Showing papers in "International Journal of Computer Applications in 2012"
TL;DR: A comparative study between some of the IaaS (Infrastructure as a Service) commonly used to select the best suited one for deployment and research development in the field of cloud computing is presented.
Abstract: Cloud computing is a quite new concept for which the resources are virtualized, dynamically extended and provided as a service on the Internet. In this paper, we present a comparative study between some of the IaaS (Infrastructure as a Service) commonly used to select the best suited one for deployment and research development in the field of cloud computing. The aim is to provide the computer industry with the opportunity to build a hosting architecture, massively scalable which is completely open source, while overcoming the constraints and the use of proprietary technologies. Then, we present the solution OpenStack retained by the comparative study. We discuss in detail its functional and architectural system. We finish by a discussion of the motivation of our choice of the IaaS solution. General Terms:
525 citations
TL;DR: This paper has analysed prediction systems for Heart disease using more number of input attributes and shows that out of these three classification models Neural Networks predicts Heart disease with highest accuracy.
Abstract: Healthcare industry is generally "information rich", but unfortunately not all the data are mined which is required for discovering hidden patterns & effective decision making. Advanced data mining techniques are used to discover knowledge in database and for medical research, particularly in Heart disease prediction. This paper has analysed prediction systems for Heart disease using more number of input attributes. The system uses medical terms such as sex, blood pressure, cholesterol like 13 attributes to predict the likelihood of patient getting a Heart disease. Until now, 13 attributes are used for prediction. This research paper added two more attributes i.e. obesity and smoking. The data mining classification techniques, namely Decision Trees, Naive Bayes, and Neural Networks are analyzed on Heart disease database. The performance of these techniques is compared, based on accuracy. As per our results accuracy of Neural Networks, Decision Trees, and Naive Bayes are 100%, 99.62%, and 90.74% respectively. Our analysis shows that out of these three classification models Neural Networks predicts Heart disease with highest accuracy.
292 citations
TL;DR: A comparative study of this tool with other commercial OCR tool Transym OCR by considering vehicle number plate as input and compared these tools based on various parameters are concluded.
Abstract: Optical character recognition (OCR) method has been used in converting printed text into editable text. OCR is very useful and popular method in various applications. Accuracy of OCR can be dependent on text preprocessing and segmentation algorithms. Sometimes it is difficult to retrieve text from the image because of different size, style, orientation, complex background of image etc. We begin this paper with an introduction of Optical Character Recognition (OCR) method, History of Open Source OCR tool Tesseract, architecture of it and experiment result of OCR performed by Tesseract on different kinds images are discussed. We conclude this paper by comparative study of this tool with other commercial OCR tool Transym OCR by considering vehicle number plate as input. From vehicle number plate we tried to extract vehicle number by using Tesseract and Transym and compared these tools based on various parameters. explained.Keywords like: Desktop OCR, Server OCR, Web OCR etc.
223 citations
TL;DR: This paper has shown the survey and summarization of previous work that investigated the clustering of time series in various application domains ranging from science, engineering, business, finance, economic, health care, and government.
Abstract: Time-Series clustering is one of the important concepts of data mining that is used to gain insight into the mechanism that generate the time-series and predicting the future values of the given time-series. Time-series data are frequently very large and elements of these kinds of data have temporal ordering. The clustering of time series is organized into three groups depending upon whether they work directly on raw data either in frequency or time domain, indirectly with the features extracted from the raw data or with model built from raw data. In this paper, we have shown the survey and summarization of previous work that investigated the clustering of time series in various application domains ranging from science, engineering, business, finance, economic, health care, to government.
222 citations
TL;DR: This paper intends to focus on the survey of application of image processing in agriculture field such as imaging techniques, weed detection and fruit grading.
Abstract: Image processing has been proved to be effective tool for analysis in various fields and applications. Agriculture sector where the parameters like canopy, yield, quality of product were the important measures from the farmers’ point of view. Many times expert advice may not be affordable, majority times the availability of expert and their services may consume time. Image processing along with availability of communication network can change the situation of getting the expert advice well within time and at affordable cost since image processing was the effective tool for analysis of parameters. This paper intends to focus on the survey of application of image processing in agriculture field such as imaging techniques, weed detection and fruit grading. The analysis of the parameters has proved to be accurate and less time consuming as compared to traditional methods. Application of image processing can improve decision making for vegetation measurement, irrigation, fruit sorting, etc.
186 citations
TL;DR: Improved version of Max-min algorithm is proposed to outperform scheduling map at least similar to RASA map in total complete time for submitted jobs and demonstrates achieving schedules with comparable lower makespan rather than R ASA and original Max- Min.
Abstract: paper, a unique modification of Max-min algorithm is proposed. The algorithm is built based on comprehensive study of the impact of RASA algorithm in scheduling tasks and the atom concept of Max-min strategy. An Improved version of Max-min algorithm is proposed to outperform scheduling map at least similar to RASA map in total complete time for submitted jobs. Improved Max-min is based on the expected execution time instead of complete time as a selection basis. Experimental results show availability of load balance in small cloud computing environment and total small makespan in large-scale distributed system; cloud computing. In turn scheduling tasks within cloud computing using Improved Max-min demonstrates achieving schedules with comparable lower makespan rather than RASA and original Max-min.
158 citations
TL;DR: This paper is compared with three methods NL Means, NL-PCA, and DCT to acquiesce better results in terms of quality and in removal of different noises.
Abstract: Image processing is an important charge in image denoising as a process and component in various other process There are many ways to denoise an image.The ultimate idea of this paper is to acquiesce better results in terms of quality and in removal of different noises. This paper is compared with three methods NL Means, NL-PCA, and DCT.PSNR and SSIM are used for quantitative study of denoising methods.
152 citations
TL;DR: The main objective of the review paper is to bring to light the progress made for ASRs of different languages and the technological viewpoint of ASR in different countries and to compare and contrast the techniques used in various stages of Speech recognition and identify research topic in this challenging field.
Abstract: paper attempts to describe a literature review of Automatic Speech Recognition. It discusses past years advances made so as to provide progress that has been accomplished in this area of research. One of the important challenges for researchers is ASR accuracy. The Speech recognition System focuses on difficulties with ASR, basic building blocks of speech processing, feature extraction, speech recognition and performance evaluation. The main objective of the review paper is to bring to light the progress made for ASRs of different languages and the technological viewpoint of ASR in different countries and to compare and contrast the techniques used in various stages of Speech recognition and identify research topic in this challenging field. We are not presenting exhaustive descriptions of systems or mathematical formulations but rather, we are presenting distinctive and novel features of selected systems and their relative merits and demerits.
144 citations
TL;DR: Results indicated that texture analysis is a useful method for discrimination of melanocytic skin tumors with high accuracy, and automatic iteration counter gives a better accuracy.
Abstract: Melanoma is considered the most dangerous type of skin cancer. Early and accurate diagnosis depends mainly on important issues, accuracy of feature extracted and efficiency of classifier method. This paper presents an automated method for melanoma diagnosis applied on a set of dermoscopy images. Features extracted are based on gray level Co-occurrence matrix (GLCM) and Using Multilayer perceptron classifier (MLP) to classify between Melanocytic Nevi and Malignant melanoma. MLP classifier was proposed with two different techniques in training and testing process: Automatic MLP and Traditional MLP. Results indicated that texture analysis is a useful method for discrimination of melanocytic skin tumors with high accuracy. The first technique, Automatic iteration counter is faster but the second one, Default iteration counter gives a better accuracy, which is 100 % for the training set and 92 % for the test set.
142 citations
Journal Article•
TL;DR: This survey describes the reasoners that can be used as plug-in for either protege or NeOn toolkit since these are most widely used ontology development tools.
Abstract: Reasoner is a software that is used to derive new facts from the existing ontologies. Some of the popular reasoners developed in the last few years are: Pellet, RACER, FACT++, Snorocket, Hermit, CEL, ELK, SWRL-IQ, TrOWL and others. This survey describes the reasoners that can be used as plug-in for either protege or NeOn toolkit since these are most widely used ontology development tools. The current study describes the reasoners with their important features such as soundness, completeness, reasoning method, incremental classification etc. Finally this paper presents comparison of the reasoners with respect to their features.
122 citations
TL;DR: This paper uses two classification algorithms J48 and Multilayer Perceptron and multilayer perceptron alias MLP of the Weka interface to choose the better algorithm based on the conditions of the datasets.
Abstract: mining is the upcoming research area to solve various problems and classification is one of main problem in the field of data mining. In this paper, we use two classification algorithms J48 (which is java implementation of C4.5 algorithm) and multilayer perceptron alias MLP (which is a modification of the standard linear perceptron) of the Weka interface. It can be used for testing several datasets. The performance of J48 and Multilayer Perceptron have been analysed so as to choose the better algorithm based on the conditions of the datasets. The datasets have been chosen from UCI Machine Learning Repository. Algorithm J48 is based on C4.5 decision based learning and algorithm Multilayer Perceptron uses the multilayer feed forward neural network approach for classification of datasets. When comparing the performance of both algorithms we found Multilayer Perceptron is better algorithm in most of the cases.
TL;DR: An Augmented Reality system for teaching spatial relationships and chemical-reaction problem-solving skills to school-level students based on the VSEPR theory is presented, based on inexpensive webcams and open-source software.
Abstract: knowledge delivery methods in education should move away from memory based learning to more motivated and creative education. This paper will emphasize on the advantages tangible interaction can bring to education. Augmented Chemistry provides an efficient way for designing and interacting with the molecules to understand the spatial relations between molecules. For Students it is very informative to see actual molecules representation 3D environment, inspect molecules from multiple viewpoints and control the interaction of molecules.We present in this paper an Augmented Reality system for teaching spatial relationships and chemical-reaction problem-solving skills to school-level students based on the VSEPR theory. Our system is based on inexpensive webcams and open-source software. We hope this willgenerate more ideas for educators and researcher to explore Augmented Reality technology in the field of interactive education.
Journal Article•
TL;DR: Various scheduler improvements possible with Hadoop are studied and some guidelines on how to improve the scheduling in Hadoops in Cloud Environments are provided.
Abstract: Cloud Computing is emerging as a new computational paradigm shift. Hadoop-MapReduce has become a powerful Computation Model for processing large data on distributed commodity hardware clusters such as Clouds. In all Hadoop implementations, the default FIFO scheduler is available where jobs are scheduled in FIFO order with support for other priority based schedulers also. In this paper we study various scheduler improvements possible with Hadoop and also provided some guidelines on how to improve the scheduling in Hadoop in Cloud Environments.
TL;DR: Wang et al. as discussed by the authors presented a comprehensive study to show the danger of botnet-based DDoS attacks on application layer, especially on the Web server and the increased incidents of such attacks that has evidently increased recently.
Abstract: Botnets are prevailing mechanisms for the facilitation of the distributed denial of service (DDoS) attacks on computer networks or applications. Currently, Botnet-based DDoS attacks on the application layer are latest and most problematic trends in network security threats. Botnet-based DDoS attacks on the application layer limits resources, curtails revenue, and yields customer dissatisfaction, among others. DDoS attacks are among the most difficult problems to resolve online, especially, when the target is the Web server. In this paper, we present a comprehensive study to show the danger of Botnet-based DDoS attacks on application layer, especially on the Web server and the increased incidents of such attacks that has evidently increased recently. Botnetbased DDoS attacks incidents and revenue losses of famous companies and government websites are also described. This provides better understanding of the problem, current solution space, and future research scope to defend against such attacks efficiently.
TL;DR: A 3D model of a concrete cube is prepared using smeared crack model and concrete damage plasticity approach and the validation of the model to the desired behavior under monotonic loading is discussed.
Abstract: Concrete is the main constituent material in many structures. The behavior of concrete is nonlinear and complex. Increasing use of computer based methods for designing and simulation have also increased the urge for the exact solution of the problems. This leads to difficulties in simulation and modeling of concrete structures. A good approach is to use the general purpose finite element software ABAQUS. In this paper a 3D model of a concrete cube is prepared using smeared crack model and concrete damage plasticity approach. The validation of the model to the desired behavior under monotonic loading is then discussed. Keywords Finite element, ABAQUS, smeared cracking, concrete damage plasticity, tension stiffening. 1. ultimate INTRODUCTION Since 1970, analyses of reinforced concrete structures using finite element method, have witnessed a remarkable advancement. Many researchers have made valuable contributions in understanding the behavior of concrete and have developed sophisticated methods of analysis. These achievements are well documented and available in various reports and technical papers but still there are many areas in which much remains to be understood and researched. The past decade with the advancement in computing techniques and the computational capabilities of the high end computers has led to a better study of the behavior of concrete. However the complex behavior of concrete sets some limitations in implementing FEM. The complexity is mainly due to non-linear stress-strain relation of the concrete under multi-axial stress conditions, strain softening and anisotropic stiffness reduction, progressive cracking caused by tensile stresses and strains, bond between concrete and reinforcement, aggregation interlocks and dowel action of reinforcement, time dependant behavior as creep and shrinkage [1]. Several researchers have documented about nonlinear analysis of reinforced concrete and prestressed concrete structures. For nonlinear analysis many commercial software are available, such as ANSYS, ABAQUS, NASTARAN, and ADINA. All these softwares are not tailor made applications which can work automatically on just feeding few data and the requirements. An acceptable analysis of any structure as a whole or a part there in, using Finite element software, and the correctness of it totally depends on the input values, especially the material properties used. However when one is working with concrete a sound technical background is required to use them in a proper manner and get the desired results. Concrete used in common engineering structures, basically is a composite material, produced using cement, aggregate and water. Sometimes, as per need some chemicals and mineral admixtures are also added. Experimental tests show that concrete behaves in a highly nonlinear manner in uniaxial compression. Figure.1 shows a typical stress-strain relationship subjected to uniaxial compression. This stress-strain curve is linearly elastic up to 30% of the maximum compressive strength. Above this point tie curve increases gradually up to about 70-90% of the maximum compressive strength. Eventually it reaches the pick value which is the maximum compressive strength
TL;DR: The findings of the study carried out to evaluate performance levels and enhance productivity of the manual warehouses by developing a WMS framework and cost benefit analysis are highlighted.
Abstract: In a supply chain, warehousing function is very critical as it acts as a node in linking the material flows between the supplier and customer. In today's competitive market environment companies are continuously forced to improve their warehousing operations. Many companies have also customized their value proposition to increase their customer service levels, which has led to changes in the role of warehouses. This paper highlights the findings of the study carried out to evaluate performance levels and enhance productivity of the manual warehouses by developing a WMS framework and cost benefit analysis.
TL;DR: Question Answering (QA) systems give the ability to answer questions posed in natural language by extracting, from a repository of documents, fragments of documents that contain material relevant to the answer.
Abstract: Question Answering (QA) is a specific type of information retrieval. Given a set of documents, a Question Answering system attempts to find out the correct answer to the question pose in natural language. Question answering is multidisciplinary. It involves information technology, artificial intelligence, natural language processing, knowledge and database management and cognitive science. From the technological perspective, question answering uses natural or statistical language processing, information retrieval, and knowledge representation and reasoning as potential building blocks. It involves text classification, information extraction and summarization technologies. In general, question answering system (QAS) has three components such as question classification, information retrieval, and answer extraction. These components play a essential role in QAS. Question classification play primary role in QA system to categorize the question based upon on the type of its entity. Information retrieval method is get of identify success by extracting out applicable answer post by their intelligent question answering system. Finally, answer extraction module is rising topics in the QAS where these systems are often requiring ranking and validating a candidate’s answer. Most of the Question Answering systems consists of three main modules: question processing, document processing and answer processing. Question processing module plays an important part in QA systems. If this module doesn't work correctly, it will make problems for other sections. Moreover answer processing module is an emerging topic in Question Answering, in which these systems are often required to rank and validate candidate answers. These techniques aiming at discovering the short and precise answers are often based on the semantic classification. QA systems give the ability to answer questions posed in natural language by extracting, from a repository of documents, fragments of documents that contain material relevant to the answer.
TL;DR: In this article, an extensive survey of security and privacy issues in a cloud computing environment is presented, which aims to elaborate and analyze the numerous unresolved issues threatening the cloud computing adoption and diffusion affecting the various stake-holders associated with it.
Abstract: Computing holds the potential to eliminate the requirements for setting up of high-cost computing infrastructure for IT-based solutions and services that the industry uses. It promises to provide a flexible IT architecture, accessible through internet from lightweight portable devices. This would allow multi-fold increase in the capacity and capabilities of the existing and new software. In a cloud computing environment, the entire data resides over a set of networked resources, enabling the data to be accessed through virtual machines. Since these data-centres may be located in any part of the world beyond the reach and control of users, there are multifarious security and privacy challenges that need to be understood and addressed. Also, one can never deny the possibility of a server breakdown that has been witnessed, rather quite often in the recent times. There are various issues that need to be addressed with respect to security and privacy in a cloud computing environment. This extensive survey paper aims to elaborate and analyze the numerous unresolved issues threatening the cloud computing adoption and diffusion affecting the various stake-holders associated with it. Keywordsas a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS), Interoperability, Denial of Service (DoS), Distributed Denial of Service (DDoS), Mobile Cloud Computing (MCC), Optical Character Recognition (OCR), Community of Interest (COI).
TL;DR: A survey on various recent gesture recognition approaches is provided with particular emphasis on hand gestures, and a review of static hand posture methods are explained with different tools and algorithms applied on gesture recognition system.
Abstract: Gestures considered as the most natural expressive way for communications between human and computers in virtual system. Hand gesture is a method of non-verbal communication for human beings for its freer expressions much more other than body parts. Hand gesture recognition has greater importance in designing an efficient human computer interaction system. Using gestures as a natural interface benefits as a motivation for analyzing, modeling, simulation, and recognition of gestures. 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.
TL;DR: Large scale path loss modeling plays a fundamental role in designing both fixed and mobile radio systems and accurate characterization of radio channel through key parameters and a mathematical model is important for predicting signal coverage, achievable data rates, BER and Antenna gain.
Abstract: Radio propagation is essential for emerging technologies with appropriate design, deployment and management strategies for any wireless network. It is heavily site specific and can vary significantly depending on terrain, frequency of operation, velocity of mobile terminal, interface sources and other dynamic factor. Accurate characterization of radio channel through key parameters and a mathematical model is important for predicting signal coverage, achievable data rates, BER and Antenna gain. Large scale path loss modeling plays a fundamental role in designing both fixed and mobile radio systems. Predicting the radio coverage area of a system is not done in a standard manner. Wireless systems are expensive systems. Therefore, before setting up a system one has to choose a proper method depending on the channel’s BTS antenna height gain. By proper selecting the above parameters there is a need to select the particular communication model which show good result by considering these parameters.
TL;DR: Various pruning methods are discussed with their features and also effectiveness of pruning is evaluated, accuracy is measured for diabetes and glass dataset with various pruning factors.
Abstract: is important problem in data mining. Given a data set, classifier generates meaningful description for each class. Decision trees are most effective and widely used classification methods. There are several algorithms for induction of decision trees. These trees are first induced and then prune subtrees with subsequent pruning phase to improve accuracy and prevent overfitting. In this paper, various pruning methods are discussed with their features and also effectiveness of pruning is evaluated. Accuracy is measured for diabetes and glass dataset with various pruning factors. The experiments are shown for this two datasets for measuring accuracy and size of the tree.
TL;DR: A comparative study has been performed to know the dielectric properties of five different substrates which affect antenna performance and will help for authors and researchers to get a fair idea of which substrate should be given preference and why for fabricating microstrip patch antenna.
Abstract: The study of microstrip patch antennas has made great progress in recent years. Compared with conventional antennas, microstrip patch antennas have more advantages and better prospects. Different researchers have used different dielectric substrates to fabricate microstrip patch antenna. So a question arises that which dielectric substrate among the common substrates available gives better performance and what are the properties of the dielectric substrates which affects antenna performance. So a comparative study has been performed to know the dielectric properties of five different substrates which affect antenna performance. The aim of the study to design and fabricate five triangular microstrip patch antennas on five different substrates and analyze their radiation characteristics. The antenna is designed to work in X-band applications. The resonant frequency is taken to be 10 GHz and height of the dielectric substrate is kept constant i.e.,1.5mm for all the five antennas. This study will help for authors and researchers to get a fair idea of which substrate should be given preference and why for fabricating microstrip patch antenna.
TL;DR: This paper presents an approach to automatic segmentation and counting of red blood cells in microscopic blood cell images using Hough Transform and discusses the results achieved by the proposed method and the conventional manual counting method.
Abstract: of red blood cells (rbc) in blood cell images is very important to detect as well as to follow the process of treatment of many diseases like anaemia, leukaemia etc. However, locating, identifying and counting of -red blood cells manually are tedious and time-consuming that could be simplified by means of automatic analysis, in which segmentation is a crucial step. In this paper, we present an approach to automatic segmentation and counting of red blood cells in microscopic blood cell images using Hough Transform. Detection and counting of rbc have been done on five microscopic images and finally discussion has been made by comparing the results achieved by the proposed method and the conventional manual counting method.
TL;DR: The transformation of traditional Distributed denial-of-service (DDoS) attack into cloud specific Economic Denial of Sustainability (EDoS) attack is explored.
Abstract: “Cloud Computing”, a new wave in the Internet revolution, transforms the kind of services provided over the Internet. The Cloud Services can be viewed from two perspectives, one as Cloud Service Provider and the other as Cloud Service Consumer. Assurance of security in the Cloud Service is a major challenge for the Providers, as it’s the biggest concern for the Consumers to opt for the service, which in turn decides the prospects of the business in Cloud Service. The Security can be administered in the Cloud at various levels and for several types of attacks. The threats and the attacks on the Cloud service can be common prevailing attacks in the internet or can be cloud specific. This paper deals about the threats and the counter measures of the prevailing DDoS attacks on the Cloud Environment as well as the Cloud Specific Vulnerabilities to these attacks. In specific, HTTP and XMLbased DDoS attacks on the cloud service are experimented under proposed security framework for EDoS Protection. A Cloud Service was hosted on Amazon EC2. The Service was targeted by HTTP, XML DDoS attacks from several nodes, which lead to the scaling of the service by consuming more Amazon EC2 resources, which in turn lead to Economic Denial of Sustainability to the Cloud Service under attack. Thus this paper explores the transformation of traditional Distributed denial-of-service (DDoS) attack into cloud specific Economic Denial of Sustainability (EDoS) attack.
TL;DR: A survey of various techniques used in credit card fraud detection and evaluates each methodology based on certain design criteria is presented.
Abstract: fraud is increasing significantly with the development of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. The companies and financial institution loose huge amounts due to fraud and fraudsters continuously try to find new rules and tactics to commit illegal actions. Thus, fraud detection systems have become essential for all credit card issuing banks to minimize their losses. The most commonly used fraud detection methods are Neural Network (NN), rule-induction techniques, fuzzy system, decision trees, Support Vector Machines (SVM), Artificial Immune System (AIS), genetic algorithms, K-Nearest Neighbor algorithms. These techniques can be used alone or in collaboration using ensemble or meta-learning techniques to build classifiers. This paper presents a survey of various techniques used in credit card fraud detection and evaluates each methodology based on certain design criteria.
TL;DR: A comprehensive review of the literature on the application of data mining techniques for the detection of financial accounting fraud and proposes a framework for data mining technique based accounting fraud detection is presented in this paper.
Abstract: With an upsurge in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection (FAFD) has become an emerging topic of great importance for academic, research and industries. The failure of internal auditing system of the organization in identifying the accounting frauds has lead to use of specialized procedures to detect financial accounting fraud, collective known as forensic accounting. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. This paper presents a comprehensive review of the literature on the application of data mining techniques for the detection of financial accounting fraud and proposes a framework for data mining techniques based accounting fraud detection. The systematic and comprehensive literature review of the data mining techniques applicable to financial accounting fraud detection may provide a foundation to future research in this field. The findings of this review show that data mining techniques like logistic models, neural networks, Bayesian belief network, and decision trees have been applied most extensively to provide primary solutions to the problems inherent in the detection and classification of fraudulent data.
Journal Article•
TL;DR: In this paper idea for use of a Genetic Algorithm (GA) based approach for generation of rules to detect Probing, DoS and R2L attacks on the system is proposed.
Abstract: Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. To control these threats, recognition of attacks is critical matter. Probing, Denial of Service (DoS), Remote to User (R2L) Attacks are some of the attacks which affects large number of computers in the world daily. Detection of these attacks and prevention of computers from it is a major research topic for researchers throughout the world. In this paper idea for use of a Genetic Algorithm (GA) based approach for generation of rules to detect Probing, DoS and R2L attacks on the system is proposed. General Terms Network Security, Genetic Algorithms.
TL;DR: This paper discusses about problems with relation databases and how different types of NOSQL Databases are used to efficiently handle the real world problems.
Abstract: Relational database is widely used in most of the application to store and retrieve data. They work best when they handle a limited set of data. Handling real time huge volume of data like internet was inefficient in relation database systems. To overcome this problem the "NO-SQL" or "Not Only SQL" Database came into existence. This paper discusses about problems with relation databases and how different types of NOSQL Databases are used to efficiently handle the real world problems.
TL;DR: This paper uses SVM and KNN algorithm to classify data and get prediction (find hidden patterns) for target and uses medical patients nominal data to classify and discover the data pattern to predict future disease.
Abstract: this age of computer science each and every thing becomes intelligent and perform task as human. For that purpose there are various tools, techniques and methods are proposed. Support vector machine is a model for statistics and computer science, to perform supervised learning, methods that are used to make analysis of data and recognize patterns. SVM is mostly used for classification and regression analysis. And in the same way k-nearest neighbor algorithm is a classification algorithm used to classify data using training examples. In this paper we use SVM and KNN algorithm to classify data and get prediction (find hidden patterns) for target. Here we use medical patients nominal data to classify and discover the data pattern to predict future disease, Uses data mining which is use to classify text analysis in future. KeywordsKNN, Patterns, Analysis, Classification.
TL;DR: This survey focuses on how to apply content mining on the web contains structured, unstructured, semi structured and multimedia data and how web content mining can be utilized in web usage mining.
Abstract: The Quest for knowledge has led to new discoveries and inventions. With the emergence of World Wide Web, it became a hub for all these discoveries and inventions. Web browsers became a tool to make the information available at our finger tips. As years passed World Wide Web became overloaded with information and it became hard to retrieve data according to the need. Web mining came as a rescue for the above problem. Web content mining is a subdivision under web mining. This paper deals with a study of different techniques and pattern of content mining and the areas which has been influenced by content mining. The web contains structured, unstructured, semi structured and multimedia data. This survey focuses on how to apply content mining on the above data. It also points out how web content mining can be utilized in web usage mining.