A rapidly emerging trend in the IT landscape is the uptake of large-scale datacenters moving storage and data processing to providers located far away from the end-users or locally deployed servers. For these large-scale datacenters, power efficiency is a key metric, with the PUE (Power Usage Effectiveness) and DCiE (Data Centre infrastructure Efficiency) being important examples. This article proposes a belief rule based expert system to predict datacenter PUE under uncertainty. The system has been evaluated using real-world data from a data center in the UK. The results would help planning construction of new datacenters and the redesign of existing datacenters making them more power efficient leading to a more sustainable computing environment. In addition, an optimal learning model for the BRBES demonstrated which has been compared with ANN and Genetic Algorithm; and the results are promising.

Acute coronary syndrome (ACS) is responsible for the obstruction of coronary arteries, resulting in the loss of lives. The onset of ACS can be determined by looking at the various signs and symptoms of a patient. However, the accuracy of ACS determination is often put into question since there exist different types of uncertainties with the signs and symptoms. Belief rule-based expert systems (BRBESs) are widely used to capture uncertain knowledge and to accomplish the task of reasoning under uncertainty by employing belief rule base and evidential reasoning. This article presents the process of developing a BRBES to determine ACS predictability. The BRBES has been validated against the data of 250 patients suffering from chest pain. It is noticed that the outputs created from the BRBES are more dependable than that of the opinion of cardiologists as well as other two expert system tools, namely artificial neural networks and support vector machine. Hence, it can be argued that the BRBES is capable of playing an important role in decision making as well as in avoiding costly laboratory investigations. A procedure to train the system, allowing its enhancement of performance, is also presented.

With the advances of liquid biopsy technology, there is increasing evidence that body fluid such as blood, urine, and saliva could harbor the potential biomarkers associated with tumor origin. Traditional correlation analysis methods are no longer sufficient to capture the high-resolution complex relationships between biomarkers and cancer subtype heterogeneity. To address the challenge, researchers proposed machine learning techniques with liquid biopsy data to explore the essence of tumor origin together. In this survey, we review the machine learning protocols and provide corresponding code demos for the approaches mentioned. We discuss algorithmic principles and frameworks extensively developed to reveal cancer mechanisms and consider the future prospects in biomarker exploration and cancer diagnostics.

A Clinical Decision Support System (CDSS) to assess suspicion of a disease would avoid unnecessary cost of medical diagnosis. Heart Failure (HF) is a complex clinical syndrome of cardiac disorder. In the present paper, a Belief rule based (BRB) CDSS has been proposed to assess suspicion of HF by using signs, symptoms and risk factors. The recently developed generic rulebased inference methodology using the evidential reasoning approach (RIMER) was considered as the methodology for developing this CDSS. Netbean7.2's GUI and MySQL server have been employed to develop the system. This belief rule based CDSS can deal with various types of uncertainties found in clinical sings, symptoms, risk factors and domain knowledge. The knowledge based for this system has been developed by taking account of real patient data, obtained in consultation with specialists. The CDSS has been tested by using the data taken from the patients with breathlessness. It has been observed that the results generated by the system is reliable and thus facilitates to take decision to avoid unnecessary costly medical diagnosis.

Asthma is a common chronic inflammatory disease. A belief rule based Clinical Decision Support System (CDSS) of asthma suspicion is enhancing the accuracy of suspicion. This research paper presents out the development of a Belief rule based (BRB) system to assess Asthma suspicion by using signs and symptoms. The recently developed generic belief rule-based inference methodology by using the evidential reasoning approach (RIMER) has been considered to develop this BRB system. This system can deal with various types of uncertainties, found in clinical sings, symptoms, and clinical domain knowledge. The knowledge base of this system has been constructed by taking account of real patient data and consultation with specialists. The practical case studies provided to test this system. It has been observed that the proposed model is effective and can generate better prediction than from manual system (usually carried out by specialist) in terms of accuracy.

Diabetes is one of the major type of disease. It is the very common disease in the modern world. Diabetes is a serious disease that affects almost every organ in the body like heart, eyes, kidney, skin, nerves, blood vassals, foot etc. If left the disease unchecked it will make serious complications including death. However, a proper diagnosis at an early stage can result in significant life saving. Unfortunately, all the physicians are not equally skilled, which can cause for time delay, inaccuracy of the diagnosis. In the present paper, a Decision Support System has been proposed for Diabetes diagnosis. The proposed system is designed and developed by using Netbean7.1's GUI and MySQL server feature with the implementation of The dataset used in this study are the signs, symptoms, risk factors associated with diabetes and the results of physical evaluation of a patient. This system provides a user interactive, menu driven environment. It will ask a bunch of questions about the signs, symptoms and risk factors to the system user and user should give yes or no answer. According to the answer the system will make Decision about the possibility of illness, how much severe it is like slight chance, moderate chance, high chance, very high chance, diabetic or not.