In this research, a new multilayered mamdani fuzzy inference system (Ml-MFIS) is proposed to diagnose hepatitis B. The proposed automated diagnosis of hepatitis B using multilayer mamdani fuzzy inference system (ADHB-ML-MFIS) expert system can classify the different stages of hepatitis B such as no hepatitis, acute HBV, or chronic HBV. The expert system has two input variables at layer I and seven input variables at layer II. At layer I, input variables are ALT and AST that detect the output condition of the liver to be normal or to have hepatitis or infection and/or other problems. The further input variables at layer II are HBsAg, anti-HBsAg, anti-HBcAg, anti-HBcAg-IgM, HBeAg, anti-HBeAg, and HBV-DNA that determine the output condition of hepatitis such as no hepatitis, acute hepatitis, or chronic hepatitis and other reasons that arise due to enzyme vaccination or due to previous hepatitis infection. This paper presents an analysis of the results accurately using the proposed ADHB-ML-MFIS expert system to model the complex hepatitis B processes with the medical expert opinion that is collected from the Pathology Department of Shalamar Hospital, Lahore, Pakistan. The overall accuracy of the proposed ADHB-ML-MFIS expert system is 92.2%.

The death ratio caused by heart diseases is threating around the world. Efficient and accurate diagnosis through information technology can turn over this picture. This article proposed Diagnosis Heart Disease using Mamdani Fuzzy Inference (DHD-MFI) based expert system which intelligently diagnoses heart disease. In an explorative pattern, the current research has taken six conducive variables for the purpose of fuzzy logic technical enhancement in the diagnosis of heart disease. The input fields comprise of age, chest pain, electrocardiography, blood pressure systolic, diabetic and cholesterol are transmitted with the help of Fuzzy rules which are framed in the light of low, normal, high and very high intensity among the input variations. The single output is obtained as a clinical decision support system for the heart diagnosis by using the Mamdani Inference method. The proposed DHD-MFI based expert system gives 94% overall accuracy.

Extreme programming (XP) is one of the widely used software process model for the development of small scale projects from agile family. XP is widely accepted by software industry due to various features it provides such as: handling frequent changing requirements, customer satisfaction, rapid feedback, iterative structure, team collaboration, and small releases. On the other hand, XP also holds some drawbacks, including: less documentation, less focus on design, and poor architecture. Due to all of these limitations, XP is only suitable for small scale projects and doesn't work well for medium and large scale projects. To resolve this issue many researchers have proposed its customized versions, particularly for medium and large scale projects. The real issue arises when XP is selected for the development of small scale and low risk project but gradually due to requirement change, the scope of the project changes from small scale to medium or large scale project. At that stage its structure and practices which works well for small project cannot handle the extended scope. To resolve this issue, this paper contributes by proposing a scaled version of XP process model called SXP. The proposed model can effectively handle such situation and can be used for small as well as for medium and large scale project with same efficiency. Furthermore, this paper also evaluates the proposed model empirically in order to reflect its effectiveness and efficiency. A small scale client oriented project is developed by using proposed SXP and empirical results are collected. For an effective evaluation, the collected results are compared with a published case study of XP process model. It is reflected by detailed empirical analysis that the proposed SXP performed well as compared to traditional XP.

With rapid population growth in cities, to allow full use of modern technology, transportation networks need to be developed efficiently and sustainability. A significant problem in the traffic motion barrier is dynamic traffic flow. To manage traffic congestion problems, this paper provides a method for forecasting traffic congestion with the aid of a Deep neural network that minimizes blockage and plays a vital role in traffic smoothing. In the proposed model, data is collected and received by using smart Internet of things enabled devices. With the help of this model, data of the previous junction of signals will send to another junction and update after that next layer named as intelligence prediction for the congestion layer will receive data from sensors and the cloud which is used to find out the congestion point. The proposed TC2S-DNN model achieved the accuracy of 98.03 percent and miss rate of 1.97 percent which is better then previous published approaches.

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