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18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Chandrashekhar, Katha Leela, P. Madhavi, K. Reddy Avanija, J. Tangudu, Naresh Indur Institute of Engineering and Technology Computer Science and Engineering Siddipet India Mohan Babu University School of Computing Dept. of Data Science Tirupati India Mohan Babu University School of Computing Tirupati India Aditya Institute of Technology and Management Tekkali India

ISBN: (纸本)9789380544519

Accurate and efficient medical image analysis is required for modern healthcare applications, facilitating timely disease diagnosis and treatment. Considering the importance of optimization algorithms and deep learning methodologies, this work presents a novel approach, Grey Wolf Optimizer-Assisted Deep Neural Networks (GWO-DNN), for feature selection and classification in medical image analysis. The proposed framework utilizes the power of the Grey Wolf Optimizer to intelligently select the most relevant features from complex medical images. The selected features are then integrated into Deep Neural Networks, to create accurate models for medical image classification tasks. This integrated approach is designed to improve diagnostic accuracy and enhance patient care. The experimentation and evaluation, reveal that the proposed GWO-DNN based model for image analysis is more efficient than the existing methods. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep Neural Networks (DNN) Disease Classification Feature Selection Grey Wolf Optimization (GWO) Medical Image Analysis

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Novel Darknet traffic data synthesis using Generative Adversarial Networks enhanced with oscillatory Growing Cosine Unit activated convolution layers

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International Journal of Cognitive computing in engineering 2024年第1期5卷 58-65页

作者： Pradeep C, Antony Amali D, Geraldine Bessie Noel, Mathew Mithra Ghalib, Muhammad Rukunuddin Subramaniam, Prabhakar Rontala Venugopal, Chitra School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Electrical Engineering Vellore Institute of Technology Vellore India School of Computing De Montfort University Dubai United Arab Emirates Information Systems and Technology Westville South Africa Electrical Engineering & Renewable Energy Oregon Institute of Technology United States

The Darknet is an anonymous, encrypted collection of websites, with a passive listening nature - accepting incoming packets, while not supporting outgoing packets. Thus, it can potentially host criminal or malicious activity and software, becoming a cyber security threat. Network Detection Systems are effective in identifying dark net traffic and mitigating its ill effects. However, capturing and extracting data from raw network traffic for training these systems can be time-intensive and costly. Using the CIC-Darknet 2020 dataset, this paper proposes using a Novel Generative Adversarial Networks (GAN) Architecture generating the required training and testing data for these systems. This uses a combination of Growing Cosine Unit (GCU) activated convolution layers and Dense layers for the Generator. Feature selection with statistical correlation methods is used to select the most relevant features. An independently trained Evaluator network is used to evaluate the generated data. The proposed system is compared to other established Tabular data GANs like Conditional Tabular GAN (CTGAN) and Copula GAN with similar parameters and on the same data. The Proposed GAN architecture outperforms CTGAN and CopulaGAN by 20 % and 10 % in ters of accuracy while also taking 90 % and 30 % less time to train respectively. Results from measuring similarity of data using the Inverted Kolmogorov - Smirnov D statistic also show significantly better results for the Proposed GAN Architecture. This shows significant promise in using Generative models to reduce the time and effort costs associated with collecting and formatting data to use in research and for training detection systems. © 2024

关键词： Generative adversarial networks

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Investigation of Decentralized Autonomous Blockchain Enabled Platform for Crowd Funding

Investigation of Decentralized Autonomous Blockchain Enabled...

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

作者： Rajmohan, R. Kumar, T. Ananth Vijai, M. SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur India IFET College of Engineering Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798331540685

This research presents a way to crowdfunding that improves security, trust, and transparency in the process by utilizing smart contracts and blockchain technology. The method gives contributors a significant say in project choices by utilizing tokenization and decentralized autonomous organizations (DAOs). The report places special emphasis on how important fraud prevention, transparency, and identity verification are in the crowdfunding environment. A thorough case study demonstrates the advantages of decentralization, transparency, and security in the context of a decentralized equity crowdfunding process. It highlights how blockchain-based crowdfunding might change the crowdfunding industry and emphasizes its disruptive potential. There is potential for both project creators and backers in this paradigm change towards decentralization and transparency, which also has wider ramifications for the digital age finance ecosystem. The proposed system is implemented as a web application and integrated with the Ethereum and meta mask platforms for simulation. © 2024 IEEE.

关键词： Decentralized finance

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IP-geolocater:a more reliable IP geolocation algorithm based on router error training

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Frontiers of Computer science 2022年第1期16卷 101-111页

作者： Shuodi ZU Xiangyang LUO Fan ZHANG State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China Zhengzhou Science and Technology Institute Zhengzhou450001China

Location based services(LBS)are widely utilized,and determining the location of users’IP is the foundation for *** by unstable delay and insufficient landmarks,the existing geolocation algorithms have problems such as low geolocation accuracy and uncertain geolocation error,difficult to meet the requirements of LBS for accuracy and reliability.A new IP geolocation algorithm based on router error training is proposed in this manuscript to improve the accuracy of geolocation results and obtain the current geolocation error ***,bootstrapping is utilized to divide the landmark data into training set and verification set,and/24 subnet distribution is utilized to extend the training ***,the path detection is performed on nodes in the three data sets respectively to extract the metropolitan area network(MAN)of the target city,and the geolocation result and error of each router in MAN are obtained by training the detection ***,the MAN is utilized to get the target’s *** on China’s 24,254 IP geolocation experiments,the proposed algorithm has higher geolocation accuracy and lower median error than existing typical geolocation algorithms LBG,SLG,NNG and RNBG,and in most cases the difference is less than 10km between estimated error and actual error.

关键词： estimated error geolocation path detection network measurement

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Cyber-Attack Defense System Enhanced by Artificial Intelligence

Cyber-Attack Defense System Enhanced by Artificial Intellige...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Rangrez, Usman Showkat Qadri, Syed Absar Ashok Kumar, C. Jothi Kumar, C. SRM Institute of Science and Technology Kattankulathur Computer Science and Engineering Tamil Nadu Chennai India SRM Institute of Science and Technology Kattankulathur Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

In today's rapidly evolving digital landscape, ensuring the security of computer systems and networks is of utmost importance. With the widespread use of internet access, the escalation of cyberattacks and infiltration attempts poses significant threats to both data integrity and user privacy. Addressing these challenges, the integration of AI-Powered Cyber Security Defender Systems has become an essential component within contemporary cybersecurity frameworks. This research delves into the realm of these advanced systems, exploring their development, classification, methodologies, challenges, and future prospects. The study commences by examining key milestones that have shaped the modern architectures of AI-Powered Cyber Security Defender Systems throughout their historical evolution. It provides a comprehensive classification of these systems, elucidating their operations and advantages. Leveraging artificial intelligence and machine learning, these systems have the capability to analyze large datasets, identify patterns, and respond to malicious activities within networks, significantly enhancing overall security. The research paper underscores the pivotal role played by AI-Powered Cyber Security Defender Systems in safeguarding digital assets and user information. In conclusion, the study offers valuable insights for cybersecurity experts, researchers, and policymakers, thoroughly addressing the development, methodologies, challenges, and future trends of these systems, advocating for a proactive approach to securing the digital sphere. © 2024 IEEE.

关键词： Cybersecurity

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Text and Voice Conversion for Machine Recognition Using NLP 8th

Text and Voice Conversion for Machine Recognition Using NLP

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Singh, Sujit Kumar Kaur, Deepinder Dhingra, Isha Computer Science and Engineering BCET Durgapur India Computer Science and Engineering SRM University Delhi NCR India University Institute of Computing Chandigarh University Mohali India

ISBN: (纸本)9789819748945

Recently, Natural Language Processing has drawn a lot of interest for its ability to computationally represent and analyze human language. The current paradigm of Information Technology makes use of natural language—the language we use every day for communication—for human–computer interaction. In recent times, NLP has drawn a lot of interest for its ability to compute a- tionally represent and analyze human language. Text present and speech deliver on various medias are unstructured in nature so we must perform data processing, text normalization, sentence segmentation, tokenization, stemming, lemmatization, bag of words, and finally, using TF-IDF, to see how words will be converted to numbers. In this work, we demonstrate the thorough processing of how, after receiving the input in the form of natural language, it transformed it into numbers using a variety of NLP techniques so that the computer can comprehend what to do next. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data handling

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Revolutionizing Public Transport: RFID-Enabled Occupancy Monitoring and Frequency Analysis System

Revolutionizing Public Transport: RFID-Enabled Occupancy Mon...

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2024 International Conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Mageswaran, J. Uma Indumathi, G. Kalpana, A.V. Elangovan, G. Lalitha, S.D. Nalini, S. Sohanda, Mayank Kumar Dept. of Computer Science Engineering Amrita School of Computing Vengal Campus Tiruvallur DistTamil Nadu India Srm Institute Of Science And Technology Ramapuram India Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur India R.M.K Engineering College Department Of Computer Science And Engineering India Srm Institute Of Science And Technolog Department Of Computing Technologies Kattankulathur India

ISBN: (纸本)9798350352689

Public bus stops in India are becoming more crowded due to the country's fast population expansion. People wait a long time for buses to come, then suddenly congregate around them when they do, packing the buses with people and pushing them up into the footboards, which causes accidents. An additional concern of overcrowding is theft. All of this is the result of numerous bus stops not having adequate information on when busses would arrive. The proposed work paves a very important role for the passengers. The passengers will be provided with the information of the arrival of next bus, seat occupancy and the total passengers inside the bus. The information about the seat occupancy, in-passengers and out-passengers is done with the help of Radio Frequency Identification (RFID) tags. The arrival of the next bus is determined by calculating the duration of the buses that are near using RFID readers placed in different bus stations. The count and the frequency will be interfaced in the web application. The passengers can check the application to know the occupancy and today's society where people can catch the right bus at the station and at the right time. This will solve the problems like waiting in bus stops and wasting time and going in a crowded bus. © 2024 IEEE.

关键词： Buses

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Securing 3D Point and Mesh Fog Data Using Novel Chaotic Cat Map

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Computers, Materials & Continua 2023年第3期74卷 6703-6717页

作者： K.Priyadarsini Arun Kumar Sivaraman Abdul Quadir Md Areej Malibari Department of Data Science and Business Systems School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChennai603203India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

With the rapid evolution of Internet technology,fog computing has taken a major role in managing large amounts of *** major concerns in this domain are security and ***,attaining a reliable level of confidentiality in the fog computing environment is a pivotal *** different types of data stored in the fog,the 3D point and mesh fog data are increasingly popular in recent days,due to the growth of 3D modelling and 3D printing ***,in this research,we propose a novel scheme for preserving the privacy of 3D point and mesh fog *** Cat mapbased data encryption is a recently trending research area due to its unique properties like pseudo-randomness,deterministic nature,sensitivity to initial conditions,ergodicity,*** boost encryption efficiency significantly,in this work,we propose a novel Chaotic Cat *** sequence generated by this map is used to transform the coordinates of the fog *** improved range of the proposed map is depicted using bifurcation *** quality of the proposed Chaotic Cat map is also analyzed using metrics like Lyapunov exponent and approximate *** also demonstrate the performance of the proposed encryption framework using attacks like brute-force attack and statistical *** experimental results clearly depict that the proposed framework produces the best results compared to the previous works in the literature.

关键词： Chaotic cat map fog computing encryption 3D point fog 3D mesh

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YOLOv8 for Pedestrian Detection: A Comparative Study for Pedestrian Detection 3

YOLOv8 for Pedestrian Detection: A Comparative Study for Ped...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Sivaraman, G. Sophiya, E. Diviya, M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369083

This research analyzes the usefulness of YOLOv8 for robust pedestrian recognition in demanding real world situations. A custom-trained YOLOv8 model is evaluated against SSD models, displaying better accuracy in detecting and localizing human classes within images characterized by occlusions and variable scales. We analyze the model's performance across confidence thresholds, demonstrating a positive balance between precision and recall. While noting computational considerations, our findings demonstrate YOLOv8's potential for advancing pedestrian detection systems, opening the way for enhanced safety and efficiency in applications such as autonomous driving, surveillance, and smart city efforts. © 2024 IEEE.

关键词： Deep learning

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A three-stage novel framework for efficient and automatic glaucoma classification from retinal fundus images

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Multimedia Tools and Applications 2024年第37期83卷 85421-85481页

作者： Singh, Law Kumar Khanna, Munish Garg, Hitendra Singh, Rekha Iqbal, Md. Department of Computer Engineering and Applications GLA University Mathura India School of Computing Science and Engineering Galgotias University Greater Noida203201 India Department of Physics Uttar Pradesh Rajarshi Tandon Open University Uttar Pradesh Prayagraj India Department of Computer Science and Engineering Meerut Institute of Engineering and Technology Meerut India

Glaucoma is one of the leading causes of visual impairment worldwide. If diagnosed too late, the disease can irreversibly cause severe damage to the optic nerve, resulting in permanent loss of central vision and blindness. Therefore, early diagnosis of the disease is critical. Recent advancements in machine learning techniques have greatly aided ophthalmologists in timely and efficient diagnosis through the use of automated systems. Training the machine learning models with the most informative features can significantly enhance their performance. However, selecting the most informative feature subset is a real challenge because there are 2n potential feature subsets for a dataset with n features, and the conventional feature selection techniques are also not very efficient. Thus, extracting relevant features from medical images and selecting the most informative is a challenging task. Additionally, a considerable field of study has evolved around the discovery and selection of highly influential features (characteristics) from a large number of features. Through the inclusion of the most informative features, this method has the potential to improve machine learning classifiers by enhancing their classification performance, reducing training and testing time, and lowering system diagnostic costs by incorporating the most informative features. This work aims in the same direction to propose a unique, novel, and highly efficient feature selection (FS) approach using the Whale Optimization Algorithm (WOA), the Grey Wolf Optimization Algorithm (GWO), and a hybridized version of these two metaheuristics. To the best of our knowledge, the use of these two algorithms and their amalgamated version for FS in human disease prediction, particularly glaucoma prediction, has been rare in the past. The objective is to create a highly influential subset of characteristics using this approach. The suggested FS strategy seeks to maximize classification accuracy while reducing the t

关键词： Image classification

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