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Joanita Dsouza

Bio: Joanita Dsouza is an academic researcher from Amity University. The author has contributed to research in topics: Process mining & Data security. The author has an hindex of 4, co-authored 8 publications receiving 31 citations.

Papers
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Proceedings ArticleDOI
01 Jul 2020
TL;DR: Data visualization technique is applied to the dataset and is used to formulate patterns for better insights on the effects of the pandemic with respect to the variables/ labels given in the dataset.
Abstract: Exploratory Data Analysis (EDA) is a field of data analysis used to visually represent the knowledge embedded deep in the given data set. The technique is widely used to generate inferences from a given data set. Data set of current pandemic, the COVID-19 is widely made available by the standard dataset repository. EDA can be applied to these standard dataset to generate inferences. In this paper, data visualization technique is applied to the dataset and is used to formulate patterns for better insights on the effects of the pandemic with respect to the variables/ labels given in the dataset. A Web application tool called Jupyter Notebook is used to generate graphs using python language as it consists of libraries which are used for the process of EDA and the visualization is depicted for the attributes showing higher correlation. Based on the graphs obtained, we can draw conclusions from the current situation based on the data available, understand why a certain variable is increasing/decreasing with respect to another and what can be done to improve the drawbacks found.

14 citations

Proceedings ArticleDOI
01 Aug 2018
TL;DR: The process mining and intrusion detection concepts are discussed and the various algorithms which have been used in process mining are compared and the application of process mining approach to detect the intrusions are discussed.
Abstract: Process mining assumes to find, screen and improve the genuine procedures through picking up understanding from the log events which are promptly accessible in the existing data framework. It gives the missing connection between one hand process, demonstrate examination and information arranged, investigation and then again execution and conformance. In this manuscript, we have discussed the process mining and intrusion detection concepts and compared the various algorithms which have been used in process mining. We have also discussed the application of process mining approach to detect the intrusions. Traditional information mining procedures, for example, characterization, grouping, relapse, affiliation administer learning, and arrangement/scene mining doesn't concentrate on the business process models and it is regularly used to advancement in the general procedure. Process mining centers around end-to-end forms and is conceivable in view of the developing accessibility of occasion information and developing the new processes and conformance checking with enhancement of the existing processes.

11 citations

Proceedings ArticleDOI
01 Aug 2018
TL;DR: In this paper, the various applications of big data mining techniques have been analyzed to improve the healthcare systems, including process mining and data mining technique have opened a new access for diagnosis of disease and also to provide effective treatment for a disease's triennial prevention.
Abstract: As technology is growing every day, the need for the technology is also becoming essential in every field. The amount of data generated by the healthcare industry is becoming tough to manage and to examine it in efficient manner for future use. In the healthcare field, massive amount of data is generated, from individual patient’s information to health history, clinical data and genetic data. The analysis of patient’s data is becoming more important, to evaluate the medical condition of patient and to prevent and take precautions for future. With the help of technology and computerized automation of machines, data can be analyzed in more efficient manner. Managing the huge volume of data has many problems interrelated to data security, data integrity and inconsistency. Process mining and data mining techniques have opened a new access for diagnosis of disease. Similarly, to provide effective treatment for a disease’s triennial prevention, data mining can be used. Big data mining can aid in analyzing medical operation indicators of hospitals for a period to help hospital administrators provide data support for medical decision-making. In this manuscript, the various applications of big data mining techniques have been analyzed to improve the healthcare systems.

9 citations

Proceedings ArticleDOI
01 Dec 2019
TL;DR: This paper explains the application of machine learning algorithms for the detection of fault in transfer nodes using preventive maintenance in an organization.
Abstract: Preventive Maintenance is the new buzzword to upkeep an enterprise application in a real world scenario. It has also become a necessity to predict the faults that could occur between the different transfer nodes of the enterprise application. The features of preventive maintenance which includes methodical study, estimation, the notion of time, the diagnosis of faults and so on can be analyzed using well designed and structured methods of relevance to the application domain. In this scenario, the technologies which are focused are diagnosis of faults and activities, prediction of the states and monitoring the conditions. There is a large amount of data which is being used for preventive maintenance. The flow of packets between transfer nodes can be stored and this data can be used for purpose of training and testing. Further, machine learning algorithms can be implemented based on the features present in the historical data. The advantage on the study of preventive maintenance is that it can aid in reduction of cost which is the primary motive of each organization existing toady. By doing so the results can be applied to various organizations to resolve failures that could possibly occur in the future well in advance. This paper explains the application of machine learning algorithms for the detection of fault in transfer noted using preventive maintenance in an organization.

9 citations

Proceedings ArticleDOI
01 Feb 2019
TL;DR: This manuscript epitomizes the general work process of cyber or digital physical frameworks, and identifies the conceivable vulnerabilities, assault issues, foes qualities and an arrangement of difficulties that are required to be addressed.
Abstract: In this manuscript, we explore the network and cyber security challenges furthermore, issues of cyber or digital physical frameworks. (1) We epitomize the general work process of cyber or digital physical frameworks, (2) identify the conceivable vulnerabilities, assault issues, foes qualities and an arrangement of difficulties that are required to be addressed. A framework has been proposed for setting situation-apprehensive security structure for general digital or cyber physical frameworks with the implementation of biometrics.

7 citations


Cited by
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Proceedings ArticleDOI
17 Mar 2021
TL;DR: In this paper, an exploration strategy called Maximum Entropy Expand (MEE) is introduced to solve the problem of misleading multiplayer games, where the recompense power is used to remove the catastrophic forgetting issue that leads to the operator's information becoming non-normalized during the off-exploitation period.
Abstract: we provided a framework for the acquisition of articulated electricity regulations for consistent states and actions, but it has only been attainable in summarised domains since then. Developers adapt our environment to learning maximum entropy policies, leading to a simple Q-learning service, which communicates the global optimum through a Boltzmann distribution. We could use previously approved amortized Stein perturbation theory logistic regression rather than estimated observations from that distribution form to obtain a stochastic diffusion network. In simulated studies with underwater and walking robots, we confirm that the entire algorithm's cost provides increased exploration or term frequency that allows the transfer of skills between tasks. We also draw a comparison to critical actor methods, which can represent on the accompanying energy-based model conducting approximate inference. Misleading multiplayer uses the recompense power to ensure that the user is further from either the evolutionary algorithms but has now evolved to become a massive task in developing intelligent exploration for deep reinforcement learning. In a misleading game, nearly all cutting-edge research techniques, including those qualify superstition yet, even with self-recompenses, which achieves enhanced outcomes in the sparse re-ward game, often easily collapse into global optimization traps. We are introducing another exploration tactic called Maximum Entropy Expand (MEE) to remedy this shortage (MEE). Based on entropy rewards but the off-actor-critical reinforced learning algorithm, we split the entity adventurer policy into two equal parts, namely, the target rule and the adventure policy. The explorer law is used to interact with the world, and the target rule is used to create trajectories, with the higher precision of the targets to be achieved as the goal of optimization. The optimization goal of the targeted approach is to maximize extrinsic rewards in order to achieve the global result. The ideal experience replay used to remove the catastrophic forgetting issue that leads to the operator's information becoming non-normalized during the off-exploitation period. To prevent the vulnerable, diverging, and generated by the dangerous triad, an on-policy form change is used specifically. Users analyse data likening our strategy with a region technique for deep learning, involving grid world experimentation techniques and deceptively recompense Dota 2 environments. The case illustrates that the MME strategy tends to be productive in escaping the current paper's coercive incentive trap and learning the correct strategic plan.

25 citations

Journal ArticleDOI
TL;DR: In this paper , the authors present the results of an extensive systematic literature review on process mining in healthcare in which 263 papers have been reviewed and highlight the evolution of the research domain by considering time trends within the review dimensions.

17 citations

Proceedings ArticleDOI
01 Jul 2020
TL;DR: Data visualization technique is applied to the dataset and is used to formulate patterns for better insights on the effects of the pandemic with respect to the variables/ labels given in the dataset.
Abstract: Exploratory Data Analysis (EDA) is a field of data analysis used to visually represent the knowledge embedded deep in the given data set. The technique is widely used to generate inferences from a given data set. Data set of current pandemic, the COVID-19 is widely made available by the standard dataset repository. EDA can be applied to these standard dataset to generate inferences. In this paper, data visualization technique is applied to the dataset and is used to formulate patterns for better insights on the effects of the pandemic with respect to the variables/ labels given in the dataset. A Web application tool called Jupyter Notebook is used to generate graphs using python language as it consists of libraries which are used for the process of EDA and the visualization is depicted for the attributes showing higher correlation. Based on the graphs obtained, we can draw conclusions from the current situation based on the data available, understand why a certain variable is increasing/decreasing with respect to another and what can be done to improve the drawbacks found.

14 citations

Journal ArticleDOI
12 Jul 2021
TL;DR: The role of Big Data technologies in enhancing the research relative to COVID-19 is presented and provides insights into the current state of knowledge within the domain and references for further development or starting new studies are provided.
Abstract: The COVID-19 pandemic has induced many problems in various sectors of human life. After more than one year of the pandemic, many studies have been conducted to discover various technological innovations and applications to combat the virus that has claimed many lives. The use of Big Data technology to mitigate the threats of the pandemic has been accelerated. Therefore, this survey aims to explore Big Data technology research in fighting the pandemic. Furthermore, the relevance of Big Data technology was analyzed while technological contributions to five main areas were highlighted. These include healthcare, social life, government policy, business and management, and the environment. The analytical techniques of machine learning, deep learning, statistics, and mathematics were discussed to solve issues regarding the pandemic. The data sources used in previous studies were also presented and they consist of government officials, institutional service, IoT generated, online media, and open data. Therefore, this study presents the role of Big Data technologies in enhancing the research relative to COVID-19 and provides insights into the current state of knowledge within the domain and references for further development or starting new studies are provided.

10 citations

Proceedings ArticleDOI
17 Mar 2021
TL;DR: In this article, the impact of COVID-19 on economy of the organizations as well world has been analyzed and the authors have found that the organizations have changed their marketing strategies and style of marketing during the pandemic and have found the new way to reach to their customers.
Abstract: COVID-19 has shown us the new dimension of the world where everything has changed. Due to new innovations in the digital world, organizations have tried their best not to get affected by covid-19 in terms of productivity and reachability to their customers. The organizations have changed their marketing strategies and style of marketing during the pandemic and have found the new way to reach to their customers. In this manuscript, the impact of COVID-19 on economy of the organizations as well world has been analyzed.

6 citations