Development of a hybrid AHP and Dempster-Shafer theory of evidence for project risk assessment problem

In this paper, a new hybrid AHP and Dempster-Shafer Theory of Evidence is presented for solving the problem of choosing the best project among a list of available alternatives while uncertain risk factors are taken into account. The aim is to minimize overall risks. For this purpose, four groups of risk factors, including Properties, Operational and Technological, Financial, Strategic risk factors, are considered. Then using an L24 Taguchi method, several experiments with various dimensions have been designed and solved by the proposed algorithm. The outcomes are then analyzed using the Validating Index (VI), Reduced Risk Indicator (R.R.I%), and Solving time. The findings indicated that, compared to the classic AHP, the results of the proposed hybrid method were different in most cases due to uncertainty of risk factors. It was observed that the method could be safely used for selecting project problems in real industries.

A New Hybrid AHP and Dempster—Shafer Theory of Evidence Method for Project Risk Assessment Problem

In this paper, a new hybrid AHP and Dempster—Shafer theory of evidence is presented for solving the problem of choosing the best project among a list of available alternatives while uncertain risk factors are taken into account. The aim is to minimize overall risks. For this purpose, a three-phase framework is proposed. In the first phase, quantitative research was conducted to identify the risk factors that can influence a project. Then, a hybrid PCA-agglomerative unsupervised machine learning algorithm is proposed to classify the projects in terms of Properties, Operational and Technological, Financial, and Strategic risk factors. In the third step, a hybrid AHP and Dempster—Shafer theory of evidence is presented to select the best alternative with the lowest level of overall risks. As a result, four groups of risk factors, including Properties, Operational and Technological, Financial, and Strategic risk factors, are considered. Afterward, using an L2^4 Taguchi method, several experiments with various dimensions have been designed which are then solved by the proposed algorithm. The outcomes are then analyzed using the Validating Index, Reduced Risk Indicator, and Solving Time. The findings indicated that, compared to classic AHP, the results of the proposed hybrid method were different in most cases due to uncertainty of risk factors. It was observed that the method could be safely used for selecting project problems in real industries.

Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources

The article concerns the problem of classification based on independent data sets—local decision tables. The aim of the paper is to propose a classification model for dispersed data using a modified k-nearest neighbors algorithm and a neural network. A neural network, more specifically a multilayer perceptron, is used to combine the prediction results obtained based on local tables. Prediction results are stored in the measurement level and generated using a modified k-nearest neighbors algorithm. The task of neural networks is to combine these results and provide a common prediction. In the article various structures of neural networks (different number of neurons in the hidden layer) are studied and the results are compared with the results generated by other fusion methods, such as the majority voting, the Borda count method, the sum rule, the method that is based on decision templates and the method that is based on theory of evidence. Based on the obtained results, it was found that the neural network always generates unambiguous decisions, which is a great advantage as most of the other fusion methods generate ties. Moreover, if only unambiguous results were considered, the use of a neural network gives much better results than other fusion methods. If we allow ambiguity, some fusion methods are slightly better, but it is the result of this fact that it is possible to generate few decisions for the test object.

Translating Islamic identity into numbers

This research aims to define various Islamic based identity profile to different individuals by identifying the various degrees of Islamic based identity profile. A scale of measurement in ordinal scale has been used to determine an Islamic based identity profile. The scale is subdivided into three main subsections, namely very rarely, average level and very frequently. By using the scale of measurement on an ordinal scale, it assists in developing a numerical hypothesis that is then used to determine an individual's Islamic based identity profile using the Dempster-Shafer theory of evidence. Using twenty-four set of questions, the research used the evidence presented to support a given Islamic based identity profile of a specific individual and filtered it using various degrees of probabilities of the evidence theory model, which have aided in proving or validating a particular hypothesis. The questions are divided into three types based on Islamic identity profile which include Fitrah, Khalifah and Din. The conclusion made is that we may be able to easily diagnose an individual’s Islamic based identity profile using Dempster-Shafer theory of evidence.

Evidence – trial and pre-trial

The article again raises the question of the concept of proof in criminal proceedings. The adoption of the Code of Criminal Procedure in 2001, based on principles different from those of earlier times, did not lead to any noticeable revision of the postulates of the theory of evidence, including the concept of proof, but further aggravated the long-known contradictions. The incompatibility of the ideas of proving, which developed in the previous period of our history, as a cognitive activity aimed at establishing objective truth, with the principles of the presumption of innocence and competition is far from obvious to everyone, so the author of the article attempts to separate two fundamentally different approaches to the concept of proof between two fundamentally different parts of the criminal process and thereby reconcile the irreconcilable sides of the scientific discussion.

Uncomfortable treatment without diagnosis

One explanation for breakdown of the traditional medical connection of diagnosis and treatment is presented. It is suggested that it was a natural process brought to life by the results of the development of genetics and the results of the application of clinical epidemiology (the theory of evidence-based medicine), which led to the beginning of the downfall of the nosological concept, so far de facto, and in the long term de jure. Medicine is painfully returning to a holistic view of a patient.