Nowadays, humans can communicate easily with others by recognizing speech and text characters, particularly facial expressions. In human communication, it is critical to comprehend their emotion or implicit expression. Indeed, facial expression recognition is vital for analyzing the emotions of conversation partners, which can contribute to a series of matters, including mental health consulting. This technique enables psychiatrists to select appropriate questions based on their patients’ current emotional state. The purpose of this study was to develop a deep learningbased model for detecting and recognizing emotions on human faces. We divided the experiment into two parts: Faster R-CNN and mini-Xception architecture. We concentrated on four distinct emotional states: angry, sad, happy, and neutral. Both models implemented using the Faster R-CNN and the mini-Xception architectures were compared during the evaluation process. The findings indicate that the mini-Xception architecture model produced a better result than the Faster R-CNN. This study will be expanded in the future to include the detection of complex emotions such as sadness.

Aspect-oriented programming (AOP) is a new programming paradigm that improves separation of concerns by decomposing the crosscutting concerns in aspect modules. Bad smells are metaphors to describe software patterns that are generally associated with bad design and bad programming of object-oriented programming (OOP). New notions and different ways of thinking for developing aspect-oriented (AO) software inevitably introduce bad smells which are specific bad design and bad programming in AO software called AO bad smells. Software metrics have been used to measure software artifact for a better understanding of its attributes and to assess its quality. Bad-smell metrics should be used as indicators for determining whether a particular fraction of AO code contains bad smells or not. Therefore, this paper proposes definition of metrics corresponding to the characteristic of each AO bad smell as a means to detecting them. The proposed bad-smell metrics are validated and the results show that the proposed bad- smell metrics can preliminarily indicate bad smells hidden in AO software.

Bad smells are software patterns that are generally associated with bad design and bad programming. They can be removed by using the refactoring technique which improves the quality of software. Aspect-oriented (AO) software development, which involves new notions and the different ways of thinking for developing software and solving the crosscutting problem, possibly introduces different kinds of design flaws. Defining bad smells hidden in AO software in order to point out bad design and bad programming is then necessary. This paper proposes the definition of new AO bad smells. Moreover, appropriate existing AO refactoring methods for eliminating each bad smell are presented. The proposed bad smells are validated. The results show that after removing the bad smells by using appropriate refactoring methods, the software quality is increased.

The growing challenges in healthcare services, such as hospital congestion and a persistent shortage of medical personnel, significantly impede effective service delivery. This particularly presents significant challenges for the continuous monitoring of patients with chronic diseases. In comparison, Internet of Things (IoT) based telemonitoring systems offer a promising solution to alleviate these challenges. However, transmitting sensitive and confidential patient health data requires a strong focus on end-to-end security. This includes securing sensitive data within the patient’s home network, during internet transmission, at the endpoint system, and managing sensitive data. In this study, we propose a secure and scalable architecture for a remote health monitoring system that integrates telemedicine technology with the IoT. The proposed solution included a portable remote health monitoring device, an IoT Gateway appliance (IoT GW) in the patient’s home, and an IoT Application Gateway Endpoint (App GW Endpoint) on a cloud infrastructure. A secure communication channel was established by implementing a multi-layered security protocol stack that uses HTTPS over Quick UDP Internet Connection (QUIC), with a focus on optimal security and compatibility, prioritizing cipher suites for data confidentiality and device authentication. The cloud architecture is designed based on the Well-Architected Framework principles to ensure security, high availability, and scalability. Our study shows that storing patient health information is reliable and efficient. Furthermore, the results for processing and transmission times clearly demonstrate that the additional encryption mechanisms have a negligible effect on data transmission latency, while significantly improving security.