Predicting death by suicide using administrative health care system data: Can recurrent neural network, one-dimensional convolutional neural network, and gradient boosted trees models improve prediction performance?

In response to the surging challenge in the number and types of mobile malware targeting smart devices and their sophistication in malicious behavior camouflage, we propose to compose a traffic behavior modeling method based on one-dimensional convolutional neural network with autoencoder and independent recurrent neural network (1DCAE-IndRNN) for mobile malware detection. The design solves the problem that most existing approaches for mobile malware traffic detection struggle with capturing the network traffic dynamics and the sequential characteristics of anomalies in the traffic. We reconstruct and apply the one-dimensional convolutional neural network to extract local features from multiple network flows. The autoencoder is applied to digest the principal traffic features from the neural network and is integrated into the independent recurrent neural network construction to highlight the sequential relationship between the highly significant features. In addition, the Softmax function with the LReLU activation function is adjusted and embedded to the neurons of the independent recurrent neural network to effectively alleviate the problem of unstable training. We conduct a series of experiments to evaluate the effectiveness of the proposed method and its performance for the 1DCAE-IndRNN-integrated detection procedure. The detection results of the public Android malware dataset CICAndMal2017 show that the proposed method achieves up to 98% detection accuracy and recall rates with clear advantages over other benchmark methods.

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A decision support healthcare system based on IoT and neural network technique

Journal of Engineering Design and Technology ◽

10.1108/jedt-08-2020-0317 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Khadeja Al_Sayed Fahmy ◽

Ahmed Yahya ◽

M. Zorkany

Keyword(s):

Neural Network ◽

Health Care ◽

Deep Learning ◽

Decision Support ◽

Health System ◽

Health Care System ◽

Virus Disease ◽

Activation Function ◽

Content Type ◽

Care System

Purpose The purpose of this paper is to develop e-health and patient monitoring systems remotely to overcome the difficulty of patients going to hospitals especially in times of epidemics such as virus disease (COVID-19). Artificial intelligence (AI) technology will be combined Internet of Things (IoT) in this research to overcome these challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the neural network (NN). Then, define the patient data sent through protocols of the IoT. NN checks the patient’s medical sensors data to make the appropriate decision. Then it sends this diagnosis to the doctor. Using the proposed solution, the patients can diagnose and expect the disease automatically and help physicians to discover and analyze the disease remotely without the need for patients to go to the hospital. Design/methodology/approach AI technology will be combined with the IoT in this research. The research aims to select the most appropriate’ best-hidden layers numbers’ and the activation function types for the NN. Findings Decision support health-care system based on IoT and deep learning techniques was proposed. The authors checked out the ability to integrate the deep learning technique in the automatic diagnosis and IoT abilities for speeding message communication over the internet has been investigated in the proposed system. The authors have chosen the appropriate structure of the NN (best-hidden layers numbers and the activation function types) to build the e-health system is performed in this work. Also, depended on the data from expert physicians to learn the NN in the e-health system. In the verification mode, the overall evaluation of the proposed diagnosis health-care system gives reliability under different patient’s conditions. From evaluation and simulation results, it is clear that the double hidden layer of feed-forward NN and its neurons contain Tanh function preferable than other NN. Originality/value AI technology will be combined IoT in this research to overcome challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the NN.

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A Fuzzy Inference-Based Spiking Neural Network for Behavior Estimation in Elderly Health Care System

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2019.p0528 ◽

2019 ◽

Vol 23 (3) ◽

pp. 528-535 ◽

Cited By ~ 1

Author(s):

Shuai Shao ◽

◽

Naoyuki Kubota

Keyword(s):

Neural Network ◽

Health Care ◽

Health Care System ◽

Fuzzy Inference ◽

Population Aging ◽

Environmental Sensors ◽

Inference System ◽

Spike Neural Network ◽

Elderly Health ◽

Care System

In recent years, population aging has become an important social issue. We hope to achieve an elderly health care system through technical means. In this study, we developed an elderly health care system. We chose to use environmental sensors to estimate the behavior of older adults. We found that traditional methods have difficulty solving the problem of excessive indoor environmental differences in different households. Therefore, we provide a fuzzy spike neural network. By modifying the sensitivity of input using a fuzzy inference system, we can solve the problem without additional training. In the experiment, we used temperature and humidity data to make an estimation of behavior in the bathroom. The results show that the system can estimate behavior with 97% accuracy and 78% sensitivity.

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Assessment of the effectiveness of Ukraine’s healthcare system in the context of accelerated development of innovative technologies

Socio-Economic Problems of the Modern Period of Ukraine ◽

10.36818/2071-4653-2019-6-6 ◽

2019 ◽

pp. 33-38

Author(s):

Zoryna Yurynets ◽

Oksana Petrukh ◽

Ivanna Myshchyshyn ◽

Marianna Kokhan ◽

Lesia Gnylianska

Keyword(s):

Neural Network ◽

Health Care ◽

Health Policy ◽

Research And Development ◽

Health Care System ◽

Development Work ◽

Public Healthcare ◽

Gdp Growth ◽

Innovative Technologies ◽

Care System

The article proposes scientific and methodological provisions regarding the evaluation of the effectiveness of the health care system of Ukraine in the conditions of accelerated development of medical innovative technologies. This model is based on the application of neural network modeling tools. The application of the developed model of the evaluation of the effectiveness of the health care system makes it possible to identify the state of public financing of health care and the research and development, to cover a large amount of data and to carry out a comparative analysis of national health policy in terms of the developed countries of the world. During the formation of the neural network model, the relationship between the different factors and the level of GDP is established. For the purposes of the present study, the results of the Ukraine’s social, economic, innovation policy for 2000-2017 have been used as the most predictable element of available data on impact on GDP growth. The proposed methodological provisions make it possible to predict the best option for the development of the health care system and the research and development work in Ukraine, facilitate the possibility of making informed decisions regarding the health policy, optimize the management decision-making regarding the future directions of the research and development work. Public healthcare financing and research and development financing have the biggest influence over the GDP growth. The increase of expenditures of the state budget on public healthcare and research and development is important for socio-economic and innovative growth of Ukraine. The main provisions can be adopted by an executive bodies of the government of Ukraine, local and regional authorities of the national economy. The analysis is the basis for formation of methodological approaches to evaluation of the effectiveness of health care system and other spheres of economic activity and creation of strategies and programs for development of health care system and innovative activity of Ukraine at different hierarchical levels.

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