Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, with arrhythmias contributing significantly to these statistics. This study proposes a machine learning framework for automated ECG classifi...
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ISBN:
(数字)9798331532215
ISBN:
(纸本)9798331532222
Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, with arrhythmias contributing significantly to these statistics. This study proposes a machine learning framework for automated ECG classification, focusing on Premature Ventricular Contraction (PVC), Atrial Fibrillation (AFib), and Ventricular Tachycardia (VTach). Normal electrocardiogram (ECG) signals from the MIT-BIH Arrhythmia database were integrated with synthetically generated abnormal signals, which were derived using mathematical models and implemented in MATLAB. Signals were segmented into 1-second intervals, with features extracted including P-wave amplitude, QRS width, and T-wave frequency. A Random Forest classifier achieved 0.88 accuracy, with high precision and recall for each class, including normal, atrial fibrillation, and ventricular ectopic beats, demonstrating balanced performance across all categories. The incorporation of synthetic data addressed dataset imbalances, particularly improving detection of minority classes like VTach. The framework demonstrates potential for real-time implementation in wearable devices and telemedicine platforms. Future work will focus on expanding the dataset and exploring deep learning techniques to enhance clinical applicability.
Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent s...
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ISBN:
(数字)9798331533366
ISBN:
(纸本)9798331533373
Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital Twin (DT) platform for intelligent Transportation Systems (ITS), designed to overcome the limitations of existing frameworks by offering a modular and scalable solution for traffic management. Built on a Domain Concept Model (DCM), the architecture systematically models key ITS components enabling seamless integration of predictive modeling and simulations. The architecture leverages machine learning models to forecast traffic patterns based on historical and real-time data. To adapt to evolving traffic patterns, the architecture incorporates adaptive Machine learning Operations (MLOps), automating the deployment and lifecycle management of predictive models. Evaluation results highlight the effectiveness of the architecture in delivering accurate predictions and computational efficiency.
The adoption of humanoid robots has surged in recent years, particularly in addressing operational limitations. This study introduces a methodology for detecting blind spots and anomalies in robot motion, using the Na...
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ISBN:
(数字)9798331532215
ISBN:
(纸本)9798331532222
The adoption of humanoid robots has surged in recent years, particularly in addressing operational limitations. This study introduces a methodology for detecting blind spots and anomalies in robot motion, using the Nao Robot as a case *** approach integrates multisensor data with deep learning techniques validated on a proprietary dataset covering diverse real-world scenarios, including dynamic non-visible obstacles. High-frequency sensor sampling (T 0 = 0.2 s) ensures accurate data, while DBSCAN clustering and ANOVA F-value-based feature selection identify critical features like obstacle distances and motion parameters for collision *** learning with Sigmoid and ReLU activations, the ADAM optimizer, and Sparse Categorical Crossentropy loss achieved a 95% F1 score and 99% AUC-ROC, demonstrating robust performance in dynamic environments also in real life environment.
The proceedings contain 21 papers. The special focus in this conference is on Software engineering Research and Practice. The topics include: MetaPix: A data-Centric AI Development Platform for Efficient Manageme...
ISBN:
(纸本)9783031866432
The proceedings contain 21 papers. The special focus in this conference is on Software engineering Research and Practice. The topics include: MetaPix: A data-Centric AI Development Platform for Efficient Management and Utilization of Unstructured Computer Vision data;A Novel Architecture That Examines Network Activity in a Docker-Based Multitenant to Verify Zero Trust Container Architecture (ZTCA) Compliance;VENUS: Designing a Validation Engine for User Stories;a Framework for Requirements Modeling of Safety Critical Systems: A Continuous Glucose Monitoring System Case Study;plan-Based and Agile Companies: A Comparison of Project Management Approaches;Service Availability Ratio (SAR): An Availability Metric for Microservice;Development of a Desktop Agent System Using GPT;navigating Challenges in E-Participation: A Comprehensive Meta-Analysis;The Shifting Landscape of Cybersecurity: The Impact of Remote Work and COVID-19 on data Breach Trends;Farmchain: Empowering Smallholder Farmers Through Blockchain and DAOs: Cryptournomic Approach;privacy Strategies for Police Personnel: Co-designing a Self-assessment Tool;cybersecurity Threats: An Analysis of the Rise and Impacts of State Sponsored Cyber Attacks;moore’s Law: What Comes Next?;use of Emerging Technologies in Africa;using Survey to Investigate the Integration of Artificial Intelligence in e-learning;students Satisfaction with the Distance Education During Covid-19 Pandemic;Improving Student Success in Math Courses Using WeBWorK;fast Food Review Online;a Systems Approach to Improving E-learning Using Theory of Constraints.
With the ever-increasing data in the world, artificial intelligence (AI) is predicted to be an essential tool for converting huge data into meaningful data. The goals of machine learning (ML) are to identify complicat...
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ISBN:
(数字)9798331542726
ISBN:
(纸本)9798331542733
With the ever-increasing data in the world, artificial intelligence (AI) is predicted to be an essential tool for converting huge data into meaningful data. The goals of machine learning (ML) are to identify complicated patterns in multi-dimensional data and utilize uncovered patterns to classify processes or predict data. Prediction accuracy is the accustomed measure of success in ML applications. In this paper, the effect of using a machine learning approach on accuracy value is studied. We compared three feature selection algorithms, namely IndFeat, MRMR, and ANOVA algorithms. The SVM classifier is applied to the selected features in order to compare the three algorithms and examine the effect of data reduction on the accuracy value. The simulation results showed that the ANOVA algorithm is better than the MRMR and IndFeat algorithms.
In recent years, the application of robotics has significantly advanced many fields, providing robust performance and efficiency in complex tasks without the need for human intervention. Robot control is a key area of...
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ISBN:
(数字)9798331542726
ISBN:
(纸本)9798331542733
In recent years, the application of robotics has significantly advanced many fields, providing robust performance and efficiency in complex tasks without the need for human intervention. Robot control is a key area of robotics that has received a lot of interest and technological development. Active Disturbance Rejection Controllers (ADRCs) are widely adopted due to several features, including their ability to maintain robust performance and effectively reject disturbances. The manual tuning of controller parameters is time-consuming and highlights the need for automation. This paper presents an innovative approach to tune the parameters of ADRC using Reinforcement learning (RL) based on Deep Deterministic Policy Gradient (DDPG) for a Differential Drive Mobile Robot (DDMR). The RL agent learns the ideal parameters of the ADRC through real-time, iterative interactions with the simulated environment, improving ADRC's performance without manual tuning. Simulation results demonstrate the effectiveness of the proposed idea in improving the trajectory tracking performance. Combining RL and ADRC provides a promising automated controller tuning solution, opening the door to more intelligent and adaptive robotic systems. 10.0pt.
As technology has advanced, using smart meters instead of conventional ones has changed. These smart meters are essential parts of intelligent grids, offering significant advantages to many stakeholders regarding soci...
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ISBN:
(数字)9798331532215
ISBN:
(纸本)9798331532222
As technology has advanced, using smart meters instead of conventional ones has changed. These smart meters are essential parts of intelligent grids, offering significant advantages to many stakeholders regarding social, environmental, and economic constraints. A detailed method of fine-grained collection and analysis of the metering data is necessary to benefit the different smart grid stakeholders. This results in collecting, transmitting, and processing a large amount of data with an exponentially raised dimension. It seriously constrains the system processing resources, latency, data management, transmission effectiveness, and analysis response time. In this regard, a new hybridization of ensemble learning and time-domain feature extraction is suggested for the automatic classification of appliances through the processing of their power consumption data. Time-domain feature mining makes up for the previously noted deficiency and can result in a considerable real-time data dimension reduction without sacrificing important information. data in compressed form can therefore be processed, examined, stored, and transmitted with efficiency. It offers a significant reduction in the post cloud-based classification latency along with computational and transmission effectiveness. The classification is carried out using the robust ensemble learning (EL) classifiers. The performance of considered EL classifiers is evaluated for the case of a real multi-class and multi-device smart meter dataset. The devised solution achieves a highest average classification accuracy of 88.33% for a 10-class problem while securing a compression gain of 32.73.
Machine learning and AI have been recently embraced by many companies. Machine learning Operations, (MLOps), refers to the use of continuous software engineering processes, such as DevOps, in the deployment of machine...
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This paper focuses on analyzing data obtained from a survey conducted among 312 management undergraduate students in China. The aim of the survey is to explore their perceptions, inclinations, and attitudes towards th...
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intelligentdata placement in hierarchical distributed storage networks (DSNs) has become crucial due to advancements in storage devices, an increase in big data applications, and strict time constraints. Inefficient ...
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ISBN:
(数字)9798331508050
ISBN:
(纸本)9798331508067
intelligentdata placement in hierarchical distributed storage networks (DSNs) has become crucial due to advancements in storage devices, an increase in big data applications, and strict time constraints. Inefficient data placement can lead to significant delays in data movement and increased service latency, impacting the overall performance of a distributed environment. It is a complex online decision-making problem due to its varying access patterns, dynamic network conditions, and ever-changing distributed environment, which traditional network optimization techniques fail to address efficiently. Reinforcement learning is best suited to address this problem as it learns from the environment dynamically and performs action. This paper presents a novel deep reinforcement learning (DRL) framework, soft actor-critic-2 (SAC-2), for heuristic data placement in the distributed network, leveraging the state-of-the-art SAC technique to improve efficiency, reduce latency, and subsequently minimize storage costs. We formulate the problem as a Markov Decision Process (MDP) model and incorporate a prioritized replay buffer for efficient learning. The heuristic data placement technique improves distributed hierarchical storage system efficiency compared to baseline strategies, as demonstrated by extensive experiments on Microsoft Research Cambridge (MSR) network traces.
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