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Opinion Analysis and Text Pattern Detection from Social Media Data Using Integrated Machine Learning Approach 2

Opinion Analysis and Text Pattern Detection from Social Medi...

引用

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

作者： Abdullah, A. Sheik Mishra, Utkarsh Bala Adithyan, G. Sriram, B. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350328202

Opinion analysis using social media data has become essential for corporations, politics, and social research because it provides insightful information about the public's attitudes toward various issues. In the realm of e-commerce, understanding consumer preferences and emerging trends is crucial for businesses to adapt and thrive in a competitive market. This paper focuses on trend detection techniques in fashion e-commerce, aiming to analyze market successes and failures. By harnessing social media data, the objective is to equip retailers with predictive tools, enabling them to anticipate trends, optimize logistics, and meet consumer demands effectively. The study utilizes Python and sentiment analysis tools, including the Vader Polarity Score, to evaluate social media content from platforms like Flipkart and Amazon. The research identifies trending keywords and assesses their relevance in the market by analyzing product names, ratings, reviews, and sentiment scores. This research empowers fashion retailers to make informed decisions by leveraging social media data and sentiment analysis. The findings spotlight current market trends and provide a framework for anticipating future consumer preferences. Implementing such techniques can revolutionize e-commerce strategies, enhance product offerings and ensure sustained market relevance. © 2024 IEEE.

关键词： Sentiment analysis

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Payable Outsourced Decryption for Functional Encryption Using BlockChain 1st

Payable Outsourced Decryption for Functional Encryption Usin...

引用

1st International Conference on Artificial Intelligence of Things, ICAIoT 2023

作者： Sendhil, R. Asifa, S. Savitha Sree, A. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9783031487736

In many new implementations that needs data storage and exchange (such as cloud storage services), the notion of Functional Encryption (FE) was initiated to label the defects of Public-key Encryption (PKE). One of the biggest issues with most FE charts is performance, as they are calculated on very expensive pairs of two lines. The general acquired solution to this issue is to load a massive load on a strong third party and let the client to do the simple calculations. However, it is unrealistic to accept that a mediator provides a free service. According to our perception, FE plans using External Decryption Scheme (FEOD) will not terminate the payment process between the user and the third party only if both are untrusted. In this article, the proposed method is to design a FE with Payable Outsourced Decryption (FEPOD) in the treaty. The payment according to FEPOD method is made by a blockchain based cryptocurrency. This allows users to pay a mediator when the allocated decoding is successfully completed. After defining the paradoxical model of the FEPOD design, the asymmetric structure of the FEPOD design is introduced. It also measures the overall performance of the proposal by executing a specific FEPOD scheme on the blockchain policy. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Integrative Framework for Heart Failure Risk Assessment and Tailored Mitigation Strategies 3

Integrative Framework for Heart Failure Risk Assessment and ...

引用

3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Yuthish Kumar, V. Goutham, S. Ir, Praveen Joe Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350391565

Heart failure is a major problem which affects many people across the *** paper proposes an integrated architecture combining advanced predictive modeling for the risk assessment of heart failure and medication recommendations concerning metabolism-related factors. By using classification algorithms, including decision trees and gradient boosting, the paper aims at accurately predicting heart failure risk. Moreover, the model will suggest remedial actions concerning diabetes stabilization, creatinine levels, and anemia management, striving to provide a complete approach to the prevention of heart failure. © 2024 IEEE.

关键词： Heart

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Advancing Heart Disease Prediction: A Hybrid Approach Integrating Machine Learning and Clinical Expertise 5

Advancing Heart Disease Prediction: A Hybrid Approach Integr...

引用

5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Dinesh, Sonakshi Tharun Raj, N. Raja, Sivakami School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331528539

Globally, cardiovascular diseases (CVDs) remain the primary cause of illness and deaths. Healthcare providers must be able to anticipate the onset of these diseases in order to make well-informed decisions about the diagnosis, treatment, and care of their patients. Thus, Machine learning (ML) models have become powerful tools in this context by offering the potential to identify critical risk factors and enhance the diagnostic process related to heart disease. The primary objective of this work is to leverage machine learning techniques to improve the prediction accuracy of heart disease. By identifying the most significant features through feature selection techniques, the study aims to enhance the predictive capability of the models. Furthermore, the research develops the possibility of building a hybrid model by merging the top two models based on their evaluation metrics. By using this hybrid model, the prediction of cardiovascular diseases should be greatly enhanced. Healthcare professionals can potentially avert the onset of severe cardiac events by appropriately identifying patients who are at high risk and taking preventative action. Additionally, this work adds to the ongoing efforts to create trustworthy and accurate algorithms for the prediction of cardiovascular disease, which is crucial for bettering patient outcomes and lowering the prevalence of heart disease worldwide. This method advances preventive healthcare and personalized medicine, which is a larger objective, in addition to increasing prediction accuracy. © 2024 IEEE.

关键词： Prediction models

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Smart Alarm System for Crime Hotspot Detection using Deep Learning 15

Smart Alarm System for Crime Hotspot Detection using Deep Le...

引用

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

作者： Pawar, Rohan Praveen Joe, I.R. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

The increasing number of surveillance cameras to watch over human activities necessitates the development of a system that can automatically identify violent and suspicious events. This is an essential piece of equipment for smart buildings. The average yearly death toll from acts of human aggression worldwide is 7.9 per 10,000 individuals. The majority of these acts of violence against people happen suddenly or in remote places. One of the main obstacles to halting these activities is the information delay in this case. In this work, the real time violence detecting technique is employed to thrive on this issue. Certain deep learning algorithms are applied in computer vision to anticipate and identify actions and attributes in videos. When police arrive at dangerous locations in real time, they analyze CCTV footage and begin an investigation before moving further. In smart buildings, this technology is mandatory. Violence Detection in real time will prevent any mis happenings from taking place by raising alerts to the security guards. Our model also has YOLO object detection inculcated in it in order to recognize any weapons or tools that an attacker may be using against people and the security guards will act accordingly. © 2024 IEEE.

关键词： CCTV surveillance technique Deep Learning MobileNetV2 Violence Detection

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AI-driven Solutions for Autonomous Maintenance and Fault Detection in Electrical Power Grids 4

AI-driven Solutions for Autonomous Maintenance and Fault Det...

引用

4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Kanimozhi, S. Enakshi Chhabra, Rohan Rai, Shashank Shekhar School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus School of Electronics Engineering Vellore Institute of Technology Chennai Campus

ISBN: (纸本)9798350354218

Efficient and reliable power grids are crucial for the functioning of modern society. However, increasing energy demand and renewable sources can lead to power failures. Traditional methods struggle. AI, particularly Random Forest Classifiers, offers a promising approach. These classifiers analyse vast amounts of grid data to identify patterns indicative of faults. This allows for real-time anomaly detection. This research investigates the efficacy of Random Forest models for fault classification, focusing on optimising data preprocessing and collection. Transparency in data collection and model development is crucial for data privacy. While simulated environments pose challenges for early fault detection, real-world data presents even greater complexities. The effectiveness of Random Forest Classifiers in early fault detection is critical for grid reliability, reducing power outages, and enhancing long-term operational efficiency. © 2024 IEEE.

关键词： Artificial Intelligence (AI) Autonomous Maintenance Data Security Fault Detection Power Grid Predictive Maintenance Random Forest

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Cybersecurity Advancements: A Comprehensive Survey of Machine Learning-based Preprocessing Techniques for Enhanced Website Phishing Detection 15

Cybersecurity Advancements: A Comprehensive Survey of Machin...

引用

15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kamble, Naresh Mishra, Nilamadhab Vellore Institute of Technology School of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798331300579

In a time of escalating digital threats, cybersecurity takes on paramount significance, as website phishing attacks pose considerable risks to individuals and organizations. This extensive survey delves into the ever-evolving realm of website phishing detection, with a specific spotlight on the pivotal role of machine learning-based preprocessing techniques in fortifying security measures. Research explores a wide spectrum of preprocessing methods, including URL analysis, content examination, and anomaly detection, all geared towards early-stage phishing detection. We also investigate the latest advancements in data feature extraction, dimensionality reduction, and data representation, all of which play a crucial part in the ongoing battle against website phishing. Through a methodical analysis of leading methodologies and illustrative case studies, this survey strives to provide a comprehensive understanding of how machine learning-based preprocessing enhances website phishing detection. Additionally, it examines the challenges, opportunities, and promising research directions within this domain. The insights presented here contribute to a knowledge base that serves as a valuable resource for security practitioners, researchers, and policymakers, helping them make informed decisions and implement effective countermeasures in the relentless fight against website phishing threats. © Grenze Scientific Society, 2024.

关键词： Phishing

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Visualization of DDoS Attack using Python Libraries 2

Visualization of DDoS Attack using Python Libraries

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Subbulakshmi, T. Mohith, G.K. Santhosh R A, Arun Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

With companies relying more and more on online services, the risk posed by Distributed Denial of Service (DDoS) attacks has notably increased. These attacks have the potential to cause significant damage to the systems they target, leading to disruptions in service, loss of data, and economic losses. To mitigate the impact of DDoS attacks, effective detection mechanisms are necessary. This research paper focuses on DDoS Attack prediction and visualisation techniques using Scapy functions of Python and its libraries to create graphs of various parameters that helps us analyse the of a given "pcap"file of which stores the packets captured in the real time network using WireShark Application. The paper uses various modules of Python to avoid human error and to give various features for the User to view the results plotted. © 2024 IEEE.

关键词： Python

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Enhancing Leukemia Diagnosis through Machine Learning: A Customized ResNet50 Approach 2

Enhancing Leukemia Diagnosis through Machine Learning: A Cus...

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

作者： Narendra, Modigari Negi, Sumit School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350371314

Leukemia is a type of blood and bone marrow cancer that continues to pose a major challenge in the field of healthcare. This is predominantly caused by the wide range of leukemia subtypes and the complexity of the classification process. Therefore, the current research aims to propose an approach to classify leukemia - more specifically, acute lymphoblastic leukemia - using a comprehensive methodology that integrates various machine learning techniques. A thorough data preprocessing is conducted and utilized Otsu's Thresholding for image segmentation and ResNet50 convolutional neural network for feature extraction. By applying the Lazy Predict library, the proposed work is further streamlined, allowing to finalize the offline model selection process. Finally, a custom ResNet50 CNN model is designed with extra layers in the feature extraction and classification resulting better performance compared to other state of the methods with an accuracy of 0.87 and a balanced accuracy of 0.83. Besides, the proposed model has faster inference times, making its application feasible for real-time leukemia diagnosis. © 2024 IEEE.

关键词： Image segmentation

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Detection of AI-Generated Image Content in News and Journalism 15

Detection of AI-Generated Image Content in News and Journali...

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

作者： Jagadish, Tarun Graceline Jasmine, S. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

This research paper explores the application of deep learning techniques for the detection of AI-generated images within the context of news and journalism. In response to the escalating risk posed by the pervasive use of sophisticated AI-generated content, particularly in the dissemination of fake news and disinformation, this study endeavors to develop a robust solution. The proposed approach involves the deployment of a high-accuracy deep learning model trained on a diverse dataset encompassing both authentic and synthetic images, with a focus on content generated by advanced AI tools such as diffusion models. The ultimate goal is to equip journalists with a tool that enables them to identify and avoid the incorporation of AI-generated images, thereby fortifying media integrity and bolstering public trust. © 2024 IEEE.

关键词： AI-generated Images Convolutional Neural Networks Deep Learning Diffusion Models Image Processing News and Journalism Synthetic Image Detection

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