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Ethical Considerations in data Mining and Analytics 1

Ethical Considerations in Data Mining and Analytics

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1st ieee international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, Priyanka Sharma, Khushboo Mahawar, Mukesh Kumar Wasim, Javed Dey, Gunadhar Nibiya, S. Anto Chandigarh University Dept. of Com. Science Engineering Chandigarh India Madhav University Dept. of Commerce & Management Pindwara India Mangalayatan University Dept. of Computer Eng. & Applications Aligarh India Usha Martin University Dept. of Computing & IT Jharkhand India Meenakshi A.H. Edu. & Research Dept. of Mathematics Chennai India

ISBN: (纸本)9798350356816

As data mining & analytics technologies advance, they offer unprecedented opportunities for extracting valuable insights from vast datasets. However, these advancements bring forth a complex array of ethical considerations that must be addressed to ensure responsible practices in the field. This paper explores the ethical dimensions of data mining & analytics, focusing on key issues such as privacy, consent, & bias. Privacy concerns arise from the extensive collection & potential misuse of personal data, necessitating stringent measures to protect individuals' information. Consent, a fundamental ethical principle, requires transparent practices where data subjects are informed & agree to the use of their data. Additionally, the challenge of algorithmic bias highlights how data-driven models can perpetuate or exacerbate existing inequalities if not carefully managed. The paper also examines the ethical implications of data ownership & the responsibilities of organizations in managing & communicating data effectively. Through a review of current practices, case studies, & regulatory frameworks, this study aims to provide a comprehensive overview of the ethical landscape in data mining & analytics. It proposes a set of best practices for professionals to navigate these challenges, emphasizing the need for ethical guidelines, ongoing education, & robust oversight mechanisms. By addressing these concerns, the paper seeks to foster a more responsible & equitable approach to data mining & analytics that balances innovation with the protection of individual rights & societal values. © 2024 ieee.

关键词： Differential privacy

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Enhancing E-commerce Recommendation Systems Through Advanced Encryption Techniques 15

Enhancing E-commerce Recommendation Systems Through Advanced...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Ghosh, Shithan Mondal, Sanjukta Laha, Arnab Mondal, Bishakha Bandyopadhyay, Atri Chakraborty, Prasun Kiit University School of Electronics Engineering Bhubaneswar India

ISBN: (纸本)9798350370249

In the domain of e-commerce recommendation systems, our research introduces an innovative strategy by incorporating secure encryption techniques. This approach aims to protect user privacy and data security while enhancing recommendation accuracy. We strive to find a delicate balance between ensuring data protection and delivering personalized user experiences in the e-commerce sphere. Through rigorous experimentation and thorough analysis, we assess how different encryption methods impact system performance, providing valuable insights for both industry practitioners and researchers. Our study not only illuminates the complex interplay between security measures and system efficiency but also serves as a foundation for advancing secure recommender systems, enabling stakeholders to navigate the intricacies of ecommerce while maintaining the utmost standards of user privacy and data security. © 2024 ieee.

关键词： data security E-commerce Recommendation systems Secure encryption User Privacy

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cognitive Vision as an Emerging Tool in Autonomous Security Systems 15

Cognitive Vision as an Emerging Tool in Autonomous Security ...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kumar, Ranjan Seth, Jyoti Venkadeshwaran, K. Department of Law ARKA JAIN University Jharkhand Jamshedpur India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Karnataka Bangalore India

ISBN: (纸本)9798350370249

cognitive vision is a rising utility of synthetic intelligence that permits machines to look and interpret pictures and movies like human beings do. Its miles proving to be a useful device in increasing the protection and protection of self-sustaining structures, particularly within the developing subject of self-sustaining security structures. This era has the potential to provide proactive protection in opposition to malicious activity with dependable accuracy and substantially reduce the want for human intervention. cognitive imaginative and prescient takes benefit of a variety of advanced algorithms and technologies, which includes system getting to know, 3-D mapping, deep getting to know, item recognition, and photo processing. This aggregate of technologies lets in for the real-time analysis of visual data, permitting self-reliant systems to fast discover and react to threats. The era may be used to discover and track shifting items, stumble on anomalies or suspicious behavior, or even recognizing people or objects. cognitive vision is specifically precious in independent security systems as it can perform non-stop historical past tracking and evaluation of visual information. This allows for the detection of malicious hobby before it escalates, which could save you unsafe situations before they even arise. This permits independent structures to mitigate safety threats whilst additionally taking into account extra autonomy and versatility in protection operations. ©2024 ieee.

关键词： Aggregate Escalates Independent Malicious Mitigate Reduce Security Versatility

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A Comparative Study of complex Query Performance on MySQL and Oracle databases for the Oil and Gas Industry 9

A Comparative Study of Complex Query Performance on MySQL an...

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9th international conference on Information Management and Technology, ICIMTech 2024

作者： Yaory, Ellyz Manuaba, Ida Bagus Kerthyayana School of Computing and Creative Arts Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

data has become an essential component in decision- making across various industries including the oil and gas sector. Before utilizing and generating data into insights, data storage and accessibility are necessary. databases which are commonly used in the oil and gas sector are MySQL and Oracle databases due to the requirement for standardized and uniform data storage. The major challenge in this industry is implementing and executing complex queries efficiently because of the volume and data complexity involved. Hence, the purpose of this study is evaluating whether the execution of complex queries on MySQL and Oracle databases for the oil and gas scenario is efficient. To reach this goal, we utilized Docker to build up the database environment. Afterwards, dummy data was generated, and a complex SQL query was formulated based on the schema. Then, the testing scenarios, variables, and analysis methods were planned. In this study, MySQL database has a better execution time and CPU time performance compared to Oracle database for all the tested scenarios. T-test is one of the analysis methods used to find out whether the performance is significantly different, and after analyzing, there is a significant difference. The results imply that MySQL database is quicker in execution time and CPU time than Oracle database. The aim of this study is to highlight the importance of selecting a database in the oil and gas industry. MySQL has a faster execution time and CPU time performance compared to Oracle database. © 2024 ieee.

关键词： Gas industry

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A Survey of Major Cybersecurity Compliance Frameworks 10

A Survey of Major Cybersecurity Compliance Frameworks

引用

10th ieee conference on Big data Security on Cloud, BigdataSecurity 2024

作者： Wang, Wenjia Sadjadi, Seyed Masoud Rishe, Naphtali Florida International University Knight Foundation School of Computing and Information Sciences Miami United States

ISBN: (纸本)9798350389524

Motivated by the challenge of navigating the complex landscape of cybersecurity compliance, this study critically examines and evaluates seven major cybersecurity frameworks: SOC 2, GDPR, PCI DSS, HIPAA, CIS Controls V8, NIST CSF, and CMMC 2.0. Our research focuses on understanding their distinct features and operational nuances, addressing a significant gap in current compliance strategies. We contribute a novel set of risk management-based evaluation criteria, offering a comprehensive analysis of these frameworks. The study further explores the Secure Controls Framework (SCF) and its effective integration with these frameworks, summarizing a unified mapping approach. This mapping facilitates streamlined compliance across multiple standards, providing a strategic tool for organizations. Our findings offer pivotal insights into the efficacy of each framework in managing cybersecurity risks, underlining the necessity for an integrated, risk-focused approach to compliance in the digital era. © 2024 ieee.

关键词： Mapping

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Enhanced data Analysis by Natural Language Query Processing 5

Enhanced Data Analysis by Natural Language Query Processing

引用

5th ieee international conference on Communication, computing and Industry 6.0, C2I6 2024

作者： Sumana, M. Kalyani, Anagha Gupta, Anushka Deepa, C. James, Diya Ramaiah Institute of Technology Dept. of Ise Bangalore India

ISBN: (纸本)9798350379884

This project aims to empower non-technical users in conducting data analysis by enabling effortless data retrieval through natural language queries. These users often lack expertise in querying languages but require access to database-stored data. Traditional methods like SQL query templates and slot-filling techniques have limitations in coverage and understanding complex queries. Models relying on LSTM or BERT may require internet connectivity, limiting offline usability. To address these challenges, transformer-based models, specifically the CodeT5 transformer, were adopted. Unlike previous methods, CodeT5 excels in understanding and generating complex queries accurately, operating offline, and enhancing accessibility. By utilizing CodeT5 and implementing natural language processing techniques for query interpretation, significant improvements were made in the user experience in data analysis tasks. The system generates Pandas queries in Python, integrating seamlessly with Python-based data analysis workflows and supporting multiple data formats for enhanced flexibility. The system streamlines data analysis workflows and increases productivity. Users can upload data in various formats, including csv and txt, and export query results for further analysis or reporting. Its scalability allows adaptation to different databases and querying languages, such as MongoDB or MySQL, catering to diverse applications. The project demonstrated significant improvements in data analysis efficiency and acessibility. The system's ability to generate and execute queries automatically reduces dependency on technical expertise, empowering users to extract insights from data effortlessly. With its scalability, cross-platform compatibility, and offline functionality, the system represents a significant advancement in democratizing data analysis. © 2024 ieee.

关键词： Structured Query Language

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Satellite data Management Using Cloud computing in Traffic Applications 6

Satellite Data Management Using Cloud Computing in Traffic A...

引用

6th Parul international conference on Engineering and Technology, PICET 2024

作者： Bedi, Harpreet Singh Mushtaq, Faiza Shukla, M.K. Bali, Jyoti School of Electronics and Electrical Engg Lovely Professional University Phagwara India Pune India School of Computing MIT Vishwaprayag University Solapur India

ISBN: (纸本)9798350369748

Managing traffic data involves strategies to keep roads running smoothly, improve safety, and make transportation more dependable. How traffic systems work and how well they perform are important for a country's economy. This research explores how cloud computing and satellite data can help in giving real-time traffic information. Satellite data has become a valuable way to get this information. However, dealing with a massive of satellite data can be complex and needs a lot of computing power and storage space. Cloud computing is a good way to handle this. Machine learning techniques have been used here to quickly deliver real-time data results, thereby, increasing the precision of traffic detection. Advanced image processing methods are also used to enhance resilience, decrease noise, improve edge detection, and eliminate unused edges from traffic patterns. This also means the data takes up less space and can be handled faster. The main goal of this study is to make traffic data more accurate and to help plan better roads for the country. The research highlights the potential of machine learning in enhancing the efficiency and effectiveness of traffic management systems that rely on satellite data by providing quick real-time data outcomes. © 2024 ieee.

关键词： data centers

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High-Order Resting-State Functional Connectivity is Predictive of Working Memory Decline After Brain Tumor Resection 46

High-Order Resting-State Functional Connectivity is Predicti...

引用

46th Annual international conference of the ieee Engineering in Medicine and Biology Society, EMBC 2024

作者： Tran, Triet M. Tran, Thi T. Khanmohammadi, Sina University of Oklahoma Data Science And Analytics Institute NormanOK73109 United States University of Oklahoma School of Computer Science NormanOK73109 United States

ISBN: (纸本)9798350371499

Surgical resection is one of the main treatment options for brain tumors. However, there is a risk of postoperative cognitive deterioration associated with resective surgery. Recent studies suggest that pre-surgery brain dynamics captured using functional Magnetic Resonance Imaging (fMRI) could provide valuable information about the risk of post-surgery cognitive decline. However, most of these studies are based on simple regression analysis of the raw fMRI signals that do not capture the underlying complex brain dynamics. Here, we investigated the role of higher-order functional brain networks in predicting cognitive decline after surgical resection of brain tumors. More specifically, we looked at the predictive power of second-order functional brain networks in estimating post-surgery working memory (WM) performance. Our results show that the second-order functional brain networks can accurately predict the working memory decline in patients with glioma and meningioma tumors. These findings suggest that there is an interesting relationship between pre-surgical higher-order brain dynamics and the risk of cognitive decline after surgery, which could potentially yield a better prognostic marker for treatment planning of brain tumor patients. © 2024 ieee.

关键词： Transplantation (surgical)

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Multimodal Steganography: A Comparative Analysis of LSB and DCT Methods for Image and Audio data Concealment 9

Multimodal Steganography: A Comparative Analysis of LSB and ...

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9th ieee international conference for Convergence in Technology, I2CT 2024

作者： Dharani, Bathula Achutha Yashaswini, Bhoomireddy Shyashyankha Reddy, Garimi Reddy Rajagopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

Multimodal steganography is a technique that involves concealing information in multiple types of media to enhance data-hiding capabilities. In this proposed work, exploration and comparison of two distinct methods for multimodal steganography are performed: the LSB method and the DCT method for the modalities. The LSB method is straightforward to implement and has a high data capacity, but it is susceptible to detection and quality degradation. In contrast, the DCT method, while more complex, offers better robustness and perceptual quality, particularly for larger data payloads. The comparison considers factors such as capacity, robustness, perceptual quality, and implementation complexity. The findings suggest that the choice between the two methods depends on specific application requirements. LSB may be suitable for scenarios prioritizing higher data capacity, while DCT is recommended for applications requiring enhanced robustness and perceptual quality. The abstract concludes by proposing the potential use of a hybrid approach for optimal trade-offs between capacity and security. © 2024 ieee.

关键词： Steganography

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Federated Learning for Alzheimer's Diagnosis: Leveraging MRI data 10

Federated Learning for Alzheimer's Diagnosis: Leveraging MRI...

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10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Roshan, J. Anitha, J. Sujitha Juliet, D. Jebapriya, S. Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

One common neurodegenerative illness that poses a serious threat to public health is Alzheimer's disease. Individuals with Alzheimer's disease manifest distinct cognitive patterns that can be identified for accurate diagnosis. Currently, diagnosis often relies on manual observations by healthcare professionals. This work trains a deep learning model on Magnetic Resonance Images (MRI) with a federated learning (FL) framework to classify the different Alzheimer's diseases. While maintaining data privacy, collaborative model training across several data sources is made possible by utilizing a federated learning approach. By aggregating insights from diverse MRI datasets, this approach strengthens the robustness and extension of deep learning models developed especially for diagnosing Alzheimer's illness. This work analyses models like Convolutional neural network (CNN), VGG16, InceptionResNetV2, and MobileNetV2 in a federated learning framework. The CNN model outperforms the other models in terms of accuracy of 81%. © 2024 ieee.

关键词： Federated learning

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