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A Tolerant and Energy Optimization Approach for Internet of Things to Enhance the QoS Using Adaptive Blended Marine Predators Algorithm

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computers, Materials & Continua 2025年第5期83卷 2449-2479页

作者： Vijaya Krishna Akula Tan Kuan Tak Pravin Ramdas Kshirsagar Shrikant Vijayrao Sonekar Gopichand Ginnela Department of Information Technology G.Narayanamma Institute of Technology and Science for WomenHyderabad500104India Engineering Cluster Singapore Institute of Technology10 Dover DriveSingapore138683Singapore Department of Electronics and Telecommunication Engineering J.D.College of Engineering&ManagementNagpur441501India Department of Computer Science and Engineering J.D.College of Engineering&ManagementNagpur441501India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014TamilnaduIndia

The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of *** paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and *** results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT *** are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient *** algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node *** study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.

关键词： Internet of things trust energy marine predators algorithm(MPA) differential evolution(DE) nodes throughput lifetime

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Tri-UnityNet: A Multifaceted Ensemble Model for Pneumonia Detection

Tri-UnityNet: A Multifaceted Ensemble Model for Pneumonia De...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Dhivyaa, C.R. Nithya, K. Shanmugapriya, P. Deebika, R. Kumar, K. Sathis Sudhakar, R. School Of Computer Science And Engineering Vellore Institute Of Technology Vellore India Srm Institute Of Science And Technology Computer Science And Engineering Trichy India Computer Science And Engineering Erode India K.S.R College Of Engineering Computer Science And Engineering Tiruchengode India Bannari Amman Institute Of Technology Computer Science And Engineering Sathyamangalam India Nandha College Of Technology Computer Science And Engineering Erode India

ISBN: (纸本)9798350364828

Pneumonia is an infection often caused by several viral infections and prediction of pneumonia requires expertise from radiotherapists, posing challenges, especially in remote areas. Developing an automatic pneumonia detection system is crucial to detect and treat pneumonia disease. Hence, an innovative approach is proposed to detect pneumonia by utilizing the ensemble of deep learning models, specifically VGG16, ResNet50, and InceptionV3. The ensemble method integrates predictions from these various architectures to enhance overall classification performance. Rather than starting the training of each model from the beginning, transfer learning techniques are utilized to fine-tune Convolutional Neural Network (CNN) models using a pre-existing knowledge base. This involves leveraging pre-trained models and adapting them specifically for the disease diagnose on Chest X-ray image dataset. The features are extracted from VGG16, ResNet50, and InceptionV3 models and the classes of disease are predicted by using softmax layers. Finally, ensemble learning is employed to fuse the predictions of multiple neural network architectures within Tri-UnityNet, enhancing its accuracy and robustness in pneumonia detection. By leveraging the complementary strengths of diverse models, the ensemble approach achieves superior performance, contributing to more reliable diagnostic outcomes. © 2024 IEEE.

关键词： Forecasting

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A Novel Approach for Secured Decentralised Data Protection Vault 5

A Novel Approach for Secured Decentralised Data Protection V...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Mathur, Alok Soni, Prasoon Vishnu, Nanduri Jayant Patel, Dhruvil Kumar, Kakelli Anil School of Computer Science Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9781665493604

If you use a data manager (passwords, documents, card details, bank details, notes, etc.) or any other vault app, your data will be stored in a specific location. It's either local storage or private storage with extreme problems. One problem is that all data is centralized. This means that anyone in control of your storage can modify or hack your data. The controller can do whatever it wants with the data, like Modify data, sell data, delete data, or prevent access without the owner's knowledge. This leaves multiple users' data vulnerable to attack, abuse, and loss. Our basic idea is to provide our users with utility software that acts as a data wallet and manager, leveraging current developments in decentralized storage architectures to provide our users with a platform that stores all their data in an impenetrable way. Signing up and signing in the user with a unique way that is with only one field that is the password field, no username, no email-id, or any additional information is required from the user. Giving the user options to upload a series of image in a particular order and then verifying the same order as another level of security. © 2023 IEEE.

关键词： Digital storage

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DeepKet- Quantum Space-Efficient Word Embedding Layer for Steganalysis 3

DeepKet- Quantum Space-Efficient Word Embedding Layer for St...

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

作者： Roshan Ahmed, N. Shridevi, S. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology Center for Advanced Data Science Chennai India

ISBN: (纸本)9798350372120

Text-based Statistical steganography is one of the most non-human detectable methods of embedding hidden messages in plain text format which is useful in concealing information. Steganalysis is its counter, the process of detecting if a text has any encrypted data in it. This paper applies quantum computing to create DeepKet Embedding, which optimizes the space requirements for word embeddings similar to Word2Vec. DeepKet is benchmarked against existing embedding layers and a significant size reduction is achieved while maintaining accuracy for steganalysis. © 2024 IEEE.

关键词： Embeddings

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Precision Monitoring of Health-Care Using Big Data and Java from Social Networking and Wearable Devices 3

Precision Monitoring of Health-Care Using Big Data and Java ...

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3rd International Conference on Intelligent Communication and Computational Techniques, ICCT 2023

作者： Goel, Rishabh Satish, C.J. Vellore Institute of Technology Department of Computer Science and Engineering Vellore India

ISBN: (纸本)9781665453578

Wearable sensors and social networking sites help gather participant information for healthcare monitoring. Wearable sensors generate a lot of healthcare data for continuous patient monitoring. Social networking sites' user-generated healthcare data is huge and unstructured. Existing healthcare monitoring systems have trouble gathering and evaluating sensor and social network data, and traditional machine learning methods are insufficient to anticipate abnormalities in big healthcare data. A novel cloud-based healthcare monitoring architecture is presented to properly save and evaluate healthcare data and increase classification results. A Wireless Sensor Network and Big Data Analytics based Intelligent Health Monitoring System (IHMS) is suggested in this paper. The suggested large data analytics engine uses data mining, ontologies, and Bidirectional Long Short-Term Memory (Bi-LSTM). Data processing approaches preprocess information and minimize dimensionality. Proposed ontologies give semantic information about diabetes and blood pressure entities, aspects, and relationships (BP). Bi-LSTM categorizes information properly to forecast medication side effects and patient abnormalities. The suggested approach defines patients' health using diabetes, BP, mental health, and medicine reviews, and this model uses Java and Protégé Web Ontology Language. The findings demonstrate that the suggested system accurately manages healthcare information and predicts pharmacological side effects. © 2023 IEEE.

关键词： Data Analytics

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TransEffiVisNet – an image captioning architecture for auditory assistance for the visually impaired

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Multimedia Tools and Applications 2024年 1-20页

作者： R, Harshitha B, Lakshmipriya Krishnamurthy, Vallidevi School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India School of Engineering Jawaharlal Nehru University New Delhi India

Insights derived out of image captioning systems have potential applications in real life, including providing auditory assistance for the visually impaired. This paper proposes TransEffiVisNet, a novel image captioning system embedded with text-to-speech generation, which can be deployed to aid individuals with visual impairments. The proposed model combines a convolutional neural network (CNN) and a transformer-based encoder and decoder to generate descriptive captions for the visual content provided as an image input. A comparative study was conducted to assess the performance of the proposed model formulated using five prominent CNN models—VGG16, ResNet50, EfficientNetB0, EfficientNetB1, and Inception V3, while maintaining the same transformer architecture. The image captioning model with EfficientNetB1 showcased phenomenal results compared to four other architectures and hence preferred to be used in TransEffiVisNet. Integration of Google Text-to-Speech technology to convert captions into speech further enhances the functionality of the model which in future can be deployed for visually impaired assistance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Digital Image Forgery Detection Using Pre-Trained Xception Model as Feature Extractor 2

Digital Image Forgery Detection Using Pre-Trained Xception M...

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2nd International Conference for Advancement in technology, ICONAT 2023

作者： Pandiyan, Niyantha Maruthu Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9781665475174

During this time of social network bloom, there has been a tremendous increase in the number of images shared over the internet. This along with the advancements in software, like photoshop, has made morphing or tampering with the original images very easy. This paper proposes a Deep Learning approach to tackle this problem and detect Fake or factored images. In this paper, we propose a method where a pre-trained Xception Model is used as the feature extractor to classify a certain image as Tampered or Real. The proposed model was trained on the CASIA-V2 dataset. The model was able to attain good Accuracy on the Test Split. © 2023 IEEE.

关键词： Feature extraction

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Optimization of Traffic System using Adaptive Response First Come First Serve

Optimization of Traffic System using Adaptive Response First...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Amutha, S. Singh, Dhruv Singh, Shashank Shekhar Jhandial, Anant Gupta Krishna, Kunal School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Electronics and Communication Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331540685

In this period of urbanization and adding vehicular traffic, the optimization of business operation systems is consummate to insure both the effectiveness of transportation networks and the safety of commuters. This exploration paper explores the operation of deep literacy ways to revise the field of business operation. By employing the power of deep learning, sensors and timers, our study aims to develop innovative results that ameliorate the delicacy, rigidity, and real- time responsiveness of business control systems. The primary focus of this exploration is to design a comprehensive deep literacy-grounded business operation system that incorporates colorful data sources. By assaying vast quantities of miscellaneous data, the system will acclimatize to dynamic business conditions and offer real- time perceptivity for effective business inflow operation, accident discovery, and traffic relief. The exploration will also explore the integration of machine literacy algorithms for intelligent business signal control, prioritizing green light allocation grounded on real- time demand and business viscosity. The proposed deep literacy result aims to significantly reduce business traffic, minimize trip times, and enhance overall road safety by furnishing optimized business operation strategies. The exploration will concentrate on the practical perpetration of the system in real- world business scripts, with the eventuality to gauge and acclimatize to colorful civic surroundings. This exploration bid seeks to lay the foundation for a future in which business systems seamlessly acclimatize to evolving business patterns, creating safer, more effective transportation networks, and contributing to a sustainable and intelligent civic ecosystem. The proposed dynamic signal control logic is 83.33 % more efficient in utilizing green time compared to the traditional fixed-time system. Estimated reduction in wasted green time is 25%. © 2024 IEEE.

关键词： Traffic signals

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Survey on Methodological Model of IoT in Digital Forensic

Survey on Methodological Model of IoT in Digital Forensic

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2023 International Conference on Intelligent Systems, Advanced Computing and Communication, ISACC 2023

作者： Roy, Ritocheta Dheeba, J. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350399387

This paper provides a synopsis of research openings in IoT-based forensic investigation. Usage of IoT in digital forensic investigation process is reviewed systematically and the methodology of IoT based forensic investigation is discussed. Problems arising from IoT-linked trials during all the forensic investigation stages are brought forward. The opportunities IoT fetches to forensic analysis are mentioned. Examples of IoT forensics applications are also shown. A thorough research outline is provided, containing data about the chief research paths and a brief overview of all the methods and models relevant to the papers. Finally, future scope and server models currently being researched are brought to light in this paper. © 2023 IEEE.

关键词： Digital forensics

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Classification and Analysis of Issues Faced by Open Source Simulation Software in the Field of Fog and Edge Computing

Classification and Analysis of Issues Faced by Open Source S...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Samuel, Jonathan R. Singh, Jaskaran Mehrotra, Shreyash Baiju, B.V. School of Computer Science Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350319088

Fog and Edge Computing is still a relatively new field, being less than a decade old. As it is under rapid development, it is of utmost importance that the tools and software used for this new field is better applied and researched upon to improve the state of education for this new technology. In this study, we aim to further the current state of simulation software by surveying open-source simulation software available in repository management applications such as GitHub. Through this analysis, we will identify potential weaknesses and drawbacks of the software, such as performance limitations, lack of scalability options, or insufficient documentation. This study aims to contribute to the open-source community within the Fog and Edge Simulation space and promote further advancements in simulation software by reviewing potential issues and better understanding the strengths and weaknesses of various simulation software by classifying them with respect to the various characteristics of each simulation software within the field of Fog and Edge Computing. © 2023 IEEE.

关键词： Open source software

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