The current study is to evaluate the patient facing problems by applying Late Acceptance Hill Climbing Algorithm (LAHC) in hospital settings. The recent proposed procedure of LAHC is based on metaheuristic algorithm which is linked with one-point clarification method. Patient’s satisfaction regarding the performance of the hospital is a composite mechanism. The optimization procedure is connected with NP-hard problems which is practically related to the problems faced by patients. These problems are concerned with assigning the group of patients visiting the hospital for receiving healthcare services. The common issues faced by patients include communication gap, response time to attend patients, early symptomatic relief, getting proper advice for dosage and usage of medicines, clean hospital environment. Moreover, patient education and guidance before discharge from hospital is also the missing element. The suggested algorithm of LAHC to PFP is developed and it has two phases: the first phase includes providing the initial feasible solution using communication-oriented methodology. The second phase uses three neighborhood framework which are implanted inside the segment of PFP based on LAHC to additionally upgrade the underlying feasible solution of the introductory phase.

In this article, an optimized structure of Photonic Crystal Fiber (PCF) has been planned that investigates the effect of dispersion and confinement loss of square shaped photonic crystal fiber, intended for wideband applications ranges from 1.35um to 1.70um. The objective of the suggested geometry is to attain such optical properties by fluctuating its parameters like pitch, number of outer circular rings and inner elliptical rings to make it flexible and simple for manufacturing. Finite Element Method and Perfectly Matched Boundary layer (PML) is set for this proposed geometry. Moreover, recommended PCF structure has unlimited application value in the fiber-optic communications such as fiber lasers, highly sensitive gas sensors, nonlinear devices, high-power transmission and much more.

Aim of this paper is to investigate the relationship among information system of digital banking, trust and customer satisfaction. For this we have applied the [1], S. J. (2009) model to Pakistani banking industry. More specifically dimensions of management information system in this study are system quality, information quality and information presentation. Total of 300 Self-administrated questionnaires were distributed among the general public of Pakistan. Smart PLS (partial least square) method were used to test the statically analysis. From statically analysis we have found that all the three dimensions of management information system significantly and positively impact the customer trust and satisfaction toward digital banking. Furthermore, this paper will surely contribute to the theoretical and managerial impactions.

Natural languages (NLs) are a simple and understandable form for presenting knowledge. However, they are ambiguous and it turns out to be quite complex to process them with machines. Controlled Natural Languages (CNLs) are usually simpler versions of NLs that are obtained by restricting the grammar and vocabulary to reduce, or even eliminate, ambiguity and complexity. CNLs look informal like NLs and are easy to read and understand and can be easily be transformed into machinereadable forms. In this paper, we present NLPT for semiautomatic translation of privacy statements, from NL to a controlled natural one, to improve machine processing. To assess the performance, we experiment on a large set of Twitter data policies. Here, we consider two main aspects i) the translation of social network data privacy policy and ii) the efficiency and efficacy of the proposed system. We also perform an empirical analysis of the results and conclude that our system can be used to translate input policies effectively and efficiently.

Automatic modulation recognition of radar waveform is a major topic and has many military applications. This paper surveys the models and the techniques used in recognizing different modulation types of intercepted radar waveform. The literature shows the outstanding performance of deep learning neural network at low SNR values and in signaloverlapped environments as well. Additionally, using different mathematical and statistical algorithms demonstrated that utilized in features extraction of the data in order to feed them into the neural network improves the performance significantly. However, reducing computation complexity is in development too