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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Kantipudi, Revathi Kumar Mallavarapu, Ajay Sai Rajagopal, Shinu M Kagolanu, Madhav Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350327533

In the era of digitalization, authentication is a vital part of modern society's security. Multi-factor authentication (MFA) is a vital solution that offers fast, simple, and trustworthy authentication in a range of scenarios, such as communications, online payments, and access rights management. It is specifically made for human-to-everything interactions. This research study investigates MFA, a constantly evolving field in authentication systems. The conventional password-based method of securing account access is susceptible to a growing number of attacks, including smudge, guessing, and shoulder-surfing. This research validates the need for a complete software solution that is reliable and easy to use, supporting Three Level Security. It addresses the shortcomings of traditional authentication methods as well as the challenges posed by ever- evolving security threats. © 2024 IEEE.

关键词： Authentication

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Post-Exploitation Insights: Dynamic Analysis of C2 Frameworks 4

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

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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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Neural Machine Translation Using a Pivot Approach: Dogri to English 15

Neural Machine Translation Using a Pivot Approach: Dogri to ...

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

作者： Nair, Aarathi Rajagopalan Sheshadri, Shailashree K. Dhanush, A. Pradyumna, N.V.S. Gupta, Deepa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

The progress of Neural Machine Translation for Indic languages is gradually achieving a noteworthy result in terms of translation accuracy. However, its advancement is impeded by the lack of rich parallel corpora, a limitation not shared by non-Indic languages. As a result, most languages of India are still categorized as low-resource or zero-resource languages. Various state-of-the-art architectures have been developed to address these challenges, with the predominant approach being the pivot-based technique. This technique, rooted in transfer learning, utilizes an auxiliary language as a pivot to assist in translation. In this study, a pivotbased Neural Machine Translation (NMT) architecture is designed for translating Dogri to English, with Hindi as the pivot. Dogri, being a low-resource language, shares linguistic similarities with Hindi. Furthermore, Hindi-to-English state-of-the-art models are accessible, ensuring high translation accuracy. Through the implementation of this pivot-based translation model, a BLEU score of 20.97, SacreBLEU of 21.54 and chrF of 50.01 is attained. © 2024 IEEE.

关键词： Neural machine translation

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Level Up ASD (Autistic Spectrum Disorder)- Interactive Learning for Autistic Children 8

Level Up ASD (Autistic Spectrum Disorder)- Interactive Learn...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Varier, Ashwin Malik, Navdeep Kumari, Khushi Gireesh, Nandana Vineetha, K.V. Ullas, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331505462

Autistic Spectrum Disorder (ASD) is a pervasive developmental disorder having issues with social communication, interests, coordination, attention, and health. People with autism are very sensitive and cannot afford a minor change in their surrounding environment. They do not know how to react according to the situation. 'Level Up ASD (Autistic Spectrum Disorder)- Interactive Learning for Autistic Children' is an innovative educational tool designed to support children with ASD. Our paper aims to foster cognitive development and enhance coping mechanisms for children with autism. We focus on providing game-like environment for children to help them to interact easily & freely. Our paper consists of few levels, after which statistics are uploaded which displays the progress of the child. © 2024 IEEE.

关键词： Structured Query Language

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Enhanced Gunshot Sound Detection using AlexNet and XGBoost from Fourier Spectrograms 4

Enhanced Gunshot Sound Detection using AlexNet and XGBoost f...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Priya, M.Yagnasri Shendre, Sanjivani P. Pati, Peeta Basa Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350349900

Gunshot detection and classification are pivotal for ensuring public safety, law enforcement, defense, and forensic investigations. This paper presents a comparitive study into the effectiveness of deep learning and traditional machine learning models in classifying gunshot sounds through Short-Term Fourier Transform (STFT) spectograms is presented. The study involves integrating Convolutional Neural Networks (CNNs) with standard machine learning techniques to extract and analyze features, particularly focusing on the penultimate layer of CNNs. By leveraging these features, this paper aims to enhance the system's ability to accurately identify complex sound patterns associated with gunshots. This analysis provides insights into the strengths and weaknesses of deep learning and machine learning approaches in gunshot classification, particularly achieving a perfect accuracy score with AlexNet and XGBoost. These findings pave the way for more effective gunshot detection systems in a variety of operational scenarios, thereby significantly contributing to public safety measures. © 2024 IEEE.

关键词： Convolutional neural networks

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Vision Based Gesture Recognition 2

Vision Based Gesture Recognition

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Hrishikesh, P. Akshay, V. Anugraha, K. Subramaniam, T.R. Hari Nair, Jyothisha J. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

The use of computers has become an integral part of our daily lives, and they are utilized in various fields. However, traditional input devices such as a mouse and keyboard can sometimes be limiting. Hand gestures can provide a more natural and efficient way of interacting with computers. However, since gestures can vary in orientation and shape from person to person, this creates a non-linear problem. Hence we propose an image-based gesture recognition method that leverages well-established models. The models were trained on a diverse dataset encompassing a wide range of gestures performed by various individuals under different environmental conditions. The experiments exhibit remarkable adaptability to different gestures resulting in a robust and accurate recognition in real-world scenarios. This research will help in creating better human-computer interaction interfaces offering users more natural and efficient ways to interact with computers. The improved gesture recognition systems elevate overall user experience and productivity. © 2024 Elsevier B.V.. All rights reserved.

关键词： Gesture recognition

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Hate Speech Detection in Marathi Tweets using Stacked Deep Learning models 15

Hate Speech Detection in Marathi Tweets using Stacked Deep L...

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

作者： Shedge, Prachi Kamalkar, Siddhi Gupta, Deepa Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Digital platforms have revolutionized communication, but they have also led to an increase in hate speech, making it challenging to create welcoming online groups. The purpose of this study is to close this gap by using deep learning models to detect hate speech in Marathi, a regional language of India. The researchers use advanced word embeddings and DL models like Long Short-Term Memory and Bidirectional LSTM to demonstrate the efficacy of these methods. The study aims to build strong hate speech detection systems that can be extended to other low-resource languages, creating safer online spaces. The study emphasizes the importance of contextually and culturally appropriate data in enhancing model accuracy. In order to strengthen their models' training and validation and guarantee reliable performance in real-world applications, the researchers also intend to integrate real-world social media data. The study closes a significant gap in regional language processing by concentrating on Marathi and opens the door for more inclusive and thorough hate speech detection frameworks across linguistic landscapes. © 2024 IEEE.

关键词： Embeddings

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All-in-one Weather-degraded Image Restoration via Adaptive Degradation-aware Self-prompting Model

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IEEE Transactions on Multimedia 2025年 27卷 3343-3355页

作者： Wen, Yuanbo Gao, Tao Li, Ziqi Zhang, Jing Zhang, Kaihao Chen, Ting Chang'an University School of Information Engineering Xi'an China Chang'an University School of Data Science and Artificial Intelligence Xi'an China Australian National University School of Computing CanberraACT Australia Harbin Institute of Technology School of Computer Science and Technology Shenzhen China

Existing approaches for all-in-one weather-degraded image restoration suffer from inefficiencies in leveraging degradation-aware priors, resulting in sub-optimal performance in adapting to different weather conditions. To this end, we develop an adaptive degradation-aware self-prompting model (ADSM) for all-in-one weather-degraded image restoration. Specifically, our model employs the contrastive language-image pre-training model (CLIP) to facilitate the training of our proposed latent prompt generators (LPGs), which represent three types of latent prompts to characterize the degradation type, degradation property and image caption. Moreover, we integrate the acquired degradation-aware prompts into the time embedding of diffusion model to improve degradation perception. Meanwhile, we employ the latent caption prompt to guide the reverse sampling process using the cross-attention mechanism, thereby guiding the accurate image reconstruction. Furthermore, to accelerate the reverse sampling procedure of diffusion model and address the limitations of frequency perception, we introduce a wavelet-oriented noise estimating network (WNE-Net). Extensive experiments conducted on eight publicly available datasets demonstrate the effectiveness of our proposed approach in both task-specific and all-in-one applications. © 1999-2012 IEEE.

关键词： Restoration

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Advanced Knee Osteoarthritis Detection using Deep Learning 9

Advanced Knee Osteoarthritis Detection using Deep Learning

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

作者： Krishna, Chetlapalli Amritha Bhuvaneswari, R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350394474

This present research investigates the use of machine learning and deep learning models for the detection of knee osteoarthritis using the X-Ray images in depth. knee osteoarthritis is one of the most common joint disorders which affects numerous percentages of people across the world. And its detection of it at an early stage is very critical for effective management. This paper presents valuable dataset of X-ray images with a dual focus on detection of knee joint and KellgrenLawrence grading [kl]. The methodology which is proposed in this paper includes basic steps such as data loading and preprocessing. It also includes feature extraction and utilization of models of both machine and deep learning. In the phase of machine learning models which are used are Decision tree, KNN, MLP, Random Forest and ensemble methods. While in deep learning models used for evaluation are Nasnetmobile, VGG19, Darknet53, Darknet19 and Googlenet. The output of all the above-mentioned models are compared and evaluated based on four most important metrics of performance. The final output of the research shows that Random Forest and the Ensemble methods have outperformed in the phase of machine learning and when it comes to deep learning Googlenet and Darknet53 have done well in comparison. This study helps in the early detection of the knee osteoarthritis and also helps in management of it by giving valuable insights to medical practitioners and researchers. © 2024 IEEE.

关键词： Grading

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Comparative Analysis of Machine Learning Models for Water Quality Prediction 4

Comparative Analysis of Machine Learning Models for Water Qu...

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4th International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Krushna, Bode Vamsi Sasikala, D. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

Water Quality plays an essential role in assessing environmental health, which has profound implications for both ecosystems and human well-being. In this work, various machine learning models including Logistic Regression, Decision Trees, Gaussian Naive Bayes, Random Forest, Support Vector Machine (SVM), and XGBoost were employed to analyze and predict water quality. The dataset used for the analysis encompasses diverse water quality parameters such as hardness, pH levels, solids, chloramines, turbidity, sulfate among others. Class imbalances in the dataset is addressed with Synthetic Minority Over-sampling Technique(SMOTE). The research findings highlight the significant improvement in classification accuracy of underrepresented data points due to SMOTE, with SVM and XGBoost achieving a training accuracy of around 78% and 75%, respectively. The study used K-fold cross-validation techniques and strategies to handle convergence warnings, enhancing the credibility and adaptability of the predictions. This study not only contributes to the growing body of knowledge in water quality analysis but also provides practical insights for researchers and practitioners seeking to employ machine learning approaches in environmental monitoring, water contamination. These findings highlight the critical role of machine learning in facilitating informed decision-making for long-term water management, ensuring the health and longevity of communities and ecosystems. © 2024 IEEE.

关键词： Decision Trees Gaussian Naive Bayes Logistic Regression Machine learning Random Forest SVM Water quality management XGBoost

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