Research on Financial Stock Market Prediction Based on the Hidden Quantum Markov Model by Xingyao Song, Wenyu Chen, Junyi Lu

“…Experimental results demonstrate that the proposed quantum model outperforms classical algorithmic models in handling higher complexity, achieving improved efficiency, reduced computation time, and superior predictive performance. …”
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Using Hybrid Artificial Intelligence Approaches to Predict the Fracture Energy of Concrete Beams by Qinghua Xiao, Congming Li, Shengxiang Lei, Xiangyu Han, Qiaofeng Chen, Zemin Qiu, Biao Sun

“…In this study, artificial intelligence approaches were tried to seek a feasible way to solve these prediction issues. Firstly, the ridge regression (RR), the classification and regression tree (CART), and the gradient boosting regression tree (GBRT) were selected to construct the predictive models. …”
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Predictor-Year Subspace Clustering Based Ensemble Prediction of Indian Summer Monsoon by Moumita Saha, Arun Chakraborty, Pabitra Mitra

“…In this article, we propose a joint-clustering of monsoon years and predictors for understanding and predicting the monsoon. This is achieved by subspace clustering algorithm. …”
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Research on Accelerated Degradation Test Design and Life Prediction for Aluminum Electrolytic Capacitor by YANG Tao, WANG Xu, XIAO Jianglin

“…By comparing the degradated capacitance predicted by BP neural network with the measured data of the degradation test and the predicted value of the least squares linear fitting of the experimental data, the results show that the prediction error of capacitance based on the BP neural network is within 3%, while the predicted value of the least squares linear fitting is around 6%, which verifies the superiority of the BP neural network life prediction algorithm, and provides strong support for development and application of the subsequent on-line monitoring technology of board level capacitors.…”
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A Stock Prediction Method Based on Deep Reinforcement Learning and Sentiment Analysis by Sha Du, Hailong Shen

“…In this paper, we use the Q-learning algorithm based on a convolutional neural network and add sentiment analysis to establish a prediction method for Chinese stock investment tasks. …”
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A Short-Term Risk Prediction Method Based on In-Vehicle Perception Data by Xinpeng Yao, Nengchao Lyu, Mengfei Liu

“…The proposed approach provides an effective solution for short-term risk prediction and offers algorithmic support for future ADAS applications.…”
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Data-Driven Neural Differential Equation Model and Stochastic Dynamics for Glioma Prediction by Mohammed Salman, Sanjay Kumar Mohanty

“…Next, we estimate the parameters involved in the mathematical model by fitting them into a set of clinical data and employing a neural ordinary differential equations algorithm. Compared to baseline mechanistic tumor models, our approach reduces prediction root mean squared error to 3.37, demonstrating improved data alignment and forecasting accuracy. …”
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Data Mining Approaches in Predicting Entrepreneurial Intentions Based on Internet Marketing Applications by Milan Krivokuća, Mihalj Bakator, Dragan Ćoćkalo, Marijana Vidas-Bubanja, Vesna Makitan, Luka Djordjević, Borivoj Novaković, Stefan Ugrinov

“…In this paper, the prediction of entrepreneurial intentions in enterprises through Internet marketing predictors is addressed. …”
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High frequency radar error classification and prediction based on K-means methods by Zhaoyi Wang, Zhaoyi Wang, Marie Drevillon, Pierre De Mey-Frémaux, Elisabeth Remy, Nadia Ayoub, Dakui Wang, Bruno Levier

“…In addition, the probabilistic relationship between observed and modeled currents was explored, obtaining the probability of occurrence of modeled current groups when each group of observed currents occurs. Finally, predictions of model and observed current errors were made based on the classification results, and it was found that the predictions based on the classification of all data had the smallest errors, with a 17% improvement over the unclassified control experiment. …”
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Ensembles of Gradient Boosting Recurrent Neural Network for Time Series Data Prediction by Shiqing Sang, Fangfang Qu, Pengcheng Nie

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Predicted distribution of Metaparasitylenchus hypothenemi (Tylenchida: Allantonematidae), parasite of the coffee berry borer by Simota-Ruiz M., Castillo A., Cisneros-Hernández J., Carmona-Castro O.

“…The geographical distribution of this parasite was predicted based on current data, using geographical information systems (GIS), the MaxEnt algorithm, and historical data to improve the prediction accuracy for other Neotropical regions. …”
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Transmission Line Icing Prediction Based on Physically Guided Fast-Slow Transformer by Feng Wang, Ziming Ma

“…Finally, the sample mixing enhancement algorithm is used to reduce the overfitting problem and improve the generalization performance of the prediction model. …”
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Evaluation of Lightning Prediction by an Electrification and Discharge Model in Long-Term Forecasting Experiments by Liangtao Xu, Shuang Chen, Wen Yao

“…Over nearly three rainy seasons of lightning activity in North China, numerical prediction experiments were carried out using the Weather Research and Forecasting model coupled with electrification and discharge schemes (WRF-Electric). …”
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Protein function prediction using GO similarity-based heterogeneous network propagation by Sai Hu, Bihai Zhao

“…Abstract Protein function prediction is a fundamental cornerstone in bioinformatics, providing critical insights into biological processes and disease mechanisms. …”
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MRI Biomarkers for Hand-Motor Outcome Prediction and Therapy Monitoring following Stroke by U. Horn, M. Grothe, M. Lotze

“…Several biomarkers have been identified which enable a considerable prediction of hand-motor outcome after cerebral damage already in the subacute stage after stroke. …”
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Radiomics analysis for prediction and classification of submucosal tumors based on gastrointestinal endoscopic ultrasonography by Hui Zhou, Guoliang Wei, Junke Wu

“…It can be used for personalized and non‐invasive prediction of the type of submucosal tumors, providing physicians with aid for early treatment and management of tumor progression.…”
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Energy Consumption Prediction Model for Electric Buses Considering Actual Quantifiable Features by Guowei Zhu, Miao Shi, Jia He

“…The new prediction model is essentially a machine learning model based on k-means clustering algorithm, which leverages feature extraction and data analysis to make predictions. …”
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Hybrid Weighted Random Forests Method for Prediction & Classification of Online Buying Customers by Umesh Kumar Lilhore, Sarita Simaiya, Devendra Prasad, Deepak Kumar Verma

“…Due to new online shopping trends over the internet, the seller needs to predict the online customer’s choice. This field is a new area of research for machine learning researchers. …”
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Integration of RSM and Machine Learning for Accurate Prediction of Surface Roughness in Laser Processing by Dragan Rodić, Milenko Sekulić, Borislav Savković, Miloš Madić, Milan Trifunović

“…Although the RSM model achieved an R² value of 0.8227, there were still some nonlinear deviations between the predicted and experimental values. To improve the prediction accuracy, a regression tree algorithm was applied to model the residuals of the RSM output. …”
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Aerodynamic Prediction and Design Optimization Using Multi-Fidelity Deep Neural Network by Bingchen Du, Ennan Shen, Jiangpeng Wu, Tongqing Guo, Zhiliang Lu, Di Zhou

“…As the insufficiency in the prediction accuracy of the optimal shapes appears when employing the non-updated MFDNN models, an update strategy is developed by tightly integrating the MFDNN models with the particle swarm optimization algorithm. …”
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