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Tweeting the Blues: Leveraging NLP and Classification Models for Depression Detection 2

Tweeting the Blues: Leveraging NLP and Classification Models...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Chereddy, Spandana Geetha, K. Sreedevi, A.G. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798350371314

Depression is a global mental health problem while Twitter is a valuable medium on which content indicating emotional affect is generated. Our research draws upon NLP to analyze tweets by the application of Naive Bayes, Support Vector Machines, and Logistic Regression classifiers. The first objective will be the tweets' classification into positive and negative sentiment categories with a secondary goal to explore the possibility of the detection of community characteristics including gender, age, and location. We investigate the tweet content by using NLP in an attempt to pinpoint the sensing patterns and the department that can reliably be used to detect depression. Results of the experiments proved comprehensive model accuracy with Support Vector Machines taking a better position on the matter of correctly classifying depression tweets. Especially, Support Vector Machines gave an accuracy of 85%, and values for precision, recall, and f1 were 0.89, 0.63, and 0.63, respectively. Therefore, the remaining ones for attaining higher accuracy and recall are difficult to achieve in models that are depressed but there is still the necessity for more data generation. This research thus clears the way for NLP in the diagnosis of mental health by providing vital assessment data that can be used by psychologists, researchers, health authorities, and policymakers to develop targeted intervention strategies and advancement of public health goals. © 2024 IEEE.

关键词： Support vector machines

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A Comprehensive Analysis on using Multifactor Authentication System for Three Level Security 2

A Comprehensive Analysis on using Multifactor Authentication...

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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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Detecting AI-generated images with CNN and Interpretation using Explainable AI 2

Detecting AI-generated images with CNN and Interpretation us...

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2nd IEEE International Conference on Contemporary computing and Communications, InC4 2024

作者： Bharathi Mohan, G. Rangarajan, Prasanna Kumar Akilesh Rao, S. Sukesh, Mandava Abinandhini, M. Jaikanth, Y. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383652

The rapid proliferation of digitally manipulated images in various media outlets has necessitated the development of robust and reliable methods for distinguishing between authentic and synthetic visual content. In response to this imperative, this research leverages state-of-the-art deep learning techniques and an extensive dataset of 'real' and 'fake' images to create an AI model for image identification. The study encompasses a meticulous data collection process, preprocessing steps, the selection of the DenseNet121 model architecture, model training, and evaluation of the model's performance through a range of metrics, including accuracy (93.42), precision, recall, and the Area Under the ROC Curve (AUC). To enhance the transparency and interpretability of the model, employed the Gradient-weighted Class Activation Mapping (Grad-CAM) technique, enabling to visualize the regions within images that influenced the model's decision-making. The research provides valuable insights into the development and evaluation of AI systems for image identification, particularly in the context of distinguishing between real and fake content. Furthermore, the interpretability techniques shed light on the model's decision-making process. This research contributes to the field of image authentication and paves the way for enhanced image classification in the era of synthetic media. © 2024 IEEE.

关键词： Cams

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Enhanced Classification of Exoplanets Using Machine Learning: Integrating Feature Selection, Ensemble Algorithms, and Explainable AI Insights 15

Enhanced Classification of Exoplanets Using Machine Learning...

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

作者： Ishvarya, G. Kammari, Kavya Sree Thambi, Sharanya Vanraj Venugopalan, Manju Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

This research delves into employing machine learning (ML) algorithms for classifying exoplanets, focusing on enhancing classification accuracy through feature selection. By utilizing data from the KOI planet candidates, the study addresses the challenge of handling large datasets by employing feature selection techniques like the chi-square method, improving the classification process's effectiveness. Principal Component Analysis (PCA) was employed for data visualization, aiding in understanding the underlying structure and relationships within the exoplanet dataset. In the implementation phase, the study initially employed standalone classifier models such as KNN, SVM, Random Forest, Naive Bayes, Decision trees and CNN. Following this, ensemble algorithms including XGBoost, GBM, CatBoost, and AdaBoost were utilized to enhance classification performance further. Among these, Random Forest emerged as the top performer, achieving an F-measure of 0.95, surpassing the others. Additionally, the study incorporates explainable AI (XAI) techniques to interpret the importance of individual features in the models' predictions, shedding light on crucial features essential for accurately classifying KOI planets. These insights serve as a foundation for developing more advanced and dependable ML models for exoplanet classification, facilitating future research endeavors that may incorporate additional datasets and features. © 2024 IEEE.

关键词： Random forests

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A Novel Approach for Phishing Website Detection using Deep Learning Models 8

A Novel Approach for Phishing Website Detection using Deep L...

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

作者： Vyshnavi, Chellu Raj, Chukka Mishal Gopakumar, Gokul Prasanna, Guddeti Bindu Palaniswamy, Suja Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331505462

Phishing website detection is a major task in cybersecurity, aimed at saving users from sites that steal personal information. The traditional approaches can hardly minimize false positives and cope with the dynamic strategies of phishing. In this work, a novel approach has been proposed to detect phishing websites using deep learning models, such as CNN, RNN, RNN with attention layers, CRNN, LSTM, and GRU, to enhance accuracy in detection. This work has addressed one vital domain challenge, which includes very rapid changes of techniques and phishing activities, and the real-time detection feature that conventional models generally may not have. The proposed approach will leverage these advanced models for automatic feature extraction and adaptability to new strategies for significant improvement of detection accuracy and reduction of false positives. The proposed method gives a robust and real-time solution for the task of phishing website detection and improving cybersecurity in general, after its evaluation on a comprehensive dataset. © 2024 IEEE.

关键词： Phishing

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Smart Surveillence Drone with Navigation System 5

Smart Surveillence Drone with Navigation System

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Padmavathi, A. Vyasasreevedansh, B. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

At present, in this time of technological advancement and an urgent necessity for advanced spying abilities, the 'Smart Surveillance Drone with Navigation System' program is leading the way in redefining the field of surveillance and safeguarding. The way in which this project is leading is through the integration of the most advanced drone technology, complex navigation systems, and real-time computer vision, striving to create an independent drone model that can be identified and followed by human subjects in real-time while travelling between predetermined waypoints. The key aims of the program involve the assembling and integrating of a range of sensors, such as GPS, IMU, cameras, ultrasonic sensors, and LiDAR, alongside the production of independent navigation algorithms and computer vision technology. By combining cutting-edge technologies such as wireless communication, real-time data processing, and machine learning, the revolutionising of surveillance in several domains is being set out by this project. The adaptability and reliability of the system will be verified by rigorous field testing, while the experience will be enhanced by user feedback and training. With such advancements, the future of disaster response, agriculture, search and rescue operations, and security is promised to be innovative and efficient. This project that's set to change how we think about surveillance and security is captured by this short summary, and the way is being paved for a safer and more secure world. © 2024 IEEE.

关键词： Autonomous Surveillance Navigation System Real-time Object Detection Security Innovation Surveillance Drone

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Interactome: A Platform for Comprehensive Drug-Drug Interaction Analysis 15

Interactome: A Platform for Comprehensive Drug-Drug Interact...

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

作者： Sivakumar, Amrutha Bhanot, Shreya Srinidhi, M. Aiswariya Milan, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350370249

Drug-drug interaction is an important aspect to be taken into consideration when administering multiple drugs to patients with severe diseases. It is important to know if the combination of the drugs causes a positive effect or an ill effect on the person's body. Thus, Interactome roots to offer a comprehensive solution to assess and find out the interactions between commonly used drugs for the treatment of different ailments such as heart diseases or diabetes and provides an understanding of how the combination of these drugs can cause side effects if not provided with enough details about the drug. Thus, it enables the user to know alternatives to using efficient drugs without any side effects by displaying graphs of the percentage of positive and negative effects and charts depicting the effects. By providing negative and positive feedback on the combination of various drugs used for heart and diabetes patients and displaying the percentage risks with the help of graphs and charts, Interactome ensures the prevention of any unnecessary attacks. Also, it saves the lives of an individual by spreading awareness about drug-drug interactions. We incorporate a Trie data structure that efficiently analyzes the input medications against a database of known interactions. It optimizes the retrieval process by organizing the drugs into a hierarchical structure. Then it ensures rapid and accurate results so that healthcare professionals and individuals receive timely and comprehensive insights into potential drug interactions. © 2024 IEEE.

关键词： charts Drug-drug interaction graphs negative effects positive effects Trie data structure

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Exploring the Techniques for Option Pricing: An Empirical Study and Performance Assessment

Exploring the Techniques for Option Pricing: An Empirical St...

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2024 International Conference on electrical, Electronics and computing Technologies, ICEECT 2024

作者： Anand, R.N. Supriya, M. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378092

The accurate pricing of financial options is crucial for effective risk management and decision-making in financial markets. Traditional option pricing models, such as the Black-Scholes model, rely on simplifying assumptions that may not fully capture the complexities of real-world market dynamics. This study aims at exploring machine learning (ML) and deep learning (DL) models in different scenarios and evaluating their performances against the conventional methods. The methodology harnesses the power of neural networks within deep learning models to discern and learn intricate patterns and relationships inherent in historical market data. By training on a diverse dataset comprising stock prices, strike prices, time to expiration, and volatility, DL models aim to capture the nuanced interactions among these variables, thus enabling more accurate and robust option pricing compared to traditional methods. Moreover, the analysis is extended to incorporate ML models, which complement DL techniques by leveraging a variety of algorithms to discern patterns and make predictions based on historical data. Through the evaluation and comparison of both DL and ML models, we aim to ascertain the efficacy and suitability of each approach for option pricing tasks. Among the models evaluated, the Multi-Layer Perceptron (MLP) demonstrated superior performance in option pricing tasks, achieving high accuracy and robustness across synthetic and real-world datasets. Additionally, ensemble learning techniques such as XGBoost and Gradient Boosting Tree Regression (GBTR) exhibited strong predictive capabilities, offering valuable insights into option pricing dynamics with R2 values of 0.9694 and 0.9474 in S&P 500 dataset respectively. Overall, the comparative analysis highlights the efficacy of machine learning and deep learning models in option pricing, with MLP emerging as a standout performer. © 2024 IEEE.

关键词： Long short-term memory

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Continuous Sign Language Recognition using Convolutional Neural Network 2

Continuous Sign Language Recognition using Convolutional Neu...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Shankara Narayanan, V. Sneha Varsha, M. Padmavathi, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350328202

Automatic Continuous Sign Language Recognition is a task that aims at associating words/phrases for sign language gestures. The scope of building a CSLR framework extends beyond bridging the communication barrier between the deaf community and the hearing majority but includes profound benefits such as bootstrapping translation systems, human-computer interaction, virtual reality environments, etc. In this study, a continuous sign language recognition framework based on a CNN architecture is proposed. Utilizing the Argentina Sign Language dataset, sign language gestures are preprocessed to extract training and testing frames. Employing transfer learning with the ResNet-50 model, a CNN is trained to classify each frame into one of 64 different words present in the dataset. The paper also conducts a thorough post-training data analysis, offering insights into areas where the model performed less effectively, identifying commonly confused pairs of sign language gestures. Additionally, a heatmap overlay technique is employed to elucidate the black-box CNN model. © 2024 IEEE.

关键词： computer vision

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Deepfake detection and classification using local surface geometrical features

Deepfake detection and classification using local surface ge...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Sivabalamurugan, M. Swapna, T.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350376876

The rise of deepfake technology presents a critical challenge to the integrity of digital media, prompting the need for advanced forgery detection techniques. This paper proposes a novel approach for detecting deepfake image forgery by integrating local surface geometry analysis into the detection process. Leveraging the inherent alterations in local surface geometry caused by deepfake generation, our method extracts and merges this information with the original image, enhancing the discriminative features essential for accurate identification of manipulated content. Using classification networks such as Resnet, Densenet, and Efficient, our method achieves a remarkable accuracy rate of above 98 % in correctly identifying deepfake images for the classification models. This high accuracy underscores the effectiveness of our approach in detecting sophisticated deepfake content. By combining local surface geometry analysis with deep learning-based classification, our method represents a significant advancement in the field of deepfake detection, offering a promising solution to combat the proliferation of manipulated media and preserve the authenticity of digital imagery. © 2024 IEEE.

关键词： Image enhancement

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