Ensemble Machine Learning for the Prediction and Understanding of the Refractive Index in Chalcogenide Glasses by Miruna-Ioana Belciu, Alin Velea

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Revolution in flow cytometry: using artificial intelligence for data processing and interpretation by Szymon Bierzanowski, Szymon Bierzanowski, Krzysztof Pietruczuk

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Data-Driven and Mechanistic Soil Modeling for Precision Fertilization Management in Cotton by Miltiadis Iatrou, Panagiotis Tziachris, Fotis Bilias, Panagiotis Kekelis, Christos Pavlakis, Aphrodite Theofilidou, Ioannis Papadopoulos, Georgios Strouthopoulos, Georgios Giannopoulos, Dimitrios Arampatzis, Evangelos Vergos, Christos Karydas, Dimitris Beslemes, Vassilis Aschonitis

“…This study introduces a novel methodology for predicting cotton yield by integrating machine learning (ML) with mechanistic soil modeling. …”
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Mitigating class imbalance in churn prediction with ensemble methods and SMOTE by R. Suguna, J. Suriya Prakash, H. Aditya Pai, T. R. Mahesh, Venkatesan Vinoth Kumar, Temesgen Engida Yimer

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TPTC: topic-wise problems’ trend clusters for smart agricultural insights extraction and forecasting of farmer’s information demand by Samarth Godara, Shbana Begam, Ram Swaroop Bana, Jatin Bedi, Rajni Jain, Md. Ashraful Haque, Rajender Parsad, Sudeep Marwaha, Madhu Patial, Saber Shirzad, Ravi Nirmal

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An analytical assessment of credit card fraud detection techniques: Supervised, Unsupervised, and Reinforcement Learning by Abdourahman Djamal Djama

“…Method. information-theoretical analysis of methods for detecting fraud with bank cards, machine learning algorithms in improving the accuracy of fraud detection. …”
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Credit card fraud detection through machine learning algorithm by Agyan Panda, Bharath Yadlapalli, Zhi Zhou

“…To help fraud investigators, more algorithms are turning to powerful machine learning methodologies. Designing fraud detection algorithms is particularly difficult because to the non-stationary distribution of data, excessively skewed class distributions, and continuous streams of transactions. …”
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BanglaTaka: A dataset for classification of Bangladeshi banknotesMendeley Data by Md. Naimul Islam Nuhash, Sadia Akter

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Data-driven approach for identifying the factors related to debt collector performance by Keerthana Sivamayilvelan, Elakkiya Rajasekar, Santhi Balachandran, Ketan Kotecha, Subramaniyaswamy Vairavasundaram

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Sentiment Analysis of Rohingya Refugees in Aceh using Support Vector Machine (SVM) and Multinomial Logistic Regression by Gigih Army Buana Baliputra, Slamet Kacung, Budi Santoso

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Exploring emotions and perspectives of families with autism spectrum disorder individuals on sports participation: a case study on Weibo using machine learning method by Shan He, Fengshi Jing, Hao Ren, Yanning Xiao, Xia Sebastiane Chen, Ruihong Tang, Yue Qiu, Junwei Zeng, Tianyu Gao, Junhao Huang

“…Objective This study investigates family sentiments and attitudes toward sports participation by ASD individuals, leveraging longitudinal data from Sina Weibo and machine learning–based sentiment analysis. …”
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Utilizing machine and deep learning algorithms to identify learning-related features in electroencephalography data during second language acquisition by Talal A. Aldhaheri, Sonali B. Kulkarni, Pratibha R. Bhise, Mohammed Tawfik

“…This study investigates EEG-based neurolinguistic patterns during the process of learning Arabic and Hindi as second languages. EEG data were collected from 10 participants (6 males and 4 females; M = 34.1, SD = 4.7 years) engaged in language acquisition tasks, and the signals underwent preprocessing steps, including noise removal, independent component analysis (ICA), and spectral feature extraction. …”
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Machine Learning Models to Predict Individual Cognitive Load in Collaborative Learning: Combining fNIRS and Eye-Tracking Data by Wenli Chen, Zirou Lin, Lishan Zheng, Mei-Yee Mavis Ho, Farhan Ali, Wei Peng Teo

“…Nine features, derived from both fNIRS and eye-tracking data, were used as input for the models. Results demonstrated that machine learning models could accurately predict individual cognitive load, with the Random Forest model achieving the highest performance (F1 score = 0.84). …”
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Enhancing Supply Chain Agility and Sustainability through Machine Learning: Optimization Techniques for Logistics and Inventory Management by Vikram Pasupuleti, Bharadwaj Thuraka, Chandra Shikhi Kodete, Saiteja Malisetty

“…<b>Methods</b>: This study leverages advanced machine learning (ML) techniques to enhance logistics and inventory man-agement. …”
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Editorial: Protecting privacy in neuroimaging analysis: balancing data sharing and privacy preservation by Rashid Mehmood, Mariana Lazar, Xiaohui Liang, Juan M. Corchado, Juan M. Corchado, Juan M. Corchado, Simon See

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Credibility-Adjusted Data-Conscious Clustering Method for Robust EEG Signal Analysis by Fatemeh Divan, Teh Ying Wah, Kheng Seang Lim, Ali Seyed Shirkhorshidi

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Researching Organic Solar Cells from the Perspective of Literature Big Data Analysis by Qing Wang, Zhixin Liu, Shengda Zhao, Yangjun Yan, Xinyi Li, Yajie Zhang, Xinghua Zhang

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Data-driven analysis and visualization of dielectric properties curated from scientific literature by Tomoki Murata, Naoto Saito, Eiji Koyama, Ton Nu Thanh Phuong, Ryusuke Misawa, Satoshi Yokomizo, Tomoya Mato, Yu Takada, Sakyo Hirose, Yukari Katsura

Subjects: “…Materials data analysis…”
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Machine learning-powered data cleaning for LEGEND: a semi-supervised approach using affinity propagation and support vector machines by E León, A Li, M A Bahena Schott, B Bos, M Busch, J R Chapman, G L Duran, J Gruszko, R Henning, E L Martin, J F Wilkerson

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Transfer learning and the early estimation of single-photon source quality using machine learning methods by David Jacob Kedziora, Anna Musiał, Wojciech Rudno-Rudziński, Bogdan Gabrys

“…Validation metrics quickly reveal that even a linear regressor can outperform standard fitting when it is tested on the same contexts it was trained on, but the success of transfer learning is less assured, even though statistical analysis, made possible by data augmentation, suggests its superiority as an early estimator. …”
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