Browsing by Author "Manoj Kumar, M.V."

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A Healthcare management using clinical decision support system
(Institute of Electrical and Electronics Engineers Inc., 2018) Thomas, L.; Manoj Kumar, M.V.; Annappa, B.
From the literature it is studied that, most of the medical error is due to faulty healthcare system. Due to this, there is treatment delay, that leads to complications in later stages of disease progression. Medical error caused due to the failure in healthcare system can be reduced by employing an appropriate clinical decision support system (CDSS). CDSS helps in identifying the severity of disease by predicting its progression. The treatment management of gallstone disease is considered as a case study in this paper.This paper presents a CDSS with the help of machine learning for improving the treatment management. CDSS with the help of a statistical comparator, identifies an efficient tool for finding the associated risk factors. These risk factors are then used to predict the disease progression and identify the cases that may need Endoscopic Retrograde Cholangio-Pancreatography (ERCP) as the treatment progresses. The model that learns and predicts accurately is selected, using the concept of Area Under Curve (AUC). For this purpose, a Modified Cascade Neural Network (ModCNN) built upon the architecture of Cascade-Correlation Neural Network (CCNN) is proposed and tested using an ADAptive LInear NEuron (ADALINE) circuit. It's performance is evaluated and compared with Artificial Neural Network (ANN) and CCNN.Using this prediction information, disease progression is analysed and proper treatment is initiated, thereby reducing the medical error. ModCNN showed better accuracy (96.42%) for predicting the disease progression when compared with CCNN (93.24%) and ANN (89.65%). Thus, CDSS presented here, assisted in reducing the medical error and providing better healthcare management. Â© 2018 IEEE.
Alphabetic cryptography: Securing communication over cloud platform
(Springer Verlag service@springer.de, 2019) Cowlessur, S.K.; Annappa, B.; Manoj Kumar, M.V.; Thomas, L.; Sneha, M.M.; Puneetha, B.H.
This paper introduces alphabetic cryptography inspired by bidirectional DNA encryption algorithm. Alphabetic cryptography first offers higher randomization and secure communication over the cloud computing platform, and second supports the exchange of complete UNICODE character set. Alphabetic cryptography has been implemented on mobile and desktop platforms. Through experimental studies, it has been observed that randomness of encryption increases exponentially with the increase in the number of alphabets of the alphabetic encryption scheme. Â© Springer Nature Singapore Pte Ltd. 2019.
An online decision support system for recommending an alternative path of execution
(Institute of Electrical and Electronics Engineers Inc., 2017) Thomas, L.; Manoj Kumar, M.V.; Annappa, B.
Traditional process execution follow the path of execution drawn by the process analyst without observing the behaviour of resource and other real time constraints. Identifying process model, predicting the behaviour of resource and recommending the optimal path of execution for a real time process is challenging. The proposed AlfyMiner: yMiner gives a new dimension in process execution with the novel techniques Process Model Analyser: PMAMiner and Resource behaviour Analyser: RBAMiner for recommending probable path of execution. PMAMiner discovers next probable activity for currently executing activity in an online process using variant matching technique for identify the set of next probable activity, among which the next probable activity is discovered using decision tree model. RBAMiner identifies the resource suitable for performing the discovered next probable activity and observe the behaviour based on; load and performance using polynomial regression model, and waiting time using queueing theory. Based on the observed behaviour yMiner recommend the probable path of execution with; next probable activity and the best suitable resource for performing it. Experiments were conducted on process logs of CoSeLoG Project1 and 72% of accuracy is obtained in identifying and recommending next probable activity and the efficiency of resource performance was optimized by 59% by decreasing their load. Â© 2017 IEEE.
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 (Î±yMiner) does this recommendation in cross organization process mining technique by comparing the variants of same process encountered in different organization. Î±yMiner proposes two novel techniques Process Model Comparator (Î±yComp) and Resource Behaviour Analyser (RBAMiner). Î±yComp 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. Î±yMiner 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.
Bankruptcy Prediction Using Bi-Level Classification Technique
(Springer Science and Business Media Deutschland GmbH, 2023) Antani, A.; Annappa, B.; Dodia, S.; Manoj Kumar, M.V.
Bankruptcy is a legal proceeding involving a person or a business, where they are unable to pay the debt. Financial investors, banks, money lenders, and the government seek to know the status of bankruptcy of firms as it carries huge financial risk. The prediction of bankruptcy will help all the stakeholders of the company. To model bankruptcy prediction, traditional statistical methods like multiple discriminant analysis and Machine Learning (ML) models like Decision Trees, Support Vector Machines, and Ensemble have been utilized. In existing works, homogeneous base estimators are used while developing ensemble algorithms. This study uses a bi-level classification technique (a heterogeneous ensemble ML technique) to predict bankruptcy. To train the classifier, the features extracted are Altman z-score parameters and market-based measures. Unlike previous studies, this study uses an indicator of corporate governance as a feature. The outcome of this study is an improvement in the performance of the ML model using the bi-level classification technique. An F1-score of 0.98 and 97.8% accuracy is achieved with features including Tobinâ€™s Q and bi-level classification technique as an ML model. It outperforms the 96% accuracy of the random forest algorithm. Â© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Best resource recommendation for a stochastic process
(International Information Institute Ltd. No. 509 Fujimi-Cho 6-64-3 Tachikawa City, Tokyo 190-0013, 2016) Thomas, L.; Manoj Kumar, M.V.; Annappa, B.
The aim of this study was to develop an Artificial Neural Network's recommendation model for an online process using the complexity of load and performance of the resources. The proposed model investigate the resource performance using stochastic gradient decent method and probabilistic cost function for learning ranking function. The test result of CoSeLoG project is presented with accuracy of 72.856%. © 2016 International Information Institute.
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.
Concept drifts detection and localisation in process mining
(International Information Institute Ltd. No. 509 Fujimi-Cho 6-64-3 Tachikawa City, Tokyo 190-0013, 2016) Manoj Kumar, M.V.; Thomas, L.; Annappa, B.
Process mining provides methods and techniques for analyzing eventlogs recorded in modern information systems that support real-world operations. While analyzing an event-log, techniques in process mining assumes that the process as a static entity. This is not often the case due to possibility of phenomenon called concept drift. During the period of execution, process can experience concept drift and can evolve with respect to any of its associated perspectives exhibiting various patterns-of-change with different pace. This paper presents the method for detecting and localizing the sudden concept drifts in control-flow perspective of the process by using features extracted by processing the traces in process-log. © 2016 International Information Institute.
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.
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. Â© 2016 IEEE.
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. Â© 2017 ACM.
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. Â© 2014 IEEE.
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.
Infant Brain MRI Segmentation Using Deep Volumetric U-Net with Gamma Transformation
(Springer Science and Business Media Deutschland GmbH, 2023) Yeshwanth, G.S.; Annappa, B.; Dodia, S.; Manoj Kumar, M.V.
The growth of the brain from infantile to adolescence is very complex and takes a very long period. There are many processes such as myelination, migration, neural induction, and many other time-taking processes to study the development of the brain. This makes it necessary to develop some automatic tools to study the development of the brain. The brain consists mainly of three parts white matter, gray matter, and cerebrospinal fluid. So, quantitative tools will be a great boon for the medical community to deal with the brain if the brain MRI images are segmented into these three different parts. Although there are some tools for segmenting adult MRI images, for 6-month child segmentation, the brain becomes challenging as the white matter and gray matter are almost indistinguishable due to the brain development process. Segmentation of brain MRI images can identify specific patterns that contribute to healthy brain development. The dataset to address this problem had been taken from the Iseg2019 challenge conducted by MICCAI. Segmentation of MRI needs expert doctors. Advancements in computer vision techniques can be used to replace present time-consuming work. This paper proposes a deep learning model for image segmentation using a three-dimensional U-net. The proposed model gives dice values of 93.75, 88.24, and 85.64 for cerebrospinal fluid, gray matter, and white matter. This paper also presents various experimental results of U-net, attention U-net with different modifications. Â© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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. Â© 2017 IEEE.
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. Â© 2014 IEEE.
Prediction of gallstone disease progression using modified cascade neural network
(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2018) Thomas, L.; Manoj Kumar, M.V.; Annappa, B.; Arun, S.; Mubin, A.
Prediction of disease severity is highly essential for understanding the progression of disease and initiating an early diagnosis, which is priceless in treatment planning. A Modified Cascade Neural Network (ModCNN) is proposed for stratification of the patients who may need Endoscopic Retrograde Cholangiopancreatography (ERCP). In this study, gallstone disease (GSD) whose prevalence is increasing in India is considered. A retrospective analysis of 100 patients was conducted and their case history was recorded along with the routine investigations. Using ModCNN, the associated risk factors were extracted for the prediction of disease progression toward severe complication. The proposed model outperformed showing better accuracy with an area under receiver operating characteristic curve (area under ROC curve) of 0.9793, 0.9643, 0.9869, and 0.9768 for choledocholithiasis, pancreatitis, cholecystitis, and cholangitis, respectively, when compared with Artificial Neural Network (ANN) showing an accuracy of 0.884. Hence, the proposed technique can be used to conduct a nonlinear statistical analysis for the better prediction of disease progression and assist in better treatment planning, avoiding future complications. Â© 2018, Springer Nature Singapore Pte Ltd.
Process Logo: An Approach for Control-Flow Visualization of Information System Process in Process Mining
(Springer Science and Business Media Deutschland GmbH, 2022) Manoj Kumar, M.V.; Bs, B.S.; Sneha, H.R.; Thomas, L.; Annappa, B.; Vishnu Srinivasa Murthy, Y.V.S.
This paper proposes a new technique named â€œProcess Logoâ€ for visualizing the causal relationship between the activities of a process (Control flow). Traditional process mining algorithms rely on representing the activity as a sequence of operations modeled using nodes and edges, as the number of activities increases, the representation of the entire control flow becomes quite tedious. Process logo is a compact yet highly informative method for visually representing the process model. It visually summarizes the number of activities, sequence of execution, relative significance, and dependency between activities. It uses a dynamic programming methodâ€”sequence alignment and clustering approach with Levenshtein measure as a distance measure. The proposed method is evaluated on the synthetic event log, the experimental result is promising. Â© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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. Â© 2017 ACM.
Simplifying spaghetti processes to find the frequent execution paths
(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2018) Manoj Kumar, M.V.; Thomas, L.; Annappa, B.
Control-flow discovery algorithms of Process Mining are capable of generating excellent process models until the process is structured (less number of activities and paths connecting between them). Otherwise, process model with Spaghetti structure will be generated. These models are unstructured, incomprehensible and cannot be used for operational support. This paper proposes the techniques for (1) converting Spaghetti (unstructured) process to Lasagna (structured) process, and (2) Identifying the frequent execution paths in the process under consideration. Â© 2018, Springer Nature Singapore Pte Ltd.