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An Effective Signcryption with Optimization Algorithm for IoT-enabled Secure Data Transmission

Computers, Materials & Continua 2022年第11期73卷 4017-4031页

作者： A.Chinnappa C.Vijayakumaran Department of Computing Technologies School of ComputingSRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia

Internet of Things(IoT)allows several low resources and controlled devices to interconnect,calculate processes and make decisions in the communication *** the heterogeneous environment for IoT devices,several challenging issues such as energy,storage,efficiency,and *** design of encryption techniques enables the transmission of the data in the IoT environment in a secured *** proper selection of optimal keys helps to boost the encryption *** this motivation,the study presents a signcryption with quantum chaotic krill herd algorithm for secured data transmission(SCQCKH-SDT)in IoT *** proposed SCQCKHSDT technique aims to effectively encrypts the data by the use of optimal keys generated by the CQKH *** proposed SCQCKH-SDT technique initially employs the signcryption technique for the encryption of *** order to optimize the secrecy,the optimal key generation process is carried out using Chaotic Krill Herd(CQKH)*** CQKH algorithm incorporates the concept of quantum computing and chaotic theory into the traditional KH *** performance validation of the SCQCKH-SDT technique is performed using benchmark *** extensive comparative analysis reported the superior performance of the SCQCKH-SDT technique over the recent approaches.

关键词： Security Internet of Things encryption optimal key generation metaheuristics

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Efficient allocation of independent gridlet on small, medium, and large grid

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Personal and Ubiquitous computing 2023年第3期27卷 1029-1037页

作者： Rajeswari, D. Ramamoorthy, S. Srinivasan, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

Gridlet allocation in a computational grid environment is a major research issue to obtain not only the efficient gridlet allocation technique but also the time needed to obtain the efficient allocation technique. Grid computing networks have various nodes to process user jobs. To achieve the high performance of computational grid, task scheduling is an important issue. The users who are using services of grid systems are more cautious about time to complete their job. Hence, this work concentrates on gridlet allocation method used for time reduction by a genetic algorithm with the MapReduce programming model for independent tasks in computational grid. In computational grid environment, multi-objective problem formulation minimization of makespan and flowtime is considered. In this proposed technique, fitness function formulation for makespan and flowtime has been formulated mathematically. The genetic algorithm with the MapReduce programming model is implemented using MapReduce written in Java and then combined with GridSim. The experimental outcome with regard to time needed, flowtime, and makespan clearly reveals that the genetic algorithm with the MapReduce model effectively optimizes time, makespan, and flowtime in computational grid environment. A comparative study of performance efficiency among genetic algorithm with the MapReduce and sequential genetic algorithm (SGA) and parallel genetic algorithm (PGA) depicts the usefulness of the model. The execution time achieved by GA with the MapReduce model in small grid is 10.48 s, medium grid is 18.76 s, and large grid is 33.73 s. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： MapReduce

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Implementing Blockchain and Smart Encryption for Immutable Purchase and Generates Digital Ownership Certificates 3

Implementing Blockchain and Smart Encryption for Immutable P...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Sodhi, Arshdeep Singh Das, Sanjana Loganathan, Sathyapriya SRM Institute of Science and Technology Kattankulathur School of Computing Department of Computing Technologies Chennai603203 India

ISBN: (纸本)9798350372120

Blockchain functions as a decentralized and distributed ledger technology, ensuring the secure recording and verification of transactions across a network of computers. It tackles trust, transparency, and resistance to tampering in conventional centralized systems by creating a clear and unalterable transaction record, eliminating the necessity for a central authority. Addressing trust-related challenges in transactions, Blockchain eliminates intermediaries, diminishes fraud risks through cryptographic security, and boosts transparency by establishing an immutable shared transaction history. The proposed system, built on a blockchain foundation, aims to securely and efficiently store purchase details by utilizing a duplicated and distributed digital ledger of transaction records across all participant computers in the network. Key features such as immutability, enhanced security through smart contracts and encryption, digital ownership certificates for authenticity verification, and a user-friendly search function contribute to the secure storage of purchase details. This innovative system extends the transformative impact of blockchain to the insurance industry, providing a decentralized alternative to counter centralization, inefficiency, and high costs. The result obtained enabling direct transactions between users and eliminating intermediaries, the system reduces frictional costs and enhances trust and transparency in insurance practices. This groundbreaking approach signifies a shift towards a more secure, efficient, and transparent future for both purchase record management and insurance processes, leveraging the full potential of blockchain technology. © 2024 IEEE.

关键词： Smart contract

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Leveraging Pre-trained ResNet-18 with Transfer Learning for Yoga Posture Classification 2

Leveraging Pre-trained ResNet-18 with Transfer Learning for ...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Aruna, M. Kaneriya, Gruhit Jain, Prashuk Srm Institute of Science and Technology Computing TechnologiesSchool of Computing Kattankulathur Chennai India

ISBN: (纸本)9798350372892

This research paper explores the application of pre-trained ResNet-18 with transfer learning for the classification of yoga postures. The study utilizes a dataset comprising images of various yoga poses taken from Kaggle. Through fine-tuning, the pre-trained model achieved impressive training accuracy of 9 8 %. Furthermore, on unseen data, the model maintained accuracy of 9 4. 9 3 %. Advantages and disadvantages of the methodology are discussed, along with experimental setup details and metrics evaluation. The findings underscore the efficacy of leveraging pre-trained models and transfer learning techniques for complex image classification tasks in specialized domains like yoga postures. However, a notable limitation arises in the difficulty of removing noise during model implementation, leading to a decrease in confidence scores. For future work, the project aims to address this limitation by focusing on noise removal techniques while maintaining accuracy and confidence scores, thus enhancing the robustness of the classification model. © 2024 IEEE.

关键词： Image classification

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Real-Time Driver Alertness Monitoring with Optimized Deep Learning and Haar-Cascade Methods

Real-Time Driver Alertness Monitoring with Optimized Deep Le...

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2024 IEEE International Conference on Electronic Systems and Intelligent computing, ICESIC 2024

作者： Rawat, Karan Singh Kumar, Shubham Kowsigan, M. Ramamoorthy, S. Madhavan, P. School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chengalpattu Kattankulathur603203 India

ISBN: (纸本)9798331522988

Driver fatigue is a leading cause of road accidents. This research presents a real-time driver drowsiness detection system using deep learning, optimized for low-power embedded systems. By compressing a complex model into a lightweight version, the system achieves an 85.5% accuracy in detecting various alertness states while maintaining energy efficiency. Utilizing facial landmark tracking and the Haar-Cascade method, the approach ensures fast and accurate detection, making it ideal for real-time vehicle safety applications. The system's integration into modern vehicles promises enhanced driver fatigue management and accident prevention. © 2024 IEEE.

关键词： Deep learning

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EmoSense: Deep Analysis of Real-Time Facial Expression for Multi-Class Emotions 2

EmoSense: Deep Analysis of Real-Time Facial Expression for M...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Sharvesh, S. Kaileshwar, M. Arthi, B. Jothikumar, C. Srm Institute of Science and Technology Computing Technologies School of computing Kattankulathur Chennai India

ISBN: (纸本)9798350372892

This study explores the efficacy of CNNs in classifying images into predefined categories, highlighting advancements in automated image recognition technology. The project aims to develop a robust CNN model that can accurately identify and classify images based on their content, which has significant implications for fields requiring automated image analysis. A sequential CNN architecture featuring layers of convolution, max pooling, and dropout, complemented by fully connected layers for feature learning and classification, developed and validated using TensorFlow and Keras. The model was trained and tested on a curated dataset comprising diverse images categorized into seven distinct classes, preprocessed to normalize image sizes and pixel values to enhance model training efficiency. 8L-CThe model used in the research includes 2L-CNN, 3L-CNN, and 4L-CNN architecture are used. In that, better accuracy of 62.2 % for 3-layer architecture received. The CNN demonstrated high accuracy and generalizability in image classification, proving capable of handling complex pattern recognition tasks, underscoring the potential of CNNs in transforming image processing applications. © 2024 IEEE.

关键词： Emotion Recognition

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Early Detection of Parkinson's Disease Through Predictive Analytics and Machine Learning 5

Early Detection of Parkinson's Disease Through Predictive An...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Lakkshmanan, Ajanthaa Karthik, Venna Venkata Prabhas, Javvadi School of Computing SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai603203 India

ISBN: (纸本)9798331530013

Millions of people worldwide are impacted by Parkinson's disease (PD), the second most prevalent neurological illness. Customized therapy, such as medication or deep brain stimulation (DBS), is essential for Parkinson's disease (PD) care. However, an accurate evaluation of the three main symptoms of the disease-bradykinesia, stiffness, and tremor-is necessary. The foundation of treatment is a clinician's diagnosis;yet, this approach only permits a cross-sectional evaluation of symptoms, which can differ hourly and are prone to inter- and intra-rater variability amongst human examiners. Although there has been a lot of interest in automating symptomatic assessment to optimize treatment plans between doctor visits, no wearable device can evaluate all three cardinal symptoms at once. Issues with stiffness measurement have hindered translation, mapping out-of-clinic muscle activity, and sensor fusion. We address all of these issues in this study using a unique wearable sensor system and machine learning methods. © 2024 IEEE.

关键词： Wearable sensors

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Breaking Barriers: Bridging Communication Gaps in Virtual Meetings with ASL Integration for Google Meet 15

Breaking Barriers: Bridging Communication Gaps in Virtual Me...

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15th International Conference on computing Communication and Networking technologies, ICCCNT 2024

作者： Seraphim, B. Ida Agarwal, Viraj Saxena, Yash Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu603203 India

ISBN: (纸本)9798350370249

In today's digital landscape, effective communication remains challenging for American Sign Language (ASL) users in virtual settings. This paper introduces a solution integrating ASL into Google Meet, enabling real-time ASL-to-text conversion for seamless communication. This study aims to address the following questions: How effective is the ASL-to-text conversion system in improving communication for ASL users in virtual meetings? What technical and ethical challenges arise during the implementation? How does the system enhance accessibility and inclusivity in online meetings? The system uses MediaPipe to detect hand signs, which are then sent to a browser via a socket connection to an extension that types the sign in the chatbox. Additionally, it incorporates text-to-speech synthesis for enhanced accessibility. User testing was conducted to validate the system's efficacy, focusing on accuracy, response time, and user satisfaction. Data was collected and analyzed to identify areas for improvement. User testing demonstrated that the system significantly improved communication accessibility and efficiency for ASL users. The ASL-to-text conversion showed high accuracy rates, and the response time was sufficient for real-time communication. Users reported increased inclusivity and a more seamless virtual meeting experience. This pioneering approach effectively addresses communication barriers for ASL users, marking a significant stride towards creating inclusive virtual environments on Google Meet. The study highlights the system's potential to enhance accessibility and inclusivity in online meetings, while also identifying technical and ethical considerations for further development. © 2024 IEEE.

关键词： Speech synthesis

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Effortless learning: leveraging summarization and tabulation for automated note making

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Multimedia Tools and Applications 2024年 1-26页

作者： Arivarasi, A. Srinivasan, R. Rajeswari, D. Govindasamy, Alagiri School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Future Connect Technologies Pvt. Ltd Tamil Nadu Kanchipuram600034 India

In this fast-paced Digital Age, Natural Language Processing (NLP) can prove beneficial in consuming quality information efficiently. With the ever-growing number of learning resources, it is becoming onerous for students to choose the best-suited learning resource. Furthermore, many students find it difficult to understand and retain highly factual information. To overcome these issues, this paper proposes a novel Automated TExt Summarization of scholastic text in Table format (Auto-TEST) to provide information at a glance by mimicking the student’s style of note-making. The Auto-TEST framework is achieved through two steps which is Summarization and Tabulation. Initially, the Extractive Single Document Text Summarization (ESDTS) method is adopted to identify the salient information from the non-fictional (scholastic) text which is extracted and grouped together to form a concise summary. A Named Entity Recognition (NER) method is implemented to extract noun chunks and then arrange them in a tabulation format that aids in memorizing essential facts and figures by enabling effortless retrieval. The performance of the Auto-TEST framework is gauged by using scholastic domain-specific dataset using ROUGE as an evaluation metric. The extracted entities gives an average Recall of 71.40%, an average Precision score of 75.80% and an average F-score of 73.54% for the dataset respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Students

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Schizophrenia Detection using Electroencephalography Signals Using Deep Learning 12

Schizophrenia Detection using Electroencephalography Signals...

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12th International Conference on Current Trends in Advanced computing, ICCTAC 2024

作者： Bajpai, Anubhav Kesaria, Anishka Briskilal, J. School of Computing SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur Chengalpattu 603203 India

ISBN: (纸本)9798350395808

The objective of the project is to see whether deep learning can be used to identify schizophrenia in teenagers by analysing their EEG data. The work takes advantage of both conventional and novel preprocessing techniques, such as the Short-Time Fourier Transform, to extract characteristic signals associated with schizophrenia from EEG data. The study highlights encouraging results with models including VGG-16, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN)[24], and Pyramidal Spatial-based Feature Attention Network (PSFAN). Notably, PSFAN presents a novel diagnostic methodology that extracts multiscale features from 4-second EEG recordings and converts them into 2-dimensional images using dilated convolutions. On two datasets, custom CNN shows statistical superiority over eleven state-of-The-Art algorithms through subject-dependent, subject-independent, and cross-dataset experiments [16]. This paper highlights the usefulness of deep learning in medical settings and highlights its potential. © 2024 IEEE.

关键词： Electroencephalography

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