Examining the Impact of Learning Management Systems in Computer Programming Courses

The development of software and communication technologies in education has led the majority of universities worldwide to integrate the functions of Learning Management Systems (LMSs) into their learning environments. LMSs offers several features that encourage their use by universities and other educational institutions, such as unlimited access to course content, easy tracking of learners’ progress and performance, and reduced costs in terms of both money and time. Most existing LMS studies have been focused on experienced LMS users who are familiar with its functions, with little consideration given to new users. Furthermore, although previous researchers have identified various means of enhancing the effectiveness of LMS use, no consensus has yet been reached on which of these features most successfully improve the learning outcomes of new learners enrolled in programming courses. The purpose of this study, therefore, was to examine the usability of particular LMS features and their impact on learning outcomes for freshman students enrolled in programming courses. Through the Virtual Programming Lab (VPL) and discussion forums, particular LMS features have been considered. For this study, a quantitative quasi-experimental design was employed, including experimental and control groups of new students enrolled in an introductory programming course that involved different LMS features. These features have been considered in the place of treatment in this experiment, in which the level of difference between participants in the two groups was compared. This study involved two main dependent variables: LMS features’ usability and learning achievement. For the first dependent variable, LMS usability, the participants completed a survey, based on the components of Shackel’s usability model (1991), to evaluate the effectiveness of the LMS features’ usability. Four constructs underpin this model: effectiveness, learnability, flexibility, a¬¬nd attitude. For the second dependent variable, learning achievement, the final grade was used to measure the impact of these two LMS features on learning achievement between the two groups. The results revealed significance differences related to LMS features’ usability and learning achievement between the experimental group and the control group. Participants in the experimental group reported greater LMS usability than did those in the control group, and overall course scores indicated improved learning performance in members of the experimental group who applied the VPL and discussion forms features of programming courses

Cyber Security against Intrusion Detection Using Ensemble-Based Approaches

The attacks of cyber are rapidly increasing due to advanced techniques applied by hackers. Furthermore, cyber security is demanding day by day, as cybercriminals are performing cyberattacks in this digital world. So, designing privacy and security measurements for IoT-based systems is necessary for secure network. Although various techniques of machine learning are applied to achieve the goal of cyber security, but still a lot of work is needed against intrusion detection. Recently, the concept of hybrid learning gives more attention to information security specialists for further improvement against cyber threats. In the proposed framework, a hybrid method of swarm intelligence and evolutionary for feature selection, namely, PSO-GA (PSO-based GA) is applied on dataset named CICIDS-2017 before training the model. The model is evaluated using ELM-BA based on bootstrap resampling to increase the reliability of ELM. This work achieved highest accuracy of 100% on PortScan, Sql injection, and brute force attack, which shows that the proposed model can be employed effectively in cybersecurity applications

Comparison of total endoscopic thyroidectomy with conventional open thyroidectomy for treatment of papillary thyroid cancer

BackgroundRecent advance of endoscopic techniques has allowed surgeons to perform thyroidectomy via an incision placement at hidden places which lead to better cosmetic acceptability compared with conventional open thyroidectomy.AimsThis study was conducted to summarize the current evidence that compare open thyroidectomy with endoscopic ‎thyroidectomy in treatment of papillary thyroid cancer‎.‎Methods An electronic literature review, including PubMed, Google Scholar, and EBSCO that examining randomized trials of endoscopic thyroidectomy (ET), conventional open thyroidectomy (COT), and management of papillary thyroid carcinoma was carried out.Results The review included 8 randomized studies that compare total endoscopic thyroidectomy versus conventional open thyroidectomy in treatment of papillary thyroid cancer. The findings showed endoscopic thyroidectomy had statically significant cosmetic appearance, less amount of blood loss and occurrence of transient hypocalcaemia than conventional open thyroidectomy in form of cosmetic outcome, amount lower blood loss.ConclusionThe current review showed that, ET has a better cosmetic outcome and lower blood loss compared with COT. While COT was associated with significantly low operation time, hospital stay, drainage time, amount of drainage fluid and transient recurrent laryngeal nerve (RLN) palsy

Stress Monitoring Using Machine Learning, IoT and Wearable Sensors

The Internet of Things (IoT) has emerged as a fundamental framework for interconnected device communication, representing a relatively new paradigm and the evolution of the Internet into its next phase. Its significance is pronounced in diverse fields, especially healthcare, where it finds applications in scenarios such as medical service tracking. By analyzing patterns in observed parameters, the anticipation of disease types becomes feasible. Stress monitoring with wearable sensors and the Internet of Things (IoT) is a potential application that can enhance wellness and preventative health management. Healthcare professionals have harnessed robust systems incorporating battery-based wearable technology and wireless communication channels to enable cost-effective healthcare monitoring for various medical conditions. Network-connected sensors, whether within living spaces or worn on the body, accumulate data crucial for evaluating patients' health. The integration of machine learning and cutting-edge technology has sparked research interest in addressing stress levels. Psychological stress significantly impacts a person's physiological parameters. Stress can have negative impacts over time, prompting sometimes costly therapies. Acute stress levels can even constitute a life-threatening risk, especially in people who have previously been diagnosed with borderline personality disorder or schizophrenia. To offer a proactive solution within the realm of smart healthcare, this article introduces a novel machine learning-based system termed "Stress-Track". The device is intended to track a person's stress levels by examining their body temperature, sweat, and motion rate during physical activity. The proposed model achieves an impressive accuracy rate of 99.5%, showcasing its potential impact on stress management and healthcare enhancement