Identification of PRKCQ-AS1 as a Keratinocyte-Derived Exosomal lncRNA That Promotes Th17 Differentiation and IL-17 secretion in Psoriasis Through Bioinformatics, Machine L... by Gao P, Gao X, Lin L, Zhang M, Luo D, Chen C, Li Y, He Y, Liu X, Shi C, Yang R

“…This study investigates key exosomal ncRNAs regulating the Th17/IL-17 axis in psoriasis and their mechanisms.Methods: We integrated bulk RNA sequencing datasets from the GEO database to construct and evaluate exosome-related patterns. Subsequently, exosome-related ncRNAs in psoriasis lesions were identified primarily through weighted gene co-expression network analysis and five machine learning algorithms. …”
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A Cloud-Based Intelligence System for Asian Rust Risk Analysis in Soybean Crops by Ricardo Alexandre Neves, Paulo Estevão Cruvinel

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An online learning method for assessing smart grid stability under dynamic perturbations by Alaa Alaerjan, Randa Jabeur

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Pattern Recognition in Urban Maps Based on Graph Structures by Xiaomin Lu, Zhiyi Zhang, Haoran Song, Haowen Yan

“…Current pattern recognition methods for map groups primarily fall into two categories: machine learning-based approaches and traditional methods. …”
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Exploring Price Patterns of Vegetables with Recurrence Quantification Analysis by Sofia Karakasidou, Athanasios Fragkou, Loukas Zachilas, Theodoros Karakasidis

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A Dual-Strategy Framework for Cyber Threat Detection in Imbalanced, High-Dimensional Data Across Heterogeneous Networks by T. Saranya, S. Indra Priyadharshini

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Marine Mammals Classification using Acoustic Binary Patterns by Maheen NADIR, Syed Muhammad ADNAN, Sumair AZIZ, Muhammad Umar KHAN

“…Prior methods for classification focused on spectral features which result in increasing bias for contour base classifiers in automatic detection algorithms. In this study, acoustic marine mammal classification is performed through the fusion of 1D Local Binary Pattern (1D-LBP) and Mel Frequency Cepstral Coefficient (MFCC) based features. …”
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ADAPTIVE VISION AI by V. Vodyanitskyi, V. Yuskovych-Zhukovska

“…Computer vision technologies rely on pattern recognition, machine learning, and neural networks to allow computers to break down images, interpret data, and identify features. …”
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Predictive Models for Educational Purposes: A Systematic Review by Ahlam Almalawi, Ben Soh, Alice Li, Halima Samra

“…The review compares the effectiveness of machine learning (ML) algorithms such as Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Decision Trees with traditional statistical models, assessing their ability to manage complex educational data and improve decision-making. …”
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Identification of novel potential biomarkers using bulk RNA and single cells to build a neural network model for diagnosis of liver cancer by Yingzheng Gao, Jiahao Chen, Weidong Du

“…Methods In this study, transcriptome and single-cell datasets related to liver cancer were downloaded from the UCSC Xena database and the Mendeley database, and differential analysis and weighted gene co-expression network analysis were used to find differentially expressed genes related to liver cancer. We used multiple machine algorithms to find hub genes related to liver cancer, and constructed new artificial neural network models based on their transcriptome expression patterns to assist in the diagnosis of liver cancer. …”
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Enhanced Kidney Stone Detection and Classification Using SVM and LBP Features by Hawkar K. Hama, Hamsa D. Majeed, Goran Saman Nariman

“…The segmentation phase follows, accurately identifying the kidney’s edges and regions of interest for effective feature extraction. The Local Binary Pattern (LBP) technique, combined with the support vector machine (SVM) algorithm serves as the primary components of the proposed model. …”
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Unveiling geological complexity in the Serra Dourada Granite using self-organizing maps and hierarchical clustering: Insights for REE prospecting in the Goiás Tin Province, Brasíli... by Guilherme Ferreira da Silva, Marcos Vinícius Ferreira, Lucy Takehara Chemale, Igor Vasconcelos Santana, Nilson Francisquini Botelho

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Advanced Leaf Classification Using Multi-Layer Perceptron for Smart Crop Management by Sara Mumtaz, Mohammed Alshehri, Yahya AlQahtani, Abdulmonem Alshahrani, Bayan Alabdullah, Haifa F. Alhasson, Hui Liu

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Data Clustering Using by Chaotic SSPCO Algorithm by OICC Press Authors

“…Data clustering is a popular analysis tool for data statistics in several fields, including includes pattern recognition, data mining, machine learning, image analysis and bioinformatics, in which the information to be analyzed can be of any distribution in size and shape. …”
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Application of machine learning to growth model in fisheries by Semra Benzer, Recep Benzer, Ali Gül

“…Their limitations in capturing nonlinear patterns necessitate alternative approaches. Machine learning (ML) techniques have recently gained attention as a powerful tool for enhancing predictive accuracy in biological studies. …”
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Ghost in the Machine: Daily Interaction with Ephemeral Computing by Sofia Pirandello

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Enhancing Heart Attack Prediction: Feature Identification from Multiparametric Cardiac Data Using Explainable AI by Muhammad Waqar, Muhammad Bilal Shahnawaz, Sajid Saleem, Hassan Dawood, Usman Muhammad, Hussain Dawood

“…However, timely diagnosis remains a challenge due to the complex and nonlinear relationships between clinical indicators. Machine learning (ML) and deep learning (DL) models have the potential to predict cardiac conditions by identifying complex patterns within data, but their “black-box” nature restricts interpretability, making it challenging for healthcare professionals to comprehend the reasoning behind predictions. …”
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Technological advances and the use of IoT in monitoring Diaphorina citri in citrus cultivation by Eduardo Goiano da Silva, Franciely da Silva Ponce, Silvia Graciele Hulse de Souza

“…Integrating data from satellite images, field sensors, and machine learning algorithms makes developing more comprehensive and predictive monitoring of agricultural conditions possible. …”
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Advancing overbreak prediction in drilling and blasting tunnel using MVO, SSA and HHO-based SVM models with interpretability analysis by Yulin Zhang, Jian Zhou, Jialu Li, Biao He, Danial Jahed Armaghani, Shuai Huang

“…Empirical methods lack universal applicability due to their reliance on specific project conditions, while statistical models struggle with inconsistent patterns across different datasets. Furthermore, traditional AI models, including single machine learning algorithms, often overlook the multifaceted nature of overbreak, and hybrid models lack comprehensive evaluation standards. …”
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Metabolomics and nutrient intake reveal metabolite–nutrient interactions in metabolic syndrome: insights from the Korean Genome and Epidemiology Study by Minyeong Kim, Suyeon Lee, Junguk Hur, Dayeon Shin

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