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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Chauhan, Aviral Singh Menon, Rohit Singh, Ananya Srinivasan, Kathiravan School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India

ISBN: (纸本)9798331542344

As digital transformation accelerates world-wide, digital forensics has become increasingly crucial in storing and administering digital evidence during forensic investigations. Although digital forensics provides a swift and effective manner to handle digital records, it also faces threats in the form of cyber-attacks, tampering, and collusion, which can undermine the integrity of forensic procedures. These issues underpin a call for better evidence-related management, that is more secure and resilient. Decentralized solutions, particularly blockchain can be used to ensure integrity, confidentiality, and traceability properties necessary for effective evidence management. Transparency throughout the case is ensured by the distributed architecture of blockchain, which contributes to the authenticity of digital evidence. We conducted a qualitative review of research on decentralization in digital forensics, identifying key topics and challenges within this niche. While several articles explored specific areas of decentralized digital forensics, a unified perspective is missing. With this review, we wish to bridge this gap by providing a broader perspective on how blockchain can improve forensic procedures in various working areas by addressing vulnerabilities and building the foundation for more secure digital forensics. © 2024 IEEE.

关键词： Blockchain Chain of Custody Digital Forensics Hyperledger information Security

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A Customizable Virtual Assistant Enhanced with Vision and Speech Capabilities 12th

A Customizable Virtual Assistant Enhanced with Vision and Sp...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Naveen Kumaran, R. Sanjay Abhishek, J. Bhuvana, S. Karthick, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science and Engineering SRM Institute of Science and Technology Uttar Pradesh Delhi–NCR Campus Ghaziabad India

ISBN: (纸本)9789819788354

Recent advancements in Smart Assistants (SAs) as well as home automation have captured the attention of both researchers and consumers. Virtual Assistants (VAs) that are speech-enabled are commonly referred to as smart speakers. They offer a wide range of network-based services. In some cases, they seamlessly link to smart environments, introducing innovative and efficient user interfaces. Nevertheless, these devices present their own set of challenges and limitations. The distinctive features of this technology, particularly its hand-free operation handled by voice and implementation of voice-user interface create a unique landscape. Existing technology adoption models, however, fall short in offering a comprehensive explanation for the adoption of this innovative technology. To gain deeper insights into the motivation behind approving and using in-home voice assistants, this work shifts its focus towards examining voice interactions. Utilization of speech recognition for converting speech into text enables constructing an attractive personalized assistant. This development facilitates seamless tasks such as sending emails without manual typing, conducting Google searches without opening the browser, and accomplishing various daily activities including playing music or launching a preferred IDE, all with a single voice command. To assess its performance, the system is seamlessly integrated with home automation (open-source) environment and operated continuously for some days. During this period, users are encouraged to interact with it, and the results demonstrate increased accuracy, reliability and appeal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Speech recognition

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Sentiment Analysis for Depression Detection Using Artificial Intelligence 3

Sentiment Analysis for Depression Detection Using Artificial...

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

作者： Choudhary, Dilip Kumar Gupta, Anisha Singh, Sriaum Abhinav, Tejashvi Agrawal, Tanvi Vellore Institute of Technology School of Electronics Engineering Vellore632014 India Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India

ISBN: (纸本)9798350372120

Depression, a leading mental health concern, affects millions globally. Early detection remains crucial for effective intervention. This study explores the potential of sentiment analysis, powered by Artificial Intelligence (AI), for depression detection. We analyse existing research, examining data sources, feature extraction techniques, and AI-based methodologies. We then conduct a preliminary review of existing AI-based depression detection systems, highlighting their strengths and limitations. Our findings indicate promising applications of sentiment analysis in identifying individuals at risk of depression. However, further research is necessary to address challenges related to data privacy and the need for robust and standardized methodologies. © 2024 IEEE.

关键词： Sentiment analysis

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Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 459-473页

作者： Ponnuviji, N.P. Nirmala, G. Kokila, M. L. Sworna Priyadharshini, S. Indra Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Thiruvallur India Department of Computer Science and Engineering RMD Engineering College Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nādu Kattankulathur Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing

关键词： Extraction

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Deep Learning-Based Histopathological Analysis for Colon Cancer Diagnosis: A Comparative Study of CNN and Transformer Models with Image Preprocessing Techniques 23th

Deep Learning-Based Histopathological Analysis for Colon Can...

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23rd International Conference on Intelligent systems Design and Applications, ISDA 2023

作者： Karthik, R. Thalanki, Vaibhav Yadav, Preyash Centre for Cyber Physical Systems Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9783031648120

Colon cancer is a serious life-threatening type of cancer that affects the colon and rectum. Manual diagnosis of this disease is time-consuming and la-bor-intensive, requiring pathologists to carefully examine multiple tissue sections and slides per patient. Deep learning techniques offer promising solutions to these challenges. This research investigates the performance of various deep learning architectures, including ResNet50, InceptionResnetV2, NasNet, EfficientNetB0, DenseNet169, ViT, and MobileViT, in histopathology. Four stain normalization methods (Vahadane, Macenko, Reinhard, and ITK) and four image-denoising techniques (Wavelet-based denoising, Gaussian Filtering, Bilateral Filtering, and Median Filtering) were employed to ad-dress staining and noise issues. An ensemble is developed integrating a CNN and a transformer. CNNs excel at extracting local features from images through the use of convolutional layers and pooling operations, while the transformer can capture long-range dependencies. This allows the ensemble to capture intricate local patterns while also considering the relationships be-tween objects, regions, or features that may span the entire image. A quantitative comparative study on the ‘EBHI’ dataset was conducted, resulting in the development of an ensemble model achieving an accuracy of up to 92.02%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Image denoising

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An efficient hybrid Hopfield convolutional neural network for detecting spam bots in Twitter platform

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International Journal of Cognitive Computing in engineering 2025年 6卷 569-587页

作者： Kumar, A.V. Santhosh Kumar, N. Suresh Devi, R. Kanniga 626126 India School of Computer Science and Engineering Vellore Institute of Technology Vandalur Kelambakkam Road Tamilnadu Chennai600127 India

Recently, social media platforms have become very popular as they offer unbelievable opportunities to their users. Twitter is one of the social media platforms on which a huge number of people exchange their messages by posting tweets. However, this platform is usually used by automated accounts called bots. Such bots are used to spread fake news, fake ideas, and products. Hence, it is essential to detect the presence of spam bots on Twitter. In order to detect spam bots on Twitter, an effective feature selection technique using a novel hybrid deep learning model is introduced in this paper. This paper proposes a novel spam bot detection system for the Twitter social network that combines profile and tweet-based features. Initially, the Twitter data are pre-processed to improve the accuracy of classification. The pre-processing stage involves various steps such as stopping word removal, tokenization, stemming, n-gram identification, user mention, and vocabulary density and richness. After pre-processing, the tweets are given to the next stage for feature extraction. In this stage, the user profile-based features such as name, screen name, location, and time, as well as the tweet-based features such as hashtags, retweeting of tweets, etc., are extracted from the tweets. The extracted features are then subjected to feature selection, where a meta-heuristic-based optimization algorithm called the Binary Golden Search Optimization algorithm (BGSO) is used. This method helps to reduce the feature dimensionality and overfitting issues. In order to improve the optimization algorithm's searching ability, an X-shaped transfer function is used. Finally, the selected features are provided to the novel Hybrid Hopfield Dilated Depthwise Separable Convolutional Neural Network (HHD2SCNN) based classification model, where the output layer classifies the given tweets as spam bots or legitimate. The proposed method is experimentally verified, and the performance metrics are evaluated

关键词： Tweets

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Speech Recognition System for Upper-Limb Impairment: A Text Editor for Programming via Voice 2

Speech Recognition System for Upper-Limb Impairment: A Text ...

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2nd International Conference on Automation, Computing and Renewable systems, ICACRS 2023

作者： Rampriya, R.S. Dharshan, R. Santhosh, S. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamilnadu Vellore India Department of Computer Technology Madras Institute of Technology Tamilnadu Chennai India

ISBN: (纸本)9798350340235

Programming is one of the most important skills to learn for the present and future generations. However, those with hand disabilities may find it difficult to program with a traditional keyboard and mouse. Disabled people would greatly benefit from assistive technology that allows them to use their voices to control computers. The existing system recognizes user input as voice and translates it into a program. However, the existing system only recognizes a limited number of keywords in a programming language. It would also be difficult to debug errors if the voice was misinterpreted. Our suggested work in speech recognition using natural language processing (NLP) would perform better than the previously available systems. It is possible to build a foundation for programming without a keyboard by creating tools that use a publicly accessible speech recognition API. Voice control of the user interface for program execution and code debugging would also be quite beneficial. This can be done with the help of modules such as Pynput. Keyboard, PyAutoGUI, which can simulate mouse cursor movements and clicks as well as keyboard button presses. The accuracy of our proposed system is 87%. When converting voice commands to text, the Word Error Rate (WER) is 18%. © 2023 IEEE.

关键词： Python Speech Recognition Speech-to-Text Vocal Programming

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Automatic text inpainting and quality elevation in video sequences

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Multimedia Tools and Applications 2024年 1-34页

作者： Palivela, Lakshmi Harika Dharmalingam, Vivekanandan Gayathri, D. Bala School of Computer Science and Engineering Vellore Institute of Technology Chennai Vandalur-Kelambakkam Road Chennai600127 India Department of Information Technology Madras Institute of Technology Anna University Chennai India Department of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute Of Science and Technology Chennai India

Scene text removal is a recent development in computer vision that replaces text patches in natural images with the appropriate background. Text removal is a difficult process leading to faulty areas of text containing text strokes with their hazy backgrounds. Text in the real world uses a variety of font kinds, some of which are difficult to localize due to their chaotic shapes, varied shading degrees, and orientation *** text erasing may include the subtasks of text detection as well as text inpainting. Both subtasks require a large amount of data to be successful;but, existing approaches were limited by insufficient real-world data for scene-text elimination. Eventhough the existing works produced considerable performance improvement in scene text removal, they often leave many text remains like text strokes, thus producinglow-quality visual outcomes. Therefore, this paper proposes an automatic text inpainting and video quality elevation model by using the Improved Convolutional Network-based ***, the video samples are collected from the diverse datasets and then converted into frames. Next, the frames are deblurred using an enhanced Convolutional Neural Network (CNN) model that has three convolutional layers for accurately localizing the texts in frames. Subsequently, the texts are detected by utilizing the CLARA-based VGG-16 network. Afterward, the text strokes are removed using a convolutional Encoder and decoder network to eliminate the presence of text on complex backgrounds and textures. Here, the coordinates of text in the deblurred frames are used to crop out the text stroke regions. So, the texts are in-painted, and then, the text in-painted regions are pasted back to their original positions in the frames. Furthermore, the video quality is elevated with the help of the DenseNet-centric Enhancement network. The experimental outcomes demonstrate that the proposed model effectively removed scene texts and enhanced the video qu

关键词： Convolutional neural networks

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Sentiment Analysis of Financial News: Implications for Corporate Strategy in Risk Management, Mergers and Marketing

Sentiment Analysis of Financial News: Implications for Corpo...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Sendhil, R. Lijo, Joshua Abinaya, E. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331509828

This research study examines the ways to use sentiment analysis on financial news for corporate strategy making. We examine the impact of sentiment in financial news on corporate decisions (beyond technology) regarding risk management, mergers and marketing strategy using recent developments from research into sentiment analysis as input and contrasts it with our findings. This study presents unsupervised and supervised sentiment analysis methods, statistical methods and AI tools for sentiment analysis, and their contributions to identifying business needs. In addition, the paper underscores existing difficulties and received a number of limitations on current techniques in sentiment analysis that have to be tended for better supporting corporate strategy. © 2025 IEEE.

关键词： Decentralized finance

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Autonomous Search and Rescue Agent Using Deep Reinforcement Learning

Autonomous Search and Rescue Agent Using Deep Reinforcement ...

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2023 International Conference on Recent Advances in science and engineering technology, ICRASET 2023

作者： Rampriya, R.S. Alam, Mohammed Junaid Jayan, Sri Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamilnadu Vellore India Madras Institute of Technology Department of Computer Technology Tamilnadu Chennai India

ISBN: (纸本)9798350306927

Disaster management is one of the areas of paramount importance for any government or organization. This process gets very tedious and tricky for some specific type of disasters. Deploying manpower for mitigating the disaster and saving the victims is a risky decision because the life of the people in the disaster management team is put to risk, which is a very unfortunate thing. So instead of using manpower, we can deploy intelligent machine agents, which are equivalent or sometimes more efficient than human beings for such highly risky tasks and avert the loss of lives. In our case, we have specifically considered the disaster wherein fire breaks out in a building and people get trapped inside due to the dangerous situation. In this case, where deploying manpower to rescue them requires a high quality protective gear and can even put their lives on risk, we have proposed to deploy intelligent machine agents that are trained to figure out the path on their own and find their way to the victims after which a rescue operation can be performed by safely escorting the victims to a nearby exit in the building. The agent in the proposed application has been evaluated by the metrics such as the time taken to reach the victims and come back to the exit, the total rewards gathered by the agent during its exploration in the environment and the number of correct decisions it made during its journey. The agent takes an average time of 145.6 seconds to complete its operation in the real-time environment. It gathered an average peak cumulative reward value of -0.2 in the real time environment. The agent on an average, records 67.3% chance of making a correct decision (that gets a positive reward) given a state vector. © 2023 IEEE.

关键词： Autonomous agents

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