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Implementing Multi-factor Authentication in Cloud Services for Enhanced Security

Implementing Multi-factor Authentication in Cloud Services f...

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2024 IEEE International Conference on Recent Advances in Science and engineering Technology, ICRASET 2024

作者： Rekha, K. Sashi Sivagami, V.M. Usharani, R. Amutha, T. Pushparani, S. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388602

The cloud services which are now the most common data transmission and endanger organizations' confidential information, it's more and more visible that security of any data should be a main priority for companies. MFA, which consists of adopting multi factor approach to authentication and thereby provides multiple types of verification thus ensures effective security, is leveraged to ensure resource access. This paper proposes a full-fledged way of implementing MFA for cloud services in order to strengthen the security given and manage the constatoing risks. The proposed methodology consists of main stages like requirements determining, technology reviewing, risks assessment, technology design, integration with cloud services, users training, pilot deployment and testing, implementation and configuration, monitoring and maintenance as well as constant improvement. Through a systemic approach, the organizations can be equipped with the capability to personalize the MFA solutions according to their security goals, to bring the cloud environments on board, as well as to ensure that the organizations comply with the security standards as set by various governing bodies. This research adds a grain of salt to the development of cloud security good practices through offering a disciplined approach to MFA implementation, which allows partners to harden their cloud assets and protect against growing cyber threats. © 2024 IEEE.

关键词： Cloud security

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Deep learning based approaches for accurate automated nuclei segmentation of Pap smear images

Deep learning based approaches for accurate automated nuclei...

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International Conference on computing and Networking Technology (ICCNT)

作者： Hridya Nair S Kasthuri A S Nair Niharika Padmanabhan Riya Ratnakaran Vinitha Panicker J Department of Computer Science and Engineering Amrita School of Computing Amritapuri India

Cervical cancer is a type of cancer that occurs among women. The cancerous cells are developed in the cells of the cervix which is the lower part of the uterus. The human papillomavirus (HPV) is responsible for the vast majority of cervical cancer cases. A Papanicolaou test (pap smear test) is the most reliable screening method for identifying cancerous cells. Pathologists visually examine the slide after performing the test to look for malignancy. But this is a time-consuming and challenging task, often limited by the availability of certified professionals. Additionally, the diagnostic efficacy of visual screening is compromised by the increased workload of pathologists, leading to decreased diagnostic accuracy. Thus, a computer-aided diagnosis system is essential to enhance the quality of the Pap test results and enable early diagnosis. Nuclei segmentation is a crucial step in automated cervical cancer detection, as the nucleus carries essential information about the alterations occurring during the disease’s progression. Thus, automated segmentation of nuclei can significantly and ultimately influence the diagnostic results. Recent surveys found that neural networks outperformed various traditional segmentation methods. We conducted experiments on the Herlev dataset using U-Net with ResNet and EfficientNet encoders, PAN (Pyramid Attention Network), and LinkNet with a ResNet encoder. For each of these models, our results showed IOU scores of 0.76, 0.938, 0.79, and 0.69, respectively.

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Accent-Enhanced Automatic Speech Recognition for Multilingual Classrooms

Accent-Enhanced Automatic Speech Recognition for Multilingua...

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International Conference on computing and Networking Technology (ICCNT)

作者： M Jagadeeshvaran H Sarah Michelle S Shruti Bala M Srinivasa Vinayak T Deepika Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In an increasingly interconnected world, universities face the challenge of hosting diverse students from different backgrounds. The biggest obstacle to effective communication and learning in multicultural classrooms is teacher and student diversity. To overcome this challenge, we propose the development of noise-enhanced automatic speech recognition (ASR), specifically for multilingual classrooms. The system is designed to provide instant feedback on lessons while preserving the subtleties of speech, thus improving understanding and creating a learning environment. Using the advanced capabilities of the Transformer model together with the convolutional neural network (CNN) highlight classifier, the proposed system provides powerful and accurate transcription capabilities. This article provides an overview of the design, implementation, and evaluation of voice-enhanced ASR, demonstrating its potential to transform communication and learning in a variety of educational settings.

关键词： Accuracy Transducers Face recognition Neural networks Transforms Speech enhancement Transformers Complexity theory Convolutional neural networks Automatic speech recognition

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Enhanced Diabetic Retinopathy Grading Using a Deep Hybrid Approach

Enhanced Diabetic Retinopathy Grading Using a Deep Hybrid Ap...

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International Conference on computing and Networking Technology (ICCNT)

作者： Gali Laharesh Gvs Sarath Chandra R Sakthi Vikkraman Sbs Mani Kumar M Suchithra Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In recent times there have been significant achievements in deep learning methodologies, the task of classifying retinal diseases is still challenging because of their minute pathological regions. The retina’s blood vessels can be compromised by diabetic retinopathy, a complication arising from diabetes falls under the distinct category of retinal diseases. Despite many attempts with classification models such as CNN and other deep learning approaches detection encounters several challenges. These include existing deep learning models are susceptible to biases towards either local or global perspectives. These biases can impact semantic-level understanding of these representations. This work proposes a hybrid framework for Diabetic retinopathy grading. Inspired from the achievements of vision transformers in image classification and its ability to capture spatial relationships in fundus images related to Diabetic retinopathy features. Augmenting this vision transformer with traditional multiple instance learning forming a hybrid model ViT-MIL. By combining ViT’s global learning capabilities with MIL’s effective local feature extraction this hybrid model achieves enhanced performance.

关键词： Deep learning Diabetic retinopathy computer vision Pathology Attention mechanisms Predictive models Transformers Feature extraction Retina Diseases

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Enhancing Disaster Awareness using BERT-Based Analysis of Twitter Chat

Enhancing Disaster Awareness using BERT-Based Analysis of Tw...

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International Conference on computing and Networking Technology (ICCNT)

作者： M Charan Kumar D. Spoorthi Keya G Bharathi Mohan M Rithani Gundala Pallavi R Prasanna Kumar Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Disaster management and response are critical for minimizing the impact of natural and man-made disasters. Social media platforms, particularly Twitter, have emerged as valuable sources of real-time information during disasters. This paper presents a novel approach to enhance disaster awareness through the analysis of Twitter chat using Bidirectional Encoder Representations from Transformers (BERT). BERT is a powerful natural language processing model that has shown remarkable performance in various text analysis tasks. This work illustrates BERT for analyzing tweets related to disasters to improve situational awareness. This work uses disaster-related tweets dataset and preprocess them for analysis and and the data is fine-tuned using pre-trained BERT model. This research demonstrate that the BERT-based approach outperforms traditional methods like BILSTM-Glove with accuracy of 84.25% and BERT-BILSTM-CNN with accuracy of 95% in classifying disaster-related tweets.

关键词： Measurement Accuracy Social networking (online) Disasters Blogs Bidirectional control Natural language processing Encoding Complexity theory Context modeling

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Sentiment Analysis of Airline Tweets Using Word Embeddings and Deep Learning Techniques

Sentiment Analysis of Airline Tweets Using Word Embeddings a...

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International Conference on computing and Networking Technology (ICCNT)

作者： Surapaneni Pavan Nikhith Popuri Varun Kumar Aira Udaybhasker Tangaturu Devendranath Reddy Parlapalli Bhargav Reddy Tripty Singh Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This project deals with the testing of different word embedding, deep learning techniques, and XAI tools for text classification tasks. It talks about the effect of Word2Vec embeddings in CNN models on text context and shows their capability of grasping it. Furthermore, this research sees the application of BERT and RoBERTa embedding and shows their influence on CNN and BiLSTM models and their comparable performance. Besides, this project also proves the significance of transformer-based embeddings in the information extraction from texts with various contextual nuances and points to their possible to increase the text classification accuracy on different deep learning architectures. We lastly, look into the function of eXplainable AI tools in the interpretation of model decisions, which ultimately helps to understand and support the model prediction outcomes in the field of text classification.

关键词： Accuracy Atmospheric modeling Text categorization Bidirectional control Predictive models Transformers Encoding Convolutional neural networks Long short term memory Context modeling

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AutoGrading Classification of Tamil Descriptive Answers FastText and Roberta-Base Sentence Embedding

AutoGrading Classification of Tamil Descriptive Answers Fast...

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Global Conference for Advancement in Technology (GCAT)

作者： Vaan Amuthu Elango Peeta Basa Pati Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Considering the challenges associated with manually grading student answer scripts and the significance of the language, an automated approach is developed for assessing Tamil descriptive answers. Three-way assessment system of correct, incorrect, and partially correct is used to improve the autograding categorization accuracy. A total of eight datasets derived from four different dataset's sentence embedding produced by sophisticated natural language processing techniques RoBERTa-Base and FastText was created. To determine the optimal combination of data input, ten models inclusive of deep learning and machine learning models were utilized to perform a grade-based classification. This classification study proved that combination of the QuestionAnswer dataset with FastText embedding yielded the most promising result of F1 Score as 92.1%. The automatic grading methods tailored to the nuances of Tamil language are improved by this research.

关键词： Support vector machines Deep learning Accuracy Data models Natural language processing Classification tree analysis

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Enhancing EEG-based emotion recognition using Semi-supervised Co-training Ensemble Learning

Enhancing EEG-based emotion recognition using Semi-supervise...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Rachel Yeo Hui Min Aung Aung Phyo Wai School of Computing National University of Singapore Singapore Singapore Center for Brain Computing Research School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Emotion recognition based on Brain-computer Interface is crucial in deepening our understanding of humans’ emotions and decision-making process. The enhanced precision in emotion measurement allows for more rigorous analysis of mental disorders and therapy effectiveness. Our method aims to solve two challenges in this field. First, existing models’ features often fail to comprehensively capture the multiple dimensions of information in EEG signals, like temporal or frequency domains. We propose two models: a CNN model trained on temporal features and a DNN model trained on differential entropy features, and ensemble their predictions with weighted voting. Second, labels can be uncertain, where data is unconfidently labelled. This is due to emotions’ subjectivity causing a lack of clear ground truth in EEG. The proposed method aims to mitigate this by using a semisupervised method that utilises data with uncertain labels as unlabelled data. Co-training is used to allow the two models to learn from each other. Our combined model achieves higher accuracy than temporal and spectral models by 8.51% and 6.87% respectively for the SEED dataset. For the MER dataset, its accuracy outperforms temporal and spectral models by 24.96% and 2.70% respectively for arousal classification, and 11.18% and 49.21% respectively for valence classification.

关键词： Training Emotion recognition Accuracy Mental disorders Predictive models Brain modeling Electroencephalography

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Clustering of Air Objects based on Path, Missile Path Prediction Using Deep Learning 2

Clustering of Air Objects based on Path, Missile Path Predic...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Surya Teja Gontumukkala, Sai Sai Varun Godavarthi, Yogeshwara Rama Ravi Teja Gonugunta, Bhanu Subramani, R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mathematics Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

For a given instance, the location of the object in air can be demonstrated by quadruples there are latitude, altitude, longitude and velocity. Trajectory is continuous stream time, latitude, altitude and longitude. The proposed work classifies the air objects based on trajectories and group them into meaningful objects such as flying objects, drones, helicopters, fighter, civilian aircraft, unmanned aerial vehicles, missiles, flying bombs, and so on. In this paper, the proposed work estimates the location of air objects by using their trajectory and these provides a guidance to the missiles to shoot the target based on its location where the missile is going to meets the object based on the trajectory. For calculating missile's path, one of the most difficult challenges is applying the missile model to various related simulations is determining the missile path. The traditional way for this task is to use models and numerical methods, that also necessitates a large computational power. The data generated by the traditional system has been used to train the network and test the network, and the defect of the network prediction result is evaluated in this study. Recurrent neural network (RNN) and long short-term memory (LSTM) have been used to perform Clustering of Air Objects based on Path and for Missile Path Prediction. For clustering of air objects, LSTM gave r2 score of 0.997 and for missile path prediction, LSTM with 7 epochs gave an r2 score of 0.791. © 2022 IEEE.

关键词： Long short-term memory

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A Framework for Tele-rehabilitation Gaming System 6

A Framework for Tele-rehabilitation Gaming System

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6th International Conference on Applied Computational Intelligence in Information Systems, ACIIS 2023

作者： Elaklouk, Ahmed M. S. Zuarni Ramli, Ratna Edris, Ibrahim Padilla-Valdez, Nena Jumaily, Adel Al Universiti Teknologi Brunei School of Computing and Informatics Bandar Seri Begawan Brunei Universiti Teknologi Mara Negeri College of Computing Informatics and Mathematics Seremban Malaysia Universiti Teknologi Brunei Centre for Communication Bandar Seri Begawan Brunei Faculty of Engineering University of Technology Brunei Brunei Department of Computer Science and Software Engineering School of Computing University of Western Australia Australia Ensta Bretagne University France

ISBN: (纸本)9798350317428

Brain damage resulting from stroke and traumatic brain injury is the primary cause of complicated disability and one of the major causes of mortality in most countries, burdening healthcare systems owing to the growth in the number of brain injury victims. Rehabilitation is required to deal with the brain damage. With fewer therapists available, it is very challenging to manage patients and even motivate them to participate actively in their rehabilitation. Patients have frequently complained that many traditional rehabilitation systems are monotonous and uninteresting. The study's objective is to propose a telerehabilitation gaming system framework that can serve as guidance for developing an affordable rehabilitation gaming system that can motivate and engage patients and increase rehabilitation effectiveness. The research methodology is based on synthesizing related research and currently available technologies. The proposed framework includes the therapist and patient attached with vital constructs: tailoring tools, instructional content, game characteristics, tailored games, and performance. It is an internet-based communication structure that connects the Rehabilitation Gaming System to the hospital and other care provider networks. The proposed framework is evaluated by a panel of five experts. The results reveal that all experts believe that the proposed framework can serve as a useful guide for creating gaming interventions that can boost the patient's motivation and engagement and increase the effectiveness of rehabilitation. © 2023 IEEE.

关键词： Serious games

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