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User Centric Block-Level Attribute Based Encryption in Cloud Using Blockchains

computer Systems Science & engineering 2022年第8期42卷 605-618页

作者： S.Godfrey Winster A.Siva Kumar R.Ramesh Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyChengalpattu603203India Department of Information Technology Sri Sai Ram Engineering CollegeChennai600044India Department of Electronics and Communication Engineering Saveetha Engineering CollegeChennai602105India

Cloud computing is a collection of distributed storage Network which can provide various services and store the data in the efficient *** advantages of cloud computing is its remote access where data can accessed in real time using Remote Method Innovation(RMI).The problem of data security in cloud environment is a major concern since the data can be accessed by any time by any *** to the lack of providing the efficient security the cloud computing they fail to achieve higher performance in providing the efficient *** improve the performance in data security,the block chains are used for securing the data in the cloud ***,the traditional block chain technique are not suitable to provide efficient security to the cloud data stored in the *** this paper,an efficient user centric block level Attribute Based Encryption(UCBL-ABE)scheme is presented to provide the efficient security of cloud data in cloud *** proposed approach performs data transaction by employing the block *** proposed system provides efficient privacy with access control to the user access according to the behavior of cloud user using Data Level Access Trust(DLAT).Based on DLAT,the user access has been restricted in the cloud *** proposed protocol is implemented in real time using Java programming language and uses IBM *** implementation results justifies that the proposed system can able to provide efficient security to the data present in and cloud and also enhances the cloud performance.

关键词： Cloud blockchain data security ABE block-level

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THz-BA: Terahertz Antenna Design for Biological Applications with a Focus on Riboflavin Detection 4

THz-BA: Terahertz Antenna Design for Biological Applications...

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4th International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Hasan, Md. Fahad Hasan, Mahbub Hossain, Arafat Faisal, Fahim Heart, Mahadi Hasan Rahman, Mahmudur American International University-Bangladesh Electrical and Electronics Engineering Dhaka Bangladesh New Mexico Institute of Mining and Technology Electrical Engineering SocorroNM United States Bangladesh Army University of Engineering and Technology Qadirabad Cantonment Information and Communication Engineering Natore Bangladesh Computer Science Brac University - Bangladesh Dhaka Bangladesh Daffodil International University Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331530938

In this work, we have designed a terahertz antenna for riboflavin detection. Three layers make up the structure of the microstrip antenna: the bottom layer models the ground plane, the middle layer models the substrate, and the top layer models the rectangular patch. The antenna design employed a FR4 substrate with a thickness of 1.7 μm and a dielectric constant of 4.3. The copper material used for the ground plane, feed line, and patch has a thickness of 0.00035μm. The suggested configuration offers an omnidirectional radiation pattern with a gain of 5.662 dBi. The antenna operates in two frequency bands. Two frequencies have been identified: the first is 2.3361 THz to 2.4622 THz with a bandwidth of 0.12605 THz, and the second is 4.4986 THz to 4.8711 THz with a bandwidth of 0.3725 THz and projected antenna return loss of -21.088dB and-21.427dB. At 2.40 THz and 4.75THz, the antenna resonates. Thus, the aforementioned antenna can be used for biological purposes, such as riboflavin detection. © 2025 IEEE.

关键词： Microstrip antennas

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Development of Authentication Framework Using GAIT for IoT communication Models

Development of Authentication Framework Using GAIT for IoT C...

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2024 International Conference on Optimization Computing and Wireless communication, ICOCWC 2024

作者： Nishat, Humaira Balaji, T.S. Shargunam, S. Sasikala, R. Rani, K. Cvr College of Engineering Department of Electronics and Communication Engineering Telangana Hyderabad India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai600026 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Srivilliputhur India Karpagam College of Engineering Department of Cyber Security Tamilnadu Coimbatore641032 India M. Kumarasamy College of Engineering Department of Information Technology Tamilnadu Karur India

ISBN: (纸本)9798350383348

The data collected by the authorised sensor devices is transferred to the cloud for safe storage. communication occurs via a secure key management system designed for a decentralised network. Subsequently, it is important to guarantee that only authorised individuals are able to get this data from the cloud. Ensuring user authentication is of utmost importance in resource-constrained IoT devices due to the large number of users accessing sensitive data. The current user authentication methods suffer from accuracy, error rate, and implementation time problems. A novel solution is offered to tackle these challenges by introducing a sophisticated stacked ensemble authentication architecture that utilises cellphones for IoT systems. The suggested authentication approach utilises gait analysis to authenticate individuals by recognising their unique patterns of human movement. A smartphone equipped with an accelerometer and gyroscope sensor is used to gather gait characteristics. The proposed system has two components: preprocessors and classifiers. The preprocessor performs noise reduction and feature extraction. Subsequently, stacked ensemble machine learning classification models, including base and meta classifiers, are used to classify the characteristics, such as linear acceleration, rotation rate, and gravity, for the purpose of user authentication. The accuracy and error rate of the outcomes are compared with the schemes that already exist. The observation indicates that the suggested framework attains a 95% accuracy rate, a lower error rate, and optimised execution time. © 2024 IEEE.

关键词： GAIT Authentication Internet of Things (IoT) KNN

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Audio Deepfakes: Feature Extraction and Model Evaluation for Detection 5

Audio Deepfakes: Feature Extraction and Model Evaluation for...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Bhukya, Ramesh K. Raj, Aditya Raja, Danish N. Department of Electronics and Communication Engineering Indian Institute of Information Technology Allahabad Uttar Pradesh Prayagraj211015 India Department of Computer Science Islamic University of Science and Technology Awantipora Jammu and Kashmir Pulwama192122 India

ISBN: (纸本)9798350361155

Cutting-edge AI-driven tools are currently employed for replicating human voices, leading to the emergence of audio deepfakes. Initially designed to enhance experiences like audiobooks, the potential misuse of audio deepfakes poses significant risks to public safety. Despite advancements in detecting video deepfakes, identifying audio deepfakes remains challenging. We employ methods to extract features such as Mel-frequency cepstral coefficients, chromagrams, spectral contrast, tonnetz, and Mel-spectrograms from audio samples. The extracted deep features are concatenated together and then employed in machine learning and deep learning models to evaluate the ASVspoof2021 dataset to categorize it into bonafide and spoofed speech utterances. The experimental results reveal that the multilayer perceptron model achieves the highest accuracy rate of 96.073%, while SVM, k-NN, XGBoost, and RF produce accuracy rates of 93.58%, 94.58%, 93.544%, and 93.58%, respectively. Additionally, DL models, including CNN, achieve an accuracy rate of 98.2%, DNN of 97.6%, and LSTM of 97.7%, showcasing the superiority of CNN in identifying deepfake audio amidst the evolving landscape of synthetic media. © 2024 IEEE.

关键词： Feature extraction

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Efficient Multi-UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach 7

Efficient Multi-UAV Path Planning for Wireless Data Harvesti...

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7th IEEE International Conference on Electronic information and communication Technology, ICEICT 2024

作者： Othman, Nawaf Qasem Hamood Al-Gunid, Haithm M. Younas, Imran Xidian University School of Telecom Engineering Xian China University of Science and Technology of China Computer Science and Technology Hefei China Xi'an Jiaotong University School of Electronics Information and Communication Eng. Xian China

ISBN: (纸本)9798350384437

This paper presents a multi-agent reinforcement learning (MARL) framework designed to optimize the path planning of multiple unmanned aerial vehicles (UAVs) for wireless data harvesting missions. The proposed approach leverages Double Deep Q-Networks (DDQN) with State-Action-Reward-State-Action (SARSA) and Q-learning algorithms to improve learning efficiency and overall *** our framework, UAVs operate in a decentralized manner, learning their policies independently through interactions with a grid-based environment that includes no-fly zones, buildings, start and landing zones, and sensor locations. Extensive simulations demonstrate that the DDQN-based algorithm significantly outperforms SARSA and Q-learning in terms of maximizing data collection, minimizing collisions, and achieving faster convergence, underscoring its potential to enhance UAV operations in dynamic © 2024 IEEE.

关键词： Deep reinforcement learning

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Leveraging AI to Unlock the Potential of Data Science 15

Leveraging AI to Unlock the Potential of Data Science

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Yadav, Rakesh Kumar Sudhamsu, Gadug Goyal, Manish Kumar Yuvaraj, S. Deshpande, Leena A. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

The application of synthetic Intelligence (AI) to records science has spread out a massive array of new possibilities. Via the combination of advanced evaluation strategies, effective understanding-pushed system gaining knowledge of algorithms, and modern computing architectures, AI is allowing researchers to quick and as it should be discover insights from huge and complicated datasets. Furthermore, AI tools are offering corporations with the ability to make higher and more knowledgeable choices from the evaluation of hundreds of thousands of statistics points. The skills of information technology powered by means of AI are revolutionizing industries which includes healthcare, finance, and manufacturing-producing savings, uncovering efficiencies, and identifying new opportunities. As AI-driven records technology maintains to strengthen, organizations that capitalize on its ability stand to gain precious insights and unlock new degrees of overall performance. © 2024 IEEE.

关键词： Smart manufacturing

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An Optimized Technique For Developing Optical Band Pass Filter

An Optimized Technique For Developing Optical Band Pass Filt...

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2024 IEEE Calcutta Conference, CALCON 2024

作者： Ghosh, Arup Kumar Garai, Gautam Bhattacharjee, Subhankar University of Calcutta A.K.C. School of Information Technology Kolkata India Saha Institute of Nuclear Physics Computer Section Kolkata India Techno International New Town Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9798331516666

In this paper we have introduced an innovative method for designing a one dimensional photonic crystal (1D PhC) based Optical Band Pass Filter (OBPF) using the Differential Evolution (DE) algorithm. These filters are essential in optical communication systems because of their ability to control light propagation. The DE algorithm efficiently optimizes the PhC structure to achieve the desired optical band pass characteristics and simplifying the design process. The OBPFs are made of multiple dielectric layers with varying thicknesses and refractive indices to enable high transmission at specific wavelengths and low transmission elsewhere. Optimization helps to find the optimal thickness and refractive index. MATLAB implementation shows encouraging results with high transmittance (99.2214%) and a narrow full width at half maximum (FWHM) of 0.6 nm. The experimental results demonstrate the superiority of this approach over the existing well-known traditional methods. © 2024 IEEE.

关键词： MATLAB

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Digital-Twin Enabled Time Ahead Resource Allocation for Integrated Fiber-Wireless Connected Vehicular Network

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Journal of communications and information Networks 2024年第3期9卷 296-308页

作者： Akshita Gupta Saurabh Jaiswal Martin Maier Vivek Ashok Bohara Anand Srivastava Wirocomm Research Group Department of Electronics&Communication EngineeringIndraprastha Institute of Information Technology Delhi(IIITD)New Delhi 110020India Department of Computer Science&Applied Mathematics Indraprastha Institute of Information Technology Delhi(IIITD)New Delhi 110020India Optical Zeitgeist Laboratory Institute National de la Recherche ScientifiqueMontreal H5A 1K6Canada

The digital twin(DT)is envisaged as a catalyst for pioneering ecosystems of service provision within an immersive environment born from the convergence of virtual and physical ***,DT could enhance the performance of edge-intelligent connected vehicular networks by allocating network resources efficiently based on the key performance indicators(KPIs)of vehicular data ***,this work addresses the key challenge of computation and spectrum resource allocation for vehicular *** allocate the optimal resource allocation,we subdivided the problem into:traffic classification,collective learning,and resource allocation *** order to do so,this paper concentrates on two crucial vehicular applications:brake application and lane-change *** utilize a random forest model to collectively learn vehicular data traffic in the upcoming time ***,a time-ahead resource allocation algorithm is proposed to improve the quality of service(QoS)by intelligently offloading vehicular data traffic to a DT-based integrated fiber-wireless(Fi-Wi)connected vehicular *** evaluate the performance of the resource allocation strategy in terms of resources required by the network alongside the packet loss *** was observed that there was a 44.74%increase in cost as the total computation resources increased from F=50 to 100 GHz,whereas the PLR of the network decreased by 71.43%.

关键词： connected vehicles digital twin edgeintelligence fiber-wireless machine learning resource allocation

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CNN-based device-free health monitoring and prediction system using WiFi signals

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International Journal of information Technology (Singapore) 2022年第7期14卷 3725-3737页

作者： Kumar, Amit Singh, Shashwat Rawal, Vibhor Garg, Shivam Agrawal, Anand Yadav, Sonal Department of Computer Science and Engineering Indian Institute of Information Technology Kota Jaipur India Department of Electronics and Communication Engineering Indian Institute of Information Technology Kota Jaipur India Department of Computer Science and Engineering National Institute of Technology Raipur India

Health monitoring has been a growing area of interest in recent times. It provides many beforehand benefits including early detection of potentially harmful illnesses. Till now, it has been achieved primarily by using devices such as wearable bands to monitor breathing rate and heart rate. Wearing and carrying devices make the person uncomfortable. In this paper, we introduce a deep learning based device-free way to monitor breathing rate and heart rate in different human positions to provide insight into whether the person is healthy. A convolutional neural network (CNN) model along with Gaussian naive bayes (GaussianNB) and support vector machine (SVM) is introduced and the device-free monitoring is implemented using WiFi signals. An individual in proximity causes a change in the channel state information (CSI) and the distortion is measured to provide corresponding vitals. The vitals give an insight into the health of the individual. The experimental results are quite encouraging with the evidence of superiority of SVM with CNN. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Channel state information (CSI) Convolution neural network (CNN) Support vector machine (SVM) WiFi signals

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Optimizing Big Data Algorithms on Cloud for Automated Disease Diagnosis in Healthcare System 2

Optimizing Big Data Algorithms on Cloud for Automated Diseas...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rai, Vijendra Sharma, Sumit Kumar Gupta, Richa Kumar, Rakesh Sethi, Tarun Information Technology Abes Institute of Technology UP Ghaziabad India Ajay Kumar Garg Engineering College Department of Information Technology UP Ghaziabad India Raj Kumar Goel Institute of Technology Electronics & Communication Engineering UP Ghaziabad India Gla University Department of Computer Engineering and Applications UP Mathura India Department of Computer Applications Iamr Duhai UP Ghaziabad India

ISBN: (纸本)9798350371055

Massive data Algorithms have the potential to revolutionize healthcare with the aid of making scientific diagnoses quicker., greater correct., and greater value efficient. Cloud-primarily based platforms are increasingly more getting used to save and procedure huge portions of scientific facts., and this allows optimized and automatic selection-making. by way of incorporating massive facts Algorithms., healthcare professionals can analyze massive volumes of scientific facts and discover diseases in a more correct and timely manner. computerized sickness diagnosis can reduce misdiagnosis and the need for useless treatments., main to higher high-quality of healthcare and reduced clinical fees. Consequently., its miles essential to optimize massive facts Algorithms on cloud for automatic ailment diagnosis in healthcare structures. Specific techniques hired include data modelling., statistics compression algorithms., and machine getting to know techniques. Facts modelling let in efficient summarization of huge volumes of information., and information compression techniques permit efficient garage., retrieval., and evaluation. Machine studying strategies such as clustering techniques and category algorithms can further beautify the accuracy of prognosis. All of these optimization strategies in the end offer healthcare expert with accelerated accuracy and progressed efficiency of prognosis. The current surge of crypto currencies and initial Coin offerings (ICOs) has created an abundance of big data.. This paper investigates the feasibility of optimizing massive information algorithms at the cloud for computerized disorder prognosis in a healthcare gadget. First., this paper explores the capacity advantages of cloud computing., along with accelerated computational power., scalability., and fee-performance at the cloud. Outcomes advocate that cloud-based totally optimization can growth the accuracy of ailment diagnoses by means of imparting extra information for extra corre

关键词： Analysis Healthcare Incorporating Predictions Selection

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