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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. -
Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications
This book serves as a bridge connecting the theoretical foundations of DRL with practical, actionable insights for implementing these technologies in a variety of industrial contexts, making it a valuable resource for professionals and enthusiasts at the forefront of technological innovation. Deep Reinforcement Learning (DRL) represents one of the most dynamic and impactful areas of research and development in the field of artificial intelligence. Bridging the gap between decision-making theory and powerful deep learning models, DRL has evolved from academic curiosity to a cornerstone technology driving innovation across numerous industries. Its core premiseenabling machines to learn optimal actions within complex environments through trial and errorhas broad implications, from automating intricate decision processes to optimizing operations that were previously beyond the reach of traditional AI techniques. Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications is an essential guide for anyone eager to understand the nexus between cutting-edge artificial intelligence techniques and practical industrial applications. This book not only demystifies the complex theory behind deep reinforcement learning (DRL) but also provides a clear roadmap for implementing these advanced algorithms in a variety of industries to solve real-world problems. Through a careful blend of theoretical foundations, practical insights, and diverse case studies, the book offers a comprehensive look into how DRL is revolutionizing fields such as finance, healthcare, manufacturing, and more, by optimizing decisions in dynamic and uncertain environments. This book distills years of research and practical experience into accessible and actionable knowledge. Whether you're an AI professional seeking to expand your toolkit, a business leader aiming to leverage AI for competitive advantage, or a student or academic researching the latest in AI applications, this book provides valuable insights and guidance. Beyond just exploring the successes of DRL, it critically examines challenges, pitfalls, and ethical considerations, preparing readers to not only implement DRL solutions but to do so responsibly and effectively. Audience The book will be read by researchers, postgraduate students, and industry engineers in machine learning and artificial intelligence, as well as those in business and industry seeking to understand how DRL can be applied to solve complex industry-specific challenges and improve operational efficiency. 2024 Scrivener Publishing LLC. -
Climate Change Impact on Water Resources, Food Production and Agricultural Practices
The greatest threat to human health that exists today is climate change. Ecosystems, societies and biodiversity are seriously at risk from the long term effects due to change in climate, primarily brought on by human activities. Rising temperatures increase evaporation, which causes drought and decreases water availability for ecosystems, drinking water supplies and agriculture. Changed precipitation patterns exacerbate floods, storms and sea levels, contaminating the water supply and harming infrastructure. The effects of rapidly changing climate on water resources must be minimised through sustainable water management techniques, conservation initiatives and International initiatives. The effects of climate change on the long run have been the focus of research because stable weather significantly influences agricultural productivity. Due to agricultures reliance on temperature and rainfall, climate change threatens world food security. Rising temperature results in lower productivity and also promotes the growth of weeds and pests, changes precipitation patterns, which will result in more crop failures and production declines. This work summarises the outcome of climate change on crop and livestock yields, water resources and the economy. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Interpreting the Evidence on Life Cycle to Improve Educational Outcomes of Students Based on Generalized ARC-GRU Approach
Research on the effects of teachers' fatigue on students' learning has been significantly less common than research on the effects of teachers' fatigue on teachers' own performance. Therefore, the purpose of this research is to see if teachers' emotional weariness has any bearing on their students' performance in the classroom. Consideration is given to a student's grades and their impressions of whether or not the system receive assistance from teachers, as well as to the student's general outlook on school, confidence in their own abilities, and faith in the availability of faculty support. Data preparation, feature extraction, and model training are the first steps in the proposed approach. Indicators of the quality of the education being provided are eliminated (by outlier removal and feature scaling). k-mean clustering approach is a technique of clustering which is commonly used in feature extraction. Following feature extraction, GARCH-GRU models are trained. The proposed approach is superior to two popular alternatives, ARCH and GRU. Using the provided method, the system were able to achieve a maximum accuracy of 97.07%. 2024 IEEE. -
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CNN-Bidirectional LSTM based Approach for Financial Fraud Detection and Prevention System
Detecting fraudulent activity has become a pressing issue in the ever-expanding realm of financial services, which is vital to ensuring a positive ecosystem for everyone involved. Traditional approaches to fraud detection typically rely on rule-based algorithms or manually pick a subset of attributes to perform prediction. Yet, users have complex interactions and always display a wealth of information when using financial services. These data provide a sizable Multiview network that is underutilized by standard approaches. The proposed method solves this problem by first cleaning and normalizing the data, then using Kernel principal component analysis to extract features, and finally using these features to train a model with CNN-BiLS TM, a neural network architecture that combines the best parts of the Bidirectional Long Short-Term Memory (BiLS TM) network and the Convolution Neural Network (CNN). BiLSTM makes better use of how text fits into time by looking at both the historical context and the context of what came after. 2023 IEEE. -
Critical Analysis of MoS2-Based Systems for Textile Wastewater Treatment
Indiscriminate discharge of toxic organic contaminant-laden wastewater into water bodies is one of the major issues posing a risk to the environment in general and aquatic living systems in particular. Widely used textile dyes are ubiquitous in the effluents emanating from industries. Photocatalysts, due to their efficiency and eco-friendliness, can be effectively used to remove pollutant dyes from the water bodies. Molybdenum disulfide (MoS2), an emerging co-catalyst, has high photocatalytic activity, strong absorptivity, non-toxicity, and low cost; with a graphene-like structure, it offers functional features similar to graphene: high charge carrier transfer, strong wear resistance, and good mechanical strength. However, in aspects such as cost, abundance, versatile morphologies, and tunable band gap with efficient visible light absorption properties, MoS2 scores over graphene. The present chapter discusses the recent advances in nanostructured MoS2 materials for applications in environmental remediation. Special emphasis has been paid to MoS2 and MoS2-based systems for the photocatalytic degradation of various organic contaminants such as malachite green, methyl orange, rhodamine B, and methylene blue that find extensive use in the textile industry. As a result, MoS2 systems play an essential role in nanocomposites, especially in speeding up photo-induced electron transport and lowering electron recombination rates, making them desirable photocatalysts for the degradation of pollutants. The chapter focuses on addressing SDG 3 (Good Health and Wellbeing), SDG 6 (Clean Water and Sanitation), SDG 7 (Clean and Affordable Energy), SDG 9 (Industry, Innovation, and Infrastructure), SDG 12 (Responsible Consumption and Production), SDG 14 (Life Below Water), and SDG 15 (Life on Land). 2025 Moharana Choudhury, Ankur Rajpal, Srijan Goswami, Arghya Chakravorty and Vimala Raghavan. -
Electrospun nanofibers of 2D Cr2CTx MXene embedded in PVA for efficient electrocatalytic water splitting
The usage of transition metal carbide-based electrocatalysts has proven to be an efficient and effective strategy for enhancing the kinetics of water splitting reactions encompassing the generation of hydrogen (hydrogen evolution reaction, HER) and oxygen (oxygen evolution reaction, OER). In this investigation, we have prepared a composite material by integrating Cr2CTx MXene (derived from Cr2AlC MAX phase) and polyvinyl alcohol (PVA) through electrospinning technique. Carbonization of the MXene-PVA nanofibers resulted in the formation of Cr2CTx/carbon nanofiber (Cr2CTx/CNF) that exhibits high porosity, stability, surface area, and electrocatalytic activity. Systematic examination and optimization for the electrocatalytic water splitting reaction reveales outstanding performance, characterized by substantially lower overpotentials of 265 mV and 250 mV at the constant current density of 10 mA cm?2 with lower Tafel slope values of 85 mV dec?1 and 52 mV dec?1 for HER and OER, respectively. Moreover, this work presents a novel strategy for fabricating non-precious electrocatalyst Cr2CTx/CNF through a cost-effective and straightforward electrospinning and carbonization process, advancing electrocatalytic water splitting applications, especially for oxygen evolution reactions. 2024 The Royal Society of Chemistry. -
The Road to Reducing Vehicle CO2 Emissions: A Comprehensive Data Analysis
In recent years, the influence of carbon dioxide (CO2) releases on the environment have become a major concern. Vehicles are one of the major sources of CO2 emissions, and their contribution to climate change cannot be ignored. This research paper aims to investigate the CO2 emissions of vehicles and compare them with different types of engines, fuel types, and vehicle models. The study was carried out by gathering information about the CO2 emissions of vehicles from the official open data website of the Canadian government. Data from a 7-year period are included in the dataset, which is a compiled version. There is a total of 220 cases and 9 variables. The data is analyzed using statistical methods and tests to identify the significant differences in CO2 emissions among different Car Models. The results indicate that vehicles with diesel engines emit higher levels of CO2 compared to those with gasoline engines. Electric vehicles, on the other hand, have zero CO2 emissions, making them the most environmentally friendly option. Furthermore, the study found that the CO2 emissions of vehicles vary depending on the type of fuel used. The study also reveals that the CO2 emissions of vehicles depend on the model and age of the vehicle. Newer models tend to emit lower levels of CO2 compared to older models. In conclusion, this study provides valuable insights into the CO2 emissions of Cars and highlights the need to adopt cleaner and more sustainable transportation options. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Abusive Words Detection on Reddit Comments Using Machine Learning Algorithms
Utilization of artificial intelligence contributes to the efficient examination of emotions, resulting in valuable insights into the psychological condition of users on a large scale. In this research endeavor, sentiment analysis is conducted on a dataset from Reddit, which was obtained through Kaggle. The feedback in this collection of data was divided into downbeat, neutral, and upbeat sentiments. Various machine learning techniques, like Random Forest, Extreme Gradient Boosting Classifier (XGB), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), were detected and examined to assess their effectiveness in sentiment classification. The review of these techniques comprised performance criteria such as F1 Score, accuracy, precision, and recall. Additionally, confusion matrices were utilized to assess the algorithms' proficiency in identifying abusive language. The investigation's conclusions indicate that, when it comes to sentiment analysis, the random forest method performs better than any other strategy, with a maximum accuracy of 0.99 that is on par with the CNN model's accuracy of 0.98. Moreover, random forest proves to be the most effective algorithm in recognizing negative comments and abusive language. This study underscores the significance of employing machine learning algorithms in sentiment analysis, content moderation, social media monitoring, and customer feedback analysis, emphasizing their role in enhancing automated systems that aim to comprehend user sentiments in online discussions. 2024 IEEE. -
Extractive Text Summarization Using Sentence Ranking
Automatic Text summarization is the technique to identify the most useful and necessary information in a text. It has two approaches 1)Abstractive text summarization and 2)Extractive text summarization. An extractive text summarization means an important information or sentence are extracted from the given text file or original document. In this paper, a novel statistical method to perform an extractive text summarization on single document is demonstrated. The method extraction of sentences, which gives the idea of the input text in a short form, is presented. Sentences are ranked by assigning weights and they are ranked based on their weights. Highly ranked sentences are extracted from the input document so it extracts important sentences which directs to a high-quality summary of the input document and store summary as audio. 2019 IEEE. -
Assessment of SERVQUAL Model in Hospitals Located in Tier II Cities of India
Service quality, being an assessment of services offered to a customer or the extent to which the services offered meets customers expectations, plays a significant role in healthcare industry. Patients pay hefty prices for the services they avail from specialty hospitals and they demand quality services. Hospitals have a larger challenge in delivering these services effectively to the patients. The current study helps us understand the role of information systems in service delivery process. Most of the hospitals have adopted healthcare information systems due to the benefit it provides. The study attempts to analyze the impact of information systems on service quality in the hospitals which are located in Tier II cities. The popular SERVQUAL model is adopted for this purpose. Patients who visit the hospitals were part of the respondent group. Gap score is found in order to observe the expected and actual experience of the patients based on five dimensions. 2018, 2018 Indian Institute of Health Management Research. -
Numerical study on magnetohydrodynamics micropolar Carreau nanofluid with Brownian motion and thermophoresis effect
The current work explores the investigation of the influence of nonlinear thermal radiation on unsteady, magnetohydrodynamics boundary layer flow of micropolar Carreau nanofluid past a stretching sheet. Viscous dissipation, internal heating, Brownian motion, heat source/sink, thermophoresis, chemical reaction, and Joule heating effects are considered in the study. To analyze the model, the governing partial differential equations are rephrased and written in the non-dimensional form with the relevant dimensionless quantities. To obtain the solutions, the nonlinear non-dimensional governing equations are numerically solved using finite difference approximation. The impact of every significant flow parameter on fluid motion, micro-rotation, temperature, concentration, surface drag, heat, and mass transfer rates are presented through plotted graphs and tables. It is noted from the study that the fluid flow and angular motion increase, whereas the temperature declines with higher values of the micropolar constant. Further, it is noticed that thermal distribution is a rising function of radiation parameter, and due to the nonlinear thermal radiation effect, there is an increase of 4.903% in temperature distribution when compared to linear thermal radiation. To support the validity of the solutions, a comparison was made with notable results from the existing literature for the specific case of this study. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
Graphs Defined on Rings: A Review
The study on graphs emerging from different algebraic structures such as groups, rings, fields, vector spaces, etc. is a prominent area of research in mathematics, as algebra and graph theory are two mathematical fields that focus on creating and analysing structures. There are numerous studies linking algebraic structures and graphs, which began with the introduction of Cayley graphs of groups. Several algebraic graphs have been defined on rings, a fast-growing area in the literature. In this article, we systematically review the literature on some variants of Cayley graphs that are defined on rings and highlight the properties and characteristics of such graphs, to showcase the research in this area. 2023 by the authors. -
Graphs on groups in terms of the order of elements: A review
Two mathematical fields that concentrate on creating and analyzing structures are algebra and graph theory. There are numerous studies linking algebraic structures like groups, rings, fields and vector spaces with graph theory. Several algebraic graphs have been defined based on the properties of the order of the group and its elements. In this paper, we systematically review the literature on such graphs to understand the research dynamics in the field. 2024 World Scientific Publishing Company. -
Coloring of n-inordinate invariant intersection graphs
In the literature of algebraic graph theory, an algebraic intersection graph called the invariant intersection graph of a graph has been constructed from the automorphism group of a graph. A specific class of these invariant intersection graphs was identified as the n-inordinate invariant intersection graphs, and its structural properties has been studied. In this article, we study the different types of proper vertex coloring schemes of these n-inordinate invariant intersection graphs and their complements, by obtaining the coloring pattern and the chromatic number associated. 2024 The Author(s) -
IoT and AI for Real-Time Customer Behavior Analysis in Digital Banking
Digital transformation has revolutionized the banking industry, ushering in an era of enhanced customer experiences and operational efficiency. The convergence of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has further propelled this evolution by providing real-time insights into customer behavior. This research explores the integration of IoT and AI for real-time customer behavior analysis in the context of digital banking. The proliferation of connected devices, ranging from smart phones to wearables, has generated an unprecedented volume of data. IoT facilitates the collection of diverse data points, such as transaction history, location information, and device interactions, creating a comprehensive digital footprint for each customer. Simultaneously, AI algorithms leverage this wealth of data to analyze, predict, and respond to customer behavior dynamically. In the realm of digital banking, understanding and adapting to customer behavior in real-time is crucial for providing personalized services, preventing fraud, and optimizing operational processes. This research delves into the mechanisms by which IoT sensors and devices, coupled with AI algorithms, enable banks to gain deeper insights into customer behavior patterns. Key components of the proposed system include data acquisition through IoT devices, secure data transmission protocols, and AI-driven analytics engines. In conclusion, this research advocates for the symbiotic relationship between IoT and AI in digital banking to enable real-time customer behavior analysis. 2024 IEEE. -
Audience perception on movies related to social issues: A case study on Ranjith movies /
Film alludes to an arrangement of pictures moving constantly, in a steady progression which makes an illusionary impact of development. Ranjith is a Malayalam screenwriter, filmmaker, producer and actor. The researcher here aims to analyse four films by Director Ranjith and study the audience perception or understanding on films related social issues. -
Covid-19 Classification and Detection Model using Deep Learning
One of the deadly diseases in recent years is covid19 which is affecting the lives of peoples. Also leading to severe adverse problems and death. Prevention is done using early diagnosis and medication which in turn helps in early detection of the disease. The basic aim of the paper is to identify and further classify the patients using the chest x-rays. From scratch the Convolutional Neural Network is diagnosed producing a very high accurate and optimum results. In recent years, researchers found out that in the radiological images such as like x-rays, the traces of covid-19 can be found. In few areas, a good accuracy of the covid-19 detection cannot be achieved due to lack of the people who test so the artificial intelligence is combined with the radiological image. In machine learning the models used are deep learning by automatizing the actions and making it certain by swift, skillful and proficient outcome produced by the chest images provided by the patients. There are several layers like convolutional layer, max pooling layer etc. which are initiated and are used with aid of ReLU activation function. These images given as inputs are also classified accordingly. There is a sequence of neurons being given as input to the active dense layer and there is a result to the input by a sigmoidal function. There is a rise in efficiency because the models are trained and there is a decline of loss at the same time. If there is a model where fitting is done earlier to the overfitting and is restricted from implementing in the data augmentation. There is a better and efficient involvement of suggestions to models of deep learning. Further there is a classification of chest images for identifying and analyzing covid19. So, to check the Covid detection, the images are used as raw. In this paper a model is proposed to have good accuracy in the classification between Covid and normal and further it can be classified into three categories like Covid, pneumonia, normal. There is a 98.08% for the first one and 87.02% for the second one. By introducing 17 convolutional layers and using the Darknet model used for classifying you only look once (YOLO) for the live identification of the objects and multiple layers of filters are used. In the model there is an initial screening. 2022 IEEE.