Development of Smart Healthcare System Based on Speech Recognition Using Support Vector Machine and Dynamic Time Warping

Ismail, Ahmed; Abdlerazek, Samir; El-Henawy, Ibrahim M.

doi:10.3390/su12062403

Cited by 83 publications

(53 citation statements)

References 24 publications

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“…Currently, more than 300 databases [48] are available for training neural networks and classification algorithms in different areas: pattern recognition in images and video data (for example, MNIST [36], CiFAR [49], Chars74 K [50]), sound recognition, weather forecasting and medical and biological research. The most critical tasks to solve on constrained devices in the IoT industry are the following: face recognition [51], speech recognition [52], health monitoring using human activity recognition on mobile devices [53] and autonomous vehicle driving [54]. This study is limited to the testing of LogNNet network on the database of handwritten numbers MNIST.…”

Section: Discussionmentioning

confidence: 99%

Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map

Velichko

2020

Electronics

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This study presents a neural network which uses filters based on logistic mapping (LogNNet). LogNNet has a feedforward network structure, but possesses the properties of reservoir neural networks. The input weight matrix, set by a recurrent logistic mapping, forms the kernels that transform the input space to the higher-dimensional feature space. The most effective recognition of a handwritten digit from MNIST-10 occurs under chaotic behavior of the logistic map. The correlation of classification accuracy with the value of the Lyapunov exponent was obtained. An advantage of LogNNet implementation on IoT devices is the significant savings in memory used. At the same time, LogNNet has a simple algorithm and performance indicators comparable to those of the best resource-efficient algorithms available at the moment. The presented network architecture uses an array of weights with a total memory size from 1 to 29 kB and achieves a classification accuracy of 80.3–96.3%. Memory is saved due to the processor, which sequentially calculates the required weight coefficients during the network operation using the analytical equation of the logistic mapping. The proposed neural network can be used in implementations of artificial intelligence based on constrained devices with limited memory, which are integral blocks for creating ambient intelligence in modern IoT environments. From a research perspective, LogNNet can contribute to the understanding of the fundamental issues of the influence of chaos on the behavior of reservoir-type neural networks.

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Section: Discussionmentioning

confidence: 99%

Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map

Velichko

2020

Electronics

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“…SVM is a supervised learning algorithm that is commonly used for classification and regression challenges. It has a simple structure, high adaptability, global optimization method, short training time, and good generalization performance [25,26]. Many researchers have employed SVM in pattern recognition, data mining, probability density estimation, and risk prediction [48].…”

Section: Support Vector Machinementioning

confidence: 99%

“…In the early stage of data collection, the level should be determined through detection and investigation. As the amount of data increases, artificial intelligence technology can be used to learn and train a risk assessment model [25].…”

Section: Risk Levelsmentioning

confidence: 99%

“…The support vector machine as an artificial intelligence learning method is used to assess and predict risks on credit scoring, healthcare, and network security assessment [25,26]. As it has been demonstrated to be more accurate than traditional methods in developing big data and cloud computing, a support vector machine (SVM) algorithm was used to evaluate the green logistics risk.…”

Section: Introductionmentioning

confidence: 99%

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Risk Assessment and Monitoring of Green Logistics for Fresh Produce Based on a Support Vector Machine

Zhang

Peng

2020

Sustainability

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The sustainability and profitability of fresh produce supply chains are contingent upon several risk factors. This work, therefore, examines several risk indicators that affect the quality and safety of fresh produce in transit, including technological, biological, sustainability, environmental, and emergency risks. Then, we developed a risk assessment and monitoring model that employs a machine learning algorithm, a support vector machine, based on historical monitoring data. The proposed methodology was then applied to simulation and numerical analysis to assess the risks incurred in the strawberry cold chain. After training, the algorithm predicted the risks incurred during transportation with an average accuracy of 90.4%. Therefore, the developed methodology can effectively and accurately perform a risk assessment. Furthermore, the risk assessment model can be applied to other fresh produce due to comprehensive risk indicators. Decision-makers in fresh produce logistics companies can use the developed methodology to identify and mitigate risks incurred, thus improving food safety, reducing product loss, maximizing profits, and realizing sustainable development.

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“…Equipped with novel communication technology and rich computing power, the sensing things are being ubiquitously deployed in many urban environments [1]. Indeed, this IoT technology has realized smart environments [2], including connected vehicles [3], smart cities [4], smart homes [5], smart campuses [6], smart health [7,8], smart factories [9,10], smart farms [11,12], smart retail [13], etc., accounting for a large portion of modern society.…”

Section: Introductionmentioning

confidence: 99%

ST-Trie: A Novel Indexing Scheme for Efficiently Querying Heterogeneous, Spatiotemporal IoT Data

Chu

Kim

et al. 2020

Sustainability

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Recently, various environmental data, such as microdust pollution, temperature, humidity, etc., have been continuously collected by widely deployed Internet of Things (IoT) sensors. Although these data can provide great insight into developing sustainable application services, it is challenging to rapidly retrieve such data, due to their multidimensional properties and huge growth in volume over time. Existing indexing methods for efficiently locating those data expose several problems, such as high administrative cost, spatial overhead, and slow retrieval performance. To mitigate these problems, we propose a novel indexing scheme termed ST-Trie, for efficient retrieval over spatiotemporal IoT environment data. Given IoT sensor data with latitude, longitude, and time, the proposed scheme first converts the three-dimensional attributes to one-dimensional index keys. The scheme then builds a trie-based index, consisting of internal nodes inserted by the converted keys and leaf nodes containing the keys and pointers to actual IoT data. We leverage this index to process various types of queries. In our experiments with three real-world datasets, we show that the proposed ST-Trie index outperforms existing approaches by a substantial margin regarding response time. Furthermore, we show that the query processing performance via ST-Trie also scales very well with an increasing time interval. Finally, we demonstrate that when compressed, the ST-Trie index can significantly reduce its space overhead by approximately a factor of seven.

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Development of Smart Healthcare System Based on Speech Recognition Using Support Vector Machine and Dynamic Time Warping

Cited by 83 publications

References 24 publications

Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map

Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map

Risk Assessment and Monitoring of Green Logistics for Fresh Produce Based on a Support Vector Machine

ST-Trie: A Novel Indexing Scheme for Efficiently Querying Heterogeneous, Spatiotemporal IoT Data

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