A Systematic Literature Review of Machine Learning-Based Personality Trait Detection Using Electroencephalographic Data by Celina Rieck, Pascal Penava, Ricardo Buettner

“…This systematic literature review examines if trait detection is possible by using electroencephalography in combination with machine and deep learning models, analyzing 58 studies since 2015. We compare their performance by a meta-analysis of weighted data, highlighting strengths and limitations. …”
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A secure and efficient encryption system based on adaptive and machine learning for securing data in fog computing by Priyanka Rajan Kumar, Sonia Goel

“…This research introduces a novel adaptive encryption framework powered by machine learning to address these security concerns. The proposed system dynamically selects and adjusts encryption methods and key strengths based on data sensitivity and the communication context, ensuring a balance between security and performance. …”
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Stacking ensemble machine learning for predicting land surface temperature hotspots using landsat 9 data by Momohjimoh Abdulsalami, Dahiru Dahuwa, Saratu Muhammad Hussaini, Yahaya Jibrin Danjuma, Michael Adewale Ibitomi, Danga Onimisi Abdulmalik, Bunmi Oyekola Isaac, Zainab Usman, Joseph Omeiza Alao, Aliyu Abdullateef

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Machine Learning-Based Harvest Date Detection and Prediction Using SAR Data for the Vojvodina Region (Serbia) by Gordan Mimić, Amit Kumar Mishra, Miljana Marković, Branislav Živaljević, Dejan Pavlović, Oskar Marko

“…In this study, the determination and prediction of harvest dates for different crops were performed by applying machine learning techniques on C-band synthetic aperture radar (SAR) data. …”
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Real-Time Intrusion Detection in Power Grids Using Deep Learning: Ensuring DPU Data Security by Maoran Xiao, Qi Zhou, Zhen Zhang, Junjie Yin

“…Deep learning technologies have revolutionized the management of energy, energy consumption, and data security within smart grids through non-intrusive load monitoring (NILM). …”
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International trade market forecasting and decision-making system: multimodal data fusion under meta-learning by Yiming Bai, Muhammad Asif

“…Traditional market analysis tools primarily rely on unidimensional data, such as historical trading records and price trends. …”
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Random Reflectance: A New Hyperspectral Data Preprocessing Method for Improving the Accuracy of Machine Learning Algorithms by Pavel A. Dmitriev, Anastasiya A. Dmitrieva, Boris L. Kozlovsky

“…This study employs machine learning (ML) algorithms, specifically Random Forest (RF) and Gradient Boosting (GB), to analyse the performance of RR in comparison to Min–Max Normalisation (MMN) and Principal Component Analysis (PCA). …”
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Site Selection of Elderly Care Facilities Based on Multi-Source Spatial Big Data and Integrated Learning by Yin Zhang, Junhong Zhu, Fangyi Li, Yingjie Wang

“…It combines topographic conditions, population distribution, economic development status, and other multi-source spatial big data at a 500 m grid scale; constructs a prediction model for the suitability of sites for elderly care facilities based on integrated learning; and carries out a comprehensive evaluation and feature importance analysis. …”
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A new machine learning approach based on spatial fuzzy data correlation for recognizing sports activities by Xu Pengru, Zhou Junhui, Kausar Nasreen, Lin Chunlei, Lu Qianqian, Ghaderpour Ebrahim, Pamucar Dragan, Zadeh Ardashir M.

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An Automated Framework for Lane Closure Detection on Highway Using Connected Vehicle Data and Machine Learning Models by Ashutosh Dumka, Raghupathi Kandiboina, Aparna Joshi, Skylar Knickerbocker, Neal Hawkins, Anuj Sharma

“…This study introduces an innovative real-time lane closure detection approach using connected vehicle (CV) data and machine learning techniques. Our methodology analyzes CV data metrics such as speed variations and lateral waypoint positioning relative to road reference lines, comparing these across road segments with and without closures. …”
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Machine learning-based feature selection for ultra-high-dimensional survival data: a computational approach by Nahid Salma, Majid Khan Majahar Ali, Raja Aqib Shamim

“…Gene interaction network analysis confirmed their role in RCC progression. Despite SCAD’s strong performance, it left 31% of data variability unexplained, suggesting hybrid ML models that integrate ensemble learning, two-component regression structures, and deep learning-based feature selection could further enhance gene selection and predictive accuracy. …”
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A Predictive Model for Perinatal Brain Injury Using Machine Learning Based on Early Birth Data by Ga Won Jeon, Yeong Seok Lee, Won-Ho Hahn, Yong Hoon Jun

“…Various machine learning models, including gradient boosting, were trained using early birth data to predict perinatal brain injury. …”
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Generation of Synthetic Density Log Data Using Deep Learning Algorithm at the Golden Field in Alberta, Canada by Sungil Kim, Kwang Hyun Kim, Baehyun Min, Jungtek Lim, Kyungbook Lee

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Hierarchical Information-Extreme Machine Learning of Hand Prosthesis Control System Based on Decursive Data Structure by Anatolii Dovbysh, Vladyslav Piatachenko, Mykyta Myronenko, Mykyta Suprunenko, Julius Simonovskiy

“…The method was developed within the information-extreme intelligent data analysis technology framework to maximize the system’s information capacity during machine learning. …”
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Research on the Inversion of Key Growth Parameters of Rice Based on Multisource Remote Sensing Data and Deep Learning by Jian Li, Jian Lu, Hongkun Fu, Wenlong Zou, Weijian Zhang, Weilin Yu, Yuxuan Feng

“…Data analysis and parameter prediction were conducted using a variety of machine learning and deep learning models including Partial Least Squares (PLSs), Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory Networks (LSTM), among which the LSTM model demonstrated superior performance, particularly at multiple critical time points. …”
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Interpretable Machine Learning for Thermospheric Mass Density Modeling Using GRACE/GRACE‐FO Satellite Data by Qian Pan, Chao Xiong, ShunZu Gao, Zhou Chen, Artem Smirnov, Chunyu Xu, Yuyang Huang

“…In this study we propose a machine‐learning approach, the bidirectional gated recurrent unit with multi‐head attention mechanism (BGMA), for modeling and predicting the TMD, based on the Gravity Recovery and Climate Experiment (GRACE) satellite data. …”
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Data-driven prediction of rate of penetration (ROP) in drilling operations using advanced machine learning models by Guoli Huang, Sarah Kanaan Hamzah, Pinank Patel, T. Ramachandran, Aman Shankhyan, A. Karthikeyan, Dhirendra Nath Thatoi, Deepak Gupta, S. AbdulAmeer, Mariem Alwan, Zahraa Saad Abdulali, Mahmood Jasem Jawad, Hiba Mushtaq, Mohammad Mahtab Alam, Hojjat Abbasi

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Deep learning super-resolution for temperature data downscaling: a comprehensive study using residual networks by Shailesh Kumar Jha, Vivek Gupta, Priyank J. Sharma, Anurag Mishra, Saksham Joshi

“…These findings suggest that advanced deep learning models employing residual networks, such as VDSR and EDSR, significantly enhance temperature data accuracy over SRCNN. …”
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Anomaly Detection Using Machine Learning in Hydrochemical Data From Hot Springs: Implications for Earthquake Prediction by Ruijie Zhu, Fengtian Yang, Xiaocheng Zhou, Jiao Tian, Yongxian Zhang, Miao He, Jingchao Li, Jinyuan Dong, Ying Li

“…Abstract This study explores the potential of machine learning algorithms for earthquake prediction, utilizing fluid chemical anomaly data from hot springs. …”
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Improving SMART learning: Course completion via AI-driven hybrid system integration in big data by Abdellah Bakhouyi, Amine Dehbi, Lahcen Amhaimar, Said Broumi, Mohamed Talea, Abderrahim Khalidi

“…In essence, when these systems are integrated, the hybrid system can accommodate the complexity and variability of educational data. Experimental analysis illustrates that the proposed model is competitive in the domain of course completion and provides a high number of accuracies, thereby enhancing its ability to boost the learning experience and academic outcomes in smart educational environments.…”
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