An Effective Method to Understand Bank Customer Retention System

Banking industry is one of those industries where data is generated every day in large amounts. This data can be used for extracting useful information. Hence it is important to store, process, manage and analyze this data. It helps in making business lucrative. This data helps in making prediction which helps in solving problems that are faced by banks these days. People are constantly working on various aspects of Banking System like fraud detection, Risk Analysis etc. Various Machine Learning algorithms like CNN, ANN etc. have been used in order to study the patterns from such datasets. Here, we are focusing on risk analysis, customer retention and customer segmentation. In this paper, we have implemented classification algorithm, namely Decision Tree, for different aspects. Training of model is done on the given data and testing is done on real time data provided by the user. This study might help various banking systems to gain knowledge about their investment scheme for a particular customer. Thus, the banking companies will have a greater control on their customer and can develop policies that will benefit both the parties.

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Big Data Business Intelligence in Bank Risk Analysis

International Journal of Business Intelligence Research ◽

10.4018/ijbir.2015070104 ◽

2015 ◽

Vol 6 (2) ◽

pp. 55-77 ◽

Cited By ~ 11

Author(s):

Nayem Rahman ◽

Shane Iverson

Keyword(s):

Big Data ◽

Risk Analysis ◽

Best Practices ◽

Business Intelligence ◽

Banking Industry ◽

Bank Risk ◽

Time Data ◽

Real Time Data ◽

Big Data Technologies ◽

Real Time Data Processing

This paper provides an overview of big data technologies and best practices from the standpoint of business intelligence (BI) applications in the banking industry. The authors discussed current challenges in banking industry that could be addressed by using big data technologies. Based on their research, they provided a list of big data tools and technologies in terms of an ecosystem that are suitable for real-time data processing and capable in bank fraud detection and prevention, and other bank risk analysis. They highlighted how business intelligence could be leveraged with the help of emerging big data technologies.

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Development of Failure Prediction Models for Subsea Blowout Preventers Using Data Analytics and AI

10.4043/31027-ms ◽

2021 ◽

Author(s):

Rodrigo Chamusca Machado ◽

Fabbio Leite ◽

Cristiano Xavier ◽

Alberto Albuquerque ◽

Samuel Lima ◽

...

Keyword(s):

Machine Learning ◽

Real Time ◽

Data Analytics ◽

Historical Data ◽

Learning Algorithms ◽

Failure Prediction ◽

Machine Learning Algorithms ◽

Time Data ◽

Real Time Data ◽

Operational Data

Objectives/Scope This paper presents how a brazilian Drilling Contractor and a startup built a partnership to optimize the maintenance window of subsea blowout preventers (BOPs) using condition-based maintenance (CBM). It showcases examples of insights about the operational conditions of its components, which were obtained by applying machine learning techniques in real time and historic, structured or unstructured, data. Methods, Procedures, Process From unstructured and structured historical data, which are generated daily from BOP operations, a knowledge bank was built and used to develop normal functioning models. This has been possible even without real-time data, as it has been tested with large sets of operational data collected from event log text files. Software retrieves the data from Event Loggers and creates structured database, comprising analog variables, warnings, alarms and system information. Using machine learning algorithms, the historical data is then used to develop normal behavior modeling for the target components. Thereby, it is possible to use the event logger or real time data to identify abnormal operation moments and detect failure patterns. Critical situations are immediately transmitted to the RTOC (Real-time Operations Center) and management team, while less critical alerts are recorded in the system for further investigation. Results, Observations, Conclusions During the implementation period, Drilling Contractor was able to identify a BOP failure using the detection algorithms and used 100% of the information generated by the system and reports to efficiently plan for equipment maintenance. The system has also been intensively used for incident investigation, helping to identify root causes through data analytics and retro-feeding the machine learning algorithms for future automated failure predictions. This development is expected to significantly reduce the risk of BOP retrieval during the operation for corrective maintenance, increased staff efficiency in maintenance activities, reducing the risk of downtime and improving the scope of maintenance during operational windows, and finally reduction in the cost of spare parts replacementduring maintenance without impact on operational safety. Novel/Additive Information For the near future, the plan is to integrate the system with the Computerized Maintenance Management System (CMMS), checking for historical maintenance, overdue maintenance, certifications, at the same place and time that we are getting real-time operational data and insights. Using real-time data as input, we expect to expand the failure prediction application for other BOP parts (such as regulators, shuttle valves, SPMs (Submounted Plate valves), etc) and increase the applicability for other critical equipment on the rig.

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Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk

Frontiers in Public Health ◽

10.3389/fpubh.2021.762303 ◽

2021 ◽

Vol 9 ◽

Author(s):

Apeksha Shah ◽

Swati Ahirrao ◽

Sharnil Pandya ◽

Ketan Kotecha ◽

Suresh Rathod

Keyword(s):

Cardiac Arrest ◽

Real Time ◽

Cox Regression ◽

Risk Classification ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Support Vector ◽

Time Data ◽

Extreme Gradient Boosting ◽

Real Time Data

Cardiovascular disease (CVD) is considered to be one of the most epidemic diseases in the world today. Predicting CVDs, such as cardiac arrest, is a difficult task in the area of healthcare. The healthcare industry has a vast collection of datasets for analysis and prediction purposes. Somehow, the predictions made on these publicly available datasets may be erroneous. To make the prediction accurate, real-time data need to be collected. This study collected real-time data using sensors and stored it on a cloud computing platform, such as Google Firebase. The acquired data is then classified using six machine-learning algorithms: Artificial Neural Network (ANN), Random Forest Classifier (RFC), Gradient Boost Extreme Gradient Boosting (XGBoost) classifier, Support Vector Machine (SVM), Naïve Bayes (NB), and Decision Tree (DT). Furthermore, we have presented two novel gender-based risk classification and age-wise risk classification approach in the undertaken study. The presented approaches have used Kaplan-Meier and Cox regression survival analysis methodologies for risk detection and classification. The presented approaches also assist health experts in identifying the risk probability risk and the 10-year risk score prediction. The proposed system is an economical alternative to the existing system due to its low cost. The outcome obtained shows an enhanced level of performance with an overall accuracy of 98% using DT on our collected dataset for cardiac risk prediction. We also introduced two risk classification models for gender- and age-wise people to detect their survival probability. The outcome of the proposed model shows accurate probability in both classes.

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A Study on Zone-based Risk Analysis System using Real-time Data

Journal of the Korean Institute of Gas ◽

10.7842/kigas.2013.17.6.83 ◽

2013 ◽

Vol 17 (6) ◽

pp. 83-89

Author(s):

Jeong Seok Oh ◽

Hyo Jung Bang

Keyword(s):

Risk Analysis ◽

Real Time ◽

Time Data ◽

Real Time Data ◽

Analysis System

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Emergent Advancements in M.tb Detection: An Artificial Intelligence Approach (Preprint)

10.2196/preprints.29050 ◽

2021 ◽

Author(s):

Jasleen Kaur ◽

Shruti Kapoor ◽

Maninder Singh ◽

Parvinderjit Singh Kohli ◽

Urvinder Singh ◽

...

Keyword(s):

Machine Learning ◽

Early Detection ◽

Real Time ◽

Automatic Detection ◽

Machine Learning Algorithms ◽

Real Time Monitoring ◽

Time Data ◽

The Real ◽

Handheld Device ◽

Real Time Data

BACKGROUND Infectious diseases are the major cause of mortality across the globe. Tuberculosis is one such infectious disease which is in the top 10 deaths causing diseases in developing as well as developed countries. The biosensors have emerged as a promising approach to attain the early detection of the pathogenic infection with accuracy and precision. However, the main challenge with biosensors is real time data monitoring preferentially reversible and label free measurements of certain analytes. Integration of biosensor and Artificial Intelligence (AI) approach would enable better acquisition of patient’s data in real time manner enabling automatic detection and monitoring of Mycobacterium tuberculosis (M.tb.) at an early stage. Here we propose a biosensor based smart handheld device that can be designed for automatic detection and real time monitoring of M.tb from varied analytic sources including DNA, proteins and biochemical metabolites. The collected data would be continuously transferred to the connected cloud integrated with AI based clinical decision support systems (CDSS) which may consist of the machine learning based analysis model useful in studying the patterns of disease infestation, progression, early detection and treatment. The proposed system may get deployed in different collaborating centres for validation and collecting the real time data. OBJECTIVE To propose a biosensor based smart handheld device that can be designed for automatic detection and real time monitoring of M.tb from varied analytic sources including DNA, proteins and biochemical metabolites. METHODS The Major challenges for control and early detection of the Mycobacterium tuberculosis were studied based upon the literature survey. Based upon the observed challenges, the biosensor based smart handheld device has been proposed for automatic detection and real time monitoring of M.tb from varied analytic sources including DNA, proteins and biochemical metabolites. RESULTS In this viewpoint, we propose an application based novel approach of combining AI based machine learning algorithms on the real time data collected with the use of biosensor technology which can serve as a point of care system for early diagnosis of the disease which would be low cost, simple, responsive, measurable, can diagnose and distinguish between active and passive cases, include single patient visits, cause considerable inconvenience, can evaluate the cough sample, require minimum material aid and experienced staff, and is user-friendly. CONCLUSIONS In this viewpoint, we propose an application based novel approach of combining AI based machine learning algorithms on the real time data collected with the use of biosensor technology which can serve as a point of care system for early diagnosis of the disease which would be low cost, simple, responsive, measurable, can diagnose and distinguish between active and passive cases, include single patient visits, cause considerable inconvenience, can evaluate the cough sample, require minimum material aid and experienced staff, and is user-friendly.

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Covid-19 Pandemic: Data Analysis and Forecasting using Machine Learning Algorithms (Preprint)

10.2196/preprints.22004 ◽

2020 ◽

Author(s):

Sohini Sengupta ◽

Sareeta Mugde

Keyword(s):

Machine Learning ◽

Time Series ◽

Real Time ◽

Machine Learning Algorithms ◽

Feature Engineering ◽

Logarithmic Scale ◽

Time Data ◽

Transfer Data ◽

Real Time Data ◽

Current Scenario

BACKGROUND India reported its first Covid-19 case on 30th Jan 2020 with no practically no significant rise noticed in the number of cases in the month of February but March2020 onwards there has been a huge escalation as has been the case with like many other countries the world over. This research paper analyses COVID -19 data initially at a global level and then drills down to the scenario obtained in India. Data is gathered from multiple data sources- several authentic government websites. Variables such as gender, geographical location, age etc. have been represented using Python and Data Visualization techniques. Getting insights on Trend pattern and time series analysis will bring more clarity to the current scenario as analysis is totally on real-time data(till 19th June). Time Series Analysis and other pattern-recognition techniques are deployed to bring more clarity to the current scenario as analysis is totally based on real-time data(till 19th June,2020) Finally we will use some machine learning algorithms and perform predictive analytics for the near future scenario. We are using a sigmoid model to give an estimate of the day on which we can expect the number of active cases to reach its peak and also when the curve will start to flatten. Strength of Sigmoid model lies in providing a count of date –this is unique feature of analysis in this paper. We are also using certain feature engineering techniques to transfer data into logarithmic scale for better comparison removing any data extremities or outliers. Certain feature engineering techniques have been used to transfer data into logarithmic scale as is affords better comparison removing any data extremities or outliers. Based on the predictions of the short-term interval, our model can be tuned to forecast long time intervals. Needless to mention there are a lot of factors responsible for the cases to come in the upcoming days. One factor being extent of adherence to the rules and restriction imposed by the Government by the citizens of the country. OBJECTIVE Prediction of the number of positive covid cases in the next few months . METHODS Machine Learning Model - Clustering Sigmoid Model RESULTS The model predicts maximum active cases at 258846. The curve flattens by day 154 i.e. 25th September and after that the curve goes down and the number of active cases eventually will decrease. CONCLUSIONS There are a lot of research works going on with respect to vaccines, economic dealings, precautions and reduction of Covid-19 cases. However currently we are at a mid-Covid situation. India along with many other countries are still witnessing upsurge in the number of cases at alarming rates on a daily basis. We have not yet reached the peak. Therefore cuff learning and downward growth are also yet to happen. Each day comes out with fresh information and large amount of data. Also there are many other predictive models using machine learning that beyond the scope of this paper. However at the end of the day it is only the precautionary measures we as responsible citizens can take that will help to flatten the curve. We can all join hands together and maintain all rules and regulations strictly. Maintaining social distancing, taking the lockdown seriously is the only key. This study is based on real time data and will be useful for certain key stakeholders like government officials, healthcare workers to prepare a combat plan along with stringent measures. Also the study will help mathematicians and statisticians to predict outbreak numbers more accurately.

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Achieving Operational Efficiencies from a Centralized Alarm Management System

10.2118/208034-ms ◽

2021 ◽

Author(s):

Mahmoud AbdulHameed Al Mahmoud ◽

Joseph Sylvester Pius David ◽

Askar Jaffer

Keyword(s):

Real Time ◽

Management System ◽

Integrated Control ◽

Information Management System ◽

Machine Learning Algorithms ◽

Time Data ◽

Alarm Management ◽

Alarm Systems ◽

Real Time Data ◽

The Times

Abstract Alarm Management Systems ("AMS") have been adopted in the oil & gas industry where several benefits were realized. Such as improved panel operator effectiveness, maintaining higher levels of plant uptime and integrity, reducing the number of abnormal situation. Which ultimately leads to higher asset productivity. Several OPCO have multiple operational assets/sites that are geographically diverse. Where each asset might have a different Integrated Control System ("ICS") due to the time and availability of technology at the time of commissioning. Requiring diverse locally implemented AMS. A unified CAMS thus reduces time and effort to develop, deploy, and maintain alarm systems and is an essential toolkit for enhanced safe operation of the plant. Some sites have multiple plants wuth common pocess control section. The process control enginners needs to visit individual plants access DCS alalrms. By carryinhour corporate alarm management, engibbers at their office PCs have the access to the DCS alarms. Implementing CAMS requires the presence of a robust data presence infrastructure in place. Notably a centralized plant information management system, where several real time data points with regards to alarms and operator inputs can be captured. A CAMS unifies the approach of how alarm management is conducted in the company. Where a CAMS system generates a set of standard and custom templates that highlight the performance of each operating asset/shift/panel operator. Providing insights into the performance of each asset, efficiency of each operational shift and response of the panel operators. That when addressed, will lead to an overall performance and production of the operational asset. With this alarm management data, it can be further enhanced through data analytics to identify areas where operational efficiencies can be achieved. Additionally, the CAMS reduces the times and effort to deploy an alarm management system for any future operating asset expansions. CAMS coupled with real time data and Machine learning algorithms, past behaviours of the plant can be correlated, which can then be utilised for future predictions on alarms. This would further enhance our data driven decision-making, and would reduce the dependence on personal driven decisions. It can be concluded, that the CAMS is worthy investment for operating companies that have geographical/ICS diverse operational assets.

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Technical challenges and perspectives in batch and stream big data machine learning

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.3.9225 ◽

2017 ◽

Vol 7 (1.3) ◽

pp. 48 ◽

Cited By ~ 1

Author(s):

KVSN Rama Rao ◽

Sivakannan S ◽

M.A. Prasad ◽

R. Agilesh Saravanan

Keyword(s):

Machine Learning ◽

Big Data ◽

Internet Of Things ◽

Social Networking ◽

Paradigm Shift ◽

Machine Learning Algorithms ◽

Smart Phones ◽

Time Data ◽

Predominant Role ◽

Real Time Data

Machine Learning is playing a predominant role across various domains. However traditional Machine Learning algorithms are becoming unsuitable for majority of applications as the data is acquiring new characteristics. Sensors, devices, servers, Internet, Social Networking, Smart phones and Internet of Things are contributing the major sources of data. Hence there is a paradigm shift in the Machine learning with the advent of Big Data. Research works are in evolution to deal with Big Data Batch and stream real time data. In this paper, we highlighted several research works that contributed towards Big Data Machine Learning.

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