• Title/Summary/Keyword: complex training

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Analysis on On-line Q&A Cases regarding Landscape Trees Management - Focused on Online Consultation Board at Tree Diagnostic Center - (조경수 관리에 관한 온라인 질의응답 사례 분석 - 수목진단센터 온라인 상담 사례를 대상으로 -)

  • Lim, Byoung-Eul;Lee, Sae-Hee
    • Journal of the Korean Institute of Landscape Architecture
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    • v.41 no.1
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    • pp.44-50
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    • 2013
  • The persons in charge of management request diagnosis and prescription to tree hospitals in order to get consultation about the problems like blight that occur in landscape tree management. This study aims to analyze what the main problems and questions raised by landscape gardeners are and those concerned in landscape tree management. This is done by investigating landscape tree-related questions and answers uploaded on the online consultation boards of the plant diagnostic centers approved in Korea including the Seoul National University Plant Clinic, the Chungbuk National University Plant Hospital, and the Kangwon Diagnostic Center. As a result, those concerned in landscape occupied the most as 81.4% among the questioners. However, only 11.5% did explain the plant management history or surrounding environment, which is essential for landscape tree diagnosis when asking questions. This shows that those concerned in landscape lack basic knowledge or interest about plant diagnosis. Among 263 questions about landscape trees, questions about physiological damage included 94 cases that were the most taking up 35.8%. Moreover, the next were damage by insects and damage by disease in order. It is thought that due to the characteristics of physiological problems that occur by various sorts of stress and with no signs, they tend to request diagnosis or prescription the most. The most frequent reasons for physiological damage are water stress and temperature stress. About damage by disease, there exist many types of diseases, and there are many complex damages accompanied by physiological causes. About damage by insects, the most common include damage by moths. In consideration of this result, universities or technician training centers should provide education for landscape tree management so that landscape technicians and students can acquire essential knowledge and information about landscape tree management and increase their interest in it. In particular, it is necessary to provide profound learning opportunities for plant physiology, and the technicians should make efforts themselves. In addition, it is needed to build organizations to which they can ask technical questions about landscape planting and management in order to understand landscape industry in general and the actual status of landscape planting technique and the actual field. Moreover, to elevate systemicity and expertise in the area of landscape tree management not yet equipped with the foundation, it is needed to cultivate the technicians intensively and conduct research by those concerned both in academic and industrial circles.

The Stakeholder's Response and Future of Mountain Community Development Program in Rep. of Korea (한국 산촌개발사업에 대한 이해관계자의 의식과 향후 발전방안)

  • Yoo, Byoung Il;Kim, So Heui;Seo, Jeong-Weon
    • Journal of Korean Society of Forest Science
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    • v.94 no.4 s.161
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    • pp.214-225
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    • 2005
  • The mountain village development program in Korea started in the mountain villages, the 45.9% of total land and one of the typical marginal region, from 1995 to achieve the equilibrium development of national land and the sustainable mountain development in Chapter 13 in Agenda 21, and it has been accelerated to increase the happiness and the quality of life of mountain community residents through the expansion by province and the improvement of related laws and regulations. This study has been aimed to analyze the response of main stakeholder's -mountain village residents and local government officials - on mountain villages development, and to provide the future plan as community development. The survey and interview data were collected from the mountain villages which already developed 59 villages and developing 15 villages in 2003. The mountain village development program has achieved the positive aspects as community development plan in the several fields, - the voluntary participation of residents, the establishment of self-support spirit as the democratic civilians, the development of base of income increasement, the creation of comfortable living environment, the equilibrium development with the other regions. Especially the mountain residents and local government officials both highly satisfy with the development of base of income increasement and the creation of comfortable living environment which are the main concerns to both stakeholder. However through the mountain development program, it is not satisfied to increase the maintenance of local community and the strengthening of traditional value of mountain villages. Also to improve the sustainable income improvement effects, it is necessary to develop the income items and technical extension which good for the each region. In the decentralization era, it is necessary for local government should have the more active and multilateral activities for these. With this, the introduction of methods which the mountain community people and the local government officials could co-participate in the mountain villages' development from the initial stages and the renovation of related local government organizations and the cooperatives will be much helpful to the substantiality of mountain development program. Also it is essential for the assistance of central government to establish the complex plan and the mountain villages network for all mountain area and the exchange of information, the education and training of mountain villages leader who are the core factor for the developed mountain villages maintenance, the composition of national mountain villages representatives. In case the development proposals which based on the interests of the main stakeholder's on mountain community could be positively accepted, then the possibility of the mountain village development as one of community development will be successfully improved in future.

The Characteristics and Landscape Meanings of Letters Carved on the Rocks of Mt. Sangdu (상두선(象頭山) 바위글씨의 특징과 경관의미)

  • Rho, Jae-Hyun;Lee, Jung-Han;Huh, Joon;Kim, Jeong-Moon
    • Journal of the Korean Institute of Traditional Landscape Architecture
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    • v.30 no.2
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    • pp.1-13
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    • 2012
  • This study aimed at learning the values and meanings of the letters carved on the rocks all over Mt. Sangdu located at the boundary between Kimje-si and Jeongeup-si of Jeollabuk-do by grasping the current state of them, investigating the patterns and contents of them, and understanding the spatial and landscape properties of the region where the rocks are scattered. The results of this study are as follows; The name of Mt. Sangdu came from the mountain with the same name located in India where Buddha were seeking the truth, and means auspicious. With the recognition of ancient maps and books, various propitious spots also made the landscape symbols of Mt. Sangdu solidify. Whoam, Chaangsuk-Kim, Weolgye Young-Cho Song and the members of Cheonggye Society like Dongcho Seok-Gon Kim led the creation of the rocks, and the 41 letter-carved rocks all over four water systems were found out and all of them were carved with Chinese characters. The letters were usually carved on flat and broad rocks, and they mainly had the shape of a small waterfall and a wide waterfall of under 1 meter height. 25(60.9%) of the carved letters were about moral training, and it seemed that they wanted to protect their pride under the shackle of the Japanese colonization over Korea. The styles of handwriting are Hangseo and Jeonseo except for names, and show various and complex styles. The mix composition of the carved letters of 'Yusubulbu(流水不腐)' of Choseo and the rocks of Takjok(濯足) is extraordinary, and the letters carved as the shape of Nakkwan(落款) have artistic value and degree of finishing. It seemed that intellectuals during the Japanese colonization over Korea in the 1930s considered Mt. Sangduasa highly valuable region because they expressed their hope and wish for the new world on the rocks. The letters on the rocks of Mt. Sangdu are invaluable cultural landscaping elements for the improvement of landscaping symbolism of Mt. Sangdu because of colliding values and spirits of the time of 'the anguish and pain of intellectuals' and 'the status of living joyfully outside of the mundane world.'

A Study on Users' Resistance toward ERP in the Pre-adoption Context (ERP 도입 전 구성원의 저항)

  • Park, Jae-Sung;Cho, Yong-Soo;Koh, Joon
    • Asia pacific journal of information systems
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    • v.19 no.4
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    • pp.77-100
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    • 2009
  • Information Systems (IS) is an essential tool for any organizations. The last decade has seen an increasing body of knowledge on IS usage. Yet, IS often fails because of its misuse or non-use. In general, decisions regarding the selection of a system, which involve the evaluation of many IS vendors and an enormous initial investment, are made not through the consensus of employees but through the top-down decision making by top managers. In situations where the selected system does not satisfy the needs of the employees, the forced use of the selected IS will only result in their resistance to it. Many organizations have been either integrating dispersed legacy systems such as archipelago or adopting a new ERP (Enterprise Resource Planning) system to enhance employee efficiency. This study examines user resistance prior to the adoption of the selected IS or ERP system. As such, this study identifies the importance of managing organizational resistance that may appear in the pre-adoption context of an integrated IS or ERP system, explores key factors influencing user resistance, and investigates how prior experience with other integrated IS or ERP systems may change the relationship between the affecting factors and user resistance. This study focuses on organizational members' resistance and the affecting factors in the pre-adoption context of an integrated IS or ERP system rather than in the context of an ERP adoption itself or ERP post-adoption. Based on prior literature, this study proposes a research model that considers six key variables, including perceived benefit, system complexity, fitness with existing tasks, attitude toward change, the psychological reactance trait, and perceived IT competence. They are considered as independent variables affecting user resistance toward an integrated IS or ERP system. This study also introduces the concept of prior experience (i.e., whether a user has prior experience with an integrated IS or ERP system) as a moderating variable to examine the impact of perceived benefit and attitude toward change in user resistance. As such, we propose eight hypotheses with respect to the model. For the empirical validation of the hypotheses, we developed relevant instruments for each research variable based on prior literature and surveyed 95 professional researchers and the administrative staff of the Korea Photonics Technology Institute (KOPTI). We examined the organizational characteristics of KOPTI, the reasons behind their adoption of an ERP system, process changes caused by the introduction of the system, and employees' resistance/attitude toward the system at the time of the introduction. The results of the multiple regression analysis suggest that, among the six variables, perceived benefit, complexity, attitude toward change, and the psychological reactance trait significantly influence user resistance. These results further suggest that top management should manage the psychological states of their employees in order to minimize their resistance to the forced IS, even in the new system pre-adoption context. In addition, the moderating variable-prior experience was found to change the strength of the relationship between attitude toward change and system resistance. That is, the effect of attitude toward change in user resistance was significantly stronger in those with prior experience than those with no prior experience. This result implies that those with prior experience should be identified and provided with some type of attitude training or change management programs to minimize their resistance to the adoption of a system. This study contributes to the IS field by providing practical implications for IS practitioners. This study identifies system resistance stimuli of users, focusing on the pre-adoption context in a forced ERP system environment. We have empirically validated the proposed research model by examining several significant factors affecting user resistance against the adoption of an ERP system. In particular, we find a clear and significant role of the moderating variable, prior ERP usage experience, in the relationship between the affecting factors and user resistance. The results of the study suggest the importance of appropriately managing the factors that affect user resistance in organizations that plan to introduce a new ERP system or integrate legacy systems. Moreover, this study offers to practitioners several specific strategies (in particular, the categorization of users by their prior usage experience) for alleviating the resistant behaviors of users in the process of the ERP adoption before a system becomes available to them. Despite the valuable contributions of this study, there are also some limitations which will be discussed in this paper to make the study more complete and consistent.

Knowledge Extraction Methodology and Framework from Wikipedia Articles for Construction of Knowledge-Base (지식베이스 구축을 위한 한국어 위키피디아의 학습 기반 지식추출 방법론 및 플랫폼 연구)

  • Kim, JaeHun;Lee, Myungjin
    • Journal of Intelligence and Information Systems
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    • v.25 no.1
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    • pp.43-61
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    • 2019
  • Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

Topic Modeling Insomnia Social Media Corpus using BERTopic and Building Automatic Deep Learning Classification Model (BERTopic을 활용한 불면증 소셜 데이터 토픽 모델링 및 불면증 경향 문헌 딥러닝 자동분류 모델 구축)

  • Ko, Young Soo;Lee, Soobin;Cha, Minjung;Kim, Seongdeok;Lee, Juhee;Han, Ji Yeong;Song, Min
    • Journal of the Korean Society for information Management
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    • v.39 no.2
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    • pp.111-129
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    • 2022
  • Insomnia is a chronic disease in modern society, with the number of new patients increasing by more than 20% in the last 5 years. Insomnia is a serious disease that requires diagnosis and treatment because the individual and social problems that occur when there is a lack of sleep are serious and the triggers of insomnia are complex. This study collected 5,699 data from 'insomnia', a community on 'Reddit', a social media that freely expresses opinions. Based on the International Classification of Sleep Disorders ICSD-3 standard and the guidelines with the help of experts, the insomnia corpus was constructed by tagging them as insomnia tendency documents and non-insomnia tendency documents. Five deep learning language models (BERT, RoBERTa, ALBERT, ELECTRA, XLNet) were trained using the constructed insomnia corpus as training data. As a result of performance evaluation, RoBERTa showed the highest performance with an accuracy of 81.33%. In order to in-depth analysis of insomnia social data, topic modeling was performed using the newly emerged BERTopic method by supplementing the weaknesses of LDA, which is widely used in the past. As a result of the analysis, 8 subject groups ('Negative emotions', 'Advice and help and gratitude', 'Insomnia-related diseases', 'Sleeping pills', 'Exercise and eating habits', 'Physical characteristics', 'Activity characteristics', 'Environmental characteristics') could be confirmed. Users expressed negative emotions and sought help and advice from the Reddit insomnia community. In addition, they mentioned diseases related to insomnia, shared discourse on the use of sleeping pills, and expressed interest in exercise and eating habits. As insomnia-related characteristics, we found physical characteristics such as breathing, pregnancy, and heart, active characteristics such as zombies, hypnic jerk, and groggy, and environmental characteristics such as sunlight, blankets, temperature, and naps.

A Convergent and Combined Activation Plan for Exercise Rehabilitation in the Era of the Fourth Industrial Revolution (4차 산업혁명시대에 운동재활분야의 융·복합적 활성화 방안)

  • Cho, Kyoung-Hwan
    • Journal of Korea Entertainment Industry Association
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    • v.14 no.8
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    • pp.407-426
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    • 2020
  • The purpose of this study was to make convergent and combined analysis of the sport industry and exercise rehabilitation in the era of New Normal based on the Fourth Industrial Revolution and devise a comprehensive plan for future activation. For this purpose, literature review was performed mainly by analyzing the environment of the sport industry in the New Normal era based on the Fourth Industrial Revolution and by carrying out convergent and combined analysis of the sport industry to present a convergent and combined activation plan for exercise rehabilitation comprehensively as follows: First, it is necessary to make a strategy of promoting exercise rehabilitation in convergent and combined ways at the sport industry level. This means development of a convergent and combined exercise rehabilitation-tourism-ICT model as well as a convergent and combined exercise rehabilitation-ICT model through collaboration among ministries, including those of health and sports. Second, it is necessary to convert into a convergent and combined way of thinking and extend and reinforce educational competitiveness in the area of exercise rehabilitation. That is, it is necessary to refine the education and training systems for reinforcing personal ICT competence of exercise rehabilitation majors and relevant ones and provide convergent and combined business commencement education. Third, it is necessary to make different types of research and development by applying practical, convergent and combined skills based on the industrial field to exercise rehabilitation and relevant areas. Efforts should be made to overcome any risk in the era of New Normal and support business commencement with convergent and combined skills for exercise rehabilitation. Fourth, it is necessary to make mid- and long-term clusters where exercise rehabilitation and relevant businesses can be accumulated. This means building an industrial hub and complex for exercise rehabilitation and requires making an R&D-based cluster with industrial-academic-governmental collaboration, maximizing the synergy effects with local infrastructures, and fulfilling the function of realizing a spontaneous profit-generating structure.

Accelerometer-based Gesture Recognition for Robot Interface (로봇 인터페이스 활용을 위한 가속도 센서 기반 제스처 인식)

  • Jang, Min-Su;Cho, Yong-Suk;Kim, Jae-Hong;Sohn, Joo-Chan
    • Journal of Intelligence and Information Systems
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    • v.17 no.1
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    • pp.53-69
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    • 2011
  • Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.

Ensemble of Nested Dichotomies for Activity Recognition Using Accelerometer Data on Smartphone (Ensemble of Nested Dichotomies 기법을 이용한 스마트폰 가속도 센서 데이터 기반의 동작 인지)

  • Ha, Eu Tteum;Kim, Jeongmin;Ryu, Kwang Ryel
    • Journal of Intelligence and Information Systems
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    • v.19 no.4
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    • pp.123-132
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    • 2013
  • As the smartphones are equipped with various sensors such as the accelerometer, GPS, gravity sensor, gyros, ambient light sensor, proximity sensor, and so on, there have been many research works on making use of these sensors to create valuable applications. Human activity recognition is one such application that is motivated by various welfare applications such as the support for the elderly, measurement of calorie consumption, analysis of lifestyles, analysis of exercise patterns, and so on. One of the challenges faced when using the smartphone sensors for activity recognition is that the number of sensors used should be minimized to save the battery power. When the number of sensors used are restricted, it is difficult to realize a highly accurate activity recognizer or a classifier because it is hard to distinguish between subtly different activities relying on only limited information. The difficulty gets especially severe when the number of different activity classes to be distinguished is very large. In this paper, we show that a fairly accurate classifier can be built that can distinguish ten different activities by using only a single sensor data, i.e., the smartphone accelerometer data. The approach that we take to dealing with this ten-class problem is to use the ensemble of nested dichotomy (END) method that transforms a multi-class problem into multiple two-class problems. END builds a committee of binary classifiers in a nested fashion using a binary tree. At the root of the binary tree, the set of all the classes are split into two subsets of classes by using a binary classifier. At a child node of the tree, a subset of classes is again split into two smaller subsets by using another binary classifier. Continuing in this way, we can obtain a binary tree where each leaf node contains a single class. This binary tree can be viewed as a nested dichotomy that can make multi-class predictions. Depending on how a set of classes are split into two subsets at each node, the final tree that we obtain can be different. Since there can be some classes that are correlated, a particular tree may perform better than the others. However, we can hardly identify the best tree without deep domain knowledge. The END method copes with this problem by building multiple dichotomy trees randomly during learning, and then combining the predictions made by each tree during classification. The END method is generally known to perform well even when the base learner is unable to model complex decision boundaries As the base classifier at each node of the dichotomy, we have used another ensemble classifier called the random forest. A random forest is built by repeatedly generating a decision tree each time with a different random subset of features using a bootstrap sample. By combining bagging with random feature subset selection, a random forest enjoys the advantage of having more diverse ensemble members than a simple bagging. As an overall result, our ensemble of nested dichotomy can actually be seen as a committee of committees of decision trees that can deal with a multi-class problem with high accuracy. The ten classes of activities that we distinguish in this paper are 'Sitting', 'Standing', 'Walking', 'Running', 'Walking Uphill', 'Walking Downhill', 'Running Uphill', 'Running Downhill', 'Falling', and 'Hobbling'. The features used for classifying these activities include not only the magnitude of acceleration vector at each time point but also the maximum, the minimum, and the standard deviation of vector magnitude within a time window of the last 2 seconds, etc. For experiments to compare the performance of END with those of other methods, the accelerometer data has been collected at every 0.1 second for 2 minutes for each activity from 5 volunteers. Among these 5,900 ($=5{\times}(60{\times}2-2)/0.1$) data collected for each activity (the data for the first 2 seconds are trashed because they do not have time window data), 4,700 have been used for training and the rest for testing. Although 'Walking Uphill' is often confused with some other similar activities, END has been found to classify all of the ten activities with a fairly high accuracy of 98.4%. On the other hand, the accuracies achieved by a decision tree, a k-nearest neighbor, and a one-versus-rest support vector machine have been observed as 97.6%, 96.5%, and 97.6%, respectively.

Sentiment Analysis of Movie Review Using Integrated CNN-LSTM Mode (CNN-LSTM 조합모델을 이용한 영화리뷰 감성분석)

  • Park, Ho-yeon;Kim, Kyoung-jae
    • Journal of Intelligence and Information Systems
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    • v.25 no.4
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    • pp.141-154
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    • 2019
  • Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.