Prediction of Interest Rate Using Artificial Neural Network and Novel Meta-Heuristic Algorithms by Milad Shahvaroughi Farahani

“…Thus, if you can forecast the interest rate, you can predict the parallel markets too. The main goal of this article, as it is clear from the title, is the prediction of interest rate using ANN and improving the network using some novel heuristic algorithms such as Moth Flame Optimization algorithm (MFO), Chimp Optimization Algorithm (CHOA), Time-varying Correlation Particle Swarm Optimization algorithm (TVAC-PSO), etc. we used 17 variables such as oil price, gold coin price, house price, etc. as input variables. …”
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Prediction of Imbalance Prices Through Gradient Boosting Algorithms: An Application to the Greek Balancing Market by Konstantinos Plakas, Nikos Andriopoulos, Dimitrios Papadaskalopoulos, Alexios Birbas, Efthymios Housos, Ioannis Moraitis

“…In the first stage, the quantiles of system imbalance are predicted employing the Quantile Regression Forest algorithm. …”
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Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms by Hu Caixiong, Zhang Lili, Li Haoran, Zhang Yaowen, Yao Yunsheng

“…A long short-term memory (LSTM) neural network combined with the gray wolf optimizer (GWO) algorithm was introduced to improve prediction accuracy. …”
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Comparative Analysis of Time Series Prediction Algorithms on Multiple Network Function Data of NWDAF by Dasheng Chen, Qi Song, Yinbin Zhang, Ling Li, Zhiming Yang

“…This diverse set of models was carefully chosen to ensure comprehensive coverage of different techniques and algorithms. Through the comparison and analysis of these models, we aim to evaluate their predictive capabilities and identify the most effective approach for network element performance prediction. …”
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Pregnancy probability prediction models based on 5 machine learning algorithms and comparison of their performance by REN Chao, REN Chao, YANG Huan, ZHOU Niya, ZHOU Niya

“…In consideration of difficulty to carry out semen parameters analysis in primary healthcare institutions, feature Set 1 including sperm parameters and feature Set 2 excluding semen parameters were constructed by including or excluding sperm quality simultaneously in the training set and the validation set. Five algorithms, that is, Logistic Regression, Naive Bayes, Random Forest, Gradient Boosting Machine, and Support Vector Machine, were used to construct preconception outcome prediction models, and the parameters of each model were optimized using random search combined with grid search. …”
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Hospital Length-of-Stay Prediction Using Machine Learning Algorithms—A Literature Review by Guilherme Almeida, Fernanda Brito Correia, Ana Rosa Borges, Jorge Bernardino

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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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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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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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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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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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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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Performance Evaluation of Hybrid Machine Learning Algorithms for Online Lending Credit Risk Prediction by Tesfahun Berhane, Tamiru Melese, Abdu Mohammed Seid

“…In this study, we used a hybrid convolutional neural network with logistic regression, a gradient-boosting decision tree, and a k-nearest neighbor to predict the credit risk in a P2P lending club. The lending clubs publicly available P2P loan data was used to train the model. …”
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Uncertainty quantification in tree structure and polynomial regression algorithms toward material indices prediction by Geng-Fu He, Pin Zhang, Zhen-Yu Yin

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Prediction of Gait Neurodegenerative Diseases by Variational Mode Decomposition Using Machine Learning Algorithms by P. Visvanathan, P.M. Durai Raj Vincent

“…Feature extraction for the chosen IMF is done with Shannon entropy technique. Prediction of different neurodegenerative disease such as Parkinsons Disease (PD), Huntingtons Disease (HD), Amyotrophic Lateral Sclerosis (ALS) and healthy subjects are carried out by Multilayer Perceptron (MLP) with the parameters optimized with Genetic Algorithm (GA). …”
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Modeling the prediction of spontaneous rupture and bleeding in hepatocellular carcinoma via machine learning algorithms by Juchao Chen, Zicheng Lei, Zongcai Duan, Zhili Wen

“…Abstract This study aimed to identify the risk factors associated with spontaneous rupture and bleeding in hepatocellular carcinoma, establish a prediction model for spontaneous rupture bleeding via a machine learning algorithm, and validate and evaluate the predictive efficacy of the model. …”
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