Insecurity is one of the major challenges that the entire world is facing now, each country having their peculiar security issues. The crime rate in every part of the society these days has become a threatening issue such that vehicles are now used for committing criminal activities more than before. The issue of vehicle theft has increased tremendously, mostly at gunpoint or car parks. In view of these, there is a need for adequate records of stolen, identified and recovered vehicles which are not readily available in our society and as such very important. The development of a vehicle theft alert and location identification system becomes more necessary for vehicle owners to ensure theft prevention and a speedy identification towards recovery efforts in situations where a vehicle is missing, stolen or driven by an unauthorized person. The theft alert function makes use of a GSM application developed and installed in a mobile phone device which is embedded in the vehicle to communicate with the vehicle owner's mobile phone. The communication is established via SMS (i.e. between the installed mobile phone device and that of the vehicle owner). The communications established include; (i). Sending an SMS alert from installed mobile phone device to vehicle owner mobile phone when the car ignition is put on. (ii). Sending an SMS from the vehicle owner's mobile phone to start and stop the installed mobile phone device application. The location identification function makes use of a web application developed to; (i). Determine the real time location of a vehicle by means of tracking using GPS. (ii). Broadcast missing or stolen vehicle information to social media and security agency. The implementation of the installed mobile phone device application was done using JAVA because of its capabilities in programming mobile applications while PHP and MySQL was used for the web application functions. Integration testing of the system was carried out using simple percentage calculation for the performance evaluation. Fifty seven (57) vehicle owners were sampled and questionnaires were distributed to them in order to ascertain the acceptability and workability of the developed system. The result obtained shows the effectiveness of the system and hence it can be used to effectively monitor vehicle as it is been driven within or outside its jurisdiction. More so, the system can be used as database of missing, identified or recovered vehicles by various security agencies.

The idea of Big Data represents a growing challenge for companies such as Google, Yahoo, Bing, Amazon, eBay, YouTube, LinkedIn, Facebook, Instagram, and Twitter. However, the challenge goes beyond private companies, government agencies, and many other organizations. It is actually an alarm clock that is ringing everywhere: newspapers, magazines, books, research papers, online, offline, it is all over the world and people are worried about it. Its economic impact and consequences are of unproportioned dimensions. This research outlines the fundamental literature required to understand the concept of Big Data. Additionally, the present work provides a conclusion and recommendations for further research on Big Data. This study is part of an ongoing research that addresses the link between Economic Growth and Big Data.

This paper illustrates a comparison study for control of highly non-linear Double Inverted Pendulum (DIP) on cart. A Matlab-Simulink model of DIP has been built using Newton's second law. The Neuro-fuzzy controllers stabilizes pendulums at vertical position while cart moves in horizontal direction. This study proposes two soft-computing techniques namely Fuzzy logic reasoning and Neural networks (NN's) for control of DIP systems. The results shows that Fuzzy controllers provides better results as compared to NN's controllers in terms of settling time (sec), maximum overshoot (degree) and steady state error. The regression (R) and mean square error (MSE) values obtained after training of Neural network were satisfactory. The simulation results proves the validity of proposed techniques.

Now a days, developing the science and technology and technology tools, the ability of reviewing and saving the important data has been provided. It is needed to have knowledge for searching the data to reach the necessary useful results. Data mining is searching for big data sources automatically to find patterns and dependencies which are not done by simple statistical analysis. The scope is to study the predictive role and usage domain of data mining in medical science and suggesting a frame for creating, assessing and exploiting the data mining patterns in this field. As it has been found out from previous researches that assessing methods can not be used to specify the data discrepancies, our suggestion is a new approach for assessing the data similarities to find out the relations between the variation in data and stability in selection. Therefore we have chosen meta heuristic methods to be able to choose the best and the stable algorithms among a set of algorithms.

Human's hand nail is analyzed to identify many diseases at early stage of diagnosis. Study of person hand nail color helps in identification of particular disease in healthcare domain. The proposed system guides in such scenario to take decision in disease diagnosis. The input to the proposed system is person nail image. The system will process an image of nail and extract features of nail which is used for disease diagnosis. Human nail consist of various features, out of which proposed system uses nail color changes for disease diagnosis. Here, first training set data is prepared using Weka tool from nail images of patients of specific diseases. A feature extracted from input nail image is compared with the training data set to get result. In this experiment we found that using color feature of nail image average 65% results are correctly matched with training set data during three tests conducted.

The domain medical and public health remains the principal preoccupation of all world population. It makes recourse at several means from various disciplines, including for instance epidemiology, to help clinicians in decision processes. This paper proposes an Assistance Platform for Epidemiological Searches and Surveillance (APESS) for service-oriented data mining in the field of epidemiology. The main aim of the present platform is to build a system that enables extracting predictive rules, flexible and scalable for aid in decision-making by trades' experts. Results showed that the current system provides prediction models of chronic diseases (epidemiological prediction rules), using classification algorithms.

In this paper we introduce a novel algorithm for counting nodes based on wireless communications and their actual position, which works for stationary nodes and in scenarios where nodes are moving at high speeds. For this, each node is endowed with a Global Positioning System (GPS) receptor, allowing it to periodically send its actual position and speed through beacon messages. These data will be received by the first-hop neighboring nodes (which are within its scope or propagation range) that will have the ability to compute the actual position of the sending node based on the last broadcasted position and speed. The algorithm is constructed on the propagation of a count request message from the originator node toward nodes that are far away from it, and response messages traveling back to the originator, in the reverse path when it is possible, otherwise using the closest node on the way to the originator. To validate and evaluate the performance of our proposal, we simulate the algorithm using a famous network simulation tool called OMNeT++/INET. The results of our simulations show that the proposed algorithm efficiently computes a number of nodes very close to the real one, even in the case of scenarios of mobile nodes moving at high speeds, with an acceptable response time.

This paper presents a new Apriori based approach for mining periodic frequent patterns from the temporal database. The proposed approach utilizes the concept of rough set theory for obtaining reduced representation of the initially considered temporal database. In order to consider only the relevant items for analyzing seasonal effects, a decision attribute festival has been considered. It has been observed that the proposed approach works fine for the analysis of the seasonal impact on buying behavior of customers. Considering the capability of approach for the analysis of seasonal profitability concern, decision making, and future marketing may use it for the important decision-making process for the uplifting of sell.

Ontology matching techniques are a solution to overcome the problem of interoperability between ontologies. However, the generated mappings suffer from logical defects that influence their usefulness. In this paper we present a detailed analysis of the problem so-called conservativity principle; alignment between ontologies should never generate new knowledge compared to those generated by reasoning solely on ontologies. We also study the sub-problems; Ontology change and Satisfiability preservation problems and compare the related works and their way to detect and repair conservativity principle. At the end we present a set of open research issues.

The recommendation is playing an essential part in our lives. Precise recommendations facilitate users to swiftly locate desirable items without being inundated by irrelevant information. In the last few years, the amount of customers, products and online information has raised speedily and results out into the huge data analysis problem for recommender systems. While handling and evaluating such large-scale data, usual service recommender systems regularly undergo scalability and inefficiency problems. Nowadays, in multimedia platform such as movie, music, games, the use of Recommender System is increased. Collaborative Filtering is a dominant filtering technique used by many RSs. CF utilizes the rating history of the user to find out "like minded" users and this set of like-minded user is then used to recommend the movies which are liked by these like-minded users but did not watch by the active user. Thus, in CF, to find out the "neighborhood" the rating history of a user is used, but the reason behind the rating is not considered at all. This will lead to inaccuracy in finding a neighborhood set and subsequently in recommendation also. To cope with these scalability and accuracy challenges, this paper proposes an innovative solution, Clustering and Review based Approach for Collaborative Filtering based Recommendation. This innovative approach is enacted with the two stages; in the first stage the clustering of the available movies for recommendation is clustered into the subclasses for further computation. In the succeeding stage, the methodology based on reviews is utilized for finding neighborhood set in User Based Collaborative Filtering.