Integration Management: A Bibliometric Study of Research Trends

Integration management has always been a topic of interest for researchers. It plays a significant role in the success of the projects. Integration management effectively incorporates all the aspects of projects and provides a roadmap towards their success. Based on the significance of the term, this paper assesses the research output of integration management since 1970 in different fields of the application using the Scopus database. The data was collected and analyzed through Microsoft Excel to analyze the research publications trend line, publication types and their access to the audience, organizations and countries involved, languages of publications, authors participating, and their respective field of research in relation to integration management. This paper will assist the researcher of this area to observe the research trends on the integration management topic as per the Scopus database until 2021

Symposium: Developments in Afghanistan: Implications for Pakistan and the Region

Last few months have seen some significant developments in Afghanistan including, but not limited to, the presidential elections and the formation of Ghani-Abdullah national government and subsequent signing of the pending Bilateral Security Agreement (BSA) with the US. These developments will undoubtedly play an important part in shaping the internal and external dynamics of the country in years to come. It is also an established fact that any development, small or big, in Afghanistan has a bearing of one sort or the other on Pakistan as well. It was in this background that Policy Perspectives sought the analysis of selected but key Pakistani opinion makers, representing various sections of society, as to what the developments taking place across the Durand line will mean for Pakistan in addition to their overall impact upon and linkages with the regional situation. This symposium is a product of the responses thus generated over past few months' period

Comparative Analysis of Classifiers for Developing an Adaptive Computer-Assisted EEG Analysis System for Diagnosing Epilepsy

Computer-assisted analysis of electroencephalogram (EEG) has a tremendous potential to assist clinicians during the diagnosis of epilepsy. These systems are trained to classify the EEG based on the ground truth provided by the neurologists. So, there should be a mechanism in these systems, using which a system’s incorrect markings can be mentioned and the system should improve its classification by learning from them. We have developed a simple mechanism for neurologists to improve classification rate while encountering any false classification. This system is based on taking discrete wavelet transform (DWT) of the signals epochs which are then reduced using principal component analysis, and then they are fed into a classifier. After discussing our approach, we have shown the classification performance of three types of classifiers: support vector machine (SVM), quadratic discriminant analysis, and artificial neural network. We found SVM to be the best working classifier. Our work exhibits the importance and viability of a self-improving and user adapting computer-assisted EEG analysis system for diagnosing epilepsy which processes each channel exclusive to each other, along with the performance comparison of different machine learning techniques in the suggested system