Wan Azani Wan Mustafa
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Preferred name
Wan Azani Wan Mustafa
Official Name
Wan Azani, Wan Mustafa
Alternative Name
Mustafa, W.
Azani Mustafa, Wan
Mustaffa, Wan Azani
Wan Mustafa, Wan Azani
Main Affiliation
Scopus Author ID
57219421621
Researcher ID
J-4603-2014

Research Output
Now showing 1 - 4 of 4
  • Publication
    Whisper of understanding: age differences in algorithmic literacy across generations
    (IEEE, 2024-07)
    Miharaini Md Ghani
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Ekram Alhafis Bin Hashim
    ;
    Hafizul Fahri Hanafi
    ;
    Haider Alabdeli
    In today's digital landscape, algorithms play a pivotal role in shaping our experiences and decision-making processes. From social media feeds to online recommendations and search results, algorithms influence our daily interactions with technology. However, the understanding and literacy surrounding these algorithms vary significantly across different age groups, leading to a potential divide in how individuals engage with and comprehend the digital world. This research delves into the age differences in algorithmic literacy, exploring the contrasting experiences and challenges faced by younger generations, often referred to as "digital natives,"and older generations, termed "digital immigrants."By examining factors such as access to technology, educational opportunities, and cultural attitudes, this study aims to shed light on the root causes of these disparities. Through a multifaceted approach, including surveys, interviews, and observational studies, the research investigates the varying levels of algorithmic literacy across different age groups. It explores the potential advantages of younger generations, who have grown up immersed in digital environments, as well as the challenges faced by older generations, who may encounter resistance or difficulties in adapting to new technologies. Furthermore, this study examines the implications of age-related algorithmic literacy gaps, including the potential for digital exclusion, ethical concerns surrounding algorithmic bias and transparency, and the need for inclusive design and education initiatives. By highlighting these issues, the research aims to foster intergenerational dialogue and collaborative efforts to bridge the algorithmic literacy divide. Ultimately, this exploration of age differences in algorithmic literacy aims to contribute to the ongoing discourse on digital literacy, promoting a more inclusive and equitable digital landscape where individuals of all ages can engage with and critically understand the algorithms that shape their daily lives.
  • Publication
    Beyond trends: Tiktok’s educational symphony by unmasking the digital revolution
    (IEEE, 2023)
    Miharaini Md Ghani
    ;
    Wan Azani Wan Mustafa
    ;
    Mohd Ekram Alhafis Bin Hashim
    ;
    Hafizul Fahri Hanafi
    ;
    Laith H. Alzubaidi
    In the fast-paced digital age, TikTok has emerged as an unlikely protagonist in the realm of education, ushering in a bite-sized learning revolution. This comprehensive study delves into the captivating phenomenon of TikTok's educational content, unveiling its transformative impact on learners across generations and disciplines. Drawing from extensive empirical research and expert insights, the article will explore the intricate interplay between TikTok's snackable video format and its ability to foster knowledge acquisition and skill development. It illuminates how this platform's unique amalgamation of entertainment and education has redefined traditional learning paradigms, empowering users to consume and share knowledge in a highly engaging and democratized manner. Beyond trends unravels the cognitive mechanisms underpinning the effectiveness of bite-sized learning, shedding light on its potential to cater to diverse learning styles and attention spans. It examines the platform's role in democratizing access to education, enabling content creators and subject matter experts to reach unprecedented global audiences. Moreover, It provides a critical analysis of the challenges and opportunities that arise as bite-sized learning gains traction, offering invaluable insights for educators, policymakers, and stakeholders invested in shaping the future of education. With its multidisciplinary approach and forward-thinking perspectives, Beyond Trends serves as a comprehensive guide to navigating the digital learning landscape, empowering readers to harness the transformative potential of TikTok and its bite-sized educational content.
      1  9
  • Publication
    Animating sustainable tourism: a literature review analysis of technology's role, challenges, and opportunities
    (IEEE, 2023)
    Suraya Md Nasir
    ;
    Mohd Ekram Alhafis Bin Hashim
    ;
    Wan Azani Wan Mustafa
    ;
    Nor Hazlen Kamaruddin
    ;
    Haider Alabdeli
    ;
    Hafizul Fahri Hanafi
    This research article explores the intricate nexus between animation technology and sustainable tourism animation, conducting an extensive literature review to scrutinize the role, hurdles, and prospects that technology presents in promoting sustainability within the realm of tourism. The outcomes enrich our comprehension of how animation technology intersects with and shapes sustainable practices in tourism, illuminating potential advancements and areas necessitating attention. By dissecting the key technologies employed in sustainable tourism animation, scrutinizing the challenges in their integration, identifying opportunities for their utilization, and assessing their impact on the sustainability of animation, each facet contributes to a comprehensive understanding of sustainable tourism animation's influence on contemporary artistic practices, technological innovations, and educational strategies. Drawing insights from diverse academic sources, peer-reviewed journals, and case studies, this study offers an integrated perspective on the transformative capacity of sustainable animation. It delves into fostering creative expression, encouraging interdisciplinary collaboration, and promoting critical engagement with emerging technologies in the digital age.
      5  16
  • Publication
    Stages Classification on Cervical Cell Images: A Comparative Study
    ( 2023)
    Wan Azani Wan Mustafa
    ;
    Mohamad Irfan Noor
    ;
    Alquran Hiam
    ;
    Miharaini Md Ghani
    ;
    Hafizul Fahri Hanafi
    ;
    Noor Hidayah Che Lah
    ;
    Mundher Adnan M.
    ;
    Hameed Abdul Hussein Abbas
    The cancer of the cervix is called cervical cancer. An element of a woman's womb is the cervix. Among other diseases that affect women, it came in at number four on the list. According to the World Health Organization's cancer report, there are currently roughly 10 million new cases of cancer recorded year, and by 2020, that number will have doubled to 20 million. With the right screening and awareness campaign, this number can be cut in half. A quarter of cancers are said to be brought on by infections, including hepatitis B, which is connected to liver cancer, and the human papillomavirus, which is connected to cervix cancer. Deep learning techniques have been successfully applied to a wide range of image classification tasks, and have the potential to be highly effective for cervical cell image classification as well. In this project, we propose to use a deep learning-based approach to classify cervical cell images into different categories, such as normal cells, abnormal cells, or cancerous cells. To achieve this goal, we will first pre-process the images to prepare them for analysis, and then extract relevant features. These features will be used to train a deep learning model, which will be fine-tuned and optimized for the specific task of cervical cell classification. In this project, transfer learning method will be by using pre-trained classifier such as ResNet-50, GoogLeNet and EfficientNet-b0. We will evaluate the performance of the model using metrics such as accuracy and compare our results to those obtained using traditional machine learning approaches. From this project, the highest accuracy achieved are 51.49%. The goal to develop a pre-trained classifier transfer learning can be used to accurately and reliably classify cervical cell images in a clinical setting are achieved.
      4  8