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7th International Conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Nagarajan, G. Atmakuri, Sukeerth Nalamati, Sri Satyendra Minu, R.I. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350372816

Cryptocurrency is becoming more mainstream, and experts predict that its market size will reach dollar 2.2 trillion by 2026. However, it's also an intangible and highly volatile asset that poses significant challenges for governments, anti-money laundering analysts, and financial crime investigators. Compliance with strict regulations is also a challenge facing virtual asset service providers. The price of Bitcoin hit an all-time high in January 2021, making headlines worldwide. As the popularity of cryptocurrency continues to grow, AML analysts and financial crime investigators face tougher challenges. VASPs are under pressure to comply with tighter regulations, and both parties need to take a proactive approach. Bitcoin visualization is a solution that involves visualizing a Bitcoin block, enabling analysts to clarify complex and dense blocks of transaction data quickly to identify high-risk activity. Instead of looking at historic Bitcoin transactions, analysts can now examine Bitcoin activity in real-time using a powerful Bitcoin network visualization chart showing key connections, combined with a dedicated Bitcoin timeline view featuring precise transaction details as they occur. We conducted a thorough and quantitative assessment of 76 analytics tools, including those proposed in academic research and those endorsed by professionals and blockchain enthusiasts online, to provide a comprehensive analysis of the facets of blockchain data that have been thoroughly investigated. We also identified areas that warrant further exploration in future research. © 2024 IEEE.

关键词： Blockchain Blockchain tools Data parsing Data Visualization Graph Modelling Graph-based analysis

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Multi-Controller Architecture Design and Features in Software - Defined Networking (SDN) 6

Multi-Controller Architecture Design and Features in Softwar...

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6th International Conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Prabhu, V. Balamurugan, P. Department of Computer Science and Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603 203 India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603 203 India

ISBN: (纸本)9798331522667

Networking paradigm known as "Software-Defined Networking" (SDN) offers more flexibility with network management and is fast gaining popularity. Separating the control plane from the data plane is largely responsible for this success. In SDN-enabled networks, a centralized controller is used to translate high-level network application requirements into low-level directives that are issued to the forwarding devices. Such the controller requires limited ability to react to the request for flow processing generated by forwarding devices. Whenever an SDN-enabled network expands in terms of size or as a network traffic rises, flow processing demands can’t be able to fulfill by a single centralized controller. This causes a controller's performance to decline, which eventually results in a malfunctioning SDN-enabled network. Implementing a multi-controller architecture, in which a number of controllers cooperate to manage a sizable amount of network traffic and flow processing requirements, would be a successful strategy. The introduction of a multi-controller architecture and a consideration of the problems with its scalability, coherence as well as accessibility make up the core contribution of this study. Additionally, it identifies areas where study in this area could still be done. © 2025 IEEE.

关键词： Scalability

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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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Detection of Brain Tumor Using U-Net Model Approach

Detection of Brain Tumor Using U-Net Model Approach

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Patra, Arunima Paul, Krishnangshu Paul, Prithwineel Department of Computer Science and Engineering Institute of Engineering and Management Kolkata India Department of Computer Science and Engineering University of Engineering and Management Kolkata India Department of Computer Science and Engineering Centre of Excellence for Quantum Computing Institute of Engineering & Management University of Engineering and Management Kolkata India

ISBN: (纸本)9789819613472

This study introduces an innovative deep learning methodology leveraging the U-Net framework for medical image segmentation and lesion detection in brain tumors. U-net architecture contains encoder and decoder blocks which in turn are capable of capturing and propagation of information of the features. In this paper, for brain tumor segmentation, a U-Net Convolutional Neural Network (CNN) is employed, utilizing multimodal magnetic resonance imaging (MRI) data and skip connections for precise tumor localization and segmentation. Through comprehensive experiments, we show that our method outperforms traditional techniques and current CNN models for medical image segmentation and lesion detection in brain tumors. Moreover, the proposed method can perform the task with higher accuracy. This tailored U-Net architecture demonstrates the potential for advancing medical image analysis, enabling precise segmentation and lesion detection, ultimately leading to improved patient care and treatment outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Artificial gannet optimization enabled deep convolutional neural network for autism spectrum disorders classification using MRI image

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Multimedia Tools and Applications 2024年第30期83卷 74757-74783页

作者： Prasad, Vadamodula Ganeshan, R. Rajeswari, R. Department of Computer Science & amp Engineering Lendi Institute of Engineering & ampTechnology Jonnada Village Andhra Pradesh Vizianagaram District 535005 India School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh Sehore466114 India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India

Autism Spectrum Disorder (ASD) is a neurodevelopment-based disability caused by variations in the brain. This may cause impact on social skills and communication of an individual. Autism is a highly challenging issue to diagnose at the early stages. ASD is one of the important problems to diagnose because it starts manifesting at low ages. This paper aims at ASD classification using MRI Images by Artificial Gannet Optimization enabled Deep Convolutional Neural Network (AGO_DCNN). Here, an MRI image is assumed that be subjected to pre-processing of the image and feature extraction phases as an input. The bilateral filter is employed to remove the noises from the input MRI image and in the pre-processing of an image stage Region of Interest (ROI) extraction is conducted. The pivotal region extraction phase receives a filtered image after which AGO is used to extract the pivotal region. AGO is a newly designed approach by merging two optimizations namely Artificial Ecosystem-based Optimization (AEO) and Gannet Optimization Algorithm (GOA). At the same time, features are extracted from the input image during the feature extraction stage. In a feature extraction stage, some features like texture and statistical features are extracted. At last, the classification of ASD is conducted using DCNN, wherein by AGO the classifier is tuned. Furthermore, AGO_DCNN attained better outcomes in terms of maximal True Positive Rate (TPR), True Negative Rate (TNR), and accuracy values of about 93.7%, 90.8%, 96.8% and False Positive Rate (FPR) and minimal False Negative Rate (FNR) and values of 78.7% and 74.7%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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A Hybrid Approach of Spider Monkey Optimization with Taylor Series for Optimal Routing in Wireless Sensor Network 7

A Hybrid Approach of Spider Monkey Optimization with Taylor ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Sindhuja, M. Srm Institute of Science And Technology School of Computing College of Engineering And Technology Department of Computing Technologies Chennai Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350359299

Clustering that facilitates routing protocols has been created in recent years to cut down on energy usage and increase the lifespan of WSNs. The LEACH cluster is well-known among WSNs. The LEACH selects a few sensor nodes at random from each cluster. Cluster heads (CHs) are the aforementioned nodes. Cluster members are the sensor nodes that make up each CH. CHs hold up extra load to coordinate diverse tasks during the cluster. Energy usage and overall network performance can both suffer if the wrong CHs are selected for the LEACH. Therefore, a significant factor in extending the WSN's lifetime is selecting an appropriate CH and an efficient routing algorithm. This work introduces a new approach to CH selection and optimal routing in WSN, combining the Spider monkey optimization technique with a Taylor-based gravitational search algorithm. The CHs are selected, and cluster-based routing algorithms are developed using Spider Monkey Optimization (SMO). The sensor nodes are first organized into clusters, with the maximum competent node being the cluster head. From there, communication is sent across several nodes in a multi-hop fashion. Taylor dependent gravitational search method (TDGSM) is introduced which uses the taylor series along with gravitational search method to find the optimal routing. The suggested algorithm has achieved superior results when compared to the existing methods. © 2024 IEEE.

关键词： Sensor nodes

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HealthScan AI-Deep Learning-Based Multi-Disease Diagnosis from Medical Imaging

HealthScan AI-Deep Learning-Based Multi-Disease Diagnosis fr...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Ganesh, Prudhvi Sai Kiriti, Chakali Murthy Gari Keerthi Rama, P. Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India College of Engineering and Technology SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies TN Kattankulathur603203 India

ISBN: (纸本)9798350375237

The HealthScan AI is a comprehensive health diagnosis AI system which utilizes deep learning algorithms and the Keras library to determine if a person has certain diseases or not using their chest x-rays and other scans. It utilizes six distinct CNN models for the identification of COVID-19 and pneumonia using the chest X-rays, Glaucoma from internal eye scans. Our methodology involves rigorous training and evaluation processes, resulting in a highly accurate and efficient model. The integration of these models into a user-friendly interface, developed with Streamlit, allows seamless interaction. This interface empowers users to upload medical images, facilitating real-time diagnostic feedback on their health status. By providing a flexible and effective tool for the identification of a range of disorders, this novel technique advances the field of medicine and has the potential to completely transform healthcare procedures. © 2024 IEEE.

关键词： COVID-19

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Secure Goods Storage and Anti-Theft Approach using Ethereum Blockchain 5

Secure Goods Storage and Anti-Theft Approach using Ethereum ...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Amasala, Likhitha Ponnuru, Mahesh Srideviponmalar, P. School of Computer Science and Engineering Vellore Institute of Technology Amaravathi522237 India School of Computing SRM Institute of Science and Technology Potheri603203 India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Potheri603203 India

At present, technological systems lack a secure and transparent method for tracking goods and preventing theft in e-commerce, leading to trust issues and data vulnerabilities. There is a pressing need for a comprehensive solution that integrates Ethereum blockchain, IPFS, and advanced cryptographic techniques to address these challenges and enhance the security and transparency of transactions. This research paper presents a robust system that harnesses the Ethereum blockchain, IPFS (Interplanetary File System), and advanced cryptographic algorithms to create a secure, decentralized approach for tracking goods and preventing theft incidents. Unique identifiers and related information will be sent to the mail of the customer and same should entered by the customer for successful transaction. By assigning unique identifiers to purchased products and employing cryptographic techniques to encrypt sensitive data, our system ensures both user privacy and the creation of an immutable transaction ledger. Users can efficiently manage their purchased goods, block stolen items, and communicate with sellers through an intuitive interface. Additionally, the system provides sellers with a comprehensive transaction history, enhancing accountability and transparency within the supply chain. Through this research, we demonstrate the effectiveness of our blockchain based anti-theft measures, underpinned by Ethereum, IPFS, and cutting-edge cryptographic algorithms, in fostering secure, trustless transactions. This work highlights the transformative potential of blockchain technology and decentralized protocols in revolutionizing security and transparency across diverse sectors. © 2024 The Authors. Published by Elsevier B.V.

关键词： Smart contract

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Activation Control of Vision Models for Sustainable AI Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3470-3481页

作者： Burton-Barr, Jonathan Fernando, Basura Rajan, Deepu Institute of High-Performance Computing The Centre for Frontier AI Research Agency for Science Technology and Research 138632 Singapore Nanyang Technological University Nanyang School of Computer Science and Engineering 639798 Singapore

As artificial intelligence (AI) systems become more complex and widespread, they require significant computational power, increasing energy consumption. Addressing this challenge is essential for ensuring the long-term sustainability of AI technology. AI-on-AI control refers to a system with a set of AI functions controlled by an upper-level AI model. Previous work in AI-on-AI control focuses on boosting accuracy or expanding system capability by increasing overall system cost. Alternatively, we focus on applying AI-on-AI control to decrease system cost and increase the sustainability and viability of a system with multiple AI functions. Our supervised image classification evaluative controller (SICEC) is a cost-reduction oriented AI-on-AI controller that learns when vision models within an AI system should be activated based on input features. The function controller (FC) preprocesses an input and activates relevant functions, functions being distinct units of AI functionality within the system. Some functions have a set of same functional models (SFMs). These models take the same input and produce the same output but have architectural differences. We introduce a same functional controller to select a SFM using the FC's decision confidence. Results are promising, with a decrease of up to 48.9% in inference time, 67.8% in floating point operations (FLOPs), and 66.4% in energy usage. With SICEC showing significant reductions in inference time and energy cost, our work contributes to limited resource computing and sustainable AI technology. © 2020 IEEE.

关键词： Energy utilization

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Oral Vision: Advanced Image Recognition for Oral Cancer 5

Oral Vision: Advanced Image Recognition for Oral Cancer

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

作者： Ghosh, Joita Mishra, Khushi Sindhuja, M. Nagadevi, S. Anand, M. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Dept. of Computing Technologies Tamil Nadu Kattankulathur India

ISBN: (纸本)9798331530013

Cancer that affects the lining of the mouth, lips, or upper throat is called oral cancer. Other names for it include tongue cancer, mouth cancer, and oral cavity cancer. There is an urgent need for early cancer detection due to the rise in cancer cases in all age groups. Alcohol consumption and tobacco usage are risk factors. The risk of oral cancer is fifteen times higher for people who use both alcohol and tobacco than for people who do not. This paper presents a deep learning-based technique that uses the ResNet50 convolution neural network model to classify the images of malignant and non-malignant oral cell images. To enhance the pretrained ResNet50 model's capacity to differentiate malignant and benign samples, it was trained using a dataset of histopathological oral tissue pictures. A special virtual voice assistant has also been integrated along with the model to make it more assistive for all kinds of users. In order to track the advancement of these methods, this paper also analyses how combining NLP with point-of-care tools makes use of a variety of algorithms and sets. © 2024 IEEE.

关键词： Lung cancer

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