Search results for: conventional learning

Authors: Nobue T. Ellis

Abstract:

This preliminary study attempts to see if a learning environment influences instructor’s teaching strategies and learners’ in-class activities in a foreign language class at a university in Japan. The class under study was conducted in a computer room, while the majority of classes of the same course were offered in traditional classrooms without computers. The study also sees if the unplanned blended learning environment, enhanced, or worked against, in achieving course goals, by paying close attention to in-class artefacts, such as computers. In the macro-level analysis, the course syllabus and weekly itinerary of the course were looked at; and in the microlevel analysis, nonhuman actors in their environments were named and analyzed to see how they influenced the learners’ task processes. The result indicated that students were heavily influenced by the presence of computers, which lead them to disregard some aspects of intended learning objectives.

Keywords: Computer-assisted language learning, actor-network theory, English as a foreign language, task-based teaching.

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Authors: Kamel Hashem, David Mioduser

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This paper describes part of a project about Learningby- Modeling (LbM). Studying complex systems is increasingly important in teaching and learning many science domains. Many features of complex systems make it difficult for students to develop deep understanding. Previous research indicates that involvement with modeling scientific phenomena and complex systems can play a powerful role in science learning. Some researchers argue with this view indicating that models and modeling do not contribute to understanding complexity concepts, since these increases the cognitive load on students. This study will investigate the effect of different modes of involvement in exploring scientific phenomena using computer simulation tools, on students- mental model from the perspective of structure, behavior and function. Quantitative and qualitative methods are used to report about 121 freshmen students that engaged in participatory simulations about complex phenomena, showing emergent, self-organized and decentralized patterns. Results show that LbM plays a major role in students' concept formation about complexity concepts.

Keywords: Complexity, Educational technology, Learning by modeling, Mental models

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Authors: Kavita Burse, Manish Manoria, Vishnu P. S. Kirar

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The back propagation algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a training algorithm consisting of a learning rate and a momentum factor. The major drawbacks of above learning algorithm are the problems of local minima and slow convergence speeds. The addition of an extra term, called a proportional factor reduces the convergence of the back propagation algorithm. We have applied the three term back propagation to multiplicative neural network learning. The algorithm is tested on XOR and parity problem and compared with the standard back propagation training algorithm.

Keywords: Three term back propagation, multiplicative neuralnetwork, proportional factor, local minima.

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Authors: Praveen Boinee, Alessandro De Angelis, Gian Luca Foresti

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Leo Breimans Random Forests (RF) is a recent development in tree based classifiers and quickly proven to be one of the most important algorithms in the machine learning literature. It has shown robust and improved results of classifications on standard data sets. Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and shown improvements in classification results for several benchmarking data sets with mainly decision trees as their base classifiers. In this paper we experiment to apply these Meta learning techniques to the random forests. We experiment the working of the ensembles of random forests on the standard data sets available in UCI data sets. We compare the original random forest algorithm with their ensemble counterparts and discuss the results.

Keywords: Random Forests [RF], ensembles, UCI.

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Authors: S. K. Tomar, R. Prasad, S. Panda, C. Ardil

Abstract:

Reduction of Single Input Single Output (SISO) discrete systems into lower order model, using a conventional and an evolutionary technique is presented in this paper. In the conventional technique, the mixed advantages of Modified Cauer Form (MCF) and differentiation are used. In this method the original discrete system is, first, converted into equivalent continuous system by applying bilinear transformation. The denominator of the equivalent continuous system and its reciprocal are differentiated successively, the reduced denominator of the desired order is obtained by combining the differentiated polynomials. The numerator is obtained by matching the quotients of MCF. The reduced continuous system is converted back into discrete system using inverse bilinear transformation. In the evolutionary technique method, Particle Swarm Optimization (PSO) is employed to reduce the higher order model. PSO method is based on the minimization of the Integral Squared Error (ISE) between the transient responses of original higher order model and the reduced order model pertaining to a unit step input. Both the methods are illustrated through numerical example.

Keywords: Discrete System, Single Input Single Output (SISO), Bilinear Transformation, Reduced Order Model, Modified CauerForm, Polynomial Differentiation, Particle Swarm Optimization, Integral Squared Error.

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Authors: A. K. Razilan, A. B. Amzari, B. Ap-azli, A. R. Safawi

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Explosion in information management and information system technology has brought dramatic changes in learning and library system environments. The use of academic digital libraries does witness the spectacular impact on academic societies’ way of performing their study in Malaysia, a country with a multi-racial people. This paper highlights a research on examining the socio-demographic differences on the preference and use of academic digital libraries as compared to physical libraries at higher learning institutions. Findings indicate that preference towards digital libraries differed between ethnicity, gender and university. However none of the socio-demographic factors is statistically significant in terms of the use of digital libraries.

Keywords: Socio-demographic, academic digital library, preference, use.

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Authors: S. A. Moeini, M. T.Ahmadian

Abstract:

Structural behavior of ring stiffened thick walled cylinders made of functionally graded materials (FGMs) is investigated in this paper. Functionally graded materials are inhomogeneous composites which are usually made from a mixture of metal and ceramic. The gradient compositional variation of the constituents from one surface to the other provides an elegant solution to the problem of high transverse shear stresses that are induced when two dissimilar materials with large differences in material properties are bonded. FGM formation of the cylinder is modeled by power-law exponent and the variation of characteristics is supposed to be in radial direction. A finite element formulation is derived for the analysis. According to the property variation of the constituent materials in the radial direction of the wall, it is not convenient to use conventional elements to model and analyze the structure of the stiffened FGM cylinders. In this paper a new cylindrical super-element is used to model the finite element formulation and analyze the static and modal behavior of stiffened FGM thick walled cylinders. By using this super-element the number of elements, which are needed for modeling, will reduce significantly and the process time is less in comparison with conventional finite element formulations. Results for static and modal analysis are evaluated and verified by comparison to finite element formulation with conventional elements. Comparison indicates a good conformity between results.

Keywords: FGMs, Modal analysis, Static analysis, Stiffened cylinders.

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Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: Deep learning, convolutional neural network, LSTM, housing prediction.

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Authors: F. Havas Beigi, M. Vafaee Yeganeh, E. Mohammadi

Abstract:

Evaluation and survey of curriculum quality as one of the most important components of universities system is necessary for different levels in higher education. The main purpose of this study was to survey of the curriculum quality of Actuarial science field. Case: University of SHahid Beheshti and Higher education institute of Eco insurance (according to viewpoint of students, alumni, employers and faculty members). Descriptive statistics (mean, tables, percentage, and frequency distribution) and inferential statistics (CHI SQUARE) were used to analyze the data. Six criteria considered for the Quality of curriculum: objectives, content, teaching and learning methods, space and facilities, Time, assessment of learning. Content, teaching and learning methods, space and facilities, assessment of learning criteria were relatively desirable level, objectives and time criterions were desirable level. The quality of curriculum of Actuarial Science field was relatively desirable level.

Keywords: Quality, curriculum, Actuarial science, higher education

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Authors: Sayed Hadi Sadeghi

Abstract:

This research study examines the current state of support services for e-network practice in an Australian and an American university. It identifies information that will be of assistance to Australian and American universities to improve their existing online programs. The study investigated the two universities using a quantitative methodological approach. Participants were students, lecturers and admins of universities engaged with online courses and learning management systems. The support services for e-network practice variables, namely academic support services, administrative support and technical support, were investigated for e-practice. Evaluations of e-network support service and its sub factors were above average and excellent in both countries, although the American admins and lecturers tended to evaluate this factor higher than others did. Support practice was evaluated higher by all participants of an American university than by Australians. One explanation for the results may be that most suppliers of the Australian university e-learning system were from eastern Asian cultural backgrounds with a western networking support perspective about e-learning.

Keywords: Support services, e-network practice, Australian universities, United States universities.

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Authors: Amna Javed, Katsuhiro Umemoto, Youji Kohda

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The purpose of this paper is to examine co-creation of non-economic values in Islamic banking services and their significance for service science by comparing Islamic and conventional banking services. Although many scholars have discussed co-creation of values in services, most of them have focused on only economic values.

Following Sharia (Islamic principles that are based on Qur’an and Sunnah) traditions, Islamic banking is more concerned with such non-economic values as well-being, partnership, fairness, trust, and justice, than such economic values as money in terms of interest.  Therefore, it may be more sustainable and suitable for today’s unpredictable socio-economic environments.

We also argue that Islamic banking is essentially a value co-creation business model that fits better with the so-called Service-Dominant Logic (SDL) than conventional banking. This paper explores a new frontier of value co-creation in services, thereby contributing to further development of service science.

Keywords: Value co-creation, Islamic banking, Non-economic values, Service science.

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Authors: Kaustubh Chakradeo, Michael Delves, Sofya Titarenko

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Malaria is a serious disease which affects hundreds of millions of people around the world, each year. If not treated in time, it can be fatal. Despite recent developments in malaria diagnostics, the microscopy method to detect malaria remains the most common. Unfortunately, the accuracy of microscopic diagnostics is dependent on the skill of the microscopist and limits the throughput of malaria diagnosis. With the development of Artificial Intelligence tools and Deep Learning techniques in particular, it is possible to lower the cost, while achieving an overall higher accuracy. In this paper, we present a VGG-based model and compare it with previously developed models for identifying infected cells. Our model surpasses most previously developed models in a range of the accuracy metrics. The model has an advantage of being constructed from a relatively small number of layers. This reduces the computer resources and computational time. Moreover, we test our model on two types of datasets and argue that the currently developed deep-learning-based methods cannot efficiently distinguish between infected and contaminated cells. A more precise study of suspicious regions is required.

Keywords: Malaria, deep learning, DL, convolution neural network, CNN, thin blood smears.

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Authors: Adnan Z. Mkhelif

Abstract:

Grammatical collocations (GCs) are word combinations containing a preposition or a grammatical structure, such as an infinitive (e.g. smile at, interested in, easy to learn, etc.). Such collocations tend to be difficult for Iraqi EFL university students (IUS) to master. To help address this problem, it is important to identify the factors causing it. This study aims at investigating the effects of L2 proficiency, frequency of GCs and their transparency on IUSs’ productive knowledge of GCs. The study involves 112 undergraduate participants with different proficiency levels, learning English in formal contexts in Iraq. The data collection instruments include (but not limited to) a productive knowledge test (designed by the researcher using the British National Corpus (BNC)), as well as the grammar part of the Oxford Placement Test (OPT). The study findings have shown that all the above-mentioned factors have significant effects on IUSs’ productive knowledge of GCs. In addition to establishing evidence of which factors of L2 learning might be relevant to learning GCs, it is hoped that the findings of the present study will contribute to more effective methods of teaching that can better address and help overcome the problems IUSs encounter in learning GCs. The study is thus hoped to have significant theoretical and pedagogical implications for researchers, syllabus designers as well as teachers of English as a foreign/second language.

Keywords: Corpus linguistics, frequency, grammatical collocations, L2 vocabulary learning, productive knowledge, proficiency, transparency.

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Authors: Shahriar Shahbazpanahi, Alaleh Kamgar

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So far, the conventional experimental and theoretical analysis in fracture mechanics have been applied to study concrete flexural- cracked beams, which are strengthened using fiber reinforced polymer (FRP) composite sheets. However, there is still little knowledge about the shear capacity of a side face FRP- strengthened shear-cracked beam. A numerical analysis is herein presented to model the fracture mechanics of a four-point RC beam, with two inclined initial notch on the supports, which is strengthened with side face FRP sheets. In the present study, the shear crack is forced to conduct by using an initial notch in supports. The ABAQUS software is used to model crack propagation by conventional cohesive elements. It is observed that the FRP sheets play important roles in preventing the propagation of shear cracks.

Keywords: Crack, FRP, shear, strengthening.

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Authors: Nurul Zahirah M.A., N. Zainul Abidin

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Green buildings have been commonly cited to be more expensive than conventional buildings. However, limited research has been conducted to clearly identify elements that contribute to this cost differential. The construction cost of buildings can be typically divided into “hard" costs and “soft" cost elements. Using a review analysis of existing literature, the study identified six main elements in green buildings that contribute to the general cost elements that are “soft" in nature. The six elements found are insurance, developer-s experience, design cost, certification, commissioning and energy modeling. Out of the six elements, most literatures have highlighted the increase in design cost for green design as compared to conventional design due to additional architectural and engineering costs, eco-charettes, extra design time, and the further need for a green consultant. The study concluded that these elements of soft cost contribute to the green premium or cost differential of green buildings.

Keywords: Green building, cost differential, soft cost, intangible cost.

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Authors: David C. White

Abstract:

This paper describes the architectural design considerations for building a new class of application, a Personal Knowledge Integrator and a particular example a Knowledge Theatre. It then supports this description by describing a scenario of a child acquiring knowledge and how this process could be augmented by the proposed architecture and design of a Knowledge Theatre. David Merrill-s first “principles of instruction" are kept in focus to provide a background to view the learning potential.

Keywords: Knowledge, personal, open data, visualization, learning, teaching

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Authors: Yanwen Li, Shuguo Xie

Abstract:

In electromagnetic imaging, because of the diffraction limited system, the pixel values could change slowly near the edge of the image targets and they also change with the location in the same target. Using traditional digital image segmentation methods to segment electromagnetic gradient images could result in lots of errors because of this change in pixel values. To address this issue, this paper proposes a novel image segmentation and extraction algorithm based on Mean-Shift and dictionary learning. Firstly, the preliminary segmentation results from adaptive bandwidth Mean-Shift algorithm are expanded, merged and extracted. Then the overlap rate of the extracted image block is detected before determining a segmentation region with a single complete target. Last, the gradient edge of the extracted targets is recovered and reconstructed by using a dictionary-learning algorithm, while the final segmentation results are obtained which are very close to the gradient target in the original image. Both the experimental results and the simulated results show that the segmentation results are very accurate. The Dice coefficients are improved by 70% to 80% compared with the Mean-Shift only method.

Keywords: Gradient image, segmentation and extract, mean-shift algorithm, dictionary learning.

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Authors: Amiel Satvedi, Richard Pyne

Abstract:

Injuries in professional sports occur on a regular basis. Some may be minor while others can cause huge impact on a player’s career and earning potential. In soccer, there is a high risk of players picking up injuries during game time. This research work seeks to help soccer players reduce the risk of getting injured by predicting the likelihood of injury while playing in the near future and then providing recommendations for intervention. The injury prediction tool will use a soccer player’s number of minutes played on the field, number of appearances, distance covered and performance data for the current and previous seasons as variables to conduct statistical analysis and provide injury predictive results using a machine learning linear regression model.

Keywords: Injury predictor, soccer injury prevention, machine learning in soccer, big data in soccer.

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Authors: Nazatul Aini Abd Majid, Faridah Yunus, Haslina Arshad, Mohammad Farhan Mohammad Johari

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Mobile technology, augmented reality, and game-based learning are some of the key learning technologies that can be fully optimized to promote pre-literacy skills. The problem is how to design an effective pre-literacy kit that utilizes some of the learning technologies. This paper presents a framework based on mobile augmented reality for the development of pre-literacy kit. This pre-literacy kit incorporates three main components which are contents, design, and tools. A prototype of a mobile app based on the three main components was developed for promoting pre-literacy. The results show that the children and teachers gave positive feedbacks after using the mobile app for the pre-literacy.

Keywords: Framework, mobile technology, augmented reality, pre-literacy skills.

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Authors: Hiromitsu Ogawa, Manato Ono, Naohiro Ban, Yoshihisa Ishida

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This paper describes the two stage control using a disturbance observer and a Kalman filter. The system feedback uses the estimated state when it controls the speed. After the change-over point, its feedback uses the controlled plant output when it controls the position. To change the system continually, a change-over point has to be determined pertinently, and the controlled plant input has to be adjusted by the addition of the appropriate value. The proposed method has noise-reduction effect. It changes the system continually, even if the controlled plant identification has the error. Although the conventional method needs a speed sensor, the proposed method does not need it. The proposed method has a superior robustness compared with the conventional two stage control.

Keywords: Disturbance observer, kalman filter, optimal control, two stage control.

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Authors: Fu E. Tang, Vun J. Ngu

Abstract:

The pollutant removal efficiency of the Intermittently Decanted Extended Aeration (IDEA) wastewater treatment system at Curtin University Sarawak Campus, and conventional activated sludge wastewater treatment system at a local resort, Resort A, is monitored. The influent and effluent characteristics are tested during wet and dry weather conditions, and peak and off peak periods. For the wastewater treatment systems at Curtin Sarawak and Resort A, during dry weather and peak season, it was found that the BOD5 concentration in the influent is 121.7mg/L and 80.0mg/L respectively, and in the effluent, 18.7mg/L and and 18.0mg/L respectively. Analysis of the performance of the IDEA treatment system showed that the operational costs can be minimized by 3%, by decreasing the number of operating cycles. As for the treatment system in Resort A, by utilizing a smaller capacity air blower, a saving of 12% could be made in the operational costs.

Keywords: Conventional Activated Sludge, IDEA, Performance Monitoring, Wastewater Treatment

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Authors: P. Acharjee, S. K. Goswami

Abstract:

Particle Swarm Optimization (PSO) with elite PSO parameters has been developed for power flow analysis under practical constrained situations. Multiple solutions of the power flow problem are useful in voltage stability assessment of power system. A method of determination of multiple power flow solutions is presented using a hybrid of Particle Swarm Optimization (PSO) and local search technique. The unique and innovative learning factors of the PSO algorithm are formulated depending upon the node power mismatch values to be highly adaptive with the power flow problems. The local search is applied on the pbest solution obtained by the PSO algorithm in each iteration. The proposed algorithm performs reliably and provides multiple solutions when applied on standard and illconditioned systems. The test results show that the performances of the proposed algorithm under critical conditions are better than the conventional methods.

Keywords: critical conditions, ill-conditioned systems, localsearch technique, multiple power flow solutions, particle swarmoptimization.

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Authors: Kathaleeya Chanda, Panupong Chanplin, Suppara Charoenpoom

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This purposes of the development of creative instruction model through digital media are to: 1) enable learners to learn from instruction media application; 2) help learners implementing instruction media correctly and appropriately; and 3) facilitate learners to apply technology for searching information and practicing skills to implement technology creatively. The sample group consists of 130 cases of secondary students studying in Bo Kluea School, Bo Kluea Nuea Sub-district, Bo Kluea District, Nan Province. The probability sampling was selected through the simple random sampling and the statistics used in this research are percentage, mean, standard deviation and one group pretest – posttest design. The findings are summarized as follows: The congruence index of instruction media for occupation and technology subjects is appropriate. By comparing between learning achievements before implementing the instruction media and learning achievements after implementing the instruction media, it is found that the posttest achievements are higher than the pretest achievements with statistical significance at the level of .05. For the learning achievements from instruction media implementation, pretest mean is 16.24 while posttest mean is 26.28. Besides, pretest and posttest results are compared and differences of mean are tested, the test results show that the posttest achievements are higher than the pretest achievements with statistical significance at the level of .05. This can be interpreted that the learners achieve better learning progress.

Keywords: Teaching learning model, digital media, creative instruction model, facilitate learners.

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Authors: Li Kewen, Su Zhaoxin, Wang Xingmou, Zhu Jian Bing

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Conventional reservoir prediction methods ar not sufficient to explore the implicit relation between seismic attributes, and thus data utilization is low. In order to improve the predictive classification accuracy of reservoir lithology, this paper proposes a deep learning lithology prediction method based on ResNet (Residual Neural Network) and SENet (Squeeze-and-Excitation Neural Network). The neural network model is built and trained by using seismic attribute data and lithology data of Shengli oilfield, and the nonlinear mapping relationship between seismic attribute and lithology marker is established. The experimental results show that this method can significantly improve the classification effect of reservoir lithology, and the classification accuracy is close to 70%. This study can effectively predict the lithology of undrilled area and provide support for exploration and development.

Keywords: Convolutional neural network, lithology, prediction of reservoir lithology, seismic attributes.

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Authors: Nilubon Kurubanjerdjit, Nattakarn Iam-On, Ka-Lok Ng

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MicroRNAs are small non-coding RNA found in many different species. They play crucial roles in cancer such as biological processes of apoptosis and proliferation. The identification of microRNA-target genes can be an essential first step towards to reveal the role of microRNA in various cancer types. In this paper, we predict miRNA-target genes for lung cancer by integrating prediction scores from miRanda and PITA algorithms used as a feature vector of miRNA-target interaction. Then, machine-learning algorithms were implemented for making a final prediction. The approach developed in this study should be of value for future studies into understanding the role of miRNAs in molecular mechanisms enabling lung cancer formation.

Keywords: MicroRNA, miRNAs, lung cancer, machine learning, Naïve Bayes, SVM.

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Authors: Taylan Kabbani, Ekrem Duman

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Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining  the financial assets price ”prediction” step and the ”allocation” step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with its environment to make optimal decisions through trial and error. This work represents a DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem as a Partially observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. We then solved the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and achieved a 2.68 Sharpe ratio on the test dataset. From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of DRL in financial markets over other types of machine learning and proves its credibility and advantages of strategic decision-making.

Keywords: Autonomous agent, deep reinforcement learning, MDP, sentiment analysis, stock market, technical indicators, twin delayed deep deterministic policy gradient.

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Authors: I. Alberdi, H. Iribas, A. Martin, N. Aginako

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This paper describes a platform that faces the main research areas for e-learning educational contents. Reusability tackles the possibility to use contents in different courses reducing costs and exploiting available data from repositories. In our approach the production of educational material is based on templates to reuse learning objects. In terms of interoperability the main challenge lays on reaching the audience through different platforms. E-learning solution must track social consumption evolution where nowadays lots of multimedia contents are accessed through the social networks. Our work faces it by implementing a platform for generation of multimedia presentations focused on the new paradigm related to social media. The system produces videos-courses on top of web standard SMIL (Synchronized Multimedia Integration Language) ready to be published and shared. Regarding interfaces it is mandatory to satisfy user needs and ease communication. To overcome it the platform deploys virtual teachers that provide natural interfaces while multimodal features remove barriers to pupils with disabilities.

Keywords: Collaborative, multimedia e-learning, reusability, SMIL, virtual teacher

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Authors: Raquel P. F. Guiné, Daniela V. T. A. Costa, Paula M. R. Correia, Moisés Castro, Luis T. Guerra, Cristina A. Costa

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The aim of this work was to characterize a potential target group of people interested in participating into a training program in organic farming in the context of mobile-learning. The information sought addressed in particular, but not exclusively, possible contents, formats and forms of evaluation that will contribute to define the course objectives and curriculum, as well as to ensure that the course meets the needs of the learners and their preferences. The sample was selected among different European countries. The questionnaires were delivered electronically for answering on-line and in the end 135 consented valid questionnaires were obtained. The results allowed characterizing the target group and identifying their training needs and preferences towards m-learning formats, giving valuable tools to design the training offer.

Keywords: Mobile-learning, organic farming, rural development, survey.

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Authors: Abdolghani Ebrahimi, Diego Klabjan, Chenxi Ge, Daniela Ladner, Parker Stride

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In this work, we use machine learning and data analysis techniques to predict the one-year mortality of cirrhotic patients. Data from 2,322 patients with liver cirrhosis are collected at a single medical center. Different machine learning models are applied to predict one-year mortality. A comprehensive feature space including demographic information, comorbidity, clinical procedure and laboratory tests is being analyzed. A temporal pattern mining technic called Frequent Subgraph Mining (FSM) is being used. Model for End-stage liver disease (MELD) prediction of mortality is used as a comparator. All of our models statistically significantly outperform the MELD-score model and show an average 10% improvement of the area under the curve (AUC). The FSM technic itself does not improve the model significantly, but FSM, together with a machine learning technique called an ensemble, further improves the model performance. With the abundance of data available in healthcare through electronic health records (EHR), existing predictive models can be refined to identify and treat patients at risk for higher mortality. However, due to the sparsity of the temporal information needed by FSM, the FSM model does not yield significant improvements. Our work applies modern machine learning algorithms and data analysis methods on predicting one-year mortality of cirrhotic patients and builds a model that predicts one-year mortality significantly more accurate than the MELD score. We have also tested the potential of FSM and provided a new perspective of the importance of clinical features.

Keywords: machine learning, liver cirrhosis, subgraph mining, supervised learning

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Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

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In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: Deep learning, indoor quality, metabolism, predictive model.

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