Ghulam Ishaq Khan Institute of Engineering Sciences and Technology

About: Ghulam Ishaq Khan Institute of Engineering Sciences and Technology is a education organization based out in Topi, Pakistan. It is known for research contribution in the topics: Quantum efficiency & Diode. The organization has 618 authors who have published 940 publications receiving 10674 citations.

An empirical investigation of the determinants of CO 2 emissions: evidence from Pakistan

Abstract: The aim of this paper is to examine the relationship between CO2 emissions and its possible determinants and their direction of causality for Pakistan over the period of 1972 to 2017. The survey of literature guides us that the most frequently discussed factors are real GDP per capita, energy consumption, urbanization, trade openness, and financial development. Testing of environmental Kuznets curve (EKC) hypothesis is the most common in environment literature so we also incorporated the real GDP per capita squared term in the model. Autoregressive distributed lag (ARDL) bound testing approach to cointegration with structural break and error correction method (ECM) are applied to the selected time series to investigate the long-run relationship between CO2 emissions and real GDP per capita, real GDP per capita squared term, energy consumption, urbanization, trade openness, and financial development. The empirical evidence confirms the cointegration among the variables and EKC holds for Pakistan support H1 of the study, which though contradictory to the previous studies conducted on Pakistan but all of previous work faces the exclusion bias and their findings were skewed. The findings also suggest that energy consumption and urbanization have a positive effect on CO2 emissions, supporting H2 and H3. However, H4 and H5 rejected as trade openness and financial development found positively significant. Moreover, bidirectional Granger causality was exists only between CO2 emissions and trade openness. The findings suggests that Pakistan need to settle the economic agenda of the nation through the resolution of economic controversies, energy mix need to tilt toward clean and renewable energy, and rural-urban migration need to manage for better air, water, and living.

Social Media for Knowledge Acquisition and Dissemination: The Impact of the COVID-19 Pandemic on Collaborative Learning Driven Social Media Adoption

Abstract: During the COVID-19 outbreak, educational institutions were closed, and students worldwide were confined to their homes. In an educational environment, students depend on collaborative learning (CL) to improve their learning performance. This study aimed to increase the understanding of social media adoption among students during the COVID-19 pandemic for the purpose of CL. Social media provides a learning platform that enables students to easily communicate with their peers and subject specialists, and is conducive to students' CL. This study addresses the key concept of CL during the COVID-19 pandemic by assessing social media use among students in higher education. The relationship between social media use and students' performance is crucial to understanding the role of social media during a pandemic. This study is based on constructivism theory and the technology acceptance model. Structural equation modeling was used to analyze the conceptual model using SmartPLS. The research findings indicate that social media plays an important role during the pandemic because it provides opportunities for students to enhance CL under the aforementioned situations. This study makes noteworthy theoretical and practical contributions.

A novel binary chaotic genetic algorithm for feature selection and its utility in affective computing and healthcare

Abstract: Genetic algorithm (GA) is a nature-inspired algorithm to produce best possible solution by selecting the fittest individual from a pool of possible solutions. Like most of the optimization techniques, the GA can also stuck in the local optima, producing a suboptimal solution. This work presents a novel metaheuristic optimizer named as the binary chaotic genetic algorithm (BCGA) to improve the GA performance. The chaotic maps are applied to the initial population, and the reproduction operations follow. To demonstrate its utility, the proposed BCGA is applied to a feature selection task from an affective database, namely AMIGOS (A Dataset for Affect, Personality and Mood Research on Individuals and Groups) and two healthcare datasets having large feature space. Performance of the BCGA is compared with the traditional GA and two state-of-the-art feature selection methods. The comparison is made based on classification accuracy and the number of selected features. Experimental results suggest promising capability of BCGA to find the optimal subset of features that achieves better fitness values. The obtained results also suggest that the chaotic maps, especially sinusoidal chaotic map, perform better as compared to other maps in enhancing the performance of raw GA. The proposed approach obtains, on average, a fitness value twice as better than the one achieved through the raw GA in the identification of the seven classes of emotions.

Watermarking of chest CT scan medical images for content authentication

Abstract: Medical image is usually comprised of region of interest (ROI) and region of non interest (RONI). ROI is the region that contains the important information from diagnosis point of view so it must be stored without any distortion. We have proposed a digital watermarking technique which avoids the distortion of image in ROI by embedding the watermark information in RONI. The watermark is comprised of patient information, hospital logo and message authentication code, computed using hash function. Earlier BCH encryption of watermark is performed to ensure inaccessibility of embedded data to the adversaries.