Sanjay Kumar Sharma

Entropy based content filtering for Mobile Web Page Adaptation

A global increase in the usage of mobile devices and the availability of internet services on the phone has increased the usage of internet on these devices. However, these devices face a major challenge of limited screen space, less bandwidth and processing capability. A majority of the website are designed to be viewed on PCs, Desktop etc. Hence mobile Internet user finds it difficult to browse the web page. In this paper we present an approach to filter the informative web page content and rearrange it on the available screen space of mobile device. In this process the web page is segmented into visual blocks and then the entropy measure of each visual block is computed in terms of content entropy and feature entropy. The model is trained using neural network classifier to segregate main content and noise content.

Parallel Performance of Numerical Algorithms on Multi-core System Using OpenMP

The current microprocessors are concentrating on the multiprocessor or multi-core system architecture. The parallel algorithms are recently focusing on multi-core system to take full utilization of multiple processors available in the system. The design of parallel algorithm and performance measurement is the major issue on today’s multi-core environment. Numerical problems arise in almost every branch of science which requires fast solution. System of linear equations has applications in fusion energy, structural engineering, ocean modeling and method of moment formulation. In this paper parallel algorithms for computing the solution of system of linear equations and approximate value of π are presented. The parallel performance of numerical algorithms on multicore system have been analyzed and presented. The experimental results reveal that the performances of parallel algorithms are better than sequential. We implemented the parallel algorithms using multithreading features of OpenMP.

Web informative content identification and filtering using machine learning technique

Internet has gained greatest acceptance as reservoirs of information. It has been observed that the web page along with main content comprises of noise advertisement, external links, which poses difficulty for various search engines crawlers to correctly classify the web page and it also provides distraction to the user interested in gathering relevant data. In this paper, we proposed a novel approach which categorises the relevant content from the web page and use this information to filter and rearrange the content of the web page. We used the web page segmentation algorithm for parsing the web page to obtain non-overlapping visual blocks and then extracted the features from these visual blocks to build the dataset. The dataset have been trained using popular machine learning classifier techniques neural network, RBF neural network to discriminate content. Finally, the classification output is used to perform main content filtering of the web page. We also analysed the importance of features on the learning process and perceive that the embedded objects from external source have highest significance for block identification.

Effect of Cow Urine and Bio-Fertilizers based Fertigation Schedule at Varying Levels of Drip Irrigation on Yield, Growth, Quality Parameters and Economics of Cucumber under Protected Condition

A native to India, cucumber (Cucumis sativus L.) is commonly grown in all parts of the country, mainly for its immature fruits. It is a good source of vitamins B and is a low calorie diet. It has 95% water content, making it a diuretic vegetable crop, which keeps the body hydrated and helps in cleansing of body toxins. It also reduces the risk of cancer, eliminates uric acid and its fiber-rich skin and high levels of potassium and magnesium helps to regulate blood pressure and promote nutrient functions. However, it is a frost susceptible species, and being a warm season vegetable, it is thermophilic and grows best under condition of high light, humidity, moisture, fertilizer and temperature (above 20 °C). Hence, growing cucumber during autumn winter and spring summer season can give off season supply to the nearby market in plains. Parthenocarpic fruits are common cucumber hybrids that can be grown in off season under protected conditions due to their ability to set fruit without pollination or fertilization even at low temperatures International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 6 Number 6 (2017) pp. 1242-1249 Journal homepage: http://www.ijcmas.com

Energy efficient resource management in cloud environment: Progress and challenges

Cloud computing is adopted everywhere because of its low cost and ease of use. Due to fast growth in demand of computational power it has become imperative for the cloud provider to manage the resources in energy efficient way, which becomes possible with virtual machine technology. This paper reviews the techniques of reducing energy consumption using virtual machine management and increasing CPU utilization. Cloud Data Centers consists of many resources like CPU, memory, bandwidth etc. As energy consumption is proportional to CPU utilization, hence CPU is considered to be the main resource in these techniques.