Determining the optimal range of angle tracking radars

Khosravi, Mohammad R.; Samadi, Sadegh; Akbarzadeh, Omid

doi:10.1109/icpcsi.2017.8392303

Cited by 10 publications

(2 citation statements)

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“…Also with using this approach, the decision time does not exceed than the permissible one (50 ms). For a future work, we can apply the approach in other types of communication networks such as cisco networks, commercial wireless networks [13], sensor networks [11], [14][15], and other types of TE networks [16] for communication of multimedia contents [17][18][19] and netted radars [20][21][22].…”

Section: Resultsmentioning

confidence: 99%

Determining the Optimum Number of Paths for Realization of Multi-path Routing in MPLS-TE Networks

et al. 2017

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Section: Resultsmentioning

confidence: 99%

Determining the Optimum Number of Paths for Realization of Multi-path Routing in MPLS-TE Networks

et al. 2017

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“…Numerous industries now profit from cutting-edge technology by disseminating technology to the populace. A recent study demonstrates the engagement of several researchers in wireless communication [1][2][3][4][5] to improve an existing system by addressing pertinent difficulties. Some areas of research, such as AI, have played a crucial part in the evolution of intelligence over these years to develop different projects in areas such as image recognization [6].…”

Section: Introductionmentioning

confidence: 99%

Predicting the total Unified Parkinson’s Disease Rating Scale (UPDRS) based on ML techniques and cloud-based update

et al. 2023

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Nowadays, smart health technologies are used in different life and environmental areas, such as smart life, healthcare, cognitive smart cities, and social systems. Intelligent, reliable, and ubiquitous healthcare systems are a part of the modern developing technology that should be more seriously considered. Data collection through different ways, such as the Internet of things (IoT)-assisted sensors, enables physicians to predict, prevent and treat diseases. Machine Learning (ML) algorithms may lead to higher accuracy in medical diagnosis/prognosis based on health data provided by the sensors to help physicians in tracking symptom significance and treatment steps. In this study, we applied four ML methods to the data on Parkinson’s disease to assess the methods’ performance and identify the essential features that may be used to predict the total Unified Parkinson’s disease Rating Scale (UPDRS). Since accessibility and high-performance decision-making are so vital for updating physicians and supporting IoT nodes (e.g., wearable sensors), all the data is stored, updated as rule-based, and protected in the cloud. Moreover, by assigning more computational equipment and memory in use, cloud computing makes it possible to reduce the time complexity of the training phase of ML algorithms in the cases we want to create a complete structure of cloud/edge architecture. In this situation, it is possible to investigate the approaches with varying iterations without concern for system configuration, temporal complexity, and real-time performance. Analyzing the coefficient of determination and Mean Square Error (MSE) reveals that the outcomes of the applied methods are mostly at an acceptable performance level. Moreover, the algorithm’s estimated weight indicates that Motor UPDRS is the most significant predictor of Total UPDRS.

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Distributed random cooperation for VBF-based routing in high-speed dense underwater acoustic sensor networks

et al. 2018

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Determining the optimal range of angle tracking radars

Cited by 10 publications

References 2 publications

Determining the Optimum Number of Paths for Realization of Multi-path Routing in MPLS-TE Networks

Determining the Optimum Number of Paths for Realization of Multi-path Routing in MPLS-TE Networks

Predicting the total Unified Parkinson’s Disease Rating Scale (UPDRS) based on ML techniques and cloud-based update

Distributed random cooperation for VBF-based routing in high-speed dense underwater acoustic sensor networks

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