Computer Intelligence in Healthcare

Low cost solutions for the development of intelligent bio-medical devices that not only assist people to live in a better way but also assist physicians for better diagnosis are presented in this chapter. Two such devices are discussed here, which are helpful for prevention and diagnosis of diseases. Statistical analysis reveals that cold and fever are the main culprits for the loss of man-hours throughout the world, and early pathological investigation can reduce the vulnerability of disease and the sick period. To reduce this cold and fever problem a household cooling system controller, which is adaptive and intelligent in nature, is designed. It is able to control the speed of a household cooling fan or an air conditioner based on the real time data, namely room temperature, humidity, and time for which system is active, which are collected from environment. To control the speed in an adaptive and intelligent manner, an associative memory neural network (Kramer) has been used. This embedded system is able to learn from training set; i.e., the user can teach the system about his/her feelings through training data sets. When the system starts up, it allows the fan to run freely at full speed, and after certain interval, it takes the environmental parameters like room temperature, humidity, and time as inputs. After that, the system takes the decision and controls the speed of the fan.

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An open source Real Time IOT based environmental sensor monitoring system

10.29007/q4cf ◽

2018 ◽

Author(s):

Ronak Vithlani ◽

Siddharth Fultariya ◽

Mahesh Jivani ◽

Haresh Pandya

Keyword(s):

Embedded System ◽

Open Source ◽

Real Time ◽

Monitoring System ◽

Information Structure ◽

Low Cost ◽

Environmental Parameters ◽

Test Bed ◽

Network Construction ◽

Embedded Sensing

In this paper, we have described an operative prototype for Internet of Things (IoT) used for consistent monitoring various environmental sensors by means of low cost open source embedded system. The explanation about the unified network construction and the interconnecting devices for the consistent measurement of environmental parameters by various sensors and broadcast of data through internet is being presented. The framework of the monitoring system is based on a combination of embedded sensing units, information structure for data collection, and intellectual and context responsiveness. The projected system does not involve a devoted server computer with respect to analogous systems and offers a light weight communication protocol to monitor environment data using sensors. Outcomes are inspiring as the consistency of sensing information broadcast through the projected unified network construction is very much reliable. The prototype was experienced to create real-time graphical information rather than a test bed set-up.

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FORECASTING AUSTRALIA’S DOMESTIC LOW COST CARRIER PASSENGER DEMAND USING A GENETIC ALGORITHM APPROACH

Aviation ◽

10.3846/16487788.2016.1171798 ◽

2016 ◽

Vol 20 (2) ◽

pp. 39-47 ◽

Cited By ~ 1

Author(s):

Panarat SRISAENG ◽

Steven RICHARDSON ◽

Glenn S. BAXTER ◽

Graham WILD

Keyword(s):

Genetic Algorithm ◽

Low Cost ◽

Training Data ◽

Percentage Error ◽

Data Sets ◽

Passenger Demand ◽

Low Cost Carrier ◽

Genetic Algorithm Approach ◽

First Time ◽

Quadratic Models

This study has proposed and empirically tested for the first time Genetic Algorithm (GA) models for forecasting Australia’s domestic low cost carriers’ demand, as measured by enplaned passengers (GAPAXDE Model) and revenue passenger kilometres performed (GARPKSDE Model). Data was divided into training and testing data sets, 36 training data sets were used to estimate the weighting factors of the GA models and 6 data sets were used for testing the robustness of the GA models. The genetic algorithm parameters used in this study comprised population size (n): 1000, the generation number: 200, and mutation rate: 0.01. The modelling results have shown that both the linear GAPAXDE and GARPKSDE models are more accurate, reliable, and have a slightly greater predictive capability compared to the quadratic models. The overall mean absolute percentage error (MAPE) of the GAPAXDE and GAR-PKSDE models are 3.33 per cent and 4.48 per cent, respectively.

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Application of Neural Networks in the Problem of Quantitative Analysis of Air Composition

Proceedings of Southwest State University ◽

10.21869/2223-1560-2020-24-1-159-174 ◽

2020 ◽

Vol 24 (1) ◽

pp. 159-174

Author(s):

O. G. Bondar ◽

E. O. Brezhneva ◽

R. E. Chernyshov

Keyword(s):

Neural Networks ◽

Mathematical Models ◽

Gas Sensors ◽

Environmental Parameters ◽

Simulation Models ◽

Training Data ◽

Data Sets ◽

Separate Determination ◽

Gas Analyzer ◽

Cross Sensitivity

Purpose of reseach is to develop a method for generating training data to enable the use of artificial neural networks (ANN) method in gas analyzer systems. The problem of increasing the accuracy of separate determination of gas concentrations in multicomponent mixtures under conditions of environmental parameters changes is considered. It is proposed to increase the accuracy of determining target gas concentrations by using the ANN method for joint processing of sensor signals.Methods: Training data for the neural network were generated using numerical experiments and mathematical simulation methods. To assess the accuracy of training, the standard deviation (SD) was used and the relative error was calculated. ANN training and research were conducted in the MATLAB environment (the Neural Networks Toolbox application). When developing mathematical models of gas sensors, the theory of electrical circuits, electronic theory of chemisorption and the adsorption theory of heterogeneous catalysis were applied.Results: A method for generating training data sets using mathematical models of gas sensors is described. The proposed training method has been tested on a specific task, in particular, a decision-making device based on ANN for a four-component gas analyzer has been developed. The efficiency of using neural networks for tuning out from the mutual cross-sensitivity of sensors was evaluated.Conclusion: A method for generating training data using simulation models is proposed, which allows automazing the process of training, research, choosing the architecture and structure of ANN and their testing. The method was tested. Based on the analysis of the obtained errors, conclusions are made about the efficiency of using neural networks to reduce errors caused by cross sensitivity at different concentrations of the main and interfering gases.

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Low-Cost IoT Enabled Embedded System for Measurement of Environmental Pollutants

Nature Environment and Pollution Technology ◽

10.46488/nept.2021.v20i04.037 ◽

2021 ◽

Vol 20 (4) ◽

Author(s):

C. Mani Kumar ◽

Shahid Ali ◽

P. Sri Lakshmi ◽

G. Raja Kullayappa ◽

K. Tanveer Alam

Keyword(s):

Embedded System ◽

High Speed ◽

Low Cost ◽

Environmental Pollutants ◽

Environmental Parameters ◽

Sensor Data ◽

User Friendliness ◽

Meteorological Systems ◽

Conventional Systems ◽

Physical Quantities

In today’s world, with ever-changing pollutants and their concentrations, the designing of low-cost meteorological systems is unavoidable for assessing environmental parameters. Wireless instrumentation is an effective way of measuring the physical quantities as it can measure and transmit the data to the targeted location at high speed. In the present work, an IoT-enabled embedded system was developed to measure the concentration of carbon dioxide, ozone, and the presence of smoke. The ARM microcontroller reads the sensor data and processes the information to calculate the pollutant parameters. The measured data is displayed on the LCD, mobile phone, and a computer simultaneously using wireless technology. With Embedded C, the Keil compiler was used to develop the interfacing software for the designed system. Portability, user-friendliness, and reliability are the significant advantages of the device compared with the conventional systems, and it can be widely used as an inexpensive solution for the monitoring of environmental conditions.

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Graphene Modified Molecular Imprinting Electrochemical Sensor for Determining the Content of Dopamine

Current Analytical Chemistry ◽

10.2174/1573411014666180730112304 ◽

2019 ◽

Vol 15 (6) ◽

pp. 628-634

Author(s):

Rong Liu ◽

Jie Li ◽

Tongsheng Zhong ◽

Liping Long

Keyword(s):

Electrochemical Sensor ◽

Molecular Imprinting ◽

Neurological Disorders ◽

Room Temperature ◽

Low Cost ◽

Differential Pulse ◽

Current Response ◽

Specific Selectivity ◽

Pulse Voltammetry

Background: The unnatural levels of dopamine (DA) result in serious neurological disorders such as Parkinson’s disease. Electrochemical methods which have the obvious advantages of simple operation and low-cost instrumentation were widely used for determination of DA. In order to improve the measurement performance of the electrochemical sensor, molecular imprinting technique and graphene have always been employed to increase the selectivity and sensitivity. Methods: An electrochemical sensor which has specific selectivity to (DA) was proposed based on the combination of a molecular imprinting polymer (MIP) with a graphene (GR) modified gold electrode. The performance and effect of MIP film were investigated by differential pulse voltammetry (DPV) and cyclic voltammetry (CV) in the solution of 5.0 ×10-3 mol/L K3[Fe(CN)6] and K4[Fe(CN)6] with 0.2 mol/L KCl at room temperature. Results: This fabricated sensor has well repeatability and stability, and was used to determine the dopamine of urine. Under the optimized experiment conditions, the current response of the imprinted sensor was linear to the concentration of dopamine in the range of 1.0×10-7 ~ 1.0×10-5 mol/L, the linear equation was I (µA) = 7.9824+2.7210lgc (mol/L) with the detection limit of 3.3×10-8 mol/L. Conclusion: In this work, a highly efficient sensor for determination of DA was prepared with good sensitivity by GR and great selectivity of high special recognization ability by molecular imprinting membrane. This proposed sensor was used to determine the dopamine in human urine successfully.

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A Fast Method for Defogging of Outdoor Visual Images

Recent Patents on Computer Science ◽

10.2174/2213275912666190819105422 ◽

2019 ◽

Vol 12 ◽

Author(s):

Tannistha Pal

Keyword(s):

Intelligent Vehicles ◽

Visual Surveillance ◽

Qualitative Assessment ◽

Computational Time ◽

Data Sets ◽

Fast Method ◽

Time Data ◽

Image Defogging ◽

Clear Vision ◽

Dark Channel

Images captured in severe atmospheric catastrophe especially in fog critically degrade the quality of an image and thereby reduces the visibility of an image which in turn affects several computer vision applications like visual surveillance detection, intelligent vehicles, remote sensing, etc. Thus acquiring clear vision is the prime requirement of any image. In the last few years, many approaches have been made towards solving this problem. In this article, a comparative analysis has been made on different existing image defogging algorithms and then a technique has been proposed for image defogging based on dark channel prior strategy. Experimental results show that the proposed method shows efficient results by significantly improving the visual effects of images in foggy weather. Also computational time of the existing techniques are much higher which has been overcame in this paper by using the proposed method. Qualitative assessment evaluation is performed on both benchmark and real time data sets for determining theefficacy of the technique used. Finally, the whole work is concluded with its relative advantages and shortcomings.

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Single–atom manganese and nitrogen co-doped graphene as low-cost catalysts for the efficient CO oxidation at room temperature

Applied Surface Science ◽

10.1016/j.apsusc.2020.147809 ◽

2021 ◽

Vol 536 ◽

pp. 147809

Author(s):

Mingming Luo ◽

Zhao Liang ◽

Chao Liu ◽

Xiaopeng Qi ◽

Mingwei Chen ◽

...

Keyword(s):

Co Oxidation ◽

Room Temperature ◽

Low Cost ◽

Single Atom ◽

Doped Graphene ◽

Co Doped

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Low-Cost Embedded System Using Convolutional Neural Networks-Based Spatiotemporal Feature Map for Real-Time Human Action Recognition

Applied Sciences ◽

10.3390/app11114940 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4940

Author(s):

Jinsoo Kim ◽

Jeongho Cho

Keyword(s):

Embedded System ◽

Real Time ◽

Action Recognition ◽

Processing Speed ◽

Recognition Accuracy ◽

Low Cost ◽

Human Action Recognition ◽

Human Action ◽

Video Data ◽

Feature Maps

The field of research related to video data has difficulty in extracting not only spatial but also temporal features and human action recognition (HAR) is a representative field of research that applies convolutional neural network (CNN) to video data. The performance for action recognition has improved, but owing to the complexity of the model, some still limitations to operation in real-time persist. Therefore, a lightweight CNN-based single-stream HAR model that can operate in real-time is proposed. The proposed model extracts spatial feature maps by applying CNN to the images that develop the video and uses the frame change rate of sequential images as time information. Spatial feature maps are weighted-averaged by frame change, transformed into spatiotemporal features, and input into multilayer perceptrons, which have a relatively lower complexity than other HAR models; thus, our method has high utility in a single embedded system connected to CCTV. The results of evaluating action recognition accuracy and data processing speed through challenging action recognition benchmark UCF-101 showed higher action recognition accuracy than the HAR model using long short-term memory with a small amount of video frames and confirmed the real-time operational possibility through fast data processing speed. In addition, the performance of the proposed weighted mean-based HAR model was verified by testing it in Jetson NANO to confirm the possibility of using it in low-cost GPU-based embedded systems.

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Smarter Open Government Data for Society 5.0: Are Your Open Data Smart Enough?

Sensors ◽

10.3390/s21155204 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5204

Author(s):

Anastasija Nikiforova

Keyword(s):

Industry 4.0 ◽

Economic Value ◽

Open Data ◽

Digital Data ◽

Open Government ◽

Data Sets ◽

Time Data ◽

Open Government Data ◽

Information And Communication ◽

Government Data

Nowadays, governments launch open government data (OGD) portals that provide data that can be accessed and used by everyone for their own needs. Although the potential economic value of open (government) data is assessed in millions and billions, not all open data are reused. Moreover, the open (government) data initiative as well as users’ intent for open (government) data are changing continuously and today, in line with IoT and smart city trends, real-time data and sensor-generated data have higher interest for users. These “smarter” open (government) data are also considered to be one of the crucial drivers for the sustainable economy, and might have an impact on information and communication technology (ICT) innovation and become a creativity bridge in developing a new ecosystem in Industry 4.0 and Society 5.0. The paper inspects OGD portals of 60 countries in order to understand the correspondence of their content to the Society 5.0 expectations. The paper provides a report on how much countries provide these data, focusing on some open (government) data success facilitating factors for both the portal in general and data sets of interest in particular. The presence of “smarter” data, their level of accessibility, availability, currency and timeliness, as well as support for users, are analyzed. The list of most competitive countries by data category are provided. This makes it possible to understand which OGD portals react to users’ needs, Industry 4.0 and Society 5.0 request the opening and updating of data for their further potential reuse, which is essential in the digital data-driven world.

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Recurrent Neural Network for Human Activity Recognition in Embedded Systems Using PPG and Accelerometer Data

Electronics ◽

10.3390/electronics10141715 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1715

Author(s):

Michele Alessandrini ◽

Giorgio Biagetti ◽

Paolo Crippa ◽

Laura Falaschetti ◽

Claudio Turchetti

Keyword(s):

Neural Network ◽

Embedded System ◽

Activity Recognition ◽

Human Activity ◽

Recurrent Neural Network ◽

Low Cost ◽

Low Complexity ◽

Human Activity Recognition ◽

Motion Artifacts ◽

Accelerometer Data

Photoplethysmography (PPG) is a common and practical technique to detect human activity and other physiological parameters and is commonly implemented in wearable devices. However, the PPG signal is often severely corrupted by motion artifacts. The aim of this paper is to address the human activity recognition (HAR) task directly on the device, implementing a recurrent neural network (RNN) in a low cost, low power microcontroller, ensuring the required performance in terms of accuracy and low complexity. To reach this goal, (i) we first develop an RNN, which integrates PPG and tri-axial accelerometer data, where these data can be used to compensate motion artifacts in PPG in order to accurately detect human activity; (ii) then, we port the RNN to an embedded device, Cloud-JAM L4, based on an STM32 microcontroller, optimizing it to maintain an accuracy of over 95% while requiring modest computational power and memory resources. The experimental results show that such a system can be effectively implemented on a constrained-resource system, allowing the design of a fully autonomous wearable embedded system for human activity recognition and logging.

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