Exploration of geo-spatial data and machine learning algorithms for robust wildfire occurrence prediction by Svetlana Illarionova, Dmitrii Shadrin, Fedor Gubanov, Mikhail Shutov, Usman Tasuev, Ksenia Evteeva, Maksim Mironenko, Evgeny Burnaev

“…The goal of this study is to explore the potential of predicting wildfire occurrences using various available environmental parameters - meteorological, geo-spatial, and anthropogenic - and machine learning (ML) algorithms. …”
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Comparative Performance Analysis of Optimization Algorithms in Artificial Neural Networks for Stock Price Prediction by Ekaprana Wijaya, Moch. Arief Soeleman, Pulung Nurtantio Andono

“…This study lays the groundwork for future research by suggesting the exploration of additional optimization algorithms and more complex neural network architectures to further improve prediction accuracy.…”
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Stacking modeling with genetic algorithm-based hyperparameter tuning for uniaxial compressive strength prediction by Tanveer Alam Munshi, Khanum Popi, Labiba Nusrat Jahan, M. Farhad Howladar, Mahamudul Hashan

“…Additionally, a hybrid stacking model that combines these algorithms was developed. Hyperparameter optimization was conducted using grid search and genetic algorithm. …”
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Research on Coal Structure Prediction Method Based on Genetic Algorithm–BP Neural Network by Cunwu Wang, Xiaobo Peng, Gang Han, Yan Zhao, Yihao Zhu, Ming Zhao

“…This paper proposes a coal structure prediction technology based on deep learning, which uses logging data to achieve single-well prediction of the coal structure. …”
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HYBRID NO-LOSS IMAGE COMPRESSION ALGORITHM BASED ON DISCRETE WAVELET TRANSFORMATION WITH ADAPTIVE PREDICTION by A. A. Boriskevich, L. A. Ruiz

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Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization. by Cheng-Wei Wang, Chao-Yang Kuo, Chi-Huang Chen, Yu-Hui Hsieh, Emily Chia-Yu Su

“…In this study, we used machine learning algorithms to construct prediction models for clinical pregnancies in IVF.…”
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Comparison of Deep Neural Networks and Random Forest Algorithms for Multiclass Stunting Prediction in Toddlers by Wulan Sri Lestari, Yuni Marlina Saragih, Caroline

“…This study aims to compare the performance of multiclass stunting prediction models using two machine learning algorithms: Deep Neural Networks and Random Forest. …”
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Prediction of room temperature in Trombe solar wall systems using machine learning algorithms by Seyed Hossein Hashemi, Zahra Besharati, Seyed Abdolrasoul Hashemi, Seyed Ali Hashemi, Aziz Babapoor

“…This study evaluated the performance of four machine learning algorithms—linear regression, k-nearest neighbors, random forest, and decision tree—for predicting the room temperature in a Trombe wall system. …”
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Retracted: Wearable Power Assistant Robot Sensor Signal Prediction Algorithm and Controller Design by Applied Bionics and Biomechanics

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Management and prediction of river flood utilizing optimization approach of artificial intelligence evolutionary algorithms by Rana Muhammad Adnan Ikram, Mo Wang, Hossein Moayedi, Atefeh Ahmadi Dehrashid

“…Four specific algorithms—black hole algorithm (BHA), future search algorithm (FSA), heap-based optimization (HBO), and multiverse optimization (MVO)—were tested for predicting flood occurrences in the Fars region of Iran. …”
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A multi-site study of clinician perspectives in the lifecycle of an algorithmic risk prediction tool by Rita Dexter, Kristin Kostick-Quenet, Jennifer Blumenthal-Barby

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SMOTE algorithm optimization and application in corporate credit risk prediction with diversification strategy consideration by Han Wei

“…Abstract Against the backdrop of dynamic transformations in the financial sector and prominent corporate diversification trends, credit risk prediction becomes significantly more challenging. On one hand, this study focuses on optimizing the Synthetic Minority Over-Sampling Technique (SMOTE) algorithm for corporate credit risk prediction, thereby enhancing financial institutions’ risk management capabilities. …”
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Alternative Possibilities of the Insolvency-Predicting-Algorithms Using. The Case of Benchmarking and Rating in Construction Sector by Krzysztof Borkowski, Waldemar Rogowski

“…In the article it has been proposed that insolvency-predicting-algorithms (pol. SWO) may be successfully used in other areas of the financial analysis. …”
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Predicting the Matching Possibility of Online Dating Youths Using Novel Machine Learning Algorithm by Karthikeyan Palanisamy, Muthumani Muralidharan

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Reversible data hiding algorithm in encrypted images based on prediction error and bitplane coding by Haiyong WANG, Mengning JI

“…With the increasing use of cloud backup methods for storing important files, the demand for privacy protection has also grown.Reversible data hiding in encrypted images (RDHEI) is an important technology in the field of information security that allows embedding secret information in encrypted images while ensuring error-free extraction of the secret information and lossless recovery of the original plaintext image.This technology not only enhances image security but also enables efficient transmission of sensitive information over networks.Its application in cloud environments for user privacy protection has attracted significant attention from researchers.A reversible data hiding method in encrypted images based on prediction error and bitplane coding was proposed to improve the embedding rate of existing RDHEI algorithms.Different encoding methods were employed by the algorithm depending on the distribution of the bitplanes, resulting in the creation of additional space in the image.The image was rearranged to allocate the freed-up space to the lower-order planes.Following this, a random matrix was generated using a key to encrypt the image, ensuring image security.Finally, the information was embedded into the reserved space.The information can be extracted and the image recovered by the receiver using different keys.The proposed algorithm achieves a higher embedding rate compared to five state-of-the-art RDHEI algorithms.The average embedding rates on BOWS-2, BOSSBase, and UCID datasets are 3.769 bit/pixel, 3.874 bit/pixel, and 3.148 bit/pixel respectively, which represent an improvement of 12.5%, 6.9% and 8.6% compared to the best-performing algorithms in the same category.Experimental results demonstrate that the proposed algorithm effectively utilizes the redundancy of images and significantly improves the embedding rate.…”
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Refinement of an Algorithm to Detect and Predict Freezing of Gait in Parkinson Disease Using Wearable Sensors by Allison M. Haussler, Lauren E. Tueth, David S. May, Gammon M. Earhart, Pietro Mazzoni

“…The purpose of this paper is to explore how the existing pFOG algorithm can be refined to improve the detection and prediction of FOG. …”
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Prediction of contact resistance of electrical contact wear using different machine learning algorithms by Zhen-bing Cai, Chun-lin Li, Lei You, Xu-dong Chen, Li-ping He, Zhong-qing Cao, Zhi-nan Zhang

“…Machine learning algorithms can predict the electrical contact performance after wear caused by these factors. …”
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Comparative Analysis of Oversampling and SMOTEENN Techniques in Machine Learning Algorithms for Breast Cancer Prediction by Tri Yulian, Erliyan Redy Susanto

“…This study aims to analyze the performance of Support Vector Machine (SVM) and Random Forest algorithms in predicting breast cancer using oversampling and SMOTEENN preprocessing techniques. …”
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SOC and SOH Prediction of Lithium‐Ion Batteries Based on LSTM–AUKF Joint Algorithm by Yancheng Song, Jiaqi Lu, Huai Zhang, Guangjun Liu

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Stock Closing Price Prediction with Machine Learning Algorithms: PETKM Stock Example In BIST by Abdullah Hulusi Kökçam, Gültekin Çağıl, Şevval Toprak

“…Random Forest Regression (RFR), Long-Short Term Memory (LSTM), and Convolutional Neural Network (CNN) algorithms are used in the prediction model. The success of these methods is compared using performance metrics such as MSE, RMSE, MAE, and R2. …”
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