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检索条件"任意字段=International Conference on Advances in Data Science and Computing Technologies, ASDC 2022"

共 6585 条记录，以下是21-30 订阅

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Evaluation of Optimum Batting Order Based on Partnership Analysis Using Ant Colony Optimization Technique

Evaluation of Optimum Batting Order Based on Partnership Ana...

引用

international conference on advances in data science and computing technologies, asdc 2022

作者： Tarafder, Mousumi Santra, Soumen Sharma, Sweta Deyasi, Arpan Department of Computer Application Techno International Newtown Kolkata700156 India Department of Electronics and Communication Engineering RCC Institute of Information Technology Kolkata700015 India

ISBN: (纸本)9789819936557

Based on the recent partnership data as available in authentic database, optimum batting order of various batters is evaluated using Ant Colony Optimization technique. Assuming batters as nodes of ant system and partnership scores is weightage of edges situated at the connecting nodes of ant paths;optimum batting order is computed for various external conditions. Cautiousness factor of the batters is considered which is most settled in the game and playing with maximum time. Un-normalized dataset over last 40 years of one-day performance is normalized by applying several conditions, where single big weight between two batters are neglected due to lack of consistency. Result will be immensely helpful for future performance optimization depending on available batters in the team. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ant colony optimization

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Breast Cancer Prediction Using Artificial Intelligence Technology 4

Breast Cancer Prediction Using Artificial Intelligence Techn...

引用

4th international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2024

作者： Akila, V. Christaline, J. Anita Srm Institute of Science and Technology Vadapalani Dept. of Electronics and Communication Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

The aim of the project is to compare the performance of four different machine learning algorithms for breast cancer prediction such as decision tree, logistic regression, XG boost, and CAT boost. We used a dataset of patient medical records containing various clinical factors to train and test the algorithms. Accurate diagnosis and early detection are essential for enhancing patient outcomes. data mining is a prominent tool in the healthcare industry for processing massive amounts of data. To examine massive amounts of complicated medical data, researchers use a variety of data mining and machine learning approaches. The use of these strategies can help medical practitioners forecast the onset of breast cancer. © 2024 IEEE.

关键词： Algorithms Breast cancer data mining Machine Learning

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Comparative Study on Educational data Mining and Learning Analytics for Improving the Teaching-Learning Environment 15

Comparative Study on Educational Data Mining and Learning An...

引用

15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Shete, Rushikesh Kathane, Kavita Gourshettiwar, Palash YCCE Department of Computer Science and Engineering Maharashtra Nagpur India Maharashtra Wardha India

ISBN: (纸本)9798331300579

Education is evolving a fast. After Covid-19 many different ways are introduced to students to learn and grow. Digital Platforms are booming in this era of self-learning. Educational data mining (EDM) gives the knowledge about the growth and understanding of students. It is important to know the outcome of the learning, irrespective of the platform. This paper gives a general review of what is known about educational data mining and how it might be used in teaching and learning. In this work we review different studies about EDM and Learning Analytics for betterment of learning environments and to enhance teaching techniques. © Grenze Scientific Society, 2024.

关键词： Contrastive Learning

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Double Encryption Technique for Sharing and Storing the Images in the Cloud Environment 4

Double Encryption Technique for Sharing and Storing the Imag...

引用

4th international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2024

作者： Anusha, P. Maruthi, R. Kumar, D. Maghesh Begum, H. Parveen Periyar Maniammai Institute of Science and Technology Department of Software Engineering Tamil Nadu Thanjavur India Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350343670

Cryptography is used by all organizations to protect the data files and ensures confidentiality mainly at the time of sharing and storing in the cloud data storage. The cloud service providers use a wide range of tools and techniques for encryption and storage technologies. The intruders are finding number of ways to break or attack the cryptographic systems, so that new ways are constantly being explored to safeguard our files from the attackers This paper explores double encryption cryptographic technique based on Elliptic curve cryptography (ECC) and Advanced Encryption Standard (AES) to perform end-to end encryption for sharing and storing files in the cloud environment. © 2024 IEEE.

关键词： Advanced Encryption Standard Cloud Cryptography Elliptic Curve Cryptography File Sharing

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Cognitive computing Stress Analysis in Aging Populations using Cloud-Connected SVM Classifier Solution with Raspberry Pi

Cognitive Computing Stress Analysis in Aging Populations usi...

引用

2024 international conference on advances in Modern Age technologies for Health and Engineering science, AMATHE 2024

作者： Raman, Ramakrishnan Mehla, Akshra Maharashtra Pune India Maharashtra Pune India

ISBN: (纸本)9798350371567

Cognitive health issues in the aging global population need innovative technologies. A Raspberry Pi-implemented SVM classifier, cognitive computing, and cloud connection provide a new elderly stress analysis method. A low-cost Raspberry Pi trains an SVM classifier to recognize stress-related cognitive activity patterns. Age-related cognitive and physiological changes train the classifier. The Raspberry Pi stores, processes, and analyzes data on the cloud. The cloud-connected stress analysis system helps healthcare professionals, caregivers, and people remotely monitor and measure stress. SVM classifiers use input data to make smart decisions and predict stress. The research concludes that monitoring cognitive health and constructing a cloud-connected stress analysis gadget increase ethics and privacy. This technology might help elders identify stress, intervene quickly, and improve health. The system improves seniors' assistive technology and healthcare cognitive computing. © 2024 IEEE.

关键词： Stress analysis

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Design of data processing scheme of 5G terminal protocol conformance test system 4

Design of data processing scheme of 5G terminal protocol con...

引用

4th international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2024

作者： Jingyao, Cao Yadav, Amit Khan, Asif Sah, Abhishek Pratap Chengdu Neusoft University School of Computer and Software Chengdu China Charles Darwin University College of Engineering It and Environment NT Australia Integral University Department of Computer Application Lucknow India University of Electronic Science and Technology of China School of Management Science and Engineering Chengdu China

ISBN: (纸本)9798350343670

Considering the fact that the test case runs abnormally and the test case execution fails during the development and debugging of the 5G terminal protocol conformance test system. Here useful approach is implemented by embedding data copy and storage module in the system, which completes the encapsulation, copy and storage of massive data generated during the operation of the system, so that equipment developers can correctly locate the errors in the system according to the data, and repair the system loopholes to ensure the stability and accuracy of the system. © 2024 IEEE.

关键词： 5G Terminal copy and storage module protocol conformance

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Quantum-Driven Big data Processing

Quantum-Driven Big Data Processing

引用

2024 international conference on advances in Modern Age technologies for Health and Engineering science, AMATHE 2024

作者： Handa, Devansh Nikhil, Kindi Krishna Duvarakanath, S. Murali, K. Amrita School Of Computing Amrita Vishwa Vidyapeetham Department Of Computer Science And Engineering Bengaluru India Amrita School Of Engineering Amrita Vishwa Vidyapeetham Department Of Mathematics Bengaluru India

ISBN: (纸本)9798350371567

The proposed study realizes a novel quantum machine learning (QML) architecture that allows heuristic function evaluation and can actually perform quantum circuits during massive data processing. The Quantum-Circuit fusion or the Quantum-data fusion approach to information technology is the latest strategy revolutionizing the major applications with vast computing needs in present time. Our method, which is task - agnostic, is aimed to increase the machine operations speed and scale as the concepts of quantum computing are applied. In this research article, the theoretical background and perspectives are described, so generally, the idea of the combination of quantum circuits is clarified. The research delivery shows as well of this combination as its importance and possibilities, offering a new and cool method in the competitive era of data-driven computational sciences, especially in the field of quantum machine learning. Within classical machine quantum-circuit fusion the potential capability to handle a large data set and obtain the new viewpoints consequently quantum machine learning progress represents a promising route. © 2024 IEEE.

关键词： Timing circuits

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Preserving Privacy in Healthcare Analytics: Federated Learning for Breast Cancer Prediction in a Collaborative Learning Framework 15

Preserving Privacy in Healthcare Analytics: Federated Learni...

引用

15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Anuvarshini, G.B. Varshaa, D. Sujithra, M. Devi, D. Sudha Department of Computing- Data Science Coimbatore Institute of Technology Coimbatore India

ISBN: (纸本)9798331300579

Federated learning has emerged as a promising technique in machine learning, enabling collaborative training across distributed datasets. Particularly in fields like healthcare, where data privacy is paramount, federated learning offers a means to improve privacy and security. This paper addresses the critical need for safeguarding sensitive medical data by proposing a privacy-preserving federated learning algorithm. Leveraging homomorphic encryption, the proposed algorithm ensures that the learning process protects both data and model integrity. By employing a secure multi-party computation protocol, the algorithm defends against potential adversaries seeking to compromise the deep learning model. Evaluation on real-world medical datasets demonstrates the effectiveness of the approach in preserving privacy and security amidst collaborative learning environments. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Steganography Tools and Their Analysis Concerning Distortion in Stego Image

Steganography Tools and Their Analysis Concerning Distortion...

引用

international conference on advances in data science and computing technologies, asdc 2022

作者： Pilania, Urmila Tanwar, Rohit Kaushik, Keshav Department of Computer Science and Engineering Manav Rachna University Haryana Faridabad121004 India Department of Systemics School of Computer Science University of Petroleum & Energy Studies Uttrakhand Dehradun248007 India

ISBN: (纸本)9789819936557

In the research work, the examination of concealing algorithms, extraction algorithms, carrier multimedia file, and other security parameters has been done for two steganography tools. From the experimental results, we strongly recommend that many of the open-source tools are not safe for embedding personal information as it can be detected with the help of the steganalysis tools that exist in the market. In this research work, stego file is analyzed based on distortion induced concerning the position of bit used for embedding, distortion pattern using histogram, spectrum analysis, similarity index, and entropy. Quantitative values of mentioned parameters are calculated in Python and emphasize the unfortunate present state of the art of realistic steganography tools. At present, there is a lack of publically available secure steganography tools. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Spectrum analysis

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data Modeling Practices for E-Commerce 15

Data Modeling Practices for E-Commerce

引用

15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Patel, Drushti Shah, Monika Computer Science and Engineering Department Institute of Technology Nirma University Gujarat Ahmedabad India

ISBN: (纸本)9798331300579

In modern times, large amounts of data with diverse characteristics, such as volume, variety, etc., are generated daily. For enterprises such as E-commerce, which is escalating nowadays, massive amounts of unstructured data have increased significantly, making it essential to manage high data availability and consistency in conventional databases. To establish extreme data availability and consistency, opting for the most effective data model is necessary to ensure that data accuracy and consistency are achieved and can be easily accessed and manipulated by the intended users. The development of data modelling approaches has made it possible to use massive quantities of data without degrading performance or loosening the conventional ACID properties. This paper emphasizes the importance of efficient data modelling. It provides practical guidelines for structuring RDBMS data models and for NoSQL data models, considering the types of data access patterns and highlighting their advantages and limitations in terms of e-commerce enterprises. © Grenze Scientific Society, 2024.

关键词： data consistency

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