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A review on current advances in machine learning based diabetes prediction

PRIMARY CARE DIABETES 2021年第3期15卷 435-443页

作者： Jaiswal, Varun Negi, Anjli Pal, Tarun Shoolini Univ Sch Elect & Comp Sci Engn Solan 173212 Himachal Prades India Gachon Univ Coll BioNano Technol Dept Food & Nutr Gyeonggi Do 13120 South Korea Vignans Fdn Sci Technol & Res Deemed Univ Dept Biotechnol Guntur 522213 Andhra Pradesh India

Diabetes is a metabolic disorder comprising of high glucose level in blood over a prolonged period in the body as it is not capable of using it properly. The severe complications associated with diabetes include diabetic ketoacidosis, nonketotic hypersmolar coma, cardiovascular disease, stroke, chronic renal failure, retinal damage and foot ulcers. There is a huge increase in the number of patients with diabetes globally and it is considered a major health problem worldwide. Early diagnosis of diabetes is helpful for treatment and reduces the chance of severe complications associated with it. Machine learning algorithms (such as ANN, SVM, Naive Bayes, PLS-DA and deep learning) and data mining techniques are used for detecting interesting patterns for diagnosing and treatment of disease. Current computational methods for diabetes diagnosis have some limitations and are not tested on different datasets or peoples from different countries which limits the practical use of prediction methods. This paper is an effort to summarize the majority of the literature concerned with machine learning and data mining techniques applied for the prediction of diabetes and associated challenges. This report would be helpful for better prediction of disease and improve in understanding the pattern of diabetes. Consequently, the report would be helpful for treatment and reduce risk of other complications of diabetes. (c) 2021 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

关键词： Diabetes Machine learning Artificial neural network SVM Bayesian network Apriori algorithm back propagation algorithm

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Pharmaceutical plant machine availability prediction using Artificial Neural Network

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2022年第2期16卷 325-335页

作者： Garg, Deepika Roy, Nihar Ranjan Khanna, Ashish GD Goenka Univ Sch Engn Gurugram Haryana India Sharda Univ Sch Engn & Technol Greater Noida UP India MAIT Delhi India

During the 2(nd) phase of COVID-19 pandemic, pharmaceutical plant industry is facing lot of production pressure and machine availability plays vital role in maximizing the manufacturing pharmacy product output. In this paper, Artificial Neural Networks (ANNs) based information processing algorithm has been used to provide a solution to this problem and it has been found suitable to predict machines availability as a prediction function. The considered pharmaceutical plants are dealing with production of medicines related common symptoms in case of COVID-19 (fever, coughing, and breathing problems). The pharmaceutical plant data corresponding to different values of repair and failure rates of different subsystems is collected from plant and analyzed with the help of validated neural network value of availability. This configuration of ANNs approach developed in this research allowed simplifying computational complexities of conventional approaches to solve a large plant machines availability problem. The ANNs methodology in the paper permitted making no assumption, no explicit coding of the problem, no complete knowledge of system configuration, only raw input and clean data found to be sufficient to determine the value of machine availability function for different value of failure and repair rates considered in the paper. The results obtained in the paper are useful for the plant leadership, as the value of failure and repair rates of various subsystems can be fine-tuned at a require clear-cut level to achieve higher availability, and avoid considerably loss of production, loss of man power, and by-pass complete breakdown of concerned system.

关键词： Availability prediction Artificial Neural Networks back propagation algorithm failure rate repair rate pandemic

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An Adaptive Fatigue Driving Detection Method Based on BP-SVM 23

An Adaptive Fatigue Driving Detection Method Based on BP-SVM

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23rd COTA International Conference of Transportation Professionals (CICTP) - Innovation-Empowered Technology for Sustainable, Intelligent, Decarbonized, and Connected Transportation

作者： Guo, Haofei Weng, Jinxian Lin, Chenjie Shanghai Maritime Univ Coll Ocean Sci & Engn Shanghai Peoples R China Shanghai Maritime Univ Coll Transport Shanghai Peoples R China

ISBN: (数字)9780784484869

ISBN: (纸本)9780784484869

This study proposes a back propagation algorithm into mechanism-Support Vector Machine model (BP-SVM), which dynamically adjusts the parameters of the model. In this paper, we used SILAB driving simulation software to simulate the driver's driving state under different weather conditions (i.e., sunny and foggy) and to detect driver fatigue by monitoring vehicle behavior (i.e., acceleration, steering wheel turns, standard deviation of speed, and longitudinal acceleration). The effectiveness of the BP-SVM model was measured by comparing the performance of RF, VGG, and SVM models in terms of accuracy and ROC curve. The results show that the average accuracy of BP-SVM, RF, SVM, and VGG are 93.97%, 89.53%, 83.88%, and 91.95%, respectively, all of which are lower than that of BP-SVM. The AUC areas of RF, SVM, VGG, and BP-SVM are 0.94, 0.92, 0.83, and 0.99, respectively, which shows that the BP-SVM model outperforms other classification models, particularly the VGG model.

关键词： Fatigue driving back propagation algorithm Adaptive mechanisms

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An approach to automated classification of epileptic seizures using Artificial Neural Network

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INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY 2009年第4期2卷 382-399页

作者： Najumnissa, D. Devi, S. Shenbaga BSA Crescent Engn Coll Dept Instrumentat & Control Engn Chennai Tamil Nadu India Anna Univ Coll Engn Ctr Med Elect Dept Elect & Commun Engn Chennai Tamil Nadu India

Epileptic seizures are important public health issues as they affect 0.8% of humans. Electroencephalograph (EEG) records provide important understanding of epileptic disorders. The conventional method is to interpret by visual inspection of common patterns. This work deals with novel method of data generation using feature extraction and classification using back propagation algorithm. Twelve patients' EEG is used for training and six patients' EEG for testing. Thus, the designed network classifies normal and types of abnormal EEG like focal, absence and tonic-clonic seizures. The network correctly classified normal and abnormal conditions. The performance of neural model has an accuracy of 96.3%.

关键词： ANN artificial neural network BPN back propagation algorithm epileptic seizures EEG electroencephalograph feature extraction hidden nodes network architecture network training performance analysis

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An improved pattern classification using back propagation neural network

Journal of the Institution of Engineers (India), Part TX: Te...

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Journal of the Institution of Engineers (India), Part TX: Textile Engineering Division 2000年第1期81卷 21-22页

作者： Paulraj, M. Sivanandam, S.N. Ramesh, A. Dept. of Elec. and Electronics Eng. P S G College of Technology Coimbatore 641 004 India

In this paper simple procedures are presented for the classification of defects in the fabric materials using a class of neural network (NN) trained by back propagation (BP) algorithm with minimum number of failures. The binary images of defects are obtained by the image processing techniques. To quicken the network response (error against epoch) with minimum number of oscillations, a slope parameter is utilized in the bipolar sigmoidal function in the hidden and output layers. The above scheme is applied to a feed-forward neural network (FFNN) to classify the defects in the fabric material. The performance of the networks in terms of local minima, learning speed is evaluated for the choice of slope parameter, learning rate and momentum factor and compared with the traditional BP algorithm.

关键词： back propagation algorithm Image processing Sigmoidal function Slope parameter

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Estimation of seasonal variation of ground resistance using Artificial Neural Networks

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ELECTRIC POWER SYSTEMS RESEARCH 2013年 94卷 113-121页

作者： Asimakopoulou, Fani E. Tsekouras, George J. Gonos, Ioannis F. Stathopulos, Ioannis A. Natl Tech Univ Athens Sch Elect & Comp Engn GR-15780 Athens Greece Hellen Naval Acad Dept Elect Engn & Comp Sci GR-18539 Athens Greece

Objective of this paper is the development of a methodological approach for estimating the ground resistance by using Artificial Neural Network. The value of the ground resistance greatly depends on the grounding system and the properties of the soil, where the system is embedded. Given that the value of soil resistivity fluctuates during the year. the ground resistance does not have one single value. The approach proposed in this paper, takes advantage of the capability of Artificial Neural Networks (ANNs) to recognize linear and non-linear relationships between various parameters. By taking into account measurements of resistivity and rainfall data accrued for previous clays, the ground resistance is estimated. On that purpose ANNs have been trained and validated by using experimental data in order to examine their ability to predict the ground resistance. The results prove the effectiveness of the proposed methodology. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Soil resistivity measurements Ground resistance Artificial Neural Networks back propagation algorithm

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Artificial Neural Network (ANN)-Based Model for In Situ Prediction of Porosity of Nanostructured Porous Silicon

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MATERIALS AND MANUFACTURING PROCESSES 2009年第1期24卷 83-87页

作者： Ray, M. Ganguly, S. Das, M. Datta, S. Bandyopadhyay, N. R. Hossain, S. M. Bengal Engn & Sci Univ Sch Mat Sci & Engn Sibpur 711103 Howrah India Bengal Engn & Sci Univ Dept Phys Sibpur 711103 Howrah India

Nanostructured porous silicon (PS) is usually formed by anodic etching in HF-based solution, and its porosity is measured by a destructive gravimetric technique. In this article, we report the development of an artificial neural network (ANN)-based model permitting in situ prediction of porosity of PS samples. The sensitive and nonlinear dependence of porosity on the formation parameters demanded a nonclassical treatment, and ANN was found suitable for handling this problem. A series of experiments were performed on p-type Si having resistivity 2-5 -cm in 24% HF solution to generate the data for development of the ANN model. The voltage fluctuations across the electrodes during the formation of PS samples were recorded and used to develop an ANN model for prediction of voltage during the transient state of PS evolution. The predicted voltages were then used to predict porosity for different values of current density (J) at any time instant. Porosity was also measured by the conventional and destructive gravimetric method for different values of J and time. The predicted porosities agreed well with gravimetrically determined values.

关键词： Anodic etching Artificial neural network back propagation algorithm Fluctuations Formation mechanism Formation parameters Gravimetric method In-homogeneity Modeling Nanostructures Nondestructive technique Porosity Porous silicon Prediction p-Type substrate

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Prediction of gamma ray spectrum for 22Na source by feed forward back propagation ANN model

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RADIATION PHYSICS AND CHEMISTRY 2023年 202卷

作者： Teke, Cagatay Akkurt, Iskender Arslankaya, Seher Ekmekci, Ismail Gunoglu, Kadir Bayburt Univ Bayburt Turkiye Suleyman Demirel Univ Sci Fac Isparta Turkiye Sakarya Univ Sakarya Turkiye Istanbul Ticaret Univ Istanbul Turkiye Isparta Appl Sci Univ Isparta Turkiye

The radiation has been used in a variety of different fields since its discovery and thus its measurement becomes vital in these industries. Different type detector may be used to measure gamma rays depends on the purposes of measurements. Gamma ray energy spectrum is an important to determine either elemental analysing of a sample or radiation shielding purposes. On the other hand, Artificial Neural Network (ANN) may be used to predict and analysing of gamma-ray spectrum. In this study, gamma ray spectrum from 22Na source detected in NaI (Tl) detector was estimated by ANN. There have been installed ten different ANN models to find the network structure that produces the best predictive value for the gamma ray spectrum NaI (Tl) Detector. Estimation study has been continued with the ANN model with be possessed of lowest error value. ANN model was created by using energy, distance and gamma-rays energy spectrum (called Io) values. In the ANN model developed using the feed forward back propagation algorithm, were used artificial neurons two in the input layer, ten in the hidden layer and one in the output layer. For the case of present work, the experimental data was used 70% for education, 20% for validation and 10% for testing. The estimated values obtained with the ANN model were compared with the experimental results and a good correlation has been found between them (R2 = 0.99).

关键词： Gamma ray spectrum 22Na source ANN back propagation algorithm

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Evaluation of Effectiveness of ANN for Feature Selection Based Electricity Price Forecasting

Evaluation of Effectiveness of ANN for Feature Selection Bas...

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International Conference on Emerging Trends in Computing and Communication Technologies (ICETCCT)

作者： Kumar, Neeraj Tripathi, M. M. Delhi Technol Univ Dept Elect Engn New Delhi India

ISBN: (纸本)9781538611470

Electricity price is one of the most concurrent aspects of power system planning. An accurate method of forecasting is required for several economic and operational advantages. In this paper effectiveness of artificial neural network (ANN) is evaluated by selecting the most influential input variable based forecasting of price in Australian electricity market using the price and total demand data of Queensland (QLD) region from January 2016 to June 2017. Using these data, monthly and weekly forecasting of electricity price is carried out. The mean absolute percentage error (MAPE) and root mean square error (RMSE) is determined by evaluating the effectiveness of proposed method. The best result shows the monthly MAPE as 1.94% and weekly MAPE as 1.06% considering only total demand as input to the ANN.

关键词： Price forecasting Artificial Neural Network Mean Absolute Percentage Error Multilayer Perceptron back propagation algorithm

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Soft Sensor for Capacitance Tracking in Quality Assurance of Fruit Ripening Process

Soft Sensor for Capacitance Tracking in Quality Assurance of...

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International Conference on Smart Engineering Materials (ICSEM)

作者： Sujatha, K. Srividhya, V. Ponmagal, R. S. Godhavari, T. Dr MGR Educ & Res Inst Dept EEE CSE ECE CEAIR Madras Tamil Nadu India Dr MGR Educ & Res Inst Dept EEE CEAIR Madras Tamil Nadu India

The highlight of this research work is to discover an efficient capacitance tracking for generation of ethylene gas (C2H4 in ppm) used for ripening of fruits by employing soft sensor built using image processing and Artificial Neural Networks (ANN) algorithms. The proposed method relies on the statistical analysis of the color which denotes the various stages in ripening and in turn indicates the amount of ethylene gas required. The changes in color, texture, intensity variation, mean, variance and standard deviation extracted from the images are the features which enable the personnel to determine the amount of ethylene gas. The Feed Forward Neural Network (FFNN) is used for ethylene gas and the corresponding capacitance value estimation. This is made possible using back propagation algorithm (BPA) for training the FFNN. As a part of image processing the intensity values in color images and its variation are tracked by dithering which is used as a unique feature input to train the FFNN. The major findings of the proposed method depends on the FFNN estimating the ethylene gas needed for ripening process in a feed forward fashion thereby providing the precision and recall values spontaneously for every instance. The improvement made in application side denotes that earlier a circuit with capacitance is used to generate ethylene gas for this purpose which is on other hand replaced by using a soft sensor. Nearly 51 images of banana are considered for training and testing respectively. Testing and confirmation result shows the required precision and recall values are in range of 80 to 89% and 100% respectively. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Feature Extraction Dithering Fruit Ripening back propagation algorithm

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