Browsing by Author "V. Vijayakumar"

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A cognitive model for adopting ITIL framework to improve IT services in Indian IT industries
(IOS Press BV, 2020) Rasbihari Dayal; V. Vijayakumar; Rahul Chandra Kushwaha; Abhishek Kumar; V.D. Ambeth Kumar; Ankit Kumar
This research paper presents a cognitive model which manages to minimize the issues of the Information Technology Infrastructure by incorporation of service management practices. The importance of this research is that this model can be replicated in other companies for the distribution of products that wish to implement improvements in their management process technological services. This work introduces the use of Information Technology Infrastructure Library or ITIL as best practice, essential methodologies for IT Management, historical evolution, methodology, service life cycle, and ITIL certifications. Service automation is widely regarded as the usefulness and improves service guarantee. One of the most useful features of automation services is that the process will run the same way every time. Such precision in the execution of repetitive executions is virtually impossible when it comes to human labor. Therefore, the automation is the best way to improve the efficiency of the service provider and the next steps of the process. © 2020 - IOS Press and the authors. All rights reserved.
The state of the art of deep learning models in medical science and their challenges
(Springer Science and Business Media Deutschland GmbH, 2021) Chandradeep Bhatt; Indrajeet Kumar; V. Vijayakumar; Kamred Udham Singh; Abhishek Kumar
With time, AI technologies have matured well and resonated in various domains of applied sciences and engineering. The sub-domains of AI, machine learning (ML), deep learning (DL), and associated statistical tools are getting more attention. Therefore, various machine learning models are being created to take advantage of the data available and accomplish tasks, such as automatic prediction, classification, clustering, segmentation and anomaly detection, etc. Tasks like classification need labeled data used to train the models to achieve a reliable accuracy. This study shows the systematic review of promising research areas and applications of DL models in medical diagnosis and medical healthcare systems. The prevalent DL models, their architectures, and related pros, cons are discussed to clarify their prospects. Many deep learning networks have been useful in the field of medical image processing for prognosis and diagnosis of life-threatening ailments (e.g., breast cancer, lung cancer, and brain tumor, etc.), which stand as an error-prone and tedious task for doctors and specialists when performed manually. Medical images are processed using these DL methods to solve various tasks like prediction, segmentation, and classification with accuracy bypassing human abilities. However, the current DL models have some limitations that encourage the researchers to seek further improvement. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.