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Effect of audiovisual aids and blended teaching on english performance and self-confidence of IX standard students in government schools of Manipur
For a productive teaching-learning process, proper planning and direction is required since the process in itself is the key to a person's progress and development. Accordingly, resources which aid in the teaching and learning process should be selected judiciously and in harmony with the concept in question. Audiovisual aids are sensory objects and images which stimulate and emphasize the learning process. The use of visual aid in teaching has the potential to increase "human bandwidth": the capacity to absorb, to comprehend, and to effectively synthesize the information into new knowledge. Blended teaching is a kind of e-learning which utilizes a scope of instruments and instructive guides to make a learning environment interactive synchronously or asynchronously and improves the learning procedure by offering projects and courses electronically by means of various mixed media specialized devices. The result indicates that there is an impact of the use of audiovisual aids and blended teaching in English performance and self-confidence of the students. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
Energy-efficient smart cities with green internet of things
With governments of different countries having a vision of smart cities, the technology adoption and implementation are at its peak and the current increase in the usage of advanced technology for a smart city has led to an increase in the carbon imprint across the globe, which needs immediate attention for the environment sustainability. Although the Internet of things (IoT)-enabled devices have changed our world by bringing an ease to our lifestyle, it has to be kept under consideration that they also have adverse effects on the environment. Over the past few years, enabling energy conservation via Internet of Things in the growth of smart cities has received a great deal of attention from researchers and industry experts and has paved the way for an emerging field called the green IoT. There are different dimensions of IoT, in which an effective energy consumption is needed to encourage a sustainable environment. This conceptual paper focuses on the key concept of green IoT and sustainability, knowledge of Smart cities' readiness to Green IoT (G-IoT)-enabled sustainable practices, and identifying the Green IoT sustainability practices for smart cities. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
Integration of technology initiatives with educational neuroscience and its impact on technology readiness to technology adoption by HSS Teachers, Kerala
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. -
IoT and wearables for detection of COVID-19 diagnosis using fusion-based feature extraction with multikernel extreme learning machine
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. -
State of local governance and urban development problems: A study of Bengaluru
[No abstract available] -
Offline Character Recognition of Handwritten MODI Script Using Wavelet Transform and Decision Tree Classifier
MODI script is derived from the N?gari family of scripts, and it was used for writing Marathi until twentieth century. Though currently not used as an official script, it has historical importance, as a large volume of manuscripts are preserved at various libraries across India. With the use of an appropriate recognition system, the handwritten documents can be transferred into digital media, so that it can be conveniently viewed, edited, or transliterated to other scripts. The research on MODI script is still in the initial stages, and there is a considerable demand for more research in this field. An implementation of wavelet transform-based feature extraction for MODI scripts character recognition is discussed in this paper. The experiment is performed using Daubechies, Haar, and Symlet wavelets, and performance comparison between these different mother wavelets is carried out. Decision tree classifier is used for the classification process, and the results indicate that the feature extraction using Daubechies wavelet yielded better character recognition result. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Fundamental Study on Electric Vehicle Model for Longitudinal Control
Stricter emission norms need to drift toward being environment friendly have shifted the concentration in the automobile sector toward electric vehicles. This research article highlights the fundamental modeling steps required for an electric vehicle control system design following a simulation approach using MATLAB/Simulink software. From an electric vehicle design perspective, this approach offers an excellent solution to give insights into EV research involving multidisciplinary engineering aspects. The study presents longitudinal control technique, relevant observations and results to bring out the differences in open-loop and closed-loop case studies. It also intends to provide better understanding toward the need for a feedback, realization of an expected path profile for students and researchers in this field of interest. The steps involved in transforming the mathematical model into a simulation model and analysis of the simulation results are detailed in this article. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Trends in Terahertz Biomedical Applications
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. -
Innovative Natural Disaster Precautionary Methods Through Virtual Space
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. -
Influence of Customer Relationship Management for the Success of E-Business
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. -
A Real-Time Approach with Deep Learning for Pandemic Management
It has never been so critical to managing pandemic situations created by a virus like COVID-19, which has brought the world almost to a standstill, claiming millions of lives. Learning from all earlier viruses and building a quick tackling mechanism is a need of the hour. There is a greater need for technology to collaborate with healthcare and leverage each of the domains expertise. With less time in hand, this collaboration must happen in a short time. There is a need to study the exiting progression in technology and the healthcare landscape to bring them to a common path for practical solutions. In the chapter, an attempt was made to put together some thoughts in both fields to relate them to pandemic managements frequent subject. Caution is drawn towards some crucial aspects, such as security and transparency, that cannot be compromised in this journey. Artificial intelligence (AI), being at the forefront of the technology supporting lives, provides a greater hope in this direction. Some of the prominent approaches can be looked at from a pandemic management point of view, which can start a more in-depth discussion on AI and healthcare going hand in hand in managing this pandemic situation. Essential areas of pandemic management, such as building on the knowledge gathered over a period, plugging in the real-time data from the society, building efficient data management systems and building transparent and interpretable solutions are the focus areas of exploration in this chapter. 2022, Springer Nature Switzerland AG. -
A Citation Recommendation System Using Deep Reinforcement Learning
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. -
Comparing Strategies for Post-Hoc Explanations in Machine Learning Models
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. -
Functionalities and Approaches of Multi-cloud Environment
Cloud computing is a paradigm that envisions fast access to any resources from anywhere at any time with the most significant advantages of enabling an intuitive environment that offers plethora of services to mankind. The exponential increase in technological advances has been an advantage for the growth of cloud computing. The advancement in technology has enabled the shift in organizations from using a single cloud toward multi-cloud strategy. Multi-cloud uses multiple services from various cloud vendors which has been advantageous in several ways. Multi-cloud strategy has been designed to enable a hybrid mode for the organizations with ample security and savings in cost. This chapter gives an overview of multi-cloud computing and the security issues with respect to cloud computing. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Internet of Things: Immersive Healthcare Technologies
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. -
Under Pressure: Integrating Policy Interventions to Save Distressed Indian SMEs of COVID-19 Aftershocks
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. -
Metal Organic Frameworks to Remove Arsenic Adsorption from Wastewater
Water is an integral part of life on earth. Rapid industrialization, urbanization, and population explosion have all contributed to the pollution of ground and surface water with, among other things, heavy metals. This has led to an acute shortage of clean drinking water. Arsenic is one of the most toxic heavy metals found in water, posing a serious threat to the environment, human beings, and aquatic life. Over the years, a considerable amount of research has been directed toward the elimination of arsenic from water via sustainable methodologies. Metal organic frameworks are a class of materials possessing exceptional features like chemical stability, high porosity, multiple functional groups, and large surface areas. These properties can be effectively channelized to make metal organic frameworks excellent adsorbents for the removal of arsenic from contaminated water and make it drinkable. We have reviewed herein, the problems of heavy metal contamination, specifically the different forms of arsenic that pollute water. The importance of metal organic frameworks and the progress made in the synthesis of materials having a metal oxide framework have been discussed. Significant properties like adsorption and mechanistic aspects of adsorption through metal organic frameworks have been described. Furthermore, the characterization of the electronic and geometric aspects of metal organic frameworks using density functional theory has been reviewed. Insight into proper scaling up and development of metal organic frameworks for practical applications have also been suggested. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
IoT Framework, Architecture Services, Platforms, and Reference Models
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. -
Performance Analysis of Logistic Regression, KNN, SVM, Nae Bayes Classifier for Healthcare Application During COVID-19
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.