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104 results on '"Kaluri, Rajesh"'

2. Blockchain for Handling the Data in Higher Education

Author

Yenduri, Gokul, Kaluri, Rajesh, Rajput, Dharmendra Singh, Lakshmanna, Kuruva, Gadekallu, Thippa Reddy, Hussain, Azham Bin, Mahmud, Mufti, Series Editor, Kaiser, M. Shamim, Series Editor, Kaluri, Rajesh, editor, Gadekallu, Thippa Reddy, editor, Rajput, Dharmendra Singh, editor, and Lakshmanna, Kuruva, editor

Published
2024
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3. From Assistive Technologies to Metaverse: Technologies in Inclusive Higher Education for Students with Specific Learning Difficulties

Author

Yenduri, Gokul, Kaluri, Rajesh, Rajput, Dharmendra Singh, Lakshmanna, Kuruva, Gadekallu, Thippa Reddy, Mahmud, Mufti, and Brown, David J.

Subjects
Computer Science - Computers and Society
Abstract

The development of new technologies and their expanding use in a wide range of educational environments are driving the transformation of higher education. Assistive technologies are a subset of cutting-edge technology that can help students learn more effectively and make education accessible to everyone. Assistive technology can enhance, maintain, or improve the capacities of students with learning difficulties. Students with learning difficulties will be greatly benefited from the use of assistive technologies. If these technologies are used effectively, students with learning difficulties can compete with their peers and complete their academic tasks. We aim to conduct this review to better understand the role of assistive technologies in providing inclusive higher education for students with learning difficulties. The review begins with the introduction of learning difficulties and their causes; inclusive education and the need for assistive technologies; the reasoning for conducting this review; and a summary of related reviews on assistive technologies for students with learning difficulties in inclusive higher education. Then, we discuss the preliminaries for the learning difficulties type and assistive technology. Later, we discuss the effects of assistive technology on inclusive higher education for students with learning difficulties. Additionally, we discuss related projects and support tools available in inclusive higher education for students with learning difficulties. We also explore the challenges and possible solutions related to using assistive technology in higher education to provide inclusive education for students with learning difficulties. We conclude the review with a discussion of potential promising future directions., Comment: Submitted to peer review

Published
2023

4. Roughsets-based Approach for Predicting Battery Life in IoT

Author

Kaluri, Rajesh, Rajput, Dharmendra Singh, Xin, Qin, Lakshmanna, Kuruva, Bhattacharya, Sweta, Gadekallu, Thippa Reddy, and Maddikunta, Praveen Kumar Reddy

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

Internet of Things (IoT) and related applications have successfully contributed towards enhancing the value of life in this planet. The advanced wireless sensor networks and its revolutionary computational capabilities have enabled various IoT applications become the next frontier, touching almost all domains of life. With this enormous progress, energy optimization has also become a primary concern with the need to attend to green technologies. The present study focuses on the predictions pertinent to the sustainability of battery life in IoT frameworks in the marine environment. The data used is a publicly available dataset collected from the Chicago district beach water. Firstly, the missing values in the data are replaced with the attribute mean. Later, one-hot encoding technique is applied for achieving data homogeneity followed by the standard scalar technique to normalize the data. Then, rough set theory is used for feature extraction, and the resultant data is fed into a Deep Neural Network (DNN) model for the optimized prediction results. The proposed model is then compared with the state of the art machine learning models and the results justify its superiority on the basis of performance metrics such as Mean Squared Error, Mean Absolute Error, Root Mean Squared Error, and Test Variance Score.

Published
2021

22. Agribot

Author

Poluru, Ravi Kumar, primary, Reddy, M. Praveen Kumar, additional, Kaluri, Rajesh, additional, Lakshmanna, Kuruva, additional, and Reddy, G. Thippa, additional

Published
2020
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24. Consumer Product Recommendation System Using Adapted PSO With Federated Learning Method

Author

Devarajan, Ganesh Gopal, Nagarajan, Senthil Murugan, Daniel, A., Vignesh, T., and Kaluri, Rajesh

Abstract

In this paper, we proposed adapted particle swarm optimization integrated federated learning-based sentiment analysis integrated deep learning (aPSO-FLSADL) model for personalized recommendations of consumer electronics that leverage SentiWordNet and BERT for word embedding, CNN-BiLSTM based Federated learning model to train a global sentiment analysis model and mutation operator based modified particle swarm optimization for learning parameter optimization in federated learning environment. SentiWordNet is a sentiment lexicon that provides sentiment scores for words, while BERT is a powerful pre-trained deep learning model for natural language processing. Our approach involves pre-processing the text data, calculating sentiment scores using SentiWordNet, converting text data into word embedding using BERT, and assigning weights to words based on a defined weighting scheme. We evaluate the performance of our approach on a separate evaluation dataset including Amazon review dataset and CNET dataset. Based on the various evaluation metrics including accuracy, loss, hit ratio, we demonstrated the effectiveness of proposed aPSO-FLSADL in generating accurate and personalized recommendations. The depicted result shows that proposed aPSO-FLSADL achieved highest training and testing accuracy for both datasets and outperform over baseline models with maximum hit ratio for consumer electronics product recommendation.

Published
2024
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28. Fog‐Sec: Secure end‐to‐end communication in fog‐enabled IoT network using permissioned blockchain system.

Author

Babu, Erukala Suresh, Rao, Mekala Srinivasa, Swain, Gandharba, Nikhath, A. Kousar, and Kaluri, Rajesh

Subjects
BLOCKCHAINS, INTERNET of things, NETWORK analysis (Communication), SOFTWARE maintenance, PRIVATE networks, ACCESS control
Abstract

The technological integration of the Internet of Things (IoT)‐Cloud paradigm has enabled intelligent linkages of things, data, processes, and people for efficient decision making without human intervention. However, it poses various challenges for IoT networks that cannot handle large amounts of operation technology (OT) data due to physical storage shortages, excessive latency, higher transfer costs, a lack of context awareness, impractical resiliency, and so on. As a result, the fog network emerged as a new computing model for providing computing capacity closer to IoT edge devices. The IoT‐Fog‐Cloud network, on the other hand, is more vulnerable to multiple security flaws, such as missing key management problems, inappropriate access control, inadequate software update mechanism, insecure configuration files and default passwords, missing communication security, and secure key exchange algorithms over unsecured channels. Therefore, these networks cannot make good security decisions, which are significantly easier to hack than to defend the fog‐enabled IoT environment. This paper proposes the cooperative flow for securing edge devices in fog‐enabled IoT networks using a permissioned blockchain system (pBCS). The proposed fog‐enabled IoT network provides efficient security solutions for key management issues, communication security, and secure key exchange mechanism using a blockchain system. To secure the fog‐based IoT network, we proposed a mechanism for identification and authentication among fog, gateway, and edge nodes that should register with the blockchain network. The fog nodes maintain the blockchain system and hold a shared smart contract for validating edge devices. The participating fog nodes serve as validators and maintain a distributed ledger/blockchain to authenticate and validate the request of the edge nodes. The network services can only be accessed by nodes that have been authenticated against the blockchain system. We implemented the proposed pBCS network using the private Ethereum 2.0 that enables secure device‐to‐device communication and demonstrated performance metrics such as throughput, transaction delay, block creation response time, communication, and computation overhead using state‐of‐the‐art techniques. Finally, we conducted a security analysis of the communication network to protect the IoT edge devices from unauthorized malicious nodes without data loss. [ABSTRACT FROM AUTHOR]

Published
2023
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32. A Review on Deep Learning Techniques for IoT Data

Author

Lakshmanna, Kuruva, primary, Kaluri, Rajesh, additional, Gundluru, Nagaraja, additional, Alzamil, Zamil S., additional, Rajput, Dharmendra Singh, additional, Khan, Arfat Ahmad, additional, Haq, Mohd Anul, additional, and Alhussen, Ahmed, additional

Published
2022
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