The article presents an approach for software development for the management of an assembly line. Basic software tools and hardware solutions that are needed for the development of the software are described. Design of specific software for the management of an assembly line for bottling liquid food products is presented. A specially developed algorithm for the management of the assembly line is described. The realization of the management of the assembly line in the programming environment Simatic Manager Step 7 and the developed user interface in the graphical environment Simatic WinCC Flexible are also presented. The design of software for the management of an assembly line for bottling liquid food products is extremely important for the development of automated manufacturing in Bulgaria. After detailed testing of a trial version of this software, it will be used in Bulgarian company for the management of an assembly line for bottling liquid food products. The developed software is an open system that can be continuously updated and improved. This software is applicable in all manufacturing plants for bottling liquid products from canning, pharmaceutical, cosmetic industries and many others.

Stemming fetches the main root word from the inflectional words called stem. Stem gives different meaning when suffix or prefix is added to it. The stem need not give perfect meaning. Lemmatization gives lemma from inflectional words. Lemma should give meaning that in the dictionary form. Natural Language processing, Information retrieval, Text mining are the areas which use the stemming as preprocessing step. Through stemming, the size of the document can be reduced and ambiguity is also removed. It makes the work easy for other process likes information retrieval, semantic analysis, text categorization etc. Though there is a need for linguistic improvements in the existing stemming algorithms, all these algorithms fail in some cases to give an exact Root word and are not able to handle informal verbs. Hence, Bruteporter Hybrid approach is proposed in order to improve the linguistic process of stemming in English Texts. It combines the Wordnet and Modified Porter Algorithm. A Wordnet is a lexical dictionary created by linguistics people. Modified porter algorithm has both suffix removal and suffix substitution functionality. This proposed approach can extract root word from both inflectional words and informal verbs. In this paper, Experiment is conducted on proposed algorithm and the accuracy is calculated.

Decision making is a crucial process which is influenced by many factors in the decisions of a person. Especially the purchase decisions are very costlier whether it is towards a product or a service. When focusing on the children products, the decisions towards the purchase of children products and brands are influenced by many factors. The study focuses on the influence of emotional and rational factors on the purchase of children products and brands. It aims to investigate the factors that influence more in the purchase decisions of children products. By exploring the results this study will help the purchasers to take wise decisions since it will throw a light on the influential factors so that it will initiate the introspection of the purchasers. The research was conducted in Coimbatore city of Tamilnadu state in India with 500 respondents. Using a well structured questionnaire, after a pilot study the required primary data have been collected from the respondents. Using excel analysis tool pack the data obtained were computed and analyzed. The results depict that the rational thinking is increasing with the age in the purchase decisions. The middle income group people are more rational in the purchase decisions towards the children products. As a whole the results show that the emotional factors have more influence than the rational factors in the purchase decision of children products and brands. It implies the purchaser must be aware of the influence of emotional factors to limit it to take wise decisions in the purchase of children products and brands.

The volume of data on the Web is increasing rapidly. The rapidly increased data in Web have brought an urgent need to develop a method to organize that data. At the same time, the level of user expectation of getting précised data is increased highly. Hence, it is tough to satisfy the user satisfaction through the existing system. In this paper, we proposed a model to organize the large volume of data over the Web and retrieve the more relevant data to the user. As an implementation of the proposed model, we built two demo search engine (one for RDF based semantic searching and another for existing searching). We use two different sets of data for testing. For every set of data, the RDF based searching returns more précised data than existing searching. The efficiency of the proposed model is better than the existing searching strategies. In the proposed model, we considered both traditional web and RDF based ontology library to organize the data effectively.

An advertisement is a notice or announcement in a public medium promoting a product. Advertising plays significant role in the introduction of a new product in the market. It stimulates the people to purchase the product. In this paper, we propose a novel personalized Online Video Advertising System which is presented to recommend product ads from ecommerce sites to users of online video hosting. First we have to find a user preference, so we have to analyze each user’s behavior of ecommerce sites. If the user wants to buy a product, user will spend more time to view the specification at the same time, user clicks that product more number of times to view the specification, price etc. These techniques are used to find the user preference. After collecting each user preference, we have to identify the semantic association between videos and products and construct the association between the key frames and products. A multi view deep learning approach is brought to view item features in different domains. When the User plays the video the user preferred advertisements are shown in the video in proper timestamps. Thus the advertisements are displayed only based on the user’s preference and the user’s will to purchase the desired product. This has been the key feature of our project. Maintaining privacy is one of the major problems for sharing personal information through social sites. Sharing video in social media may lead to unwanted problem and less privacy. As a result we need some tools for secured transmission. For satisfying this need, we propose a system called Adaptive Privacy Policy Prediction (A3P) to enhance privacy settings for user’s data. This system provides a two-level framework for securing the data based on users browsing history on shopping site. It determines the best available privacy policy for users. Our solution relies on image classification which is associated with policies to upload images and also to user's social features. Hence by using this adaptive privacy policy our sharing of shopping and recommended videos can be secured to only desire people.

The aim of this study is to find an efficient and robust model for building temperature information systems in South-East Nigeria. The study obtained daily mean temperature data records for a period of 10years of the capture cities of Enugu, Abakaliki and Owerri, and applied the data to several forecasting models: 3 & 4 point moving averages (MA), the Single Exponential Smoothing (SES) and the time dependent regression model for intercept and non-intercept models as well as linear and non-linear models. The comparison of various forecasting models was made based on the following performance evaluation methods: F-values, Mean Square Error (MSE) and Root Mean Square Error (RMSE) where applicable. The findings show that the power model with statistical characteristics of F-values = 1513.71(Enugu), 1523.622(Abakaliki) and 1514.103(Owerri), MSE = 0.655(Enugu), 0.6495(Abakaliki), and 0.5925(Owerri), and RMSE = 0.80524(Enugu), 0.80292(Abakaliki) and 0.76703(Owerri), is the best model for temperature information systems because of its consistency in minimizing errors, and largeness of F-values. This is followed by the single exponential smoothing technique and logarithmic model. This study therefore presents and recommends the power regression model as the most robust model for temperature forecasting in South-East Nigeria.