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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Aanandha Saravanan, K. Prabha, M. Ambika Bhuvaneswari, C. MuthuKumaran, d. Aloy Anuja Mary, G. Sathyasri, B. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798350384208

Online marketing and advertising have expanded quickly as a result of the high-speed networking industry's exponential growth. deals are offered by internet retailers to both in-person and online shoppers. E-commerce also involves taking precautions against security risks, such as credit card fraud, data breaches, and the disclosure of personal information. This study suggests visual cryptography and steganography as two-way authentication mechanisms to combat e-commerce fraud. In terms of information transfer, identity theft, and authentication, this method offers higher security. Cell wallets are extremely practical and helpful for routine financial transactions, and enhancing their level of security is crucial. The current perspective uses the mobile wallet framework to create digital pockets and a comfortable and convenient m-commerce application for smartphones. The suggested method involves carrying out financial activities via a mobile wallet, such as receiving money, giving money to someone else, paying bills, etc. An encryption key is used to protect the parameters of the transaction when a person chooses an option from their wallet. An identical consultation key and cipher text are received when utilizing the AES-256 method. depending on the type of transaction the person chooses, the parameters will change. After that, the cloud server receives the obtained encrypted text content, and the fee gateway built into the suggested program facilitates the money transaction. The total effectiveness of the suggested techniques was compared to the effectiveness of the existing strategies. The overall performance of the suggested solution is found to be much improved in terms of operations performance and transaction security after examining various factors. © 2023 IEEE.

关键词： Cryptography

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IoT based Energy Management System for Buildings 5

IoT based Energy Management System for Buildings

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Natarajan, Vignesh Prasanna Aggarapu, Naveena Metta, Sai Laxman Kota, Siva Tejaswini Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331504403

This research study presents an IoT-based energy management system designed for buildings, integrating human detection technology and a pre-paid energy metering system. The system optimizes energy usage by dynamically adjusting devices based on human presence and allows users to monitor and manage their energy consumption in real-time. This approach enhances building energy efficiency, reduces wastage, and provides users with greater control over their energy usage. The combination of IoT technology, human detection, and pre-paid metering offers a comprehensive solution for efficient, cost-effective, and userfriendly energy management in buildings. In order to ensure that energy is used efficiently, the system dynamically adjusts appliances like air conditioning, heating, and lighting based on human presence. Users may also top up energy credits online or using mobile apps, and it gives them the freedom to monitor and manage their energy consumption in real-time. The system's all-encompassing strategy improves building energy efficiency, lowers waste, and gives users more control over how much energy they use. By providing occupancy information, the system's human detecting capabilities not only minimizes energy usage but also improves safety and security. By automating security and lighting systems, this data can enhance building management as a whole. Pre-paid energy metering system integration promotes energy conservation in addition to helping consumers budget their energy costs. All things considered, this Internet of Things (IoT)based energy management system provides a complete answer for effective, affordable, and user-friendly building energy management. © 2024 IEEE.

关键词： Budget control

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Analyzing and Forecasting Financial Services using Packet Transform through Wavelet Transitions 2

Analyzing and Forecasting Financial Services using Packet Tr...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Manjunathan, N. Prabaharan, G. Prathik, A. Jaganathan, d. Manikandan, N.K. Muthulingam, S. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Dept. of Cse Avadi Chennai India Madanapalle Institute of Technology & Science Department of Artificial Intelligence India Alliance University Dept. of Cse Bangalore India

ISBN: (纸本)9798350368413

Financial analysis plays a pivotal role in understanding market trends and making informed investment decisions. This research leverages the power of Wavelet Packet Transform (WPT) to extract valuable insights from financial time series data. By applying WPT to financial data, complex patterns and trends can be uncovered, enabling more accurate forecasting and analysis. The study utilizes Plotly's interactive line charts to visualize these insights effectively. The proposed approach has demonstrated exceptional performance, achieving an accuracy rate of 99.5%. This high level of accuracy empowers stakeholders and financial advisors to navigate the dynamic financial landscape with greater confidence. By providing deeper insights into market behavior, this research contributes to improved decision-making and risk management in the financial industry. © 2024 IEEE.

关键词： decentralized finance

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Transmission Binary Mapping Algorithm with deep Learning for Underwater Scene restoration

Transmission Binary Mapping Algorithm with Deep Learning for...

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2023 International Conference on Circuit Power and Computing technologies, ICCPCT 2023

作者： Ashwini, A. Purushothaman, K.E. Gnanaprakash, V. deva Shahila, d Ferlin Vaishnavi, T. rosi, A. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of ECE Chennai India

ISBN: (纸本)9798350333244

deep learning based image resolution reconstruction has gained popularity recently. Using sensors, underwater pictures are captured. due to the water's exponential attenuation of the transmitted light, underwater photographs have poor visibility, including low contrast and color blurring. The identification of targets and further evaluation are made more difficult by the underwater images' degradation. This paper offers a hybrid underwater image processing method called deep learning based transmission binary mapping algorithm that combines a color balance algorithm with an underwater picture restoration technique to solve these issues. This research work proposes an enhanced picture super-resolution reconstruction approach. It is based on transmission based binary mapping algorithm for enhancement in the efficiency of machine learning concepts. To calculate transmission maps, rGB attenuation for each channels are decomposed. These suggested approach can efficiently determine light at the background and combined transmission binary mapping at underwater images, which can then be used to recover these image. In addition, color attenuation balancing technique helps to provide upgraded scenes that seems realistic. Finally, thorough testing are put into place to evaluate how well our method works. The suggested method surpasses a number of popular underwater image enhancement and restoration techniques, according to tests on photographs taken in various underwater situations. © 2023 IEEE.

关键词： Color

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Lightweight Cryptography Model for Overhead and delay reduction in the Network 4th

Lightweight Cryptography Model for Overhead and Delay Reduct...

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4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Yamparala, rajesh Kamaleshwar, T. Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India

ISBN: (纸本)9783031488870

Mobile Ad hoc Network (MANET) refers to a group of multi-hop wireless networks that can configure themselves. The success of MANET-based applications hinges on a number of criteria, with reliability being a key one. While many security procedures already exist, the new characteristics and vulnerabilities of this networking paradigm may make the old ones obsolete. The ability of a network to scale up in size without sacrificing performance is known as scalability. Proactive routing algorithms in mobile wireless networks face a significant scalability difficulty due to the frequent need to send and receive control signals. The key to fixing the scalability issue is cutting down on overhead. In order to lessen the burden of data transmission, a overhead and delay reduction model is designed in this research. The suggested technique is based on a weight function that integrates the node's connection and battery life. In this research, MANET network latency and traffic load reduction model is designed. The proposed technique is based on the idea of only transmitting certain, predetermined packets avoiding loss. Congestion in the network can be avoided by first ensuring that all neighbor nodes are valid before sending a packet. When it comes to the evolution of MANET, Quality of Service (QoS) is a crucial factor. This research proposes a Trusted Node Feedback based Node Authentication Model with Node Transmission Analysis (NAM-NTA) model for decreasing the network overhead and delay levels in the MANET. The emphasis of this research is on constraints based on latency and neighbor connectivity. This technique is able to detect superfluous connections and eliminate them from the network structure. The proposed achieved 98.3% accuracy in network delay reduction. The proposed model is compared with the traditional model and the results show that the proposed model performance in overhead and delay reduction is high. © 2024, ICST institute for Computer sciences, Social Informa

关键词： Quality of service

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Siamese capsule gorilla troops network-based multimodal sentiment analysis for car reviews

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Soft Computing 2024年第13-14期28卷 7627-7647页

作者： Kothuri, Sri raman rajaLakshmi, N.r. Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India

Sentiment analysis of online car reviews is a significant process in natural language processing. Numerous car enthusiasts and influencers create video reviews of automobiles on platforms for example YouTube. In realistic settings for sentiment analysis within this context, the task includes evaluating spoken reviews, the video content, and the emotions expressed by both the content creators and the viewers as reflected in the comments section. The vulnerability to facing circumstances in real-world scenarios where critical components are necessary for performing sentiment analysis on multimedia car reviews may unexpectedly be absent. To overcome this issue, siamese capsule gorilla troops network-based multimodal sentiment analysis for car reviews is proposed in this research work. Initially, pre-processing and feature extraction are performed on the audio, video and text information. The feature fusion is performed by enhancing spiking neural networks with geometric attention fusion network. The fused feature is fed as an input to siamese capsule gorilla troops network which classifies the emotion states as valence, arousal, and trustworthiness. Also, the algorithm called gorilla troop optimization (GTO) optimizes the classification error of the proposed siamese capsule network. Finally, this research work assesses high- and low-level features across modalities, introduces a novel enhancing spiking neural networks with geometric attention fusion network, enhances classification accuracy through new audio, video, and text embeddings, and boosts system robustness by integrating incomplete multimodal data. The achieved accuracy, precision, recall, and F-score values are 99%, 98.55%, 98.75%, and 98.02%, respectively, for the active multimodal sentimental analysis in car review (MuSe-Car) dataset. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Sentiment analysis

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design and development of Human Identification and Obstacle detection System for Blind Using Machine Learning 2nd

Design and Development of Human Identification and Obstacle ...

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2nd International Conference on deep sciences for Computing and Communications, IcondeepCom 2023

作者： Pandithurai, O. dhiyaneswaran, P. raj, S. B. Gokul Sriman, B. Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9783031689048

According to the World Health Organization (WHO) estimates that the millions of people worldwide suffer from visual impairment, whether partial or complete, which makes it challenging for them to detect obstacles and identify people around them. However, recent advancements in information technology and spatial cognition theory have created an opportunity for researchers and developers to explore innovative solutions that can empower visually impaired individuals, enhance inclusivity, and improve accessibility. To this end, a novel framework has been proposed that utilizes artificial vision, specifically an intelligent system based on a convolutional neural network (CNN) algorithm. This system automatically recognizes human and scene objects or obstacles in real-time, even in difficult situation with large moving object. The system provides the user with a complete information, including the position of located targets, and alerts the user via a voice message about obstaclesor known or unknown individuals. The proposed work aims to create a user-friendly technology that fulfills the essential needs of physically disabled individuals, providing them with an easy-to-use interface, convenience, portability, and cost-effectiveness. Ultimately, this approach enables blind users to navigate and manage indoor and outdoor locations that they may not have been aware of otherwise, enhancing their autonomy and quality of life. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Mathematical Modeling of Social Networks using Hypergraphs 1

Mathematical Modeling of Social Networks using Hypergraphs

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Meenakshi, A. Mythreyi, O. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Mathematics Chennai India

ISBN: (纸本)9798350342796

Graph theory plays a major role in all fields of research and technology. Graph data modeling establishes a new standard for data model visualisation based on network representation. These network models are made up of nodes and connections. The mathematical analysis of social networks is done by modeling a collection of nodes and connections that describe their interactions. In this research, we employ hypergraphs to describe a social network, which provides an intuitive modeling language for exploring polyadic interactions between a collection of entities. We describe our findings from research on modeling social network structures with hypergraphs which make benefit of the rich representation. Hypergraphs provide a highly broad structure for constructing information into graph models, which may provide insights into the data that would not seem feasible otherwise. © 2023 IEEE.

关键词： centrality cloud computing hypergraphs influence social network analysis

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Pneumonia detection Using deep Learning techniques

Pneumonia Detection Using Deep Learning Techniques

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2023 IEEE International Conference on research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, rMKMATE 2023

作者： Kavitha, M. Srinivsan, r. Triveni, Kandimalla Chowdary, Chilukuri Pavan Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798350305708

during this time of COVId-19, pneumonia needs to be diagnosed and treated properly. An accurate diagnosis of pneumonia requires an analysis of chest X-rays (CXrs). This requires the expertise of a radiologist. The fuzziness of CXr images makes diagnosis quite challenging and time-consuming, even for experienced radiologists. Human judgment can also lead to erroneous identification. The system can therefore play a significant role here if it is authentic and automated. Every sector uses deep Learning (dL) in this era of cutting-edge technology. detecting diseases affecting the chest is possible through chest radiography. Medical imaging research is incorporating computer vision techniques to detect disease. The literature on pneumonia detection is summarised and guidelines for medical practitioners are lacking, despite the fact that several algorithms exist for its detection. In this paper, CNN(Convolution Neural Networks) and VGG-16(Visual Geometry Group) are the algorithms used. The accuracy obtained when training CNN is 89% and for VGG-16 is 85%. The Adam Optimizer is used to assist in improving the accuracy and reduce the total loss. The precision and recall for CNN is 0.79, 0.81 and for VGG-16 is 0.83, 0.81. The dataset is taken from the Kaggle with the size of 2GB containing 16, 000 images of chest Xrays. © 2023 IEEE.

关键词： Adam Optimizer Chest X-ray CNN deep Learning Precision recall VGG-16

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Integrating Convolutional and recurrent Networks for Image Caption Generation: A Unified Approach 15

Integrating Convolutional and Recurrent Networks for Image C...

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

作者： Nandhini, I. Leo Prasanth, L. Nagalakshmi, T. Manjula, d. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics & Communication Engineering Chennai India Anna University Department of Information Science and Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mathematics Chennai India Vit University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

Within the realm of natural language processing and computer vision, the synergy between Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) systems has emerged as some powerful paradigm for image captioning. This paper presents a comprehensive solution for visual and temporal understanding in the image captioning domain by presenting a unified framework that efficiently blends the strengths of CNN and LSTM architectures. Our proposed model takes advantage of the spatial hierarchies captured by CNNs in images and the sequential dependencies modeled by LSTMs in natural language. The CNN component efficiently retrieves high-degree features to result in coherent and contextually rich image captions. The hybridization of both of these networks creates a symbiotic relationship, addressing the challenges associated with both spatial and temporal aspects of image interpretation. We carried out in-depth tests on benchmark datasets, demonstrating the superiority of our hybrid CNN-LSTM architecture over standalone models. Our results showcase improved captioning exactness, fluency, and contextual relevance. Our proposed model achieved highest BLEU score 0. 8 1 when compared with existing methods. In addition, we investigated the interpretability of our model, shedding light on the visual and temporal cues learned by the hybrid architecture. © 2024 IEEE.

关键词： Computer Vision deep Learning Image Captioning Long Term Short Memory Natural Language Processing Neural Networks

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