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Intelligent Optimized Delay Algorithm for Improved Quality of Service in Healthcare Social Internet of Things
Internet of Things (IoT) interconnects billions of devices by establishing a network that adheres to International Organization of Standardization (ISO) standards. These devices communicate with each other by sharing data regulated by the application. This is performed to accomplish a task or service that the application demands. The social or human-like behaviors are adapted in the IoT environment forming the Social IoT (SIoT). The SIoT integrates social networks in IoT-connected devices, making them unique and identifiable. Recent advancements in networking, intelligent network management, battery management, remote sensing, sensors, and other related technologies convinced users and designers to adopt IoT even for large-scale applications where the data involved is enormous. Leveraging the advancements in medical IoT, which focuses on healthcare to patients, can improve its service by removing redundant manual processes, long wait times, and providing other automated services. The advancements in real-time healthcare IoT devices and wearables make a strong case for implementing SIoT in the healthcare domain. SIoT in the healthcare domain has the potential to benefit users on a large scale. This chapter comprehends the challenges and solutions of using SIoT in medical and healthcare solutions from a networking quality of service (QoS) perspective. In addition, this chapter compares the intelligent algorithm, which can be used to improve the QoS of SIoT. Achieving higher QoS is necessary for healthcare services, especially while handling data from emergency and intensive care units. These data cannot tolerate errors and delays. Intelligent network management has become unavoidable in the health and medical services to achieve a higher degree of QoS system, which indirectly decreases data transfer time. The data from the sensor devices sent across the network leads to data loss and delay in data transmission due to congestion in the network and gateway devices. The optimized algorithms incorporated with the delay-based algorithm improves the QoS predominantly and reduces the delay in data transfer. Similarly, the particle swarm optimization algorithm allocates resources over the network and dynamically makes the network adapt to increased and reduced data flow, which reduces the delay and improves the QoS. Intelligent optimized delay algorithm (IODA) is proposed to improve the network performance by reducing the delay and using available bandwidth for data transfer in SIoT. 2023 selection and editorial matter, Gururaj H L, Pramod H B, and Gowtham M; individual chapters, the contributors. -
Fintech Innovations in the Financial Service Industry
Digital transformation underscored by the fourth industrial revolution has led to the emergence of sophisticated technology-enabled financial services known as fintech, that has swiftly altered traditional financial services space. Global adoption of fintech is rapidly increasing due to its disruptive nature and is largely embraced by participants who are underserved by traditional financial service providers. Global investments in fintech are growing rapidly year by year owing to increased interconnectivity with the digital revolution. Fintech is expansive, engulfing a plethora of innovative applications in various services including payments, financing, asset management, insurance, etc. There exists a gap in the literature and visualization research on impact and future pathway of fintech innovations in payments and financial services and role of financial regulations. This study aims to enrich the understanding of fintech innovations in payments and financing and investigate the correlation and significance of regulatory framework in maintaining a fair ecosystem. With this objective, an extant systematic review was performed using research articles published in peer-reviewed journals for the period 20142022 when there has been a burgeoning of interest in fintech globally. The findings of this study contribute to the theoretical constructs of fintech innovations in the financial services industry and show that such innovations play a crucial role in shaping the nature of future of business. The results of this study have implications for researchers who could deploy this research as a reference point to get a holistic insight and a detailed mapping of innovations in fintech. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Chromatic bounds of some (P5, banner)-free graphs
Let ?(G) and ?(G), respectively, denote the chromatic number and clique number of a graph G. A P5 is a path on five vertices, a banner (paw) is the graph obtained by joining a new vertex to a single vertex of C4 (C3) and a hammer is obtained by subdividing the pendant edge of a paw exactly once. Recently, (P5,banner)-free graphs have received wide attention. In 2019, Karthick, Maffray and Pastor, gave a structural characterisation of (P5,banner)-free graphs, which when combined with a result by Bourneuf and Thomass[Bounded twin-width graphs are polynomially ?-bounded, Adv. Comb. (2025)] implies that for a (P5,banner)-free graph G, ?(G) ? ?(G)5. Geir [Colourings of P5-Free Graphs, PhD Thesis, Technische Universit at Bergakademie Freiberg (2022)] showed that the ?-binding function of the class of (P5,banner)-free graphs is bounded by the ?-binding function of 3K1-free graphs. By a result of Kim [The Ramsey number R(3,t) has order of magnitude t2/log t, Random Structures and Algorithms 7(3) (1995) 173207], the chromatic number ?(G) of a 3K1-free graph G has order of magnitude ?(?(G)2/log ?(G)). Recently, Song and Xu [Divisibility and coloring of some P5-free graphs, Discrete Appl. Math. 348 (2024) 144151] proved that every (P5, C5, banner, hammer)-free graph G is ?(G)3/2-colorable. This motivates us to study the subclasses of (P5, banner)-free graphs. We prove that for any (P5, banner, F ? K1)-free graph G where F ?{C4,K4 ? e,K3 ? K1,paw}, ?(G) ? ?(G)2/2 for ?(G) ? 3. Moreover, the bound is tight for ?(G) = 3. 2026 World Scientific Publishing Company. -
Towards Optimal ?-Binding Functions of (2K1?K2)-Free Graphs and (P3?K1)-Free Graphs
A function f:N?R is called a ?-binding function for a hereditary family G of graphs, if ?(G)?f(?(G)) for every G?G where ?(G) and ?(G) denote the chromatic number and clique number respectively. In his influential work, Gya?fa? (1987) showed that the family of (2K1?K2)-free graphs and the family of (P3?K1)-free graphs are ?-bounded. Randerath and Schiermeyer (2004) improved the ?-binding functions of both these classes to x+12. In this paper, we further improve the ?-binding function of both these classes to x22 for x?3. Furthermore, we obtain a tight chromatic bound for (P3?K1)-free graphs with clique number 4. The Author(s), under exclusive licence to Springer Nature Japan KK 2025. -
A comparative evaluation of machine learning and deep learning models across diverse datasets for early detection of lung cancer
Lung cancer is among the most fatal types of cancer, accounting for millions of fatalities globally. The capacity for its early detection has the potential to greatly enhance the outcome of treatments, and in recent times, machine learning (ML) and deep learning (DL) algorithms have emerged as mighty resources in aiding radiologists and doctors. This article describes a comparison study of research articles in which they employed different ML and DL models in lung cancer detection on a wide range of datasets. The analysis establishes that the variety, quality, and source of the dataset are central to determining how reliable reported model performance is. Reproducibility has been made possible with public datasets such as LIDC-IDRI, NSCLC, and Kaggle datasets, whereas private clinical datasets typically lead to improved accuracy since they consist of high-quality curated annotations. Subsequent research has shown that DL models, especially state-of-the-art architectures such as convolutional neural networks (CNNs) and EfficientNet-B3, are well-suited to image classification tasks and consistently outperform classical ML models when large, well-balanced datasets are available. Hybrid approaches that blend CNN-based feature learning with traditional classifiers like support vector machines have also proven highly promising, particularly when applied to overcome challenges such as small sample sizes and noisy images. Directions for future work point toward the integration of standardized, multicenter datasets, explainable AI models, and multimodal learning techniques to reach reliable in-clinic deployment. 2026 Elsevier Inc. All rights reserved. -
Enhanced supercapacitors and LPG sensing performance of reduced graphene oxide/cobalt chromate pigments for energy storage applications
It is imperative that an initial inquiry be conducted as soon as possible since the production of monolayer of carbon atoms (rGO) composites is the root cause of their poor performance in supercapacitor and LPG sensors. Here, an effort is undertaken to construct a cobalt chromate pigments-reduced graphene oxide (CoCr2O4/rGO) by solution combustion method for the supercapacitor and LPG sensor. The proposed method is efficient and easy in terms of its application to the production of CoCr2O4/rGO polycrystalline composite on a wide scale. Within the scope of this work is an investigation into the improved supercapacitor and LPG sensing behaviour of CoCr2O4/rGO polycrystalline composite. We have implemented a simple method that has been identified for mass-producing reduced graphene oxide. The Solution combustion technique was used, and it was successful in achieving this goal for the very first time. X-ray diffraction technique is used analyse crystallinity, phase, and structural investigation. The nature of gas sensing behaviour with a step function of LPG gas at 500 ppb was studied at room temperature for rGO, The CoCr2O4 pigments and 0.5CoCr2O4+0.5rGo polycrystalline composite samples. The gas response is maximum for 0.5CoCr2O4+0.5rGo polycrystalline composite in the order of 97% in compare with the reduced graphene oxide sample which shows the lowest sensitivity in the order of 26% on exposure of liquified petroleum gas (LPG). The recorded response and recovery times of 0.5CoCr2O4+0.5rGo polycrystalline composite is found to be 40 s and 52 s respectively in comparison to the rGO sample about 58 and 74 s respectively. By adding rGo to the material, the cyclic voltammetry (CV) findings demonstrate improved current density and area of CV loop with increased scan rate. In three-electrode reveals the system, a CoCr2O4-rGo material exhibits a specific capacitance of 226 F/g. Thus, the results reveals that rGo is contributing significantly to the enhancement of a supercapacitor's performance of CoCr2O4. 2023 Elsevier Ltd and Techna Group S.r.l. -
Synthesis of ZnO and NiO nano ceramics composite high-performance supercapacitor and its catalytic capabilities
NiO and ZnO mixed nanocomposites were manufactured using the solution combustion process. As-prepared samples were analyzed using XRD. The XRD shows an average crystallite size of 3540 nm. The elemental composition determined by EDS indicates a nearly equal proportion of Ni and Zn, with an atomic ratio of Ni/Zn = 0.96. The specific capacitances of NiO is 295.5 Fg-1, ZnO is 117.3 Fg-1 and ZnO/NiO nanocomposites is 561.75 Fg-1 which are more than NiO and ZnO alone. This study shows that constructing binary oxide nanocomposites is an approach for developing high-performance supercapacitor electrode materials. Experimental observations on catalytic activity revealed that NiO/ZnO increased catalytic activity. Furthermore, adding NiO to ZnO in the composite increased the overall amount of oxygen vacancies in the samples. Our research lays the door for a simple, inexpensive, nontoxic, and quick technique to synthesize binary transition metal oxide-based electrode materials for high-performance supercapacitors. 2024 Elsevier Ltd and Techna Group S.r.l. -
Malicious URL Detection Using Machine Learning Techniques
Cyber security is a very important requirement for users. With the rise in Internet usage in recent years, cyber security has become a serious concern for computer systems. When a user accesses a malicious Web site, it initiates a malicious behavior that has been pre-programmed. As a result, there are numerous methods for locating potentially hazardous URLs on the Internet. Traditionally, detection was based heavily on the usage of blacklists. Blacklists, on the other hand, are not exhaustive and cannot detect newly created harmful URLs. Recently, machine learning methods have received a lot of importance as a way to improve the majority of malicious URL detectors. The main goal of this research is to compile a list of significant features that can be utilized to detect and classify the majority of malicious URLs. To increase the effectiveness of classifiers for detecting malicious URLs, this study recommends utilizing host-based and lexical aspects of the URLs. Malicious and benign URLs were classified using machine learning classifiers such as AdaBoost and Random Forest algorithms. The experiment shows that Random Forest performs really well when checked using voting classifier on AdaBoost and Random Forest Algorithms. The Random Forest achieves about 99% accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Does the green finance initiatives transform the world into a green economy? A study of green bond issuing countries
Green finance initiatives have received global support in modern times, relatively in response to safeguard the environment and preserve natural resources through channelizing the investments to create a green economy. This paper attempts to evaluate and compare the green finance growth in green bond issuing nations across the world. This study also assesses the effect of green finance growth on the dependence of non-renewable energy resources especially fossil fuels that have been creating several environmental issues for the past years. This study develops a pressure-state-response framework to evaluate the comprehensive system of green finance growth that depicts the interaction of sub-aspects. We employ the entropy technique to calculate the weights at each level within the evaluation system. We also constructed empirical models to assess the relationship between green finance growth and dependence on fossil fuel consumption and found that there exists a negative relationship between the two. The results convey that proliferation of green finance instruments can reduce the dependence on fossil fuels and smoothen the transition towards a carbon negative world. 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Investment Decisions : Behavioral Biases in Selected Less Volatile Asset Classes
This study investigates the behavioral biases in selected less volatile asset classes and their influence on investment decisions(IDs). This study compares and contrasts demographic factors(DF) that influence behavioral biases(BB), examines the relationship between behavioral biases(BB) and risk-taking newlinebehaviors (RTB), determines whether BBs can be used to predict RTB and IDs, and looks at covariance patterns between factors that influence BBs, RTB, and IDs.A comprehensive analysis was conducted, considering various DF such as age, gender, education, annual income, marital status, total annual savings newlinepercentage, and the number of dependents in the family. The findings revealed no statistically significant interaction effects between these demographic variables and the combined dependent variables. Additionally, no significant main effects of age, gender, annual income, education, marital status, or paying tax were observed on the combined dependent variables. The study identified several correlations among the behavioral biases examined, including overconfidence(OC), representativeness(R), anchoring(A), herding(H), mental accounting(MA), and conservatism bias(CB). Positive correlations were found between OC and R, A and OC, A and R, H and OC, H newlineand R, MA and OC, MA and R, CB and OC, CB and R, CB and A, CB and H, CB and MA, risk-taking behaviors and overconfidence, risk-taking behaviors and representativeness, risk-taking behaviors and anchoring, risk-taking newlinebehaviors and herding, risk-taking behaviors and mental accounting, and risktaking behaviors and conservatism bias. Furthermore, herding and conservatism bias was significantly associated with risk-taking behaviors, while anchoring, herding, mental accounting, and conservatism bias were associated considerably with IDs. As part of the assessment techniques utilized in this study, seven characteristics or latent constructs were examined using various observable variables or scale items. -
Wage Collapse and Gender Differences in Earning in India
The study found that the average daily wages almost increased three times between 1983 and 202122 in rural and urban areas of the country. The average wages rose more rapidly for women than men. We have observed that the wage growth of casual workers increased much faster over the years, reflecting less fluctuation than regular workers. However, at the same time, the growth rate of regular workers has collapsed several times, and in the recent period, the collapse was almost complete. From the analysis of Gini coefficient and decomposition, we observed that wage inequality has come down in India between 201112 and 202122, and much of the differences in earnings are explained by within-group factors. The Author(s), under exclusive licence to Indian Society of Labour Economics 2026. -
Education Inequality in India: An Empirical Analysis Using National Sample Survey Data
This research examines the ruralurban differences in educational inequality of major states in India. Using National Sample Survey Office (NSSO) data and decomposition methods, this study finds that overall educational inequality has come down but still very high in rural areas. We found that factors such as limited access to higher education, financial constraints and social factors are responsible for the high inequality in rural areas. This study highlights the need for government intervention to enhance educational access by increasing institutions and providing financial aid. It also notes that non-financial barriers like English proficiency further exclude lower socio-economic groups. Hence, we argue for inclusive education policies to improve the existing situation. 2024 Institute for Human Development. -
Changes in Wage Trends and Earnings Differences in Kerala
In this article, the weekly earnings gap between men and women in Kerala is examined by a number of inequality indices such as the percentile ratio and the Gini coefficient. The entropy measures of inequality are used to decompose wage inequality into within-group and between-group inequalities. The earnings inequality between men and women has been increasing, even though their wage grows faster than mens wage. The indices of inequality suggest the growing wage disparity in the regular and casual labour market. The result reveals that the levels of education and earnings are positively correlated, but women with the same level of education earn much less than men in regular salaried work. The rising wage inequality of men and women during 20042009 were associated with the growth rate of wages in the same period. That is, the wage rates of both regular and casual workers have increased more than four per cent during the period that experienced the highest inequality. 2019, Indian Society of Labour Economics. -
Quality of Drinking Water and Sanitation in India
Wide disparity exists in access to drinking water across social groups in rural and urban India. This article shows that the economically weaker sections or the lower quintile class does not have access to water within the premises both in rural and urban areas. This indicates that low income or wealth would mean poor access to basic amenities for households. Similarly, access to toilets and incidence of open defaecation reflect social disparities. The regression results show that an increase in the household income increases the predicted probability of maintaining an exclusive latrine. Further, compared to the General Category, the Scheduled Castes and Other Backward Classes have a lower probability of constructing an exclusive latrine facility, in the rural and urban areas. 2021 Institute for Human Development. -
Predictive Modeling of Substance Abuse Risks using Big Data Analytics and Social Media Mining
The worldwide increase in substance abuse among teenagers and young adults has become serious concern in recent times. One way this pattern has developed is through the evolution of social media. Social media has transformed people's attitudes towards certain behaviors and has encouraged risky behavior to the point of actually causing addiction by exposing them to drug-related material. Despite the existence of preventative measures, such as education programs in schools, many children and youth have not had adequate access to educational interventions or evidence-based measures due to barriers created by geography, economic circumstances, and social factors, particularly in less developed countries. The research proposed is focusing on addressing this gap using a big data approach. This research employs a unique analytical framework that integrates multiple large data sets from a variety of sources to better identify and assess the effectiveness of interventions. This model employs an analytical approach that uses statistical learning techniques and predictive analytics to identify historical patterns and anticipate future trends, and assess the effectiveness of various interventions conducted in different countries. The results of the analysis suggest that this big data approach will provide decision-makers with clearly documented evidence related to various risk-taking behaviors as they relate to available prevention interventions, and will assist decision-makers in developing targeted prevention intervention strategies. This study demonstrates the revolutionary aspect behind the application of computational intelligence in preventing substance abuse and informing evidence-based community health interventions. 2025 IEEE. -
Educational Perspectives of the Metaverse
The article presents a comprehensive review of the transformational potential of metaverses in the field of modern education. A multidimensional analysis of this technology is carried out, considering it not only as a technical innovation, but also as a fundamentally new educational paradigm. The paper explores in detail the unique capabilities of metaverses for creating immersive learning environments where traditional educational formats are combined with innovative approaches such as gamification and project activities in virtual space. The article pays special attention to the practical aspects of the use of metaverses in the educational process. Methodologically, the article identifies three key areas for the successful realization of the educational potential of the metaverses: technical (providing infrastructure and accessibility), organizational (developing a regulatory framework and standards) and pedagogical (creating effective didactic models). In conclusion, the article emphasizes that the full realization of the educational potential of the metaverse is possible only with a systematic approach that comprehensively takes into account technological, organizational and pedagogical aspects. Such a multi-level approach will make it possible to transform the metaverse from a promising, but still experimental technology into an effective educational tool of a new generation. Only in this case will the metaverses be able to become a catalyst for profound changes in the learning system, ensuring its adaptation to the requirements of the rapidly developing digital reality. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Research on Effectiveness and Assistants in the Educational Process for Student Learning
The article discusses the topical issue of the use of AI assistants in the educational process of universities, which is becoming especially important in the context of the rapid development of digital technologies. Artificial intelligence, as one of the key tools of our time, is being actively introduced into the educational sphere, allowing us to optimize routine tasks and increase the effectiveness of the educational process. AI assistants are able not only to automate tasks such as checking assignments, drawing up curricula and analyzing student performance, but also to provide personalized recommendations for students, which significantly improves the quality of learning and creates an immersive environment for students. This article examines the role of AI assistants as a tool to support teachers in the educational process of universities. The main advantages of using them are considered, including improving the effectiveness of teachers, reducing the burden on teaching staff and the possibility of a more flexible approach to student learning. Attention is paid to the difficulties and limitations associated with the introduction of AI technologies, such as the need to adapt existing educational programs and the ethical aspects of using artificial intelligence. The purpose of the study is to analyze the effectiveness of using AI assistants in various educational environments, as well as to identify key factors for the successful integration of these technologies into the educational process. Based on the analysis, recommendations are offered for teachers on the optimal use of AI assistants, taking into account the specifics of educational tasks. The article will be useful both for researchers in the field of educational technologies using IT tools, and for practitioners interested in optimizing and improving the quality of the educational process. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Research of Approaches to Assessing the Information Security Risks of an Organization:Qualitative Risk Assessment Methods
This article explores modern approaches to assessing information security risks within organizations, focusing on qualitative, quantitative, and hybrid risk management methodologies. The study examines the core principles of each approach, their practical applicability in different organizational contexts, the tools and frameworks commonly employed, and key implementation challenges. A comparative analysis of these methods highlights their respective strengths and limitations, providing insights for selecting the most suitable risk assessment strategy based on organizational needs, industry requirements, and available resources. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Does FDI Determine Sustainable Development? Evidence from India
This study aims to evaluate if foreign direct investment (FDI) impacts sustainable development in India. The study uses time series data from World Bank database for India for a period spanning 19902022. For sustainable development, an index representing the three dimensions of sustainability is created using principal component analysis (PCA). Descriptive statistics complemented with autoregressive distributive lag (ARDL) model is implemented on the data collected. The study finds that FDI significantly and positively affects sustainable development in India. This relationship exists not only in the short run but also in the long run. To the best of our knowledge, not many studies have identified the impact of FDI on sustainable development in India, taking indicators of all dimensions of sustainability. The study calls attention to creating policies that focus on attracting and incentivizing FDI that aligns with sustainable development goals. This includes investments in infrastructure, renewable energy, and technology transfer. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Large-Scale Proteomics Reveals New Candidate Biomarkers for Late-Onset Preeclampsia
BACKGROUND: Preeclampsia is classified as either a more severe early onset or a more prevalent late-onset form. Lower PlGF (placental growth factor) and increased sFlt-1 (fms-like tyrosine kinase-1) in maternal circulation are promising biomarkers, yet they lack specificity for preeclampsia. METHODS: We quantified ?7000 proteins in 673 samples collected from 89 patients with late-onset preeclampsia and 91 controls at T1 (1522), T2 (2230), and T3 (3042) weeks. Elastic net and random forest models were fitted and evaluated by cross-validation. Differential abundance analysis followed by functional profiling, was used to identify and interpret protein changes. RESULTS: An increase in protein differential abundance in late-onset preeclampsia was observed with advancing gestation, reaching 806 proteins at T3 related to angiogenesis, cell adhesion, and extracellular matrix remodeling. FAAH2 (fatty acid amide hydrolase 2), SIGLEC6 (sialic acid-binding Ig-like lectin-6), IL17RC (interleukin-17 receptor C), HTRA1 (serine protease), sFlt-1, and 47 other proteins dysregulated at T3 were validated in a reanalysis of a ?5000 protein Norwegian data set. Random forest models with 20 proteins showed high accuracy at T3 (area under the curve [AUC], 0.83 [0.770.89], sensitivity 59%) even in cases not yet diagnosed at sampling (n=31, AUC, 0.80 [0.710.90], sensitivity 58%), outperforming sFlt-1 and PlGF. Moderate accuracy was obtained at T1 (AUC, 0.63 [0.540.72], sensitivity 33%) and T2 (AUC, 0.59 [0.500.68], sensitivity 17%). Combining maternal characteristics and obstetric history with proteomics data increased accuracy at T1 (AUC, 0.68 [0.590.77], sensitivity 28%), T2 (AUC, 0.68 [0.600.77], sensitivity 31%), and T3 (AUC, 0.87 [0.810.92], sensitivity 69%). CONCLUSIONS: The findings confirm the involvement of abnormal trophoblast invasion, angiogenesis, and extracellular matrix remodeling in late-onset preeclampsia, while highlighting new protein alterations consistent across diverse cohorts. 2025 American Heart Association, Inc.