The objective of this paper is to propose three modified versions of the Gravitational Search Algorithm for continuous optimization problems. Although the Gravitational Search Algorithm is a recently introduced promising memory-less heuristic but its performance is not so satisfactory in multimodal problems particularly during the later iterations. With a view to improve the exploration and exploitation capabilities of GSA, it is hybridized with well-known real coded genetic algorithm operators. The first version is the hybridization of GSA with Laplace Crossover which was initially designed for real coded genetic algorithms. The second version is the hybridization of GSA with Power Mutation which also was initially designed for real coded genetic algorithms. The third version hybridizes the GSA with both the Laplace Crossover and the Power mutation. The performance of the original GSA and the three proposed variants is investigated over a set of 23 benchmark problems considered in the original paper of GSA. Next, all the four variants are implemented on 30 rotated and shifted benchmark problems of CEC 2014. The extensive numerical, graphical and statistical analysis of the results show that the third version incorporating the Laplace Crossover and Power mutation is a definite improvement over the other variants.

In recent years, the mining research over data stream has been prominent as they can be applied in many alternative areas in the real worlds. In [20], a framework for mining frequent itemsets over a data stream is proposed by the use of weighted slide window model. Two algorithms of single pass (WSW) and the WSW-Imp (improving one) using weighted sliding model were proposed in there to solve the data stream problems. The disadvantage of these algorithms is that they have to seek all data stream many times and generate a large set of candidates. In this paper, we have proposed a process of mining frequent itemsets with weights over a data stream. Based on the downward closure property and FP-Growth method [8,9] an alternative algorithm called WSWFP-stream has been proposed. This algorithm is proved working more efficiently regarding to computing time and memory aspects.

The suggested new approach based on B-Event formal technics consists of suggesting aspects and constraints related to the reliability of NoC (Network-On-chip) and the over-cost related to the solutions of tolerances on the faults: a design of NoC tolerating on the faults for SoC (System-on-Chip) containing configurable technology FPGA (Field Programmable Gates Array), by extracting the properties of the NoC architecture. We illustrate our methodology by developing several refinements which produce QNoC (Quality of Service of Network on chip) switch architecture from specification to test. We will show how B-event formalism can follow life cycle of NoC design and test: for example the code VHDL (VHSIC Hardware Description Language) simulation established of certain kind of architecture can help us to optimize the architecture and produce new architecture; we can inject the new properties related to the new QNoC architecture into formal B-event specification. B-event is associated to Rodin tool environment. As case study, the last stage of refinement used a wireless network in order to generate complete test environment of the studied application.

Today, many individuals are used to dine-out. However, they are unaware of the business operation on that particular moment of the day. Several times, we end up arriving at the restaurant only to find that it is closed/having a break. Hence, we propose a framework for a system of related applications which solves the above problem by being informative regarding the business operability to the customers. Firstly, a trader side framework that allows food stall operators to inform the status and nature of their business to their customers whether they are open for business or not. Secondly, a customer side framework for the food stall operator customers to view restaurant status, menu and to place booking. Mobile applications are developed based on the proposed framework for both trader and customer. And lastly, a website is developed for the general public to view the business status of the stall operators. By being able to inform customers the status of the business, it will provide convenience to many people in our society. Our contribution will be the aforementioned framework as well as mobile apps and website which provides convenience to many people in our society, in terms of reducing time wastage as well as fuel costs to the stall’s destination.

We present a method to find the most influential rock guitarist by applying Google PageRank algorithm to information extracted from Wikipedia articles. The influence of a guitarist was estimated by the number of guitarists citing him/her as an influence and the influence of the latter. We extracted this who-influenced-whom data from the Wikipedia biographies and converted them to a directed graph where a node represented a guitarist and an edge between two nodes indicated the influence of one guitarist over the other. Next we used Google PageRank algorithm to rank the guitarists. The results are most interesting and provide a quantitative foundation to the idea that most of the contemporary rock guitarists are influenced by early blues guitarists. Although no direct comparison exist, the list was still validated against a number of other best-of lists available online and found to be mostly compatible.

In information retrieval, Question Answering (QA) is the task of answering a question posed in natural language (NL) using either a pre-structured database or a collection of natural language documents without human intervention. Question Answering systems are categorized on their available resource for answers. The domain specific Question Answering System gives more exact and correct answers than web based Question Answering system as it is limited for only one domain resource to answer. This paper proposes the closed domain Question Answering System for handling the legal documents of Indian Penal Code (IPC) sections and Indian Amendment Laws to retrieve more precise answers. This system tries to retrieve the exact answers from stored knowledge-base for the query related to Indian Penal Code (IPC) sections and Indian Amendment Laws asked by user. This Keyword based Question Answering System works on structured, unstructured and non-question form queries. The closed domain Question Answering system gives more accurate answer than other open domain system as it restricted single resource. Keywords from both queries and answer corpus play important role for extracting answer.

Software Quality Assurance Testing time computer vision based automation tools are used to test the window based application and window based application is combined of many objects. Among them most of the automation tool detect window objects by comparing images. Most of the objects are visible in the window screen but some objects which are not visible to the screen at the first time. Proper interaction with the window application hidden objects get visible to the screen like dropdown list item, editor text object, list box item and slider. With the automation tools these hidden objects cannot be searched directly. In this paper proposes some methods which will enhance the automation tools to access the window application hidden objects.

There is an increase in death rate yearly as a result of heart diseases. One of the major factors that cause this increase is misdiagnoses on the part of medical doctors or ignorance on the part of the patient. Heart diseases can be described as any kind of disorder that affects the heart. In this research work, causes of heart diseases, the complications and the remedies for the diseases have been considered. An intelligent system which can diagnose heart diseases has been implemented. This system will prevent misdiagnosis which is the major error that may occur by medical doctors. The dataset of statlog heart disease has been used to carry out this experiment. The dataset comprises attributes of patients diagnosed for heart diseases. The diagnosis was used to confirm whether heart disease is present or absent in the patient. The datasets were obtained from the UCI Machine Learning. This dataset was divided into training, validation set and testing set, to be fed into the network. The intelligent system was modeled on feed forward multilayer perceptron, and support vector machine. The recognition rate obtained from these models were later compared to ascertain the best model for the intelligent system due to its significance in medical field. The results obtained are 85%, 87.5% for feedforward multilayer perceptron, and support vector machine respectively. From this experiment we discovered that support vector machine is the best network for the diagnosis of heart disease.