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  • Open Access

    ARTICLE

    Deep Learning Method to Detect the Road Cracks and Potholes for Smart Cities

    Hong-Hu Chu1, Muhammad Rizwan Saeed2, Javed Rashid3,4,*, Muhammad Tahir Mehmood5, Israr Ahmad6, Rao Sohail Iqbal4, Ghulam Ali1

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1863-1881, 2023, DOI:10.32604/cmc.2023.035287 - 06 February 2023

    Abstract The increasing global population at a rapid pace makes road traffic dense; managing such massive traffic is challenging. In developing countries like Pakistan, road traffic accidents (RTA) have the highest mortality percentage among other Asian countries. The main reasons for RTAs are road cracks and potholes. Understanding the need for an automated system for the detection of cracks and potholes, this study proposes a decision support system (DSS) for an autonomous road information system for smart city development with the use of deep learning. The proposed DSS works in layers where initially the image of… More >

  • Open Access

    ARTICLE

    LexDeep: Hybrid Lexicon and Deep Learning Sentiment Analysis Using Twitter for Unemployment-Related Discussions During COVID-19

    Azlinah Mohamed1,3,*, Zuhaira Muhammad Zain2, Hadil Shaiba2,*, Nazik Alturki2, Ghadah Aldehim2, Sapiah Sakri2, Saiful Farik Mat Yatin1, Jasni Mohamad Zain1

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1577-1601, 2023, DOI:10.32604/cmc.2023.034746 - 06 February 2023

    Abstract The COVID-19 pandemic has spread globally, resulting in financial instability in many countries and reductions in the per capita gross domestic product. Sentiment analysis is a cost-effective method for acquiring sentiments based on household income loss, as expressed on social media. However, limited research has been conducted in this domain using the LexDeep approach. This study aimed to explore social trend analytics using LexDeep, which is a hybrid sentiment analysis technique, on Twitter to capture the risk of household income loss during the COVID-19 pandemic. First, tweet data were collected using Twint with relevant keywords… More >

  • Open Access

    ARTICLE

    Hybrid Deep Learning Method for Diagnosis of Cucurbita Leaf Diseases

    V. Nirmala1,*, B. Gomathy2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2585-2601, 2023, DOI:10.32604/csse.2023.027512 - 01 August 2022

    Abstract In agricultural engineering, the main challenge is on methodologies used for disease detection. The manual methods depend on the experience of the personal. Due to large variation in environmental condition, disease diagnosis and classification becomes a challenging task. Apart from the disease, the leaves are affected by climate changes which is hard for the image processing method to discriminate the disease from the other background. In Cucurbita gourd family, the disease severity examination of leaf samples through computer vision, and deep learning methodologies have gained popularity in recent years. In this paper, a hybrid method More >

  • Open Access

    ARTICLE

    Optimized Deep Learning Methods for Crop Yield Prediction

    K. Vignesh1,*, A. Askarunisa2, A. M. Abirami3

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1051-1067, 2023, DOI:10.32604/csse.2023.024475 - 15 June 2022

    Abstract Crop yield has been predicted using environmental, land, water, and crop characteristics in a prospective research design. When it comes to predicting crop production, there are a number of factors to consider, including weather conditions, soil qualities, water levels and the location of the farm. A broad variety of algorithms based on deep learning are used to extract useful crops for forecasting. The combination of data mining and deep learning creates a whole crop yield prediction system that is able to connect raw data to predicted crop yields. The suggested study uses a Discrete Deep… More >

  • Open Access

    ARTICLE

    An Enhanced Deep Learning Method for Skin Cancer Detection and Classification

    Mohamed W. Abo El-Soud1,2,*, Tarek Gaber2,3, Mohamed Tahoun2, Abdullah Alourani1

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1109-1123, 2022, DOI:10.32604/cmc.2022.028561 - 18 May 2022

    Abstract The prevalence of melanoma skin cancer has increased in recent decades. The greatest risk from melanoma is its ability to broadly spread throughout the body by means of lymphatic vessels and veins. Thus, the early diagnosis of melanoma is a key factor in improving the prognosis of the disease. Deep learning makes it possible to design and develop intelligent systems that can be used in detecting and classifying skin lesions from visible-light images. Such systems can provide early and accurate diagnoses of melanoma and other types of skin diseases. This paper proposes a new method… More >

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