Classification of Image and Text Data Using Deep Learning-Based LSTM Model

The advent of social networking and the internet has resulted in a huge shift in how consumers express their loyalty and where firms acquire a reputation. Customers and businesses frequently leave comments, and entrepreneurs do the same. These write-ups may be useful to those with the ability to analyse them. However, analysing textual content without the use of computers and the associated tools is time-consuming and difficult. The goal of Sentiment Analysis (SA) is to discover client feedback, points of view, or complaints that describe the product in a more negative or optimistic light. You can expect this to be a result based on this data if you merely read and assess feedback or examine ratings. There was a time when only the use of standard techniques, such as linear regression and Support Vector Machines (SVM), was effective for the task of automatically discovering knowledge from written explanations, but the older approaches have now been mostly replaced by deep neural networks, and deep learning has gotten the job done. Convolution and compressing RNNs are useful for tasks like machine translation, caption creation, and language modelling, however they suffer from gradient disappearance or explosion issues with large words. This research uses a deep learning RNN for movie review sentiment prediction that is quite comparable to Long Short-Term Memory networks. A LSTM model was well suited for modelling long sequential data. Generally, sentence vectorization approaches are used to overcome the inconsistency of sentence form. We made an attempt to look into the effect of hyper parameters like dropout of layers, activation functions and we also tested the model with different neural network settings and showed results that have been presented in the various ways to take the data into account. IMDB is the official movie database which serves as the basis for all of the experimental studies in the proposed model.

Beastly Condemnation: The Representation of Oldenbarnevelt’s Twenty-Four Judges as Animals

The Rijksmuseum’s History Department holds a remarkable early eighteenthcentury album titled Regtspleging van Oldenbarnevelt (The Trial of Oldenbarnevelt). The album contains a collection of thirty-eight watercolour drawings on parchment with written explanations on paper and deals with the infamous trial of the Land’s Advocate. At its heart are cartoons of the twenty-four judges who signed Oldenbarnevelt’s death warrant, with the judges depicted as animals. The Rijksmuseum album is similar to albums in the National Library of the Netherlands and Rotterdam City Archives. In this article we show that Oldenbarnevelt’s judges continued to be subjects of general interest for more than a century. We locate the satirical portrayal of the judges as animals in the broader tradition of animal allegories used as a vehicle for political criticism, and explore the function of the album. It probably served as a key to a painting – not Cornelis Saftleven’s famous work Satire op de berechting van Johan van Oldenbarnevelt (Satire of the Trial of Johan van Oldenbarnevelt) in the Rijksmuseum, but a later composition by an anonymous artist now in the Six Collection. Finally, we come to the conclusion that the album is part of a game of concealment and revelation that is typical of the Remonstrants’ memorial culture.

Automate Identification and Recognition of Handwritten Text from an Image

Handwritten text acknowledgment is yet an open examination issue in the area of Optical Character Recognition (OCR). This paper proposes a productive methodology towards the advancement of handwritten text acknowledgment frameworks. The primary goal of this task is to create AI calculation to empower element and information extraction from records with manually written explanations, with an, expect to distinguish transcribed words on a picture. The main aim of this project is to extract text, this text can be handwritten text or it can machine printed text and convert it into computer understandable or wNe can say computer editable format. To implement thais project we have used PyTesseract which is an open-sourcemOCR engine used to recognize handwritten text and OpenCV a library in python used to solve computer vision problems. So the input image is executed in various steps, first there is pre-processing of an image then there is text localization after that there is character segmentation and character recognition and finally we have post-processing of image. Further image processingalgorithms can also be used to deal with the multiple characters input in a single image, tilt image, or rotated image. The prepared framework gives a normal precision of more than 95 % with the concealed test picture.

Blended Learning - The Efficiency of Video Resources and YouTube in the Modern Dental Education

The media-enhanced teaching techniques became popular in the last years because they stimulate the students' engagement and motivation. The educational movies uploaded on YouTube became nowadays a highly used resource for learning in the academic environment. We conducted an opinion survey among the students at the Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, Romania, about their agreement and interest in using educational movies as didactic tools, compared with the traditional teaching methods, and about the features they prefer in such materials. The study group included 170 students with average age 20.93 ± 2.751. Most students (91.2%) use didactic movies in their practical training; 77.6% think that the didactic movies from YouTube contain correct scientific information. 74.6% of the students like to learn using didactic movies, 39.7% of them appreciate the oral speech accompanied by PowerPoint Presentations and 46.6% like the oral speech accompanied by written explanations, with no significant differences between genders and age groups. The most popular features of the educational movies are: human narrator (94.7%), who speaks in Romanian (82.4%). The narrator’s speech must be accompanied by animated schemes with explanations (90.6%) and drawings (93.5%). The movie must have a clear structure, evolving from basic to complex (85.9%). The educational movies uploaded on YouTube are an adequate source of information for the dental students; the professionals in universities must survey better the scientific quality of such materials.

What Can Student-Generated Animations Tell Us About Students’ Conceptions of Evolution?

Background A large body of research investigate student´s conceptions and emphasizes that students have alternative conceptions about causes of evolutionary changes. The conventional way to monitor students’ conceptions are through inventories where researchers analyze their written answers. However, textbooks are being increasingly complemented with, or even replaced by, various multimedia materials and multiple modes are used to communicate evolutionary processes. This has profound implications for students’ learning, and the test format may influence which knowledge they present. The goal of this exploratory study is therefore to expand the understanding of students’ conceptions of evolution through natural selection by applying student-generated stop-motion animations to disclose students’ conceptions. Forty-seven Swedish upper secondary school students generated eighteen animations concerning evolution through natural selection. We analysed the animations qualitatively using content analysis recording key concepts, alternative conceptions and connections in organizational levels and time. This analysis was related to the analysis of the students written explanations of a case of evolutionary change. Results Our study highlights some of the benefits and limitations of using these two assessment forms. Concerning alternative concepts, a clear difference between the results of the two methods of assessment was observed. In particular, the alternative conception essentialism was show to a lesser extent in the student’s animations than in their written responses, while natural selection as an event became more prevalent. ConclusionsThese findings support the view that students’ expression of different misconceptions is influenced by the context and representational form. The work also reveals that generating stop-motion animations to explain scientific concepts is an engaging approach that stimulates students to explore their understanding in a creative and personal manner, which potentially is positive for engagement and learning. The potential for complementing standard paper and pen tests with tasks that encompass stop-motion animations is discussed further.

Consumption Patterns under a Universal Basic Income

In this paper, we challenge one of the criticisms against the idea of a universal basic income (UBI), namely, that people will waste the support on high-end consumption. We rely on the literature from various disciplines from which we developed high- and low-UBI scenarios for respondents to decide what they would do if the state were to provide an unconditional stipend. We analyzed the multiple-choice responses, using an ordered probit, and the written explanations of the respondents’ choices, using content analysis. The results suggest that while a higher UBI would increase consumption, it would likely be done responsibly. People with low incomes showed restraint in all categories. The qualitative analysis captures some of the complexities of people’s socioeconomic circumstances that support the notion of responsible consumption. The policy implication is that a UBI could be introduced at a low level and gradually increased to a level that maximizes societal benefits.

Student-generated PowerPoint animations: a study of student teachers’ conceptions of molecular motions through their expressed models

Student-generated drawings are known to be effective in building and revealing students’ conceptions of chemistry. Some chemistry concepts, moreover, include changes and processes that cannot be merely represented by static drawings. Computer-based animations are needed to represent the dynamics. In this study, 25 chemistry student teachers, who had studied the concept of molecular motions and had taken the course of Chemistry Instructional Media and Technology, were assigned to make expressed models of water molecules’ motions in the form of animations with PowerPoint, the most familiar program and installed on students’ computers. Students were also assigned to give written explanations of the three molecular motions. Within one month, both tasks were due simultaneously. Students’ expressed models were analysed based on Custom Animation features used for the animations, while students’ written explanations were analysed based on the typology of the sentences. It was found that all students appeared to hold correct conceptions of translation; many students appeared to hold correct conceptions of rotation; and almost all students appeared to hold misconceptions of vibration. There was no substantial difference between PowerPoint Animations and written explanations in revealing students’ conceptions of molecular motions. However, there were several inconsistencies of students’ conceptions that occurred in both tasks. For example, several students who incorrectly explained rotation as circular movements displayed a spinning of the particle on its own axis in the animation. Students’ expressed models in PowerPoint Animations provided other information unrevealed in their written explanations. These pieces of information included types of molecular motion in different phases, simultaneous motions, and deflections of molecules after collisions. The analysis of students’ expressed models in PowerPoint Animations can be an effective approach to reveal students’ conceptions of molecular dynamics if accompanied by adequate tutorials on the animation program, clear instructions, and guidance to get learning resources.

The Roles of Different Design Techniques in Learning Tactical Scenes of Play through Dynamic Visualizations: A Brief Review

Dynamic visualizations have been developed to exchange information that transforms over time across a broad range of professional and academic contexts. However, these visual tools may impose substantial demands on the learner’s cognitive resources that are very limited in current knowledge. Cognitive load theory has been used to improve learning from dynamic visualizations by providing certain design techniques to manage learner cognitive load without adding any oral/written explanations. This systematic review examined a series of experimental studies assessing the roles of these design techniques in learning tactical scenes of play through dynamic visualizations. Electronic databases PubMed and Google Scholar were used to search relevant articles. Eleven studies were eventually included for the systematic review based on the eligibility criteria. The present review revealed that adapting design techniques to the level of learners’ expertise, type of depicted knowledge, and level of content complexity is a crucial part of effective learning.

Sociohydrologic Systems Thinking: An Analysis of Undergraduate Students’ Operationalization and Modeling of Coupled Human-Water Systems

One of the keys to science and environmental literacy is systems thinking. Learning how to think about the interactions between systems, the far-reaching effects of a system, and the dynamic nature of systems are all critical outcomes of science learning. However, students need support to develop systems thinking skills in undergraduate geoscience classrooms. While systems thinking-focused instruction has the potential to benefit student learning, gaps exist in our understanding of students’ use of systems thinking to operationalize and model SHS, as well as their metacognitive evaluation of systems thinking. To address this need, we have designed, implemented, refined, and studied an introductory-level, interdisciplinary course focused on coupled human-water, or sociohydrologic, systems. Data for this study comes from three consecutive iterations of the course and involves student models and explanations for a socio-hydrologic issue (n = 163). To analyze this data, we counted themed features of the drawn models and applied an operationalization rubric to the written responses. Analyses of the written explanations reveal statistically-significant differences between underlying categories of systems thinking (F(5, 768) = 401.6, p < 0.05). Students were best able to operationalize their systems thinking about problem identification (M = 2.22, SD = 0.73) as compared to unintended consequences (M = 1.43, SD = 1.11). Student-generated systems thinking models revealed statistically significant differences between system components, patterns, and mechanisms, F(2, 132) = 3.06, p < 0.05. Students focused most strongly on system components (M = 13.54, SD = 7.15) as compared to related processes or mechanisms. Qualitative data demonstrated three types of model limitation including scope/scale, temporal, and specific components/mechanisms/patterns excluded. These findings have implications for supporting systems thinking in undergraduate geoscience classrooms, as well as insight into links between these two skills.