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Malicious Query Detection Using Lexical Analyzer and Machine Learning 3

Malicious Query Detection Using Lexical Analyzer and Machine...

引用

3rd International Conference on electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Prahalya, S.P. Samhitha, Sankepalli Sai Ratan, Vadapalli Sanjana Belwal, Meena Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350369083

SQL injection attacks continue to pose a serious threat in web security that calls for methods of creative solving. With database systems becoming more dynamic and complex, threat landscapes continuing to change, old techniques often do not work well. To address this issue, the study proposes a framework that involves combining machine learning techniques with a custom lexical analyser to predict normal and abnormal SQL queries that target MySQL data tables. This study uses six machine learning classifiers-Support vector machines, Logistic Regression, Linear Regression, Naïve Bayes, K-Nearest Neighbors and Random Forest for accurately telling apart malicious and non-malicious queries and to identify potential risk of SQL injection, contributing to overall web security. © 2024 IEEE.

关键词： Adversarial machine learning

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An Extensive Analysis of ML Techniques for Predicting and Analysing Medical Data 5

An Extensive Analysis of ML Techniques for Predicting and An...

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Rajan, Akshay Manoj, Milan Santhosh, Gouri Sarath, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350379945

The study aims to examine the effectiveness of three machine learning algorithms: Random Forest, Support Vector Machine (SVM), and Logistic Regression, navigating through the complex terrain of medical-related datasets. Through an in-depth analysis of three major datasets including demographics, health indicators, and assessments of Parkinson's disease, this study reveals the subtle advantages and disadvantages of each algorithm. Whereas Random Forest has unmatched flexibility in figuring out complex correlations, Logistic Regression stands out as an example of simplicity and openness. SVM struggles with computing loads and parameter complexities at the same time. This thorough investigation avoids claiming a model is supe-rior, allowing practitioners to identify the complex interactions amongst machine learning giants when it comes to healthcare datasets. © 2024 IEEE.

关键词： Contrastive Learning

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Automated Short Answer Grading With Word Embedding-Based Semantic Similarity Using PySpark 6

Automated Short Answer Grading With Word Embedding-Based Sem...

引用

6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Akhilesh, P. Amal Krishna, K. Bharadwaj, S Karthick Venugopalan, Manju Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350381689

Automated short answer grading, a pivotal advancement in educational assessment methodologies, addresses scalability challenges and streamlines evaluation processes using natural language processing and machine learning. This technology ensures timely, consistent, and objective feedback, replacing manual grading to mitigate biases and variations, thus enhancing the educational experience. The proposed model employs Word2Vec to generate word embeddings for each student response and the corresponding reference answer. By leveraging cosine similarity, the model calculates semantic similarity between the vectors, producing a score ranging from 0 to 1. This cosine similarity score is then scalable, allowing for the computation of final grades based on question-specific weightage, providing a robust and scalable approach for automated short-answer grading. In the context of this work, regression-based evaluation metrics yielded a Mean Squared Error (MSE) of 0.2727 & R2 score of 0.67, demonstrating the efficacy of the automated grading system in achieving accurate and reliable assessments. © 2024 IEEE.

关键词： Semantics

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Game Therapy for Specially-Abled Individuals with PPO Reinforcement Learning in VR-based Educational Games 5

Game Therapy for Specially-Abled Individuals with PPO Reinfo...

引用

5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Joypriyanka, M. Surendran, R. Sathish Kumar, P.J. Sivasangari, A. Saveetha Institute of Medical and Techinical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Sathyabama Institue of Science and Technology School of Computing Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

This research study presents a new application of reinforcement learning (RL), specifically Proximal Policy Optimization (PPO), in creating immersive virtual reality (VR) games aimed at aiding skill acquisition and emotional regulation for individuals with anger management difficulties. People with Down syndrome, Autism Spectrum Disorder, Cerebral Palsy (CP), Intellectual Disabilities (ID), and Stroke Survivors are mainly considered. The study examines how PPO-trained agents improve cognitive, social, and emotional regulation. Techniques include thorough hyperparameter optimization for the PPO algorithm with two sets. Iterative agent training in Unity and AWS Sagemaker uses cloud computing resources for parallelized training. Emotional regulation and positive behaviour change are assessed empirically and through user studies. Virtual reality (VR) cooperative RL games may offer comprehensive treatments for anger management, according to the results, which show promising gains in emotional regulation and teamwork. By enhancing interest and effectiveness in satisfying emotional regulation demands, the study demonstrates that AI-driven therapies and "gamified therapy,"which mixes game components into therapy, can revolutionize mental health. © 2024 IEEE.

关键词： Reinforcement learning

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A Hybrid Deep Learning Model For Emotion Recognition For Discrete Speech 15

A Hybrid Deep Learning Model For Emotion Recognition For Dis...

引用

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

作者： Sohel, Mohammad Aman Madhukar, Raghupatruni Sai Oyyavuru, Puneeth Kumar Rahul, Sriramoju Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370249

Human emotion recognition is recognized as essential for effective communication, understanding development, and developing meaningful connections. Its importance depends on assisting individuals in recognizing social signs, interpreting varied situations, and fostering psychological well-being. Furthermore, it helps with decision-making, conflict resolution, and general community cohesiveness. Recognizing human emotions in speech is especially important for supporting effective communication, raising awareness, and understanding complex nuances in interpersonal dynamics. This proficiency not only improves mental processes, but it also encourages deeper relationships and more compassionate reactions. The model is based on supervised deep learning principles and was particularly designed for emotion identification using discrete inputs. It employs data augmentation techniques to resolve imbalances in lower instance classes and experimentation using multiple deep learning models implemented that include CNN, LSTM and CLSTM. CLSTM Model reported the best performance with an F1-score of 0.77. © 2024 IEEE.

关键词： Deep learning Model discrete speech emotion detection human-machine interactions RAVDESS dataset voice tone

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Speech and Text-based Depression Detection On Social Media For Early Intervention 5

Speech and Text-based Depression Detection On Social Media F...

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

作者： Reddy, Gona Sai Tejeshwar Chandan, Kalisetty Hari Kiran, Karri Venkat Jaswanth, Allu Bhaskar, Jasmine Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350376685

Depression is a significant mental health and SDG3 issue with far-reaching social and economic impacts. While early detection and intervention are crucial, traditional screening methods are often not accessible or accurate. This paper presents a method for detecting depression through a combination of voice and text analysis. By using log mel spectrograms and convolutional neural networks (CNNs) for voice analysis, and bi-directional long short-term memory (BiLSTM) networks for text analysis, the model improves detection accuracy and provides insights into mental health and well-being. The model structure, data collection, processing techniques, and training processes for both CNN and Bi-LSTM models are detailed. This study employs the "Suicide and Depression Detection"dataset from Kaggle for text and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) for voice. A user interface is developed to ensure system accessibility. The experimental results demonstrate the high accuracy of the proposed approach for depression detection. This research highlights the importance of an accessible interface that combines multiple analysis methods and the need for interdisciplinary collaboration to address complex societal and mental health issues. © 2024 IEEE.

关键词： Convolutional neural networks

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Unleashing power of Vision Transformers for disease prediction in Chest X ray images 15

Unleashing power of Vision Transformers for disease predicti...

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

作者： Chowdary, Chava Sriya Ruthwik, Dovala Sai, Kancharla Nititsh Singh, Rimjhim Padam Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

This work develops and evaluates a deep learning model to properly categorise chest X-ray pictures into three categories: normal, pneumonia, and COVID-19. The main idea is to help physicians diagnose illnesses at an early stage and provide accurate treatment, which eventually leads to critical patient care and enhanced healthcare system efficiency. Hence, this research work makes use of various state-of-the-art architectures like Vision Transformers, InceptionNetV3, EfficientNetV2, MobileNetV2, NasnetMobile, ConvNext and DenseNet169, to fully evaluate their performance on the dataset of Chest-Xray-14. These models have shown good results and their usefulness in detecting chronic chest conditions thereby, proving their efficient applicability in the medical field. This study has great reported efficient results with Vision Transformers clearly to be the best model for diagnosing chest diseases like Pneumonia and COVID-19 speedily and effectively. © 2024 IEEE.

关键词： ConvNext COVID-19 DenseNet169 EfficientNetV2 InceptionNetV3 MobileNetV2 NasNetMobile Pneumonia Vision Transformers

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Pan-Sharpening Using Fusion of 2D-DFRFT, 2D-DWT, and CNN

Pan-Sharpening Using Fusion of 2D-DFRFT, 2D-DWT, and CNN

引用

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Varma, Gadiraju Hemanth Sai Phaniveer, Gudivada Lohit Sai Srivallabh, Tippa Pramodh, Tripuramallu Nidhin Prabhakar, T.V. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331517953

In recent years, the demand for high-resolution remote sensing imaging has increased in a variety of fields, including environmental monitoring, urban planning, agriculture, and disaster management. Multispectral imaging, which gathers information at several wavelengths, can provide useful insights for such applications. However, the spatial resolution of multi- spectral images frequently falls short of the necessary criteria for thorough analysis. Pansharpening approaches overcome this issue by combining high-resolution panchromatic (PAN) images with lowerresolution multispectral data, thereby improving spa- tial details while keeping spectral features. In this article, we present a unique pan-sharpening technique that makes use of the synergies between the two-dimensional discrete fractional Fourier transform (2D-DFRFT), the twodimensional discrete wavelet transform (2D-DWT), and convolutional neural network (CNN) models. By combining these complex mathematical transforms with deep learning approaches, we provide a holistic solution for improving both spectral and spatial resolution. Our method's main innovation is its multi-stage procedure, which starts with converting the PAN and multispectral images using 2DDFRFT and 2D-DWT, then extracts features using a CNN model. Subse- quent high-pass filtering of the converted images refines spatial information, allowing for a more precise fusion process. The inclusion of angle parameters associated with both 2D-DFRFT and 2D-DWT provides an additional degree of freedom, allowing for finer control over the fusion process and producing better results. We test the performance of our proposed pan-sharpening system using real-world satellite pictures from IKONOS and GeoEye-1 via extensive experiments in MATLAB. © 2024 IEEE.

关键词： High pass filters

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A decentralized organ donation registry enabling transparency in medical field 15

A decentralized organ donation registry enabling transparenc...

引用

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

作者： Gonuguntla, Surya Reddy, M. Aditya Vardhan Sridhar, Vaidehi Saketh, Challagundla Neethu, M.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

Recent advancements in technology have resulted in the creation of novel approaches for administering organ donation systems, with an objective of overcoming the restrictions of traditional centrally controlled systems. Blockchain technology is a potential replacement to the existing centralised methods for better transparency, security, and efficiency in organ donation. Previous research on organ donation systems mostly employed centralized database solutions, which have limited interoperability, a predefined set of protocols, and do not permit distributed control due to lack of security, transparency and accountability. A further significant component of the transplantation procedure is the matching of organ donor/recipient based on compatibility. Despite traditional centralised database solutions, blockchain promotes decentralized control, improving interoperability and accountability. Crucially, it facilitates the critical donor-recipient matching process, improving compatibility and possibly revolutionising transplantation operations. © 2024 IEEE.

关键词： Blockchain Technology Data Privacy Decentralization Organ Donation System Security Transparency

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Application of Generative Adversarial Network for Data Augmentation in Diabetic Retinopathy 15

Application of Generative Adversarial Network for Data Augme...

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

作者： Kumar, Anuj Singh, Rimjhim Padam Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Image generation through generative adversarial network (GAN) has been a well-studied problem. It has been specifically implemented in medical domain for a number of use cases but training GAN has its own challenges. Application of GAN on generation of eye fundus images for the disease diabetic retinopathy (DR) has not been explored in depth. This paper shows a novel approach to generate low resolution eye fundus images for DR using GAN and validate it though a CNN classifier using samplers and schedulers module. The proposed approach beats the current benchmark in terms of generated image quality as well as classification accuracy. The low-quality generated images of 224∗224 pixels manages to retain the key aspects of DR which aids in identifying the DR grade during classification predominantly from generated images only. © 2024 IEEE.

关键词： Generative adversarial networks

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