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A Sentence-Level Risk Estimator for Identifying Hallucinations in Generative AI
Hallucination, defined as the generation of factually incorrect or ungrounded content, represents a critical challenge in large language models and summarization systems. Existing evaluation metrics often operate at the document level and fail to pinpoint erroneous sentences with sufficient granularity. This work introduces Sentence-Level Risk Estimation (SRE), a unified framework for detecting hallucinations at fine granularity by integrating three complementary signals: semantic alignment using BERT-based embedding similarity, QA-based factuality verification through question-answer pair generation and validation, and Natural Language Inference (NLI) entailment assessment using pre-trained models such as DeBERTa-MNLI. These signals are aggregated into a unified Sentence Risk Score (SRS) via weighted calibration. Experimental evaluation on CNN/DailyMail and XSum datasets demonstrates that the proposed method achieves precision of 0.85, recall of 0.75, F1-score of 0.80, and correlation with human judgments of 0.85, representing substantial improvements over existing approaches including FactCC, QAGS, and SummaC. The proposed framework enables AI systems to flag risky sentences for review or regeneration, thereby improving trust and safety in generative applications. 2026 IEEE. -
Volatility Prediction in the Indian Share Market Using Sentiment Analysis
In addressing the challenge of accurate volatility prediction in the Indian share market, the study explores the performance of deep learning-based models using sentimentdriven features. A demo model was deployed using data from five major Nifty 50 stocks-RELIANCE, HDFCBANK, INFOSYS, ITC, and MARUTI-for the financial years 2020 to 2023. We compared the results of traditional ARIMA model and standalone LSTM and its hybrid variants: LSTM + CNN and LSTM+RNN. Sentiment scores were gathered from financial news using FinBERT and NLTK, and combined with stock price data to generate time-series features. While all models demonstrated promising results, the LSTM+RNN hybrid model consistently achieved the lowest MAE and RMSE, indicating improved learning of temporal dependencies. The standalone LSTM and LSTM+RNN models also showed positive results for Sharpe ratio and Maximum drawdown indicating strong economic significance of the models. The study emphasizes the potential of hybrid LSTM architectures in modeling market volatility driven by investor sentiment. Limitations included limited dataset size, exclusion of other volatility factors, and overfitting in early hybrid GARCH trials. Future work aims to expand data coverage, integrate hybrid GARCH models more effectively, and explore additional market indicators. This research highlights a scalable and effective approach for sentiment-informed volatility forecasting in financial domains. 2025 IEEE. -
Optimized Fake News Detection in Social Networks Using Boosting Algorithms andMachine Learning Classifiers
Rising incidence of fake news on social media has turned verifying information into an imperative issue; hence, fact-checking information is becoming an important task. The traditional machine learning-based models like Logistic Regression, Nae Bayes, Support Vector Machines, and Random Forest suffer from the high-dimensional textual data, and the model may not yield optimal results in fake news detection classification. This paper suggests a better detection framework incorporating Gradient Boosting, CatBoost, and AdaBoost, along with Multinomial Nae Bayes for comparative study. This research uses TF-IDF vectorization and advanced text preprocessing, such as stopword removal, tokenization, and feature engineering,are done for better classification accuracy. The research was carried out on public dataset, including the Fake Job Posting dataset of Kaggle, to ensure model flexibility. The findings show remarkable performance enhancement with CatBoost posting the best accuracy of 98.23% and an ROC-AUC score of 0.9739, surpassing traditional models. A statistical significance test (t-test) validates the improvements as significant. Results have shown that ensemble-based approaches perform well in handling imbalanced and high-dimensional text data, and they should be generalizable to real-world fake news detection tasks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
The elements of culture and their effectiveness in advertising /
Advertisements are considered to be one of the most effective ways of promoting a product or a service. Though it has a high impact on people on its own, it uses different techniques to attract its viewers. One of the effective ways of penetrating a consumer's mind has been through his/her culture. Culture has been present in all advertisements since years. Cultures in advertisements were either set to change the audiences' or the local culture was incorporated in advertisements for a better impact. -
An Integrated Pythagorean Fuzzy Delphi-AHP Framework for Optimizing Foreign Direct Investment: Key Drivers for Success
Foreign Direct Investment (FDI) plays a pivotal role in global economic development, fostering cross-border collaborations and driving economic growth. Recognizing the significance of optimizing FDI drivers, this study employs a novel approach by integrating the Pythagorean Fuzzy Delphi (PFD) and Pythagorean Fuzzy Analytic Hierarchy Process (PFAHP). The Pythagorean Fuzzy Delphi methodology was used to identify and classify drivers into Technological, Political, Environmental, Social, and Cultural categories. Subsequently, the PFAHP was employed to rank these drivers. The top three prioritized drivers are: advocating for favorable foreign investment policies and trade agreements; implementing advanced cybersecurity measures to safeguard sensitive technology and data; and developing cutting-edge research and development facilities to foster innovation and attract technology-intensive investments. The study concludes by discussing how implementing these top-ranked drivers can significantly enhance FDI by creating a conducive environment for international investment, thereby contributing to economic prosperity and technological advancement. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Mechanisms Linking Gratitude to Life Satisfaction among Adults : A Mixed - Methods Study
The study examined the relationship between gratitude and life satisfaction in educated adults in an Indian context and the mediation of affect, schema and coping. The sample comprised 711 males and females (18-45 yrs). The research utilised a sequential explanatory mixed methods design, incorporating a follow-up explanation model (Creswell & Creswell, 2017). The initial quantitative phase addressed research questions concerning how the selected variables mediate the relationship between gratitude and life satisfaction. Mediation analysis revealed that positive affect and positive self/others partially mediated the relationship between gratitude and life satisfaction. There is no influence of gender in the role of gratitude in life satisfaction. The quantitative data held significance as it served as the foundation for subsequent qualitative analysis. The two-phased data collection facilitated a comprehensive exploration of the research questions, and integrating quantitative and qualitative data provided a better understanding of the relationships under investigation. A semi-structured interview was designed in the qualitative phase, incorporating insights from the survey results. The interview questions explored participants' perceptions and experiences regarding how various factors contribute to connecting gratitude with life satisfaction. A thematic analysis was performed to recognise the themes expressed by the participants, as outlined by Braun and Clarke in 2013. Three broader themes were derived, incorporating the 14 categories identified through coding. The three identified themes from the qualitative analysis are: 1. Life satisfaction through positive emotions; 2. Self-oriented schema promotes a sense of satisfaction, and 3. Positive connections with others enhance happiness. The qualitative data enrich our understanding by illustrating how participants who prioritise others' well-being and maintain meaningful social connections experience enhanced happiness. The quantitative findings are reinforced by the qualitative insights, which highlight that positive emotions serve as an emotional bridge that connects the feelings of gratitude to an overall sense of happiness, enhancing life satisfaction. This integrated approach enhances our understanding of how gratitude influences emotional well-being, ultimately contributing to overall life satisfaction. The identified themes of life satisfaction through positive emotions, self-oriented schema, and positive connections with others yield valuable implications. Implementing gratitude-focused interventions offers actionable steps for individuals, educators, and mental health practitioners to enhance overall well-being. -
Integrating k-Means++ with ARCANE: A Scalable Framework for Exact Cluster Unlearning
To address the demand for exact data removal in unsupervised clustering, a novel framework for exact machine unlearning is proposed that integrates the K-Means++ algorithm with ARCANE. This framework combines high-quality cluster initialization with targeted partitioning, allowing a more efficient method for removing data without the need for a naive retraining of the model. The proposed model is compared to a SISA-based approach against synthetic and Iris datasets. The ARCANE K-Means++ model demonstrated superior clustering quality, achieving a Silhouette Score of 0.841 to the baseline's performance of 0.263. ARCANE framework also demonstrated better speedup and predictable unlearning times for typical deletion requests than the SISA model. This is a strong, scalable, and provably-exact method for machine unlearning, providing a new and intuitive framework for developing privacy-preserving AI. 2025 IEEE. -
A prospective study on portrayal of rural theme in selected Tamil films /
Tamil film industry is popularly known as Kollywood. It has its own unique elements which makes it different from other film industry in Indian cinema. The researcher has tried to find out if there is any kind of rural element in the films which she has chosen in chronological manner. The researcher has analyzed based on certain concrete parameters. -
Indian Wives of Incarcerated Men Tell Their Own Stories: An Intersectional Narrative Analysis of Disenfranchisement and Resilience
Objective: Guided by intersectional feminism and symbolic interactionism, the purpose of this study was to document the untold stories of women with incarcerated spouses in India. Background: When a family member is incarcerated, the task of emotionally and financially supporting the family often falls upon women, who are likely to be underresourced and overwhelmed. Women whose husbands are incarcerated in India are likely to possess multiple marginalized identities, increasing their vulnerability to intersecting forms of oppression. Empirical research is lacking on wives of incarcerated men in India, contributing to their invisibility in policy-making and programmatic interventions. Method: In-depth, semi-structured interviews were conducted with 14 wives of prison inmates who resided in or around the capital city of Delhi, all of whom either held a lower caste identity or a Muslim religious identity. Transcribed interviews were analyzed following the steps of narrative analysis. Results: Results illustrate the diversity of storied experiences of wives of incarcerated husbands in India. Participants' narratives represented three types of stories: Ambivalent but Hanging On, Unconditionally Devoted, and Independent and Disillusioned. Four overarching themes characterized women's experiences with spousal incarceration: gendered care work, being stigmatized and sexualized, staying in the marriage, and ceilings of aspiration. Conclusion: This study renders visible women on the margins of Indian society, illustrating how they make meaning of extraordinary life circumstances and persevere through dire hardship. 2025 The Author(s). Journal of Marriage and Family published by Wiley Periodicals LLC on behalf of National Council on Family Relations. -
The Impactful Learning-Empowering Education Beyond Classrooms
Todays education is a dynamic ecosystem influenced by emerging technologies, human skills & iterative learning. The idea of learning beyond classrooms is introduced in this chapter, fostering an influential learning environment that aids in the process of continuous development. Both motivated students and empowered teachers are essential to ensuring the achievement of the impactful learning process. This chapters section offers an analysis of how instructors motivation, co- creation & continual training are essential for maintaining innovation. Education must extend beyond academic excellence to prepare learners for life. A beyond- classrooms learning approach is not a pedagogy but a mindset that integrates the competencies & prepares students to thrive in challenging, real-world situations in addition to achieving academic success. This chapter aims at rethinking education as a concept that expands classrooms, transforms communities, and prepares learners for impactful futures. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Building Partnerships and Networks for Collaborative Practice-Led Research Initiatives
This chapter delves into the strategic development of partnerships and networks aimed at enhancing collaborative practice-led research initiatives. It underscores the significance of these collaborations in fostering professional development across various disciplines. By analyzing successful models and theoretical frameworks, the chapter provides actionable insights for building and maintaining effective partnerships that drive innovation and professional growth. It also addresses critical challenges such as organizational culture differences, misaligned goals, and communication barriers, offering practical solutions for sustaining long-term collaborations and ensuring continuous improvement. The insights presented are grounded in rigorous academic research and aim to guide practitioners and researchers in creating impactful and sustainable collaborative networks. 2025 by IGI Global Scientific Publishing. -
Advance Data Ingestion Framework - Integration, Processing, Transformation, and Loading
The research introduces a new concept known as the Advanced Data Ingestion Framework, which is aimed at enhancing the process of getting into stored information through some intelligent methods like data preprocessing, transformation and loading. By making use of Azure services, the platform considers distributed computing and parallel processing so that structured as well as unstructured data can be incorporated from various origins without any difficulty. To begin with, the proposed framework starts with setting up scalable Azure infrastructure and integrating SAP S4/HANA for secure and efficient data transfer purposes. Within Azure Data Factory the ingestion occurs while Delta Lake ensures proper housekeeping & integrity within the system. It includes creating Power BI dashboards which allow users to see patterns easily and make better decisions based on what they know or can learn. The study brings out the flaws of current data input solutions and emphasizes the urgent requirement for a highly scalable low latency system that can support real time data processing efficiently. It tests the framework under different performance environments showing that it can effectively manage modern data within it. Finally, there is discussion about future improvements such as incorporating more sophisticated analytics or ML models thereby strengthening the decisionmaking process based on available facts. 2025 IEEE. -
Risk and Resilience in Human Emergencies: Pedagogical Directions from a Psychosocial and Neuropsychological Paradigm
This chapter will furnish an introductory sketch of theoretical perspectives and current empirical findings on risk and resilience in human emergencies. While risk is an inherent part of human emergencies, resilience, the ability of individuals and systems to maintain functioning levels post adversity and adapt is equally important. The goal will be to collate conceptual framework and evidence to provide evidence-informed practices and directions for pedagogy. We will review a wide range of theoretical expositions and focus them on the level to explore how risk and resilience influence and are influenced by the socio-political, environmental, and psychological experiences of learners. Practical examples and best practice recommendations for pedagogy and andragogy to reduce risk and develop resilience at the individual and collective levels will be discussed. We will propose a model to include psychological science in pedagogical experiences to improve conceptualisation, experience, analysis, and application of the teaching and learning process to cope with human emergencies. 2025 selection and editorial matter, Kennedy Andrew Thomas and Joseph Varghese Kureethara; individuals, the contributors. -
EEG Signatures of Resilience Across Individuals With High and Low Anxiety
Background. Over the past decade, psychological resilience has become a key focus in psychological science. However, most research relies on self-report and psychosocial assessments to explore resilience across different populations and contexts. Methods. This two-phased study examined resilience using self-reported measures and EEG recordings. Phase 1 involved a cross-sectional analysis of resilience and anxiety in young adults using correlation and regression analysis. Phase 2 utilized a grouped experimental design with EEG resting-state recordings to compare high- and low-resilience individuals. EEG data were collected using a 64-channel Geodesic Sensor Net, NetAmps 400 Amplifiers, and NetStation Acquisition 5.0 Software. Spectral analysis was performed for group comparisons. Results. Significant EEG differences emerged between high- and low-resilience groups in the anterior midline, right frontal, right central, left parietal, and right parietal regions. Alpha band differences were predominantly frontal and right-sided, while beta band differences were posterior and left-sided. Conclusions. Results of the two phased study bridge the gap between psychosocial measures and electrophysiological measures in the study of resilience and anxiety. A conceptual model based on the findings is outlined to guide future research to investigate the mechanism between resilience and clinical presentations of anxiety and/or depression at the psychosocial and electrophysiological level. Copyright: 2025. Gupta and Reddy. -
Optimizing Healthcare: Enhancing Disease Management with Recommendation Systems
This paper explores a data-driven disease recommendation system for medical professionals based on symptoms. The technology examines symptom patterns to recommend diseases from large datasets by utilizing collaborative filtering and data analytics. To provide individualized disease recommendations based on symptom severity, it goes through data preprocessing and uses techniques like collaborative filtering and cosine similarity. Even if the technology is promising, disease predictions might be strengthened. It seeks to support early disease prediction and offer patients and healthcare professionals individualized guidance. This system demonstrates the potential of technology in healthcare decision-making using a basic Tkinter application. More improvements are anticipated as a result of data-driven approach advancements, which will improve patient care and optimize healthcare procedures. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Unveiling Patterns, Visualizations, and Trends from Patient Diabetes Data
The important role that exploratory data analysis, or EDA, plays in the context of diabetes prediction is explored in this work. EDA is used as a key component of a multimodal strategy to identify unique characteristics linked to diabetes. EDA offers insights that aid in the creation of prediction models by sifting through the complex patterns present in the medical data. The focus is on using EDA to fully grasp the data landscape while also comprehending the distinct features of diabetes. This investigation is critical to accurately categorizing people into discrete risk groups and emphasizes the use of domain-specific knowledge in enhancing diabetes prediction techniques. The research suggests using specific EDA techniques to gain deep insights and lead proactive responses. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
An Improved AI-Based Low Latency Data Transmission in 5G Communication Systems
This paper devised an advanced artificial intelligence (AI) solution for ultra-low latency data transmission in 5G networks. With increasing data rates and lower latency required in 5G networks, efficient methods for transmitting the maximum amount of data are necessary. We have developed an approach that uses AI algorithms so that data transmission can be done more optimally and help reduce latency, providing better overall performance. Our approach consists of several steps, in which we predict the traffic patterns using machine learning techniques in step 1 and allocate network resources accordingly. That helps reduce network congestion and speeds up data transmission. We also introduce deep learning algorithms to adjust the transmission parameters according to network conditions, reducing latency. We simulate our algorithm in 5G network scenarios to assess its performance. The comparison of the results shows that a very low latency was achieved for this design over the earlier methods. Our developed AI-based improved solution provides a potential key to low latency data transmission in 5G communication systems. Integrating AI methods makes the system not only perform better but also be able to adapt more easily when network conditions change. The next steps are to explore the improvements of algorithms and implement them practically in 5G networks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Evaluation and analysis of quality in e commerce (B2C Website) /
There has been a phenomenal growth in Ecommerce in the last few years and it is still growing. As the market is expanding, more and more organizations want to have web presence. Due to sudden and rapid demand in Ecommerce website, website development companies are ignoring or skipping the quality factor. Apart from this, due to popularity of Internet, more and more consumers are buying products and services through ecommerce websites. The software development companies want to delivery the products quickly, to make fast money. To meet the deadlines, usually the quality of the product is not taken as a priority, resulting in a product full of bugs, being shipped to the customers. Once the software becomes live, more and more bugs keep coming resulting in loss of business and credibility of the organization is affected. Lack of quality Ecommerce websites result in consumers moving back to the alternate methods of shopping or switching to another website. With the increase in the competition, companies have started studying the consumer behavior and determining the factors that affect the quality of Ecommerce websites form the consumers perspective. During these years, several lessons have been learned about the technology, business and economy of Ecommerce. -
Impact on economic activities by adoption of international financial reporting standards by Indian companies
The importance of international accounting practice studies has grown over the past few years in order to meet economic agent demands and to facilitate international business practices. It is essential to understand that international accounting convergence is an important topic for capital market regulators, investors, markets, governments and all others who deal with financial information of public companies. This brings out the importance of accounting as being an essential fiscal tool for various economic agents. The merit of international accounting convergence lies in its ability to minimize negative effects resulting from diversity of accounting practices in different countries (Cordeiro et al. 2007). In such a scenario, the introduction of International Financial Reporting Standards (IFRS) for listed companies in many countries around the world is viewed as one of the most significant regulatory changes in accounting history (Daske et al. 2008).