Showing papers in "International Journal of Computer Applications Technology and Research in 2013"

A Review of Machine Learning based Anomaly Detection Techniques

Abstract: Intrusion detection is so much popular since the last two decades where intrusion is attempted to break into or misuse the system. It is mainly of two types based on the intrusions, first is Misuse or signature based detection and the other is Anomaly detection. In this paper Machine learning based methods which are one of the types of Anomaly detection techniques is discussed.

Performance Comparison of Digital Image Watermarking Techniques: A Survey

Abstract: Digital watermarking is the processing of combined information into a digital signal. A watermark is a secondary image, which is overlaid on the host image, and provides a means of protecting the image. In order to provide high quality watermarked image, the watermarked image should be imperceptible. This paper presents different techniques of digital image watermarking based on spatial & frequency domain, which shows that spatial domain technique provides security & successful recovery of watermark image and higher PSNR value compared to frequency domain.

Detect Breast Cancer using Fuzzy C means Techniques in Wisconsin Prognostic Breast Cancer (WPBC) Data Sets

Abstract: Medical data mining is very much valuable to medical experts. The main task of data mining is diagnosing the patient's disease Classification. Breast cancer is a severe and life threatening disease very commonly found in woman. An unusual growth of cells in breast is the main source of breast cancer those cells can be of two types malignant (Cancerous) and benign (Non-Cancerous) these types must be diagnosed taking proper meditation and for proper treatment. Modern medical diagnosis scheme is totally based on data taken through clinical and/or other test; most of the decision related to classification of a disease is a very crucial and challenging job. In this research work, using intelligent techniques of data mining is Fuzzy C Means; we have focused on breast cancer diagnosis by fuzzy systems. Fuzzy rules are desirable because of their interpretability by human experts. It has been applied to classify data related to breast cancer from UCI repository site. Experimental works were done using MATLAB in order to reduce dimensionality of breast cancer data set a ranking based feature selection technique. Results on breast cancer diagnosis data set from UCI machine learning repository show that this approach would be capable of classifying cancer cases with high accuracy rate in addition to adequate interpretability of extracted rules.

Applying Data Mining Techniques on Soil Fertility Prediction

Abstract: The techniques of data mining are very popular in the area of agriculture. The advancement in Agricultural research has been improved by technical advances in computation, automation and data mining. Now a days ,data mining is being used in a vast areas .The products of data mining system and domain specific data mining application soft ware’s are available for tailor made use, but data mining in agricultural on soil datasets is a relatively a young and contemporary research domain. Larger volume of data are harvested along the with the crop harvest in agriculture. Inferring the knowledge from huge volume of data is virtually a difficult task in the current scenario. This research uses the data mining techniques for analysis of soil dataset. This data mining algorithms are used for analysing the soil datasets for classification purposes. The various techniques of data mining is used and compared in this research.

A Review on Wireless Sensor Network Protocol for Disaster Management

Abstract: Disasters management and emergency services used to protect a person or society from the cost of disasters such as tsunami warning , landslide monitoring, earthquake rescue operation , volcano monitoring, and fire protection. Timely report and responses are especially important for reducing the number of sufferers and damages from incidents. In such cases, the communication structure that may not function well. This makes it hard to gain information about the incident, and then to respond to the incident rapidly and properly. Sensor networks can provide a good solution to these problems through actively monitoring and well-timed reporting emergency incidents to base station. Our objective on this topic aim to study different sensor network protocols to resolve some key technical problems in this area, thus identify the energy efficient wireless sensor network architecture for significant improvement of disaster management . We also analyze the WSN protocol based on metrics such as Energy efficiency, location awareness, network lifetime. It furthermore focuses the advantages and performance for disaster management.

Shape Oriented Feature Selection for Tomato Plant Identification

Abstract: Selection of relevant features for classification from a high dimensional data set by keeping their class discriminatory information intact is a classical problem in Machine Learning. The classification power of the features can be measured from the point of view of redundant information and correlations among them. Choosing minimal set of features optimizes time, space complexity related cost and simplifies the classifier design, resulting in better classification accuracy. In this paper, tomato (Solanum Lycopersicum L) leaves and fruiting habits were chosen with a futuristic goal to build a prototype model of leaf & fruit classification. By applying digital image processing techniques, tomato leaf and fruit images were pre-processed and morphological shape based features were computed. Next, supervised filter and wrapper based feature selection techniques were adopted to choose the optimal feature set leading to small within-class variance and large among-class distance which may be of utter importance in building the model for recognition system of the tomato leaf and fruiting habit genre.

High Performance Computing for Satellite Image Processing and Analyzing - A Review

Abstract: High Performance Computing (HPC) is the recently developed technology in the field of computer science, which evolved due to meet increasing demands for processing speed and analysing/processing huge size of data sets. HPC brings together several technologies such as computer architecture, algorithm, programs and system software under one canopy to solve/handle advanced complex problems quickly and effectively. It is a crucial element today to gather and process large amount of satellite (remote sensing) data which is the need of an hour. In this paper, we review recent development in HPC technology (Parallel, Distributed and Cluster Computing) for satellite data processing and analysing. We attempt to discuss the fundamentals of High Performance Computing (HPC) for satellite data processing and analysing, in a way which is easy to understand without much previous background. We sketch the various HPC approach such as Parallel, Distributed & Cluster Computing and subsequent satellite data processing & analysing methods like geo-referencing, image mosaicking, image classification, image fusion and Morphological/neural approach for hyper- spectral satellite data. Collective, these works deliver a snapshot, tables and algorithms of the recent developments in thos e sectors and offer a thoughtful perspective of the potential and promising challenges of satellite data processing and analysing using HPC paradigms.

Survey on Dynamic Resource Allocation Strategy in Cloud Computing Environment

Abstract: Cloud computing becomes quite popular among cloud users by offering a variety of resources. This is an on demand service because it offers dynamic flexible resource allocation and guaranteed services in pay as-you-use manner to public. In this paper, we present the several dynamic resource allocation techniques and its performance. This paper provides detailed description of the dynamic resource allocation technique in cloud for cloud users and comparative study provides the clear detail about the different techniques.

Automatic Seed Classification by Shape and Color Features using Machine Vision Technology

Abstract: In this paper the proposed system uses content based image retrieval (CBIR) technique for identification of seed e.g. wheat, rice, gram etc. on the basis of their features. CBIR is a technique to identify or recognize the image on the basis of features present in image. Basically features are classified in to four categories 1.color 2.Shape 3. texture 4. size .In this system we are extracting color, shape feature extraction. After that classifying images in to categories using neural network according to the weights and image displayed from the category for which neural network shows maximum weight. category1 belongs to wheat and category2 belongs to gram. Experiment was conducted on 200 images of wheat and gram by using Euclidean distance(ED) and artificial neural network techniques. From 200 images 150 are used for training purpose and 50 images are used for testing purpose. The precision rate of the system by using ED is 84.4 percent By using Artificial neural network precision rate is 95 percent.

Analysis of Cryptographic Algorithms for Network Security

Abstract: Cryptography plays a major role in securing data. It is used to ensure that the contents of a message are confidentially transmitted and would not be altered. Network security is most vital component in information security as it refers to all hardware and software function, characteristics, features, operational procedures, accountability, access control, and administrative and management policy. Cryptography is central to IT security challenges, since it underpins privacy, confidentiality and identity, which together provide the fundamentals for trusted e-commerce and secure communication. There is a broad range of cryptographic algorithms that are used for securing networks and presently continuous researches on the new cryptographic algorithms are going on for evolving more advanced techniques for secures communication.

Reduction of types of Noises in dental Images

Abstract: --This paper presents a filter for restoration of Dental images that are highly corrupted by salt and pepper noise and speckle noise, Poisson noise. After detecting and correcting the noisy pixel, the proposed filter is able to suppress noise level. In this paper for each noise proposed different type of filter and compare these three types of filter with their PSNR value and MSE value and SNR value. After filtering stage maximum detected noise pixels will be filtered and simulation results show the filtered image.

Occlusion and Abandoned Object Detection for Surveillance Applications

Abstract: Object detection is an important step in any video analysis. Difficulties of the object detection are finding hidden objects and finding unrecognized objects. Although many algorithms have been developed to avoid them as outliers, occlusion boundaries could potentially provide useful information about the scene’s structure and composition. A novel framework for blob based occluded object detection is proposed. A technique that can be used to detect occlusion is presented. It detects and tracks the occluded objects in video sequences captured by a fixed camera in crowded environment with occlusions. Initially the background subtraction is modeled by a Mixture of Gaussians technique (MOG). Pedestrians are detected using the pedestrian detector by computing the Histogram of Oriented Gradients descriptors (HOG), using a linear Support Vector Machine (SVM) as the classifier. In this work, a recognition and tracking system is built to detect the abandoned objects in the public transportation area such as train stations, airports etc. Several experiments were conducted to demonstrate the effectiveness of the proposed approach. The results show the robustness and effectiveness of the proposed method. Keyword: Occlusion, Histograms of Oriented Gradients descriptors, Support Vector Machine, mixture of Gaussians techniques, Blob, abandoned object.

Swarm Intelligence Based Optimization for Web Usage Mining in Recommender System

Abstract: Nowadays, the web has become one of the most effective and efficient platform for information change and retrieval .Due to heterogeneity and unstructured nature of the data available on the WWW, web mining uses various data mining techniques to discover useful knowledge from web hyperlinks, page content and usage log. This research introduces the theoretical foundations of Swarm Intelligence and Design, implementation of swarm optimization algorithm. The Swarm Intelligence optimization and data mining technique can be used together to form a method which often leads to the result. Design and implementation of a web mining system based on multi-agents technology will reduce the information overload and search depth. This is helpful to users using the web within a platform for e-commerce or e-learning.Swarm Intelligence is an efficient technology that deals with natural and artificial system. It provides an efficient way for finding optimal solution. During the past few decades researches are trying to use these techniques to solve many problems in various fields. Recommender System is the one of the most important application of e-commerce and it plays vital role in understanding the user’s behaviour or interest by which it increases the profit of sales or usage of services of website. This paper describes a swarm intelligence optimization for web mining to find the optimal solution and based on that process is done.

Design of an Intelligent and Efficient Light Control System

Abstract: Recently, many researches has been carried out to save the energy in many aspects such as producing a device which consumes very less energy or designing a system which helps to reduce the power consumption using the existing devices. In this paper, a room light control system is proposed which is named as light control system (LCS). This proposed system will able to provide the needed light which provides the satisfaction of users and will provide energy saving and management. In this paper the Lighting Control System and the decision making algorithm, are discussed. As per the algorithm the system will first check any occupant is there in the room. If so then the system will check the intensity of light in the room and if it is low then it will switch on the light. Our proposed system can able to minimize the energy consumed for lighting in a room and can able to provide it efficiently.

Natural Hand Gestures Recognition System for Intelligent HCI: A Survey

Abstract: Gesture recognition is to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. Hand Gestures have greater importance in designing an intelligent and efficient human–computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation to virtual reality. In this paper a survey on various recent gesture recognition approaches is provided with particular emphasis on hand gestures. A review of static hand posture methods are explained with different tools and algorithms applied on gesture recognition system, including connectionist models, hidden Markov model, and fuzzy clustering. Challenges and future research directions are also highlighted.

Objects Tracking in Images Sequence Using Center-Symmetric Local Binary Pattern (CS-LBP)

Abstract: In this paper we present a method for objects tracking in images sequence. This approach is achieved into two main steps. In the first one, we constructed the Center-Symmetric Local Binary Pattern (CS-LBP) histogram pattern of each image in the sequence and the reference pattern. In the second one, we perform the algorithm by the pattern selected based on a distance measures to find similarity between two histograms. The maximum CS-LBP histogram distance gives best results than the chi-square one. The proposed approach has been tested on synthetic and real sequence images and the results are satisfactory.

A Novel Color Image Fusion for Multi Sensor Night Vision Images

Abstract: In this paper presents a simple and fast color fusion approach for night vision images. Image fusion involves merging of two or more images in such a way, to get the most advantageous characteristics of each image. Here the Visible image is fused with the InfraRed (IR) image, so the desired result will be single, highly informative image providing full information. This paper focuses on color constancy and color contrast problem. Firstly the contrast of the infrared and visible image is enhanced using Local Histogram Equation. Then the two enhanced images are fused in three compounds of a LAB image using aDWT image fusion. This paper adopts an approach which transfer color from the reference image to the fused image using Color Transfer Technology. To enhance the contrast between the target and the background, a scaling factor is introduced in the transferring equation in the b channel. Finally our approach gives the Multiband Fused image with the natural day-time color appearance and the hot targets are popped out with intense colors while the background details present with the natural color appearance.

Profit Maximization for Service Providers using Hybrid Pricing in Cloud Computing

Abstract: Cloud computing has recently emerged as one of the buzzwords in the IT industry. Several IT vendors are promising to offer computation, data/storage, and application hosting services, offering Service-Level Agreements (SLA) backed performance and uptime promises for their services. While these „clouds‟ are the natural evolution of traditional clusters and data centers, they are distinguished by following a pricing model where customers are charged based on their utilization of computational resources, storage and transfer of data. They offer subscription-based access to infrastructure, platforms, and applications that are popularly termed as IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and SaaS (Software as a Service). In order to improve the profit of service providers we implement a technique called hybrid pricing , where this hybrid pricing model is a pooled with fixed and spot pricing techniques.

Evolving CSP Algorithm in Predicting the Path Loss of Indoor Propagation Models

Abstract: Constraint programming is the study of system which is based on constraints. The solution of a constraint satisfaction problem is a set of variable value assignments, which satisfies all members of the set of constraints in the CSP. In this paper the application of constraint satisfaction programming is used in predicting the path loss of various indoor propagation models using chronological backtrack algorithm, which is basic algorithm of CSP. After predicting the path loss at different set of parameters such as frequencies (f), floor attenuation factor (FAF), path loss coefficient (n), we find the optimum set of parameter frequency (f), floor attenuation factor (FAF), path loss coefficient(n) at which the path loss is minimum. The Branch and bound algorithm is used to optimize the constraint satisfaction problem.

Performance Evaluation of DSDV, DSR AND ZRP Protocol in MANET

Abstract: Mobile Ad-Hoc Networks are rapidly deployable and self-configuring networks. In MANET all the network node work as a router and must be capable to relay traffic from one to another since communicating nodes might be out of range. MANET is characterized by dynamic topology, possibly unidirectional links, constrained resources and network partitions. The main two attributes are mobility and multi-hop.[1][3] The size of MANET can be varied from small static network to highly large dynamic network. MANET use dynamic changing network topologies that are proactive, reactive and hybrid protocol.[1][2] In this paper, an attempt has been made to compare performance of Proactive, reactive and hybrid protocol for the MANET. A comparative study of DSDV (proactive) DSR (reactive) and ZRP (hybrid) has been done on the basis of their performance in MANETs using NS2 simulator. Packet delivery fraction ratio and throughput are considered as a performance parameter for evaluating the performance of DSDV,DSR and ZRP protocol.

On the Adjacency Matrix and Neighborhood Associated with Zero-divisor Graph for Direct Product of Finite Commutative Rings

Abstract: The main purpose of this paper is to study the zero-divisor graph for direct product of finite commutative rings. In our present investigation we discuss the zero-divisor graphs for the following direct products: direct product of the ring of integers under addition and multiplication modulo p and the ring of integers under addition and multiplication modulo p for a prime number p, direct product of the ring of integers under addition and multiplication modulo p and the ring of integers under addition and multiplication modulo 2p for an odd prime number p and direct product of the ring of integers under addition and multiplication modulo p and the ring of integers under addition and multiplication modulo p – 2 for that odd prime p for which p – 2 is a prime number. The aim of this paper is to give some new ideas about the neighborhood, the neighborhood number and the adjacency matrix corresponding to zero-divisor graphs for the above mentioned direct products. Finally, we prove some results of annihilators on zerodivisor graph for direct product of A and B for any two commutative rings A and B with unity

Multiple Downlink Fair Packet Scheduling Scheme in Wi-Max

Abstract: IEEE 802.16 is standardization for a broadband wireless access in network metropolitan area network (MAN). IEEE 802.16 standard (Wi-Max) defines the concrete quality of service (QoS) requirement, a scheduling scheme and efficient packet scheduling scheme which is necessary to achieve the QoS requirement. In this paper, a novel waiting queue based on downlink bandwidth allocation architecture from a number of rtps schedule has been proposed to improve the performance of nrtPS services without any impaction to other services. This paper proposes an efficient QoS scheduling scheme that satisfies both throughput and delay guarantee to various real and non-real applications corresponding to different scheduling schemes for k=1,2,3,4. Simulation results show that proposed scheduling scheme can provide a tight QoS guarantee in terms of delay for all types of traffic as defined in WiMax standards. This process results in maintaining the fairness of allocation and helps to eliminate starvation of lower priority class services. The authors propose a new efficient and generalized scheduling schemes for IEEE 802.16 broadband wireless access system reflecting the delay requirements.

Secure Communication Using Generalized Digital Certificate

Abstract: A digital certificate is the combination of a statement and a signature of the statement, signed by a trusted certification authority. This work proposes using generalized digital certificate (GDC), for user authentication and key agreement for efficient secure communication. A GDC contains user’s public information, such as the information of user’s digital driver’s license, digital birth certificate, etc., and a digital signature of public information signed by a trusted certificate authority(CA). In GDC, user does not have any public and private key pairs, therefore key management in using GDC is much simpler than public key digital certificate. The digital signature component of GDC is used as the secret token of each user that will never be exposed to any verifier. Instead, the owner proves to the verifier that he has the knowledge of the signature by correctly responding to the verifier’s challenge. The session key established using proposed approach can be used for secure communication between the entities.

Detection of Outliers and Reduction of their Undesirable Effects for Improving the Accuracy of K-means Clustering Algorithm

Abstract: Clustering is an unsupervised categorization technique and also a highly used operation in data mining, in which, the data sets are divided into certain clusters according to similarity or dissimilarity criterions so that the assigned objects to each cluster would be more similar to each other comparing to the objects of other clusters. The k-means algorithm is one of the most well-known algorithms in clustering that is used in various models of data mining. The k-means categorizes a set of objects into certain number of clusters. One of the most important problems of this algorithm occurs when encountering to outliers. The outliers in the data set lead to getting away from the real cluster centers and consequently a reduction in the clustering algorithm accuracy. In this paper, we separate outliers from normal objects using a mechanism based on dissimilarity of objects. Then, the normal objects are clustered using kmeans algorithm process and finally, the outliers are assigned to the closest cluster. The experimental results show the accuracy and efficiency of the proposed method.

Modified Visual Cryptography Scheme for Colored Secret Image Sharing

Abstract: Intent of this paper is to prove the better performance of the XOR Based visual cryptography schemes and traditional VCS on the basis of quality of reconstructed image and type of shares generated for colored images. The visual cryptography scheme (VCS) is a scheme which encodes a secret image into several shares. Here we are working with (2, 2) VCS. XOR-Based visual cryptography is capable to overcome the drawbacks of the visual cryptography scheme (VCS) the small contrast of the recovered secret image.

RW-Closed Maps and RW-Open Maps in Topological Spaces

Abstract: -----------------------------------------------------------------------------------------------------------Abstract:In this paper we introduce rw-closed map from a topological space X to a topological space Y as the image of every closed set is rw-closed and also we prove that the composition of two rw-closed maps need not be rw-closed map. We also obtain some properties of rw-closed maps. Mathematics Subject Classification: 54C10

A New Design of Multiplier using Modified Booth Algorithm and Reversible Gate Logic

Abstract: In this paper we propose a new concept for multiplication by using modified booth algorithm and reversible logic functions. Modified booth algorithm produces less delay compare to normal multiplication process. Modified booth algorithm reduces the number partial products which will reduces maximum delay count a the output. by combining modified booth algorithm with reversible gate logic it will produces further less delay compare to all other. In the past years reversible logic functions has developed as an important research area. Implementing reversible logic has the advantage of reducing the gate count, garbage outputs as well as constant inputs. Addition subtraction operations are realized using reversible DKG gate. This modified booth algorithm with reversible gate logic are synthesized and simulated by using Xilinx 13.2 ISE simulator.

Advanced Authentication Scheme using Multimodal Biometric Scheme

Abstract: Fingerprint recognition has attracted various researchers and achieved great success. But, fingerprint alone may not be able to meet the increasing demand of high accuracy in today’s biometric system. The purpose of our paper is to inspect whether the integration of palmprint and fingerprint biometric can achieve performance that may not be possible using a single biometric technology. Pre-processing is done for fingerprint and palmprint images separately in order to remove any noise. The next step is feature extraction. Minutiae algorithm is used for fingerprint feature extraction and Local Binary pattern for palmprint. Wavelet fusion is applied in order to fuse the extracted features and Support Vector Machine is used for matching. The main highlight of the project is multimodal biometrics which will give a better security and accuracy comparing to unimodel system.