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E-government development is often complex, with multiple stakeholders, large user bases and complex goals. Consequently, even experts have difficulties in evaluating these systems, especially in an integrated and comprehensive way, as well as on an aggregate level, and thus, there is currently little knowledge about the actual impact and results of e-government. Expert systems are a candidate solution to evaluate such complex e-government systems. However, it is difficult for expert systems to cope with uncertain evaluation data that are vague, inconsistent, highly subjective or in other ways challenging to formalize. This paper presents an approach that 2 can handle uncertainty in e-government evaluation: The combination of Belief Rule Base (BRB) knowledge representation and Evidential Reasoning (ER). This approach is illustrated with a concrete prototype, known as the Belief Rule Based Expert System (BRBES) and implemented in the local e-government of Bangladesh. The results have been compared with a recently developed method of evaluating e-government, showing that the BRBES approach is more accurate and reliable. The BRBES can be used to identify the factors that need to be improved in e-government projects and can juxtapose different scenarios. Thus, the system can be used to facilitate decision making processes under uncertainty.

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Protein structure prediction and analysis are more significant for living organs to perfect asses the living organ functionalities. Several protein structure prediction methods use neural network (NN). However, the Hidden Markov model is more interpretable and effective for more biological data analysis compared to the NN. It employs statistical data analysis to enhance the prediction accuracy. The current work proposed a protein prediction approach from protein images based on Hidden Markov Model and Chapman Kolmogrov equation. Initially, a preprocessing stage was applied for protein images' binarization using Otsu technique in order to convert the protein image into binary matrix. Subsequently, two counting algorithms, namely the Flood fill and Warshall are employed to classify the protein structures. Finally, Hidden Markov model and Chapman Kolmogrov equation are applied on the classified structures for predicting the protein structure. The execution time and algorithmic performances are measured to evaluate the primary, secondary and tertiary protein structure prediction.