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Kalpdrum Passi
Laurentian University

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Conference paper
Published: 20 May 2021 in Communications in Computer and Information Science
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This Research presents an innovative approach towards detecting fraudulent credit card transactions. A commonly prevailing yet dominant problem faced in detection of fraudulent credit card transactions is the scarce occurrence of such fraudulent transactions with respect to legitimate (authorized) transactions. Therefore, any data that is recorded will always have a stark imbalance in the variety of minority (fraudulent) and majority (legitimate) class samples. This imbalanced distribution of the training data among classes makes it hard for any learning algorithm to learn the features of the minority class. In this thesis, we analyze the impact of applying class-balancing techniques on the training data namely oversampling (using SMOTE algorithm) for minority class and under sampling (using CMTNN) for majority class. The usage of most popular classification algorithms such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGB), Logistic Regression (LR), Random Forest (RF) are processed on balanced data and which results to quantify the performance improvement provided by our approach.

ACS Style

Vrushal Shah; Kalpdrum Passi. Data Balancing for Credit Card Fraud Detection Using Complementary Neural Networks and SMOTE Algorithm. Communications in Computer and Information Science 2021, 3 -16.

AMA Style

Vrushal Shah, Kalpdrum Passi. Data Balancing for Credit Card Fraud Detection Using Complementary Neural Networks and SMOTE Algorithm. Communications in Computer and Information Science. 2021; ():3-16.

Chicago/Turabian Style

Vrushal Shah; Kalpdrum Passi. 2021. "Data Balancing for Credit Card Fraud Detection Using Complementary Neural Networks and SMOTE Algorithm." Communications in Computer and Information Science , no. : 3-16.

Journal article
Published: 10 October 2020 in IoT
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In the derived approach, an analysis is performed on Twitter data for World Cup soccer 2014 held in Brazil to detect the sentiment of the people throughout the world using machine learning techniques. By filtering and analyzing the data using natural language processing techniques, sentiment polarity was calculated based on the emotion words detected in the user tweets. The dataset is normalized to be used by machine learning algorithms and prepared using natural language processing techniques like word tokenization, stemming and lemmatization, part-of-speech (POS) tagger, name entity recognition (NER), and parser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK). A derived algorithm extracts emotional words using WordNet with its POS (part-of-speech) for the word in a sentence that has a meaning in the current context, and is assigned sentiment polarity using the SentiWordNet dictionary or using a lexicon-based method. The resultant polarity assigned is further analyzed using naïve Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and random forest machine learning algorithms and visualized on the Weka platform. Naïve Bayes gives the best accuracy of 88.17% whereas random forest gives the best area under the receiver operating characteristics curve (AUC) of 0.97.

ACS Style

Ravikumar Patel; Kalpdrum Passi. Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning. IoT 2020, 1, 218 -239.

AMA Style

Ravikumar Patel, Kalpdrum Passi. Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning. IoT. 2020; 1 (2):218-239.

Chicago/Turabian Style

Ravikumar Patel; Kalpdrum Passi. 2020. "Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning." IoT 1, no. 2: 218-239.

Conference paper
Published: 12 December 2019 in Transactions on Petri Nets and Other Models of Concurrency XV
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Sign gesture recognition is an important problem in human-computer interaction with significant societal influence. However, it is a very complex task, since sign gestures are naturally deformable objects. Gesture recognition contains unsolved problems for the last two decades, such as low accuracy or low speed, and despite many proposed methods, no perfect result has been found to explain these unsolved problems. In this paper, we propose a deep learning approach to translating sign gesture language into text. In this study, we have introduced a self-generated image data set for American Sign language (ASL). This dataset is a collection of 36 characters containing A to Z alphabets and 0 to 9 number digits. The proposed system can recognize static gestures. This system can learn and classify specific sign gestures of any person. A convolutional neural network (CNN) algorithm is proposed for classifying ASL images to text. An accuracy of 99% on the alphabet gestures and 100% accuracy on digits was achieved. This is the best accuracy compared to existing systems.

ACS Style

Kalpdrum Passi; Sandipgiri Goswami. Real Time Static Gesture Detection Using Deep Learning. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 408 -426.

AMA Style

Kalpdrum Passi, Sandipgiri Goswami. Real Time Static Gesture Detection Using Deep Learning. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():408-426.

Chicago/Turabian Style

Kalpdrum Passi; Sandipgiri Goswami. 2019. "Real Time Static Gesture Detection Using Deep Learning." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 408-426.

Chapter
Published: 01 January 2018 in Information Retrieval and Management
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This paper offers insights into evolving a decision support system (DSS) to aid primary care physicians and/or nurses in the post-surgical care of patients with Colorectal Cancer in a clinical setting. Presently, the oncologists in the cancer center, who are familiar with the Clinical Practice Guidelines (CPGs), are primarily responsible for the provision of follow-up care to their patients on the basis of the CPGs; in contrast, the attending primary care physician and/or nurse assisting the oncologist may be unfamiliar with these guidelines. These caregivers would, therefore, either require hardcopies of the CPGs or can be aided via a DSS for them to be able to provide the appropriate follow-up care for the respective cancer patients. Clearly, the Colorectal Cancer follow-up CPGs have to be analyzed and the ontology representing the knowledge embedded in the guidelines designed prior to realizing such a DSS. The designed ontology is often coded into Web Ontology Language (OWL) as a standard ontology that can be accessed through the Web. The authors' research team designed and presented the semantic framework of the web application, using the designed ontology that combines the current Web technology with database storage to achieve a unified development of the DSS. The authors also designed a user-friendly interface of the Web application to provide medical practitioners the functionality of the CPGs and the flexibility to customize the desired follow-up care schedule. The resulting DSS provides the physicians with follow-up program for the Colorectal Cancer patients based on the CPGs. The system was built using the semantic framework for the follow-up program and queries on the system are executed through SPARQL query engine.

ACS Style

Kalpdrum Passi; Hongtao Zhao. A Decision Support System (DSS) for Colorectal Cancer Follow-Up Program via a Semantic Framework. Information Retrieval and Management 2018, 746 -770.

AMA Style

Kalpdrum Passi, Hongtao Zhao. A Decision Support System (DSS) for Colorectal Cancer Follow-Up Program via a Semantic Framework. Information Retrieval and Management. 2018; ():746-770.

Chicago/Turabian Style

Kalpdrum Passi; Hongtao Zhao. 2018. "A Decision Support System (DSS) for Colorectal Cancer Follow-Up Program via a Semantic Framework." Information Retrieval and Management , no. : 746-770.

Journal article
Published: 01 January 2015 in International Journal of Healthcare Information Systems and Informatics
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This paper offers insights into evolving a decision support system (DSS) to aid primary care physicians and/or nurses in the post-surgical care of patients with Colorectal Cancer in a clinical setting. Presently, the oncologists in the cancer center, who are familiar with the Clinical Practice Guidelines (CPGs), are primarily responsible for the provision of follow-up care to their patients on the basis of the CPGs; in contrast, the attending primary care physician and/or nurse assisting the oncologist may be unfamiliar with these guidelines. These caregivers would, therefore, either require hardcopies of the CPGs or can be aided via a DSS for them to be able to provide the appropriate follow-up care for the respective cancer patients. Clearly, the Colorectal Cancer follow-up CPGs have to be analyzed and the ontology representing the knowledge embedded in the guidelines designed prior to realizing such a DSS. The designed ontology is often coded into Web Ontology Language (OWL) as a standard ontology that can be accessed through the Web. The authors' research team designed and presented the semantic framework of the web application, using the designed ontology that combines the current Web technology with database storage to achieve a unified development of the DSS. The authors also designed a user-friendly interface of the Web application to provide medical practitioners the functionality of the CPGs and the flexibility to customize the desired follow-up care schedule. The resulting DSS provides the physicians with follow-up program for the Colorectal Cancer patients based on the CPGs. The system was built using the semantic framework for the follow-up program and queries on the system are executed through SPARQL query engine.

ACS Style

Kalpdrum Passi; Hongtao Zhao. A Decision Support System (DSS) for Colorectal Cancer Follow-Up Program via a Semantic Framework. International Journal of Healthcare Information Systems and Informatics 2015, 10, 17 -38.

AMA Style

Kalpdrum Passi, Hongtao Zhao. A Decision Support System (DSS) for Colorectal Cancer Follow-Up Program via a Semantic Framework. International Journal of Healthcare Information Systems and Informatics. 2015; 10 (1):17-38.

Chicago/Turabian Style

Kalpdrum Passi; Hongtao Zhao. 2015. "A Decision Support System (DSS) for Colorectal Cancer Follow-Up Program via a Semantic Framework." International Journal of Healthcare Information Systems and Informatics 10, no. 1: 17-38.

Conference paper
Published: 01 January 2013 in Computer Vision
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Follow-up care for Cancer patients is provided by the oncologist at the cancer center. There are administrative and cost advantages in providing the follow-up care by family physicians or nurses. This paper presents a Semantic Web approach to develop a decision support system for the Colorectal Cancer Follow-up care that can be used to provide the follow-up care by the physicians. The decision support system requires the development of Ontology for the follow-up care suggested by the Clinical Practice Guidelines (CPG). We present the ontology for the Colorectal Cancer based on the follow-up CPG. This formalized and structured CPGs ontology can then be used by the semantic web framework to provide patient specific recommendation. In this paper, we present the details on the design and implementation of this ontology and querying the ontology to generate knowledge and recommendations for the patients.

ACS Style

Hongtao Zhao; Kalpdrum Passi. Semantic Web and Ontology Engineering for the Colorectal Cancer Follow-Up Clinical Practice Guidelines. Computer Vision 2013, 53 -64.

AMA Style

Hongtao Zhao, Kalpdrum Passi. Semantic Web and Ontology Engineering for the Colorectal Cancer Follow-Up Clinical Practice Guidelines. Computer Vision. 2013; ():53-64.

Chicago/Turabian Style

Hongtao Zhao; Kalpdrum Passi. 2013. "Semantic Web and Ontology Engineering for the Colorectal Cancer Follow-Up Clinical Practice Guidelines." Computer Vision , no. : 53-64.