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                <text>Smartphone Usage, Social Media Engagement, and Academic Performance: Mediating Effect of Digital Learning</text>
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                <text>Academic performance; Digital learning; India; Private university; Smartphone; Social media engagement; Students</text>
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                <text>Smartphone has penetrated across all regions of India and all sections of the society, especially among the higher education student community. Smartphones with the internet, in turn, provide access to social media platforms. Smartphones and social media (SM) together provide several benefits to the users, and they also cause unwanted repercussions on the users such as adverse mental health, addiction, and laziness. But one of the important gains of smartphone and social media usage (SMU) is digital learning through them. Smartphones and SM are inevitable in the academic process of higher education students. The students use extensively the smartphone and SM for sharing information and content, for having peer discussion, for managing assignments, and for communicating with the instructors. So, this study tries to study the mediating effect of digital learning through smartphones and SM on the educational accomplishment of private university students in Bangalore, India. This study adopted the survey method of research which collected primary data through structured questionnaires from the private university students. The collected data were analyzed using students t test, One-way ANOVA, Chi-Square, and Structural Equation Modeling. The study results found that smartphones and SM affect the academic performance (AP) of the sample students significantly through digital learning.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Ravikumar T.; Anuradha R.; Rajesh R.; Prakash N.</text>
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                <text>Lecture Notes on Data Engineering and Communications Technologies, Vol-116, pp. 351-364.</text>
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                <text>Ravikumar T., School of Business and Management, Christ (Deemed to be University), Bangalore, 560029, India; Anuradha R., School of Business and Management, Christ (Deemed to be University), Bangalore, 560029, India; Rajesh R., School of Business and Management, Christ (Deemed to be University), Bangalore, 560029, India; Prakash N., School of Business and Management, Christ (Deemed to be University), Bangalore, 560029, India</text>
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                <text>Nanomaterials for A431 Epidermoid Carcinoma Treatment</text>
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                <text>Malignancy is the ancient sickness that causes an increased rate of mortality worldwide. Traditional malignant growth treatments that are clinically utilized comprise chemotherapy, radiotherapy, and medical procedure. Despite the fact that there have been motivating enhancements in the nanotechnology and biomedical field, malignant growth remains the most urgent condition to treat, as the central reason for mortality. Nanotechnology has the possibility to improve medication transport and delivery by modifying pharmacokinetics and conveyance, resulting in reduced negative reactions and in this manner improving precision. Some issues exist regarding destinations and the difficulties that occur, and the potential for success becomes closer with every discovery. Nanomaterials are smaller in size than organic macromolecules. More correctly, they as a rule have a width of many nanometers (nm), which makes them from 100 up to multiple times smaller than even one malignancy cell. Nanoparticles can occur in sizes running from 10 up to 400nm, and can likewise be used with a simple set up or a blend of pharmacologically dynamic medications, depending on a superficial level of properties. The various aspect of nanotechnology for malignant growth treatment include exact targeting of the lively segments in cell/tissues, producing upgrades responsive medication discharge, defeating natural obstructions, interfacing against disease dynamic system with imaging atoms, improving disease examination, and imaging. For the most part, nanoparticles burdened with mending operators are conveyed experimentally for firm malignancy treatment. Todays nanotechnology is a magnificent platform for the treatment of differing malignant growths.  2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.</text>
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                <text>Christobher S.; Kalitha Parveen P.; Easwaran M.; Bhotla H.K.; Kaliannan D.; Balasubramanian B.; Meyyazhagan A.</text>
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                <text>Nanotechnology in the Life Sciences, pp. 211-235.</text>
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                <text>Christobher S., Department of Zoology, Nallamuthu Gounder Mahalingam College, Tamil Nadu, Pollachi, India; Kalitha Parveen P., PG Department of Zoology, Hajee Karutha Rowther Howdia College of Arts and Science, Tamil Nadu, Uthamapalayam, India; Easwaran M., Bioknowl Insights Private Limited, Tamil Nadu, Coimbatore, India; Bhotla H.K., Bioknowl Insights Private Limited, Tamil Nadu, Coimbatore, India; Kaliannan D., Zoonosis Research Center, Department of Infection Biology, School of Medicine, Wonkwang University, Iksan, South Korea; Balasubramanian B., Department of Food Science and Biotechnology, College of Life Sciences, Sejong University, Seoul, South Korea; Meyyazhagan A., Department of Life Sciences, CHRIST (Deemed to be University), Bangalore, India</text>
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                <text>Performance Analysis of Logistic Regression, KNN, SVM, Nae Bayes Classifier for Healthcare Application During COVID-19</text>
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                <text>Coronavirus; COVID-19; Heart disease; Nae Bayes; Pandemic; SVM</text>
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                <text>Heart disease is one of the main causes of mortality in India and the USA. According to statistics, a person dies out of a heart-related disease every 36s. COVID-19 has introduced several problems that have intensified the issue, resulting in increased deaths associated to heart disease and diabetes. The entire world is searching for new technology to address thesechallenges. Artificial intelligence [AI] and machine learning [ML] are considered as the technologies, which are capable of implementing a remarkable change in the lives of common people. Health care is the domain, which is expected to get the desirable benefit to implement a positive change in the lives of common people and the society at large. Previous pandemics have given enough evidence for the utilization of AI-ML algorithm as an effective tool to fight against and control the pandemic. The present epidemic, which is caused by Sars-Cov-2, has created several challenges that necessitate the rapid use of cutting-edge technology and healthcare domain expertise in order to save lives. AI-ML is used for various tasks during pandemic like tracing contacts, managing healthcare-related emergencies, automatic bed allocation, recommending nearby hospitals, recommending vaccine centers nearby, drug-related information sharing, recommending locations by utilizing their mobile location. Prediction techniques are used to save lives as early detections help to save lives. One of the problems that might make a person suffering from COVID-19 extremely sick is heart disease. In this research, four distinct machine learning algorithms are used to try to detect heart disease earlier. Many lives can be saved if heart disease can be predicted earlier.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Goswami M.; Sebastian N.J.</text>
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                <text>Lecture Notes on Data Engineering and Communications Technologies, Vol-96, pp. 645-658.</text>
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                <text>Goswami M., CHRIST (Deemed to be University), Bangalore, India; Sebastian N.J., CHRIST (Deemed to be University), Bangalore, India</text>
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                <text>Internet of things (IoT) is spawning a twirl in the world of connected devices by aiding the devices to connect, compute, and coordinate with each other. While the concept of IoT is still embryonic, its outcomes are trailblazing. IoT acts as a facilitator in creating a smart world by connecting devices through sensors and actuators to the Internet. The acceptance of IoT in various sectors indicates that the partakers in an IoT ecology are diverse. This demands common functionalities, interoperability standards, and network protocols across sectors. But there exists an extremity of incongruency in devices, capabilities, and network protocols, and therefore it is imperative to have a complete reference architecture model that necessitates the existing diversities and defines a new monody for the IoT environment. The lack of standard and uniform architectural knowledge, frameworks, and platforms is presently resisting the researchers to reap the benefits that the Internet of things (IoT) offers. This chapter summarizes various Internet of things frameworks, architectures, platforms, and reference models and thereby paves way for businesses to build IoT on it.  2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.</text>
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                <text>Rajasekaran S., Department of Chemistry, CHRIST (deemed to be University), Bangalore, Karnataka, 560029, India; Devi K.R.S., Department of Chemistry, CHRIST (deemed to be University), Bangalore, Karnataka, 560029, India; Pinheiro D., Department of Science and Humanities School of Engineering and Technology, CHRIST (deemed to be University)) Kengeri Campus, Bangalore, Karnataka, 560074, India; Mohan M.K., Department of Science and Humanities School of Engineering and Technology, CHRIST (deemed to be University)) Kengeri Campus, Bangalore, Karnataka, 560074, India; Iyyappa Rajan P., Asia Pacific Center for Theoretical Physics, POSTECH Campus, Pohang, 37673, South Korea</text>
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                <text>The Micro, Small, Medium Enterprises (MSME) sector has one of Indias highest employment Indexes and is the launchpad for all genres and innovators. This sector is inclusive in integrating grass root level workers into tech innovators. There are about 63 million MSMEs in India, employing 110 million individuals. According to 2019 MSME reports, the sector contributed 29% to the overall GDP catalyzing socio-economic development. The Covid-19 pandemics have left world economies and business entities to redefine and rethink policy regulations and business models. The pandemic has created socio-economic displacement across business sectors, and no country is free from the socio-economic exclusions that has triggered. The Indian economy has been badly affected by a projection of over a seven percent decline in quarterly GDP in 2021.The coronavirus pandemic has impacted MSME earnings by 2050 percent, with micro and small organizations being the worst hit due to liquidity crunch. According to the survey conducted by Endurance International Group, many MSMEs have temporarily shut their operations or laid off their staff due to the inability to pay salaries. Further, due to slip in demand and halted production, many had to vacate the rented premises where they were functioning. MSMEs seek government support to tide over the situation with policy interventions on tax discounts or exemptions and loans distributed at cheaper rates or zero interest rates. With the economic slowdown and global restrictions on business outsourcing, and border tensions with China, India revived its Swadeshi (ethnic) dream of Mahatma Gandhi. The Government launched Atmanirbhar Bharat Mission to boost MSMEs and thrust indigenous industries and processes to reduce our foreign nations resilience. Indian government policies are favourable because they have committed $50 billion to help small businesses survive and provide low-income workers with a $266 billion stimulus package of around two percent of Indias annual economic output. Aatmanirbharta which means self-reliance, has been chosen by Oxford Languages as its Hindi word of the year 2020 as it authenticated the everyday achievements of the countless Indians who survived the perils of a pandemic, as stated in one of the popular daily newspaper. The paper focuses on the issues and challenges faced by MSMEs in India due to the pandemic. Further, an analysis of changes in MSME definition presented in the Union Budget 2021 and various policy interventions by the Government and their impact on reviving in the MSME sector is presented.  2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.</text>
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                <text>Contributions to Management Science, pp. 107-121.</text>
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                <text>Internet of Things (IoT) can be defined as a system that consists of a group of things where information is exchanged with the help of the internet, sensors, and devices. The boom of IoT is mainly because of the factor that it does not require human influence and can take place independently in utilizing digital information from physical devices. The main concern is how the integration of these technologies creates unique applications for the ease of human life. This chapter discusses various technologies of IoT in healthcare and their numerous applications in medical field. It also introduces the involvement of augmented reality that is acquiring a new dimension in the Internet of Things.  2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.</text>
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                <text>Comparing Strategies for Post-Hoc Explanations in Machine Learning Models</text>
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                <text>Most of the machine learning models act as black boxes, and hence, the need for interpreting them is rising. There are multiple approaches to understand the outcomes of a model. But in order to be able to trust the interpretations, there is a need to have a closer look at these approaches. This project compared three such frameworksELI5, LIME and SHAP. ELI5 and LIME follow the same approach toward interpreting the outcomes of machine learning algorithms by building an explainable model in the vicinity of the datapoint that needs to be explained, whereas SHAP works with Shapley values, a game theory approach toward assigning feature attribution. LIME outputs an R-squared value along with its feature attribution reports which help in quantifying the trust one must have in those interpretations. The R-squared value for surrogate models within different machine learning models varies. SHAP trades-off accuracy with time (theoretically). Assigning SHAP values to features is a time and computationally consuming task, and hence, it might require sampling beforehand. SHAP triumphs over LIME with respect to optimization of different kinds of machine learning models as it has explainers for different types of machine learning models, and LIME has one generic explainer for all model types.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Vij A., Christ (Deemed To Be University), Lavasa, India; Nanjundan P., Christ (Deemed To Be University), Lavasa, India</text>
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                <text>Recommender systems have seen tremendous growth in the last few years due to the emergence of web services like YouTube, Netflix, and Amazon, etc. An excessive amount of data is being utilized to give proper recommendations to the users. The number of research articles getting published every day is increasing exponentially and thus an efficient model is required to provide accurate and relevant recommendations to the research scholars. The proposed Deep Reinforcement Recommender for Citations (DRRC) model uses reinforcement learning to train the available citation network to achieve the most relevant recommendations. The proposed DRRC model outperforms the state-of-the-art models.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Raghavendra Rao A., Data Science Department, CHRIST (Deemed to be) University and Specialist-QMS, First American India Private Ltd., Bengaluru, India; Samanta D., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India</text>
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                <text>Customer relationship management has recently been one of the key factors in the success of many organizations. Organizations have realized the importance of customer satisfaction and are integrating their operations with that of customer relationship to serve the customers in a better way. This paper seeks to understand the importance of CRM in e-business. It also talks about the importance of customer relationship for an organization in its growth. Relationship marketing has been studied to show how customer relationship management software can be made use of for the benefit of an organization.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>ISSN: 23673370</text>
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                <text>Siby A., CHRIST (Deemed to be University), Bengaluru, India; George J.P., CHRIST (Deemed to be University), Bengaluru, India</text>
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                <text>Innovative Natural Disaster Precautionary Methods Through Virtual Space</text>
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                <text>3D environment; Humancomputer interaction; Immersion; Interaction; Natural disasters; Precautionary; Simulation; Virtual reality</text>
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                <text>Humancomputer interaction is the study of a human and computer interaction in which we analyze and create an interface between the humans and the computer to decide to which extent it is possible to interact with computers which change the way of the usual lifestyle that can evolve the future generations according to the humans convenience. Virtual reality environments in natural disasters are to train people to overcome or prevent their lives from risky situations. When it comes to natural disasters, people never know when such disasters strike in their daily lives, so it is necessary to be prepared to face such consequences. Though the rescuers are there to save the lives of the people, it is not possible to wait for the rescuers all the time, and the situations may also be even worse than the expected. It becomes highly impossible to take precautionary measures; therefore, after the warning of the disaster, people can prepare themselves to survive such situations without the help of rescuers. Different disasters happen in different landscapes; for example, Tsunami occurs in the sea, floods occur as a temporary disaster that covers the land with water, usually not covered by water, and many other disasters that cause life and damage property. Therefore, with the help of virtual reality simulation, people can be trained according to the scenarios or the natural disaster created by the computer-generated 3D environment where the trainee can interact and perform actions generated based on the scenarios. In the virtual world, provided in the head-mounted display, the user can be trained upon by first instructing what to be done and later, after understanding the situation, the trainee is put into a natural disaster scenario where he performs the precautionary measures that need to be done based on the scenario and prepare accordingly in such situations so that before the arrival of the rescuers, people would be more aware of what measures to be taken and react accordingly in such a way that it reduces the risk of life. The chapter further explains in detail about humancomputer interaction (HCI), virtual reality (VR), advantages and disadvantages of virtual reality, various natural disasters, and the role and impact of VR environment in creating awareness and providing precautionary measures for preventing natural disasters. When it comes to immersive technology and smart cities, it is equally important to make everything smart according to the changing generations and technologies in our day-to-day lives. On the other hand, when dealing with people to make them understand and educate things, we must also enhance teaching and make them feel interested in whatever we impose on them. So, when we give the people a 360-degree view or a three-dimensional view of the scenarios, it helps them experience like they are actually into the scenario to understand and make immediate decisions. The advantage of using such immersive technology is that when errors or misjudgments are made to learn from the mistakes and correct it, it helps them understand the scenario and take spot and efficient decision at the time of disasters which will have a significant impact on rescuing the lives of the people.  2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.</text>
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                <text>Surya S.; Aurelia S.</text>
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                <text>Surya S., Department of Computer Science, Christ University, Bangalore, India; Aurelia S., CHRIST (Deemed to be University), Bangalore, Karnataka, India</text>
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                <text>Trends in Terahertz Biomedical Applications</text>
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                <text>Terahertz radiation has drawn enormous attention in recent times due to its various application possibilities. This chapter reviews various emerging trends and well-established technologies in Terahertz biomedical. Due to its extraordinary sensing capabilities, non-invasive, non-ionizing properties, sensitive instrumentations for spectroscopy and imaging, Terahertz has found various biomedical sensing applications from biomolecules, proteins to cells and tissues. This chapter highlights terahertz device engineering, system technologies, range of materials, aiming at various biomedical applications. It also includes emerging topics such as terahertz biomedical imaging, pattern recognition and tomographic reconstruction by machine learning and artificial intelligence, for possible biomedical imaging applications.  2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.</text>
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                <text>Transnational Railway Cultures: Trains in Music, Literature, Film, and Visual Art, pp. 162-178.</text>
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                <text>Chatterjee A., Department of English and Cultural Studies, CHRIST (Deemed to Be University), Bengaluru, India</text>
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                <text>IoT and wearables for detection of COVID-19 diagnosis using fusion-based feature extraction with multikernel extreme learning machine</text>
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                <text>COVID-19; Deep Learning; Feature Extraction; Fusion model; Radiological Images</text>
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                <text>Presently, wearables act as a vital part of healthcare sector and they are able to offer exclusive perceptions about the person's health conditions. In contrast to traditional diagnosis in a hospital environment, wearables can give unrestricted access to real-time physiological data. COVID-19 epidemic is increasing at a faster rate with limited test kits. Hence, it becomes essential to develop a novel COVID-19 diagnostic model. Numerous studies were based on the utilization of artificial intelligence techniques on radiological images to precisely identify the disease. This chapter presents an efficient fusion-based feature extraction with multikernel extreme learning machine (FFE-MKELM) for COVID-19 diagnosis using internet of things (IoT) and wearables. Primarily, the wearables and IoT are used to capture the radiological images of the patient. The presented FFE-MKELM model incorporates Gaussian filtering based preprocessing for removing the noise that exists in the radiological image. Besides, directional local extreme patterns with deep features based on Inception v4 model are applied for the FFE process. In addition, MKELM model is utilized as a classification model to determine the appropriate class label of the input radiological images. Moreover, monarch butterfly optimization algorithm is applied to fine tune the parameters involved in the MKELM model. Experimental validation of the FFE-MKELM model is performed against benchmark dataset and the outcomes are inspected under different measures. The resultant simulation outcome ensured the betterment of the FFE-MKELM method by demonstrating an increased sensitivity of 97.34%, specificity of 97.26%, accuracy of 97.14%, and F-measure of 97.01%.  2022 Elsevier Inc. All rights reserved.</text>
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                <text>Ganesh Kumar R.; Toomula S.; Paulraj D.; Jebin Bose S.; Bikku T.; Sivabalaselvamani D.</text>
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&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127957821&amp;amp;doi=10.1016%2FB978-0-323-85854-0.00008-3&amp;amp;partnerID=40&amp;amp;md5=ff173f59d33519a0fc67527e8e08a462" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127957821&amp;amp;doi=10.1016%2fB978-0-323-85854-0.00008-3&amp;amp;partnerID=40&amp;amp;md5=ff173f59d33519a0fc67527e8e08a462&lt;/a&gt;</text>
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                <text>Ganesh Kumar R., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST, Bangalore, Karnataka, India; Toomula S., Department of Computer Science, RBVRR Women's College, Hyderabad, TELANGANA, India; Paulraj D., Department of CSE, RMK College of Engineering and Technology (RMKCET), Thiruvallur, Tamil Nadu, India; Jebin Bose S., Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu, India; Bikku T., Computer Science and Engineering, Vignan's Nirula Institute of Technology and Science for Women, Guntur, Andhra Pradesh, India; Sivabalaselvamani D., Department of Computer Applications, Kongu Engineering College, Erode, Tamil Nadu, India</text>
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                <text>Integration of technology initiatives with educational neuroscience and its impact on technology readiness to technology adoption by HSS Teachers, Kerala</text>
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                <text>Education change model; Educational neuroscience; Techno-learning and memory; Technology Adoption; Technology Readiness</text>
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                <text>The technology-enabled education process remoulded the modern education systems. The facelift of education 4.0 process harmonized the education systems with industrial demands and technology advancements. The education reforms of the State of Kerala with the tools of technology and neuroscience could achieve remarkable milestones in the education sector. This case study analyses the digital initiatives of KITE and its role on providing uninterrupted-effective education during the Covid-19 pandemic in Kerala. This study is affirmed with quantitative study on how these integrated technology initiatives impact on Technology Adoption of the HSS teachers with respect to their Technology Readiness. Responses of 857 teachers from six education districts of Kerala were used for this study. This study is relevant as it could connect the pre-Covid digital initiatives which could successfully empower the teachers to face the Covid-19 pandemic situation without interrupting the education process amidst the Covid-19 restrictions in Kerala. The study identified that the technology learning initiatives with tools of educational neurosciences have partially mediated teachers' Technology Readiness to Technology Adoption. The multiple learning initiatives integrated with the tools of technology and educational neuroscience could fully support the virtual learning throughout the State of Kerala during the Covid-19 pandemic situations.  The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved.</text>
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                <text>Joseph G.V.; Thomas K.A.</text>
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                <text>Neuro-Systemic Applications in Learning, pp. 423-444.</text>
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&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122462328&amp;amp;doi=10.1007%2F978-3-030-72400-9_21&amp;amp;partnerID=40&amp;amp;md5=65a45c07cc05b4d51ebad2ffb80ace31" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122462328&amp;amp;doi=10.1007%2f978-3-030-72400-9_21&amp;amp;partnerID=40&amp;amp;md5=65a45c07cc05b4d51ebad2ffb80ace31&lt;/a&gt;</text>
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                <text>Joseph G.V., Vimal Jyothi Institute of Management and Research, Chemperi PO, Kannur, Kerala, India; Thomas K.A., CEDBEC, CHRIST (Deemed to be University), Banglore, Karnataka, India</text>
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