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CTG-Based Fetal Health Prediction: A Comparative Study of Machine Learning Models

CTG-Based Fetal Health Prediction: A Comparative Study of Ma...

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International Conference on computing and Networking Technology (ICCNT)

作者： Vishnu Vardhan Kosuru Sree Virajitha Ramaraju Akshay Rajan Gouri Santhosh T Anjali Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This study explores the use of machine learning approaches for fetal health classification utilizing Cardiotocogram (CTG) data to improve prenatal treatment and maternal-fetal health. The study systematically examines five machine learning algorithms: Random Forest, Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression, using an extensive record with 2,126 well-annotated CTG recordings. This work is peculiar since it uses a wide range of feature extraction and preprocessing techniques to ensure high model correctness and dependability. With a 95% accuracy rate, the Random Forest algorithm proven to be the most effective, surpassing the other models by far. This study emphasizes how machine learning, especially ensemble approaches, may effectively predict health problems in fetuses. The study lays the groundwork for future developments in fetal health detection using cutting-edge technologies and machine learning algorithms by emphasizing interpretability and transparency.

关键词： Support vector machines Logistic regression Machine learning algorithms Accuracy Search methods Nearest neighbor methods Prediction algorithms Boosting Classification algorithms Random forests

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Empowering IoT Security: Leveraging Deep Neural Networks Against Ransomware and DDoS Attacks

Empowering IoT Security: Leveraging Deep Neural Networks Aga...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： K Geetha Dharmindar Chaudhary K. Shrivarshini Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Using Internet of Things (IoT) devices has become more efficient and convenient, but it has also increased the potential of security breaches, particularly from ransomware and Distributed Denial of Service (DDoS) assaults. This study looks into the ever-changing threats posed by ransomware on the Internet of Things, emphasizing human-centered detection and mitigation techniques. It highlights attack methods like supply chain breaches and device exploitation that jeopardize privacy and service continuity. To enhance IoT security, a deep neural network-based approach is proposed for automated reaction and real-time monitoring. The superior precision and efficacy of the suggested approach in mitigating ransomware and DDoS assaults in contrast to existing techniques underscores the significance of robust security protocols and active stakeholder engagement. This paper’s goal is to provide a thorough study of these dangers along with a novel solution that will improve security protocols in IoT ecosystems. This study seeks to close the gap between sophisticated technological protections and workable, user-oriented tactics by highlighting the need of human-centered approaches, guaranteeing that IoT devices continue to be dependable and safe in an increasingly interconnected world. This study emphasizes the vital need for an integrated strategy to IoT security that incorporates both cutting-edge technology and proactive human engagement through thorough evaluation and suggested solutions.

关键词： Deep learning Protocols Ecosystems Denial-of-service attack Internet of Things Security Ransomware Stakeholders computer crime Random forests

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PyroCyclone Eye: Satellite Detection System For Wildfire And Cyclone

PyroCyclone Eye: Satellite Detection System For Wildfire And...

引用

International Conference on computing and Networking Technology (ICCNT)

作者： Anirudh C Vinayan P Pankaj Sreenadh Venugopal T Anjali Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cyclones and wildfires are deadly; they do significant damage to human life and property. This requires an effective early warning system that would ensure a timely intervention since the normal methods are not efficient. Frequently fall short of the necessary pace and precision in successful detection as well as signaling: this results in response lag times and escalation of loss. In addition, most current systems often work on their own without any cooperation. It lacks integration and immediate communication means to the concerned authorities. However, in this study, we put forward a novel system that combines detection with early warning for cyclones and wild fires through satellite images using advanced computer vision techniques. We apply the YOLOv9 object detection algorithm on the satellite pictures which allows us to detect cyclones and wildfires with a high level of precision as well as speed. In addition, we design an alert system Using the Twilio WhatsApp API to report detected incidents to the relevant authorities without delay. Through this we aim to improve disaster management potential minimizing loss to life and property and ensure safety to the communities vulnerable to natural disasters.

关键词： Freeware Wildfires Satellites Disasters Object detection Alarm systems Cyclones Real-time systems Internet telephony Faces

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Cloud-Native CDN Monitoring Using CI/CD

Cloud-Native CDN Monitoring Using CI/CD

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International Conference on computing and Networking Technology (ICCNT)

作者： Richa Vivek Savant S Navin Sunder Samarth Seshadri Niharika Panda Shinu M Rajagopal Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In a time where digital content consumption reigns, it is essential to efficiently deliver multimedia resources. This research seeks to transform how content is delivered by implementing and overseeing a secure, monitored Content Delivery Network (CDN) on a scalable Kubernetes cluster, managed through a secure CI/CD pipeline built on Jenkins, using the Amazon Web Services (AWS) and essential DevOps tools like Docker, SonarQube, Trivy, Prometheus, Grafana, Argo CD and Helm. The results obtained in this study are derived from an emulation of a CDN. The SonarQube analysis confirms whether the system meets all code quality gates, Trivy identifies and resolves critical container vulnerabilities and OWASP vulnerability checks identify and mitigate security risks in software dependencies. The system is monitored by analyzing the performance metrics visualized in Grafana dashboards, which offer detailed insights into metrics including and not limited to: active jobs, job queue duration, queued rate, memory usage, CPU usage, executor health metrics, system load, RAM usage, root filesystem usage, disk space usage, disk I/O activities, time synchronized drift and time synchronized status. Additionally, SMTP e-mail notifications are configured to improve responsiveness to anomalies identified via monitoring. This study emphasizes making content delivery mechanisms more robust, providing advanced tools for developers, and enabling easy access to digital resources for the community.

关键词： Chaos Codes Content distribution networks Web services Pipelines Random access memory Security Synchronization Monitoring Queueing analysis

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Parser for TINY Language

Parser for TINY Language

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Ch. Durga Priya Ch. Vikram Raju A. Issac Daniel G. Rishik Patel Meena Belwal Department of Computer science and Engineering Amrita School of Computing Bangalore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

This study provides a novel method to generate a compiler, which translates a program using TINY language. TINY language is a simple programming language which is designed to understand the basic concepts of compiler. A compiler is a type of program which is designed to convert the code written in high-level language into machine language that is understandable by the computer. The procedure for building a parser requires various sub-tasks, there are a list of operations that include parsing or splitting the code into tokens, checking's the code's syntax using grammar rules, understanding, interpreting and optimizing the provided code and generating the execution-ready code by the performance of last assembling. In order to face the challenges of compiler construction, which includes rule-based complex grammar, error handling, and fast tokenization, the python programming language is implemented for developing pyLexer(tokenization) and pyParsing (syntactic analysis) libraries. Lexer is used for matching regular expressions to return corresponding tokens(lexemes), while parser is supposed to check whether the given syntax is according to the grammar rules or not through recursion. Web application is created for this using Streamlit, which provides users with many useful features like text rendering capability, error highlighting and code suggestions.

关键词： Phased arrays Codes Program processors Translation Source coding Syntactics Rendering (computer graphics) Tokenization Grammar Python

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

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

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Communication, Control and Intelligent Systems (CCIS)

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

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

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.

关键词： computer vision Tracking Pose estimation Anxiety disorders Control systems Real-time systems Intelligent systems

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Malicious URL Detection: Comparative Study of Machine Learning Algorithms

Malicious URL Detection: Comparative Study of Machine Learni...

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International Conference on computing and Networking Technology (ICCNT)

作者： Thadakaluru Jaswanthi Talluri Harshitha Simhadri Tanya Meena Belwal Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Malicious URLs can be detected by leveraging lexical analysis and machine learning, where the parsed components of the URL are parsed, and its features include domain reputation, URL length, and the presence of suspicious characters. Research on the detection of malicious URLs suffers challenges in keeping pace with the changing structural landscape of URLs. Adaptation to dynamic patterns and addressing the technical issues of efficient processing and scalability become important to stay one step ahead of the evolving threat landscape. This paper concentrates on addressing these challenges with the integration of Machine Learning and Lexical Analysis. Features extracted in this process are then fed into machine learning algorithms for the classification of URLs and are tested on unseen data. Research performance is measured using several metrics including Precision, Recall, F1-score and Accuracy during the Model evaluation. Out of all the models experimented, Extra Trees classifier stands out the best with 92% accuracy, showing effectiveness in accurate detection of malicious URL’s.

关键词： Uniform resource locators Measurement Accuracy Machine learning algorithms Computational modeling Scalability Forestry Feature extraction Data mining Random forests

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Data-Driven Insights: Mall Customer Segmentation through Fuzzy C-Means Clustering

Data-Driven Insights: Mall Customer Segmentation through Fuz...

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BIT Conference (BITCON), International

作者： Tina Babu Ebin P M Rekha R Nair Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

This study, employed the Fuzzy C-Means clustering algorithm to segment mall customers based on various demographic and spending attributes. Using a dataset of 200 mall customers, the study analyzed their annual income, spending score, and other relevant factors to identify distinct customer groups. The Fuzzy C-Means algorithm, which allows data points to belong to multiple clusters with varying degrees of membership, provided a nuanced segmentation that traditional clustering methods might miss. To enhance the interpretability of the results, the authors utilized Plotly, an interactive visualization tool, to create dynamic and insightful visual representations of the segmented data. The analysis revealed five distinct customer segments, each with unique characteristics and behaviors. The interactive plots facilitated a deeper understanding of the clustering results, enabling the identification of key patterns and trends within each segment. This segmentation can inform targeted marketing strategies and personalized customer experiences, potentially driving increased customer satisfaction and loyalty. The combination of Fuzzy C-Means clustering and Plotly visualization proved effective in uncovering complex relationships within the data, demonstrating the potential of these techniques in retail analytics and customer segmentation.

关键词： Visualization Time-frequency analysis Consumer behavior Heuristic algorithms Time series analysis Data visualization Clustering algorithms Machine learning Predictive models Market research

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MediLocker: Centralized Electronic Health Records System

MediLocker: Centralized Electronic Health Records System

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： N Harini R S Karishma M Pranav Ananya Duvvuri Marrivada Akhil Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

The transition from traditional paper-based patient records to advanced digital formats has not been without its challenges. While Electronic Health Records (EHRs) offer instantaneous access to detailed patient health information, there are drawbacks too. One significant drawback that was identified from the literature survey was the lack of interoperability and uniformity in patients' medical information across different healthcare facilities. This fragmentation hampers communication and collaboration among healthcare providers, leading to inefficiencies and gaps in patient care. Additionally, concerns about the security and privacy of patient data in EHRs persist, with instances of data breaches and unauthorized access raising alarm within the healthcare community. In response to these challenges, this paper proposes the development of a secure, privacy-ensuring Medilocker system aimed at investigating electronic health management systems comprehensively by assessing the status of EHR systems and efficiently managing electronic health records while safeguarding patient data. The implementation of role-based access control, coupled with data anonymization for preserving confidentiality, results in the development of a cohesive solution that facilitates the systemization, integration, and fortification of the healthcare system, while ensuring prompt access to information in compliance with privacy and security standards.

关键词： Surveys Data privacy Hospitals Data breach Information filtering Reliability Electronic medical records

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Dissecting Digital Dangers: A MITRE-Aligned Analysis of Two Prominent Malware Instances

Dissecting Digital Dangers: A MITRE-Aligned Analysis of Two ...

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International Conference on computing and Networking Technology (ICCNT)

作者： Subhiksha Suresh Rao Richa Vivek Savant Sahil Mishra B Sreevidya M Rajesh Department of Computer Science & Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The increasing complexity of malware requires a more thorough comprehension of how they operate. Using the MITRE ATT&CK framework, this project delves into the TrickBot and AsyncRAT malwares, offering a thorough examination of their tactics, techniques, and procedures (TTPs) across the cyber kill chain. This study uncovers trends in how these malicious software gain entry, carry out tasks, remain active, and extract information from a network by correlating TTPs with ATT&CK methods. Key analyses indicate that TrickBot utilizes elevated entropy values to avoid being detected, incorporates modular structures to enhance its capabilities, and utilizes spear-phishing and network spreading to achieve broad infection. Asyncrat is found to employ sophisticated anti-analysis methods, reflective code loading, and achieves persistence by altering the system registry while avoiding sandbox environments. Additionally, it establishes command and control (C2) connections to maintain long-term access over compromised systems. The observations uncovered in this research emphasize the importance of having strong cybersecurity policies and comprehensive threat intelligence to combat changing threats effectively. Understanding the complex behaviours and evasion methods of these malwares is crucial for creating specific defences and improving security overall.

关键词： Command and control systems Prevention and mitigation Loading Market research Malware Entropy Trojan horses Servers IP networks Data mining

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