ML that creates a global framework by gathering knowledge from a number of different dispersed edge clients. FL allows on-device training, keeps client information in private, and updates the frameworks. FL approaches...
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Story generation stands as a crucial, yet formidable task, necessitating a profound grasp of subtle, often unspoken knowledge, along with context-specific cues to craft compelling narratives. The core challenges invol...
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The widespread adoption of online exams has highlighted the need for robust security and integrity measures. This research proposes a scalable architecture that integrates blockchain technology to address these challe...
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The research aims to demonstrate the susceptibility of Machine Learning Algorithms to adversarial attacks. Machine Learning is pivotal in diverse applications across homogeneous and heterogeneous environments, includi...
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With the use of a Novel Random Forest Classifier with K-Fold Cross Validation and a comparison of the findings with those of a Stochastic Gradient Classifier for the Chicago Crime dataset, the purpose of this endeavou...
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As technology is evolving and changing day by day many possibilities have become possible such as the development of smart and natural interface interactive systems between the user and computer using hand gestures. R...
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ISBN:
(数字)9789819725083
ISBN:
(纸本)9789819725076
As technology is evolving and changing day by day many possibilities have become possible such as the development of smart and natural interface interactive systems between the user and computer using hand gestures. Robots are trying these techniques to mimic humans and amaze everyone with the new technological advancement and innovations helping to make communication with smart machines easier. Also, the technology has the capabilities to be used in the medical industry for the distribution of hospital resources and help to reduce dependency on translators. Using such technology is also promoting the things which were considered impossible can be achieved now with the help of integration with AR/XR platforms and more futuristic technologies for better results. Gesture recognition is playing a major role in making life easier and smart for making our day-to-day tasks with the integration with IoT devices and cloud-based systems. The hand gesture recognition system is an example of a gesture recognition system that could help speech-impaired people to reduce their dependability on a translator for communicating their ideology. With the help of a hand recognition system, non-verbal communication can be easily achieved with the control of computers. With the help of technology interacting with humans becomes very easy and convenient. Further, the technology can be extended when combined with augmented reality, cloud computing, and IoT for more practicality and real-life implementation. With the help of AI and machine learning previous records can be maintained and further improved for increasing the overall experience of the user. The main objective of the system will give the system ability to perform different functions of computer and mouse cursor functions like drag and drop and webcam functionalities with the help of virtual mouse system functionality. Techniques like image recognition of deep learning are used for performing real-time tracking. The existing syste
Denoising (LDCT) low dose computed-tomographical imaging is essential for improving diagnostic preciseness and image clarity, especially in the presence of gaussian noise. In LDCT imaging, gaussian noise adversely aff...
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Simulation is crucial for autonomous driving technology evolution. Radar, as an essential sensor in this field, significantly influences decision-making with its outputs. High-fidelity autonomous driving simulations r...
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Simulation is crucial for autonomous driving technology evolution. Radar, as an essential sensor in this field, significantly influences decision-making with its outputs. High-fidelity autonomous driving simulations require radar models that replicate radar outputs, including false alarms, missed alarms, and measurement errors, both in real-time and with high fidelity. The radar detection process is highly complex, and false and miss alarms add significant uncertainty to the detection results. Current radar models cannot accurately predict radar outputs. To address these issues, this study introduces a data-driven radar modeling approach. Initially, an analysis of factors influencing radar detection outcomes was conducted. Then proposes a labeling method for radar output objects, identify the corresponding scene targets, and distinguish between ghost and real objects. Following this, it introduces a modeling technique that separates radar output status and parameters, aiming to accurately predict radar outputs in the presence of false and missed alarms. It further decouples output parameters to boost prediction accuracy. Radar data is then collected to create a dataset. The radar model is developed and validated against conventional models. The model achieves a 96.5% accuracy in predicting false and missed alarms, with its predictions for radar output parameters closely approximating actual values. Compared to traditional models, there are improvements exceeding 70.60% and 93.68% respectively. Its 5-millisecond processing speed is substantially faster than actual radar speeds. This demonstrates the method's ability to create high-fidelity, real-time models. IEEE
Various applications, including space exploration, transportation, factories, and the military, demand the presence of mobile robots. In those applications, navigation algorithms are essential for enabling mobile robo...
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Various systems were developed to assist those with vision impairments and enhance their standard of life. Regrettably, the majority of these systems have restricted functionality. We decided to build a model that det...
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