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Crypto Prophets: Machine Learning Oracles for Price Forecasting

Crypto Prophets: Machine Learning Oracles for Price Forecast...

引用

Integration of Emerging Technologies for the Digital World (ICIETDW), International Conference on

作者： S Baghavathi Priya Janani Srinivasan Anusha Priyanga S Veda Chatiyode Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

Cryptocurrency has emerged as a revolutionary financial asset, captivating investors and researchers alike with its volatile nature and potential for high returns. As the market continues to evolve, there is an increasing need for sophisticated tools to predict price movements and inform investment decisions. This research work delves into a predictive assessment of cryptocurrency price fluctuations, using thorough machine learning techniques to project future prices for Monero, Ethereum, Wrapped Bitcoin, and Bitcoin—the four leading cryptocurrencies. Using historical price data, our methodology includes data preprocessing, feature selection, and the application of various machine learning models such as Linear Regression, Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), Random Forest, and Support Vector Regression (SVR). The models’ predictive performance was meticulously evaluated using Mean Squared Error (MSE), R-squared (R 2 ), and Mean Absolute Percentage Error (MAPE). Linear Regression achieved superior performance for Bitcoin with an R 2 score of 0.9823 and Wrapped Bitcoin with an R 2 score of 0.9780, while XG-Boost excelled in Monero predictions with the lowest MSE of 325.33. This research offers critical insights for investors, market strategists, and financial analysts, underscoring the prominent impact of machine learning in enhancing the market analysis of cryptocurrencies and informing decision-making processes. The work immensely contributes to the flourishing area of financial technology, suggesting various ways to move forward with integrated advanced predictive models and sentiment analysis of markets and economic indicators to redefine investment strategies in the highly volatile cryptocurrency domain.

关键词： Sentiment analysis Fluctuations Biological system modeling Linear regression Machine learning Bitcoin Predictive models Nearest neighbor methods Forecasting Investment

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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...

引用

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

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

引用

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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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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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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Real-Time Facial Expression Recognition Using Deep Learning for Enhanced Human-Machine Interactions

Real-Time Facial Expression Recognition Using Deep Learning ...

引用

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

Real-time facial emotion recognition is a rapidly advancing field in computer vision and artificial intelligence with wide-ranging applications. This project implements a system for detecting and classifying facial expressions in real-time video streams using deep learning techniques. A convolutional neural network (CNN) model was developed and trained on the FER-2013 dataset to recognize seven basic emotions: anger, disgust, fear, happiness, sadness, surprise, and neutral. The system architecture integrates face detection, facial landmark extraction, and emotion classification components. Data augmentation and preprocessing techniques were applied to improve model performance. Two CNN architectures were evaluated - a baseline model and an enhanced model with additional layers and data augmentation. The enhanced model achieved 75.97% accuracy on the test set, surpassing the current state-of-the-art for the FER-2013 dataset. A graphical user interface was developed using Python Tkinter to create an accessible application. Real-time testing demonstrated the system’s ability to detect faces and classify emotions in live video feeds from a webcam. The system shows promise for applications in fields such as human-computer interaction, security, marketing, and mental health monitoring.

关键词： Human computer interaction Deep learning Emotion recognition Webcams Face recognition Streaming media Data augmentation Real-time systems Data models Convolutional neural networks

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Enhancing Software Test Effort Estimation using Ensemble Learning Algorithms

Enhancing Software Test Effort Estimation using Ensemble Lea...

引用

Intelligent Technologies (CONIT), International Conference on

作者： K Venkatraman M Akashvarma S Siddharth Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

In software engineering, accurate test effort prediction is critical to project schedule and resource efficiency. Conventional approaches, which depend on past performance or expert opinion, frequently fail to provide a reliable estimate of the testing effort. These approaches might offer some insight at first, but as project complexity rises, they become less scalable and more error-prone. This study explores the use of machine learning to improve the estimation of software test effort in response. This work intends to overcome the shortcomings of conventional methods and create more accurate and scalable solutions by utilising the power of machine learning algorithms. The goal of the research is to determine the best machine learning strategy for software test effort estimation through extensive testing and comparative analysis. The results of this study are compared using metrics such as RMSE, MSE, MAE, and R2 score, with Random Forest algorithm demonstrating superior performance over other algorithms.

关键词： Schedules Machine learning algorithms Accuracy Software algorithms Estimation Software Software reliability

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Swarm-Optimized Deep Convolutional Networks for Precise Detection and Grading of Diabetic Retinopathy

Swarm-Optimized Deep Convolutional Networks for Precise Dete...

引用

Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： U Sadhana Tripty Singh Beena. B. M Dept. of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Diabetic Retinopathy (DR) is a very critical problem of diabetes that can lead to blindness if not detected and treated in the early stage. This research was conducted on this difficult disease based on image processing and Machine intelligence. In this research, our model detects and classifies the DR using the swarm-optimized deep convolutional networks. The proposed model integrated the convolutional neural networks (CNNs) along with Particle Swarm Optimization (PSO) to enrich the performance of the model execution and results with fine-tuned hyperparameters. This custom-optimized CNN model, designed for image classification, was evaluated on the Kaggle diabetic retinopathy dataset. The swarm-optimized deep CNN model presented balanced performance across all DR classes and achieved a precision of 0.93, recall of 0.97, and F1-score of 0.95 for all classes. The model's overall accuracy reached 0.84 on the selected dataset.

关键词： Diabetic retinopathy Accuracy Machine learning Retina Robustness Real-time systems Convolutional neural networks Particle swarm optimization Machine intelligence Image classification

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

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

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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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Smart Surveillence Drone with Navigation System

Smart Surveillence Drone with Navigation System

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computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

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

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.

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