Does a Meta-Combining Method Lead to More Accurate forecasts in the decision-making process? by Emrah Gulay, Serkan Aras

“…When researchers deal with time series data by using dissimilar models, the combined forecasts of these models are expected to be superior. …”
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Using baseline MRI radiomics to predict the tumor shrinkage patterns in HR-Positive, HER2-Negative Breast Cancer by Lijia Wang, Yongchen Wang, Li Yang, Jialiang Ren, Qian Xu, Yingmin Zhai, Tao Zhou

“…The combined clinical-radiomics model further improved prediction accuracy, with an AUC of 0.831 in the training cohort and 0.810 in the validation cohort.ConclusionRadiomics features based on baseline MRI significantly enhance the prediction of tumor shrinkage patterns in HR+/HER2- breast cancer patients. …”
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MSASGCN :  Multi-Head Self-Attention Spatiotemporal Graph Convolutional Network for Traffic Flow Forecasting by Yang Cao, Detian Liu, Qizheng Yin, Fei Xue, Hengliang Tang

“…Experiments on two real datasets verify the stability of our proposed model, obtaining a better prediction performance than the baseline algorithms. …”
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Stock Price Prediction Using Machine Learning: Evidence from Pakistan Stock Exchange by Zafar Akhter, Dr. Hassan Raza

“…The report additionally proposes potential avenues for future research, such as exploring alternate data sources, employing sentiment analysis techniques, and developing more advanced algorithms. The findings of this study hold significant significance for investors and financial institutions, as they highlight the potential of machine learning in facilitating informed investment decisions and improving financial forecasts and analysis. …”
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Dynamic load balancing in cloud computing using predictive graph networks and adaptive neural scheduling by K. Rajammal, M. Chinnadurai

“…Additionally, comparative analyses with existing optimization algorithms exhibit the proposed model ability in managing the loads in cloud computing. …”
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Drone Swarm for Distributed Video Surveillance of Roads and Car Tracking by David Sánchez Pedroche, Daniel Amigo, Jesús García, José M. Molina, Pablo Zubasti

“…The proposal is to assess how a system consisting of multiple cooperating UAVs can enhance performance by utilizing fast detection algorithms. Within the study, the differences in one-stage and two-stage detection models have been considered, revealing that while two-stage models offer improved accuracy, their increased computation time renders them impractical for real-time applications. …”
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Experimental Study on Glass Deformation Calculation Using the Holographic Interferometry Double-Exposure Method by Yucheng Li, Yang Zhang, Deyu Jia, Song Gao, Muqun Zhang

“…This approach (1) eliminates object wave amplitude interference via dual-exposure superposition, establishing submicron linear mapping between fringe displacement and deformation amplitude; (2) introduces a fringe gradient-based direction detection algorithm resolving deformation vector ambiguity; and (3) implements an error-compensated fusion framework integrating theoretical modeling, MATLAB 2015b simulations, and experimental validation. …”
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Efficiency Optimization of PMSM-Type Elevator Machine: An Industrial Comparison by Mucahit Soyaslan, Osman Eldogan, Ahmet Fenercioglu, Yusuf Avsar, Nurdogan Ceylan, Muhammed Salih Sarıkaya

“…This study presents the design, modeling, and prototyping of an external rotor permanent magnet synchronous motor (ER-PMSM) specifically for elevator traction systems. …”
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Explainable AI-Based Skin Cancer Detection Using CNN, Particle Swarm Optimization and Machine Learning by Syed Adil Hussain Shah, Syed Taimoor Hussain Shah, Roa’a Khaled, Andrea Buccoliero, Syed Baqir Hussain Shah, Angelo Di Terlizzi, Giacomo Di Benedetto, Marco Agostino Deriu

“…To address these limitations, this study proposes a comprehensive pipeline combining transfer learning, feature selection, and machine-learning algorithms to improve detection accuracy. Multiple pretrained CNN models were evaluated, with Xception emerging as the optimal choice for its balance of computational efficiency and performance. …”
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PPLA-Transformer: An Efficient Transformer for Defect Detection with Linear Attention Based on Pyramid Pooling by Xiaona Song, Yubo Tian, Haichao Liu, Lijun Wang, Jinxing Niu

“…When compared to the Swin-Transformer model on the public PKU-Market-PCB dataset, our model achieves an improvement of 1.7% mAP and 51 FPS. …”
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Artificial Intelligence in Virtual Screening: Transforming Drug Research and Discovery—A Review by Sayantani Roy, Karuppiah Nagaraj, Amit Mittal, Flora C. Shah, Kaliyaperumal Raja

“…Machine learning-based scoring systems and quantitative structure–activity relationship (QSAR) models improved binding affinity predictions.…”
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An Enhanced Phenology Dataset for Global Drylands from 2001 to 2019 by Yuqi Dong, Yu Zhou, Li Zhang, Feng Tian, Qiaoyun Xie, Yiyang Chen, Linlin Ruan, Bo Zhang

“…We highlight the importance of high-resolution data in improving dryland phenology retrieval. This dataset improves our understanding of how dryland ecosystems respond to climate change and supports the development of Earth system models.…”
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Artificial Intelligence-Based Tools for Process Optimization: Case Study—Bromocresol Green Decolorization with Active Carbon by Gabriel Dan Suditu, Elena Niculina Drăgoi, Alexandra Georgiana Apostică, Andra Maria Mănăilă, Veronica Mădălina Radu, Adrian Cătălin Puițel, Mircea Teodor Nechita

“…In addition, a modified bacterial foraging optimization algorithm was applied as an alternative optimizer in combination with artificial neural networks in order to determine multiple combinations of parameters that can lead to maximum process efficiency. …”
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A multiobjective continuation method to compute the regularization path of deep neural networks by Augustina Chidinma Amakor, Konstantin Sonntag, Sebastian Peitz

“…For linear models, it is well known that there exists a regularization path connecting the sparsest solution in terms of the ℓ1 norm, i.e., zero weights and the non-regularized solution. …”
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SLPOD: superclass learning on point cloud object detection by Xiaokang Yang, Kai Zhang, Yangyue Feng, Beibei Su, Yiming Cai, Kaibo Zhang, Zhiheng Zhang

“…To tackle this challenge, we introduce SLPOD, a Superclass-based point cloud object detection algorithm. Employing a siamese network structure, SLPOD conducts unsupervised clustering of samples within the same category to enhance the extraction of individual-specific features, thereby improving detection accuracy when confronted with complex datasets. …”
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A Prototype for Computing the Distance of Features of High-Pressure Die-Cast Aluminum Products by Luis Alberto Arroniz Alcántara, Óscar Hernández-Uribe, Leonor Adriana Cárdenas-Robledo, José Alejandro Fernández Ramírez

“…Automotive manufacturers are changing their product models faster due to the customization of users’ demands. …”
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Using Segmentation to Boost Classification Performance and Explainability in CapsNets by Dominik Vranay, Maroš Hliboký, László Kovács, Peter Sinčák

“…These results demonstrate a significant performance improvement over traditional CapsNet and CNN models. …”
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An efficient method for identifying surface damage in hydraulic concrete buildings by Libo Yang, Dawei Zhu, Xuemei Liu

“…Furthermore, the paucity of data diversity leads to a singular function of the model that cannot provide comprehensive decision support for improving maintenance measures. …”
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Hardware Implementation of Next Generation Reservoir Computing with RRAM‐Based Hybrid Digital‐Analog System by Danian Dong, Woyu Zhang, Yuanlu Xie, Jinshan Yue, Kuan Ren, Hongjian Huang, Xu Zheng, Wen Xuan Sun, Jin Ru Lai, Shaoyang Fan, Hongzhou Wang, Zhaoan Yu, Zhihong Yao, Xiaoxin Xu, Dashan Shang, Ming Liu

“…Reservoir computing (RC) possesses a simple architecture and high energy efficiency for time‐series data analysis through machine learning algorithms. To date, RC has evolved into several innovative variants. …”
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Control and management in a complex biotechnical system of a dairy farm by V. V. Kirsanov, R. A. Baisheva

“…The paper presents the scheme of transformation and expansion of machine functionality in a biotechnical system; mathematical modeling of the dependence of technological signals controlled by the functions of local biotechnological systems (LBTS); mathematical model of diagnostic signals about the corresponding parameters of technical blocks of LBTS. …”
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