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

共 21694 条记录，以下是751-760 订阅

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Enhancing Disaster Awareness using BERT-Based Analysis of Twitter Chat 15

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

引用

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

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

ISBN: (纸本)9798350370249

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. © 2024 IEEE.

关键词： BERT Disaster Management Disaster Response Natural Language Processing Text Analysis Twitter

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An Ensemble Approach to Tuberculosis Prediction using Shenzhen and Montgomery Datasets 15

An Ensemble Approach to Tuberculosis Prediction using Shenzh...

引用

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

作者： Varshith, Tummala Koneri, Thanush S. Reddy, Tetali Syam Kumar Singh, Rimjhim Padam Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Chest X-rays are critical for detecting a variety of lung disorders, with tuberculosis (TB) being a particular priority due to its global impact on public health. TB must be identified quickly and accurately using medical imaging to begin treatment and stop the spread of the illness. This work examines the importance of chest X -ray analysis and its role in TB identification. A novel convolutional neural network (CNN) based ensemble approach is proposed, thoroughly investigating the performance of state-of-the-art sophisticated deep learning models, such as Xception, InceptionV3, DenseNet201, ResNet50, EfficientNetB2, ResNet50-InceptionV3, EfficientNetB2-Xception, DenseNet201-Xception, and DenseNet201-InceptionV3, for differentiating between normal and tuberculosis-affected chest X-rays. The standard Shenzhen and Montgomery datasets were used for training and testing the efficacy of the proposed model. The findings of this study promote the development of accurate and effective TB detection technologies, ultimately enhancing global efforts to combat this deadly illness. Notably, the DenseNet201InceptionV3 ensemble produced the best results for TB detection, achieving a precision of 0.91, a recall of 0.85, an F score of 0.89, and an accuracy of 95.14%. © 2024 IEEE.

关键词： Lung cancer

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Artificial Intelligence in Healthcare: Predicting AIDS Progression with Machine Learning Models 8

Artificial Intelligence in Healthcare: Predicting AIDS Progr...

引用

8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Rajan, Akshay Santhosh, Gouri Manoj, Balu Manohar, Nandana Anjali, T. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350367904

A comprehensive analysis was performed using the AIDS Clinical Trials Group 175 dataset to improve the accuracy of predicting AIDS disease progression. The primary objective was to integrate machine learning techniques to predict AIDS disease outcomes based on clinical, demographic, and treatmentrelated variables. Several machine learning models, including advanced neural network architectures, were developed and thoroughly evaluated. The study highlighted the ability of machine learning to accurately identify patterns and risk factors associated with AIDS disease progression, thereby improving treatment strategies and patient management. Extensive comparisons of several machine learning models were performed to evaluate their performance and robustness. The aim is to demonstrate the important role of artificial intelligence in predicting and diagnosing AIDS early, thereby contributing to better healthcare outcomes. The comprehensive evaluation of model performance in this study is expected to support future advances in prediction models for AIDS and other chronic diseases. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancement of Cassava Leaf Disease Detection Using InceptionNet and Squeeze and Excitation Attention Mechanism

Enhancement of Cassava Leaf Disease Detection Using Inceptio...

引用

2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Alagawadi, Anusha P Arunbalaji, C.G. Shivdutt, Sahen Singh, Rimjhim Padam Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331535476

Automated cassava leaf disease detection using images has gained immense importance as it is consumed by 800 million people as a staple crop across the globe. This paper proposes a novel approach to cassava leaf diseases classification based on InceptionV3 convolutional model augmented with Squeeze and Excitation attention mechanism for enhanced feature representation. The proposed work also attempts to improve robustness through extensive data augmentation, balanced sampling techniques and adaptive learning rate adjustment mechanism. For the evaluation of the performance of the proposed model, different techniques for explainability have been adopted, including Grad- CAM and LIME, which bring to the fore the mechanism of decision-making by our model. The proposed model is compared against six fine-tuned convolutional architectures and the results demonstrate the highest accuracy and F-score values for disease classification, thus showing the efficacy of the proposed approach. Over-all this research contributes to the general area of agricultural disease diagnosis but also sheds light on the interpretability of deep learning models for complex classification tasks. © 2024 IEEE.

关键词： Plant diseases

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Advancing Personality Detection through Word Embedments and Deep Learning: An Examination Using the MBTI Dataset 6

Advancing Personality Detection through Word Embedments and ...

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

作者： Shanmukha, Aditya G Shamyuktha, R.S. Karan, S. Gupta, Deepa Palaniswamy, Suja Amrita School of Computing Bengaluru Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350381689

Personality detection and classification hold significant potential for enhancing decision-making processes and optimizing outcomes across various sectors. This interdisciplinary field finds applicative relevance in numerous domains such as business, healthcare, education, and personal development. In an effort to contribute to this burgeoning area of research, this paper introduces a comprehensive model specifically designed for personality detection. The model is rigorously tested on the Myers-Briggs Type Indicator (MBTI) dataset, a widely recognized framework in personality typology. The core of this proposed model lies in its innovative integration of four advanced word embeddings: Keras, Word2Vec, Glove, and FastText. These embeddings are coupled with cutting-edge deep learning architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Bidirectional LSTM (BI-LSTM), a hybrid of CNN and LSTM, and Gated Recurrent Units (GRU). This amalgamation results in a suite of 18 distinct models, each offering unique insights into personality classification. The implementation of these models is meticulously undertaken, and the outcomes are documented in detail. The results of this research which is 82% of accuracy, not only demonstrate the efficacy of combining various word embeddings with deep learning techniques but also pave the way for future explorations in the field of personality detection and classification. © 2024 IEEE.

关键词： Convolutional neural networks

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Aural Signatures: Audio Fingerprinting Techniques for Real-Time Audio Recognition 3

Aural Signatures: Audio Fingerprinting Techniques for Real-T...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Sharan Nripesh Reddy, B. Sai Venkat, B. Manohar, I. Abhishek, S. Anjali, T. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350396157

Audio fingerprinting is an essential technique for copyright protection and music recognition. The capacity to accurately categorize audio content and spot possible copyright violations is vital for musicians, content creators, and streaming services. With its valuable solutions for copyright protection, content monitoring, and music recommendation, this study has significant implications for the music industry. In the research, the research focused on developing robust audio fingerprinting methods for precise audio content recognition. These tactics have shown to be remarkably accurate and quite effective. The approach is based on the use of strong hashing techniques and the extraction of various audio aspects. Long Short-Term Memory (LSTM) along with SVM, KNN, and CNN were employed in the research. Notably, LSTM outperformed other models, showcasing an outstanding accuracy rate of 93 %. The results hold particular promise for massive music platforms and instantaneous content matching. The proposed methods have demonstrated the effectiveness of these strategies in identifying audio material, even under challenging conditions. © 2024 IEEE.

关键词： Long short-term memory

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Human Emotion Recognition in Education LearningSoftware 7

Human Emotion Recognition in Education LearningSoftware

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Geethani, V. Deepthi, G. Eniyan, M.K. Tangavel, Senthil Kaumar Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350359299

Emotions and facial expressions are continuous, variable entities. Researchers from a variety of disciplines have focused on emotion recognition techniques to better understand how users interact with, use, and benefit from the system. Our goal is to understand and extrapolate the learner's emotional state throughout a learning interaction. One instance is human emotion recognition in Education learning software, which communicates how well a person understands the concepts and their emotions during the assignment of tasks, reaction to different kinds of topics, etc. Assessing a learner's emotions can gradually improve their learning experience and update the course materials. It is important to take into account our own limitations in this area because they will only be amplified in a machine learning system designed around them because human facial emotion recognition systems lack an innate understanding of human emotion and rely on our capacity to correctly label expressions. The idea is to create a CNN model to categorize the fundamental emotions and thereafter determine the pupils' psychological states. © 2024 IEEE.

关键词： Emotion Recognition

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Enhancing Software Test Effort Estimation using Ensemble Learning Algorithms 4

Enhancing Software Test Effort Estimation using Ensemble Lea...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Venkatraman, K. Akashvarma, M. Siddharth, S. Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798350349900

In software engineering, accurate test effort prediction is critical to project schedule and resource efficiency. Conventional approaches, which depend on past performance or expert opinion, frequently fail to provide a reliable estimate of the testing effort. These approaches might offer some insight at first, but as project complexity rises, they become less scalable and more error-prone. This study explores the use of machine learning to improve the estimation of software test effort in response. This work intends to overcome the shortcomings of conventional methods and create more accurate and scalable solutions by utilising the power of machine learning algorithms. The goal of the research is to determine the best machine learning strategy for software test effort estimation through extensive testing and comparative analysis. The results of this study are compared using metrics such as RMSE, MSE, MAE, and R2 score, with Random Forest algorithm demonstrating superior performance over other algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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Generation of Photorealistic Face Images Using Deep Convolutional Generative Adversarial Networks 8

Generation of Photorealistic Face Images Using Deep Convolut...

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8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Anvitha, Sagala Sai Reddy, Sagam Prashamsa Sunaini, Yerramachu Nidhin Prabhakar, T.V. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331531539

In present time, the area of generating photorealistic images using Generative Adversarial Networks (GAN s) has seen considerable interest due to their capability to make high-quality, high-efficient and diverse visual content. This project mainly aims on proceeding the generation of photorealistic face images by addressing existing vulnerabilities in already existing face generation methods. The main objective is to examine and enhance the detection of generated faces by discriminator in GAN sand customizing the generator of a Deep Convolutional GAN model and the evaluation of its efficiency in producing less vulnerable to naked eye, visually clear, and realistic images. By utilizing diverse datasets such as CelebA, this study also aims to develop powerful methodologies that can reliably generate photorealistic facial images. Here the approach involves a thorough examination of the strengths and weaknesses of existing GAN models in generating realistic facial images. Through experimentation with diversified Deep Convolutional GAN architecture and optimization techniques, this study aims to attain significant improvements in image fidelity and realism. By utilizing the large and diverse datasets available, the work intends to traverse various strategies to mitigate common artifacts and inconsistencies observed in GAN -generated faces. Here using different variants of DCGAN s, significant results were obtained indistinguishable from real photographs. © 2024 IEEE.

关键词： Generative adversarial networks

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Fortifying Digital Security: A Machine Learning and Explainable AI Framework for Password Strength Assessment 8

Fortifying Digital Security: A Machine Learning and Explaina...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Shreya, Kuthati Divakarla, Lasya Priya Kumar, Kanderi Johith Vishwas, H.N. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350367904

In the current era where almost anything is accessible over the internet, passwords have become most important to ensure security and authentication. To provide authentication many other mechanisms exist like smart locks, biometrics, voice recognition, face recognition, etc. but still, passwords exist as the widely preferred method for authentication because of its ease of use and as many people are familiar with passwords. With the ease of use that it comes with there is a possible threat of guessing the passwords that exists. Usually, the passwords are some patterns, names, important years, etc. These are sometimes repetitive or easily guessed by the attacker because of their simplicity, and having common and weak passwords. The proposed model is a machine-learning algorithms that classifies the passwords as weak, medium, and strong based on their strength. Machine learning models like Random Forest (RF) classifier, Logistic Regression, XGBoost (XGB), and Gradient Boosting Machines (GBM). © 2024 IEEE.

关键词： Authentication Protocol

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