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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Kundu, Protyasha Saha, Swarnadeep Kundu, Anindita Barai, Smritikona Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350383201

An Opportunistic Network (OppNet), as opposed to a ubiquitous centralized network, relies on sporadic and opportunistic encounters between nodes to facilitate communication. The uncertainty about the node's nature presents challenges when nodes with malicious intents are present in the network. One category of malicious nodes includes blackhole nodes, which receive messages but drop them intentionally. In this paper, we introduce a novel trust function designed to detect blackhole nodes within the network. Our function has been widely tested and simulated to validate and increase the efficacy of transmission of messages in the network. We have performed a comparative analysis with various protocols (PROPHET, Epidemic and Spray And Wait) on several parameters such as Delivery Ratio, Overhead Ratio and Number of Messages dropped. The results demonstrate that our trust function is able to correctly detect and avoid blackhole nodes. We observed significant improvement in delivery ratio in Epidemic Routing (47%), Prophet (41%), and Spray And Wait Routing (17%) protocols when adjusting blackhole nodes while keeping the total network nodes constant. © 2023 IEEE.

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Benchmarking Semantic Segmentation Approaches for Polyp and Lesion Detection in Medical Imaging 2

Benchmarking Semantic Segmentation Approaches for Polyp and ...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Pandiyaraju, V. Sunil, Meghana Shravya, V. Venkatraman, Shravan Kannan, A. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Anna University Department of Information Science and Technology Chennai India

ISBN: (纸本)9798331534950

In medical imaging, semantic segmentation is essential because it can precisely locate and isolate regions of interest, such lesions or tumours, from intricate anatomical systems. Deep learning (DL) has led to a substantial evolution in segmentation approaches, which have improved medical diagnostics' accuracy and efficiency. The performance of six cutting-edge models is assessed in this paper: LinkNet, U-Net with ResNet50, LinkNet with ResNet50, FPN Net, Simple U-Net, and SegNet-Transformer across four segmentation tasks - polyp detection, lung lesion segmentation from Computed Tomography (CT) scans, breast lesion detection from ultrasound images, and Segmenting brain tumours with magnetic resonance imaging (MRI). With results like 0.9618 for brain tumour segmentation and 0.9483 for lung lesion segmentation, SegNet-Transformer consistently obtained the highest Dice Coefficient across all tests, demonstrating superior ability in capturing intricate boundaries. Other models, such as U-Net with ResNet50, performed competitively with Dice scores of 0.9375 for lung lesion segmentation and 0.9472 for brain tumors but required slightly more memory and longer training times. This study underscores that model performance varies across different tasks, revealing specific strengths and weaknesses. Ultimately, while SegNet-Transformer demonstrates robustness, the choice of model should be informed by the unique requirements of each segmentation task, balancing accuracy, efficiency, and computational demands. © 2024 IEEE.

关键词： Semantic Segmentation

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A novel optimised GWOAN algorithm for scheduling task and power consumption in MEC network

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International Journal of Intelligent systems Technologies and Applications 2023年第4期21卷 386-401页

作者： Sivakumar, P. Murugan, Murali Somasundaram, S.K. Prabhu, S. Nagendra School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Engineering Retail Business Professional Georgia United States School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Chennai632014 India

In recent decades, the 5G and internet of things (IoT) are occupied with several applications like face recognition, traffic control, video surveillance and telecommunication, etc. Mobile-edge computing (MEC) is a promising paradigm in wireless communication which is carried by the computational scheduling of mobile devices. To enhance the computation design, the AlexNet of the DL model is applied which is based on the convolutional neural network (CNN) used to train a large number of attributes. To provide an optimal solution, the metaheuristics algorithm of grey wolf optimisation (GWO) is combined with an AlexNet which is named a novel grey wolf optimisation-based AlexNet (GWOAN) algorithm. In the proposed GWOAN algorithm, the AlexNet hyperparameters (weights, biases and other parameters) are fine-tuned by GWO and then performed a classification. As a result, the GWOAN has achieved a higher scheduling task and low latency than the standard AlexNet, ResNet-18 and VGGNet-16 respectively. © 2023 Inderscience Enterprises Ltd.

关键词： Optimization

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TizenRT OS Based Data Compression Algorithms for IoT Devices 4

TizenRT OS Based Data Compression Algorithms for IoT Devices

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4th IEEE International Conference on Computing, Communication, and Intelligent systems, ICCCIS 2023

作者： Chamak, Tanmoy Roy Kaushik, Sachi Singhal, Shiven Dadheech, Aditya Anitha, Prof. A. Jenicka, Prof. S. Thapa, Vidisha School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Samsung Research Institute Karnataka Bangalore India

ISBN: (纸本)9798350306118

The paper investigates the integration and adaptation of the LZ4 compression algorithm within the context of TizenRT, in addition to existing algorithms Miniz and LZMA. TizenRT, a real-Time operating system tailored for Internet of Things (IoT) devices, presents unique challenges in balancing compression efficiency and resource constraints. The study evaluates the performance of LZ4 alongside Miniz and LZMA in terms of compression ratios, processing overhead, and memory utilization. Experimental results demonstrate the feasibility of LZ4's adoption in TizenRT, highlighting its potential to enhance data compression and decompression operations while adhering to the stringent requirements of IoT devices. The findings contribute to the optimization of data management strategies within the TizenRT ecosystem. © 2023 IEEE.

关键词： Internet of things

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Detecting AI-Generated Deep Fakes Using ResNext CNN and LSTM-Based RNN: A Robust Approach for Real-Time Video Manipulation Detection

Detecting AI-Generated Deep Fakes Using ResNext CNN and LST...

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International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Dhar, Akanksha Agrawal, Ekansh School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9789819706402

The increasing computational power has greatly empowered deep learning algorithms, making the creation of highly realistic and virtually undetectable synthetic videos, commonly known as deep fakes, remarkably simple. These deep fakes, often featuring convincing face swaps, can be employed in situations such as generating political turmoil, fabricating terrorism events, distributing revenge porn, and engaging in blackmail. In this study, we introduce an innovative deep learning-based technique designed to effectively differentiate between AI-generated fake videos and genuine ones. Our approach excels at automatically identifying manipulated videos, including those involving content replacement and reenactment deep fakes. Our endeavor involves leveraging Artificial Intelligence (AI) to combat its own creations. The central component of our system utilizes a ResNext Convolutional Neural Network (CNN) to extract features from individual frames of videos. These features are then used to train a Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN). This RNN helps classify videos by determining if they have been manipulated, such as being deepfakes, or if they are genuine recordings. To ensure our model performs well with real-time data and reflects real-world situations, we evaluate it on a large and balanced dataset created by combining various existing datasets like FaceForensic++, Deepfake Detection Challenge, and Celeb-DF. Furthermore, we showcase that our system achieves competitive results using a straightforward yet effective approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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Hardware Acceleration for Embedded Scripting Programming Language ‘mruby/c’

Computer Software

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computer Software 2024年第2期41卷 60-79页

作者： Maeda, Hiroyuki Tanaka, Kazuaki Fukuoka Industrial Technology Center Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Japan

In this paper, we propose hardware acceleration to improve a performance of scripting programming languages for embedded developments. Scripting programming languages enable more efficient software developments and scripting programming languages are used in several system developments such as IoT developments. However, scripting programming languages are an interpreted language and the program is executed by a virtual machine, so it takes a long time to execute. Therefore, we implemented hardware acceleration for scripting programming language "mruby/c" targeting small embedded devices. The hardware acceleration supports bottleneck processing of a virtual machine of mruby/c, execution speeds are improved. This paper describes the configuration of the hardware acceleration for mruby/c and the performance evaluation. © 2024 Japan Society for Software science and technology. All rights reserved.

关键词： Virtual machine

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Real Time Bimodal Emotion Recognition using Hybridized Deep Learning Techniques 2

Real Time Bimodal Emotion Recognition using Hybridized Deep ...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Bhat, Akshata A. Kavitha, S. Satapathy, Shashank Mouli Kavipriya, J. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

Emotion detection has become an important study subject due to the potential uses that it has in many other domains. It incorporates data from various sources, such as facial expressions, voice patterns, physiological indications, and textual signals. To identify human emotions, a real-time bimodal image and audio-based detection-based approach has been proposed. The approach is termed as "bimodal" since images and audio data are considered as inputs to the proposed model. In this study, hybridized CNN-LSTM for the image-based model and Multi-Layer Perceptron is used for the developing audio model. For the image-based model, two combined layers of CNN, and LSTM along with regularization techniques like drop-out, and batch normalization have been adopted for model development. Similarly, for the audio model, the dataset has been fine-tuned using a standard scaler and PCA before training. Based on the combined results of both models, the user's final emotion is determined. Both the audio-based model and the video-based model outperform the existing models discussed in the literature with higher performance accuracy of 79.34% and 77.27% respectively. © 2024 Elsevier B.V.. All rights reserved.

关键词： Emotion Recognition

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Phishtor: Phishing Detection Using Machine Learning

Phishtor: Phishing Detection Using Machine Learning

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Rangasamy, Gokul Primya, T. Kumar, Gurupriya Sivakumar, Harinath Kanagaraj, G. Sathish Kumar, D. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Kumaraguru College of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Vellore Institute of Technology School of Computer Science Engineering and Information Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350309140

Nowadays, the majority of people rely heavily on the internet. Online transactions, online sign-ups, and online purchasing are used by everyone. One kind of threat to websites is phishing, which is an illegal activity that involves obtaining user data, including credit/debit card numbers, passwords, and login credentials. An efficient phishing detection method based on ensemble learning is presented in this study. The technique proposed, which combines three classifiers (K-Nearest Neighbours, KNN, Support Vector Machines, and Gradient Boosting Classifiers, GBC) to achieve the highest accuracy performance, has been shown to yield positive results overall (97.4%). The proposed technique uses feature extraction for processing the Uniform Resource Locator (URL) and predicts the website whether it's phishing or not. Therefore, it is preferable and used to employ the suggested technique to identify phishing using machine learning. © 2024 IEEE.

关键词： Phishing

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Tackling E-Waste Management Using Decentralized Marketplace Leveraging Ethereum Blockchain

Tackling E-Waste Management Using Decentralized Marketplace ...

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

作者： Allu, Himasri Bharti, Anjali Satapathy, Shashank Mouli School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (纸本)9798331540685

This study explores the implementation of a decen-tralized marketplace using blockchain technology for e-waste management. Through stakeholder-driven validation using the proof of stake and on-chain attestation, the platform ensures transparency and accountability in the e-waste supply chain. Integrating the Meta Mask wallet facilitates secure transactions, enhancing user experience and adoption. Results demonstrate a 95% reduction in transaction confirmation time and a 25% cost reduction in Ethereum transactions compared to SWIFT transfers, enhancing economic viability and user engagement. Leveraging blockchain for e-waste management presents a promising avenue for addressing complex supply chain challenges and advancing toward a more sustainable future. © 2024 IEEE.

关键词： Ethereum

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Optimal feature extraction and classification-oriented medical insurance prediction model: machine learning integrated with the internet of things

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International Journal of computers and Applications 2022年第3期44卷 278-290页

作者： Chowdhury, Subrata Mayilvahanan, P. Govindaraj, Ramya School of Computer Science Vels Institute of Science Technology & Advanced Studies TN Chennai India School of Information Technology and Engineering Vellore Institute of Technology TN Vellore India

This paper plans to develop an effective machine learning system integrated with the Internet of Things (IoT) to predict the health insurance amount. IoT in healthcare enables interoperability, machine-to-machine communication, information exchange, and data movement that make healthcare service delivery effective. The model includes three phases (a) Feature Extraction, and (b) Weighted Feature Extraction, and (c) Prediction. The feature extraction process computes two statistical measures: First Order Statistics like mean, median, standard deviation, the maximum value of entire data, and minimum value of entire data, and Second-Order Statistics like Kurtosis, skewness, correlation, and entropy. The prediction process deploys a renowned machine learning algorithm called Neural Network (NN). As the main contribution, the weighted feature vector is developed here, where the weight optimally tuned by Modified Whale Optimization Algorithm (WOA). Also, the contribution relies on NN, where the training algorithm replaced with the same modified WOA for weight update. The modified WOA developed here is termed as Fitness dependent Randomized Whale Optimization Algorithm (FR-WOA). At last, the valuable experimental analysis using three datasets confirms the efficient performance of the suggested model. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Internet of things

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