In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task in part because various objects reflect and disperse signals. Ultra WideBand (UWB) is an emerging technology in the field of indoor positioning that has shown better performance compared to others. In order to set the stage for this work, we provide a survey of the state-of-the-art technologies in indoor positioning, followed by a detailed comparative analysis of UWB positioning technologies. We also provide an analysis of strengths, weaknesses, opportunities, and threats (SWOT) to analyze the present state of UWB positioning technologies. While SWOT is not a quantitative approach, it helps in assessing the real status and in revealing the potential of UWB positioning to effectively address the indoor positioning problem. Unlike previous studies, this paper presents new taxonomies, reviews some major recent advances, and argues for further exploration by the research community of this challenging problem space.

Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authentication, especially in spreading infectious diseases like coronavirus disease 2019 (COVID-19). Even though there is a number of available security systems, they suffer from one or more of issues, such as identity fraud, loss of keys and passwords, or spreading diseases through touch authentication tools. To overcome these issues, IoT-based intelligent control medical authentication systems using DL models are proposed to enhance the security factor of medical and healthcare places effectively. This work applies IoT with DL models to recognize human faces for authentication in smart control medical systems. We use Raspberry Pi (RPi) because it has low cost and acts as the main controller in this system. The installation of a smart control system using general-purpose input/output (GPIO) pins of RPi also enhanced the antitheft for smart locks, and the RPi is connected to smart doors. For user authentication, a camera module is used to capture the face image and compare them with database images for getting access. The proposed approach performs face detection using the Haar cascade techniques, while for face recognition, the system comprises the following steps. The first step is the facial feature extraction step, which is done using the pretrained CNN models (ResNet-50 and VGG-16) along with linear binary pattern histogram (LBPH) algorithm. The second step is the classification step which can be done using a support vector machine (SVM) classifier. Only classified face as genuine leads to unlock the door; otherwise, the door is locked, and the system sends a notification email to the home/medical place with detected face images and stores the detected person name and time information on the SQL database. The comparative study of this work shows that the approach achieved 99.56% accuracy compared with some different related methods.

Social media is referred to as active global media because of its seamless binding thanks to COVID-19. Connecting software such as Facebook, Twitter, WhatsApp, WeChat, and others come with a variety of capabilities. They are well-known for their low-cost, quick, and effective communication. Because of the seclusion and travel constraints caused by COVID-19, concerns, such as low physical involvement in many possible activities, have arisen. Depending on their information, knowledge, nature, experience, and way of behavior, various types of human beings have diverse responses to any scenario. As the number of net subscribers grows, inappropriate material has become a major concern. The world's most prestigious and trustworthy organizations are keenly interested in conducting practical research in this field. The research contributes to using Artificial Intelligence (AI) as a service (AIaaS) for preventing the spread of immoral content. As software as a service (SaaS) and infrastructure as a service (IaaS), AIaaS for immoral content detection and eradication can use effective cloud computing models to leverage this service. It is highly adaptable and dynamic. AIaaS-based immoral content detection is mostly effective for optimizing the outcomes based on big data training data samples. Immoral content is identified for semantic and sentiment evaluation, and content is divided into immoral, cyberbullying, and dislike components. The suggested paper's main issue is the polarity of immoral content that can be processed using an AI-based optimization approach to control content proliferation. To finish the class and statistical analysis, support vector machine (SVM), selection tree, and Naive Bayes classifiers are employed.

The number of individuals who suffer from visual impairment is increasing rapidly. The most significant barrier to improving the lives of the blind and visually impaired people is their inability to navigate independently and safely. Indoor navigation systems for blind and visually impaired people aim to guide them in navigating independently in familiar and unfamiliar environments. Our system aims to provide an assistive technology for blind and visually impaired individuals by exploiting popular existing technologies that are often used by blind individuals, such as the smartphone. The system provides users with guidance statements that help them navigate from their current positions to desired destinations. The system consists of four main components: mapping, positioning, navigation, and interface. In order to implement these components, three main applications need to be developed: application in localization server, application in communication server, and application in smartphone, each of which is located in a different place but connected to the others. Functionalities test and blind test were conducted to evaluate the system. The system proved its ability to aid blind individuals effectively.

This paper comprehensively reviews medical VQA models, structures, and datasets, focusing on combining vision and language. Over 75 models and their statistical and SWOT (Strengths, Weaknesses, Opportunities, Threats) analyses were compared and analyzed. The study highlights whether the researchers in the general field influence those in the medical field. According to an analysis of text encoding techniques, LSTM is the approach that is utilized the most (42%), followed by non-text methods (14%) and BiLSTM (12%), whereas VGGNet (40%) and ResNet (22%) are the most often used vision methods, followed by Ensemble approaches (16%). Regarding fusion techniques, 14% of the models employed non-specific methods, while SAN (13%) and concatenation (10%) were frequently used. The study identifies LSTM-VGGNet and LSTM-ResNet combinations as the primary approaches in medical VQA, with 18% and 15% usage rates, respectively. The statistical analysis of medical VQA from 2018 to 2023 and individual yearly analyses reveals consistent preferences for LSTM and VGGNet, except in 2018 when ResNet was more commonly used. The SWOT analysis provides insights into the strengths and weaknesses of medical VQA research, highlighting areas for future exploration. These areas include addressing limited dataset sizes, enhancing question diversity, mitigating unimodal bias, exploring multi-modal datasets, leveraging external knowledge, incorporating multiple images, ensuring practical medical application integrity, improving model interpretation, and refining evaluation methods. This paper’s findings contribute to understanding medical VQA and offer valuable guidance for future researchers aiming to make advancements in this field.

Complex software systems that support organizations are updated regularly, which can erode system architectures. Moreover, documentation is rarely synchronized with the changes to the software system. This creates a slew of issues for future software maintenance. To this goal, information extraction tools use exact approaches to extract entities and their corresponding relationships from source code. Such exact approaches extract all features, including those that are less prominent and may not be significant for modularization. In order to resolve the issue, this work proposes an enhanced approximate information extraction approach, namely, fact extractor system for Java applications (FESJA) that aims to automate software modularization using a fact extraction system. The proposed FESJA technique extracts all the entities along with their corresponding more dominant formal and informal relationships from a Java source code. Results demonstrate the improved performance of FESJA, by extracting 74 (classes), 43 (interfaces), and 31 (enumeration), in comparison with eminent information extraction techniques.