International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management最新文献

Business and IT strategy alignment is a complex dynamic process in which organizations are in a position to enable extensive IT capabilities to achieve their business objectives. This interdependence is amplified by the COVID-19 crisis, which makes the integration of IT and business strategies more important than ever. This paper mainly aims to contribute to the understanding of strategic alignment from a practical perspective, as well as to demonstrate the applicability and robustness of the Strategic Alignment Model (SAM). Moreover, potential opportunities and risks associated with the strategic alignment of business and IT strategies are analysed. Findings are discussed after a qualitative analysis of 31 participants (semi-structured survey and interviews). Results indicated several difficulties affecting the strategic alignment implementation transcend the business and IT strategies like communication, corporate culture, governance, resource prioritization, and effective leadership. The study contends that there is a need to align and harmonize different agendas and interests within an organization and improve the understanding of the value of Strategic Alignment.

COVID-19 is a severe epidemic that has put the world in a global crisis. Over 42 Million people are infected, and 1.14 Million deaths are reported worldwide as on Oct 23, 2020. A deeper understanding of the epidemic suggests that a person's negligence can cause widespread harm that would be difficult to negate. Since no vaccine is yet developed, social distancing must be practiced to detain COVID-19 spread. Therefore, we aim to develop a framework that tracks humans for monitoring the social distancing being practiced. To accomplish this objective of social distance monitoring, an algorithm is developed using object detection method. Here, CNN based object detector is explored to detect human presence. The object detector's output is used for calculating distances between each pair of humans detected. This approach of social distancing algorithm will red mark the persons who are getting closer than a permissible limit. Experimental results prove that CNN based object detectors with our proposed social distancing algorithm exhibit promising outcomes for monitoring social distancing in public areas.

Even after 13 months, our world is still battling with global pandemic COVID-19. The pandemic has already infected more than one hundred and three million people, killing about two and a quarter million of them. Recently, several vaccines to inoculate people against the ongoing pandemic have been approved. People in most of the countries are being injected with these vaccines. While the world awaits for the outcome of the ongoing vaccinations, a more aggressive strain of coronavirus is killing many people in several countries, including the UK, the USA, Netherland, South Africa, and Australia. The ongoing pandemic has severely affected almost all aspects of our lives; education being one of them. As a result, majority of students in the world have completely lost access to the traditional way of institutional learning, while others have suffered in different ways and to a lesser degree. Many universities, colleges, and schools were closed during the initial pandemic lockdown during March-May, 2020. Luckily, some higher education institutions were better equipped to switch over to online teaching, either using a Learning Management System (LMS) or use some other online tools to connect with their students. With the help of surveys of students and teachers from several countries, this article aims to analyze the damage caused to the education sector by the pandemic at the global level. Guided by our literature review, and analysis of responses to our two surveys, we also provide a framework for a national education system, which could withstand future global crises like the one created by the COVID-19, and ensure continuity of education to all, especially the poor sections of the society.

The golden rule developed by Gordon E. Moore in 1965 stands forth and upholds its perception, which is observant with trending technology and making organizations, groups and individuals extract benefits from machine. AI, Robotics, Business Intelligence, Big Data and Analytics, Edge Computing, Hyperautomation, Blockchain, Democratization, Human Augmentation, Multiexperience are technical domains and trends supporting ongoing technical progress making mankind to innovate and create superhuman capabilities leaving HRs to fight the battle of replacing technology-literate people with people-literate technology. The likeliness towards analytics and complex algorithms made a breakthrough into a creative zone extending manageable workforce with the rising trends. The primary study with 108 h of leading Service Organizations of India was made to examine the recent tools and techniques for HR analytics which are adopted by them. As we recognized that analytics is driving force for HRs to be strategic business partner and step further for transforming roles. In addition we identified the implication of analytics on various HR data and decisions made by them.

Internet of Medical Things (IoMT) and embedded systems have improved the healthcare systems by enabling remote monitoring the patients' health conditions anywhere and anytime especially during novel COVID-19 pandemic. In this paper, an IoT-based predicting model is proposed to predict colorectal cancer (CRC) in elderlies. It provides a CRC predicting model for the involved medical team to continuously trace an elderly's biological indicators using smart wearable embedded systems and medical IoT devices. In this model, vital medical data is collected by IoMT devices and sensors, then analytical information is derived via machine learning (ML) methods for early CRC diagnosis and elderly's health parameters changes. The experimental results confirm that the suggested model meets the proper accuracy of predicting the CRC in aged people.

In this Post-COVID-19 "New Normal" era, the importance of Telemedicine is immense. In telemedicine, the patients' sensitive medical data security is the matter of highest demands. There exists data transmission between the psychiatric patients and psychiatrists through web-based portal. Cryptography is the essential component where medical data is transmitted securely. Insomnia, Depression, Mood swings, Fear of death, etc are the common mental conditions observed in this COVID-19 pandemic. A symmetric key based on randomization of numbers were generated by a reliable and third party Key Distribution Centre. A set of robust binary sequences of 128 bits long were generated, known as secret session keys, and will be transported to both the parties. The web-portal will capture all the patients' symptoms. Then homeopathy E-Prescriptions would be generated by the psychiatrists. This transmission of E-Prescription would be done using the proposed secret session key using Advanced Encryption Standards. Ignatia, Natrum Sulphuricum, Aconite, Arsenicum Album, Belladonna, etc are the important homeopathy medicines as prescribed by the homeopathy psychiatrists in COVID-19. Mathematical calculations were done on this proposed technique with positive efficacy. Hence, homeopathy telemedicine is the pivotal way to cure psychiatric patients remotely in this Post-COVID-19 "New Normal" context.

Outbreak of COVID-19, created a disastrous situation in more than 200 countries around the world. Thus the prediction of the future trend of the disease in different countries can be useful for managing the outbreak. Several data driven works have been done for the prediction of COVID-19 cases and these data uses features of past data for future prediction. In this study the machine learning (ML)-guided linear regression model has been used to address the different types of COVID-19 related issues. The linear regression model has been fitted into the dataset to deal with the total number of positive cases, and the number of recoveries for different states in India such as Maharashtra, West Bengal, Kerala, Delhi and Assam. From the current analysis of COVID-19 data it has been observed that trend of per day number of infection follows linearly and then increases exponentially. This property has been incorporated into our prediction and the piecewise linear regression is the best suited model to adopt this property. The experimental results shows the superiority of the proposed scheme and to the best of our knowledge this is a new approach towards the prediction of COVID-19.

This paper presents an efficient mechanism for secured encryption of intraoral information in the emerging field of Teledental. Due to global rapid surge in the (Coronavirus Disease) COVID patients, the services of Teledental are best suited in the newer post-COVID era. A devised perceptron has been intelligently embedded with de-multiplexing ability to transmit data to the dentists has been proposed. Exact session key has been developed through learning rules applied on the perceptrons by both the patient and dentist. For simplicity, gingivitis data is highly recommended to transmit in a highly secured manner with patients' data integrity. Gingivitis is an important dental disease which is primarily caused by the bacterial colonization. It shows gum bleeding and inflammations in the gingiva. Encrypted transmission is required to the Dentist for early diagnosis and treatment in Teledental system in this pandemic context. Gingivitis data are then broken into parts by the demultiplexer followed by individual proposed header generation. It is predominantly done to confuse the intruders about the originality of the intraoral data. Chi-square, Avalanche, Strict Avalanche, etc. were carried on the proposed partial shares to generate good outcomes when compared to classical algorithms. To confuse the intruders, character frequency, floating frequency, and autocorrelation were tested extensively. It is a newer approach to avail the secured Teledental features in post-COVID time.

Initial stage detection of malaria is very helpful in reducing the human death rate. Generally manual diagnosis is used for detection of malaria using 100 × to 600 × microscope but time required for this process is very large and false report chances are more, which results in death of a person. A high speed, low cost and result accurate biosensor plays a key role in diagnosis of malaria. When malaria parasite's infects RBC's, its mechanical, physical and biochemical structure will get modified results in change of refractive index of RBC. Therefore, refractive index varies from normal RBC to infected RBC. This factor is utilized to design the photonic biosensor for detection of malaria in humans and it is label free detection method. The proposed photonic crystal sensor has 10 µm × 10 µm dimension. The extracted sample is placed in the sensor holes and light beam with a wavelength of 1.85-1.95 µm is fed inside the bio sensor. If the malaria parasites are present then there will be variation in RI from normal sample results in the wavelength shift. FDTD technique is used for the simulation of this model. Quality factor achieved for this design is 214 and the sensitivity for change in refractive index is 225 nm/RIU.