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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Janet, S. Shaji, Ayda Sara D'Cruz, Brian Cedric Abraham, Eldhose Philip, Abin Oommen Kamal, Leda Toc H Institute of Science and Technology Department of Computer Science and Engineering Kochi India Indian Institute of Information Technology Kottayam IIIT-K Department of Computer Science and Engineering Cybersecurity India

ISBN: (纸本)9798350314090

A metaverse is a rapidly evolving concept in the realm of virtual reality that refers to a network of three-dimensional virtual environments centered on social interaction. The word is widely used in futurism and science fiction to depict a potential iteration of the internet as a single, universal virtual environment assisted by the usage of virtual and augmented reality headgear. There are several Metaverse currently available, such as Sandbox and Decentraland, each offering unique and immersive experiences for users. Non-Fungible Tokens (NFTs) are a novel form of digital assets that are stored on a blockchain, a decentralized digital ledger that keeps track of transactions. NFTs can prove ownership and validity for these digital assets via a blockchain, increasing their value and collectability. NFTs can be linked to digital assets that can be reproduced, such as images, movies, and audio. To give a public certificate of authenticity or proof of ownership, NFTs use a digital ledger. As part of our ongoing project, the creation of the Metarealm, a metaverse for Kochi city, promises to provide an engaging and cutting-edge platform for users to buy, sell, and trade virtual land and assets. This Metaverse will enable users to explore the city and its landmarks in a fully immersive 3D environment, fostering a sense of community and engagement among its users. As the Metaverse and NFTs continue to evolve, the possibilities for virtual experiences and digital ownership are limitless. © 2023 IEEE.

关键词： Blockchain

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Detection and Decryption of Ransomware 2

Detection and Decryption of Ransomware

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Jayanthi, Ms. M. Ajay, Alwin J Vijayakumar, Kavya Prakash, Surya G KIT-Kalaignar Karunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India KIT-Kalaignar Karunanidhi Institute of Technology Iv Cse Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665456302

The increasing threat of ransomware attacks is a concern for the entire online community. From software firms and universities to companies and organizations, everyone is trying to take proactive measures to protect themselves from such attacks. Governments around the world, including the United States, have issued warnings and issued guidelines to help users stay alert and take precautions. The problem of ransomware attacks is not limited to any particular country and even the most tech-savvy users can become victims. In India, the number of ransomware attacks has also been on the rise. This paper aims to provide a better understanding of the issue by combining research, personal experiences of a ransomware attack, and the use of cybersecurity tools. By illustrating the working of ransomware and presenting it graphically, society can be made more aware of the risk of a ransomware attack and take the necessary steps to prevent it. © 2023 IEEE.

关键词： Cybersecurity

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Exploring the Future of Key Management and Authentication in Public Key Infrastructures

Exploring the Future of Key Management and Authentication in...

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

作者： Aswini, Kilaru Rajalakshmi, B. Singh, Navdeep Suman, Bashetty Rana, Ajay Al-Allak, Mustafa Abdulhussein Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India New Horizon College Of Engineering Department Of Computer Science Engineering Bangalore India Lovely Professional University Phagwara India Mlr Institute Of Technology Department Of Computer Science And Engineering Telangana Hyderabad India Amity University Greater Noida India College Of Medical Technology The Islamic University Najaf Iraq

ISBN: (纸本)9798350366846

The five-algorithm design in this study covers authentication, advanced key management, post-quantum security, and integrating blockchain. The Lifecycle Management Model Algorithm uses a cyclical process to make encryption keys. This process is built on rotating keys and updating parameters to keep things flexible. Lattice-based encryption is used by the Post-Quantum Encryption Integration Algorithm to protect against quantum threats. For post-quantum security, it is always changing to use technology that is not affected by quantum mechanics. To make a dynamic and reliable authentication system, the Advanced Authentication Protocols Algorithm collects physiological data, looks at behavioral trends, and uses quantum-resistant authentication protocols. Proof of Work and quantum-resistant protections are used by the Blockchain-based Key Management Algorithm to build decentralized trust in key transfers. Lastly, the Scalable PKI Infrastructure Algorithm makes sure that it can grow as needed, respond to new technical settings, and register entities dynamically by combining many technologies. As part of the ablation study, each program was taken apart. Key rotation, behavioral analytics, Proof of Work, and quantum-resistant integration were the main topics of the works. These algorithms keep cryptographic systems safe from new threats and give today's complicated digital ecosystems a flexible base. © 2024 IEEE.

关键词： Authentication

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A novel feature selection approach with integrated feature sensitivity and feature correlation for improved prediction of heart disease

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12005-12019页

作者： Saranya, G. Pravin, A. Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai600119 India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai603203 India

This paper presents a random forest-feature sensitivity and feature correlation (RF-FSFC) technique for enhanced heart disease prediction. The proposed methodology is implemented using the Cleveland heart disease dataset which comprises a total of 120 heart disease patient records. Data imputation was utilized for missing values, and min–max normalization was utilized for data transformation. We attempted to construct an RF-based classifier for coronary heart disease in this paper by combining feature sensitivity and correlation analysis. The sensitivity-based feature selection process ranks features according to their value in assessing CHD risk, and the feature correlation analysis phase analyses if there are any correlations between features. The heart disease prediction accuracy of 81.16% was obtained using the proposed RF-FSFCA technique by omitting five features (sex, hemoglobin, TD, CRF, and cirrhosis). When compared to the Naïve Bayes, decision tree, regression analysis, and support vector machine models, the proposed model offered a higher accuracy of 86.141% without omitting any features. It also offered sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) scores of 87.321%, 87.364%, 91.23, and 91.02 respectively. Experiment findings demonstrated that the proposed RF-FSFC approach significantly improves prediction accuracy as compared to other approaches that do not use the integrated Feature selection method. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Sensitivity analysis

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Smart Health Management Application Using IoT and Smart Sensors 2

Smart Health Management Application Using IoT and Smart Sens...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Arnika Shaikh, Asmat Ara Kumar, Mukesh Sharma, Bhasker Yadav, Dinesh Kumar Aeri, Manisha Sharda School of Engineering and Technology Sharda University Department of Computer Science and Applications Up Greater Noida India Parle Tilak Vidyalaya Association's Institute of Management Department of Marketing Management Maharashtra Mumbai India Graphic Era Hill University Department of Computer Science & Engineering Uttarakhand Dehradun India Hitech Institute of Engineering and Technology Department of Computer Science UP Ghaziabad India Lloyd Institute of Engineering & Technology Department of Mechanical Engineering UP Greater Noida India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350371055

Clever fitness management software using the Internet of Factors (IoT) and smart sensors are a modern answer for powerful and personalized health care. This technology integrates innovative sensors with IoT, creating a community of connected gadgets that can music and screen a character's health and well-being. The utility collects facts in actual time, which are then analyzed and processed to provide valuable insights and recommendations for stepped-forward fitness management. The software uses diverse clever sensors, including health trackers, blood strain monitors, and glucose video display units, to constantly display the consumer's vital signs, symptoms, and physical activities. This record is then transmitted to the cloud for garage and analysis. © 2024 IEEE.

关键词： Garage Health care Management Sumptoms Transmitted

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Utilizing Machine Learning for Sport Data Analytics in Cricket: Score Prediction and Player Categorization 3

Utilizing Machine Learning for Sport Data Analytics in Crick...

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3rd IEEE Mysore Sub Section International Conference, MysuruCon 2023

作者： Suguna, R. Praveen Kumar, Y. Suriya Prakash, J. Neethu, P.S. Kiran, S. Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vidhya Jyothi Institute of Technology Department of Computer Science and Engineering Hyderabad India Department of Computer Science and Engineering Bengaluru India School of Engineering and Technology Bengaluru India Nitte Meenakshi Institute of Technology Department of Mathematics Bangalore India

ISBN: (纸本)9798350340358

Cricket is a popular sport with complex gameplay and numerous variables that contribute to team performance. In recent years, sports analytics has gained significant attention, aiming to extract valuable insights from large volumes of cricket data. Cricket has many fans in India. With a strong fan following, many try to use their cricket intuition to predict the outcome of a match. A set of rules and a points system govern the game. The venue and the performance of each player greatly affect the outcome of the match. The game is difficult to predict accurately as the various components are closely related. The CRR (Current Run Rate) approach is used to predict the final score of the first innings of a cricket match. Total points are calculated by multiplying the average number of runs scored in each over by the total number of overs. For ODI cricket, these methods are useless as the game can change very quickly regardless of the current run rate. The game may be decided by 1 or 2 overs. For more accurate score predictions, a system is needed that can more accurately predict the outcome of an inning. This research paper explores the application of machine learning techniques to predict scores and classify players based on their roles in the squad. The study utilizes a comprehensive dataset comprising various attributes of cricket matches, including player statistics, match conditions, and historical performance. Linear Regression, Logistic Regression, Naive Bayes, Support Vector Machines (SVM), Decision Tree, and Random Forest regression models are employed to predict scores. Additionally, player categorization is performed using a classification approach. The results demonstrate the effectiveness of machine learning techniques in enhancing performance analysis and decision-making in the game of cricket. © 2023 IEEE.

关键词： Forecasting

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IntegraSec: Securing IoT with an Integrated Lightweight Privacy-Aware Publish-Subscribe Framework 2

IntegraSec: Securing IoT with an Integrated Lightweight Priv...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Chamarajappa, Rashmi H. Dyamanna, Guruprakash C. Sri Siddhartha Institute of Technology Ssahe Department of Information Science & Engineering Tumkur India Sri Siddhartha Institute of Technology Ssahe Department of Computer Science and Engineering Tumkur India

ISBN: (纸本)9798350306415

Publish/subscribe(pub/sub) communication in IoT healthcare systems enables efficient and real-time dissemination of patient data, facilitating timely medical interventions and remote monitoring. Moreover, the pub/sub model has characteristics of loose coupling which could lead to security and privacy;this paper introduces a comprehensive security framework IntegraSec which integrates the lightweight design, LAA (Lightweight Authentication based on anonymity), LAC(lightweight access control), principle based on the AE(Asymmetric Encryption) based model integrated with different model such as anonymous authentication through, user registration and so on to enhance the access control and data privacy. Moreover, the proposed model secures the pub/sub model by safeguarding the content, securing the subscriber identity and publication authenticity for lightweight secure communication. Considering the lightweight approach IntegraSec improvises the overhead in terms of storage and communication. © 2024 IEEE.

关键词： IntegraSec IoT lightweight pub/sub model security

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Controlled Drugs Administration using Block Chain 2

Controlled Drugs Administration using Block Chain

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Lokhande, Meghana Sawalkar, Amey Shelke, Gaurav Shewale, Kartik Shaikh, Ayan Symbiosis International University Symbiosis Institute of Technology Department of Computer Engineering Lavale Pune India Pimpri Chinchwad College of Engineering Department of Computer Engineering India

ISBN: (纸本)9798350346640

Controlled drugs are drugs that when used ethically may prove life-saving for patients but these drugs also have the potential for abuse, misuse, and addiction. Controlled drugs are under strict government regulations, but still few people manage to diverge these drugs from the ethical system. Few recent techniques that are considered for administration of controlled drugs are Electronic Health Records (EHR), Machine Learning and Advanced Analytics, IOT, etc. But the techniques mentioned above have some disadvantages such as EHR is centralized, ML requires high computing power and is less scalable, IOT devices are expensive and are vulnerable to cyber-attacks thus compromising sensitive information. The system proposed in this paper uses an Ethereum blockchain for administration of controlled drugs. Blockchain ensures transparency, traceability, immutability, decentralization and security in the system. © 2023 IEEE.

关键词： Data Analytics

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Dual-Model Approach for Lung Disease Classification using Convolutional Neural Networks and Support Vector Machines 2

Dual-Model Approach for Lung Disease Classification using Co...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Shaik, Mohammed Ali Fatima, Afshan Parveen, Masrath Rani, A. Soumya Mohammad, Akheel Rahim, Abdul School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computer Science & Engineering CMR Institute of Technology Hyderabad India Department of Information Technology Vidya Jyothi Institute of Technology Hyderabad India Hyderabad India Department of Computer Science & Engineering Dr. Vrk women's College of Engineering and Technology Hyderabad India Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women Secunderabad India

ISBN: (纸本)9798331504960

This paper proposes a solution of integrating the use of Support Vector Machine (SVM) and Convolutional Neural Networks (CNNs) for categorizing lung conditions of people with and without lung disease. The proposed model has achieved a high F1-Score of 97.4%, a high Precision of 93.17% and high Recall of 93. 63 for the Healthy class. The F1 Scores of this study for the Infected Lung class were as follows: the accuracy of F1 Score was 97.4%;Precision was 94.12%;and Recall was 92.06%. The combining of SVM and CNN model was quite accurate with the accuracy of 97% and the model was accurate in multiple domains of assessment. These results suggest that the application of CNNs and SVMs to the task of medical image analysis constitutes a promising and accurate approach for diagnosing lung diseases. © 2024 IEEE.

关键词： Lung cancer

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FACE UNLOCK SYSTEM FOR VEHICLES 2

FACE UNLOCK SYSTEM FOR VEHICLES

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Navya, Soleti Suresh, Juttu Sangeetha, A. Praveen, G. Nabi, Shaik Abdul Kumar, Dilip Mlr Institute of Technology Department of Computer Science and Information Technology India Malla Reddy Engineering College and Management Sciences Department of Information Technology Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Cse Dept Avniet Jntuh India Mlr Institute of Technology Dept. of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798350370188

Currently, all vehicles are key operated. Our intention is therefore to develop an intelligent unlocking system based on facial recognition technology. In Today's era of automation and smart devices, privacy and security are important issues in information technology so to information system, so it is urgent to change system security measures. In previous systems, many vehicle doors had mechanical locks with a limited number of keys. To enhance security, we propose an intelligent vehicle door unlocking system based on facial recognition the system uses camera sensor to recognize faces and image matching algorithms to recognize authenticated faces. Only people with matching faces can open the door this system not only provides additional security but also useful for systems without physical keys. © 2024 IEEE.

关键词： Network security

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