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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Sudhakar, A.V.V. Sharma, Upasana Visala, K. Kumar, K. Suresh Arora, Chirag Chouhan, Kuldeep Sr University School of Engineering Department of Eee Telangana Warangal India Abes Engineering College Department of Electronics and Communication Engineering UP Ghaziabad India Vignan's Institute of Information Technology Computer Science Engineering Andhra Pradesh Vishakapatnam India Panimalar Engineering College Mba Department Varadarajapuram Chennai600123 India Kiet Group of Institutions Department of Electronics and Communication Engineering U.P Ghaziabad India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida201310 India

ISBN: (纸本)9798331508685

One of the most effective and widely used technological advancements for increasing capacity, enhancing quality, and expanding coverage in wireless communications systems is the smart antenna. Smart antennas are essential for increasing the effectiveness and dependability of wireless communication systems because they make use of cutting-edge signal processing techniques. Smart antennas' beamforming capability, which concentrates signal transmission and reception in particular directions, increases signal strength, lowers interference, and expands network capacity. As wireless networks become more complicated, machine learning (ML) has become a potent tool for beamforming optimisation. ML algorithms can proactively adjust antenna characteristics by using real-time data, learning from fluctuations in the environment to optimise performance. In order to improve beamforming capabilities, this research investigates the incorporation of ML with smart antenna systems. We go over several ML techniques, such as supervised and reinforcement learning, for resource allocation, user localisation, and signal-to-noise ratio (SNR) optimisation. Additionally, we draw attention to how deep learning might enhance spatial filtering, facilitating more effective communication in 5G and beyond. The potential direction for next-generation wireless communication networks is presented by the convergence of ML and smart antennas. © 2025 IEEE.

关键词： Smart antennas

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Development of Machine Learning Framework for the Protection of IoT Devices 7

Development of Machine Learning Framework for the Protection...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Mohandas, R. Sivapriya, N. Sanyasi Rao, A. Radhakrishna, K. Sahaai, Madona B. Department of Electronics and Communication Engineering Balaji Institute of Technology & Science Telangana Warangal India Department of Computer Applications Cauvery College of Women Tamil Nadu Trichy India Tamil Nadu Chennai India

ISBN: (纸本)9781665464086

Internet of Things (IoT) has a wide range of threats to businesses, according to security experts. Organizations need an intelligent system that can automatically detect suspicious IoT devices linked to their networks. This study introduces a unique security framework powered by machine learning (ML) that automatically adapts to the growing security needs of the IoT sector. There should be a way to identify IoT devices that aren't on a trusted white list. In this article, a machine learning method has been used to recognize IoT device types from a white list by using network traffic data. Seventeen separate IoT devices, each representing one of nine different categories of IoT devices, were manually tagged to train and assess multi-class classifiers. The majority rule was used to classify block listed devices accurately using unidentified in 86% of trial forms, while authorized expedient categories stayed appropriately identified through the real kinds with 88% of forms. The detection times varied for different types of IoT devices. In addition, it shows how the machine learning-based IoT white-listing system can defend itself against hostile attacks. © 2023 IEEE.

关键词： Internet of things

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Deep Learning Empowered Natural Language Understanding Voicebot’s for farmers: Kissan Samvaad Initiative

Deep Learning Empowered Natural Language Understanding Voice...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： V Jayalakshmi R Deepa R Ganesan N Sriram R Surendran Department of Computer Science and Applications SRM Institute of Science and Technology Ramapuram Chennai Tamilnadu Department of Computing Technologies School of Computing SRM institute of science and technology Computer science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D institute of science and technology Avadi chennai Department of Computer Applications KG College of Arts and Science Coimbatore Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

The ‘Kissan Samvaad’ work focuses on implementing a closed-domain Chatbot using the RASA Open Source Framework to address the lack of timely access to expert agricultural advice for farmers. Despite the rapid growth of Deep Learning and NLU based Chatbots in various fields, farmers often rely on peer knowledge to solve agricultural problems. Kissan Samvaad aims to bridge this gap by allowing farmers to interact with the Chatbot to specify farmers need. By identifying intents and entities in user inputs, the Chatbot retrieves relevant information from its database to provide remedies and solutions using BERT. Initial testing of Kissan Samvaad has shown promising results, indicating its potential to serve as a valuable resource for farmers.

关键词： Deep learning Training Scalability Streaming media Chatbots Agriculture Real-time systems Multilingual Text to speech Testing

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Blockchain Breakthrough: A Review of its Role in Modern Technology

Blockchain Breakthrough: A Review of its Role in Modern Tech...

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IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： Sameer Asthana Ravi Prakash Chaturvedi Annu Mishra Poorva Nayyar Md. Shaliyar Department of Computer Science and Engineering Greater Noida Institute of Technology Greater Noida Department of Computer Science and Applications Sharda School of Engineering and Technology Greater Noida Department of Computer Science and Engineering Sharda School of Engineering and Technology Greater Noida

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

This article summarizes the Blockchain technology along with the artificial intelligence. The cutting edge technology is described in a elaborated manner along with its advantages, disadvantages and its impact with the artificial intelligence. There are various sectors in which it is used and the outcomes are discussed. The proficiency of blockchain has proved its reliability and threshold in different domains.

关键词： Automation Reviews Blockchains Security Reliability Artificial intelligence

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Deep Learning Technique for computer Vision-Based Pose Estimation for Augmented Reality 3rd

Deep Learning Technique for Computer Vision-Based Pose Estim...

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3rd International Conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Palanimeera, J. Ponmozhi, K. Jyothi, Kanagaraj Dargar, Shashi Kant Birla, Shilpi Department of Computer Applications Sathyabama Institute of Science and Technology Chennai India Department of Computer Applications Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Biotechnology Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Electronics and Communication Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819784288

Pose estimation approaches of open pose were suggested. Pose estimation in real-time is a difficult research subject with many computer vision applications. Pose estimation applications are Human Activity Estimation, Motion Transfer and Augmented Reality, Training Robots, Motion Tracking for Consoles, and Human Fall Detection. This paper describes the Augmented Reality (AR) of pose estimation. AR is an enhanced version of the real physical world that is achieved through the use of digital visual elements, sound, or other sensory stimuli delivered via technology. One interesting use for obtaining the Open-Pose discovered important locations in each pose is in the field of augmented reality. A technique for 3D model overlays on an image using pose key point information that is presented in this study. To extract crucial information from the data, the fully convolutional networks deep learning technique is used. The technique used in this work comprises the real-time intersection of the picture plane with the intended 3D model and accurate model orientation based on the key point data. The performance will greatly depend on the Graphics Processing Unit specifications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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DCT-CNN Hybrid Model for High-Capacity and Secure Data Concealment in Encrypted Images

DCT-CNN Hybrid Model for High-Capacity and Secure Data Conce...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Adithya Pothan Raj V Yogita Dayanand Patil Madhurikkha S V. Srithar Vidhya Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences Department of Engineering Science Vishwakarma University pune Department of Artificial Intelligence & Data Science Jaya Sakthi Engineering College Department of Computer Science and Applications Periyar Maniammai Institute of Science and Technology Department Of Computer Science and Engineering Jeppiaar Engineering College

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

This research article presents a revolutionary approach to address the challenges of reversible data embedding in encrypted images within cloud networks. Our pioneering algorithm combines the potency of the Discrete Cosine Transform (DCT) and Convolutional Neural Network (CNN) to introduce a novel DCT-CNN hybrid model, specifically designed for efficient and high-capacity reversible data hiding. Diverging from conventional methods, our approach eliminates the need for trial data embedding during predictor selection. The initial phase involves estimating the optimal predictor using CNN, where training labels are forged through a trial embedding procedure. The selected predictor, providing the highest embedding rate, is assigned as the class label for the corresponding encrypted image. Users secure their images through a three-tier encryption process before transferring them to the cloud. During the testing phase, the trained CNN model processes encrypted images uploaded by users to determine the predictor class with the maximum embedding capacity. Data embedding is then executed using the classified predictor. The proposed predictor storage demonstrates impressive results, achieving an accuracy, recall, and precision of $93.9 \%, 92.6 \%$, and $95.3 \%$ in average, respectively, particularly for the BossBase dataset. Additionally, our reversible data embedding algorithm, employing the DCT-CNN paradigm, achieves a commendable embedding rate of 69.1bpp, along with an average PSNR of 38.8 dB and SSIM of 0.93 in average. Evaluations conducted on datasets, including BossBase and BOWS-2, convincingly demonstrate the stoutness and effectiveness of the proposed method. This research introduces a unique perspective to the realm of reversible data embedding in cloud networks, offering a promising solution for secure and high-capacity data concealment within encrypted images.

关键词： Measurement Training Accuracy Computational modeling Prediction algorithms Data models Encryption

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Enhanced satellite imagery analysis for post-disaster building damage assessment using integrated ResNet-U-Net model

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Multimedia Tools and applications 2025年第5期84卷 2689-2714页

作者： Bhardwaj, Diwakar Nagabhooshanam, N. Singh, Ajeet Selvalakshmi, B. Angadi, Sanjeevkumar Shargunam, S. Guha, Tapas Singh, Gurkirpal Rajaram, A. Computer Engineering and Applications GLA University Chaumuhan India Department of Research Analytics Saveetha Dental College and Hospitals Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Aditya University Surampalem Andhra Pradesh India VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of Computing Science and Engineering Tagore Engineering College Rathinamangalam Tamil Nadu Chennai600127 India Department of Computing Science and Engineering Nutan College of Engineering and Research Talegaon Dabhade Maharashtra 410507 India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu Srivilliputhur626126 India Department of CSE Techno International New Town West Bengal Kolkata700156 India Department of Architecture PTU Punjab Mohali140307 India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Tamil Nadu Nagapattinam611002 India

In the aftermath of natural disasters, timely and accurate building damage assessment is crucial for effective disaster response. This research introduces an innovative integrated ResNet-U-Net ensemble model that significantly enhances the accuracy and efficiency of damage detection from satellite imagery. The model combines the deep feature extraction capabilities of ResNet with the precise localization advantages of U-Net, supplemented by attention mechanisms to focus on critical damage indicators. Methodologically, the research employs a dataset of high-resolution satellite images from post-Hurricane Iota, processed through normalization and augmentation techniques. The model demonstrates exceptional accuracy, achieving 96.91% on an unbalanced dataset representative of real-world conditions, and 89.35% on a balanced dataset. This performance indicates a substantial improvement over existing approaches, offering a rapid and reliable tool for aiding disaster relief efforts. The proposed model's computational efficiency and generalizability underscore its potential to transform disaster management practices. Future work will explore the model's application to various disaster types and integration with real-time data processing systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Satellite imagery

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Collaborative and Integrated Platform to Support Distributed Manufacturing System Using a Service-Oriented Approach Based on Cloud Computing Paradigm

Collaborative and Integrated Platform to Support Distributed...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Nainwal, Ankita Sharma, Vishal Varshney, Neeraj Shirish Boob, Nandini Reddy, N.V. Uma Reddy, K. Jyothsna Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India GLA University Department of Computer Engineering and Applications Mathura India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India New Horizon College of Engineering Department of Artificial Intelligence & Machine Learning Bangalore India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

Increasing interest in distributed manufacturing systems is a result of the dynamic nature of today's business environment and with that comes the popularity of their use. This has given rise in the interest in distributed manufacturing systems. The indirect consequence of this has led to much higher use of distributed manufacturing systems. A large number of production processes, dispersed across a multitude of locations geographically separate, must be controlled and integrated in order to achieve production agility, flexibility and efficiency of costs. In our most recent project, we developed an integrated, collaborative manufacturing platform, the centerpiece of which was LAYMOD. To address the issues posed by such systems, this research presents a collateral and integrated platform based on a service oriented approach that can operate within the cloud computing paradigm. These systems present problems which the study is conducted in order to solve. Utilising the features of cloud computing, it aims to serve as a platform in solving process coordination and hence improving efficiency and scalability of distant manufacturing processes and potential real time communications among various stake holders. Furthermore, statistical research on the rates of platform module adoption in different settings provides specifics of (a) use patterns of some platform modules and (b) what capabilities are provided by some platform modules. When extrapolated to a wider size, our work is applied towards the creation of collaborative and integrated platforms for a number of the various manufacturing processes which are currently in use. © 2025 IEEE.

关键词： Cloud platforms

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Enhancing Cloud Storage Security with Homomorphic Encryption and ML-Based Access Control 2

Enhancing Cloud Storage Security with Homomorphic Encryption...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Shnain, Ammar H. Varshney, Neeraj Afreen, Sana Suganya, M. Babu, Bulipe Sankara Aravind, T. The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq GLA University Department of Computer Engineering & Applications Mathura India India New Prince Shri Bhavani College of Engineering and Technology Department of ECE Tamil Nadu Chennai600073 India GRIET Department of CSE Telangana Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350368109

In this research a new approach of improving security of cloud storage through the use of homomorphic encryption and machine learning access control is proposed. One of the most important deficiencies the proposed system relevant to the critical issue of protecting data during its life cycle while enabling computations on them in an encrypted form without decrypting them. Thus, the system minimizes both the unauthorized access rights and the user behavior affecting the data integrity and confidentiality relying on the approaches of the supervised learning. It uses partially homomorphic encryption based on the Paillier cryptosystem as well as RandomForest and NeuralNetwork models. Experimental evaluation performed in a cloud-like environment shows that a system achieves desired security while providing low latency and high accuracy under high working load. The provided research can be considered as a step to the enhancement of the cloud security technologies and presents efficient solution for the protection of the cloud data storage. © 2024 IEEE.

关键词： Cloud security

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An Enhanced Prediction Logic to Forecast Bitcoin Price Variations Using Elevated Machine Learning Principles 9

An Enhanced Prediction Logic to Forecast Bitcoin Price Varia...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Bhuvaneswari, J. Nair, M. Saranya Devi, S. Durga Santhiya, J. Juliet, N. Rmk College of Engineering And Technology Department of Ai&ds Chennai Puduvoyal India Vellore Institute of Technology Chennai India Vel Tech High Tech Dr.Rangarajan Dr. Sakunthala Engineering College Chennai India Dr.Mahalingam College of Engineering And Technology Department of Computer Science And Engineering Pollachi India Computer Applications S.A.Engineering College Chennai India

ISBN: (纸本)9798350365092

The volatility of Bitcoin prices presents both challenges and opportunities for investors and researchers seeking to understand and forecast cryptocurrency market dynamics. In this study, we propose a Hybrid Recurrent Neural Network and Autoregressive Integrated Moving Average (RNN-ARIMA) model to predict Bitcoin price variations. Leveraging minute-to-minute Bitcoin price data sourced from Bitstamp exchange, our methodology integrates the capabilities of RNNs and ARIMA models to capture both temporal dependencies and linear trends in the data. Through rigorous experimentation, we evaluate the performance of the hybrid model against baseline models and alternative configurations. Our results demonstrate the superiority of the Hybrid RNN-ARIMA model, with a Mean Absolute Error (MAE) of 120.54, Mean Squared Error (MSE) of 25098.23, Root Mean Squared Error (RMSE) of 158.45, R-squared (R2) of 0.87, and Mean Absolute Percentage Error (MAPE) of 3.8%. These findings underscore the effectiveness of the hybrid modeling approach in accurately predicting Bitcoin price variations and provide valuable insights for stakeholders navigating the volatile cryptocurrency market. © 2024 IEEE.

关键词： Recurrent neural networks

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