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A Comparative Exploration of Machine Learning and Graph Deep Learning Models for Discriminatory Speech Detection 2

A Comparative Exploration of Machine Learning and Graph Deep...

引用

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

作者： Kavya, J. Pragathy Reddy, K.N. Maddipatla, Phanindra Kesanapalli, Anjali Arya, Arti PES University Dept. of Computer Science Engineering Karnataka Bengaluru560100 India

ISBN: (纸本)9798350371314

In an era marked by digital expansion, discriminatory speech remains a widespread concern, exerting significant societal implications. This study presents an overview of discriminatory speech detection techniques and explores the complexities involved in identifying discriminatory speech by utilizing a variety of datasets from various fields. The importance of preprocessing methods and feature engineering is next examined, opening the door for more in-depth study. Furthermore, the paper delves into traditional Machine Learning (ML) algorithms and state-of-the-art Deep Learning (DL) models along with a crucial component of using graph-based structures with Natural Language Processing (NLP) techniques and the use of graphs to extract contextual information. Using both graphs and NLP algorithms enhances the identification process by uncovering contextual connections within textual data. © 2024 IEEE.

关键词： Speech recognition

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Stress Level Analysis Using EEG Signals: A Comparative Study of EEGNET and CNN Deep Learning Models 5

Stress Level Analysis Using EEG Signals: A Comparative Study...

引用

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

作者： Khanaganni, Anulaxmi S. Monisha, H.M. Kumar, R. Ashok Bms College of Engineering Dept Information Science and Engineering Bangalore India Bms College of Engineering Dept Computer Science and Business Systems Bangalore India

ISBN: (纸本)9798350376685

Understanding the brain's response to stress is pivotal for advancing mental health monitoring technologies. In this research paper, we leverage deep learning techniques to predict stress levels using electroencephalography (EEG) data from 10 students. This data captures brain activity during both rest and stress-induced conditions, with the stress induced through a visually demanding Stroop test. We focus on categorizing stress levels into four distinct categories: no stress, low stress, medium stress, and high stress. For analysis, we employ two advanced deep learning models, EEGNet and Convolutional Neural Network (CNN), which have demonstrated significant potential in managing the complexities of EEG data and identifying intricate patterns related to different stress *** methodology in this study includes rigorous data preprocessing, segmentation, and labeling to ensure the reliability and accuracy of the model training process. The EEG data from the 10 students are combined into a single dataset, with 70% allocated for training and 30% for testing. This approach ensures a robust evaluation of the models' predictive capabilities. We systematically compare EEGNet and CNN based on accuracy, precision, recall, and other relevant metrics. Our goal is to determine which model more effectively predicts stress levels and to understand their respective strengths and limitations. This comparative analysis provides valuable insights and paves the way for future applications in real-time stress monitoring. © 2024 IEEE.

关键词： Convolutional neural networks

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Cardiac Disease Diagnosis from Echocardiogram using Random Forest Classifier 5

Cardiac Disease Diagnosis from Echocardiogram using Random F...

引用

5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Shruthi, T. Srimathi, P. Janani, S. St. Joseph's College of Engineering Dept. of Computer Science Engineering Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Cardiovascular diseases (CVDs) represent a leading global health issue, necessitating early detection and accurate diagnosis for effective treatment. Electrocardiography (ECG) serves as an essential tool that provides valuable insights into the heart's electrical activity, aiding in the identification of potential abnormalities. This project leverages machine learning techniques to enhance the diagnostic potential of ECG data, paving the way for improved cardiovascular care through precision medicine. The methodology focuses on training machine learning models to analyze ECG signals and classify heartbeats into categories that reflect various cardiac conditions. A com- prehensive dataset of approximately 20,000 ECG recordings, encompassing a wide range of heart rhythms and anomalies, establishes a robust foundation for model training. The Decision Tree classifier achieves an outstanding accuracy of 97%, demonstrating the ability to detect subtle patterns in ECG data. Furthermore, the Random Forest classifier showcases strong performance in distinguishing between different cardiac rhythms. Key metrics, such as precision, recall, and F1-score, provide a deeper understanding of the classifier's diagnostic effectiveness. The confusion matrix serves as a visualization tool to pinpoint areas where model adjustments are necessary, further enhancing accuracy. These promising results highlight the potential of machine learning in transforming cardiac health monitoring and improving diagnostic processes, ultimately contributing to better patient care and outcomes. By harnessing advanced algorithms, this project illustrates the immense possibilities for integrating machine learning into modern healthcare systems. © 2024 IEEE.

关键词： Electrocardiography

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Real-Time Network Monitoring: A Big Data Approach 14

Real-Time Network Monitoring: A Big Data Approach

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Jaswanth, Kunisetty Sruthi, S. Ramachandrula, Pranav Saravanan, S. Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India Amrita School of Computing Dept. of Computer Science Engineering Chennai India

ISBN: (纸本)9798350335095

Real-time network monitoring is a critical requirement for tracking user activities and ensuring optimal network performance. In this paper, we propose a big data approach to real-time network monitoring that leverages the capabilities of Apache Spark, MongoDB, and Apache Kafka with Python. Our approach utilizes distributed stream processing of network data, enabling real-time analytics for user activity monitoring and network performance optimization. Our approach demonstrates the effectiveness of our system through experiments with real network traffic data, showing how it can identify and track user activities in real-time. Our system can be used by network administrators and IT professionals to optimize network performance, improve user experience, and enhance security through real-time monitoring and analysis of user activity data. This approach can also be opted as a feature in SDNs. © 2023 IEEE.

关键词： Network performance

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Career Craft AI: A Personalized Resume Analysis and Job Recommendations System 1

Career Craft AI: A Personalized Resume Analysis and Job Reco...

引用

1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Rajput, Akshay Dubey, Akanksha Thakur, Rishabh Singh, Deepak Singh, Upendra Pratap Graphic Era Deeemed to Be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350384246

In the today's era of endless career options, the challenge to get that one ideal job that aligns best with individual's skills and interests is absent due to lack of personalized recommendations and lack of guidance about resume making it becomes challenging to find best jobs and get shortlisted for the hiring process of a company. This paper introduces a novel approach to address these challenges by the development of a Career Crafter AI - A Resume analysis and Job Recommendation System. Knowing the limitations of existing recommendations systems that primarily rely on individuals provided interests, our system, implemented on the e-learning platform for skill building, Level Up, goes beyond the traditional approach of recommendation system. Using Vector Machine algorithm and the cosine similarity our system classifies the users based on their skills and profile mentioned on the platform and Natural Language Processing enables to provide the tips and recommendation to improve our resume and get the insights about the user for better recommendation. The recommendation engine utilizes these insights to provide accurate and best fit jobs to the users and as well as provide potential candidate for job roles to the recruiters This research paper aims to provide a one stop destination for learners to improve their resume for getting an edge during shortlisting and interviews and to transform and optimize the job recommendation systems by offering a more personalized and effective approach, empowering individuals to navigate their career paths with confidence and precision. © 2024 IEEE.

关键词： Support vector machines

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'Vulnerabilities in Smart Contracts: A Detailed Survey of Detection and Mitigation Methodologies'

'Vulnerabilities in Smart Contracts: A Detailed Survey of De...

引用

2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Kumar, Nayantara K Honnungar, Niranjan V Sharwari Prakash, M. Lohith, J.J. B.M.S. College of Engineering Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350372502

In the ever-changing world of blockchain technology, the emergence of smart contracts has completely transformed the way agreements are executed, offering the potential for automation and trust in decentralized systems. Despite their built-in security features, smart contracts still face persistent vulnerabilities, resulting in significant financial losses. While existing studies often approach smart contract security from specific angles, such as development cycles or vulnerability detection tools, this paper adopts a comprehensive, multidimensional perspective. It delves into the intricacies of smart contract security by examining vulnerability detection mechanisms and defense strategies. The exploration begins by conducting a detailed analysis of the current security challenges and issues surrounding smart contracts. It then delves into established frameworks for classifying vulnerabilities and common security flaws. The paper examines existing methods for detecting, and repairing contract vulnerabilities, evaluating their effectiveness. Additionally, it provides a comprehensive overview of the existing body of knowledge in smart contract security-related research. Through this systematic examination, the paper aims to serve as a valuable reference and provide a comprehensive understanding of the multifaceted landscape of smart contract security. © 2024 IEEE.

关键词： Smart contract

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Enacting Rural Empowerment: Implementing Cooperative Commerce in Communities using Machine Learning 2

Enacting Rural Empowerment: Implementing Cooperative Commerc...

引用

2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Skandarsini, R.R. Sangavi, M.S. Harika, Ravitla Varun, C.M. RMK Engineering College Dept. of Computer Science and Business Systems Thiruvallur India

ISBN: (纸本)9798350365269

In order to revitalize rural economies through the empowerment of self-help organizations and the promotion of economic growth, this study offers a revolutionary cooperative commerce platform. The platform integrates cutting-edge features such as digital ledger and inventory management for sellers, demand forecasting, and dynamic pricing, thereby fostering optimized production and efficient marketplaces. Central to the platform's efficacy is the implementation of user segmentation, which categorizes users based on income and spending behaviors, enabling targeted marketing strategies and continuous application improvements to enhance user engagement and profitability. Leveraging machine learning algorithms, personalized user experiences are cultivated, elevating buyer satisfaction and overall engagement. Additionally, administrative tools provide valuable insights for informed decision-making. Preliminary results demonstrate improved financial management for self-help groups, heightened economic activities, and an enriched user-centric experience. Identified challenges and proposed future enhancements underscore the platform's commitment to adaptability and responsiveness to the evolving needs of rural communities, ensuring sustained relevance and impact. © 2024 IEEE.

关键词： Predictive analytics

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Prediction Model for Suicidal Behavior Disorder Risk Analysis by Correlating Cyber and Real World Data 4

Prediction Model for Suicidal Behavior Disorder Risk Analysi...

引用

4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Neelakantam, Gone Ecole Centrale School of Engineering Mahindra University Dept. Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350387063

In the era of internet of things (IoT), people are attentive to express their non verbal behavior on cyber world such as social media by using smart phone, laptop or tablet. People are more active in sharing their daily activities status such as current activity the person is into and personal life situations such as achievement, problem, stress and even any hazardous desire (i.e. suicide). In contrast with real world such as smart home environment, people generally spent their non verbal behavior in term of Activity Daily Livings (ADLs) in associating with smart home sensors. Based on non verbal and verbal behaviors, data are generated from both Cyber and Real world, which are big in volume and variety. However, there is lack of investigating Cyber and Real world data especially in analyzing the risk of committing suicide, considering suicide is a big issues and threats in the society. Therefore, it motivate us to propose prediction model to determine high and low risk of committing suicide by combining two separate approaches such as Activity Recognition (AR) for real world and Sentiment Analysis (SA) for cyber world. © 2024 IEEE.

关键词： Risk assessment

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A Brief Survey on Feature Extraction Models for Brain Tumor Detection 3

A Brief Survey on Feature Extraction Models for Brain Tumor ...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Janapati, Malathi Akhtar, Shaheda Acharya Nagarjuna University Dept. of Computer Science and Engineering Guntur India Guntur and Research Supervisor Dept. of Computer Science and Engineering Guntur India

ISBN: (纸本)9781665462167

Today, tumours are the second leading cause of cancer deaths. Cancer poses a significant threat to a large population of patients. The medical community needs a quick, automated, efficient, and trustworthy method for detecting tumours like brain tumours. Detection is crucial to effective treatment. If doctors are able to catch a tumour in its earliest stages, they have a better chance of preserving the patient's health. To do this, several distinct image processing methods are used. Through this method, doctors have been able to effectively treat tumours and save the lives of many patients. Tumors are simply abnormal growths of cells that cannot be stopped. As brain tumour cells multiply, they eventually deplete the brain's supply of nutrients. Clinicians currently use MR images (MRI) of the patient's brain to manually pinpoint the location and extent of a brain tumour. Brain tumours can develop at any age in both children and adults. However, this is not the case if detection is timely and accurate. This investigation focuses on three subtypes of brain cancer: gliomas, meningiomas, and pituitary tumours. While there have been numerous publications on the topic of brain tumour classification and prediction, very few have focused on the importance of feature extraction. Manual diagnosis and conventional feature extraction methods have their limitations, and new approaches are needed to overcome them. An automated diagnostic system is necessary for extracting features and making an accurate diagnosis of brain cancer. Although advancements are being made, automatic brain tumour diagnosis continues to struggle with issues like low accuracy and a high proportion of false-positive findings. In this research work, a brief survey is provided on feature extraction for brain tumor detection using machine learning and deep learning techniques. © 2023 IEEE.

关键词： Feature extraction

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Quora Question Sincerity Detection Using BERT-Based Framework 14

Quora Question Sincerity Detection Using BERT-Based Framewor...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Arora, Drishti Aggarwal, Garima Asif, Farhan Amity University Dept. of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350344837

In the dynamic landscape of online knowledge exchange, platforms such as Quora have become vital arenas for diverse discussions and information dissemination. However, distinguishing sincere from insincere questions poses a challenge, impacting user experience and content credibility. The research seeks to utilize BERT-based frameworks to classify Quora questions, with a specific focus on discerning between sincere and insincere inquiries. The study underscores the significance of automated insincere content identification in fostering a trustworthy and constructive online environment on Quora, thereby contributing to the improvement of online interaction quality. The research delves into a comprehensive methodology, including data preprocessing, model training, fine-tuning, and performance evaluation. Extensive experimentation demonstrates that the proposed BERT-based approach achieves an impressive F1-score of 96.1%, outperforming existing methods. The contribution of the study lies in its potential to enhance online interactions by automating the identification of insincere content, fostering a conducive environment for meaningful conversations. © 2024 IEEE.

关键词： Information dissemination

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