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Internet of Things Security and Privacy Issues in Healthcare Industry
The Internet of Things (IoT) is an imagines unavoidable, associated, and hubs connecting independently while offering a wide range of administrations. Wide conveyance, receptiveness and moderately high handling intensity of IoT objects made them a perfect focus for digital assaults. Additionally, the same number of IoT center points is assembling and taking care of private data, they are changing into a goldmine of information for malignant on-screen characters. Subsequently, security and particularly the capacity to recognize traded off hubs, together with gathering and safeguarding confirmations of an assault or malignant exercises develop as a need in effective arrangement of IoT systems. This paper is deal with some major security problems and challenging factors of IoT. This IoT security issues on really challenging factor in current world. 2019, Springer Nature Switzerland AG. -
Group Key Management Techniques forSecure Load Balanced Routing Model
Remote sensor organizations (WSNs) assume a vital part in giving ongoing information admittance to IoT applications. Be that as it may, open organization, energy limitation, and absence of brought together organization make WSNs entirely defenseless against different sorts of pernicious assaults. In WSNs, recognizing vindictive sensor gadgets and dispensing with their detected data assume a vital part for strategic applications. Standard cryptography and confirmation plans cannot be straightforwardly utilized in WSNs on account of the asset imperative nature of sensor gadgets. In this manner, energy productive and low idleness procedure is needed for limiting the effect of malignant sensor gadgets. In this research work presents a secured and burden balanced controlling contrive for heterogeneous bunch-based WSNs. SLBR shows a predominant trust-based security metric that beats the issue when sensors proceed to influence from extraordinary to terrible state and the other way around; besides, SLBR alters stack among CH. In this way, underpins fulfilling superior security, allocate transmission, and vitality efficiency execution. Trials are driven to calculate this presentation of developed SLBR demonstrate over existing trust-based controlling show, particularly ECSO. The result accomplished appears SLBR demonstrate fulfills favored execution over ECSO as distant as vitality capability (i.e., arrange lifetime considering to begin with sensor contraption downfall and total sensor contraption passing), correspondence overhead, throughput, allocate planning idleness, and harmful sensor contraption mis-classification rate and recognizable verification. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Role of medicinal plants against lung cancer
Nowadays for treatment of various diseases, scientific studies are conducted using the medicinal plants of both domestic and wild for curing purpose. Every plant contain compounds that have medicinal properties and can be isolated from the plants parts. Due to plants diversity in India and use in Ayurveda, Unani and Siddha, India is known as medicinal hub. Lung cancer is the third most common cancer, that develops in lung tissue and are of two type's non-small cell lung cancer and small cell lung cancer. Many factors cause lung cancer; tobacco smoking is the prominent cause of lung cancer. The individuals who smoke have 20-30% more chance of developing lung cancer than non-smokers. The conventional treatment of lung cancer, are chemotherapy, stem cell therapy, and electrochemical treatments. Plants and the compounds present can be used for treating lung cancer. So in this chapter will focus on plants Acalypha indica, Solanum trilobatum, Justicia adhatoda, Coleus amboinicus and Piper nigrum in lung cancer treatment and on the medicinal properties. 2024, IGI Global. -
A Comparative Study of Machine Learning Techniques for Credit Card Customer Churn Prediction
A customer is a churner when a customer moves from one service provider to another. Nowadays, with an increasing number of severe competition with inside the market, essential banks pay extra interest on customer courting management. A robust and real-time credit card holders churn evaluation is vital and valuable for bankers to preserve credit cardholders. Much research has been observed that retaining an old customer is more than five times easier compared to gaining a new customer. Hence, this paper proposes a method to predict churns based on a bank dataset. In this work, Synthetic Minority Oversampling Technique (SMOTE) has been used for handling the imbalanced dataset. Credit card customer churn is predicted using random forest, k-nearest neighbor, and two boosting algorithms, XGBoost and CatBoost. Hyperparameter tuning using grid search has been used to increase the accuracy. The experimental result shows Catboost has achieved an accuracy of 97.85% and tends to do better than the other models. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Machine learning insights into mental health risk factors associated with climate change: Impact on schoolchildren's cognitive abilities
In this chapter, we use machine learning techniques to investigate how the effects of climate change and certain risk factors for mental health affect students' cognitive skills in the classroom. The mental health of at-risk populations, especially students, must be considered in light of the fact that the world's environment is changing significantly. Using state-of-the-art machine learning algorithms, we analyze large datasets that include environmental variables, socio-economic characteristics, and markers of mental health among school-aged persons. We are primarily interested in identifying key relationships and trends that might help us understand the complex relationship between climate change and cognitive health in this population. In order to uncover complex insights, the chapter takes a holistic approach by combining feature selection, model training, and interpretability analysis. The cognitive capacities of school-aged children may be significantly impacted by some climate- related stresses, according to preliminary results. The findings add to our knowledge of the interconnected webs of environmental shifts, psychological susceptibilities, and cognitive consequences. Educators, legislators, and healthcare providers can benefit from this study's use of machine learning insights into the possible effects of climate change on students' mental health. It also paves the way for the creation of tailored treatments and adaptive techniques to deal with the highlighted dangers, fostering resilience and prosperity in the face of a changing environment. 2024, IGI Global. All rights reserved. -
Plant-Derived Nanocellulosic Material: A Promising Technology Application in Environmental Bioremediation
Nanocellulose (NC) polymers derived from plant sources are gaining enormous interest in environmental remediation owing to their low cost and potential for renewable adsorption. Plant-derived nanocellulose is applied in waste water treatment because of its unique features and functionality. The word nanocellulose refers to cellulosic materials having a dimension of nanoscopic scale/or nanoscale. One such nanomaterial is a cellulose-based material with a well-aligned nanocellulose composition indicating its structural hierarchy. Nanocellulose has been recognized as a remarkable natural biomaterial adsorbent which is obtained from renewable sources such as wood, plants, fruit peel, can be found abundantly on earth, and biodegradable and can be easily used in the surface fabrication. Due to its increased surface area, nanocellulose has gained considerable advantage over conventional cellulose fibers. Application of nanocellulosic material in environmental remediation and wastewater treatment has recently emerged as a potential adsorbent generating, and aroused much attention in addressing the environmental issue. Nanocellulose may adsorb a wide range of contaminants, such as heavy metals, dissolved pollutants (organic), dyes, petroleum oil, and unwanted effluents. This review provides focus on the structure, properties, isolation, and adsorbent classes of nanocellulosic materials, as well as their applications in environmental remediation. 2025 by Apple Academic Press, Inc. -
Bionanoparticles Impact on Human Health, an In Vitro and In Vivo Status
In the hunt for a safe replacement for hazardous conventional nanoparticles that are applied in biomedicine field, bionanoparticles are known to be the ideal choice. The term bionanoparticles refers to nanoparticles made using biomolecules or that use a biomolecule to enclose or immobilize a more conventional nanomaterial. For the creation of bionanoparticles, biomolecules are taken from bacteria, plants, agricultural wastes, insects, marine life, and some mammals. Bionanoparticles, possess unique qualities with lot of potential that make them applicable in different field such as, pharmacy, aerospace engineering, biosensors, material sciences and so on. These bionanoparticles have improved biocompatibility, bioavailability, and bioreactivity and display minimal or insignificant toxic effects in humans, animals, and at the environment level. Nanoparticles can be introduced into the body either by biomedical procedures as a part of treatment, diagnosis, or the application of cosmetics. The mode of entry is usually via intravenous, intradermal, intramuscular and peritoneal injections. Unintentional entry of nanoparticles is a result of environmental pollution or accidental release. The effect of bionanoparticles on human health received much importance as they are biologically synthesized and biocompatible. The goal of this chapter is to review human exposure to bionanoparticles with an emphasis on the effects on human cells and animal models. 2025 selection and editorial matter, Shakeel Ahmed; individual chapters, the contributors. -
Securing Automated Systems with BT: Opportunities and Challenges
The use of automated systems is becoming increasingly prevalent in various industries; however, they pose significant security risks. In order to enhance the security of these systems, Blockchain Technology (BT) provides a promising solution. This chapter discusses the opportunities and challenges associated with using BT to secure automated systems. The role of BT in securing automated systems is discussed, emphasizing its ability to improve security and transparency. Additionally, BT-based systems with enhanced security are examined, such as decentralized data management, immutable and transparent ledgers, reduced cyber-attacks, and secure data sharing. Despite these opportunities, challenges such as high computational power requirements, integration challenges, BT scalability, and regulatory challenges must be addressed. Utilizing BT can create a more secure and transparent system that can help to prevent fraud, hacking, and other forms of cyber-attacks, ultimately enhancing the reliability and safety of automated systems. In conclusion, this paper highlights the potential of using BT for securing automated systems and the need for continued research and development to overcome the challenges associated with its implementation. 2024 selection and editorial matter, Nidhi Sindhwani, Rohit Anand, A. Shaji George and Digvijay Pandey; individual chapters, the contributors. -
Bioinformatics applications for evaluating health and pharmacological properties of tea: Use of computer-assisted drug discovery tools
Bioinformatics has emerged as a crucial tool in tea research, enabling the exploration of the genetic and molecular intricacies underlying tea cultivation, quality, and health benefits. By leveraging bioinformatics, researchers have extensively explored, inferred, and evaluated the pharmacological properties of tea. This groundbreaking approach has unveiled a myriad of possibilities for utilizing the bioactive compounds present in tea. Metabolomics studies have unraveled the intricate metabolic pathways within tea plants, providing insights into the synthesis and accumulation of bioactive compounds. Bioinformatics in tea research opens new avenues for the tea industry, benefiting both producers and consumers worldwide. These advancements not only deepen our understanding of tea biology but also hold immense potential for sustainable tea production, the discovery of novel bioactive compounds, and the optimization of tea flavors and health benefits. This chapter explains the bioinformatic tools used to identify various therapeutic properties of tea biocompounds. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Disproportionate impact of climate change: Housing crisis and displacement among the transgender community in India
India is a nation that is threatened by climate change. Climate change and the housing crisis are inextricably linked, they are associated with exacerbated mental and physical health conditions. It often affects individuals differently based on various factors shaped by social norms. So, marginalized sections like transgender persons are disproportionately affected. Individuals with inadequate housing are significantly affected by natural disasters. However, most transgender individuals cannot rent due to a lack of documents and unemployment. Thus, housing is an essential social determinant of physical and mental health. The book chapter discusses the various intersecting identities and the geographical and ecological contexts. The current revised climate laws in India have emphasized incorporating gender but there is a need to focus on gender beyond the binary to formulate more sensitive and equitable methods to address climate change. It also discusses the psycho-social impact on the community and the unique challenges they face as extreme weather events increase. 2024, IGI Global. All rights reserved. -
Synthesis, Structure, and Physical Properties of Bulk MoS2
With the discovery of graphene by Novoselov and Geim in 2004, two-dimensional (2D) materials have been extensively researched due to their bizarre promise in the fields of electronics, optics, medical, mechanics, energy conversion, and storage. Especially, 2D-layered materials consisting of atomic sheets stacked together by weak van der Waal forces have received intriguing research interest in recent years. Cutting-edge 2D materials being investigated by researchers include 2D oxides (V2O5, MoO3, LixCoO2), topological insulators (Bi2Se3, Bi2Te3, HfBr), nitrides (h-BN, MoN, Ti4N3Tx, W2N, V2N), carbides (Ti3C2, Ta4AlC3), and transition-metal dichalcogenides (MoS2, WS2). Research has proved that these materials could counterpart graphene in a variety of fields and applications. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Recent development on self-powered and portable electrochemical sensors: 2D materials perspective
Electrochemical sensors have attracted tremendous research interest due to their simplicity and compatibility to be integrated with standard electronic technologies and capability to produce electrical signals that can be effectively acquired, processed, stored, and analyzed. Due to the incredible electronic and physical properties derived from the 2D structure, two dimensional (2D) nanomaterials such as graphene, phosphorene black phosphorus, transition metal dichalcogenides (TMDCs), and others have proven to be attractive for the fabrication of high-performance electrochemical sensors. The book chapter is focused in the unique characteristics of 2D materials leading toward excellent sensing performance, the structural and molecular designing of various 2D materials, structure-property relationships, various sensing applications employing disparate 2D nanostructures with an emphasis on highlighting various prototypical and prominent research paths. 2023 Elsevier Inc. All rights reserved. -
MoS2, a new perspective beyond graphene
Owing to the fascinating structural, optical, electrical, chemical properties, graphene has created new paradigm in the field of nanoscience and the common crystalline structures that can be exfoliated include the layered van der Waals (vdW) solids such as boron nitride, transition metal dichalcogenides (TMDCs), black phosphorus, and the layered ionic solids. Here, we bring forth the state-of-art-of materials dominated by their two-dimensional (2D) geometry beyond graphene. Being one of the most well-studied families of vdW layered materials, molybdenum disulphide (MoS2) belonging to TMDC family has gained considerable research interest. The present work is focused on attempts to optimize and characterize this material with unique properties for a host of applications. The work resolves the hydrothermal growth of hexagonal MoS2 nanoflakes with attracting optical and magnetic properties providing strong evidence for the spin orbit split valence bands of these nanostructures. The enhanced electrocatalytic activity, excitation wavelength dependent down-conversion and up-conversion photoluminescence, growth of structural polymorphs using simple hydrothermal method, and the efficient anticancer properties of MoS2 nanostructures providing greater insight into energy and biomedical applications are also discussed. The improved catalytic activity of MoS2-based nanostructures reveals the increasing number of accessible active sites, formation of large surface area and is greatly beneficial for accomplishing a clean, environmental-friendly, inexpensive hydrogen mission for energy storage and conversion applications. The synergistic effect of the MoS2 nanocomposites was able to impede angiogenesis, tumor growth, and epithelial to mesenchymal transition, elucidating the anticancer efficacy. Understanding and exploiting such unique properties of these 2D materials paves new horizons toward novel technological advances in electronic and medical field. 2021 Elsevier Inc. All rights reserved. -
Food Quality Indicator-Based Intelligent Food Packaging
Foodborne illnesses caused by microbial growth and consumption of spoiled food items can lead to severe health issues. Monitoring real-time food quality through indicators/sensors has been an important priority for food industries, researchers, consumers, and regulatory bodies in this context. Intelligent packaging (IP), a type of food packaging, uses an indicator component to track and alert consumers on the quality of packaged food from the stage of manufacture to consumption in real time. Intelligent packaging helps reduce food waste and ensure consumer safety. This book chapter will discuss various food quality indicators, including humidity, oxygen, carbon dioxide, pH, and microbial indicators, and their applications in IP. 2025 John Wiley & Sons Ltd. All rights reserved. -
Children Witnessing Violence in India: Nature, Risk Factors, Impact and Prevention Strategies
Children witness various degrees and intensities of violations and violence along with a hoard of environmental stressors. Such a spectrum of violence includes disturbing family environments, witnessing adults, including parents and family members, indulge in violence and abusive behaviours and direct or vicarious exposure to violence outside the home. The chapter aims to provide an overview of the nature and impact of witnessing violence. The frequency, type, intensity and the child's relationship with the people involved or impacted by the violence can determine the impact on a child's mental health and development. Children may witness distressing events in their daily lives like the loss of a loved one or watching adults take up challenging tasks, which may help them be resilient and learn coping skills with appropriate support. Long-term exposure to witnessing violence and trauma can lead to severe emotional and developmental difficulties. Such direct or vicarious exposure to varying degrees of violence may cultivate a culture of fear, repression and silence around the children. These difficulties may be similar to those of children who are direct victims of abuse. Witnessing violence has also been linked to anxiety and depression. Children growing up in such environments are at higher risk of normalizing violence and growing into abusive adults. Poverty, cultural factors, parenting, schooling, and policies can largely determine such risks for children. The paper discusses the preventive and promotive approaches at the school, family and community levels. Education and empowerment of adults in the child's environment can be the best preventative approach. Existing policies and programmes in India for children need to bring in more robust initiatives to identify, report, prevent and protect children witnessing violence. The needs and concerns of children witnessing violence and prevention approaches should be part of courses in helping professionals training and curriculum. The chapter calls for the necessity of individual and community-based interventions in terms of need-based models for addressing the mental health needs of children. The chapter strongly recommends the need for addressing mental health education for families and schools. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Research Intention Towards Incremental Clustering
Incremental clustering is nothing but a process of grouping new incoming or incremental data into classes or clusters. It mainly clusters the randomly new data into a similar group of clusters. The existing K-means and DBSCAN clustering algorithms are inefficient to handle the large dynamic databases because, for every change in the incremental database, they simply run their algorithms repeatedly, taking lots of time to properly cluster those new ones coming data. It takes too much time and has also been realized that applying the existing algorithm frequently for updated databases may be too costly. So, the existing K-means clustering algorithm is not suitable for a dynamic environment. Thats why incremental versions of K-means and DBSCAN have been introduced in our work to overcome these challenges.To address the aforementioned issue, incremental clustering algorithms were developed to measure new cluster centers by simply computing the distance of new data from the means of current clusters rather than rerunning the entire clustering procedure. Both the K-means and the DBSCANDBSCAN algorithms use a similar approach. As a result, it specifies the delta change in the original database at which incremental K-means or DBSCANDBSCAN clustering outperforms prior techniques. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Data Mining-Based Variant Subset Features
A subset of accessible variants data is chosen for the learning approaches during the variant selection procedure. Itincludes the important one with the fewest dimensions and contributes the most to learner accuracy. The benefit of variant selection would be that essential information about a particular variant isnt lost, but if just a limited number of variants are needed,and the original variants are extremely varied, there tends to be a risk of information being lost since certain variants must be ignored. Dimensional reduction, also based on variant extraction, on the other hand, allows the size of the variant space to be reduced without losing information from the original variant space.Filters, wrappers, and entrenched approaches are the three categories of variant selection procedures. Wrapper strategies outperform filter methods because the variation selection procedure is suited for the classifier to be used. Wrapper techniques, on the other hand, are too expensive to use for large variant spaces due to their high computational cost;therefore each variant set must be evaluated using the trained classifier, which slows down the variant selection process. Filter techniques have a lower computing cost and are faster than wrapper procedures, but they have worse classification reliability and are better suited to high-dimensional datasets. Hybrid techniques, which combine the benefits of both filters and wrappers approaches, are now being organized. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Feature Subset Selection Techniques with Machine Learning
Scientists and analysts of machine learning and data mining have a problem when it comes to high-dimensional data processing. Variable selection is an excellent method to address this issue. It removes unnecessary and repetitive data, reduces computation time, improves learning accuracy, and makes the learning strategy or data easier to comprehend. This chapterdescribes various commonly used variable selection evaluation metrics before surveying supervised, unsupervised and semi-supervised variable selection techniques that tend to be often employed in machine learningtasks including classification and clustering. Finally, ensuing variable selection difficulties are addressed. Variant selection is an essential topic in machine learning and pattern recognition, and numerous methods have been suggested. This chapter scrutinizesthe performance of various variable selection techniques utilizing public domain datasets. We assessed the quantity of decreased variants and the increase in learning assessment with the selected variable selection techniques and then evaluated and compared each approach based on these measures. The evaluation criteria for the filter model are critical. Meanwhile, the embedded model selects variations during the learning model's training process, and the variable selection result is automatically outputted when the training process is concluded. While the sum of squares of residuals in regression coefficients is less than a constant, Lasso minimizes the sum of squares of residuals, resulting in rigorous regression coefficients. The variables are then trimmed using the AIC and BIC criteria, resulting in a dimension reduction. Lasso-dependent variable selection strategies, such as the Lasso in the regression model and others, provide a high level of stability. Lasso techniques are prone to high computing costs or overfitting difficulties when dealing with high-dimensional data. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Introduction to Data Mining and Knowledge Discovery
Data mining is a process of discovering some necessary hidden patterns from a large chunk of data that can be stored in multiple heterogeneous resources. It has an enormous use to make strategic decisions by business executives after analyzing the hidden truth of data. Data mining one of the steps in the knowledge-creation process. A data mining system consists of a data warehouse, a database server, a data mining engine, a pattern analysis module, and a graphical user interface. Data mining techniques include mining the frequent patterns and association learning rules with analysis, sequence analysis. Data mining technique is applicable on the top of various kinds of intelligent data storage systems such as data warehouses. It provides some analysis processes to make some useful strategic decisions. There are various issues and challenges faced by a data mining system in large databases. It provides a great place to work for data researchers and developers. Data mining is the process of classification, which can be executed based on the examination of training data (i.e., objects whose class label is predefined). With the help of an expert set of previous class objects with known class labels, it can find a model that can predict a class object with an unknown class label. These classification models can be classified into a variety of categories, including nearest neighbor, neural network, and others. Bayesian model, decision tree, neural network Random forest, decision trees Support vector machine, random forest SVM (support vector machine), for example. By analyzing the most common class among k closest samples, the K-Nearest Neighbor (KNN) technique aids in predicting of the class object with the unknown class label. Its an easy-to-use strategy that yields a solid classification result from any distribution. The Naive Bayes theory helps to perform the classification. It is one of the fastest classification algorithms, capable of efficiently handling real-world discrete data. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.