The Rubik’s cube is 3D puzzle with 6 different colored faces. The classis puzzle is a 3x3x3 cube with 43 quintillion possible permutations having a complexity of NP-Hard. In this paper, new metaheuristic approaches based on Simulated Annealing (SA) and Genetic Algorithm (GA) are proposed for solving the cube. The proposed algorithms are simulated in Matlab software and tested for 100 random test cases. The simulation results show that the GA approach is more effective in finding shorter sequence of movements than SA, but the convergence speed and computation time of the SA method is considerably less than GA. Besides, the simulation of GA confirms the claim that the cube can be solved with maximum 22 numbers of movements.

Today E-commerce websites provide customers with the needed product information by giving various kinds of services to choose from. One such service is to allow the customer to read the end user online reviews. Online reviews contain features which are useful for the analysis in opinion mining. Converting these unstructured reviews into useful information require extracting the product features from them. Natural Language Processing (NLP) based technique extracts various kinds of product features including the low frequency features. Topic Modeling based approach also identifies specific product features from the online reviews. The effective number of product features is made available to the customer when these two approaches are combined. This results in the expanded product feature set so that the customer makes wise decisions without having to compromise on the feature set.

To observe the beauty of nature and to visit various places around the world, a vast number of tourists visit different countries and many tourist attraction sites now-a-days. But Most of the tourist places have failed to introduce itself as a tourist destination to the visitor due to lack of proper information and proper guideline to visit there. This paper tries to focus on some problems in the tourism industry and try to solve those problems using crowd sourced data with some customized algorithms. Some of the main problems are the lack of information about a destination tourist spot, combination on budget to visit the spot, time of travels etc. We proposed a customize algorithm which will provide maximum suggestion to visit a place with nearest all sub place based on user destination within their given budget and time. Using our method, user can choose the most suitable plan for them to visit those places.

This paper presents a careful study and analysis of an existing manual sales and inventory control system of a pharmaceutical store and aimed at designing and developing a computer-based system in order to increase the efficiency and accuracy of the business operations of the pharmaceutical store. The study was carried out to reduce the problems of inconsistency and inaccuracy of sales and drug data inherent in the existing manual system of the pharmacy. An analysis of the current manual system was done to get a better understanding of the system. The Rapid Application Development (RAD) methodology was used in this work to implement an iterative program which is suitable for stand-alone applications that can be updated from time to time as may be required by the computer -based system. Testing was done in every phase of the development life cycle to ensure that the new system works properly. The software was developed and implemented using Visual Basic 6.0 programming language and Microsoft Access for the database platform. The software developed provides alert of every expired drug and minimum quantity of each drug available in stock. Other activities such as product sales, drugs and users registration, and periodic reports generation can also be carried out with the new system.

With the advancement in the web technology it is considered as one of the vast repository of information. However this information is in the hidden form.  Various data mining techniques need to be applied for extracting the meaningful information from the web. In this paper the various techniques are discussed that have been used by many researchers for extracting the information and also shown the disadvantages with the existing approaches. The paper put forward a novel concept of mining the association rule from the web data by using Quine-McCluskey algorithm. This algorithm is an optimization technique over the existing algorithm like Apriori, reverse Apriori, k-map. This paper exhibits the working of the Quine- McCluskey algorithm that can extract the frequently accessed web pages with minimum number of candidate sets generation. However the limitation of Quine-McCluskey algorithm is that it cannot find the infrequent patterns.

Keyphrase extraction from news web pages is an important task for news documents retrieval and summarization. Keyphrases are like index terms that enclose the important information about document content. Keyphrases actually offer concise and precise description of document content. Key phrases are considered as a single word or a combination of more than one word that represent the important concepts in a text documents. The aim of this paper is to develop and evaluate an automatic keyphrases extraction approach for news web pages. Our approach identifies the candidate keyphrases from documents and chooses those candidate keyphrase having highest weight score. Weight formula combines the feature set that includes TF*IDF, phrase disatnce in documents and lexical chain that is based on WordNet to represent semantic relations between words. The experimental results show that the performance of our approach is better than the contemporary approaches today.