Ketan Kotecha
Biographic Data
| ID | 6628149 |
|---|---|
| NAME | Ketan Kotecha |
| GIVEN NAMES | Ketan |
| FAMILY NAME | Kotecha |
| SIGNATURE | KOTECHA K |
| AFFILIATIONS | Symbiosis International University |
| ORCID | 0000-0003-2653-3780 |
| VERIFIED | Yes |
| TOTAL WORKS | 11 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 11 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Blueprint for Capacity Building
Potential and Current Status of AI Use for Accessible, Affordable, and Adaptable Healthcare
Visiting and exploring digital transformation management
Purpose This study aims to explore and understand the literature on digital transformation (DT) research that will lead to developing a conceptual and thematic structure of DT management. Design/methodology/approach The research approach employed a hybrid approach of bibliometric analysis and a structured review of DT management research studies from 2017 to 2023. Although systematic reviews and bibliometric analysis are distinct methods, they co…
Digital citizenship and paradigm shift in generation z’s emotional communication
This study examines the intricate interplay between the digital environment and the evolving communication dynamics of Generation Z, specifically focusing on the impact of social media on familial bonds. The research objective is to explore the ways in which Generation Z’s social media consumption patterns shape their relationships and lives, providing insights into the intricate interplay between technology and human connections. Adopting Hirsch…
Employing Co-Learning to Evaluate the Explainability of Multimodal Sentiment Analysis
Deep neural nets are opaque black-box models with little to no understanding of underlying model dynamics. This issue is more prevalent in the case of multimodal artificial intelligence (AI) systems, where model explainability and interpretability are prime concerns due to data integration from heterogeneous data streams and complex inter and intramodal interactions. However, the traditional explainable models are challenging to apply in the mult…
Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder
A breast tissue biopsy is performed to identify the nature of a tumour, as it can be either cancerous or benign. The first implementations involved the use of machine learning algorithms. Random Forest and Support Vector Machine (SVM) were used to classify the input histopathological images into whether they were cancerous or non-cancerous. The implementations continued to provide promising results, and then Artificial Neural Networks (ANNs) were…
Improving the Accuracy of Ensemble Machine Learning Classification Models Using a Novel Bit-Fusion Algorithm for Healthcare AI Systems
Healthcare AI systems exclusively employ classification models for disease detection. However, with the recent research advances into this arena, it has been observed that single classification models have achieved limited accuracy in some cases. Employing fusion of multiple classifiers outputs into a single classification framework has been instrumental in achieving greater accuracy and performing automated big data analysis. The article propose…
Hybrid Deep Neural Network for Handling Data Imbalance in Precursor MicroRNA
Over the last decade, the field of bioinformatics has been increasing rapidly. Robust bioinformatics tools are going to play a vital role in future progress. Scientists working in the field of bioinformatics conduct a large number of researches to extract knowledge from the biological data available. Several bioinformatics issues have evolved as a result of the creation of massive amounts of unbalanced data. The classification of precursor microR…
An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment
Human emotions affect psychological health to a great level. Positive emotions relate to health improvement; whereas negative emotions may aggravate psychological disorders such as anxiety, stress, and depression. Although there exist several computational methods to predict psychological disorders, most of them provide a black-box view of uncertainty. This research involves developing a novel predictive model for multi class psychological risk r…
Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk
Cardiovascular disease (CVD) is considered to be one of the most epidemic diseases in the world today. Predicting CVDs, such as cardiac arrest, is a difficult task in the area of healthcare. The healthcare industry has a vast collection of datasets for analysis and prediction purposes. Somehow, the predictions made on these publicly available datasets may be erroneous. To make the prediction accurate, real-time data need to be collected. This stu…
Machine learning techniques and older adults processing of online information and misinformation
No prominent works on this page.
Hybrid Deep Neural Network for Handling Data Imbalance in Precursor MicroRNA
Over the last decade, the field of bioinformatics has been increasing rapidly. Robust bioinformatics tools are going to play a vital role in future progress. Scientists working in the field of bioinformatics conduct a large number of researches to extract knowledge from the biological data available. Several bioinformatics issues have evolved as a result of the creation of massive amounts of unbalanced data. The classification of precursor microR…
An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment
Human emotions affect psychological health to a great level. Positive emotions relate to health improvement; whereas negative emotions may aggravate psychological disorders such as anxiety, stress, and depression. Although there exist several computational methods to predict psychological disorders, most of them provide a black-box view of uncertainty. This research involves developing a novel predictive model for multi class psychological risk r…
Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk
Cardiovascular disease (CVD) is considered to be one of the most epidemic diseases in the world today. Predicting CVDs, such as cardiac arrest, is a difficult task in the area of healthcare. The healthcare industry has a vast collection of datasets for analysis and prediction purposes. Somehow, the predictions made on these publicly available datasets may be erroneous. To make the prediction accurate, real-time data need to be collected. This stu…
Machine learning techniques and older adults processing of online information and misinformation
Improving the Accuracy of Ensemble Machine Learning Classification Models Using a Novel Bit-Fusion Algorithm for Healthcare AI Systems
Healthcare AI systems exclusively employ classification models for disease detection. However, with the recent research advances into this arena, it has been observed that single classification models have achieved limited accuracy in some cases. Employing fusion of multiple classifiers outputs into a single classification framework has been instrumental in achieving greater accuracy and performing automated big data analysis. The article propose…
Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder
A breast tissue biopsy is performed to identify the nature of a tumour, as it can be either cancerous or benign. The first implementations involved the use of machine learning algorithms. Random Forest and Support Vector Machine (SVM) were used to classify the input histopathological images into whether they were cancerous or non-cancerous. The implementations continued to provide promising results, and then Artificial Neural Networks (ANNs) were…
Digital citizenship and paradigm shift in generation z’s emotional communication
This study examines the intricate interplay between the digital environment and the evolving communication dynamics of Generation Z, specifically focusing on the impact of social media on familial bonds. The research objective is to explore the ways in which Generation Z’s social media consumption patterns shape their relationships and lives, providing insights into the intricate interplay between technology and human connections. Adopting Hirsch…
Employing Co-Learning to Evaluate the Explainability of Multimodal Sentiment Analysis
Deep neural nets are opaque black-box models with little to no understanding of underlying model dynamics. This issue is more prevalent in the case of multimodal artificial intelligence (AI) systems, where model explainability and interpretability are prime concerns due to data integration from heterogeneous data streams and complex inter and intramodal interactions. However, the traditional explainable models are challenging to apply in the mult…
Visiting and exploring digital transformation management
Purpose This study aims to explore and understand the literature on digital transformation (DT) research that will lead to developing a conceptual and thematic structure of DT management. Design/methodology/approach The research approach employed a hybrid approach of bibliometric analysis and a structured review of DT management research studies from 2017 to 2023. Although systematic reviews and bibliometric analysis are distinct methods, they co…
Blueprint for Capacity Building
Potential and Current Status of AI Use for Accessible, Affordable, and Adaptable Healthcare
Computer Science (9 works) · Artificial Intelligence (7 works) · Machine learning (6 works) · Artificial neural network (4 works) · Artificial Intelligence in Healthcare (3 works) · Data mining (3 works) · Decision tree (3 works) · Deep learning (3 works) · Medicine (3 works) · Psychology (3 works)