Students' Behavioural Intention Regarding E-Learning During the COVID-19 Pandemic

Due to the appearance of COVID-19, the newly emerged situation has provoked numerous reactions in the field of education, both in the world and in Serbia. Prompted by this problem, the authors of this paper conducted a survey to determine students' behavioural intention, as well as their readiness to use e-learning during the COVID-19 pandemic. E-learning has integrated technology and education and has proven to be a powerful tool that enables the education system to respond to the challenges of modern society. In this research, an online questionnaire was distributed to the students of the University of Belgrade. To process the results, the SEM methodology was employed, which enabled the testing of the proposed hypotheses. The obtained results showed the students' behavioural intention can be directly and indirectly predicted by the joint influence of the role of authority, innovative orientation, user-friendly learning, expected performance, and quality of e-learning. This knowledge enabled a comprehensive analysis that encompassed the e-learning experiences students gained during a state of emergency. © 2023, FACTEACHEREDUCATION. All rights reserved.

Successful hysteroscopic management of two cases of interstitial pregnancy

Introduction. Interstitial pregnancy (IP) is the rarest type of tubal pregnancy with a high rupture rate and often remains asymptomatic in the first 10-12 gestational weeks. Therefore, the timing of the diagnosis is crucial for successful management. Case report. Two patients, aged 28 and 22, were diagnosed with IP using transvaginal ultrasound. Both patients were asymptomatic, with initial serum betahCG of 6,664 mIU/mL and 4,641 mIU/mL, respectively. Since they refused treatment with methotrexate and wanted to preserve their fertility, we performed operative hysteroscopy with resection and evacuation of the gestational tissue. The procedures were uneventful. The betahCG levels dropped significantly, and the patients were discharged after three and four hospital days, respectively. Conclusion. Using hysteroscopic procedures, we successfully treated two asymptomatic patients with IP of gestational age < 10 weeks by ultrasonography and levels of serum betahCG < 7,000 mIU/mL. With the occurrence of IP but also the numerous advantages of hysteroscopy, large, multicenter studies are necessary to further investigate the place of this approach as a single treatment method for IP. Trends and consequences observed during the COVID-19 pandemic correlate with the importance of timely diagnosis of ectopic pregnancies, the benefits of a minimally invasive approach in their treatment, and epidemiologically justified shorter hospital stays.Copyright © 2023 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

Chaotic Binary Ant Lion Optimizer Approach for Feature Selection on Medical Datasets with COVID-19 Case Study

Binary version of the ant lion optimizer (ALO) are suggested and utilized in wrapper-mode to pick the best feature subset for classification. ALO is a recently developed bio-inspired optimization approach that mimics ant lion hunting behavior. Furthermore, ALO balances exploration and exploitation utilizing a unique operator to explore the space of solutions adaptively for the best solution. The difficulties of a plethora of noisy, irrelevant, and misleading features, as well as the capacity to deal with incorrect and inconsistent data in real-world subjects, provide rationale for feature selection to become one of the most important requirements. A difficult machine learning problem is to choose a subset of important characteristics from a vast number of features that characterize a dataset. Choosing the most informative markers and conducting a high-accuracy classification across the data may be a difficult process, especially if the data is complex. The feature selection task is usually expressed as a bio-objective optimization challenge, with the goal of enhancing the performance of the prediction model (data training fitting quality) while decreasing the number of features used. Various evaluation criteria are employed to determine the success of the suggested approach. The findings show that the suggested chaotic binary algorithm can explore the feature space for optimum feature set efficiently. © 2022 IEEE.

Feature Selection by Hybrid Binary Ant Lion Optimizer with COVID-19 dataset

Metaheuristic optimization is becoming increasingly popular as a way for addressing complicated issues that are difficult to tackle using standard approaches. The capacity to solve issues that may occur as a result of the interactions of basic information processing units is known as swarm intelligence. Swarm intelligence is a type of artificial intelligence methods, that is based on indirect communications between individual from population. The ant lion optimizer algorithm is recently proposed swarm intelligence algorithm. However, basic ant lion version suffers from some deficiencies which are addressed in this study by performing hybridization with well-known firefly algorithm. Proposed hybrid approach was adapted for solving important feature selection problem from machine learning domain. Hybrid metaheuristics was first validated against 10 UCL datasets and afterwards it was applied to a novel COVID-19 dataset. Moreover, comparative analysis with similar methods tested under the same condition was presented. Obtained experimental results prove the efficiency of proposed hybrid ant lion optimizer for tackling feature selection challenge. © 2021 IEEE.