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Real-Time Crisis Management: Deep Learning Analysis on Tweets From X 15

Real-Time Crisis Management: Deep Learning Analysis on Tweet...

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

作者： Sulochana, Balla Charishma Pragada, Bhavya Sri Kiran, Boya Chaitanya Reddy, Gaddam Anvith Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350370249

In recent times social media platforms serve as the main source for communication, especially on public relations or on any economical crisis. During such situations, many organizations depend on tweet conversations on X platform (earlier Twitter), to know the public sentiment and reactions, and provide responsive strategies. This research mainly focuses on using machine learning and deep learning techniques for analyzing the tweet conversations on PR. The novelty in this research is to use the power of natural language processing (NLP) techniques, to analyze every word and its occurrence to know the semantic meaning and understand the sentiment of that conversation. The proposed methodology begins with preprocessing the conversation data, building deep learning models namely LSTM, BiLSTM and machine learning models like logistic regression, Naive Bayes, SVM and XGBoost, which is followed by evaluation through certain metrics. This study helps in providing automated tools for improving the organizations to know the public sentiment during crisis and respond as fast as possible with effective strategies to address the public needs. © 2024 IEEE.

关键词： Tweets

来源：评论

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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...

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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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Enhancing Computation Offloading In Wireless-Powered Mobile-Edge computing Networks With Deep Reinforcement Learning For Online Optimization 4

Enhancing Computation Offloading In Wireless-Powered Mobile-...

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

作者： Kumar, R. Prasanna Spandana, Chereddy Srisurya, Ippatapu Venkata Priyadharshini, A.R. Krithika, S. Sai, Gudivada Dhyana Venkatraman, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

Edge computing at the mobile frontier, enhanced by the integration of wireless energy, represents a cutting-edge strategy to boost processing performance in networks with limited energy resources, such as wireless sensor networks and the Internet of Things (IoT). This study investigates a mobile edge computing (MEC) framework powered by wireless energy, employing a dual-mode offloading scheme. In this paradigm, tasks from wireless devices (WD) may either be processed on-device or entirely shifted to an MEC server. In this approach, tasks from a wireless device (WD) are either processed locally or completely transferred to an MEC server. The objective is to create an online algorithm that can adjust task offloading and wireless resource allocation adaptively according to the variable conditions of the wireless channel. Conventional numerical optimization methods are inadequate due to the swift variations within the channel's coherence time. Our aim is to develop an algorithm that operates online and can dynamically adjust both offloading and resource distribution in response to the fluctuating state of the wireless channel. Traditional numerical optimization approaches fall short because they cannot swiftly adapt to the rapid changes in the channel's coherence. The solution we propose is a framework based on Deep Reinforcement Learning for Online Offloading that utilizes deep neural networks to incrementally learn from offloading decisions, thereby circumventing the need for complex combinatorial optimization. This leads to a significant reduction in the computational load, particularly in expansive networks. We've further enhanced this system with a method that enables real-time modification of the DROO algorithm's parameters. Our experiments demonstrate that this novel algorithm nearly achieves optimal efficiency and significantly reduces computation times - by more than ten times relative to existing techniques. For example, in a network with 30 users, DROO achiev

关键词： access points component Mobile-edge computing radio-frequency reinforcement learning resource allocation Wireless Devices WPT

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Emotion Recognition Using Meta-Learning based on Facial Expressions 5

Emotion Recognition Using Meta-Learning based on Facial Expr...

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

作者： Prashanth, V.J. Sri Naga Jathin, P. Gopinath, Suraj Udith, S. Kavitha, C.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Emotion recognition is essential in many real-life problems and applications such as computer-human interaction, health monitoring, etc. In this project, we propose a meta-learning approach for developing a computer visionbased emotion recognition system. The aim of the work is to find human emotions from visual inputs and sort them accordingly. Meta learning techniques are used to allow the model to quickly adapt itself to emotion patterns using little training data that is short and compact. Therefore, meta learning technique is important since it allows the algorithm to perform well among different datasets with diverse data sets including unseen emotions (unknown emotions). Emotion recognition should address generalization problems while having ability to handle adaptation issues. It therefore seeks transparency in emotion recognition in addition to overcoming challenges of adaptability. Its objective is to offer answers to practical cases where emotion detection can be applied within dynamic environments. Thus at the end of this project we expect a reliable, faster, more dynamic way of classifying and recognizing human emotions based on visual inputs which highly valuable in understanding human emotional state for multiple other applications. © 2024 IEEE.

关键词： Contrastive Learning

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Autonomous Car Driving: Advanced Maneuvers Training 3

Autonomous Car Driving: Advanced Maneuvers Training

引用

3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Chethana, Savarala Charan, Sreevathsa Sree Srihitha, Vemula Radha, D. Amudha, J. Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering India

ISBN: (纸本)9798350364569

As the demand for autonomous driving systems continues to rise, the need for proficient highway navigation becomes paramount. This study presents a comprehensive approach to training autonomous cars for proficient highway driving using deep reinforcement learning. The research focuses on critical maneuvers of switching lanes on the road. A sophisticated simulation environment is then employed for training, enabling safe and efficient iterations. The primary objective of this project is to develop a more adaptive and efficient navigation system using Deep Reinforcement Learning (DRL). Utilizing the HighwayEnv environment and Stable Baselines' Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms, the project aims to create a model capable of real-time decision-making and multi-objective optimization, thereby improving the overall efficiency and safety of highway navigation. A comparative analysis has been performed on these two algorithms to know the best algorithm suitable for the task of switching lanes. The developed system demonstrates promising results in mastering complex highway scenarios, showcasing the potential for a safer and more efficient autonomous driving future. © 2024 IEEE.

关键词： Deep reinforcement learning

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TweetFeel: Analyzing Emotions in the Twittersphere

TweetFeel: Analyzing Emotions in the Twittersphere

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Adithya Gokulan, S. Reddy, Mahati Varshith, M. Venugopalan, Manju Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

The majority of people on the planet now have access to the Internet for text, image, audio, and video communication. Through social media, people from many backgrounds share knowledge about current events and express their own opinions on them. Analyzing people's emotions is necessary to comprehend and identify the behavior of such vast amounts of textual data about them. The study focuses on information gathered from Twitter, one of the most widely used social media platforms, by examining both historical and real-time feeds and extracting emotions from them. The necessary English-language Twitter data is transformed into a vector of six emotions, and supervised learning methods like Logistic Regression, Naive Bayes, SVM, Decision Tree, Random Forest, Gradient Boosting, and Multi-Layer Perceptron are utilized to identify the label of one of the basic human emotions. Gradient Boosting and SVM are the top-performing models that reported the highest F1-Score of 0.88 among the other models. Sadness and joy came out to be the best-performing emotions whereas it was comparatively difficult to predict surprise emotion. © 2024 IEEE.

关键词： Tweets

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AI-Based Posture Correction, Real-Time Exercise Tracking and Feedback using Pose Estimation Technique 3

AI-Based Posture Correction, Real-Time Exercise Tracking and...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Kaushik, M. Kandala, Mahita Vignesh, G.S. Vithyatharshana, N. Palaniswamy, Suja Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331528201

Physical activity is an essential component of physical and mental health-being. Although physical activity offers numerous advantages, maintaining proper posture could be difficult, especially when it comes to complex movements or poses. This task is even challenging for the ones who exercise at home, those who have disabilities, or social anxiety. In order to deal with this issue, this work proposes an AI gym tracker app powered by advanced pose estimation technology through OpenCV and Mediapipe which are two popular frameworks being used in computer vision to access and solve several real-life problems through pose-estimation. This paper describes an AI-powered gym tracker app built with MediaPipe and OpenCV. It uses real-time pose estimation and joint angle calculation to provide feedback on exercise form as well as expanded rep count. This application employs a unique method by using dynamic angle thresholds and joint distance computations to provide precise feedback about user posture. It demonstrates the application's effectiveness in assisting users in maintaining proper form and tracking their workout progress by demonstrating a counter. Future work is to broaden its functionality to embrace more exercises and to further add other forms of decision feedback system, thus exploring the possible incorporation of this application into various fitness apps which would in-turn enhance the overall experience of the users. © 2024 IEEE.

关键词： mHealth

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Integrative Day Trading Stock Trend Prediction using Web Scraping and Sentiment Analysis 9

Integrative Day Trading Stock Trend Prediction using Web Scr...

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

作者： Chennupati, Anjali Prahas, Bhamidipati Ghali, Bharadwaj Aaditya Venugopalan, Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

In the digital era, data is pivotal across disciplines like business, marketing, engineering, and social sciences. This research proposes a new method of acquiring intraday stock data from the National Stock Exchangeand sentiment-laden news articles, via the web-scrapping technology. Spark's distributed computing framework that provides powerful processing capabilities has been employed to perform sentiment analysis and quantify sentiment scores for the chosen stock over the past month. Concurrently, the XGBoost algorithm processes the historical stock data to predict future values along with the cross-validation process. With the integration between the XGBoostpredictions and the sentiment score, this model offers stock recommendations to the user to either buy or sell a certain stock. The proposed model performance is evaluated for future value predictionsbased on the average RMSE score reported as *** extremely systematic take reaches across financial prediction, natural language processing, and machine learning to supply recommendations for investors. © 2024 IEEE.

关键词： Sentiment analysis

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Malware Image Classification with Enhanced Feature Extraction Using VGG16 and Spatial Pyramid Pooling 8

Malware Image Classification with Enhanced Feature Extractio...

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

作者： Ryan, Collin Diviya, M. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798331531539

In the present era, the rise in number and complexity of malware poses a major threat leading to the rise in the importance of smarter Malware detection systems to understand and detect modern malwares. Modern systems are often very slow and inefficient with outdated machine learning models and feature selection techniques. These inefficiencies are overcome by the use of a multiclass detection system with CNN application to automatically classify malware. The system represents malware binaries in image format and uses VGG16 based network architecture with Spatial Pyramid Pooling (SPP) and ReLU activation to efficiently retrieve multiscale features from images of variable dimensionality. The model also includes dropout regularization, categorical cross entropy and Adam optimizer for better performance, adaptability and generalization. This approach works with a well balanced dataset with extensive training and achieved an accuracy of 95.94% making it a reliable and scalable solution for real time threat detection in a dynamic environment. © 2024 IEEE.

关键词： Convolutional neural networks

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Evaluating uniXcoder Embeddings for Automated Grading: A Study Across Varied Code Perspectives 15

Evaluating uniXcoder Embeddings for Automated Grading: A Stu...

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

作者： Narmada, Nakka Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Automated grading systems play a pivotal role in modern educational settings, offering efficiency and scalability in assessing student submissions. In this study, we investigate the potential of uniXcoder embeddings of C language codes. A comparative analysis across four scenarios: code-only, codequestion, code-question-comment, and code-question-comment-reference is conducted. The dataset deals with C programs that are evaluated by Subject Matter Experts (SMEs). This study explores the analysis of code embeddings using K-Means approach and to determine the influence of Principal Component Analysis (PCA) and scaling techniques. The study compares clustering outcomes with and without PCA, with and without scaling techniques looking at measures including silhouette score, Davies-Bouldin index, Kullback-Leibler divergence and visualization techniques to draw conclusions. The findings suggest that uniXcoder embeddings demonstrate that 'code-question-comment-reference' scenario is best suited for automated grading tasks. It also looks into the ability of the embedding to capture the level of error present in the code. This research contributes to the advancement of automated grading systems by providing insights into the suitability of uniXcoder embeddings for assessing student code submissions in educational environments. © 2024 IEEE.

关键词： C (programming language)

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