Reinforced liquid state machines—new training strategies for spiking neural networks based on reinforcements by Dominik Krenzer, Martin Bogdan, Martin Bogdan, Martin Bogdan

“…IntroductionFeedback and reinforcement signals in the brain act as natures sophisticated teaching tools, guiding neural circuits to self-organization, adaptation, and the encoding of complex patterns. This study investigates the impact of two feedback mechanisms within a deep liquid state machine architecture designed for spiking neural networks.MethodsThe Reinforced Liquid State Machine architecture integrates liquid layers, a winner-takes-all mechanism, a linear readout layer, and a novel reward-based reinforcement system to enhance learning efficacy. …”
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Machine Learning and Multilayer Perceptron-Based Customized Predictive Models for Individual Processes in Food Factories by Byunghyun Lim, Dongju Kim, Woojin Cho, Jae-Hoi Gu

“…This makes it difficult to identify usage patterns for individual operations. This study identifies steam energy consumption patterns across four stages of food processing. …”
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A guide to neural ordinary differential equations: Machine learning for data-driven digital engineering by Joseph M. Worsham, Jugal K. Kalita

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Enhancing Mobile App Recommendations With Crowdsourced Educational Data Using Machine Learning and Deep Learning by Naadiya Mirbahar, Kamlesh Kumar, Asif Ali Laghari

“…In the rapidly evolving digital landscape, personalized recommendations have become essential for enhancing user experience. Machine learning models analyze user behavior patterns to suggest relevant entertainment, education, or e-commerce content. …”
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Predictive maintenance based on anomaly detection in photovoltaic system using SCADA data and machine learning by Agussalim Syamsuddin, Andrew Cahyo Adhi, Amie Kusumawardhani, Toni Prahasto, Achmad Widodo

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Parameter estimation of submarine power cables in offshore applications using machine learning-based methods by Felipe P. de Albuquerque, Rafael Nascimento, Gabriel de Castro Biage, Rooney R.A. Coelho, Ronaldo F. Ribeiro Pereira, Eduardo C. Marques da Costa, Mario L. Pereira Filho, Cassio G. Lopes, José R. Cardoso

“…In practical conditions, the training dataset takes into account noise patterns using well-established modeling methods for phasor measurements. …”
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Comprehensive machine learning analysis of PANoptosis signatures in multiple myeloma identifies prognostic and immunotherapy biomarkers by Yashu Feng, Shuoting Wang, Jingwen Zhang, Chengcheng Liu, Ling Zhang, Jiajun Liu

“…Quantitative PCR (qPCR) further validated differential expression patterns of the seven core PRGs between MM patients and healthy controls. …”
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Multimodal machine learning for analysing multifactorial causes of disease—The case of childhood overweight and obesity in Mexico by Rosario Silva Sepulveda, Magnus Boman, Magnus Boman

“…Public health interventions have shown only moderate success, possibly from relying on knowledge extracted using limited types of statistical data analysis methods.PurposeTo explore if multimodal machine learning can enhance identifying predictive features from obesogenic environments and investigating complex disease or social patterns, using the Mexican National Health and Nutrition Survey.MethodsWe grouped features into five data modalities corresponding to paediatric population exogenous factors, in two multimodal machine learning pipelines, against a unimodal early fusion baseline. …”
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Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case Study by Maryam Abbasi, Marco V. Bernardo, Paulo Váz, José Silva, Pedro Martins

“…This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. …”
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The future of spatial epidemiology in the AI era: enhancing machine learning approaches with explicit spatial structure by Nima Kianfar, Benn Sartorius, Colleen L. Lau, Robert Bergquist, Behzad Kiani

“…Research in spatial epidemiology relies on both conventional approaches and Machine- Learning (ML) algorithms to explore geographic patterns of diseases and identify influential factors (Pfeiffer & Stevens, 2015). …”
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Myo‐Guide: A Machine Learning‐Based Web Application for Neuromuscular Disease Diagnosis With MRI by Jose Verdu‐Diaz, Carla Bolano‐Díaz, Alejandro Gonzalez‐Chamorro, Sam Fitzsimmons, Jodi Warman‐Chardon, Goknur Selen Kocak, Debora Mucida‐Alvim, Ian C. Smith, John Vissing, Nanna Scharff Poulsen, Sushan Luo, Cristina Domínguez‐González, Laura Bermejo‐Guerrero, David Gomez‐Andres, Javier Sotoca, Anna Pichiecchio, Silvia Nicolosi, Mauro Monforte, Claudia Brogna, Eugenio Mercuri, Jorge Alfredo Bevilacqua, Jorge Díaz‐Jara, Benjamín Pizarro‐Galleguillos, Peter Krkoska, Jorge Alonso‐Pérez, Montse Olivé, Erik H. Niks, Hermien E. Kan, James Lilleker, Mark Roberts, Bianca Buchignani, Jinhong Shin, Florence Esselin, Emmanuelle Le Bars, Anne Marie Childs, Edoardo Malfatti, Anna Sarkozy, Luke Perry, Sniya Sudhakar, Edmar Zanoteli, Filipe Tupinamba Di Pace, Emma Matthews, Shahram Attarian, David Bendahan, Matteo Garibaldi, Laura Fionda, Alicia Alonso‐Jiménez, Robert Carlier, Ali Asghar Okhovat, Shahriar Nafissi, Atchayaram Nalini, Seena Vengalil, Kieren Hollingsworth, Chiara Marini‐Bettolo, Volker Straub, Giorgio Tasca, Jaume Bacardit, Jordi Díaz‐Manera, the Myo‐Guide Consortium

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APPLYING SOME MATCHING ALGORITHMS FOR SEQUENCE IGNATURE TO ANALYZE AND DETECT ENTRIES INTO SYSTEM NETWORKS by Pham Nguyen Huy Phuong, Dang Le Nam

“…Additionally, tools for network monitoring such as open source munintools,are also used to analyze and evaluate the performance of network-attack. Next, the time of pattern identification in the Snort's machine, and the performance of Snort as well as the number of packets passing through Snort, the amount of alerts per second, connection speed in real time, the percentage of received data in pattern matching process, etc. are also measured based on intelligent algorithms built in Snort. …”
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Prediction of delirium occurrence using machine learning in acute stroke patients in intensive care unit by Hyungjun Kim, Hyungjun Kim, Min Kim, Da Young Kim, Dong Gi Seo, Ji Man Hong, Ji Man Hong, Dukyong Yoon, Dukyong Yoon

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Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validation by Han Chen, Han Chen, Enguang Chen, Enguang Chen, Ting Cao, Feifan Feng, Min Lin, Xuan Wang, Yu Xu

“…Validation confirmed the diagnostic efficiency and expression patterns of the PANoptosis-related hub genes, supported by in vitro and the GSE60436 dataset analysis. …”
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On Training Spiking Neural Networks by Means of a Novel Quantum Inspired Machine Learning Method by Jean Michel Sellier, Alexandre Martini

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Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals by Sindhuja Tirumalai Govindarajan, Elizabeth Mamourian, Guray Erus, Ahmed Abdulkadir, Randa Melhem, Jimit Doshi, Raymond Pomponio, Duygu Tosun, Murat Bilgel, Yang An, Aristeidis Sotiras, Daniel S. Marcus, Pamela LaMontagne, Tammie L. S. Benzinger, Mark A. Espeland, Colin L. Masters, Paul Maruff, Lenore J. Launer, Jurgen Fripp, Sterling C. Johnson, John C. Morris, Marilyn S. Albert, R. Nick Bryan, Susan M. Resnick, Mohamad Habes, Haochang Shou, David A. Wolk, Ilya M. Nasrallah, Christos Davatzikos

“…To address this, we developed and validated machine learning models to quantify the distinct spatial patterns of atrophy and white matter hyperintensities related to hypertension, hyperlipidemia, smoking, obesity, and type-2 diabetes mellitus at the patient level. …”
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Integrating Dimensional Analysis and Machine Learning for Predictive Maintenance of Francis Turbines in Sediment-Laden Flow by Álvaro Ospina, Ever Herrera Ríos, Jaime Jaramillo, Camilo A. Franco, Esteban A. Taborda, Farid B. Cortes

“…Key variables, including power output, water flow rate, and operational time, were extracted and analyzed to identify patterns and correlations affecting turbine performance. …”
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Machine Learning for the Photonic Evaluation of Cranial and Extracranial Sites in Healthy Individuals and in Patients with Multiple Sclerosis by Antonio Currà, Riccardo Gasbarrone, Davide Gattabria, Nicola Luigi Bragazzi, Giuseppe Bonifazi, Silvia Serranti, Paolo Missori, Francesco Fattapposta, Carlotta Manfredi, Andrea Maffucci, Luca Puce, Lucio Marinelli, Carlo Trompetto

“…We sought to identify the diagnostic accuracy of wavelength-specific patterns in distinguishing MS from normal controls and spectral markers associated with disability (e.g., Expanded Disability Status Scale scores). …”
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Social Factors Influencing Healthcare Expenditures: A Machine Learning Perspective on Australia’s Fiscal Challenges by Wei Gu, Zhantian Zhang, Ou Liu

“…This study employs machine learning techniques to investigate the key determinants of healthcare expenditures in Australia from 2011 to 2021. …”
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Predicting the Energy Consumption in Chillers: A Comparative Study of Supervised Machine Learning Regression Models by Mohamed Salah Benkhalfallah, Sofia Kouah, Saad Harous

“…This paper examines the application of artificial intelligence and supervised machine learning techniques to modeling and predicting the energy consumption patterns in the smart grid sector of a commercial building located in Singapore. …”
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