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Analyzing The Effect of Edge computing on Real-Time data processing and Latency Reduction 10

Analyzing The Effect of Edge Computing on Real-Time Data Pro...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Sampath, T. Dutt, Amit Veeraiah, Vivek Aishwarya, B.K. Lal, Karu Kapila, Dhiraj School of Cs & Ai Sr University Telangana Warangal India Lovely Professional University Phagwara India Sri Siddhartha Academy of Higher Education Department of Computer Science Karnataka Tumkur572107 India Institute of Aeronautical Engineering Hyderabad India Ohio National Financial Services United States Lovely Professional University Department of Computer Science & Engineering Punjab Phagwara India

ISBN: (纸本)9798350382471

The concept of edge computing is a fresh approach that has garnered notable attention in recent times, primarily driven by the use of the Internet of Thingsand the requirement for instantaneous data processing and examination. Edge computing technology offers various benefits, including diminished latency, enhanced efficiency, and heightened dependability. Nonetheless, it also introduces a range of obstacles, such as the safeguarding of security and the management of dispersed computational assets. Edge computing minimizes latency and cuts down on bandwidth expenditures in the realm of Health Information Technology by relocating a portion of the data processing and analytical tasks from the central server or cloud to the periphery of the network, precisely where data originates or is utilized. This approach ensures that only indispensable or pertinent data is dispatched to the cloud or server, while the remaining data is either managed locally or disregarded. As a result, the volume of data requiring transmission is reduced, leading to bandwidth preservation and heightened swiftness. Furthermore, edge computing facilitates real-time or nearly real-time data processing and analysis, contributing to latency reduction and overall improved responsiveness. Edge gateways function as intermediaries connecting edge devices and cloud servers, aiding in data routing and supplying supplementary processing capabilities whenever required. Cloud servers furnish extra computational potency and storage aptitude, affording enterprises the ability to handle and scrutinize substantial data quantities instantaneously. Edge computing architecture latency reduction accuracy. The main contribution of the study is to analyze the effects of edge computing on latency reduction and real-time data processing. © 2023 IEEE.

关键词： bandwidth data processing latency reduction Real-time responsiveness

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Symmetric Key Cryptography: Review, Algorithmic Insights, and Challenges in the Era of Quantum Computers 14

Symmetric Key Cryptography: Review, Algorithmic Insights, an...

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14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Hasija, Taniya Ramkumar, K.R. Singh, Bhupendra Kaur, Amanpreet Mittal, Sudesh Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Defence Research and Development Organisation Centre for Artificial Intelligence & Robotics Bangalore India

ISBN: (纸本)9798350335095

In an increasingly digital world, security has become paramount. Whether it's personal information, financial transactions, or sensitive communications, the need to safeguard data from unauthorized access and malicious activities has never been more critical. Security plays a vital role in protecting individuals, organizations, and even nations from potential threats and risks. In this context, cryptography emerges as a fundamental tool for ensuring security in digital systems. Cryptography involves the use of mathematical algorithms and techniques to encode information, making it unintelligible to unauthorized parties. To secure the information symmetric key cryptography algorithms and asymmetric key cryptography algorithms are implemented from last five decades. This review examines symmetric key cryptography algorithms and the challenges they face in the era of quantum computing. Symmetric key cryptography relies on the computational complexity of mathematical problems to secure sensitive information. This review aims to provide valuable insights and awareness to researchers and practitioners in the field of cryptography, enabling them to address these challenges effectively and maintain the security of sensitive information in the quantum era. © 2023 IEEE.

关键词： Sensitive data

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Advances and Predictions in Predictive Auto-Scaling and Maintenance algorithms for Cloud computing 2

Advances and Predictions in Predictive Auto-Scaling and Main...

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2nd international conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Rathinam, Anantha Raman Vathani, B. Santha Komathi, A. Lenin, J. Bharathi, B. Urugan, S.M. Department of Computer Science and Engineering - Honors KL University Guntur Andhra Pradesh Vijayawada India Department of Management Studies Mahalashmi Women’s College of Arts and Science Tamil Nadu Chennai India Department of Computer Science Nadar Saraswathi College of Arts and Science College Tamilnadu Theni India Department of ComputerScience and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India Department of Computer Science and Engineering R.M.K. Engineering College Tamil Nadu Chennai India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350340235

The use of sophisticated algorithms has radically altered cloud computing predictive auto-scaling and upkeep approaches. Recurrent Neural Networks (RNNs), the Prophet Algorithm, K-Means Clustering, and Seasonal Autoregressive Integrated Moving-Average (SARIMA) all play a role in improving cloud infrastructures, and their interactions are studied here. By capitalizing on their superiority in processing sequential data, RNNs can deduce accurate workload forecasts from past use patterns. Concurrently, the Prophet Algorithm records seasonal and annual patterns, which adds depth to forecasts. By grouping servers into clusters with similar consumption patterns, K-Means Clustering improves resource allocation efficiency and paves the way for precise auto-scaling. SARIMA models capture nuanced seasonal fluctuations, which lead to reliable demand forecasts. This explores the state-of-the-art and future directions of these techniques, illuminating their potential to revolutionize current approaches to cloud management. When these methods are combined, cloud service providers are better able to proactively scale their resources, hence reducing the likelihood of bottlenecks and outages. It foresees the development of these algorithms and their subsequent widespread use in a variety of fields outside of cloud computing, such as Internet of Things (IoT) networks and edge computing infrastructures. © 2023 IEEE.

关键词： Auto-Scaling Cloud computing data-driven Insights K-Means Clustering Maintenance algorithms Predictive Analytics Prophet Algorithm Recurrent Neural Networks Scalability Seasonal Autoregressive Integrated Moving-Average

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Generating Muslim Name using Character-Level Language Model in Deep Learning

Generating Muslim Name using Character-Level Language Model ...

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2023 international conference on Smart computing and Application, ICSCA 2023

作者： Ali Shah, Syed Atif Yafooz, Wael M.S. Emara, Abdel-Hamid M. Sajid, Hashim Air University Department of Cs Islamabad Pakistan Al-Madinah International University Department of Cs Malaysia College of Computer Science and Engineering Taibah University Department of Computer Science Saudi Arabia Al-Azhar University Faculty of Engineering Department of Computers and Systems Engineering Cairo11884 Egypt Abasyn University Department of Cs Pakistan

ISBN: (纸本)9798350347050

this paper proposes novel deep-learning models that can generate Muslim names. A recurrent neural network (RNN) approach is used as the machine learning model. Generate new names, using the character-level language model. This technique has learned the different name patterns and generates new names. In order to achieve this objective, thousands of Muslim names have been collected from popular domains and then trimmed for the data set. After processing the whole dataset, 1000 pure and most common names are selected for a dataset. The deployed system is using some important functions provided by neural network libraries. Here, the argument also reveals that if a huge data set is used to employ this deep learning technique, more accurate results are expected. These outcomes will be better when compared with the traditional machine learning algorithms. The efficacy of the proposed model is proved by the similarity of artificial names with real names. © 2023 IEEE.

关键词： Recurrent neural networks

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A Comparative Analysis of IoT data Using Machine Learning algorithms 15th

A Comparative Analysis of IoT Data Using Machine Learning Al...

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15th international conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Nagashree, B.A. Satheesh Kumar, S. Muthukumaran, V. Munrathnam, Meram Angeline Kavitha, M. Department of Computer Science and Applications School of CSA REVA University Bangalore India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R.K. Valley Kadapa District Andhra Pradesh Idupulapaya516330 India Department of Mathematics Voorhees College Vellore632001 India

ISBN: (纸本)9783031889912

IoT (Internet of Things) is revolutionizing the commercial environment by acquiring vast amounts of data from various sources. However, processing and analyzing this data is challenging due to the large volume of IoT devices. AI-powered IoT creates intelligent machines that mimic human intelligence, aiding decision-making with minimal human input. Both individuals and professionals’ benefit from this combination, as IoT is concerned with communicating online, but AI allows machines to learn from data and experience. This study demonstrates the analysis of IoT data using machine learning algorithms and compares the accuracy of various machine learning algorithms used. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Artificial Intelligence in Natural Disaster: data Crawling- and Text Mining-Based Literature Review 8

Artificial Intelligence in Natural Disaster: Data Crawling- ...

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8th international conference on Informatics and computing, ICIC 2023

作者： Ramadhan, Zhafran Yunita, Ariana Universitas Pertamina Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350342604

A literature review is an essential part of research. Beginning researchers who would like to conduct research in any field commonly review previous papers to identify trends and gaps in research. However, conducting a literature review is challenging, particularly for novice researchers. First, synthesising previous related works can be time-consuming. Second, accessing expensive databases such as Scopus and Web of Science might be inaccessible for novice researchers in developing countries. Thus, the purpose of this paper is to present a review performed by data crawling ArXiv and using latent Dirichlet allocation (LDA), a topic modelling algorithm. In this paper, we provide an overview of the topic models of natural disasters and artificial intelligence. Papers from 2017-2022 were crawled using Python with specific keywords, and only papers written in English were eligible for further analysis. Then, the papers were pre-processed by lowering the text, removing punctuation and stop words, tokenising, and lemmatising with POS tagging techniques. Pre-processed papers were then trained using the LDA algorithm. For evaluation, coherence metrics were used to determine the optimal number of topics. There were five topics resulting from the topic modelling algorithm. The clusters were qualitatively analysed and labelled as image classification, algorithms, social media analysis, natural language processing, and machine learning. Further findings are also discussed. © 2023 IEEE.

关键词： Natural language processing systems

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Sentiment Analysis across Modalities: A Comprehensive Review of Text and Audio Approaches 6

Sentiment Analysis across Modalities: A Comprehensive Review...

引用

6th international conference on Recent Trends in Advance computing, ICRTAC 2023

作者： Elle, Krisha Dhobale, Nikita Deshmukh, Pratiksha Nirne, Sakshi Jarali, Ashwini International Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350394702

Sentiment analysis, an essential component of natural language processing, reveals the complex web of human emotions and viewpoints present in a variety of communication formats. Text-based as well as audio-based sentiment analysis are two areas where this survey that closely examines sentiment analysis. The digital era's explosion of textual and auditory data has sparked a great deal of research in these fields, producing a wide range of methods and models. Sentiment analysis algorithms have become increasingly complex as a result of the availability of textual data from sources like social media and news articles. Concurrently, there has been a surge in interest in audio-based sentiment analysis because of the increasing use of audio content in podcasts, customer service interactions, and multimedia platforms. This survey provides an in-depth understanding of the recent methods, techniques and approaches in sentiment analysis using text and audio. It carefully describes in detail various developments in these fields, encouraging interdisciplinary idea-sharing. This paper presents a comprehensive overview of sentiment analysis advancements by offering a comprehensive review of research papers, with a focus on relevant models and techniques. © 2023 IEEE.

关键词： Sentiment analysis

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Comparative Analysis of data Compression using Canonical Huffman and Golomb Rice Encoding in Verilog HDL and Implementation in FPGA 14

Comparative Analysis of Data Compression using Canonical Huf...

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14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Ajay, Lekshmi S Prabha Rajeev, Sreenidhi Amrita Vishwa Vidyapeetham Department of Electronics and Communication Amritapuri India

ISBN: (纸本)9798350335095

The world revolves around data. Hence the importance of storing data becomes highly essential, but in order to store the data as such requires a lot of storage space which brings up the role of data Compression. It becomes necessary to compress the data before storing it. Compressing the data without losing any information is highly important. data Compression can be lossy or lossless. It is always essential to follow lossless compression for textual data. The project aims to compress textual data using two lossless compression techniques such as Canonical Huffman Encoding and Golomb Rice Encoding [1]. 64-bit input data is compressed using both algorithms in Verilog HDL and analyzed which algorithm encodes the textual data with the least number of bits. Efficiency of the algorithms are analyzed using parameters such as Compression Ratio, Area, Power Consumption and Delay. The better algorithm between the two is found and is implemented in FPGA. In order to reduce power consumption, clock gating is added to the better design. data compression reduces the hardware cost drastically [2]. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Research on the application of big data information crawling technology in network Public opinion processing

Research on the application of big data information crawling...

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2023 international conference on Networking, Informatics and computing, ICNETIC 2023

作者： Wang, Shuo Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350313314

In the era of new media, the high-speed communication of information brings new challenges to the guidance of network public opinion, and the characteristics of network public opinion communication change with the development of The Times. It is necessary to build a big data security governance system and framework design to meet the compliance requirements of data security. If the network public opinion appears uncontrollable phenomenon, will cause extremely serious influence. Python language and Scrapy crawler framework are used to establish relevant data analysis system and K-means clustering algorithm is used to classify public *** digitalization of public opinion information can effectively reduce the waste of resources. © 2023 IEEE.

关键词： Big data

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Accelerating Causal algorithms for Industrial-scale data: A Distributed computing Approach with Ray Framework 3

Accelerating Causal Algorithms for Industrial-scale Data: A ...

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3rd international conference on AI-ML Systems, AIMLSystems 2023

作者： Verma, Vishal Reddy, Vinod Ravi, Jaiprakash Dream11 Mumbai India

ISBN: (纸本)9798400716492

The increasing need for causal analysis in large-scale industrial datasets necessitates the development of efficient and scalable causal algorithms for real-world applications. This paper addresses the challenge of scaling causal algorithms in the context of conducting causal analysis on extensive datasets commonly encountered in industrial settings. Our proposed solution involves enhancing the scalability of causal algorithm libraries, such as EconML, by leveraging the parallelism capabilities offered by the distributed computing framework Ray. We explore the potential of parallelizing key iterative steps within causal algorithms to significantly reduce overall runtime, supported by a case study that examines the impact on estimation times and costs. Through this approach, we aim to provide a more effective solution for implementing causal analysis in large-scale industrial applications. © 2023 ACM.

关键词： Machine learning

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