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Breaking the Bias: Assessing Nudge Effectiveness in Overcoming Decision-Making Prejudices
Influencing consumer decision-making processes in the digital age is vital as E-commerce continues dominating the market. This research work explores the application of nudging to e-commerce or any digital environment involving consumer choices, investigating its efficacy in mitigating cognitive biases that affect online purchasing decisions. We employed a 2 x 2 within-group experimental design to examine how different nudges influence e-commerce choices. Data collected from 88 participants reveal that status-quo nudges can influence decision-making more than social proof nudges or salience nudges in online shopping. These results significantly impact digital marketing strategies, suggesting that carefully designed nudges can guide consumer choices by overcoming ingrained prejudices. This research also provides practical insights into consumer behaviour through subtle interventions in e-commerce settings. While nudging has been studied in offline contexts, applying it to e-commerce and digital consumer choices represents a growing area of study. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Breaking the Cycle of Child Labor in Dehradun: A Multidimensional Study on Causes and Challenges
Child labor remains a critical issue, particularly in developing nations like India, where millions of children are deprived of their right to education and safe childhood due to poverty, marginalization, and inadequate policy implementation. This study explores the underlying causes, occupational patterns, and harmful consequences of child labor in Dehradun, Uttarakhand. It highlights that children are primarily engaged in agriculture, domestic work, and small industries, often facing physical and mental exploitation. Despite existing legal frameworks and initiatives like the Child Labor (Prohibition and Regulation) Act and National Child Labor Projects, enforcement challenges persist. The study uses cross-sectional and triangulation methods to analyze data from 42 child laborers and suggests a holistic approach, including poverty alleviation, quality education, community empowerment, and rehabilitation services, as key to combating child labor. It emphasizes the need for multi-stakeholder participation to ensure child rights and reduce labor dependency among vulnerable families. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development. -
Breaking the Glass Ceiling: Will the Role of Organizational Workplace Policies Perpetuate or Mitigate Gender Bias?
Despite significant global progress in narrowing gender gaps, inequality persists across many countries. Organizations like the Global Gender Gap Index and the European Institute for Gender Equality monitor improvements in political leadership, economic opportunities, health, and education. However, women continue to face challenges, including unequal pay, limited career advancement, and imbalanced household labor. The "glass ceiling" refers to invisible barriers that prevent women from achieving top positions despite equal qualifications. Long-term effects include temporary employment and lower retirement savings. True gender equality requires more than quotas-it demands equitable opportunities, flexible work policies, pay transparency, and mentorship programs. Tackling unconscious bias and fostering inclusive environments is essential for sustainable change and women's holistic success. 2026, IGI Global Scientific Publishing. All rights reserved. -
Breaking the Taboo: Addressing Menstrual Health Challenges in India
Although, menstrual hygiene is a topic as ancient as mankind, it has recently garnered attention because society is more willing to face its difficulties. Adolescents seldom talk about issues related to menstruation, menstruation disorders, menstrual cleanliness, and customs of their culture. There is little data on the hardships that teenage females bear from menstruation and their social norms. Adolescent health education must include information about menstruation. Menstrual behaviors are often greatly influenced by culture, awareness, and social condition. However, periods, behaviors, and problems are seldom included in health education programs for the younger girls in impoverished nations. International health organizations such as WHO and UNICEF have advised developing culturally responsive menstrual health management (MHM) as well as water, sanitation and hygiene (WASH) programs for the adolescent girls. Without an awareness of the preconceived notions and prejudices that teenage girls in poor nations currently have about menstruation, these programs cannot be implemented. The goal of this review from India was to record the myths that are currently in circulation concerning menstruation, menarche, and other understudied menstrual constraints. Our goal in conducting this review was to characterize and assess the effectiveness of menstruation education programs designed to provide early teenage girls the information and abilities they need to support menstrual health. RJPT All right reserved. -
Breast Cancer Classification Using Machine Learning A Study
Nowadays, breast cancer is the most common disease found in women. Although many researchers and experts have aimed to discover the solution to this widespread disease, they have not determined it. In this study, the techniques that are used to find the early signs of breast cancer with the use of machine learning (ML) are discussed. ML is an emerging technology in the field of computer science and information technology, especially in disclosing medical diagnoses. ML is also used, for example, in image recognition, speech recognition, traffic prediction, virtual personal assistants, and online fraud detection. There are plenty of algorithms and techniques that are used in ML. Some of the most popular techniques are discussed in this study. 2025 selection and editorial matter, A. Malini, Surbhi Bhatia Khan, S. Kayalvizhi, and Mohammed Saraee; individual chapters, the contributors. -
Breast Cancer Detection in Mammography Images Using Deep Convolutional Neural Networks and Fuzzy Ensemble Modeling Techniques
Breast cancer has evolved as the most lethal illness impacting women all over the globe. Breast cancer may be detected early, which reduces mortality and increases the chances of a full recovery. Researchers all around the world are working on breast cancer screening tools based on medical imaging. Deep learning approaches have piqued the attention of many in the medical imaging field due to their rapid growth. In this research, mammography pictures were utilized to detect breast cancer. We have used four mammography imaging datasets with a similar number of 1145 normal, benign, and malignant pictures using various deep CNN (Inception V4, ResNet-164, VGG-11, and DenseNet121) models as base classifiers. The proposed technique employs an ensemble approach in which the Gompertz function is used to build fuzzy rankings of the base classification techniques, and the decision scores of the base models are adaptively combined to construct final predictions. The proposed fuzzy ensemble techniques outperform each individual transfer learning methodology as well as multiple advanced ensemble strategies (Weighted Average, Sugeno Integral) with reference to prediction and accuracy. The suggested Inception V4 ensemble model with fuzzy rank based Gompertz function has a 99.32% accuracy rate. We believe that the suggested approach will be of tremendous value to healthcare practitioners in identifying breast cancer patients early on, perhaps leading to an immediate diagnosis. 2022 by the authors. -
Breast cancer detection: A comparative review on passive and active thermography
Breast cancer is the main cause of death among women due to cancer. Early detection is crucial in controlling the disease. Thermography is a non-invasive imaging method that uses temperature differences on the breast surface to identify tumors. This paper focuses on the various aspects of thermography as a diagnostic tool for detecting breast cancer. It includes a review of the currently existing active thermography approaches used to energize the tumor cell to enhance the thermal contrast on the surface. The comparison of passive and active thermography showed that active thermography was more effective, increasing depth-dependent performance from 3 mm to 9 mm for 1.5 mm sized tumors and accuracy from 54% to 82% without a rise in false positive rates. The contrast between malignant and benign tissue also improved from 0.6 C to 0.9 C, indicating that active thermography increases the performance of passive thermography in various aspects. A comparative study of active thermography reveals that healthy tissues are likely to be damaged if the input parameters are not regulated properly. A comprehensive comparison of various tumor estimation algorithms in the paper concludes that the dynamic analysis using an active approach outperforms static analysis due to a significant decrease in error percentage. 2023 Elsevier B.V. -
Breast Cancer Diagnosis: Feature Selection and Ensemble Machine Learning
Breast cancer diagnosis requires accurate diagnostic tools that are both efficient and interpretable for clinical deployment. This study presents an integrated pipeline combining Recursive Feature Elimination with Cross-Validation (RFECV), Synthetic Minority Over-sampling Technique (SMOTE), and ensemble learning methods applied to the Wisconsin Breast Cancer Diagnostic dataset. RFECV achieved dimensionality reduction from 30 to 17 features, representing a 43% reduction while maintaining predictive performance. SMOTE transformed the class imbalance ratio from 1.68:1 to a perfect 1:1 balance. A comprehensive evaluation of twelve machine learning models revealed that LightGBM attained an F1-score of 0.9722, accuracy of 96.5%, and ROC-AUC of 0.9914 with strong cross-validation stability (0.9681 0.0179). Feature importance analysis identified worst perimeter, area, and concave points as the most discriminative features for differentiating malignant from benign tumors. The proposed approach achieved a 35% reduction in training time compared to full-featured models without sacrificing performance. This reproducible pipeline demonstrates practical clinical relevance for automated breast cancer diagnosis with improved computational efficiency and model interpretability. 2025 IEEE. -
Breast Cancer Prediction using a Stacked Ensemble of XGBoost and LightGBM with Logistic Regression Meta-Learning
Breast cancer remains one of the major reasons for cancer deaths in women, which is why it is key to develop and improve diagnostic systems for accurate predictions. Currently, the advent of Machine learning has helped in providing powerful algorithms to achieve advancements in cancer detection. However, the main motivation of this research is to focus on building more complex ensemble architectures, as they are known for significantly improving predictive accuracy, robustness, and generalisation, especially in performing complex tasks such as medical diagnosis. In this research, a Hybrid stacking ensemble was built using two gradient boosting techniques, XGBoost and LightGBM, with a Logistic Regression meta-learner to predict breast cancer and compare their performance with standard classifiers. The Breast Cancer Wisconsin (Diagnostic) dataset, which consists of 569 patient records, was utilised for model training and analysis. The data was preprocessed using Z-score normalisation and stratified 5-fold cross-validation. The machine learning algorithms, such as Decision Tree, Logistic Regression, and Random Forest, were compared with the hybrid model, and the metrics used for comparison were accuracy, precision, recall, F1-score, and ROC-AUC. The proposed hybrid model performed well, achieving a high accuracy rate of 97.37% and a recall rate of 93.00% for malignant cases. McNemar's test (p > 0.05) confirms that this accuracy rate is statistically equivalent to the Random Forest classifier. These findings proved that the proposed model can perform optimally in predicting complex data with the same degree of precision as the standard models. Therefore, the hybrid model can be considered a robust and reliable new alternative for breast cancer prediction. 2026 IEEE. -
Breast Cancer Survival Prediction using Gene Expression Data
Breast cancer is one of the most common forms of cancer in the world.[1]. Breast, skin, colon, pancreatic, and other 100 types of cancer have founded globally. An accurate breast cancer prognosis can save many patients from having unnecessary treatment and the huge medical costs that come with it. Multiple gene mutations can possibly transform a normal cell into a cancerous one. Genomic variations and traits have a significant effect on cancer. Genetic abnormalities caused by various circumstances drive numerous efforts to find biomarkers of breast cancer advancement. Early Detection of Cancer types is the only way to recover the patients from this acute disease. In this paper, a proposed Deep learning algorithm and Machine learning algorithms are used to predict the survival of cancer patients using clinical data and gene expression data. The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset is split into clinical and gene data for detailed preprocessing. This proposed method gives a better understanding of the condition and assesses how effective treatment methods are by using Deep Learning and Machine Learning models on gene data. Logistic Regression is the most accurate method identified. Grenze Scientific Society, 2022. -
Breeding distrust during artificial intelligence (AI) era: howtechnological advancements, jobinsecurity and job stress fuel organizational cynicism?
Purpose: This study examines how technological advancements and psychological capital contribute to job stress. Furthermore, the paper examines how job insecurity, job stress and job involvement influence the cynicism of recently laid-off employees. Despite various research studies, there is a lack of understanding of employees views on their work future and its probable influence on their job behaviors in this era of technology. Design/methodology/approach: A quantitative method was used to collect a sample of 403 recently laid-off employees. The research tool of this study was a questionnaire, and the sampling technique was stratified random sampling. IBM SPSS and AMOS software were utilized to ensure the trustworthiness and accuracy of constructs via factor analysis. The proposed hypotheses were tested using structural equation modeling. Findings: The analysis showed that technological advancements, specifically in job-related stress, job involvement and job insecurity, significantly affect organizational cynicism. Job involvement is negatively associated with employees cynicism. Practical implications: The current study adds to the comprehension of shifts in the perceived behavior of employees toward their organizations due to factors like the adoption of new technology in the organization, job stress, job insecurity and job involvement. Accordingly, there will be a need to form a favorable working atmosphere so that employees can perform their jobs with positive psychology and without any insecurity or stress. Originality/value: The study is thought to contribute to the literature in terms of measuring organizational cynicism while layoffs continue due to AI advancements. 2024, Emerald Publishing Limited. -
Breeding Potential of Crosses Derived from Parents Differing in Overall GCA Status for Productivity per se Traits and Powdery Mildew Disease Response in Blackgram [Vigna mungo (L.) Hepper]
Background: Predicting the breeding potential of crosses in terms traits means, genetic variability and frequency of desirable transgressive segregants in early segregating generations is crucial in breeding programme. Therefore, an experiment was carried out to assess breeding potential of crosses involved parents with varying overall GCA status and contrasting responses to powdery mildew disease (PMD) in blackgram. Methods: Total of 40 F1 s developed by following Line Tester design; among, nine crosses were selected based on gca status of parents and responses to PMD. F1, F2 and F3 along with parents of six and three crosses were evaluated for 10 productivity per se traits and responses to PMD separately during kharif, 2016 and rabi, 2016-17 respectively. The traits means, absolute and standardized range, PCV and frequency of transgressive segregants in F2 and F3 were compared to assess the breeding potential of the crosses. Result: F2 and F3 generations derived from six crosses (for productivity traits) and three crosses (for PDI) were differed for means, absolute and standardized range, PCV and the frequency of transgressive segregants. This is may be due to the contribution of diverse genes from female and male parent. Though considerable number of transgressive segregants were also identified in F2 and F3 of all the crosses, high frequency of desirable transgressive segregants was observed in crosses involved parents with overall high GCA status. 2024, Agricultural Research Communication Centre. All rights reserved. -
BRICS VS. G7: A COMPARATIVE ANALYSIS OF ECONOMIC AND POLITICAL EFFICIENCY IN SHAPING GLOBAL ORDER
The global distribution of power is increasingly shaped by the competing influences of two major blocs: BRICS (Brazil, Russia, India, China, and South Africa) and the G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States). This paper investigates how BRICS and the G7 shape the emerging multipolar global order. Using comparative analysis of key indicators: GDP, trade flows, investment patterns, diplomatic engagement, and strategic alliances. The paper examines each blocs structure and internal cohesion. The analysis underscores the G7's historical supremacy, which stems from its economic strength and political unity, in contrast to BRICS rising role as a representative for the Global South and a platform for alternative governance models. Important metrics include trade flows, investment trends, diplomatic efforts, and strategic alliances. The research also assesses the internal dynamics within each bloc, including challenges to cohesion and the effectiveness of decision-making. By comparing the advantages and drawbacks of BRICS and G7, this paper provides insights into their respective functions in a multipolar world order, evaluating their ability to promote transformative global agendas. Lastly, the paper concludes that both alliances embody divergent approaches to global governance, reflecting deeper shifts in international collaboration, competition, and the balance of power. 2025, Observare. All rights reserved. -
BRICS: Advancing Cooperation and Strengthening Regionalism
In the era of regional international relations and more interdependence, organisations like the Brazil, Russia, India, China and South Africa (BRICS) can play a meaningful role in international level as well as regional in years to come. The recent summit of the BRICS reiterates that more cooperation is needed at various levels. In Delhi declaration, it is called for a more representative international financial architecture, with an increase in the voice and representation of developing countries and the establishment and improvement of a just international monetary system that can serve the interests of all countries and support the development of emerging and developing economies. Moreover, these economies having experienced broad-based growth are now significant contributors to global recovery. This is true. One must acknowledge the fact that the roles of the BRICS countries are composed of various political systems, various subcontinent, but in the changed context, all these countries are coming under the purview of the developing countries in broader terms. That makes the BRICS beyond the regional boundaries to set a benchmark in the regional cooperation. Chinas permanent status in the United Nations makes the BRICS more strategically oriented and pragmatic aspects of foreign policy engagement in the twenty-first century. The political leadership and vision is equally important with economic engagement. The four major theories of the international relations (IR) are striking in this respect which includes liberalism, realism, constructivism and Marxism. Theoretical framework relevant to regionalism in focusing on IR theories is also analysed in this article. The main argument of the article is that there is no prescribed regional model and BRICS has to tune to the member countries regional and political frameworks to engage with. Therefore, the framework of analysis is more or less critical about the Western engagement and it is region focused. 2012 Indian Council of World Affairs. -
Bridging Academia and Communities Through Service-Learning Praxis at Christ University
The Service Learning (SL) experiences of students in higher education institutions play a pivotal role in shaping future generations to be more inclusive and responsible. This chapter delves into the institutionalisation of SL, community partnerships, and specific departmental SL experiences, focusing on the initiatives at Christ University. At the core of these efforts is the Centre for Service Learning at Christ University, which actively monitors, recommends policies, and establishes frameworks for departments. The chapter introduces the ADORED model, elucidating the phases of SL at the institution: Assess, Develop, Organise, Reflect, Execute, and Demonstrate. This chapter describes SL practices in the School of Commerce, Education, and Psychology, offering tangible examples. By examining these specific cases, the chapter provides insights into the successful integration and sustainability of SL within a university setting. 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Bridging Compliance and Sustainability: The Role of Integrated Reporting Under the Companies Act 2013 in Advancing the SDGs
The Companies Act 2013 has played a transformative role in reshaping corporate reporting in India by mandating greater transparency, accountability, and disclosure, especially in areas related to environmental, social, and governance (ESG) performance. These legal provisions have laid the groundwork for adopting Integrated Reporting (IR). While IR is not yet mandatory for all Indian companies, its principles align closely with the intent of Indian corporate law to promote responsible and sustainable business practices. The research highlights how Indian Corporate Laws can encourage more companies to adopt the integrated reporting framework, as it is more holistic as compared to the Business Responsibility and Sustainability Reporting (BRSR) and helps in the attainment of SDGs. This research offers meaningful insights for policymakers, regulators, corporate leaders, and investors on the potential of Integrated Reporting to serve as a bridge between compliance and sustainability, thus reinforcing India's commitment to global development goals and sustainable economic growth. 2026 by IGI Global Scientific Publishing. -
Bridging Digital Divide in India: Positive Outlook Amid COVID-19
The digital divide is described as the gap in access to, knowledge of, use of, or ability to comprehend information and communication technology (ICT) between different societal groups. The digital divide can often give way to an upsurge in social inequalities. This study intended to comprehend the extent of the rural-urban digital divide in India regarding access to the internet and to analyze the increase or decrease in the same due to the global coronavirus pandemic. The analysis of the paper was primarily based on secondary data collected from the report on The Indian Telecom Services Performance Indicators issued by the Telecom Regulatory Authority of India for June 2019 and June 2020. Percentage analysis was employed to comprehend the trend of the digital divide in terms of access for the years 2019 and 2020. The results disclosed that there was an increase in internet access in the rural population during the time frame of COVID-19, and this increase has led to a decrease in the digital divide in terms of access to the internet. Moreover, the study revealed that COVID-19, to some extent, has resulted in bridging the rural-urban digital divide in India in terms of access. The study further highlighted the importance of digital literacy and access to ICT, and suggested ways to improve digital literacy in India. 2022, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Bridging Financial and Operational Gaps in Supply Chain Finance: An Information Processing Theory Perspective
This paper explores the integration of financial and operational flows in Supply Chain Finance (SCF) through the lens of Information Processing Theory (IPT). Despite increasing adoption of SCF solutions like reverse factoring and trade credit, existing literature lacks a unified theoretical framework that captures both financial and organizational complexities. Drawing from 47 peer-reviewed articles in leading supply chain journals, this study identifies key SCF dimensionstask characteristics, environment, and interdependenceas primary sources of uncertainty and information processing needs. It then examines how IT systems, coordination mechanisms, and organizational design enhance processing capacity, enabling firms to build SCF capabilities such as risk assessment, supplier onboarding, and financial process standardization. These capabilities facilitate financial supply chain integration through data connectivity, embedded flows, and collaborative planning. The study contributes a comprehensive conceptual model that connects SCF uncertainties, processing strategies, and performance outcomes, addressing theoretical and managerial gaps. It further provides a foundation for future empirical research and strategic design of SCF systems to enhance supply chain resilience and financial efficiency. 2025 by the authors. -
Bridging neural technologies with financial decision-making in neurofinance
This book chapter delves into neurofinance, an interdisciplinary domain merging neuroscience, psychology, and financial economics to investigate the neural mechanisms driving financial decision-making. It highlights the role of brain regions such as the prefrontal cortex, amygdala, and striatum in processing emotions, risks, and rewards, alongside the influence of neurotransmitters like dopamine and serotonin. The chapter discusses technological tools like fMRI, EEG, and BCIs, emphasizing their application in studying biases, risk tolerance, and reward anticipation. Practical implications include personalized investment strategies, improved financial planning, and enhanced market stability. Addressing ethical concerns and methodological challenges, the chapter underscores the potential of neurofinance to transform financial systems by integrating neural insights with AI and emerging technologies for better decision-making. 2025, IGI Global Scientific Publishing. All rights reserved.