The distributed embedded systems paradigm is a promising platform for high-performance embedded applications. We present a distributed algorithm and system based on cost-effective devices. The proof of concept shows h...
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ISBN:
(纸本)9798400705977
The distributed embedded systems paradigm is a promising platform for high-performance embedded applications. We present a distributed algorithm and system based on cost-effective devices. The proof of concept shows how a parallelized approach leveraging a distributed embedded platform can address the computational of the machinelearning K-Nearest Neighbors (K-NN) algorithm with large and heterogeneous datasets.
Health has a significant influence on one's wellbeing and quality of life. By assisting medical practitioners, suggesting possible therapies, and allowing early illness identification, machinelearning techniques ...
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Every data packet must pass through a few intermediate nodes toreach its destination. Among other reasons, tremendous growth in internetdevices encourages those intermediate nodes to drop the data packets. Optimizing ...
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machinelearning based intelligent approach is applied to finding spam in YouTube videos. Spam comments are ones that are promotional or unrelated. A growing number of users have been drawn to the idea of making money...
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In contemporary society, mental health concerns are gaining heightened attention, and accurately forecasting individuals' mental well-being has become a pivotal research focus. Traditional predictive models for me...
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The subfield of artificial intelligence (AI) within computer science aims to create intelligent machines capable of performing tasks typically requiring human intelligence. This foundational concept posits that human ...
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ISBN:
(纸本)9798350391558;9798350379990
The subfield of artificial intelligence (AI) within computer science aims to create intelligent machines capable of performing tasks typically requiring human intelligence. This foundational concept posits that human intelligence can be sufficiently defined for machines to emulate. machinelearning (ML), a branch of AI, enables software programs to enhance their predictive accuracy without explicit programming, using historical data to forecast new output values. Deep learning, a subset of ML, involves training models to organize sounds, text, or images using neural networks and substantial labeled data. In some cases, deep learning models surpass human performance, achieving state-of-the-art accuracy. This research study explores the principles and advancements in AI, ML, and deep learning, emphasizing their transformative potential and applications.
Introducing machinelearning (ML) into cooperative spectrum sensing (CSS) in cognitive radio has yielded effective data-driven solutions for the spectrum shortage problem. Significant quantities of labeled data are re...
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ISBN:
(纸本)9798350371000;9798350370997
Introducing machinelearning (ML) into cooperative spectrum sensing (CSS) in cognitive radio has yielded effective data-driven solutions for the spectrum shortage problem. Significant quantities of labeled data are required for ML-based CSS model training. However, collecting and annotating data repeatedly under dynamic spectrum environmental conditions is time-consuming, costly, and impractical. To this end, we propose a novel non-parametric dual transfer framework for CSS (DTCSS) to solve the poor generalization sensing performance caused by insufficient labeled data in the target environment with different wireless signals and propagation. DTCSS features a unique design that employs a two-stage learning approach. The offline training stage aims to transfer domain-level and class-level knowledge from the existing environment to the target environment and train a target detector. The objective of the online sensing phase is to utilize the trained detector to deduce the spectrum status of the target environment. DTCSS is robust and effective without hyperparameter tuning. Results from simulations indicate that DTCSS can achieve competitive sensing performance.
AI-Powered Edge computing is accelerating the integration of the cyber world with the ever-growing list of new physical IoT devices and will fundamentally change and empower the way humans interact with the world. In ...
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ISBN:
(纸本)9798350376975;9798350376968
AI-Powered Edge computing is accelerating the integration of the cyber world with the ever-growing list of new physical IoT devices and will fundamentally change and empower the way humans interact with the world. In this paper, we prototyped and analyzed three edge computing architectures for running SmartFall, a real-time fall detection application that uses accelerometer data from the watch, to compare the trade-off in relationship to battery consumption, potential data loss, machinelearning model's prediction accuracy, and latency in model inferencing. Our experiments show that running the machinelearning prediction on the server using the TensorFlow native model format has achieved the best model accuracy without draining the battery power of the smartwatches. However, the optimal selection of the software architecture depends on the intended deployment environment, projected user numbers, users' privacy concerns, and network stability.
The convergence of machinelearning and edge computing has led to the development of scalable solutions that bring computation closer to the data source. However, optimizing machinelearning models efficiently for edg...
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In digital era vast quantity of data is produced by smart machines and it is challenging work to store and evaluate the Big data. The frequent pattern mining in Big data plays extremely vital part in industry for deci...
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