Pneumonia, COVID-19, and tuberculosis are some of the most fatal and common lung diseases in the current era. Several approaches have been proposed in the literature for the diagnosis of individual diseases, since each requires a different feature set altogether, but few studies have been proposed for a joint diagnosis. A patient being diagnosed with one disease as negative may be suffering from the other disease, and vice versa. However, since said diseases are related to the lungs, there might be a likelihood of more than one disease being present in the same patient. In this study, a deep learning model that is able to detect the mentioned diseases from the chest X-ray images of patients is proposed. To evaluate the performance of the proposed model, multiple public datasets have been obtained from Kaggle. Consequently, the proposed model achieved 98.72% accuracy for all classes in general and obtained a recall score of 99.66% for Pneumonia, 99.35% for No-findings, 98.10% for Tuberculosis, and 96.27% for COVID-19, respectively. Furthermore, the model was tested using unseen data from the same augmented dataset and was proven to be better than state-of-the-art studies in the literature in terms of accuracy and other metrics.

With the rapid advances in Information Technology (IT), the focus on engaging computing students to gain practical experience in the IT industry before graduation is becoming increasingly complex without incorporating pedagogical strategies of success in curricula. The goal is to enable computing major students to gain in-depth knowledge and practical understanding of the IT working environment before graduating through essential industry-driven practical skills based on international standards and best practices. Unfortunately, tracking and analyzing students’ practical skills performance during their IT field training programs, which are conducted primarily off-campus at various public and private organizations, before, during, and after the training period, is a daunting task for both the college instructors and the industry trainers. To overcome these challenges, this paper introduces a Sustainable University Field Training (SUNFIT) framework, which is a pedagogical approach towards mining the educational data using machine learning to integrate and measure the field training programs against the internationally recognized accreditation standards such as Accreditation Board for Engineering and Technology (ABET). The study employs machine learning models aimed at continuously measuring and monitoring international ABET accreditation requirements on computing major courses’ academic data, elucidating student performance across various semesters, integrating best practices, and producing an evidence-based rationale approach for evaluating weak learning outcomes (LOs) with minimal manual intervention, as well as preventing faculty-specific portfolio errors. The proposed approach could be easily developed by academics, researchers, or even students, and for a variety of purposes, including enhancing poor student outcomes (SOs). In addition, various data mining and machine learning approaches have been investigated over field training assessment data for successful prediction in subsequent cycles. The results are promising, with Naïve Bayes obtaining the highest accuracy of 90.54% followed by J48 and PART algorithms at 87.83%.

Device-to-Device (D2D) communication underlaying cellular network is a capable system for advancing the spectrum’s efficiency. However, in this condition, D2D generates cross-channel and co-channel interference for cellular and other D2D users, which creates an excessive technical challenge for allocating the spectrum. Despite this, massive connectivity is another issue in the 5G and beyond networks that need to be addressed. To overcome this problem, non-orthogonal multiple access (NOMA) is integrated with the D2D groups (DGs). In this paper, our target is to maximize the sum throughput of the overall network while maintaining the signal-to-interference noise ratio (SINR) of the cellular and D2D users. To achieve the target, a discriminated spectrum distribution framework dependent on multi-agent deep reinforcement learning (MADRL), termed a deep deterministic policy gradient (DDPG) is proposed. Here, it shares the global historical states, actions, and policies using the duration of central training. Furthermore, the proximal online policy scheme (POPS) is used to decrease the computation complexity of training. It utilized the clipping substitute technique for the modification and reduction of complexity at the training stage. The simulation results demonstrated that the proposed scheme POPS attains 16.67%, 24.98%, and 59.09% higher performance than the DDPG, Deep Dueling and deep Q-network (DQN).

In medical treatments and care, medical health is a critical area that requires immediate attention of and can benefit greatly from current day technology. Advancements in IOT development is transforming all facets of our everyday lives and it’s our belief that it can play an important role in mental healthcare too. This paper proposes a human centered model which incorporates the use of the latest IOT wearable technologies along with a fully encrypted cloud computing algorithm that can potentially help in improving the identification and treatment of mental healthcare patients by integrating suggested measures and means of data acquisition, collection, transmission, processing and protection. If done right, with further detailed research that focuses on mental health, we can expect to see significant improvements in treatment and diagnosis methods for patient suffering from mental conditions in the very near future. Our emphasis is on creating a human centered model that can be converted into an interactive app with responsive features that can suggest different treatment measures based on the information collected from the Big Data gathered through the IOT sources utilized and depending on the patient’s condition with utmost security protection of the confidential data.

Healthcare facilities in Developing Countries are not sufficient, particularly in rural or remote areas. These areas show poorer health outcomes than urban areas due to lack of trained physicians, clinics and healthcare services. This is mainly because, most of the healthcare practitioners don’t like to practice in these areas. In contrast, a huge number of lady doctors don’t practice after their graduation due to various reasons, thus major portion of medical human resource is not utilized. Moreover, the information and communication technologies (ICT) in Developing Countries are becoming popular and affordable day by day. Many developing countries have started the implementation of telemedicine projects. This research work focuses on connecting the remote areas of Developing Countries to the urban areas’ practices and doctors especially the non-practicing lady doctors through ICT by establishing virtual clinics in remote areas. In addition to that, the virtual clinic decision support system (VCDSS) facilitates the users in two ways. Firstly, automatically assigning the appropriate most doctor for patient based on his ranking, experience and specialization etc. using a fuzzy rule based system (FRBS). Secondly, it helps the registered doctors at the time of prescribing a patient by suggesting the medicines according to the patient’s symptoms and history. It is done by using the knowledge-base of past diagnoses/prescriptions by the same doctor as well as other doctors and British National Formulary (BNF) using Apriori Algorithm and Inductive Learning Algorithm. The results are shown using sample dataset.