How low can you go: evaluating electrode reduction methods for EEG-based speech imagery BCIs by Maurice Rekrut, Maurice Rekrut, Johannes Ihl, Tobias Jungbluth, Antonio Krüger, Antonio Krüger

“…In this study, we evaluated several electrode reduction algorithms in combination with various feature extraction and classification methods across three distinct EEG-based speech imagery datasets to identify the optimal number and position of electrodes for SI-BCIs. …”
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Bio inspired feature selection and graph learning for sepsis risk stratification by D. Siri, Raviteja Kocherla, Sudharshan Tumkunta, Pamula Udayaraju, Krishna Chaitanya Gogineni, Gowtham Mamidisetti, Nanditha Boddu

Subjects: “…Wolverine optimization algorithm…”
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Assessment of Information predictability of stochastic processes by A. V. Ausiannikau

“…The necessary theoretical information for parameter estimation algorithms informational predictability of stochastic processes. …”
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Predicting dementia in people with Parkinson’s disease by Mohamed Aborageh, Tom Hähnel, Patricia Martins Conde, Jochen Klucken, Holger Fröhlich

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Review of pedestrian trajectory prediction methods by Linhui LI, Bin ZHOU, Weiwei REN, Jing LIAN

“…With the breakthrough of deep learning technology and the proposal of large data sets, the accuracy of pedestrian trajectory prediction has become one of the research hotspots in the field of artificial intelligence.The technical classification and research status of pedestrian trajectory prediction were mainly reviewed.According to the different modeling methods, the existing methods were divided into shallow learning and deep learning based trajectory prediction algorithms, the advantages and disadvantages of representative algorithms in each type of method were analyzed and introduced.Then, the current mainstream public data sets were summarized, and the performance of mainstream trajectory prediction methods based on the data sets was compared.Finally, the challenges faced by the trajectory prediction technology and the development direction of future work were prospected.…”
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ON DESIGN OF PREDICTIVE MODEL FOR HEART DISEASE by Soumyendu Bhattacharjee, Susmita Das, Sangita Roy, Arpita Santra, Anasuya Sarkar, Moumita Pa, Biswarup Neogi

“…We used several artificial intelligence (AI) techniques, such as the critical backslide and KNN, to predict and group patients with cardiovascular sickness. …”
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Genomic selection in pig breeding: comparative analysis of machine learning algorithms by Ruilin Su, Jingbo Lv, Yahui Xue, Sheng Jiang, Lei Zhou, Li Jiang, Junyan Tan, Zhencai Shen, Ping Zhong, Jianfeng Liu

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Diabetes Mellitus Disease Prediction and Type Classification Involving Predictive Modeling Using Machine Learning Techniques and Classifiers by B. Shamreen Ahamed, Meenakshi S. Arya, S. K. B. Sangeetha, Nancy V. Auxilia Osvin

“…Various Machine-Learning (ML) algorithms are being used in order to predict and detect the disease to avoid further complications of health. …”
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Research on Short-term Load Forecasting Algorithm Based on VMD and TCN by WANG Qing, CHEN Zhiru, LI Guimin, JING Zhen, ZHANG Zhi, WANG Pingxin, CUI Qi

“…The simulation results show that the forecasting effect of VMD-TCN is the best, MAPE and RMSE are 1. 65% and 15. 05kW, respectively, indicating that the algorithm can be used to achieve accurate short-term forecasting of the station load, so as to facilitate the dispatch management, optimization operation, energy saving and emission reduction of the station. …”
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Predicting and Preventing Crime: A Crime Prediction Model Using San Francisco Crime Data by Classification Techniques by Muzammil Khan, Azmat Ali, Yasser Alharbi

“…The study proposes a crime prediction model by analyzing and comparing three known prediction classification algorithms: Naive Bayes, Random Forest, and Gradient Boosting Decision Tree. …”
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An Optimized Power Load Forecasting Algorithm Based on VMD‐SMA‐LSTM by Wei Liu, Fan Hua, Yongping Cui, Yangchao Xu, Han Liu

“…Case studies demonstrate that the proposed algorithm outperforms other power load forecasting methods in prediction accuracy.…”
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An Integrated Framework for Cryptocurrency Price Forecasting and Anomaly Detection Using Machine Learning by Hani Alnami, Muhammad Mohzary, Basem Assiri, Hussein Zangoti

“…The accurate prediction of cryptocurrency prices is crucial due to the volatility and complexity of digital asset markets, which pose significant challenges to traders, investors, and researchers. …”
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A lightweight detection algorithm of PCB surface defects based on YOLO. by Shiwei Yu, Feng Pan, Xiaoqiang Zhang, Linhua Zhou, Liang Zhang, Jikui Wang

“…The results indicated that when comparing our model with the original model, there was a 47.2% reduction in the model's parameter count, a 48.5% reduction in GFLOPs, a 42.4% reduction in Weight, a 2.0% reduction in FPS, and a 2.4% rise in mAP. …”
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Mapping and interpretability of aftershock hazards using hybrid machine learning algorithms by Bo Liu, Haijia Wen, Mingrui Di, Junhao Huang, Mingyong Liao, Jingyuan Yu, Yutao Xiang

“…By employing the stacking algorithm to optimize and combine XGBoost and LightGBM models, the proposed model significantly improves the prediction performance. …”
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Active noise control of refrigerator based on cascaded notch feedback algorithm by Chaoping Gui

“…The experimental test platform is set up to carry out the actual refrigerator noise reduction experiments under different conditions. The algorithm has the effect of noise reduction under different robust algorithms.…”
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The malaria secretome: from algorithms to essential function in blood stage infection. by Christiaan van Ooij, Pamela Tamez, Souvik Bhattacharjee, N Luisa Hiller, Travis Harrison, Konstantinos Liolios, Taco Kooij, Jai Ramesar, Bharath Balu, John Adams, Andrew P Waters, Chris J Janse, Kasturi Haldar

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Optimization based machine learning algorithms for software reliability growth models by Myeongguen Shin, Juwon Jung, Jihyun Lee, Insoo Ryu, Sanggun Park

“…However, many previous studies have relied on single optimization methods or deep learning approaches, which are prone to local optima and extrapolation issues, reducing prediction accuracy. To fill this gap, current study employs a broader range of optimization algorithms based on the Least Squares Method (LSM) and Maximum Likelihood Estimation (MLE) to approximate global optima. …”
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Inversion algorithm of black carbon mixing state based on machine learning by Z. Tian, Z. Tian, J. Wang, J. Wang, J. Wang, J. Wang, C. Liu, C. Liu, J. Xing, J. Wang, J. Wang, Z. Zhang, Z. Zhang, Y. Jin, Y. Jin, S. Shen, S. Shen, B. Wang, B. Wang, W. Nie, W. Nie, X. Huang, X. Huang, A. Ding, A. Ding

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Kinematics Analysis of Grasping Manipulator based on ART-RBF Learning Algorithm by Kai Wang, XiaoJin Wan

“…The results show that the soft competition algorithm can improve the prediction accuracy to a certain extent. …”
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Research and application of adaptive algorithm for 5G voice quality evaluation by Yuxiang ZHAO, Yaxin JI, Li YU, Tianyi ZHOU, Hang ZHOU

“…MOS (mean opinion score) is usually used to evaluate voice quality in the industry.It can objectively and fairly reflect the user’s voice service perception.It is difficult and costly to obtain data by road test, so a trained supervised learning model is usually used to predict the MOS score.However, the operator voice data has the characteristics of low percentage of MOS low score data and time sequence change, which affects the accuracy and generalization of the model prediction.Based on the study of existing data acquisition systems and machine learning algorithms of operators, an adaptive algorithm for MOS evaluation of 5G speech quality was proposed.Firstly, POLQA algorithm test equipment based on full parameter evaluation obtained training data to ensure the accuracy of training samples.Secondly, by means of data enhancement, the difficulty of acquiring poor quality samples was solved.Finally, based on the adaptive algorithm selection, the optimal MOS prediction model could be selected periodically and dynamically according to the timing changes of data features, so as to achieve large-scale and intelligent evaluation of 5G voice quality.…”
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