Search results for: machine learning and natural language processing

Authors: Selma Karabinar

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Lifelong learning can be described as a system where learning takes place for a person over the course of a lifespan and comprises formal, non-formal and informal learning to achieve the maximum possible improvement in personal, social, and vocational life. 21st century is marked with the digital technologies and people need to learn and adapt to new literacies as part of their lifelong learning. Our current knowledge gap brings to mind several questions: Do people with digital mindsets have different assumptions about affordances of digital technologies? How do digital mindsets lead language learners use digital technologies within and beyond classrooms? Does digital literacies have different significance for the learners? The presentation is based on a study attempted to answer these questions and show the relationship between lifelong learning and digital literacies. The study was conducted with learners of English language at a state university in Istanbul. The quantitative data in terms of participants' lifelong learning perception was collected through a lifelong learning scale from 150 students. Then 5 students with high and 5 with low lifelong learning perception were interviewed. They were questioned about their personal sense of agency in lifelong learning and how they use digital technologies in their language learning. Therefore, the qualitative data was analyzed in terms of their knowledge about digital literacies and actual use of it in their personal and educational life. The results of the study suggest why teaching new literacies are important for lifelong learning and also suggests implications for language teachers' education and language pedagogy.

Keywords: digital mindsets, language learning, lifelong learning, new literacies

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Authors: Thapanee Musiget

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Ethnic minority children’s language learning is believed that it can be developed through school system. However, many cases prove that these kids are left to challenge with multicultural context at school and sometimes decreased the ability to acquire new learning. Consequently, it is significant for ethnicity parents to consider that prompting their children at home before their actual school age can eliminate negative outcome of children's language acquisition. This paper discusses the approach of instructional use of parents and children language learning in the context of minority language group in Thailand. By conducting this investigation, secondary source of data was gathered with the purpose to point out some primary methods for parents and children in ethnicity. The process of language learning is based on the sociocultural theory of Vygotsky, which highlights expressive communication among individuals as the best motivating force in human development and learning. The article also highlights the role of parents as they lead the instruction approach. In the discussion part, the role of ethnic minority parents as a language instructor is offered as mediator.

Keywords: ethnic minority, language learning, multicultural context, sociocultural theory

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Authors: Rita Chattopadhyay, Vivek Anand Thoutam

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Speech is the most acceptable means of communication where we can quickly exchange our feelings and thoughts. Quite often, people can communicate orally but cannot interact or work with computers or devices. It’s easy and quick to give speech commands than typing commands to computers. In the same way, it’s easy listening to audio played from a device than extract output from computers or devices. Especially with Robotics being an emerging market with applications in warehouses, the hospitality industry, consumer electronics, assistive technology, etc., speech-based human-machine interaction is emerging as a lucrative feature for robot manufacturers. Considering this factor, the objective of this paper is to design the “Audio-Visual Co-Data Processing Pipeline.” This pipeline is an integrated version of Automatic speech recognition, a Natural language model for text understanding, object detection, and text-to-speech modules. There are many Deep Learning models for each type of the modules mentioned above, but OpenVINO Model Zoo models are used because the OpenVINO toolkit covers both computer vision and non-computer vision workloads across Intel hardware and maximizes performance, and accelerates application development. A speech command is given as input that has information about target objects to be detected and start and end times to extract the required interval from the video. Speech is converted to text using the Automatic speech recognition QuartzNet model. The summary is extracted from text using a natural language model Generative Pre-Trained Transformer-3 (GPT-3). Based on the summary, essential frames from the video are extracted, and the You Only Look Once (YOLO) object detection model detects You Only Look Once (YOLO) objects on these extracted frames. Frame numbers that have target objects (specified objects in the speech command) are saved as text. Finally, this text (frame numbers) is converted to speech using text to speech model and will be played from the device. This project is developed for 80 You Only Look Once (YOLO) labels, and the user can extract frames based on only one or two target labels. This pipeline can be extended for more than two target labels easily by making appropriate changes in the object detection module. This project is developed for four different speech command formats by including sample examples in the prompt used by Generative Pre-Trained Transformer-3 (GPT-3) model. Based on user preference, one can come up with a new speech command format by including some examples of the respective format in the prompt used by the Generative Pre-Trained Transformer-3 (GPT-3) model. This pipeline can be used in many projects like human-machine interface, human-robot interaction, and surveillance through speech commands. All object detection projects can be upgraded using this pipeline so that one can give speech commands and output is played from the device.

Keywords: OpenVINO, automatic speech recognition, natural language processing, object detection, text to speech

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Authors: Hani Mustafa, Cristina Gonzalez Ruiz, Estelle Bech

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The growing influence of digital technologies has made learning and interaction more accessible, resulting in effective collaboration if properly managed. Technology enabled learning has become an important conduit for learning, including collaborative learning. The use of wiki technology, for example, has opened a new learning platform that enables the integration of social, linguistic, and cognitive processes of language learning. It encourages students to collaborate in the construction, analysis, and understanding of knowledge. But to what extent is the use of wikis effective in promoting collaborative learning among students. In addition, how do students perceive this technology in enhancing their language learning? In this study, students were be given a wiki project to complete collaboratively with their group members. Students had to write collaboratively to produce and present a seven-day travel plan in which they had to describe places to visit and things to do to explore the best historical and cultural aspects of the country. The study involves students learning French, Malay, and Spanish as a foreign language. In completing this wiki project, students will move from passive learning of language to real engagement with classmates, requiring them to collaborate and negotiate effectively with one another. The objective of the study is to ascertain to what extent does wiki technology helped in promoting collaborative learning and improving language skills from students’ perception. It is found that while there was improvement in students language skills, the overall experience was less positive due to unfamiliarity with a new learning tool.

Keywords: collaborative learning, foreign language, wiki, teaching

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Authors: Carlos Roncero-Parra, Alfonso Parreño-Torres, Jorge Mateo Sotos, Alejandro L. Borja

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In this research, different methods based on machine/deep learning algorithms are presented for the classification and diagnosis of patients with mental disorders such as alzheimer. For this purpose, the signals obtained from 32 unipolar electrodes identified by non-invasive EEG were examined, and their basic properties were obtained. More specifically, different well-known machine learning based classifiers have been used, i.e., support vector machine (SVM), Bayesian linear discriminant analysis (BLDA), decision tree (DT), Gaussian Naïve Bayes (GNB), K-nearest neighbor (KNN) and Convolutional Neural Network (CNN). A total of 668 patients from five different hospitals have been studied in the period from 2011 to 2021. The best accuracy is obtained was around 93 % in both ADM and ADA classifications. It can be concluded that such a classification will enable the training of algorithms that can be used to identify and classify different mental disorders with high accuracy.

Keywords: alzheimer, machine learning, deep learning, EEG

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Authors: Joao Carlos Koch Junior, Risa Takashima

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Learning disabilities have been extensively and increasingly studied in recent times. In spite of this, there is arguably a scarce number of studies addressing a key issue, which is the impact of foreign-language requirements on students with learning disabilities in higher education, and the lack of training or awareness of teachers regarding language learning disabilities. This study is an attempt to address this issue. An extensive review of the literature in multiple fields will be summarised. This, paired with a case-analysis of a university adopting a more inclusive approach towards special-needs students in its foreign-language programme, this presentation aims to establish a link between different studies and propose a number of suggestions to make language classrooms more inclusive.

Keywords: foreign language teaching, higher education, language teacher education, learning disabilities

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Authors: Anli Du Preez, James Bekker

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Data is currently one of the most critical and influential emerging technologies. However, the true potential of data is yet to be exploited since, currently, about 1% of generated data are ever actually analyzed for value creation. There is a data gap where data is not explored due to the lack of data analytics infrastructure and the required data analytics skills. This study developed a decision support framework for data analytics by following Jabareen’s framework development methodology. The study focused on machine learning algorithms, which is a subset of data analytics. The developed framework is designed to assist data analysts with little experience, in choosing the appropriate machine learning algorithm given the purpose of their application.

Keywords: Data analytics, Industrial engineering, Machine learning, Value creation

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Authors: Peter Adekunle, Clinton Aigbavboa, Matthew Ikuabe, Opeoluwa Akinradewo

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The future of machine learning (ML) in building may seem like a distant idea that will take decades to materialize, but it is actually far closer than previously believed. In reality, the built environment has been progressively increasing interest in machine learning. Although it could appear to be a very technical, impersonal approach, it can really make things more personable. Instead of eliminating humans out of the equation, machine learning allows people do their real work more efficiently. It is therefore vital to evaluate the factors influencing the implementation and challenges of implementing machine learning techniques in the South African built environment. The study's design was one of a survey. In South Africa, construction workers and professionals were given a total of one hundred fifty (150) questionnaires, of which one hundred and twenty-four (124) were returned and deemed eligible for study. Utilizing percentage, mean item scores, standard deviation, and Kruskal-Wallis, the collected data was analyzed. The results demonstrate that the top factors influencing the adoption of machine learning are knowledge level and a lack of understanding of its potential benefits. While lack of collaboration among stakeholders and lack of tools and services are the key hurdles to the deployment of machine learning within the South African built environment. The study came to the conclusion that ML adoption should be promoted in order to increase safety, productivity, and service quality within the built environment.

Keywords: machine learning, implementation, built environment, construction stakeholders

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Authors: Gabriel Montague, Max Vilgalys, Catherine H. Crawford, Jorge Ortiz, Dava Newman

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There has never been so much publicly accessible atmospheric and environmental data. The possibilities of these data are exciting, but the sheer volume of available datasets represents a new challenge for researchers. The task of identifying and working with a new dataset has become more difficult with the amount and variety of available data. Datasets are often documented in ways that differ substantially from the common English used to describe the same topics. This presents a barrier not only for new scientists, but for researchers looking to find comparisons across multiple datasets or specialists from other disciplines hoping to collaborate. This paper proposes a method for addressing this obstacle: creating a knowledge graph to bridge the gap between everyday English language and the technical language surrounding these datasets. Knowledge graph generation is already a well-established field, although there are some unique challenges posed by working with Earth data. One is the sheer size of the databases – it would be infeasible to replicate or analyze all the data stored by an organization like The National Aeronautics and Space Administration (NASA) or the European Space Agency. Instead, this approach identifies topics from metadata available for datasets in NASA’s Earthdata database, which can then be used to directly request and access the raw data from NASA. By starting with a single metadata standard, this paper establishes an approach that can be generalized to different databases, but leaves the challenge of metadata harmonization for future work. Topics generated from the metadata are then linked to topics from a collection of English queries through a variety of standard and custom natural language processing (NLP) methods. The results from this method are then compared to a baseline of elastic search applied to the metadata. This comparison shows the benefits of the proposed knowledge graph system over existing methods, particularly in interpreting natural language queries and interpreting topics in metadata. For the research community, this work introduces an application of NLP to the ecological and environmental sciences, expanding the possibilities of how machine learning can be applied in this discipline. But perhaps more importantly, it establishes the foundation for a platform that can enable common English to access knowledge that previously required considerable effort and experience. By making this public data accessible to the full public, this work has the potential to transform environmental understanding, engagement, and action.

Keywords: earth metadata, knowledge graphs, natural language processing, question-answer systems

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Authors: Jamshid Qodirov, Sirojiddin Komolov, Ravilov Mirahmad, Olimjon Mirzayev

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Nowadays, natural language processing tools play a crucial role in our daily lives, including various techniques with text processing. There are very advanced models in modern languages, such as English, Russian etc. But, in some languages, such as Uzbek, the NLP models have been developed recently. Thus, there are only a few NLP models in Uzbek language. Moreover, there is no such work that could show which Uzbek NLP model behaves in different situations and when to use them. This work tries to close this gap and compares the Uzbek NLP models existing as of the time this article was written. The authors try to compare the NLP models in two different scenarios: sentiment analysis and sentence similarity, which are the implementations of the two most common problems in the industry: classification and similarity. Another outcome from this work is two datasets for classification and sentence similarity in Uzbek language that we generated ourselves and can be useful in both industry and academia as well.

Keywords: NLP, benchmak, bert, vectorization

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Authors: Nabil Shawkat, Jamil Saquer

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Social media has accelerated our ability to engage with others and eliminated many communication barriers. On the other hand, the widespread use of social media resulted in an increase in online hate speech. This has drastic impacts on vulnerable individuals and societies. Therefore, it is critical to detect hate speech to prevent innocent users and vulnerable communities from becoming victims of hate speech. We investigate the performance of different deep learning and machine learning algorithms on three different datasets. Our results show that the BERT model gives the best performance among all the models by achieving an F1-score of 90.6% on one of the datasets and F1-scores of 89.7% and 88.2% on the other two datasets.

Keywords: hate speech, machine learning, deep learning, abusive words, social media, text classification

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Authors: Olukemi E. Adetuyi-Olu-Francis

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Learning is natural for living. One stops learning when life ends. Hence, there is no negotiating life-long learning. An individual has the innate ability to learn as many languages as he/she desires as long as life exists. French language education to every Nigerian teacher-trainee is a necessity. Nigeria’s geographical location requires that the French language should be upheld for economic and cultural co-operations between Nigeria and the francophone countries sharing borders with her. The French language will enhance the leadership roles of the teacher-trainees and their ability to function across borders. The 21st century learning tools are basically digital, and many apps are complementing the actual classroom interactions. This study examined the communicative competence in the French language to equip Nigerian teacher-trainees in the new-normal society using mobile apps as a lifelong learning tool. Three research questions and hypotheses guided the study, and the researcher adopted a pre-test, a post-test experimental design, using a sample size of 87 teacher-trainees in South-south geopolitical zone of Nigeria. Results showed that the use of mobile apps is effective for learning the French language. One of the recommendations is that the use of mobile apps should be encouraged for all Nigerian youths to learn the French language for enhancing leadership roles in the world of work and for international interactions for socio-economic co-operations with Nigerian neighboring countries.

Keywords: communicative competence, french language, life long learning, mobile apps, new normal society, teacher trainees

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Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

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Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as vital physiological signs, images, and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision-making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful in influencing the judgement of clinical sentiment in ICU clinical notes. This paper introduces two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of clinical transformer models that can reliably predict clinical sentiment for notes of COVID patients in the ICU. We train the model on clinical notes for COVID-19 patients, a type of notes that were not previously seen by clinicalBERT, and Bio_Discharge_Summary_BERT. The model, which was based on clinicalBERT achieves higher predictive accuracy (Acc 93.33%, AUC 0.98, and precision 0.96 ). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and precision 0.92 ).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation

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Authors: May George

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Inference plays an important role in the learning process and it can lead to a rapid acquisition of a second language. When learning a non-native language, i.e., a critical language like Arabic, the students depend on the teacher’s support most of the time to learn new concepts. The students focus on memorizing the new vocabulary and stress on learning all the grammatical rules. Hence, the students became mechanical and cannot produce the language easily. As a result, they are unable to predict the meaning of words in the context by relying heavily on the teacher, in that they cannot link their prior knowledge or even identify the meaning of the words without the support of the teacher. This study explores how the teacher guides students learning during the inference process and what are the processes of learning that can direct student’s inference.

Keywords: inference, reading, Arabic, language acquisition

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Authors: Lucia Silveira Alda

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This research aims to analyze critically a set of studies published in the Proceedings of the International Conference on Mobile Learning of IADIS, from 2008 until 2012, which addresses the issue of foreign language learning mediated by mobile phones. The theoretical review of this study is based on the Vygotskian assumptions about tools and mediated learning and the concepts of mobile learning, CALL and MALL. In addition, the diffusion rates of the mobile phone and especially its potential are considered. Through systematic review and meta-analysis, this research intended to identify similarities and differences between the identified characteristics in the studies on the subject of language learning and mobile phone. From the analysis of the results, this study verifies that the mobile phone stands out for its mobility and portability. Furthermore, this device presented positive aspects towards student motivation in language learning. The studies were favorable to mobile phone use for learning. It was also found that the challenges in using this tool are not technical, but didactic and methodological, including the need to reflect on practical proposals. The findings of this study may direct further research in the area of language learning mediated by mobile phones.

Keywords: language learning, mobile learning, mobile phones, technology

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Authors: Vincenzo Capalbo, Gianpaolo Ghiani, Emanuele Manni

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Global e-commerce sales have reached unprecedented levels in the past few years. As this trend is only predicted to go up as we continue into the ’20s, new challenges will be faced by companies when planning and controlling e-commerce logistics. In this paper, we survey the related literature on Optimization and Machine Learning as well as on combined methodologies. We also identify the distinctive features of next-generation planning algorithms - namely scalability, model-and-run features and learning capabilities - that will be fundamental to cope with the scale and complexity of logistics in the next decade.

Keywords: e-commerce, hardware acceleration, logistics, machine learning, mixed integer programming, optimization

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Authors: Benyamin Ahmadnia, Javier Serrano

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In this paper we compare two different approaches for translating from Persian to Spanish, as a language pair with scarce parallel corpus. The first approach involves direct transfer using an statistical machine translation system, which is available for this language pair. The second approach involves translation through English, as a pivot language, which has more translation resources and more advanced translation systems available. The results show that, it is possible to achieve better translation quality using English as a pivot language in either approach outperforms direct translation from Persian to Spanish. Our best result is the pivot system which scores higher than direct translation by (1.12) BLEU points.

Keywords: statistical machine translation, direct translation approach, pivot language translation approach, parallel corpus

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Authors: Sakshi V. Tantak, Vishap K. Malik, Neelanjney Pilarisetty

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A patent is an exclusive right granted for an invention. It can be a product or a process that provides an innovative method of doing something, or offers a new technical perspective or solution to a problem. A patent can be obtained by making the technical information and details about the invention publicly available. The patent owner has exclusive rights to prevent or stop anyone from using the patented invention for commercial uses. Any commercial usage, distribution, import or export of a patented invention or product requires the patent owner’s consent. It has been observed that the central and important parts of patents are scripted in idiosyncratic and complex linguistic structures that can be difficult to read, comprehend or interpret for the masses. The abstracts of these patents tend to obfuscate the precise nature of the patent instead of clarifying it via direct and simple linguistic constructs. This makes it necessary to have an efficient access to this knowledge via concise and transparent summaries. However, as mentioned above, due to complex and repetitive linguistic constructs and extremely long sentences, common extraction-oriented automatic text summarization methods should not be expected to show a remarkable performance when applied to patent documents. Other, more content-oriented or abstractive summarization techniques are able to perform much better and generate more concise summaries. This paper proposes an efficient summarization system for patents using artificial intelligence, natural language processing and deep learning techniques to condense the knowledge and essential information from a patent document into a single summary that is easier to understand without any redundant formatting and difficult jargon.

Keywords: abstractive summarization, deep learning, natural language Processing, patent document

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Authors: Pawel Twardowski, Maciej Tabaszewski, Jakub Czyżycki

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The main goal of the work was to use machine learning to predict cutting-edge wear. The research was carried out while milling hardened steel with sintered carbide cutters at various cutting speeds. During the tests, cutting-edge wear was measured, and vibration acceleration signals were also measured. Appropriate measures were determined from the vibration signals and served as input data in the machine-learning process. Two approaches were used in this work. The first one involved a two-state classification of the cutting edge - suitable and unfit for further work. In the second approach, prediction of the cutting-edge state based on vibration signals was used. The obtained research results show that the appropriate use of machine learning algorithms gives excellent results related to monitoring cutting edge during the process.

Keywords: milling of hardened steel, tool wear, vibrations, machine learning

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Authors: Yizhen Zhao, Adam S. Z. Belloum, Goncalo Maia Da Costa, Zhiming Zhao

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Machine learning has evolved from an area of academic research to a real-word applied field. This change comes with challenges, gaps and differences exist between common practices in academic environments and the ones in production environments. Following continuous integration, development and delivery practices in software engineering, similar trends have happened in machine learning (ML) systems, called MLOps. In this paper we propose a framework that helps to streamline and introduce best practices that facilitate the ML lifecycle in an industrial setting. This framework can be used as a template that can be customized to implement various machine learning experiment. The proposed framework is modular and can be recomposed to be adapted to various use cases (e.g. data versioning, remote training on cloud). The framework inherits practices from DevOps and introduces other practices that are unique to the machine learning system (e.g.data versioning). Our MLOps practices automate the entire machine learning lifecycle, bridge the gap between development and operation.

Keywords: cloud computing, continuous development, data versioning, DevOps, industrial setting, MLOps

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Authors: Federico Pittino, Thomas Arnold

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The shredding of waste materials is a key step in the recycling process towards the circular economy. Industrial shredders for waste processing operate in very harsh operating conditions, leading to the need for frequent maintenance of critical components. Maintenance optimization is particularly important also to increase the machine’s efficiency, thereby reducing the operational costs. In this work, a monitoring system has been developed and deployed on an industrial shredder located at a waste recycling plant in Austria. The machine has been monitored for one year, and methods for predictive maintenance have been developed for two key components: the cutting knives and the drive belt. The large amount of collected data is leveraged by statistical machine learning techniques, thereby not requiring very detailed knowledge of the machine or its live operating conditions. The results show that, despite the wide range of operating conditions, a reliable estimate of the optimal time for maintenance can be derived. Moreover, the trade-off between the cost of maintenance and the increase in power consumption due to the wear state of the monitored components of the machine is investigated. This work proves the benefits of real-time monitoring system for the efficient operation of industrial shredders.

Keywords: predictive maintenance, circular economy, industrial shredder, cost optimization, statistical machine learning

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Authors: Elleuch Imen

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In this paper, it propose a new approach for learning Arabic as a foreign language via audio-visual translation, particularly subtitling. The approach consists of developing video sequences appropriate to different levels of learning (from A1 to C2) containing conversations, quizzes, games and others. Each video aims to achieve a specific objective, such as the correct pronunciation of Arabic words, the correct syntactic structuring of Arabic sentences, the recognition of the morphological characteristics of terms and the semantic understanding of statements. The subtitled videos obtained can be incorporated into different Arabic second language learning tools such as Moocs, websites, platforms, etc.

Keywords: arabic foreign language, learning, audio-visuel translation, subtitled videos

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Authors: Lu Xi, Li Pan, Wen Mengmeng

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The stiffness of each component has different influences on the stiffness of the machine tool. Taking the five-axis gantry machining center as an example, we made the modal analysis of the machine tool, followed by raising and lowering the stiffness of the pillar, slide plate, beam, ram and saddle so as to study the stiffness matching among these components on the standard of whether the stiffness of the modified machine tool changes more than 50% relative to the stiffness of the original machine tool. The structural optimization of the machine tool can be realized by changing the stiffness of the components whose stiffness is mismatched. For example, the stiffness of the beam is mismatching. The natural frequencies of the first six orders of the beam increased by 7.70%, 0.38%, 6.82%, 7.96%, 18.72% and 23.13%, with the weight increased by 28Kg, leading to the natural frequencies of several orders which had a great influence on the dynamic performance of the whole machine increased by 1.44%, 0.43%, 0.065%, which verified the correctness of the optimization method based on stiffness matching proposed in this paper.

Keywords: machine tool, optimization, modal analysis, stiffness matching

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Authors: Abdulwakeel B. Raji

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This paper introduces a concept of an intelligent virtual learning environment that involves communication between learners and an artificially intelligent teaching agent in an attempt to replicate classroom learning interactions. The benefits of this technology over current e-learning practices is that it creates a virtual classroom where real time adaptive learning interactions are made possible. This is a move away from the static learning practices currently being adopted by e-learning systems. Over the years, artificial intelligence has been applied to various fields, including and not limited to medicine, military applications, psychology, marketing etc. The purpose of e-learning applications is to ensure users are able to learn outside of the classroom, but a major limitation has been the inability to fully replicate classroom interactions between teacher and students. This study used comparative surveys to gain information and understanding of the current learning practices in Nigerian universities and how they compare to these practices compare to the use of a developed e-learning system. The study was conducted by attending several lectures and noting the interactions between lecturers and tutors and as an aftermath, a software has been developed that deploys the use of an artificial intelligent teaching-agent alongside an e-learning system to enhance user learning experience and attempt to create the similar learning interactions to those found in classroom and lecture hall settings. Dialogflow has been used to implement a teaching-agent, which has been developed using JSON, which serves as a virtual teacher. Course content has been created using HTML, CSS, PHP and JAVASCRIPT as a web-based application. This technology can run on handheld devices and Google based home technologies to give learners an access to the teaching agent at any time. This technology also implements the use of definite clause grammars and natural language processing to match user inputs and requests with defined rules to replicate learning interactions. This technology developed covers familiar classroom scenarios such as answering users’ questions, asking ‘do you understand’ at regular intervals and answering subsequent requests, taking advanced user queries to give feedbacks at other periods. This software technology uses deep learning techniques to learn user interactions and patterns to subsequently enhance user learning experience. A system testing has been undergone by undergraduate students in the UK and Nigeria on the course ‘Introduction to Database Development’. Test results and feedback from users shows that this study and developed software is a significant improvement on existing e-learning systems. Further experiments are to be run using the software with different students and more course contents.

Keywords: virtual learning, natural language processing, definite clause grammars, deep learning, artificial intelligence

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Authors: Talal Alshammari, Nasser Alshammari, Mohamed Sedky, Chris Howard

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With the widespread adoption of the Internet-connected devices, and with the prevalence of the Internet of Things (IoT) applications, there is an increased interest in machine learning techniques that can provide useful and interesting services in the smart home domain. The areas that machine learning techniques can help advance are varied and ever-evolving. Classifying smart home inhabitants’ Activities of Daily Living (ADLs), is one prominent example. The ability of machine learning technique to find meaningful spatio-temporal relations of high-dimensional data is an important requirement as well. This paper presents a comparative evaluation of state-of-the-art machine learning techniques to classify ADLs in the smart home domain. Forty-two synthetic datasets and two real-world datasets with multiple inhabitants are used to evaluate and compare the performance of the identified machine learning techniques. Our results show significant performance differences between the evaluated techniques. Such as AdaBoost, Cortical Learning Algorithm (CLA), Decision Trees, Hidden Markov Model (HMM), Multi-layer Perceptron (MLP), Structured Perceptron and Support Vector Machines (SVM). Overall, neural network based techniques have shown superiority over the other tested techniques.

Keywords: activities of daily living, classification, internet of things, machine learning, prediction, smart home

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Authors: Elham Sharifineyestani, Mohammad Farshchin

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Tropical cyclones are one of the most expensive and deadliest natural disasters. They cause heavy rainfall and serious flash flooding that result in billions of dollars of damage and considerable mortality each year in the United States. Prediction of the frequency of tropical cyclone-induced rainfall events can be helpful in emergency planning and flood risk management. In this study, we have developed a machine-learning model to predict the exceedance frequencies of tropical cyclone-induced rainfall events in the United States. Model results show a satisfactory agreement with available observations. To examine the effectiveness of our approach, we also have compared the result of our predictions with the exceedance frequencies predicted using a physics-based rainfall model by Feldmann.

Keywords: flash flooding, tropical cyclones, frequencies, machine learning, risk management

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Authors: Nicholas V. Scott, Jack McCarthy

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Military, law enforcement, and counter terrorism organizations are often tasked with target detection and image characterization of scenes containing explosive materials in various types of environments where light scattering intensity is high. Mitigation of this photonic noise using classical digital filtration and signal processing can be difficult. This is partially due to the lack of robust image processing methods for photonic noise removal, which strongly influence high resolution target detection and machine learning-based pattern recognition. Such analysis is crucial to the delivery of reliable intelligence. Polarization filters are a possible method for ambient glare reduction by allowing only certain modes of the electromagnetic field to be captured, providing strong scene contrast. An experiment was carried out utilizing a polarization lens attached to a hyperspectral imagery camera for the purpose of exploring the degree to which an imaged polarized scene of potassium, phosphorus, and nitrogen mixture allows for improved target detection and image segmentation. Preliminary imagery results based on the application of machine learning algorithms, including competitive leaky learning and distance metric analysis, to polarized hyperspectral imagery, suggest that polarization filters provide a slight advantage in image segmentation. The results of this work have implications for understanding the presence of explosive material in dry, desert areas where reflective glare is a significant impediment to scene characterization.

Keywords: explosive material, hyperspectral imagery, image segmentation, machine learning, polarization

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Authors: Ng Liang Shen, Hau Yuan Wen

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Arrhythmia analysis of ECG signal plays a major role in diagnosing most of the cardiac diseases. Therefore, a single arrhythmia detection of an electrocardiographic (ECG) record can determine multiple pattern of various algorithms and match accordingly each ECG beats based on Machine Learning supervised learning. These researchers used different features and classification methods to classify different arrhythmia types. A major problem in these studies is the fact that the symptoms of the disease do not show all the time in the ECG record. Hence, a successful diagnosis might require the manual investigation of several hours of ECG records. The point of this paper presents investigations cardiovascular ailment in Electrocardiogram (ECG) Signals for Cardiac Arrhythmia utilizing examination of ECG irregular wave frames via heart beat as correspond arrhythmia which with Machine Learning Pattern Recognition.

Keywords: electrocardiogram, ECG, classification, machine learning, pattern recognition, detection, QRS

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Authors: Suwaree Yordchim

Abstract:

The objective is to study the satisfaction on English with an online learning. Online learning system mainly consists of English lessons, exercises, tests, web boards, and supplementary lessons for language practice. The sample groups are 80 Thai students studying English for Business Communication, majoring in Hotel and Lodging Management. The data are analyzed by mean, standard deviation (S.D.) value from the questionnaires. The results were found that the most average of satisfaction on academic aspects are technological searching tool through E-learning system that support the students’ learning (4.51), knowledge evaluation on prepost learning and teaching (4.45), and change for project selections according to their interest, subject contents including practice in the real situations (4.45), respectively.

Keywords: English language learning, online system, online learning, supplementary lessons

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Authors: Sarayu Agarwal

Abstract:

This paper aims to teach English (secondary language) by bridging the understanding between the Regional language (primary language) and the English Language (secondary language). Here primary language is the one a person has learned from birth or within the critical period, while secondary language would be any other language one learns or speaks. The paper also focuses on evolving old teaching methods to a contemporary participatory model of learning and teaching. Pilot studies were conducted to gauge an understanding of student’s knowledge of the English language. Teachers and students were interviewed and their academic curriculum was assessed as a part of the initial study. Extensive literature study and design thinking principles were used to devise a solution to the problem. The objective is met using a holistic learning kit/card game to teach children word recognition, word pronunciation, word spelling and writing words. Implication of the paper is a noticeable improvement in the understanding and grasping of English language. With increasing usage and applicability of English as a second language (ESL) world over, the paper becomes relevant due to its easy replicability to any other primary or secondary language. Future scope of this paper would be transforming the idea of participatory learning into self-regulated learning methods. With the upcoming govt. learning centres in rural areas and provision of smart devices such as tablets, the development of the card games into digital applications seems very feasible.

Keywords: English as a second language, vocabulary-building card games, learning through gamification, rural education

Procedia PDF Downloads 223