Showing papers in "International Journal of Computer Applications in 2011"

Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction

Abstract: The successful application of data mining in highly visible fields like e-business, marketing and retail has led to its application in other industries and sectors. Among these sectors just discovering is healthcare. The healthcare environment is still „information rich‟ but „knowledge poor‟. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. This research paper intends to provide a survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today‟s medical research particularly in Heart Disease Prediction. Number of experiment has been conducted to compare the performance of predictive data mining technique on the same dataset and the outcome reveals that Decision Tree outperforms and some time Bayesian classification is having similar accuracy as of decision tree but other predictive methods like KNN, Neural Networks, Classification based on clustering are not performing well. The second conclusion is that the accuracy of the Decision Tree and Bayesian Classification further improves after applying genetic algorithm to reduce the actual data size to get the optimal subset of attribute sufficient for heart disease prediction.

Fast and Accurate Detection and Classification of Plant Diseases

Abstract: We propose and experimentally evaluate a software solution for automatic detection and classification of plant leaf diseases. The proposed solution is an improvement to the solution proposed in [1] as it provides faster and more accurate solution. The developed processing scheme consists of four main phases as in [1]. The following two steps are added successively after the segmentation phase. In the first step we identify the mostlygreen colored pixels. Next, these pixels are masked based on specific threshold values that are computed using Otsu's method, then those mostly green pixels are masked. The other additional step is that the pixels with zeros red, green and blue values and the pixels on the boundaries of the infected cluster (object) were completely removed. The experimental results demonstrate that the proposed technique is a robust technique for the detection of plant leaves diseases. The developed algorithm‟s efficiency can successfully detect and classify the examined diseases with a precision between 83% and 94%, and can achieve 20% speedup over the approach proposed in [1].

Particle Swarm Optimization: Technique, System and Challenges

Abstract: Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method developed in 1995 by Eberhart and Kennedy based on the social behaviors of birds flocking or fish schooling. A number of basic variations have been developed due to improve speed of convergence and quality of solution found by the PSO. On the other hand, basic PSO is more appropriate to process static, simple optimization problem. Modification PSO is developed for solving the basic PSO problem. The observation and review 46 related studies in the period between 2002 and 2010 focusing on function of PSO, advantages and disadvantages of PSO, the basic variant of PSO, Modification of PSO and applications that have implemented using PSO. The application can show which one the modified or variant PSO that haven’t been made and which one the modified or variant PSO that will be developed.

A Review of Anomaly based Intrusion Detection Systems

Abstract: With the advent of anomaly-based intrusion detection systems, many approaches and techniques have been developed to track novel attacks on the systems. High detection rate of 98% at a low alarm rate of 1% can be achieved by using these techniques. Though anomaly-based approaches are efficient, signature-based detection is preferred for mainstream implementation of intrusion detection systems. As a variety of anomaly detection techniques were suggested, it is difficult to compare the strengths, weaknesses of these methods. The reason why industries don‟t favor the anomaly-based intrusion detection methods can be well understood by validating the efficiencies of the all the methods. To investigate this issue, the current state of the experiment practice in the field of anomalybased intrusion detection is reviewed and survey recent studies in this. This paper contains summarization study and identification of the drawbacks of formerly surveyed works.

Load Balanced Min-Min Algorithm for Static Meta-Task Scheduling in Grid Computing

Abstract: Grid computing has become a real alternative to traditional supercomputing environments for developing parallel applications that harness massive computational resources. However, the complexity incurred in building such parallel Grid-aware applications is higher than the traditional parallel computing environments. It addresses issues such as resource discovery, heterogeneity, fault tolerance and task scheduling. Load balanced task scheduling is very important problem in complex grid environment. So task scheduling which is one of the NP-Complete problems becomes a focus of research scholars in grid computing area. The traditional Min-Min algorithm is a simple algorithm that produces a schedule that minimizes the makespan than the other traditional algorithms in the literature. But it fails to produce a load balanced schedule. In this paper a Load Balanced Min-Min (LBMM) algorithm is proposed that reduces the makespan and increases the resource utilization. The proposed method has two-phases. In the first phase the traditional Min-Min algorithm is executed and in the second phase the tasks are rescheduled to use the unutilized resources effectively.

VANET Routing Protocols: Pros and Cons

Abstract: Vehicular Ad-hoc Network) is a new technology which has taken enormous attention in the recent years. Due to rapid topology changing and frequent disconnection makes it difficult to design an efficient routing protocol for routing data among vehicles, called V2V or vehicle to vehicle communication and vehicle to road side infrastructure, called V2I. The existing routing protocols for VANET are not efficient to meet every traffic scenarios. Thus design of an efficient routing protocol has taken significant attention. So, it is very necessary to identify the pros and cons of routing protocols which can be used for further improvement or development of any new routing protocol. This paper presents the pros and cons of VANET routing protocols for inter vehicle communication.

Automatic text classification: a technical review

Abstract: Automatic Text Classification is a semi-supervised machine learning task that automatically assigns a given document to a set of pre-defined categories based on its textual content and extracted features. Automatic Text Classification has important applications in content management, contextual search, opinion mining, product review analysis, spam filtering and text sentiment mining. This paper explains the generic strategy for automatic text classification and surveys existing solutions to major issues such as dealing with unstructured text, handling large number of attributes and selecting a machine learning technique appropriate to the text-classification application.

Data and Information Visualization Methods, and Interactive Mechanisms: A Survey

Abstract: availability of enough visualization techniques it can be very confusing to know what and when should be appropriate technique to use in order to convey maximum possible understanding. The basic purpose of visual representation is to efficiently interpret what is insight, as easy as possible. Different available visualization techniques are use for different situation which convey different level of understanding. This document is guide for the young researchers who wants to start work in visualization. The purpose of this piece of document is to collect all visualization techniques with their brief introduction. This paper deals with many definitions and aspects of visualization, how visualization take place i.e. different steps of visualization process, problems that are confront in visualization, categorization of visualization techniques on the bases of distinct perspective, typically known common data and information visualization techniques, basic interactive methods for visualization their advantages and disadvantages, interactivity process, and the scope of visualization up to some extent in different field of research. General Terms Visualization, Visualization techniques, Challenges, Interactive techniques.

A Comparative Study of Adaptive Crossover Operators for Genetic Algorithms to Resolve the Traveling Salesman Problem

Abstract: enetic algorithm includes some parameters that should be adjusting so that the algorithm can provide positive results. Crossover operators play very important role by constructing competitive Genetic Algorithms (GAs). In this paper, the basic conceptual features and specific characteristics of various crossover operators in the context of the Traveling Salesman Problem (TSP) are discussed. The results of experimental comparison of more than six different crossover operators for the TSP are presented. The experiment results show that OX operator enables to achieve a better solutions than other operators tested.

Fruit Detection using Improved Multiple Features based Algorithm

Abstract: icient locating the fruit on the tree is one of the major requirements for the fruit harvesting system. This paper presents the fruit detection using improved multiple features based algorithm. To detect the fruit, an image processing algorithm is trained for efficient feature extraction. The algorithm is designed with the aim of calculating different weights for features like intensity, color, orientation and edge of the input test image. The weights of different features represent the approximate locations of the fruit within an image. The Detection Efficiency is achieved up to 90% for different fruit image on tree, captured at different positions. The input images are the section of tree image. The proposed approach can be applied for targeting fruits for robotic fruit harvesting.

A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services

Abstract: arketing literature states that it is more costly to engage a new customer than to retain an existing loyal customer. Churn prediction models are developed by academics and practitioners to effectively manage and control customer churn in order to retain existing customers. As churn management is an important activity for companies to retain loyal customers, the ability to correctly predict customer churn is necessary. As the cellular network services market becoming more competitive, customer churn management has become a crucial task for mobile communication operators. This paper proposes a neural network (NN) based approach to predict customer churn in subscription of cellular wireless services. The results of experiments indicate that neural network based approach can predict customer churn with accuracy more than 92%. Further, it was observed that medium sized NNs perform best for the customer churn prediction when different neural network's topologies were experimented.

Performance Evaluation of Decision Tree Classifiers on Medical Datasets

Abstract: In data mining, classification is one o f the significant techniques with applications in fraud detection, Artificial intelligence, Medical Diagnosis and many other fields. Classification of objects based on their features into predefined categories is a widely studied problem. Decision trees are very much useful to diagnose a patient problem by the physicians. Decision tree classifiers are used extensively for diagnosis of breast tumour in ultrasonic images, ovarian cancer and heart sound diagnosis. In this paper, performance of decision tree induction classifiers on various medical data sets in terms of accuracy and time complexity are analysed. Keywords— Data Mining, Classification, Decision Tree Induction, Medical Datasets.

"Evaluation of BER for AWGN, Rayleigh and Rician Fading Channels under Various Modulation Schemes"

Abstract: Several transmission modes are defined in IEEE 802.11 a/b/g WLAN standards. A very few transmission modes are considering for IEEE 802.11 a/b/g in physical layer parameters and wireless channel characteristics. In this paper, we evaluated the performance of available transmission modes in IEEE 802.11b [1]. However, the performance analysis can be done straightforward using the evaluation of IEEE 802.11b. The performance of transmission modes are evaluated by calculating the probability of Bit Error Rate (BER) versus the Signal Noise Ratio (SNR) under the frequently used three wireless channel models (AWGN, Rayleigh and Rician) [2]. We consider the data modulation and data rate to analyze the performance that is BER vs. SNR. We also consider multipath received signals. The simulation results had shown the performance of transmission modes under different channel models and the number of antennas. Based on simulation results, we observed that some transmission modes are not efficient in IEEE 802.11b. The evaluation of performance confirms the increase in the coverage area of the physical layer in the 802.11b WLAN devices. General Terms Digital Modulation, Fading, BER (Bit Error Ratio), SNR (Signal to Noise Ratio)

A Survey of Ontology Learning Approaches

Abstract: The problem that ontology learning deals with is the knowledge acquisition bottleneck, that is to say the difficulty to actually model the knowledge relevant to the domain of interest. Ontologies are the vehicle by which we can model and share the knowledge among various applications in a specific domain. So many research developed several ontology learning approaches and systems. In this paper, we present a survey for the different approaches in ontology learning from semi-structured and unstructured date

Efficient Cluster Head Selection Scheme for Data Aggregation in Wireless Sensor Network

Abstract: A wireless sensor network is a resource constraint network, in which all sensor nodes have limited resources. In order to save resources and energy, data must be aggregated, and avoid amounts of traffic in the network. The aim of data aggregation is that eliminates redundant data transmission and enhances the life time of energy in wireless sensor network. Data aggregation process has to be done with the help of effective clustering scheme .in this paper we gives new scheme related to clustering for data aggregation called “Efficient cluster head selection scheme for data aggregation in wireless sensor network” (ECHSSDA), also we compare our propose scheme to the LEACH clustering algorithm. Comparison is based on the energy consumption, cluster head selection and cluster formation. In which we predict that, our propose algorithm is better than LEACH in the case of consume less energy by the cluster node and cluster head sending data to the base station consume less energy as better then LEACH.

Application based Study on Wireless Sensor Network

Abstract: A wireless sensor network is type of wireless network. Basically it consist a collection of tiny device are called sensor node, sensor node has a resource constraint means battery power, storage and communication capability. These sensor nodes are set with radio interface with which they communicated with one another to form a network. Wireless sensor network has very necessary application like remote has remote environmental monitoring and target tracking. The goal of our survey is to present a comprehensive review of the recent literature on various aspects of WSNs.And also discuss how wireless sensor network works and advantages and disadvantages over the traditional network

Grey Relational Analysis based Intuitionistic Fuzzy Multi-Criteria Group Decision-Making Approach for Teacher Selection in Higher Education

Abstract: selection is a group decision-making process under multiple criteria involving subjectivity, imprecision, and vagueness, which are best represented by intuitionistic fuzzy sets. An intuitionistic fuzzy set, which is characterized by membership function (degree of acceptance), non-membership function (degree of rejection) and the degree of indeterminacy or the degree of hesitancy, is a more general and suitable way to deal with imprecise information, when compared to a fuzzy set. The purpose of this study is to develop an intuitionistic fuzzy multi criteria group making method with grey relational analysis for teacher selection in higher education. Intuitionistic fuzzy weighted averaging operator is used to aggregate individual opinions of decision makers into a group opinion. Eight criteria obtained from expert opinions are considered for selection process. The criteria are namely academic performances, teaching aptitude, research experience, leadership quality, personality, management capacity, and values. Weights of the criteria are obtained by using a questionnaire. The weights of decision makers are considered as equal i.e. their importance are equal. The rating of an alternative with respect to certain criteria offered by decision maker is represented by linguistic variable that can be expressed by intuitionistic fuzzy sets. Grey relational analysis is used for ranking and selection of alternatives to constitute a panel of selected candidates. An educational problem for teacher selection is provided to illustrate the effectiveness of the proposed model.

Decision Support System for Congenital Heart Disease Diagnosis based on Signs and Symptoms using Neural Networks

Abstract: Congenital Heart Disease is one of the major causes of deaths in children. However, a proper diagnosis at an early stage can result in significant life saving. Unfortunately, all the physicians are not equally skilled, which can cause for time delay, inaccuracy of the diagnosis. A system for automated medical diagnosis would enhance the accuracy of the diagnosis and reduce the cost effects. In the present paper, a Decision Support System has been proposed for diagnosis of Congenital Heart Disease. The proposed system is designed and developed by using MATLAB’s GUI feature with the implementation of Backpropagation Neural Network. The Backpropagation Neural Network used in this study is a multi layered Feed Forward Neural Network, which is trained by a supervised Delta Learning Rule. The dataset used in this study are the signs, symptoms and the results of physical evaluation of a patient. The proposed system achieved an accuracy of 90%.

Performance Prediction of Engineering Students using Decision Trees

Abstract: mining can be used for decision making in educational system. A decision tree classifier is one of the most widely used supervised learning methods used for data exploration based on divide & conquer technique. This paper discusses use of decision trees in educational data mining. Decision tree algorithms are applied on engineering students' past performance data to generate the model and this model can be used to predict the students' performance. It will enable to identify the students in advance who are likely to fail and allow the teacher to provide appropriate inputs.

A Survey on Routing Protocols and its Issues in VANET

Abstract: Vehicular ad-hoc networks (VANETs) offer a vast number of applications without any support from fixed infrastructure. These applications forward messages in a multi-hop fashion. Designing an efficient routing protocol for all VANET applications is very hard. Hence a survey on routing protocols based on various parameters of VANET is a necessary issue in vehicle-tovehicle (V2V) and infrastructure-tovehicle (IVC) communication. This paper gives a brief overview of different routing algorithms in VANET along with major classifications. The protocols are also compared based on their essential characteristics and tabulated.

A New Method for Dimensionality Reduction using K- Means Clustering Algorithm for High Dimensional Data Set

Abstract: Clustering is the process of finding groups of objects such that the objects in a group will be similar to one another and different from the objects in other groups. Dimensionality reduction is the transformation of high-dimensional data into a meaningful representation of reduced dimensionality that corresponds to the intrinsic dimensionality of the data. K-means clustering algorithm often does not work well for high dimension, hence, to improve the efficiency, apply PCA on original data set and obtain a reduced dataset containing possibly uncorrelated variables. In this paper principal component analysis and linear transformation is used for dimensionality reduction and initial centroid is computed, then it is applied to K-Means clustering algorithm.

Parkinson Disease Classification using Data Mining Algorithms

Abstract: discovery in databases has established its success rate in various prominent fields such as e-business, marketing, retail and medical. Medical data mining has great potency for exploring the out of sight patterns in the respective medical data sets. This paper intends to provide a survey of current techniques of knowledge discovery in databases using data mining techniques that are in use today for the classification of Parkinson Disease. Parkinson Disease is a chronic malady of the central nervous system where the key indications can be captivated from the Mentation, Activities of Daily Life (ADL), Motor Examination and Complications of Therapy. The speech symptom which is an ADL is a common ground for the progress of the disease. The dataset for the disease is acquired from UCI, an online repository of large data sets. A comparative study on different classification methods is carried out to this dataset by applying the feature relevance analysis and the Accuracy Analysis to come up with the best classification rule. Also the intention is to sieve the data such that the healthy and people with Parkinson will be correctly classified.

Price Prediction of Share Market using Artificial Neural Network (ANN)

Abstract: Share Market is an untidy place for predicting since there are no significant rules to estimate or predict the price of share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc are all used to attempt to predict the price in the share market but none of these methods are proved as a consistently acceptable prediction tool. Artificial Neural Network (ANN), a field of Artificial Intelligence (AI), is a popular way to identify unknown and hidden patterns in data which is suitable for share market prediction. For predicting of share price using ANN, there are two modules, one is training session and other is predicting price based on previously trained data. We used Backpropagation algorithm for training session and Multilayer Feedforward network as a network model for predicting price. In this paper, we introduce a method which can predict share market price using Backpropagation algorithm and Multilayer Feedforward network. General Terms Artificial Neural Network, Machine Learning, Back propagation Algorithms, Share Market.

Noise Reduction of Speech Signal using Wavelet Transform with Modified Universal Threshold

Abstract: In this paper, Discrete-wavelet transform (DWT) based algorithm are used for speech signal denoising. Here both hard and soft thresholding are used for denoising. Analysis is done on noisy speech signal corrupted by babble noise at 0dB, 5dB, 10dB and 15dB SNR levels. Simulation & results are performed in MATLAB 7.10.0 (R2010a). Output SNR (Signal to Noise Ratio) and MSE (Mean Square Error) is calculated & compared using both types of thresholding methods. Soft thresholding method performs better than hard thresholding at all input SNR levels. Hard thresholding shows a maximum of 21.79 dB improvement whereas soft thresholding shows a maximum of 35.16 dB improvement in output SNR. General Terms Thresholding, multi-resolution analysis, wavelet.

Literature Review of Wavelet based Digital Image Watermarking Techniques

Abstract: In image watermarking, information is embedded into cover media to prove ownership. Various watermarking techniques have been proposed by many authors in the last several years which include spatial domain and transform domain watermarking. Wavelet based image watermarking is gaining more popularity because of its resemblance with the human visual system. This paper elaborates suitability of wavelet transform for image watermarking, wavelet transform based image watermarking process, classification and analysis of wavelet based watermarking techniques. The purpose of this paper is to provide a comprehensive review of the existing literature available on wavelet based image watermarking methods. It will be useful for researchers to implement effective image watermarking method.

A Review of various k-Nearest Neighbor Query Processing Techniques

Abstract: the queried object, from a large volume of given uncertain dataset, is a tedious task which involves time complexity and computational complexity. To solve these complexities, various research techniques were proposed. Among these, the simple, highly efficient and effective technique is, finding the K-Nearest Neighbor (kNN) algorithm. It is a technique which has applications in various fields such as pattern recognition, text categorization, moving object recognition etc. Different kNN techniques are proposed by various researchers under various situations. In this paper, we classified these techniques into two ways: (1) structure based (2) non-structure based kNN techniques. The aim of this paper is to analyze the key idea, merits, demerits and target data behind each kNN techniques. The structure based kNN techniques such as Ball Tree, k-d Tree, Principal Axis Tree (PAT), Orthogonal Structure Tree (OST), Nearest Feature Line (NFL), Center Line (CL) and Non-structured kNN techniques such as Weighted kNN, Condensed NN, Model based k-NN, Ranked NN (RNN), Pseudo/Generalized NN, Clustered k- NN(CkNN), Mutual kNN (MkNN), Constrained RkNN etc., are analyzed in this paper. It is observed that the structure based kNN techniques suffer due to memory limit whereas the Non-structure based kNN techniques suffer due to computation complexity. Hence, structure based kNN techniques can be applied to small volume of data whereas Non-structure kNN techniques can be applied to large volume of data.

Overview and Analysis of the Performances of ZigBee- based Wireless Sensor Networks

Abstract: Wireless sensor networks (WSN) consists of light-weight, lowpower and small size sensor nodes (SNs). They have ability to monitor, calculate and communicate wirelessly. In this paper we present a performance evaluation of ZigBee which is IEEE 802.15.4 standard, including the Physical (PHY) layer and Media Access Control (MAC) sub-layer, which allow a simple interaction between the sensors. We provide an accurate simulation model with respect to the specifications of IEEE 802.15.4 standard. We simulate and analyzed two different scenarios, where we examine the topological features and performance of the IEEE 802.15.4 standard using OPNET simulator. We compared the three possible topologies (Star, Mesh and Tree) to each other.

A Real Time Analysis of PPG Signal for Measurement of SpO2 and Pulse Rate

Abstract: Continuous measurement of oxygen level and pulse rate is very important for aged people, pregnant women and in many other critical situations. This is commonly monitored by a pulse oxymeter. This paper presents a low-cost and a miniaturized pulse oxymeter to continuously measure patient‟s blood-oxygen saturation level (SpO 2 ) and pulse rate. Change in intensity of light transmitted through tissue due to arterial blood pulse can be measured as a voltage signal called the photoplethysmographm (PPG). Oxygenated blood has different light absorption characteristics than deoxygenated blood under red and infra red wavelengths. So the hardware implementation is included placing of two LEDs (red and infra red) on the patient‟s finger and a photo detector on opposite side of the LEDs to get the corresponding PPG signals which are used to estimate the SpO 2 by comparing the absorption characteristics of the two different colored light (red and infra red). As the PPG signal is mostly corrupted by patient‟s hand movement, it is given to LabView window by DAQ card for further signal processing. arterial blood and path length of light travelling through the In this paper a low pass filter is used for removing motion artifacts and a moving average algorithm is applied to remove high frequency noise content. The SpO

A Perspective Analysis of Traffic Accident using Data Mining Techniques

Abstract: database. It is commonly used in a marketing, surveillance, fraud detection and scientific discovery. In data mining, machine learning is mainly focused as research which is automatically learnt to recognize complex patterns and make intelligent decisions based on data. Nowadays traffic accidents are the major causes of death and injuries in this world. Roadway patterns are useful in the development of traffic safety control policy. This paper deals with the some of classification models to predict the severity of injury that occurred during traffic accidents. I have compared Naive Bayes Bayesian classifier, AdaBoostM1 Meta classifier, PART Rule classifier, J48 Decision Tree classifier and Random Forest Tree classifier for classifying the type of injury severity of various traffic accidents. The final result shows that the Random Forest outperforms than other four algorithms.

Diagnosis of Heart Disease for Diabetic Patients using Naive Bayes Method

Abstract: objective of our paper is to predict the chances of diabetic patient getting heart disease. In this study, we are applying Naive Bayes data mining classifier technique which produces an optimal prediction model using minimum training set. Data mining is the analysis step of the Knowledge Discovery in Databases process (KDD). Data mining involves use of techniques to find underlying structures and relationships in a large database. Diabetes is a set of related diseases in which body cannot regulate the amount of sugar specifically glucose (hyperglycemia) in the blood. The diagnosis of diseases is a vital role in medical field. Using diabetic"s diagnosis, the proposed system predicts attributes such as age, sex, blood pressure and blood sugar and the chances of a diabetic patient getting a heart disease.