Mohammad Ali Jinnah University

About: Mohammad Ali Jinnah University is a education organization based out in Karachi, Pakistan. It is known for research contribution in the topics: Nonlinear system & Sliding mode control. The organization has 602 authors who have published 929 publications receiving 11198 citations. The organization is also known as: Capital University of Science & Technology & Muhammad Ali Jinnah University.

Evidence of the COVID-19 Virus Targeting the CNS: Tissue Distribution, Host–Virus Interaction, and Proposed Neurotropic Mechanisms

Abstract: The recent outbreak of coronavirus infectious disease 2019 (COVID-19) has gripped the world with apprehension and has evoked a scare of epic proportion regarding its potential to spread and infect ...

Role of transformational and transactional leadership on job satisfaction and career satisfaction

Abstract: Effective leadership always plays an important role in the growth and better performance of the organization. This research has been conducted to determine the impact of transformational and transactional leadership style on job success and career satisfaction. A total of 240 responses (n = 240) from various private organizations working in the capital city of Pakistan were collected using various measures of TLI Questionnaire along with items of job success and career satisfaction. Results showed positive trends of all variables. Transactional leadership is found significantly related to job success while transformational leadership and job success are found highly related with career satisfaction. The results of regression analysis show that job success is more dependent on transformational and transactional leadership as compared to career satisfaction. Managerial implications are presented based upon these results.

DBSCAN: Past, present and future

Abstract: Data Mining is all about data analysis techniques. It is useful for extracting hidden and interesting patterns from large datasets. Clustering techniques are important when it comes to extracting knowledge from large amount of spatial data collected from various applications including GIS, satellite images, X-ray crystallography, remote sensing and environmental assessment and planning etc. To extract useful pattern from these complex data sources several popular spatial data clustering techniques have been proposed. DBSCAN (Density Based Spatial Clustering of Applications with Noise) is a pioneer density based algorithm. It can discover clusters of any arbitrary shape and size in databases containing even noise and outliers. DBSCAN however are known to have a number of problems such as: (a) it requires user's input to specify parameter values for executing the algorithm; (b) it is prone to dilemma in deciding meaningful clusters from datasets with varying densities; (c) and it incurs certain computational complexity. Many researchers attempted to enhance the basic DBSCAN algorithm, in order to overcome these drawbacks, such as VDBSCAN, FDBSCAN, DD_DBSCAN, and IDBSCAN. In this study, we survey over different variations of DBSCAN algorithms that were proposed so far. These variations are critically evaluated and their limitations are also listed.