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检索条件"机构=MTech Computer Science and Engineering School of Computing Science and Engineering"

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SQL Query Recommendation Based on Matrix Factorization 4th

SQL Query Recommendation Based on Matrix Factorization

引用

4th International Conference on Innovations in Computational Intelligence and computer Vision, ICICV 2024

作者： Anantatmula, Sri Harshitha Krishnan, Rohith Harikumar, Sandhya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789819769919

The primary objective of this study is to enhance query processing performance through the integration of sophisticated data reduction techniques within databases. Operating within the domain of in-database analytics, the focus is on evaluating efficient methods for traversing extensive data sets, expediting query processing, and deriving valuable insights using decomposition techniques, specifically QR decomposition. This study aims to achieve two primary goals: firstly, the implementation of decomposition techniques such as QR within a relational database using SQL as an operator, and secondly, the utilisation of column pivoting mechanisms on the reduced data for query recommendation. This amalgamation serves as an experimental pilot space for the innovative hypothesis of integrating query recommendation through the matrix factorization algorithm, QR factorization, using SQL. The core motivation behind the in-database approach for the query recommendation lies in the principle that analysing data where it resides is optimal for the most effective analysis. In this context, query recommendation within in-database analytics streamlines the process of data analysis and exploration by suggesting appropriate queries derived from user behaviour. These recommendations are instrumental in improving efficiency, providing relevant answers, and optimising the overall analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Matrix factorization

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Multimodal Lung Cancer Image Analysis 15

Multimodal Lung Cancer Image Analysis

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Pallithodiyil, Akshita Prasanth Srikrishna, Atyam Preetham Sai, Gutha Venkat Paripoorna, D. Sathiya, R.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

This paper addresses the significant global impact of lung cancer, the primary cause of cancer-related deaths worldwide and the second leading cause of mortality following cardiovascular disease. The challenge lies in detecting lung cancer, often diagnosed at an advanced stage, complicating treatment efforts. The proposed model integrates CT (Computed Tomography) and PET (Positron-Emission Tomography) scans to enhance lung cancer prediction. CT scans offer detailed anatomical insights, while PET scans excel in identifying abnormal metabolic activity and precise tumour localization. This combined approach provides a comprehensive assessment, empowering healthcare professionals with improved diagnostic accuracy and better-informed treatment planning for lung cancer patients. © 2024 IEEE.

关键词： classification CNN (Convolutional Neural Network) CT Early Fusion NIfTI (Neuroimaging Informatics Technology Initiative) Non-small Cell Lung Cancer (NSCLC) PET ResNet Structural Similarity Index Measure (SSIM) SVM

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An Investigation of Federated Learning Strategies for Disease Diagnosis 15

An Investigation of Federated Learning Strategies for Diseas...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Das, Abhay Krishnadas, Aishwarya Krishnan, Vaishakh S. Farida, Avani Sarath, Greeshma Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350370249

Traditional healthcare systems utilize centralized approaches for building machine-learning models for disease diagnosis. It requires sharing raw data to the centralized server, which is practically difficult due to patient's privacy. This leads to significant vulnerabilities and challenges. As a result, switching to approaches for scalable and distributed AI at the network edge for privacy-preserving smart healthcare applications is essential. Deep learning models need to train on data, on any nodes, in a privacy-preserving manner, but not give up the distinction of patients. This paper aims to investigate the application of FL in providing health diagnosis while enhancing the patient's data privacy, by adopting the Flower platform. It employs the following four algorithms for federated learning that can facilitate collaborative model training with shared patient data: FedAvg, FedProx, FedAdagrad, and FedAdam. To evaluate FL's effectiveness, deep learning models trained on decentralized COVID-19 and Pneumonia datasets are used from different sources. FL methods were compared to a Centralized Convolutional Neural Network (CNN) to check the performance. The analysis shared in this paper shows that implementing FL contributes to establishing the subject's accurate diagnostics concerning the patient's data protection in the healthcare industry. © 2024 IEEE.

关键词： Adaptive Optimization Methods Federated Learning (FL) Flower (FL framework) Healthcare Diagnostics

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Post-Exploitation Insights: Dynamic Analysis of C2 Frameworks 4

Post-Exploitation Insights: Dynamic Analysis of C2 Framework...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Rao, Subhiksha Suresh Mishra, Sahil Seshadri, Samarth Kavitha, C.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331540364

Recognizing and reducing the risks presented by malevolent actors in the realm of cyber security requires an awareness of post exploitation techniques. However, deThe study investigates post operational challenges through a dynamic analysis of command-and-control frameworks (C2) fenders are finding it difficult to keep up with the rapid development of C2 frameworks. Using the MITRE ATT&CK framework as a guide, this study explores a popular C2 server: Metasploit. Through extensive research, deployment, and exploitation in a controlled environment, the strengths and weaknesses of each framework are assessed. Key metrics such as ease of installation, performance, security features, and community support are carefully researched to provide comprehensive insights. In addition, the project explores future frontiers by identifying potential modules that can enhance the capabilities of the selected C2 framework. By offering a detailed analysis, this study helps to understand the importance of the C2 frameworks in cyber security and provides valuable insights for security practitioners and researchers. © 2024 IEEE.

关键词： Cyber attacks

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SecureComment: Safeguarding Online Discussions with Intelligent Toxic Comment Filtering 2

SecureComment: Safeguarding Online Discussions with Intellig...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Rayani, Reddy Kowshik Tekula, Samhitha Vattigunta, Subhash Kovid Kovi, Naveen Kumar Namitha, Kalakunnath Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350360523

Toxic comments, such as hate speech and abuse, are a widespread issue online, disrupting healthy conversation and user safety. Identifying and filtering these comments is crucial for ensuring respectful online communications. The primary challenge arises from the need to obtain accurate detection while addressing emerging tactics and balancing between false positives and negatives. This concern affects user interactions and platform credibility and raises legal and ethical risks. Therefore, this study examines the effectiveness of numerous machine-learning models and implements an ensemble method for analyzing and classifying toxic comments. We analyze the effectiveness of various algorithms using evaluation metrics like precision, recall, accuracy, and F1-score. Ensemble methods are utilized to combine the capabilities of several models, enhancing the classification of toxic comments. The study aspires to deliver insights into the comparative performance of various machine learning models in classifying toxic comments accurately and the advantages of ensemble methods in enhancing classification accuracy. © 2024 IEEE.

关键词： Classification (of information)

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Code Confabulator: Harnessing LLMs to Compile Code for Visualization 15

Code Confabulator: Harnessing LLMs to Compile Code for Visua...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Rayvanth, Nannapaneni Pranavi, Pasupuleti Suryaa, E. Challa, Venkatahemant Kumar Reddy Belwal, Meena Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Code visualization tools play a core role for visualization of the code and helping novices in understanding, analyzing, and optimization of their programs. Tools such as Algorithm Visualizer and PythonTutor are developed to make algorithm visualization possible across different programming languages like Python, Java, C, and C++. Despite their advantages, these tools suffer from unnecessary data collection, increased time complexity, and limitation in visualizing a few data structures. In our research, we want to introduce a more humanlike view of understanding and solve the shortcomings mentioned above by proposing a framework, which will make use of LLMs for extracting data from the code in Python, Java, C, or C++ to be visualized. In our framework, we have used LLMs instead of the traditional compiler for feasibility and adaptability. Our objective is the appropriateness evaluation of LLMs for this task, starting from freeform prompts. The LLM is asked to generate outputs in a given format;hence, these outputs are taken and converted into visualizations with a rule-based approach. The task will be done to enhance quality visualization and reduce time complexity while allowing the user to view the code at a human level view. We have also publicly released the framework so that people are able to provide further contributions and improvements. © 2024 IEEE.

关键词： Code visualization Large language Models

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Automatic Assessment of Quadratic Equation Solutions Using MathBERT and RoBERTa Embeddings 15

Automatic Assessment of Quadratic Equation Solutions Using M...

引用

15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Rao, Subhiksha Suresh Mishra, Sahil Akhilesh, S. Balakrishnan, Roshni M. Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350370249

This project aims to create a machine learning classification system that classifies solutions to quadratic equations into three different groups: 'Incorrect,' 'Partially Correct,' and 'Correct.' MathBERT and RoBERTa embeddings are utilized in this system. A multi-class classification technique is used by the system, with an emphasis on feature engineering, data preparation, and model selection. To improve model performance, methods like Principal Component Analysis (PCA), LIME (Local Interpretable Model-Agnostic Explanations), and ensemble meta models with Boosting, Voting, and Stacking are used. The efficacy of many classification models in correctly classifying quadratic solutions is examined in this work. To evaluate the performance of the model, evaluation measures including F1-score, accuracy, precision, and recall are used. The results show that MathBERT embeddings outperform RoBERTa embeddings. Ensemble models, especially those that include SVM and MLP classifiers, achieve the best performance, with the stacking approach yielding an accuracy of approximately 65%. This model accurately classifies solutions into the correct categories, demonstrating the potential for practical applications in mathematics education. © 2024 IEEE.

关键词： Automated Evaluation Classification Educational Technology Machine Learning MathBERT Quadratic Equations RoBERTa Solution Accuracy

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Modelling Complex Associations for Image Captioning Using Vision Transformers 9

Modelling Complex Associations for Image Captioning Using Vi...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Ritul, Chavda Sampath, Nalini Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350394474

Captioning an image is the method of writing a plain English the description of an image. This is a difficult assignment since it calls for the model to comprehend the image's information and to produce correct and fluid text. It has been demonstrated that a novel class of neural network called vision transformers is useful for picture captioning. Vision transformers are able to learn long-range dependencies in images, which is essential for generating accurate and fluent captions. In this project, will explore the use of vision transformers for modelling complex associations in images. The work will investigate how vision transformers can be used to generate captions that are more informative and comprehensive. The work will also explore how vision transformers can be used to generate captions that are more creative and *** work is based on Flickr8k Dataset, which is a large dataset of images and captions. The vision transformer model will be trained on this dataset. The model is then assessed using a held-out test set. Compared to conventional picture captioning models, the vision transformer model will be able to provide captions that are more detailed and insightful. It will also be able to generate more creative and engaging captions. This project has implications for the development of new image captioning models. Vision transformers are a promising new approach for image captioning, and they have the potential to generate more accurate, fluent, informative, creative, and engaging captions. © 2024 IEEE.

关键词： computer vision

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Enhanced Heart Disease Prediction Using Grid Search Optimized XGBoost Classifier 3

Enhanced Heart Disease Prediction Using Grid Search Optimize...

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3rd Odisha International Conference on Electrical Power engineering, Communication and computing Technology, ODICON 2024

作者： Babu, Tina Nair, Rekha R Pavithra, K. Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350354379

Since heart disease is still one of the major causes of death worldwide, there has been a lot of study done on early diagnosis and prevention techniques. Thus, an XGBoost classifier tuned for Grid Search is used to carry out a novel prediction. Finding the best characteristics in the dataset is aided by Grid Search optimization. The preprocessed dataset is subjected to suitable feature selection procedures in order to extract the most pertinent predictors. The accuracy, precision, recall, and F1-score are among the performance indicators used to assess the suggested method. Comparing the suggested model to other techniques as the Random Forest model, Decision Tree model, and Multi-Layer Perceptron (MLP), it yielded an accuracy of 97.56% and a Fi_Score of 97.69%. By enabling early identification and preventive actions, this research adds to the ongoing efforts to develop accurate and dependable predictive models for cardiac disease, which in turn improves patient outcomes and lessens the strain on healthcare systems. © 2024 IEEE.

关键词： Cardiology

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Encryption scheme using Mealy Machine 5

Encryption scheme using Mealy Machine

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Ramasubbu, Hemanth Silaparasetty, Sricharan Bhakuni, Mehul Singh Panda, Niharika Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

This novel encryption scheme leverages a Mealy machine for dynamic encryption. To significantly enhance data security, it enforces strong key requirements (with mixed uppercase letters, digits, and special characters). The encryption process involves pre-processing data, creating a character mapping matrix, integrating a Mealy machine based on the matrix sum for additional complexity, generating and validating a strong key, and manipulating the data with the key matrix and Mealy machine outputs. Decryption follows a similar approach to recover the original data, making the system efficient for both encryption and decryption. This work paves the way for a secure and versatile encryption solution. © 2024 IEEE.

关键词： cryptography data security data transmission dynamic encryption Mealy machine Secure communication

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