basic types of consumer churn, complete churn and partial churn. …”
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Purchasing Prediction Using Machine Learning Algorithms for Optimizing Inventory Management by Reza Hamdi Prayetno, Rani Destika Purba, Kyrene Wirawan, Kelvin Sweet, Evta Indra

“…However, the challenges that companies often face are the uncertainty of market demand and changes in trends that are difficult to predict. Along with technological developments, traditional methods of inventory management are starting to be replaced by data-based approaches and machine learning algorithms. …”
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Fruit size prediction of tomato cultivars using machine learning algorithms by Masaaki Takahashi, Yasushi Kawasaki, Hiroki Naito, Hiroki Naito, Unseok Lee, Koichi Yoshi

“…We aimed to develop a method for early prediction of tomato fruit size at harvest with machine learning algorithm, and three machine learning models (Ridge Regression, Extra Tree Regrreion, CatBoost Regression) were compared using the PyCaret package for Python. …”
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Prediction of Middle School Students’ Recycling Behaviors with Machine Learning Algorithms by Fatma Merve Mustafaoğlu, Fatma Alkan

“…Environmentally responsible behaviors, shaped during early ages, are closely linked to environmental attitudes, as demonstrated by prior research. This study aims to predict middle school students’ recycling behaviors using machine learning algorithms. …”
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PERFORMANCE COMPARISON OF CLASSICAL ALGORITHMS AND DEEP NEURAL NETWORKS FOR TUBERCULOSIS PREDICTION by Gilgen Mate Landry, Rodolphe Nsimba Malumba, Fiston Chrisnovi Balanganayi Kabutakapua, Bopatriciat Boluma Mangata

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Algorithms for Effector Prediction in Plant Pathogens and Pests: Achievements and Current Challenges by César De los Santos-Briones, Karla Gisel Carreón-Anguiano, Sara E. Vila-Luna, Jewel Nicole Anna Todd, Ignacio Islas-Flores, Luis Sáenz-Carbonell, Pablo Alejandro Gamas-Trujillo, Blondy Canto-Canché

Subjects: “…algorithms…”
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Machine learning algorithms to predict the tensile strength of novel composite materials by S. Sathees Kumar, P. Shyamala, Pravat Ranjan Pati

“…Future studies will extend this methodology to multi-property prediction, integrating microstructural descriptors for improved real-world applicability.…”
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Comparative Analysis of Supervised Machine Learning Algorithms for COVID-19 Prediction by Rubina Shaheen, Beenish Akram, Amna Zafar, Talha Waheed

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Smart Agriculture: Predicting Diseases in olive using Deep Learning Algorithms by Rahman F., Raghatate Kapesh Subhash

“…In this study, olive tree diseases are managed by improving prediction and management using a deep learning algorithm, with the aim of efficiency and accuracy of the process. …”
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Development and external validation of prediction algorithms to improve early diagnosis of cancer by J. Hippisley-Cox, CA Coupland

“…Here we develop and externally validate two diagnostic prediction algorithms to estimate the probability of having cancer for 15 cancer types. …”
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FEATURE-BASED IMPLEMENTATION OF MACHINE LEARNING ALGORITHMS FOR CARDIOVASCULAR DISEASE PREDICTION by H. Singh, R. Tripathy, P. Kumar Sarangi, U. Giri, S. Kumar Mohapatra, N. Rameshbhai Amin

“…After applying all algorithms to our data set, the experimental results concluded that the Logistic Regression model has predicted well with highest accuracy of 92% in comparison with other algorithms.…”
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Comparative performance analysis of neoepitope prediction algorithms in head and neck cancer by Leila Y. Chihab, Leila Y. Chihab, Julie G. Burel, Aaron M. Miller, Aaron M. Miller, Luise Westernberg, Brandee Brown, Jason Greenbaum, Michael J. Korrer, Stephen P. Schoenberger, Sebastian Joyce, Sebastian Joyce, Young J. Kim, Zeynep Koşaloğlu-Yalçin, Bjoern Peters, Bjoern Peters

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Prediction Model of Late Fetal Growth Restriction with Machine Learning Algorithms by Seon Ui Lee, Sae Kyung Choi, Yun Sung Jo, Jeong Ha Wie, Jae Eun Shin, Yeon Hee Kim, Kicheol Kil, Hyun Sun Ko

“…Two sets of variables from the first trimester until 13 weeks (E1) and the early third trimester until 28 weeks (T1) were used to develop the FGR prediction models using a machine learning algorithm. …”
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Machine Learning Algorithms for Prediction of Survival Curves in Breast Cancer Patients by Roqia Saleem Awad Maabreh, Malik Bader Alazzam, Ahmed S. AlGhamdi

“…The main goal of this study is to find the best machine learning algorithms for predicting the individualised survival of breast cancer patients, as well as the most appropriate treatment, and to propose new numerical variable stratifications. …”
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Postpartum depression risk prediction using explainable machine learning algorithms by Xudong Huang, Lifeng Zhang, Chenyang Zhang, Jing Li, Chenyang Li

“…Feature selection was performed using LASSO regression and the Boruta algorithm. Eight machine learning algorithms were then employed to construct the prediction models. …”
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PREDICTIVE CONTROL SYSTEM SYNTHESIS OF DISTRICT HEATING SYSTEM by Dobrotin S.A., Prokopchuk E.L.

Subjects: “…predictive control…”
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Explainable Predictive Factors for Inter-Agency Partnership Success by Nicola Drago, Goncalo Afonso Braz, Tullio Vardanega

“…Opaque algorithms reached 0.780. Both compare well with the current success rates of 81% for project co-financing partnerships and 75% for knowledge partnerships. …”
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Numerical benchmarking of dual decomposition-based optimization algorithms for distributed model predictive control by Vassilios Yfantis, Achim Wagner, Martin Ruskowski

“…This paper presents a benchmark study of dual decomposition-based distributed optimization algorithms applied to constraint-coupled model predictive control problems. …”
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Predictive modelling of sustainable concrete compressive strength using advanced machine learning algorithms by Tejas Joshi, Pulkit Mathur, Parita Oza, Smita Agrawal

“…The results show that ML algorithms are highly effective in predicting CS, with the random forest algorithm achieving the highest accuracy (R² = 0,95; error = 3,74). …”
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