Application of nine point logic control and predictive pid control algorithms in smart grid decision-making by Zhixin Ou

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An enhanced adaptive dynamic metaheuristic optimization algorithm for rainfall prediction depends on long short-term memory by Ahmed M. Elshewey, Amel Ali Alhussan, Doaa Sami Khafaga, Marwa Radwan, El-Sayed M. El-kenawy, Nima Khodadadi

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Predicting Financial Distress through Ranking Working Capital Management Components Using Random Forest Algorithm by Pouya Sadeghi, Daryush Farid, Hamid Reza Mirzaei, Abolfazl Dehghani

“…In the first step, a random forest algorithm was employed to assess the predictive power of the seven working capital management indicators in classifying companies as distressed or healthy. …”
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Novel deep learning algorithm based MRI radiomics for predicting lymph node metastases in rectal cancer by Weiqun Ao, Sikai Wu, Neng Wang, Guoqun Mao, Jian Wang, Jinwen Hu, Xiaoyu Han, Shuitang Deng

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A preference random walk algorithm for link prediction through mutual influence nodes in complex networks by Kamal Berahmand, Elahe Nasiri, Saman Forouzandeh, Yuefeng Li

“…Predicting links in complex networks has been one of the essential topics within the realm of data mining and science discovery over the past few years. …”
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Feature selection using a multi-strategy improved parrot optimization algorithm in software defect prediction by Qi Fei, Guisheng Yin, Zhian Sun

“…This study proposes a novel feature selection and defect prediction classification algorithm based on a multi-strategy enhanced Parrot Optimization (PO) algorithm. …”
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Development of a LASSO machine learning algorithm-based model for postoperative delirium prediction in hepatectomy patients by Yu Zhu, Renrui Liang, Ying Wang, Jian-Jun Yang, Ning Zhou, Cheng-Mao Zhou

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A Water Quality Prediction Model Based on Long Short-Term Memory Networks and Optimization Algorithms by Aihua Yu, Qingjie Xiao

“…In comparison with the previous prediction models such as SVR, LSTM, CNN-LSTM and CNN-GRU, obviously, the prediction effect of AWPSO-LSTMAT is significantly improved by means of modifying and optimizing the original algorithm. …”
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HPRNA: Predicting synergistic drug combinations for angina pectoris based on human pathway relationship network algorithm. by Mengyao Zhou, Mengfan Xu, Xiangling Zhang, Xiaochun Xing, Yang Li, Guanghui Wang, Guiying Yan

“…Compared to lengthy medical drugs experimental screening, mathematical models and algorithms show great potential in synergistic drug combinations prediction. …”
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Promising algorithms to perilous applications: a systematic review of risk stratification tools for predicting healthcare utilisation by Hutan Ashrafian, Jessica Morley, Joe Zhang, Christopher Oddy

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Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms by Anjan Kumar Pradhan, Prasad Gandham, Kanniah Rajasekaran, Niranjan Baisakh

“…However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …”
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Prediction of Soil Liquefaction Using a Multi-Algorithm Technique: Stacking Ensemble Techniques and Bayesian Optimization by Long Tsang, Mahdi Akbari, Pouyan Fakharian

“…A Bayesian optimization method is also used to improve the accuracy of the predictions of soil liquefaction by adjusting the hyperparameters of these four classification algorithms. …”
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Learning unbiased risk prediction based algorithms in healthcare: A case study with primary care patients by Vibhuti Gupta, Julian Broughton, Ange Rukundo, Lubna J. Pinky

“…Additionally, effective strategies are proposed to address gender, race, and age biases, ensuring that risk prediction outcomes are equitable and impartial. Through experiments with various machine learning algorithms leveraging the Fairlearn tool, we have identified biases in the dataset, compared the impact of these biases on the prediction performance, and proposed effective strategies to mitigate these biases. …”
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Artificial Intelligence Algorithms in Asthma Management: A Review of Data Engineering, Predictive Models, and Future Implications by Shayma Alkobaisi, Muhammad Farhan Safdar, Piotr Pałka, Najah Abed Abu Ali

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WCN25-867 BAYESIAN DEEP LEARNING ALGORITHMS TO PREDICT THE RATE OF DETERIORATION IN CHRONIC KIDNEY DISEASE by Laxmi Priya Kasi, Sujoy Kar, Sanjay Maitra, Tameem Ansari, Bharath Potla, Sangita Reddy

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Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea) by Degninou Yehadji, Degninou Yehadji, Geraldine Gray, Carlos Arias Vicente, Petros Isaakidis, Petros Isaakidis, Abdourahimi Diallo, Saa Andre Kamano, Thierno Saidou Diallo

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Quantifying Healthcare Provider Perceptions of a Novel Deep Learning Algorithm to Predict Sepsis: Electronic Survey by Karthik Ramesh, BS, Aaron Boussina, PhD, Supreeth P. Shashikumar, PhD, Atul Malhotra, MD, Christopher A. Longhurst, MD, MS, Christopher S. Josef, MD, Kimberly Quintero, RN, MS, Jake Del Rosso, MD, Shamim Nemati, PhD, Gabriel Wardi, MD, MPH

“…COnformal Multidimensional Prediction Of SEpsis Risk (COMPOSER), a deep learning algorithm, is used at two EDs of a large academic medical center to predict sepsis before clear clinical presentation. …”
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PM2.5 concentration prediction using machine learning algorithms: an approach to virtual monitoring stations by Ahmad Makhdoomi, Maryam Sarkhosh, Somayyeh Ziaei

“…The ML models performed very well in predicting the concentrations of PM2.5 with around 95% of their predictions falling within the factor of the observed concentration. …”
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Comparative Analysis of the Proportional Distribution Method and the Random Forest Algorithm for Predicting Pedestrian Traffic Accident Risk by Hristo V. Uzunov, Plamen G. Matzinski, Vasil H. Uzunov, Silvia V. Dechkova

“…This study presents a comparative analysis of two methodologies for predicting the risk of pedestrian traffic accidents: a methodology based on proportional risk distribution and the Random Forest algorithm. …”
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Use of responsible artificial intelligence to predict health insurance claims in the USA using machine learning algorithms by Ashrafe Alam, Victor R. Prybutok

“…Methods: Six ML algorithms were used to predict health insurance claims, and their performance was evaluated using various metrics. …”
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