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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kumar, M. Ashok Jasim, Laith H. Sudhakar, K. Reddy, R. Archana Saranya, N. Naga Skyline University Nigeria Department Of Computer Science Software Engineering And Cyber Security Kano State Nigeria College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Dept. Of Ai & Ds Bengaluru India School Of Computer Science And Engineering Sr University Warangal India Saveetha College Of Liberal Arts & Science Simats Department Of Computer Applications Chennai India

ISBN: (纸本)9798350318609

As sports video collections continue to expand at a rapid pace, there is a need for advanced classification methods that can accurately and efficiently categorize and manage the massive amount of content available. These methods must be able to dynamically adapt to changes in the collection, ensuring that the videos are properly organized and easily searchable for users. Traditional classification methods are not able to keep up with the rapid growth and complexity of sports video collections, leading to incomplete classification and impacting search and retrieval processes. This paper addresses this challenge using the UCF sports dataset and proposes a Visual Geometry Group-19 (VGG-19) based CNN classifier and Equiangular Basis Vector (EBV) representation introduced for efficient classification and category representation. Furthermore, a 3D Convolutional Neural Network (3D-CNN) architecture extracts both spatial and temporal features from videos for accurate action classification. The Adam optimizer in the optimization algorithm is employed to optimize the learning process. This paper shows that the proposed 3D-CNN model outperforms existing methods a Hybrid Convolutional Neural Network with a Bidirectional Recurrent Neural Network (HCNN-BiRNN) model, Deformable Convolution Networks (DCN), 2D-Convolutional Neural Network (2D-CNN), Convolutional Neural Network - Long Short-Term Frequency (CNN-LSTM) in terms of accuracy (99.45%), precision (98.00%), sensitivity (96.98%), and F1-score (97.05%). © 2024 IEEE.

关键词： Convolutional neural networks

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An Improved Blockchain Based Security Framework for IoT Enabled Wireless Network

An Improved Blockchain Based Security Framework for IoT Enab...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Shrivastava, Kapil Singh, Sandeep Chaudhary, Preeti Singh, Ruchi Saraswat, Birendra Kumar Garg, Ashish Gla University Department Of Computer Engineering And Applications UP Mathura India Gl Bajaj Institute Of Technology And Management Department Of Information Technology UP Greater Noida India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Srm Institute Of Science And Technology Department Of Management UP Modinagar India Raj Kumar Goel Institute Of Technology Computer Science & Engineering UP Ghaziabad India Graphic Era Deemed To Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350366846

The net of factors (Iota) has opened new opportunities for connecting a diverse range of devices and making them handy over the net. However, it has additionally accelerated the vulnerability of these gadgets to cyberassaults. One capability technique to cope with It trouble is implementing Block chain generation, which offers allotted and relaxed facts storage and transaction validation. It paper advocates a progressed Block chain-based safety framework for Iota-enabled wireless networks. Our framework uses clever contracts, which might be self-executing virtual contracts that implement the agreed-upon policies and conditions among parties. Those clever contracts ease communication among Iota gadgets and the Wi-Fi community, ensuring that the most effective legal and demonstrated devices can access the network. Our framework incorporates a consensus mechanism for validating transactions on the Block chain, ensuring the integrity and immutability of statistics. Our proposed framework contains a dynamic key control gadget, which often updates and rotates keys to prevent unauthorized admission to the community. It selection also permits relaxed communication among devices, even in the case of a compromised key. In conclusion, our improved Block chainbased total protection framework presents a strong answer for securing Iota-enabled Wi-Fi networks. © 2024 IEEE.

关键词： Wi-Fi

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Learning Techniques for Depression Detection: A Comparative Studies

Learning Techniques for Depression Detection: A Comparative ...

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

作者： Das, Arnab Debbarama, Mrinal Kanti Barik, Rabindra K. National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798350399387

Depression is a common mental problem that can fundamentally affect individuals' emotional wellness as well as their everyday lives. After COVID-19 other pandemics and subsequent social isolation this issue is more potent than ever. Numerous research works have been going on searching for methods that effectively recognize depression in order to detect depression. In this regard, a number of studies have been proposed. In this study, it examines a number of previous ones utilizing various Machine Learning (ML) and Artificial Intelligence (AI) methods for depression detection. In addition, various methods for determining an individual's mood and emotion are discussed. This study also discusses how facial expression, voice, gesture can be understood by chatbot and classified it as a depressed person or not. Addition to this, it reviews all the related research works and evaluates their methods to detect depression. © 2023 IEEE.

关键词： COVID-19

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Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation Graph Convolutional Networks and Structural MRI Data 5

Enhancing Alzheimer's Disease Diagnosis Using Multi-Relation...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kanna, P. Rajesh Gunasundari, C. Senthamarai, M. Pandiaraja, P. Nithin, P. Chitra, K. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Trichy India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Erode India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamil Nadu Erode India Kongu Engineering College Department of Computer Applications Tamil Nadu Erode India

ISBN: (纸本)9798331518097

Alzheimer's disease (AD) has substantial obstacles to early detection, which frequently leads to therapy delays. In this article a unique method that uses structural MRI data and Multi-Relation Graph Convolutional Networks (MR-GCN) to improve AD diagnostic accuracy. Which can capture the nuanced interactions that characterize neurodegenerative disorders by representing the intricate relationships between various brain regions as a multi-relational graph. This strategy provides a comprehensive picture of brain function and pathology through the combination of relational data and structural MRI features. Using a large dataset, the resusts are evaluated and contrasted in MR-GCN method with other machine-learning techniques and single-relation graph models. The findings demonstrate that this method outperforms current models in terms of classification accuracy and resilience, achieving higher sensitivity and specificity in distinguishing between individuals in good health and various stages of AD. Then for further exploration the interpretability of the model is identified in critical brain regions that support the diagnosing process. This work provides a promising framework for the integration of sophisticated graph-based medical imaging tools, which could lead to more sophisticated clinical applications in the diagnosis and monitoring of neurodegenerative diseases in addition to improving our understanding of how AD progresses. © 2024 IEEE.

关键词： Convolutional neural networks

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Implementation of Clustering Approaches in Fuzzy Database through Weka Tools 14

Implementation of Clustering Approaches in Fuzzy Database th...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Rani, Santosh Singh, Tarkeshwar Singh, Sandeep Kumar Kumar, Santosh Galgotias College of Engineering & Technology Department of Computer Applications Greater Noida India School of Computing Science & Engineering Galgotias University Greater Noida India

The extraction of knowledge and useful information from large amounts of data stored in databases is known as data mining. Given the growing use of fuzzy object-oriented database systems in complex and uncertain database applications, studying data mining techniques for fuzzy object-oriented databases is essential. Users may be able to better comprehend, organize, and utilize the data in these databases as a result. Fuzzy object-oriented databases are used to implement data mining techniques in order to extract the useful latent regularities from the enormous, complicated, and uncertain database. In this study, we develop a knowledge-based system for a fuzzy object-oriented database using a variety of data mining methods. In addition, the application of this learned information may significantly improve the adaptability and strength of database browsing, database organization, and data and knowledge querying in fuzzy object-oriented databases. © Grenze Scientific Society, 2023.

关键词： Data mining

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Signal Detection of MIMO System with One Bit ADCS Based on Deep Learning 7

Signal Detection of MIMO System with One Bit ADCS Based on D...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Shrivastava, Anurag Maindola, Meenakshi Alabdeli, Haider Gopal, K. Thandra, Nagarjuna Bansal, Saloni Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Gla University Department of Computer Engineering and Applications Uttar Pradesh Mathura India

ISBN: (纸本)9798350350067

Due to the quantization noise created by analog-to-digital converters (ADCs) with just one bit of resolution, signal identification in Multiple-Input Multiple-Output (MIMO) systems is a very difficult task to perform. This is due to the fact that MIMO systems have many inputs in addition to a large number of outputs. The conventional methods of detection have a difficult time properly handling this noise. In this study, we describe a novel strategy based on deep learning for improving signal identification performance in MIMO systems like these that employ ADCs with just one bit of resolution. The method of deep learning that we recommend takes advantage of the power of neural networks to generate intricate connections from information that has been quantized to one bit. We provide the model with the ability to learn to differentiate between the several separate delivered signals while simultaneously decreasing the impacts of quantization noise by training it on a massive dataset consisting of MIMO channel realisations. This is done by providing it with the opportunity to learn from a variety of MIMO channel realisations. In-depth simulations are carried out in order to evaluate how well our deep learning-based approach to identifying signals works, and the results obtained from these simulations are compared to the findings obtained from more conventional detection methods. The results of our research indicate that deep learning algorithms perform better than traditional ones in terms of bit error rate (BER) and symbol recognition accuracy, particularly in challenging conditions with low signal-to-noise ratios (SNR). © 2024 IEEE.

关键词： Quantization (signal)

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Lips and Tongue Cancer Classification Using Deep Learning Neural Network 6

Lips and Tongue Cancer Classification Using Deep Learning Ne...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Bansal, Satish Jadon, Rakesh S. Gupta, Sanjay Kumar Prestige Institute of Management and Research Gwalior India Mits Department of Computer Engineering Gwalior India Sos in Computer Science and Applications Jiwaji University Gwalior India

ISBN: (纸本)9798350346961

One of the major diseases in developing countries is Oral Cancer, caused by alcohol, tobacco product and smoking which creates uncontrolled and abnormal cells in parts of human body. Recent Convolution Neural Network (CNN) has helped in medical industry to used medical images for finding the different types of diseases. The objective of research to build new CNN model which use for analysis the oral cancer images and determine the cancerous and noncancerous image. In this paper, CNN technique and image processing are used to categorize cancer or non-cancer lips and tongue image dataset. Deep Learning approaches is used to develop and check the performance of proposed CNN model. For experiment small Kaggle dataset that contains cancerous and non-cancerous lips and tongue images and apply proposed CNN model (Oral_Cancer_Detection). The result of Oral_Cancer_Detection model is very effective and accurate with low complexity. The Oral_Cancer_Detection model found 94% accuracy in our experiment and achieved better accuracy results in less time.. © 2023 IEEE.

关键词： Neural network models

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FinTech Edge: Utility Computing & Artificial Intelligence Technologies for Smart Financial Acquisition & Blockchain in the Financial Industries 7

FinTech Edge: Utility Computing & Artificial Intelligence Te...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Singh, Sandeep Sarkar, Tiyas Mangla, Monika Rakhra, Manik Singh, Amanpreet Jairath, Kapil Lyallpur Khalsa College Department of Computer Science Punjab Jalandhar India Lovely Professional University School of Computer Science & Engineering Punjab Phagwara India Dwarkadas J Sanghvi College of Engineering Department of Information Technology Mumbai India Lovely Professional University School of Computer Science & Applications Punjab Phagwara India Trinity College Department Of Sciences Punjab Jalandhar India

ISBN: (纸本)9798350350067

The study ventures into the changing environment of blockchain, artificial intelligence (AI), and cloud computing as key components of financial technology (FinTech). This research examines how these technologies are transforming the financial system, with a focus on intelligent finance investment including blockchain's disruptive potential. It begins with a brief description of cloud computing, explaining how it promotes extensibility, formability & cost-efficiency inside financial Institutions. The focus then shifts to AI applications, including the impact of robot advisers, trading using algorithms as well as statistical research on the reinvention of investing processes. The tale probes further into complex architecture of blockchain technology, giving light on its ability to increase the security and openness of the financial system. It reveals linkages that open the path for novel methods and significant developments, emphasizing the convergence of cloud, AI & blockchain. As we explore these FinTech territories, the paper acknowledges the inherent obstacles and dangers while also providing thoughts on legislative and ethical implications. It examines emerging trends, predicting the long-term influence of novel innovations on the financial environment. © 2024 IEEE.

关键词： Decentralized finance

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Multi-Criteria Decision-Making Model to Achieve Sustainable Developmental Goals in Industry 4.0 for Smart City Infrastructure

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第4期5卷 1135-1153页

作者： Akila, D. Pal, Souvik Sarkar, Bikramjit Jayalaksshmi, S. Muthaiyah, Saravanan Anbananthen, Kalaiarasi Sonai Muthu Department of Computer Applications Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Management Information Systems Saveethas College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences India Department of Computer Science and Engineering JIS College of Engineering Kalyani India Department of Information Technology Vels Institute of Science Technology and Advanced Studies India School of Business and Technology International Medical University Kuala Lumpur57000 Malaysia Faculty of Information Science and Technology Multimedia University Melaka75450 Malaysia

Due to a shortage of funding and other market challenges, Small and Medium-sized Enterprises (SMEs) face difficulties in adopting new technologies. Numerous technological obstacles negatively impact the long-term commercial achievement of SMEs. The deployment of Industry 4.0hopes to resolve these technological challenges. A sustainable city is a complex structure where economic, societal, and ecological components interact and compete. There is a scarcity of l methodologies for measuring interactions in this complex structure. Industry 4.0 aims to obtain higher performance effectiveness, profitability, and automation. The main goal is to develop a reliable method of evaluating small and medium-sized enterprises (SMEs) adopting Industry 4.0 technologies, particularly concerning smart city applications. This paper aims to determine the influence of Industry 4.0 in fostering economic efficiency and sustainability amongst these SMEs. The study introduces a multi-criteria decision-making (SC-MCDM) system designed to test an SME’s achievement of their targeted sustainable developmental goals. A technique for computing the interaction between various standards, i.e., (static interactions and dynamical pattern resemblance), as well as the weightage of variables of every indicator generated by the connection, is included within SC-MCDM. Furthermore, applying the suggested technique is validated by assessing the sustainable development goals of twelve Chinese cities within the Triple Bottom Line (TBL) paradigm. From a geographic-temporal viewpoint, spatial variations in city sustainability reveal regional sustainable inequalities. Indicator scores suggest that the most significant factors for most communities are the lack of research spending, falling financing in stationary assets, shortage of financial development, and inadequate shared transit. Furthermore, the growth of tertiary industries, improvement of energy performance, expansion of green areas, and reduction of poll

关键词： Sustainable city

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Comparative Analysis of Different Types of WSN 10

Comparative Analysis of Different Types of WSN

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Bajaj, Shilpa Bala, Manju Angurala, Mohit I. K. Gujral Punjab Technical University Department of Computer Applications Kapurthala India Khalsa College of Engineering and Technology Department of Computer Science and Engineering Amritsar India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India

ISBN: (纸本)9789380544472

Wireless sensor networks (WSNs) have been widely adopted in various fields such as environmental monitoring, industrial control, and military applications. Different types of WSNs have been developed to cater to the specific needs of these applications. This comparative study aims to examine the different types of WSNs including terrestrial, underwater, and underground, and their respective advantages and limitations. The study will also explore the unique challenges faced by each type of WSN and the solutions that have been proposed to overcome these challenges. The main aim of carrying out the comparative study is to make researchers aware of the advantages and challenges of each type of WSN so that they can overcome the limitations with innovative approaches in the future. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Wireless sensor networks

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