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Logistic Regression Based Approach for Human Sentiment Analysis Across Domains

Logistic Regression Based Approach for Human Sentiment Analy...

引用

Advances in Computing research on science engineering and technology (ACROSET), International Conference on

作者： Sameer Nagar Praveen Bhanodia Kamal Kumar Sethi Narendra Pal Singh Rathore Department of Computer Science and Engineering Acropolis Institute of Technology and Research Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

This research paper presents a pioneering approach to cross-domain sentiment analysis utilizing logistic regression, a widely employed technique for binary classification tasks. Sentiment analysis, crucial for understanding subjective information in text data, finds applications in market analysis, customer feedback processing, and brand management. Our methodology involves extracting sentiment features from text data, leveraging techniques such as bag-of-words or word embeddings, and training logistic regression models on labeled datasets from diverse domains. Unlike conventional methods relying solely on domain-specific features, our approach incorporates domain-independent features to facilitate knowledge transfer across domains. Through extensive experiments on various datasets including product reviews, social media posts, and news articles, we demonstrate the effectiveness of our approach in achieving competitive performance compared to state-of-the-art methods. Additionally, we explore the impact of domain adaptation techniques such as domain adversarial training and transfer learning on model performance, providing insights into the interpretability of sentiment models and enabling more accurate sentiment prediction in real-world scenarios across diverse domains.

关键词： Training Logistic regression Sentiment analysis Adaptation models Analytical models Computational modeling Transfer learning Predictive models Feature extraction Robustness

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Evolutionary Optimization for Biometric Feature Identification and Recognition

Evolutionary Optimization for Biometric Feature Identificati...

引用

Advances in Computing research on science engineering and technology (ACROSET), International Conference on

作者： Shraddha Verma Praveen Kumar Bhanodia Kamal Kumar Sethi Narendra Pal Singh Rathore Shiv Shankar Rajput Anita Mahajan Department of Computer Science and Engineering Acropolis Institute Of Technology And Research Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

Performance variability is a major difficulty for multimodal biometric systems, affecting their consistency and dependability in person identification. In order to solve this issue, the use of ensemble learning techniques is suggested in this work. The predictions of several classifiers are combined in ensemble learning to increase system consistency and performance as a whole. The unpredictability inherent in multimodal biometric systems is lessened by ensemble approaches, which integrate many classifiers trained on various subsets of data. By producing identification results that are more reliable and stable under many circumstances, this method improves system reliability. Improved accuracy and consistency can be achieved by integrating complementing data from many biometric modalities using ensemble learning techniques including bagging, boosting, and stacking. In practical applications, the suggested method shows promise in reducing performance variability and improving the dependability of multimodal biometric systems.

关键词： Biometrics Accuracy Biological system modeling Stacking Boosting Teamwork Ensemble learning Reliability Bagging Optimization

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Privacy Preserving Techniques in Data science : A Review

Privacy Preserving Techniques in Data Science : A Review

引用

Advances in Computing research on science engineering and technology (ACROSET), International Conference on

作者： Yuvraj Panchal Rashid Sheikh Kamal Kumar Sethi Praveen Kumar Bhanodia Shiv Shankar Rajput Narendra Pal Singh Rathore Department of Computer Science And Engineering Acropolis Institute Of Technology And Research Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

In the field of data science, privacy-preserving methods are essential to guaranteeing the moral and secure use of data. This extensive overview study offers an in-depth examination of privacy-preserving approaches, including federated learning, homomorphic encryption, secure multi-party computation (SMPC), anonymization strategies, and differential privacy. This paper explains the theoretical underpinnings, real-world applications, difficulties, and future possibilities of privacy-preserving strategies in data science. The study explores anonymization strategies such generalization, suppression, k-anonymity, l-diversity, and t-closeness after providing an outline of privacy risks in data science. The concepts and uses of federated learning, homomorphic encryption, SMPC, and differential privacy are then examined, emphasizing how these techniques protect sensitive data and promote teamwork in analysis. Examples from the fields of healthcare, banking, telecommunications, social media, and government are shown to show how useful privacy-preserving techniques are in the real world. We hope to give scholars, practitioners, and policymakers a thorough knowledge of privacy-preserving strategies and their implications for ethical and long-term data-driven decision-making through this extensive review.

关键词： Surveys Ethics Technological innovation Privacy Reviews Decision making Data science Information filtering Homomorphic encryption Information integrity

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Design Thinking and Innovative Tool Use in Business Model

Design Thinking and Innovative Tool Use in Business Model

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Singhi, Nitika Verma, Ankush Agrawal, Atul Khan, Irfan Computer Science Engineering Shri Vaishnav Vidhyapeeth Vishwavidyalava Indore India Data Analytics and Information System Prestige Institute of Management and Research Indore India Computer Science Engineering Sushila Devi Bansal College of Technology Indore India Computer Science Engineering Bansal Institute of Science and Technology Bhopal India

ISBN: (纸本)9798350375237

In past few year designs thinking has worked on several new effective problem and has generated innovative solution for new business model. For the scenario of today's demand, to better understand how design thinking can work on different perspectives in business models and how it can bring new innovative solutions for any problem is essential However, studies of existing literature explore the application, potential, and challenges of Design Thinking towards the comprehensive aim of this paper, which regards business model innovation as a fundamental subject of Design Thinking. The paper provides practical tools for exploring Design Thinking along with various critical aspects for the researchers to explore how Design Thinking can be applied to innovative business models. The main focus of this paper is to explain the basics of Design Thinking, how to frame the mind through different techniques used, the Design Techniques used for a business model, the methodology of a design thinker, and different tools like BPM, Blue Work Live, Mind Mapping, Brainstorming, and RICPS. © 2024 IEEE.

关键词： Mapping

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Advancements and Challenges in Federated Learning : A Survey

Advancements and Challenges in Federated Learning : A Survey

引用

Advances in Computing research on science engineering and technology (ACROSET), International Conference on

作者： Vivek Prajapat Narendra Pal Singh Rathore Kamal Kumar Sethi Shiv Shankar Rajput Department of Computer Science and Engineering Acropolis Institute of Technology and Research Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

Machine Learning (ML) has significantly impacted daily life by automating tasks and enhancing decision-making across diverse sectors such as healthcare, finance, and transportation. However, concerns regarding data privacy, particularly in sensitive domains like healthcare and finance, have impeded its widespread adoption. Federated Learning (FL), pioneered by Google in 2016, presents a promising solution by enabling devices to collaborate on model training without sharing raw data. In FL, each device retains its data locally, and only model updates are exchanged with a central server for aggregation. This decentralized approach preserves data privacy while still improving model performance through collaborative learning. FL has garnered interest across industries, with approximately 32% of companies intending to integrate it into their systems soon. Furthermore, investment in FL is projected to rise substantially from $107 million in 2020 to $538 million by 2025, as indicated by forecasts from KPMG. This paper provides an overview of FL, its applications across various sectors, current adoption trends, and future growth prospects, highlighting its significance in addressing data privacy concerns while advancing machine learning capabilities.

关键词： Training Surveys Performance evaluation Federated learning Finance Transportation Medical services Market research Data models Servers

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Differential Privacy in Federated Learning Using Noise Multipliers: An Analysis on MNIST Dataset

Differential Privacy in Federated Learning Using Noise Multi...

引用

International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Yuvraj Panchal Rashid Sheikh Kamal Kumar Sethi Department of Computer Science and Engineering Acropolis Institute of Technology and Research Indore India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Federated learning enables decentralized learning to run on multiple clients by training locally on each client’s data and sharing new instances only on a central server. However, if privacy is not protected, sensitive information about any customer can be accessed via model updates. Differential privacy (DP) provides a robust method of preserving data privacy by combining controlled noise with gradient updates sent to a central server This paper explores the application of differential privacy to federated learning using different noise factors on the MNIST dataset. We investigate the trade-off between accuracy and privacy protection by analyzing test loss and accuracy over several communication channels, with noise factors of 0.2, 0.3, and 0.7. The results show that increased noise reduces model accuracy but provides better privacy protection. We also emphasize the importance of balancing accuracy and confidentiality in official learning processes to ensure for example safe yet efficient sampling operations.

关键词： Training Differential privacy Privacy Accuracy Federated learning Noise Government Focusing Servers Protection

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Demystifying XAI: Understanding of Applications, Limitations, Challenges, and Future Perspectives

Demystifying XAI: Understanding of Applications, Limitations...

引用

Advances in Computing research on science engineering and technology (ACROSET), International Conference on

作者： Pratham Varshney Narendra Pal Singh Rathore Kamal Kumar Sethi Shiv Shankar Rajput Department of Computer Science and Engineering Acropolis Institute of Technology and Research Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

Artificial Intelligence (AI) appears to be omnipresent, but its usability is currently a pain point as seen with automation bias. The General Data Protection Regulation (GDPR) has started solving this problem. The premise behind Explainable AI (XAI) is described as making AI decisions interpretable, gaining user trust and robustness of applications in healthcare and finance. This paper gives a survey of recent XAI advancements, providing types of algorithms, and examples of tasks. Despite the concept of XAI, we still have the black-box issue of AI, and especially in critical areas like healthcare with sensitive information, we need to protect user privacy. It is proposed that XAI can expose the decision-making process of the AI, to make the AI decision-making more reliable and trustworthy. Evaluation metrics and potential future directions for XAI are discussed, with a guiding principle: AI needs to be ethical, trustworthy, and user-friendly.

关键词： Surveys Privacy Explainable AI Pain Decision making Medical services Robustness Security General Data Protection Regulation Usability

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Automated Subject Allotment to Faculty in an Academic institute Using Maxflow Problem

Automated Subject Allotment to Faculty in an Academic Instit...

引用

Advances in Computing research on science engineering and technology (ACROSET), International Conference on

作者： Neha Sehta Naman Shah Department of Computer Science and Engineering Acropolis Institute of Technology and Research Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

presently, educational organizations are utilizing manual procedures to assign subjects to faculties depending on their own preferences, which gives rise to prejudice and therefore, discontentment. The best approach would be to allocate the most qualified individual to instruct the appropriate course, taking into account their knowledge and experience. Many instructional units rely on manual trial-and-error methods, which are time-consuming, inefficient, and prone to biased judgment. Moreover, the distribution is likely disadvantageous for several academic personnel. This study introduces an automated allocation of subjects to the faculties, taking into account the preferences in an effective way. The subject allocation problem is mapped into a maxflow problem and solved using Edmonds-Karp algorithm. A satisfaction metric can be computed to evaluate the effectiveness of the solution proposed which is average calculated on the distribution of subject as per the choice of faculty.

关键词： Productivity Measurement Heuristic algorithms Scalability Manuals Organizations Resource management Personnel research and development Testing

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Glaucoma Detection Using Convolutional Neural Network

Glaucoma Detection Using Convolutional Neural Network

引用

Advances in Computing research on science engineering and technology (ACROSET), International Conference on

作者： Divya Khichi Priyanka Jangde Kamal Kumar Sethi Department of Computer Science and Engineering Acropolis Institute of Technology and Research Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

One of the quintessential examples in ophthalmology is the early detection of glaucoma, a disease that accounts for significant irrecoverable vision loss when caught later. Conventional diagnostic methods are based on visual inspection, which may be time-consuming and open to subjective interpretation. We suggest a deep learning method using the Convolutional Neural Network (CNN) model to solve this. With the ORIGA data (a labeled fundus image collection where each has been identified as containing glaucoma), we trained CNN to learn about complex spatial patterns that may discriminate against the disease. We can see a reduction in loss and improvement in accuracy through the graphs; our model is learning well. A confusion matrix with highly accurate favorable rates also shows the effectiveness of CNN, and its predictions individually were accurate. Our CNN model reduces loss by up to 49.3% and improves validation accuracy by nearly 3.97%, which is highly competitive compared to standard logistic regression and machine learning techniques.

关键词： Glaucoma Training Deep learning Logistic regression Visualization Accuracy Computational modeling Convolutional neural networks Standards Diseases

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Accelerating Cloud Deployment Through Containerization Technologies

Accelerating Cloud Deployment Through Containerization Techn...

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Advances in Computing research on science engineering and technology (ACROSET), International Conference on

作者： Aayushya Lakkadwala Prashant Lakkadwala Dept. of Computer Science and Engineering Acropolis Institute of Technology and Research Indore India Dept. of Information Technology Acropolis Institute of Technology and Research Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

This research investigates the effectiveness of containerization technologies, with a focus on Docker, in accelerating cloud application deployments. The study examines diverse methodologies aimed at optimizing container image size, refining container orchestration mechanisms for expedited deployments, and evaluating the integration of serverless functions within containers to reduce deployment durations. By synthesizing existing scholarly literature with empirical analyses, this paper aims to elucidate the efficacy of containerization in streamlining cloud deployment processes. It offers insights into optimal practices and identifies prospective avenues for future research and development in the field.

关键词： Protocols Refining Serverless computing Containers Streaming media Market research Stakeholders Optimization research and development Pragmatics

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