All over the world, deaf people use sign language as the only reliable source of communication with each other as well as with normal people. These communicating signs are made up of the shape of the hand and movement. In Pakistan, deaf people use Pakistan sign language (PSL) as a means of communication with people. In scientific literature, many studies have been done on PSL recognition and classification. Most of these work focused on colored-based hands while some others are sensors and Kinect-based approaches. These techniques are costly and also avoid user-friendliness. In this paper, a technique is proposed for the recognition of thirty-six static alphabets of PSL using bare hands. The dataset is obtained from the sign language videos. At a later step, four vision-based features are extracted i.e. local binary patterns, a histogram of oriented gradients, edge-oriented histogram, and speeded up robust features. The extracted features are individually classified using Multiple kernel learning (MKL) in support vector machine (SVM). We employed a one-to-all approach for the implementation of basic binary SVM into the multi-class SVM. A voting scheme is adopted for the final recognition of PSL. The performance of the proposed technique is measured in terms of accuracy, precision, recall, and F-score. The simulation results are promising as compared with existing approaches.

All over the world, deaf use sign language to communicate each other. The signs are built and communicated through movements and the shapes of the hands. Pakistan sign language (PSL) is used by the deaf community of Pakistan. Automatic recognition of PSL alphabets into the predefined categories is presented here. The seven categories are defined using the visibility, shape, and the orientations of both the fingers and the hand. The histograms of the uniform local binary (lbp) for the neighboring distance of one, two, and three are computed and concatenated to form a single vector. At a later step, six statistical features of the combined histogram are computed, i.e., standard deviation, variance, skewness, kurtosis, entropy, and energy. The classification is achieved using support vector machines (SVMs). The modality of one-verses-one is used for the adoption of binary SVM into the multi-class SVM. The proposed technique is validated over the data set of 3414 PSL signs, taken through the help of seven native signers. The performance of the proposed technique is evaluated by precision, recall, accuracy, and f-measure, while it is elaborated through the classification matrix, tabular, and graph representations.

The recent advancements in cyber technology have facilitated the global electronic sharing of healthcare data among various healthcare professionals, including clinicians, researchers, business administrators, and others. Fog computing is gaining popularity as a means of disseminating information across industries, especially in medicine and healthcare, due to its capability to manage vast amounts of information and data. The evolution of fog computing now allows it to be adaptable to various budget constraints within healthcare organizations, thanks to technological progress. This adaptation includes features like real-time data breach alerts and automated surveillance. As new communication and computing models emerge, fresh security and privacy concerns arise within medical and health-related data. Existing data privacy and security protocols have proven inadequate in preventing data leaks and theft attacks, especially those orchestrated by malicious parties. While medical data is stored in the cloud, legitimate entities’ authorized access to medical documents remains a significant concern. Therefore, this article designs an authentication and authorization scheme that safeguards the privacy of users’ medical information documents. In response to this challenge, we propose an innovative approach that leverages Decoy information technology and User Behavior profiling. This methodology aims to enhance the security of authorized data access within the Fog environment, ensuring the confidentiality of medical data. The results show that the proposed protocol is more secure against unauthorized access and security attacks and more computationally efficient than its counterparts.

Automated formalization of legal text is a time- and effort-consuming task, but human-based validation consumes even more of both. The exchange of healthcare data in compliance with the medical privacy law requires experts with deep familiarity of its intricate provisions for verification. The article presents a medical relational model (MRM) for the extraction of logical rules from medical law, required to design a medical decision support system (MDSS) that facilitates the process of exchanging data electronically with minimum human intervention. The division of medical law into small concept classes makes it easier to formalize the legal text of medical law into logical rules. These logical rules are then used to make a precise decision in compliance with the law, after evaluating requests from different entities for different purposes in MDSS. Our methodology is to analyze the legal text and release records in compliance with the medical law. For developing countries where medical laws are not as mature as HIPAA (Health Insurance Portability and Accountability Act) in the USA, the proposed methodology can be adapted to build their MDSS based on MRM.