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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Saravanan G Ramy AlFatlawy Kirtanjot Kaur K. Kavitha Devi Anita Sofia Liz. D. R Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq University Centre for Research & Development Chandigarh University Mohali India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

In this era, resource allocation in 5th Generation (5G) and beyond networks establishes an effective, dynamic and intelligent management for resources of networks. However, the traditional Machine Learning (ML) and Deep Learning (DL) approaches exhibit challenges like latency optimization, user mobility and spectrum efficiency for improved network performance. In order to solve these issues, the proposed model utilizes Binary Sparse Uniform Modem (BSUM) for resource allocation. Primarily, the Deep Slice and Secure 5G & Long Term Evolution (LTE) Wireless dataset is used for allocating the resources. Subsequently, the preprocessing technique accompanies Normalization, Scaling and Transformation which helps for aligning feature scales, avoiding from dominant features that leads to equitable and optimized allocation of resources like spectrum and power. Next step involves extraction of features using Deep Convolutional Neural Networks (DCNN) that helps in identification of patterns and relationships and enhancing the scalability. Further, Classification model is incorporated with BSUM for adapting seamless dynamic environment of networks and preliminaries required for resource allocation. The results of the proposed model are superior to the existing models by achieving 35.92 Megabits Per Second (MBPS) and 89.23% Interactivity score.

关键词： Wireless communication Adaptation models 5G mobile communication Feature extraction Dynamic scheduling Modems Resource management Convolutional neural networks Communication system security Long Term Evolution

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Contribution Analysis of ESS Connecting to Urban Railroad Substation

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Transactions of the Korean institute of Electrical Engineers 2021年第12期70卷 1792-1798页

作者： Yun, Chi-Myeong Cho, Gyu-Jung Kim, Hyungchul Kim, Chul-Hwan Jung, Hosung Smart Electrical & Signaling Division Korea Railroad Research Institute Korea Republic of Dept. of Transportation Engineering Korea University of Science and Technology Korea Republic of Smart Electrical & Signaling Division Korea Railroad Research Institute Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In accordance with global efforts to reduce carbon, energy storage system(ESS) application and research are rapidly progressing in Korea. There are many advantages when ESS is connected to DC electric railway for regenerative braking energy utilization and peak reduction. In this paper, the di/dt and ∆I characteristics of the 50F relay were analyzed according to the presence of failure resistance, ESS operation condition, and train operating condition by connecting the ESS to the DC-link of a DC electric railway substation. To analyze the contribution of ESS, power analysis program, PSCAD/EMTDC was used. © The Korean institute of Electrical Engineers This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://***/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

关键词： Railroads

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QoE-Based Resource Allocation in Wireless Networks: An Analysis 3

QoE-Based Resource Allocation in Wireless Networks: An Analy...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Agarwal, Trapty Grover, Madhur Sahu, Madhusmita Ojha, Varun Sunil, M.P. Saravanan, M. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Applications Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331542375

Wireless networks are developing quickly and can avoid providing an increasing number of services such as video streaming, online games, or real-time communication. However, the quality of experience when accessing content over these networks is still at stake;end-users are concerned with constraints like network congestion, channel quality, and resources provided. In the literature, several proposals, such as QoE-aware resource allocation strategies, have been suggested to tackle this issue. This technical abstract considers QoE-driven approaches for resource allocation in wireless networks. These approaches are employed to facilitate users' perceived QoE by selectively provisioning the users with adequate network resources according to their QoS demands. For example, the analysis takes a look at some of these strategies as well as the main factors that inform them when designing for user preferences, network conditions, and resource availability. We then discuss the benefits and limitations of other approaches, such as Fuzzy logic-based methods and game theory-based methods. © 2025 IEEE.

关键词： Resource allocation

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Intelligent IoT-Based Water Quality Monitoring System Using TD-CNN-LSTM Approach

Intelligent IoT-Based Water Quality Monitoring System Using ...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： M. Velammal Vittam Rakesh K. Kartheeban Khalid Ali Qidwai G Shyamala Thulasimani T Department of Computer Science and Technology Karpagam College of Engineering Coimbatore Tamil Nadu India Electrical and Electronics Engineering Geethanjali College of Engineering and Technology Hyderabad Telangana India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Anand Nagar Krishnankoil Tamilnadu India Computer Science College of Computer Science and Information Technology Jazan University Japan Saudi Arabia Civil Engineering School of Engineering SR University Warangal Telangana India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Tamilnadu India

Today, water contamination affects a large portion of the world's population. Manually collecting water samples from various sites and analyzing them in a lab as part of the routine monitoring technique. These approaches used to be effective, but they are now too slow and laborious to be practical. In addition, cutting-edge methods consider a wide range of physical and chemical elements. The standard method of quality monitoring and data sharing is time-consuming, unreliable, and costly. This emphasizes the need for constant, around-the-clock water quality monitoring. Basic Data Preprocessing, feature selection, and model training are the three main pillars of the proposed approach. The proposed method employs the preprocessing tools. The proposed system used the SOFM method (Structure-From-Function-Movement) to identify and eliminate unnecessary features selection. A model's accuracy can be measured with the help of prediction tools like utilizing CNN, LSTM, and TD-CNN-LSTM, models are trained. The proposed model outperforms two current top-tier contenders. The proposed method achieves higher accuracy (about 98.37%) when compared to other approaches like CNN and LSTM.

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A CNN-Based Framework for Detection and Recognition of Objects Using Transfer Learning: A Case Study on Flower Recognition

A CNN-Based Framework for Detection and Recognition of Objec...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Shivam Tiwari Vikrant Sharma Yogesh Singh Rathore Parul Gandhi Durgesh Srivastava Department of Computer Science & Engineering Sharda University Greater Noida Computer Science and Engineering Graphic Era Hill University Dehradun UK Computer Science and Application Sharda University Greater Noida UP Department of Computer Applications Manav Rachna International Institute of Research and Studies Faridabad Haryana Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India

The vast variety of floral species present on Earth poses a considerable challenge in memorizing their individual names. Acquiring knowledge about specific flower types inhabiting diverse environments like forests, mountains, and dense regions becomes crucial for their recognition and differentiation. Consequently, the development of a technique for accurately determining flower categories becomes imperative. The task of distinguishing and categorizing a particular blossom amidst the extensive array of floral species presents a formidable task. Hence, the automation of flower species identification has been a subject of extensive research interest. This research domain is characterized by the utilization of features extracted from floral images in conjunction with specialized algorithms aimed at classifying flower species. A prominent aspect of these endeavors is the employment of three widely recognized flower image datasets. The present study constitutes a comprehensive survey of various methodologies proposed by diverse researchers with the objective of accomplishing precise identification of flower species.

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Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-Resolution 27

Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Xu, Tianyi Zhou, Yijie Hu, Xiaotao Zhang, Kai Zhang, Anran Qiu, Xingye Xu, Jun School of Statistics and Data Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China School of Intelligence Science and Technology Nanjing University Suzhou China Tencent Data Platform Beijing China Zhejiang University Hangzhou China Systems Engineering Research Institute China State Shipbuilding Corporation Limited Beijing China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781643685489

Arbitrary-scale super-resolution (ASSR) aims to learn a single model for image super-resolution at arbitrary magnifying scales. Existing ASSR networks typically comprise an off-the-shelf scale-agnostic feature extractor and an arbitrary scale upsampler. These feature extractors often use fixed network architectures to address different ASSR inference tasks, each of which is characterized by an input image and an upsampling scale. However, this overlooks the difficulty variance of super-resolution on different inference scenarios, where simple images or small SR scales could be resolved with less computational effort than difficult images or large SR scales. To tackle this difficulty variability, in this paper, we propose a Task-Aware Dynamic Transformer (TADT) as an input-adaptive feature extractor for efficient image ASSR. Our TADT consists of a multi-scale feature extraction backbone built upon groups of Multi-Scale Transformer Blocks (MSTBs) and a Task-Aware Routing Controller (TARC). The TARC predicts the inference paths within feature extraction backbone, specifically selecting MSTBs based on the input images and SR scales. The prediction of inference path is guided by a new loss function to trade-off the SR accuracy and efficiency. Experiments demonstrate that, when working with three popular arbitrary-scale upsamplers, our TADT achieves state-of-the-art ASSR performance when compared with mainstream feature extractors, but with relatively fewer computational costs. The code is available at https://***/Tillyhere/TADT. © 2024 The Authors.

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Analyzing the behaviors of pedestrians and cyclists in interactions with autonomous systems using controlled experiments: A literature review

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Transportation research Part F: Traffic Psychology and Behaviour 2025年 114卷 270-307页

作者： Danya Li Wencan Mao Francisco C. Pereira Yu Xiao Xiang Su Rico Krueger Department of Technology Management and Economics Technical University of Denmark (DTU) Denmark Information Systems Architecture Science Research Division National Institute of Informatics (NII) Japan Department of Information and Communications Engineering Aalto University Finland Department of Computer Science and Department of Agricultural Sciences University of Helsinki Finland

Urban transportation is set to undergo a profound transformation with the advent of autonomous systems such as autonomous vehicles, automated buses, and sidewalk delivery robots. To promote safe, sustainable, and inclusive urban mobility, understanding and predicting the behaviors of pedestrians and cyclists, including their intentions, decisions, and movements, when they interact with autonomous systems becomes crucial. Gaining a thorough understanding of these complex interactions can not only improve the safety, efficiency, and acceptance of autonomous systems but also enhance the design and implementation of these technologies. Through a comprehensive review of the literature spanning the years 2014 to 2023, we identify 99 articles that empirically investigate the interactions of humans and autonomous systems. Based on our overview of progress and challenges within this field, we further identify five research gaps that future research should address to enhance human-autonomous system interactions, including: (1) scaling up experimental scenarios to multi-user and multi-modal setups to better represent real-world challenges, (2) emphasizing safety-critical scenarios that are difficult to achieve in real-world environments by applying virtual reality, (3) incorporating diverse behavioral data from the human perspective to deepen the understanding of vulnerable road user behavior and decisions, (4) embracing continuous and real-time interaction to better predict dynamic future environments, and (5) enhancing the generalization ability to ensure realism and broad applicability. This review article offers valuable insights for the growing human-autonomous system research community, specifically those interested in leveraging controlled experiments to enhance the understanding and prediction of pedestrians' and cyclists' behaviors in future urban environments.

关键词： Human-autonomous system interaction Human behavioral modeling Autonomous vehicles Virtual reality Controlled experiment

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A Novel Stacked Regression Method for Telemonitoring Parkinson’s Disease Outcome Prediction Using Biomedical Voice Features

A Novel Stacked Regression Method for Telemonitoring Parkins...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Puneet Bawa Manisha Rajput Virender Kadyan Monika Sethi Centre of Excellence for Speech and Multimodal Laboratory Chitkara University Institute of Engineering & Technology Chitkara University Punjab India Chitkara University Institute of Engineering & Technology Chitkara University Punjab India Machine Intelligence Research Centre School of Computer Science UPES Dehradun Uttarakhand India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Parkinson’s Disease (PD) is a complex neurodegenerative disorder characterized by both motor and non-motor symptoms. The development of traditional diagnostic approaches for detecting PD has limitations in terms of accuracy and accessibility, particularly in regard to continuous monitoring. This paper make an effort by proposing a stacked regression model combining Random Forest (RF) and Extreme Gradient Boosting (XGBoost) with an effort of improving predictive accuracy for Unified Parkinson’s Disease Rating Scale (UPDRS) using biomedical voice data as a non-invasive monitoring method. The stacked model achieved lower MSE and higher accuracy compared to individual models demonstrating superior predictive performance. Thus, the combined model effectively captures nonlinear relationships and reduces overfitting through the balanced bias-variance tradeoff with Mean Squared Error (MSE) score of 25.027 an R 2 of 0.621 offering a scalable and non-invasive solution for predicting UPDRS scores. Overall, the proposed approach has significant implications for real-time monitoring and proactive patient management in PD with potential applications across other neurodegenerative diseases.

关键词： Radio frequency Analytical models Accuracy Biological system modeling Computational modeling Predictive models Motors Data models Monitoring Diseases

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Automatic Modulation Classification in Wireless Communications Using Multi-Aspect Graph Attention Network with Avg-TopK Pooling and Osprey Optimization

Automatic Modulation Classification in Wireless Communicatio...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： R. Mohandas Bharath Elangovan Soorya Sivasubramanian Sridhar. S Trupti Dhanadhya Department of Electronics & Communication Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Artificial Intelligence & Data Science C K College of Engineering & Technology Cuddalore Tamil Nadu India Department of Computer Science and Engineering V.R.S College of Engineering & Technology Arasur Tamil Nadu India Department of Research Rajalakshmi Institute of Technology (Autonomous Chennai Tamil Nadu India Department of Electrical Engineering Dr. D Y Patil Institute of Technology Pune Maharashtra India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

AMC stands for automatic modulation classification, a significant tool in wireless communication for identification of the signal modulations of immense relevance to dynamic spectrum management, interference identification and cognitive radio networks. Many of the previous AMC approaches include feature extraction and manual classification which are not particularly portable and accurate in other contexts of communications. Recently, with the development of deep leaning, attention based methods have brought obviously enhanced performance in perceiving comprehensive signal characteristics; but there are still problems in focusing to multi-aspect features and training at a large scale signal data with high computational complexity. In order to address this problem, this research introduces a Avg-TopK pooling layer along with a Multi-Aspect Graph Attention Network (MAGAN) for the purpose of the AMC while using the Osprey Optimization Algorithm for the optimization of the proposed model. The Avg-TopK pooling only keeps K most important feature representations and decreases noise levels for better signal modulation classification. MAGAN uses multi-aspect attention mechanism allowing the emphasize various properties of the signal while perceiving mutual connections between the features of the signal. Additionally, the proposed Osprey Optimization Algorithm improves the training time of the model through optimization of hyperparameters. The presented results obtained from experimental analysis of standard AMC performance metrics show that the proposed model provides better AMC classification capability and insensitivity to the noise and existence of multiple types of modulation modes in contrast to other AMC models. In light of this, this work aims at solving some of the major challenges facing AMC through presenting a highly adaptive, efficient and accurate solution for the ever evolving wireless communication systems. The introduced approach attains higher accuracy as 99

关键词： Training Analytical models Adaptation models Accuracy Wireless networks Modulation Classification algorithms Cognitive radio Optimization Radio spectrum management

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Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks

arXiv

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arXiv 2024年

作者： Hou, Junlin Liu, Sicen Bie, Yequan Wang, Hongmei Tan, Andong Luo, Luyang Chen, Hao The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong The Department of Engineering Shenzhen MSU-BIT University Shenzhen China The Department of Biomedical Informatics Harvard University Cambridge United States The Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science Hong Kong University of Science and Technology Hong Kong HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China

The increasing demand for transparent and reliable models, particularly in high-stakes decision-making areas such as medical image analysis, has led to the emergence of eXplainable Artificial Intelligence (XAI). Post-hoc XAI techniques, which aim to explain black-box models after training, have raised concerns about their fidelity to model predictions. In contrast, Self-eXplainable AI (S-XAI) offers a compelling alternative by incorporating explainability directly into the training process of deep learning models. This approach allows models to generate inherent explanations that are closely aligned with their internal decision-making processes, enhancing transparency and supporting the trustworthiness, robustness, and accountability of AI systems in real-world medical applications. To facilitate the development of S-XAI methods for medical image analysis, this survey presents a comprehensive review across various image modalities and clinical applications. It covers more than 200 papers from three key perspectives: 1) input explainability through the integration of explainable feature engineering and knowledge graph, 2) model explainability via attention-based learning, concept-based learning, and prototype-based learning, and 3) output explainability by providing textual and counterfactual explanations. This paper also outlines desired characteristics of explainability and evaluation methods for assessing explanation quality, while discussing major challenges and future research directions in developing S-XAI for medical image analysis. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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