Using machine learning to forecast conflict events for use in forced migration models by Yani Xue, Thomas Schincariol, Thomas Chadefaux, Derek Groen

“…Accurate predictions of displacement patterns are crucial for improving the delivery of aid to refugees and other forcibly displaced individuals. …”
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MLA-Machine Learning Approach for Dependable Battery Condition Monitoring in Electric Vehicles by Tirgar Pravin, Priya R Karpaga, Sampath Kumar Vankadara, Lakhanpal Sorabh, Raj R Gowtham, N K Rayaguru

“…The GRU model study real-time variables such as voltage, current, and temperature to identify physical patterns. The monitoring process help out the model focus on the most important data, leading to more accurate predictions. …”
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Machine Learning-Enhanced Attribute-Based Authentication for Secure IoT Access Control by Jibran Saleem, Umar Raza, Mohammad Hammoudeh, William Holderbaum

“…This research presents the SmartIoT Hybrid Machine Learning (ML) Model, a novel integration of Attribute-Based Authentication and a lightweight machine learning algorithm designed to enhance security while minimising computational overhead. …”
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Machine learning-driven benchmarking of China's wastewater treatment plant electricity consumption by Minjian Li, Chongqiao Tang, Junhan Gu, Nianchu Li, Ahemaide Zhou, Kunlin Wu, Zhibo Zhang, Hui Huang, Hongqiang Ren

“…The findings not only enhance understanding of WWTP electricity consumption patterns and provide a scalable model for wider application, but also demonstrate a novel methodology for addressing multi-variable problems.…”
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Integration of Deep Learning and Machine Learning Techniques for Advancing the Detection of Plant Diseases by Abdul Karim, Touqeer Ahmed Jumani, Muhammad Amir Raza, Shadi Khan Baloch, Nouman Qadeer Soomro, Muhammad Masud, Muhammad Salman Saeed, Mouaaz Nahas, Aamir Ali Patoli

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From literature to biodiversity data: mining arthropod organismal traits with machine learning by Joseph Cornelius, Harald Detering, Oscar Lithgow-Serrano, Donat Agosti, Fabio Rinaldi, Robert Waterhouse

“…Testing and using such approaches to annotate articles in machine-actionable formats is, therefore, necessary to enable the exploitation of existing knowledge in new biology, ecology and evolution research. …”
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A Comparative Study of Machine Learning Models for Short-Term Load Forecasting by Etna Vianita, Henri Tantyoko

“…This study presented a comparative analysis of five machine learning models namely XGBoost, Random Forest, Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), and LightGBM using real-world electricity demand data collected over a four-month period. …”
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Machine Learning-Based Diabetes Risk Prediction Using Associated Behavioral Features by Ayodeji O. J. Ibitoye, Joseph D. Akinyemi, Olufade F. W. Onifade

“…With different uncertainties in human lifestyles, it is difficult to predict diabetes while assuming that the risk patterns are the same for all. The likelihood of diabetes in a patient is mostly predicted using machine learning (ML) models on features explicitly available in datasets, while the intrinsic relationship between features viz-a-viz their potential relevance to the presence of diabetes is oftentimes neglected. …”
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Comparing machine learning models with a focus on tone in grooming chat logs by Leonie Hamm, Steve McKeever

“…The results measured through precision, recall and F1 score show that the large language model performs better in grooming detection than traditional machine learning. Moreover, performance differences between the positive and negative sentiment are captured and indicate that positive tone improves detection while negative toned grooming conversations have nuanced patterns that are harder to distinguish from non-grooming. …”
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A hybrid model of machine learning for classifying household water-consumption behaviors by Miao Wang, Zonghan Li, Yi Liu, Lu Lin, Chunyan Wang

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A Review of the Application of Data Science and Machine Learning in Agricultural Water Management by Reza Delbaz, Hamed Ebrahimian

“…New technologies and innovations can improve water management in agriculture. Data science and machine learning are emerging technologies. Data science is a growing field in the world of technology that helps analyze, extract information, and understand patterns and relationships in big data. …”
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Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects by A. Presno Vélez, M. Z. Fernández Muñiz, J. L. Fernández Martínez

“…Structural health monitoring (SHM) systems used sensors to detect damage indicators such as vibrations and cracks, which were crucial for predicting service life and planning maintenance. Machine learning (ML) enhanced SHM by analyzing sensor data to identify damage patterns often missed by human analysts. …”
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Evaluating the Effectiveness of Dimensionality Reduction on Machine Learning Algorithms in Time Series Forecasting by Rida Zaheer, Muhammad Kashif Hanif, Muhammad Umer Sarwar, Ramzan Talib

“…Certain methods excel at uncovering underlying patterns and improving predictive accuracy, while others offer computational advantages. …”
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Optimizing Sensitivity in Machine Learning Models for Pediatric Post-operative Kyphosis Prediction by Raja Ayu Mahessya, Dian Eka Putra, Rostam Ahmad Efendi, Rayendra, Rozi Meri, Riyan Ikhbal Salam, Dedi Mardianto, Ikhsan, Ismael, Arif Rizki Marsa

“…This study developed and evaluated machine learning models for kyphosis prediction using a dataset of 81 pediatric patients by comparing the logistic regression and decision tree approaches. …”
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Applications of Machine Learning-Driven Molecular Models for Advancing Ophthalmic Precision Medicine by Rahul Kumar, Joshua Ong, Ethan Waisberg, Ryung Lee, Tuan Nguyen, Phani Paladugu, Maria Chiara Rivolta, Chirag Gowda, John Vincent Janin, Jeremy Saintyl, Dylan Amiri, Ansh Gosain, Ram Jagadeesan

“…Advanced artificial intelligence (AI) and machine learning (ML) models offer a novel lens to analyze these diseases by integrating diverse datasets, identifying patterns, and enabling precision medicine strategies. …”
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Advanced Fault Diagnosis in Milling Machines Using Acoustic Emission and Transfer Learning by Muhammad Umar, Zahoor Ahmad, Saif Ullah, Faisal Saleem, Muhammad Farooq Siddique, Jong-Myon Kim

“…The accurate diagnosis of faults in milling machines is important to ensure manufacturing efficiency and minimize downtime. …”
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Exploring machine learning trends in poverty mapping: A review and meta-analysis by Badri Raj Lamichhane, Mahmud Isnan, Teerayut Horanont

“…Machine Learning (ML) has rapidly advanced as a transformative tool across numerous fields, offering new avenues for addressing poverty-related challenges. …”
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Wireless Patch Antenna Characterization for Live Health Monitoring Using Machine Learning by Dominic Benintendi, Kevin M. Tennant, Edward M. Sabolsky, Jay Wilhelm

“…Temperature monitoring in extreme environments, such as coal-fired power plants, was addressed by designing and testing wireless patch antennas for use in machine learning-aided temperature estimation. The sensors were designed to monitor the temperature and health of boiler systems. …”
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Exploring Regional Determinants of Tourism Success in the Eurozone: An Unsupervised Machine Learning Approach by Charalampos Agiropoulos, James Ming Chen, George Galanos, Thomas Poufinas

“…Utilizing an extensive dataset that includes economic, demographic, and tourism-specific indicators, we employ unsupervised machine learning techniques, primarily K-means clustering and Principal Component Analysis (PCA), to unearth underlying patterns and relationships. …”
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Machine learning-assisted decoding of temporal transcriptional dynamics via fluorescent timer by Nobuko Irie, Naoki Takeda, Yorifumi Satou, Kimi Araki, Masahiro Ono

“…Here, we introduce an integrative approach combining molecular biology and machine learning to elucidate Foxp3 transcriptional dynamics through flow cytometric Timer analysis. …”
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