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Internet of Things Based Autonomous Borewell Management System
Water is a basic need for all living beings. At present, due to a large population, water level is getting depleted at an alarming rate particularly in urban region. During summer season, there is no continuous flow of water or availability of water. In electrical contingency situations, bore-wells are prone to damages. The utilization of power at dry run condition affects the economy of the consumers. Despite having no water in the bore-well, if the motor runs, the motor windings may burdened and gives rise to unnecessary power loss. In the present scenario, conservation of energy is a major concern. The conservation of energy as a whole will take place when an individual take an active part by using autonomous and effective methodologies or controllers. The issue is solved by managing the borewell using Internet of Things (IoT) as a platform to automate and manage. The IoT based borewell management system is designed to provision scheduling, manual operation, avoidance of borewell motor running at dry run condition and also nullifies energy loss. The automated borewell operations can be executed from a remote control and measurement unit by the measurement of electrical parameters and analytics. The proposed system minimizes man power, saves time and conserve energy loss. The paper presents operating the conventional borewell by deployment of smart controller which handles the information and communication technology at client and base units. Springer Nature Switzerland AG 2020. -
Internet of Things Enabled Device Fault Prediction System Using Machine Learning
Internet of Things (IOT) started as a niche market for hobbyists and has evolved into a huge industry. This IoT is convergence of manifold technologies, real-time analytics, machine learning and Artificial Intelligence. It has given birth to many consumer needs like home automation, prior device fault detection, health appliances and remote monitoring applications. Programmed recognition and determination of different kinds of machine disappointment is a fascinating process in modern applications. Different sorts of sensors are utilized to screen flaws that is discovers vibration sensors, sound sensors, warm sensors, infrared cameras, light cameras, and other multispectral sensors. The modern devices are becoming ubiquitous and pervasive in day to day life. This device is need for reliable and predicate algorithms. This article is primarily emphases on the prediction of faults in real life appliances making our day to day life easier. Here, the database of the device includes previous faults which are restored in online by using cloud computing technology. This will help in the prediction of the faults in the devices that are to be ameliorated. It additionally utilizes Nae Bayes calculation for shortcoming location in the gadgets. The proposed model of this article is involves the monitoring of each and every home appliance through internet and thereby detect faults without much of human intervention. Springer Nature Switzerland AG 2020. -
Internet of things for building a smart and sustainable environment: A survey
In the previous decade, internet of things (IoT) has emerged as a transformative force in the quest to create smarter and more sustainable environments. By interconnecting a large array of sensors, devices, and infrastructure, IoT technology enables the real-time collection, analysis, and utilization of data to optimize resource management, improve decision-making, and reduce environmental impact. In smart cities, homes, industries, and agricultural settings, IoT plays a pivotal role in achieving resource efficiency, environmental preservation, and economic growth. However, its widespread adoption also poses several challenges related to privacy, security, and interoperability. As IoT continues to evolve, it promises to shape a future where sustainability and technological innovation go hand in hand, making a path toward more resilient, efficient, and livable environments. 2024, IGI Global. All rights reserved. -
Internet of Things Security and Privacy Issues in Healthcare Industry
The Internet of Things (IoT) is an imagines unavoidable, associated, and hubs connecting independently while offering a wide range of administrations. Wide conveyance, receptiveness and moderately high handling intensity of IoT objects made them a perfect focus for digital assaults. Additionally, the same number of IoT center points is assembling and taking care of private data, they are changing into a goldmine of information for malignant on-screen characters. Subsequently, security and particularly the capacity to recognize traded off hubs, together with gathering and safeguarding confirmations of an assault or malignant exercises develop as a need in effective arrangement of IoT systems. This paper is deal with some major security problems and challenging factors of IoT. This IoT security issues on really challenging factor in current world. 2019, Springer Nature Switzerland AG. -
Internet of Things-Based Smart Agriculture Advisory System
The Internet era provides a lot of automation tools for data analysis, and it is the need of the hour to develop new analytical tools to manage the big data. For task automation, machine learning and expert systems are of primary importance to study the behavior of computer thinking to involve computers in sensible work, known as computational intelligence. The data involves varied formats such as structured, unstructured, as well as semi-structured, and it is an automation tool that uses computational intelligence to extract valid and potential information from the sources. The specific purpose of this proposed work is to meet out computing demands which highly rely on computational intelligence. Computational intelligenceinvolves the design and deployment of an analytical tool for multidimensional data analytics. The proposed integrated framework focuses on multidimensional data analytics, for crop and plant data, especially plants that contain medicinal values and components. This research works main aim is to create a secured data tool for agriculture crop data management through big data (crops and plants) analytics. The data security is enhanced through applied cryptography, and the final phase prediction on crops is done by various machine and deep learning algorithms. The specific objective of this research work is to help farmers in making informed decisions for the enhancement of cultivation and information. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
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. -
Internet of things: Service-oriented architecture opportunities and challenges
Internet of Things is now a subject that is increasingly growing on both the job and modern devices. It is a concept that maybe not just get the potential to influence how we live but in addition how we work. Intelligent systems in IoT machines in many cases are used by various events; consequently, simultaneous information collection and processing are often anticipated. Such a characteristic that is exclusive of systems has imposed brand new challenges towards the designs of efficient data collection processes. This article is to be discussing various layers in Internet of things. Those layers are sensing layer, network layer, service layer and application layer. Various data processing techniques are integrated along with data filtering and data conversion. Protocol transformation is also feeling the major challenges faced by enterprises wanting to shift to the style in brand new technology. Springer Nature Singapore Pte Ltd. 2020. -
Interventions to help students find a deeper purpose during their academic journey
This comprehensive exploration delves deeply into academic interventions aimed at guiding students toward a more profound sense of purpose throughout their educational journeys. It emphasizes that education offers a unique opportunity for students to discover their passions, interests, and aspirations beyond textbooks and exams. The interventions discussed, including mentorship, career counseling, experiential learning, and self-discovery exercises, are meticulously designed to empower students to recognize their distinct strengths and interests. These interventions not only aim to facilitate academic excellence but also enable students to pursue careers aligned with their core values and aspirations. The exploration scrutinizes effective strategies, programs, and support mechanisms, addressing challenges students face when making career choices, and culminates in recommendations for educators, career counselors, and policymakers interested in enriching students' educational experiences and fostering purpose-driven learners. 2024, IGI Global. -
Introduction
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Introduction
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Introduction
In the digital era, signal processing has found application in daily life from medical diagnosis to social networking. The digital domain has evolved as the preferred choice in communication system design due to its advantages over analog systems, such as high-speed transmission, improved quality, and effortless copying with high precision. The advancement of digital media brings new opportunities. The internet boom of this millennium has allowed digital data to move around the world in real time. Efficient segmentation, recognition, and analysis of multidimensional data such as hyperspectral images, medical imaging, data analysis in social media, and audio signals are still challenging issues. Digital data produced through data-processing algorithms has the fundamental advantages of transportability, proficiency, and accuracy of information content; but such data is also at significant risk because perfect illegal replicas can be made in unlimited numbers. 2021 John Wiley & Sons Ltd. -
Introduction to blockchain for internet of things
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Introduction to Data Mining and Knowledge Discovery
Data mining is a process of discovering some necessary hidden patterns from a large chunk of data that can be stored in multiple heterogeneous resources. It has an enormous use to make strategic decisions by business executives after analyzing the hidden truth of data. Data mining one of the steps in the knowledge-creation process. A data mining system consists of a data warehouse, a database server, a data mining engine, a pattern analysis module, and a graphical user interface. Data mining techniques include mining the frequent patterns and association learning rules with analysis, sequence analysis. Data mining technique is applicable on the top of various kinds of intelligent data storage systems such as data warehouses. It provides some analysis processes to make some useful strategic decisions. There are various issues and challenges faced by a data mining system in large databases. It provides a great place to work for data researchers and developers. Data mining is the process of classification, which can be executed based on the examination of training data (i.e., objects whose class label is predefined). With the help of an expert set of previous class objects with known class labels, it can find a model that can predict a class object with an unknown class label. These classification models can be classified into a variety of categories, including nearest neighbor, neural network, and others. Bayesian model, decision tree, neural network Random forest, decision trees Support vector machine, random forest SVM (support vector machine), for example. By analyzing the most common class among k closest samples, the K-Nearest Neighbor (KNN) technique aids in predicting of the class object with the unknown class label. Its an easy-to-use strategy that yields a solid classification result from any distribution. The Naive Bayes theory helps to perform the classification. It is one of the fastest classification algorithms, capable of efficiently handling real-world discrete data. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Introduction to quantum machine learning
Quantum Machine Learning (QML) is popularly known to be an integrative approach to learning of the Quantum Physics (QP) and Machine Learning (ML). In this chapter, an outline of the fundamental ideas and features related to quantum machine learning is laid out. The different facets of quantum algorithms are discussed in this chapter. In addition to this, the basic features of quantum reinforcement learning and quantum annealing are also provided in this chapter. Finally, the chapter deliberates about the advancement of quantum neural networks to through light in the direction of QML. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved. -
INTRODUCTION TO TEACHING INTERNSHIP: An Analytical Approach
The introduction chapter presents the transition that teaching internship models underwent from the 20th century to the 21st century. It addresses the need for acquiring the teaching knowledge required of a teacher trainee suitable to the present time. It provides a glimpse of internship practices in other professions like medicine, law and business to provide an analytical base to understand the teaching internship. A successful teaching practicum is determined by its outcome and the experiences offered at the internship site. The chapter also sheds light on the expectations and benefits of teaching internship. It also clarifies the terms used in teaching internship for a common understanding from the stakeholders. It explores the idea of mentor and mentorship in teaching internship. The chapter concludes by providing an overview of the succeeding chapters in the volume. 2023 selection and editorial matter G.S. Prakasha and Anthony Kenneth; individual chapters, the contributors. -
Introduction, scope and significance of fermentation technology
Fermentation technology is a field which involves the use of microorganisms and enzymes for production of compounds that have applications in the energy, material, pharmaceutical, chemical and food industries. Though fermentation processes have been used for generations as a requirement for sustainable production of materials and energy, today it has become more demanding for continuous creations and advancement of novel fermentation processes. Efforts are directed both towards the advancement of cell factories and enzymes, as well as the designing of new processes, concepts, and technologies. The global market of microbial fermentation technology was valued at approximately USD 1,573.15 million in 2017 and which is expected to generate revenue of around USD 2,244.20 million by end of 2023. However, regular supply of materials, such as nutrients, microorganisms, the complex nature of production process, and high manufacturing cost hinder the market growth. 2019 Scrivener Publishing LLC. All rights reserved. -
Introduction: Tourism at a crossroads
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Investigation into the Mechanical, Fatigue and Superplastic Characteristics of Shape Memory Alloys (SMA) in CuAlMn, CuAlBeMn, and CuAlFeMn Compositions and Their Composite Variants
Shape memory alloys (SMAs) exhibit high sensitivity to compositional changes in terms of their super elasticity, shape memory effect, and transition temperatures. A deeper comprehension of SMA composition and its impact on mechanical properties can be attained by differential scanning calorimetry. The current study uses experimental work to assess the energy absorption capacity, mean fracture width, residual strength, and cracking strength of samples made of short shape memory alloy (SMA) fibers that are randomly distributed on the specimens tensile side. In this investigation, three samples were synthesized based on the Cu, Al, and Mn proportions found in CuAlMn shape memory alloys (SMA1, SMA2, and SMA3). Moreover, three samples with different ratios of Cu, Al, Mn, Be, and Fe were synthesized for the shape memory alloys CuAlBeMn and CuAlFeMn (SMA2, and SMA3). The synthesized CuAlMn, CuAlBeMn, and CuAlFeMn SMA alloys showed good strain recovery, ranging from 90 to 95%. The martensite that forms and changes when the alloys are heated and quenched mostly controls the strain recovery by the corresponding SMAs. SMA 2 of the CuAlBeMn has a greater strain recovery rate, rising by 8.5% and 44.38%, respectively, in comparison to SMA 1 and SMA 3. CuAlBiMn shape memory alloys demonstrated superior super elasticity and martensite stability in comparison to SMA 1 and SMA 2 respectively. SMA 1 and SMA 2 demonstrated greater residual strength, cracking strength, and energy absorption capacity for all fiber volume fractions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Investor behaviour, market efficiency, and regulatory challenges in digital currency investments: A comprehensive literature review
The rapid proliferation of digital currencies has ignited significant interest in understanding investor behaviour and the regulatory challenges within this evolving financial landscape. The study seeks to elucidate the factors that influence investor behaviour in the cryptocurrency/digital currency market and explore the regulatory implications associated with digital currency investments. A systematic approach was adopted to identify, review, and analyze relevant scholarly articles from reputable databases. The selected articles span various dimensions of the cryptocurrency/digital currency ecosystem, including investor behaviour, market efficiency, technological innovation, and regulatory measures. It reveals that investor behaviour in the digital currency market is influenced by a complex interplay of emotions, market sentiment, and information asymmetry. Additionally, market efficiency theories are being reconsidered in light of the highly volatile nature of cryptocurrencies. Regulatory challenges encompass issues related to fraud, pump-and-dump schemes, and legal ambiguity. 2024, IGI Global. All rights reserved. -
Involvement and implementation of corporate social responsibility (CSR): A case of COVID-19 evidence from various countries
Industry performance is a pivotal indicator for assessing the robustness of economic growth of a country. Any fluctuations in this indicator wield considerable influence over economic growth. Previous research has established that the industry performance is not a static phenomenon but rather exhibits variations across different time periods. Amongst the multitude of crises, the COVID-19 pandemic reverberated across every nation, spreading to all strata of society. It had an adverse impact on both public and private sector impacting the lives of millions. Industries were struggling with dwindling profits, thus trying vehemently to cut costs in diverse realms. This challenging financial predicament made it difficult for companies to uphold their corporate social responsibilities. Hence, only a few companies initiated CSR activities due to the paucity of funds. Having this in the backdrop, this case study analysis aims to examine corporate involvement and implementation of CSR activities during the COVID-19 pandemic and assesses the extent to which these activities have helped improve people's lives. 2024, IGI Global. All rights reserved.