Improving parking availability prediction in smart cities with IoT and ensemble-based model by Stéphane Cédric Koumetio Tekouabou, El Arbi Abdellaoui Alaoui, Walid Cherif, Hassan Silkan

“…We propose in this paper a new system that integrates the IoT and a predictive model based on ensemble methods to optimize the prediction of the availability of parking spaces in smart parking. …”
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Prediction model for oil seal performance parameters based on PSO-MLP-KAN by Weixing Yan, Mingshuo Shi, Pengbo Xiao, Kui Zhang, Xin Wu

“…To accurately, efficiently, and stably predict the sealing performance of oil seals, this study proposes a prediction method based on a combination of the Kolmogorov–Arnold network (KAN) and multi-layer perceptron (MLP) optimized by particle swarm optimization (PSO) algorithm. …”
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Vehicle Position Updating Strategy Based on Kalman Filter Prediction in VANET Environment by Yuanfu Mo, Dexin Yu, Jun Song, Kun Zheng, Yajuan Guo

“…This paper proposes vehicle position data updating strategy with packet repetition based on Kalman filter predicting. Firstly, we design a position data updating model based on Kalman filter difference predicting equations. …”
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Gaze cluster analysis reveals heterogeneity in attention allocation and predicts learning outcomes by Nathalie John, Sebastian P. Korinth, Mareike Kunter, Franziska Baier-Mosch

“…We show that low ISC values (neuronal and eye tracking data) during multiple meaningful foci do not necessarily indicate a lack of attention. Additionally, GCM predicts participants’ self-reported mental effort and their tested knowledge. …”
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Transforming Cardiac Care: Machine Learning in Heart Condition Prediction Using Phonocardiograms by Sandra D’Souza, Niranjan Reddy S, Saikonda Krishna Tarun, Sohan P, aneesha acharya k

“…In the research, machine learning-based prediction methods work on the audio recordings of heartbeats known as phonocardiograms (PCG) to develop an algorithm that differentiates a normal healthy heart from an abnormal heart based on the heart sounds. …”
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Prediction of Ground-Level Air Pollution Using Artificial Neural Network in Tehran by Afshin Khoshand, Mahshid Shahbazi Sehrani, Hamidreza Kamalan, Siamak Bodaghpour

“…Training the models was on the basis of Multi-Layer Perceptron (MLP) with the Back Propagation (BP) algorithm using MATLAB program. The results indicated appropriate agreement between the observed and predicted concentrations, as the values of the coefficient of multiple determinations (R2) for all models were more than 0.83. …”
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Quantitative phase imaging with temporal kinetics predicts hematopoietic stem cell diversity by Takao Yogo, Yuichiro Iwamoto, Hans Jiro Becker, Takaharu Kimura, Reiko Ishida, Ayano Sugiyama-Finnis, Tomomasa Yokomizo, Toshio Suda, Sadao Ota, Satoshi Yamazaki

“…By analyzing the cellular kinetics of individual HSCs, we discovered previously undetectable diversity that snapshot analysis cannot resolve. The QPI-driven algorithm quantitatively evaluates stemness at the single-cell level and leverages temporal information to significantly improve prediction accuracy. …”
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Stability Prediction of Residual Soil and Rock Slope Using Artificial Neural Network by Mahesh Paliwal, Himkar Goswami, Arunava Ray, Ashutosh Kumar Bharati, Rajesh Rai, Manoj Khandelwal

“…Nonlinear equations have been formulated by coding the artificial neural network algorithm. An android application has also been developed to predict the stability of residual soil and rock slope instantly. …”
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A novel approach for predicting the standardised precipitation index considering climatic factors by Mustafa A. Alawsi, Salah L. Zubaidi, Laith B. Al-badranee

“…Also, it was found that the PSO algorithm precisely predicts the parameters of the proposed model. …”
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Passenger Flow Prediction of Integrated Passenger Terminal Based on K-Means–GRNN by Yifan Tan, Haixu Liu, Yun Pu, Xuemei Wu, Yubo Jiao

“…In this paper, the passenger flow GRNN prediction model is proposed, based on the K-means cluster algorithm, and an improved index named BWPs (Between-Within Proportion-Similarity) is proposed to improve the clustering effect of K-means so that the clustering effect of the new index is verified. …”
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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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End-to-End Collaborative Optimization Method for Microgrid Power Prediction and Optimal Scheduling by ZHANG Li, WANG Bao, JIA Jianxiong, SONG Zhumeng, YE Yutong, YU Yue, LIN Jiaqing, XU Xiaoyuan

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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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