Energy-saving algorithm considering idle light-path prediction in SDM-EON by Yu XIONG, Jinyou HE, Baohua WANG, Bin ZHOU

“…To effectively reduce the energy consumption,blocking rate and crosstalk between adjacent cores in a multi-core fiber for space division multiplexing elastic optical network (SDM-EON),an energy-saving algorithm considering idle light-path prediction was proposed.The extreme learning machine model was used to predict the traffic volume of each light-path in the network.Thus the idle light-path set and the maintenance time threshold of each idle light-path were obtained.Then,the actual maintenance time of the idle light-path was perceived by the prediction algorithm.Finally,in the light-paths where the actual maintenance time do not exceed the minimum maintenance time threshold and the inter-core crosstalk are lower than the crosstalk threshold,the idle light-path with the least loading energy consumption was selected to carry the new traffic.The simulation results show that compared with the traditional energy-saving algorithm,when the SDM-EON crosstalk limitation is satisfied,the proposed algorithm can lead better energy-saving while maintain the blocking rate at levels compatible.…”
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Dynamic Multiobjective Optimization Algorithm Based on Average Distance Linear Prediction Model by Zhiyong Li, Hengyong Chen, Zhaoxin Xie, Chao Chen, Ahmed Sallam

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Retracted: Exercise Behavior Prediction and Injury Assessment Based on Swarm Intelligence Algorithm by Discrete Dynamics in Nature and Society

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Predicting and controlling the reactivity of immune cell populations against cancer by Kfir Oved, Eran Eden, Martin Akerman, Roy Noy, Ron Wolchinsky, Orit Izhaki, Ester Schallmach, Adva Kubi, Naama Zabari, Jacob Schachter, Uri Alon, Yael Mandel‐Gutfreund, Michal J Besser, Yoram Reiter

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Prediction of super-large diameter shield attitude based on LSTM-Transformer by Linfabao Dai, Wenming Chen, Mingqing Xiao, Wenhao Sun, Zhengzheng Wang

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Enhancing the Prediction of Dam Deformations: A Novel Data-Driven Approach by Jonas Ziemer, Gideon Stein, Carolin Wicker, Jannik Jänichen, Daniel Klöpper, Katja Last, Joachim Denzler, Christiane Schmullius, Maha Shadaydeh, Clémence Dubois

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Application of machine learning in depression risk prediction for connective tissue diseases by Leilei Yang, Yuzhan Jin, Wei Lu, Xiaoqin Wang, Yuqing Yan, Yulan Tong, Dinglei Su, Kaizong Huang, Jianjun Zou

“…Addressing the limitations of traditional assessment tools, six ML models were constructed using univariate analysis and the LASSO algorithm, with the categorical boosting (Catboost) model emerging as the best performer, demonstrating strong predictive ability across different depression severity levels (none_F1 = 0.879, mild_F1 = 0.627, moderate and severe_F1 = 0.588). …”
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Advancing sustainable mobility: Dynamic predictive modeling of charging cycles in electric vehicles using machine learning techniques and predictive application development by Biplov Paneru, Durga Prasad Mainali, Bishwash Paneru, Sanjog Chhetri Sapkota

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Construction and validation of a predictive model for suicidal ideation in non-psychiatric elderly inpatients by Shuyun Xiong, Dongxu Si, Meizhu Ding, Cuiying Tang, Jinling Zhu, Danni Li, Ying Lei, Lexian Huang, Xiaohua Chen, Jicai Chen

Subjects: “…Machine learning algorithms…”
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Machine learning algorithms to predict epidural-related maternal fever: a retrospective study by Xiaohui Guo, Xiaohui Guo, Xiaohui Guo, Haixia Zhang, Haixia Zhang, Haixia Zhang, Hongliang Mei, Hongliang Mei, Hongliang Mei

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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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Production Capacity Prediction for Tight Gas Reservoirs Based on ADASVRLGBM by MENG Sihai, ZHANG Zhansong, GUO Jianhong, HAN Zihao, ZENG Weijie, LYU Hengyang

Subjects: “…production capacity prediction…”
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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…This study investigates and predicts the likelihood of operational risk occurrence in the banking industry using machine learning algorithms. …”
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Predicting Restroom Dirtiness Based on Water Droplet Volume Using the LightGBM Algorithm by Sumio Kurose, Hironori Moriwaki, Tadao Matsunaga, Sang-Seok Lee

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Software Defect Prediction Based On Deep Learning Algorithms : A Systematic Literature Review by Akhlas Hasan, Shayma Mohi-Aldeen

Subjects: “…prediction…”
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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 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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