In modern era, due to the broad applications of uncertain data, mining itemsets over uncertain databases has paying much more attention. Association Rule Mining (ARM) is a well known and most popular technique of Data Mining. It identifies itemsets from the dataset which appears frequently and generates association rules. This is the procedure which is followed by the traditional ARM it does not consider the utility of an itemsets. In real-world applications such as retail marketing, medical diagnosis, client segmentation etc., utility of itemsets is varied on various constraints such as based on cost, profit or revenue. Utility Mining intend to discover itemsets with their utilities by considering profit, quantity, cost or other user preferences.[22]High-utility itemset mining (HUIM) has thus emerged as an important research topic in data mining. But most HUIM algorithms only handle precise data, even though big data collected in reallife applications using experimental measurements or noisy sensors is often uncertain. High-Utility Rare Itemset (HURI) mining finds itemsets from a database which have their utility no less than a given minimum utility threshold and have their support less than a given frequency threshold. Identifying high-utility rare itemsets from a database can help in better business decision making by highlighting the rare itemsets which give high profits so that they can be marketed more to earn good profit. Koh and Rountree (2005) proposed a modified apriori inverse algorithm to generate rare itemsets of user interest. In this paper we propose an efficient algorithm named Mining High Utility Rare Itemsts over Uncertain Database (HURIU) .This novel approach uses the concept of apriori inverse over uncertain databases. This paper will also give the new version or extension of the algorithm HURI proposed by Jyothi et al. The implementation of an algorithm for the analysis is done on JDK 6.1 and referred the sample dataset presented by Lan Y.et al,2015[15] for uncertain database.

There is huge amount of data which is being processed daily. According to statistic world’s population is 7 billion and 6 billion people has smart phones. So, having smart phones there are various application which connects world. It has made world easier and difficult at the same time. Social media has made people to converse with each other and add few knowledgeable information on it. This huge exploration of data has to be maintained and it is necessary to know the context in which it is used. Discource interpretation of the word is very important a particular world can be used in positive context or in negative context .For example “great” can be used in positive context as well as negative context. . So Natural Language processor is used (NLP) to get the context of the word which is being used. So in this paper we can exploit the knowledge of Natural language processing the context in greater way.

Pregnancy and motherhood are natural processes in the lives of women. One of the great feelings for every woman is begin a Mother, but maternal period is very much susceptible for health issues. Some old illnesses may increases or new illnesses can start during this period. Recently Indian health status has improved. Child mortality rates and maternal mortality are declined in current scenario but when Indian situation is compared with other developed countries it is not so good. Advance in data analytics has become one of the driving forces in multiple fields to turn them into technology dependent ones. One of the fields where this impact is still at lower level is maternal health care. There is a need to analyze the symptoms, take steps towards the precautions and dealing with the complication which women fights during this period. This paper discuss various complication women suffer during maternal period, various approaches proposed in past with their findings and it also proposed a model which can be used for predication of parameters which lead to the complication in pregnancy

Mobile Ad Hoc Networks (MANETs) are the wireless networks which can be deployed instantly without requiring any fixed wired infrastructure. MANETs are specifically very much useful in military, commercial and civilian applications. Since infrastructure less MANETs have dynamic topology and battery powered mobile nodes, it is a challenging task to provide secure data transmission between any pair of nodes in MANET. Multipath on Demand Routing is one possible solution to provide security in MANET. This paper proposes a new method (SDNMR) of providing secure communication by integrating trust based mechanism with multipath on demand routing approaches in MANETs. The simulation analysis of proposed method reveals the facts that the method provides significant security to the data compared to previous related work.

The world of Computing has been completing 80 years in applying scientific methodologies to understand basic principles of Emerging Technologies. In recent times, IT has great influence on how different Indian Government Departments operate. Security is one of the most important issues in E-governance projects. Egovernance applications will be increasingly used by the citizens of many countries to access a set of government services. Currently, the use of the E-government applications arises many challenges; one of these challenges is the security issues. E-government applications security is a very important characteristic that should be taken into account. This paper analyses about new secured model developed for Treasury Information System by using Quantum Key Distribution based verifiable and traceable cipher text attributes based encryption (QKD-VTCP ABE) algorithm that support an integrated internet-based E-governance applications

In current time Cloud Computing is the most recent pattern where IT applications and foundations are provided as administrations under a use based installment model to its end-clients. Normally two issues happen amid relocation of CPU or memory concentrated VMs which are known as complete movement time and memory utilization at host which results in corruption in the general execution of the framework. In this paper we have proposed a method that removes the memory utilization, utilizing improved pre-copy methodology. So as to expand the security of the data and to limit the information loss, a safe relocation has been accomplished by utilizing the Elgamal cryptographic technique.

In wireless sensor networks (WSNs), the node distribution in the unequal clustering is rapidly used for distributing the load and increase the network lifetime. In tradition unequal clustering mechanism, the nodes which are nearer to the base station suffers with energy depletion. To overcome this issue, this paper proposes the multilevel unequal clustering algorithm with efficient cluster head selection procedure based on the residual energy and maximum transmission capacity of the nodes. The proposed algorithm uses threshold function to resist the waiting time of the node for receiving the cluster head notification message. The simulation results show the efficiency of the proposed algorithm compared with the counterpart algorithms.

Lack of Flexibility, Centralized Control, and Cost are limitations of the traditional network. Software defined networking (SDN) adds flexibility and programmability in network management by separating the control plane from the data plane. Distributed controllers with SDN are logically centralized at control plane and physically distributed at data plane. They are deployed to improve the adeptness and accuracy of the control plane, which could isolate network into few subdomains with independent SDN controllers. Traffic is dynamic and configuration between switch and controller is static. If one of the controllers fails, load imbalance arises. To address this problem of fault tolerance in distributed controller DCFT (Distributed Controller Fault Tolerance) model is proposed in this paper. A novel switch migration method with coordinator controller in a distributed SDN controller is proposed for providing fault tolerance through load balancing. The system architecture of the proposed model with different modules such as coordinator controller election, load collection, decision taking, switch migration, Inter controller messenger designed. On failure of coordinator controller switch migration discussed. Implement DCFT model in Mininet, derived results, The results show that our design could achieve load balancing among distributed controllers while fault occurs, regardless network traffic variation and outperforms static binding controller system with communication overhead, controller load balance rate, and packet delay. We compare our model with CRD (controller redundancy decision), MUSM (maximum utilization switch migration) and ZSM (Zero switch migration) techniques. Simulation analysis performed on custom topology. We compare packet delay, communication overhead and load balancing rate in a custom topology with before and after migration of switches. It’s revealed that the DCFT model produces better performance in fault tolerance.

Modern multi-core systems allow concurrent execution of different applications on a single chip. Such multicores handle the large bandwidth requirement from the processing cores by employing multiple levels of caches with one or two levels of private caches along with a shared last-level cache (LLC). In shared LLC, when applications with varying access behavior compete with each other for space, conventional single core cache replacement techniques can significantly degrade the system performance. In such scenarios, we need an efficient replacement policy for reducing the off-chip memory traffic as well as contention for the memory bandwidth. This paper proposes a novel Application-aware Cache Replacement (ACR) policy for the shared LLC. ACR policy considers the memory access behavior of the applications during the process of victim selection to prevent victimizing a low access rate application by a high-access rate application. \textcolor{red}{ It dynamically tracks the maximum life-time of cache lines in shared LLC for each concurrent application and helps in efficient utilization of the cache space. Experimental evaluation of ACR policy for 4-core systems, with 16-way set associative 4MB LLC, using SPEC CPU 2000 and 2006 benchmark suites shows a geometric mean speed-up of 8.7% over the least recently used (LRU) replacement policy. We show that the ACR policy performs better than recently proposed thread-aware dynamic re-reference interval prediction (TA-DRRIP) and protecting distance based (PDP) techniques for various 2-core, 4-core and 8-core workloads.

In today’s era educational organization strongly needs devices which are ready to access and use and also various operating system platforms are required for different learning courses. To achieve this type of environment hardware availability come in front as an important issue along with lots of money required to purchase them. Many educational courses required to run particular software or application on particular operating system platform along with specific hardware configuration. The maintenance of this different versions of operating systems and their installation stuff is hectic process and also required man power. To overcome all this problem there is solution called Cloud OpenStack. Cloud OpenStack allows us to develop an environment on commodity hardware or on existing system present in the educational organization. It’s easily handle different version of OS platforms and also monitor and maintain them. Another important thing is many educational organizations still used Virtual machine method to used different OS platforms for various learning courses. This paper come up with implementation of Cloud OpenStack in Educational Organization and analyzing the results. The outcomes of Cloud OpenStack compare with the Virtual Machines method and find out which technique is better and more suitable for academics.

This paper focused on the study of various feature extraction techniques applied for fingerprint identification, verification and classification as it is the most important step for image processing. Feature extraction techniques are classified into local (low level) and global (high level) features. Global features such as arch, loop, delta and whorl where as local features such as ridge end and bifurcation called minutiae are majors of automatic fingerprint recognition system. In this study, it has been observed that most of the fingerprint recognition systems are based on minutiae features. In this paper we analyze the various feature extraction methods used so far with their mathematical background to the readers

Let ??(??, ??) be a unicyclic graph. A unicyclic graph is a connected graph that contains exactly one cycle. A dominating set of a graph G = (V, E) is a subset D of V, such that every vertex which is not in D is adjacent to at least one member of D. The domination number is the number of vertices in a smallest dominating set for G. In this paper I have presented an algorithmic approach to compute the domination number and the minimum domination set for the unicyclic graph. The algorithm has polynomial time complexity of ??(??).

Cowry game, also known as Chowka Bhara, is an ancient board game originated in India. It is a 2-4 player strategic race game which contains an element of chance due to the throw of special dice (cowry shells). This game involves reasoning under uncertainty and stochasticity, and the decisions have to be made based on incomplete knowledge. Therefore, it may be regarded as a suitable domain for exploring various approaches in real-time strategic decision-making. This research investigates the potential for an artificially intelligent Cowry game player, capable of playing on a high-performing level against human opponents. We propose and implement a few advanced playing strategies for the game, and analyze their effectiveness based on different performance measures. We also provide an experimental comparison between the proposed advanced strategies and the basic strategies that we developed in our earlier work. The analysis of playing strategies presented in this paper can be used for evolving more sophisticated strategies, and may eventually lead to the development of an adaptive artificial player for Cowry game and similar race games.

Processors are an integral part of the computer and electronics industry. Every computational unit contains some sort of processing circuit, designed to perform multiple operations on a single device and can be categorized based on its speed, flexibility and adaptability. RISC, CISC, Harvard and Von Neumann are some of these philosophies, emphasizing on a generalized approach for the design. This paper describes the design and implementation of a custom 8-bit single cycle processor on Xilinx Vivado Design suite, supporting 16 instructions, 256 bytes of internal storage, an Arithmetic Logic Unit and control unit. The module functionalities, Instruction set design and Data flow of the processor have also been discussed for a subset of instructions along with the simulation results.

Software Performance Testing(SPT) is the kind of Non-Functional Testing(NFT) to prove the to be delivered system by the project team/product team is working as expected with respect to system health, consistency, endurance robustness etc. This testing is a very critical part of system going live. Now a days customers/stakeholders are very keen to carry on this test to get absolute confidence to the software product they are going to use for their business. It has been seen several instances in the history of software industry ,live application has been demoted or scrapped due to the system not able to behave as it is expected with its features and functionalities when the system is used concurrently in high load .This type of verification can be only simulated when we do performance testing. Now workload distribution for performance testing till date has been generally performed by manually capturing load details like list of very frequently performed critical transactions, no. of call/hours, no. of concurrent users running from the log or details from business. And it’s a laborious time consuming job. With the advent of Robotic Process Automation (RPA) we can overcome the drawbacks of manual hurdles workload model with a futuristic reusable and reliable one, about which I discussed in following sections.

In a Multi-Agent System existing formalisms for implementing organizational policies assign specific roles to each agent. Examples are hierarchical organization, contract net protocol, social reasoning mechanism, and the use of matchmaker agents. These policies allow the problem solving roles of the agents to change dynamically but do not adapt to variations in computational load on the multi-agent system. They are designed to operate for a predefined maximum problem-solving load and fail to respond, when the number of task requests, exceed this limit. This paper proposes an adaptive organizational policy, called E-RTA (Efficient Resource and Task Allocation), for Multi-Agent-Systems that operate under time constraints and varying computational loads. We consider MAS (Multi-AgentSystems) as consisting for several problem-solving organizations where each organization is comprises multiple agents that may be grouped into teams for specific problem solving.

The world of e-commerce and e-business has opened many horizons to explore customers on web. Consumers are expecting businesses to approach and please them with their expectations. This has given rise to recommender systems. Many of the recommender systems are generalized in nature which are often based on market stratum and user predictions. However, extensive research is being carried out in providing personalized recommendations using association rules, customer segmentation, social media ontologies and demographics. There are many issues in the implementation of these systems. This paper discusses diverse recommender approaches proposed in the past with a comparative study and gap analysis. It also proposes a Hybrid Personalized Recommender system by using a probabilistic model.

Self-Organizing Maps are widely used unsupervised neural network architecture to discover group of structures in a dataset. Feature Selection plays a major role in Machine Learning. “An Appropriate Feature Classification Model using Kohonen Network (AFCM)” is based on Recurrent Neural Network approach for feature selection which clusters relevant and irrelevant features from the dataset present in cloud environment. The proposed model not only clusters relevant and irrelevant features but also refine the clustering process by minimizing the errors and irrelevant features. The AFCM consists of Feature Selection Organizer and Convergence SOM. In the Feature Selection Organizer, features are clusters into Relevant and Irrelevant Feature classes. The Convergence SOM helps to improve the prediction accuracy in the Relevant Feature set and to reduce the irrelevant features. The efficiency of the proposed model is extensively tested upon real world medical datasets. The experimental result on standard medical dataset shows that the AFCM is better than the Traditional models.

The occurrence of the congestion has an extremely deleterious impact on the performance of Wireless Sensor Network (WSNs). Some novel blockage control method utilizing computerized reasoning for remote sensor systems. Remote Sensor Systems are a class of remote systems estimated for checking objective and ecological wonders. Our paper provides opening to wireless sensor network as well as for artificial intelligence, which aims to act both proactively, in order to avoid the creation of congestion in WSNs, and reactively, so as to mitigate the diffusion of upcoming congestion through alternative path routing. We make use of Artificial Intelligence (AI) which play an important role in our society, give rise to systems that can manage themselves. Many advanced AI techniques can be utilized to improve the performance and reliability. Investigating the AI algorithm applied to WSN may improve network management, security or routing in WSN which may result in a more reliable network. We will look at AI algorithm applied in WSN and discuss the possible use of these AI in WSN to address the WSN challenge and improve its performance and reliability. And due to the constraint on data processing and power consumptions, use of artificial intelligence has been historically discarded in these kind of network. The use of the AI in WSN makes system error free, fast and efficient in almost many aspects. This paper attempts to encourage the use of artificial intelligence techniques in wireless sensor nodes.

Remote sensing and analysis of an object or specific area of the earth at different distances with very large number of bands forms a major part of hyperspectral imaging technology. Currently, a wide range of data sets are obtained continuously from hyperspectral remote sensing, in addition to conventional multispectral remote sensing images, and presented to users by institutions for both commercial and research purposes. These data sets give a vast opportunity to explore number of areas where research can be carried out. Areas such as subpixel mapping, super resolution, target detection, compression and retrieval find a vast scope for research work. However, among all the research topics retrieval forms an important topic for discussion due to large amount of data sets being processed at very small interval of time. Excessiveness of information revealed from these data sets also complicates access of users to images they are interested in. In this study, an in-depth review of current research challenges in Hyperspectral Image retrieval techniques has been done to specify research gaps and trends in this subject.

Nowadays, research are being directed in the field of large system models. These large system models generally manages the distributed environment to break down and understand behavior of the systems. A large number of the system give simulation of the distributed environment to run their experiments. Hence, when there is an expansion in demand of cloud and distributed system, side by side it is important to maintain the power consumption and efficiency of the framework. This is a most challenging assignment to perform. In this paper, we give energy optimizing model with the help of migration manager to manage the productivity of the entire system. We will contrast DVFS algorithm with on Demand and Conservative mode of operation in cloud. We will exhibit our experiment in CloudSim software

Data Classification is the application of data mining techniques to discover patterns from the micro array and biological datasets. This research entitled “PREDICTING SAFTEY INFORMATION OF DRUGS USING DATA MINING TECHNIQUE” incorporates information theory, which is the process of deriving the information from the unsupervised dataset through feature selection. Finding the best features that are similar to a test data is challenging task in current data era. This research presents a framework for discovering best feature selection from unsupervised datasets. The proposed research work presents a new approach to measure the features (attributes) in drug prediction dataset using the methodologies namely, data cleaning, Adaptive Relevance Feature Discovery and Random Forest Classification. There are number of pharmacy companies are available in the market with multiple medicines for same problem.This prediction of drugs is used to prescribe the medicines for the patient’s disease by analyzing the history of the patient’s health. Feature selection and dimensionality reduction is characterized by a regularity analysis where the feature values correspond to the number times that term appears in the dataset. The relevance feature discovery method gives a useful measure is used to find the similarity features between data points are likely to be in terms of their features property. Some of the challenges faced in finding the best feature selection include positive, negative and inconsistency. This Proposed work proposes an enhanced Drug prediction based on Random Forest classification method to estimate the feature searching that is measured using minimal redundancy optimization method corresponding to drug prediction dataset

The problem of restoration of digital images plays a central role in multitude important applications. A particularly challenging instance of this problem occurs in the case when the degradation phenomenon is modelled by ill-conditional operator. In such situation, the presence of noise makes it impossible to recover a valuable approximation of the image of interest without using some priori information called as simply priors is essential for image restoration, rendering it stable and robust to noise. Particularly, if the original image is known to be a piecewise smooth function, a total variation (TV) based image restoration can be applied. This paper proposes an algorithm for unconstrained optimization problem where the objective function includes a data fidelity term and a nonsmooth regulaizer.Total Variation method is employed to find solution of the problem based on the Improved Iterative Shrinkage Thresholding Algorithm (IISTA). IISTA is performed through a recursive application of two simple procedures linear filtering and soft thresholding. An experimental result shows that proposed algorithm performs well when compared with the existing methods.

India is the agrarian country. The overall economy of our country is based on agriculture. Although the methods of cultivation are traditional and not hi-tech thus more over 75% of our farmers are dependent on monsoon. Prediction of actual monsoon is a challenge for meteorological scientists. Since the climatic data time series shows highly non-linear and chaotic behavior thus its forecast is still an enigma. Thus, forecasting of climate phenomenon is a challenging issue for the researchers round the globe. However, it is a prime necessity to forecast climatic changes such as Rainfall (daily rainfall, monthly rainfall, heavy rainfall etc.), Flood, Drought, minimum and maximum Temperature, River flow etc. To recognize applications of Artificial Neural Network (ANNs) in weather forecasting, especially in drought forecasting a comprehensive literature review from 2000 to 2017 is done and presented in this paper. In the study, more over 90 contributions have been surveyed and it has been observed that the architecture of ANN such as BPN, RBFN, MLP, ANFIS, ARIMA etc. are found best to forecast chaotic behavior and have efficient enough to forecast drought as well as other weather phenomenon over broader or smaller homogeneous region.