Feasibility of Using Wavelet Analysis and Machine Learning Method in Technical Diagnosis of Car Seats by Cezary BARTMAŃSKI, Alicja BRAMORSKA

“…The method is based on the analysis of acoustic signals produced during the operation of the drive. Pattern recognition and machine learning processes were used in the diagnosis. …”
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Identification of Relevant ECG Features for Epileptic Seizure Prediction Using Interpretable Machine Learning by Azra Abtahi, Philippe Ryvlin, Amir Aminifar

“…We employ SHAP (SHapley Additive exPlanations), an interpretability framework, to interpret the prediction patterns. Based on our analysis, multifractality features are among the most important features in seizure prediction, capturing patterns that are not captured by the HRV and Lorenz features.…”
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Detecting Fraudulent Transaction in Banking Sector Using Rule-Based Model and Machine Learning by Cut Dinda Rizki Amirillah

“…The results showed that the IF model could detect anomalous patterns not covered by RBM, thereby improving the accuracy of fraud transaction identification. …”
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Research on artificial intelligence, machine and deep learning in medicine: global characteristics, readiness, and equity by Doris Klingelhöfer, Markus Braun, Janis Dröge, David A. Groneberg, Dörthe Brüggmann

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High-throughput behavioral screening in Caenorhabditis elegans using machine learning for drug repurposing by Antonio García-Garví, Antonio-José Sánchez-Salmerón

“…However, these methods present certain limitations in detecting subtle and non-linear patterns. In this study, we propose a high-throughput screening method based on machine learning, using classifiers that provide a recovery percentage as a measure of treatment effect. …”
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Detecting Subtle Cyberattacks on Adaptive Cruise Control Vehicles: A Machine Learning Approach by Tianyi Li, Mingfeng Shang, Shian Wang, Raphael Stern

“…The proposed approach is observed to outperform contemporary neural network models in detecting irregular driving patterns of ACC vehicles.…”
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Driven early detection of chronic kidney cancer disease based on machine learning technique. by Wafa Almukadi, Sayed Abdel-Khalek, Adel A Bahaddad, Ahmed Mohammed Alghamdi

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Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain by Ali Asghar Rostami, Mohammad Taghi Sattari, Halit Apaydin, Adam Milewski

“…In this case study, flood susceptibility patterns in the Marand Plain, located in the East Azerbaijan Province in northwest Iran, were analyzed using five machine learning (ML) algorithms: M5P model tree, Random SubSpace (RSS), Random Forest (RF), Bagging, and Locally Weighted Linear (LWL). …”
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Transformer-Based Amharic-to-English Machine Translation With Character Embedding and Combined Regularization Techniques by Surafiel Habib Asefa, Yaregal Assabie

“…It is also an under-resourced language, presenting significant challenges for natural language processing tasks like machine translation. The primary challenges include the scarcity of parallel data, which increases the risk of overfitting and limits the model’s ability to generalize effectively, and the complex morphology of Amharic, which further complicates learning patterns in translation tasks. …”
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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. …”
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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. …”
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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. …”
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Optimized Motion Capture for Cricket Shot Classification Using Minimal Hardware and Machine Learning by J. Ishan Randika, Kanishka Rajamanthri, Avishka Kothalawala, Niroshan Gunawardana, Ashan Induranga, Pathum Weerakkody, Kaveendra Maduwantha, B. T. G. S. Kumara, Kaveenga Koswattage

“…Motion data collected from the system was analyzed to extract distinct angle variation patterns associated with different batting shots. These patterns were used to train a hybrid machine learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. …”
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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. …”
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PREDICTION INTERVALS IN MACHINE LEARNING: RESIDUAL BOOTSTRAP AND QUANTILE REGRESSION FOR CASH FLOW ANALYSIS by Wa Ode Rahmalia Safitri, Farit Mochamad Afendi, Budi Susetyo

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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

“…The global distributions of ozone bias predicted by ML revealed systematic patterns influenced by meteorological conditions, geographic features, anthropogenic activities, and biogenic emissions. …”
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Immune Microenvironment Characterization and Machine Learning-Guided Identification of Diagnostic Biomarkers for Ulcerative Colitis by Zheng Q, Wang L, Zhang Y, Peng J, Hou J, Wang H, Ma Y, Tang P, Li Y, Li H, Chen Y, Li J, Chen Y

“…Likewise, cytokines such as IL-10, TGF-β, RORγ, and IL-21 exhibited abnormal expression patterns in UC tissues. WGCNA identified three immune cell-associated gene modules, among which the MEblue, MEturquoise, and MEgrey modules were highly correlated with aberrant immune cells. …”
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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). …”
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Design of an Iterative Method for Malware Detection Using Autoencoders and Hybrid Machine Learning Models by Rijvan Beg, R. K. Pateriya, Deepak Singh Tomar

“…This configuration allows the model to be able to detect and adapt to evolving attack patterns, thus, by 25%, significantly improving the zero-day attack detection.…”
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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. …”
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