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  • Open Access

    ARTICLE

    Impact of Fuzzy Normalization on Clustering Microarray Temporal Datasets Using Cuckoo Search

    Swathypriyadharsini P1,∗, K.Premalatha2,†

    Computer Systems Science and Engineering, Vol.35, No.1, pp. 39-50, 2020, DOI:10.32604/csse.2020.35.039

    Abstract Microarrays have reformed biotechnological research in the past decade. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks with larger volume of genes also increases the challenges of comprehending and interpretation of the resulting mass of data. Clustering addresses these challenges, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding… More >

  • Open Access

    ARTICLE

    Rough Set Based Rule Approximation and Application on Uncertain Datasets

    L. Ezhilarasi1,*, A.P. Shanthi2, V. Uma Maheswari1

    Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 465-478, 2020, DOI:10.32604/iasc.2020.013923

    Abstract Development of new Artificial Intelligence related data analy sis methodologies w ith rev olutionary information technology has made a radical change in prediction, forecasting, and decision making for real-w orld data. The challenge arises w hen the real w orld dataset consisting of v oluminous data is uncertain. The rough set is a mathematical formalism that has emerged significantly for uncertain datasets. It represents the know ledge of the datasets as decision rules. It does not need any metadata. The rules are used to predict or classify unseen ex amples. The objectiv e of this research is to dev elop… More >

  • Open Access

    ARTICLE

    C5.0 Decision Tree Model Using Tsallis Entropy and Association Function for General and Medical Dataset

    Uma K.V1,*, Appavu alias Balamurugan S2

    Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 61-70, 2020, DOI:10.31209/2019.100000153

    Abstract Real world data consists of lot of impurities. Entropy measure will help to handle impurities in a better way. Here, data selection is done by using Naïve Bayes’ theorem. The sample which has posterior probability value greater than that of the threshold value is selected. C5.0 decision tree classifier is taken as base and modified the Gain calculation function using Tsallis entropy and Association function. The proposed classifier model provides more accuracy and smaller tree for general and Medical dataset. Precision value obtained for Medical dataset is more than that of existing method. More >

  • Open Access

    ARTICLE

    A Recommendation Method for Highly Sparse Dataset Based on Teaching Recommendation Factorization Machines

    Dunhong Yao1, 2, 3, Shijun Li4, *, Ang Li5, Yu Chen6

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1959-1975, 2020, DOI:10.32604/cmc.2020.010186

    Abstract There is no reasonable scientific basis for selecting the excellent teachers of the school’s courses. To solve the practical problem, we firstly give a series of normalization models for defining the key attributes of teachers’ professional foundation, course difficulty coefficient, and comprehensive evaluation of teaching. Then, we define a partial weight function to calculate the key attributes, and obtain the partial recommendation values. Next, we construct a highly sparse Teaching Recommendation Factorization Machines (TRFMs) model, which takes the 5-tuples relation including teacher, course, teachers’ professional foundation, course difficulty, teaching evaluation as the feature vector, and take partial recommendation value as… More >

  • Open Access

    ARTICLE

    Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content

    Muhammad Zubair Asghar1, Fazli Subhan2, Muhammad Imran1, Fazal Masud Kundi1, Adil Khan3, Shahboddin Shamshirband4, 5, *, Amir Mosavi6, 7, 8, Peter Csiba8, Annamaria R. Varkonyi Koczy8

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1093-1118, 2020, DOI:10.32604/cmc.2020.07709

    Abstract Emotion detection from the text is a challenging problem in the text analytics. The opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine… More >

  • Open Access

    ARTICLE

    Comparative Variance and Multiple Imputation Used for Missing Values in Land Price DataSet

    Longqing Zhang1, Liping Bai1,*, Xinwei Zhang2, Yanghong Zhang2, Feng Sun2, Changcheng Chen2

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1175-1187, 2019, DOI:10.32604/cmc.2019.06075

    Abstract Based on the two-dimensional relation table, this paper studies the missing values in the sample data of land price of Shunde District of Foshan City. GeoDa software was used to eliminate the insignificant factors by stepwise regression analysis; NORM software was adopted to construct the multiple imputation models; EM algorithm and the augmentation algorithm were applied to fit multiple linear regression equations to construct five different filling datasets. Statistical analysis is performed on the imputation data set in order to calculate the mean and variance of each data set, and the weight is determined according to the differences. Finally, comprehensive… More >

  • Open Access

    ARTICLE

    Image Augmentation-Based Food Recognition with Convolutional Neural Networks

    Lili Pan1, Jiaohua Qin1,*, Hao Chen2, Xuyu Xiang1, Cong Li1, Ran Chen1

    CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 297-313, 2019, DOI:10.32604/cmc.2019.04097

    Abstract Image retrieval for food ingredients is important work, tremendously tiring, uninteresting, and expensive. Computer vision systems have extraordinary advancements in image retrieval with CNNs skills. But it is not feasible for small-size food datasets using convolutional neural networks directly. In this study, a novel image retrieval approach is presented for small and medium-scale food datasets, which both augments images utilizing image transformation techniques to enlarge the size of datasets, and promotes the average accuracy of food recognition with state-of-the-art deep learning technologies. First, typical image transformation techniques are used to augment food images. Then transfer learning technology based on deep… More >

  • Open Access

    ARTICLE

    An Improved Memory Cache Management Study Based on Spark

    Suzhen Wang1, Yanpiao Zhang1, Lu Zhang1, Ning Cao2, *, Chaoyi Pang3

    CMC-Computers, Materials & Continua, Vol.56, No.3, pp. 415-431, 2018, DOI: 10.3970/cmc.2018.03716

    Abstract Spark is a fast unified analysis engine for big data and machine learning, in which the memory is a crucial resource. Resilient Distribution Datasets (RDDs) are parallel data structures that allow users explicitly persist intermediate results in memory or on disk, and each one can be divided into several partitions. During task execution, Spark automatically monitors cache usage on each node. And when there is a RDD that needs to be stored in the cache where the space is insufficient, the system would drop out old data partitions in a least recently used (LRU) fashion to release more space. However,… More >

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