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Application of New Companding Techniques on the DWT-Based SC-FDMA System

Annals of Data Science 2022年第6期9卷 1149-1159页

作者： Abd El-Rahman, Amal Fawzy Abd Elnaby, Mustafa M. Khamis, Salah Napoleon, Sameh Abd El-Samie, Fathi E. Electronics and Communications Department Faculty of Engineering Tanta University Tanta Egypt Department of Electronics and Electrical CommunicationsEngineering Faculty of Electronic Engineering MenoufiaUniversity Menouf32952 Egypt Department of Information Technology Collegeof Computer and Information Sciences Princess Nourah BintAbdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Electronics and Electrical Communication Engineering Higher Institute for Engineering and Technology KafrElsheikh Egypt

Orthogonal Frequency Division Multiplexing (OFDM) is the most popular multicarrier communication technique, but its disadvantage is the large Peak-to-Average Power Ratio (PAPR). In recent years, different researchers presented several techniques to avoid this problem such as companding techniques. Moreover, the Single-Carrier Frequency Division Multiple Access (SC-FDMA) system is a popular system in mobile communications because of its advantage of low PAPR, but reducing its PAPR is still an open research issue. So, an extension of the work applied on OFDM to SC-FDMA is adopted in this paper to reduce the PAPR, while achieving a low Bit Error Rate (BER). New companding schemes are adopted in this paper with the help of the Discrete Wavelet Transform (DWT) in the presence of channel degradations for lowering the PAPR of the SC-FDMA system, while achieving a low BER. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Frequency division multiple access

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An Effective Cyber Security Threat Detection in Smart Cities Using Dueling Deep Q Networks 4

An Effective Cyber Security Threat Detection in Smart Cities...

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4th IEEE International Conference on Mobile Networks and Wireless communications, ICMNWC 2024

作者： Stanly Jayaprakash, J. Kodati, Sarangam Kanchana, A. Al-Farouni, Mohammed Ramachandra, A.C. Mahendra Institute of Technology Department of Computer Science and Engineering Namakkal India Cvr College of Engineering Department of Information Technology Hyderabad India Mahendra Engineering College for Women Department of Computer Science and Engineering Namakkal India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350352931

In recent days, smart city technologies are developing rapidly with the incorporation of IoT devices and AI-driven systems has modernized and thus facilitated for enhancing productivity, sustainability, and connectivity. The motive is to mitigate the rising cyber threats which are affecting the interrelated organizations of smart cities. Despite advancements in threat detection technologies, challenges persist, including real-time anomaly detection in vast data streams, managing resource constraints on IoT devices, and ensuring adaptability against evolving attack vectors. Therefore, this research suggests a novel approach by using Dueling Deep Q Networks (DDQN) method. Initially, the data is collected from the dataset and then Inter Quartile Range (IQR) is imposed at the preprocessing stage for detection of outliers. Then, extraction of optimal features is done by using Term Frequency-Inverse Document Frequency (TF-IDF) and Recursive Feature Elimination (RFE) which transforms the data into statistical vectors and eliminates the least significance feature respectively. Finally, the obtained optimal features are detected using DDQN method as it has the ability to detect effectively with respect to the state of action. The proposed DDQN has resulted with accuracy of 99.32% when compared with Improved Bacterial Foraging Optimization using optimum deep learning for Anomaly detection (IBFO-ODLAD). © 2024 IEEE.

关键词： Cyber attacks

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The Cloud's Balancing Confidentiality and Collaboration

The Cloud's Balancing Confidentiality and Collaboration

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Vivek, V. Sahoo, Girija Shankar Kehkeshan Jallal, S. Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

Group data sharing in cloud settings has become more popular recently as a result of cloud computing's broad acceptance. The need for efficient and safe data exchange in such settings is currently urgent. Another particular problem in cloud-based exchange of data is finding a balance of anonymity and traceability. In order to preserve anonymity, this study focuses on providing safe and effective data exchange and storage for organizations via the cloud. In order to accommodate numerous users surreptitiously in public clouds, it suggests a unique traceable collective data sharing system that makes use of key collaboration and group signature techniques. This system ensures that member identities may be tracked when required while enabling group members to converse anonymously using group signatures. Additionally, to allow for safe data exchange and preservation among group members, a shared conference key is obtained via key agreement. Notably, the key generation process for the system uses a symmetrically balanced incomplete block architecture, greatly easing the load on participants while determining a shared conference key. The suggested approach for collective information sharing on the internet of things is shown to be secure and effective via theoretical investigation and empirical assessments. © 2023 IEEE.

关键词： (KA-ABE) access control anonymity Cloud computing data breaches data integrity fault tolerance group data sharing multiple-to-many data sharing traceability

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CardioNet: An Integrative AI-Machine Learning Framework for Enhanced Prediction and Management of Cardiovascular Diseases Using Deep Data Analytics and Clinical Insights 4

CardioNet: An Integrative AI-Machine Learning Framework for ...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Senthil, G.A. Prabha, R. Thamarai, I. Roopa, D. Rajesh Kanna, R. Srimathi, C. Agni College Of Technology Department of Information Technology Chennai India Sri Sairam Institute of Technology Department of Electronics and Communication Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai City Campus India Sri Sairam Institute of Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Campus Chennai India

ISBN: (纸本)9798331508845

Cardio Vascular Disease (CVD) causes dysfunction in our heart and blood arteries, which frequently results in death or paralysis. Therefore, many lives can be prevented by the early and automatic recognition of CVD. Although this goal has been the subject of numerous studies, performance and dependability can yet be enhanced. This work represents another advancement in that direction. Given that cardiovascular diseases (CVDs) remain the primary cause of death worldwide, effective strategies for early detection and management are necessary. To help with CVD prediction and management, we introduce CardioNet, an integrated machine learning framework with strong data analytics and clinical insights. To accurately predict the risk and progression of CVD, CardioNet integrates state-of-the-art machine learning algorithms with a variety of medical data sources, including genetic profiles, imaging data, and electronic health records. CardioNet integrates clinical information and feedback mechanisms to offer customized risk assessment and therapy suggestions. This, in turn, improves patient outcomes and lowers the cost of healthcare. The potential of CardioNet as a comprehensive strategy to counter the widespread threat of CVDs is highlighted. © 2024 IEEE.

关键词： Electronic health record

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A Comprehensive Image Processing Framework for Early Diagnosis of Diabetic Retinopathy

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computers, Materials & Continua 2024年第11期81卷 2665-2683页

作者： Kusum Yadav Yasser Alharbi Eissa Jaber Alreshidi Abdulrahman Alreshidi Anuj Kumar Jain Anurag Jain Kamal Kumar Sachin Sharma Brij BGupta College of Computer Science and Engineering University of Ha’ilHa’il81481Saudi Arabia Department of CSE Chandigarh UniversityMohaliPunjab140413India Department of Electronics and Communication Engineering Amity UniversityNoida201301India Department of Information Technology Indira Gandhi Delhi Technical University for WomenNew Delhi110006India Chief Manager State Bank of IndiaPanchkulaHaryana134109India Department of Computer Science and Information Engineering Asia UniversityTaichung City413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

In today’s world,image processing techniques play a crucial role in the prognosis and diagnosis of various diseases due to the development of several precise and accurate methods for medical *** analysis of medical images is essential for doctors,as manual investigation often leads to inter-observer *** research aims to enhance healthcare by enabling the early detection of diabetic retinopathy through an efficient image processing *** proposed hybridized method combines Modified Inertia Weight Particle Swarm Optimization(MIWPSO)and Fuzzy C-Means clustering(FCM)*** FCM does not incorporate spatial neighborhood features,making it highly sensitive to noise,which significantly affects segmentation *** method incorporates a modified FCM that includes spatial functions in the fuzzy membership matrix to eliminate *** results demonstrate that the proposed FCM-MIWPSO method achieves highly precise and accurate medical image ***,segmented images are classified as benign or malignant using the Decision Tree-Based Temporal Association Rule(DT-TAR)*** analysis with existing state-of-the-art models indicates that the proposed FCM-MIWPSO segmentation technique achieves a remarkable accuracy of 98.42%on the dataset,highlighting its significant impact on improving diagnostic capabilities in medical imaging.

关键词： Image processing biological data PSO Fuzzy C-Means(FCM)

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Powering Development Wirelessly: Improvements in Far-Field Instrument Power

Powering Development Wirelessly: Improvements in Far-Field I...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kumar Sinha, Deepak Singh, Swati Sivaperumal Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

In this study, far-field mobile electrical power options for inexpensively mobile sensors are thoroughly investigated. Emphasis is placed on situations in which sensor placements are uncertain and battery replacement is not practicable. Low-power nondirectional mobile battery backup becomes a possibility for sensors with occasional data transmission demands, estimated radiofrequency (RF) power consumption ranging from 20 to 200 W/cm2, and a necessity for small sensor dimensions. The sensor system uses an antenna to collect coming GHz electromagnetic waves, then transforms this energy into electricity for a storage device, which could be a thin-film battery, using a rectifier circuit. This method makes it possible for the full platform-which includes gauges, an inexpensively Bluetooth transmitter, including a low-power CPU for management-to function continuously. To maximize performance, particularly at reduced incident energy levels, RF power receipt and power control circuits must be designed in concert. Both hybrid and modular power management circuitry options are described and evaluated in this research. © 2023 IEEE.

关键词： Bluetooth wireless Energy harvesting and Efficiency Optimization Far-field power Low-power sensors Mobile sensors Power management Rectenna design System integration Wireless energy transfer

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Understanding the Journey to 6G Development

Understanding the Journey to 6G Development

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Agrawal, Trapty Kumar, Ajit Gowrishankar, J. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

The fifth-generation (5G) mobile communication technology is now being widely deployed in a number of nations, along with a significant rise in the number of 5G customers. However, it is becoming more and more clear that it is time for both academics and business to shift their emphasis towards the next generation as the mobile telecommunications market continues to change. At this point, it is crucial to provide a thorough review of the state of the art and look forward to the future of communications. This article tries to achieve this goal by providing a thorough analysis designed to outline the sixth generation (6G) system's characteristics. The motivations, use cases, use scenarios, needs, key performance indicators (KPIs), architecture, and enabling technologies that will characterize 6G are all covered in this investigation. © 2023 IEEE.

关键词： 6G AI architecture disruptive services enabling technologies generation Key Performance Indicators (KPIs) latency mobile communication mobile traffic mobility network efficiency peak data rate performance indicators technology terahertz communications use cases user-experienced data rate

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Secure and Tailored Health Solutions for People with Specific Requirements Leveraging Advanced AI Techniques 3

Secure and Tailored Health Solutions for People with Specifi...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Singh, Vaishali Veena, C.S. Raja Praveen, K.N. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350343274

Our proposed method aims to revolutionize healthcare by leveraging advanced AI techniques to tailor health solutions for individuals with specific health requirements. This innovative approach integrates three powerful AI algorithms: Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and Generative Adversarial Network (GAN). These algorithms work collaboratively to develop secure, precise, and individualized healthcare services. The DNN mimics the brain's structure to extract intricate patterns, the LSTM handles sequential data and long-term dependencies, and the GAN generates data resembling real distributions. Security and privacy are ensured through data encryption and secure communication. The tailored health solution is a result of integrating these techniques, providing enhanced healthcare services. © 2023 IEEE.

关键词： Long short-term memory

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Effective Co-Operation in Edge-Cloud Computation Networking

Effective Co-Operation in Edge-Cloud Computation Networking

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2023 International Conference on Advances in Computation, communication and information Technology, ICAICCIT 2023

作者： Mahesh, T.R. Savita Bhagwat, Renuka Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka 562112 India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350344387

To improve the performance of mobile devices, both the edge of the network and the cloud provide computing services. While the cloud can manage large processing needs, the edge can minimize latency by offering local computing services. Their cooperation is essential for edge-cloud computational business models since it increases the usage of computing resources and assures Quality of Service (QoS). To facilitate resource sharing between the network's edge and the cloud, this study proposes an effective architecture for mobility edge-cloud computing networks. It uses wholesale and buyback techniques. Study conducted design resource management issues for edge servers to manage wholesale and buyback agreements and for cloud services to establish wholesale rates and distribute local computing resources in order to maximize this sharing process. From the perspectives of both optimizing societal welfare and maximizing profitability in both the perimeter and the cloud, study conducted approach these issues. Study conducted illustrate the concavity of the social welfare for i) and suggest an ideal cloud resource management approach to maximize it. Study conducted first develop the conical shape of wholesale computing power with respect to the wholesale rate for option ii), given the difficulty of directly demonstrating the conical shape of the key issue. Study conducted then create an optimal pricing while computing administration approach for maximizing the resulting profits. According to numerical analyses, maximizing overall profit is consistent with promoting societal welfare, whereas maximizing individual earnings is achieved through maximizing pricing using cloud computing resource management. © 2023 IEEE.

关键词： Cloud computing Edge-cloud computation Mobile Edge Computing (MEC) Modelling and Performance analysis Profit transfers Profitability Quality of Service (QoS) Queueing mechanism Resource management Resource sharing social welfare

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SB-AGT: A stochastic beam search-enhanced attention-based Gumbel tree framework for drug-drug interaction extraction from biomedical literature

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computers in Biology and Medicine 2025年 189卷 110011-110011页

作者： Saad, Eman Kishk, Sherif Ali-Eldin, Amr Saleh, Ahmed I. Communication and Information Technology Center Mansoura University Egypt Electronics and Communication Engineering Dept. Mansoura University Egypt Computer Engineering & Control Systems Dept. Mansoura University Egypt

Drug-Drug Interactions (DDI) and Chemical-Protein Interactions (CPI) detection are crucial for patient safety, as unidentified interactions may lead to severe Adverse Drug Reactions (ADRs). While extensive DDI and CPI information exists within biomedical literature, manual extraction by experts proves time-intensive and resource-demanding. This study presents SB-AGT: Stochastic Beam Search-enhanced Attention-based Gumbel Tree an innovative approach to automated DDI and CPI extraction through an attention-based architecture incorporating a modified Gumbel-Tree method with stochastic beam search optimization. SB-AGT leverages multi-head attention mechanisms and enhanced latent tree structures to effectively capture complex syntactic features and drug relationship patterns. Also, the methodology includes comprehensive preprocessing protocols and addresses dataset imbalance through hybrid sampling techniques. Experimental validation on the DDIExtraction 2013 and CHEMPROT datasets demonstrates the model's effectiveness, achieving precision, surpassing traditional approaches and showing competitive performance compared to contemporary pre-trained models. Through parametric analysis, we establish optimal beam search configurations that maximize the model's extraction accuracy. © 2025 Elsevier Ltd

关键词： Stochastic models

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