Application of Machine Learning Algorithms to Predict Gas Sorption Capacity in Heterogeneous Porous Material by Tasbiha Ibad, Syed Muhammad Ibad, Haylay Tsegab, Rabeea Jaffari

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Application of machine learning algorithm incorporating dietary intake in prediction of gestational diabetes mellitus by Tianze Ding, Peijie Liu, Jie Jia, Hui Wu, Jie Zhu, Kefeng Yang

“…Conclusion: XGBoost and LightGBM algorithms outperform logistic regression in predicting GDM among Chinese pregnant women. …”
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An algorithm for variational inclusion problems including quasi-nonexpansive mappings with applications in osteoporosis prediction by Raweerote Suparatulatorn, Wongthawat Liawrungrueang, Thanasak Mouktonglang, Watcharaporn Cholamjiak

“…Furthermore, we applied this algorithm for data classification to osteoporosis risk prediction, utilizing an extreme learning machine. …”
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Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss by Rui Zhang, Yuanbing Guo, Xiaonan Zhai, Juan Wang, Xiaoyan Hao, Liu Yang, Lei Zhou, Jiawei Gao, Jiayun Liu

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Research on Spatial Prediction of Precipitation in Guangdong Province Based on Gaussian Process Regression Algorithm by ZENG Xiongzhi, YU Chao

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A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data by Zakaria Mokadem, Mohamed Djerioui, Bilal Attallah, Youcef Brik

“…This study investigates the predictive performance of nine supervised machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, Gaussian Naïve Bayes, Multi-Layer Perceptron, eXtreme Gradient Boost, and Gradient Boosting—using neuropsychological assessment data. …”
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Smart prediction-planning algorithm for connected and autonomous vehicle based on social value orientation by Donglei Rong, Yuefeng Wu, Wenjun Du, Chengcheng Yang, Sheng Jin, Min Xu, Fujian Wang

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Prediction of rolling bearing performance degradation based on whale optimization algorithm and backpropagation model by Jingyue Wang, Yuntong Han, Haotian Wang, Jianming Ding, Cai Yi

“…The dissertation proposes a prediction model that enhances the BP (Backpropagation) neural network using the WOA (Whale Optimization Algorithm) to address the issue of local convergence during prediction. …”
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Application of machine learning algorithm for prediction of abortion among reproductive age women in Ethiopia by Angwach Abrham Asnake, Alemayehu Kasu Gebrehana, Hiwot Altaye Asebe, Beminate Lemma Seifu, Bezawit Melak Fente, Meklit Melaku Bezie, Mamaru Melkam, Sintayehu Simie Tsega, Yohannes Mekuria Negussie, Zufan Alamrie Asmare

“…Therefore, this study employed machine learning algorithms to predict abortion in Ethiopia and identify its predictors using nationally representative data. …”
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Constructing and Predicting Solutions for Different Families of Partial Differential Equations: A Reliable Algorithm by Mubashir Qayyum, Amna Khan

“…This algorithm estimates convergent series with an easy-to-use way of finding solution components through symbolic computation. …”
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Construction and Demolition Waste Generation Prediction by Using Artificial Neural Networks and Metaheuristic Algorithms by Ruba Awad, Cenk Budayan, Asli Pelin Gurgun

“…To address this gap, this study aims to predict C&DW quantities in construction projects more accurately by integrating the gray wolf optimization algorithm (GWO) and the Archimedes optimization algorithm (AOA) into an artificial neural network (ANN). …”
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Adaptive random early detection algorithm based on network traffic level grade prediction by Debin WEI, Chengsheng PAN, Li YANG, Zuoren YAN

“…In view of the problem that the calculation of average queue length and maximum packet drop probability in random early detection algorithm and its variants reflect the changes of network traffic slowly, an adaptive random early detection algorithm based on network traffic level grade prediction was proposed.Based on the statistical characteristics of self-similar network traffic, the transition probability table of network traffic level grade was established, and a grade prediction method of self-similar network traffic level with low complexity and high accuracy was proposed.Furthermore, the prediction results were applied to calculate the average queue length in equal interval and adjust the maximum packet drop probability.Under the condition of fixed and variable bottleneck link capacity, it is found that regardless of the degree of self-similarity of network traffic, the proposed algorithm can improve the throughput and packet loss rate, especially when the Hurst parameter is large and the traffic is light.…”
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Green Ground: Construction and Demolition Waste Prediction Using a Deep Learning Algorithm by Wadha N. Alsheddi, Shahad E. Aljayan, Asma Z. Alshehri, Manar F. Alenzi, Norah M. Alnaim, Maryam M. Alshammari, Nouf K. AL-Saleem, Abdulaziz I. Almulhim

“…Different types of waste lack an efficient and accurate method for classification, especially in cases that require the rapid processing of materials. A deep learning prediction model based on a convolutional neural network algorithm was developed to classify and predict the types of construction and demolition waste (CDW). …”
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Traffic Prediction of Space-Integrated-Ground Information Network Based on Improved LSTM Algorithm by Chengsheng PAN, Yufu WANG, Li YANG

“…The space-integrated-ground information network is easy to interrupt and the traffi c fl uctuation is not stable due to the problems of high traffi c burst and topological time-varying, which makes the traffi c prediction diffi cult much higher than the ground.In order to solve this problem, an improved LSTM algorithm was put forward.Firstly, the traffi c autocorrelation was judged by analyzd the infl uence of the lag variable of traffi c sequence on the predicted value; Secondly, the noise and breakpoint of the training set were eliminated by replacing the interruption with the predicted value; Finally, Dropout algorithm was used to reduce the impact of noise and neural network over fi tting, and accurately predict the traffi c data of the integrated intelligent network.The simulation results showed that in OPNET simulation environment, compared with other algorithms, the accuracy of this algorithm was improved by 59.21%, and the training speed of the algorithm was improved by 11.11%, which could provide eff ective data support for the overall scheduling of the integrated intelligent network.…”
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Maize Kernel Broken Rate Prediction Using Machine Vision and Machine Learning Algorithms by Chenlong Fan, Wenjing Wang, Tao Cui, Ying Liu, Mengmeng Qiao

“…Rapid online detection of broken rate can effectively guide maize harvest with minimal damage to prevent kernel fungal damage. The broken rate prediction model based on machine vision and machine learning algorithms is proposed in this manuscript. …”
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Predictive PID Control for Automated Guided Vehicles Using Genetic Algorithm and Machine Learning by Kinza Nazir, Yong-Woon Kim, Yung-Cheol Byun

“…This study introduces a hybrid framework combining traditional Proportional-Integral-Derivative (PID) control with advanced machine learning to optimize AGV performance. A genetic algorithm (GA) was employed to generate ground truth PID parameters for diverse track configurations, ensuring superior path-tracking accuracy. …”
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Predicting the availability of power line communication nodes using semi-supervised learning algorithms by Kareem Moussa, Khaled Mostafa Elsayed, M. Saeed Darweesh, Abdelmoniem Elbaz, Ahmed Soltan

“…Machine Learning has solved this by predicting a node having optimum readings. The more the machine learning models learn, the more accurate they become, as the model becomes always updated with the node’s continuous availability status, so self-training algorithms have been used. …”
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Predictive modeling of electrochromic performance in ammonium metatungstate solutions using machine learning algorithms by Bocheng Jiang, Honglong Ning, Muyun Li, Rihui Yao, Chenxiao Guo, Yucheng Huang, Zijie Guo, Dongxiang Luo, Dong Yuan, Junbiao Peng

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Application of the Different Machine Learning Algorithms to Predict Dry Matter Intake in Feedlot Cattle by Hayati Köknaroğlu, Özgür Koşkan, Malik Ergin

“…The main objective of this study was to compare different machine learning algorithms to predict daily dry matter intake (DMI) in feedlot cattle. …”
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Predictive modelling of aquaculture water quality using IoT and advanced machine learning algorithms by Md. Abdullah Al Mamun Hridoy, Chiara Bordin, Andleeb Masood, Khalid Masood

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