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Faculty acceptance of virtual teaching platforms for online teaching: Moderating role of resistance to change
Under this new normal world scenario, online teaching has been essential rather than a choice in continuing learning activities. During the COVID-19 period, virtual teaching platforms played an important role in the success of online teaching in various higher educational institutions. Thus, the current study attempted to predict faculty adoption of online platforms by introducing a set of essential drivers for engaging in online teaching. Following the theory of reasoned action, the study broadened the technology acceptance model variables and security and trust as extrinsic determinants and included resistance to change as moderators to invigorate the research model. Data were collected through an online survey with a sample size of 418 Indian respondents. Our results posit that perceived ease of use, usefulness, security and trust positively influence the faculty's intentions to adopt online platforms. In addition, the study also reported that positive intention leads to the actual use of virtual platforms. Furthermore, the research found the moderating role of the resistance to change dimension in the association of intention and actual use of virtual teaching platforms. The findings provide both theoretical and practical applications of educational technology. Implications for practice or policy The first step for accepting virtual teaching platforms is to help faculty to reduce their resistance for effective online teaching. Higher education institutions should have a policy promising faculty that online teaching using virtual teaching platforms will offer a safer and more trustworthy environment. Higher education institutions should undertake intense organisational renewal and implement bottom-up processes for synchronous learning. Regulators could frame a policy including virtual teaching platforms to provide interactive professional development opportunities. Articles published in the Australasian Journal of Educational Technology (AJET) are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant AJET right of first publication under CC BY-NC-ND 4.0. -
FACVO-DNFN: Deep learning-based feature fusion and Distributed Denial of Service attack detection in cloud computing
Cloud computing offers a broad range of resource pools for conserving a huge quantity of information. Due to the intrusion of attackers, the information that exists in the cloud is threatened. Distributed Denial of Service (DDoS) attack is the main reason for attacks in the cloud. In this study, a Fractional Anti Corona Virus Optimization-based Deep Neuro-Fuzzy Network (FACVO-based DNFN) is devised for detecting DDoS in the cloud. The production of log files, feature fusion, data augmentation, and DDoS attack detection is the processing stages involved in this phase of the DDoS attack detection process. The feature fusion is carried out by RV coefficient and Deep Quantum Neural Network (Deep QNN), and the data augmentation is performed. Then, the Anti Corona Virus Optimization (ACVO) method and Fractional Calculus (FC) are both incorporated to create the FACVO algorithm. The DNFN is trained by the created FACVO algorithm, which identifies the DDoS attack. The proposed approach achieved testing accuracy, TPR, TNR, and precision values of 0.9304, 0.9088, 0.9293, and 0.8745 for using the NSL-KDD dataset without attack, and 0.9200, 0.8991, 0.9015, and 0.8648 for using the BoT-IoT dataset without attack. 2022 Elsevier B.V. -
FADA: Flooding Attack Defense AODV Protocol to counter Flooding Attack in MANET
The intrinsic nature of a Mobile Ad hoc Network (MANET) makes it difficult to provide security and it is more vulnerable to network attacks. Denial of Service (DoS) attack can be executed using Flooding attack, that has the potential to bring down the entire network. This attack works by delivering an excessive number of unwanted packets that consumes too much battery life, storage space, and bandwidth, that eventually lowers the system's performance. In order to flood the network, the attacker injects fake packets into it. Both Control Packet flooding and Data flooding attacks are taken into account in this study. FADA (Flooding Attack Defense AODV) protocol is proposed to counter flooding attack that promotes greater utilization of existing resources. This research identifies the attack-causing node, isolates it and protects the network against flooding attack. Attack Detection Rate, Attack Detection Accuracy, End-to-end delay and Throughput are few metrics used for evaluation of the proposed model. NS-2.35 is used to demonstrate the efficiency of the suggested protocol and the results prove that the proposed model increases system's throughput while decreasing attack. The simulation results have shown that the proposed FADA protocol performs better than the existing models taken into consideration. 2023 IEEE. -
Fair and Inclusive Customer Segmentation in AI- Driven Marketing
This chapter explains how artificial intelligence has evolved customer segmentation from a marketing tool into a socio- technical decision mechanism with implications for fairness, inclusion, and cultural representation. In the chapter, algorithmic segmentation is analyzed using clustering methods, explainable frameworks such as LIME and SHAP, and fairness metrics to identify or alleviate structural bias in multicultural markets. It discusses accuracy fairness trade- offs, transparency, emotional trust, and organizational capability gaps, especially when segmentation outputs flow into generative AI driven personalization. Through case studies on multicultural targeting, AI sales agents, misinformation flows, and exclusion in finance, employment, and welfare, the authors show how segmentation systems affect society. The chapter concludes with strategic, ethical, and policy recommendations for responsible, inclusive AI marketing grounded in fairness aware segmentation. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Fair skin, unfair trade and invisible victims
[No abstract available] -
Fair skin, unfair trade and invisible victims
[No abstract available] -
Fairness-Aware and Interpretable Depression Detection on Social Media Using BERT with Gender Bias Mitigations
Reddit and similar social media platforms offer substantial information regarding mental health issues. The automatic detection of depression raises different issues pertaining to fairness and transparency. This paper presents a Fairness Aware and Interpretable Depression Detection framework that utilizes BERT and incorporates an explicit gender bias mitigation mechanism. Data were obtained from gender-specific forums on Reddit. The Mistral language model based classifier was used to set a high confidence threshold, which helped in inferring gender labels while both depressed and non-depressed were among the patients assigned the labels. A balanced dataset with four groups (Depressed-Male, Depressed-Female, NonDepressed-Male, NonDepressed-Female) was prepared. Two pipelines were carried out where one involved a baseline BERT classifier while the other employed a fairness aware BERT model that incorporated gender embeddings during the training phase. The models were assessed using accuracy, precision, recall, F1 score, and confusion matrices and the fairness metrics applied were Demographic Parity Difference (DPD) and Equal Opportunity Difference (EOD). To enhance the model's reasoning transparency, SHAP was applied due to its capability to provide clear and comprehensive explanations. The results indicated that the fairness centered model effectively reduced gender biasness and equalized error rates among the different groups without losing its original accuracy. The essential point is that the model had learned to give precedence to clinical indicators over gender specific language. This study suggests a roadmap for the creation of ethical AI by combining fairness, interpretability and high performance into a seamless framework. 2025 IEEE. -
Faith and culture in education: Fostering inclusive environments
This chapter explores the role of faith and religious diversity in educational practices, focusing on creating and sustaining inclusive environments. Educators can foster a sense of belonging for students from diverse backgrounds by understanding the importance of faith-based education, creating safe spaces for discussion around faith and culture, and promoting inclusivity. The chapter examines the impact of exclusion and discrimination on students from diverse faith backgrounds and offers strategies for promoting diversity and inclusion in the classroom and school community. By embracing diversity and promoting inclusivity, we can create positive and productive learning environments that celebrate diversity and encourage respect for all students. The chapter concludes with a call to action for educators to create inclusive environments that value diversity and promote respect for all students. 2023 by IGI Global. All rights reserved. -
FaithfulNet: An explainable deep learning framework for autism diagnosis using structural MRI
Explainable Artificial Intelligence (XAI) can decode the black box models, enhancing trust in clinical decision-making. XAI makes the predictions of deep learning models interpretable, transparent, and trustworthy. This study employed XAI techniques to explain the predictions made by a deep learning-based model for diagnosing autism and identifying the memory regions responsible for children's academic performance. This study utilized publicly available sMRI data from the ABIDE-II repository. First, a deep learning model, FaithfulNet, was developed to aid in the diagnosis of autism. Next, gradient-based class activation maps and the SHAP gradient explainer were employed to generate explanations for the model's predictions. These explanations were integrated to develop a novel and faithful visual explanation, Faith_CAM. Finally, this faithful explanation was quantified using the pointing game score and analyzed with cortical and subcortical structure masks to identify the impaired brain regions in the autistic brain. This study achieved a classification accuracy of 99.74% with an AUC value of 1. In addition to facilitating autism diagnosis, this study assesses the degree of impairment in memory regions responsible for the children's academic performance, thus contributing to the development of personalized treatment plans. 2025 Elsevier B.V. -
Fake news and its impacts on businesses: Political communication, propaganda, and economic implications
Fake news and disinformation have become powerful tools in political communication, particularly in the context of populism and digital propaganda. While misinformation has always been part of political strategy, digital platforms have intensified its spread, allowing political actors to construct narratives that manipulate public opinion, mobilize support, and delegitimize opponents. This study examines fake news as a politically constructed phenomenon, exploring how digital populism and propaganda use disinformation to reinforce ideological divides and influence democratic processes. Using a constructivist framework, we analyze how fake news is socially produced and legitimized within political discourse, shaping perceptions of truth and power. We also investigate how major social media platforms profit from misinformation while simultaneously positioning themselves as arbiters of truth. By focusing on the intersection of political communication, propaganda, and economic incentives, this research highlights the role of digital media in sustaining misinformation ecosystems. 2025, IGI Global Scientific Publishing. -
Fake news and social media: Indian perspective
The unlimited freedom made social media platforms are susceptible to misuse, misinformation, and thus, fake news. In the last few years, social media has turned out to be a massive player in shaping public discourse in a democratic space (Marda & Milan, 2018). Though there have been pressures from policymakers on service/platform providers, nothing concrete has built up towards accountability of the user or platform proprietors. In India, there has been a consistent increase of social media users and instances of the misuse of this medium. This paper seeks to examine how the propagation of fake news has disrupted the public sphere and possible policies that can be implemented to curb the plague of fake news. The relationship between various events of violence reported in India media and the role of fake news in instigating chaos are discussed in this paper. It also tries to review policies initiatives taken by various countries, especially in Europe and possible measures which India could take to restrict the flow of fake news. Media Watch. -
Fake News Detection and Classify the Category
A new type of disinformation has emerged: fake news, or untrue stories that have been presented as actual occurrences. We can no longer tell whether the information is true from fraudulent since so much information is published on social media these days. Artificial intelligence algorithms are helpful in resolving the fake news identification issue. In the field of natural language processing, fake news identification is a crucial yet difficult issue (NLP). In this article, we discuss similar duties as well as the difficulties associated with finding bogus news. Based on these findings, we suggest intriguing avenues for future study, such as developing more accurate, thorough, fair, and useful detection models. The average public's life is impacted by mass media since it happens regularly. Because of this, news stories are written that are somewhat true or even entirely untrue. Using online social networking sites, people deliberately promote these fake goods. It is crucial to decide whether the news is false owing to its potential to have detrimental social and national effects. The false news identification process made use of many criteria, including the headline and body content of the news piece. The suggested method works effectively in terms of producing results with excellent accuracy, precision, and memory. Comparing all the models employed in this study, it was discovered that Distillbert and multinomial nae bayes models perform better than Logistic and others ml models. The credibility of the story may be evaluated using a larger dataset for better results and additional variables like the author and publisher of the news. Grenze Scientific Society, 2023. -
Fake News Detection and Classify the Category
Everyone depends on numerous sources of E-news in today's world when the internet is ubiquitous. Online content abounds, especially social media feeds, many of which are unreliable and may not always be factual. For people to utilise social media platforms like Facebook, Twitter, and others, fake news is a topic that may be studied through Natural Language Processing techniques. Using ideas from natural language processing and machine learning applied to social media, our goal in this work is to conduct categorization of different news items that are available online. Our intention is to empower the user to utilise NLP (Natural Language Processing) methods to identify 'fake news,' which refers to misinformed material that may be categorised as genuine or false using software like Python. The model focuses on identifying false news sources based on several articles from a website, categorising the news as false or true, and determining its veracity using unreliable sources like scikit-learn and NLP for textual analysis of the website distributing the news. When a source is identified as a publisher of false news, which can be predicted with high vectorization and also suggested using the Python scikit-learn module to do tokenization and feature development, biased viewpoints may be identified and categorised in any subsequent articles from that source. 2022 IEEE. -
Fake News Detection in Healthcare Using Machine Learning
The internet has revolutionary power in todays society, acting as an unmatched catalyst for technical innovation, worldwide connectedness, and information dissemination. It has transformed communication and made knowledge more accessible to all, and given people, companies, and society the tools they need to prosper in the connected digital world. However, this power is responsible for navigating issues, such as the proliferation of fake news and safeguarding information integrity. As peoples health comes first, false information about it might have disastrous consequences. Even for the most knowledgeable professionals in the field, identifying false information about health can be difficult because of the variety of factors that must be considered. New advances in machine learning have enabled automatic classification of bogus news. For the detection of fake news correctly, we must train the automation in such a way that it captures the bogus correctly, and for that the data we input is of at most importance and, in fact, the most important as well. To enhance the models capacity to discern between authentic and fake news, this study investigates the extraction of structural and semantic information from text using a combination of named entity recognition and syntactic parsing. Utilizing these characteristics, we trained a variety of machine learning algorithms, assessed their effectiveness, and found that the Random Forest classifier outperformed the others in classification. 2025 Scrivener Publishing LLC. -
Fake News Detection using Machine Learning and Deep Learning Hybrid Algorithms
Spreading misinformation or fake news for personal, political, or financial gain has become very common these days. The influence of this misinformation on peoples opinions can be significant, i.e., the 2016 presidential election in the United States was a perfect illustration of how false news may be used to deceive people. In todays fast-paced world, automatic detection of fake news has become an importantrequirement. In this paper, multiple machine learning algorithms have been implemented to perform classification. A proposition of a hybrid architecture consisting of CNN along with LSTM has also been made. The proposed model outperforms the other traditional approaches. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Fake News Detection Using TF-IDF Weighted with Word2Vec: An Ensemble Approach
Social media platforms' utilization for news consumption is steadily growing due to their accessibility, affordability, appeal, and ability to propagate misinformation. False information, whether intentionally or unintentionally created, is being disseminated across the internet. Certain individuals spread inaccurate information on social media to gain attention, financial benefits, or political advantage. This has a detrimental impact on a substantial portion of society that is heavily influenced by technology. It is imperative for us to develop better discernment in distinguishing between fake and genuine news. In this research paper, we present an ensemble approach for detecting fake news by using TF-IDF Weighted Vector with Word2Vec. The extracted features capture specific textual characteristics, which are converted into numerical representations for training the models and balanced dataset with the Random over Sampling technique. The implementation of our proposed framework utilized the ensemble approach with majority voting which combines 2 machine learning models like Random Forest and Decision Tree. The proposed strategy was adopted empirically evaluated against contemporary techniques and basic classifiers, including Gaussian Nae Bayes, Logistic Regression, Multilayer Perceptron, and XGBoost Classifier. The effectiveness of our approach is validated through the evaluation of the accuracy, F1-Score, Precision, Recall, and Auc curve, yielding an impressive accuracy score of 94.24% on the FakeNewsNet dataset. 2023, Ismail Saritas. All rights reserved. -
Fake News Detection: An Effective Content-Based Approach Using Machine Learning Techniques
Fake news is any information fabricated to mislead readers to spread an idea for certain gains (usually political or financial). In today's world, accessing and sharing information is very fast and almost free. Internet users are growing significantly than ever before. Therefore, online platforms are perfect grounds to spread information to a broader section of society. What could circulate between a relative few can now circulate globally overnight. This advantage also marked the increase in the number of fake news attacks by its users, which is unsuitable for a healthy society. Therefore, there is a need for good algorithms to identify and take down fake information as soon as they appear. This paper aims at solving the problem by automating the process of identifying fake news using its content. Evaluation metrics like the accuracy of correct classification, precision, recall and f1-score assess the performance of the approach. The machine learning approach achieved its best performance with 96.7 percentage accuracy, 96.2 percentage precision, 97.5 percentage recall and 96.9 percentage f1 score on the ISOT dataset. 2022 IEEE. -
Family Caregiving in Dementia in India: Challenges and Emerging Issues
This chapter would provide an overview of the caregiving scenario in India with a focus on families as the mainstay of support and care for people with dementia. The various aspects of caregiving in dementia would be discussed in the light of the Indian sociocultural context. The impact of caregiving and challenges faced by the family and informal caregivers would be described in the light of the changing demographics and urbanization in India. The need for different kinds of caregiver education and training programmes tailored to the domiciliary and socioeconomic status of the family would be discussed. The resources available for family caregivers, like existing programmes for psychoeducation, family self-care, online and other resources for support would be described. We would discuss the challenges faced in developing culturally appropriate interventions for India that can be delivered within existing resources, such as supporting families in their role as caregivers and providing training and support for them. The chapter would discuss the emerging issues in the models of care for low- and middle-income countries like India, where the care is primarily home-based. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021. -
Family Factors Associated with Problematic Use of the Internet in Children: A Scoping Review
Background: Problematic use of the internet (PUI) is a growing concern, particularly in the young population. Family factors influence internet use among children in negative ways. This study examined the existing literature on familial or parental factors related to PUI in children. Methods: A scoping review was conducted in EBSCOhost, PubMed, ScienceDirect, JSTOR, Biomed Central, VHL Regional Portal, Cochrane Library, Emerald Insight, and Oxford Academic Journal databases. Studies reporting data on family factors associated with PUI in children, published in English in the 10 years to July 2020 were included. The following data were extracted from each paper by two independent reviewers: methodology and demographic, familial, psychiatric, and behavioral correlates of PUI in children. Results: Sixty-nine studies fulfilled the eligibility criteria. Three themes emerged: parenting, parental mental health, and intrafamilial demographic correlates of PUI in children. Parenting styles, parental mediation, and parentchild attachment were the major parenting correlates. Conclusion: Literature on significant familial and parental factors associated with PUI in children is scarce. More research is required to identify the interactions of familial and parental factors with PUI in children, to develop informed management strategies to address this issue. 2022 Indian Psychiatric Society - South Zonal Branch. -
Family Functioning and Differentiation in Indian Homeschooling Families: A Systems Perspective on Stress and Coping
Family system and functioning play an integral part in individuals emotional development and channel the various coping mechanisms adopted by individuals to deal with multiple situations in life. The study explored family functioning, family differentiation, family stress, and family coping strategies among homeschooling families. A sample of 115 homeschooling families was selected by snowball sampling from India. A quantitative approach using correlation and regression analysis was used for the study. There has been a surge in families opting for home-based education post-pandemic. There is a need for an in-depth exploration of how these families function, develop differentiation, experience stress, and cope in a collectivistic culture like India. The results indicate that balanced cohesion in families predicts better differentiation in the motherchild subsystem, while family satisfaction predicts better differentiation in the husband and wife subsystem. The research findings can form a basis for developing family therapy specific to homeschooling and enhance the knowledge and understanding regarding the benefits and costs of homeschooling. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.