Enhancing multiclass brain tumor diagnosis using SVM and innovative feature extraction techniques by Mustafa Basthikodi, M. Chaithrashree, B. M. Ahamed Shafeeq, Ananth Prabhu Gurpur

“…This research addresses the challenge of multiclass categorization by employing Support Vector Machine (SVM) as the core classification algorithm and analyzing its performance in conjunction with feature extraction techniques such as Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP), as well as the dimensionality reduction technique, Principal Component Analysis (PCA). …”
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Development of an Automated Management System for Agricultural Technologies in Horticulture by D. O. Khort, A. I. Kutyrev, I. G. Smirnov, I. V. Voronkov

“…They created the system ability to operate in a dialogue mode with the user through forms, based on the algorithm for choosing the optimal options for technological processes in the horticultural products production. …”
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Towards a computer-assisted assessment of imitation in children with autism spectrum disorder based on a fine-grained analysis by Rujing Zhang, Jingying Chen, Xiaodi Liu, Yanling Gan, Guangshuai Wang

“…In this process, several quantitative indicators were applied to quantify the children’s imitation ability based on a fine-grained analysis of their visual attention and motor execution patterns. Then, three classic machine-learning algorithms were employed to explore whether the indicators could efficiently classify children with imitation difficulties. …”
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Analysis of the capabilities of an information system for improving sleep quality based on biometric data analysis by M.S. Graf, A.V. Yakoniuk, D.V. Krant, I.I. Golovach

“…The system is able to function independently thanks to the use of machine learning algorithms, including LSTM for prediction, Kalman filter for data cleaning, Isolation Forest for anomaly detection, and K-means for clustering sleep patterns.…”
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基于IMU信号的人工智能上肢多关节运动状态识别系统构建——卒中后人工智能运动功能评估与检测系统建设前导研究 Construction of an Artificial Intelligence Upper Limb Multi-Joint Motion State Recognition System Based on IMU Signals—A Pre... by 程相鑫1，张烁1，杜松骏1，刘子阳1，周宏宇2，3，贾伟丽2，3，李子孝2，3，刘涛1 (CHENG Xiangxin1, ZHANG Shuo1, DU Songjun1, LIU Ziyang1, ZHOU Hongyu2,3, JIA Weili2,3, LI Zixiao2,3, LIU Tao1 )

“…The results showed that deep learning algorithms were superior to traditional machine learning in decoding complex motion patterns, and a single-wrist IMU could replace the dual-sensor configuration in specific tasks, providing a basis for hardware optimization. …”
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Classification Model for Bot-IoT Attack Detection Using Correlation and Analysis of Variance by Firgiawan Faira, Dandy Pramana Hostiadi

“…The classification process utilized Random Forest, k-NN, Naïve Bayes, and Decision Tree algorithms, with 100 iterations and an 80:20 training-testing split. …”
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Energy Demand Forecasting Scenarios for Buildings Using Six AI Models by Khaled M. Salem, Francisco J. Rey-Martínez, A. O. Elgharib, Javier M. Rey-Hernández

“…This research addresses a significant gap in energy demand forecasting across three building types by comparing six machine learning algorithms: Artificial Neural Networks, Random Forest, XGBoost, Radial Basis Function Network, Autoencoder, and Decision Trees. …”
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Integrating dimension reduction and out-of-sample extension in automated classification of ex vivo human patellar cartilage on phase contrast X-ray computed tomography. by Mahesh B Nagarajan, Paola Coan, Markus B Huber, Paul C Diemoz, Axel Wismüller

“…However, the large size of feature sets extracted in such studies motivates an investigation into algorithmic feature reduction for computing efficient feature representations without compromising their discriminatory power. …”
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Enhancing Crop Yield Prediction Using IoT-Based Soil Moisture and Nutrient Sensors by Alsalami Zaid, Mohammed G., Srinivas Tummala

“…Crop yield prediction is crucial for ensuring food security by enabling farmers to optimize resource use, manage risks, and plan for market demands, ultimately leading to increased agricultural productivity and sustainability..The IoT-based crop yield prediction system integrates advanced sensing technologies, communication protocols, machine learning algorithms, and real-time monitoring to optimize crop production. …”
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Prediction of early breast cancer patient survival using ensembles of hypoxia signatures. by Inna Y Gong, Natalie S Fox, Vincent Huang, Paul C Boutros

“…As such, these classification patterns further confirm that there is a subset of patients whose prognosis is consistently challenging to predict.…”
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Integrative Role of RNA N7-methylguanosine in epilepsy: Regulation of neuronal oxidative phosphorylation, programmed death and immune microenvironment. by Jiangli Zhao, Qingyuan Sun, Xuchen Liu, Jiwei Wang, Ning Yang, Chao Li, Xinyu Wang

“…Our findings also suggested that active m7G levels could promote oxidative phosphorylation in the neurons of epilepsy patients and decrease neuronal necroptosis activity. Machine learning algorithms were used to identify key m7G regulators (EIF4E3, NUDT3, SNUPN, LSM1, and METTL1), and a nomogram model was constructed based on these findings. …”
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Identification and experimental verification of biomarkers related to butyrate metabolism in osteoarthritis by Yi Zhang, Youliang Shen, Dewei Kou, Tengbo Yu

“…Six candidate biomarkers (IL1B, IGF1, CXCL8, PTGS2, SERPINE1, MMP9) were identified through two machine-learning algorithms. IL1B, CXCL8, and PTGS2 were upregulated in controls, exhibiting consistent patterns across validation datasets. …”
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Research Progress on Sequence Recommendation Based on Deep Learning and Large Language Model by XU Fengru, LI Bohan, XU Shuai

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High-speed threat detection in 5G SDN with particle swarm optimizer integrated GRU-driven generative adversarial network by R. Shameli, Sujatha Rajkumar

“…The attack detection in 5G SDN involves Machine learning (ML) and Deep learning (DL) algorithms to analyze large volumes of network data and identify patterns indicative of attacks. …”
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TATPat based explainable EEG model for neonatal seizure detection by Turker Tuncer, Sengul Dogan, Irem Tasci, Burak Tasci, Rena Hajiyeva

“…In this EFE model, there are four essential phases and these phases: (i) automaton and transformer-based feature extraction, (ii) feature selection deploying cumulative weight-based neighborhood component analysis (CWNCA), (iii) the Directed Lobish (DLob) and Causal Connectome Theory (CCT)-based explainable result generation and (iv) classification deploying t algorithm-based support vector machine (tSVM). In the first phase, we have used a channel transformer to get channel numbers and these values have been divided into three levels and these levels are named (1) high, (2) medium and (3) low. …”
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VGGBM-Net: A Novel Pixel-Based Transfer Features Engineering for Automated Coffee Bean Diseases Classification by Muhammad Shadab Alam Hashmi, Azam Mehmood Qadri, Ali Raza, Saleem Ullah, Aseel Smerat, Changgyun Kim, Muhammad Syafrudin, Norma Latif Fitriyani

“…These enhanced features are then used as inputs for advanced machine-learning algorithms. Unlike traditional models, this feature extraction enhances classification accuracy and robustness. …”
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Abnormal eye movement, brain regional homogeneity in schizophrenia and clinical high-risk individuals and their associated gene expression profiles by Zhaobin Chen, Yangpan Ou, Yudan Ding, Ying Wang, Huabing Li, Feng Liu, Ping Li, Dongsheng Lv, Yong Liu, Bing Lang, Jingping Zhao, Wenbin Guo

“…Twenty-seven drug-naïve FSZ, 25 CHR, and 28 healthy controls (HCs) were recruited for eye-tracking tasks and resting-state functional magnetic resonance imaging to evaluate eye movement and regional homogeneity (ReHo) differences. Machine-learning algorithms were used to differentiate FSZ from CHR. …”
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Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma. by José Fernando García Molina, Lei Zheng, Metin Sertdemir, Dietmar J Dinter, Stefan Schönberg, Matthias Rädle

“…Robust detection of prostatic cancer is a challenge due to the multitude of variants and their representation in MR images. We propose a pattern recognition system with an incremental learning ensemble algorithm using support vector machines (SVM) tackling this problem employing multimodal MR images and a texture-based information strategy. …”
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Exploration of Epigenetic Mechanisms and Biomarkers Among Patients with Very-Late-Onset Schizophrenia-Like Psychosis by Gan Y, Yue W, Sun J, Yang D, Fang C, Zhou Z, Yin J, Zhou H

“…Machine learning algorithms generated diagnostic models, with classification performance evaluated using Area Under the Curve (AUC) metrics.Results: Analysis revealed distinct DNA methylation signatures in VLOSLP patients compared to controls. …”
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Early Warning for Stepwise Landslides Based on Traffic Light System: A Case Study in China by Shuangshuang Wu, Zhigang Tao, Li Zhang, Song Chen

“…Furthermore, leveraging the C5.0 machine learning algorithm, a comparison between the predictive capabilities of the TLS model and a pure rate threshold model reveals that the TLS model achieves a 93% accuracy rate, outperforming the latter by 7 percentage points. …”
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