Interactions between the gut microbiome and DNA methylation patterns in blood and visceral adipose tissue in subjects with different metabolic characteristics by José Ignacio Martínez-Montoro, Gracia M. Martín-Núñez, Andrés González-Jiménez, Lourdes Garrido-Sánchez, Isabel Moreno-Indias, Francisco J. Tinahones

“…., participants without obesity -healthy controls-, and participants with obesity and normal insulin sensitivity/insulin resistance/ type 2 diabetes-T2DM-) were included in this study. A machine learning approach was performed in order to identify distinctive patterns in three omics (gut microbiome, blood DNA methylome, and visceral adipose tissue-VAT- DNA methylome) according to the different study groups. …”
Get full text

Capturing drug use patterns at a glance: An n-ary word sufficient statistic for repeated univariate categorical values. by Gabriel J Odom, Laura Brandt, Clinton Castro, Sean X Luo, Daniel J Feaster, Raymond R Balise, CTN-0094 Team

“…Further, machine readable use pattern summaries are a standardized method to calculate treatment outcomes and are therefore useful to all future SUD clinical trials. …”
Get full text

Exploring the role of armed conflict in progress toward Sustainable Development Goals: Global patterns, regional differences, and driving mechanisms by Di Wang, Zhenci Xu, Unai Pascual, Lei Liu, Waqar Ahmad, Dong Jiang

Get full text

QuadTPat: Quadruple Transition Pattern-based explainable feature engineering model for stress detection using EEG signals by Veysel Yusuf Cambay, Irem Tasci, Gulay Tasci, Rena Hajiyeva, Sengul Dogan, Turker Tuncer

“…Therefore, EEG signal processing is crucial for neuroscience and machine learning (ML). Therefore, a new EEG stress dataset has been collected, and an explainable feature engineering (XFE) model has been proposed using the Directed Lobish (DLob) symbolic language. …”
Get full text

Exploring the best fit: A comparative analysis of AFINN, Textblob, VADER, and Pattern on Arabic reviews for optimal dictionary extraction by Shakeel Ahmad, Sheikh Muhammad Saqib, Asif Hassan Syed, Nashwan Alromema, Ali Kararay

“…While deep learning models tailored for Arabic text have garnered attention, there exists a considerable gap in integrating widely used tools like AFINN, TextBlob, VADER, and Pattern.en for text polarity due to compatibility issues with Arabic text. …”
Get full text

Solar Flare Prediction Using Long Short-term Memory (LSTM) and Decomposition-LSTM with Sliding Window Pattern Recognition by Zeinab Hassani, Davud Mohammadpur, Hossein Safari

“…Among approximately possible patterns, 7552 yearly pattern windows are identified, highlighting the challenge of long-term forecasting due to the Sun’s complex, self-organized-criticality-driven behavior. …”
Get full text

Fingerprint patterns of human brain activity reveal a dynamic mix of emotional responses during virtual intergroup encounters by Gloria Mendoza-Franco, Inga Jasinskaja-Lahti, Matthias B. Aulbach, Ville J. Harjunen, Anna Peltola, J. Niklas Ravaja, Matilde Tassinari, Saana Vainio, Iiro P. Jääskeläinen

“…In this study, we used machine learning to identify emotional brain patterns using functional magnetic resonance imaging. …”
Get full text

Flower Automata Pattern-Based Discrimination of Fibromyalgia From Control Subjects Using Fusion of Sleep EEG and ECG Signals by Prabal Datta Barua, Makiko Kobayashi, Sengul Dogan, Mehmet Baygin, Turker Tuncer, Jose Kunnel Paul, Thomas Iype, U. R. Acharya

“…The novelty of the model is the use of dynamic and interpretable feature engineering framework comprising of two innovations: 1) Flower Automata Pattern (FAP) for self-organized pattern-based feature extraction, and 2) Attention-Driven Wavelet Transform and Absolute Maximum Pooling (ADWTAMP) method for signal decomposition and compression. …”
Get full text

Predicting Three-Dimensional (3D) Printing Product Quality with Machine Learning-Based Regression Methods by Ahmet Burak Tatar

“…This dataset includes input parameters such as layer height, wall thickness, infill density, infill pattern, nozzle temperature, bed temperature, print speed, printing material (PLA and ABS), and fan speed. …”
Get full text

Effective Machine Learning Techniques for Non-English Radiology Report Classification: A Danish Case Study by Alice Schiavone, Lea Marie Pehrson, Silvia Ingala, Rasmus Bonnevie, Marco Fraccaro, Dana Li, Michael Bachmann Nielsen, Desmond Elliott

“…Background: Machine learning methods for clinical assistance require a large number of annotations from trained experts to achieve optimal performance. …”
Get full text

Knowledge Driven Machine Learning Towards Interpretable Intelligent Prognostics and Health Management: Review and Case Study by Ruqiang Yan, Zheng Zhou, Zuogang Shang, Zhiying Wang, Chenye Hu, Yasong Li, Yuangui Yang, Xuefeng Chen, Robert X. Gao

“…Abstract Despite significant progress in the Prognostics and Health Management (PHM) domain using pattern learning systems from data, machine learning (ML) still faces challenges related to limited generalization and weak interpretability. …”
Get full text

Biomarkers of Eating Disorders Using Support Vector Machine Analysis of Structural Neuroimaging Data: Preliminary Results by Antonio Cerasa, Isabella Castiglioni, Christian Salvatore, Angela Funaro, Iolanda Martino, Stefania Alfano, Giulia Donzuso, Paolo Perrotta, Maria Cecilia Gioia, Maria Carla Gilardi, Aldo Quattrone

“…Support Vector Machine (SVM) technique, combined with a pattern recognition method, was employed utilizing structural magnetic resonance images. …”
Get full text

Supervised machine learning classification algorithms for detection of fracture location in dissimilar friction stir welded joints by Akshansh Mishra, Apoorv Vats

“…�Machine Learning focuses on the study of algorithms that are mathematical or statistical in nature in order to extract the required information pattern from the available data. …”
Get full text

THE TECHNOLOGY OF INTERNAL THREAD CUTTING OF HIGH PRECISION IN PARTS OF SHIP MACHINE COMPONENTS, MACHINERY AND SHIP ARMATURE by N. M. Vagabov, A. Z. Kurbanov, M. A. Magomedova

“…Noted are shortcomings of their designs resulting in creating a modern cutting pattern, allowing to minimize the shortages identified in the processing of hard materials. …”
Get full text

A STUDY ON THE MONITORING MANAGEMENT SYSTEM FOR ANALYZING THE HEALTH STATUS OF LIVESTOCK CATTLE BASED ON MACHINE LEARNING by Cho Jeong Hyun

Get full text

Algorithm for link prediction in self-regulating network with adaptive topology based on graph theory and machine learning by Evgeny Y. Pavlenko

Get full text

The LETBP feature descriptor based fish species classification using Kepler optimization with Extreme Learning Machine by Annalakshmi GANESAN, Sakthivel Murugan SANTHANAM

“…Then, the novel feature descriptor method called local energy triangular binary pattern (LETBP) is proposed to extract features from the images, which effectively extracts the pixel information from all directions. …”
Get full text

Analysis of Structural Internal Forces and Stratum Deformation in Shaft Construction Using Vertical Shaft Sinking Machine by Hexige Baoyin, Zhibing Xu, Long Yu, Xu Zhang, Xiaoxiao Wang, Yang Liu

“…The use of the vertical shaft sinking machine (VSM) for shaft construction can effectively improve construction safety and efficiency. …”
Get full text

An Adaptive Vehicle Location Prediction Using Machine Learning: A Case Study of Campus Shuttle Bus by Tarak Nandy, Raenu Kolandaisamy, Rafidah Md Noor, Sananda Bhattacharyya

“…On the other hand, the stateless predictive model is unsuitable due to lower accuracy and mismatched pattern analysis. This paper proposed a vehicle location prediction model using machine learning based on a case study of a campus shuttle bus scenario. …”
Get full text

Predicting visual field global and local parameters from OCT measurements using explainable machine learning by Md Mahmudul Hasan, Jack Phu, Henrietta Wang, Arcot Sowmya, Erik Meijering, Michael Kalloniatis

“…To evaluate the models, a total of 268 glaucomatous eyes (86 early, 72 moderate, 110 advanced) and 226 normal eyes were included. The machine learning models outperformed recent OCT-based VF prediction deep learning studies, with correlation coefficients of 0.76, 0.80 and 0.76 for mean deviation, visual field index and pattern standard deviation, respectively. …”
Get full text