The advent of online social networks (OSN) has transformed a common passive reader into a content contributor. It has allowed users to share information and exchange opinions, and also express themselves in online virtual communities to interact with other users of similar interests. However, OSN have turned the social sphere of users into the commercial sphere. This should create a privacy and security issue for OSN users. OSN service providers collect the private and sensitive data of their customers that can be misused by data collectors, third parties, or by unauthorized users. In this paper, common security and privacy issues are explained along with recommendations to OSN users to protect themselves from these issues whenever they use social media.

Social media applications, such as Twitter and Facebook, allow users to communicate and share their thoughts, status updates, opinions, photographs, and videos around the globe. Unfortunately, some people utilize these platforms to disseminate hate speech and abusive language. The growth of hate speech may result in hate crimes, cyber violence, and substantial harm to cyberspace, physical security, and social safety. As a result, hate speech detection is a critical issue for both cyberspace and physical society, necessitating the development of a robust application capable of detecting and combating it in real-time. Hate speech detection is a context-dependent problem that requires context-aware mechanisms for resolution. In this study, we employed a transformer-based model for Roman Urdu hate speech classification due to its ability to capture the text context. In addition, we developed the first Roman Urdu pre-trained BERT model, which we named BERT-RU. For this purpose, we exploited the capabilities of BERT by training it from scratch on the largest Roman Urdu dataset consisting of 173,714 text messages. Traditional and deep learning models were used as baseline models, including LSTM, BiLSTM, BiLSTM + Attention Layer, and CNN. We also investigated the concept of transfer learning by using pre-trained BERT embeddings in conjunction with deep learning models. The performance of each model was evaluated in terms of accuracy, precision, recall, and F-measure. The generalization of each model was evaluated on a cross-domain dataset. The experimental results revealed that the transformer-based model, when directly applied to the classification task of the Roman Urdu hate speech, outperformed traditional machine learning, deep learning models, and pre-trained transformer-based models in terms of accuracy, precision, recall, and F-measure, with scores of 96.70%, 97.25%, 96.74%, and 97.89%, respectively. In addition, the transformer-based model exhibited superior generalization on a cross-domain dataset.

The systems of healthcare are being transmuted by scientific improvements in the information of medical systems, electronic records in medical, smart, and wearable devices, and handheld devices. This growth in medical big data, together with the expansion of computational approaches in the area of healthcare, has aided researchers and practitioners to excerpt and visualize medical big data in a novel scale of research. The role of scientific programming in providing solutions to existing and forthcoming issues exists in the organization of large-scale data in healthcare, such as by assisting in the handing out of vast data volumes, modelling of composite systems, and sourcing derivations from healthcare data and simulations. Visualization is a significant tool in producing diagrams, images, or animations to transfer healthcare messages and improve understandings. Programming tools, including Apache Hadoop, Informatica PowerCenter, and Tableau, analyze data extremely efficiently and enable the visualization of meaningful insights extracted from big data.

Protection of medical images over the internet is a challenging task regarding its communication and usefulness. This work proposes a novel mechanism for the protection of DICOM images. The technique provides the facility to embed the iris (image) as a watermark to protect its integrity. After retrieval of the watermark, the mechanism is so robust that it recognizes the iris efficiently and correctly. Similarly, the system protects the Region of Interest (ROI) before the extraction of watermark as it is quite difficult to find volume, visualize or analyze the ROI. The technique not only protects the watermark but also gives the protection to ROI, which may be different in various medical images. Experimental results provide a deep view about the robustness of the technique in real time against both the intentional and unintentional attacks. The empirical results also show that after extraction of the watermark, the system has the capability to accurately find the volume, visualize and analyze the ROI.