A case study of predicting banking customers behaviour by using data mining

Data Mining (DM) is a technique that examines information stored in large database or data warehouse and find the patterns or trends in the data that are not yet known or suspected. DM techniques have been applied to a variety of different domains including Customer Relationship Management CRM). In this research, a new Customer Knowledge Management (CKM) framework based on data mining is proposed. The proposed data mining framework in this study manages relationships between banking organizations and their customers. Two typical data mining techniques - Neural Network and Association Rules - are applied to predict the behavior of customers and to increase the decision-making processes for recalling valued customers in banking industries. The experiments on the real world dataset are conducted and the different metrics are used to evaluate the performances of the two data mining models. The results indicate that the Neural Network model achieves better accuracy but takes longer time to train the model

Ensemble neural network approach detecting pain intensity from facial expressions

This paper reports on research to design an ensemble deep learning framework that integrates fine-tuned, three-stream hybrid deep neural network (i.e., Ensemble Deep Learning Model, EDLM), employing Convolutional Neural Network (CNN) to extract facial image features, detect and accurately classify the pain. To develop the approach, the VGGFace is fine-tuned and integrated with Principal Component Analysis and employed to extract features in images from the Multimodal Intensity Pain database at the early phase of the model fusion. Subsequently, a late fusion, three layers hybrid CNN and recurrent neural network algorithm is developed with their outputs merged to produce image-classified features to classify pain levels. The EDLM model is then benchmarked by means of a single-stream deep learning model including several competing models based on deep learning methods. The results obtained indicate that the proposed framework is able to outperform the competing methods, applied in a multi-level pain detection database to produce a feature classification accuracy that exceeds 89%, with a receiver operating characteristic of 93%. To evaluate the generalization of the proposed EDLM model, the UNBC-McMaster Shoulder Pain dataset is used as a test dataset for all of the modelling experiments, which reveals the efficacy of the proposed method for pain classification from facial images. The study concludes that the proposed EDLM model can accurately classify pain and generate multi-class pain levels for potential applications in the medical informatics area, and should therefore, be explored further in expert systems for detecting and classifying the pain intensity of patients, and automatically evaluating the patients’ pain level accurately

Performance improvement of decision trees for diagnosis of coronary artery disease using multi filtering approach

The heart is one of the strongest muscular organs in the human body. Every year, this disease can kill many people in the world. Coronary artery disease (CAD) is named as the most common type of heart disease. Four well-known decision trees (DTs) are applied on the Z-Alizadeh Sani CAD dataset, which consists of J48, BF tree, REP tree, and NB tree. A multi filtering approach, named MFA, was used to modify the weight of attributes to improve the performance of DTs in this study. The model was applied on three main coronary arteries including the Left Anterior Descending (LAD), Left Circumflex (LCX), and Right Coronary Artery (RCA). The obtained results show that data balancing has a valuable impact on the performance of DTs. The comparison results show that this study provides the best results applied on the Z-Alizadeh Sani dataset compared to previous studies. The proposed MFA could improve the performance of the classic DTs algorithms significantly, with the highest accuracies obtained by NB tree for LAD, LCX, and RCA are 94.90%, 92.97% and 93.43%, respectively

Enhanced deep learning predictive modelling approaches for pain intensity recognition from facial expression video images

Automated detection of pain intensity from facial expressions remains a significant challenge in medical diagnostics and health informatics for providing a more intelligent pathway for the treatment of disease. Artificial intelligence methodologies, that have the ability to analyze facial expression images, utilizing an automated machine learning algorithm, can be a promising approach for pain intensity analysis. As a rapidly emerging machine learning technique, deep neural network algorithms have made significant progress in both feature identification, mapping, and modelling of the pain intensity from human facial images, with a strong potential to aid the health practitioners in the diagnosis of certain medical conditions from observable symptoms and signs of disease. While there is a significant amount of research within the pain recognition and management area that adopts facial expression datasets into deep learning algorithms to detect the pain intensity in binary classes, and identifying the pain and non-pain faces, the volume of research in identifying pain intensity levels in multi-classes remains rather limited. Although the effectiveness of deep learning models has been demonstrated, obtaining accurate algorithms to automatically detect pain in multi-class levels is still a challenging task and needs major improvement in the predictive skill of such techniques. In addition to this challenge, there exists individual behaviors, such as smiling or crying in pain situations by some patients that can make it potentially more difficult to measure the actual pain arising from a disease condition using the patient’s facial expressions through deep learning models. The PhD Thesis reports on the design, statistical validation and the practical testing of new enhanced deep neural-network algorithms tailored for the effective and efficient detection of pain intensity in humans by means of using a facial expression video image. To explore the robustness of the proposed deep learning algorithms, reliable information sourced from the UNBC-McMaster Shoulder Pain Archive Database, and the MIntPAIN database, comprised of human facial images, were used for training and testing of the proposed pain classification model. To provide enhanced model performance, the models were coupled with the fine-tuned VGGFace pre-trainer as a feature extraction ancillary tool. To reduce the dimensionality of the classification model input dataset and to extract the most relevant facial features in modeling the pain intensity, the Principal Component Analysis (PCA) was applied to improve its computational efficiency. The pre-screened facial image features, used as potential model inputs, were then transferred to generate the newly enhanced deep learning models. In this project, three variants of the enhanced deep learning-based classifier algorithms were developed and evaluated , including the joint hybrid CNN-BiLSTM (EJH-CNN-BiLSTM) algorithm, the ensemble deep learning model (EDML), and a temporal neural network (TCN) with the Hue, Saturation, Value (HSV) color space as (HSV-TCN) algorithm. All algorithms were tested on human facial image dataset to model pain intensity. The EJH-CNN-BiLSTM deep learning algorithm comprised of convolutional neural networks, linked to the joint bidirectional-long-short-term memory (BiLSTM), for multi-classification of human pain. The resulting EJH-CNN-BiLSTM classification model, tested to estimate four levels of pain, revealed high accuracy (90%) and AUC (98.4%) on the balanced UNBC-McMaster Shoulder Pain database, benchmarked by a diverse suite of model performance evaluation indicators. The proposed classifier was improved by applying in a stacked ensemble deep learning model (EDLM). This ensemble deep learning model has three deep learning models based on CNN-LSTM and their output were merged to classify 5 levels. The results show the model accurately classifies pain to identify multi classes of pain level and its performance is high in compare with other baseline models and the state-of-the-art methodologies. The accuracy reached to 86 % and AUC of 90.5% for UNBC-McMaster Shoulder Pain database and AUC of 93.67% and accuracy of 92.26% for MIntPAIN database. Although the proposed models outperform pain detection from facial images in multi levels, the speed of the algorithm need improvement. To speed up the deep learning based pain recognition systems from human facial videos’ images a new algorithm based on the temporal convolutional network with HSV color space inputs was developed and the evaluation results shows its effectiveness and efficiency of it is noticeable in compare with other models. The obtained results show accuracy of 94.14% and AUC of 91.3% in UNBC-McMaster Shoulder Pain database and accuracy 89% and AUC 92% in MIntPAIN database for 5 classes and the algorithm run 6 times faster than the above models. In summary, the results from these experiments clearly prove that the proposed deep learning approaches were able to generate accurate performance for the recognition of pain intensity levels from the videos’ images of facial expressions and could be adopted in health care systems. The newly developed techniques provide key contributions to health informatics area, as prominent artificial intelligence tools to evaluate a patient’s pain level more accurately that manual methods. Subsequently, these techniques could be applied in the management and treatment of pain in patients by using a more coherent, accurate, and effortlessness methodology

A relational study of supply chain agility and firms’ performance in the services providers

The link between agile supply chain dimensions and business performance in the Iranian service industry is assessed in this study. A questionnaire covering important agility criteria identified in the literature was designed and administered to a sample of 420 managers and users of supply chain in service providers. The response rate achieved was 16%. Validity and reliability were statistically tested. Line regression analysis was also used and all tests confirm normal distribution of data. By testing the full supply chain related with agile practices, the findings indicated there was a significant connection between supply chain agility and business performance such as user satisfactions, information systems and advertisement.</p

Business inteligence technology implimentation readiness factors

Business Intelligence technology implementation can bring capabilities to making decisions faster and better for organizations. It usually faces on significant rate of failure and leads to a large wasting of time and resources. Therefore, identifying critical readiness factors of it helps IT managers in implementation successfully and prevents from failure in this issue. This study conducted in Shahrvand retail company in Iran that wants to implement BI technology successfully. The purpose of this study is to recognize potential readiness factors of business intelligence technology implementation in this organization. On the base of previous study the most important factors were distinguished and one conceptual model was designed. The quantitative method was selected to evaluate factors and totally259 questionnaires were collected among respondents. The SPSS software used to analysis of data. The result shows that robust &amp; extensible framework as a technology's factor has positive and strong significant relationship with business intelligence technology readiness implementation. Therefore, The results show that the technology dimension factors are important in this implementation successfully. Also, findings demonstrated that the clear business visions and planning, committed management support and sponsorship, and map the solutions to the users were the important factors in this issue.</p

The effect of information technology on the agility of the supply chain in the Iranian power plant industry

Purpose An agile supply chain (ASC) includes companies that are operationally linked to each other, such as supply, design, manufacturing and distribution centers that respond and react quickly and effectively to change markets. Information systems and technology have a main role in achieving this objective. Therefore, the purpose of this paper is to examine the relationship between information integration, information infrastructure flexibility and the ASC in the Iranian power plant industry (IPPI). Design/methodology/approach The quantitative method was employed in this study. Survey questionnaires were sent to 87 managers in the IPPI to examine the relationship between information integration, information infrastructure flexibility, and the ASC. Findings The final results indicated that information sharing and responsibility were strongly related with the ASC; accessibility and connectivity had important relations with the ASC; while the relationships between compatibility and adaptableness as IT flexibility variables and ASC were positive but not significant. Research limitations/implications This study focussed on the impact of IT on the IPPI specifically companies that manufacture boilers, electronic control tools, turbines, turbine blades, generators and other power plant-related components. Practical implications A new research model was developed to assess the impact of the interrelationships among IT capabilities and the ASC and results should assist managers as well as academicians. Originality/value An investigation was carried out through this study based on the current situation in IPPI to empirically examine and evaluate the effect of IT integration and flexibility on ASC. Besides, a very limited number of studies have been done on the implementation of information technology in the IPPI

The effective factors on user acceptance in mobile business intelligence

Mobile business intelligence used for business intelligence mobile service applications increasingly. According to Gartner (2011), global smartphone sales had arrived at 630 million in 2012, and are supposed to reach 1,105 million items in 2015. As a result, business intelligence users not only rely on desktop computers, while they as well want mobile access to joint and used data. Nevertheless, few studies have been consummate on mobile business intelligence services and the user acceptance rate of mobile BI is still moderately low. For these reasons, the current article centred on the significant of the factors and levels of mobile business intelligence user acceptance that affect the mobile business intelligence user Acceptance. The conceptual model planned and data collected between mobile business intelligence users and quantitative method used. The collected data, analysed by SPSS software. The result of data analysis exposed that how factors such as organization climate, information quality, system quality, society effect and individual effect were influenced user acceptance in mobile business intelligence applications.</p

Enhanced deep learning algorithm development to detect pain intensity from facial expression images

Automated detection of pain intensity from facial expressions, especially from face images that show a patient's health, remains a significant challenge in the medical diagnostics and health informatics area. Expert systems that prudently analyse facial expression images, utilising an automated machine learning algorithm, can be a promising approach for pain intensity analysis in health domain. Deep neural networks and emerging machine learning techniques have made significant progress in both the feature identification, mapping and the modelling of pain intensity from facial images, with great potential to aid health practitioners in the diagnosis of certain medical conditions. Consequently, there has been significant research within the pain recognition and management area that aim to adopt facial expression datasets into deep learning algorithms to detect the pain intensity in binary classes, and also to identify pain and non-pain faces. However, the volume of research in identifying pain intensity levels in multi-classes remains rather limited. This paper reports on a new enhanced deep neural network framework designed for the effective detection of pain intensity, in four-level thresholds using a facial expression image. To explore the robustness of the proposed algorithms, the UNBC-McMaster Shoulder Pain Archive Database, comprised of human facial images, was first balanced, then used for the training and testing of the classification model, coupled with the fine-tuned VGG-Face pre-trainer as a feature extraction tool. To reduce the dimensionality of the classification model input data and extract most relevant features, Principal Component Analysis was applied, improving its computational efficiency. The pre-screened features, used as model inputs, are then transferred to produce a new enhanced joint hybrid CNN-BiLSTM (EJH-CNN-BiLSTM) deep learning algorithm comprised of convolutional neural networks, that were then linked to the joint bidirectional LSTM, for multi-classification of pain. The resulting EJH-CNN-BiLSTM classification model, tested to estimate four different levels of pain, revealed a good degree of accuracy in terms of different performance evaluation techniques. The results indicated that the enhanced EJH-CNN-BiLSTM classification algorithm was explored as a potential tool for the detection of pain intensity in multi-classes from facial expression images, and therefore, can be adopted as an artificial intelligence tool in the medical diagnostics for automatic pain detection and subsequent pain management of patients