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Mar'21

The IUP Journal of Information Technology

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Machine Learning (ML) plays a very crucial role in everyday life. During the ongoing coronavirus pandemic, scientists and healthcare experts use Artificial Intelligence (AI) and ML to tackle the spread of the virus infection. A large number of countries have been using digital contact tracing applications to trace and monitor the people infected with the virus. Various forecasting models have been used to generate short-term forecasts on the virus spread to assist healthcare experts and policy-makers in many countries. Deep learning-based tools have been used to analyze X-ray and CT scan images to diagnose and screen people infected with coronavirus. ML models have been very instrumental in the development of drug and vaccine for Covid-19. Machine Learning (ML) plays a very crucial role in everyday life. During the ongoing coronavirus pandemic, scientists and healthcare experts use Artificial Intelligence (AI) and ML to tackle the spread of the virus infection. A large number of countries have been using digital contact tracing applications to trace and monitor the people infected with the virus. Various forecasting models have been used to generate short-term forecasts on the virus spread to assist healthcare experts and policy-makers in many countries. Deep learning-based tools have been used to analyze X-ray and CT scan images to diagnose and screen people infected with coronavirus. ML models have been very instrumental in the development of drug and vaccine for Covid-19.

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ML in general has a lot of potential for the healthcare industry. ML algorithms are used to diagnose and predict various diseases such as Parkinson's disease, hepatitis, dengue, heart diseases and neurological disorders. Deep learning is very effective in medical imaging. It handles complex image data and can diagnose diseases at an early stage. Diagnosis of diabetic retinopathy, breast cancer, cardiovascular diseases, rheumatoid arthritis, etc. is being automated using deep learning models. ML models are very much useful in medical prognosis. Medical prognosis improves treatment process and avoids unnecessary expenditure on treatment. Robotic surgery is more effective than the traditional process and uses sensors and 3D images to guide the surgical procedure. ML models have been successfully used in genomics and proteomics to study the structure and function of genetic materials. Drug discovery and clinical trials also use ML models heavily to reduce the cost and time of development of a new drug. The development of several Covid-19 vaccines in a record time is an example of it. However, mainstream adoption of AI and ML in healthcare is slow due to several factors. The black-box nature of ML models creates a trust deficit among doctors and patients in spite of several breakthroughs achieved in the field. Handing over the critical care to a machine is not convincing to a large number of stakeholders. Several issues, both technical and nontechnical, need to be addressed to realize the full potential of AI and ML in the healthcare sector.

The first paper, "Behavioral IT^® - Coping with IT Disruptions", by Prem Kamble, presents a study of human psychology from industrial-age to information-age and draws useful conclusions to ensure a smooth change. The study reflects the author's wide experience as a CIO in implementing various Information Technology (IT) solutions and his observations on psychology of IT users.

The next paper, "Reshaping the Paradox of Information Power and Overload: Data Mining Strategies for Knowledge Workers", by Umesh Arya, discusses information overload and data retrieval strategies. As suggested by the author, in addition to the conventional research skills, the new-age researchers should also learn the data mining and web scrapping skills as well as data analysis and visualization methods.

The last paper, "Applications of Genetic Algorithm in Water Resources Management and Optimization", by Garima Tyagi, Rohit Singh and Abid Hussain, provides a refreshing review of Genetic Algorithm (GA) and its application in water resource planning and optimization. The authors describe the different stages of genetic algorithm and how it can be applied to water resources management.

-A C Ojha
Consulting Editor

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Behavioral IT® - Coping with IT Disruptions

Prem Kamble

For sustaining businesses in a VUCA World, or a world with Vulnerability, Uncertainty, Complexity and Ambiguity, it is important to address the primary cause of VUCA. The culprit is the rapidly changing technology, more significantly the Information Technology (IT). Though the primary driver of change today is IT, not many change management courses discuss how to manage IT-driven change. Technology changes fast, but it takes generations to change the minds and behavior of people. The vehicle of businesses runs on two uneven wheels - one wheel (technology) runs at jet speed and the other wheel (people) runs at bullock cart speed. It is extremely important to address this "inertia of the human mind" to sustain businesses. To deal with the social sustainability problems arising out of IT, you need a solution with a behavioral approach. The author has coined a new term called "Behavioral IT^®" to address the social issues of IT (Prem Kamble, 2010a and 2012a). Behavioral IT is both a managerial skill and a strategy which deals with the psychological, behavioral and attitudinal aspects of technological change. This paper draws lessons from the disruption and turmoil of the industrial revolution and concludes that we need a change in mindset to tackle the information revolution. It takes a multidisciplinary approach with major stress on psychology of change. It looks at the key features of IT in contrast to the industrial one to draw useful conclusions about what we need to learn and (more importantly) unlearn from the past to ensure a smoother change. Over 70% failures in IT projects indicate that something is seriously wrong. This paper is useful for all CXO's, managers, heads of companies and heads of departments-in short, for all the change drivers or change catalysts in businesses. It is of course useful for students of management too. At the outset, it is important to inform the reader that this is not an academic research paper, it is based on-the-job 'research' and experience of over 25 years of enabling IT transitions as a CIO. During this period, the author has closely observed the psychology of IT users during IT transitions, right from CEO to the lowest clerk.

Article Price : Rs.50

Reshaping the Paradox of Information Power and Overload: Data Mining Strategies for Knowledge Workers

Umesh Arya

Researchers and journalists comprise a good part of the "knowledge workers", i.e., the profession where most of the people are engaged in the knowledge intensive industries. The digital workplace offers unprecedented opportunities, while decimating the strongly guarded 'traditional' tower of working and learning. The same is quite evident with huge disruptions in industry and academia. The tsunami of information overload has created different power relations and negotiated hierarchies in scholarship, citizenship and governance. Different actors are now asserting their identities and influencing the knowledge sphere with their tech-enabled approach to data mining and info scraping. The data, being the "new oil" of information economy, seem to lubricate the knowledge sphere for enhanced understanding, mapping and better feeling of the events happenings day in and day out. The paper dwells on the concept of information overload, data retrieval strategies and finally the data mining or the data scraping strategies.

Article Price : Rs.50

Applications of Genetic Algorithm in Water Resources Management and Optimization

Garima Tyagi, Rohit Singh and Abid Hussain

It is the need of current scenario that water resource is to be retained and maintained. Different types of models have been applied for the optimization of Water Resource Management. Different researchers have applied and used many algorithms to solve optimization problems. Genetic Algorithm (GA) is one of the evolutionary algorithms and research has been done on the use of GA in the Water Resource Management Optimization (WRMO). The paper reviews different applications applied so far, their usefulness and also their scope in the development of future models for water resource planning and optimization.

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