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    Selection for Upland Rice Varieties Under Different Levels of Light Intensity
    (2022-04-01)
    Nokkoul, Raumjit
    ;
    Unsup, Karnchanaporn
    ;
    Krutsuwan, Apichart
    ;
    Wichitparp, Thirayut
    Upland rice varieties were selected under varying levels of light intensity. This study identified shade-tolerant upland rice varieties with a high yield. From November 2016 to March 2017, the experiment was conducted at King Mongkut's Institute of Technology in Ladkrabang, Prince of Chumphon, Thailand. For this, ten varieties of upland rice i.e. Dawk Pa-yawm, Nangdam, Dokkam, Nangchuan, Lebnokrai, Samduen, Maepeung, Damgatondom, Lebmuenang, Pukaotong were grown at different levels of 0%, 50%, 60%, and 70% shading levels. The experiment was conducted in a randomized complete block design with four replications. The results of qualitative characteristics indicated that shadowing conditions didn’t have any significant effect on plant height, 50% flowering age, harvesting age, and panicle length. On the other hand, shaded conditions have a substantial effect on the number of plants per tiller, the number of panicles per tiller, the number of perfect grains per panicle, and the amount of empty grain per panicle since there was a quantitative character. Among the tested varieties, Lebmuenang, Pukaotong, Damgatondom, and Nangchuan varieties are suited for growing under 50% shade conditions while Samduen was only suited for cultivation in 70 % shaded conditions, whereas Pukaotong was good for growing in 50% and 60% shade situations. The results of this experiment did not encourage farmers to use the other varieties as cultivars under shaded conditions due to low yields. However, the outcomes of this study can be used as a reference for breeding programs in low-light conditions.
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    A novel technique for feature subset selection based on Cosine similarity
    (2012-11-27)
    Suebsing, Anirut
    ;
    Hiransakolwong, Nualsawat
    Nowadays, data mining has been playing an important role in the various disciplines of sciences and technologies. Data mining is composed of many tasks but one of the essential procedures of data mining is feature selection, which is the technique mostly based on the machine learning for selecting a subset consisted of significant features, building a stronger learning model, and enhancing the efficiency of prediction rate. Normally, a processing of building a learning model from the huge amount of collected data needs high computation cost. Therefore, with feature selection, the computation cost can be reduced by selecting relevant features. In the previous researches on feature selection, the criteria and algorithms for selecting the features from the raw data are mostly complicated and difficult to implement. Therefore, this paper presents a novel method by applied Cosine similarity to feature selection method. The proposed algorithm begins with selecting a robust feature subset using the Cosine similarity. This method is the simple algorithm using smallerstorage space, reducing computation time and gaining higher predictive performance. During the evaluation phase, the tendata sets from UCI benchmark data sets are used to evaluate the performance of proposed approach by using the C5.0, CARTand Neural Networks classifiers. Experimental results show that the method based on the Cosine similarity can improve the performance of accuracy detection rate with less error rate.