National University of Computer and Emerging Sciences

About: National University of Computer and Emerging Sciences is a education organization based out in Islamabad, Pakistan. It is known for research contribution in the topics: Computer science & The Internet. The organization has 1506 authors who have published 2438 publications receiving 26786 citations.

SWISE: Semantic Web based intelligent search engine

Abstract: Most of the search engines search for keywords to answer the queries from users. The search engines usually search web pages for the required information. However they filter the pages from searching unnecessary pages by using advanced algorithms. These search engines can answer topic wise queries efficiently and effectively by developing state-of-art algorithms. However they are vulnerable in answering intelligent queries from the user due to the dependence of their results on information available in web pages. The main focus of these search engines is solving these queries with close to accurate results in small time using much researched algorithms. However, it shows that such search engines are vulnerable in answering intelligent queries using this approach. They either show inaccurate results with this approach or show accurate but (could be) unreliable results. With the keywords based searches they usually provide results from blogs (if available) or other discussion boards. The user cannot have a satisfaction with these results due to lack of trusts on blogs etc. To get the trusted results search engines require searching for pages that maintain such information at some place. This requires including domain knowledge in the web pages to help search engines in answering intelligent queries. The layered model of Semantic Web provides solution to this problem by providing tools and technologies to enable machine readable semantics in current web contents.

Identification of significant risks in pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) approach

Abstract: Pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) technique was analyzed to determine the significance of clinical and phenotypic variables as well as environmental conditions that can identify the underlying causes of child ALL. Fifty pediatric patients (n = 50) included who were diagnosed with acute lymphoblastic leukemia (ALL) according to the inclusion and exclusion criteria. Clinical variables comprised of the blood biochemistry (CBC, LFTs, RFTs) results, and distribution of type of ALL, i.e., T ALL or B ALL. Phenotypic data included the age, sex of the child, and consanguinity, while environmental factors included the habitat, socioeconomic status, and access to filtered drinking water. Fifteen different features/attributes were collected for each case individually. To retrieve most useful discriminating attributes, four different supervised ML algorithms were used including classification and regression trees (CART), random forest (RM), gradient boosted machine (GM), and C5.0 decision tree algorithm. To determine the accuracy of the derived CART algorithm on future data, a ten-fold cross validation was performed on the present data set. The ALL was common in children of age below 5 years in male patients whole belonged to middle class family of rural areas. (B-ALL) was most frequent as compared with T-ALL. The consanguinity was present in 54% of cases. Low levels of platelets and hemoglobin and high levels of white blood cells were reported in child ALL patients. CART provided the best and complete fit for the entire data set yielding a 99.83% model fit accuracy, and a misclassification of 0.17% on the entire sample space, while C5.0 reported 98.6%, random forest 94.44%, and gradient boosted machine resulted in 95.61% fitting. The variable importance of each primary discriminating attribute is platelet 43%, hemoglobin 24%, white blood cells 4%, and sex of the child 4%. An overall accuracy of 87.4% was recorded for the classifier. Platelet count abnormality can be considered as a major factor in predicting pediatric ALL. The machine learning algorithms can be applied efficiently to provide details for the prognosis for better treatment outcome.

The potential role of renewable energy sources in robot's power system: A case study of Pakistan

Abstract: The domain of ‘robotics’ is undergoing a major transformation in dimension as well as scope. Recent advances in various disciplines of technology have revolutionized this domain at an incredible pace far beyond the contemporary state of the art. Highlighting the forecasted population of robots in future and the resulting demand of massive energy consumption, this review addresses the application of renewable energy sources in uninterruptible supply for robots. The study analyzes the extensive field of renewable energy technology and discovers that comparing with traditional sources; green energies like solar, wind and biological have significant potential to drive robots. Listing the world-wide achievements attained by the applications of renewable energy technology for robots, the review finally presents the subject matter in context of Pakistan. It is shown that being rich in renewable energy resources, broad possibilities for robot technologies exist here. With a discussion on challenges to exploit this potential, suggestions are outlined. It is anticipated that wider dissemination of research developments in the integration of these streams will stimulate more exchanges and collaborations among the research community and contribute to further advancements.

Recent trends and advances in solving the inverse problem for EEG source localization

Abstract: This paper addresses the recent advancements and trends in the field of electroencephalography (EEG) using inverse problem solutions. Using the EEG data of the brain to gather the informati...

Entropy generation in nonlinear mixed convective flow of nanofluid in porous space influenced by Arrhenius activation energy and thermal radiation

Abstract: Here, nonlinear mixed convective flow of nanomaterials over a porous stretching sheet is discussed. The Buongiorno model is used in the mathematical modeling. Important aspects of Buongiorno model, i.e., Brownian movement and thermophoresis are addressed. Further impact of activation energy, viscous dissipation, Joule heating and nonlinear thermal radiation retained in energy and concentration expressions. Optimization of entropy generation rate is discussed. The governing systems are modeled through dimensionless variables. The series solutions are constructed via OHAM algorithm. Features of various sundry variables are interpreted and deliberated. Our analysis reveals that entropy enhances via higher estimation of Reynolds number, radiation and magnetic variables. Our analysis reveals that Bejan number shows decaying feature via Brinkman number and magnetic parameter.