DETECTING URBAN SLUMS IN DKI JAKARTA: A KOTAKU DATA APPROACH WITH ENSEMBLE METHODS by Muhammad Muawwad MS, Rani Nooraeni, Ananda Galuh Intan Prasetya

“…Modeling is done using the Random Forest algorithm. Data sourced from the KOTAKU program website established by the Ministry of PUPR RI. …”
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A Comparative Study of Machine Learning Techniques for Predicting Mechanical Properties of Fused Deposition Modelling (FDM)-Based 3D-Printed Wood/PLA Biocomposite by Prashant Anerao, Atul Kulkarni, Yashwant Munde, Namrate Kharate

“…Four distinct machine learning algorithms have been selected for predictive modeling: Linear Regression, Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost). …”
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Decoding PM_2.5 Prediction in Nanning Urban Area, China: Unraveling Model Superiorities and Drawbacks Through SARIMA, Prophet, and LightGBM by Minru Chen, Binglin Liu, Mingzhi Liang, Nini Yao

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Severity Classification of a Seismic Event based on the Magnitude-Distance Ratio Using Only One Seismological Station by Luis Hernán Ochoa Gutiérrez, Luis F Niño, Carlos A. Vargas

“…We trained a Support Vector Machine (SVM) algorithm with seismograph data recorded by INGEOMINAS's National Seismological Network at a three-component station located near Bogota, Colombia. …”
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CD79A and GADD45A as novel immune-related biomarkers for respiratory syncytial virus severity in children: an integrated machine learning analysis and clinical validation by Juan Juan Chen, Zhang Ze Lu, Yu Xin Jing, Xing Mei Nong, Yi Qin, Jin Yang Huang, Na Lin, Jie Wei

“…Machine learning models, particularly SVM (area under the curve, AUC = 0.950), prioritized CD79A and GADD45A as key predictors of hospitalization. …”
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A novel deep learning-based 1D-CNN-optimized GRU approach for heart disease prediction by Jini Mol G., Ajith Bosco Raj T.

“…This is completely evaluated against other deep learning algorithms.…”
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Real-time mobile broadband quality of service prediction using AI-driven customer-centric approach by Ayokunle A. Akinlabi, Folasade M. Dahunsi, Jide J. Popoola, Lawrence B. Okegbemi

“…The highlighted gap can be addressed by machine learning (ML), as it has been effectively used in the past to support the analysis and knowledge discovery of communication systems’ traffic data through identification of intricate and hidden patterns. …”
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Investigating the contributory factors influencing speeding behavior among long-haul truck drivers traveling across India: Insights from binary logit and machine learning technique... by Balamurugan Shandhana Rashmi, Sankaran Marisamynathan

“…While conventional statistical methods like binary logit technique lacked prediction capabilities, machine learning (ML) algorithms including decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) were employed to model speeding behavior among LHTDs. …”
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Introduction to Computational Creativity by Anna Longo, ChatGPT

“…By deconstructing the cognitive processes involved in human creativity, researchers can design algorithms that simulate these processes. This involves machine learning, neural networks, evolutionary algorithms, and other AI techniques that enable computers to recognize patterns, generate new ideas, and refine them through iterative processes.  …”
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The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy by Szandra László, Ádám Nagy, József Dombi, Emőke Adrienn Hompoth, Emese Rudics, Zoltán Szabó, András Dér, András Búzás, Zsolt János Viharos, Anh Tuan Hoang, Vilmos Bilicki, István Szendi

“…Several types of features are extracted from both datasets. Machine learning algorithms using different feature sets achieved nearly 90-95% for the CS group and 70-85% accuracy for the PSF. …”
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Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipel... by Nalinda D. Liyanagedera, Corinne A. Bareham, Heather Kempton, Hans W. Guesgen

“…For two of those pipelines, Common Spatial Patterns (CSP) and Short Time Fourier Transform (STFT) were successfully used as feature extraction algorithms where both these algorithms are significantly new for meditation EEG. …”
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Computational intelligence investigations on evaluation of salicylic acid solubility in various solvents at different temperatures by Adel Alhowyan, Wael A. Mahdi, Ahmad J. Obaidullah

“…Abstract This research shows the utilization of various tree-based machine learning algorithms with a specific focus on predicting Salicylic acid solubility values in 13 solvents. …”
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CTSS in the tumor microenvironment links immune escape and immunotherapy sensitivity in kidney renal clear cell carcinoma by Hanjing Zhou, Jun Ying, Xuchun Xu, Jian Huang

“…Employing advanced machine learning (ML) algorithms, we identified Cathepsin S (CTSS) as the most pivotal tumor suppressor, with elevated CTSS expression consistently predicting improved survival across multiple independent cohorts. …”
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Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunothera... by Shenjun Huang, Tingting Tu

“…We analyzed various datasets, applying negative matrix factorization alongside machine learning algorithms to reveal gene expression patterns and construct diagnostic models. …”
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Land cover changes in grassland landscapes: combining enhanced Landsat data composition, LandTrendr, and machine learning classification in google earth engine with MLP-ANN scenari... by Cecilia Parracciani, Daniela Gigante, Onisimo Mutanga, Stefania Bonafoni, Marco Vizzari

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The ZTF Source Classification Project. III. A Catalog of Variable Sources by Brian F. Healy, Michael W. Coughlin, Ashish A. Mahabal, Theophile Jegou du Laz, Andrew Drake, Matthew J. Graham, Lynne A. Hillenbrand, Jan van Roestel, Paula Szkody, LeighAnna Zielske, Mohammed Guiga, Muhammad Yusuf Hassan, Jill L. Hughes, Guy Nir, Saagar Parikh, Sungmin Park, Palak Purohit, Umaa Rebbapragada, Draco Reed, Daniel Warshofsky, Avery Wold, Joshua S. Bloom, Frank J. Masci, Reed Riddle, Roger Smith

“…We also identify the most important features for XGB classification and compare the performance of the two ML algorithms, finding a pattern of higher precision among XGB classifiers. …”
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Three Decades of Land Cover Dynamics in a Boreal Coastal Basin: A Multisensor Spectral Index and Machine Learning Approach Using Landsat Data and GB-SAR Data by Jinsong Zhang, Bochi Zou, Yifei Yuan, Asad Khan, Muhammad Bilawal Junaid, Qaiser Abbas, Rana Muhammad Zulqarnain, Nazih Y. Rebouh, Olga D. Kucher, Hassan Alzahrani

“…Landsat surface reflectance and spectral index data were fused and subsequently classified using a random forest machine learning algorithm, allowing for enhanced land cover classification accuracy across seven time intervals spanning three decades. …”
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IDEEA: information diffusion model for integrating gene expression and EEG data in identifying Alzheimer’s disease markers by Enes Ozelbas, Tuba Sevimoglu, Tamer Kahveci

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Machine Learning-Based Analysis of Travel Mode Preferences: Neural and Boosting Model Comparison Using Stated Preference Data from Thailand’s Emerging High-Speed Rail Network by Chinnakrit Banyong, Natthaporn Hantanong, Supanida Nanthawong, Chamroeun Se, Panuwat Wisutwattanasak, Thanapong Champahom, Vatanavongs Ratanavaraha, Sajjakaj Jomnonkwao

“…These findings underscore the effectiveness of machine learning approaches in capturing complex behavioral patterns, providing empirical evidence to guide high-speed rail policy development in low- and middle-income countries. …”
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Building Consumption Data Systems Driven by AI Plus Expert for Scientific and Technical Literature Information Resources by Guanghui YE, Kai TU, Lina HU, Li HAN, Zhiming FENG

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