Abstract is a part of document has an important role in explaining the whole document. Words that frequently appear can be used as a reference in grouping the final project document into categories. Text mining method can be used to group the abstracts. The purpose of this study is to apply the method of association rule mining namely ECLAT algorithm to find most common terms combination and to group a collection of abstracts. The data used in this study is documents of final project abstract in English of undergraduate computer science student of IPB from 2012 to 2014. This research used stopwords about common computer science terminology, applied association rule mining with support of 0.1, 0.15, 0.2, 0.25, 0.3, and 0.35, and used k-Means clustering with number of cluster (k) of 10 because it gives the lowest SSE. This research compared the value of support, SSE, the number of cluster members, and purity value in each cluster. The best clustering result is data with additional stopwords and without applying association rule mining, and with k is 10. The SSE result is 23 485.03, and with purity of 0.512

Superior garbage collection algorithms are needed for deterministic runtime system in complex embedded systems to explore the benefits of contemporary and conquered application programming language. Android embedded operating system is greatly used world wide as a mobile platform without denying this fact it also attracted researchers and engineers to integrate in other embedded real-time systems. It exploits Java language for embedded application development and it can also enhance a certain real time capability with the adoption of real-time support at Dalvik Virtual Machine (DVM). Need for Real-time garbage collection algorithms in embedded systems is identified by achieving new insights into the existing garbage collection algorithms through finding blemishes in it. The space based technique is used in proposed new Real-time GC algorithm for execution runtime system and Real time Garbage Collection (GC) schedulability issue is also addressed. The intuitive performance analysis result demonstrates reduction in the response time and also describes the determinism characteristic of the real time applications using proposed solution.

Three classes of documents, based on their data, circulate in the web: Unstructured documents (.Doc, .html, .pdf ...), semi-structured documents (.xml, .Owl ...) and structured documents (Tables database for example). A semi-structured document is organized around predefined tags or defined by its author.
However, many studies use a document classification by taking into account their textual content and underestimate their structure. We attempt in this paper to propose a representation of these semi-structured web documents based on weighted vectors allowing exploiting their content for a possible treatment. The weight of terms is calculated using: The normal frequency for a document, TF-IDF (Term Frequency - Inverse Document Frequency) and logic (Boolean) frequency for a set of documents. To assess and demonstrate the relevance of our proposed approach, we will realize several experiments on different corpus.

Data mining is the process of analyzing different aspects of data and aggregating it into useful information. Classification is a data mining task generally used in medical data mining. The goal here is to discover new and useful patterns to provide meaningful and useful information for the users about the diabetes. Here a diabetes prediction and monitoring system is designed and implemented using ID3 classification algorithm. The symptoms causing diabetes are identified and are applied to the prediction model based on which the prediction is done. The monitoring module analyzes the laboratory test reports of the blood sugar levels of the patient and provides proper awareness messages to the patient through mail and bar chart.

Polarimetric radar images suffer from the presence of speckles that degrade the received signal and introduce untruthful indications about the nature of the objects. In this study, we proposed a new framework to filter polarimetric images in which the edges and the channel correlation are preserved. Through a proposed scheme, the image is segmented into groups of regular and irregular pixels. The segmentation process is based on the homogeneity of the texture variation throughout the image. In the homogeneous area, speckle reduction is performed using the adaptive local mean of the neighboring pixels. For non-homogeneous surfaces, the scheme works independently for each set of resolution cells using the general product model containing both intensity and texture information. Quantitative and qualitative assessments confirmed that the proposed filter achieved highly ranked order; it has the ability to preserve fine details, polarimetric information, and to maintain the scattering mechanism of the different objects.

Marine vessels in today's age are fitted with a number of state of the art systems required for their smooth operation. The compartments which house such systems along with the restricted compartments onboard ships such as the ships galley, dry rations store, cold rooms, battery compartments etc are required to be monitored on real time basis for temperature, pressure, humidity for detecting various hazards like fire, flooding etc. In addition, military platforms also need to monitor compartments such as the armory and magazines to avoid damage to munitions and prevent unauthorized access. The present project aims to develop a proof of concept prototype real time parameter monitoring and motion detection system for critical/restricted compartments on marine platforms with data logging capability.
Various sensors forming a sensor suite have been interfaced to the Raspberry Pi board, forming the Data Acquisition Console which is the nodal control center. As most marine vessels are fitted with a shipboard Local Area Network, the project utilizes this existing network for relaying data. The console is placed in the compartment where parameters are to be monitored and the measured data is acquired and transferred via wireless (using Access Points (APs) operating on Wi-Fi/ 802.11 network) or via wired connectivity with the nearest switch and be accessed by concerned personnel at various nodes/ computer on the . The performance of the DAC was successfully ascertained by comparison of sensor performance with other independent sensor readings. The measurement errors were found to be within the permissible accuracy limits of the sensors. Motion detection was achieved by using PIR motion The probability of detection (Pd) for the motion sensor was calculated by conducing iterative motion tests with favorable results. Data is displayed in a web-based dashboard Graphical User Interface. Further, provision has also been made to set visual alarms whenever a particular sensor reading crosses a pre-designated safe limit.

The steady rise of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. Consequently, the location based services has now become one of the most exiting features of the next generation wireless systems. This paper presents the work related to both person oriented and device oriented location based services, where the focus is not only to find the location of person but also to find the location of object (mobile phone).This work is valuable to a user to find the location in cases of kidnapping, loss of phone, security of girl child, location of elder members of the family having weak memory. Also, this type of work is also considered as an active location based service where the user is aware of being tracked with the agreement.

This paper presents solution of multi-objective optimal dispatch (MOOD) problem of solar-wind-thermal system by improved stochastic fractal search (ISFSA) algorithm. Stochastic fractal search (SFSA) is inspired by the phenomenon of natural growth called fractal. It utilizes the concept of creating fractals for conducting a search through the problem domain with the help of two main operations diffusion and updating. To improve the exploration and exploitation capability of SFSA, scale factor is used in place of random operator. The SFSA and proposed ISFSA is implemented and tested on six different multi objective complex test systems of power system. TOPSIS is used here as a decision making tool to find the best compromise solution between the two conflicting objectives. The outcomes of simulation results are also compared with recent reported methods to confirm the superiority and validation of proposed approach.

There is a tremendous number of Arabic text documents available online that is growing every day. Thus, categorizing these documents becomes very important. In this paper, an approach is proposed to enhance the accuracy of the Arabic text categorization. It is based on a new features representation technique that uses a mixture of a bag of words (BOW) and two adjacent words with different proportions. It also introduces a new features selection technique depends on Term Frequency (TF) and uses Frequency Ratio Accumulation Method (FRAM) as a classifier. Experiments are performed without both of normalization and stemming, with one of them, and with both of them. In addition, three data sets of different categories have been collected from online Arabic documents for evaluating the proposed approach. The highest accuracy obtained is 98.61% by the use of normalization.

In the present world internet and web search engines have become an important part in one's day-to-day life. For a user query, more than few thousand web pages are retrieved but most of them are irrelevant. A major problem in search engine is that the user queries are usually short and ambiguous, and they are not sufficient to satisfy the precise user needs. Also listing more number of results according to user make them worry about searching the desired results and it takes large amount of time to search from the huge list of results. To overcome all the problems, an effective approach is developed by capturing the users' click through and bookmarking data to provide personalized query recommendation. For retrieving the results, Google API is used. Experimental results show that the proposed method is providing better query recommendation results than the existing query suggestion methods.