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Exploring Unique Techniques to Preserve Confidentiality and Authentication 2

Exploring Unique Techniques to Preserve Confidentiality and ...

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Asmitha, M. Rajgopal, Shinu M Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

In the realm of interconnected networks, effective security measures are essential for an increase in digital and networked society. The dangers linked to cyberattacks intensify as long as people and businesses depend on online platforms for storing information, business transactions, and interaction. Protecting personal and corporate data requires maintaining confidentiality, which is the guarantee that sensitive information stays secret. Cyberattacks are an ongoing danger to this confidentiality;they can take many different forms, including phishing a advanced exploitation. In order to reduce these dangers and guard against unwanted access, it becomes essential to boost safety precautions including encryption, reliable communication standards, and information access *** paper addresses the need to improve verification and secrecy in the field of cybersecurity. The foundation of safe online verification is authentication, which is the process of confirming the authenticity of individuals and organizations. Conventional authentication techniques are vulnerable to hacking, which can result in illegal access and compromised data. In order to strengthen the defenses against unwanted access, this paper suggests implementation of sophisticated verification techniques, such as Message Authentication with SHA-1, Blowfish Algorithm, Digital Signature Algorithm, RSA. Besides personal privacy issues, bolstering security is vital because it directly affects the stability of finance networks and vital infrastructures. This paper highlights the different methodology to protect data by confidentiality and authentication beforehand that can be exploited. © 2024 IEEE.

关键词： Authentication Blowfish Confidentiality Data Encryption Standard Rivest-Shamir-Adleman

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Decentralized Ledger Technologies: Exploring the Landscape and Diverse applications of Blockchain 4

Decentralized Ledger Technologies: Exploring the Landscape a...

引用

4th International Conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Sharma, Nandini Khenwar, Medha Agrawal, Shobit Sonika G.L Bajaj Group of Institutions Dept. of Computer Science & Engineering Mathura India

ISBN: (纸本)9798350389128

The purpose of this paper is to explore and illuminate the diverse applications of blockchain technology across various sectors, including banking, crowdsourcing, finance and insurance, healthcare, copyright management, e-government, the public sector, distribution systems, logistics and supply chain management, and education. By examining its uses, advantages, and disadvantages, this paper aims to provide a comprehensive overview of how blockchain is revolutionizing these fields. Known for its unparalleled integrity and security, blockchain offers a reliable and secure framework that operates without the need for third parties. This paper not only aims to enhance the understanding of blockchain's current applications but also to identify emerging trends and potential new uses, providing valuable insights for future researchers. © 2024 IEEE.

关键词： Distributed ledger

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Design and Implementation of a Real-time IoT Solution for Smart Meter Data Analysis in Microsoft Azure 15

Design and Implementation of a Real-time IoT Solution for Sm...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bhavaniperi, Sai Deepa Ravi, Abhishek Supriya, M. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

This research presents an end-to-end IoT solution designed as a project, not a traditional lab setup. It simulates high-velocity data generation from smart meters and focuses on real-time data visualization within Azure's cloud ecosystem. An integral part of this project is an AI-based spike and dip anomaly detection system. The project creates an IoT ecosystem that replicates smart meter telemetry data. It utilizes Microsoft Azure's robust services to cover the entire IoT data lifecycle, from data ingestion to real-time visualization. Key project components involve Azure resource provisioning, IoT Hub setup, and the deployment of a Device Provisioning Service. The project implements a lambda architecture, filtering and processing a subset of telemetry data in real-time. This data undergoes immediate visualization, showcasing Azure's agility in handling high-velocity streams. This paper is a valuable reference for interests in implementing IoT solutions with Microsoft Azure, particularly emphasizing real-time data processing and visualization within a lambda architecture while incorporating AI-based anomaly detection for spikes and dips. © 2024 IEEE.

关键词： Data streams

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Smart Recruitment System 15

Smart Recruitment System

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Dugyala, Rakshitha Gaddam, Vinay Kumar Eroju, Harika Dantuluri, Mohana Varma Madhubabu, C.H. Bv Raju Institute Of Technology Dept. of Computer Science Engineering Medak Telangana Narsapur India

ISBN: (纸本)9798350370249

This project proposes the development of an advanced job applicants assessment and recruitment system that harnesses the power of artificial intelligence (AI) to streamline and enhance the hiring process. The system integrates cuttingedge AI algorithms, including natural language processing (NLP) and machine learning, to automate and optimize key stages of recruitment. The system begins with an AI-driven resume screening process that analyzes candidate resumes, extracting relevant information and evaluating qualifications against predefined criteria. Additionally, NLP algorithms are employed to comprehend the context and nuances of the resumes, enabling a more accurate assessment of candidates' skills and experiences. Furthermore, the system incorporates machine learning models to dynamically adapt and learn from past recruitment data, continually improving its ability to identify suitable candidates. This iterative learning process ensures a more effective and efficient selection of candidates over time. The intelligent-based system also facilitates automated interview scheduling, taking into account both candidate availability and interviewer preferences. By automating this process, the system optimizes the coordination of interviews, reducing the time and effort required from human resources personnel. Collaboration with HR professionals and data scientists is crucial for refining the system's algorithms and ensuring alignment with industry standards and ethical considerations. The proposed solution aims to revolutionize the traditional recruitment process by providing a robust, intelligent, and data-hy7driven approach to talent acquisition. © 2024 IEEE.

关键词： Artificial intelligence(AI) Data-driven approach Ethical considerations HR collaboration Intelligent recruitment Iterative learning Job applicants assessment Machine learning Natural language processing (NLP) Resume screening Talent acquisition

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Compiler Design for recognizing different Programming Languages 15

Compiler Design for recognizing different Programming Langua...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Ch Rahul A N Reddy, Bedadala Mohaneeswar Swetchana, Dutta Panda, Niharika Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Compiler design for programming language recognition is a tedious process with crucial phases. These phases include lexical analysis, syntax parsing, semantic validation, intermediate code generation, optimization, and final code generation. The initial step involves linguistic analysis, breaking source code into elemental components like keywords, identifiers, literals, operators, etc. This step is tailored to each programming language, considering the specific constructs and syntax rules of that particular language. Leveraging the Python-Lex-Yacc library, this research seeks to design a compiler to identify multiple programming languages, primarily focusing on the abovementioned lexical analysis phase. The proposed compiler is engineered to recognize various programming languages, including C++, Java, and Python. The need for a compiler to recognize multiple programming languages concurrently arises from the evolving land of software development. With modern applications often written in various languages, a unified compiler capable of recognizing multiple languages is essential. This research aims to tackle this challenge by proposing a compiler that identifies multiple programming languages concurrently. © 2024 IEEE.

关键词： Program compilers

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FMRI Data Analysis Preserving Map Variability Via Unsupervised Object-Centric Learning 46

FMRI Data Analysis Preserving Map Variability Via Unsupervis...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Jin, Rui Kim, Seung-Jun University of Maryland Dept. of Computer Science & Electrical Engineering BaltimoreMD21250 United States

ISBN: (纸本)9798350371499

A novel data-driven functional magnetic resonance imaging (fMRI) data analysis method is proposed using a deep object-centric learning paradigm. The method can faithfully estimate the variabilities in the spatial neural activation maps, which capture functional interconnections in the brain, over fMRI volumes. The key idea is to treat the component maps composing individual fMRI volumes as "objects,"whose latent representations are separately learned by a set of autoencoders. Numerical tests using synthetic and real data sets verify the advantages of the proposed method compared to existing matrix factorization-based approaches. © 2024 IEEE.

关键词： Matrix factorization

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Optimizing Compiler for Quantum Computing Using Qiskit Terra 15

Optimizing Compiler for Quantum Computing Using Qiskit Terra

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Balaji, Peddi Gowtham Chamikar, Peraboina Chowdary, Omkar Belwal, Meena Dept. of Computer Science and Engineering Amrita School of Computing Bangalore Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350370249

The recent times of fast development of quantum computers has been followed by many researchers who want to run the larger and more difficult quantum algorithms on it and they have promised such speedup several times. Notwithstanding this, commercial quantum computers are currently fraught with instability and errors. This paper is centered on development of the customized compiler for quantum computing with aid of Qiskit Terra, a QIS framework. We target central classes such as circuit representation structures, programming techniques based on OpenQASM, challenges in the QC architecture, high-level routing optimization, and the structure of the Qiskit Terra compiler. In every circuit, the optimization is provided by methods of Qiskit Terra, including Trivial Layout, Noise Adaptive Layout, Dense Layout, and Custom Layout. These optimization levels address many problems that emerge when circuits are being compiled and executed, and thus, the optimization levels seek to boost circuit efficiency, reduce the count of gates, and enhance the overall performance. The introduction of numerous optimization passes starting from unroller passes that translate gates, the layout passes that map virtual to physical qubits, and the optimization algorithm aimed at the runtime reduction. Modern quantum compilation technologies are being currently developed based on economic (and theoretical) analyses to underpin the investigation and development of optimal quantum computation procedures. © 2024 IEEE.

关键词： Quantum computers

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Classification of News Category Using Contextual Features

Classification of News Category Using Contextual Features

引用

2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Jahnavi, M. Chandana, K. Nair, Priyanka C. Dheeraj, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

This study focuses on the crucial task of news category classification using a dataset consisting of 175,995 instances spanning across 30 distinct categories. The system utilizes a preprocessing pipeline to collect intricate linguistic features. Pre-trained word embeddings such as GloVe, FastText and Word2Vec are utilized to gather semantic information. The dataset is balanced using SMOTE analysis, and semantic information is gathered using pre-trained word embeddings. The study investigates various machine learning algorithms such as K-Nearest Neighbors, Random Forest, XGBoost, Decision Tree, Logistic Regression, Gaussian Naive Bayes, and AdaBoost. The primary emphasis lies in attaining exceptional precision, F1 score, and comprehending the interpretability of the models' predictions. The study also highlights the need of model interpretability, utilizing LIME. The framework for real-world news classification employs a variety of embeddings, with KNN standing out as particularly effective. It improves precision and efficiency, especially when combined with Word2Vec embeddings, demonstrating machine learning's potential in a wide range of news categories. © 2024 IEEE.

关键词： Embeddings

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Advanced Latency Compensation Techniques for Seamless Online Gaming Experiences 9

Advanced Latency Compensation Techniques for Seamless Online...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Soorya, Vijayakumar Bhat, Suhas S Shaik, Sajjad Rajagopal, Shinu M. Bhaskaran, Sreebha Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350394474

Online multiplayer games have become increasingly popular in the modern world, attracting a worldwide audience and creating virtual communities that cut across national borders. This research offers strategies for improving multiplayer gaming experiences and addresses the issues of lag and synchronization in networked game environments by introducing a novel combination of latency exposure, latency concealing techniques and prediction models. The use of visual and auditory effects to conceal latency, the display of ping time for player awareness of latency, and the use of algorithms like k-Nearest Neighbors, Decision Trees, and Random Forests for player input prediction in the event of network outages are effective techniques for enhancing gaming experiences. In addition to providing a thorough understanding of the technological nuances of latency control in online multiplayer games, the research presents solutions for scenarios involving real-time gaming. By implementing these techniques, the study demonstrates significant improvements in player experience, characterized by reduced lag, enhanced fairness, and greater responsiveness across various game genres. © 2024 IEEE.

关键词： Decision trees

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Prediction of Bank Performance Using Machine Learning and Genetic Algorithm Hybrid Models

Prediction of Bank Performance Using Machine Learning and Ge...

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2022 International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2022

作者： Ainan, Ummey Hany Nur-E-Arefin, Md. Dept. of Computer Science & Engineering Chittagong Bangladesh Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (数字)9781665469449

ISBN: (纸本)9781665469449

Now-a-days banking sector is considered as the back-bone of modern economy of a country. Predicting correct performance of banks of a country can show the nearest future of a country. In past statistical measurement is used to predict bank performance. Nowadays Machine Learning (ML) approaches are used in banking sector for better accuracy. Different Hybrid models are also widely used for better performance. In this work three famous Machine Learning classifiers named Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR) are combined with Genetic Algorithm (GA) to make three hybrid models named GA+RF, GA+SVM and GA+LR. The dataset used in this work are consist of 50 Turkish banks, 30 American banks and 20 European banks. The data have 24 performance indicators that measures performance from the year of 2010 to 2020. CAMEL technique is applied in this dataset in order to find ratings of the banks. In this study Genetic Algorithm is also used as optimizer and feature selector. At the end the models are evaluated with and without feature selection as well as with and without optimization. In this study GA+SVM hybrid model with optimization but without feature selection provides best accuracy among all the models which is 100% test accuracy. On the other hand, GA+LR model provide 81.81 % test accuracy with feature selection but without optimization which is lowest in the whole study. © 2022 IEEE.

关键词： Genetic algorithms

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