International Journal of Intelligent Systems and Applications @ijisa
Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1173
Detection of Diabetes using Combined ML Algorithm
Статья научная
Recently data clustering algorithm under machine learning are used in ‘real-life data’ to segregate them based on the outcome of a phenomenon. In this paper, diabetes is detected from pathological data of 768 patients using four clustering algorithms: Fuzzy C-Means (FCM), K-means clustering, Fuzzy Inference system (FIS) and Support Vector Machine (SVM). Our main objective is to make binary classification on the data table in a sense that presence or absence of diabetes of a patient. We combined the four machine learning algorithms based on entropy-based probability to enhance accuracy of detection. Before applying combining scheme, we reduce the size of variables applying multiple linear regression (MLR) on the table then logistic regression is again applied on the resultant data to keep the outlier within a narrow range. Finally, entropy based combining scheme with some modification is applied on the four ML algorithms and we got the accuracy of detection about 94% from the combining technique.
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Detection of Metamorphic Malware based on HMM: A Hierarchical Approach
Статья научная
Recent research have depicted that hidden Markov model (HMM) is a persuasive option for malware detection. However, some advanced metamorphic malware are able to overcome the traditional methods based on HMMs. This proposed approach provides a two-layer technique to overcome these challenges. Malware contain various sequences of opcodes some of which are more important and help detect the malware and the rest cause interference. The important sequences of opcodes are extracted by eliminating partial sequences due to the fact that partial sequences of opcodes have more similarities to benign files. In this method, the sliding window technique is used to extract the sequences. In this paper, HMMs are trained using the important sequences of opcodes that will lead to better results. In comparison to previous methods, the results demonstrate that the proposed method is more accurate in metamorphic malware detection and shows higher speed at classification.
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Статья научная
This research aims to study a development of a forecasting model to predict a monthly paddy price in Thailand with 2 datasets. Each of datasets is the univariate time series that is a monthly data, since Jan 1997 to Dec 2017. To generate a forecasting model, we present a forecasting model by using the Artificial Neural Network technique and determine its structure with Autoregressive form of the ARIMA model and Genetic Algorithm, it’s called AR-GA-ANN model. To generate the AR-GA-ANN model, we set 1 to 3 hidden layers for testing, determining the number of input nodes by an Autoregressive form of the ARIMA model and determine the number of neurons in hidden layer by Genetic Algorithm. Finally, we evaluate a performance of our AR-GA-ANN model by error measurement with Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) and compare errors with the ARIMA model. The result found that all of AR-GA-ANN models have lower RMSE and MAPE than the ARIMA model and the AR-GA-ANN with 1 hidden layer has lowest RMSE and MAPE in both datasets.
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Determination of status of family stage prosperous of Sidareja District using data mining techniques
Статья научная
Family welfare is a family formed in legitimate marriage, spiritual needs and material worthy, devoted to God YME, have a harmonious relationship, harmonious and balanced with society and the environment. The government has implemented various family development programs prosperous. To support this, every year the government implements the family data collection process. Family data collection is considered an important step because it has many functions, primarily to understand the target group and to determine solutions to solve the problems of each target group. The search or discovery process of information and knowledge contained in the number of data can be done with data mining technology. Data mining is a term used to describe the discovery of knowledge in a database. In this case data mining can be used to determine the status of the prosperous family stage. The K-Nearest Neighbor (KNN) method, the Naive Bayes method and the Principal Component Analysis (PCA) are used for the proper classification of status stages. Based on the test results, the performance test of classification algorithm for case of determining status of prosperous family of Sidareja District for Naïve Bayes method using confusion matrix obtained 98.12% accuracy after added PCA feature selection to 97.73% while KNN method obtained accuracy of 98.86%, then after added PCA feature selection increased to 98.96%.
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Статья научная
The paper is dedicated to the problem of efficiency increasing in case of applying multilayer perceptron in context of parameters estimation for technical systems. It is shown that the increase of efficiency is possible by adaptation of structure of the multilayer perceptron to the problem specification set. It is revealed that the structure adaptation lies in the determination the following parameters: 1. The number of hidden neuron layers; 2. The number of neurons within each layer. In terms of the paper, we introduce mathematical apparatus that allows conducting the structure adaptation for minimization of the relative error of the neuro-network model generalization. A numerical experiment to demonstrate efficiency of the mathematical apparatus was developed and described in terms of the article. Further research in this sphere lies in the development of a method for calculation of optimum relationship between the number of the hidden neuron layers and the number of hidden neurons within each layer.
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Devanagri Handwritten Numeral Recognition using Feature Selection Approach
Статья научная
In this paper novel feature selection approach is used for the recognition of Devanagri handwritten numerals. The numeral images used for the experiments in the study are obtained from standard benchmarking data-set created by CVPR (ISI)Kolkata. The recognition algorithm consists of four basic steps; pre-processing, feature generation, feature subset selection and classification. Features are generated from the boundary of characters, utilizing the direction based histogram of segmented compartment of the character image. The feature selection algorithm is utilizing the concept of information theory and is based on maximum relevance minimum redundancy based objective function. The classification results are obtained for a single neural network based classifier as well as for the committee of Neural Network based classifiers. The paper reports an improvement in recognition result when decision combiner based committee is used along with class related feature selection approach.
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Development a Model for Drug Interaction Prediction Based on Patient State
Статья научная
Drug interactions prediction is one of the health critical issues in drug producing and use. Proposing computational model for classifying and predicting interactions of drugs with high precision is a difficult problem. Medicines are classified into two classes: overlapping, non-overlapping. It was suggested an expert system for classifying and predicting interactions of drugs using various information about drugs, interference reasons and common factors between patients and active substance that causes interference, such as: effective dose of the drug, maximum dose, times of use per day and age of patients considering that only adult category selected. The proposed model can classify and predict interactions of drugs through patient's state taking into consideration that when changing one of mentioned factors, the effect of drugs will be changed and it may lead to appear new symptoms on the patients. There is a desktop application related with the mentioned model, which helps users to know drugs and drugs families and its interactions. Proposed model will be implemented in Python using following classifiers: Logistic Regression (LR), Support Vector Machine (SVM) and Neural Network (NN), which divided data according to their similarity related to the factors of occurrence of drug interference. As these techniques showed good results, NN technology is considered one of the best techniques in giving results where MLPClassifier achieved superior performance with 97.12%.
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Development a New Crossover Scheme for Traveling Salesman Problem by aid of Genetic Algorithm
Статья научная
This research work provides a detailed working principle and analysis technique of multi- offspring crossover operator. The proposed approach is an extension of the basic partially- mapped crossover (PMX) based upon survival of the fittest theory. It improves the performance of the genetic algorithm (GA) for solving the well-known combinatorial optimization problem, the traveling salesman problem (TSP). This study is based on numerical experiments of the proposed with other traditional crossover operators for eighteen benchmarks TSPLIB instances. The simulation results show a considerable improvement because the proposed operator enhances the opportunity of having better offspring. Moreover, the t-test also establishes the improved significance of the proposed operator. Its preferable results not only confirm the advantages over others, but also show the long run survival of a generation having a number of offspring more than the number of parents with the help of mathematical ecology theory.
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Статья научная
Higher Order Neural Networks (HONN) are characterized with fast learning abilities, stronger approximation, greater storage capacity, higher fault tolerance capability and powerful mapping of single layer trainable weights. Since higher order terms are introduced, they provide nonlinear decision boundaries, hence offering better classification capability as compared to linear neuron. Nature-inspired optimization algorithms are capable of searching better than gradient descent-based search techniques. This paper develops some hybrid models by considering four HONNs such as Pi-Sigma, Sigma-Pi, Jordan Pi-Sigma neural network and Functional link artificial neural network as the base model. The optimal parameters of these neural nets are searched by a Particle swarm optimization, and a Genetic Algorithm. The models are employed to capture the extreme volatility, nonlinearity and uncertainty associated with stock data. Performance of these hybrid models is evaluated through prediction of one-step-ahead exchange rates of some real stock market. The efficiency of the models is compared with that of a Radial basis functional neural network, a multilayer perceptron, and a multi linear regression method and established their superiority. Friedman's test and Nemenyi post-hoc test are conducted for statistical significance of the results.
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Статья научная
Time Series data is large in volume, highly dimensional and continuous updating. Time series data analysis for forecasting, is one of the most important aspects of the practical usage. Accurate rainfall forecasting with the help of time series data analysis will help in evaluating drought and flooding situations in advance. In this paper, Artificial Neural Network (ANN) technique has been used to develop one-month and two-month ahead forecasting models for rainfall prediction using monthly rainfall data of Northern India. In these model, Feed Forward Neural Network (FFNN) using Back Propagation Algorithm and Levenberg- Marquardt training function has been used. The performance of both the models has been assessed based on Regression Analysis, Mean Square Error (MSE) and Magnitude of Relative Error (MRE). Proposed ANN model showed optimistic results for both the models for forecasting and found one month ahead forecasting model perform better than two months ahead forecasting model. This paper also gives some future directions for rainfall prediction and time series data analysis research.
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Статья научная
The structure of the technical accident prevention subsystem for the smart home system has been developed in the article. The subsystem model based on Petri network, model based on neural network and physical model using the Arduino microcontroller have been realized in the development process. The subsystem research results with the use of the developed models, soft- and hardware tools are also presented.
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Статья научная
This paper proposes an adaptive traffic control system that dynamically manages traffic phases and durations at cross-intersection. The developed model optimally schedules green light timing in accordance with traffic condition on each lane in order to minimize the Average Waiting Time (AWT) at the cross intersection. A MATLAB based Graphic User Interface (GUI) traffic control simulator was developed. Three scenarios of vehicular traffic control were simulated and the results presented. The results show that scenario one and two demonstrated the variation of the AWT and Performance of the developed algorithm with changes in the maximum allowable green light timing over the simulation interval. In the third scenario, an AWT of 38sec was recorded against a maximum allowable green light duration of 120sec, during which 1382 vehicles were evacuated from the intersection, leaving 22 vehicles behind. The algorithm also had a performance of 98.43% over a simulation duration of 1800sec.
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Статья научная
This research introduces machine learning models using the Gaussian Process Regression (GPR) depicting the association between paddy yield and weather in Sri Lanka. All major regions in the island with most contribution to the total paddy production were considered in this research. The climatic factors of rainfall, relative humidity, minimum temperature, maximum temperature, average wind speed, evaporation, and sunshine hours were considered as input (independent) variables, while the paddy yield was the output (dependent) variable. The collinearity within each pair of independent and dependent variables was determined using Spearman’s and Pearson’s correlation coefficients. Data sets corresponding to the two main annual paddy cultivation seasons since 2009 were trained in MATLAB to develop crop-weather models. The most appropriate Kernel function was chosen from among four types of Kernels viz. Rational Quadratic, Exponential, Squared Exponential, and Matern 5/2 based on their degree of coherence in modeling. This approach exploits the full potential of GPR in developing highly accurate crop-weather models. The performance of the crop-weather models was measured by the Correlation Coefficient, Mean Absolute Percentage Error, Mean Squared Error, Root Mean Squared Error Ratio, Nash Number and the BIAS. All the GPR-based models proposed in this paper are highly accurate in terms of the aforementioned evaluation metrics. Accordingly, when the climatic data are known or projected, the paddy yield and thereby the harvest of Sri Lanka can be predicted precisely by using the proposed crop-weather models.
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Статья научная
Surveillance systems are useful in the identification of patients that contract infections during their hospitalization period. Despite still being at infancy, electronic information control surveillance systems for Hospital Acquired Infections (HAIs) are improving and becoming more commonplace as the acceptance levels rise. There are crucial gaps in existing knowledge concerning the best ways for implementing electronic surveillance systems especially in the context of the Intensive Care Unit (ICU). To bridge this gap, the aim of this paper was to provide a comprehensive review of various electronic surveillance approaches and to highlight the requisite data components and offer guidelines. This review revealed denominator, numerator, and discrete data requirements and guidelines for the surveillance of four main ICU HAIs, including Central Line–Associated Bloodstream Infection (CLABSI), Urinary Tract Infection (UTI), Surgical Site Infections (SSIs) and Ventilator-Associated Conditions/Events (VACs/VAEs).
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Development of Regression Models for Assessing Fire Risk of Some Indian Coals
Статья научная
Spontaneous combustion of coals leading to mine fires is a major problem in Indian coal mines that creates serious safety and mining risk. A number of experimental techniques based on petrological, thermal and oxygen avidity studies have been used for assessing the spontaneous heating liability of coals all over the world. Crossing point temperature (CPT) is one of the most common methods in India to assess the fire risk of coal so that appropriate strategies and effective action plans could be made in advance to prevent occurrence and spread of fire and hence minimize coal loss. In this paper, the spontaneous heating risks of some of the Indian coals covering few major coalfields were assessed using CPT apparatus. Statistical analysis was carried out between CPT and the proximate analysis parameters and it was found that the Mixture Surface Regression (MSR) model was more effective and gave very good residual values as compared to the polynomial and simple multiple regression models. The performance of Anderson-Darling testing was done between the prediction results of MSR model and measured value of CPT showed that the residual follows normal distribution hence justifies the suitability of model for the prediction of spontaneous heating liability of coal.
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Development of a Computational Model for Cassava Food Processing Using Coloured Petri Net
Статья научная
A food system is composed of a complex network of activities and processes for production, distribution, transportation and consumption, which interact with each other, thus leading to changeable behaviour. Most existing empirical studies on cassava processing have focused on the technical efficiency analysis of the cassava crop processing techniques among processors indicating that the modelling of the events and operations involved in the processing of the cassava crop is highly limited. In this context, different strategies have been used to solve difficult environmental and agro-informatic systems model-based problems such as system dynamics, agent based, rule-based knowledge and mathematical modeling. However, the structural comprehension and behavioral investigation of this modeling are constrained. In this regard, formal computational modeling is a method that enables modeling and simulation of the dynamical characteristics of these food systems to be examined. In this study, the system specification is designed using Unified Modelling language (UML) to show the structural process and system design modelled and simulated using Coloured Petri Net (CPN), a formal method for analyzing the behavioural properties of complex system because of its efficient analysis. For the purpose of observing and analyzing the behaviour of the cassava food process, a series of simulation runs was proposed.
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Development of a Facenet Enhanced Secured Smart Office System
Статья научная
A secured smart office system is the one that is capable of recognizing and granting access to authorized persons only and manage the office appliances autonomously. The goals are access control, security and automation. Over the years, several studies have been carried out to meet these needs using RFID cards, access codes and biometrics resulting in weak security with long computational period. Switching of electrical appliances and smoke detection in case of fire outbreak were used but real time electrical appliances management that could prevent fire outbreak is yet to be achieved. This research focused its attention on the design and implementation of a smart office system that meet these needs. The system was developed using a raspberry pi 4 board. Ultrasonic sensor, camera, servo motor, relay, current and voltage sensors were interfaced with the raspberry pi for image capturing, opening the door, switching and power monitoring respectively. The system captures the image of an approaching person and process it for recognition using FaceNet; an open source model for face recognition. Information was transmitted via SIM800L GSM module as SMS to the administrator. The system shuts down the office electrical network once the supply voltage exceeds 220v ac or less than 161v ac, thus preventing any chance of fire outbreak due to irregular power supply. The accuracy of image recognition model was 93.13%. This research has shown a simple way of implementing an autonomous smart office system that is capable of providing adequate security, efficiency and convenience in offices.
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Development of a Low-Cost GSM-Bluetooth Home Automation System
Статья научная
In today's age of digital technology and intelligent systems, home automation has become one of the fastest developing technology in the world as more and more people begin to see the idea of remotely monitoring and controlling their home appliances more as a necessity rather than a luxury. This paper presents the design and development of a smart home system that allows control of home appliances using both Bluetooth and GSM technology. The use of multiple control mediums gives more robustness to the system as appliance control and monitoring is made cheaper and possible regardless of the distance from which control is being effected. The system is controlled using a dedicated android based application which ensures convenience and ease of use. In addition, it is equipped with a security feature which is activated when the user is away from home. This enables the user to detect intrusion while the user is away.
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Development of a Model for Electronic Toll-Collection System
Статья научная
The paper presents a new method for electronic toll collection system and to match car number plate detection system. The electronic toll collection system (ETS) has been fabricated based on microcontroller. Here a system is developed to collect tolls according to the weight of the vehicle. The car number plate detection method utilizes template matching technique to approximate the location of car number plate. Then, using this output from the template matching method, color information is used to eliminate the unwanted color areas from the approximate number plate region without affecting the correct color regions. Hence, the number plate region can be determined more accurately. This work can easily be done by image processing system using MATLAB. The method has low complexity and reduced the processing time magnificently. This automated system also shows a better performance in highway traffic management. This paper shows the gateway to fabricate a highly automated toll-plaza.
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Development of hybrid learning machine in complex domain for human identification
Статья научная
This paper presents a hybrid learning machine for human identification. It is a merger of eigenface with fisherface method, genetic fuzzy clustering and complex neural network. The non-linear aggregation based summation and radial basis function neural networks (NLA-SRBF NNs) are proposed as one of the functional component of the novel learning machine. The architecture of NLA-SRBF NNs incorporates hidden neurons, with summation and radial basis aggregation, and output neurons with only summation aggregation, along with complex resilient propagation (ČRPROP) learning procedure. The improved learning and speedy convergence of NLA-SRBF NN enables the hybrid machine to provide better recognition accuracy. The learning machine consists of feature extraction, unsupervised clustering and supervised classification module. The aim of our proposal is to enhance the performance of biometric based recognition system. The efficacy and potency of our hybrid learning machine demonstrated on three benchmark biometric datasets-extended Cohn-Kanade, FERET and AR face datasets to comprehend the motivation. The performance comparisons of different variations of hidden neuron and learning algorithm thoroughly presented the superiority of the proposed NN based hybrid learning machine.
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