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  • Open Access


    Calcul de similarité sémantique entre trajectoires

    Clément Moreau, Thomas Devogele, Laurent Etienne

    Revue Internationale de Géomatique, Vol.29, No.1, pp. 107-128, 2019, DOI:10.3166/rig.2019.00077

    Abstract Understanding mobility, whether physical in the spatial sense or virtual in the web navigation sense, raises many challenges in terms of monitoring individuals, spatial planning or activity recommendations. Having access today to many resources on the contextual nature of this mobility, one of the current concerns is to succeed in identifying groups of similar individuals with regard to their mobility. To do this, we propose a semantic trajectory model enriched by ontologies at the level of contextual data and allowing us to calculate the similarity between each episode of individual mobility. Subsequently, an editing distance is used to evaluate in… More >

  • Open Access


    A New Hybrid Feature Selection Sequence for Predicting Breast Cancer Survivability Using Clinical Datasets

    E. Jenifer Sweetlin*, S. Saudia

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 343-367, 2023, DOI:10.32604/iasc.2023.036742

    Abstract This paper proposes a hybrid feature selection sequence complemented with filter and wrapper concepts to improve the accuracy of Machine Learning (ML) based supervised classifiers for classifying the survivability of breast cancer patients into classes, living and deceased using METABRIC and Surveillance, Epidemiology and End Results (SEER) datasets. The ML-based classifiers used in the analysis are: Multiple Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine and Multilayer Perceptron. The workflow of the proposed ML algorithm sequence comprises the following stages: data cleaning, data balancing, feature selection via a filter and wrapper sequence, cross validation-based training, testing and… More >

  • Open Access


    Bi-LSTM-Based Deep Stacked Sequence-to-Sequence Autoencoder for Forecasting Solar Irradiation and Wind Speed

    Neelam Mughees1,2, Mujtaba Hussain Jaffery1, Abdullah Mughees3, Anam Mughees4, Krzysztof Ejsmont5,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6375-6393, 2023, DOI:10.32604/cmc.2023.038564

    Abstract Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050. However, they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions. In microgrids, smart energy management systems, such as integrated demand response programs, are permanently established on a step-ahead basis, which means that accurate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids. With this in mind, a novel “bidirectional long short-term memory network” (Bi-LSTM)-based, deep stacked, sequence-to-sequence autoencoder (S2SAE) forecasting model… More >

  • Open Access


    High Utility Periodic Frequent Pattern Mining in Multiple Sequences

    Chien-Ming Chen1, Zhenzhou Zhang1, Jimmy Ming-Tai Wu1, Kuruva Lakshmanna2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 733-759, 2023, DOI:10.32604/cmes.2023.027463

    Abstract Periodic pattern mining has become a popular research subject in recent years; this approach involves the discovery of frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic pattern mining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodic patterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequences is more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences is important. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. To address existing problems, three… More >

  • Open Access


    An Efficient Color-Image Encryption Method Using DNA Sequence and Chaos Cipher

    Ghofran Kh. Shraida1, Hameed A. Younis1, Taief Alaa Al-Amiedy2, Mohammed Anbar2,*, Hussain A. Younis3,4, Iznan H. Hasbullah2

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2641-2654, 2023, DOI:10.32604/cmc.2023.035793

    Abstract Nowadays, high-resolution images pose several challenges in the context of image encryption. The encryption of huge images’ file sizes requires high computational resources. Traditional encryption techniques like, Data Encryption Standard (DES), and Advanced Encryption Standard (AES) are not only inefficient, but also less secure. Due to characteristics of chaos theory, such as periodicity, sensitivity to initial conditions and control parameters, and unpredictability. Hence, the characteristics of deoxyribonucleic acid (DNA), such as vast parallelism and large storage capacity, make it a promising field. This paper presents an efficient color image encryption method utilizing DNA encoding with two types of hyper-chaotic maps.… More >

  • Open Access


    Sequence-Based Predicting Bacterial Essential ncRNAs Algorithm by Machine Learning

    Yuan-Nong Ye1,2,3,*, Ding-Fa Liang2, Abraham Alemayehu Labena4, Zhu Zeng2,*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2731-2741, 2023, DOI:10.32604/iasc.2023.026761

    Abstract Essential ncRNA is a type of ncRNA which is indispensable for the survival of organisms. Although essential ncRNAs cannot encode proteins, they are as important as essential coding genes in biology. They have got wide variety of applications such as antimicrobial target discovery, minimal genome construction and evolution analysis. At present, the number of species required for the determination of essential ncRNAs in the whole genome scale is still very few due to the traditional methods are time-consuming, laborious and costly. In addition, traditional experimental methods are limited by the organisms as less than 1% of bacteria can be cultured… More >

  • Open Access


    Video Transmission Secrecy Improvement Based on Fractional Order Hyper Chaotic System

    S. Kayalvizhi*, S. Malarvizhi

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 1201-1214, 2023, DOI:10.32604/csse.2023.032381

    Abstract In the Digital World scenario, the confidentiality of information in video transmission plays an important role. Chaotic systems have been shown to be effective for video signal encryption. To improve video transmission secrecy, compressive encryption method is proposed to accomplish compression and encryption based on fractional order hyper chaotic system that incorporates Compressive Sensing (CS), pixel level, bit level scrambling and nucleotide Sequences operations. The measurement matrix generates by the fractional order hyper chaotic system strengthens the efficiency of the encryption process. To avoid plain text attack, the CS measurement is scrambled to its pixel level, bit level scrambling decreases… More >

  • Open Access


    Implementation of Rapid Code Transformation Process Using Deep Learning Approaches

    Bao Rong Chang1, Hsiu-Fen Tsai2,*, Han-Lin Chou1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 107-134, 2023, DOI:10.32604/cmes.2023.024018

    Abstract Our previous work has introduced the newly generated program using the code transformation model GPT-2, verifying the generated programming codes through simhash (SH) and longest common subsequence (LCS) algorithms. However, the entire code transformation process has encountered a time-consuming problem. Therefore, the objective of this study is to speed up the code transformation process significantly. This paper has proposed deep learning approaches for modifying SH using a variational simhash (VSH) algorithm and replacing LCS with a piecewise longest common subsequence (PLCS) algorithm to faster the verification process in the test phase. Besides the code transformation model GPT-2, this study has… More > Graphic Abstract

    Implementation of Rapid Code Transformation Process Using Deep Learning Approaches

  • Open Access


    Structural characterization of four Rhododendron spp. chloroplast genomes and comparative analyses with other azaleas


    BIOCELL, Vol.47, No.3, pp. 657-668, 2023, DOI:10.32604/biocell.2023.026781

    Abstract Azalea is a general designation of Rhododendron in the Ericaceae family. Rhododendron not only has high ornamental value but also has application value in ecological protection, medicine, and scientific research. In this study, we used Illumina and PacBio sequencing to assemble and annotate the entire chloroplast genomes (cp genomes) of four Rhododendron species. The chloroplast genomes of R. concinnum, R. henanense subsp. lingbaoense, R. micranthum, and R. simsii were assembled into 207,236, 208,015, 207,233, and 206,912 bp, respectively. All chloroplast genomes contain eight rRNA genes, with either 88 or 89 protein-coding genes. The four cp genomes were compared and analyzed… More >

  • Open Access


    Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences

    Umair Muneer Butt1,2,*, Hadiqa Aman Ullah2, Sukumar Letchmunan1, Iqra Tariq2, Fadratul Hafinaz Hassan1, Tieng Wei Koh3

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5017-5033, 2023, DOI:10.32604/cmc.2023.035512

    Abstract Human Activity Recognition (HAR) is an active research area due to its applications in pervasive computing, human-computer interaction, artificial intelligence, health care, and social sciences. Moreover, dynamic environments and anthropometric differences between individuals make it harder to recognize actions. This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications. It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network. Moreover, the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information. Six state-of-the-art pre-trained models… More >

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