To get an idea of the modern gadgets that are available in the market and to select/ buy a product, a visit to the mall/ shop/ exhibition is needed till now. However, the products are now presented to the individual by online and he/ she need not take the trouble of going out. Recommender systems are alternative to search algorithms, as they help users discover item they might not have found by themselves. These systems use search engines indexing non-traditional data. Recommender systems are developed to get the pulse of the customer and to present the items, in which he/ she is interested and thus the manufacturer is able to sell his products and the customer is able to buy the items with less effort. Should it be also possible at this rate, using the recommender systems, to diagnose and prescribe the medicine to the ailing patient, to be experimented/ studied?
Information and Communications Technology (ICT) has changed our society remarkably in the last few years, one key feature is its great and continuously-increasing size. There seems no end in sight to the proliferation of mobile communications as over 120,000 new base stations are deployed yearly, and there is growth across every demographic from teenagers to businessmen to the poorest Indian village. The intersection of two undeniably increasing trends, escalating energy costs and meteoric growth in voice and data communications usage, creates an urgent need to address the development of more energy efficient ‘green’ communications. According to BCC Research, the ICT global market was worth $38.4 billion in 2010; the forecast is a growth to $58.4 billion and the downlink traffic from cellular handsets is expected to grow more than eight fold rising from 56MB per month to 455MB by 2015. This meteoric rise in users and data demand alone does not create a crisis; however when one evaluates the communications ecosystem from a carbon footprint and energy cost perspective, the results are startling. Although its effects on our everyday lives are obvious, the hazardous effects that this technology may have on the environment are much less clear and seldom talked about and are often neglected or overlooked. These impacts can be expressed throughout the lifecycle (from the manufacturing, use and disposal) of the ICT product. This paper presents a research vision for Green Communications. The core tenant of our Green Communications vision is to reduce overall energy consumption within framework of optimizing system capacity and maintaining user Quality of Service.
With the rapid growth of cloud computing developers are being concern about the massive power consumption bythe data centers. The huge amount of power consume by the data centers does not only results in high operational cost but also the production of heat and carbon footprints on the environment. Researchers are making efforts to maximize the power efficiency and minimizing energy consumption of data centers. This paper presents a study of the researches and methods that have been proposed to increase the power efficiency and minimize power consumption of the data centers.
Image fusion is technique in which two or more images of same scene are merged together such that new image formed suitable for better interpretation. Image fusion result into a fused image that continue to have the most useful information. This fused Image has characteristics of each input image. Various standard method used for image fusion produce good result spatially but cause spatial noise. To remove this distortion and enhance quality of fused image, various fusion schemes have been proposed. In this paper, we focus on the fusion methods based on various standard techniques and compare it with wavelet transform used for image fusion .Wavelet transform is majorly used in the area of image fusion because it provide better results in temporal domain as well as spatial domain .Performance of any fusion technique is measured by mean standard deviation, entropy, average gradient, peak signal to noise ratio (PSNR) and correlation coefficients.
The web is an enormous information space where a large number of an individual article or unit such as documents, images, videos or other multimedia can be retrieve. In this context, several information technologies have been developed to assist users to gratify their searching needs on web, and the most used by users are search engines as Yahoo, Google, Netscape, e-Bay, e-Trade, Expedia, Amazon, Bing, Ask, and so on. The search engines allow users to find web relevant resources by setting up their queries and reviewing a list of answers. In this paper a search result optimization method for search engine optimization by page rank updating, query recommendation and query reformulation are proposed. It explores the users queries registered in the search engine’s query logs in order to learn how users search and also in order to design algorithms that can improve the correctness of the answers suggested to users. The proposed method starts by exploring the query logs to find query clusters and identify session of queries then it examine query logs to discover useful relationship among pages, keywords and queries within clusters using association rule mining algorithms such as an apriori algorithm and automated apriori algorithm. We also showed that automated apriori algorithm generates more strong rules as compare to apriori algorithm.
