• The Journal of Asian Finance, Economics and Business
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• v.5 no.3
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• pp.157-167
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• 2018

This research aims to generalize the conceptual basis of precariousness of employment, study the factors and scale of unsustainable of employment in the countries of the Eurasian Economic Union (EAEU). The concept of precariousness of employment is formed in social and economic studies about 40 years ago, but objective and subjective conditions and forms of unsustainable employment existed before. This study proposes a classification of forms of precariousness of employment on 16 criteria: the duration and timing of agreements, contract terms, the nature of income; the degree of labor autonomy; the level of formality; the level of openness; the level of vulnerability; the conditions of growth of qualification; the level of flexibility; the level of stability; regularity; the severity of the danger of work; in relation to the workplace; the quality of employment, the level of social security. In this research highlighted factors (globalization, demography, migration, structure factors, shadow and informal economy, social development and living standards, unemployment), and systematized certain trends of precariousness of employment, channels and means, forms of manifestation. The empirical analysis identified of the labor potential of the Eurasian countries and new quantitative estimates of the levels of unsustainable employment in the Eurasian Economic Space.

• Journal of the Korean Geosynthetics Society
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• v.13 no.4
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• pp.133-141
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• 2014

This study was carried out to establish the database system by using the safety inspection and in-depth inspection results for infrastructures such as bridges, tunnels, dams, and water supplies. A classification system of each facility was proposed by standardizing items for inspection & diagnosis in order to automatize work process. Also, it justifies data structure based on database from pre-investigation to field survey, evaluation of facilities, and report making. In addition to this, it suggests improvement plans of relative regulations and guidelines such as Facility Management System(FMS), operational regulation, and inspection detailed guideline to make inspection result database of infrastructures which can be used effectively.

• Structural Monitoring and Maintenance
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• v.2 no.3
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• pp.181-197
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• 2015

Assessing the condition of paint on civil structures is an important but challenging and costly task, in particular when it comes to large and complex structures. Current practices of visual inspection are labour-intensive and time-consuming to perform. In addition, this task usually relies on the experience and subjective judgment of individual inspectors. In this study, hyperspectral imaging and classification techniques are proposed as a method to objectively assess the state of the paint on a civil or other structure. The ultimate objective of the work is to develop a technology that can provide precise and automatic grading of paint condition and assessment of degradation due to age or environmental factors. Towards this goal, we acquired hyperspectral images of steel surfaces located at long (mid-range) and short distances on the Sydney Harbour Bridge with an Acousto-Optics Tunable filter (AOTF) hyperspectral camera (consisting of 21 bands in the visible spectrum). We trained a multi-class Support Vector Machines (SVM) classifier to automatically assess the grading of the paint from hyperspectral signatures. Our results demonstrate that the classifier generates highly accurate assessment of the paint condition in comparison to the judgement of human experts.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.97-106
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• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.203-208
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• 2022

The occurrence of Type 2 Diabetes Mellitus (T2DM) is hoarding globally. All kinds of Diabetes Mellitus is controlled to disrupt over 415 million grownups worldwide. It was the seventh prime cause of demise widespread with a measured 1.6 million deaths right prompted by diabetes during 2016. Over 90% of diabetes cases are T2DM, with the utmost persons having at smallest one other chronic condition in UK. In valuation of contemporary applications of Big Data (BD) to Diabetes Medicare by sighted its upcoming abilities, it is compulsory to transmit out a bottomless revision over foremost theoretical literatures. The long-term growth in medicine and, in explicit, in the field of "Diabetology", is powerfully encroached to a sequence of differences and inventions. The medical and healthcare data from varied bases like analysis and treatment tactics which assistances healthcare workers to guess the actual perceptions about the development of Diabetes Medicare measures accessible by them. Apache Spark extracts "Resilient Distributed Dataset (RDD)", a vital data structure distributed finished a cluster on machines. Machine Learning (ML) deals a note-worthy method for building elegant and automatic algorithms. ML library involving of communal ML algorithms like Support Vector Classification and Random Forest are investigated in this projected work by using Jupiter Notebook - Python code, where significant quantity of result (Accuracy) is carried out by the models.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.120-128
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• 2023

In this work, we have presented ATSA, a hierarchical attention deep learning model for Arabic sentiment analysis. ATSA was proposed by addressing several challenges and limitations that arise when applying the classical models to perform opinion mining in Arabic. Arabic-specific challenges including the morphological complexity and language sparsity were addressed by modeling semantic composition at the Arabic morphological analysis after performing tokenization. ATSA proposed to perform phrase-chunks sentiment embedding to provide a broader set of features that cover syntactic, semantic, and sentiment information. We used phrase structure parser to generate syntactic parse trees that are used as a reference for ATSA. This allowed modeling semantic and sentiment composition following the natural order in which words and phrase-chunks are combined in a sentence. The proposed model was evaluated on three Arabic corpora that correspond to different genres (newswire, online comments, and tweets) and different writing styles (MSA and dialectal Arabic). Experiments showed that each of the proposed contributions in ATSA was able to achieve significant improvement. The combination of all contributions, which makes up for the complete ATSA model, was able to improve the classification accuracy by 3% and 2% on Tweets and Hotel reviews datasets, respectively, compared to the existing models.

• Research in Community and Public Health Nursing
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• v.11 no.1
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• pp.67-79
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• 2000

In recent years, the recipients of the services of the health centers in Korea have been shifted from individual sick persons to families as a unit. As a result, the home visiting care records which are all filled out manually, will be increased. Since there is virtually no increase in the number of community health nurses, the CHNs are required to work more efficiently. One of the ways to make the CHNs' work more efficient is to reduce recording time by using a computer. However, a computer system that can manage the families as a unit has not yet been developed. In response to this need, we developed a database system that can be utilized in home visiting care service. The family assessment data is collected. diagnosed. and evaluated according to the family diagnosis classification. The system for family diagnosis consists of seven areas. Those areas are family structure. maintenance of the family system, interaction and interchange. support. coping and adaptation, health management. and housing environment. The areas of the family diagnosis consists of 99 items in all. We expect the following from this system. First. the CHNs will be able to identify family problems more easily. Second. the community's health level can be confirmed by the statistics the system produces. Thirdly, the CHNs' nursing services will be cost effective via reduced recording time. Finally, the family problems of the sick individuals which have been neglected under the health system oriented on individual persons can be effectively managed.

• Journal of Biomedical Engineering Research
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• v.38 no.5
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• pp.237-241
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• 2017

Early detection of the pulmonary nodule is important for diagnosis and treatment of lung cancer. Recently, CT has been used as a screening tool for lung nodule detection. And, it has been reported that computer aided detection(CAD) systems can improve the accuracy of the radiologist in detection nodules on CT scan. The previous study has been proposed a method using Convolutional Neural Network(CNN) in Lung CAD system. But the proposed model has a limitation in accuracy due to its sparse layer structure. Therefore, we propose a Deep Convolutional Neural Network to overcome this limitation. The model proposed in this work is consist of 14 layers including 8 convolutional layers and 4 fully connected layers. The CNN model is trained and tested with 61,404 regions-of-interest (ROIs) patches of lung image including 39,760 nodules and 21,644 non-nodules extracted from the Lung Image Database Consortium(LIDC) dataset. We could obtain the classification accuracy of 91.79% with the CNN model presented in this work. To prevent overfitting, we trained the model with Augmented Dataset and regularization term in the cost function. With L1, L2 regularization at Training process, we obtained 92.39%, 92.52% of accuracy respectively. And we obtained 93.52% with data augmentation. In conclusion, we could obtain the accuracy of 93.75% with L2 Regularization and Data Augmentation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.213-218
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• 2016

Music copyright began in 1850 in France and, unlike other copyrighted works such as architecture or arts, which are based on visual conditions, copyrighted music is based the on trends of the times. The appropriate range of protection for musical works is not the entire music, but the part that is determined to be a creative expression deduced from the analysis of the musical structure. Concerning the issue of plagiarism in popular music, the determination of creativity plays an important role in whether a piece of music encroaches on the original copyrighted works or not. However, determining whether a work is an element of a previously copyrighted work should be achieved through a consensus formed by members of the relevant industry and academia rather than the court. The purpose of this study is to classify the creative and non-creative elements of popular music, in order to create a classification that can enable musical creators to provide a consensus on the elements of creative expression.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.