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Introduction to blockchain for internet of things
[No abstract available] -
A novel secured ledger platform for real-time transactions
The present disclosure relates to a new centralized ledger technology with a centralized validation process. It offers a single platform for all categories of real-time transactions and validations, unlike existing conventional blockchain technology. It offers three levels of hashing placed at the generator, server, and validator end for data security from data tampering and two levels of encryption for communication lines between generator-server and server-validator for packet security. This system ensures trustworthiness, authenticity, and CIA (confidentiality, integrity, and availability) to its end users while being real-time in execution. The proposed system does not follow a chain-based file architecture. Due to this, no concept of chain break arises, and the problems that arise as a result of chain break in the blockchain are avoided. 2022 Elsevier Inc. All rights reserved. -
A Review of Deep Learning Methods in Automatic Facial Micro-expression Recognition
Facial expression analysis to understand human emotion is the base for affective computing. Until the last decade, researchers mainly used facial macro-expressions for classification and detection problems. Micro-expressions are the tiny muscle moments in the face that occur as responses to feelings and emotions. They often reveal true emotions that a person attempts to suppress, hide, mask, or conceal. These expressions reflect a persons real emotional state. They can be used to achieve a range of goals, including public protection, criminal interrogation, clinical assessment, and diagnosis. It is still relatively new to utilize computer vision to assess facial micro-expressions in video sequences. Accurate machine analysis of facial micro-expression is now conceivable due to rapid progress in computational methodologies and video acquisition methods, as opposed to a decade ago when this had been a realm of therapists and assessment seemed to be manual. Even though the research of facial micro-expressions has become a longstanding topic in psychology, this is still a comparatively recent computational science with substantial obstacles. This paper a provides a comprehensive review of current databases and various deep learning methodologies to analyze micro-expressions. The automation of these procedures is broken down into individual steps, which are documented and debated. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Activity Classifier: A Novel Approach Using Naive Bayes Classification
Activity movements have been recognized in various applications for elderly needs, athletes activities measurements and various fields of real time environments. In this paper, a novel idea has been proposed for the classification of some of the day to day activities like walking, running, fall forward, fall backward etc. All the movements are captured using a Light Blue Bean device incorporated with a Bluetooth module and a tri-axial acceleration sensor. The acceleration sensor continuously reads the activities of a person and the Arduino is designed to continuously read the values of the sensor that works in collaboration with a mobile phone or computer. For the effective classification of a persons activity correctly, Nae Bayes Classifier is used. The entire Arduino along with acceleration sensor can be easily attached to the foot of a person right at the beginning of the user starts performing any activity. For the evaluation purpose, mainly four protocols are considered like walking, running, falling in the forward direction and falling in the backward direction. Initially five healthy adults were taken for the sample test. The results obtained are consistent in the various test cases and the device showed an overall accuracy of 90.67%. Springer Nature Switzerland AG 2020. -
Future Perspectives of Microplastic towards Environmental Assessment
Microplastic (MP) pollution is an outcome of the widespread use of non-biodegradable plastic and improper disposal. This leads to contamination of environmental resources, such as landfills, and all kinds of water reservoirs including but not limited to sea, fresh water, drinking water, and even wastewater. Recent reports have highlighted the presence of MPs in the human body, including blood, lungs, placentas, and breast milk, indicating the severity of the issue. It is thus crucial to eliminate these hazardous contaminants from the environment. One of the effective methods to address the concern while reducing the adverse effects is to remove the MPs at their discharge points. Nanomaterials with exceptional properties like high surface area, ease of functionalization, and high affinity toward various pollutants act as excellent adsorbents. In this chapter, we present an overview of emerging nanomaterial-based adsorbents, such as photocatalysts, metal-organic frameworks, carbon-based nanomaterials, and nanocomposites, for effective removal of MPs from aqueous media via adsorption, photo-catalysis, and membrane filtration. However, considering that the research in the area of MP pollution is still in its infant stage, we aim to provide a brief account of the strengths, weaknesses, and future research dimensions of nanomaterial-based adsorbents for removing MPs from aqueous media. 2025 selection and editorial matter, Nirmala Kumari Jangid and Rekha Sharma; individual chapters, the contributors. -
Inkjet printing of MOx-based heterostructures for gas sensing and safety applicationsRecent trends, challenges, and future scope
Volatile organic compounds (VOCs) are pollutants that affect air quality and human health. Detection of VOCs is important for environmental safety. Metal oxide semiconductor (MOS) is a promising product for gas sensors due to its advantages of easy fabrication, low cost, and good portability. Their performance is greatly affected by microstructure, defects, catalysts, heterojunctions, and moisture. Metal oxidebased nanomaterials serve as a platform to identify various VOCs with high sensitivity due to their wide bandgap, n-type transport, and excellent electrical properties. Gas detection devices based on doping, altered morphology, and heterostructure have been shown to be effective against VOCs. Inkjet printing (IJP) is a promising process for the room-temperature deposition of functional metal oxides for sensing applications. However, the development of metal oxide ink requires a careful selection of the precursors, solvents, and additives. This section will focus on the production of various metal oxide (MOx)-based sensors such as ZnO, SnO2, MoO3, CuO, Cu2O, Mn3O4, and WO3 for the detection of VOCs such as acetylene, toluene, ethanol, formaldehyde, and acetone. It will summarize recent research and advances in large-scale printing of MOx-based nanocomposites. This work illustrates the need to explore new composite materials, structures, and morphology as well as other methods for better and faster transformation. The role of solvents in ink stabilization and printing and the behavior of ink rheological parameters in the IJP spraying process will also be discussed. Ink formulations for the synthesis of functional nanocomposites will be analyzed and presented for future scope and challenges. 2024 Elsevier Inc. All rights reserved. -
Graphitic carbon nitride (GCN) for solar cell applications
There is an eminent global energy crisis and photovoltaics as one of the primary renewable energy sources is playing an important part in offsetting the dependency on fossil fuels. Current solar cells technology is dominated by silicon, and researchers are trying to replace it with organic and nanocrystalline semiconducting materials. Graphitic carbon nitride (g-C3N4, GCN) has gained interest as a visible light driven photocatalyst with a unique 2D structure, excellent chemical stability and tunable electronic structure along with attractive optoelectronic properties. Pure GCN suffers from low surface area and rapid recombination of photo-generated electron-hole pairs resulting in low photovoltaic and photocatalytic activity and hence modification by doping with other atoms is required. Photocatalytic applications of GCN based nanomaterials for water splitting, hydrogen production, CO2 reduction and pollutant degradation has been extensively investigated and systematically reviewed. However, their applications as energy storage has been explored recently and there is a lack of comprehensive review that systematically summarizes the application of GCN and GCN-based heterostructures for solar cell applications. Heterojunctions with superior light absorption and appropriate conduction band and valence band alignment is a promising approach for the applications in efficient environmental remediation and solar energy storage. This critical review summarizes the synthesis and advances of GCN nanocomposites modified with semiconductors (TiO2, ZnO), bismuth titanate, strontium titanate and rare earth metals for solar cell applications. GCN-based heterostructures with perovskite and polymer based materials are also presented. The characteristics and transfer mechanism within the various heterojunctions is also reviewed and presented. The review ends with a summary and some perspectives on the challenges and new directions in exploring GCN-based advanced nanomaterials particularly towards photovoltaics and energy storage applications. 2022 Elsevier Inc. All rights reserved. -
Nanoarchitectures as photoanodes
This chapter looks into providing detailed information on the state-of-the-art and recent trends on materials and nanoarchitectures for improved photoanode device. It provides a roadmap for researchers toward optimization of photoanodes using advanced material engineering. The chapter casts some light on the performance of various photoanode materials and nanostructures, such as TiO2, ZnO, SnO2, Nb2O5, Al2O3, ZrO2, CeO2, SrTiO3, Zn2SnO4, and carbon in dye-sensitized solar cells (DSSCs). Plasmonic photoanodes are an emerging field in DSSC spanning a wide range of materials where the paramount challenge is coming up with effective strategies to incorporate suitable plasmonic structures into nanocrystalline and nanostructured electrodes. Optical excitation of the dye is the basis of DSSC operation, where an electron is excited from the dye molecule into the conduction band of a wideband metal oxide. 2020 JohnWiley & Sons Inc. All rights reserved. -
Machine Learning and Signal Processing Methodologies to Diagnose Human Knee Joint Disorders: A Computational Analysis
Computer-aid diagnostic (CAD) has emerged as a highly innovative research topic in diverse fields which includes medical imaging systems, radiology diagnostics, and so on. These are the systems that majorly assist doctors by the way of interpretation of medical data or images. In the diagnosis of knee joint disorder technique, both time and frequency-based analysis can be done. These non-stationary and non-linear signals are processed into three important methods, namely VMD, TVF-EMD, and CEEMDAN. To analyze the vibroarthrographic (VAG) signal, the initial stage is to compute the mode strategies termed as intrinsic mode functions (IMFs) which can be attained only after performing the transformations. In our chapter, we analyzed Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for computing the mode signals. The CEEMDAN method utilized the time and frequency data for the available features. The feature extraction depends purely on pixel intensity and the statistical parameters. The classification of available data samples is done through the Least Square Support Vector Machine (LS-SVM) and SVM-Recursion of Feature Elimination (SVM-RFE) for the efficient analysis of healthy and unhealthy data samples. 2024 selection and editorial matter, Hemachandran K., Raul V. Rodriguez, Umashankar Subramaniam, and Valentina Emilia Balas; individual chapters, the contributors. -
Navigating resource scarcity in a changing climate: AI-powered perspectives on mental health
In the backdrop of the extensive global impact of the COVID-19 pandemic, environmental crises have, to a certain degree, taken a back seat. The pandemic-induced scarcity mindset, emphasizing immediate short-term needs over long-term considerations, has played a role in this shift in priorities. This scarcity mindset, prevalent during the pandemic, poses a risk to pro-environmental behavior and may contribute to environmental degradation, thereby heightening the likelihood of future pandemics. This chapter advocates for a reevaluation of pro-environmental actions, emphasizing their role in addressing various human needs, especially during periods of scarcity. AI-driven chatbots possess the capability to significantly enhance accessibility to affordable and efficient mental health services by complementing the efforts of clinicians. To safeguard pro-environmental behavior, we propose a reconceptualization that positions these actions not merely as value-laden or effortful but as pragmatic measures essential for resource conservation, particularly in times of scarcity. The study explores, the intricate dynamics of resource scarcity, climate change, and mental health, employing AI-powered perspectives to navigate this complex interplay. 2024, IGI Global. All rights reserved. -
Unveiling the Emotions: A Sentiment Analysis of Amazon Customer Feedback
This study explores sentiment analysis in the context of diverse regions and contemporary customer feedback, aiming to address research questions related to consolidation based on polarity scores and sentiments. The research utilizes multinomial regression for a comprehensive analysis of customer feedback worldwide. The investigation incorporates confusion matrices, statistics, and class-specific metrics to evaluate the models performance. Results indicate a highly accurate model with perfect sensitivity, specificity, and overall accuracy. The analysis further includes a breakdown of key metrics such as accuracy, confidence intervals, no information rate, p-value, kappa, and prevalence, emphasizing the models robustness. In conclusion, the multinomial logistic regression model demonstrates exceptional performance in predicting sentiment across diverse classes, highlighting its effectiveness in sentiment analysis on a global scale. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
ML in drug delivery-current scenario and future trends
Machine learning (ML) has enabled transformative applications and emerged as a domain-agnostic decision-making tool as a virtue of its rapid democratization. The authors believe that a systematic assortment of important publications on this issue is indispensable in this context. In terms of data ingestion, data curation, data preprocessing, data handling, and model cross-validation, this review gathers together several studies that have demonstrated a minimum ML framework approach. In general, ML models are described as black-box models, with limited information supplied about their transparency. The authors propose techniques based on the US Food and Drug Administration (FDA)'s current good ML practice (GMLP) in order to improve the ML framework and minimize the aforementioned gap, especially for data. Considering this, the conversation around a model's logic and interpretability are additionally provided. Explicitly, the authors explore the challenges and constraints that ML execution confronts throughout the development of pharmaceuticals. In this context, a structural approach in statistics is presented to allow the scientist to assess the quality of data and incorporate important ideas and techniques that would be implemented in modern ML. The data analytics tetrahedron proposed here can be applied to data of any size. To further contextualize, selected case studies capturing good practices are highlighted to provide pharmaceutical scientists, pharmaceutical ML enthusiasts, readers, reviewers, and regulatory authorities an exposure to fundamental and cuttingedge techniques of ML and data science with respect to chemistry, manufacture, and control (CMC) of drug products. In addition, the authors believe that leveraging ML within CMC procedures can assist in improving decision-making, increasing quality, and enhancing the speed of pharmaceutical product development. IOP Publishing Ltd 2023. All rights reserved. -
Narratives on using critical approaches in teacher education
Using the approach of autoethnographic narrative, three teacher educators from a cosmopolitan city in South India discuss how they use critical approaches in preparing preservice teachers and educational psychologists in the courses that they teach at a private university. The students are sensitized about the marginalized and the privileged sections in a multicultural and multilingual nation as India and to become culturally responsive in their classrooms or with their clientele in terms of their dispositions, knowledge, and skills. The chapter also describes the integration of critical approaches in the doctoral program aimed at addressing educational disparities and promoting social justice in education. 2024, IGI Global. All rights reserved. -
Phytochemicals for neurodegeneration and neuroinflammation: medicine of the future or a mirage?
Dietary polyphenols cease to be mere nutrients but have immense health enhancing and disease modifying effects. Phytochemical-based therapeutic approaches for neurodegenerative diseases are becoming increasingly popular. This may be attributed to the lack of long-term benefits or adverse effects of current pharmacotherapy. Polyphenols target multiple pathways and their long-term use could prove beneficial for diseases involving multiple etiological factors. While polyphenols are nontoxic and oral route is the preferred mode of administration, bioavailability in the brain is limited rendering the neuroprotective efficacy questionable. Methods employing synthetic biopolymers, nanoformulations, liposomal carriers, or conjugation have been explored to enhance the bioavailability. While results have been promising in experimental models, translation to human neurodegenerative conditions is limited. It can therefore be surmised that the present knowledge on dietary polyphenols is only the tip of the iceberg and extensive translational research is warranted to fill the gap for their therapeutic use. 2023 Elsevier Inc. All rights reserved. -
Sumset valuations of graphs and their applications
Graph labelling is an assignment of labels to the vertices and/or edges of a graph with respect to certain restrictions and in accordance with certain predefined rules. The sumset of two non-empty sets A and B, denoted by A+B, is defined by A+B=\(a=b: a\inA, b\inB\). Let X be a non-empty subset of the set \Z and \sP(X) be its power set. An \textit of a given graph G is an injective set-valued function f: V(G)\to\sP_0(X), which induces a function f+: E(G)\to\sP_0(X) defined by f+(uv)=f(u)+f(v), where f(u)+f(v) is the sumset of the set-labels of the vertices u and v. This chapter discusses different types of sumset labeling of graphs and their structural characterizations. The properties and characterizations of certain hypergraphs and signed graphs, which are induced by the sumset-labeling of given graphs, are also done in this chapter. 2020, IGI Global. -
Phytochemistry and antigenotoxic properties of six ethnobotanically important members from the family Zingiberaceae
Genotoxicity is considered as a potential cause of various diseases including cancer. During the last decade, herbal extracts attained a great deal of attention due to its safe and effective applications against various DNA damaging agents. However, the mechanism of DNA strand breaks by various mutagens and genotoxins is often correlated with the generation of Reactive Oxygen Species (ROS). Herbal extracts constitute a number of phytochemicals and those are reported to have considerable antioxidant properties, which are in turn capable of neutralizing ROS mediated DNA damage. The botanical family Zingiberaceae is reported to have significant antioxidant and antigenotoxic potential by various researchers. Among a number of species belonging to this family, six species, namely Alpinia galanga, A. zerumbet, Curcuma amada, C. caesia, Zingiber officinale, and Z. zerumbet, attract notable attention due to their remarkable ethnobotanical and medicinal importance. This chapter deals with phytochemical composition, antioxidant, and antigenotoxic properties of these six Zingiberaceous plant extracts. 2020 by IGI Global. All rights reserved. -
Green Data Centers: A Review of Current Trends and Practices
A green data center is a facility that makes use of eco-friendly techniques and technologies to lessen its carbon footprint and environmental impact. A data center can consume as much electricity as a small city and contains thousands of servers. These server farms require an enormous amount of processing power to operate, which presents numerous difficulties, including high energy costs, greenhouse gas emissions, backups, and recovery. This paper clarifies the various green data center best practices, including energy efficiency, cooling systems, renewable energy, sustainable building techniques, and carbon footprint. The need for green data centers in todays internet, commercial, financial, and business applications is also covered in the paper. The reality and myths of green data centers are alsoexamined. The paper delves into the metrics for each characteristic used to gauge how green and effective data centers are. The discussion has concluded with case studies of companies that have implemented green data centers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Strategic Data Analytics for Sustainable Competitive Advantage
Data and analytics have become major assets for all organizations to leverage into superior strategic positions in this cut-throat competitive world with buzzwords like data crunch, metrics, and dark data. This chapter discusses the structural and economic reasons of why business analytics is necessary for organizations. The ability to collect different resources and entities such as talent, process, data, and information technology to bring out a valuable output is crucial for business analytics success. The most common difficulty of big data begins when organizations are in the journey of business analytics. Since a number of organizations are still in the baby steps of basic, tackling data challenges is humongous for them. This situation calls for the need to foster a business analytics ecosystem by every organization. This paper discusses how optimizing analytics could lead to a sustainable competitive advantage, building data strategy, and setting Key Performance Indicators (KPI) for business analytics. The chapter further explores how analytics is used across business domains and the challenges in crafting a business analytics strategy. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Exploring challenges in online higher education for AI integration using MICMAC analysis
The consequence of Covid-19 has affected the traditional higher education system. Acknowledging the significant role of online education in national development for accessibility and quality education, countries around the world have understood its importance in current digital era. Indian policymakers have been giving due importance to enhancing the education quality, however the progress made by the country in higher education is not adequate. Amidst all the inadequacies of traditional education system, artificial intelligence (AI) technologies are bringing new ray of hope to democratize education system. This chapter is subjected to identify the challenges in online education and suggest specific ways to address each of them. The challenges are categorized into internal and external challenges/barriers. These challenges have been modeled with the expertise of educationalist's opinions and interpretive structural modeling to create a hierarchy of the barriers using MICMAC analysis and categorize these barriers into four clusters. 2024, IGI Global. All rights reserved.