Research and application of prediction model for returning home development population based on machine learning technology by DU Zhao, XIE Guocheng, CHEN Jingxuan, ZHANG Weibin

“…To efficiently serve users and improve their product usage experience, the use of algorithms such as LightGBM and CatBoost was proposed to predict the returning population, thereby providing a basis for services and products, and improving user market retention rates.…”
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Detection and Classification of Emotional State Based on Speech Signal by Hiba Younis, Mrewan Mustafa, Rahma Raad

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Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods by Bin Zhang, Shengsheng Huang, Chenxing Zhou, Jichong Zhu, Tianyou Chen, Sitan Feng, Chengqian Huang, Zequn Wang, Shaofeng Wu, Chong Liu, Xinli Zhan

“…Background Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. …”
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Machine Learning-Based Prediction of Postoperative Pneumonia Among Super-Aged Patients With Hip Fracture by Tang M, Zhang M, Dang Y, Lei M, Zhang D

“…In addition, the eXGBM model demonstrated the optimal prediction performance in terms of accuracy (0.858), precision (0.870), F1 score (0.855), Brier score (0.104), and log loss (0.349). …”
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Short-term PV power prediction based on meteorological similarity days and SSA-BiLSTM by Yikang Li, Wei Huang, Keying Lou, Xizheng Zhang, Qin Wan

“…Therefore, the algorithm in this paper has better accuracy in short-term PV power prediction under different seasons and different weather conditions.…”
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A novel integrated TDLAVOA-XGBoost model for tool wear prediction in lathe and milling operations by Zhongyuan Che, Chong Peng, Chi Wang, Jikun Wang

“…This study proposes an integrated model for tool wear prediction in CNC machining that combines improved algorithms with XGBoost. …”
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Satellite imagery, big data, IoT and deep learning techniques for wheat yield prediction in Morocco by Abdelouafi Boukhris, Antari Jilali, Abderrahmane Sadiq

“…We are the first paper that has combined spatial data and temporal data to predict crop yield based on deep learning algorithms, unlike other works that uses only remote sensing data or temporal data. …”
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Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach by Lingyu Xu, Siqi Jiang, Chenyu Li, Xue Gao, Chen Guan, Tianyang Li, Ningxin Zhang, Shuang Gao, Xinyuan Wang, Yanfei Wang, Lin Che, Yan Xu

“…Predictive models were constructed using eight machine learning algorithms and two ensemble algorithms, with the optimal model identified through AUROC. …”
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Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy. by Xiaohua Zeng, Changzhou Liang, Qian Yang, Fei Wang, Jieping Cai

“…Stock price prediction is a challenging research domain. The long short-term memory neural network (LSTM) widely employed in stock price prediction due to its ability to address long-term dependence and transmission of historical time signals in time series data. …”
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Lipid-Metabolism-Related Gene Signature Predicts Prognosis and Immune Microenvironment Alterations in Endometrial Cancer by Zhangxin Wu, Yufei Nie, Deshui Kong, Lixiang Xue, Tianhui He, Kuaile Zhang, Jie Zhang, Chunliang Shang, Hongyan Guo

“…Tumor immune infiltration patterns were evaluated using single-sample Gene Set Enrichment Analysis (ssGSEA), Estimation of Stromal and Immune Cells in Malignant Tumors using Expression Data (ESTIMATE), and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms. Results: Multivariate analysis indicated that the prognostic model had robust predictive value, with AUCs of 0.701, 0.746, and 0.790 for 1-, 3-, and 5-year overall survival predictions. …”
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Prediction of Psychoacoustic Metrics Using Combination of Wavelet Packet Transform and an Optimized Artificial Neural Network by Mehdi POURSEIEDREZAEI, Ali LOGHMANI, Mehdi KESHMIRI

“…The presented SQE model is a signal processing technique, which can be implemented in current microphones for predicting the sound quality. The proposed method extracts objective psychoacoustic metrics including loudness, sharpness, roughness, and tonality from sound samples, by using a special selection of multi-level nodes of the WPT combined with a trained ANN. …”
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Intelligent predictive risk assessment and management of sarcopenia in chronic disease patients using machine learning and a web-based tool by Ke Rong, Gu li jiang Yi ke ran, Changgui Zhou, Xinglin Yi

“…Abstract Background Individuals with chronic diseases are at higher risk of sarcopenia, and precise prediction is essential for its prevention. This study aims to develop a risk scoring model using longitudinal data to predict the probability of sarcopenia in this population over next 3–5 years, thereby enabling early warning and intervention. …”
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PENGENALAN SUARA MANUSIA MENGGUNAKAN JARINGAN SYARAF TIRUAN DENGAN METODE LINEAR PREDICTIVE CODING DAN FAST FOURIR TRANSFORM by Nirwan Sinuhaji, Benar, Romulo P. Aritonang

“…This study will use an artificial neural network using the linear predictive coding and fast methods as an initial processor. …”
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Prediction of the discharge coefficient of steeply crested inclined weirs using different neural network techniques by Adnan A. Ismael, Abdulnaser A. Ahmed, Raid Rafi Omar Al-Nima, Mohammed Khaire Hussain

“… The main objective of this work is to accurately predict in irrigation and hydraulic systems the discharge coefficient of the used sharp-crested inclined dams. …”
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Deep learning-based multimodal trajectory prediction methods for autonomous driving: state of the art and perspectives by Jun HUANG, Yonglin TIAN, Xingyuan DAI, Xiao WANG, Zhixing PING

“…Although deep learning methods have achieved better results than traditional trajectory prediction algorithms, there are still problems such as information loss, interaction and uncertainty difficulties in modelling, and lack of interpretability of predictions when implementing multimodal high-precision prediction for autonomous vehicles in heterogeneous, highly dynamic and complex changing environments.The newly developed Transformer's long-range modelling capability and parallel computing ability make it a great success not only in the field of natural language processing, but also in solving the above problems when extended to the task of multimodal trajectory prediction for autonomous driving.Based on this, the aim of this paper is to provide a comprehensive summary and review of past deep neural network-based approaches, in particular the Transformer-based approach.The advantages of Transformer over traditional sequential network, graphical neural network and generative model were also analyzed and classified in relation to existing challenges, simultaneously.Transformer models can be better applied to multimodal trajectory prediction tasks, and that such models have better generalisation and interpretability.Finally, the future directions of multimodal trajectory prediction were presented.…”
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Maximizing spatial–temporal coverage in mobile crowd-sensing based on public transports with predictable trajectory by Chaowei Wang, Chensheng Li, Cai Qin, Weidong Wang, Xiuhua Li

“…The results show that our algorithm achieves a near optimal coverage and outperforms existing algorithms.…”
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Machine-Learning Insights from the Framingham Heart Study: Enhancing Cardiovascular Risk Prediction and Monitoring by Emi Yuda, Itaru Kaneko, Daisuke Hirahara

“…This study utilized the Framingham Heart Study dataset to develop and evaluate machine-learning models for predicting mortality risk based on key cardiovascular parameters. …”
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jti and sparta: Time and Space Efficient Packages for Model-Based Prediction in Large Bayesian Networks by Mads Lindskou, Torben Tvedebrink, Poul Svante Eriksen, Søren Højsgaard, Niels Morling

“…In Bayesian networks, the computation of conditional probabilities is fundamental for model-based predictions. This is usually done based on message passing algorithms that utilize conditional independence structures. …”
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Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia by Caitlin A. Finney, David A. Brown, Artur Shvetcov

“…Abstract Existing dementia prediction models using non-neuroimaging clinical measures have been limited in their ability to identify disease. …”
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Machine learning approaches for predicting the structural number of flexible pavements based on subgrade soil properties by Asadullah Ziar

“…Abstract This study presents a machine learning approach to predict the structural number of flexible pavements using subgrade soil properties and environmental conditions. …”
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