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

作者： Kale, Kiran Raja Praveen, K.N. Savita Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India 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

ISBN: (纸本)9798350357769

Fifth-generation wireless networks are ready to handle the increasing volume of mobile traffic and provide critical technological solutions to suit various service needs. Utilizing the millimeter-wave (mm-wave) spectrum is a crucial strategy for achieving large data speeds. Additionally, analogue radio-over-fiber (ARoF) has a wide range of benefits, such as affordability, little power usage, and exceptional spectrum efficiency. As a result, mm-wave ARoF becomes a strong candidate for providing becomes the standard waveform for 5G, it is crucial that mm-wave ARoF systems support it. However, in mm-wave OFDM ARoF systems, an existence of phase interference represents a substantial performance-limiting constraint. Through a painstakingly constructed test setup, we perform a thorough examination of phase noise in this study. We are able to systematically modify the system's overall last phase noise level thanks to this configuration. In addition, we provide a fresh and unique approach for OFDM transmitter phase noise compensation. Our testing confirms the algorithm's effectiveness under these circumstances and highlights the potential advantages of mm-wave OFDM ARoF for 5G to meet by evaluating how well it works over a range of period noise values and subcarrier spacings. © 2023 IEEE.

关键词： Analogue Radio-over-Fiber Beyond 5G Distortion Frequency-Selective Channels Investigation Millimeter-Wave MM-Wave OFDM Phases Spectral Efficiency and Signal-to-Noise Ratio Subcarrier Spacings

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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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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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Visualizing Wellness: The Impact of Deep Learning on Healthcare Image Analysis

Visualizing Wellness: The Impact of Deep Learning on Healthc...

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2023 International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2023

作者： Sinha, Deepak Kumar Naval, Preeti Fiza, Samreen Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore 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

Deep convolution networks have quickly established itself as the main method for deciphering medical pictures due to their widespread use. This extensive study examined almost 300 recent papers, mostly from the preceding year, that summarized the basic artificial intelligence ideas related to medical picture analysis. Applications of deep learning were explored in detail in a number of disciplines, including object recognition, picture grouping, classification, and registering. This book presents a succinct summary of studies conducted in the fields of neurological disorders, retinal cells, asthma, malignant scans, breasts, cardiovascular and cardiac pelvic, and nerve imaging. Study conducted concludes our assessment with a review of the state-of-the-art at this time, a thorough investigation of outstanding problems, and suggestions for further research. All areas, including computerized pathology, musculoskeletal, pulmonary, the heart, breasts, muscles and tendons, retinal, and neurology imaging, are summarized in a summary of current studies. This thorough review ends with an assessment of the field's current state, a critical analysis of ongoing issues, and specific suggestions for further research and development. © 2023 IEEE.

关键词： Classification Convolutional Neural Networks (CNNs) Deep Learning Detection Healthcare Image Analysis Image Processing and Medical Image Analysis Localization Machine Learning Medical Imaging

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Efficient Segmentation for wsn Connectivity Technologies

Efficient Segmentation for wsn Connectivity Technologies

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

作者： Vivek, V. Savita Dore, Aruna 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 wireless sensors networks, the chargeable capacity of sensor nodes serves as the primary energy source. In WSNs, grouping has emerged as a crucial strategy to save energy use and lengthen network lifespan. Transport energy is inversely correlated to the amount of distance across transmitter and destination nodes, which supports clustering. In this paper, we provide a novel fuzzy logic model for the cluster head (CH) choice, a key step in clustering in WSNs. The recommended model includes five characteristics to assess each node's suitability for CH status. The closeness to the starting place, volume, topographical suitability, and remaining power are some of these criteria. This fuzzy logic methodology is used to propose the Fuzzy Reasoning-based Energy-Efficient Grouping for WSN, with emphasis on establishing the shortest possible distance across CHs (FL-EEC/D). As a further metric of clustered methods' energy use, we also evaluate how efficiently they distribute energy among the sensors inside the WSN using the Gini index. We compare our proposed FL-EEC/D approach with the Low Energy Adaptation Clustered Architecture (LEACH), a fuzzy logic-based clustering approach, and various techniques. For various network sizes and topologies, simulation findings show considerable improvements in energy efficiency, network longevity, and balanced energy consumption across sensor nodes. Our results show that initial node depletion and half node depletion have significantly improved on average. © 2023 IEEE.

关键词： Cluster Head Election Clustering Algorithms Energy Efficiency Fuzzy Inference Systems Fuzzy Logic Model Gini Index Measurement Network Longevity Routing Protocols Separation Distance Wireless Sensor Networks

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Intelligent Forensic Investigation Using Optimal Stacked Autoencoder for Critical Industrial Infrastructures

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computers, Materials & Continua 2022年第8期72卷 2275-2289页

作者： Abdullah S.AL-Malaise AL-Ghamdi Mahmoud Ragab F.J.Alsolami Hani Choudhry Ibrahim Rizqallah Alzahrani Information Systems Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Computer Science Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Biochemistry Department Faculty of ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science and Engineering Department College of Computer Science and EngineeringUniversity of Hafr Al BatinAl JamiahHafar Al Batin39524Saudi Arabia

Industrial Control Systems(ICS)can be employed on the industrial processes in order to reduce the manual labor and handle the complicated industrial system processes as well as communicate *** of Things(IoT)integrates numerous sets of sensors and devices via a data network enabling independent *** incorporation of the IoT in the industrial sector leads to the design of Industrial Internet of Things(IIoT),which find use in water distribution system,power plants,*** the IIoT is susceptible to different kinds of attacks due to the utilization of Internet connection,an effective forensic investigation process becomes *** study offers the design of an intelligent forensic investigation using optimal stacked autoencoder for critical industrial *** proposed strategy involves the design of manta ray foraging optimization(MRFO)based feature selection with optimal stacked autoencoder(OSAE)model,named MFROFS-OSAE *** primary objective of the MFROFS-OSAE technique is to determine the presence of abnormal events in critical industrial ***-OSAE approach involves several subprocesses namely data gathering,data handling,feature selection,classification,and parameter ***,the MRFO based feature selection approach is designed for the optimal selection of feature ***,the OSAE based classifier is derived to detect abnormal events and the parameter tuning process is carried out via the coyote optimization algorithm(COA).The performance validation of the MFROFS-OSAE technique takes place using the benchmark dataset and the experimental results reported the betterment of the MFROFS-OSAE technique over the recent approaches interms of different measures.

关键词： Industrial control systems internet of things artificial intelligence feature selection deep learning

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Traffic Anomaly Detection Using Optimized Faster R-CNN

Traffic Anomaly Detection Using Optimized Faster R-CNN

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2024 International Conference on Electronics, Electrical and information engineering, ICEEIE 2024

作者： Karim, Shahid Qadir, Akeel Han, Gao Li, Jing Ibrar, Muhammad Bibi, Irfana School of Information Engineering Xi'an Eurasia University 710065 China Software College Shenyang Normal University Shenyang China Department of Computer Science Faculty of Computing and Information Technology University of Punjab Punjab Lahore Pakistan

ISBN: (数字)9781510686809

ISBN: (纸本)9781510686793

This paper introduces a new method for traffic anomaly identification within the Faster R-CNN framework by combining the DenseNet module with the region proposals (RPs) processing block. The suggested approach seeks to improve feature extraction and RP creation in aerial imagery or CCTV frames to get beyond the drawbacks of the current methods. Discriminative features are extracted through DenseNet, and the RPs block makes it easier to provide a wide range of accurate and varied area proposals. Contrast correction techniques are applied to overcome the difficulties presented by low-resolution and low-contrast images frequently seen in surveillance scenarios. Comparing experimental results to state-of-the-art techniques, one can observe notable resilience and detection accuracy increases. The suggested method works exceptionally well in various traffic scenarios and environmental circumstances, indicating its potential for practical implementation in traffic surveillance systems. Overall, the study contributes to advancing the efficacy and reliability of anomaly detection in urban traffic environments. © 2024 SPIE.

关键词： Aerial photography

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Cloud Deployment Unveiled: A Security Model Focused on Industry

Cloud Deployment Unveiled: A Security Model Focused on Indus...

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2023 International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2023

作者： Yadav, Rakesh Kumar Natya, S. Mahesh, T.R. Maharishi University of Information Technology Maharishi School of Engineering & 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)9798350344387

The Cloud Compute Adoption Architecture (CCAF), a unique architecture designed to improve safety of the business cloud installations, is introduced in this research article. Using router, authentication and authorization, and encryption methods embedded in Industrial File Sync/Share systems, CCAF aims to provide a multi-layered privacy strategy. Our paper describes the creation of this security A system, examines relevant literature, and explains the reasoning behind CCAF. Through testing and thorough analysis of the key technologies, the study assesses the effectiveness of the CCAF security layered approach. The intricate security system in place at CCAF proved to be an outstanding tool during penetration testing, detecting and eliminating 99.95% of worms including trojans, and even deactivating over 85% of them after 100 hours of continuous attack. Of particular note was the operating system's lightning-fast reaction time of less than 0.012 seconds per trojan and virus, which allowed for the early detection and mitigation of threats. Moreover, each try at SQL injection was successfully thwarted by the CCAF's extensive security defenses, guaranteeing strong security of data. The article highlights the usefulness of CCAF by exploring how its multi-layered security is seamlessly integrated into current regulations, services, and corporate processes. The research highlights how important CCAF's complex security is for Big Data applications running in the cloud, especially when it comes to scalability, speed, and veracity. The study's conclusions highlight CCAF's legitimacy and significant contributions to the field, demonstrating its applicability and effectiveness in protecting the cloud. © 2023 IEEE.

关键词： Cloud Compute Adoption Framework (CCAF) encryption Enterprise File Sync firewall identity management multilayered security approach penetration testing and SQL injection assessment security technologies Share solutions

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Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

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computers, Materials & Continua 2022年第11期73卷 3757-3769页

作者： Olfat M.Mirza GJose Moses R.Rajender E.Laxmi Lydia Seifedine Kadry Cheadchai Me-Ead Orawit Thinnukool Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeNorway Program in Statistics and Information Management Faculty of ScienceMaejo UniversityChiang Mai50290Thailand Department of Modern Management and Information Technology College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

关键词： Churn prediction customer retention deep learning machine learning archimedes optimization algorithm

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Conceptual Framework for the Optimization of Edge Device Workload Allocation in Wireless Body Area Networks

Conceptual Framework for the Optimization of Edge Device Wor...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Alahakoon, Sachinthani Alobaedy, Mustafa Muwafak Fazea, Yousef City University Malaysia Faculty of Information Technology Petaling Jaya46100 Malaysia Marshall University Department of Computer Sciences and Electrical Engineering HuntingtonWV25755 United States

ISBN: (纸本)9798350364910

Wireless body area networks (WBANs), a relatively new technology that has emerged in response to the exponential growth in the demand for healthcare, have shown themselves to be promising and are already being utilized extensively in the field of human health monitoring. Within the scope of this paper, a conceptual framework of an optimal workload allocation algorithm for edge computing-based WBAN systems is presented. Users can enhance their WBAN system by optimizing the workload allocation algorithms, which will result in a reduction in latency, energy consumption, job failure, and communication load. A final evaluation of the effectiveness of the proposed strategy was carried out, and the results of the experiment demonstrate that the proposed approach was successful in improving the quality of the system by lowering the number of transmission failures and the amount of power that was consumed. © 2024 IEEE.

关键词： Electronic health record

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