Search results for: supervised machine learning algorithm

Authors: M. Al-Shamery, R. Young, P. Birch, C. Chatwin

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Computer Generated Holography (CGH) is employed to create digitally defined coherent wavefronts. A CGH can be created by using different techniques such as by using a detour-phase technique or by direct phase modulation to create a kinoform. The detour-phase technique was one of the first techniques that was used to generate holograms digitally. The disadvantage of this technique is that the reconstructed image often has poor quality due to the limited dynamic range it is possible to record using a medium with reasonable spatial resolution.. The kinoform (phase-only hologram) is an alternative technique. In this method, the phase of the original wavefront is recorded but the amplitude is constrained to be constant. The original object does not need to exist physically and so the kinoform can be used to reconstruct an almost arbitrary wavefront. However, the image reconstructed by this technique contains high levels of noise and is not identical to the reference image. To improve the reconstruction quality of the kinoform, iterative techniques such as the Gerchberg-Saxton algorithm (GS) are employed. In this paper the GS algorithm is described for the optimisation of a kinoform used for the reconstruction of a complex wavefront. Iterations of the GS algorithm are applied to determine the phase at a plane (with known amplitude distribution which is often taken as uniform), that satisfies given phase and amplitude constraints in a corresponding Fourier plane. The GS algorithm can be used in this way to enhance the reconstruction quality of the kinoform. Different images are employed as the reference object and their kinoform is synthesised using the GS algorithm. The quality of the reconstructed images is quantified to demonstrate the enhanced reconstruction quality achieved by using this method.

Keywords: computer generated holography, digital holography, Gerchberg-Saxton algorithm, kinoform

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Authors: Sara Fallahi

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This paper reviews the importance of content familiarity of receptive skills and offers solutions to the issue of content unfamiliarity in language learning materials. Presently, language learning materials are mainly comprised of global issues and target language speakers’ culture(s) in receptive skills. This might leadlearners to focus on content rather than the language. As a solution, materials on receptive skills can be developed with a focus on learners’culture and social concerns, especially in the beginner levels of learning. Language learners often learn their target language through the receptive skills of listening and reading before language production ensues through speaking and writing. Students’ journey from receptive skills to productive skills is mainly concentrated on by teachers. There are barriers to language learning, such as time and energy, that can hinder learners’ understanding and ability to build the required background knowledge of the content. This is generated due to learners’ unfamiliarity with the skill’s content. Therefore, materials that improve content familiarity will help learners improve their language comprehension, learning, and usage. This presentation will conclude with practical solutions to help teachers and learners more authentically integrate language and culture to elevate language learning.

Keywords: language learning, listening content, reading content, content familiarity, ESL books, language learning books, cultural familiarity

Procedia PDF Downloads 97

Authors: Angel Daniel Muñoz Guzmán

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E-learning is becoming more and more common every day. For online, hybrid or traditional face-to-face programs, there are some e-teaching platforms like Google classroom, Blackboard, Moodle and Canvas, and there are platforms for full e-learning like Coursera, edX or Udemy. These tools are changing the way students acquire knowledge at schools; however, in today’s changing world that is not enough. As students’ needs and skills change and become more complex, new tools will need to be added to keep them engaged and potentialize their learning. This is especially important in the current global situation that is changing everything: the Covid-19 pandemic. Due to Covid-19, education had to make an unexpected switch from face-to-face courses to digital courses. In this study, the students’ learning experience is analyzed by applying different e-tools and following the Tec21 Model and a flexible and digital model, both developed by the Tecnologico de Monterrey University. The evaluation of the students’ learning experience has been made by the quantitative PrEmo method of emotions. Findings suggest that the quantity of e-tools used during a course does not affect the students’ learning experience as much as how a teacher links every available tool and makes them work as one in order to keep the student engaged and motivated.

Keywords: student, experience, e-learning, e-teaching, e-tools, technology, education

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Authors: Carlos Oliveira

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This paper is a criticism of the traditional model of teaching and presents alternative teaching methods, different from the traditional lecture. These methods are accompanied by reports of experience of their application in a class. It was concluded that in the lecture, the student has a low learning rate and that other methods should be used to make the most engaging learning environment for the student, contributing (or facilitating) his learning process. However, the teacher should not use a single method, but rather a range of different methods to ensure the learning experience does not become repetitive and fatiguing for the student.

Keywords: educational practices, experience report, IT in education, teaching methods

Procedia PDF Downloads 381

Authors: Hitesh Tahbildar

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The cost of test data generation for a program is computationally very high. In general case, no algorithm to generate test data for all types of algorithms has been found. The cost of generating test data for different types of algorithm is different. Till date, people are emphasizing the need to generate test data for different types of programming constructs rather than different types of algorithms. The test data generation methods have been implemented to find heuristics for different types of algorithms. Some algorithms that includes divide and conquer, backtracking, greedy approach, dynamic programming to find the minimum cost of test data generation have been tested. Our experimental results say that some of these types of algorithm can be used as a necessary condition for selecting heuristics and programming constructs are sufficient condition for selecting our heuristics. Finally we recommend the different heuristics for test data generation to be selected for different types of algorithms.

Keywords: ongest path, saturation point, lmax, kL, kS

Procedia PDF Downloads 388

Authors: Hind Bouami

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Handling life-critical systems complexity requires to be equipped with appropriate technology and the right human agents’ functions such as knowledge, experience, and competence in problem’s prevention and solving. Human agents are involved in the management and control of human-machine system’s performance. Documenting human agent’s situation awareness is crucial to support human-machine designers’ decision-making. Knowledge about risks, critical parameters and factors that can impact and threaten automation system’s performance should be collected using preventive and retrospective approaches. This paper aims to document operators’ situation awareness through the analysis of automated organizations’ feedback. The analysis of automated hospital pharmacies feedbacks helps to identify and control critical parameters influencing human machine interaction in order to enhance system’s performance and security. Our human machine system evaluation approach has been deployed in Macon hospital center’s pharmacy which is equipped with automated drug dispensing systems since 2015. Automation’s specifications are related to technical aspects, human-machine interaction, and human aspects. The evaluation of drug delivery automation performance in Macon hospital center has shown that the performance of the automated activity depends on the performance of the automated solution chosen, and also on the control of systemic factors. In fact, 80.95% of automation specification related to the chosen Sinteco’s automated solution is met. The performance of the chosen automated solution is involved in 28.38% of automation specifications performance in Macon hospital center. The remaining systemic parameters involved in automation specifications performance need to be controlled.

Keywords: life-critical systems, situation awareness, human-machine interaction, decision-making

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Authors: Abrar Al-Hasan

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Web 2.0 has significantly increased the amount of information available to users not only about firms and their offerings, but also about the activities of other individuals in their networks and markets. It is widely acknowledged that this increased availability of ‘social’ information, particularly about other individuals, is likely to influence a user’s behavior and choices. However, there are very few systematic studies of how such increased information transparency on the behavior of other users in a focal users’ network influences a focal users’ behavior in the emerging marketplace of online language learning. This study seeks to examine the value and impact of ‘social activities’ – wherein, a user sees and interacts with the learning activities of her peers – on her language learning efficiency. An online experiment in a peer-to-peer language marketplace was conducted to compare the learning efficiency of users with ‘social’ information versus users with no ‘social’ information. The results of this study highlight the impact and importance of ‘social’ information within the language learning context. The study concludes by exploring how these insights may inspire new developments in online education.

Keywords: e-Learning, language learning marketplace, peer-to-peer, social network

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Authors: Michael Moeti, Khuliso Sigama, Thapelo Samuel Matlala

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One of the principal challenges that the world is confronted with is insecurity. The crime rate is increasing exponentially, and protecting our physical assets especially in the motorist industry, is becoming impossible when applying our own strength. The need to develop technological solutions that detect and report theft without any human interference is inevitable. This is critical, especially for vehicle owners, to ensure theft detection and speedy identification towards recovery efforts in cases where a vehicle is missing or attempted theft is taking place. The vehicle theft detection system uses Convolutional Neural Network (CNN) to recognize the driver's face captured using an installed mobile phone device. The location identification function uses a Global Positioning System (GPS) to determine the real-time location of the vehicle. Upon identification of the location, Global System for Mobile Communications (GSM) technology is used to report or notify the vehicle owner about the whereabouts of the vehicle. The installed mobile app was implemented by making use of python as it is undoubtedly the best choice in machine learning. It allows easy access to machine learning algorithms through its widely developed library ecosystem. The graphical user interface was developed by making use of JAVA as it is better suited for mobile development. Google's online database (Firebase) was used as a means of storage for the application. The system integration test was performed using a simple percentage analysis. Sixty (60) vehicle owners participated in this study as a sample, and questionnaires were used in order to establish the acceptability of the system developed. The result indicates the efficiency of the proposed system, and consequently, the paper proposes the use of the system can effectively monitor the vehicle at any given place, even if it is driven outside its normal jurisdiction. More so, the system can be used as a database to detect, locate and report missing vehicles to different security agencies.

Keywords: CNN, location identification, tracking, GPS, GSM

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Authors: Alexander Guzman Urbina, Atsushi Aoyama

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The sustainability of traditional technologies employed in energy and chemical infrastructure brings a big challenge for our society. Making decisions related with safety of industrial infrastructure, the values of accidental risk are becoming relevant points for discussion. However, the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables and with large amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI), and more specifically using hybrid systems such as Neuro-Fuzzy algorithms. Therefore, this paper aims to introduce a hybrid algorithm for risk assessment trained using near-miss accident data. As mentioned above the sustainability of traditional technologies related with energy and chemical infrastructure constitutes one of the major challenges that today’s societies and firms are facing. Besides that, the adaptation of those technologies to the effects of the climate change in sensible environments represents a critical concern for safety and risk management. Regarding this issue argue that social consequences of catastrophic risks are increasing rapidly, due mainly to the concentration of people and energy infrastructure in hazard-prone areas, aggravated by the lack of knowledge about the risks. Additional to the social consequences described above, and considering the industrial sector as critical infrastructure due to its large impact to the economy in case of a failure the relevance of industrial safety has become a critical issue for the current society. Then, regarding the safety concern, pipeline operators and regulators have been performing risk assessments in attempts to evaluate accurately probabilities of failure of the infrastructure, and consequences associated with those failures. However, estimating accidental risks in critical infrastructure involves a substantial effort and costs due to number of variables involved, complexity and lack of information. Therefore, this paper aims to introduce a well trained algorithm for risk assessment using deep learning, which could be capable to deal efficiently with the complexity and uncertainty. The advantage point of the deep learning using near-miss accidents data is that it could be employed in risk assessment as an efficient engineering tool to treat the uncertainty of the risk values in complex environments. The basic idea of using a Near-Miss Deep Learning Approach for Neuro-Fuzzy Risk Assessment in Pipelines is focused in the objective of improve the validity of the risk values learning from near-miss accidents and imitating the human expertise scoring risks and setting tolerance levels. In summary, the method of Deep Learning for Neuro-Fuzzy Risk Assessment involves a regression analysis called group method of data handling (GMDH), which consists in the determination of the optimal configuration of the risk assessment model and its parameters employing polynomial theory.

Keywords: deep learning, risk assessment, neuro fuzzy, pipelines

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Authors: Ann-May Marais

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South African higher education institutions are following the global adoption of a blended approach to teaching and learning. Blended learning is viewed as a transformative teaching-learning approach, as it provides students with the optimum experience by mixing the best of face-to-face and online learning. Although academics realise the benefits of blended learning, they find it challenging and time-consuming to implement blended strategies. Professional development is a critical component of the adoption of higher education teaching-learning approaches. The Institutional course for higher education academics offered at a South African University was designed in a blended model, implemented and evaluated. This paper reports on a study that investigated the experiences of academics on the blended approach used during the induction course. A qualitative design-based research methodology was employed, and data was collected using participant feedback and document analysis. The data gathered from each of the four ICNL offerings were used to inform the design of the next course. Findings indicated that lecturers realised that blended learning could cater to student diversity, different learning styles, engagement, and innovation. Furthermore, it emerged that the course has to cater for diversity in technology proficiency and readiness of participants. Participants also require ongoing support in technology usage and discipline-specific blended learning workshops. This paper contends that the modelling of a blended approach to professional development can be an effective way to motivate academics to apply blended learning in their teaching-learning experiences.

Keywords: blended learning, professional development, induction course, integration of technology

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Authors: Ahmad Akrad, Rabia Sehab, Fadi Alyoussef

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Nowadays, induction machines are widely used in industry thankful to their advantages comparing to other technologies. Indeed, there is a big demand because of their reliability, robustness and cost. The objective of this paper is to deal with diagnosis, detection and isolation of faults in a three-phase induction machine. Among the faults, Inter-turn short-circuit fault (ITSC), current sensors fault and single-phase open circuit fault are selected to deal with. However, a fault detection method is suggested using residual errors generated by the root mean square (RMS) of phase currents. The application of this method is based on an asymmetric nonlinear model of Induction Machine considering the winding fault of the three axes frame state space. In addition, current sensor redundancy and sensor fault detection and isolation (FDI) are adopted to ensure safety operation of induction machine drive. Finally, a validation is carried out by simulation in healthy and faulty operation modes to show the benefit of the proposed method to detect and to locate with, a high reliability, the three types of faults.

Keywords: induction machine, asymmetric nonlinear model, fault diagnosis, inter-turn short-circuit fault, root mean square, current sensor fault, fault detection and isolation

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Authors: Liang Wang, Wei Zhang

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There have been some existing researches on design-based engineering learning (DBEL) and project-based or problem-based engineering learning (PBEL). Those findings have greatly promoted the reform of engineering education in China. However, the engineering with a big E means that more and more engineering activities are designed and operated by communities of practice (CoPs), namely community-based engineering learning. However, whether community-based engineering learning can promote students' innovation has not been verified in published articles. This study fills this gap by investigating the relationship between community-based learning approach and students’ creativity, using engineering identity as an intermediary variable. The goal of this study is to discover the core features of community-based engineering learning, and make the features more beneficial for students’ creativity. The study created and adapted open survey items from previously published studies and a scale on learning community, students’ creativity and engineering identity. Firstly, qualitative content analysis methods by MAXQDA were used to analyze 32 open-ended questionnaires. Then the authors collected data (n=322) from undergraduate students in engineering competition teams and engineering laboratories in Zhejiang University, and structural equation modelling (SEM) was used to understand the relationship between different factors. The study finds: (a) community-based engineering learning has four main elements like real-task context, self-inquiry learning, deeply-consulted cooperation and circularly-iterated design, (b) community-based engineering learning can significantly enhance the engineering undergraduate students’ creativity, and (c) engineering identity partially moderated the relationship between community-based engineering learning and undergraduate students' creativity. The findings further illustrate the value of community-based engineering learning for undergraduate students. In the future research, the authors should further clarify the core mechanism of community-based engineering learning, and pay attention to the cultivation of undergraduate students’ engineer identity in learning community.

Keywords: community-based engineering learning, students' creativity, engineering identity, moderate effect

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Authors: Konstanca Nikolajevic, Nicolas Belanger, David Duvivier, Rabie Ben Atitallah, Abdelhakim Artiba

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Finding the optimal 3D path of an aerial vehicle under flight mechanics constraints is a major challenge, especially when the algorithm has to produce real-time results in flight. Kinematics models and Pythagorian Hodograph curves have been widely used in mobile robotics to solve this problematic. The level of difficulty is mainly driven by the number of constraints to be saturated at the same time while minimizing the total length of the path. In this paper, we suggest a pragmatic algorithm capable of saturating at the same time most of dimensioning helicopter 3D trajectories’ constraints like: curvature, curvature derivative, torsion, torsion derivative, climb angle, climb angle derivative, positions. The trajectories generation algorithm is able to generate versatile complex 3D motion primitives feasible by a helicopter with parameterization of the curvature and the climb angle. An upper ”motion primitives’ concatenation” algorithm is presented based. In this article we introduce a new way of designing three-dimensional trajectories based on what we call the ”Dubins gliding symmetry conjecture”. This extremely performing algorithm will be soon integrated to a real-time decisional system dealing with inflight safety issues.

Keywords: robotics, aerial robots, motion primitives, helicopter

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Authors: Nihad Alem

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Nowadays, distance learning (DL) is widely perceived as a reformed type of education that takes advantage of technology to give more appealing opportunities especially for learners whose life conditions impede their attendance to regular classrooms however, creating interactional environment for students to expand their learning community and alleviate the feeling of loneliness and isolation should receive more attention when designing a distance learning course. This research aims to explore whether the audio/video peer learning can offer pedagogical add-ons to the Algerian distance learners and what are the pros and cons of its application as an educational experience in a synchronous environment mediated by Skype. Data were collected using video recordings of six sessions, reflective logs, and in-depth semi-structured interviews and will be analyzed by qualitatively identifying and measuring the three constitutional elements of the educational experience of peer learning namely the social presence, the cognitive presence, and the facilitation presence using a modified community of inquiry coding template. The findings from this study will provide recommendations for effective peer learning educational experience using the facilitation presence concept.

Keywords: audio/visual peer learning, community of inquiry, distance learning, facilitation presence

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Authors: Bilel Chalghaf, Mathieu Varin

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Forest characterization in Quebec, Canada, is usually assessed based on photo-interpretation at the stand level. For species identification, this often results in a lack of precision. Very high spatial resolution imagery, such as DigitalGlobe, and Light Detection and Ranging (LiDAR), have the potential to overcome the limitations of aerial imagery. To date, few studies have used that data to map a large number of species at the tree level using machine learning techniques. The main objective of this study is to map 11 individual high tree species ( > 17m) at the tree level using an object-based approach in the broadleaf forest of Kenauk Nature, Quebec. For the individual tree crown segmentation, three canopy-height models (CHMs) from LiDAR data were assessed: 1) the original, 2) a filtered, and 3) a corrected model. The corrected CHM gave the best accuracy and was then coupled with imagery to refine tree species crown identification. When compared with photo-interpretation, 90% of the objects represented a single species. For modeling, 313 variables were derived from 16-band WorldView-3 imagery and LiDAR data, using radiance, reflectance, pixel, and object-based calculation techniques. Variable selection procedures were employed to reduce their number from 313 to 16, using only 11 bands to aid reproducibility. For classification, a global approach using all 11 species was compared to a semi-hierarchical hybrid classification approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were used: (1) support vector machine (SVM), (2) classification and regression tree (CART), (3) random forest (RF), (4) k-nearest neighbors (k-NN), and (5) linear discriminant analysis (LDA). Each model was tuned separately for all approaches and levels. For the global approach, the best model was the SVM using eight variables (overall accuracy (OA): 80%, Kappa: 0.77). With the semi-hierarchical hybrid approach, at the tree type level, the best model was the k-NN using six variables (OA: 100% and Kappa: 1.00). At the level of identifying broadleaf and conifer species, the best model was the SVM, with OA of 80% and 97% and Kappa values of 0.74 and 0.97, respectively, using seven variables for both models. This paper demonstrates that a hybrid classification approach gives better results and that using 16-band WorldView-3 with LiDAR data leads to more precise predictions for tree segmentation and classification, especially when the number of tree species is large.

Keywords: tree species, object-based, classification, multispectral, machine learning, WorldView-3, LiDAR

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Authors: Akshay Paranjape, Nils Plettenberg, Robert Schmitt

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Traditional optimization methods like evolutionary algorithms are widely used in production processes to find an optimal or near-optimal solution of control parameters based on the simulated environment space of a process. These algorithms are computationally intensive and therefore do not provide the opportunity for real-time optimization. This paper utilizes the Deep Reinforcement Learning (DRL) framework to find an optimal or near-optimal solution for control parameters. A model based on maximum a posteriori policy optimization (Hybrid-MPO) that can handle both numerical and categorical parameters is used as a benchmark for comparison. A comparative study shows that DRL can find optimal solutions of similar quality as compared to evolutionary algorithms while requiring significantly less time making them preferable for real-time optimization. The results are confirmed in a large-scale validation study on datasets from production and other fields. A trained XGBoost model is used as a surrogate for process simulation. Finally, multiple ways to improve the model are discussed.

Keywords: reinforcement learning, evolutionary algorithms, production process optimization, real-time optimization, hybrid-MPO

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Authors: Bogdan Walek, Vladimir Bradac, Radim Farana

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Distance learning systems offer useful methods of learning and usually contain final course test or another form of test. The paper proposes web application for evaluating tests using expert system in distance learning systems. Proposed web application is appropriate for didactic tests or tests with results for subsequent studying follow-up courses. Web application works with test questions and uses expert system and LFLC tool for test evaluation. After test evaluation the results are visualized and shown to student.

Keywords: distance learning, test, uncertainty, fuzzy, expert system, student

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Authors: Ayesha Sanjana Kawser Parsha

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S-acylation is an irreversible bond in which cysteine residues are linked to fatty acids palmitate (74%) or stearate (22%), either at the COOH or NH2 terminal, via a thioester linkage. There are several experimental methods that can be used to identify the S-palmitoylation site; however, since they require a lot of time, computational methods are becoming increasingly necessary. There aren't many predictors, however, that can locate S- palmitoylation sites in Arabidopsis Thaliana with sufficient accuracy. This research is based on the importance of building a better prediction tool. To identify the type of machine learning algorithm that predicts this site more accurately for the experimental dataset, several prediction tools were examined in this research, including the GPS PALM 6.0, pCysMod, GPS LIPID 1.0, CSS PALM 4.0, and NBA PALM. These analyses were conducted by constructing the receiver operating characteristics plot and the area under the curve score. An AI-driven deep learning-based prediction tool has been developed utilizing the analysis and three sequence-based input data, such as the amino acid composition, binary encoding profile, and autocorrelation features. The model was developed using five layers, two activation functions, associated parameters, and hyperparameters. The model was built using various combinations of features, and after training and validation, it performed better when all the features were present while using the experimental dataset for 8 and 10-fold cross-validations. While testing the model with unseen and new data, such as the GPS PALM 6.0 plant and pCysMod mouse, the model performed better, and the area under the curve score was near 1. It can be demonstrated that this model outperforms the prior tools in predicting the S- palmitoylation site in the experimental data set by comparing the area under curve score of 10-fold cross-validation of the new model with the established tools' area under curve score with their respective training sets. The objective of this study is to develop a prediction tool for Arabidopsis Thaliana that is more accurate than current tools, as measured by the area under the curve score. Plant food production and immunological treatment targets can both be managed by utilizing this method to forecast S- palmitoylation sites.

Keywords: S- palmitoylation, ROC PLOT, area under the curve, cross- validation score

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

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Authors: M. K. Wijayarathna, S. M. Buddika Harshanath

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Monitoring student engagement has become a crucial part of the educational process and a reliable indicator of the capacity to retain information. As online learning classrooms are now more common these days, students' attention levels have become increasingly important, making it more difficult to check each student's concentration level in an online classroom setting. To profile student attention to various gradients of engagement, a study is a plan to conduct using machine learning models. Using a convolutional neural network, the findings and confidence score of the high accuracy model are obtained. In this research, convolutional neural networks are using to help discover essential emotions that are critical in defining various levels of participation. Students' attention levels were shown to be influenced by emotions such as calm, enjoyment, surprise, and fear. An improved virtual learning system was created as a result of these data, which allowed teachers to focus their support and advise on those students who needed it. Student participation has formed as a crucial component of the learning technique and a consistent predictor of a student's capacity to retain material in the classroom. Convolutional neural networks have a plan to implement the platform. As a preliminary step, a video of the pupil would be taken. In the end, researchers used a convolutional neural network utilizing the Keras toolkit to take pictures of the recordings. Two convolutional neural network methods are planned to use to determine the pupils' attention level. Finally, those predicted student attention level results plan to display on the graphical user interface of the System.

Keywords: HTML5, JavaScript, Python flask framework, AI, graphical user

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Authors: R. Herscu-Kluska

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The rise of the flipped or inverted classroom meet the conceptual needs of our time. The evidence of increased student satisfaction and course grades improvement promoted the flipped learning approach. Due to the successful outcomes of the inverted classroom, the flipped learning became a pedagogy and educational rising strategy among all education sciences. The aim of this study is to analyze the effect of flipped classroom on higher order learning in chemistry courses since it has been suggested that in higher education courses, class time should focus on knowledge application. The results of this study indicate improving meta-cognitive thinking and learning skills. The students showed better ability to cope with higher order learning assignments during the actual class time, using inverted classroom strategy. These results suggest that flipped learning can be used as an effective pedagogy and educational strategy for developing higher order thinking skills, proved to contribute to building lifelong learning.

Keywords: chemistry education, flipped classroom, flipped learning, inverted classroom, science education

Procedia PDF Downloads 327

Authors: Ambuj Kumar Gautam, V. Vasu

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This paper introduces a head movement based Human Machine Interface (HMI) that uses the right and left movements of head to control a robot motion. Here we present an approach for making an effective technique for real-time face orientation information system, to control a robot which can be efficiently used for Electrical Powered Wheelchair (EPW). Basically this project aims at application related to HMI. The system (machine) identifies the orientation of the face movement with respect to the pixel values of image in a certain areas. Initially we take an image and divide that whole image into three parts on the basis of its number of columns. On the basis of orientation of face, maximum pixel value of approximate same range of (R, G, and B value of a pixel) lie in one of divided parts of image. This information we transfer to the microcontroller through serial communication port and control the motion of robot like forward motion, left and right turn and stop in real time by using head movements.

Keywords: electrical powered wheelchair (EPW), human machine interface (HMI), robotics, microcontroller

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Authors: D. S. Nagesh, G. L. Datta

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In the field of welding, various studies had been made by some of the previous investigators to predict as well as optimize weld bead geometric descriptors. Modeling of weld bead shape is important for predicting the quality of welds. In most of the cases, design of experiments technique to postulate multiple linear regression equations have been used. Nowadays, Genetic Algorithm (GA) an intelligent information treatment system with the characteristics of treating complex relationships as seen in welding processes used as a tool for inverse mapping/optimization of the process is attempted.

Keywords: smaw, genetic algorithm, bead geometry, optimization/inverse mapping

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Authors: D. S. Nagesh, G. L. Datta

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In the field of welding, various studies had been made by some of the previous investigators to predict as well as optimize weld bead geometric descriptors. Modeling of weld bead shape is important for predicting the quality of welds. In most of the cases design of experiments technique to postulate multiple linear regression equations have been used. Nowadays Genetic Algorithm (GA) an intelligent information treatment system with the characteristics of treating complex relationships as seen in welding processes used as a tool for inverse mapping/optimization of the process is attempted.

Keywords: SMAW, genetic algorithm, bead geometry, optimization/inverse mapping

Procedia PDF Downloads 411

Authors: Tushar Bhardwaj

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Internet of Things (IoT) possesses a dynamic network where the network nodes (mobile devices) are added and removed constantly and randomly, hence the traffic distribution in the network is quite variable and irregular. The basic but very important part in any network is route searching. We have many conventional route searching algorithms like link-state, and distance vector algorithms but they are restricted to the static point to point network topology. In this paper we propose a model that uses the Ant Colony Algorithm for route searching. It is dynamic in nature and has positive feedback mechanism that conforms to the route searching. We have also embedded the concept of Non-Deterministic Finite Automata [NDFA] minimization to reduce the network to increase the performance. Results show that Ant Colony Algorithm gives the shortest path from the source to destination node and NDFA minimization reduces the broadcasting storm effectively.

Keywords: routing, ant colony algorithm, NDFA, IoT

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Authors: Geeta Goyal

Abstract:

Teaching-learning of English as a second language is a challenge for the learner as well as the teacher. Whereas a student may find it hard and get demotivated while communicating in a language other than mother tongue, a teacher, too, finds it difficult to integrate necessary teaching material in lesson plans to maximize the outcome. Studies reveal that task-based teaching can be useful in diverse contexts in a second language classroom as it helps in creating opportunities for language exposure as per learners' interest and capability levels, which boosts their confidence and learning efficiency. The present study has analysed the impact of various activities carried out in a heterogenous group of second language learners at tertiary level in a semi-urban area in Haryana state of India. Language tasks were specifically planned with a focus on engaging groups of twenty-five students for a period of three weeks. These included language games such as spell-well, cross-naught besides other communicative and interactive tasks like mock-interviews, role plays, sharing experiences, storytelling, simulations, scene-enact, video-clipping, etc. Tools in form of handouts and cue cards were also used as per requirement. This experiment was conducted for ten groups of students taking bachelor’s courses in different streams of humanities, commerce, and sciences. Participants were continuously supervised, monitored, and guided by the respective teacher. Feedback was collected from the students through classroom observations, interviews, and questionnaires. Students' responses revealed that they felt comfortable and got plenty of opportunities to communicate freely without being afraid of making mistakes. It was observed that even slow/timid/shy learners got involved by getting an experience of English language usage in friendly environment. Moreover, it helped the teacher in establishing a trusting relationship with students and encouraged them to do the same with their classmates. The analysis of the data revealed that majority of students demonstrated improvement in their interest and enthusiasm in the class. The study revealed that task-based teaching was an effective method to improve the teaching-learning process under the given conditions.

Keywords: communication skills, English, second language, task-based teaching

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Authors: Latifa Sabri, Mohammed Benzirar, Mimoun Zazoui

Abstract:

The installation of photovoltaic systems is one of future sources to generate electricity without emitting pollutants. The photovoltaic cells used in these systems have demonstrated enormous efficiencies and advantages. Several researches have discussed the maximum efficiency of these technologies, but only a few experiences have succeeded to right weather conditions to get these results. In this paper, two types of cells were selected: crystalline and amorphous silicon. Using the method of genetic algorithm, the results show that for an ambient temperature of 25°C and direct irradiation of 625 W/m², the efficiency of crystalline silicon is 12% and 5% for amorphous silicon.

Keywords: PV, maximum efficiency, solar cell, genetic algorithm

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Authors: Chandra Mukherjee

Abstract:

The present paper is a representation of the work done in the field of ECG signal analysis using MATLAB 7.1 Platform. An accurate and simple ECG feature extraction algorithm is presented in this paper and developed algorithm is validated using BIOPAC software. To detect the QRS peak, ECG signal is processed by following mentioned stages- First Derivative, Second Derivative and then squaring of that second derivative. Efficiency of developed algorithm is tested on ECG samples from different database and real time ECG signals acquired using BIOPAC system. Firstly we have lead wise specified threshold value the samples above that value is marked and in the original signal, where these marked samples face change of slope are spotted as R-peak. On the left and right side of the R-peak, faces change of slope identified as Q and S peak, respectively. Now the inbuilt Detection algorithm of BIOPAC software is performed on same output sample and both outputs are compared. ECG baseline modulation correction is done after detecting characteristics points. The efficiency of the algorithm is tested using some validation parameters like Sensitivity, Positive Predictivity and we got satisfied value of these parameters.

Keywords: first derivative, variable threshold, slope reversal, baseline modulation correction

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Authors: Nahıd Naderı Anarı, Mojdeh Shafıee

Abstract:

Languaging has been identified as a contributor to language learning. Compared to oral languaging, written languaging seems to have been less explored. In order to fill this gap, this paper examined the effect of ‘metanotes’, namely metatalk in a written modality to identify whether written languaging actually facilitates language learning. Participants were instructed to take metanotes as they performed a translation task. The effect of metanotes was then analyzed by comparing the results of these participants’ pretest and posttest with those of participants who performed the same task without taking metanotes. The statistical tests showed no evidence of the expected role of metanotes in foreign language learning.

Keywords: EFL learners, foreign language learning, language teaching, metanotes

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Authors: Jinan Fiaidhi, Sabah Mohammed

Abstract:

Cross-domain learning in the medical field is a research challenge as many conditions, like in oncology imaging, use different imaging modalities. Moreover, in most of the medical learning applications, the sample training size is relatively small. Although few-shot learning (FSL) through the use of a Siamese neural network was able to be trained on a small sample with remarkable accuracy, FSL fails to be effective for use in multiple domains as their convolution weights are set for task-specific applications. In this paper, we are addressing this problem by enabling FSL to possess the ability to shift across domains by designing a two-layer FSL network that can learn individually from each domain and produce a shared features map with extra modulation to be used at the second layer that can recognize important targets from mix domains. Our initial experimentations based on mixed medical datasets like the Medical-MNIST reveal promising results. We aim to continue this research to perform full-scale analytics for testing our cross-domain FSL learning.

Keywords: Siamese neural network, few-shot learning, meta-learning, metric-based learning, thick data transformation and analytics

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