Supervised machine learning for nutrient and contaminant profiling and benefit-risk assessment of Arctic seafood by Quang Tri Ho, Ole Jakob Nøstbakken, Monica Sanden, Lene Secher Myrmel, Martin Wiech, Annette Bernhard, Bente M. Nilsen, Amund Maage, Lisbeth Dahl

“…This study aimed to profile concentration patterns including interspecies and regional variations between Barents Sea and Norwegian Sea, and to explore correlations of nutrients and contaminants in five commercially important Arctic marine fish species using supervised machine learning applied to analytical data. …”
Get full text

Electricity Theft Detection Using Rule-Based Machine Leaning (rML) Approach by Sheyda Bahrami, Erol Yumuk, Alper Kerem, Beytullah Topçu, Ahmetcan Kaya

“…Even though consumption-based models have been applied extensively to the detection of power theft, it can be difficult to reliably identify theft instances based only on patterns of usage. In this paper, a novel rule-based combined machine learning (rML) technique is developed for power theft detection to address the drawbacks of systems that rely just on consumption patterns. …”
Get full text

Discriminating and classifying odontocete echolocation clicks in the Hawaiian Islands using machine learning methods. by Morgan A Ziegenhorn, Kaitlin E Frasier, John A Hildebrand, Erin M Oleson, Robin W Baird, Sean M Wiggins, Simone Baumann-Pickering

“…Passive acoustic monitoring (PAM) has proven a powerful tool for the study of marine mammals, allowing for documentation of biologically relevant factors such as movement patterns or animal behaviors while remaining largely non-invasive and cost effective. …”
Get full text

Bioclimatic Drivers of Amur Falcon Habitat Dynamics Using Advanced Machine Learning Models by B. Ghale, S. Mamgain, K. Gupta, A. Roy, H. C. Karnatak

“…To date, no comprehensive study has analyzed how bio-climatic factors influence migration patterns across such a broad range. This study explores the bio-climatic factors influencing the falcon's migration and habitat suitability using remote sensing, GIS, and machine learning models—Maximum Entropy (MaxEnt) and Random Forest (RF). …”
Get full text

Applying a Machine Learning Method to Detect Changing Neuronal Activity in Seizure Disorders by Cengiz Gunay, Krishan Bhalsod

“…Using MATLAB, we are training a machine learning model on electrophysiological data to recognize patterns of post-synaptic events that show potential seizure activity. …”
Get full text

Machine learning-based model for behavioural analysis in rodents applied to the forced swim test by Andrea Della Valle, Sara De Carlo, Gregorio Sonsini, Sebastiano Pilati, Andrea Perali, Massimo Ubaldi, Roberto Ciccocioppo

“…Therefore, they are often unable to accurately differentiate the major subtypes of movement patterns, such as swimming and climbing. To address these limitations, we propose a novel approach based on machine learning (ML) using a three-dimensional residual convolutional neural network (3D RCNN) that processes video pixels directly, capturing the spatiotemporal dynamics of rodent behaviour. …”
Get full text

PREDICTION INTERVALS IN MACHINE LEARNING: RESIDUAL BOOTSTRAP AND QUANTILE REGRESSION FOR CASH FLOW ANALYSIS by Wa Ode Rahmalia Safitri, Farit Mochamad Afendi, Budi Susetyo

Get full text

Identifying drivers of surface ozone bias in global chemical reanalysis with explainable machine learning by K. Miyazaki, Y. Marchetti, J. Montgomery, S. Lu, K. Bowman

“…<p>This study employs an explainable machine learning (ML) framework to examine the regional dependencies of surface ozone biases and their underlying drivers in global chemical reanalysis. …”
Get full text

Design of an Iterative Method for Malware Detection Using Autoencoders and Hybrid Machine Learning Models by Rijvan Beg, R. K. Pateriya, Deepak Singh Tomar

“…In this context, we propose a comprehensive framework that applies machine learning methods to enhance evidence collection and malware activity analysis. …”
Get full text

Improving Machine Learning-Based Robot Self-Collision Checking with Input Positional Encoding by Kulecki Bartłomiej, Belter Dominik

“…The results demonstrate the benefits of incorporating positional encoding, which enhances classification accuracy by enabling the model to better capture high-frequency variations, leading to a more detailed and precise representation of complex collision patterns. The manuscript shows that machine learning-based techniques, such as lightweight multilayer perceptrons (MLPs) operating in a low-dimensional feature space, offer a faster alternative for collision checking than traditional methods that rely on geometric approaches, such as triangle-to-triangle intersection tests and Bounding Volume Hierarchies (BVH) for mesh-based models.…”
Get full text

Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery by Fouad Trad, Bassel Isber, Ryan Yammine, Khaled Hatoum, Dana Obeid, Mohammad Chahine, Rachid Haidar, Ghada El-Hajj Fuleihan, Ali Chehab

“…To ensure the models’ decision-making is compatible with clinical decisions and common practices, we applied explainability techniques such as SHAP to reveal the patterns learned by the models. These patterns were found to be clinically plausible. …”
Get full text

Organ-system-based subclassification of preeclampsia using machine learning predicts pregnancy outcomes by Yanhong Xu, Yizheng Zu, Xiaosi Lu, Yiping Wang, Jiaying Zheng, Xia Xu, Jianying Yan

“…Heatmap and sankey diagram analyses revealed significant overlap between high-risk clusters, with the most frequent combination being H-Cluster 1, K-Cluster 1, L-Cluster 1 and C-Cluster 5. Conclusions Machine learning identified distinct PE subclasses based on organ system dysfunction patterns, each demonstrating unique pregnancy outcomes. …”
Get full text

Non-invasive detection of Parkinson’s disease based on speech analysis and interpretable machine learning by Huanqing Xu, Wei Xie, Mingzhen Pang, Ya Li, Luhua Jin, Fangliang Huang, Xian Shao

“…ObjectiveParkinson’s disease (PD) is a progressive neurodegenerative disorder that significantly impacts motor function and speech patterns. Early detection of PD through non-invasive methods, such as speech analysis, can improve treatment outcomes and quality of life for patients. …”
Get full text

Optimising Manufacturing Efficiency: A Data Analytics Solution for Machine Utilisation and Production Insights by Saleh Seyedzadeh, Vyron Christodoulou, Adam Turner, Saeid Lotfian

“…This paper proposes a non-invasive, data-driven methodology for monitoring and optimising machine utilisation in manufacturing environments. …”
Get full text

Identifying Human Factors in Aviation Accidents with Natural Language Processing and Machine Learning Models by Flávio L. Lázaro, Tomás Madeira, Rui Melicio, Duarte Valério, Luís F. F. M. Santos

“…The use of machine learning techniques to identify contributing factors in air incidents has grown significantly, helping to identify and prevent accidents and improve air safety. …”
Get full text

Nuclear morphometrics coupled with machine learning identifies dynamic states of senescence across age by Sahil A. Mapkar, Sarah A. Bliss, Edgar E. Perez Carbajal, Sean H. Murray, Zhiru Li, Anna K. Wilson, Vikrant Piprode, You Jin Lee, Thorsten Kirsch, Katerina S. Petroff, Fengyuan Liu, Michael N. Wosczyna

“…Here we show that this method reveals dynamic, age-associated patterns of senescence in regenerating skeletal muscle and osteoarthritic articular cartilage. …”
Get full text

MACHINE LEARNING AND DEEP LEARNING: A COMPARATIVE ANALYSIS FOR APPLE LEAF DISEASE DETECTION by Anupam Bonkra, Sunil Pathak, Amandeep Kaur

“…Accurate disease diagnosis depends on identifying the distinctive patterns that illnesses leave on foliage. Specialists or cultivators have frequently performed plant inspections, which may be costly and time-consuming. …”
Get full text

Design Maintenance System on Mixer Machine to Prevent the Breakdown Using Reliability Centered Maintenance by Budhi Santri Kusuma, Mhd. Ardian Syahputra, Roaida Yanti, Dede Ibrahim Muthawali

“…However, many manufacturing companies neglect maintenance, leading to frequent machine breakdowns that can result in machine downtime and financial losses. …”
Get full text

ADVANCED CLUSTERING TECHNIQUES FOR TIN DEPOSIT CLASSIFICATION IN MALAYSIA: A MACHINE LEARNING APPROACH by Meisam Saleki, Kamran Mostafaei, Zakaria Bin Endut, Mohammad Nabi Kianpour

Get full text

Machine Learning Algorithms in EEG Analysis of Kleefstra Syndrome: Current Evidence and Future Directions by Katerina D. Tzimourta

“…Given the growing role of machine learning (ML) in extracting patterns from EEG data in related disorders—such as Angelman, Rett and Fragile X syndromes—this review explores how similar approaches could be adapted for KS. …”
Get full text