Effective Prediction on Time Series Data Using Deep Learning: An Incisive Review by Rupa Rajakumari, Ujwal Ambadas Lanjewar

“…Concurrently, DL (Deep Learning) algorithms are capable of offering promising solution to predict time-series due to their advantages in automatic temporal learning. …”
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Modification of Multilayer Perceptron Using Detection Rate Model for Prediction of Nominal Exchange Rate by Al-Khowarizmi Al-Khowarizmi, Romi Fadillah Rahmat, Michael J Watts, Akrim Akrim, Arif Ridho Lubis, Muhammad Basri

“…The results obtained with absolute error achieve an accuracy of 99.73% while the accuracy based on the detection rate achieves an accuracy of 99.49%. this can be seen in the case of the prediction of (Indonesian Rupiah) IDR exchange rate against United State Dollar (USD) with the MLP algorithm by testing using MAPE to achieve sensitivity with absolute error. …”
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Risk prediction method for power Internet of Things operation based on ensemble learning by Chao Hong, Xiaoyun Kuang, Yiwei Yang, Yixin Jiang, Yunan Zhang

“…It has high prediction accuracy and fast speed than other algorithms. …”
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Overview of Applications and Research Directions of Deep Learning Methods for Wind Power Prediction by LIU Tan, LIU Na, LIU Guiping, LIU Kunjie, LIU Min, ZHUANG Xufei, ZHANG Zhonghao

“…In addition, research progress in deep learning-based wind power prediction is outlined in data processing, parameter optimization algorithms, and optimization methods for wind power prediction models. …”
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Leveraging machine learning to predict residential location choice: A comparative analysis by Vahid Noferesti, Hamid Mirzahossein

“…By applying this method and different machine learning models, the study provides a detailed comparison of their performance in predicting residential choices. A comparative analysis of various machine learning algorithms reveals that XGBoost and gradient boosting models significantly outperform traditional methods, achieving a 42 % accuracy rate in predicting residential location choices on the 33 % validation data of household travel survey data from MWCOG. …”
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Contact prediction is hardest for the most informative contacts, but improves with the incorporation of contact potentials. by Jack Holland, Qinxin Pan, Gevorg Grigoryan

“…Thus, the remaining limitations of contact prediction algorithms are most noticeable in conjunction with geometrically restrictive contacts-precisely those that contribute more information in structure prediction. …”
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Vessel Trajectory Prediction Method Based on the Time Series Data Fusion Model by Xinyun WU, Jiafei CHEN, Caiquan XIONG, Donghua LIU, Xiang WAN, Zexi CHEN

“…Experiments are conducted on real public datasets, and the results show that the TCC model proposed in this paper outperforms the existing baseline algorithms with high accuracy and robustness in vessel trajectory prediction.…”
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Software Defect Prediction Using Extreme Gradient Boosting(XGBoost) with Tune Hyperparameter by Tariq AL-Hadidi, Safwan Omar Hasoon

“…This study aims to classify software defect prediction using machine learning techniques, specifically classification techniques. …”
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Using a Machine Learning Approach to Predict the Thailand Underground Train’s Passenger by Wuttipong Kusonkhum, Korb Srinavin, Narong Leungbootnak, Tanayut Chaitongrat

“…Analysis approaches included the analysis phase, classification, and regression algorithm. However, the regression algorithm’s accuracy is poor and therefore cannot be used. …”
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Prediction of Enthalpy of Mixing of Binary Alloys Based on Machine Learning and CALPHAD Assessments by Shuangying Huang, Guangyu Wang, Zhanmin Cao

“…The enthalpy of mixing, a critical thermodynamic property in the liquid phase reflecting element interaction strength and pivotal for studying phase equilibria, can now be predicted efficiently using machine learning. This study proposes a model combining machine learning with the Calculation of Phase Diagram (CALPHAD) to predict the enthalpy of mixing. …”
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Prediction of tea leaf characteristics using spectral data and machine learning techniques by Sum Tateh, Suyog Balasaheb Khose, Damodhara Rao Mailapalli, Chandranath Chatterjee, Narendra Singh Raghuwanshi

“…The remote sensing spectral reflectance has the potential to detect variations in leaf characteristics. This study predicts tea leaf characteristics and detects leaf infestation using spectral reflectance data and machine learning (ML) algorithms. …”
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Video Analysis and Frame Prediction Based on Improved Object Detection and ConvGRU by Xijuan Wang, Ru Chen

“…To further improve video processing technology and increase video fluency, this article innovatively uses improved object detection algorithms for video analysis. The study also improves neural networks to construct video frame prediction models with the help of motion perception, synthetic streaming, and other technologies. …”
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Comparative Analysis of Machine Learning Techniques for Prediction of the Compressive Strength of Field Concrete by Omobolaji Opafola, Abisola Olayiwola, Ositola Osifeko, Adekunle David, Ajibola Oyedejı

“…The GB model trained and evaluated was deployed to a web application using Streamlit for real-time prediction of the concrete compressive strength. The results of this research offer a precise and practical method for judging the quality of concrete constructions.…”
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Genetic fuzzy system for prediction of respiratory rate of chicks subject to thermal challenges by Patrícia F. P. Ferraz, Tadayuki Yanagi Junior, Yamid F. Hernandez-Julio, Gabriel A. e S. Ferraz, Maria A. J. G. Silva, Flavio A. Damasceno

“…The Fuzzy Inference System had mean percentage error of 2.77, and for Fuzzy Inference System and Genetic Fuzzy Rule Based System it was 0.87, thus indicating an improvement in the accuracy of prediction of respiratory rate when using the tool of genetic algorithms.…”
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A Comparative Study of Loan Approval Prediction Using Machine Learning Methods by Vahid Sinap

“…According to the performance measures, Random Forest was the most successful algorithm with an accuracy rate of 97.71% in loan approval prediction. …”
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The Prediction of Storm‐Time Thermospheric Mass Density by LSTM‐Based Ensemble Learning by Peian Wang, Zhou Chen, Xiaohua Deng, Jingsong Wang, Rongxing Tang, Haimeng Li, Sheng Hong, Zhiping Wu

“…In this paper, an available prediction model is established by Long Short‐Term Memory (LSTM)‐based ensemble learning algorithms. …”
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Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds by Wu Qiao, Tong Xie, Jing Lu, Tinghan Jia

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An Efficient Deep Learning-Based Framework for Predicting Cyber Violence in Social Networks by Younes Fayand Fathabad, Mohammad Ali Balafar, Amin Golzari Oskouei, Kamal Koohi

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An Integrated Supply Chain Model for Predicting Demand and Supply and Optimizing Blood Distribution by Pooria Bagher Niakan, Mehdi Keramatpour, Behrouz Afshar-Nadjafi, Alireza Rashidi Komijan

“…<i>Method</i>: Classic time-series models are applied to predict future supply chain circumstances, addressing uncertainty in blood demand and the need for timely supply. …”
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Scalable earthquake magnitude prediction using spatio-temporal data and model versioning by Rahul Singh, Bholanath Roy

“…Abstract Earthquake magnitude prediction is critical for natural calamity prevention and mitigation, significantly reducing casualties and economic losses through timely warnings. …”
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