scholarly journals CLASSIFICATION MODEL FOR LEARNING DISABILITIES IN ELEMENTARY SCHOOL PUPILS

2019 ◽  
Vol 17 (1) ◽  
pp. 9-20
Author(s):  
I. O. AWOYELU ◽  
I. A. AGBOOLA
Keyword(s):  
Learning Disabilities ◽  
Learning Disability ◽  
Primary Schools ◽  
Binary Classification ◽  
Back Propagation ◽  
Principal Component ◽  
General Term ◽  
Classification Model ◽  
Back Propagation Algorithm

Learning disability is a general term that describes specific kinds of learning problems.  Although, Learning Disability cannot be cured medically, there exist several methods for detecting learning disabilities in a child. Existing methods of classification of learning disabilities in children are binary classification – either a child is normal or learning disabled. The focus of this paper is to extend the binary classification to multi-label classification of learning disabilities. This paper formulated and simulated a classification model for learning disabilities in primary school pupils. Information containing the symptoms of learning disabilities in pupils were elicited by administering five hundred (500) questionnaire to teachers of Primary One to Four pupils in fifteen government owned elementary schools within Ife Central Local Government Area, Ile-Ife of Osun State. The classification model was formulated using Principal Component Analysis, rule based system and back propagation algorithm. The formulated model was simulated using Waikatto Environment for Knowledge Analysis (WEKA) version 3.7.2. The performance of the model was evaluated using precision and accuracy. The classification model of primary one, primary two, primary three and primary four yielded precision rate of 95%, 91.18%, 93.10% and 93.60% respectively while the accuracy results were 95.00%, 91.18%, 93.10% and 93.60% respectively. The results obtained showed that the developed model proved to be accurate and precise in classifying pupils with learning disabilities in primary schools. The model can be adopted for the management of pupils with learning disabilities.  

scholarly journals Simulation and detection of tamil speech accent using modified mel frequency cepstral coefficient algorithm

2018 ◽  
Vol 7 (3.3) ◽  
pp. 426
Author(s):  
Swagata Sarkar ◽  
Sanjana R ◽  
Rajalakshmi S ◽  
Harini T J
Keyword(s):  
Online Courses ◽  
Hidden Markov ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Mel Frequency Cepstral Coefficients ◽  
Propagation Algorithm ◽  
Accent Recognition ◽  
Speech Reconstruction ◽  
Mel Frequency Cepstral Coefficient

Automatic Speech reconstruction system is a topic of interest of many researchers. Since many online courses are come into the picture, so recent researchers are concentrating on speech accent recognition. Many works have been done in this field. In this paper speech accent recognition of Tamil speech from different zones of Tamilnadu is addressed. Hidden Markov Model (HMM) and Viterbi algorithms are very popularly used algorithms. Researchers have worked with Mel Frequency Cepstral Coefficients (MFCC) to identify speech as well as speech accent. In this paper speech accent features are identified by modified MFCC algorithm. The classification of features is done by back propagation algorithm.  

Biomimetic Pattern Recognition for Classification of Proteomic Profile

2013 ◽  
Vol 791-793 ◽  
pp. 1961-1964
Author(s):  
Xiao Li Yang ◽  
Qiong He
Keyword(s):  
Pattern Recognition ◽  
Cancer Diagnosis ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Proteomic Profile ◽  
Satisfactory Model ◽  
Over Time

We propose a biomimetic pattern recognition (BPR) approach for classification of proteomic profile. The proposed approach preprocess profile using iterative minimum in adaptive setting window (IMASW) method for baseline correction, discrete wavelet transform (DWT) for fitting and smoothing, and average total ion normalization (ATIN) for remove the influence of vary amount of sample and degradation over time. Then principal component analysis (PCA) and BPR build classification model. With an optimization of the parameters involved in the modeling, we obtain a satisfactory model for cancer diagnosis in three proteomic profile datasets. The predicted results show that BPR technique is more reliable and efficient than support vector machine (SVM) method.

scholarly journals Identification and Classification of Animal Kingdom using Image Processing and Artificial Neural Networks

2019 ◽  
Vol 8 (3) ◽  
pp. 4645-4650
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Bilateral Symmetry ◽  
Automated System ◽  
Back Propagation Algorithm ◽  
Animal Kingdom ◽  
Artificial Neural ◽  
Eukaryotic Organisms

The biological kingdom ‘Animalia’ is composed of multi cellular eukaryotic organisms. Most of the animal species exhibit bilateral symmetry. The hierarchy of biological classification has eight taxonomy ranks. The top position in the hierarchy is occupied by the ‘domain’ and ending with the lowest position occupied by ‘species’. The classification of animal kingdom includes, Porifera, Coelenterata, Platyhelminthes, Aschelminthes, Annelida, Arthropoda, Mollusca, Echinodermata and Chordata. Manual identification of Phylum or class for each and every species, is very tedious, because there exists nearly a millions of species categorized under various classes. Hence an automated system is proposed to be developed using image segmentation and Artificial Neural Networks (ANN) trained with Back Propagation Algorithm (BPA) which is capable of assisting the scientists and researchers for class identification. This system will be useful in Museums and Archeological departments, where a huge variety of species are maintained. The classification efficiency of the proposed system is 89.1%.

scholarly journals Modeling of Speech Recognition Using Artificial Neural Network

2019 ◽  
Vol 8 (9) ◽  
pp. 1225-1229
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Pattern Matching ◽  
Back Propagation ◽  
Experimental Result ◽  
Communication Link ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
The Voice

Automatic speech recognition has attained a lot of significance as it can act as easy communication link between machines and humans. This mode of communication is easy for man to use as it is effortless and easy. Many approaches for extraction of the features of the speech and classification of speech have been considered. This paper unveils the importance of neutral network and the way it can be used for recognition of speech. Mel Frequency Cepstrum Coefficients is made use of for extraction of the features from the voice. For pattern matching neural network has been used. MATLAB has been used to show how the speech is recognized. In this paper the speech recognition has been done firstly by multilayer feed forward neural network using Back propagation algorithm. Then the process of speech recognition is shown by using Radial basis function neural network. The paper then analyzes the performance of both the algorithms and experimental result shows that BPNN outperforms the RBFNN.

scholarly journals Phishing Website Detection using Neural Network and PCA based on Feature Selection

2020 ◽  
Vol 8 (6) ◽  
pp. 1150-1152
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Feature Selection ◽  
Computing Time ◽  
Back Propagation ◽  
Principal Component ◽  
Criminal Activity ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Network Method

Phishing is a criminal activity that tries to steal user account password or other confidential information by tricking user into believing they are on the actual website. In order to phishing, they must get user to go from an email to a website. User can also land on phishing site by mistyping a URL (web address). However, the numbers of phishing attacks have been growing and need the protection technique. Neural network and Principal Component Analysis (PCA) can be combined to detect phishing website. This study uses back-propagation algorithm based on neural network method and PCA based on feature selection to reduce large attributes into small attributes. Neural network without using PCA will be compared with neural network using PCA. The result shows that neural network using PCA has better accuracy in 55.67% and neural network without using PCA only reaches 54.43% accuracy. However neural network without using PCA has faster computing time than neural network using PCA. This study can be used as a phishing protection technique.

scholarly journals Calcium Spark Detection and Event-Based Classification of Single Cardiomyocyte Using Deep Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Shengqi Yang ◽  
Ran Li ◽  
Jiliang Chen ◽  
Zhen Li ◽  
Zhangqin Huang ◽  
...  
Keyword(s):  
Binary Classification ◽  
Detection Algorithm ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Detection Methods ◽  
Cardiac Pathology ◽  
Powerful Approach ◽  
Whole Heart ◽  
Event Based

Ca2+ sparks are the elementary Ca2+ release events in cardiomyocytes, altered properties of which lead to impaired Ca2+ handling and finally contribute to cardiac pathology under various diseases. Despite increasing use of machine-learning algorithms in deciphering the content of biological and medical data, Ca2+ spark images and data are yet to be deeply learnt and analyzed. In the present study, we developed a deep residual convolutional neural network method to detect Ca2+ sparks. Compared to traditional detection methods with arbitrarily defined thresholds to distinguish signals from noises, our new method detected more Ca2+ sparks with lower amplitudes but similar spatiotemporal distributions, thereby indicating that our new algorithm detected many very weak events that are usually omitted when using traditional detection methods. Furthermore, we proposed an event-based logistic regression and binary classification model to classify single cardiomyocytes using Ca2+ spark characteristics, which to date have generally been used only for simple statistical analyses and comparison between normal and diseased groups. Using this new detection algorithm and classification model, we succeeded in distinguishing wild type (WT) vs RyR2-R2474S± cardiomyocytes with 100% accuracy, and vehicle vs isoprenaline-insulted WT cardiomyocytes with 95.6% accuracy. The model can be extended to judge whether a small number of cardiomyocytes (and so the whole heart) are under a specific cardiac disease. Thus, this study provides a novel and powerful approach for the research and application of calcium signaling in cardiac diseases.

scholarly journals Application of Neural Networks to the Classification of Pancreatic Intraductal Proliferative Lesions

2001 ◽  
Vol 23 (3-4) ◽  
pp. 129-136 ◽  
Author(s):  
Krzysztof Okoń ◽  
Romana Tomaszewska ◽  
Krystyna Nowak ◽  
Jerzy Stachura
Keyword(s):  
Neural Networks ◽  
Back Propagation ◽  
Ccd Camera ◽  
Maximum Diameter ◽  
Network Simulator ◽  
Image Analysis System ◽  
Back Propagation Algorithm ◽  
Mean Values ◽  
Starting Point

The aim of the study was to test applycability of neural networks to classification of pancreatic intraductal proliferative lesions basing on nuclear features, especially chromatin texture. Material for the study was obtained from patients operated on for pancreatic cancer, chronic pancreatitis and other tumours requiring pancreatic resection. Intraductal lesions were classified as low and high grade as previously described. The image analysis system consisted of a microscope, CCD camera combined with a PC and AnalySIS v. 2.11 software. The following texture characteristics were measured: variance of grey levels, features extracted from the grey levels correlation matrix and mean values, variance and standard deviation of the energy obtained from Laws matrices. Furthermore we used moments derived invariants and basic geometric data such as surface area, the minimum and maximum diameter and shape factor. The sets of data were randomly divided into training and testing groups. The training of the network using the back‐propagation algorithm, and the final classification of data was carried out with a neural network simulator SNNS v. 4.1. We studied the efficacy of networks containing from one to three hidden layers. Using the best network, containing three hidden layers, the rate of correct classification of nuclei was 73%, and the rate of misdiagnosis was 3%; in 24% the network response was ambiguous. The present findings may serve as a starting point in search for methods facilitating early diagnosis of ductal pancreatic carcinoma.

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