Research on CSI feedback of RIS-assisted massive MIMO system based on manifold learning by QIAN Mujun, YU Shunchi, LIU Chen, SONG Yunchao, LU Feng

“…Then, the framework combined the manifold learning to train two set of dictionaries to achieve dimension reduction and reconstruction of incremental CSI. …”
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An efficient leakage power optimization framework based on reinforcement learning with graph neural network by Peng Cao, Yuhan Dong, Zhanhua Zhang, Wenjie Ding, Jiahao Wang

“…However, it poses great challenge in large scale circuit design as an NP-hard problem. Machine learning-based approaches have been proposed to solve this problem, aiming to achieve well tradeoff between leakage power reduction and runtime speed up without new induced timing violation. …”
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Data-oriented optimized nonuniform quantization for CR-enhanced communication efficiency in federated learning by Shuai Luo, Qiming Wan, Hongrui Wang, Tianchun Xiang, Yang Wang, Xin He, Wei Zhang

“…Experimental results demonstrate that Non-QuanFL achieves up to a 29.4% reduction in the communication cost compared to SLMQ while maintaining comparable model performance, which is helpful to implement an energy-efficient distributed learning network oriented on carbon reduction (CR). …”
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Deep learning can reduce acquisition time of T2-weighted image in brain imaging by Eman Hassan El-Saeed Abou-ELMagd, Sabry Alameldin Elmogy, Dina Gamal Abdelzaher

“…Abstract Background We used the deep learning-based reconstruction algorithm to reduce the scan time for brain T2-weighted images (T2WI) with reduction of image noise and preservation of image quality. …”
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Signal-piloted processing and machine learning based efficient power quality disturbances recognition. by Saeed Mian Qaisar

“…Therefore, a remarkable reduction can be secured in the data storage, processing and transmission requirement towards the post classifier. …”
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Application of Ensemble Learning and VISSIM in Intersection Traffic Flow Prediction and Signal Timing Optimization by Yutong Rou, Chao Liang, Zhizhan Lu

“…Initially, an ensemble learning algorithm accurately predicts intersection traffic flow. …”
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Convolutional neural networks with transfer learning for natural river flow prediction in ungauged basins by Henrique Echternacht, Luciana Campos, Alfeu Dias de Martinho, Danilo Pinto Moreira de Souza, Rodrigo Barbosa de Santis, Tiago Silveira Gontijo, Matteo Bodini, Angela Gorgoglione, Camila Martins Saporetti, Leonardo Goliatt

“…The present study introduces a novel approach to streamflow prediction involving the development of a Deep Learning (DL) model that combines a convolutional neural network with Transfer Learning (TL) techniques to predict streamflow in river systems. …”
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Flexible Edge-AI Software Execution Architecture Based on Cloud-Connected Incremental Learning by Myeongjin Kang, Daejin Park

“…The proposed system demonstrates an average accuracy improvement of over 10% in biased input scenarios, a 49% reduction in training time compared to the Jetson Nano board with learning capability, and a 70% reduction in communication volume compared to cloud code-streaming edge devices. …”
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MACHINE LEARNING BASED STRATEGY FOR MITM ATTACK MITIGATION UTILIZING HYBRID FEATURE SELECTION by Pratik Kumar Swain, Suneeta Satpathy, Srinivasa Rao Pokuri

“…This study integrates machine learning classification, dimensionality reduction, and hybrid feature selection to present a novel approach for MITM threat identification. …”
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Manufacturing agglomeration and carbon emissions: an ensemble learning approach with evidence from South Korea by Zhen Wu, Su-Han Woo, Pairach Piboonrungroj, Po-Lin Lai

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Federated Learning-Driven IoT Request Scheduling for Fault Tolerance in Cloud Data Centers by Sheeja Rani S, Raafat Aburukba

“…The quantitatively analyzed results show that the MRKFL-FTS technique achieved an 8% improvement in task scheduling efficiency and fault prediction accuracy, a 36% improvement in throughput, and a 14% reduction in makespan and time complexity. In addition, the MRKFL-FTS technique resulted in a 13% reduction in response time. …”
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Machine learning-based assessment of regional-scale variation of landslide susceptibility in central Vietnam. by Raja Das, Pham Van Tien, Karl W Wegmann, Madhumita Chakraborty

“…The post-event landslide susceptibility models of these three climate extreme events were developed using nine causative factors and a Random Forest machine learning algorithm. The results indicate a notable areal expansion of high to very high landslide susceptibility in the northern and eastern regions and a moderate reduction in the central and southern areas during the post-Molave period compared to the post-Ketsana period. …”
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A Hierarchical Evolutionary Search Framework with Manifold Learning for Powertrain Optimization of Flying Vehicles by Chenghao Lyu, Nuo Lei, Chaoyi Chen, Hao Zhang

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An Empirical Evaluation of Supervised Learning Methods for Network Malware Identification Based on Feature Selection by C. Manzano, C. Meneses, P. Leger, H. Fukuda

“…The classifying network traffic method using machine learning shows to perform well in detecting malware. …”
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Software Defect Prediction Using Deep Q-Learning Network-Based Feature Extraction by Qinhe Zhang, Jiachen Zhang, Tie Feng, Jialang Xue, Xinxin Zhu, Ningyang Zhu, Zhiheng Li

“…Moreover, without proper feature reduction, the interpretability and generalization ability of machine learning models in SDP may be compromised, hindering their practical utility in diverse software development environments. …”
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Physics-informed deep learning model for line-integral diagnostics across fusion devices by Cong Wang, Weizhe Yang, Haiping Wang, Renjie Yang, Jing Li, Zhijun Wang, Yixiong Wei, Xianli Huang, Chenshu Hu, Zhaoyang Liu, Xinyao Yu, Changqing Zou, Zhifeng Zhao

“…Prediction results demonstrate that the additional input of PI improves the deep learning model’s ability, leading to a reduction in the average relative error ${E_1}$ between the reconstruction profiles and the target profiles by approximately $0.84 \times {10^{ - 2}}$ on synthetic datasets and about $0.06 \times {10^{ - 2}}$ on experimental datasets. …”
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Deep Learning-Based Infrared Image Segmentation for Aircraft Honeycomb Water Ingress Detection by Hang Fei, Hongfu Zuo, Han Wang, Yan Liu, Zhenzhen Liu, Xin Li

“…Through rigorous experimentation, our model surpasses existing benchmarks, yielding a commendable 22.44% reduction in computational effort and a substantial 38.89% reduction in parameter count. …”
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Evaluating machine learning pipelines for multimodal neuroimaging in small cohorts: an ALS case study by Shailesh Appukuttan, Shailesh Appukuttan, Aude-Marie Grapperon, Aude-Marie Grapperon, Mounir Mohamed El Mendili, Hugo Dary, Maxime Guye, Annie Verschueren, Jean-Philippe Ranjeva, Shahram Attarian, Wafaa Zaaraoui, Matthieu Gilson

“…In this study, we systematically evaluated the impact of various machine learning pipeline configurations, including scaling methods, feature selection, dimensionality reduction, and hyperparameter optimization. …”
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Digitization of Medical Device Displays Using Deep Learning Models: A Comparative Study by Pedro Ferreira, Pedro Lobo, Filipa Reis, João L. Vilaça, Pedro Morais

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Research on Iterative Learning Semi-active Control for Curve Passing of High-speed Train by Yongjun AI, Chunjun CHEN, Xin LI, Jianrong ZHOU

“…The results showed that after 10 iterations, the stationarity index, derailment coefficient, wheel load reduction rate and wheel-rail lateral force were improved.…”
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