Classroom interaction tools or student response systems are rapidly expanding, and advanced technologies like distributed processing, microservice architecture, container orchestration, and artificial intelligence (AI) have enabled these systems to scale significantly with a certain need to sustain high performance, extensibility, and data visualization. A combination of interactive technologies with effective pedagogical practices can create an active learning environment that promotes reflection, critical thinking, and higher-level learning processes. The research presented in this paper aimed at designing and evaluating a novel architecture for a classroom interaction tool that allows third parties to expand certain functionalities. The system provides an infrastructure with basic functionalities that can be leveraged by third-party developers who can focus on creative ways to process and visualize the responses from the audience/students. This study applied an iterative architecture design evaluated using the Architectural Tradeoff Analysis Method and semi-structured interviews. The architecture is illustrated using$4+1$views to transform the models and state the operational semantics. The design and implementation aimed at using best practices for microservices, container-orchestrated clusters, message queues, and sharded databases. The results of the trade-off analysis consist of detailed, measurable scenarios with risks, sensitivity, and tradeoff points. In addition, student interviews provided insight into their expectations of the architecture. A prototype has also been implemented as a proof of concept for quantitative evaluation (performance and reliability testing) against expected results. This study establishes a solid foundation for an evaluated architecture for developers and researchers looking to design similar tools, ensuring scalability and high performance.

Technological advancements enabled new possibilities for interacting within a classroom or, in general, with an audience. Starting with dedicated devices called clickers, the technology leveraged personal general-use devices such as smartphones and tablets. Terminology varies, but this is generally called response technology, response system, or student response system. This research aimed to explore the students' perceptions concerning technology-mediated opentext questions and the various factors that influence students' learning during lectures. This research adopts a case study approach, employing qualitative, in-depth interviews with ten students from a large science and technology university in northern Europe. In this exploratory research, interview sessions were digitally recorded, audios were converted to text through Microsoft Word, and NVivo was used for thematic analysis. The study uncovered student engagement and interactivity, focus and attention, learning and knowledge retention, and challenges as key themes that impact student learning. Findings indicated that students generally had positive experiences with this approach, though they also encountered several challenges. Notable examples from the findings: the technology-mediated questions combined with anonymity allow increased freedom of expression for the students; the modality for collecting responses can feel quite limited, especially when using a smartphone or similar device as input. Some identified themes, e.g., challenges encountered, can be further classified into additional subthemes, e.g., technological, pedagogical, and content-related. Combining open-text questions with other types in the context of a lecture opens opportunities for active participation, formative assessment, reflections, scaffolding discussions, and, in general, higher-level learning activities and processes. Teachers and technologists could consider these findings to optimize the implementation of such tools in classroom settings.

Abstract Disease diagnosis is an exciting task due to many associated factors. Inaccuracy in the measurement of a patient’s symptoms and the medical expert’s expertise has some limitations capacity to articulate cause affects the diagnosis process when several connected variables contribute to uncertainty in the diagnosis process. In this case, a decision support system that can assist clinicians in developing a more accurate diagnosis has a lot of potentials. This work aims to deploy a fuzzy inference-based decision support system to diagnose various diseases. Our suggested method distinguishes new cases based on illness symptoms. Distinguishing symptomatic disorders becomes a time-consuming task in most cases. It is critical to design a system that can accurately track symptoms to identify diseases using a fuzzy inference system (FIS). Different coefficients were used to predict and compute the severity of the predicted diseases for each sign of disease. This study aims to differentiate and diagnose COVID-19, typhoid, malaria and pneumonia. The FIS approach was utilized in this study to determine the condition correlating with input symptoms. The FIS method demonstrates that afflictive illness can be diagnosed based on the symptoms. Our decision support system’s findings showed that FIS might be used to identify a variety of ailments. Doctors, patients, medical practitioners and other healthcare professionals could benefit from our suggested decision support system for better diagnosis and treatment.

Abstract In today’s world, lung cancer is a significant health burden, and it is one of the most leading causes of death. A leading type of lung cancer is malignant mesothelioma (MM). Most of the MM patients do not show any symptoms. Etiology plays a vital factor in the diagnosis of any disease. Positron emission tomography (PET), magnetic resonance imaging (MRI), biopsies, X-rays and blood tests are essential but costly and invasive MM risk factor identification methods. In this work, we mainly focused on the exploration of the MM risk factors. The identification of mesothelioma symptoms was carried out by utilizing the data of mesothelioma patients. However, the dataset was comprised of both healthy and mesothelioma patients. The dataset is prone to a class imbalance problem in which the number of MM patients significantly less than healthy individuals. To overcome the class imbalance problem, the synthetic minority oversampling technique has been utilized. The association rule mining-based Apriori algorithm has been applied to a preprocessed dataset. Before using the Apriori algorithm, both duplicate and irrelevant attributes were removed. Moreover, the numerical attributes were also classified into nominal attributes and the association rules were generated in the dataset. Our results show that erythrocyte sedimentation rate, asbestos exposure and its duration time, and pleural and serum lactic dehydrogenase ratio are major risk factors of MM. The severe stages of MM can be avoided by earlier identification of risk factors of the disease. The failure of identification of risk factors can lead to increased risk of multiple medical conditions, including cardiovascular diseases, mental distress, diabetes and anemia.

Abstract The area of corporate bankruptcy prediction attains high economic importance, as it affects many stakeholders. The prediction of corporate bankruptcy has been extensively studied in economics, accounting and decision sciences over the past two decades. The corporate bankruptcy prediction has been a matter of talk among academic literature and professional researchers throughout the world. Different traditional approaches were suggested based on hypothesis testing and statistical modeling. Therefore, the primary purpose of the research is to come up with a model that can estimate the probability of corporate bankruptcy by evaluating its occurrence of failure using different machine learning models. As the dataset was not well prepared and contains missing values, various data mining and data pre-processing techniques were utilized for data preparation. Within this research, the task of resolving the issues induced by the imbalance between the two classes is approached by applying different data balancing techniques. We address the problem of imbalanced data with the random undersampling and Synthetic Minority Over Sampling Technique (SMOTE). We used five machine learning models (support vector machine, J48 decision tree, Logistic model tree, random forest and decision forest) to predict corporate bankruptcy earlier to the occurrence. We use data from 2009 to 2013 on Poland manufacturing corporates and selected the 64 financial indicators to be broken down. The main finding of the study is a significant improvement in predictive accuracy using machine learning techniques. We also include other economic indicators ratios, along with Altman’s Z-score variables related to profitability, liquidity, leverage and solvency (short/long term) to propose an efficient model. Machine learning models give better results while balancing the data through SMOTE as compared to random undersampling. The machine learning technique related to decision forest led to 99% accuracy, whereas support vector machine (SVM), J48 decision tree, Logistic Model Tree (LMT) and Random Forest (RF) led to 92%, 92.3%, 93.8% and 98.7% accuracy, respectively, with all predictive financial indicators. We find that the decision forest outperforms the other techniques and previous techniques discussed in the literature. The proposed method is also deployed on the web to assist regulators, investors, creditors and scholars to predict corporate bankruptcy.

In a wireless sensor network (WSN), node localization is a key requirement for many applications. The concept of mobile anchor-based localization is not a new concept; however, the localization of mobile anchor nodes gains much attention with the advancement in the Internet of Things (IoT) and electronic industry. In this paper, we present a range-free localization algorithm for sensors in a three-dimensional (3D) wireless sensor networks based on flying anchors. The nature of the algorithm is also suitable for vehicle localization as we are using the setup much similar to vehicle-to-infrastructure- (V2I-) based positioning algorithm. A multilayer C-shaped trajectory is chosen for the random walk of mobile anchor nodes equipped with a Global Positioning System (GPS) and broadcasts its location information over the sensing space. The mobile anchor nodes keep transmitting the beacon along with their position information to unknown nodes and select three further anchor nodes to form a triangle. The distance is then computed by the link quality induction against each anchor node that uses the centroid-based formula to compute the localization error. The simulation shows that the average localization error of our proposed system is 1.4 m with a standard deviation of 1.21 m. The geometrical computation of localization eliminated the use of extra hardware that avoids any direct communication between the sensors and is applicable for all types of network topologies.