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3rd International Conference on Bio-engineering for Smart Technologies (BioSMART)

作者： Cherifi, Dalila Kaddari, Redouane Zair, Hamza Nait-Ali, Amine Univ Boumerdes Inst Elect & Elect Engn Boumerdes Algeria Paris Est Creteil UPEC LiSSi EA 3956 Creteil France

ISBN: (纸本)9781728135786

With the rapid development of computer vision applications, infrared face recognition is receiving much attention because of miniscule sensitivity to the face variations due to illumination changes. The infrared image is merely a gray level image but the recognition is interestingly good enough to look at. In this article we present an infrared face recognition system where we used two methods and two databases to train and test our system. We used the Histogram of Gradient (HOG) algorithm along with the Support Vector Machine (SVM) classifier as a first method. In the second method, we implemented the backpropagation algorithm. Although the results of the HOG-SVM were very promising and we were able to get a better results compared with a previous work, neural network gave us a perfect results by using the same number of training examples.

关键词： Face Recognition HOG-SVM back propagation algorithm Biometric

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Automatic text classification using BPLion-neural network and semantic word processing

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IMAGING SCIENCE JOURNAL 2018年第2期66卷 69-83页

作者： Ranjan, Nihar M. Prasad, Rajesh S. Sinhgad Inst Technol & Sci Pune Maharashtra India NBN Sinhgad Sch Engn Pune Maharashtra India

Text mining has become a major research topic in which text classification is the important task for finding the relevant information from the new document. Accordingly, this paper presents a semantic word processing technique for text categorization that utilizes semantic keywords, instead of using independent features of the keywords in the documents. Hence, the dimensionality of the search space can be reduced. Here, the back propagation Lion algorithm (BP Lion algorithm) is also proposed to overcome the problem in updating the neuron weight. The proposed text classification methodology is experimented over two data sets, namely, 20 Newsgroup and Reuter. The performance of the proposed BPLion is analysed, in terms of sensitivity, specificity, and accuracy, and compared with the performance of the existing works. The result shows that the proposed BPLion algorithm and semantic processing methodology classifies the documents with less training time and more classification accuracy of 90.9%.

关键词： Text classification back propagation algorithm Lion optimization algorithm semantic words hyponymy

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Fuzzy Neural Network Control of Thermostatically Controlled Loads for Demand-Side Frequency Regulation

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ENERGIES 2019年第13期12卷 2463-2463页

作者： Qu, Zhengwei Xu, Chenglin Ma, Kai Jiao, Zongxu Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China

In this paper, a fuzzy neural network controller for regulating demand-side thermostatically controlled loads (TCLs) is designed with the aim of stabilizing the frequency of the smart grid. Specifically, the balance between power supply and demand is achieved by tracking the automatic generation control (AGC) signal in an electric power system. The particle swarm optimization (PSO) and error back propagation (BP) algorithms are used to optimize the control parameters and consequently reduce the tracking errors. The fuzzy neural network can be applied to solve load control problems in power systems, since its self-learning and associative storage functions can deal with the highly nonlinear relationship between input and output. Simulation results show the advantage of the fuzzy neural network control scheme in terms of frequency regulation error and consumer comfort.

关键词： automatic generation control fuzzy neural network control thermostatically controlled loads back propagation algorithm particle swarm optimization

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New approaches to determine age and gender in image processing techniques using multilayer perceptron neural network

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APPLIED SOFT COMPUTING 2018年 70卷 157-168页

作者： Avuclu, Emre Basciftci, Fatih Aksaray Univ Dept Comp Technol Aksaray Turkey Selcuk Univ Fac Technol Dept Comp Engn Konya Turkey

As a result of some events (disasters, inheritance, disappearances etc.), age and gender determination can be vital for people. Forensic medical institutions make the determination of age by examining the structures such as teeth and bones. Procedures for forensic science are currently estimated manually according to certain morphological findings on the tooth. In this study, 1313 panoramic dental images were used automatically to estimate age. Image preprocessing is applied on these images. Trapezoidal teeth are corrected in the coordinate plane to obtain more accurate and standard results. In the study, the correction process is done with original and novel developed algorithm. Dental images are automatically and dynamically segmented and feature vectors are created by extracting their features. The generated feature vectors are dynamic and presented as inputs to the Multilayer Perceptron Neural Network. Depending on the request, the number of input count reduction process can be performed. In this study, age and gender were determined from dental x-ray images with novel and originally developed algorithm. The application is written in C # programming language. In some tooth groups, the highest classification rate of 100% and age determination with 0 error were performed. With this study, age determination in forensic science will be more accurate. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Age and gender estimation Panoramic dental images Image processing techniques Multi-layer 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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Real time fixed point adaptive chaotic system generator for deep brain stimulation using FPGA 16

Real time fixed point adaptive chaotic system generator for ...

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16th International Conference on Frontiers of Information Technology (FIT)

作者： Shah, Syed Aamir Ali Zhang, Lei Bais, Abdul Univ Regina Fac Engn & Appl Sci Regina SK Canada

ISBN: (纸本)9781538693551

Parkinson disease (PD) is a neurodegenerative disorder which is caused by the untimely death of dopamine generating neurons situated in substantia nigra pars compacta and striatum. The depletion of striatal dopamine induces the synchronization in brain activity in motor regions which results in tremors. Deep brain stimulation (DBS) is used as an obvious choice in therapy when the medication stops working. Traditional DBS systems are open loop systems in which stimulation parameters need to be adjusted manually from among a large pool of available combinations. This makes the whole process tedious for the doctors and traumatic for the patients. We have developed an FPGA based device which can monitor, learn and mimic the behavior of chaotic systems on the go. The purpose of this device is to learn the chaotic behavior of brain, when in normal state, and generate the chaotic stimulation signal during PD based tremors.

关键词： Chaotic system FPGA Fixed point implementation Parkinson disease Deep brain stimulation Artificial Neural Networks back propagation algorithm

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Comparison of BPA-MLP and LSTM-RNN for Stocks Prediction 6

Comparison of BPA-MLP and LSTM-RNN for Stocks Prediction

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6th International Symposium on Computational and Business Intelligence (ISCBI)

作者： Achkar, Roger Elias-Sleiman, Fady Ezzidine, Hasan Haidar, Nourhane Amer Univ Sci & Technol Dept Comp & Commun Engn Beirut Lebanon

ISBN: (纸本)9781538694503

Neural networks is considered one of the most developed concept in artificial intelligence, due to its ability to solve complex computational tasks, and its efficiency to find solutions. There is a wide range of applications that adopt this technique, one of which is in the financial investment issues. This paper presents an approach to predict stock market ratios using artificial neural networks. It considers two different techniques-BPA-MLP and LSTM-RNN-their potential, and their limitations. Tests were conducted on different data sets, such as Facebook (TM) stocks, Google (TM) stocks, and Bitcoin (TM) stocks. We achieve a best-case accuracy of 97% for MLP algorithm, and 99.5% for LSTM algorithm. While the results appear to be promising, a web interface is presented in order to accept a certain amount of money, and accordingly checks the best stock to invest in.

关键词： back propagation algorithm Multi-layer Perceptron Long Short-Term Memory Recurrent Neural Networks

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An Artificial Intelligence Approach for Predicting Different Types of Stroke 2

An Artificial Intelligence Approach for Predicting Different...

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International Conference on Inventive Communication and Computational Technologies (ICICCT)

作者： Haridas, Hima Wilson, Aswathy Jyothi Engn Coll Dept Comp Sci Trichur India

ISBN: (纸本)9781538619742

Stroke is a second leading cause of death and they have been serious, long-term incapacity. Stroke is the sudden demise of cerebrum cells because of a nonattendance of oxygen, caused by blockage of bloodstream or break of a supply route to the mind. According to World Health Organization in the upcoming year's stroke will continue growth death rate. The many works have been carried out for detecting stroke *** artificial intelligence approach for predicting stroke and its types using deep learning. Types are Ischemic stroke, Hemorrhagic Stroke, Transient Ischemic *** our work, dataset collection from the medical institute. The preprocessing method expels copy records, missing information, and conflicting information. Principle component analysis algorithm is computation is used for reducing the estimations and deep learning utilizing predicts whether the patient is encountering stroke illness or not. So as to foresee the stroke illness, it actualizes classification by deep learning. When the patient details are entered, it checks with trained model and forecasting of different types of *** this work mainly focuses better way for predicting stroke and different type of stroke.

关键词： artificial intelligence artificial neural network back propagation algorithm

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Soft Sensor for Flame Temperature Measurement and IoT based Monitoring in Power Plants 2nd

Soft Sensor for Flame Temperature Measurement and IoT based ...

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2nd International Conference on Large Area Flexible Microelectronics (ILAFM) - Wearable Electronics

作者： Sujatha, K. Bhavani, Nallamilli P. G. Cao, Su-Qun Kumar, K. S. Ram Dr MGR Educ & Res Inst Mech & Mat Engn Fac Madras Tamil Nadu India Dr MGR Educ & Res Inst CEAIR Dept EEE ECE Madras Tamil Nadu India Huaiyin Inst Technol Huaian Jiangsu Peoples R China JNN Inst Engn Madras Tamil Nadu India

Investigation of temperature measurement from the flame colour in thermal and gas turbine power plants is of enormous significance in the realm of vision machine technology. The primary objective for this work relies on detection, recognition and understanding of colour image processing for flame colour analysis. In this effort, soft computing methods using Artificial Neural Network (ANN) model with back propagation algorithm (BPA) and Ant Colony Optimisation (ACO) are used for this purpose. The central theme of this work uses the fact that the colour of the flame images is dependent on the temperature. The initial move is to describe a facet quantity for each flame image together with 10 facet rudiments, which are the brightness of flame, the area of the high temperature flame, the brightness of high temperature flame, the rate of area of the high temperature flame, the flame centroid about X and Y, orientation and the two discriminant vectors correspondingly. The superiority of the images used is improved using Curvelet transform. The conception of flame detection and classification is conceded to compute the temperature from its colour. The specimen incorporates 51 flame images, a portion of which is used for trail and testing the ANN and ACO model. Ultimately, the whole specimen flame images are recognized and classified based on the temperatures corresponding to the core of the fire ball. The results are being validated by comparing with the conventional Euclidean classifier. Demonstrations establish an effective and indigenous system for flame temperature measurement. The elucidation states that the Internet of Things (IoT) with the proposed intelligent temperature sensor is connected to the embedded computing system to monitor the fluctuation in flame temperature with respect to colour changes in order to ensure complete combustion. This scheme utilizes wearable electronics technology which constantly monitors and controls the improvement of productivity in p

关键词： Soft Sensor back propagation algorithm Ant Colony Optimization Euclidean classifier Flame Temperature Feature extraction Curvelet transform Fisher's Linear Discriminant Analysis Arduino UNO and Internet of Things

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A Neural Network Based Expert System for the Diagnosis of Diabetes Mellitus

A Neural Network Based Expert System for the Diagnosis of Di...

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International Conference on Information Technology Science (MosITS)

作者： Alade, Oluwatosin Mayowa Sowunmi, Olaperi Yeside Misra, Sanjay Maskeliunas, Rytis Damasevicius, Robertas Covenant Univ Ota Nigeria Kaunas Univ Technol Kaunas Lithuania

ISBN: (纸本)9783319749808;9783319749792

Diabetes is a disease in which the blood glucose, or blood sugar levels in the body are too high. The damage caused by diabetes can be very severe and even more pronounced in pregnant women due to the tendency of transmitting the hereditary disease to the next generation. Expert systems are now used in medical diagnosis of diseases in patients so as to detect the ailment and help in providing a solution to it. This research developed and trained a neural network model for the diagnosis of diabetes mellitus in pregnant women. The model is a four-layer feed forward network, trained using back-propagation and Bayesian Regulation algorithm. The input layer has 8 neurons, two hidden layers have 10 neurons each, and the output layer has one neuron which is the diagnosis result. The developed model was also incorporated into a web-based application to facilitate its use. Validation by regression shows that the trained network is over 92% accurate.

关键词： Expert system Diabetes diagnosis Neural network back propagation algorithm

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