An Advanced Recomposition-Based Displaying Technique: Maximizing Image Reconstruction for Virtual Museum Applications by Jingjie Zhao, Xin Shi, Olga Yezhova, Qinchuan Zhan, Xijing Zhang

“…These methods, combined with a multi-layer aggregation algorithm that encodes deep feature representations in a Gaussian Mixture Model (GMM), enable seamless scene reconstruction with improved precision. …”
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Enhancing Consumer Decision-Making in Skincare: Implementation of the VIKOR Method for Product Recommendation Systems by Diah Arifah Prastiningtyas, Greta Septy Purwiantono, Febry Eka Purwiantono, Addin Aditya

“…By demonstrating the efficacy of the VIKOR method, this research paves the way for its future application in various industries, offering a replicable and adaptable model for improving decision processes in consumer goods selection across diverse markets. …”
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MAT-FHE: arbitrary dimension matrix multiplication scheme for floating point over fully homomorphic encryption by Yatao Yang, Zhaofu Li, Yucheng Ding, Man Hu

“…After analysis, it is secure under the CPA model, composable, and supports floating matrix continuous multiplication. …”
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A Secure Data Collection Method Based on Deep Reinforcement Learning and Lightweight Authentication by Yunlong Wang, Jie Zhang, Guangjie Han, Dugui Chen

“…To address such challenges, we propose a lightweight chain authentication protocol for scalable IoT environments (LCAP-SIoT), which uses Physical Unclonable Functions (PUFs) and distributed authentication to secure communications, and a secure data collection algorithm, named LS-QMIX, which fuses the LCAP-SIoT and Q-learning Mixer (QMIX) algorithm to optimize the path planning and cooperation efficiency of the multi-UAV system. …”
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Neural network technologies for forecasting and controlling electricity consumption in energy systems by the genetic method by Nikolay K. Poluyanovich, Oleg V. Kachelaev, Marina N. Dubyago, Talia Hernandez Falcón

“…Based on the results of training and testing, the genetic algorithm confirmed the possibility of automating the selection of optimal hyperparameters and obtaining forecasts of greater accuracy and the possibility.…”
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Machine Learning-Based Interpretable Screening for Osteoporosis in Tuberculosis Spondylitis Patients Using Blood Test Data: Development and External Validation of a Novel Web-Based... by Yasin P, Ding L, Mamat M, Guo W, Song X

“…The optimal model was further refined through threshold tuning to enhance performance metrics. …”
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Torque capability enhancement of commercial interior permanent magnet motors using t-shaped notching and merged barrier rotor topology by H.N. Lin, P. Seangwong, N. Fernando, A. Siritaratiwat, P. Khunkitti

“…A multi-objective genetic algorithm with sensitivity-based constraints is employed to optimizes key design parameters, and the electromagnetic performance is evaluated using 2D finite element analysis under no-load and on-load conditions. …”
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A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI by Oishi Jyoti, Hafsa Binte Kibria, Zareen Tasnim Pear, Md Nahiduzzaman, Md. Faysal Ahamed, Khandaker Reajul Islam, Jaya Kumar, Muhammad E. H. Chowdhury

“…This study focuses on optimizing and comparing various machine learning models for ASD diagnosis, while incorporating explainable AI techniques to ensure model transparency and interpretability. …”
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Surface Soil Organic Carbon Estimation Based on Habitat Patches in Southwest China by Jieyun Xiao, Wei Zhou, Ting Wang, Yao Peng, Zhan Shi, Saibo Li, Yang Li, Tianxiang Yue

“…Using the RF feature selection (FS) method to select optimal factors as model input variables improved simulation accuracy compared with using all factors or selecting based on Pearson correlation analysis (R² increased by 1.75%–64.71%). …”
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Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis by Fernando Pedro Silva Almeida, Mauro Castelli, Nadine Côrte-Real

“…This study addresses the gap in existing research by comprehensively analyzing the performance of various machine learning algorithms, including ensemble learning and deep learning models, to improve prediction accuracy. …”
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Analysis of Blockchain-Technology by Danylo Dvorchuk, Iryna Shpinareva

“…The research also highlights emerging trends in blockchain development, particularly hybrid models and AI-driven optimization techniques, which can enhance blockchain efficiency and security. …”
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A hybrid approach to predicting and classifying dental impaction: integrating regularized regression and XG boost methods by Asok Mathew, Pradeep K. Yadalam, Ahmed Radeideh, Shrouk Hady, Rona Swed, Reyyan Cheema, Majd Mousa AL-Mohammad, Mohammed Alsaegh, SR Shetty

“…Enhancing data quality, refining feature selection, and using advanced modeling techniques are crucial for improving predictive capabilities. …”
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Evaluating the performance and feasibility of integrating thermoelectric generators with solar photovoltaic panels: A case study by D Lakshmi Satya Nagasri, ram kumar Alajingi, Ponnambalam Pathipooranam, S. Senthil raja, Mohd Faiz bin Mohd Salleh, R. Marimuthu

“…Further, a conclusion is drawn from the techno-economic analysis to focus on optimizing the number of TEGs placed under SPV and improving MPPT algorithms to enhance performance and reduce costs.…”
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Comparative Analysis of Machine Learning Techniques for Fault Diagnosis of Rolling Element Bearing with Wear Defects by Devendra Sahu, Ritesh Kumar Dewangan, Surendra Pal Singh Matharu

“…This optimization of the signal enhancement methodology significantly improved the fault diagnosis accuracy. …”
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Image-Based Detection and Classification of Malaria Parasites and Leukocytes with Quality Assessment of Romanowsky-Stained Blood Smears by Jhonathan Sora-Cardenas, Wendy M. Fong-Amaris, Cesar A. Salazar-Centeno, Alejandro Castañeda, Oscar D. Martínez-Bernal, Daniel R. Suárez, Carol Martínez

“…Leukocyte detection employed optimal thresholding segmentation utility (OTSU) thresholding, binary masking, and erosion, followed by the connected components algorithm. …”
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Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study by Rishishankar E. Suresh, M S Zobaer, Matthew J. Triano, Brian F. Saway, Parneet Grewal, Nathan C. Rowland

“…Certain movement phases are more responsive to NIBS, so a system that auto-detects these phases would optimize stimulation timing. This study assessed the effectiveness of various machine learning models in identifying movement phases in hemiparetic individuals undergoing simultaneous NIBS and EEG recordings. …”
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ES-Net Empowers Forest Disturbance Monitoring: Edge–Semantic Collaborative Network for Canopy Gap Mapping by Yutong Wang, Zhang Zhang, Jisheng Xia, Fei Zhao, Pinliang Dong

“…Canopy gaps are vital microhabitats for forest carbon cycling and species regeneration, whose accurate extraction is crucial for ecological modeling and smart forestry. However, traditional monitoring methods have notable limitations: ground-based measurements are inefficient; remote-sensing interpretation is susceptible to terrain and spectral interference; and traditional algorithms exhibit an insufficient feature representation capability. …”
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MoNetViT: an efficient fusion of CNN and transformer technologies for visual navigation assistance with multi query attention by Liliek Triyono, Liliek Triyono, Rahmat Gernowo, Prayitno

“…Our study introduces MoNetViT (Mini-MobileNet MobileViT), a lightweight model combining CNNs and MobileViT in a dual-path encoder to optimize global and spatial image details. …”
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Energy and Reserve Scheduling of Distribution Network in Presence of Electric Vehicle Aggregators: A Decentralized Approach by Atena Tazikeh Lemeski, Reza Ebrahimi, Alireza Zakariazadeh, Keyvan Firuzi

“…The goal is the maximization of AGG profit from energy transactions along with the least network operation costs and improvement of network indices. The objective function of the proposed approach also includes AGG capability to provide the network with spinning reserve which brings incentive payments. …”
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Flexible imputation toolkit for electronic health records by Alireza Vafaei Sadr, Jiang Li, Wenke Hwang, Mohammed Yeasin, Ming Wang, Harold Lehmann, Ramin Zand, Vida Abedi

“…Our findings validate that Pympute’s Flexible method significantly improves imputation performance compared to the single model approach. …”
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