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FC-Kit: an intelligent fact-checking system for preventing fake news spread in social media through active plugins
The rapid expansion of social media has intensified the spread of misinformation, threatening public trust, informed decision-making and societal stability. This paper introduces the Fact-Checking Kit (FC-Kit), a plugin-based, real-time misinformation detection framework designed for seamless integration into social media platforms. At its core, the system employs the proposed CanineNet News Sentinel (CnNS) model, which incorporates advanced algorithms for detecting fake news while also assessing bias indicators, identifying clickbait headlines, detecting poor text framing and calculating an article credibility rate. Experimental evaluations on benchmark datasets Twitter and Twitch demonstrate that FC-Kit achieves 99% detection accuracy and reduces computational time by 41.4% compared to state-of-the-art methods. Unlike conventional fact-checking systems, FC-Kit actively tracks the news dissemination chain, enabling early intervention before misinformation gains traction. Its modular plugin architecture supports real-time analysis, ensuring media literacy promotion and fostering critical thinking among users. By combining content credibility scoring with advanced detection features, FC-Kit offers a scalable and practical solution for social media platforms, fact-checking organizations and researchers committed to combating online misinformation. This work advances the state-of-the-art in misinformation detection and emphasizes the necessity of embedding automated fact-checking tools directly into social media platforms. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
FC-Kit: an intelligent fact-checking system for preventing fake news spread in social media through active plugins
The rapid expansion of social media has intensified the spread of misinformation, threatening public trust, informed decision-making and societal stability. This paper introduces the Fact-Checking Kit (FC-Kit), a plugin-based, real-time misinformation detection framework designed for seamless integration into social media platforms. At its core, the system employs the proposed CanineNet News Sentinel (CnNS) model, which incorporates advanced algorithms for detecting fake news while also assessing bias indicators, identifying clickbait headlines, detecting poor text framing and calculating an article credibility rate. Experimental evaluations on benchmark datasets Twitter and Twitch demonstrate that FC-Kit achieves 99% detection accuracy and reduces computational time by 41.4% compared to state-of-the-art methods. Unlike conventional fact-checking systems, FC-Kit actively tracks the news dissemination chain, enabling early intervention before misinformation gains traction. Its modular plugin architecture supports real-time analysis, ensuring media literacy promotion and fostering critical thinking among users. By combining content credibility scoring with advanced detection features, FC-Kit offers a scalable and practical solution for social media platforms, fact-checking organizations and researchers committed to combating online misinformation. This work advances the state-of-the-art in misinformation detection and emphasizes the necessity of embedding automated fact-checking tools directly into social media platforms. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
FDI in Developing Nations: Unveiling Trends, Determinants and Best Practices for India
In the recent UNCTAD World Investment Report 2023, China has the highest FDI inflows among the developing countries, following Brazil, India, Mexico, and Indonesia. These five developing countries attracted more FDI inflows in the year 2022. However, among these five countries, China and the other four countries have a lot of differences in FDI inflows. So, this study investigates the factors helping China get more FDI inflows by analyzing the trends and determinants of FDI inflows. The study also compares all the selected countries to suggest the best practices India can adopt to enhance its FDI attractiveness. So, the study considered economic indicators like GDP, infrastructure, trade openness, and natural resources. Further, panel data analysis was used to investigate the determinants influencing FDI inflows, utilizing the Panel Autoregressive Distributed Lag (P-ARDL) model for the data from 1990 to 2022. The findings showed that trade openness, market size, and quality of infrastructure explain the attraction of FDI inflows in selected countries in the long run. Thus, it is important to implement policies that encourage international collaboration by raising trade, lowering corporate expenses, and making infrastructural investments. India's availability of a large consumer market, developed infrastructure, and government initiatives like 'Make in India,' and "Skill India"have pulled major FDI inflows. India should prioritize manufacturing, IT, and healthcare while improving infrastructure and streamlining regulations. 2024 IEEE. -
Fe doped ZnO nanomaterials for energy storage applications as high-capacitance supercapacitor electrodes
Enhancing the performance of electrode materials is essential for developing high-capacitance supercapacitors, and transition-metal-doped metal oxides have shown particular promise in this regard. In this work, Fe-doped ZnO nanostructures were synthesized using a sonochemical method and systematically examined through XRD, SEM, TEM, XPS and UVvis analyses to verify Fe incorporation and the resulting changes in crystallinity, morphology and optical behaviour. The structural modifications induced by Fe were evident in the electrochemical response, with the optimized ZnOFe sample delivering a specific capacitance of 11.4 F g?1 at 0.1 A g?1 in the two-electrode system and 462 F g?1 in the three-electrode system, both measured in 3 M KOH electrolyte. A CR2032 coin cell assembled with this material achieved an energy density of 1.6 Wh kg?1 and a power density of 2890.93 W kg?1, demonstrating an effective balance between energy storage and power output. These findings highlight the suitability of Fe-doped ZnO as a tunable electrode material and support its further exploration in advanced supercapacitor systems. This journal is The Royal Society of Chemistry, 2026. -
Fear estimation evidence from BRICS and UK /
International Journal Of Applied Business And Economic Research, Vol.15(4), pp.195-207, ISSN: 0972-7302. -
Fear estimation-evidence from BRICS and UK
The paper aims to build a composite Fear Index for the BRICS countries and UK by adding new dimensions to the initial structure, such as overbought/oversold conditions and commodity impacts. The main purpose is to identify the degree in which fear really percolates down to all the market participants, respectively if this generates a certain asset transfer to Gold. The results point out the GMM model as the best fit for explaining the link between the Fear Index and the behaviour of market participants. It also confirms the transfer of assets to a safer asset class during the phases of high volatility on the market. Serials Publications Pvt. Ltd. -
Fear of COVID-19, workplace phobia, workplace deviance and perceived organizational support: A moderated mediation model
This paper aims to test a moderated-mediation model examining therelationships between Fear of COVID-19, workplace phobia, work deviance behaviourand perceived organizational support among hotel employees. An online questionnaire was administered to collect data, to which 481 responded. Data was collected from full-time frontline employees working in the Maldivian hospitality industry. The moderated-mediation model explained 44% of the variance in workplace deviance behaviourscan be predicted bythe fear of COVID-19, perceived organisational support and workplace phobia. The findingsshowthat perceived organizational support reduces the negative impact of COVID-19 fear on workplace phobia and deviance. Results suggest that to reduce the negative effect of the pandemic, organisations should adopt support measures across different managerial levels at different scales rather than providing one-size-fits-all solutions. 2023 The Authors. Stress and Health published by John Wiley & Sons Ltd. -
Fear of Missing Out and Aggression: Role of Fatigue, Daytime Sleepiness and Self-regulationA Serial Mediation Model
Background: Fear of missing out (FOMO) is a recent psychological phenomenon and has been constantly linked with aggression, disturbed sleeping habits and deficits in self-regulatory skills. It is important to understand the mechanism through which FOMO influences sleepiness, self-regulation and aggression. Purpose: The objective of the study was to investigate the relationship between FOMO and aggression in young adults and examine the mediating roles of fatigue, daytime sleepiness and self-regulation in the relationship between FOMO and aggression. Method: A cross-sectional correlational research design was employed to collect data from 455 young adults aged 1824 years (M = 20.71; SD = 1.61). Data were collected through standardised self-report measures. The obtained data were analysed using the Statistical Package for the Social Sciences v23, and sequential mediation analysis using AMOS v22. Results: Findings indicated significant relationships between FOMO and the outcome variables like aggression, daytime sleepiness and self-regulation. However, no significant relationship was found between FOMO and fatigue; therefore, fatigue was not considered for further analysis. Sequential mediation analysis revealed that elevated levels of FOMO predicted lower daytime sleepiness (? = 0.26, p < .001), which in turn predicted low self-regulation (? = 0.39, p < .001) and consequently led to elevated levels of aggression (? = 0.26, p < .001). The indirect route (FOMO ? sleepiness ? self-regulation ? aggression) was statistically significant with excellent model fit (?2(2) = 3.86, RMSEA = 0.02, CFI = 0.99, SRMR = 0.01). Conclusion: The study indicates a full sequential mediation: greater FOMO levels reduce daytime sleepiness, possibly due to heightened arousal, which in turn leads to poor self-regulatory skills and increased aggression. It can be concluded that psychological interventions to improve self-regulation can help manage aggression in people with high levels of FOMO. The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). -
Fear of Missing Out and Executive Functions: The Role of Self-regulation as a Mediator
Background: The fear of missing out (FoMO) is characterised by constant worry about missing out on rewarding experiences. However, the worry arising from FoMO affects both self-regulation and executive functioning. Purpose: The main purpose of the study was to explore the relationship between FoMO and executive functioning, keeping self-regulation as the mediating variable. Methods: A cross-sectional correlational design was used. The sample size was 455 university students with an age range of 1824 years. The Fear of Missing Out Scale, Short Self-Regulation Questionnaire and Executive Skills Questionnaire were used. Regression analysis was done using Statistical Package for the Social Sciences v26, and mediation analysis results were obtained through PROCESS Macro (Model 4). Results: Regression results pointed out that FoMO explained 18% of variance in executive functioning (? = 0.43, p < .001) and 16% of variance in self-regulation (? = 0.39, p < .001). Higher FoMO was related to poorer self-regulation and executive functions. Mediation analysis revealed that self-regulation partially mediated the FoMOexecutive functioning relationship, with significant indirect effects (B = 0.66, SE = 0.07, 95% CI [0.83, 0.52]) and direct effects (B = 0.83, SE = 0.13, 95% CI [1.10, 0.56]). Conclusion: FoMO negatively affects both self-regulation and executive functions, with self-regulation acting as a significant mediator through both direct and indirect pathways. These findings suggest the need for building self-regulatory skills to protect oneself from FoMOs negative effects. The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). -
Feature Based Fuzzy Framework for Sentimental Analysis of Web Data
Social mass media has emerged as a projectile platform for the evolution of web data. The sentimental Analysis where the huge textual online reviews are analyzed to extract the actual sentiment or emotions hidden in the reviews. In this paper an effective approach for sentimental analysis of web data is proposed which deploys the fuzzy based machine learning algorithm to accomplish fine-level sentiment analysis of huge online opinions by assimilating the fuzzy linguistic hedges influence on opinion descriptors. The seven layered categories are designed that uses SentiWordNet which has three stages: Pre-processing phase, Feature Selection Phase and Fuzzy based Sentiment Analysis phase. Various machine learning algorithms like AdaBoost, (IBK) K-Nearest Neighbour, (NB) Nae Bayes and (SVM)/SMO Support Vector Machine are used for classification. Jsoup is implemented for gathering web opinions which are subjected to initial processing task later applied with stemming and tagging. This fuzzy based methodology is investigated for Mobile, Laptops dataset, also compared with state-of-the-art approaches which demonstrate upper indication of 94.37% accurateness through Kappa indicators showcasing lesser error rates. The investigational outcomes are tested on training data using Ten-Fold cross validation which concludes that this approach can be efficaciously used in Sentimental analysis as an aid for online decision. 2019 IEEE. -
Feature Engineering for Epileptic Seizure Classification Using SeqBoostNet
Epileptic seizure, a severe neurological condition, profoundly impacts patients social lives, necessitating precise diagnosis for classification and prediction. This study addresses the need for reliable automated seizure detection in epilepsy by employing Artificial Intelligence (AI) driven analysis of Electroencephalography (EEG) signals. Key innovations include combining spectral and temporal features using Uniform Manifold Approximation and Projection (UMAP) with Fast Fourier Transformation (FFT), and the introduction of the Sequential Boosting Network (SeqBoostNet), a robust stacking model integrating machine learning and deep learning for effective seizure classification. Validated on benchmark datasets such as the BONN dataset from the UCI repository and the BEED from the Bangalore EEG Epilepsy Dataset, this approach achieved high accuracy, distinguishing Focal and Generalized seizure onsets with 95.91% accuracy and overall average accuracies of 96.71% on BEED and 97.11% on BONN. Existing models frequently struggle with the variability of seizure events. However, these findings underscore the models strength in distinguishing between seizure onset types, even with the inherent fluctuations in seizure patterns. This research not only advances automated seizure detection but also underscores the value of integrating AI with EEG analysis to improve neurological diagnostics, offering the potential for significant enhancements in diagnostic accuracy and patient outcomes. 2025 University of Bahrain. All rights reserved. -
Feature extraction and classification techniques of modi script character recognition
Machine simulation of human reading has caught the attention of computer science researchers since the introduction of digital computers. Character recognition is the process of recognizing either printed or handwritten text from document images and converting it into machine-readable form. Character recognition is successfully implemented for various foreign language scripts like English, Chinese and Latin. In the case of Indian language scripts, the character recognition process is comparatively difficult due to the complex nature of scripts. MODI script-an ancient Indian script, is the shorthand form for the Devanagari script in which Marathi was written. Though at present, the script is not used officially, it has historical importance. MODI character recognition is a very complex task due to its variations in the writing style of individuals, shape similarity of characters and the absence of word stopping symbol in documents. The advances in various machine learning techniques have greatly contributed to the success of various character recognition processes. The proposed work provides an overview of various feature extraction and classification techniques used in the recognition of MODI script till date and also provides evaluation and comparison of these techniques. 2019, Universiti Putra Malaysia Press. All rights reserved. -
Feature extraction and diagnosis of dementia using magnetic resonance imaging
Dementia is a state of mind in which the sufferer tends to forget important data like memories, language, etc.. This is caused due to the brain cells that are damaged. The damaged brain cells and the intensity of the damage can be detected by using Magnetic Resonance Imaging. In this process, two extraction techniques, Gray Level Co-Occurrence Matrix (GLCM) and the Gray Level Run-Length matrix (GLRM), are used for the clear extraction of data from the image of the brain. Then the data obtained from the extraction techniques are further analyzed using four machine learning classifiers named Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and the combination of two classifiers (SVM+KNN). The results are further analyzed using a confusion matrix to find accuracy, precision, TPR/FPR - True and False Positive Rate, and TNR/FNR - True and False Negative Rate. The maximum accuracy of 93.53% is obtained using the GLRM Feature Extraction (FE) technique with the combination of the SVM and KNN algorithm. 2023, Bentham Books imprint. All rights reserved. -
Feature extraction and fusion techniques for multimodal data
Integrating multimodal information has become crucial in the big data era for developing a thorough knowledge of complex systems and enhancing decision-making in a variety of fields. The importance of feature extraction and fusion strategies in multimodal learning is examined in this chapter, with particular attention paid to the difficulties and approaches involved in merging several data modalities, including text, pictures, audio, and sensor data. It talks about how conventional feature extraction approaches have evolved into more sophisticated ones like deep learning models for picture and audio data and neural embeddings for text. The chapter also explores several fusion tactics, such as early, late, and intermediate fusion, and focuses on how they are used in domains including sentiment analysis, autonomous cars, healthcare, and multimodal search engines. The chapter highlights future directions, such as lightweight architectures and privacy-preserving techniques, while also addressing contemporary issues, such as managing missing data, scalability, and privacy concerns. The chapter provides a thorough grasp of how feature extraction and fusion aid in the creation of multimodal systems that are more precise, effective, and interpretable by looking at these factors. 2026 Elsevier Inc. All rights reserved. -
Feature Extraction for Collaborative Filtering: A Genetic Programming Approach
International Journal of Computer Science Issues, Vol. 9, Issue, 5, No. 1, pp. 348-354, ISSN No. 1694-0814 -
Feature extraction of clothing texture patterns for classification
Different features are extracted for Pattern Recognition using an efficient algorithms like Scale Invariant Feature Transform, Rotation invariant Radon Transform and extracting statistical features of a texture image. Support vector machine with RBF kernel in Weka is used in this paper for classification. This paper shows method to classify the clothing texture patterns like strips, plaid, pattern less and irregular pattern. This paper also proposes a method which can be efficient method to apply for the real time natural texture patterns and colors recognition systems. This paper gives the experiments results and the proposed method to enhance the experiments accuracy in future scope. 2015 IEEE. -
Feature extraction of optical character recognition: Survey
Optical Character Recognition is still prevailing even after many decades of implementation. The challenges faced here are increasing day by day so as its applications. From Punched cards to Handwritten Text, from images to video, from uniform font to universal font, from English text to Global language, from researchers to visually handicapped are the transformations obtained from an era of the 1980s to 2010. This paper has covered the advancement of acknowledging the characters, how are features are extracted, various methodologies used and more importantly what is the use of OCR. Research India Publications. -
Feature films as pedagogy in higher education: A case study of Christ University, Bengaluru
Contemporary education system in India was initiated by the British for the maintenance of their imperial administration. After India became an independent country, conscious efforts were made to overhaul the educational system to produce proper administrators and contributors for Indian polity, economy and culture. To assess dynamics of Indian education, various committees and commissions were formed. It also meant change in education programs, curricula and syllabi to meet national needs and global challenges. Most universities in India have limited infrastructure, thus the role of private or deemed to be university becomes crucial. Christ University attending to the social structure, internationalization and employability demands, offers a number of quality educational programs to ensure employable graduates. This has led the way in devising pedagogy and curricula to align with the global higher education practices. Here we discuss the use of commercial feature film as a pedagogical tool in the classrooms within the Deanery of Humanities and Social Sciences and its implication. 2018, IGI Global. -
Feature Fusion Classification for Emotional Intelligence Using Peripheral Signals
Real-time emotion identification is an innovation in the field of humancomputer interaction, which is an essential and challenging task. The existing studies methods for identifying emotions include face, audio, and physiological signals. The study aims to develop a model for emotion classification to identify and interpret human emotions through skin temperature, respiration, and plethysmography. The study also includes analyzing and interpreting emotional states through ensemble models. The classification is based on the frequency domain signal components extracted using the Fast Fourier Transform (FFT), such as amplitude and frequency, to identify emotional states. Ensemble-based machine learning algorithms such as XGBoost and LGBM achieved the highest accuracy in classifying various emotional states. The study involves unimodal and ensemble methods to analyze the signals. The comparative classification rate of unimodal results with ensemble shows that it is the highest at 85.99%, achieved for sad emotions by XGBoost. Fusing modules like respiration, skin temperature, and plethysmography maintains the accuracy level for all four emotions. The unimodal temperature has the highest accuracy of 86.1% for calm, whereas the fusion model has maintained accuracy for all the emotional states. The feature amplitude is the most promising feature for the classification method, which attains an average of 83.2% for XGBoost. The applications enhance user experiences and contribute valuable help in psychology, mental health care, and HumanComputer Interaction. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
FEATURE SELECTION AND CLASSIFICATION OF LEUKEMIC CELLS USING IOT AND MACHINE LEARNING
Machine learning and the Internet of Things (IoT) have affected every step of the leukemia process, from diagnosis to understanding to therapy. Consequently, this study delves into the planning of an innovative system that employs IoT and machine learning techniques to precisely differentiate leukemic cells. Depending on the patient's samples, the system uses different ways to feature selection and cell classification. To pick the most informative collection of features that enables stable and accurate cell categorization into suitable categories, the offered research relies on strong machine-learning approaches for feature selection. Next, a classification model is used to classify cells based on their properties using the attributes that have been chosen. There is evidence that the suggested approach can classify leukemic cells with an identification rate of up to 99%, which is greater than the current methods. As a novel strategy for managing massive volumes of biological and medical samples, the suggested method will be an invaluable tool for doctors treating leukemia patients. The system's ability to process data from various Internet of Things (IoT) sources should aid its ability to learn and adapt to real-world clinical settings. With the results of this study in hand, we may be able to detect leukemia sooner, with greater precision, and maybe use more tailored treatments for each patient, leading to better results while reducing healthcare expenditures. 2025, Institute of Mechanics of Continua and Mathematical Sciences. All rights reserved.