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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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
Redefining traditional education using augmented reality and virtual reality
[No abstract available] -
Review of Medical Drones in Healthcare Applications
Drone technology has an immense potential to provide various efficient solutions in the healthcare sector. It has been used in different applications ranging from transportation to rescue. The recent benefits of drone technology to assist emergency situations have achieved academic attention, which results in extensive practical implementation and simulation. However, it still needs to come into a reality beyond a certain extent. The involvement of drones in the healthcare industry has grown to be smart and intelligent for providing better medical care, fast transport and delivery of medicines, and search and rescue operations. The most important reason behind the evolution of drones is the replacement of human service, which is required during the global health crisis. Recent technology such as the Internet of Things, artificial intelligence, and the 5G network joined hands with drone technology, resulting in a great benefit to the healthcare industry. Drones are needed to battle the coronoavirus by delivering medicines and test samples. It is also involved in remote pandemic management in the form of helping the authorities to monitor social gatherings, by broadcasting the awareness messages and nearby hospitals details, for spraying virus protection liquid on the street, and many more. Therefore, this chapter proposes the intricacies of the Internet of medical drones in the healthcare sector, in hopes of empowering and eliciting more aggressive investigation. 2023 selection and editorial matter, Saravanan Krishnan and M. Murugappan; individual chapters, the contributors. -
A comprehensive study on the assessment of chemically modified Azolla pinnata as a potential cadmium sequestering agent
The major environmental issue raised throughout the world is the egression of toxic pollutants in water bodies. Hence, employment of novel technological interventions such as bioremediation and phytoremediation for mitigating the toxic effects caused by the pollutants has gained attention. The aquatic macrophyte, Azolla pinnata is utilized as a biofiltering agent in the present study for the chelation of metal toxicants from the artificial wastewater system. The nutritive value of A. pinnata was determined to be 268.99Kcal/100g energy and the mineral profiling showed the highest amount of calcium (54.7ppm), iron (14.04ppm) and manganese (7.96 ppm). The quantitative screening of total phenolic and total flavonoid contents showed a maximum of 402.334.29 mg/g GAE and 105.253.81 mg/g QE respectively and the sample exhibited strong antioxidant activity in quenching the DPPH radicals with an IC50 value of 88.27?g/ml. Similarly, the highest bioactivity was observed in methanolic and chloroform extract of A. pinnata biomass showing the zone of growth inhibition against E. coli (17mm) and S. aureus (18mm). The results recorded from the SEM-EDX, GCMS, FTIR and XRD confirmed the adsorptive properties of biomass. The chemically modified and unmodified Azolla exposed to cadmium metal solution showed the maximum adsorption of about 0.470.001 and 0.480.003 ppm in 60mins using the unmodified biomass with dosage of 0.75 and 1.0g respectively. Moreover, the results recorded from the instrumental characterization for the adsorptive properties of Azolla biomass proved that cadmium chelation is due to the modifications caused in porosity, surface structure and the addition of functional groups in the treated biomass surface. 2023 The Ceramic Society of Japan. -
Synergistic Effect of Chemical and Physical Treatments on Azolla pinnata for Cadmium Ions Removal from Synthetic Wastewater Systems
Azolla pinnata, an aquatic fern has been utilized as an effective biofiltering and ad-sorbent agent to complement many convention-al treatment methods for the removal of environmental pollutants. This study is designed to develop an effective regime to treat metal pollutants of industrial and urban waste discharge using a novel strategy involving Azolla pinnata. In the present study, cell surface modification by physical treatments that include heating (muffle furnace), and mechanical waves (ultrasonication) and chemical treatments as sulphuric acid and ethanol were employed to enhance the adsorption of metal pollutants. Factors such as biosorbent dose, contact time, initial metal ion concentration, temperature, and solution pH were optimised in batch mode. The point of zero charge of the adsorbent was determined to be at 5.85 pH. The results of surface morphology, elemental analysis, crystallinity, recorded through SEM, FTIR and XRD confirmed the ad-sorptive properties in both modified and unmod-ified biomass. The intensity peaks linked to O-H, C-H, C-N, N-H and C=O stretching bands was intense in the treated A. pinnata groups indicat-ing the induction of the active groups. Out of the two chemical pre-treatments, the batch ad-sorption experiment with ethanol found to che-late Cd+2 metal ions to a higher extent (94.36%) in contrast to the results obtained from H2SO4 treated biomass. Whereas, the physical treat-ments exhibited the strong adsorption (83.28 and 96.920.55%) for ultrasonicated and muf-fle furnace pre-treated biomass respectively for the dosage of 0.25g. The adsorption efficiency of physically modified sorbent revealed the cent percent removal of Cd+2 ions from the aqueous phase with the dosage of 1.0g in 15min of con-tact time which is due to the incorporation of new binding sites. Moreover, these results proved that the highest rate of cadmium adsorption onto A. pinnata is in result of the modifications caused onto surface structure, porosity and the addition of functional groups on the surface of the treated biomass. 2024, Curr. Trends Biotechnol. Pharm. All rights reserved. -
A Methodology to Formulate Attainment Process of Outcome-based Education for Undergraduate Engineering Degree Programme
The Outcome-Based Education (OBE) has important role in accreditation of any engineering programme. The OBE involves attainment of programme mission, objectives and outcomes. The paper discusses a methodology to calculate attainment of programme educational objectives and programme outcomes. The results of particular batch 2020 were shown. The process would help in implementing OBE in any technical institution approved by AICTE, India. 2024 IEEE. -
Analysis and Actions Planned for Programme Outcomes in Outcome Based Education for a Particular Course
In India many of the technical institutions are NBA (National Board of Accreditation) accredited and the accreditation is a way to maintain quality of education. The outcome-based education (OBE) plays an important role in technical education across the world. So, in this research we will show how we can implement the attainment process related to OBE for a particular course. In this paper we will discuss how the course outcome and mapping of course outcome with program outcome can be defined. Then we will discuss the process to calculate the attainment. Finally, the program gaps were identified for that course and actions were suggested. 2024 IEEE. -
Process of identity development and psychological functioning: A critical narrative review for the Indian context
Background: Identity is a crucial milestone achievement for adolescents to become contributing adult members in society. This narrative research focused on exploring the link between identity development and psychological functioning and understanding the process of Indian adolescents' and adults' identity development and psychological functioning. Often, the Indian identity researchers use the theories of identity development conceptualized by Erikson, James Marcia and Michael Berzonsky which have been primarily conceptualized to understand the process of individual's identity development in the western individualistic cultural context. These theorists based their theories on certain essential contextual conditions, for the individuals' identity development. This review article critically explored the availability and applicability of those contextual conditions for Indian adolescents' and adults' identity development. Methods: The articles for the review were mainly collected from the online databases such as PROQUEST Research Library, Taylor and Francis, the archives of the Indian Journal of Social Psychiatry, the archives of the Indian Journal of Psychiatry, EBSCO, and Google. A narrative review method was used to examine various elements of the process of identity development conceptualized by the mainstream identity development theorists Erikson, James Marcia, and Michael Berzonsky and their applicability to the process of Indian adolescents' and adults' identity development. Results: The review found that the processes of mainstream identity development theories have some serious limitations in their applicability to the Indian context. Conclusions: This article identified alternative identity development processes and interventions that could be used to enhance Indian adolescents' and adults' identity development. 2022 The Author(s). -
Attachment to God: Narratives of Roman Catholic Priests
This narrative analysis was aimed at exploring the attachment to God narratives of 28 middle-aged Roman Catholic Religious priests rendering their service in various settings in South India. The study found that majority of the Roman Catholic priests had developed representations of a secure attachment to God. Twenty-six priests had developed representations of a secure attachment to God, and two priests of an insecure attachment to God. The Majority of the Roman Catholic priests had developed representations of a secure attachment to more than one spiritual attachment figures. Along with God, most priests had also developed representations of a secure attachment to the Virgin Mary. All the major themes related to attachment to God were found in the narratives of the Roman Catholic Priests. Author(s) 2020. -
Minimizing Energy Depletion Using Extended Lifespan: QoS Satisfied Multiple Learned Rate (ELQSSM-ML) for Increased Lifespan of Mobile Adhoc Networks (MANET)
Mobile Adhoc Networks (MANETs) typically employ with the aid of new technology to increase Quality-of-Service (QoS) when forwarding multiple data rates. This kind of network causes high forwarding delays and improper data transfer rates because of the changes in the nodes vicinity. Although an optimized routing technique to transfer energy has been used to lessen the delay and improve the throughput by assigning a proper data rate, it does not consider the objective of minimizing the energy use, which results in less network lifetime. The goal of the proposed work is to minimize the energy depletion in a MANET, which results in an extended Lifespan of the network. In this research paper, an Extended Life span and QSSM-ML routing algorithm is proposed, which minimizes energy use and enhances the network lifetime. First, an optimization problem is formulated with the purpose of increasing the networks lifetime while limiting the energy utilization and stability of the path along with residual. Second, an adaptive policy is applied for the asymmetric distribution of energy at both origin and intermediate nodes. In order to achieve maximum network lifespan and minimal energy depletion, the optimization problem was framed when power usage is a constraint by allowing the network to make use of the leftover power. An asymmetric energy transmission strategy was also designed for the adaptive allocation of maximum transmission energy in the origin. This made the network lifespan extended with the help of reducing the nodes energy use for broadcasting the data from the origin to the target. Moreover, the nodes energy use during packet forwarding is reduced to recover the network lifetime. The overall benefit of the proposed work is that it can achieve both minimal energy depletion and maximizes the lifetime of the network. Finally, the simulation findings reveal that the ELQSSM-ML algorithm accomplishes a better network performance than the classical algorithms. 2023 by the authors.