Muhammad Shaheen

Mining sustainability indicators to classify hydrocarbon development

The role of energy in economic, social and ecological development of a country defines its significance in sustainable development. We propose here a method to classify a nations hydrocarbon development into one of five classes: (1) futuristic; (2) conforming; (3) sustainable; (4) unsustainable; or, (5) critical. K means clustering is a method of unsupervised classification in which the clusters cannot be labeled due to their lack of a class value. We propose a unique method to label unsupervised classes which is then used to divide the energy data of nations into five clusters. The labeled clusters are structured in an ID3 decision tree which provides a hierarchical structure to evaluate the hydrocarbon development in a given country. The results indicate some useful and interesting patterns in sustainability indicators.

Data mining applications in hydrocarbon exploration

This paper presents a review of the use of intelligent data analysis techniques in Hydrocarbon Exploration. The term “intelligent” is used in its broadest sense. The process of hydrocarbon exploration exploits data which have been collected from different sources. Different dimensions of data are analyzed by using Statistical Analysis, Data Mining, Artificial Neural Networks and Artificial Intelligence. This review is meant not only to describe the evolution of intelligent data analysis techniques used in different phases of hydrocarbon exploration but also signifying the growing use of Data Mining in various application domains; we avoided a general review of Data Mining and other intelligent data analysis techniques in this paper. The volume of general literature might affect the precision of our view regarding the application of these techniques in hydrocarbon exploration. The review reveals the suitability of existing techniques to data collected from diverse sources in addition to the use of analytical techniques for the process of hydrocarbon exploration.

Situation-Awareness and Sensor Stream Mining for Sustainable Human Life

Criminal activities cause a huge amount of loss both financially and in terms of human lives. Because of these acts, business and social sectors are struggling. This paper illustrates the development of an online sensor stream mining system that is able to analyze the situational behavior of all of the persons in specific areas and in turn propose real-time alert systems to take countermeasures. This system gathers different information from heterogeneous sensors, fuse that information, and generate real-time alerts to minimize the likelihood of disaster. These alerts and alarms assist security personnel in making appropriate decisions in real-time scenarios. The novelty of this approach comprises context-awareness with online diagnoses to take countermeasures in real-time to reduce the loss of lives, and damage to societies and economies. This technique makes the sensor stream mining process more dependable and increases the reliability of the overall system. To fulfill the objectives of this research, we incorporate lightweight online mining algorithms to extract useful but hidden information from the data gathered. Contextual information such as a person’s pattern of movement, current location, personal profile, and area of residence are exploited to detect anomalous behaviors. The major goal of this research is to detect those persons performing malicious activities and in turn minimize society’s exposure to risks and vulnerabilities.

Situation-awareness and sensor stream mining for sustainable society

Criminal activities are causing a huge amount of loss both in term of financial and human lives. Due to these acts, business and social sectors are striving. This paper is aimed to develop an online sensor stream mining system, able to analyze situational behavior of all persons in some specific vicinity and proposes real-time alert system to take countermeasures. This system is designed to gather different information from heterogeneous sensors and fuse that information to generate realtime alerts to minimize chances of disaster. These alerts and alarms assist security personnel to take necessary decisions in real-time scenarios. The novelty of this approach comprises context-awareness with online diagnoses to take countermeasures in real-time which will in turn reduce losses of lives, society and economy. This technique enables sensor stream mining process more dependable and increases reliability of the overall system. To fulfill the objectives of this research, we have incorporated light weight online mining algorithms and link analysis to extract useful but hidden information from the gathered data. Context information like persons movement pattern, current location of that person, profile of the specific person and area of residence as well as importance of current location are exploited to detect anomalous behaviors. The major goal of this research is to detect those persons performing malicious activities and in turn minimizing exposure of society to risks and vulnerabilities.

Critical Skills for Computer Academicians Course Proposal

The numbers of Computer Science professionals are rapidly increasing in Pakistan. Earlier revisions of the CS curriculum made by Higher Education Commission (HEC), Pakistan were based upon the critical skills needed for the professionals according to the demands of market. Unfortunately no effort was made to determine the critical skills needed for computer academicians. As part of the course development process for academicians, a study was conducted to determine the expected skills and knowledge required for these academicians. The academicians are divided into three main groups: Computer Programming instructors, Databases Instructors and Computer Networks instructors. T hese groups were made after the survey of demand from IT industry in Pakistan. It was concluded from the survey that More than 65% Computer professional jobs are required for the mentioned groups. An online survey tool (http://www.qnaire.netfirms.com) was developed to collect the data from respondents about the importance of various skills for computer science academicians. Ph.D professors, Ph.D Assistant Professors and Lecturers having Bachelors degree in Computer Science or relevant discipline are included in the list of respondents. The results indicate that the conceptual knowledge about three groups will be important with handsome emphasis on advanced applications. By applying principal component analysis and correlation analysis on the data collected by online survey the prominent factors were identified on the basis of which a course plan was developed. In coming years the better results will produce by the proposed curriculum. Ke ywords: curriculum development, computer academician, programming instructors, database instructors, networks instructors, teaching assistants.

An Algorithm of Association Rule Mining for Microbial Energy Prospection

The presence of hydrocarbons beneath earth’s surface produces some microbiological anomalies in soils and sediments. The detection of such microbial populations involves pure bio chemical processes which are specialized, expensive and time consuming. This paper proposes a new algorithm of context based association rule mining on non spatial data. The algorithm is a modified form of already developed algorithm which was for spatial database only. The algorithm is applied to mine context based association rules on microbial database to extract interesting and useful associations of microbial attributes with existence of hydrocarbon reserve. The surface and soil manifestations caused by the presence of hydrocarbon oxidizing microbes are selected from existing literature and stored in a shared database. The algorithm is applied on the said database to generate direct and indirect associations among the stored microbial indicators. These associations are then correlated with the probability of hydrocarbon’s existence. The numerical evaluation shows better accuracy for non-spatial data as compared to conventional algorithms at generating reliable and robust rules.

Frequency of rifampicin resistance in re-treatment cases of pulmonary tuberculosis using Gene-Xpert in a tertiary care hospital tuberculosis clinic

Objective: To evaluate the frequency of Rifampicin resistance in re-treatment cases of pulmonary tuberculosis using Gene-Xpert. Method: This is a descriptive study. Location of the study is the chest clinic of tertiary care hospital and an attached DOTS Tuberculosis Center and Drug Resistance TB center .We enrolled 812 patients over a period of two years who fulfilled eligibility criteria. Sputum for AFB smear was done in all patients and Gene-xpert was conducted for MTB (Mycobacterium Tuberculosis) and Rifampicin resistance. Results: Of the total (N=812) cases, 356 (43.84%) were sputum smear positive and 456 (56.16%) were sputum smear negative. In sputum smear positive patients, Gene-Xpert MTB was positive in 351 (98.60%) patients and negative in 5 (1.40%) patients. Among 351 patients whose Gene-Xpert MTB was positive, Rifampicin resistance was positive in 130 (37.03%) and negative in 221 (62.96%) patients.In sputum smear negative patients (456), Gene-Xpert MTB was positive in 84 (18.42%) patients and negative in 372 (81.57%) patients. Among 84 patients who were sputum smear negative and Gene-Xpert MTB positive, Rifampicin resistance was positive in 17 (20.23%) patients and negative in 67 (79.76%) patients.Overall frequency of Rifampicin resistance was found in 147 (18.10%) patients. Conclusion: Frequency of drug resistance is high among re-treatment cases of Tuberculosis. Early and rapid detection of drug resistance Tuberculosis is important to avoid delay in starting treatment. Gene-Xpert has a potential to be used as a primary diagnostic tool for early detection of for Multi-drug resistance Tuberculosis.

An intelligent assistant for mathematical production

Many functionalities like text auto filling, spell checking, thesaurus, translation, grammar, and many others, have been introduced to check textual document production but no feature have been integrated to any computer application which can help a user to produce own mathematical formula or from the existing reusable shared resources. We address the issue by developing an assistant to be incorporated into computer applications which is used by different users to produce equations and statements. Our main objective is to facilitate a mathematician to build a database of well structured formulae that can be reused either by him or by any of his colleagues. The assistance is provided on the basis of a context stored in the form of a knowledge base which is dynamically enhanced /updated with an online learning functionality. Concurrent Versioning System (CVS) approach will be incorporated to facilitate users for sharing of knowledge and exploiting the potential advantage of internet and distributed computing.