Assessing farmer’s Pesticide Safety Knowledge in cotton growing area of Punjab, Pakistan

A pesticide safety knowledge test was developed to assess farmer’s knowledge related to pesticide safety. Yes-No (true-false) type 25 item, test, was constructed and used in a sample of 162 pesticide applicator in two districts of southern Punjab Pakistan. The overall mean score was 17.2(72%). More educated and adult respondents performed better than younger and illiterate. Similarly large land holder scored higher than small landholders, indicating their more access to information and extension. Overall ten Items received less than 50% correct response. The result shows that farmers have reasonably good knowledge but it still has to see, to what extent that knowledge is being used practically. It could possibly be the future research topic.Health cost, Environmental cost, Pesticide knowledge, pesticide safety

E-BANKING: A CASE STUDY OF ASKARI COMMERCIAL BANK PAKISTAN

This paper has covered the operational issues related to e-banking as well as customer’s perception on usage of e-banking a case study of Askari Bank, Pakistan. 40 staff members and four customers are selected as sample for this study. Both qualitative and quantitative methods are used to present the results. Descriptive statistics is applied to describe the demographic variables while for operational problems correlation was used. Finally cross case analysis present customers’ perception about e-banking practices. Analysis shows that customer is not ready to adopt new technology that why their satisfaction level with e-banking is low. Internet speed and government policies are not supportive for e-banking in Pakistan. Due to lack of trust on technology and low computer literacy rate, customer hesitates to adopt new technology. : In order to promote IT culture in Pakistan, government has to reduce the internet rate. to promote the benefits of e-banking on media so that more user get facilitated from e-banking services.E-banking, Internet, ATM, Online transaction, E-readiness, Technology Acceptance Models

An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach

Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists and affects their performance. Several automatic techniques have been proposed using traditional approaches to assist neurologists in detecting binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal. These methods do not perform well when classifying ternary case e.g. ictal vs. normal vs. inter-ictal; the maximum accuracy for this case by the state-of-the-art-methods is 97+-1%. To overcome this problem, we propose a system based on deep learning, which is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model, the bottleneck is the large number of learnable parameters. P-1D-CNN works on the concept of refinement approach and it results in 60% fewer parameters compared to traditional CNN models. Further to overcome the limitations of small amount of data, we proposed augmentation schemes for learning P-1D-CNN model. In almost all the cases concerning epilepsy detection, the proposed system gives an accuracy of 99.1+-0.9% on the University of Bonn dataset.Comment: 18 page