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检索条件"任意字段=3rd IEEE Intelligent Data Acquisition and Advanced Computing Systems"

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Investigations of PAPR reduction methods in FBMC System 3

Investigations of PAPR reduction methods in FBMC System

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3rd International Conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Samantaray, Rashmi Rani Siddappaji Hkbk College Of Engineering Vtu Department of Electronics and Communication Bmsce Bengaluru India Bms College of Engineering Department of Electronics and Communication Bengaluru India

ISBN: (纸本)9798331515911

The primary concern of the 5G radio network has sparked interest in the development of new waveforms beyond 4G (5th generation networks). Filter Bank Multicarrier (FBMC) waveforms have recently been suggested as the desired one for 5G networks. It has both advantages and disadvantages. One of the biggest drawback of the FBMC broadcast signal is its high Peak to Average Power Ratio (PAPR). It has been widely used in wireless communication to achieve high speed and data rates. A high PAPR system results in substantial distortion of the signal because of the nonlinear power amplifier. Therefore, methods that effectively reduce PAPR are necessary for the realization of the FBMC system. The FBMC system uses the some common techniques as the OFDM system. Comparative analysis of various approaches such as PTS, SLM, Companding, etc are performed by considering their simulation outcomes when it comes to effective spectrum utilization, improved PAPR, along with minimum complexity. © 2025 ieee.

关键词： 5G mobile communication systems

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD) 3

Blockchain Integration for Robust Intellectual Property Prot...

引用

3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 ieee.

关键词： Smart contract

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The Effect of Algorithmic Trading systems on Stock Market Volatility 3

The Effect of Algorithmic Trading Systems on Stock Market Vo...

引用

3rd International Conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Charithra, C.M. Vikas, Bhavya Mukund Sharma, N. Bnm Institute of Technology Department of Business Administration Bengaluru India

ISBN: (纸本)9798331515911

For many people, trading in stock market is a popular investment option. Stock price trends has always been an important area of interest for machine learning researchers. Modelling the stock market has always stood as a challenge, despite of many Algorithmic Trading system being available. Algorithmic trading is a mechanism involving use of Artificial Intelligence(AI) for quick trading in stock *** work focuses on understanding the role of algorithmic trading on the capital markets and examines the existence of volatility and test the homogeneousness in the degree of volatility of the chosen Emerging and the developed markets from ***, PP tests and Jarque-Bera tests were conducted to examine the stationarity and normality of the time series data. Returns, SD, ARIMA, ARCH, GARCH, BEKK-GARCH, Granger causality test were applied for the study. The empirical analysis depicted that volatility is present in all stock markets, with its level varying between different markets over time. This will be beneficial to the investors to arrive at right decision. © 2025 ieee.

关键词： Marketplaces

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A Comparative Study of Various Clustering Methods on Dengue Microarray Gene Expression dataset and Pathway Enrichment Analysis 3

A Comparative Study of Various Clustering Methods on Dengue ...

引用

3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Das, Epsita Chatterjee, Piyali Netaji Subhash Engineering College Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798331523893

Recently, disease progression caused by dengue virus has intensely increased and affected almost 80 countries of WHO. As a result, in the year of 2023, 6.5 million cases of dengue infection followed by 7300 dengue-related deaths have been reported. The global spread of dengue is driven by the lack of vaccines and insufficient vector control efforts. Human and dengue virus interaction affects gene expression value which needs to be investigated for biomarker prediction. Using clustering methods, it is possible to identify genes contributing to a deeper understanding to know how the virus manipulates host cell machinery and gain insight of the same biological pathways or cellular processes. Clustering results may help in identification of biomarkers, uncovering potential therapeutic targets. This study aims to analyze dengue gene expression dataset GSE51808 through various unsupervised clustering algorithms leveraging the identification of new biomarkers through enrichment analysis. © 2025 ieee.

关键词： Clustering methods Dengue Microarray data

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Hybrid AI-Electronic systems for Real-Time Edge Processing in IoT Networks 3

Hybrid AI-Electronic Systems for Real-Time Edge Processing i...

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3rd International Conference on intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Sweety Prasanna Kiruba, G. Esakki Madura, E. Kumar, Bura Vijay Kavitha, M. Reddy, T. Srinivas Athiraja, A. Bannari Amman Institute of Technology Department of Computer Technology Tamil Nadu Sathyamangalam India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Sathyamangalam India School of Computer Science and Artificial Intelligence Sr University Warangal India Kathir College of Engineering Department of Artificial Intelligence and Data Science Neelambur Coimbatore India Malla Reddy Engineering College Department of Ece Main Campus Maisammaguda Telangana Medchal India Hyderabad India

ISBN: (纸本)9798331515911

Real-time data processing is facing many issues as a result of the Internet of Things' rapid expansion, especially at the network's edge where latency, bandwidth, and compute resources are limited. The creation of hybrid AI-electronic systems for real-time edge processing is investigated as a solution to these problems. To improve the effectiveness and precision of data processing in Internet of Things contexts, the research combines machine learning algorithms with sophisticated electronic circuits. The techniques include creating a brand-new hybrid framework that blends edge computing and AI models and focuses for low latency and low power consumption. When compared to conventional edge processing systems, extensive simulations and real-world research show a 25% reduction in processing latency and a 15% gain in data categorization accuracy. According to the findings, the suggested hybrid AI-electronic system can successfully handle the requirements of contemporary IoT networks and offer a scalable real-time edge processing solution. This strategy has potential for use in smart city applications, industrial automation, and other fields in the future. © 2025 ieee.

关键词： Adversarial machine learning

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Artificial Intelligence (AI) Assisted Literature Review 3

Artificial Intelligence (AI) Assisted Literature Review

引用

3rd International Conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Hilda, Jabanjalin Jeevaa, S.V. Bhadhrinath, U. Solanki, Darshini Sr. School of Computer Science Engineering Vellore Institute of Technology Vellore India Vellore Institue of Tehnology Vellore Vellore India

ISBN: (纸本)9798331527549

This research aims to address the inefficiencies and inaccuracies in traditional literature review methodologies by integrating advanced Artificial Intelligence (AI) technologies, specifically transformer-based models. The study focuses on optimizing PDF-to-text conversion through image and character recognition, thereby minimizing resource wastage and errors associated with manual transcription. Additionally, the research employs advanced Natural Language Processing (NLP) techniques to automate the identification of critical phrases such as keywords and organizational entities, significantly reducing manual errors and improving accuracy. The methodology also includes the use of sophisticated summarization algorithms and sentiment analysis, streamlining the process of summarizing extensive literature and identifying study limitations. By leveraging state-of-the-art transformer models and industry-standard libraries, this research introduces a novel, efficient framework for literature analysis. The outcome is a comprehensive, AI-powered toolkit that enhances the speed, accuracy, and precision of literature reviews, ultimately providing researchers with a more reliable and resource-efficient approach to navigating scholarly content. © 2025 ieee.

关键词： Natural language processing systems

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intelligent Battery Management System for Electric Vehicle with IoT-based Monitoring, AI-Driven Multiple Fault Diagnosis and Energy Optimization 3

Intelligent Battery Management System for Electric Vehicle w...

引用

3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Arun Kumar, P. Shalini, S. Sneha, R. Vasunthra, P. Suganyadevi, S. M Kumarasamy College of Engineering Department of Ece Karur India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Battery related issues have a significant effect on both the performance and safety of electric vehicle (EV). Overheating Battery Lifespan is meant to be reduced by simply the dangers of overheating, as they can even cause thermal runaway, as well as from over and deep revolution. Besides, EV batteries have poor performance even in extreme temperatures, capacity loss, and internal defects. In order to cope with these problems, a proposed IoT based EV battery monitoring and fault detection system provides the real time analysis to promptly notify the battery health. Built in IoT sensors are integrated in this system which monitors critical parameters constantly i.e. temperature, voltage, current and charge levels. For 95-98% battery parameters monitoring accuracy, the collected data is transferred to a centralized cloud platform for real time cloud processing. The system utilizes advanced edge computing techniques and continuous monitoring capable of detecting a fault with 95-98% probability. Electrochemical processes are analyzed with 90-95% accuracy using predictive analytics and machine learning algorithms to ensure optimal battery performance. Moreover, the system achieves energy efficiency of more than 95% through power management and control mechanisms. In 95% of the cases, intelligent charging algorithms will prevent overcharging. This cutting-edge technology dramatically improves EV battery safety, longevity as well as efficiency, assuring reliability and sustainability. © 2025 ieee.

关键词： Predictive analytics

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Decoding Voice Authenticity: An Exploration of Deep Learning and Audio Feature Techniques 3

Decoding Voice Authenticity: An Exploration of Deep Learning...

引用

3rd International Conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Yalla, Swathi Tejah Raju, Morampudi Gopi Prashanth Nagaraju, D. Ali, Mohammad Farhaan Department of AI & Data Science CBIT Hyderabad India

ISBN: (纸本)9798331527549

The increasing sophistication of spoofing, mimicry, and deepfake technologies exposes critical vulnerabilities in voice authentication systems, including the inability to generalize across diverse attack types, reliance on narrow feature sets, and a lack of interpretability, which seriously limits their applicability in the real world. This review will analyze the state-of-the-art techniques for Logical Access, Presentation Attacks, and Deepfake manipulations. It points out the limitations of the traditional models and progress based on modern frameworks. It explores using advanced audio features Mel-Spectrogram and MFCC along with LightGBM classifiers and incorporates SHAP (Shapley Additive Explanations) for model transparency and cosine similarity matrices for mimicry detection. The study identifies a dual-layered framework that attains high accuracy and robustness across datasets while ensuring adaptability to emerging attack strategies. It addresses the critical need for scalable, interpretable, and adaptive voice authentication solutions and provides a foundation for effectively mitigating evolving audio spoofing threats. © 2025 ieee.

关键词： Deep learning

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The Application of Digital Empowerment Combined with intelligent Algorithms of Computer Technology in Decision Support systems 3

The Application of Digital Empowerment Combined with Intelli...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Lu, Wan Chen, Li Gao, Fei Wang, Chang Wuhan Windoor Information & Technology Co. Ltd Hubei Wuhan China

ISBN: (纸本)9798331508456

Traditional decision support systems (DSS) show obvious limitations in dealing with increasingly complex and dynamic decision-making scenarios. By integrating graph neural networks (GNNs) and expert systems (ESs), this paper constructs a new IDSS framework aimed at improving decision-making efficiency and accuracy. In terms of research methods, large-scale distributed computing is performed in the cloud based on parallel computing technology;the logical reasoning of expert systems and the self-learning ability of graph neural networks are combined to achieve dynamic updates and efficient processing of data by unstructured knowledge bases. Compared with traditional DSS, the accuracy of IDSS based on the fusion of GNNs and ES in financial forecasting and risk assessment scenarios is improved to more than 90%, and resource utilization is significantly optimized. This study shows that the integrated application of intelligent algorithms has broad prospects in improving the dynamic adaptability of decision support systems and coping with complex decision-making environments. © 2025 ieee.

关键词： Distributed cloud

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Integrative Personalized Oncology: Leveraging Multi-Source data and Drug Solubility for Optimal Cancer Treatment 3

Integrative Personalized Oncology: Leveraging Multi-Source D...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Manju, M. Sujataroy Anuratha, T. Dhanasri, S. Thirumalaivasan, V. Rajalakshmi Engineering College Department of AIML Chennai India Rajalakshmi Engineering College Department of Biotechnology Chennai India

ISBN: (纸本)9798331523893

The rise of personalized medicine in oncology has highlighted the need for tailored treatment strategies, as traditional approaches often overlook the unique genetic and molecular profiles of cancer patients. Approximately 30-40 percentage of patients experience inadequate responses to standard therapies, underscoring the critical importance of understanding tumor heterogeneity. Despite advancements in cancer research, there remains a gap in effectively integrating diverse patient data - such as genomic, proteomic, lifestyle, and environmental factors - into actionable treatment plans. This project aims to develop a comprehensive, machine learning-driven framework for personalized oncology that leverages multi-source data to optimize treatment recommendations. Employing ensemble modeling techniques, including random forests and deep neural networks, the framework enhances predictive accuracy while addressing challenges related to drug solubility and bioavailability through cheminformatics. By continuously monitoring patient outcomes and refining predictive models, the research seeks to provide dynamic, evidence-based treatment plans tailored to individual needs. Ultimately, the key message of this study is that integrating advanced computational methods with diverse data sources can significantly improve the efficacy of cancer treatments and patient outcomes, paving the way for a more personalized approach in oncology. © 2025 ieee.

关键词： Drug Solubility Machine Learning Oncology Personalized Medicine Tumor Heterogeneity

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