Machine learning approach for power consumption model based on monsoon data for smart cities applications

The spread of COVID-19 worldwide continues despite multidimensional efforts to curtail its spread and provide treatment. Efforts to contain the COVID-19 pandemic have triggered partial or full lockdowns across the globe. This paper presents a novel framework that intelligently combines machine learning models and the Internet of Things (IoT) technology specifically to combat COVID-19 in smart cities. The purpose of the study is to promote the interoperability of machine learning algorithms with IoT technology by interacting with a population and its environment to curtail the COVID-19 pandemic. Furthermore, the study also investigates and discusses some solution frameworks, which can generate, capture, store, and analyze data using machine learning algorithms. These algorithms can detect, prevent, and trace the spread of COVID-19 and provide a better understanding of the disease in smart cities. Similarly, the study outlined case studies on the application of machine learning to help fight against COVID-19 in hospitals worldwide. The framework proposed in the study is a comprehensive presentation on the major components needed to integrate the machine learning approach with other AI-based solutions. Finally, the machine learning framework presented in this study has the potential to help national healthcare systems in curtailing the COVID-19 pandemic in smart cities. In addition, the proposed framework is poised as a pointer for generating research interests that would yield outcomes capable of been integrated to form an improved framework.

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An Optimized FPGA Architecture for Sleep Level Categorization Using Fuzzed Utilized Machine Learning Approach

Advances in Medical Technologies and Clinical Practice - Advancing the Investigation and Treatment of Sleep Disorders Using AI ◽

10.4018/978-1-7998-8018-9.ch012 ◽

2021 ◽

pp. 214-224

Author(s):

Britto Pari J. ◽

Mariammal Karuthapandian ◽

Vaithiyanathan Dhandapani

Keyword(s):

Machine Learning ◽

Power Consumption ◽

Reference Data ◽

Learning Approach ◽

Use Of Self ◽

Self Regulated Learning ◽

Fpga Architecture ◽

Machine Learning Approach ◽

Fir Filtering ◽

Sensitivity Specificity

In this chapter, an efficient FPGA architecture is proposed to categorize and analyze the sleep level. This proposed architecture is implemented using four sub parts which are namely preprocessing unit, FIR filtering, self-regulated learning, and fuzzy deduction. The EEG (electro encephalo gram) and EMG (electro myogram) are signal samples are considered for the analysis of this sleep level. The signals are initially preprocessed to remove undesired signal components. Further, a reconfigurable multichannel multiply accumulate (MAC)-based FIR filter is utilized for achieving the desired signal. Then the signal is classified based on the reference data with the use of self-regulated machine learning and fuzzy deduction schemes which involves averaging and thresholding process. Further, the signals are categorized into completely awake level, partially awake level, and sleep level using fuzzy if-then rules. The performance parameters are analyzed in terms of sensitivity, specificity, latency, area occupied, power consumption, and speed enhancement.

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