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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Singh, Pardeep Chaudhary, Vipin Kumar Bhatia, Arvind Kumar Sharma, Gaurav Kumbhawat, Amit Singh, Suchita Lovely Professional University Dept. of Computer Science Engineering Punjab Phagwara India Buddha Institute of Technology Dept. of Computer Science Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350385199

Globally, heart disease poses a significant challenge to public health. Early identification is crucial for effectively managing and treating heart conditions. Machine learning has shown promising results in predicting medical problems for the past few years. In this study, A system was developed that is capable of detecting cardiovascular diseases using machine learning methods. The project consists of Data loading, Exploratory data analysis, preprocessing, training, evaluation and storage. A dataset was introduced that contained an array of characteristics related to the cardiovascular health status (such as age, gender, cholesterol level.). However, training measured the accuracy of 3 algorithms: K Nearest Neighbours (KNN), Decision Tree, and Random Forest in identifying heart ailment. The article also returns an ensemble graph that considers the forecasts of both these models together, saving the best GNB model for future use. Results showed that the machine learning methods are proficient in heart problem identification, as in this hybrid model, with a 97.32% accuracy rate. © 2024 IEEE.

关键词： Adversarial machine learning

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ElHa Net: A Pattern Recognition Neural Network 8

ElHa Net: A Pattern Recognition Neural Network

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8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Babiker, Elsadig Ahmed Mohamed Adlan, Hanan Hassan Ali Prince Sattam Bin Abdulaziz University College of Computer Engineering and Science Dept. of Computer Science Al-Kharj Saudi Arabia Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Dept. of Computer Science Riyadh Saudi Arabia Dept. of Computer Science Faculty of Mathematical Sciences United Kingdom

ISBN: (纸本)9798331540104

Neural Networks are the state-of-the-art models that derive intelligent systems. Generalization of the neural network model is a judge of how well an architecture mimic human intelligence. Self-extraction neural networks become the dominant neural networks, especially in autonomous systems such as intelligent robotics, autonomous vehicles, ṫ etc. This paper presents a neural network based on distance measure, ElHaNet Neural Network. ElHaNet network is a self-extraction neural network and is capable of pattern recognition. ElHaNet architecture composed of an extraction phase and a classification phase. Neurons in the extraction network capture patterns in the presented input. The classification network performs on the input patterns to be classified according to the problem in hand. The developed architecture is bench marked against well-known architectures for hand written digit recognition. The performance of the network is found to compete favorably with state-of-the-art models in the literature. © 2024 IEEE.

关键词： Neural network models

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Envisioning Revolutionary Technological Changes: The Future Terrain of Fuzzy Logic 2

Envisioning Revolutionary Technological Changes: The Future ...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Chalamalasetty, Sarvani Bikku, Thulasi Mukherjee, Papiya Tyagi, Nitin Kesarwani, Jyoti Kesharwani, Abhishek Kl University Koneru Lakshmaiah Education Foundation Vaddeswaram Dept of Computer Science and Engineering Andhra Pradesh Guntur India Amrita School of Computing Amrita Vishwa Vidyapeetham Dept of Computer Science and Engineering Andhra Pradesh Amaravati India Dronacharya Group of Institutions Dept of Computer Science and Engineering Uttar Pradesh Gr. Noida India Gl Bajaj Institute of Technology & Management Dept of Computer Science and Engineering Uttar Pradesh Greater Noida India United College of Engineering and Research Upsidc Industrial Area Naini Dept of Computer Science and Engineering Uttar Pradesh Prayagraj India Abes Engineering College Dept of Computer Science Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350371055

The realm of intelligent systems empowers the development of applications that play a vital role in our real-world scenarios. Fuzzy logic, a valuable asset in contemporary times, finds application across diverse industries. This research aims to delve into the trends of fuzzy logic, examining current advancements through the application of fuzzy theory and computational capabilities. Additionally, the study offers recommendations for future research endeavors. The survey culminates with insights into potential design and development considerations for the future. © 2024 IEEE.

关键词： Artificial Intelligence Fuzzy Logic Intelligent systems

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A Comparative Study on Keyword Extraction and Generation of Synonyms in Natural Language Processing

A Comparative Study on Keyword Extraction and Generation of ...

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2023 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2023

作者： Dhala, Rasmi Rani Pavan Kumar, A.V.S. Panda, Soumya Priyadarsini Giet University Dept. of Computer Science and Engineering Gunupur India Sit Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350339369

Natural Language Processing (NLP) is an emerging field that aims to enable machines to understand and interpret human language. Keyword extraction and synonym generation are essential tasks in natural language processing. They play a significant role in information retrieval, text classification, and sentiment analysis. In this paper, we explore three different approaches to keyword extraction and synonym generation: rule-based model, statistical model, and extreme learning machine (ELM) model. We compare the performance of each method on a corpus of text and analyze the strengths and weaknesses of each approach. Our results show that the ELM model outperforms the other two methods in terms of accuracy and efficiency. © 2023 IEEE.

关键词： Neural network models

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Artificial Intelligence and Blockchain-enabled Secure Birth Certificate Management System

Artificial Intelligence and Blockchain-enabled Secure Birth ...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Raiput, Aaryaveer Prajapati, Kalp Hathaliya, Jigna J. Jadav, Nilesh Kumar Tanwar, Sudeep Garg, Deepak Institute of Technology Nirma University Dept.of Cse Ahmedabad India School of Computer Science and Ai Sr University Telangana Warangal India

ISBN: (纸本)9798350375237

With the recent advancement in technology, forgery activities have become more sophisticated and challenging to detect. This necessitates the development of advanced technologies that can counteract the illicit practices over digital platforms to streamline their operation. This article confronts the birth certificate forgery activities to strengthen the document verification system. In that context, we proposed an artificial intelligence (AI) and blockchain-based reliable framework for securing an individual's birth records. For that, we utilized a standard birth record dataset comprised of tampered and non-tampered data, which is trained on different machine learning (ML) algorithms, such as K-nearest neighbours (KNN), decision tree (DT), logistic regression (LR), and perceptron (PT) for binary classification tasks. Further, the predicted data from the AI models are forwarded to the blockchain network for secure data storage. In that context, we first designed a smart contract that validates the legitimacy of the predicted data;upon validation, it is forwarded to the interplanetary file system (IPFS) for hash computation. Integrating IPFS improves the scalability and response time of the blockchain network. Lastly, the proposed work is evaluated by considering different evaluation metrics, such as training accuracy, statistics measures (precision, recall, and F1 score), blockchain transaction and execution cost. Integrating blockchain and AI addresses data security, confidentiality, and accuracy in birth registration, providing a robust solution against potential digital and offline threats. © 2024 IEEE.

关键词： Blockchain

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Framework for Cloud-Based Data Management in Autonomous Vehicles

Framework for Cloud-Based Data Management in Autonomous Vehi...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Pallapothu, Harshith Gopi Krishna, G. Sunil, Digvijay Teja Yadav, S. Nagasundari, S. Pes University Dept. of Computer Science & Engineering Bengaluru India Research Center for Information Security Forensics and Cyber Resilience Pes University Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331529833

The increasing adoption of autonomous vehicles has driven the need for robust data management solutions that support real-time operations and ensure vehicle safety and efficiency. This work introduces a cloud-based framework for management of sensor data from autonomous vehicles, focusing on optimizing payload transmission rates. The framework leverages AWS IoT Core and AWS IoT Analytics to ensure efficient data flow from vehicle sensors to cloud storage. This framework demonstrates its potential for scalable deployment in real-world autonomous vehicle networks, contributing to the evolution of connected vehicle technologies and intelligent transport systems. This framework ensures reliable data transmission up to 250 payloads per second, beyond which data loss occurs. © 2025 IEEE.

关键词： Cloud platforms

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Cloud Cryptography: Mechanism of Different Encryption Standards 18

Cloud Cryptography: Mechanism of Different Encryption Standa...

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

作者： Lanke, Ravikumar Md Zubair Rahman, A.M.J. Bhardwaj, Rajat Somashekhara Reddy, D. Jain, Parth Mahesh, T.R. Dept. of Computer Science and Engineering Bangalore India Al-Ameen Engineering College Karundevanpalayam Erode India Aset Amity University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9789380544519

The term 'cloud cryptography' describes the application of cryptographic methods and protocols to protect communications and data in cloud computing settings. The provision of computing services, including storage, processing power, and applications, via the internet is known as cloud computing. Although cloud computing has numerous advantages, such as cost-effectiveness and scalability, it also poses security risks because sensitive data is processed and stored outside of the conventional network borders of an organization. Cloud cryptography plays a pivotal role in cloud security by safeguarding against unauthorized access, data disclosure, modification, or destruction. Keeping up with the most recent cryptographic methods and protocols is crucial to addressing new threats and weaknesses in this industry, which is always changing. Organizations can safeguard their data from unauthorized access, modification, and destruction by utilizing encryption techniques. This article explores the cloud cryptography along with its different encryption standards. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Architecture Benefits Cloud Computing Cryptography Encryption

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Fuzzy Logic-based Energy Management for WSNs in Smart Grid Applications 4

Fuzzy Logic-based Energy Management for WSNs in Smart Grid A...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Vengatesan, K. Vijayalakshmi, R. Samee, Sayyad Chopade, Neha Mahalle, Shilpa Bashkaran, K. Department of AIDS Sanjivani University Kopargaon India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Dept.of Computer Science Al Sadara School Al Ain Abu Dhabi United Arab Emirates SIES College of Management Studies India Kongunadu College of Engineering and Technology Thottiam India

ISBN: (纸本)9798331515720

Fuzzy Logic-Based Energy Management (FLBEM) is an innovative approach for optimizing energy consumption in Wireless Sensor Networks (WSNs) utilized in Smart Grid applications. This technique employs fuzzy logic principles to dynamically adjust network parameters, such as transmission power and data sampling rates, to enhance energy efficiency while maintaining reliable communication. By intelligently adapting to network conditions and environmental factors, FLBEM extends the operational lifetime of sensor nodes and ensures the stability of the WSN, thus facilitating real-Time monitoring and control of Smart Grid systems. The proposed FLBEM approach encompasses a comprehensive framework that includes the design of fuzzy logic controllers, the development of energy-Aware routing protocols, and the integration of renewable energy sources for powering sensor nodes. Simulation results demonstrate significant improvements in energy efficiency, network lifetime, and overall performance, validating the effectiveness of the FLBEM strategy in the context of Smart Grid applications. As energy management remains a critical concern for WSNs, the implementation of FLBEM offers a promising solution to address the challenges of energy conservation and sustainability in modern power systems. © 2024 IEEE.

关键词： Smart power grids

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BWFSO: Hybrid Black-widow and Fish swarm optimization Algorithm for resource allocation and task scheduling in cloud computing

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Materials Today: Proceedings 2022年 62卷 4903-4908页

作者： Manikandan, N. Divya, P. Janani, S. Dept of Computer Science Engineering Saveetha School of Engineering Chennai 600059 India Dept of Computer Science St. Joseph's College of Engineering Chennai 600024 India Dept of Computer Science of Engineering St. Joseph's College of Engineering Chennai 600119 India

Cloud computing has been booming technology in recent years. It is used to share resources over the internet. Though cloud has many advantages and is used worldwide. It also has some disadvantages and issues. The major problem in cloud computing is scheduling and resource allocation. Allocating resources and tasks is one of the highly critical challenges. There are no proper methods or techniques to improve task scheduling and resource allocation. Previous methods used Virtual machine (VM) instances for scheduling. The major drawback of using Virtual machine instances is that it takes a lot of startup time and consumes all the resources to perform the task. In this paper, we have proposed a solution with fuzzy C-means clustering hybrid algorithms of using Black widow optimization for task scheduling and fish swarm optimization for efficient resource allocation to reduce cost, energy, resource utilization. © 2022

关键词： Resource allocation

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Secured Release through an Agile Security Growth Framework 29

Secured Release through an Agile Security Growth Framework

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29th ISSAT International Conference on Reliability and Quality in Design, RQD 2024

作者： Bhattacharya, Saikath Williams, Laurie Singh, Munindar P. Electrical Engineering and Computer Science Milwaukee School of Engineering Milwaukee United States Dept. of Computer Science North Carolina State University Raleigh United States

ISBN: (纸本)9798986576138

Software development teams need to produce and assess secure software products so that they can perform critical functionality. Because no software product can ever be "perfectly secure, " development teams need to decide when a software product is "secure enough" to be released, that is, to make a decision on the product’s release readiness. The goal of this paper is to aid software practitioners in predicting whether a software product is "secure enough" to be released through the evolution and evaluation of software security growth models (SSGM). In this paper, we adapt and evolve software reliability growth models (SSGMs) for the purpose of SSGM using empirical software vulnerability analysis. We quantitatively compare the quality of agile software versions by comparing the security correction and detection process over testing time and software testing cost. The framework is generic to incorporate software vulnerability data into different time frames and multiple agile software versions. We evaluated our SSGM using data from Google Chromium, which uses an agile process with releases every 50 days. © Conference Proceedings of the 29th ISSAT International Conference on Reliability and Quality in Design, RQD 2024.

关键词： Software quality

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