Comparative Analysis of Diabetes Prediction Models Using the Pima Indian Diabetes Database by Zhao Yize

“…The K-means model operates by grouping data points into separate clusters according to their characteristics, achieving an accuracy of 90.04% in diabetes prediction. In comparison, the random forest model, which builds multiple decision trees (DT) to do their predictions, demonstrates superior performance over several widely used algorithms such as K-Nearest Neighbours (KNN), Logistic Regression (LR), DT, Support Vector Machines (SVM), and Gradient Boosting (GB). …”
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Using machine learning to predict gamma shielding properties: a comparative study by T A Nahool, A M Abdelmonem, M S Ali, A M Yasser

“…This study employed machine learning (ML) algorithms to predict the linear attenuation coefficients (LACs) of materials in inorganic scintillation detectors, which are crucial for evaluating self-shielding properties. …”
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Application Research of Cross-Attention Mechanism for Traffic Prediction Based on Heterogeneous Data by Feng Zhihao

“…Through an analysis of these methods, the research demonstrates how applying advanced deep learning algorithms and cross-attention processes has significantly improved prediction robustness and accuracy. …”
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SVM-Based Spectrum Mobility Prediction Scheme in Mobile Cognitive Radio Networks by Yao Wang, Zhongzhao Zhang, Lin Ma, Jiamei Chen

“…Numerical results validate that SVM-SMP gains better short-time prediction accuracy rate and miss prediction rate performance than the two algorithms just depending on the location and speed information. …”
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Programming Languages Prediction from Stack Overflow Questions Using Deep Learning by Razowana Khan Mim, Tapu Biswas

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Interval price prediction of livestock product based on fuzzy mathematics and improved LSTM. by Weimin Ma, Lingling Peng, Hu Chen, Haisheng Yan

“…An empirical study was conducted on the weekly price data of pork, beef, and mutton in China from 2009 to 2023, incorporating discussions on different embedding dimensions, prediction step, fuzzy granulation window sizes, decomposition techniques, and prediction algorithms. …”
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Strength prominence index: a link prediction method in fuzzy social network by Sakshi Dev Pandey, Sovan Samanta, A. S. Ranadive, Leo Mrsic, Antonios Kalampakas, Tofigh Allahviranloo

“…In our experiments, we used three well-known estimators to evaluate the accuracy of link prediction algorithms: precision, area under the precision-recall curve, and area under the receiver operating characteristic curve. …”
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Predicting Subcontractor Performance Using Web-Based Evolutionary Fuzzy Neural Networks by Chien-Ho Ko

“…This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs). …”
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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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Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information by Sungyeol Lee, Jaemo Kang, Jinyoung Kim

“…The random forest, XGBoost, and LightGBM machine learning algorithms were used to develop the prediction model, and the SMOTE sampling technique was employed to address data imbalance. …”
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AI-Driven Drought Monitoring: Advanced Machine Learning Techniques for Early Prediction by Vij Priya, Tiwari Ankita

“…Amid the escalating impacts of climate change, droughts are becoming increasingly frequent and severe, necessitating advanced monitoring and predictive strategies to mitigate their adverse effects on agriculture, water resources, and ecosystems. …”
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An optimized machine learning framework for predicting and interpreting corporate ESG greenwashing behavior. by Fanlong Zeng, Jintao Wang, Chaoyan Zeng

“…The IHPO algorithm was then employed to optimize the hyperparameters of the XGBoost model, forming an IHPO-XGBoost ensemble learning model for predicting corporate ESG greenwashing behavior. …”
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Efficient Air Quality Prediction Models Based on Supervised Machine Learning Techniques by Oumoulylte Mariame, El Allaoui Ahmad, Farhaoui Yousef, Boughrous Ali Ait

“…To tackle these issues, it's crucial to set up prediction systems allowing officials to act before high pollution levels occur. …”
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Research on Default Prediction for Credit Card Users Based on XGBoost-LSTM Model by Jing Gao, Wenjun Sun, Xin Sui

“…The resulting XGBoost-LSTM model showed good classification performance in default prediction. The results of this study can provide a reference for the application of deep learning algorithms in the field of finance.…”
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Optimizing Photovoltaic Power Prediction Using Computational Methods and Artificial Neural Networks by Cempaka Amalin Mahadzir, Ahmad Fateh Mohamad Nor, Siti Amely Jumaat, Noor Syahirah Ahmad Safawi

“… This paper focuses on utilizing an Artificial Neural Network (ANN) to predict photovoltaic (PV) panel output power. Since solar power output is fluctuating and depends on climatic, geographical and temporal factors, precise prediction requires the implementation of computational approaches. …”
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Prediction of Large Springback in the Forming of Long Profiles Implementing Reverse Stretch and Bending by Mohammad Reza Vaziri Sereshk, Hamed Mohamadi Bidhendi

“…Comparing the results of this algorithm for different sheet metal forming processes with experimental measurements demonstrates that this technique successfully predicts a wide range of springback with reasonable accuracy. …”
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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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Exploring the VAK model to predict student learning styles based on learning activity by Ahmed Rashad Sayed, Mohamed Helmy Khafagy, Mostafa Ali, Marwa Hussien Mohamed

“…Our results show that the Random Forest algorithm achieved the highest accuracy with 98 %.This research shows how machine learning techniques embedded in learning analytics could expand the functionalities of VLEs toward greater personalization and effectiveness, with every student receiving the best educational experience that suits their learning styles.…”
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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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