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An image compression method eradicates redundant and/or unrelated information, and resourcefully encodes leftovers. Practically, it is frequently essential to toss away both non redundant information and relevant information to attain the essential compression. In any case, the ploy is discovering methods that permit important information to be resourcefully extracted and represented. This paper copes with dissimilar compression methods for comprising the information in an image. The information can be compressed by means of Lossy techniques such as Quantization, Transform coding, Block Transform Coding or Lossless techniques such as Run Length Coding, Lossless Predictive Coding, Multi-resolution Coding. All these techniques have been discussed in this paper and the performance of any technique/method is analyzed on various parameters like MSE and PSNR.

the increasing problems of uncertainty, vagueness and imprecision during the modeling of various control system, the fuzzy logic comes into account. Fuzzy logic was first introduced by a Polish philosopher, Jan Lukasiewicz, in 1930. As the classical logic operates with only two values- 1 (true) and 0 (false), Lukasiewicz introduced a new logic which has more than two truth values. Lukasiewicz extends the range of classical logic to all the real numbers in the interval between 0 and 1 and named it as fuzzy logic. Fuzzy logic is a powerful tool which represents and process human knowledge in the form of fuzzy if-then rules. As the fuzzy logic systems is based on human thinking and natural language and also has a good stability, fast response and less complexity, the applications based on fuzzy logic have been increased significantly. To follow the trend, this paper presents the basic introduction of fuzzy logic, fuzzy sets and its operations. This paper provides a huge description of fuzzy logic system and fuzzy inference system and also provides comparison between fuzzy logic system and conventional control system. In this paper, the various applications of fuzzy logic have been simulated using the MATLAB.

Out of all body parts face is one of the most important part of body by which everyone can show its emotions, feelings etc. Most of the humans can easily predict a person’s current age just by gazing their faces. Facial recognition is a part of biometric software application which is used to identify a particular and individual thing in an image by analysis and evaluation of patterns. There are various face prediction models which are based on different techniques like PCA, ANN etc. Age plays an important role to predict the face of any person. Most of the models are built on the basis of age parameter. Recently, image processing has played a major role in this area of research and has widely used for the face prediction. These ages based models have various useful applications like security purpose, to find a missing person. This paper presents a survey of various face prediction systems using image processing techniques in recent times. A comprehensive study of a number of face prediction systems are done in this paper, with different methodologies and their performances.

Variability of semantic expression is a fundamental phenomenon of a natural language where same meaning can be expressed by different texts. The process of inferring a text from another is called textual entailment. Textual Entailment is useful in a wide range of applications, including question answering, summarization, text generation, and machine translation. The recognition of textual entailment is one of the recent challenges of the Natural Language Processing (NLP) domain. This paper summarizes key ideas from the area of textual entailment recognition by considering in turn the different recognition models. The paper points to prominent testing data, training data, resources and Performance Evaluation for each model. Also this paper compares between textual entailment models according to the method which used, the result of each method and the strong and weakness of each method.

Cloud computing is recent blooming technology which provides various services like storage, platform, applications etc. through internet. In cloud computing, data owner hosts (stores) their data on cloud servers and data users access those data through cloud servers any time and from any place. This leads to data outsourcing on cloud in high amount. But because of vast amount of data on cloud, it increases security challenges again on data integrity, authenticity and confidentiality in the form of data hacking. Here data owner has no idea about the whereabouts of storage locations. To check for the data loss, data owner has to be online continuously and monitor the data all the time; hence increases the overhead on data owner which owner surely wants to avoid because that’s the reason of using cloud services. So the strong need of security mechanism, introduces the concept of Third Party Auditor (TPA) to the world. Cloud’s public audit ability lets third party auditor (TPA) checks for data integrity. Here in this paper, various issues and challenges come across when data is stored on cloud, has been analyzed. Various papers discussed here, describes various techniques for secure cloud storage to provide privacy preserving public auditing.

Although the computing devices in today's era can be compromised in a variety of ways, this research paper delves deep into different types of cyber attacks that strip out security from the innocent clients. The impact of these attacks depend more on the broad spectrum of opportunities that a novice user serves to an attacker unknowingly. Though a client sometimes has minimum power to withstand the technical prowess of a skilled attacker, still the probability of such attacks can be nullified by reducing the vulnerabilities of a particular computing system. Thus conscious risk management strategies need to be implemented to save organizations and billions of users across the world surfing the web wirelessly. The paper starts with a brief introduction about various wireless attacks that are frequently carried out in a local area network and their classifications. The research presents several schemes for detecting, mitigating and preventing such type of precarious attacks. Role of user awareness in diminishing the threatening effects of such assaults has been specially taken into consideration. The important features and limitations of the already reported methods against protection of such attacks have been examined.

Intrusion detection is a detection of encroachment on the personal network or the private network to breach the security systems. This system provides analytical measures to gather information from various networks or computers to identify the cracks in the security systems caused by intruders. The sudden tremendous growth in the amount of internet users network intrusion detection has gained a huge amount of attention/need towards the research of network. Today, cyberattacks have become a vital issue for any organization or individual in the network against preserving significant data and information in their personal computers connected to a network. In this paper, a comparative study was done on two different data mining techniques: decision tree and support vector machine algorithms. These techniques are implemented on the dataset for the experiment, since decision tree C5.0 technique and support vector machine (SVM) in general widely used in intrusion experiment data i.e. KDD CUP99 data set downloaded from UCI repository site. The better performance of C5.0 algorithm in terms of accuracy, sensitivity and specificity error measures are to be proved in this paper. General Terms C5.0 algorithm, SVM algorithm, Classification, Clustering, Network Intrusion, KDD cup99, Cyber-Attacks, DoS, Probe, R2L, U2R.

Online Social Networking in these days is most powerful way to share the Information, thought, event and many more. In the Definition of technology we usually follow the Industry of Information Technology where, we describe the high-level contributions of this paper and discuss potential future research directions. Despite the massive popularity of online social networks, surprisingly little is known about how people are using them to connect and share content. To better understand the structure of online social networks, in this paper we conducted a large-scale measurement study that collected data on the social networks of four popular sites, covering over 12 million users and 400 million links, in order to remove the unwanted messages. Machine Learning Text Categorization is also used to categorize the short text messages.

Sentiment analysis is an ongoing research area in the field of text mining. People post their review in form of unstructured data so opinion extraction provides overall opinion of reviews so it does best job for customer, people, organization etc. The main aim of this paper is to find out approaches that generate output with good accuracy. This paper presents recent updates on papers related to classification of sentiment analysis of implemented various approaches and algorithms. The main contribution of this paper is to give idea about that careful feature selection and existing classification approaches can give better accuracy.

The main objective of any educational organization is to provide quality education and improve the overall performance of an institution by looking at individual performances. One way to analyze learners' performances is to identify the areas of weakness and guide their students to a better future. Although data mining has been successful in many areas, its use in student performance analysis is still relatively new, i.e. the knowledge is hidden in educational data set and it is extracted using data mining techniques. This paper discusses about a learning model for predicting student performance using classification techniques. Also the paper shows the comparative performance analysis of J48, Naive Bayesian classifier and Random forest algorithm.

computing is a revolutionary technology that has brought about new luxuries in the field of Information Technology. It provides a source of data and application software storage in the form of huge data hubs called "clouds", which can be accessed with the help of a network connection. These clouds help maximize the capabilities of enterprises without any extra set-up, personnel or licensing costs. Clouds are generally deployed using Public, Private or Hybrid models depending upon the requirements of the user. In this paper, the cloud computing architecture has been assessed, focusing on the various features of the Public, Private and Hybrid cloud models relevant to most individuals and organizations and how they support or hinder the operation of various cloud computing models. This paper intends to provide a bird"s eye view of various characteristics of aforesaid models of cloud; which are put together side by side in order to facilitate the research study in this environment.

In this paper, have implemented the isolated digit recognition in Kannada language using Hidden Markov Model Toolkit (HTK). Hidden Markov models used as pattern recognizer with the help of MFCC as a featured vector of the wave samples. The paper focuses on all isolated digits of Kannada i.e. Sonne to Ommbattu (0 to 9), The system helps in interaction of rural people and the computer or any system. The system data structure is defined at word level and its performance is evaluated.

When developing software, the selection of an appropriate software development methodology (SDM) is an essential decision. There are many SDM exist that are used to control the process of developing a software process. No exact system was found which could guide software engineers for selection of a proper methodology during software development. This paper show factor affecting the selection of SDM and the consistency in which the methodology selection carried out. Based on Fuzzy AHP, we evaluate the consistency in SDM selection. This paper presents a framework of Fuzzy AHP approach for selection of three different SDM with their three conflicting selecting criteria alternatives.

Temperature prediction is one of the most important and challenging task in today‟s world. Temperature prediction is the attempt by meteorologists to forecast the state of the atmospheric parameters such as: Temperature, Humidity, etc. The paper presents research on weather forecasting by using historical dataset. Because atmosphere pattern is complex, nonlinear system, traditional methods aren‟t effective and efficient. Artificial Neural Network is an influential method for resolving such problems. The proposed ANN evaluates the performance of the developed models by applying different neurons, hidden layers and transfer functions to predict temperature for 365 days of the year. The criteria used for appropriate model selection is mean square error (MSE). Contrary to similar researches the data model and workflow suggested in the paper generated lesser MSE (i.e. more accurate results) that too with reduced computational complexity (i.e. better performance). General Terms Algorithm, Forecast, Prediction, Pattern Recognition

Wireless network is fast growing area. Wireless network has an ability to provide a good quality of service i.e voice, data, video conferencing etc. Different mobile generations are presented i.e 1G to 4G. In this research paper, 4G technology is used. WiMAX is 4G technology. In this research work, create a WiMAX network and that network consist of 2 base stations, app configuration, profile configuration, WiMAX configuration and 16 mobile nodes. After creating a network check the quality of network i.e Jitter (sec) and delay (sec). In this research paper we conclude that when the speed of moving mobile node is increased we obtain the low delay and low jitter in network.

With the modern use of services of the cloud computing, the users need higher security. Therefore safety of the cloud computing is a first consideration of the users to select. In the establishment of the cloud computing, application proportion of the virtualization increases gradually, scope and the depth of safety also expanded gradually. The associated concepts of the cloud computing and its establishment conditions are discussed in this paper. In this paper the foundation of the internationalcertification-institute is suggested for service providing companies to raise the trust and improve the likeliness of utilizing this advance and valuable technique among the people. If the security is not fixed and powerful, then the flexibility and the benefits that a cloud computing has to provide will have the few prospect. This will facilitates the researchers and the security professionals to understand about the users concerns and the vendors concerns and the critical analysis regarding the various research types suggested.

The existence of mobile devices in today’s modern society has generated greater advantages to many users especially within the African continent with regard to how information is being disseminated. However, the efficacy of mobile device technology has not come without some drawbacks, especially with the proliferation of Internet enabled mobile devices among young people. Trends of cyber-bullying, for example, are on the increase and recent studies in South Africa have shown that the use of mobile devices among school pupils between the ages of 2 to 17 years is at 60%. This can only be attributed to mobile device proliferation, increased cyber technologies like social network and change of communication modes. For this reason, one might end up being excluded from this environment if one excludes oneself from the modern communication culture. However, with all this advances in mobile communication technologies, there lacks proper guidance on the side of young people with regard to the proper use of mobile devices and their forensic readiness capability. This is aggravated by the fact that a bigger percentage of parents also have no knowledge of what young people do online especially with their mobile device features such as online chat rooms, cyber-bullying, and sexting. Therefore, the problem being addressed in this paper is that: by the time of writing this paper, there exist no reliable and effective mobile forensic readiness models that can be used to create awareness to both young people and their parents on how to minimize cyber-bullying. This paper thus proposes a mobile forensic readiness model that can minimize cyber bullying, enhance awareness on issues of cyber bullying as well as the creation of parental guidance information on cyber bullying. The model can also act as a monitoring tool that can be embedded on different mobile devices assigned to, for example, learners and minors. The embedded model can then help parents and guardians to identify and maintain control of the various information and online activities that children engage in. Mobile forensic readiness model aimed at minimizing cyber bullying, as well as enhance awareness on issues of cyber bullying, constitutes the main contribution of this paper. General Terms Mobile forensic readiness model, cyber bullying.

The increase in city traffic is one of the major effects of population growth especially in urban areas. Due to this searching for a vacant parking area has become difficult during peak hours. In this paper, we have conducted a survey on different available smart parking techniques, their drawbacks and proposed solutions. As smart parking has become the need of the day, in this paper we present different innovative techniques which exploit latest technology to provide better services to the end users as well as reduce the workload of the parking administrator. Sections II to V describe existing systems and sections VI to VIII describe suggestions for a cloud application.

Classification is a data exploration and learning mechanism, which has been widely studied and a wide range of applications subject. Supervised Classification is based on association rules and if we increase number of association rule degree of accuracy of classification is also being increase but larger number of rule take longer time to classify. Recently researcher is focus to develop an model that increase the accuracy in minimum time. In this paper Genetic based multi class classification model is proposed. Proposed model also use Dumpster shafer theorem for confining resultant rule set generated by GA algorithm. This paper used wine data set available at UCI machine learning website for classification and applies 3 cross fold mechanism for cross validation.

In vision of the importance of researcher’s sharing the same theory of network security, this paper suggests a novel security approach for network security in which network security issues can be treated essentially. The meaning of network security has been identified for some time, but provides networks (especially public networks) with security utilities has proven difficult. The act of the network itself in computer network security has been minimum , as systems certainly preserve their own security. To resolve such problems, this paper recommends a network security approach for the access model.

The technology has advanced so fast and has attracted almost all the business organizations to move in single dimension called cloud. Green Cloud Computing (or Green Computing) is the energy-efficient and environment-friendly form of cloud computing. Cloud computing is a rising trend employing the concept of large data centers but suffering from the concerns such as exorbitant heat emissions, superfluous consumption of energy, release of tremendous amounts of injurious compounds and gases like methane, carbon-dioxide, nitrous oxide etc. that cause green-house effect. By overcoming all of the stated issues of cloud computing by the help of virtualization and energy-efficiency, green computing evolved as a better technology to adopt. In this paper, a review of all the techniques and approaches utilized to implement virtualization, security and energy-efficiency in green computing has been done. Additionally, the comparison of all these techniques is provided for better understanding. Thus, this paper encourages green computing for cloud computing, due to its better performance and security reasons, to be implemented by all the businesses that are planning to migrate to cloud.

Today’s real world faces different kinds of complex optimization problems. The existing methodologies can’t cope of with such complex problems. This paper presents classification rule mining as a multi-objective problem rather than a single objective one. Multi-Objective optimization is a challenging area and focus for research. Here two modern domains of research are discussed one is swarm intelligence and other is data mining. In this paper PSO is taken as taken as a swarm intelligence algorithm and classification rule mining is taken as the problem domain. In classification rule discovery, classifiers are designed through the following two phases: rule extraction and rule selection. In the rule extraction phase, a large number of classification rules are extracted from training data. This phase is based on two rule evaluation criteria: support (coverage) and confidence. An association rule mining technique is used to extract classification rules satisfying pre-specified threshold values of minimum support (coverage) and confidence. In second phase, a small number of rules are targeted from the extracted rules to design an accurate and compact classifier. In this paper, I used PSO for multiple objective rule selection to maximize the accuracy of the rule sets and minimize their complexity.

n nature of the transmission medium the broadcast, Wireless sensor networks are vulnerable to security attacks. The nodes are placed in a hostile or dangerous environment where they are not tangibly safe in the MANETs. In many application, the data obtained from the sensing nodes need a false, or malicious node could intercept private information or could send false messages to nodes in the network. Among the major attacks Eavesdropping, Spoof Attack, Denial of Service, Wormhole attack, Sinkhole attack, Sybil attack, Selective Forwarding attack, Passive information gathering, Node capturing, and False or malicious node, Hello flood attack are common. In this paper, authors have proposed and implemented an efficient light weighted authentication secure routing protocol on top of an AODV. The focused area of the proposed routing protocol is increasing the network security of the MANET. Additionally, the paper evaluates the implemented protocol using NS2 simulator in different networks with SecAODV.

Now a days, skin diseases are mostly found in animals, humans and plants. A skin disease is a particular kind of illness caused by bacteria or an infection. These diseases like alopecia, ringworm, yeast infection, brown spot, allergies, eczema etc. have various dangerous effects on the skin and keep on spreading over time. It becomes important to identify these diseases at their initial stage to control it from spreading. These diseases are identified by using many technologies such as image processing, data mining, artificial neural network (ANN) etc. Recently, image processing has played a major role in this area of research and has widely used for the detection of skin diseases. Techniques like filtering, segmentation, feature extraction, image pre-processing and edge detection etc. are part of image processing and are used to identify the part affected by disease, the form of affected area, its affected area color etc. This paper presents a survey of various skin disease diagnosis systems using image processing techniques in recent times. A comprehensive study of a number of skin disease diagnosis systems are done in this paper, with different methodologies and their performances.

Cloud computing involves sharing computing resources rather than having individual servers or personal devices to handle applications. Cloud computing architectures include the delivery of software, infrastructure, storage and technology enabled services over internet to the people and organizations on demand. Cloud scheduling is the process of allocating resources to the job requests in the form of Virtual Machines. In this paper we designed and developed a novel, efficient cloud scheduling algorithm based on load balancing analytics for allocation of physical resources in the form of virtual machine to the incoming job requests. In this paper we measured various cloud performance metrics like mean turnaround time and mean waiting time. The results obtained with this method compared with traditional methods like First Come First Serve (FCFS), two stage scheduling algorithms and observed considerable increase in the performance Metrics. General Terms Scheduling algorithm, resource allocation, Cloud computing, optimization, Virtual Machine.

development and evaluation is the most difficult task in software engineering. The future of the software industry depends upon the development of software. For this purpose quality is considered essential step in designing software. Quality of software is dependable upon different aspects but the dream of software quality is not feasible without software reliability. Reliability of software is the chance of performing operation without any failure in a particular environment for a specific period. This paper is basically concerned with improving quality of software. The quality is the instrument that can predict future of the software in industry. This paper presents the strategy to measure and maintain quality of software. The research is based on the reviews collected through questionnaire and visits to persons concerned with the designing, maintaining and utilizing software. It also discusses some techniques like CMM, Process Maturity Hierarchy used for measuring the reliability of software. The solution to this problem is to focus on the weaknesses found through the research. The first step towards formulating reliable software is to trace all the facts that are responsible for failure of software. Later each issue will be given individual attention for its resolution. At the end reliable software will be generated.

Virtual Machine (VM) Live Migration improves system manageability through VMs rearrangement in their physical hosts. System managers can redistribute VMs to tackle different challenges as: VMs consolidation for energy consumption reduction, load balancing and software rejuvenation. VM live migration consists in successive transfers of memory pages from one host to another. Thus, system capacity planning may consider VM live migration overhead. This paper presents impacts of VM live migration operations on system performance of different RAM-sized VMs (512MB, 1GB and 2GB). These experiments aims to observe how RAM size changes can affect responsiveness and reliability of a Web Server hosted on a single VM. The experiment has external client which sends web requests in a constant rate through Autobench (httperf) benchmark tool. This benchmark tool provides results of Web Server errors and response time during VM live migration process. Experiments results also consider network throughput during VM live migration. The results presents that network overhead due to VM live migration causes major system performance degradation in VMs with more amount of RAM. Therefore, this paper results may be useful for managers to consider VM live migration impacts in capacity planning.

Because of the advancement of Web technologies and the inconsistency of Web standards to be consistent with every single technology evolution, Web developers are been faced with certain challenges. It become difficult for them to keep track of their applications that are been correctly rendered across wide range of browsers and platforms. To find out cross-browser inconsistency, developers keep checking that every document produced by the application is appropriately passed across all useful browser-platform combinations. But this requires more time and results are error-prone. The existing cross-browser compatibility testing tools speed up this process by automating the rendering of a Web document in multiple browsers and platforms, and using either image analysis or Document Object Model (DOM) analysis to feature out cross-browser inconsistency. This paper comprises the problem of cross-browser inconsistent testing of the modern web applications as a functional accuracy check of web application’s behaviour with various web browsers and present a solution for it. Also, reasons of cross-browser inconsistency issues and solutions to them are been presented in the paper.

This paper proposes a novel application of Fireworks Algorithm in Artificial Neural Network training. Fireworks Algorithm is a recently developed Swarm Intelligence algorithm for function optimization. Fireworks Algorithm mimics the explosion process of fireworks. In this paper, Fireworks Algorithm is applied in training of Multi-Layer Perceptron for classification task in medical data mining. The classification task is carried out on 5 well-known medical data sets from UCI machine learning repository. A comparative study has been made with classical optimization algorithm Levenberg-Marquardt method and another Swarm Intelligence algorithm Particle Swarm Optimizer. The experimental results show that the proposed method performs better than other algorithms in classification.

Speech recognition is an area of Natural Language Processing and Artificial Intelligence. To achieve good accuracy and efficiency of Automatic Speech Recognition (ASR) system for Indian Gujarati language is challenging task due to its morphology, language barriers, different dialects, and unavailability of resources. This paper presents proposed architecture of ASR for Gujarati language. Raw input data have been collected from 4 male and 2 female who belongs from age between 18 to 36 years to prepare dataset for training purpose. The goal of Speech recognition system is to make machines capable enough to operate in natural languages. ASR is a system to convert vocalized form to visualized form using different computational devices. This convincing approach is useful to the people having disabilities deaf or inability to use input device. In this paper we have used Hidden Markov Model Toolkit HTK Tool to measure performance and error parameters. The system implementation analyzed WR (Word Recognition Rate) 95.9% and WER (Word Error Rate) as 5.85 % in Lab environment. For the open noisy environment calculated WR was 95.1% and WER found 7.40%.

JPEG is a widely used image compression method. Though it is very efficient, it introduces certain artifacts and quantization noise. This paper is a detailed survey about various existing methods for the reduction of these artifacts. The paper explains each method and their advantages and drawbacks. Some of the methods mentioned are Weiner filtering, Image Optimization, Zero-masking, Local Edge regeneration, Multiple dictionary learning, Hybrid Filtering, Fuzzy filtering, Total Variation Regularization, Offset and Shift Technique, Post-processing et al. Also, a comparative study is made as to which method is suitable for which scenario.

The paper explores the Genetic Algorithm approach to generate adequate and accurate test data for a specific target path. Software plays an important role in many of the systems, where the usage of software for a variety of purposes in different domains of modern life is rapidly increasing. With advancements in technology, it becomes quite complex whereas, software often contains errors. So testing consumes more money and time, which leads to automation that reduces human effort in finding bugs and errors. Actually, Automation in the last phase of system development is similar to manual testing. In both cases, bugs are detected only after code has been complete. Software testing is the most important component of software development process. Path testing is a popular structural testing method which uses the source code of a program to find every possible executable path. Test data generation is a key problem in software testing and its automation will improve the efficiency and effectiveness of software testing. Genetic Algorithm is an adaptive heuristic search algorithm that premise on evolutionary ideas of natural selection and genetic. In this paper, Genetic Algorithm is used to generate path by converting the program into its corresponding Control Flow Graph and then automatically generates the test data for the target path using different sets of GA operators.

Cloud computing has bring a revolution in the field of computing. Many algorithms are proposed to make it even more efficient. In cloud computing Virtualization plays an important role and whole performance of cloud depends on VM allocation and Migration. As lots of energy is consumed in this technology so algorithms to save energy and improve efficiency are proposed called Green algorithms. In this paper a green algorithm for VM Migration is proposed using metaheuristic algorithm called ACO. The variant of ACO used in this paper is Max-Min Ant System. Results show that MaxMin Ant System gives best result as compared to other approaches in terms of VM Migrations, VM consolidation and energy consumptions.

The Healthcare trade usually clinical diagnosis is ended typically by doctor’s knowledge and practice. Computer Aided Decision Support System plays a major task in medical field. Data mining provides the methodology and technology to alter these mounds of data into useful information for decision making. By using data mining techniques it takes less time for the prediction of the disease with more accuracy. Among the increasing research on heart disease predicting system, it has happened to significant to categories the research outcomes and gives readers with an outline of the existing heart disease prediction techniques in each category. Data mining tools can answer trade questions that conventionally in use much time overriding to decide. In this paper we study different papers in which one or more algorithms of data mining used for the prediction of heart disease. As of the study it is observed that Fuzzy Intelligent Techniques increase the accuracy of the heart disease prediction system. The generally used techniques for Heart Disease Prediction and their complexities are summarized in this paper.

Now a days thousands of malware samples are received by anti-malware companies on daily basis. And these large numbers are send for analysis by a number of automated analysis tools. These tool automatically execute a program in a controlled environment and generate a report describing the program’s behaviour. This research paper is a contribution towards the Dynamic Malware analysis. The aim is to provide the general malware features found in recent malware by performing dynamic malware analysis using cuckoo sandbox executed on Windows XP (SP3). This paper also discusses the detailed information about techniques & tools used in dynamic malware analysis.

means illegal entry or unwelcome addition of the system. So, Intrusion detection system is used to find out the signatures of an intrusion. The goal of the system is to protect system for various network attacks like Dos, U2R, R2L, Probing etc. Intrusion detection system (IDS) collects information from various parts of network and system. This paper introduces the Anomaly Intrusion Detection System that can detect various network attacks.The aim of this work is to identify those attacks with the support of supervised neural network, i.e. back propagation artificial neural network algorithm and make complete data safe. In this paper, system comprises experimenting neural networks that use only the (17 of 41) most significant features of the KDD 99 dataset. The proposed IDS use a supervised neural network to study system's performance.

Information security has been an essential part of human life from old time. In computer society, information protection turns out to be more vital for humankind and new rising technologies are developing in a perpetual stream. Cryptography is one of the most important techniques used for securing transmission of messages and protection of data. It provides privacy and security for the secret information by hiding it from unauthorized users. In this paper authors have proposed a method of cryptography, in which authors have used Laplace transform for encrypting the plain text and corresponding inverse Laplace transform for decryption. This paper is based on the work of [1,2,3,4]. General Terms Encryption, Decryption, Plain Text, Cipher Text, Key

Testing is performed after modification of the program or software; it classified the existing test cases into re-used test cases and affected test cases after modification of the code. Test case prioritization is an approach of arranging the existing test cases in manner that most affected test cases (that generated maximum number of faults) test first after the other one. There are many techniques used for prioritization test cases at the time of regression testing. This paper present a new approach for prioritization of test cases using static executable program slices for regression testing. Program slicing is a process to classify the program into number of parts based on various types of dependencies between program statements. This paper presents an overview of basic concept of generating static program slices and on the basis of these programs slices prioritization of test cases at the time of regression testing Keywordstesting, Program dependency, static program slices, test cases prioritization and execution history

Interface of java used as a design pattern for object oriented software development mostly used with inheritance, in java multiple inheritance is possible only through the interface this approach is widely used with modern software development approach apart from that there is also a rough side of java’s Interface which discussed in this paper, interface doesn’t allow the any kind of definition under it, but there is also some other concept like parent and child class concept if such concept apply with the interface then the theory of interface has been changed in this paper a practical approach has been used for this research problem.

with data each passing day. There needs to be efficient development in the area of information retrieval so the required data can be fetched accurately and efficiently. In this paper the two promising areas Natural Language Processing and Web technologies which can be combined together to enable the enterprise to combine the unstructured and structured data in ways that was not handled efficiently by traditional tools. The better understanding of web information can be done by integrating NLP and Web portal. The paper also explores various techniques used both areas. Also the NLP frameworks which can be used for the future work in this area.

Machine Translation is area of research since six decades. It is gaining popularity since last decade due to better computational facilities available at personal computer systems. This paper presents different Machine Translation system where Sanskrit is involved as source, target or key support language. Researchers employ various techniques like Rule based, Corpus based, Direct for machine translation. The main aim to focus on Sanskrit in Machine Translation in this paper is to uncover the language suitability, its morphology and employ appropriate MT techniques.

Cloud computing becomes the next-generation architecture of IT Enterprises. Cloud computing is a technology. It enables clients to use high-end services in a form of software as a service. These reside on different servers all over the world. There are many security threats in cloud computing. Data security is one of them. Data security raises client concerns. There are many issues of data security. It named maintenance of data integrity, data hiding and data safety. These threats dominate clients concerns when the issue of cloud come up. Cloud computing has a big data. Traditional encryption method is time-consuming in this environment. Cloud computing has a single security architecture. It has also many customers with different demands of security. In this case, data security is considered one of the most important issues in cloud computing. The proposed work focused on accessing data securely in cloud and desktop environment. It depends on public key cryptosystem. Generally, Data security is an important factor for both cloud computing and traditional desktop applications. Client needs to have the highest possible level of privacy. Public key cryptosystem is a good candidate for this purpose. It plays a vital role in cloud computing security and desktop. The paper presents an evaluation for selected public key cryptosystem techniques. It named ElGamal cryptosystem, modified chaotic cryptosystem and chaotic cryptosystem. A modified chaotic cryptosystem was presented. The modification enabled the same message to have different cipher version. The experiments implemented at two independent platforms namely desktop computer and Amazon EC2 Micro Instance cloud computing environment. In this paper, the selected algorithms compared according to randomness testing. A standardized NIST statistical testing is used in both desktop environment and cloud computing environment. The algorithms are implemented using python charm Cryptography framework. Simulation results are shown to demonstrate the effectiveness of each algorithm. NIST statistical tests are used to determine suitable technique for cloud computing environment and desktop. It also used to study the performance of the selected encryption techniques in both environment. General Terms Cloud computing, Security. NIST statistical testing, chaotic, a Modified chaotic, ElGamal

Quantum cryptography is a knowledge that guarantees ultimate safety. Associated to present cryptography that might be overcome by the expansion of an ultra-high-speed computer, quantum cryptography safeguards secure message since it is founded on the important bodily laws. It is an emergent technology in which two parties may concurrently produce shared, secret cryptographic key substantial using the broadcast of quantum conditions of light. Quantum cryptography is a novel way for secret communications present the final security pledge of the holiness of a Law of Nature. The quantum cryptography conviction on two innermost rudiments of quantum technicalities-the Heisenberg Uncertainty attitude and the standard of photon partition[19]. This research paper distillates on the trust of quantum cryptography, and in what method this knowledge contribute to the system security. This paper tactics the real world demand procedure of this know-how and the future itinerary in which quantum cryptography hasten.

This paper proposes a novel application of Grammatical Bee Colony for classification of medical data. Grammatical Bee Colony is a Swarm Programming algorithm generally used for automatic computer program generation in any arbitrary language. In this paper, Grammatical Bee Colony based classifier is designed and applied in medical data mining. The proposed method is applied on ten medical data sets and obtained results are compared with Multi-Layer Perceptron classifier trained with Levenberg-Marquardt algorithm. The proposed method statistically outperforms other method.

This paper presents various Data Hiding techniques of video in compressed domain. Data is embedded into digital media for the purpose of identification, annotation, copy right and tampering detection. The identification purpose mainly falls into the area of cloud computing- making sure that the sender is the one intended; annotation refers to the property of tagging or giving captions to the video; copyright protection, which is used to prevent the recreation of the video; tampering detection is the method in which the receiver checks whether the video is tampered or disturbed during the transmission by a third party. Several factors affect the data hiding process-the quantity of data to be hidden, whether the embedding technique can handle large payloads without affecting the quality of the video, embedding in a lossy compression environment and the extent to which the data hidden is subject to modification or removal by a third party. This paper discusses the various data hiding techniques of video in the compressed environment and compares the techniques based on the quantities mentioned above. Furthermore they are analyzed in terms of their method, robustness and capacity. A comparison graph is plotted based on the efficiency analysis of the data hiding techniques and conclusions are drawn based on the analysis.

This paper provides a review on the IDMA (Interleave Division Multiple Access) technology in wireless communication system based on Interleaver. In this paper, IDMA technique is proposed in AWGN channel. IDMA is a multiuser scheme in which chip Interleavers are the only means of user separation. The receiver involves a chip-bychip iterative multi-user detection. The IDMA performance in terms of bit error rate, bandwidth efficiency and complexity reduction is discussed. However, we observe that during iterative process, the IDMA output performs well with ISI Cancellation when numbers of users increases. Indeed, the increase in the number of users requires the independent processing of MAI and ISI that is carried out in IDMA.

Electrocardiogram (ECG) is a signal with unique, valuable information about the functional aspects of the heart with respect to time. The automatic analysis of ECG signals is an important application since the early detection of heart diseases/abnormalities can prolong life and enhance the quality of living through appropriate treatment. The ECG is collected using a number of electrodes placed in different positions on the body. Multi-lead ECGs acquired simultaneously helps in better diagnosis of heart diseases. This paper focuses on classification of healthy and Myocardial infarction signals. The identification of acute myocardial infarction with symptoms of Ischemia is critical to delivering appropriate medical care. In this paper decision tree based classifiers are implemented for the classification of ECG signals. The signals were analyzed for 34 normal and 33 myocardial infarction patients in the database PTB from the domain Physionet.org. The classifiers, J48 and Classification and Regression Trees (CART) are compared with respect to accuracy measures. The J48 classifier performs better with the correct classification rate of 98% and 0.9 Kappa statistics. General Terms Signal Processing, Classification, Data Mining.

This paper presents challenges being faced in designing automatic translation software. There are many approaches to automatic translation like Direct, Rule based, Transfer based, Statistical based and Interlingua. A brief description of all the approaches and their advantages and drawbacks are discussed. Universal Networking Language (UNL) based on Interlingua approach can be used especially for translation among multiple languages because it requires knowledge of UNL and of the language which user wants UNL to support. User can then get translated text in any of the languages supported by UNL without even being oblivious to any other language. It is less expensive approach also. This paper also gives brief introduction to UNL and how it can overcome many of the challenges in translation.

Cloud computing is interesting era of research where the multiple component, heavy hardware and architecture take part to execute the data and user processes. To maintain different virtual machines, data centers at different locations are required. Sometimes may be over burden on any datacenter and failure or downtime of the datacenter occurred. Thus in order to provide solution a load balancing technique is used which directs the request to an appropriate server for fast computation. Various traditional techniques such as Throttle, Round Robin were introduced but having some drawback with computation time and communication cost which makes them less efficient. In this paper we present CELBT (Cost Effective Load Balancing Technique) which compute the same data transfer in less computation time and cost. In this paper an evaluation is performed on CloudAnalyst tool to compute the parameters like time, cost and found the algorithm proven its effectiveness while compared with existing algorithms.

mining is a process of extracting information from a dataset and transform it into understandable structure for further use, also it discovers patterns in large data sets . Data mining has number of important techniques such as preprocessing, classification. Classification is one such technique which is based on supervised learning.. diabetic is a life threatening disease prevalent in several developed as well as developing countries like India. the data classification is diabetic patients data set is developed by collecting data from hospital repository consists of 1865 instances with different attributes. The instances in the dataset are two categories of blood tests, urine tests. In this paper we discuss various algorithm approaches of data mining that have been utilized for diabetic disease prediction. Data mining is a well known technique used by health organizations for classification of diseases such as diabetes and cancer in bioinformatics research. In the proposed approach we have used WEKA with 10 cross validation to evaluate data and compare results. Weka has an extensive collection of different machine learning and data mining algorithms. In this paper we have firstly classified the diabetic data set and then compared the different data mining techniques in weka through Explorer, knowledge flow and Experimenter interfaces. Furthermore in order to validate our approach we have used a diabetic dataset with 108 instances but weka used 99 rows and 18 attributes to determine the prediction of disease and their accuracy using classifications of different algorithms to find out the best performance. The main objective of this paper is to classify data and assist the users in extracting useful information from data and easily identify a suitable algorithm for accurate predictive model from it. From the findings of this paper it can be concluded that Naive Bayes the best performance algorithms for classified accuracy because they achieved maximum accuracy= 76.3021% correctly classified instances, maximum ROC = 0.819 , had least mean absolute error and it took minimum time for building this model through Explorer and Knowledge flow results.