There is considerable interest in using tangible user interfaces (TUIs) to support teaching children programming, but evidence for the benefits is mixed, and their deployment in school environments presents more challenges than graphical user interfaces (GUIs). This study investigates the effect of GUIs and TUIs on learning outcomes, attitudes toward computing, and reported enjoyment in a computer-programming activity with primary-school students aged 6-7 in Saudi Arabia. Forty-two students engaged in a 45minute learning activity using either a TUI or GUI programming environment. The study used a between-groups design, and quantitative data were collected, including pre-test and post-test results, and ratings on attitudinal and enjoyment surveys. Learning gains were significantly higher for the GUI group than the TUI group. However, post-activity increases in reported attitude toward computing were significantly higher for the TUI group. There was no difference in activity enjoyment scores, which were high for both groups. CCS CONCEPTS• Human-centered computing ~Empirical studies in HCI • Applied computing ~Interactive learning environments • Social and professional topics~K-12 education

This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and transfer learning in the context of medical imaging. Medical imaging plays a critical role in the diagnosis and treatment of diseases, and CNN-based models have demonstrated significant improvements in image analysis and classification tasks. Transfer learning, which involves reusing pre-trained CNN models, has also shown promise in addressing challenges related to small datasets and limited computational resources. This paper reviews the advantages of CNN and transfer learning in medical imaging, including improved accuracy, reduced time and resource requirements, and the ability to address class imbalances. It also discusses challenges, such as the need for large and diverse datasets, and the limited interpretability of deep learning models. What factors contribute to the success of these networks? How are they fashioned, exactly? What motivated them to build the structures that they did? Finally, the paper presents current and future research directions and opportunities, including the development of specialized architectures and the exploration of new modalities and applications for medical imaging using CNN and transfer learning techniques. Overall, the paper highlights the significant potential of CNN and transfer learning in the field of medical imaging, while also acknowledging the need for continued research and development to overcome existing challenges and limitations.

The aedes mosquito-borne dengue viruses cause dengue fever, an arboviral disease (DENVs). In 2019, the World Health Organization forecasts a yearly occurrence of infections from 100 million to 400 million, the maximum number of dengue cases ever testified worldwide, prompting WHO to label the virus one of the world’s top ten public health risks. Dengue hemorrhagic fever can progress into dengue shock syndrome, which can be fatal. Dengue hemorrhagic fever can also advance into dengue shock syndrome. To provide accessible and timely supportive care and therapy, it is necessary to have indispensable practical instruments that accurately differentiate Dengue and its subcategories in the early stages of illness development. Dengue fever can be predicted in advance, saving one’s life by warning them to seek proper diagnosis and treatment. Predicting infectious diseases such as dengue is difficult, and most forecast systems are still in their primary stages. In developing dengue predictive models, data from microarrays and RNA-Seq have been used significantly. Bayesian inferences and support vector machine algorithms are two examples of statistical methods that can mine opinions and analyze sentiment from text. In general, these methods are not very strong semantically, and they only work effectively when the text passage inputs are at the level of the page or the paragraph; they are poor miners of sentiment at the level of the sentence or the phrase. In this research, we propose to construct a machine learning method to forecast dengue fever.

Spontaneous gestures produced during mathematics learning have been widely studied, however, research on the role of gesture in computing education is limited. This paper presents an investigation into children's use of spontaneous gestures when learning programming using either a tangible user interface (TUI) or a graphical user interface (GUI). The study explored the relationship between spontaneous gestures, interface type and learning outcomes in a programming lesson for primary school students aged 6-7. In the study, 34 participants engaged in a learning activity lasting approximately 37 minutes, using a TUI or a GUI. The study used a between-subjects design, and mixed methods. Pre-test and post-test data were collected, and sessions were video recorded and subsequently coded and analysed. A video analysis scheme, adapted from mathematics education research, was used to code the spontaneous gestures produced during the learning session. We found a statistically significant difference between the mean learning gains of high-frequency gesturers and low-frequency gesturers, with the top quartile showing significantly greater learning gains. There was no significant difference in the frequency of gestures between interface types. A qualitative analysis of representational gestures showed that some children use spontaneous hand gestures to demonstrate abstract computational concepts, providing evidence for the embodiment of children's offline thinking in the computing domain.

Soft sensors are data-driven devices that allow for estimates of quantities that are either impossible to measure or prohibitively expensive to do so. DL (deep learning) is a relatively new feature representation method for data with complex structures that has a lot of promise for soft sensing of industrial processes. One of the most important aspects of building accurate soft sensors is feature representation. This research proposed novel technique in automation of manufacturing industry where dynamic soft sensors are used in feature representation and classification of the data. Here the input will be data collected from virtual sensors and their automation-based historical data. This data has been pre-processed to recognize the missing value and usual problems like hardware failures, communication errors, incorrect readings, and process working conditions. After this process, feature representation has been done using fuzzy logic-based stacked data-driven auto-encoder (FL_SDDAE). Using the fuzzy rules, the features of input data have been identified with general automation problems. Then, for this represented features, classification process has been carried out using least square error backpropagation neural network (LSEBPNN) in which the mean square error while classification will be minimized with loss function of the data. The experimental results have been carried out for various datasets in automation of manufacturing industry in terms of computational time of 34%, QoS of 64%, RMSE of 41%, MAE of 35%, prediction performance of 94%, and measurement accuracy of 85% by proposed technique.

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Politeness is an essential part of a conversation. Like verbal communication, politeness in textual conversation and social media posts is also stimulating. Therefore, the automatic detection of politeness is a significant and relevant problem. The existing literature generally employs classical machine learning-based models like naive Bayes and Support Vector-based trained models for politeness prediction. This paper exploits the state-of-the-art (SOTA) transformer architecture and transfer learning for respectability prediction. The proposed model employs the strengths of context-incorporating large language models, a feed-forward neural network, and an attention mechanism for representation learning of natural language requests. The trained representation is further classified using a softmax function into polite, impolite, and neutral classes. We evaluate the presented model employing two SOTA pre-trained large language models on two benchmark datasets. Our model outperformed the two SOTA and six baseline models, including two domain-specific transformer-based models using both the BERT and RoBERTa language models. The ablation investigation shows that the exclusion of the feed-forward layer displays the highest impact on the presented model. The analysis reveals the batch size and optimization algorithms as effective parameters affecting the model performance.

There is persistent interest in tangible approaches to supporting young children's learning of programming, but there has been mixed evidence about its benefits, and few studies have investigated the specific features that offer learning benefits in classroom contexts. This research will involve the use of programming blocks that can be instantiated in two contrasting types of interface: a tangible user interface (TUI) and a graphical user interface (GUI). The system will be designed to minimize extraneous differences between the two interfaces in order to isolate the variables of interest. Using a between-subjects design, the study will investigate the impact of interface type on learning and on attitudinal outcomes for children aged 6 to 7. From an embodied interaction perspective, the study will analyse the cognitive advantages of each interface, including identifying how and why each interface type might affect learning outcomes. The study will also investigate children's spontaneous gestures as indicators of understanding. Finally, the research will explore the relationships between interface types, attitudes towards computing, engagement and gender.