Using cutting-edge computer vision and machinelearning techniques, this research study suggests a system that can recognize American Sign Language (ASL) in real-time motions. The suggested approach classifies ASL ges...
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This study explores the effectiveness, ethical considerations, and demographic influences of AI-based interventions in managing stress and anxiety. AI technologies, including machinelearning, Natural Language Process...
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In the previous decade, Internet of Things (IoT) systems have grown into a worldwide behemoth that has encompassed every element of everyday existence by enhancing human existence with uncountable intelligent assistan...
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In the previous decade, Internet of Things (IoT) systems have grown into a worldwide behemoth that has encompassed every element of everyday existence by enhancing human existence with uncountable intelligent assistance. Due to the ease of usage and increasing need for smart gadgets and networks, IoT is experiencing more security concerns today than ever before. As a result, a powerful constantly improved and current security solution is necessary for contemporary IoT systems. A significant technological improvement in machinelearning (ML) has been observed, opening up several potential study avenues for tackling existing and prospective IoT concerns. The fundamental goal of this study is to implement an ML-based model for IoT security enhancement. In the initial phase of this study approach, feature scaled has been performed on the UNSW-NB15 database utilizing the Minimum-maximum idea of normalizing to reduce data leaks on the experimental statistics. Principal Components Assessment (PCA) has been utilized to reduce dimensions in the following phase. Finally, for the investigation, 6 suggested ML solutions have been applied. The outcomes from experiments have been assessed using a validating database. The outcomes have been compared to previous research, and the outcomes have been compatible with an accuracy of 99.99 percent and an MCC-Mathew correlation coefficient of 99.97 percent.
In order to improve our understanding of brain systems, monitoring the depth of unconsciousness during anesthesia is essential in clinical settings and neuroscience research. EEG data offers a dependable way to charac...
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The increasing frequency and intensity of natural disasters, such as earthquakes, tsunamis, floods, and forest fires, necessitate the development of advanced early warning systems. Current disaster prediction systems ...
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We develop provably safe and convergent reinforcement learning (RL) algorithms for control of nonlinear dynamical systems, bridging the gap between the hard safety guarantees of control theory and the convergence guar...
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We develop provably safe and convergent reinforcement learning (RL) algorithms for control of nonlinear dynamical systems, bridging the gap between the hard safety guarantees of control theory and the convergence guarantees of RL theory. Recent advances at the intersection of control and RL follow a two-stage, safety filter approach to enforcing hard safety constraints: model-free RL is used to learn a potentially unsafe controller, whose actions are projected onto safe sets prescribed, for example, by a control barrier function. Though safe, such approaches lose any convergence guarantees enjoyed by the underlying RL methods. In this paper, we develop a single-stage, sampling-based approach to hard constraint satisfaction that learns RL controllers enjoying classical convergence guarantees while satisfying hard safety constraints throughout training and deployment. We validate the efficacy of our approach in simulation, including safe control of a quadcopter in a challenging obstacle avoidance problem, and demonstrate that it outperforms existing benchmarks.
In every nation, agriculture has boosted the economy. Agriculture is currently dealing with a number of difficulties, such as irrigation and water management. Crop irrigation plays a crucial role in agricultural produ...
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Power systems, known as the hearth of satellites, have a direct impact on how long the satellite will operate. An on-orbit mission might fail due to several problems with the satellite power system. Thus, during the e...
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ISBN:
(纸本)9798350323023
Power systems, known as the hearth of satellites, have a direct impact on how long the satellite will operate. An on-orbit mission might fail due to several problems with the satellite power system. Thus, during the entire lifespan of the satellite, the diagnostics of power system faults is crucial. In this study, a new machinelearning-based fault diagnosis approach has been proposed for geosynchronous (GEO) satellite power systems. In the feature extraction step of the approach, principal component analysis (PCA) technique is used. Then, LogitBoost with random forest classifier is utilized for the aim of classification. The experimental results show that the proposed model is effective and can be used for fault diagnosis of GEO satellite power systems.
This paper employs machinelearning techniques to combat the escalating threat of phishing attacks in the digital realm. The research builds a predictive model capable of differentiating between phishing and legitimat...
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The surge in digital transactions has paved the way for an alarming rise in credit card fraud, compelling the need for robust detection systems. The swift progress of technology has transformed customer payment habits...
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