Online-Journals.org (International Association of Online Engineering)
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ScanSavant: Malware Detection for Android Applications with Explainable AI
Get PDFMobile devices face SQL injection, malware, and web-based threats. Current solutions lack real-time detection. This paper introduces an Android app with advanced algorithms for real-time threat scanning. During testing, our application detected 94% of SQL injection attempts, outperforming the 86% average detection rate in similar studies. For malware analysis, it achieved a 97% detection accuracy on a dataset of infected files, higher than the industry standard of 93%. Additionally, our app can detect 85 malware variants and assign 15 attributes (Trojan.Gen.8, Worm.Autorun, Adware.Elex, Spyware.Zbot, Ransom.Cryptolocker, Rootkit.ZeroAccess, Exploit.CVE-2017-0143, Virus.MSIL.CoinMiner, Trojan.Emotet, Backdoor. DarkComet, PUP.Optional.Conduit, Adware.MyWebSearch, Virus.Win32.Sality, Trojan.Win32. Necurs, and Ransom.WannaCry) to some malwares, providing detailed analysis for better threat management. The application effectively scans both EXE and APK files, ensuring comprehensive protection. When assessing website links, the application identified security risks with 96% accuracy, demonstrating its capability in managing web-based threats. This app detects SQL injections, analyses malware, and assesses website security, bolstering cyber defence with user-friendly features and top-notch threat mitigation
Deciphering Patterns in Student Emotional Fluctuations: A Big Data Approach in Educational Psychology
Get PDFWith the rapid advancement of big data and information technology, the analysis of student emotional fluctuations has emerged as a new frontier in the field of educational psychology. This study aims to explore the patterns of student emotional fluctuations through big data analysis techniques and to use these patterns to predict students’ psychological tendencies, thereby providing educators with real-time, accurate teaching references. The importance of student emotions in the learning process and the potential of big data technology in perceiving and analyzing student emotions are elucidated in the background section. The current state of study discusses the limitations of traditional methods in analyzing student emotions, such as small sample sizes, short data collection time spans, and the lack of timeliness and accuracy in analysis. A student emotional fluctuation identification model based on big data is first established, capable of integrating multi-source data and effectively capturing subtle changes in student emotions. Furthermore, a psychological tendency mixed-frequency prediction model is constructed, utilizing the mixed data sampling (MIDAS) mixed-frequency model, aimed at achieving accurate predictions of trends in student emotional fluctuations. The development and validation of these two models demonstrate the application value of big data analysis in the field of educational psychology, supporting personalized learning and promoting the effectiveness of student emotional management and teaching interventions
Inquiring Students’ Alternative Conceptions about Floating and Sinking Objects
Get PDFThe aim of this study is to determine the misconceptions of Greek junior high school students regarding the floating and sinking of a body through the concept of density. Density is included in most international curricula for this age group and is revisited in Greek junior high schools after being introduced in primary school. After interviews with teachers who teach the physics course, in order to discover the students’ way of thinking and their common misconceptions, the researchers of this study created targeted questionnaires for students aged 11–12 years old. During the 2022–23 school year, the questionnaires were handed out to 47 first-grade students at a junior high school in Athens, Greece. Before being administered to the students, the questionnaires were subjected to a content validity test by five physics experts according to Aiken’s V index. Then, they were completed by the students before the lesson was taught. After the students had completed the initial questionnaire, a teaching proposal focused on the density-based approach was presented to them. Subsequently, the students filled out the same questionnaire again following the instructional session. Statistical analysis demonstrated a notable enhancement in the comprehension of the misconceptions addressed in this study, with the rates of improvement varying between 18.08% and 52.13%. Consequently, the instructional proposal proved to be instrumental in advancing students’ conceptual understanding of floating and sinking within the framework of density interpretation
Earthquake Footprints for Predicting Events
Get PDFThis paper considers the problem of predicting earthquakes. It uses a small amount of information to create a descriptive key that can be used as a footprint to describe an event. A frequency grid clusters events that occurred at the same time and then the algorithm averages the history of these events over preceding days, in particular the gaps when the events did not occur. The gaps are measured for the clustered events only and can be used to create a description that is quite unique. Results suggest that seismic events can in fact be traced using this key and subsequently recognised again, if the same conditions reoccur. They also suggest that force direction may be more important than magnitude, for this type of earthquake. Greek and USA datasets have been looked at and the prediction accuracy can be 70% or better. The author therefore suggests that this is an interesting method that deserves attention
The Development of Teacher Training Curriculum for Organizing of Learning Experiences to Enhance Social Development for Persons with Autism Spectrum Disorder
Get PDFThe present study sought to (1) assess the needs in the development of the teacher training curriculum on providing learning experiences for promoting social development among persons with autism spectrum disorder (ASD), (2) develop the curriculum, and (3) examine its effectiveness. A mixed-method approach and research and development were adopted. The data were collected during July 2022–April 2023. The results are reported: 1. A review of documents, literature and related studies showed that teacher preparation was required for promoting persons with ASD’s social development. Additionally, the survey indicated that the school directors’ primary need for self-improvement was the frequency of attending training programs (PNIModified = 0.54), while that of the teachers was knowledge and understanding of promoting social development (PNIModified = 0.21). Also, the school directors’ strongest need in providing learning experiences was continuous promotion of social development (PNIModified = 0.47), and that of the teachers was implementation of learning activities based on lessons plans (PNIModified = 0.21). 2. The developed curriculum comprised eight components: rationale, objectives of the curriculum, contents, training activities and methods, training materials, durations, venues, and measurement and evaluation. Concerning its quality, the curriculum achieved a high level of suitability (x = 4.38, S.D. = 0.16). 3. After the curriculum implementation, the teachers achieved higher post-training scores (x = 22) than their pre-training scores (x = 17.20) and were most satisfied (x = 4.71, S.D. = 0.08). Provision of learning experiences achieved a mean score of 84.67%
Improving Data Delivery in Unreliable Networks Using Network Coding and Ant-Colony Optimization
Get PDFWireless Sensor Networks (WSNs) comprise interconnected wireless nodes that receive and transmit data across applications and platforms. This paper addressed the problem of link failures in WSNs that potentially could lead to the loss of data packets while still in transit. This was achieved through the use of network coding which is known to address capacity bottleneck problems in WSNs. In particular, a technique called Ant Agent-Assisted Network Coding (AAANC) is proposed that employs the ant colony optimization technique in addition to network coding operations. The main aim of AAANC is to facilitate the successful delivery and decoding of coded data packets in the presence of link failures. AAANC employs a packet route selection technique that is inspired by the social behavior of natural ants. For natural ants, a strong pheromone trail along a path indicates a promising route to a food source, and this is analogous to a reliable communication link for routing data packets in this paper. Through simulations, AAANC was compared to diagonal pseudorandom network coding (DNC) and triangular pseudorandom network coding (TNC), and it proved to have a superior performance in terms of packet delivery ratio and number of decoded packets. Significant performance gain can be achieved if AAANC algorithm is made to dynamically adapt the ant colony and network coding parameters in response to traffic changes
The Development of Problem-Based Mobile Augmented Reality Application to Enhance Creative Problem-Solving Skills for Undergraduate Students
Get PDFThis study aims to develop a problem-based mobile augmented reality (AR) application to enhance creative problem-solving skills among undergraduate students. The research involves an experiment conducted on a sample group of 30 undergraduate students enrolled in the maintenance of computers and audio-visual equipment course, selected through a simple random sampling method. The instruments used in the study included structured interview questions, needs assessment questionnaires, quality evaluation of mobile applications, evaluation of the learning plan, measurement of creative problem-solving ability, and satisfaction surveys. The data obtained were analyzed using mean, standard deviation, dependent t-test, and effect size statistics. The findings demonstrated that the developed mobile application achieved the highest quality level, with an average value of 4.62 (SD = 0.64). The mobile application efficiency reached 75.48 out of 75.16, meeting the established threshold of 75 out of 75. Using the mobile application led to statistically significant improvements in creative problem-solving skills after the learning process, with scores higher than those before learning at the 0.05 level. The effect size was 6.61, indicating a large impact. Additionally, student satisfaction with the mobile application was reported as the highest
Big Data Analytics in Higher Education: A New Adaptive Learning Analytics Model Integrating Traditional Approaches
Get PDFDespite the explosion of interest in adaptive learning and learning analytics (LA) for higher education (HE), there has been relatively little research integrating educational approaches’ indicators to build an adaptive learning analytics model. Adaptive learning analytics (ALA) models have grown in favor of HE due to their claims of enhancing student learning outcomes, providing personalized learning paths, and allowing students to interact with course material at their own pace. With focus on using data to personalize the learning experience and the environment in which the experience of learning occurs, LA centers on enhancing education through meticulous data analysis, while big data (BD) in education addresses the overarching challenges and opportunities arising from extensive and varied datasets. These concepts are interconnected, where LA and ALA specifically apply BD principles within the educational context. In this paper, we explain some BD concepts used in HE, define the essential perceptions related to LA, and analyze educational approaches to define the fundamental implications. Besides, we try to connect some LA model with educational approaches based on the big educational data collected in order to establish an efficient educational model. We include all steps cited before we try to build an ALA model in HE that resolves the limitations of the oldest models, thus improving the learner’s learning process by adding and treating additional indicators
Dual Spectral Attention Model for Iris Presentation Attack Detection
Get PDFThe widespread use of iris recognition systems has led to a growing demand for enhanced security measures to counter potential iris presentation attacks, also known as anti-spoofing. To enhance the security and reliability of iris recognition systems, researchers have developed numerous methods for detecting presentation attacks. Most of these methods lack precision in detecting unknown attacks compared to known attacks. In addition, most literature on iris presentation attack detection (PAD) systems utilizes near-infrared (NIR) samples as inputs. These samples produce superior-quality and robust images with less reflection in the cornea of the eye. Despite this, due to the widespread use of smartphones and the necessity for unsupervised identity verification, visible-light samples play a crucial role in detecting presentation attacks. These samples can be easily captured using smartphone cameras. In this paper, a dual-spectral attention model has been developed to train a unified model for multiple real-world attack scenarios. Two different scenarios were tested. In the first scenario, the model was trained as a one-class anomaly detection (AD) approach, while in the second scenario, it was trained as a normal two-class detection approach. This model achieved the best result for the attack presentation classification error rate (APCER) of 4.87% in a one-class AD scenario when tested on the attack dataset, outperforming most studies on the same test dataset. These experimental results suggest that future research opportunities in areas such as working with visible light images, using an AD approach, and focusing on uncontrolled environment samples and synthetic iris images may improve iris detection accuracy
Effectiveness of Mobile Virtual Laboratory Based on Project-Based Learning to Build Constructivism Thinking
Get PDFConstructivism as a theoretical basis in education is key, considering that this approach views learning as an active process where students play a role in constructing their own knowledge. Apart from that, the development of mobile and virtual technology is increasingly growing in the educational environment. This research explores the effectiveness of this approach in building constructivist thinking in learning electrical installation practices. The method used in this research is the 4D Define, Design, Develop and Disseminate model. The research instruments include an expert review validity questionnaire and an evaluation instrument for students’ perceptions of the effectiveness of the Mobile Virtual Laboratory Based on Project-Based Learning in building constructivism. The research results show that the Mobile Virtual Laboratory is valid based on expert assessment, in addition there has been a significant increase in students’ understanding of concepts and practical skills, with a high level of satisfaction with the use of the Mobile Virtual Laboratory. So, it can be concluded that the Mobile Virtual Laboratory based on Project-Based Learning can be an effective tool to support constructivist learning in electrical measurement practice. The results of this research open opportunities for further research on how Artificial Intelligence can build constructive thinking