A Monocyte-Driven Prognostic Model for Multiple Myeloma: Multi-Omics and Machine Learning Insights by Xie L, Gao M, Tan S, Zhou Y, Liu J, Wang L, Li X

“…Through multi-omics analyses and machine learning algorithms, we established a robust monocyte-related prognostic signature. …”
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Predicting changes of incisor and facial profile following orthodontic treatment: a machine learning approach by Jing Peng, Yan Zhang, Mengyu Zheng, Yanyan Wu, Guizhen Deng, Jun Lyu, Jianming Chen

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Comparative analysis of force sensitive resistor circuitry for use in force myography systems for hand gesture recognition by Giancarlo K. Sagastume, Peyton R. Young, Marcus A. Battraw, Justin G. Kwong, Jonathon S. Schofield

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A deep learning approach for epilepsy seizure detection using EEG signals by Manoj Kaushik, Divyanshu Singh, Malay Kishore-Dutta, Carlos M. Travieso

“…EEG signals could be helpful in imminent seizure prediction if the machine could detect changes in EEG patterns. In this study, we have proposed a one-dimensional Convolutional Neural network (CNN) for the automatic detection of epilepsy seizures. …”
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The Comparison of Diffeomorphic Images Based on the Construction of Persistent Homology by Sergey N. Chukanov

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Textile Development (TED) Method – a new method for the sustainable development of new textile products by Isa Bettermann, Debolina Mukherjee-Kleiber, Rahel Heesemann, Thomas Gries

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A Methodology for Acceleration Signals Segmentation During Forming Regular Reliefs Patterns on Planar Surfaces by Ball Burnishing Operation by Stoyan Dimitrov Slavov, Georgi Venelinov Valchev

“…In the present study, an approach for determining the different states of ball burnishing (BB) operations aimed at forming regular reliefs’ patterns on planar surfaces is introduced. The methodology involves acquiring multi-axis accelerometer data from CNC-driven milling machine to capture the dynamics of the BB tool and workpiece, mounted on the machine table. …”
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Magnetic Resonance Imaging Texture Analysis Based on Intraosseous and Extraosseous Lesions to Predict Prognosis in Patients with Osteosarcoma by Yu Mori, Hainan Ren, Naoko Mori, Munenori Watanuki, Shin Hitachi, Mika Watanabe, Shunji Mugikura, Kei Takase

“…<b>Objectives:</b> To construct an optimal magnetic resonance imaging (MRI) texture model to evaluate histological patterns and predict prognosis in patients with osteosarcoma (OS). …”
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Microbiome and fragmentation pattern of blood cell-free DNA and fecal metagenome enhance colorectal cancer micro-dysbiosis and diagnosis analysis: a proof-of-concept study by Zhongkun Zhou, Yunhao Ma, Dekui Zhang, Rui Ji, Yiqing Wang, Jianfang Zhao, Chi Ma, Hongmei Zhu, Haofei Shen, Xinrong Jiang, Yuqing Niu, Juan Lu, Baizhuo Zhang, Lixue Tu, Hua Zhang, Xin Ma, Peng Chen

“…Machine learning models based on these differential characteristics achieve high diagnostic accuracy, especially when they are integrated with fragmentation patterns. …”
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Graph-based machine learning for high-resolution assessment of pedestrian-weighted exposure to air pollution by Feifeng Jiang, Jun Ma

“…The results reveal significant divergences between traditional exposure assessments and pedestrian-specific exposure patterns, uncovering previously overlooked high-risk zones. …”
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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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Transcriptomic analysis and machine learning modeling identifies novel biomarkers and genetic characteristics of hypertrophic cardiomyopathy by Feng Zhang, Chunrui Li, Lulu Zhang

“…Immune cell infiltration patterns were quantified via single-sample gene set enrichment analysis (ssGSEA). …”
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Symbolic Machine Learning: A Different Answer to the Problem of the Acquisition of Lexical Knowledge from Corpora by Pascale Sébillot

“…Among them, the symbolic machine learning (ML) techniques can infer efficient and expressive patterns of a target relation from examples of elements that verify this relation. …”
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Real-Time Acoustic Measurement System for Cutting-Tool Analysis During Stainless Steel Machining by Tom Salm, Kourosh Tatar, José Chilo

“…Using the TreeBagger machine-learning algorithm, the system accurately predicts tool wear, detecting both gradual and abrupt wear patterns. …”
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In-Process Monitoring of Inhomogeneous Material Characteristics Based on Machine Learning for Future Application in Additive Manufacturing by André Jaquemod, Marijana Palalić, Kamil Güzel, Hans-Christian Möhring

“…The algorithms are trained to recognize patterns, anomalies, or deviations from expected behavior, which can aid in evaluating the effect of detected defects on the machining process and the resultant component quality. …”
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Discrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study by Stanford Martinez, Carolina Ramirez-Tamayo, Syed Hasib Akhter Faruqui, Kal Clark, Adel Alaeddini, Nicholas Czarnek, Aarushi Aggarwal, Sahra Emamzadeh, Jeffrey R Mock, Edward J Golob

“…To mitigate this problem, radiologists use personalized and high-dimensional visual search strategies, otherwise known as search patterns. Qualitative descriptions of these search patterns, which involve the physician verbalizing or annotating the order he or she analyzes the image, can be unreliable due to discrepancies in what is reported versus the actual visual patterns. …”
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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 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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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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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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