Information Technology

A new hybrid approach with Genetic Algorithm and Simulated Annealing is proposed to solve Multi Job-Shop Scheduling problem, which is one of the well-known hardest combinatorial optimization problems. The main objective of multi job shop scheduling problem is to find a schedule of operations of each job in a set of jobs that can minimize the maximum completion time called makespan. To improve the makespan, SA algorithm has been designed and combined with genetic algorithm . Thus, hybrid GA is implemented over MJSSP and the effectiveness and efficiency is proved by getting promising results for different benchmark job-shop scheduling problem instances.

Association rule mining is one of the well established fields in data mining. This paper has surveyed the research papers in this field from 1993 to 2013. This paper describes the fundamental algorithms and its advantages and disadvantages. This also provides brief overview of current trends of association and frequent pattern mining and medical applications.

Voice morphing means the transition of one speech signal into another. Like image morphing, speech morphing aims to preserve the shared characteristics of the starting and final signals, while generating a smooth transition between them. Speech morphing is analogous to image morphing. In image morphing the in-between images all show one face smoothly changing its shape and texture until it turns into the target face. It is this feature that a speech morph should possess. One speech signal should smoothly change into another, keeping the shared characteristics of the starting and ending signals but smoothly changing the other properties. The major properties of concern as far as a speech signal is concerned are its pitch and envelope information. These two reside in a convolved form in a speech signal. Hence some efficient method for extracting each of these is necessary. We have adopted an uncomplicated approach namely cepstral analysis to do the same. Pitch and formant information in each signal is extracted using the cepstral approach. Necessary processing to obtain the morphed speech signal include methods like Cross fading of envelope information, Dynamic Time Warping to match the major signal features (pitch) and Signal Re-estimation to convert the morphed speech signal back into the acoustic waveform.

A method and system for finding a person using a mobile messenger service in a mobile communication terminal including a global positioning system (GPS) module are provided. The method includes providing a location finding method using a mobile messenger service, the method comprising: providing a mobile messenger service to a mobile communication terminal in which a position detecting module is provided; receiving information on access to the mobile communication terminal and information on registration of counterpart mobile communication terminals registered with the mobile messenger service, so as to generate information on locations of the mobile communication terminal and the counterpart mobile communication terminals and to generate map information to which the location information is mapped; and providing the map information to the mobile communication terminal.

Vehicle traffic is experienced everyday by millions of people in day-to-day life. To overcome the problems there emerged a need for an effective traffic prediction system. An efficient prediction method reduces the traffic congestion and allows us for optimal usage of resources. In this paper we propose a traffic prediction system that uses a series of clustering and prediction techniques on the data set and has been used to predict the traffic flow over a road point. This is performed by finding the similarity value, we can quantify the influence of that Road points over others and this method will provide you a prior knowledge about traffic congestion so that user can plan to avoid vehicle traffic.

With the huge upsurge of information in day-to-day’s life, it has become difficult to assemble relevant information in nick of time. But people, always are in dearth of time, they need everything quick. Hence clustering was introduced to gather the relevant information in a cluster. There are several algorithms for clustering information out of which in this paper, we accomplish K-means and K-Medoids clustering algorithm and a comparison is carried out to find which algorithm is best for clustering. On the best clusters formed, document summarization is executed based on sentence weight to focus on key point of the whole document, which makes it easier for people to ascertain the information they want and thus read only those documents which is relevant in their point of view.

Mining frequent item sets from data streams is of great interest recently in many applications. Sliding windows are used in many applications to overcome an important problem with data streams i.e. unbound size. In this paper we propose an effective transaction based one-pass algorithm that uses a circular queue to implement the transaction-sensitive sliding window. The proposed algorithm, FIM_CQTransSWin has three phases representation of transaction, maintenance of the sliding window and generation of frequent item sets in the current sliding window. In the first phase, each transaction is read from the data stream and is converted into a decimal number based on the items present in this transaction. In the second phase, the decimal numbers representing the transactions are put in a circular queue with the front pointer indicating the oldest transaction and the rear pointer indicating the latest transaction. When the circular queue becomes full the oldest transaction is removed using dequeue operation and then the new transaction is appended at the rear end. The first and second phases are repeated indefinitely as long as the transactions keep arriving in the data stream. The third phase gets activated when the user requests for the frequent item sets. When the user request arrives, the frequent item sets in the current sliding window are generated using the candidate generation approach of the apriori algorithm and MASK operation.

Software risk management is the practice of assessing risks that affects the software development projects, process or products. Most software development confronts great risks and risks might occur in the whole development process. This paper explores the different risks involved in various phases of the software development process and defines mitigation steps after analyzing risks. The objective of this research is to construct a visualization tool for software risk assessment for all phases in software development process.

The issue of copyright infringement spans across different sectors of the economy: ranging from the film industry, music, literary, as well as academics (plagiarism); just to mention but few. Software industry is another sector that is plagued with the menace of the software piracy. The practice started date back to late 1970s, and has eaten deep into the industry. The losses recorded by the software developers and the benefits users are been deprived of, prompted many researcher to suggest different measures (legal, physical and technical) to controlling this illegality. Employing means of battling this unscrupulous act has two-edged benefits. On the side of the developers, their intellectual properties are guarded, and the gains of their labours are not been reap by another. For the users, the originality of the product is assured and the users can benefits from regular update and maintenance routines. In this paper, our focus is on the technological control of this ‘unwanted stranger’. Previous models were studied and adequately examined. The existing serial key and image splitting models were adopted to form a hybrid model type. The outcome of our design, testing and implementation, shows a reduction in the level of possible access by pirates or unauthorised users.

The use of document summarization allows a user to get a sense of the content of full document, or to know its information content without reading all sentences within the document. Data reduction helps user to find the required information quickly without having to waste time in reading the whole document. This paper presents a method to generate a summary from the original document. And the method includes several characteristics such as sentence-id, position of each term in a sentence, term frequency, sentence similarity measure and weight of each and every sentence. To solve the optimization problem differential evolution (DE) algorithm is used, which can choose the optimal summary. DE algorithm is based on a fitness function and selection of fitness function is crucial for the good performance of DE algorithm.