• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.271-281
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• 2022

Manual seed classification challenges can be overcome using a reliable and autonomous seed purity identification and classification technique. It is a highly practical and commercially important requirement of the agricultural industry. Researchers can create a new data mining method with improved accuracy using current machine learning and artificial intelligence approaches. Seed classification can help with quality making, seed quality controller, and impurity identification. Seeds have traditionally been classified based on characteristics such as colour, shape, and texture. Generally, this is done by experts by visually examining each model, which is a very time-consuming and tedious task. This approach is simple to automate, making seed sorting far more efficient than manually inspecting them. Computer vision technologies based on machine learning (ML), symmetry, and, more specifically, convolutional neural networks (CNNs) have been widely used in related fields, resulting in greater labour efficiency in many cases. To sort a sample of 3000 seeds, KNN, SVM, CNN and CNN-SVM hybrid classification algorithms were used. A model that uses advanced deep learning techniques to categorise some well-known seeds is included in the proposed hybrid system. In most cases, the CNN-SVM model outperformed the comparable SVM and CNN models, demonstrating the effectiveness of utilising CNN-SVM to evaluate data. The findings of this research revealed that CNN-SVM could be used to analyse data with promising results. Future study should look into more seed kinds to expand the use of CNN-SVMs in data processing.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.1
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• pp.1-7
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• 2005

Cluster analysis of microarray data has been often used to find biologically relevant Broups of genes based on their expression levels. Since many functionally related genes tend to be co-expressed, by identifying groups of genes with similar expression profiles, the functionalities of unknown genes can be inferred from those of known genes in the same group. In this Paper we address a novel clustering approach, called seed clustering, and investigate its applicability for microarray data analysis. In the seed clustering method, seed genes are first extracted by computational analysis of their expression profiles and then clusters are generated by taking the seed genes as prototype vectors for target clusters. Since it has strong mathematical foundations, the seed clustering method produces the stable and consistent results in a systematic way. Also, our empirical results indicate that the automatically extracted seed genes are well representative of potential clusters hidden in the data, and that its performance is favorable compared to current approaches.

• Language and Information
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• v.10 no.1
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• pp.21-46
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• 2006

This study proposes a Word Sense Disambiguation (WSD) algorithm, based on concept learning with special emphasis on statistically meaningful lowest frequency words. Previous works on WSD typically make use of frequency of collocation and its probability. Such probability based WSD approaches tend to ignore the lowest frequency words which could be meaningful in the context. In this paper, we show an algorithm to extract and make use of the meaningful lowest frequency words in WSD. Learning method is adopted from the Find-Specific algorithm of Mitchell (1997), according to which the search proceeds from the specific predefined hypothetical spaces to the general ones. In our model, this algorithm is used to find contexts with the most specific classifiers and then moves to the more general ones. We build up small seed data and apply those data to the relatively large test data. Following the algorithm in Yarowsky (1995), the classified test data are exhaustively included in the seed data, thus expanding the seed data. However, this might result in lots of noise in the seed data. Thus we introduce the 'maximum a posterior hypothesis' based on the Bayes' assumption to validate the noise status of the new seed data. We use the Naive Bayes Classifier and prove that the application of Find-Specific algorithm enhances the correctness of WSD.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.5
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• pp.83-90
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• 2020

In this paper, a web-based seed phenotype visualization support system is proposed to extract and visualize data such as the surface color, length, area, perimeter and compactness of seed, which is phenotype information from the image of soybean/rice seeds. This system systematically stores data extracted from seeds in databases, and provides a web-based user interface that facilitates the analysis of data by researchers using data tables and charts. Conventional seed characteristic studies have been manually measured by humans, but the system developed in this paper allows researchers to simply upload seed images for analysis and obtain seed's numerical data after image processing. It is expected that the proposed system will be able to obtain time efficiency and remove spatial restriction, if it is used in seed characterization research, and it will be easy to analyze through systematic management of research results and visualization of the phenotype characteristics.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.2
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• pp.39-56
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• 2021

The seed zone is a map that describes the areas where plant material can be transferred with little risk for properly adapting to a new location. The seed zone study is largely divided into studies based on genetic data and studies based on climatic data. Can be. This study was conducted to establish a temporary domestic seed zone applicable to the entire Korean Peninsula and evaluate its possibility based on the US climate-based seed zone establishment methodology. The temporary seed zone was constructed in the same way as the US case by superimposing the data obtained by dividing the winter minimum temperature into 12 grades and the data obtained by dividing the annual heat: moisture index into 6 grades. As a result of the analysis, 65 temporary seed zones were formed throughout the Korean Peninsula, and the areas of the seed zones representing the smallest and largest areas were 3.0km² and 29,423.0km², respectively, and it was confirmed that they had an average size of about 5,064.9km². Temporary seed zones applied in Korea show a pattern of changes in temperature according to the relatively horizontal forest zone, and it was confirmed that the area where the Baekdu-daegan ecological axis is located has a tendency to show lower dryness than other areas. This study applied the US climate-based seed zone methodology in Korea as a pilot, and confirmed the climatic similarity across the Korean Peninsula. Furthermore, it is expected to provide an optimal seed map that improves the success rate of restoration in the future by revising the seed zone grade suitable for the domestic environment in consideration of the results of this study and the possibility of seed adaptation to the field survey and environmental space.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.9 s.339
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• pp.41-50
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• 2005

As the size of circuits becomes larger, the test method needs more test data volume and larger test application time. In order to reduce test data volume and test application time, a new test data compression/decompression method is proposed. The proposed method is based on an XOR network uses don't-care-bits to improve compression ratio during seed vectors generation. After seed vectors are produced seed vectors can be merged using two prefix codes. It only requires 1 clock time for reusing merged seed vectors, so test application time can be reduced tremendously. Experimental results on large ISCAS '89 benchmark circuits prove the efficiency of the proposed method.

• Korean Journal of Organic Agriculture
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• v.22 no.1
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• pp.1-23
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• 2014

The purpose of this paper was to measure technical efficiencies and scale efficiencies of Korean seed industry by DEA method, and to identify the factors affecting the efficiencies by Tobit regression model. Survey data of 50 seed companies nationwide were applied for the analysis. The average score of overall technical efficiency for the surveyed companies in 2012 was 0.44, which is decomposed into pure technical efficiency 0.68 and scale efficiency 0.63. A majority of the seed companies exhibited at least one form of inefficiency except a few companies in optimal scale. It was also shown the most companies were operating in the stage of increasing returns to scale, which implies Korean seed companies are mainly in smaller scale than optimal. Additional results suggest that the Center for Private Seed Companies, which will be established at Gimje in 2015, plays an important role to make domestic seed companies improve their scale efficiency as well as pure technical efficiency by way of enlarging their size and co-using the high technology in the Center.

• Journal of the East Asian Society of Dietary Life
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• v.9 no.1
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• pp.74-80
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• 1999

The proximate composition, pH, vitamins and minerals in sunflower seed sprout were investigated to furnish basic data for utilization in health food or processed food. The pH of sunflower seed sprout was 5.70. The contents of moisture, crude protein, crude fat, crude ash and crude fiber of sunflower seed sprout were 94.7%, 1.3%, 0.3%, 1.3% and 1.6%, respectively. The vitamin A, vitamin B$_1$, vitamin B$_2$, vitamin C and niacin contents in sunflower seed sprout were 114.411. U%, 0.06mg%, 0.05mg%. 5.90mg% and 0.80mg%, respectively. The contents of Ca, P, Fe, Na, K, Mn, Cu, Zn and Mg in sunflower seed sprout per 100g were 80.00mg, 4.85mg, 3.63mg, 8.25mg, 180.90mg, 1.35mg, 0.43mg, 1.85mgand 66.35mg, respectively. The crude ash and crude fiber content of sunflower seed sprout were 3 or 4 times higher than those in the sprout of radish seed, mung bean, soybean or alfalfa, respectively.

• Journal of the Korea Society for Simulation
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• v.28 no.4
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• pp.67-74
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• 2019

As the market for Internet of Things (IoT) service grows, the security issue of the data from IoT devices becomes more important. In this paper, we implemented a cryptographic library for confidentiality of sensor data from Android Things based IoT services. The library made use of the SEED algorithm for encryption/decryption of data and we verified the library by implementing a service environment. With the library, the data is securely encrypted and stored in the database and the service environment is able to represent the current sensing status with the decrypted sensor data. The contribution of this work is in verifying the usability of SEED based encryption library by implementation in IoT sensor based service environment.

• Journal of the Korea Society for Simulation
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• v.29 no.2
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• pp.73-81
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• 2020

HCCL stands for Heterogenous Container Class Library. HCCL is a library that allows heterogeneous types of data to be stored in a container as a single record and to be constructed as a list of the records to be stored in database. With HCCL, encryption/decryption can be done based on the unified data type. Recently, IoT sensor which is embedded in smartphone enables developers to provide various convenient services to users. However, it is also true that infringement of personal information may occur in the process of transmitting sensor information to API and users need to be prepared for this situation in some sense. In this study, we developed a data model that enhances existing security using SEED cryptographic algorithms while managing information of sensors based on HCCL. Due to the fact that the Android environment does not provide permission management function for sensors, this study decided whether or not to encrypt sensor information based on the user's choice so that the user can determine the creation and storage of safe data. For verification of this work, we have presented the performance evaluation by comparing with the situation of storing the sensor data in plaintext.