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  • Open Access

    ARTICLE

    Video Summarization Approach Based on Binary Robust Invariant Scalable Keypoints and Bisecting K-Means

    Sameh Zarif1,2,*, Eman Morad1, Khalid Amin1, Abdullah Alharbi3, Wail S. Elkilani4, Shouze Tang5

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3565-3583, 2024, DOI:10.32604/cmc.2024.046185

    Abstract Due to the exponential growth of video data, aided by rapid advancements in multimedia technologies. It became difficult for the user to obtain information from a large video series. The process of providing an abstract of the entire video that includes the most representative frames is known as static video summarization. This method resulted in rapid exploration, indexing, and retrieval of massive video libraries. We propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint (BRISK) and bisecting K-means clustering algorithm. The current method effectively recognizes relevant frames using BRISK… More >

  • Open Access

    ARTICLE

    Genetic Algorithm Combined with the K-Means Algorithm: A Hybrid Technique for Unsupervised Feature Selection

    Hachemi Bennaceur, Meznah Almutairy, Norah Alhussain*

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2687-2706, 2023, DOI:10.32604/iasc.2023.038723

    Abstract The dimensionality of data is increasing very rapidly, which creates challenges for most of the current mining and learning algorithms, such as large memory requirements and high computational costs. The literature includes much research on feature selection for supervised learning. However, feature selection for unsupervised learning has only recently been studied. Finding the subset of features in unsupervised learning that enhances the performance is challenging since the clusters are indeterminate. This work proposes a hybrid technique for unsupervised feature selection called GAk-MEANS, which combines the genetic algorithm (GA) approach with the classical k-Means algorithm. In… More >

  • Open Access

    ARTICLE

    Plant Leaf Diseases Classification Using Improved K-Means Clustering and SVM Algorithm for Segmentation

    Mona Jamjoom1, Ahmed Elhadad2, Hussein Abulkasim3,*, Safia Abbas4

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 367-382, 2023, DOI:10.32604/cmc.2023.037310

    Abstract Several pests feed on leaves, stems, bases, and the entire plant, causing plant illnesses. As a result, it is vital to identify and eliminate the disease before causing any damage to plants. Manually detecting plant disease and treating it is pretty challenging in this period. Image processing is employed to detect plant disease since it requires much effort and an extended processing period. The main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases, More >

  • Open Access

    ARTICLE

    An Adaptive Parameter-Free Optimal Number of Market Segments Estimation Algorithm Based on a New Internal Validity Index

    Jianfang Qi1, Yue Li1,3, Haibin Jin1, Jianying Feng1, Dong Tian1, Weisong Mu1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 197-232, 2023, DOI:10.32604/cmes.2023.026113

    Abstract An appropriate optimal number of market segments (ONS) estimation is essential for an enterprise to achieve successful market segmentation, but at present, there is a serious lack of attention to this issue in market segmentation. In our study, an independent adaptive ONS estimation method BWCON-NSDK-means++ is proposed by integrating a new internal validity index (IVI) Between-Within-Connectivity (BWCON) and a new stable clustering algorithm Natural-SDK-means++ (NSDK-means++) in a novel way. First, to complete the evaluation dimensions of the existing IVIs, we designed a connectivity formula based on the neighbor relationship and proposed the BWCON by integrating… More >

  • Open Access

    ARTICLE

    A Novel Cluster Analysis-Based Crop Dataset Recommendation Method in Precision Farming

    K. R. Naveen Kumar1, Husam Lahza2, B. R. Sreenivasa3,*, Tawfeeq Shawly4, Ahmed A. Alsheikhy5, H. Arunkumar1, C. R. Nirmala1

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3239-3260, 2023, DOI:10.32604/csse.2023.036629

    Abstract Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making information. Precision agriculture uses data mining to advance agricultural developments. Many farmers aren’t getting the most out of their land because they don’t use precision agriculture. They harvest crops without a well-planned recommendation system. Future crop production is calculated by combining environmental conditions and management behavior, yielding numerical and categorical data. Most existing research still needs to address data preprocessing and crop categorization/classification. Furthermore, statistical analysis receives less attention, despite producing more accurate and valid results. The study was conducted on… More >

  • Open Access

    ARTICLE

    Research on Short-Term Load Forecasting of Distribution Stations Based on the Clustering Improvement Fuzzy Time Series Algorithm

    Jipeng Gu1, Weijie Zhang1, Youbing Zhang1,*, Binjie Wang1, Wei Lou2, Mingkang Ye3, Linhai Wang3, Tao Liu4

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2221-2236, 2023, DOI:10.32604/cmes.2023.025396

    Abstract An improved fuzzy time series algorithm based on clustering is designed in this paper. The algorithm is successfully applied to short-term load forecasting in the distribution stations. Firstly, the K-means clustering method is used to cluster the data, and the midpoint of two adjacent clustering centers is taken as the dividing point of domain division. On this basis, the data is fuzzed to form a fuzzy time series. Secondly, a high-order fuzzy relation with multiple antecedents is established according to the main measurement indexes of power load, which is used to predict the short-term trend More >

  • Open Access

    ARTICLE

    Cardiac CT Image Segmentation for Deep Learning–Based Coronary Calcium Detection Using K-Means Clustering and Grabcut Algorithm

    Sungjin Lee1, Ahyoung Lee2, Min Hong3,*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2543-2554, 2023, DOI:10.32604/csse.2023.037055

    Abstract Specific medical data has limitations in that there are not many numbers and it is not standardized. to solve these limitations, it is necessary to study how to efficiently process these limited amounts of data. In this paper, deep learning methods for automatically determining cardiovascular diseases are described, and an effective preprocessing method for CT images that can be applied to improve the performance of deep learning was conducted. The cardiac CT images include several parts of the body such as the heart, lungs, spine, and ribs. The preprocessing step proposed in this paper divided… More >

  • Open Access

    ARTICLE

    An Improved Encoder-Decoder CNN with Region-Based Filtering for Vibrant Colorization

    Mrityunjoy Gain1, Md Arifur Rahman1, Rameswar Debnath1, Mrim M. Alnfiai2, Abdullah Sheikh3, Mehedi Masud3, Anupam Kumar Bairagi1,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 1059-1077, 2023, DOI:10.32604/csse.2023.034809

    Abstract Colorization is the practice of adding appropriate chromatic values to monochrome photographs or videos. A real-valued luminance image can be mapped to a three-dimensional color image. However, it is a severely ill-defined problem and not has a single solution. In this paper, an encoder-decoder Convolutional Neural Network (CNN) model is used for colorizing gray images where the encoder is a Densely Connected Convolutional Network (DenseNet) and the decoder is a conventional CNN. The DenseNet extracts image features from gray images and the conventional CNN outputs a * b * color channels. Due to a large number of desaturated… More >

  • Open Access

    ARTICLE

    Application of Federated Learning Algorithm Based on K-Means in Electric Power Data

    Weimin He, Lei Zhao*

    Journal of New Media, Vol.4, No.4, pp. 191-203, 2022, DOI:10.32604/jnm.2022.032994

    Abstract Accurate electricity forecasting is the key basis for guiding the power sector to arrange operation plans and guaranteeing the profitability of electric power companies. However, with the increasing demand of enterprises and departments for data security, the phenomenon of “Isolated Data Island” becomes more and more serious, resulting in the accuracy loss of the traditional electricity prediction model. Federated learning, as an emerging artificial intelligence technology, is designed to ensure data privacy while carrying out efficient machine learning, which provides a new way to solve the problem of “Isolated Data Island” in terms of electricity… More >

  • Open Access

    ARTICLE

    Micro Calcification Detection in Mammogram Images Using Contiguous Convolutional Neural Network Algorithm

    P. Gomathi1,*, C. Muniraj2, P. S. Periasamy3

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1887-1899, 2023, DOI:10.32604/csse.2023.028808

    Abstract The mortality rate decreases as the early detection of Breast Cancer (BC) methods are emerging very fast, and when the starting stage of BC is detected, it is curable. The early detection of the disease depends on the image processing techniques, and it is used to identify the disease easily and accurately, especially the micro calcifications are visible on mammography when they are 0.1 mm or bigger, and cancer cells are about 0.03 mm, which is crucial for identifying in the BC area. To achieve this micro calcification in the BC images, it is necessary… More >

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