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The paper introduces a novel watermarking scheme to enhance robustness and integrity in medical images using the Knight's Tour algorithm. Medical image digitization and sharing necessitate robust techniques for data security. Existing watermarking methods often lack resilience against attacks and maintain imperceptibility. To address these issues, the proposed scheme employs the Knight's Tour algorithm to determine the optimal pixel locations for watermark embedding. Extensive evaluations demonstrate its effectiveness against geometric attacks, specifically image rotation and scaling. The study's significance lies in providing a reliable solution for safeguarding medical image data, ensuring authenticity, and bolstering trust in healthcare applications. Moreover, the scheme's adaptability extends its significance to multimedia data protection and content authentication. [ABSTRACT FROM AUTHOR]

The integration of Augmented Reality (AR) technology in indoor navigation has transformed spatial awareness and user interaction. This research focuses on "AR@Campus," an AR app designed for campus navigation, aligned with Industry 4.0 principles. It leverages AR to enhance human perception and dynamic digital interaction. The primary goal is to provide an intuitive campus navigation experience using IndoorAtlas positioning technology. Key features include overlaying virtual elements like paths and landmarks onto real-world views, improving spatial understanding. The study covers software development, interface design, and user evaluation, utilizing the IndoorAtlas SDK and Android Studio for precise positioning. The user interface prioritizes ease of use and personalized experiences. User testing is integral, confirming the app's effectiveness, praised for its user-friendly interface and spatial awareness enhancements. AR@Campus represents a significant step forward in campus navigation, benefiting users by seamlessly integrating AR technology into their daily experiences. [ABSTRACT FROM AUTHOR]

Sentiment analysis (SA) is the study of people's emotions and attitudes toward a particular topic. It is beneficial for monitoring and analyzing social media text in order to gather public opinion. Despite the fact that there are SA applications for monolingual text such as English and non-English languages like Hindi, Chinese and French, the Malay language has far fewer works, not to mention the mixed language such as Malay-English (also known as Manglish). Other than comments and posts from websites and social media, the emoji used by internet users can also help to provide better insights into how they truly feel about a particular topic. Our work focuses on Malay-English mixed language comments and posts on how Malaysians feel about daily new cases of Covid-19 in Malaysia. We proposed a neural network framework to perform SA on languages spoken by Malaysians, namely Malay, English, and Malay-English, by also taking into account the emoji used by internet users. The data was pre-processed to remove noises and then transformed into word vector representation using word embedding technique. Then we propose a framework that involves training and testing mixed language textual data along with emoji analysis by using bidirectional Long Short Term Memory (biLSTM) neural network. To compare with the proposed method, several machine learning models and Long Short Term Memory (LSTM) with word vectorization was used. Finally, compared to the machine learning model such as Naïve Bayes and Logistic Regression, neural networks such as LSTM, the proposed method; biLSTM with tuned hyper-parameter for Malay-English mixed language achieved the highest accuracy of 76.6%, and macro F1-score of 69.6%. [ABSTRACT FROM AUTHOR]