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  • Open Access

    ARTICLE

    Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

    Anwer Mustafa Hilal1,2,*, Aisha Hassan Abdalla Hashim1, Heba G. Mohamed3, Mohamed K. Nour4, Mashael M. Asiri5, Ali M. Al-Sharafi6, Mahmoud Othman7, Abdelwahed Motwakel2

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 607-621, 2023, DOI:10.32604/cmc.2023.031371

    Abstract Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era. Malicious Uniform Resource Locators (URLs) can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems. This may result in compromised security of the systems, scams, and other such cyberattacks. These attacks hijack huge quantities of the available data, incurring heavy financial loss. At the same time, Machine Learning (ML) and Deep Learning (DL) models paved the way for designing models that can detect malicious URLs accurately and classify them. With this motivation,… More >

  • Open Access

    ARTICLE

    Stacked Gated Recurrent Unit Classifier with CT Images for Liver Cancer Classification

    Mahmoud Ragab1,2,3,*, Jaber Alyami4,5

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2309-2322, 2023, DOI:10.32604/csse.2023.026877

    Abstract Liver cancer is one of the major diseases with increased mortality in recent years, across the globe. Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis (CAD) models have been developed to detect the presence of liver cancer accurately and classify its stages. Besides, liver cancer segmentation outcome, using medical images, is employed in the assessment of tumor volume, further treatment plans, and response monitoring. Hence, there is a need exists to develop automated tools for liver cancer detection in a precise manner. With this motivation, the current study introduces an Intelligent… More >

  • Open Access

    ARTICLE

    Hyperparameter Tuning Bidirectional Gated Recurrent Unit Model for Oral Cancer Classification

    K. Shankar1, E. Laxmi Lydia2, Sachin Kumar1,*, Ali S. Abosinne3, Ahmed alkhayyat4, A. H. Abbas5, Sarmad Nozad Mahmood6

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4541-4557, 2022, DOI:10.32604/cmc.2022.031247

    Abstract Oral Squamous Cell Carcinoma (OSCC) is a type of Head and Neck Squamous Cell Carcinoma (HNSCC) and it should be diagnosed at early stages to accomplish efficient treatment, increase the survival rate, and reduce death rate. Histopathological imaging is a wide-spread standard used for OSCC detection. However, it is a cumbersome process and demands expert’s knowledge. So, there is a need exists for automated detection of OSCC using Artificial Intelligence (AI) and Computer Vision (CV) technologies. In this background, the current research article introduces Improved Slime Mould Algorithm with Artificial Intelligence Driven Oral Cancer Classification (ISMA-AIOCC) model on Histopathological images… More >

  • Open Access

    ARTICLE

    KGSR-GG: A Noval Scheme for Dynamic Recommendation

    Jun-Ping Yao1, Kai-Yuan Cheng1,*, Meng-Meng Ge2, Xiao-Jun Li1, Yi-Jing Wang1

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5509-5524, 2022, DOI:10.32604/cmc.2022.030150

    Abstract Recommendation algorithms regard user-item interaction as a sequence to capture the user’s short-term preferences, but conventional algorithms cannot capture information of constantly-changing user interest in complex contexts. In these years, combining the knowledge graph with sequential recommendation has gained momentum. The advantages of knowledge graph-based recommendation systems are that more semantic associations can improve the accuracy of recommendations, rich association facts can increase the diversity of recommendations, and complex relational paths can hence the interpretability of recommendations. But the information in the knowledge graph, such as entities and relations, often fails to be fully utilized and high-order connectivity is unattainable… More >

  • Open Access

    ARTICLE

    Sport-Related Activity Recognition from Wearable Sensors Using Bidirectional GRU Network

    Sakorn Mekruksavanich1, Anuchit Jitpattanakul2,*

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1907-1925, 2022, DOI:10.32604/iasc.2022.027233

    Abstract Numerous learning-based techniques for effective human activity recognition (HAR) have recently been developed. Wearable inertial sensors are critical for HAR studies to characterize sport-related activities. Smart wearables are now ubiquitous and can benefit people of all ages. HAR investigations typically involve sensor-based evaluation. Sport-related activities are unpredictable and have historically been classified as complex, with conventional machine learning (ML) algorithms applied to resolve HAR issues. The efficiency of machine learning techniques in categorizing data is limited by the human-crafted feature extraction procedure. A deep learning model named MBiGRU (multimodal bidirectional gated recurrent unit) neural network was proposed to recognize everyday… More >

  • Open Access

    ARTICLE

    Air Pollution Prediction Via Graph Attention Network and Gated Recurrent Unit

    Shun Wang1, Lin Qiao2, Wei Fang3, Guodong Jing4, Victor S. Sheng5, Yong Zhang1,*

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 673-687, 2022, DOI:10.32604/cmc.2022.028411

    Abstract PM2.5 concentration prediction is of great significance to environmental protection and human health. Achieving accurate prediction of PM2.5 concentration has become an important research task. However, PM2.5 pollutants can spread in the earth’s atmosphere, causing mutual influence between different cities. To effectively capture the air pollution relationship between cities, this paper proposes a novel spatiotemporal model combining graph attention neural network (GAT) and gated recurrent unit (GRU), named GAT-GRU for PM2.5 concentration prediction. Specifically, GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities, and GRU is to extract the temporal dependence of the long-term… More >

  • Open Access

    ARTICLE

    Spatio-temporal Model Combining VMD and AM for Wind Speed Prediction

    Yingnan Zhao1,*, Peiyuan Ji1, Fei Chen1, Guanlan Ji1, Sunil Kumar Jha2

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1001-1016, 2022, DOI:10.32604/iasc.2022.027710

    Abstract This paper proposes a spatio-temporal model (VCGA) based on variational mode decomposition (VMD) and attention mechanism. The proposed prediction model combines a squeeze-and-excitation network to extract spatial features and a gated recurrent unit to capture temporal dependencies. Primarily, the VMD can reduce the instability of the original wind speed data and the attention mechanism functions to strengthen the impact of important information. In addition, the VMD and attention mechanism act to avoid a decline in prediction accuracy. Finally, the VCGA trains the decomposition result and derives the final results after merging the prediction result of each component. Contrasting experiments for… More >

  • Open Access

    ARTICLE

    Optimized Gated Recurrent Unit for Mid-Term Electricity Price Forecasting

    Rashed Iqbal1, Hazlie Mokhlis1, Anis Salwa Mohd Khairuddin1,*, Syafiqah Ismail1, Munir Azam Muhammad2

    Computer Systems Science and Engineering, Vol.43, No.2, pp. 817-832, 2022, DOI:10.32604/csse.2022.023617

    Abstract Electricity price forecasting (EPF) is important for energy system operations and management which include strategic bidding, generation scheduling, optimum storage reserves scheduling and systems analysis. Moreover, accurate EPF is crucial for the purpose of bidding strategies and minimizing the risk for market participants in the competitive electricity market. Nevertheless, accurate time-series prediction of electricity price is very challenging due to complex nonlinearity in the trend of electricity price. This work proposes a mid-term forecasting model based on the demand and price data, renewable and non-renewable energy supplies, the seasonality and peak and off-peak hours of working and non-working days. An… More >

  • Open Access

    ARTICLE

    A Deep Two-State Gated Recurrent Unit for Particulate Matter (PM2.5) Concentration Forecasting

    Muhammad Zulqarnain1, Rozaida Ghazali1,*, Habib Shah2, Lokman Hakim Ismail1, Abdullah Alsheddy3, Maqsood Mahmud4

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3051-3068, 2022, DOI:10.32604/cmc.2022.021629

    Abstract Air pollution is a significant problem in modern societies since it has a serious impact on human health and the environment. Particulate Matter (PM2.5) is a type of air pollution that contains of interrupted elements with a diameter less than or equal to 2.5 m. For risk assessment and epidemiological investigations, a better knowledge of the spatiotemporal variation of PM2.5 concentration in a constant space-time area is essential. Conventional spatiotemporal interpolation approaches commonly relying on robust presumption by limiting interpolation algorithms to those with explicit and basic mathematical expression, ignoring a plethora of hidden but crucial manipulating aspects. Many advanced… More >

  • Open Access

    ARTICLE

    A Hybrid Deep Learning Scheme for Multi-Channel Sleep Stage Classification

    Wei Pei1, Yan Li1, Siuly Siuly1,*, Peng Wen2

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 889-905, 2022, DOI:10.32604/cmc.2022.021830

    Abstract Sleep stage classification plays a significant role in the accurate diagnosis and treatment of sleep-related diseases. This study aims to develop an efficient deep learning based scheme for correctly identifying sleep stages using multi-biological signals such as electroencephalography (EEG), electrocardiogram (ECG), electromyogram (EMG), and electrooculogram (EOG). Most of the prior studies in sleep stage classification focus on hand-crafted feature extraction methods. Traditional hand-crafted feature extraction methods choose features manually from raw data, which is tedious, and these features are limited in their ability to balance efficiency and accuracy. Moreover, most of the existing works on sleep staging are either single… More >

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