caste; education; equalization of opportunity; gender; groups; probability distribution; zonotope
This paper provides a robust criterion for comparing lists of probability distributionsinterpreted as allocations of opportunitiesfaced by different social groups. We axiomatically argue in favor of comparing those lists of probability distributions on the basis of a uniformamong groupsvaluation of their expected utility. We identify an empirically implementable criterion for comparing allocations of opportunities that coincides with the unanimity of all such uniform valuations of expected utility that exhibit aversion to inequality of opportunity. We illustrate our criterion by evaluating allocations of educational opportunities among castes and genders in 14 Indian states. 2025 The Author(s). International Economic Review published by Wiley Periodicals LLC on behalf of The Economics Department of the University of Pennsylvania and the University of Osaka Institute of Social and Economic Research Association.
Andreoli, Francesco; Faure, Mathieu; Gravel, Nicolas; Kundu, Tista
International Economic Review;Volume;67;Issue;1;pp.365-397
All Open Access; Green Open Access; Hybrid Gold Open Access
https://doi.org/10.1111/iere.70020 https://www.scopus.com/pages/publications/105016132849?origin=resultslist
Andreoli F., Department of Economics, University of Verona, Verona, Italy, Luxembourg Institute of Socio-Economic Research, LISER, Luxembourg; Faure M., Aix-Marseille School of Economics, Aix-Marseille University, CNRS, Cedex, Marseille, France; Gravel N., Aix-Marseille School of Economics, Aix-Marseille University, CNRS, Aix-en-Provence, France; Kundu T., CHRIST University, Karnataka, Bangalore, India
Large-Scale Proteomics Reveals New Candidate Biomarkers for Late-Onset Preeclampsia
biomarkers; humans; pregnancy; proteomics; random forest
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.
Andresen, Ina J.; Romero, Roberto; Westerberg, Ane C.; Than, Ndor Gor; Gomez-Lopez, Nardhy; Bhatti, Gaurav; Ahmodu, Oladejo; Gudicha, Dereje W.; Meyyazhagan, Arun; Awonuga, Awoniyi; Chaiworapongsa, Tinnakorn; Bryant, David R.; Michelsen, Trond M.; Tarca, Adi L.
https://doi.org/10.1161/HYPERTENSIONAHA.125.25189 https://www.scopus.com/pages/publications/105028322951?origin=resultslist
Andresen I.J., Department of Obstetrics, Division of Obstetrics and Gynecology, Oslo University Hospital Rikshospitalet, Norway; Romero R., Pregnancy Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, United States Department of Health and Human Services, Bethesda, MD, United States, Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, United States, Department of Epidemiology and Biostatistics, Michigan State University, East Lansing (R.R.), United States; Westerberg A.C., Department of Obstetrics, Division of Obstetrics and Gynecology, Oslo University Hospital Rikshospitalet, Norway; Than N.G., Pregnancy Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, United States Department of Health and Human Services, Bethesda, MD, United States, Systems Biology of Reproduction Research Group, Institute of Molecular Life Sciences, HUN-REN Research Centre for Natural Sciences, Budapest, Hungary, Department of Obstetrics and Gynecology, Semmelweis University, Budapest, Hungary, Maternity Private Clinic of Obstetrics and Gynecology, Budapest, Hungary, Genesis Theranostix Group, Budapest, Hungary; Gomez-Lopez N., Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI, United States, Center for Reproductive Health Sciences, Departments of Obstetrics and Gynecology and Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, United States; Bhatti G., Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, United States; Ahmodu O., Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, United States; Gudicha D.W., Pregnancy Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, United States Department of Health and Human Services, Bethesda, MD, United States; Meyyazhagan A., Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI, United States, Department of Life Sciences, Christ University, Bengaluru, India; Awonuga A., Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI, United States; Chaiworapongsa T., Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI, United States; Bryant D.R., Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI, United States; Michelsen T.M., Department of Obstetrics, Division of Obstetrics and Gynecology, Oslo University Hospital Rikshospitalet, Norway, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Norway; Tarca A.L., Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI, United States, Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, United States
Does FDI Determine Sustainable Development? Evidence from India
ARDL model; Climate; Economic advancement; Economic growth; Environmental quality; Foreign direct investment; India; Principal component analysis; Social growth; Sustainable development
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.
Sustainable Development Goals Series;Volume;Part F993;pp.47-60
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-3-031-95103-9_4 https://www.scopus.com/pages/publications/105018201464?origin=resultslist
Andrews A.M., Department of Economics, Christ University, Karnataka, Bengaluru, India; Saigal T., Department of Economics, Christ University, Karnataka, Bengaluru, India
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.
Andrusenko, Julia; G. S. Prakasha, D.; Golovin, Ivan; Lapin, Vladimir
Navigating Technological Advancement in the VUCA and BANI World;pp.47-60
https://doi.org/10.4018/979-8-3373-2240-7.ch004 https://www.scopus.com/pages/publications/105027349111?origin=resultslist
Andrusenko J., North Caucasian Federal University, Russian Federation; G. S. Prakasha D., Christ University, Bangalore, India; Golovin I., North Caucasian Federal University, Russian Federation; Lapin V.
Research on Effectiveness and Assistants in the Educational Process for Student Learning
AI assistants; Artificial intelligence; automation of knowledge verification; digitalization of higher education; educational technologies (EdTech); integration of AI tools; learning efficiency; methods of effectiveness assessment; pedagogical innovations
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.
Andrusenko, Yulia; Kosogov, Egor; Movzalevskaya, Vitaliya; Jenefa, J.; Usanov, Aleksandr
Lecture Notes in Networks and Systems;Volume;1616 LNNS;pp.13-31
Springer Science and Business Media Deutschland GmbH
https://doi.org/10.1007/978-3-032-04365-8_2 https://www.scopus.com/pages/publications/105019504175?origin=resultslist
Andrusenko Y., North Caucasus Federal University, Stavropol, 355000, Russian Federation; Kosogov E., North Caucasus Federal University, Stavropol, 355000, Russian Federation; Movzalevskaya V., North Caucasus Federal University, Stavropol, 355000, Russian Federation; Jenefa J., Christ University, Karnataka, Bangalore, 560074, India; Usanov A., Federal State Budgetary Educational Institution of Higher Education Financial University, St. Petersburg, Russian Federation
Educational Perspectives of the Metaverse
artificial intelligence in education; digital educational environment; digital transformation of learning; educational metaverse; gamification; pedagogical innovation; Roblox in learning; VR/AR
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.
Andrusenko, Yulia; Kosogov, Egor; Movzalevskaya, Vitaliya; Vinodha, D.
Lecture Notes in Networks and Systems;Volume;1616 LNNS;pp.32-43
https://doi.org/10.1007/978-3-032-04365-8_3 https://www.scopus.com/pages/publications/105019535875?origin=resultslist
Andrusenko Y., North Caucasus Federal University, Stavropol, 355000, Russian Federation; Kosogov E., North Caucasus Federal University, Stavropol, 355000, Russian Federation; Movzalevskaya V., North Caucasus Federal University, Stavropol, 355000, Russian Federation; Vinodha D., Christ University, Karnataka, Bangalore, 560074, India
Predictive Modeling of Substance Abuse Risks using Big Data Analytics and Social Media Mining
Addiction Prevention; Big Data Analytics; Predictive Modelling; Public Health Informatics; Risk Detection; Social Media Influence; Substance Abuse; Treatment Barriers
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.
Anees Moidheen, M.M.; Parande, Athreya S; Jomy, Rayal; Presannakumar, Krishna; Souza, Mithun D
Proceedings of the 2025 International Conference on Computational Innovations and Sustainable Technologies, ICCIST 2025;
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICCIST67338.2025.11438824 https://www.scopus.com/pages/publications/105037460726?origin=resultslist
Anees Moidheen M.M., Department Of Computer Science, Christ (Deemed to be University) Bangalore Yeshwanthpur Campus, Bangalore, India; Parande A.S., Department Of Computer Science, Christ (Deemed to be University) Bangalore Yeshwanthpur Campus, Bangalore, India; Jomy R., Department Of Computer Science, Christ (Deemed to be University) Bangalore Yeshwanthpur Campus, Bangalore, India; Presannakumar K., Department Of Computer Science, Christ (Deemed to be University) Bangalore Yeshwanthpur Campus, Bangalore, India; Souza M.D., Department Of Computer Science, Christ (Deemed to be University) Bangalore Yeshwanthpur Campus, Bangalore, India
Quality of Drinking Water and Sanitation in India
disparity in income; Drinking water; hygiene; sanitation; unequal distribution
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.
Indian Journal of Human Development, Vol-15, No. 1, pp. 138-152.
Aneesh M.R, Department of Economics, CHRIST (Deemed to be University), Bangalore, Karnataka, India
Changes in Wage Trends and Earnings Differences in Kerala
Earnings; Employment; Inequality; Labour; Wage differences
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.
Indian Journal of Labour Economics, Vol-61, No. 4, pp. 623-638.
Aneesh M.R., Christ (Deemed University), Bannerghatta Road Campus, Hulimavu, Bengaluru, 560076, Karnataka, India
Education Inequality in India: An Empirical Analysis Using National Sample Survey Data
decomposition; disparity; Education; Gini ratio; inequality
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.
Indian Journal of Human Development, Vol-18, No. 2, pp. 267-279.
Aneesh M.R., CHRIST University, Karnataka, Bengaluru, India; Maya K., CHRIST University, Karnataka, Bengaluru, India; Aneesh K.A., CHRIST University, Ghaziabad Campus, Uttar Pradesh, India
Wage Collapse and Gender Differences in Earning in India
Gender difference; Gini coefficient; Wage collapse; wage decomposition; wage growth; Wage inequality
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.
https://doi.org/10.1007/s41027-026-00606-3 https://www.scopus.com/pages/publications/105034451303?origin=resultslist
Aneesh M.R., Department of Economics, Christ University, Karnataka, Bengaluru, 560029, India; Jose A., Rajagiri Business School, Rajagiri Valley, Kakkanad, Kerala, Ernakulam, 682039, India, Rajagiri College of Social Sciences, Kerala, Kochi, India
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.
Does the green finance initiatives transform the world into a green economy? A study of green bond issuing countries
Fossil fuel dependence; Green bonds; Green finance; PSR model
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.
Aneja R.; Kappil S.R.; Das N.; Banday U.J.
Environmental Science and Pollution Research, Vol-30, No. 14, pp. 42214-42222.
ISSN: 9441344; PubMed ID: 36645606; CODEN: ESPLE
Aneja R., Department of Economics, Central University of Haryana, Mahendragarh, India; Kappil S.R., Department of Data Science, Christ University, Karnataka, Bangalore, India; Das N., Indian Institute of Technology, Kharagpur, India; Banday U.J., Indian Institute of Technology, New Delhi, India
Malicious URL Detection Using Machine Learning Techniques
AdaBoost; Binary features; Count features; Cyber security; Lexical features; Machine learning; Malicious URL detection; Random forest; Voting classifier
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.
Lecture Notes in Networks and Systems, Vol-458, pp. 657-669.
Angadi S., Christ University, Bangalore, India; Shukla S., Christ University, Bangalore, India
Synthesis of ZnO and NiO nano ceramics composite high-performance supercapacitor and its catalytic capabilities
Energy storage; Methane combustion; Nio/ZnO composites; Super capacitor
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.
Angadi V J.; Molahalli V.; Soman G.; Hegde G.; Wang S.; Roy N.; Joo S.W.; Pattar V.; Shaikh S.F.; Prakash C.; Kumar A.; Ubaidullah M.; Zhang M.
Ceramics International, Vol-50, No. 20, pp. 39732-39738.
https://doi.org/10.1016/j.ceramint.2024.07.352 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199959723&doi=10.1016%2fj.ceramint.2024.07.352&partnerID=40&md5=e0dfb2198c4df8a762356d49c1ff2f8b
Angadi V J., Department of Physics K.L.E's P.C.Jabin Science College, Hubli-31, Karnataka, India; Molahalli V., Department of Chemistry, Christ University, Hosur Road, Bengaluru, 560029, India; Soman G., Department of Chemistry, Christ University, Hosur Road, Bengaluru, 560029, India; Hegde G., Department of Chemistry, Christ University, Hosur Road, Bengaluru, 560029, India; Wang S., School of Electronic and Information Engineering, Chongqing Three Gorges University, Wanzhou, Chongqing, 404000, China; Roy N., Department of Physics, Yeungnam University, Gyeongsan, 38541, South Korea; Joo S.W., School of Mechanical and IT Engineering, Yeungnam University, Gyeongsan, 38541, South Korea; Pattar V., Education Technology Unit, JNCASR, Bengaluru, India; Shaikh S.F., Department of Chemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia; Prakash C., University Centre for Research & Development, Chandigarh University, Punjab, Mohali, 140413, India; Kumar A., Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh, 174103, India; Ubaidullah M., Department of Chemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia; Zhang M., School of Electronic Engineering, Yangzhou Polytechnic College, Hanjiang, YangZhou, 225009, China
Enhanced supercapacitors and LPG sensing performance of reduced graphene oxide/cobalt chromate pigments for energy storage applications
CoCr2O4; Composite; rGo; Supercapacitors
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.
Angadi V J.; Pasha A.; Molahalli V.; K Pattar V.; Kumar A.; Hegde G.; Prakash C.; Pandit B.; Sehgal S.; Ubaidullah M.
Ceramics International, Vol-49, No. 22, pp. 35333-35339.
https://doi.org/10.1016/j.ceramint.2023.08.206 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168727030&doi=10.1016%2fj.ceramint.2023.08.206&partnerID=40&md5=0706908f337c6f5ea33ffb463718aea5
Angadi V J., Department of Physics, P. C. Jabin Science College, Karnataka, Hubballi, 580031, India; Pasha A., Department of Physics, Ghousia College of Engineering, Karnataka, Ramanagara, India; Molahalli V., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, 560029, India; K Pattar V., Education Technology Unit, JNCASR, Jakkur, Karnataka, Bengaluru, 560064, India; Kumar A., Department of Chemistry, GLA University, Mathura, 281406, India; Hegde G., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, 560029, India, Centre for Advanced Research and Development (CARD), CHRIST (Deemed to be University), Bangalore, 560029, India; Prakash C., School of Mechanical Engineering, Lovely Professional University, Punjab, Phagwara, 144411, India; Pandit B., Department of Materials Science and Engineering and Chemical Engineering, Universidad Carlos III de Madrid, Avenida de la Universidad 30, Legan, Madrid, 28911, Spain; Sehgal S., Division of Research Innovation, Uttaranchal University, Dehradun, India; Ubaidullah M., Department of Chemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia
A comparative evaluation of machine learning and deep learning models across diverse datasets for early detection of lung cancer
artificial intelligence; comparative analysis; deep learning; early detection; ensemble learning; lung cancer; Machine learning
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.
Angeline, A.; Margaret Savitha, P.; Sahayam, Norra
Explainable AI in Clinical Practice: Methods, Applications, and Implementation;pp.45-67
https://doi.org/10.1016/B978-0-443-44111-0.00001-9 https://www.scopus.com/pages/publications/105036918135?origin=resultslist
Angeline A., Christ University, Karnataka, Bangalore, India; Margaret Savitha P., Christ University, Karnataka, Bangalore, India; Sahayam N., Data Solution Architect DXC Technologies, United Kingdom
Towards Optimal ?-Binding Functions of (2K1?K2)-Free Graphs and (P3?K1)-Free Graphs
(2K1?K2)-free graphs; (P3?K1)-free graphs; Chromatic number; Clique number; ?-binding function
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.
Angeliya, C.U.; Choudum, Sheshayya; Joseph, Mayamma
Graphs and Combinatorics;Volume;41;Issue;2;Article No.;39;
https://doi.org/10.1007/s00373-025-02897-7 https://www.scopus.com/pages/publications/85218683073?origin=resultslist
Angeliya C.U., Department of Mathematics, Christ University, Bangalore, 560029, India; Choudum S., Department of Mathematics, Christ University, Bangalore, 560029, India; Joseph M., Department of Mathematics, Christ University, Bangalore, 560029, India
Chromatic bounds of some (P5, banner)-free graphs
(P5, banner)-free graphs; chromatic number; clique number; Graph classes
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.
Asian-European Journal of Mathematics;Issue;;Article No.;2650025;
https://doi.org/10.1142/S1793557126500257 https://www.scopus.com/pages/publications/105032237008?origin=resultslist
Angeliya C.U., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Choudum S., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Joseph M., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India
Fintech Innovations in the Financial Service Industry
financial services; financing; fintech; innovations; payments
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.
Anifa M.; Ramakrishnan S.; Joghee S.; Kabiraj S.; Bishnoi M.M.
Journal of Risk and Financial Management, Vol-15, No. 7
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.3390/jrfm15070287 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135497908&doi=10.3390%2fjrfm15070287&partnerID=40&md5=3d50f78a981cc1cb7b58b42303e7f057
Anifa M., Department of Business Administration, CHRIST (Deemed to be University), Bengaluru, 560029, India; Ramakrishnan S., Amity Business School, Amity University, Dubai, 73000, United Arab Emirates; Joghee S., International Business and Marketing, School of Business, Skyline University, P.O. Box 1797, Sharjah, United Arab Emirates; Kabiraj S., Strategy and International Business, School of Entrepreneurship and Business, Hame University of Applied Sciences Ltd., (HAMK), Valkeakoski, FI-37630, Finland; Bishnoi M.M., School of Humanities, Arts and Applied Sciences, Amity University, Dubai, 73000, United Arab Emirates