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Redefining traditional education using augmented reality and virtual reality
[No abstract available] -
Smart cities: Redefining urban life through IoT
A smart city is designed using acceptable internet of things (IoT) technologies that solve urban life problems and provide quality of life to the residents. IoT refers to a network of physical devices that are capable of gathering and sharing data and expediting numerous functions without human assistance. IoT supports smart home builders and managers by providing an efficient ecosystem in terms of less operating cost and improvising residence services. In recent days, the initiative of smart homes/buildings/ cities is increasing gradually around the globe. The inclined population in an urban area also expects well-managed automated services in their everyday life. 2023, IGI Global. -
Sustainable IoT for smart environmental control
The network of physical items/things/objects, that are implanted with sensors, software, and other networking technologies to communicate and exchange data with other devices and systems through the internet, is referred to as the Internet of Things (IoT). Environmental control in the context of IoT systems refers to the use of connected devices and sensors to manage and regulate various aspects of the environment, such as temperature, lighting, air quality, water quality, and more. The goal is to create an intelligent environment that is more efficient, comfortable, and sustainable. 2023, IGI Global. -
Agricultural Internet of Things (AIoT) Architecture, Applications, and Challenges
The internet of things (IoT) is a system that involves adding sensors, software, and network connectivity to physical devices, enabling them to collect and exchange data. This technology has the potential to bring significant advancements to various sectors, including agriculture. In farming, the agricultural internet of things (AIoT) utilizes IoT to improve efficiency, sustainability, and productivity. Through the real-time collection and analysis of data, AIoT can optimize growing conditions, prevent diseases and pests, and ultimately increase crop yields. By monitoring factors such as soil moisture, temperature, and nutrient levels, AIoT technology can effectively track crop health and detect potential issues in advance. In this way, AIoT technology is helping farmers to make more informed decisions and take more effective actions to improve crop yields, reduce waste, and lower costs. AIoT in agriculture finds practical applications in smart irrigation systems, precision agriculture, livestock monitoring systems, and climate control systems. Smart irrigation systems utilize weather data and soil moisture sensors to efficiently manage water consumption. Precision agriculture employs sensors and data analysis techniques to optimize planting, fertilization, and pest control practices. Livestock monitoring systems aid in monitoring and managing the well-being of farm animals. Climate control systems utilize AIoT to regulate and optimize environmental conditions for crops and livestock. Livestock monitoring systems use sensors to track the health and well-being of animals. Climate control systems for greenhouses and barns use AIoT devices to monitor temperature, humidity, and other environmental factors to optimize growing conditions. Sensors can be used to monitor various environmental factors in a farm, by connecting the sensors to a cloud-based platform for storing and analyzing data. The wireless sensor networks can be used to calculate the dew point on leaves and adjust the greenhouse environment to prevent and control plant diseases. Drones equipped with sensors, cameras, and other imaging technology can also be used to monitor crop conditions, as this allows farmers to take proactive measures to address these issues, preventing crop loss and reducing the need for pesticides and other chemicals. IoT/sensor nodes are vital components in precision agriculture as they gather real-time data. Integrating data analytics and machine learning into the agricultural system improves its practicality and efficiency. Real-time data availability enhances precision in agriculture, and combining data analytics with this information leads to notable progress in the field. However, AIoT technology is gradually advancing in agriculture, but there is a need for a more rigorous research approach in this area. Additionally, the current literature lacks coherence and solid research on the interconnectedness of technology and agriculture. 2024 selection and editorial matter, Alex Khang, Vugar Abdullayev, Vladimir Hahanov and Vrushank Shah; individual chapters, the contributors. -
Internet of Things (IoT) as a Game Changer to the Education Sector
This study examines the use of internet of things (IoT) technology in the field of education, concentrating on its uses, advantages, and difficulties. The current educational system frequently fails to provide individualized learning opportunities since it is characterized by traditional classroom settings and teacher-centered learning. But the emergence of IoT and its companion technologies, including big data, artificial intelligence, and network communication, offers fresh chances to transform education. The IoT architecture in the education sector is covered in the opening section of the paper, with an emphasis on the function of IoT devices in building a networked environment. These tools, such as smart HVAC (heating, ventilation, and air conditioning) systems, make it easier to gather and analyze enormous volumes of data. Institutions of higher learning can get important insights and create individualized learning strategies, thanks to the integration of big data and artificial intelligence. The report also examines a variety of IoT uses in education. It emphasizes the importance of IoT in remote learning, which has become more popular recently. It also demonstrates how the internet of things has influenced the development of smart campuses with interactive whiteboards and other IoT gadgets. The importance of personalized learning in contemporary education is also discussed, with IoT acting as a catalyst for experiences that are specifically suited for students. The study also looks at how IoT might benefit students with disabilities and improve staff and student health monitoring. The use of augmented reality and virtual reality tools in teaching is also investigated. The study explores Edutech-based IoT solutions, concentrating on their function in the processes of teaching, learning, and evaluation. It examines management and government initiatives on both a national and international scale, including those from Ireland's Future Schools, Jharkhand's DigiSAT, and Assam's online job advisory portal. The Kajeet Smart Bus, C-Pen, and Ipevo VZ-X Wireless Document Camera are just a few examples of IoT deployments in education that are highlighted in the study. These instances highlight the concrete contribution of IoT to improving educational practices. IoT tools are also examined in relation to several educational contexts, such as primary, secondary, and higher secondary education. The study also examines the distinct needs of special schools and universities and emphasizes the importance of IoT in STEAM teaching at the university level. The chapter discusses the advantages, disadvantages, possibilities, and difficulties that players in the education sector would have when implementing IoT. It highlights how crucial it is to take advantage of the capabilities of big data, artificial intelligence, and network communication to enhance teaching and learning results. The article also highlights problems with the research and suggests potential fixes, noting areas that could use more investigation. This chapter's conclusion highlights the IoT's disruptive potential in the field of education. Education can be revolutionized by integrating IoT devices, utilizing big data and artificial intelligence, and utilizing network communication. It makes it possible to create individualized, engaging, and data-driven learning experiences that get students ready for the digital era. 2024 selection and editorial matter, Alex Khang, Vugar Abdullayev, Vladimir Hahanov and Vrushank Shah; individual chapters, the contributors. -
IoVST: Internet of vehicles and smart traffic - Architecture, applications, and challenges
The internet of things (IoT) is the network of sensors, devices, processors, and software, enabling connection, communication, and data transfer between devices. IoT is able to collect and analyze large amounts of data which can then be used to automate daily tasks in various fields. IoT holds the potential to revolutionise and create many opportunities in multiple industries like smart cities, smart transport, etc. Autonomous vehicles are smart vehicles that are able to navigate and move around on their own on a well-planned road. 2023, IGI Global. -
Aspect based sentiment analysis using fine-tuned BERT model with deep context features
Sentiment analysis is the task of analysing, processing, inferencing and concluding the subjective texts along with sentiment. Considering the application of sentiment analysis, it is categorized into document-level, sentence-level and aspect level. In past, several researches have achieved solutions through the bidirectional encoder representations from transformers (BERT) model, however, the existing model does not understand the context of the aspect in deep, which leads to low metrics. This research work leads to the study of the aspect-based sentiment analysis presented by deep context bidirectional encoder representations from transformers (DC-BERT), main aim of the DC-BERT model is to improvise the context understating for aspects to enhance the metrics. DC-BERT model comprises fine-tuned BERT model along with a deep context features layer, which enables the model to understand the context of targeted aspects deeply. A customized feature layer is introduced to extract two distinctive features, later both features are integrated through the interaction layer. DC-BERT mode is evaluated considering the review dataset of laptops and restaurants from SemEval 2014 task 4, evaluation is carried out considering the different metrics. In comparison with the other model, DC-BERT achieves an accuracy of 84.48% and 92.86% for laptop and restaurant datasets respectively. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Aspect based sentiment analysis using a novel ensemble deep network
Aspect-based sentiment analysis (ABSA) is a fine-grained task in natural language processing, which aims to predict the sentiment polarity of several parts of a sentence or document. The essential aspect of sentiment polarity and global context have deep relationships that have not received enough attention. This research work design and develops a novel ensemble deep network (EDN) which comprises the various network and integrated to enhance the model performance. In the proposed work the words of the input sentence are converted into word vectors using the optimised bidirectional encoder representations from transformers (BERT) model and an optimised BERT-graph neural networks (GNN) model with convolutions is built that analyses the ABSA of the input sentence. The optimised GNN model with convolutions for context-based word representations is developed for the word-vector embedding. We propose a novel EDN for an ABSA model for optimised BERT over GNN with convolutions. The proposed ensemble deep network proposed system (EDN-PS) is evaluated with various existing techniques and results are plotted in terms of metrics for accuracy and F1-score, concluding that the proposed EDN-PS ensures better performance in comparison with the existing model. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Enhancing social cognition in individuals with ADHD: An eastern approach
With the increasing prevalence of ADHD in the global front, it is essential to explore different effective methods for providing support and intervention. The difficulties with social cognition are reflected in their limitations in emotional self-regulation, emotion recognition, and empathy. Though several interventions exist for ADHD, many at times, the effectiveness of eastern approaches are overlooked due to the limited awareness about its nature. Research suggests that systematic and regular practice of yoga helps to improve attention, control emotion, and reduce restlessness among them. Several asanas are found to be especially helpful for managing ADHD symptoms including cobra (bhujangasana) pose, cat-cow pose (bitilasana marjaryasana), downward-facing dog (adho mukha shvanasana), tree pose (vrikshasana), mountain pose (tadasana), among many others. The chapter gives a comprehensive summary on the application of yoga techniques on the improvement of social cognition in individuals with ADHD. 2024, IGI Global. -
Smartphones in churches: An affective negotiation around digital disruptions and opportunities in Delhi Christian Churches
Interpersonal relations in a world of digital ubiquity have brought religious institutions into personal digital networks. The development of personal-religious networks has affected individual faith practices and altered religious responses. This article explores patterns of behaviour emerging from a collective sense of belonging and affective responses in online church networks where smartphones acts as an extension of self. It analyses the relationship between faith and visual practices/aesthetics impacting today's religious experience. It also explains affective loops that are consciously constructed by church authorities to shape collective action. The function of these loops is to create a deeper sense of connectedness between practitioners and the church through harnessing technology and its affective power. 2019 Journal of Content, Community & Communication. -
Data Mining Techniques to Enhance Customer Segmentation and Targeted Marketing Strategies
The retail industry is facing an ever-increasing challenge of effectively identifying and targeting its customers. Using traditional segmentation techniques to fully capture the intricate and ever-changing character of customer behavior is difficult. This project will examine sales data from a general shop using an assortment of data mining technologies in order give insights into customer habits and purchasing trends. Retail sales records builds the dataset. K-means clustering, association rule mining, and regency, the frequency, and monetary (RFM) analysis will all be employed to look into the data. This study contributes to create something of focused marketing strategies and consumer segmentation by identifying high-value and atrisk clients. Association rule mining illuminates consumer taste and actions by identifying hidden patterns and correlations in large datasets. These discoveries extend the scope of our comprehension of consumer purchasing habits and offer data for more targeted advertising initiatives. Additionally, the K-means clustering algorithm divides customers according to their purchasing habits and behavior, allowing profound knowledge to enhance marketing and sales strategies. Findings from the research will give an extensive awareness of customer behavior and purchasing dynamics, which will improve the efficacy of the general store's marketing and sales campaigns. The most effective technique for exploiting insights from sales data will be discovered by contrasting the outcomes of RFM analysis, K-means clustering, and association rule mining. This work promises to make substantial improvements to data mining and buyer behavior research algorithms, and it has the capacity to be implemented across an extensive selection of corporate restrictions intended to improve their sales strategies. 2024 IEEE. -
Exploring Advances in Machine Learning and Deep Learning for Anticipating Air Quality Index and Forecasting Ambient Air Pollutants: A Comprehensive Review with Trend Analysis
India and the rest of the world are growing more and more worried about polluted atmosphere on a daily basis. A comprehensive prevision and prognostication of air quality parameters is vital due to the major harm that air pollution causes to both the environment and public health, causing concern on a global scale. In-depth analyses of the methods for predicting ambient air pollutants, like carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter with diameters less than 10? (PM10) and less than 2.5? (PM2.5), and ozone (O3), are provided in this work in tandem with the modeling of the Air Quality Index (AQI).To further enhance the anticipated precision and applicability of these models, the assessment additionally employs trend analysis to determine precedents and new trends in air quality. This paper offers insights into recent advances in algorithms using deep learning and machine learning for anticipating AQI and forecasting pollutant concentrations by combining current research in this topic. In order to inform policy decisions and measures aimed at reducing air pollution and its adverse effects on public health, trend analysis integration affords a more thorough comprehension of the dynamics of air quality. 2024 IEEE. -
Development and Validation of Emotion Recognition Software in the Indian Population
Though written extensively, recent debates on universality of emotions have shown that age, gender, and ethnicity have greater implications in the ability to identify expressions from faces. Facial emotion recognition deficits have been consistently shown in psychiatric conditions, which necessitates the need to construct a culturally sensitive tool. Fourteen actors depicted emotions such as happy, sad, anger, fear, surprise, disgust, and neutrality. From a total of 126 images, participants rated in terms of intensity and accuracy. Final software was developed with 28 images, and mean accuracy and reaction time were obtained. Friedmans significance test revealed a significant effect of emotion on its different dimensions. This study helped establish a culturally sensitive emotion recognition tool with the Indian population, which can be used in mental health settings for screening purposes and aid in developing rehabilitation modules. 2020, National Academy of Psychology (NAOP) India. -
Ecotourism a Sustainable Development Approach: A Case Study of Bandipur Forest
Bandipur Tiger Reserve is geographically speaking, it is an ecological confluence since the Western and Eastern Ghats intersect here, making this region unique and exceptional in terms of its flora and fauna. The community land areas of all the border settlements as well as the nearby notified and unnotified forests have been included in the buffer of this tiger reserve. The scrub jungle along the park's eastern boundaries is made up of stunted trees, scattered bushes, and open grassland patches. The Eco-tourism activity is run in the two Ranges of Bandipur (54 km2) and GS Betta (28 km2), covering a total area of 82.00 km2, or around 9.40% of the Reserve's total size. From the above analysis, it could be concluded that the government should provide that there are administrative facilities, halting facilities, etc. just next to National Highway 67, which cuts through the eco-tourism region. Additionally, the village community people agree that the regions where some Private Tourist Resorts have situated border the Kundu Range's Eco-tourism area. The Reserve benefits from having almost year-round operations. The usual methods of stopping poaching, such as arresting and prosecuting offenders, have obviously failed; conservation education aiming at altering local attitudes will greatly reduce the ongoing threats to the integrity of biological systems in the Bandipur forest. Operationalizing sustainable ecotourism within protected areas ultimately relies on management and operations that maximize the industry's potential positive advantages while minimizing its negative ones. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Decent Work Deficit: A Challenge on the Women Empowerment in Indian Agricultural Sector
Women play a crucial role in Indian agriculture, but they also confront several obstacles that reduce their productivity and prevent them from fully engaging in the sectors development. The majority of women in India are employed in agriculture, which is one of the sectors that contributes most to the GDP and is essential to the economic development of the nation. Although women continue to have a significant and recognized role in agriculture, their function is frequently overlooked. Women make up about 75% of the full-time labor force on Indian farms. The nation wont develop unless its women farmers are empowered. Only through decent work labour the agriculture sector will be developed which will help in the empowermentof women agricultural Labourers in India. So the government should take all steps to implement the decent work concept of ILO in the Indian agricultural sector. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
A review on serverless architectures-Function as a service (FaaS) in cloud computing
Emergence of cloud computing as the inevitable IT computing paradigm, the perception of the compute reference model and building of services has evolved into new dimensions. Serverless computing is an execution model in which the cloud service provider dynamically manages the allocation of compute resources of the server. The consumer is billed for the actual volume of resources consumed by them, instead paying for the pre-purchased units of compute capacity. This model evolved as a way to achieve optimum cost, minimum configuration overheads, and increases the application's ability to scale in the cloud. The prospective of the serverless compute model is well conceived by the major cloud service providers and reflected in the adoption of serverless computing paradigm. This review paper presents a comprehensive study on serverless computing architecture and also extends an experimentation of the working principle of serverless computing reference model adapted by AWS Lambda. The various research avenues in serverless computing are identified and presented. Universitas Ahmad Dahlan. -
Serverless Architecture - A Revolution in Cloud Computing
Emergence of cloud computing as the inevitable IT computing paradigm, the perception of the compute reference model and building of services has evolved into new dimensions. Serverless computing is an execution model in which the cloud service provider dynamically manages the allocation of compute resources of the server. The consumer is billed for the actual volume of resources consumed by them, instead paying for the pre-purchased units of compute capacity. This model evolved as a way to achieve optimum cost, minimum configuration overheads, and increases the application's ability to scale in the cloud. The prospective of the serverless compute model is well conceived by the major cloud service providers and reflected in the adoption of serverless computing paradigm. This review paper presents a comprehensive study on serverless computing architecture and also extends an experimentation of the working principle of serverless computing reference model adapted by AWS Lambda. The various research avenues in serverless computing are identified and presented. 2018 IEEE. -
Text summarization using residual-based temporal attention convolutional neural network
To address the computational complexity and limited to large data Enhanced Residual based Temporal Attention Convolutional Neural Network (ERTACNN) with Improved Initialization strategy-based Aquila Optimization Algorithm (IIAOA) is proposed. Initially the document is pre-processed to get structured data and given to feature extraction. Then the features are selected with Aquila Optimization Algorithm to remove redundant or unrelated features from high-dimensional data, from which the entropy values are calculated and given to proposed classifier. In this classification, the temporal attention mechanism is combined with classifier to compute attention weight and accompanied with important time points for classifying the documents. Finally, the proposed method is implemented in python and evaluated against existing works which achieves 70.34, 55.6 and 72.4 Recall Oriented Understudy for Gisting Evaluation (ROUGE) score than existing approaches. 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management. -
A Survey on Domain-Specific Summarization Techniques
Automatic text summarization using different natural language processing techniques (NLP) has gained much momentum in recent years. Text summarization is an intensive process of extracting representative gist of the contents present in a document. Manual summarization of structured and unstructured text is a tedious task that involves immense human effort and time. There are quite a number of successful text summarization algorithms for generic documents. But when it comes specialized for a particular domain, the generic training of algorithms does not suffice the purpose. Hence, context-aware summarization of unstructured and structured text using various algorithms needs specific scoring techniques to supplement the base algorithms. This paper is an attempt to give an overview of methods and algorithms that are used for context-aware summarization of generic texts. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Tangent Search Long Short Term Memory with Aadaptive Reinforcement Transient Learning based Extractive and Abstractive Document Summarization
Text summarization is the process of creating a shorter version of a longer text document while retaining its most important information. There have been a number of methods proposed for text summarization, but the existing method does not provide better results and has a problem with sequence classification. To overcome these limitations, a tangent search long short term memory with adaptive reinforcement transient learning-based extractive and abstractive document summarization is proposed in this manuscript. In abstractive phase, the features of the extractive summary are extracted and then the optimal features are selected by Adaptive Flamingo Optimization (AFO). With these optimal features, the abstractive summary is generated. The proposed method is implemented in python. For extractive text summarization, the proposed method attains 42.11% ROUGE-1 Score, 23.55% ROUGE-2 score and 41.05% ROUGE-L score using Gigaword. Additionally, 57.13% ROUGE-1 Score, 28.35% ROUGE-2 score and 52.85% ROUGE-L score using DUC-2004 dataset. For abstractive text summarization the proposed method attains 47.05% ROUGE-1 Score, 22.02% ROUGE-2 score and 48.96% ROUGE-L score using Gigaword. Also, 35.13% ROUGE-1 Score, 20.35% ROUGE-2 score and 35.25% ROUGE-L score using DUC-2004 dataset. 2023, Modern Education and Computer Science Press. All rights reserved.