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Retail Business Convenience Segmentation using Clustering and data Visualization 2

Retail Business Convenience Segmentation using Clustering an...

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2nd IEEE international conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Thirunavukkarasu, J. Sanjanaa, J. Sivarakshana, M. Yuvashree, R. Sri Sai Ram Institute of Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315905

The conventional approach to launching a business is to research and gather data regarding the past performance of rival businesses unless they were profitable or unsuccessful. Innovation is the ethos of the modern day, as everyone is engaged in a struggle to outperform one another. The objective of our suggested research is to create knowledge that will be helpful to aspiring business owners and small companies that are losing money. Our main aim is to assist small-scale manufacturers in becoming successful marketers. In return for the dataset, which must be provided as input, we will provide them with clear instructions on how to start a profitable business and recover from their loss. In order to analyse data more effectively, our planned work will segment clients based on stock input, weekly updates of stocks sold, and waste products. In this work, two different clustering techniques (k-Means and hierarchical) are used to classify the products into subsets, and their respective results are compared. data will be segmented using clustering algorithms, allowing for much more focused production of the final result. © 2023 IEEE.

关键词： data visualization

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Optimizing Machine Learning Model Accuracy via OBNT Algorithm: Advanced data Preprocessing Technique

Optimizing Machine Learning Model Accuracy via OBNT Algorith...

引用

2023 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Ramkumar, M. Malathi, K. Pavithra, K. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798350319200

Machine learning has emerged as a focal point in the current landscape of data analytics. It involves the utilization of diverse ML algorithms to analyze data and extract valuable insights. These insights serve multiple industries, such as retail, petroleum, stock markets, and various organizations, resulting in increased sales, productivity, and profitability. Building an effective ML model to analyze and predict future data patterns is contingent upon high accuracy, which is directly dependent on the quality of the underlying data. Techniques like data preprocessing and feature selection play crucial roles in ensuring data quality. In this research, a methodology is proposed to meticulously select the most optimal data from raw datasets to construct an ML training model, thereby bolstering the accuracy percentage. The study involves comparing the accuracy of models built without employing the proposed technique against those utilizing it, alongside a comparative analysis of machine learning algorithm accuracy post-application of the proposed technique in contrast to existing preprocessing methodologies. The findings unequivocally demonstrate the efficacy of the proposed OBNT Algorithm in enhancing the accuracy of ML training models. The results indicated a statistically significant difference between the two groups, with a P value of 0.003 (p © 2023 IEEE.

关键词： algorithms data Preprocessing data Science Feature Selection Machine Learning

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Efficient Prescription data Entry by EMR Software 2

Efficient Prescription Data Entry by EMR Software

引用

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

作者： Shukla, Tanmay Suri, Khushi Sudhakaran, Pradeep Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu District Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350340235

Efficient and accurate recording of electronic prescription data is of paramount importance in the ever-evolving field of healthcare. As this investigation delves deeper, a groundbreaking solution emerges, adept at bridging the speed gap between traditional handwritten methods and the digital landscape. This innovative technique is founded on a profound understanding of the cognitive processes underpinning manual prescription creation, resulting in a significant overhaul of the digital interface. This approach revolves around the integration of predictive algorithms and tailored templates into the electronic prescription system, with the goal of matching the speed of traditional handwritten methods. This substantial enhancement not only streamlines workflow but also enhances patient care, optimizes pharmacy operations, and provides valuable data for research, promising a transformative impact on the healthcare industry. © 2023 IEEE.

关键词： Application programming interface Encryption Express MongoDB Node (MERN) Stack React RxNorm

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DPFCFI: A Hardware-Based Forward Control-Flow Integrity for Architecture and Microarchitecture 21

DPFCFI: A Hardware-Based Forward Control-Flow Integrity for ...

引用

21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： She, Cairui Chen, Liwei Shi, Gang Institute of Information Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350329223

Spectre attacks and their many variants pose a security risk to modern processors. Branch Target Injection (BTI or Spectre v2) is one of the most dangerous Spectre attacks, as it enables an attacker to leak sensitive data by exploiting indirect branch mispredictions. Some works, like SPECCFI and Intel IBT, propose that the indirect branch should also confirm the control flow integrity (CFI) in the microarchitecture. They use CFI to defend the microarchitecture against BTI attacks. However, SPECCFI follows the traditional CFI label policy, which causes compatibility and security issues. Intel IBT also can not provide sufficient security *** this paper, we propose Double Protection based on Forward Control-Flow Integrity (DPFCFI). DPFCFI is a hardware-based security mechanism for microarchitecture and architecture that combines CFI with transparent label design. DPFCFI can stop BTI attacks and traditional forward control-flow hijack attacks. DPFCFI's transparent label design enables it to support high-precision CFG and improve security. DPFCFI also adds no new ISA (Instruction Set Architecture) instructions and maintains program compatibility. We implement DPFCFI on CVA6 (Ariane) RISC-V processor. The experiment on FPGA demonstrates that DPFCFI incurs low performance overhead (2.60% on average) © 2023 IEEE.

关键词： Sensitive data

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Security Vulnerability and Key Points of Encryption Technology of Computer Information Technology data 2

Security Vulnerability and Key Points of Encryption Technolo...

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2nd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Sun, Min Wuhan Institute of Design and Sciences Wuhan China

ISBN: (纸本)9798350347456

With the continuous development of computer information technology, data storage and processing methods have changed, which has a great impact on information security. This paper describes the work done in this field at home and abroad. In combination with the emergence of new technologies such as network technology and cloud computing in the current era, it provides us with convenient conditions. At the same time, it also discusses how to apply these new science and technology to reality, and puts forward corresponding measures for reference and suggestions. This paper first introduces the data storage and processing methods of computer information technology and related concepts, and then briefly describes and analyzes computer information technology and information security. An encryption technology model for security vulnerabilities is designed to test the encryption technology performance of the model. The test results show that when the key is public key encryption, the running time is less than that of private key encryption, which means that with the increase of the number of files, the computational complexity increases at a relatively fast linear speed. © 2023 IEEE.

关键词： Security of data

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Big data Mining And Clustering Using Distributed Bayesian Matrix Decomposition

Big Data Mining And Clustering Using Distributed Bayesian Ma...

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2023 international conference on Evolutionary algorithms and Soft computing Techniques, EASCT 2023

作者： Das, Bappaditya Murari, Thejovathi Das, Chandan Rajeswari, P. Dr. B.C. Roy Engineering College Computer Science and Engineering West Bengal Durgapur India Acharya Nagarjuna University Computer Science and Engineering Andhra Pradesh Guntur India Artificial Intelligence & Data Science Panimalar Engineering College Chennai India

ISBN: (纸本)9798350313413

Technological advancement has enabled us to store and process massive amounts of data in a much shorter period of period. The phrase 'Big data' merely describes the enormous amounts of data that are now regularly utilized in business and academic settings. The emphasis here is not only on data collection, but also on careful data analysis in order to obtain meaningful results. There are several approaches to dealing with massive data streams. data clustering into compact units is one such method. This not only reduces data size but also allows it to be used more efficiently. Because all of these devices are from different manufacturers, the data generated is heterogeneous in nature and contains noise. A fundamental problem is gaining knowledge from heterogeneous, noisy data. Several strategies for mining knowledge from big data have already been proposed. Because big data is frequently acquired from dispersed locations and stored on multiple machines, handling these extremely large data sets in a single machine is either inefficient or impossible. We provide a distributed Bayesian matrix decomposition model (DBMD) for big data mining and clustering. Statistical inference, alternate direction method of multipliers (ADMM), and accelerated gradient descent (AGD) are the three techniques utilized to implement distributed computing. Investigated are the theoretical convergence tendencies of various approaches. We offer a perfect plug-in weighted average that minimizes estimate variance to get over noise heterogeneity. In real-world experiments, our algorithms outperform or are on par with two popular distributed approaches, Scalable- nonnegative matrix factorization (NMF) and Scalable- k-means++, which are confirmed by our theoretical results from synthetic studies. © 2023 IEEE.

关键词： Big data Mining Clustering Distributed Bayesian Matrix Decomposition (DBMD) Scalable-k-means++ Scalable-nonnegative matrix factorization (NMF)

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Research on data Storage Security in Cloud computing Environment 4

Research on Data Storage Security in Cloud Computing Environ...

引用

4th international conference on Information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Guo, Chen Su, Mohan Cui, Fang China Mobile Group Device Co. Ltd China

ISBN: (纸本)9798350337181

This paper conducts in-depth research and discussion on data storage security in cloud computing environment. This study collected 1000 dummy data from different organizations as the basic data set for the study. This data covers various types of sensitive information, including personally identifiable information, financial data, and medical records. We generate these data using appropriate data generation methods to ensure realistic characteristics and distributions. Based on existing backup technologies and algorithms, we evaluate the impact of different backup strategies on data security. Experimental results show that our data backup and recovery strategy and the application of encryption technology effectively improve the security of data in cloud storage. By selecting backup strategies and recovery mechanisms reasonably, we can ensure data availability and integrity. At the same time, employing a proper encryption scheme can protect data from the risk of unauthorized access and disclosure. © 2023 IEEE.

关键词： Cloud storage

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Internet threats and ways to protect against them: A brief review 13

Internet threats and ways to protect against them: A brief r...

引用

13th international conference on Cloud computing, data Science and Engineering, Confluence 2023

作者： Mambetov, Saken Begimbayeva, Yenlik Joldasbayev, Serik Kazbekova, Gulnur Al-Farabi Kazakh National University Almaty Kazakhstan Al-Farabi Kazakh National University Associate the Department of Information Systems Almaty Kazakhstan International It University The Department of Computer Engineering Almaty Kazakhstan Khoja Akhmet Yassawi International Kazakh-Turkish University Department of Computer Engineering Turkistan Kazakhstan

ISBN: (纸本)9781665462631

Since the 21st century is the century of information technology, there are now a large number of Internet users, and the number of these users is steadily growing. Most people know the useful aspects of this network, but there are dangerous aspects of the Internet, which are reviewed in this paper. Today there are many types of threats such as phishing attacks, spam messages, malware, worms, spyware, Trojans, rooters, botnets. The main principle of protection against these threats is information and cyber literacy of using the Internet. The main purpose of this article is a summary of Internet threats based on a review of articles by previous researchers, as well as the definition of types of threats and attacks. As a result of the study, threats were filtered into two main types: threats of social engineering, where the main emphasis is on the information carrier, and technical threats, where various methods, algorithms and software implementations are used to hack directly into a computing device containing information. © 2023 IEEE.

关键词： Information filtering

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Finding patterns in employee attrition rates using self-organizing Kohonen maps and decision trees

Finding patterns in employee attrition rates using self-orga...

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2023 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Degtyareva, Ksenia Ageev, Daniel Alikhanovich Kukartsev, Vladislav Viktorovich Reshetnev Siberian State University of Science and Technology Department of Information Economic System Krasnoyarsk Russia Moscow State Technical University Informatics Artificial Intelligence and Control Systems Faculty Bauman Moscow Russia

ISBN: (纸本)9798350319200

This text presents a study of the causes of employee termination in organizations. Using employee data such as age, gender, marital status, monthly income, education, and others, an analysis was conducted using neural network algorithms (Kohonen maps) and Decision Trees. The results showed that both methods were suitable for analysis, but the neural network method showed the lowest error (7.4%). The analysis allowed to identify the most significant factors such as monthly income, age of employees, marital status and years of employment with the company. The analysis will help to optimize the process of predicting employee dismissal and save resources on unnecessary analyses. © 2023 IEEE.

关键词： data analysis data set economy finance Kohonen map

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Green IoT Edge computing Towards Sustainable and Distributed data processing 10

Green IoT Edge Computing Towards Sustainable and Distributed...

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

作者： Ananth, K.R. Punna, Hari Shankar Selvaraj, Krishnamoorthy Rajagopal, K. Mahajan, Vaishali Sakthivel, S. Tamilnadu Erode India Department of Cse Telangana India Department of Erp Keller India Maharashtra Pune India K.S.Rangasamy College of Arts and Science Department of Computer Applications Tamilnadu Tiruchengode India

ISBN: (纸本)9798350382471

data creation in the network's periphery has increased exponentially as IoT devices have become more commonplace. To address the challenges this large data deluge poses, sustainable and efficient data processing technologies are required. The term 'Green IoT Edge computing' refers to a new strategy that uses distributed data processing and renewable energy to improve the efficiency and longevity of Internet of Things (IoT) applications. In the Internet of Things (IoT), edge computing is used to process data locally, at the network's periphery, rather than forwarding it to a centralized data center. This method improves real-time decision-making, minimizes network latency, and helps preserve bandwidth. However, in locations with limited access to power or other resources, the high power consumption of edge computing devices continues to be a major issue. Green IoT edge computing addresses this challenge by combining renewable energy sources like solar panels and wind turbines with traditional edge computing facilities. This guarantees low- impact and long-term power for edge devices, cutting down on their environmental impact and operational expenses. This strategy harmonizes IoT operations with environmental sustainability targets by utilizing renewable energy sources. The ability to handle data in a distributed manner is also crucial to green IoT edge computing. Rather than a centralized server, a distributed group of edge devices processes data. This improves energy efficiency as well as fault tolerance and dependability. Edge devices working together to properly spread processing activities can reduce the demand for powerful, energy-hungry servers. Green IoT Edge computing's primary invention is the 'Renewable Edge Optimizer.' This clever piece of code automatically adjusts data processing tasks in accordance with the availability of renewable power. When clean energy is plentiful, the optimizer may shift more data processing to distributed nodes, relieving press

关键词： Distributed data processing Edge computing etc Green IoT Renewable Edge Optimizer Renewable Energy Sustainability

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