Bank credit plays an important role in the economy of any nation. The current study examined the association among bank credit to private sector and economic growth in Pakistan. Economic growth was taken as dependent variable, while bank credit to private sector, interest rate, inflation, investment to GDP and government consumptions were taken as independent variables. Secondary data were collected from World Bank Indicator, ranging for the period 1973 to 2013. Descriptive research and correlation were used to check the normality of data. Unit root test was used to check the stationarity of variables. Co-integration VECUM and Granger Casuality test were statistically used to test the variable relationship and casuality effect of the variable. Regression analysis was used to analyze the impact of bank credit on economic growth. The findings of the study showed that bank credit had extensive relationship with economic progression; in short term the relationship was also significant. Regression analysis showed that there was adverse impact of bank credit on economic growth in Pakistan. However, problem associated with bank credit facility is the constraint and regulation imposed by SBP on the percentage of credit to be given to the Entrepreneurs. For solitary in the meantime bank lending has a casual influence on economic growth, there is a policy need to give devotion to liberalization the monetary sector.
The importance of unsupervised clustering methods is well established in the statistics and machine learning literature. Many sophisticated unsupervised classification techniques have been made available to deal with a growing number of datasets. Due to its simplicity and efficiency in clustering a large dataset, the k-means clustering algorithm is still popular and widely used in the machine learning community. However, as with other clustering methods, it requires one to choose the balanced number of clusters in advance. This paper’s primary emphasis is to develop a novel method for finding the optimum number of clusters, k, using a data-driven approach. Taking into account the cluster symmetry property, the k-means algorithm is applied multiple times to a range of k values within which the balanced optimum k value is expected. This is based on the uniqueness and symmetrical nature among the centroid values for the clusters produced, and we chose the final k value as the one for which symmetry is observed. We evaluated the proposed algorithm’s performance on different simulated datasets with controlled parameters and also on real datasets taken from the UCI machine learning repository. We also evaluated the performance of the proposed method with the aim of remote sensing, such as in deforestation and urbanization, using satellite images of the Islamabad region in Pakistan, taken from the Sentinel-2B satellite of the United States Geological Survey. From the experimental results and real data analysis, it is concluded that the proposed algorithm has better accuracy and minimum root mean square error than the existing methods.
Industrial automation or assembly automation is a strictly monitored environment, in which changes occur at a good speed. There are many types of entities in the focusing environment, and the data generated by these devices is huge. In addition, because the robustness is achieved by sensing redundant data, the data becomes larger. The data generating device, whether it is a sensing device or a physical device, streams the data to a higher-level deception device for calculation, so that it can be driven and configured according to the updated conditions. With the emergence of the Industry 4.0 concept that includes a variety of automation technologies, various data is generated through numerous devices. Therefore, the data generated for industrial automation requires unique Information Architecture (IA). IA should be able to satisfy hard real-time constraints to spontaneously change the environment and the instantaneous configuration of all participants. To understand its applicability, we used an example smart grid analogy. The smart grid system needs an IA to fulfill the communication requirements to report the hard real-time changes in the power immediately following the system. In addition, in a smart grid system, it needs to report changes on either side of the system, i.e., consumers and suppliers configure and reconfigure the system according to the changes. In this article, we propose an analogy of a physical phenomenon. A point charge is used as a data generating device, the streamline of electric flux is used as a data flow, and the charge distribution on a closed surface is used as a configuration. Finally, the intensity changes are used in the physical process, e.g., the smart grid. This analogy is explained by metaphors, and the structural mapping framework is used for its theoretical proof. The proposed analogy provides a theoretical basis for the development of such information architectures that can represent data flows, definition changes (deterministic and non-deterministic), events, and instantaneous configuration definitions of entities in the system. The proposed analogy provides 3986 CMC, 2022, vol.71, no.2 a mechanism to perform calculations during communication, using a simple concept on the closed surface to integrate two-layer cyber-physical systems (computation, communication, and physical process). The proposed analogy is a good candidate for implementation in smart grid security.
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