Browse Items (9798 total)
Sort by:
-
Factors influencing purchase intention of online shopping customers: A review of the existing literature
The rapid digital transformation, especially on the internet, has provided businesses with unprecedented opportunities for global expansion. This shift has revolutionized marketing, replacing costly and labour-intensive efforts with cost-effective digital strategies. E-commerce platforms facilitate this process by offering an interactive interface for users to post reviews, comments, and questions, thus enhancing the decision-making experience. Understanding customer purchase intention is vital in this digital age, as it's shaped by various antecedent factors, with digital word-ofmouth (eWOM) being a significant influencer. However, the relationship between eWOM and purchase intention remains underexplored. This chapter reviews 60 previous studies, shedding light on the factors affecting online customers' purchase intentions. The study identifies research gaps, setting a clear direction for future investigations. 2024, IGI Global. All rights reserved. -
Factors influencing student mentoring: Insights from higher education institutions
Mentoring has a close relationship to the word journey, symbolic of a goal-oriented process. Goal 4 of the Sustainable Development Goals (SDGs) stress on quality education and mentoring in educational institutions is a vehicle in this direction. Higher education is a crucial phase when students seek coaching and guidance for professional development. Higher Education Institutions (HEI) focusing on quality education assign mentors for students' academic and professional enhancement. The present study involving 92 participants describes the mentoring process followed in two social science courses of two different HEI by following a mixed-method and employing descriptive statistics and grounded theory. The emergent themes include continued communication channels between mentor and mentee, goals, duration, frequency, conflict mitigation, age and inclusion. These are structured as a model which is akin to the Rhodes Model of Youth Mentoring but more comprehensive and suited to the Indian context. 2021 SCMS Group of Educational Institutions. All rights reserved. -
Factors influencing user perception on mobile social networking apps /
Sumedha Journal of Management, Vol.4, Issue 2, pp.429-448, ISSN No: 2277-6753. -
Factors influencing user perception on mobile social networking apps /
Sumedha Journal Of Management, Vol.4, Issue 2, pp.429-448, ISSN No: 2277-6753. -
Factors leading to the early exit of women chefs and their limited presence in the hotel industry of Bengaluru /
Research Review International Journal of Multidisciplinar, Vol.3, Issue 10, pp.24-31, ISSN No: 2455-3085. -
Factors of reintegration of children in conflict with law
Building an ethical society involves lifelong learning and training, individually and collectively. On many occasions, crime and offence happen in the life of children. Juvenile Justice Act 2015 of India covers two categories of children: Children Need Care and Protection (CNCP) and Children in Conflict with Law (CCL). The behaviour of CCL is one of the most complex areas of behavioural science. Recidivism proves that the present reintegration is insufficient to arrest crime. This study focuses on the factors that support the reintegration of the CCL who had undergone the procedures of the Juvenile Justice Board (JJB). This is an exploratory study conducted in Kerala, India, to find the significant factors that contribute to successful reintegration, making children part of an ethical society. The methodology of the study is qualitative in nature and using data collected from boy offenders who have undergone the procedure of JJB and their parents and officials through different individual case studies. All children who participated in the survey have been rehabilitated, but reintegration seems yet to be completed. 2020 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore),. -
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. -
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. -
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 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 OF CONGRUENCES FOR (2, ?)?REGULAR BIPARTITION TRIPLES
Though congruences have their limitations, they have significant importance in the field of number theory and helps in proving many interesting results. Thus, this article has adopted the technique and properties of congruences to identify and prove a set of congruent properties for integer partition. The partition of a positive integer is a way of expressing the number as a sum of positive integers. One such partitions known as regular bipartition triple are discussed in this article. New congruences modulo even integers and modulo prime (p ? 5) powers are derived for (2, ?)?regular bipartition triples. Also infinite families of congruences modulo 2 for some (2, ?)?regular bipartition triples are derived. The theorems stated in this article are proved using the q?series notation and some of the prominent results such as Eulers pentagonal number theorem and Jacobis triple product identities. There are certain lemmas which are derived using these results that help in proving the major results of this article. 2022, RAMANUJAN SOCIETY OF MATHEMATICS AND MATHEMATICAL SCIENCES. All rights reserved.