Development, Monitoring, and Management Approaches of Machine Learning Implementations for the Effective Delivery of Government Services

Development, Monitoring, and Management Approaches of Machine Learning Implementations for the Effective Delivery of Government Services

DOI: 10.4018/978-1-6684-9716-6.ch008
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Abstract

New age technologies like machine learning, artificial intelligence, and deep learning are playing a crucial role in many applications. The chapter focuses on development, monitoring, and management approaches to machine learning implementation for effective government service deliveries. Government forms different policies and aims to successfully pass them to the citizens. The government service deliveries include many sectors like healthcare system, education policies, foreign policies, infrastructure and construction policies, public transportation policies, etc. Machine learning (ML), deep learning (DL), and artificial intelligence (AI) provide many methods like time series analysis, regression, classification, reinforcement learning, clustering, dimensionality reduction, long short-term memory, etc. These methods help retrieving meaningful data from the large volume and predict the desired results. These technologies already revolutionized many sectors like automating the application processes, fetching the relevant and required data instantly, etc.
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Machine Learning and Data Science Techniques for Effective Government Service Delivery

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1. Introduction

This chapter gives brief introduction of machine learning techniques like classification, regression, time series analysis etc. These techniques are widely used in many applications for the better accuracy and results. Governments provide services to the citizens with the changing trend of digitization & it is necessary to convert the same into e-services (e-portals). Citizens can access these services from any location, upload required documents and track their requests (applications), administrators or the concerned authorities can have a ‘dashboard’ to analyze, and process the submitted data. There are many benefits of applying the recent machine learning (ML) and artificial intelligence (AI) methods for the development of e-services. With the help of ML methods, one can develop e-portals, monitor and manage the developed e-services (e-portals) and contribute in effective delivery of these services to the citizens. With the digitization of services there are few risks associated with the data privacy, and cyber-attacks. Hence, stringent mechanisms can be integrated (as a security layer) in every services to prevent the cyber-attack and maintain the data privacy. Actually, every e-service (e-portal) must be developed including a CIA security i.e. confidentiality, integrity and availability. With the help of AI and ML techniques it is possible to cater various services to the citizens (large population) within minimum time and efforts. Hence, using recent AI and ML methods one can develop prescriptive, descriptive and decision making models. However, at the same time it is the responsibility of the governments to securely and ethically establish the proposed framework of policies.

As mentioned earlier, there are many sectors where new age technologies can help governments to effectively apply their polices are managing the traffic flow, incident prevention (unwanted incident), cyber-attack prevention, providing world class healthcare facilities, automating task in many fields, linking various documents and representing them into a unique format etc. Recently world has faced a pandemic situation like Covid-19, hence using such newly age technologies one can develop a model predicting if any pandemic situation could occur in the future? There are many methods like regression and classification which can be used to predict the future values. Even it is possible to analyze the historical climate related data & predictive model can be prepared forecasting the tentative temperature, rainfall etc.

Logistics, infrastructure and construction fields can be revolutionize with the help of these technologies. It is also necessary to work on transportation i.e. by road, by rail and using by sea transportation. It means smoothing the transportation by minimizing the accidents, connecting the maximum cities and once this phase successfully completes it benefits the import-export businesses. Such revolution automatically increases the gross domestic product (GDP) of the citizens. Once the GDP of citizens increases it positively affects (increases) the economy of the country. As discussed these technologies can be used for infrastructure and civil construction, it enables government agencies to carefully observe if there are any illegal constructions or people are changing their properties & not paying the taxes. Autonomous computing can be introduced in the small to large applications where a citizen can file any application from home (anywhere) and can track it further. The most beautiful advantage of this digital automation is ‘paperless’ applications in many sectors. Therefore, paper and application cost significantly reduces and approval process becomes more transparent and easier. However it must be noted, there are many challenges in application of these new age technologies. With the help of these technologies it is possible to store, process and make available the required data to the citizens at any time (access transparency). Even it is possible to develop a ‘guide to citizen’ (gov-bot) application guiding citizens about any queries. It can also guide the citizens in application process with all the necessary requirements. Decision support systems (DSS) are the best examples of these technologies and a good governance based on DSS can be develop to take better decision by evaluating large volume of data. Knowledge management, predictive modeling, threat intelligence (cyber-attack prevention), automation and visualization etc. are the few approaches of machine learning which can be used in setting good governance.

Key Terms in this Chapter

Underfitting: It is expected that machine learning model learn from the training data and analyze the patterns. However, if the ML model doesn’t work as expected then underfitting occurs.

Dimension Reduction: It minimizes the dimensions and represents only the necessary variables, features in feature vector space.

Artificial Intelligence (AI): Ability of the computer systems, machines to automate the tasks intelligently with less human intervention.

Feature: An input variable in model is known as ‘features’.

Big Data: Big data means max. Volume of data with characteristics like volume, variety and veracity, etc.

Autoencoder: It is a combination of encoder & decoder. It processes by extracting the valuable information from the given input.

Overfitting: It occurs when model can process the training data but fails to process the testing data.

Machine Learning: It is a branch of artificial intelligence (AI) trains the machines to automatically train and tests the data, learns from the past experiences with less human intervention.

Gradient Boosting: Iteratively trains the models & integrate them to have a strong model.

Classification: A supervised learning method from machine learning classifying the input data.

Regression: A method from machine learning predicting the relationship between independent and dependent variables.

Feature Extraction: A process to retrieve the features with the help of proposed machine learning model.

Reinforcement Learning (RL): Model learns by studying the outcome of the iteration. It helps in better decision making and get most possible output i.e. maximum reward.

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