Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (468)
  • Open Access

    ARTICLE

    Feature Point Detection for Repacked Android Apps

    M. A. Rahim Khan*, Manoj Kumar Jain

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1359-1373, 2020, DOI:10.32604/iasc.2020.013849

    Abstract Repacked mobile applications and obfuscation attacks constitute a significant threat to the Android technological ecosystem. A novel method using the Constant Key Point Selection and Limited Binary Pattern Feature (CKPS: LBP) extraction-based Hashing has been proposed to identify repacked Android applications in previous works. Although the approach was efficient in detecting the repacked Android apps, it was not suitable for detecting obfuscation attacks. Additionally, the time complexity needed improvement. This paper presents an optimization technique using Scalable Bivariant Feature Transformation extract optimum feature-points extraction, and the Harris method applied for optimized image hashing. The experiments produced better results than the… More >

  • Open Access

    ARTICLE

    Fused and Modified Evolutionary Optimization of Multiple Intelligent Systems Using ANN, SVM Approaches

    Jalal Sadoon Hameed Al-bayati1,*, Burak Berk Üstündağ2

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1479-1496, 2021, DOI:10.32604/cmc.2020.013329

    Abstract The Fused Modified Grasshopper Optimization Algorithm has been proposed, which selects the most specific feature sets from images of the disease of plant leaves. The Proposed algorithm ensures the detection of diseases during the early stages of the diagnosis of leaf disease by farmers and, finally, the crop needed to be controlled by farmers to ensure the survival and protection of plants. In this study, a novel approach has been suggested based on the standard optimization algorithm for grasshopper and the selection of features. Leaf conditions in plants are a major factor in reducing crop yield and quality. Any delay… More >

  • Open Access

    ARTICLE

    Deep Learning-Based Classification of Fruit Diseases: An Application for Precision Agriculture

    Inzamam Mashood Nasir1, Asima Bibi2, Jamal Hussain Shah2, Muhammad Attique Khan1, Muhammad Sharif2, Khalid Iqbal3, Yunyoung Nam4, Seifedine Kadry5,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1949-1962, 2021, DOI:10.32604/cmc.2020.012945

    Abstract Agriculture is essential for the economy and plant disease must be minimized. Early recognition of problems is important, but the manual inspection is slow, error-prone, and has high manpower and time requirements. Artificial intelligence can be used to extract fruit color, shape, or texture data, thus aiding the detection of infections. Recently, the convolutional neural network (CNN) techniques show a massive success for image classification tasks. CNN extracts more detailed features and can work efficiently with large datasets. In this work, we used a combined deep neural network and contour feature-based approach to classify fruits and their diseases. A fine-tuned,… More >

  • Open Access

    ARTICLE

    Reference Gene Selection for qRT-PCR Normalization in Iris germanica L.

    Yinjie Wang, Yongxia Zhang, Qingquan Liu, Liangqin Liu, Suzhen Huang, Haiyan Yuan*

    Phyton-International Journal of Experimental Botany, Vol.90, No.1, pp. 277-290, 2021, DOI:10.32604/phyton.2020.011545

    Abstract Quantitative real-time PCR (qPCR) is an effective and widely used method to analyze expression patterns of target genes. Selection of stable reference genes is a prerequisite for accurate normalization of target gene expression by qRT-PCR. In Iris germanica L., no studies have yet been published regarding the evaluation of potential reference genes. In this study, nine candidate reference genes were assessed at different flower developmental stages and in different tissues by four different algorithms (GeNorm, NormFinder, BestKeeper, and RefFinder). The results revealed that ACT11 (Actin 11) and EF1α (Elongation factor 1 alpha) were the most stable reference genes in different… More >

  • Open Access

    REVIEW

    Applications of Molecular Markers in Fruit Crops for Breeding Programs—A Review

    Riaz Ahmad1, Muhammad Akbar Anjum1,*, Safina Naz1, Rashad Mukhtar Balal2

    Phyton-International Journal of Experimental Botany, Vol.90, No.1, pp. 17-34, 2021, DOI:10.32604/phyton.2020.011680

    Abstract Selection and use of molecular markers for evaluation of DNA polymorphism in plants are couple of the most important approaches in the field of molecular genetics. The assessment of genetic diversity using morphological markers is not sufficient due to little differentiating traits among the species, genera or their individuals. Morphological markers are not only highly influenced by environmental factors but skilled assessment is also prerequisite to find the variations in plant genetic resources. Therefore, molecular markers are considered as efficient tools for detailed DNA based characterization of fruit crops. Molecular markers provide new directions to the efforts of plant breeders… More >

  • Open Access

    ARTICLE

    A Novel Image Retrieval Method with Improved DCNN and Hash

    Yan Zhou, Lili Pan*, Rongyu Chen, Weizhi Shao

    Journal of Information Hiding and Privacy Protection, Vol.2, No.2, pp. 77-86, 2020, DOI:10.32604/jihpp.2020.010486

    Abstract In large-scale image retrieval, deep features extracted by Convolutional Neural Network (CNN) can effectively express more image information than those extracted by traditional manual methods. However, the deep feature dimensions obtained by Deep Convolutional Neural Network (DCNN) are too high and redundant, which leads to low retrieval efficiency. We propose a novel image retrieval method, which combines deep features selection with improved DCNN and hash transform based on high-dimension features reduction to gain lowdimension deep features and realizes efficient image retrieval. Firstly, the improved network is based on the existing deep model to build a more profound and broader network… More >

  • Open Access

    ARTICLE

    Optimizing Bidders Selection of Multi-Round Procurement Problem in Software Project Management Using Parallel Max-Min Ant System Algorithm

    Dac-Nhuong Le1,2,3,*, Gia Nhu Nguyen2,4, Harish Garg5, Quyet-Thang Huynh6, Trinh Ngoc Bao7, Nguyen Ngoc Tuan8

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 993-1010, 2021, DOI:10.32604/cmc.2020.012464

    Abstract This paper presents a Game-theoretic optimization via Parallel MinMax Ant System (PMMAS) algorithm is used in practice to determine the Nash equilibrium value to resolve the confusion in choosing appropriate bidders of multi-round procurement problem in software project management. To this end, we introduce an approach that proposes: (i) A Game-theoretic model of multiround procurement problem (ii) A Nash equilibrium strategy corresponds to multi-round strategy bid (iii) An application of PSO for the determination of global Nash equilibrium. The balance point in Nash Equilibrium can help to maintain a sustainable structure not only in terms of project management but also… More >

  • Open Access

    ARTICLE

    Fingerprint-Based Millimeter-Wave Beam Selection for Interference Mitigation in Beamspace Multi-User MIMO Communications

    Sangmi Moon1, Hyeonsung Kim1, Seng-Phil Hong2, Mingoo Kang3, Intae Hwang1,*

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 59-70, 2021, DOI:10.32604/cmc.2020.013132

    Abstract Millimeter-wave communications are suitable for application to massive multiple-input multiple-output systems in order to satisfy the ever-growing data traffic demands of the next-generation wireless communication. However, their practical deployment is hindered by the high cost of complex hardware, such as radio frequency (RF) chains. To this end, operation in the beamspace domain, through beam selection, is a viable solution. Generally, the conventional beam selection schemes focus on the feedback and exhaustive search techniques. In addition, since the same beam in the beamspace may be assigned to a different user, conventional beam selection schemes suffer serious multi-user interference. In addition, some… More >

  • Open Access

    REVIEW

    Importance of Features Selection, Attributes Selection, Challenges and Future Directions for Medical Imaging Data: A Review

    Nazish Naheed1, Muhammad Shaheen1, Sajid Ali Khan1, Mohammed Alawairdhi2,*, Muhammad Attique Khan3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 315-344, 2020, DOI:10.32604/cmes.2020.011380

    Abstract In the area of pattern recognition and machine learning, features play a key role in prediction. The famous applications of features are medical imaging, image classification, and name a few more. With the exponential growth of information investments in medical data repositories and health service provision, medical institutions are collecting large volumes of data. These data repositories contain details information essential to support medical diagnostic decisions and also improve patient care quality. On the other hand, this growth also made it difficult to comprehend and utilize data for various purposes. The results of imaging data can become biased because of… More >

  • Open Access

    ARTICLE

    Least-Square Support Vector Machine and Wavelet Selection for Hearing Loss Identification

    Chaosheng Tang1, Deepak Ranjan Nayak2, Shuihua Wang1,3,4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 299-313, 2020, DOI:10.32604/cmes.2020.011069

    Abstract Hearing loss (HL) is a kind of common illness, which can significantly reduce the quality of life. For example, HL often results in mishearing, misunderstanding, and communication problems. Therefore, it is necessary to provide early diagnosis and timely treatment for HL. This study investigated the advantages and disadvantages of three classical machine learning methods: multilayer perceptron (MLP), support vector machine (SVM), and least-square support vector machine (LS-SVM) approach and made a further optimization of the LS-SVM model via wavelet entropy. The investigation illustrated that themultilayer perceptron is a shallowneural network,while the least square support vector machine uses hinge loss function… More >

Displaying 391-400 on page 40 of 468. Per Page