Joint reduction of peak-to-average power ratio and out-of-band power based on subcarrier weighting in OFDM systems by Qing LI, Han-ying HU

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Development of a high-performing, cost-effective and inclusive Afrocentric predictive model for stroke: a meta-analysis approach  by M Nweke, P Oyirinnaya, P Nwoha, SB Mitha, N Mshunqane, N Govender, M Ukwuoma, SC Ibeneme

“…Abstract Background Predicting stroke risk is critical for preventive interventions. …”
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Intelligent System for Reducing Waste and Enhancing Efficiency in Copper Production Using Machine Learning by Bagdaulet Kenzhaliyev, Timur Imankulov, Aksultan Mukhanbet, Sergey Kvyatkovskiy, Maral Dyussebekova, Nurdaulet Tasmurzayev

“…This study addresses this challenge by leveraging advanced machine learning (ML) techniques to enhance the efficiency of pyrometallurgical operations such as slag optimization, composition prediction, and waste minimization. Using a combination of real-world and synthetic data, we developed models capable of both forward prediction, estimating slag and matte compositions from ore characteristics, and backward prediction, inferring ore compositions from output characteristics. …”
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Machine learning models in enhancing prediction of health-related indices among older adults: A scoping review by Raoof Nopour

“…Health status is indicated by a relatively good predictive performance based on ANNs. Conclusion: This review demonstrated that various machine learning algorithms and data types significantly impact predictive ability and preventive strategies in clinical environments.…”
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A student academic performance prediction model based on the interval belief rule base by Wenkai Zhou, Yunsong Li, Jiaxing Li, Tianhao Zhang, Xiping Duan, Ning Ma, Yuhe Wang

“…Abstract Student performance prediction (SPP) constitutes one of the pivotal tasks in educational data analysis. …”
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Link stability metric based on mobility prediction model in mobile ad hoc networks by ZHANG Hui1, DONG Yu-ning1

“…A mobility prediction model based stable link selection algorithm was proposed in which stable neighbor met-ric and local movement metric were defined.Mobility prediction model was applied to predict stability probabilities be-tween each local node and its neighbors by using those two metrics and LZ78 algorithm so as to find the most stable neighbor of each local node and most stable route in a route discovery.The simulation results show that the algorithm outperforms the histogram algorithm and the lowest ID algorithm in selecting stable links.…”
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A Novel Evolutionary Deep Learning Approach for PM_2.5 Prediction Using Remote Sensing and Spatial–Temporal Data: A Case Study of Tehran by Mehrdad Kaveh, Mohammad Saadi Mesgari, Masoud Kaveh

“…Concurrently, long short-term memory (LSTM) models have shown considerable promise in enhancing air quality predictions, often outperforming other prediction techniques. …”
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Optimising the Selection of Input Variables to Increase the Predicting Accuracy of Shear Strength for Deep Beams by Mohammed Majeed Hameed, Faidhalrahman Khaleel, Mohamed Khalid AlOmar, Siti Fatin Mohd Razali, Mohammed Abdulhakim AlSaadi

“…The feature-section algorithm based on the combination of genetic algorithm and information theory (GAITH) was used to select the most important input combinations and introduce them into the prediction models. …”
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Short-term traffic flow prediction based on adaptive rank dynamic tensor analysis by Lingchao HE, Dong LIN, Xinxin FENG

“…Short-term traffic flow prediction in intelligent transportation system can provide data support in areas such as route planning,traffic management,public safety and so on.In order to improve the prediction accuracy with missing and abnormal data,a short-term traffic flow prediction method based on the adaptive rank dynamic tensor analysis was proposed.Firstly,a four dimensional tensor consisted of week,day,time and space was constructed,which could excavate the multimodal correlation of traffic flow data.Secondly,tensor flow data with dynamic structure was formed by using sliding window model.The principal component analysis (PCA) algorithm was extended to an offline tensor analysis algorithm that could accept tensor input.Then the adaptive rank and the forgetting factor were introduced to generate an adaptive rank dynamic tensor analysis algorithm.Finally,the tensor stream data was inputted into the adaptive rank dynamic tensor analysis algorithm to realize the short-term traffic flow prediction.The experimental results show that a good prediction can be achieved even with data missing.…”
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Fault Prediction of Bearing Based on Dual Dimensional Perception and Composite Gated Recurrent Network by Wang Weiping, Xue Shibei

“…Finally, by using actual bearing degradation data, the proposed algorithm’s ability to perceive and identify early degradation states of bearings was verified, demonstrating the effectiveness and superiority of the proposed method for bearing fault prediction research.…”
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Short-Term Wind Power Prediction Based on MVMD-AVOA-CNN-LSTM-AM by Xiqing Zang, Zehua Wang, Shixu Zhang, Mingsong Bai

“…Thereafter, the African vultures algorithm is used to optimize the hyperparameters of the CNN-LSTM algorithm, and the AM is added to increase the prediction effect, and the decomposed subsequences are predicted separately, and the predicted values of each subsequence are superimposed to obtain the final prediction value. …”
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Feasibility of Tunnel TEM Advanced Prediction: A 3D Forward Modeling Study by Jiannan Fu, Xiaodong Yang, Guanqun Zhou, Xueliang Jin, Fuqin Zhai, Fanbin Meng, Yafei Wang

“…The transient electromagnetic (TEM) method has long been applied in tunnel advanced prediction. However, it remains questionable to what extent a geologic anomaly body will influence the induced electromagnetic response in front of the heading face. …”
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Prediction acrylamide contents in fried dough twist based on the application of artificial neural network by Xinyu Wu, Haiyang Yan, Yue Cao, Yuan Yuan

“…Detection measures like LC-MS, HPLC are time-consuming and costly, which inspired us to use back propagation-artificial neural networks (BP-ANN) based on a genetic algorithm to establish an acrylamide prediction model in fried dough twist. …”
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A Location Predicting Method for Indoor Mobile Target Localization in Wireless Sensor Networks by Peng Gao, Weiren Shi, Wei Zhou, Hongbing Li, Xiaogang Wang

“…Then, one certain localization result can be obtained using MLE algorithm. After that, based on the path-planning model and some previous localization results, the most likely position of the target in the next time interval can be predicted with the proposed predicting approach. …”
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Optimizing Assembly Error Reduction in Wind Turbine Gearboxes Using Parallel Assembly Sequence Planning and Hybrid Particle Swarm-Bacteria Foraging Optimization Algorithm by Sydney Mutale, Yong Wang, De Tian

“…Specifically, the PSBFO algorithm reduced errors from an initial value of 50 to a final value of 5 across 20 iterations, with components such as the low-speed shaft and planetary gear system showing the most substantial reductions. …”
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Prediction model for psychological disorders in ankylosing spondylitis patients based on multi-label classification by Kun Yang, Yifan Gong, Xiaohan Xu, Tiantian Sun, Xinning Qu, Xiaxiu He, Hongxiao Liu

“…The Boruta algorithm was applied to select predictive factors, and a multi-label classification learning algorithm based on association rules (AR) was developed. …”
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Smart IoT-driven precision agriculture: Land mapping, crop prediction, and irrigation system. by Gourab Saha, Fariha Shahrin, Farhan Hasin Khan, Mashook Mohammad Meshkat, Akm Abdul Malek Azad

“…The ML model-based crop prediction showed 97.35% accuracy utilizing random forest algorithm. …”
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A prediction model for soil heavy metal content based on improved tensor completion by Zhangang Wang, Wenjie Li, Tianhe Yun, Jiaxiang Qi

“…This paper presents a novel method for predicting soil heavy metal content using an advanced tensor completion algorithm. …”
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Classification Prediction of Rockburst in Railway Tunnel Based on Hybrid PSO-BP Neural Network by Min Zhang

“…In order to accurately predict the rockburst intensity level of the railway tunnel, the rock stress coefficient σθ/σc, rock brittleness coefficient σc/σt, and elastic energy index Wet are used as evaluation indexes of rockburst intensity, and a BP neural network rockburst prediction model based on hybrid particle swarm optimization algorithm is proposed. …”
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Research on Mode Shift Control of Multimode Hybrid Systems Based on Hybrid Model Prediction by Xinxin Zhao, Jiadian Liu, Bing Li

“…In order to improve the performance of the hybrid mining dump truck in the mode switching process as much as possible, this paper takes power-split hybrid special transmission as the research object and proposes a hybrid model predictive control (HMPC) strategy. However, the simulation time of the HMPC algorithm is about 27% less than that of the MPC algorithm. …”
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