One of the major causes of death in the world is cardiac arrhythmias. In the field of healthcare, physicians use the patient’s electrocardiogram (ECG) records to detect arrhythmias, which indicate the electrical activity of the patient’s heart. The problem is that the symptoms do not always appear and the physician may be mistaken in the diagnosis. Therefore, patients need continuous monitoring through real-time ECG analysis to detect arrhythmias in a timely manner and prevent an eventual incident that threatens the patient’s life. In this research, we used the Structured Streaming module built top on the open-source Apache Spark platform for the first time to implement a machine learning pipeline for real-time cardiac arrhythmias det

BackgroundThere is an increasing trend in using network science methods and algorithms, including community detection methods, in different areas of healthcare. These areas include protein networks, drug prescriptions, healthcare fraud detection, and drug abuse. Counterfeit drugs, off-label marketing issues, and finding the healthcare community structures in a network of hospitals, are examples of using community detection in healthcare.ObjectiveThis paper attempts to find physicians’ real medical specialties based on their prescription history. As a novel application of community detection in the healthcare field, this knowledge can be used as an alternative for missing values of the healthcare databases. Therefore, it could help scienti

Human resource performance evaluation is one of the main activities in human resource management that is critical for organisational development. In this paper, a new approach of using data mining techniques is proposed for HR evaluation from CRM perspective. In fact, a two-step association rule analysis is presented and implemented on the data of a public transportation organisation in Iran. The data relates to a call centre of this organisation, which is established to hear the citizens’ voice about the performance of the human resource. At the first step of the proposed technique, the results indicate the sectors with a dominant pattern of negative human resource performance and the ones with a positive performance. At the second step,

Purpose – The purpose of this paper is to detect different behavioral groups and the dominant patterns of customer shifts between segments of different values over time. Design/methodology/approach – A new hybrid methodology is presented based on clustering techniques and mining top-k and distinguishing sequential rules. This methodology is implemented on the data of 14,772 subscribers of a mobile phone operator in Tehran, the capital of Iran. The main data include the call detail records and event detail records data that was acquired from the IT department of this operator. Findings – Se

One of the most important issues in the domain of customer relationship management is identifying the factors that affect customer‘s satisfaction. Accordingly, we focus on this subject and try to propose a new approach on using association rule technique in this domain. This technique provides us with identifying the relationship between different effective factors and the CSI index thorough if-then rules and also detecting the most effective factors which influence customer’s satisfaction. The results of implementing the proposed approach in “Bahman Diesel” company imply that customer’s satisfaction of mobile services is the most effective factor. The behavior of the company’s employees and the waiting time of reception have al

With increasing use of point of sale terminals at stores, banks are seeking to achieve a bigger portion in such financial exchanges. An important problem for banks is to identify the most profitable professions. For this purpose, a new application using recency, frequency, and monetary (RFM)-based clustering and customer lifetime value analysis containing two extensions of RFM is proposed for guild segmentation. The methodology is applied on a real data from an Iranian state bank. The findings reveal that this methodology is applicable in practice and could be very effective for bank managers of any other banks.

One of the main problems in dynamic customer segmentation is finding the dominant patterns of customer movements between different segments via time. Accordingly, we concentrate on the customer dynamics in this paper and try to find different groups of customers in transmissions between segments via time. The dominant characteristics of these groups are also investigated. To obtain this objective, a new hybrid technique based on the K-means algorithm, hierarchical clustering and association rule mining is presented and implemented on the data of one of the main telecommunication corporations in Iran. The results show that there are seven different groups of customers. Furthermore, the impact of customer dynamics on segments’ changes via t

Nowadays knowledge of organizations is their most important assets. The importance of this intellectual property is very mush since the organizations' executive success without the management and proper use of this valuable resource, is difficult and sometimes impossible. So the only way to survive in the current competitive situations, is to implement appropriate knowledge management system and institutionalize it. In this research, a knowledge management model using system dynamics approach is presented and implied in a transportation as a case study. The reasons for the distance between the desired state of knowledge management in the company and its current state, has been extracted and analyzed by the presented model. In fact, it has b