Online-Journals.org (International Association of Online Engineering)
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Enhancing Fake News Detection via Stance Analysis: Leveraging Advanced NLP Techniques and Machine Learning Models
Get PDFFake news detection is still a field of research that is in its infancy, and this is clearly evident as it has only recently gained significant attention from society. The use of machine learning algorithms and natural language processing (NLP) techniques offers valuable problem-solving opportunities to address these complex challenges. This study explores stance detection as a method to identify misinformation by examining the connection between article headlines and their corresponding body text. Utilizing the FNC-1 and FARN datasets, we apply advanced NLP methods and machine learning (ML) models, including logistic regression, XGBoost, and DistilBERT. Key preprocessing techniques such as lemmatization, named entity recognition (NER), sentiment analysis, and semantic similarity are employed to capture both linguistic and contextual features. The experimental results show that transformer-based models such as DistilBERT achieve superior performance compared to traditional approaches, particularly in accurately classifying nuanced stances. These findings highlight the crucial role of context-aware models in improving the accuracy of misinformation detection and demonstrate their potential for scalable, real-world applications
Making Museum Collections Culturally Accessible: A Training Activity for Professionals at the National Roman Museum
Get PDFThis study was conducted as part of an ongoing Ph.D. project at the National Roman Museum (MNR) in Rome, Italy. The project focuses on promoting mental well-being, social inclusion, and active citizenship for museum visitors, particularly those at risk of marginalization. This contribution aimed to equip MNR personnel with tools to provide inclusive educational activities that are easily applicable to every visit. The study assessed the impact of specific training activities on museum professionals’ well-being, 4 C’s skills (Communication, Collaboration, Critical Thinking, Creativity), and purpose at work. Museum professionals participated in tours of the permanent collections and were demonstrated how to use Visual Thinking Strategies (VTS), an inclusive teaching and learning methodology. The collected data was analyzed using descriptive statistics and content analysis. The study provides insights into potential ways of enhancing the professional role of museum staff and improving the educational offer of the museum
The Design of Emotionally Resonant Learning Experiences: Prospect-Refuge, Framing, and Friction
Get PDFAs learning professionals, we often neglect the emotional aspects of the learning process, especially for adults and even more particularly in the context of corporate learning and performance. Anxiety, uncertainty, and vulnerability can become stumbling blocks as learners work through the transition from who they are to who they are in the process of becoming. The principles of prospect-refuge, framing, and friction – applied in fields such as architecture, urban design, and visual art – can provide powerful insight into the design of meaningful, supportive, and emotionally resonant learning experiences
Smart Defense: Harnessing Hybrid Deep Learning Models for Resilient IoT Intrusion Detection
Get PDFInternet of Things (IoT) networks have transformed various industries by enabling seamless connectivity and automation, yet they also pose significant security challenges. Traditional intrusion detection systems (IDS) struggle to protect these complex and diverse networks due to the vast variability in IoT devices, protocols, and communication patterns. This study explores the integration of deep learning (DL) and adversarial techniques to enhance IDS performance for IoT network security. We propose a DL-based IDS framework utilizing hybrid models, including convolutional neural network and long-short term memory (CNN-LSTM), bidirectional LSTM (B-LSTM), and bidirectional GRU (B-GRU). Experiments on the ToN-IoT dataset achieved accuracy levels exceeding 98% in non-adversarial scenarios. Among the models, B-LSTM exhibited outstanding resilience to adversarial attacks, such as FGSM, PGD, and Deep Fool, demonstrating its suitability for real-world IoT network security applications. This study highlights the need for robust IDS models to secure IoT networks effectively and emphasizes the importance of rigorous testing against adversarial threats, even when high accuracy is achieved
Enhancing Lung Cancer Detection in CT Imaging through Wavelet Multi-Layer Perceptron and Dragonfly Algorithm Optimization
Get PDFGlobally, lung cancer continues to be the primary cause of cancer-related mortality. Reducing the death rates associated with this dangerous illness requires prompt, precise diagnosis and efficient treatment. An enhanced deep learning (DL) framework for lung cancer classification utilizing computed tomography (CT) scan images is presented in this paper. A multi-layer perceptron (MLP) is used for classification after a variety of picture preparation techniques, including wavelet transformations and Canny edge detection, are used to improve feature extraction. Additionally, the dragonfly algorithm (DA) is used to increase the optimization. This approach’s remarkable 98.6% accuracy rate shows how reliable and successful it is in identifying lung cancer
Edge Computing and Blockchain-Based Data Security in IoMT
Get PDFThe Internet of Medical Things (IoMT), also known as healthcare IoT, consists of interconnected medical devices and applications that enable remote monitoring of patients with chronic conditions. In existing healthcare systems, data from IoMT devices is stored in the cloud for analysis. However, major challenges include ensuring data privacy and prioritising critical health information. Rapid processing and transmission of emergency health data to hospitals are crucial for timely care, while strict privacy measures are necessary to prevent risks like data breaches, fraud, and unauthorised access to medical services. To overcome these challenges, the proposed system implements Ethereum blockchain technology and an edge AI classification algorithm on data collected in real-time. Edge computing enables instant analysis, classification, and prioritisation of health data, minimising latency and facilitating quick decision-making. Simultaneously, blockchain technology ensures robust data privacy through a secure access control mechanism. Patient information is securely stored on the blockchain and accessed via an Aadhaar card number and unique tokens. These tokens enable role-based access control, allowing authorised individuals— like doctors, nurses, patients, and relatives—to view, update, or delete specific records as needed
The Engineer as a Teacher: Professional Teaching Identity of Engineers in Spain
Get PDFThe professional teaching identity of engineers who teach has been scarcely studied. This study employed a narrative approach to analyze the teaching identity of 17 engineers across three groups: those who train future engineers, those who prepare future science, technology, engineering, and mathematics (STEM) teachers, and those who are training to become STEM educators. Three dimensions of teaching identity were examined: personal, professional, and situational. Findings indicate that university professors construct their identity based on their personal trajectories, whereas future STEM educators emphasize their individual characteristics as key to their performance. Additionally, engineers who teach recognize the need for specific pedagogical training, yet they often rely on peer learning due to limited institutional support. Moreover, among those training to become teachers, professional experience in engineering is valued more highly than teaching practice. These results highlight the significance of context, career trajectory, and institutional support in shaping the teaching identity of engineers
Integration of Cybersecurity into Computer Science Teachers’ Training: A Systematic Review
Get PDFThis paper addresses the integration of cybersecurity into the training programmes of computer science teachers in higher education institutions. Given the growing digital threats such as phishing, malware, and data leakages, attaining cybersecurity knowledge and skills becomes critical for educators. Based on a systematic review of 51 empirical studies selected from databases such as Scopus, Web of Science and Springer Link, different approaches to integrating cybersecurity into educational programmes and the results of such interventions are examined. This study shows that the systematic integration of cybersecurity topics and the use of interdisciplinary methods and tailored programmes contribute to the development of professional competencies, critical thinking and ethical skills. Key challenges include different levels of technological readiness among teachers and the need to regularly update curricula in response to rapidly changing cyber threats. The study also identifies the lack of practice-orientated training and specialised courses as significant barriers to effective integration. The results emphasise the importance of improving curricula to enhance the effectiveness of cybersecurity education and adapt to the requirements of today’s digital environment
Comparing Students’ Perspectives on Online Learning during the COVID-19 Pandemic: A Cross-Cultural Study of Undergraduate Students in the U.S. and Saudi Arabia
Get PDFThe global educational landscape has grappled with unprecedented challenges amid the COVID-19 pandemic, prompting a seismic shift from traditional instruction to ubiquitous online learning. This study, employing an exploratory mixed-methods approach with 890 participants from Saudi and United States (U.S.) higher education institutions, scrutinizes and compares the perspectives of undergraduate students on online learning during the pandemic. Multiple regression analyses reveal divergent associations between U.S. and Saudi participants, highlighting varied attributes such as interactivity, learning environment, time management, cost, flexibility, and resource accessibility. Despite these differences, commonalities emerge in participants’ views on teacher roles, learning quality, academic success, and social interaction in the online milieu. Thematic analysis uncovers challenges related to the swift transition, including resource inadequacies and perceptions of online teaching quality. Nevertheless, positive aspects of online learning, such as cost-effectiveness and flexibility, are emphasized, underscoring its growing importance in higher education amid the pandemic’s challenges
The Use of ChatGPT as a Learning Tool Among Jordanian Students in Technical Universities: Attitudes and Perceptions
Get PDFThe rapid advancement of technology, particularly with the emergence of artificial intelligence (AI) language models such as Chat Generative Pre-Trained Transformer (ChatGPT), has significantly transformed various domains, especially in education. While ChatGPT has revolutionized complex task performance by enabling personalized learning and automated assessments, its application raises significant concerns about academic integrity. This study investigates the attitudes and perspectives of undergraduate students at Al Hussein Technical University (HTU) toward using ChatGPT in educational settings and the challenges they encounter. Data were collected through a questionnaire administered to 110 undergraduate students, comprising 15 items rated on a three-point Likert scale. The study’s findings indicate that 66% of students are aware of the use of ChatGPT, with 56% expressing comfort in its use, indicating a substantial integration of this technology within educational settings. However, concerns about its reliability persist, with only 43% affirming its accuracy and 26% expressing disagreement. Nonetheless, 67.5% recognize its efficiency, time-saving benefits, and potential to enhance learning. While ChatGPT is useful for brainstorming and idea generation, 79% of students reported hesitance to use it for at-home assessments, indicating persistent apprehensions related to academic integrity. The findings suggest that integrating ChatGPT in educational settings requires addressing reliability and ethical issues through targeted training and clear guidelines. This study advocates incorporating ChatGPT into the HTU curriculum as a complementary resource, balanced with human intellectual engagement, to optimize academic performance and student learning