Faculty Publications

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Author: search.filters.author.Manoj Kumar, M.V.

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    Foundations of healthcare informatics
    (Elsevier, 2021) Annappa, B.; Manoj Kumar, M.V.; Thomas, L.
    Health informatics fundamentally deals with the acquisition (recording), processing, interpreting, and using of healthcare (patient) data by domain experts. Healthcare informatics generally refers to the management of data/information in healthcare rather than the application of computers in it-which is centered on patient care. The sheer amount of data and imperfection in decision making imply the usage of information systems (particularly process-aware information systems, called PAIS) in managing the healthcare process. Health informatics mainly offers tools for controlling the healthcare process and facilitating the acquisition of medical knowledge (recording). It offers a reliable and fast communication path among the people involved in the healthcare process. © 2021 Elsevier Inc. All rights reserved.
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    Deep Learning for COVID-19
    (Springer Science and Business Media Deutschland GmbH, 2022) Bs, B.S.; Manoj Kumar, M.V.; Thomas, L.; Ajay Kumar, M.A.; Wu, D.; Annappa, B.; Hebbar, A.; Vishnu Srinivasa Murthy, Y.V.S.
    Ever since the outbreak in Wuhan, China, a variant of Coronavirus named “COVID 19” has taken human lives in millions all around the world. The detection of the infection is quite tedious since it takes 3–14 days for the symptoms to surface in patients. Early detection of the infection and prohibiting it would limit the spread to only to Local Transmission. Deep learning techniques can be used to gain insights on the early detection of infection on the medical image data such as Computed Tomography (CT images), Magnetic resonance Imaging (MRI images), and X-Ray images collected from the infected patients provided by the Medical institution or from the publicly available databases. The same techniques can be applied to do the analysis of infection rates and do predictions for the coming days. A wide range of open-source pre-trained models that are trained for general classification or segmentation is available for the proposed study. Using these models with the concept of transfer learning, obtained resultant models when applied to the medical image datasets would draw much more insights into the COVID-19 detection and prediction process. Innumerable works have been done by researchers all over the world on the publicly available COVID-19 datasets and were successful in deriving good results. Visualizing the results and presenting the summarized data of prediction in a cleaner, unambiguous way to the doctors would also facilitate the early detection and prevention of COVID-19 Infection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Efficient process mining through critical path network analysis
    (IEEE Computer Society, 2014) Thomas, L.; Manoj Kumar, M.V.; Annappa, B.
    Process mining is emerging scientific research discipline, concentrates on discovering, monitoring and enhancing the operational processes using the operational traces of the process documented in log. Process mining enables the process centric analysis of the data and aims at bridging gap between data mining, business process modeling and analysis. This article analyses use of Critical Path Method used in project management, in the context of process mining in order to find critical paths in process model. This article aims in leveraging process mining practices with the application of CPM and study its feasibility. Critical path identifies the minimum time possible to finish the project. Extra care must be taken while executing activities on critical path. Delay in any of the activities on critical path would definitely delay the process completion time and collapse overall process plan. © 2014 IEEE.
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    Phenomenon of concept drift from process mining insight
    (IEEE Computer Society, 2014) Manoj Kumar, M.V.; Thomas, L.; Annappa, B.
    Process mining is originated form the fact that the modern information systems systematically record and maintain history of the process which they monitor and support. Systematic study of the recorded information in process centric manner will help to understand the process in a better way. Process mining acts as enabling technology by facilitating process centric analysis of data, which other available data science like data mining etc. fails to provide. Process mining algorithms are able to provide excellent insights on the process which they analyze, but they fail to handle the change in the process. Concept drift is a phenomenon of change in the process while it is being analyzed and it is a non-stationary learning problem. As the process changes while it is being analyzed, end result of the analysis becomes obsolete. Process mining algorithms are static biased, they assume that process at the beginning of analysis period will remain as same at the end of analysis period. There is at most requirement to effectively deal with the change in process to conduct optimal analysis. The main focus of this paper is to identify different factors to be considered while designing the solution for the problem of concept drift and explain each of the identified factors briefly. As the phenomenon of concept drift is extensively under consideration for research in other scientific research disciplines, this article considers restricting the content strictly concerning to the context of process mining. © 2014 IEEE.
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    An optimal process model for a real time process
    (CEUR-WS, 2015) Thomas, L.; Manoj Kumar, M.V.; Annappa, B.; Vishwanath, K.P.
    Recommending an optimal path of execution and a complete process model for a real time partial trace of large and complex organization is a challenge. The proposed AlfyMiner (αyMiner) does this recommendation in cross organization process mining technique by comparing the variants of same process encountered in different organization. αyMiner proposes two novel techniques Process Model Comparator (αyComp) and Resource Behaviour Analyser (RBAMiner). αyComp identifies Next Probable Activity of the partial trace along with the complete process model of the partial trace. RBAMineridentifies the resources preferable for performing Next Probable Activity and analyse their behaviour based on performance, load and queue. αyMiner does this analysis and recommend the best suitable resource for performing Next Probable Activity and process models for the real time partial trace. Experiments were conducted on process logs of CoSeLoG Project1 and 72% of accuracy is obtained in identifying and recommending NPA and the performance of resources were optimized by 59% by decreasing their load.
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    Capturing the sudden concept drift in process mining
    (CEUR-WS, 2015) Manoj Kumar, M.V.; Thomas, L.; Annappa, B.
    Concept drift is the condition when the process changes during the course of execution. Current methods and analysis techniques existing in process mining are not proficient of analyzing the process which has experienced the concept drift. State-of-the-art process mining approaches consider the process as a static entity and assume that process remains same from beginning of its execution period to end. Emphasis of this paper is to propose the technique for localizing concept drift in control-flow perspective by making use of activity correlation strength feature extracted using process log. Concept drift in the process is localized by applying statistical hypothesis testing methods. The proposed method is verified and validated on few of the real-life and artificial process logs, results obtained are promising in the direction of efficiently localizing the sudden concept drifts in process-log.
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    Discovery of optimal neurons and hidden layers in feed-forward Neural Network
    (Institute of Electrical and Electronics Engineers Inc., 2016) Thomas, L.; Manoj Kumar, M.V.; Annappa, B.
    Identifying the number of neurons in each hidden layers and number of hidden layers in a multi layered Artificial Neural Network (ANN) is a challenge based on the input data. A new hypothesis is proposed for organizing the synapse from x to y neuron. The synapse of number of neurons to fire between the hidden layer is identified. By the introduction of this hypothesis, an effective number of neurons in multilayered Artificial Neural Network can be identified and self organizing neural network model is developed which is referred as cognitron. The normal brain model has 3 layered perceptron; but the proposed model organizes the number of layers optimal for identifying an effective model. Our result proved that the proposed model constructs a neural model directly by identifying the optimal weights of each neurons and number of neurons in each dynamically identified hidden layers. This optimized model is self organized with different range of neurons on different layer of hidden layer, and by comparing the performance based on computational time and error at each iteration. An efficient number of neurons are organized using gradient decent. The proposed model thus train large model to perform the classification task by inserting optimal layers and neurons. © 2016 IEEE.
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    Recommending an alternative path of execution using an online decision support system
    (Association for Computing Machinery acmhelp@acm.org, 2017) Thomas, L.; Manoj Kumar, M.V.; Annappa, B.
    Prediction of disease severity is highly essential for understanding the progression of disease and initiating an alternative path of execution, which is priceless in treatment planning. An online decision support system (ODeSS) is proposed here for stratification of the patients who may need Endoscopic Retrograde CholangioPancreatography (ERCP) and recommend an alternate path of execution. By this an immediate intervention can be avoided. In this study gallstone disease (GSD) whose prevalence is increasing in India is considered. ODeSS is a versatile non-linear information model which clustered the traces based on the duration of its completion. This is a Retrospective analyses of 575 traces. ODeSS applied the technique of longest common subsequence for identifying the sequence of an online execution and discovering to which cluster of variants it may belong. This discovery assist in taking appropriate clinical decision by recommending an alternative path of execution for such cases which may need emergency interventions. ODeSS performance was evaluated using area under receiver operating characteristic curve (area under ROC curve). This showed an accuracy of 0.9653 in prediction. The proposed model was validated using ROC curve in k-fold cross validation. Hence the proposed ODeSS can be used to conduct a non-linear statistical analysis since, the relationships between the predictive variables are not linear. It can be used as a clinical practice to recommend the path of execution. This would assist in better treatment planning, avoiding future complications. © 2017 ACM.
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    Distilling lasagna from spaghetti processes
    (Association for Computing Machinery acmhelp@acm.org, 2017) Manoj Kumar, M.V.; Thomas, L.; Annappa, B.
    If the operational process is flexible, control flow discovery methods in process mining tend to produce Spaghetti (unstructured) models. Spaghetti models generally consist of large number of activities and paths. These models are unstructured, incomprehensible difficult to analyse, impossible to use during operational support and enhancement. Due The structural complexity of Spaghetti processes majority of techniques in process mining can not be applied on them. There is a at most necessity to design and develop methods for simplifying the structure of Spaghetti process to make them easily understandable and reusable. The methods proposed in this paper concentrates on offering the tools and techniques for analysing the Spaghetti process. The problems addressed in this paper are 1) converting the unstructured Spaghetti to structured and simplified Lasagna process, 2) identifying the list of possible, significant, and impossible paths of execution in Lasagna process. The proposed technique is verified and validated on real-life road traffic fine management event-log taken from standard repository. © 2017 ACM.
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    On predicting the frequent execution patterns in information systems
    (Institute of Electrical and Electronics Engineers Inc., 2017) Manoj Kumar, M.V.; Thomas, L.; Annappa, B.
    Process mining research discipline offers a spectrum of techniques for analysing event logs. Event logs represent the history of process execution. This information can be used for monitoring, analysing and improving the operational processes. The currently available methods in process mining emphasise on constructing the static process model. These models depict various dimensions of the process under analysis. But, models can only represent the past execution history and can't be used to guide and control the prospectus execution of the process. There is a need for the methods and techniques which guide the future execution of process in the light of recorded information. This paper introduces a technique for identifying and predicting the frequent control-flow execution patterns in information systems. The proposed Position Weight Matrix proven to be efficient during experimentation and validation studies. © 2017 IEEE.