Search results for: style classification
2203 How Best Mentors mentor: A Metadiscursive Study of Mentoring Styles in Teacher Education
Authors: Cissy Li
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Mentorship is a commonly used strategy for career development that has obvious benefits for students in undergraduate pre-service teacher training programs. In contrast to teaching practicum, which generally involves pedagogical supervision and performance evaluation by teachers, mentorship is more focused on sharing experiences, supporting challenges, and nurturing skills in order to promote personal and professional growth. To empower pre-service teachers and prepare them for potential challenges in the context of local English language teaching (ELT), an alumni mentoring program was established in the framework of communities of practice (CoP), with the mentors being in-service graduates working in local schools and mentees being students on the teacher-training programme in a Hong Kong university. By triangulating audio transcripts of mentoring sessions delivered by three top mentors with data from questionnaire responses and mentor logs, this paper examines the mentoring styles of the three best mentors from the metadiscursive perspective. It was found that, in a community of practice, mentors who may seem to enjoy a relative more dominant position, in fact, had to strategically and pragmatically employ metadiscursive resources to manage relationships with the mentees and organize talks in the mentoring process. Other attributing factors for a successful mentoring session include mentor personality and prior mentorship experiences, nature of the activities in the session, and group dynamics. This paper concludes that it is the combination of all the factors that constitute a particular mentoring style. The findings have implications for mentoring programs in teacher preparation.Keywords: mentoring, teacher education, mentoring style, metadiscourse
Procedia PDF Downloads 872202 Information and Communication Technology (ICT) Education Improvement for Enhancing Learning Performance and Social Equality
Authors: Heichia Wang, Yalan Chao
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Social inequality is a persistent problem. One of the ways to solve this problem is through education. At present, vulnerable groups are often less geographically accessible to educational resources. However, compared with educational resources, communication equipment is easier for vulnerable groups. Now that information and communication technology (ICT) has entered the field of education, today we can accept the convenience that ICT provides in education, and the mobility that it brings makes learning independent of time and place. With mobile learning, teachers and students can start discussions in an online chat room without the limitations of time or place. However, because liquidity learning is quite convenient, people tend to solve problems in short online texts with lack of detailed information in a lack of convenient online environment to express ideas. Therefore, the ICT education environment may cause misunderstanding between teachers and students. Therefore, in order to better understand each other's views between teachers and students, this study aims to clarify the essays of the analysts and classify the students into several types of learning questions to clarify the views of teachers and students. In addition, this study attempts to extend the description of possible omissions in short texts by using external resources prior to classification. In short, by applying a short text classification, this study can point out each student's learning problems and inform the instructor where the main focus of the future course is, thus improving the ICT education environment. In order to achieve the goals, this research uses convolutional neural network (CNN) method to analyze short discussion content between teachers and students in an ICT education environment. Divide students into several main types of learning problem groups to facilitate answering student problems. In addition, this study will further cluster sub-categories of each major learning type to indicate specific problems for each student. Unlike most neural network programs, this study attempts to extend short texts with external resources before classifying them to improve classification performance. In short, by applying the classification of short texts, we can point out the learning problems of each student and inform the instructors where the main focus of future courses will improve the ICT education environment. The data of the empirical process will be used to pre-process the chat records between teachers and students and the course materials. An action system will be set up to compare the most similar parts of the teaching material with each student's chat history to improve future classification performance. Later, the function of short text classification uses CNN to classify rich chat records into several major learning problems based on theory-driven titles. By applying these modules, this research hopes to clarify the main learning problems of students and inform teachers that they should focus on future teaching.Keywords: ICT education improvement, social equality, short text analysis, convolutional neural network
Procedia PDF Downloads 1182201 The Impact of Social Media to Indonesian Muslim Fashion Trend
Authors: Siti Dewi Aisyah
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Islamic Muslim fashion has become a trend in Indonesia. It is said that social media has a huge impact on its development. Indonesia is ranked among the most users of social media. That is why people who wear hijab also use social media for different purposes, one of this is to introduce hijab fashion. Consequently, they are becoming famous in social media. Social media has become a tool for communicating their beliefs as a Muslim as well as personal branding as a good hijabi yet with a fashionable style. This research will examine the social media such as Blog and Instagram, how it triggers the consumer culture to hijabi, what is the actual meaning behind of their feed posts in their social media, how they produce good photograph in their social media and for what reason they use social media. This research had been conducted through in-depth interviews with several bloggers who created Hijabers Community who have made a new trend in Muslim fashion and also Instagrammers who made their feeds as a style inspiration. The methodology used for this research is by analyzing Blog and Instagram through visual analysis that also examines the semiotic meaning behind the photographs that are posted by the people on the social media especially about the Islamic Modest Fashion trend. The theoretical framework for this research is about studying social media that is examined through visual analysis. The Muslim fashion trend was lead by several bloggers and continued to Instagram which then created a consumption pattern. From colourful colors, pastel colors, monochrome colors to neutral coffee tone colors, it was influenced by the Muslim fashion designers that also become digital influencers in social media. It was concluded that social media had been a powerful promotional and effective tool to change the trend in Indonesian Muslim Fashion trend.Keywords: blog, instagram, consumer culture, muslim fashion, social media, visual analysis
Procedia PDF Downloads 3602200 Effectiveness of Cognitive and Supportive-Expressive Group Therapies on Self-Efficiency and Life Style in MS Patients
Authors: Kamran Yazdanbakhsh, Somayeh Mahmoudi
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Multiple sclerosis is the most common chronic disease of the central nervous system associated with demyelination of neurons and several demyelinated parts of the disease encompasses throughout the white matter and affects the sensory and motor function. This study compared the effectiveness of two methods of cognitive therapy and supportive-expressive therapy on the efficacy and quality of life in MS patients. This is an experimental project which has used developed group pretest - posttest and follow-up with 3 groups. The study included all patients with multiple sclerosis in 2013 that were members of the MS Society of Iran in Tehran. The sample included 45 patients with MS that were selected volunteerily of members of the MS society of Iran and randomly divided into three groups and pretest, posttest, and follow-up (three months) for the three groups had been done.The dimensions of quality of life in patients with multiple sclerosis scale, and general self-efficiency scale of Schwarzer and Jerusalem was used for collecting data. The results showed that there was a significant difference between the mean of quality of life scores at pretest, posttest, and follow-up of the experimental groups. There was no significant difference between the mean of quality of life of the experimental groups which means that both groups were effective and had the same effect. There was no significant difference between the mean of self-efficiency scores in control and experimental group in pretest, posttest and follow-up. Thus, by using cognitive and supportive-expressive group therapy we can improve quality of life in MS patients and make great strides in their mental health.Keywords: cognitive group therapy, life style, MS, self-efficiency, supportive-expressive group therapy
Procedia PDF Downloads 4782199 Probabilistic Crash Prediction and Prevention of Vehicle Crash
Authors: Lavanya Annadi, Fahimeh Jafari
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Transportation brings immense benefits to society, but it also has its costs. Costs include such as the cost of infrastructure, personnel and equipment, but also the loss of life and property in traffic accidents on the road, delays in travel due to traffic congestion and various indirect costs in terms of air transport. More research has been done to identify the various factors that affect road accidents, such as road infrastructure, traffic, sociodemographic characteristics, land use, and the environment. The aim of this research is to predict the probabilistic crash prediction of vehicles using machine learning due to natural and structural reasons by excluding spontaneous reasons like overspeeding etc., in the United States. These factors range from weather factors, like weather conditions, precipitation, visibility, wind speed, wind direction, temperature, pressure, and humidity to human made structures like road structure factors like bump, roundabout, no exit, turning loop, give away, etc. Probabilities are dissected into ten different classes. All the predictions are based on multiclass classification techniques, which are supervised learning. This study considers all crashes that happened in all states collected by the US government. To calculate the probability, multinomial expected value was used and assigned a classification label as the crash probability. We applied three different classification models, including multiclass Logistic Regression, Random Forest and XGBoost. The numerical results show that XGBoost achieved a 75.2% accuracy rate which indicates the part that is being played by natural and structural reasons for the crash. The paper has provided in-deep insights through exploratory data analysis.Keywords: road safety, crash prediction, exploratory analysis, machine learning
Procedia PDF Downloads 1042198 Automatic Classification of the Stand-to-Sit Phase in the TUG Test Using Machine Learning
Authors: Yasmine Abu Adla, Racha Soubra, Milana Kasab, Mohamad O. Diab, Aly Chkeir
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Over the past several years, researchers have shown a great interest in assessing the mobility of elderly people to measure their functional status. Usually, such an assessment is done by conducting tests that require the subject to walk a certain distance, turn around, and finally sit back down. Consequently, this study aims to provide an at home monitoring system to assess the patient’s status continuously. Thus, we proposed a technique to automatically detect when a subject sits down while walking at home. In this study, we utilized a Doppler radar system to capture the motion of the subjects. More than 20 features were extracted from the radar signals, out of which 11 were chosen based on their intraclass correlation coefficient (ICC > 0.75). Accordingly, the sequential floating forward selection wrapper was applied to further narrow down the final feature vector. Finally, 5 features were introduced to the linear discriminant analysis classifier, and an accuracy of 93.75% was achieved as well as a precision and recall of 95% and 90%, respectively.Keywords: Doppler radar system, stand-to-sit phase, TUG test, machine learning, classification
Procedia PDF Downloads 1542197 Using Machine Learning to Predict Answers to Big-Five Personality Questions
Authors: Aadityaa Singla
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The big five personality traits are as follows: openness, conscientiousness, extraversion, agreeableness, and neuroticism. In order to get an insight into their personality, many flocks to these categories, which each have different meanings/characteristics. This information is important not only to individuals but also to career professionals and psychologists who can use this information for candidate assessment or job recruitment. The links between AI and psychology have been well studied in cognitive science, but it is still a rather novel development. It is possible for various AI classification models to accurately predict a personality question via ten input questions. This would contrast with the hundred questions that normal humans have to answer to gain a complete picture of their five personality traits. In order to approach this problem, various AI classification models were used on a dataset to predict what a user may answer. From there, the model's prediction was compared to its actual response. Normally, there are five answer choices (a 20% chance of correct guess), and the models exceed that value to different degrees, proving their significance. By utilizing an MLP classifier, decision tree, linear model, and K-nearest neighbors, they were able to obtain a test accuracy of 86.643, 54.625, 47.875, and 52.125, respectively. These approaches display that there is potential in the future for more nuanced predictions to be made regarding personality.Keywords: machine learning, personally, big five personality traits, cognitive science
Procedia PDF Downloads 1432196 The Increasing of Unconfined Compression Strength of Clay Soils Stabilized with Cement
Authors: Ali̇ Si̇nan Soğanci
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The cement stabilization is one of the ground improvement method applied worldwide to increase the strength of clayey soils. The using of cement has got lots of advantages compared to other stabilization methods. Cement stabilization can be done quickly, the cost is low and creates a more durable structure with the soil. Cement can be used in the treatment of a wide variety of soils. The best results of the cement stabilization were seen on silts as well as coarse-grained soils. In this study, blocks of clay were taken from the Apa-Hotamış conveyance channel route which is 125km long will be built in Konya that take the water with 70m3/sec from Mavi tunnel to Hotamış storage. Firstly, the index properties of clay samples were determined according to the Unified Soil Classification System. The experimental program was carried out on compacted soil specimens with 0%, 7 %, 15% and 30 % cement additives and the results of unconfined compression strength were discussed. The results of unconfined compression tests indicated an increase in strength with increasing cement content.Keywords: cement stabilization, unconfined compression test, clayey soils, unified soil classification system.
Procedia PDF Downloads 4172195 Administrative Determinants of Students' Sports Participation in Private and Public Secondary Schools in Kwara State, Nigeria
Authors: Danjuma Moudu Momoh
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Participation in sports is of immense benefit to the soundness of individual mental and social wellness, particularly among youngsters. The 1980’s and 1990’s compared to 2000’s witnessed great involvement of youngsters in school games arising from the high administrative supports attached to sports. Previous studies in an attempt to increase youngster’s participation in sports had focused more on other factors rather than on administrative factors. This study, therefore, investigated the importance of administrative factors (availability of facilities, availability of equipment, funding, scheduling of sports programme and administrative style of school principals) on students’ sports participation in private and public secondary schools in Kwara State, Nigeria. Descriptive survey research design using validated and structured questionnaire, was adopted. Stratified random sampling technique was used to pick the students both male and female. A total of two thousand five hundred and sixty participants were involved in the study. A reliable coefficient of r=0.82 was obtained for the instruments using Cronbach Alpha. Data were analyzed using multiple regressions to test the hypotheses at 00.5 significant levels. At the end of the study, it was discovered that the relative contributions of administrative factors among the students were: availability of facilities (β=0.314), availability of equipment (β=0.444), funding (β=0.301), scheduling of sports programme (β=0.447), made relative contributions to the dependent variable, administrative style of school principal (β=0.077) did not make significant but minimal contribution to the student’s sports participation.Keywords: administrative determinants, secondary school students, physical activity, sports participation
Procedia PDF Downloads 5392194 Classification of Health Information Needs of Hypertensive Patients in the Online Health Community Based on Content Analysis
Authors: Aijing Luo, Zirui Xin, Yifeng Yuan
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Background: With the rapid development of the online health community, more and more patients or families are seeking health information on the Internet. Objective: This study aimed to discuss how to fully reveal the health information needs expressed by hypertensive patients in their questions in the online environment. Methods: This study randomly selected 1,000 text records from the question data of hypertensive patients from 2008 to 2018 collected from the website www.haodf.com and constructed a classification system through literature research and content analysis. This paper identified the background characteristics and questioning the intention of each hypertensive patient based on the patient’s question and used co-occurrence network analysis to explore the features of the health information needs of hypertensive patients. Results: The classification system for health information needs of patients with hypertension is composed of 9 parts: 355 kinds of drugs, 395 kinds of symptoms and signs, 545 kinds of tests and examinations , 526 kinds of demographic data, 80 kinds of diseases, 37 kinds of risk factors, 43 kinds of emotions, 6 kinds of lifestyles, 49 kinds of questions. The characteristics of the explored online health information needs of the hypertensive patients include: i)more than 49% of patients describe the features such as drugs, symptoms and signs, tests and examinations, demographic data, diseases, etc. ii) these groups are most concerned about treatment (77.8%), followed by diagnosis (32.3%); iii) 65.8% of hypertensive patients will ask doctors online several questions at the same time. 28.3% of the patients are very concerned about how to adjust the medication, and they will ask other treatment-related questions at the same time, including drug side effects, whether to take drugs, how to treat a disease, etc.; secondly, 17.6% of the patients will consult the doctors online about the causes of the clinical findings, including the relationship between the clinical findings and a disease, the treatment of a disease, medication, and examinations. Conclusion: In the online environment, the health information needs expressed by Chinese hypertensive patients to doctors are personalized; that is, patients with different background features express their questioning intentions to doctors. The classification system constructed in this study can guide health information service providers in the construction of online health resources, to help solve the problem of information asymmetry in communication between doctors and patients.Keywords: online health community, health information needs, hypertensive patients, doctor-patient communication
Procedia PDF Downloads 1142193 Early-Warning Lights Classification Management System for Industrial Parks in Taiwan
Authors: Yu-Min Chang, Kuo-Sheng Tsai, Hung-Te Tsai, Chia-Hsin Li
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This paper presents the early-warning lights classification management system for industrial parks promoted by the Taiwan Environmental Protection Administration (EPA) since 2011, including the definition of each early-warning light, objectives, action program and accomplishments. All of the 151 industrial parks in Taiwan were classified into four early-warning lights, including red, orange, yellow and green, for carrying out respective pollution management according to the monitoring data of soil and groundwater quality, regulatory compliance, and regulatory listing of control site or remediation site. The Taiwan EPA set up a priority list for high potential polluted industrial parks and investigated their soil and groundwater qualities based on the results of the light classification and pollution potential assessment. In 2011-2013, there were 44 industrial parks selected and carried out different investigation, such as the early warning groundwater well networks establishment and pollution investigation/verification for the red and orange-light industrial parks and the environmental background survey for the yellow-light industrial parks. Among them, 22 industrial parks were newly or continuously confirmed that the concentrations of pollutants exceeded those in soil or groundwater pollution control standards. Thus, the further investigation, groundwater use restriction, listing of pollution control site or remediation site, and pollutant isolation measures were implemented by the local environmental protection and industry competent authorities; the early warning lights of those industrial parks were proposed to adjust up to orange or red-light. Up to the present, the preliminary positive effect of the soil and groundwater quality management system for industrial parks has been noticed in several aspects, such as environmental background information collection, early warning of pollution risk, pollution investigation and control, information integration and application, and inter-agency collaboration. Finally, the work and goal of self-initiated quality management of industrial parks will be carried out on the basis of the inter-agency collaboration by the classified lights system of early warning and management as well as the regular announcement of the status of each industrial park.Keywords: industrial park, soil and groundwater quality management, early-warning lights classification, SOP for reporting and treatment of monitored abnormal events
Procedia PDF Downloads 3152192 Preparation on Sentimental Analysis on Social Media Comments with Bidirectional Long Short-Term Memory Gated Recurrent Unit and Model Glove in Portuguese
Authors: Leonardo Alfredo Mendoza, Cristian Munoz, Marco Aurelio Pacheco, Manoela Kohler, Evelyn Batista, Rodrigo Moura
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Natural Language Processing (NLP) techniques are increasingly more powerful to be able to interpret the feelings and reactions of a person to a product or service. Sentiment analysis has become a fundamental tool for this interpretation but has few applications in languages other than English. This paper presents a classification of sentiment analysis in Portuguese with a base of comments from social networks in Portuguese. A word embedding's representation was used with a 50-Dimension GloVe pre-trained model, generated through a corpus completely in Portuguese. To generate this classification, the bidirectional long short-term memory and bidirectional Gated Recurrent Unit (GRU) models are used, reaching results of 99.1%.Keywords: natural processing language, sentiment analysis, bidirectional long short-term memory, BI-LSTM, gated recurrent unit, GRU
Procedia PDF Downloads 1502191 Classification of Digital Chest Radiographs Using Image Processing Techniques to Aid in Diagnosis of Pulmonary Tuberculosis
Authors: A. J. S. P. Nileema, S. Kulatunga , S. H. Palihawadana
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Computer aided detection (CAD) system was developed for the diagnosis of pulmonary tuberculosis using digital chest X-rays with MATLAB image processing techniques using a statistical approach. The study comprised of 200 digital chest radiographs collected from the National Hospital for Respiratory Diseases - Welisara, Sri Lanka. Pre-processing was done to remove identification details. Lung fields were segmented and then divided into four quadrants; right upper quadrant, left upper quadrant, right lower quadrant, and left lower quadrant using the image processing techniques in MATLAB. Contrast, correlation, homogeneity, energy, entropy, and maximum probability texture features were extracted using the gray level co-occurrence matrix method. Descriptive statistics and normal distribution analysis were performed using SPSS. Depending on the radiologists’ interpretation, chest radiographs were classified manually into PTB - positive (PTBP) and PTB - negative (PTBN) classes. Features with standard normal distribution were analyzed using an independent sample T-test for PTBP and PTBN chest radiographs. Among the six features tested, contrast, correlation, energy, entropy, and maximum probability features showed a statistically significant difference between the two classes at 95% confidence interval; therefore, could be used in the classification of chest radiograph for PTB diagnosis. With the resulting value ranges of the five texture features with normal distribution, a classification algorithm was then defined to recognize and classify the quadrant images; if the texture feature values of the quadrant image being tested falls within the defined region, it will be identified as a PTBP – abnormal quadrant and will be labeled as ‘Abnormal’ in red color with its border being highlighted in red color whereas if the texture feature values of the quadrant image being tested falls outside of the defined value range, it will be identified as PTBN–normal and labeled as ‘Normal’ in blue color but there will be no changes to the image outline. The developed classification algorithm has shown a high sensitivity of 92% which makes it an efficient CAD system and with a modest specificity of 70%.Keywords: chest radiographs, computer aided detection, image processing, pulmonary tuberculosis
Procedia PDF Downloads 1132190 Graph Neural Network-Based Classification for Disease Prediction in Health Care Heterogeneous Data Structures of Electronic Health Record
Authors: Raghavi C. Janaswamy
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In the healthcare sector, heterogenous data elements such as patients, diagnosis, symptoms, conditions, observation text from physician notes, and prescriptions form the essentials of the Electronic Health Record (EHR). The data in the form of clear text and images are stored or processed in a relational format in most systems. However, the intrinsic structure restrictions and complex joins of relational databases limit the widespread utility. In this regard, the design and development of realistic mapping and deep connections as real-time objects offer unparallel advantages. Herein, a graph neural network-based classification of EHR data has been developed. The patient conditions have been predicted as a node classification task using a graph-based open source EHR data, Synthea Database, stored in Tigergraph. The Synthea DB dataset is leveraged due to its closer representation of the real-time data and being voluminous. The graph model is built from the EHR heterogeneous data using python modules, namely, pyTigerGraph to get nodes and edges from the Tigergraph database, PyTorch to tensorize the nodes and edges, PyTorch-Geometric (PyG) to train the Graph Neural Network (GNN) and adopt the self-supervised learning techniques with the AutoEncoders to generate the node embeddings and eventually perform the node classifications using the node embeddings. The model predicts patient conditions ranging from common to rare situations. The outcome is deemed to open up opportunities for data querying toward better predictions and accuracy.Keywords: electronic health record, graph neural network, heterogeneous data, prediction
Procedia PDF Downloads 812189 Towards Real-Time Classification of Finger Movement Direction Using Encephalography Independent Components
Authors: Mohamed Mounir Tellache, Hiroyuki Kambara, Yasuharu Koike, Makoto Miyakoshi, Natsue Yoshimura
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This study explores the practicality of using electroencephalographic (EEG) independent components to predict eight-direction finger movements in pseudo-real-time. Six healthy participants with individual-head MRI images performed finger movements in eight directions with two different arm configurations. The analysis was performed in two stages. The first stage consisted of using independent component analysis (ICA) to separate the signals representing brain activity from non-brain activity signals and to obtain the unmixing matrix. The resulting independent components (ICs) were checked, and those reflecting brain-activity were selected. Finally, the time series of the selected ICs were used to predict eight finger-movement directions using Sparse Logistic Regression (SLR). The second stage consisted of using the previously obtained unmixing matrix, the selected ICs, and the model obtained by applying SLR to classify a different EEG dataset. This method was applied to two different settings, namely the single-participant level and the group-level. For the single-participant level, the EEG dataset used in the first stage and the EEG dataset used in the second stage originated from the same participant. For the group-level, the EEG datasets used in the first stage were constructed by temporally concatenating each combination without repetition of the EEG datasets of five participants out of six, whereas the EEG dataset used in the second stage originated from the remaining participants. The average test classification results across datasets (mean ± S.D.) were 38.62 ± 8.36% for the single-participant, which was significantly higher than the chance level (12.50 ± 0.01%), and 27.26 ± 4.39% for the group-level which was also significantly higher than the chance level (12.49% ± 0.01%). The classification accuracy within [–45°, 45°] of the true direction is 70.03 ± 8.14% for single-participant and 62.63 ± 6.07% for group-level which may be promising for some real-life applications. Clustering and contribution analyses further revealed the brain regions involved in finger movement and the temporal aspect of their contribution to the classification. These results showed the possibility of using the ICA-based method in combination with other methods to build a real-time system to control prostheses.Keywords: brain-computer interface, electroencephalography, finger motion decoding, independent component analysis, pseudo real-time motion decoding
Procedia PDF Downloads 1332188 The Stylistic Representation of Subjectivity in Exemplary Written and Audiovisual Biographical Records about the Brazilian Modernist Artist Tarsila Do Amaral
Authors: Juliane Noack Napoles, Vivian Martins Nogueira Napoles
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This paper operates at the stylistic level of biographical records about the artist Tarsila do Amaral (1886-1973) and the various biographical modes of representation of her subjectivity. Tarsila do Amaral was a Brazilian nationalistic painter, who took part in the first half of the last century in the Antoprofágico Moviment and in the Surrealistic Movement - artistic movements that emerged in the 1920’s. The paper will be developed in the field of Cultural and Media Science and based on an understanding of biography as a subgenre of historical records that will be discussed. Doing that, the theoretical principles about the history genre will also be discussed. In this context, the analytical focus of the present project is the stylistic forms of representation of subjectivity in the postmodern period as expressed in written and audiovisual biographical representation of Tarsila do Amaral. Some exemplary audiovisual biographical records about Tarsila do Amaral will be first analyzed on their own. Then, they will be related to some written biographical records about the painter. At the end, both written and audiovisual records and their stylistic forms of representation of Tarsila do Amaral’s subjectivity are going be analyzed. Tarsila do Amaral will be considered as a Subject Form, following actual concepts about this term in Cultural Studies. For these purposes, it will also be discussed about cultural identity – gender and national identity – and developed a heuristic model so that different understandings and conceptual proposals correlate, including those pertaining to the terms biography, gender, identity, mediality, style, subject and subjectivity. This model will finally be used for the analysis of the selected biographical records.Keywords: biography, gender, identity, modernism, postmodernism, style, subject, subjectivity, surrealism, Tarsila do Amaral
Procedia PDF Downloads 1672187 An Application to Predict the Best Study Path for Information Technology Students in Learning Institutes
Authors: L. S. Chathurika
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Early prediction of student performance is an important factor to be gained academic excellence. Whatever the study stream in secondary education, students lay the foundation for higher studies during the first year of their degree or diploma program in Sri Lanka. The information technology (IT) field has certain improvements in the education domain by selecting specialization areas to show the talents and skills of students. These specializations can be software engineering, network administration, database administration, multimedia design, etc. After completing the first-year, students attempt to select the best path by considering numerous factors. The purpose of this experiment is to predict the best study path using machine learning algorithms. Five classification algorithms: decision tree, support vector machine, artificial neural network, Naïve Bayes, and logistic regression are selected and tested. The support vector machine obtained the highest accuracy, 82.4%. Then affecting features are recognized to select the best study path.Keywords: algorithm, classification, evaluation, features, testing, training
Procedia PDF Downloads 1152186 Analysis, Evaluation and Optimization of Food Management: Minimization of Food Losses and Food Wastage along the Food Value Chain
Authors: G. Hafner
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A method developed at the University of Stuttgart will be presented: ‘Analysis, Evaluation and Optimization of Food Management’. A major focus is represented by quantification of food losses and food waste as well as their classification and evaluation regarding a system optimization through waste prevention. For quantification and accounting of food, food losses and food waste along the food chain, a clear definition of core terms is required at the beginning. This includes their methodological classification and demarcation within sectors of the food value chain. The food chain is divided into agriculture, industry and crafts, trade and consumption (at home and out of home). For adjustment of core terms, the authors have cooperated with relevant stakeholders in Germany for achieving the goal of holistic and agreed definitions for the whole food chain. This includes modeling of sub systems within the food value chain, definition of terms, differentiation between food losses and food wastage as well as methodological approaches. ‘Food Losses’ and ‘Food Wastes’ are assigned to individual sectors of the food chain including a description of the respective methods. The method for analyzing, evaluation and optimization of food management systems consist of the following parts: Part I: Terms and Definitions. Part II: System Modeling. Part III: Procedure for Data Collection and Accounting Part. IV: Methodological Approaches for Classification and Evaluation of Results. Part V: Evaluation Parameters and Benchmarks. Part VI: Measures for Optimization. Part VII: Monitoring of Success The method will be demonstrated at the example of an invesigation of food losses and food wastage in the Federal State of Bavaria including an extrapolation of respective results to quantify food wastage in Germany.Keywords: food losses, food waste, resource management, waste management, system analysis, waste minimization, resource efficiency
Procedia PDF Downloads 3992185 Issues in Translating Hadith Terminologies into English: A Critical Approach
Authors: Mohammed Riyas Pp
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This study aimed at investigating major issues in translating the Arabic Hadith terminologies into English, focusing on choosing the most appropriate translation for each, reviewing major Hadith works in English. This study is confined to twenty terminologies with regard to classification of Hadith based on authority, strength, number of transmitters and connections in Isnad. Almost all available translations are collected and analyzed to find the most proper translation based on linguistic and translational values. To the researcher, many translations lack precise understanding of either Hadith terminologies or English language and varieties of methodologies have influence on varieties of translations. This study provides a classification of translational and conceptual issues. Translational issues are related to translatability of these terminologies and their equivalence. Conceptual issues provide a list of misunderstandings due to wrong translations of terminologies. This study ends with a suggestion for unification in translating terminologies based on convention of Muslim scholars having good understanding of Hadith terminologies and English language.Keywords: english language, hadith terminologies, equivalence in translation, problems in translation
Procedia PDF Downloads 1812184 Diversity in Finance Literature Revealed through the Lens of Machine Learning: A Topic Modeling Approach on Academic Papers
Authors: Oumaima Lahmar
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This paper aims to define a structured topography for finance researchers seeking to navigate the body of knowledge in their extrapolation of finance phenomena. To make sense of the body of knowledge in finance, a probabilistic topic modeling approach is applied on 6000 abstracts of academic articles published in three top journals in finance between 1976 and 2020. This approach combines both machine learning techniques and natural language processing to statistically identify the conjunctions between research articles and their shared topics described each by relevant keywords. The topic modeling analysis reveals 35 coherent topics that can well depict finance literature and provide a comprehensive structure for the ongoing research themes. Comparing the extracted topics to the Journal of Economic Literature (JEL) classification system, a significant similarity was highlighted between the characterizing keywords. On the other hand, we identify other topics that do not match the JEL classification despite being relevant in the finance literature.Keywords: finance literature, textual analysis, topic modeling, perplexity
Procedia PDF Downloads 1602183 A Framework for Auditing Multilevel Models Using Explainability Methods
Authors: Debarati Bhaumik, Diptish Dey
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Multilevel models, increasingly deployed in industries such as insurance, food production, and entertainment within functions such as marketing and supply chain management, need to be transparent and ethical. Applications usually result in binary classification within groups or hierarchies based on a set of input features. Using open-source datasets, we demonstrate that popular explainability methods, such as SHAP and LIME, consistently underperform inaccuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution to the outcome). Besides accuracy, the computational intractability of SHAP for binomial classification is a cause of concern. For transparent and ethical applications of these hierarchical statistical models, sound audit frameworks need to be developed. In this paper, we propose an audit framework for technical assessment of multilevel regression models focusing on three aspects: (i) model assumptions & statistical properties, (ii) model transparency using different explainability methods, and (iii) discrimination assessment. To this end, we undertake a quantitative approach and compare intrinsic model methods with SHAP and LIME. The framework comprises a shortlist of KPIs, such as PoCE (Percentage of Correct Explanations) and MDG (Mean Discriminatory Gap) per feature, for each of these three aspects. A traffic light risk assessment method is furthermore coupled to these KPIs. The audit framework will assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit businesses deploying multilevel models to be future-proof and aligned with the European Commission’s proposed Regulation on Artificial Intelligence.Keywords: audit, multilevel model, model transparency, model explainability, discrimination, ethics
Procedia PDF Downloads 872182 Large Neural Networks Learning From Scratch With Very Few Data and Without Explicit Regularization
Authors: Christoph Linse, Thomas Martinetz
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Recent findings have shown that Neural Networks generalize also in over-parametrized regimes with zero training error. This is surprising, since it is completely against traditional machine learning wisdom. In our empirical study we fortify these findings in the domain of fine-grained image classification. We show that very large Convolutional Neural Networks with millions of weights do learn with only a handful of training samples and without image augmentation, explicit regularization or pretraining. We train the architectures ResNet018, ResNet101 and VGG19 on subsets of the difficult benchmark datasets Caltech101, CUB_200_2011, FGVCAircraft, Flowers102 and StanfordCars with 100 classes and more, perform a comprehensive comparative study and draw implications for the practical application of CNNs. Finally, we show that VGG19 with 140 million weights learns to distinguish airplanes and motorbikes with up to 95% accuracy using only 20 training samples per class.Keywords: convolutional neural networks, fine-grained image classification, generalization, image recognition, over-parameterized, small data sets
Procedia PDF Downloads 812181 Developing an Advanced Algorithm Capable of Classifying News, Articles and Other Textual Documents Using Text Mining Techniques
Authors: R. B. Knudsen, O. T. Rasmussen, R. A. Alphinas
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The reason for conducting this research is to develop an algorithm that is capable of classifying news articles from the automobile industry, according to the competitive actions that they entail, with the use of Text Mining (TM) methods. It is needed to test how to properly preprocess the data for this research by preparing pipelines which fits each algorithm the best. The pipelines are tested along with nine different classification algorithms in the realm of regression, support vector machines, and neural networks. Preliminary testing for identifying the optimal pipelines and algorithms resulted in the selection of two algorithms with two different pipelines. The two algorithms are Logistic Regression (LR) and Artificial Neural Network (ANN). These algorithms are optimized further, where several parameters of each algorithm are tested. The best result is achieved with the ANN. The final model yields an accuracy of 0.79, a precision of 0.80, a recall of 0.78, and an F1 score of 0.76. By removing three of the classes that created noise, the final algorithm is capable of reaching an accuracy of 94%.Keywords: Artificial Neural network, Competitive dynamics, Logistic Regression, Text classification, Text mining
Procedia PDF Downloads 1162180 Enhancing the Interpretation of Group-Level Diagnostic Results from Cognitive Diagnostic Assessment: Application of Quantile Regression and Cluster Analysis
Authors: Wenbo Du, Xiaomei Ma
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With the empowerment of Cognitive Diagnostic Assessment (CDA), various domains of language testing and assessment have been investigated to dig out more diagnostic information. What is noticeable is that most of the extant empirical CDA-based research puts much emphasis on individual-level diagnostic purpose with very few concerned about learners’ group-level performance. Even though the personalized diagnostic feedback is the unique feature that differentiates CDA from other assessment tools, group-level diagnostic information cannot be overlooked in that it might be more practical in classroom setting. Additionally, the group-level diagnostic information obtained via current CDA always results in a “flat pattern”, that is, the mastery/non-mastery of all tested skills accounts for the two highest proportion. In that case, the outcome does not bring too much benefits than the original total score. To address these issues, the present study attempts to apply cluster analysis for group classification and quantile regression analysis to pinpoint learners’ performance at different proficiency levels (beginner, intermediate and advanced) thus to enhance the interpretation of the CDA results extracted from a group of EFL learners’ reading performance on a diagnostic reading test designed by PELDiaG research team from a key university in China. The results show that EM method in cluster analysis yield more appropriate classification results than that of CDA, and quantile regression analysis does picture more insightful characteristics of learners with different reading proficiencies. The findings are helpful and practical for instructors to refine EFL reading curriculum and instructional plan tailored based on the group classification results and quantile regression analysis. Meanwhile, these innovative statistical methods could also make up the deficiencies of CDA and push forward the development of language testing and assessment in the future.Keywords: cognitive diagnostic assessment, diagnostic feedback, EFL reading, quantile regression
Procedia PDF Downloads 1442179 Modeling Engagement with Multimodal Multisensor Data: The Continuous Performance Test as an Objective Tool to Track Flow
Authors: Mohammad H. Taheri, David J. Brown, Nasser Sherkat
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Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential.Keywords: affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, student engagement
Procedia PDF Downloads 832178 Improved Rare Species Identification Using Focal Loss Based Deep Learning Models
Authors: Chad Goldsworthy, B. Rajeswari Matam
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The use of deep learning for species identification in camera trap images has revolutionised our ability to study, conserve and monitor species in a highly efficient and unobtrusive manner, with state-of-the-art models achieving accuracies surpassing the accuracy of manual human classification. The high imbalance of camera trap datasets, however, results in poor accuracies for minority (rare or endangered) species due to their relative insignificance to the overall model accuracy. This paper investigates the use of Focal Loss, in comparison to the traditional Cross Entropy Loss function, to improve the identification of minority species in the “255 Bird Species” dataset from Kaggle. The results show that, although Focal Loss slightly decreased the accuracy of the majority species, it was able to increase the F1-score by 0.06 and improve the identification of the bottom two, five and ten (minority) species by 37.5%, 15.7% and 10.8%, respectively, as well as resulting in an improved overall accuracy of 2.96%.Keywords: convolutional neural networks, data imbalance, deep learning, focal loss, species classification, wildlife conservation
Procedia PDF Downloads 1792177 Spatial Data Mining by Decision Trees
Authors: Sihem Oujdi, Hafida Belbachir
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Existing methods of data mining cannot be applied on spatial data because they require spatial specificity consideration, as spatial relationships. This paper focuses on the classification with decision trees, which are one of the data mining techniques. We propose an extension of the C4.5 algorithm for spatial data, based on two different approaches Join materialization and Querying on the fly the different tables. Similar works have been done on these two main approaches, the first - Join materialization - favors the processing time in spite of memory space, whereas the second - Querying on the fly different tables- promotes memory space despite of the processing time. The modified C4.5 algorithm requires three entries tables: a target table, a neighbor table, and a spatial index join that contains the possible spatial relationship among the objects in the target table and those in the neighbor table. Thus, the proposed algorithms are applied to a spatial data pattern in the accidentology domain. A comparative study of our approach with other works of classification by spatial decision trees will be detailed.Keywords: C4.5 algorithm, decision trees, S-CART, spatial data mining
Procedia PDF Downloads 6072176 A Robust System for Foot Arch Type Classification from Static Foot Pressure Distribution Data Using Linear Discriminant Analysis
Authors: R. Periyasamy, Deepak Joshi, Sneh Anand
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Foot posture assessment is important to evaluate foot type, causing gait and postural defects in all age groups. Although different methods are used for classification of foot arch type in clinical/research examination, there is no clear approach for selecting the most appropriate measurement system. Therefore, the aim of this study was to develop a system for evaluation of foot type as clinical decision-making aids for diagnosis of flat and normal arch based on the Arch Index (AI) and foot pressure distribution parameter - Power Ratio (PR) data. The accuracy of the system was evaluated for 27 subjects with age ranging from 24 to 65 years. Foot area measurements (hind foot, mid foot, and forefoot) were acquired simultaneously from foot pressure intensity image using portable PedoPowerGraph system and analysis of the image in frequency domain to obtain foot pressure distribution parameter - PR data. From our results, we obtain 100% classification accuracy of normal and flat foot by using the linear discriminant analysis method. We observe there is no misclassification of foot types because of incorporating foot pressure distribution data instead of only arch index (AI). We found that the mid-foot pressure distribution ratio data and arch index (AI) value are well correlated to foot arch type based on visual analysis. Therefore, this paper suggests that the proposed system is accurate and easy to determine foot arch type from arch index (AI), as well as incorporating mid-foot pressure distribution ratio data instead of physical area of contact. Hence, such computational tool based system can help the clinicians for assessment of foot structure and cross-check their diagnosis of flat foot from mid-foot pressure distribution.Keywords: arch index, computational tool, static foot pressure intensity image, foot pressure distribution, linear discriminant analysis
Procedia PDF Downloads 4922175 Modified Naive Bayes-Based Prediction Modeling for Crop Yield Prediction
Authors: Kefaya Qaddoum
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Most of greenhouse growers desire a determined amount of yields in order to accurately meet market requirements. The purpose of this paper is to model a simple but often satisfactory supervised classification method. The original naive Bayes have a serious weakness, which is producing redundant predictors. In this paper, utilized regularization technique was used to obtain a computationally efficient classifier based on naive Bayes. The suggested construction, utilized L1-penalty, is capable of clearing redundant predictors, where a modification of the LARS algorithm is devised to solve this problem, making this method applicable to a wide range of data. In the experimental section, a study conducted to examine the effect of redundant and irrelevant predictors, and test the method on WSG data set for tomato yields, where there are many more predictors than data, and the urge need to predict weekly yield is the goal of this approach. Finally, the modified approach is compared with several naive Bayes variants and other classification algorithms (SVM and kNN), and is shown to be fairly good.Keywords: tomato yield prediction, naive Bayes, redundancy, WSG
Procedia PDF Downloads 2272174 Earthquake Classification in Molluca Collision Zone Using Conventional Statistical Methods
Authors: H. J. Wattimanela, U. S. Passaribu, A. N. T. Puspito, S. W. Indratno
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Molluca Collision Zone is located at the junction of the Eurasian plate, Australian, Pacific, and the Philippines. Between the Sangihe arc, west of the collision zone, and to the east of Halmahera arc is active collision and convex toward the Molluca Sea. This research will analyze the behavior of earthquake occurrence in Molluca Collision Zone related to the distributions of an earthquake in each partition regions, determining the type of distribution of a occurrence earthquake of partition regions, and the mean occurrence of earthquakes each partition regions, and the correlation between the partitions region. We calculate number of earthquakes using partition method and its behavioral using conventional statistical methods. The data used is the data type of shallow earthquakes with magnitudes ≥ 4 SR for the period 1964-2013 in the Molluca Collision Zone. From the results, we can classify partitioned regions based on the correlation into two classes: strong and very strong. This classification can be used for early warning system in disaster management.Keywords: molluca collision zone, partition regions, conventional statistical methods, earthquakes, classifications, disaster management
Procedia PDF Downloads 487