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检索条件"机构=Computer Science and Engineering Acropolis Institute of Technology and Research Indore"

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Transfer Learning for Multiclass Classification of Wheat Leaf Diseases

Transfer Learning for Multiclass Classification of Wheat Lea...

引用

2024 International Conference on Advances in Computing research on science engineering and technology, ACROSET 2024

作者： Verma, Riya Singh Rathore, Narendra Pal Sethi, Kamal Kumar Acropolis Institute of Technology and Research Artificial Intelligence and Data Science Indore India Acropolis Institute of Technology and Research Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

In the following paper, we explore a study focusing on the detection of wheat leaf diseases using transfer learning techniques combined with cutting-edge convolutional neural network (CNN) architectures, namely VGG16, ResNet152V2, and Inception. The aim is to enhance the accuracy of disease detection while minimizing computational resources and training time. We conducted experiments on a dataset consisting of wheat leaf images affected with several diseases. Through transfer learning, we fine-tuned pre-trained models on our dataset to leverage their learned features. Our results demonstrate that VGG16 consistently outperforms ResNet152V2 and Inception, achieving the highest accuracy of 98.66% in disease detection. This indicates the efficacy of VGG16 for wheat leaf disease classification tasks, highlighting its potential for agricultural applications requiring robust and accurate detection of plant diseases. © 2024 IEEE.

关键词： Adversarial machine learning

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Glaucoma Detection Using Convolutional Neural Network

Glaucoma Detection Using Convolutional Neural Network

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2024 International Conference on Advances in Computing research on science engineering and technology, ACROSET 2024

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

ISBN: (纸本)9798350388800

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. © 2024 IEEE.

关键词： Convolutional neural networks

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Advancements and Challenges in Federated Learning : A Survey

Advancements and Challenges in Federated Learning : A Survey

引用

2024 International Conference on Advances in Computing research on science engineering and technology, ACROSET 2024

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

ISBN: (纸本)9798350388800

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. © 2024 IEEE.

关键词： Federated learning

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Differential Privacy in Federated Learning Using Noise Multipliers: An Analysis on MNIST Dataset 4

Differential Privacy in Federated Learning Using Noise Multi...

引用

4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

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

ISBN: (纸本)9798331518981

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. © 2024 IEEE.

关键词： Federated learning

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Evaluation of Classifiers Based on Neural Network for Handwritten Digit Recognition

Evaluation of Classifiers Based on Neural Network for Handwr...

引用

2024 International Conference on Advances in Computing research on science engineering and technology, ACROSET 2024

作者： Sehta, Neha Saraf, Krupi Vinchi, Aditya Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Handwritten Digit Recognition (HDR) refers to the computer' s ability to identify and categorize hand written digits from various sources into 10 predetermined categories, ranging from 0 to 9. The issue of digit recognition is significant and crucial. Due to variations in size, thickness, location, and orientation of manually written digits, several challenges need to be addressed in order to evaluate the problem of recognizing handwritten digits. The diversity and variety in the composition techniques of different persons also impact the pattern and appearance of the digits. It refers to the approach of recognizing and organizing written numbers. It offers a diverse array of uses, such as automated bank checks, postal addresses, and tax paperwork, among others. The objective of this research is to employ a classification system to accurately identify handwritten numerals. The consequences of the most widely employed deep learning algorithms, such as multilayered perceptron network, in conjunction with convolution neural networks, are being discussed. Comparison of five neural network classifier shows that accuracy up to 99% can be achieved only in few epochs of training with default configuration of hyper-parameters. © 2024 IEEE.

关键词： Deep learning

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Privacy Preserving Techniques in Data science : A Review

Privacy Preserving Techniques in Data Science : A Review

引用

2024 International Conference on Advances in Computing research on science engineering and technology, ACROSET 2024

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

ISBN: (纸本)9798350388800

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. © 2024 IEEE.

关键词： Anonymity

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

Demystifying XAI: Understanding of Applications, Limitations...

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2024 International Conference on Advances in Computing research on science engineering and technology, ACROSET 2024

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

ISBN: (纸本)9798350388800

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. © 2024 IEEE.

关键词： Decentralized finance

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Logistic Regression Based Approach for Human Sentiment Analysis Across Domains

Logistic Regression Based Approach for Human Sentiment Analy...

引用

2024 International Conference on Advances in Computing research on science engineering and technology, ACROSET 2024

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

ISBN: (纸本)9798350388800

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. © 2024 IEEE.

关键词： Logistic regression

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Evolutionary Optimization for Biometric Feature Identification and Recognition

Evolutionary Optimization for Biometric Feature Identificati...

引用

2024 International Conference on Advances in Computing research on science engineering and technology, ACROSET 2024

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

ISBN: (纸本)9798350388800

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. © 2024 IEEE.

关键词： Contrastive Learning

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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...

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2024 International Conference on Advances in Computing research on science engineering and technology, ACROSET 2024

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

ISBN: (纸本)9798350388800

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. © 2024 IEEE.

关键词： Automation

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