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  • Open Access

    ARTICLE

    Twitter Arabic Sentiment Analysis to Detect Depression Using Machine Learning

    Dhiaa A. Musleh, Taef A. Alkhales, Reem A. Almakki*, Shahad E. Alnajim, Shaden K. Almarshad, Rana S. Alhasaniah, Sumayh S. Aljameel, Abdullah A. Almuqhim

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3463-3477, 2022, DOI:10.32604/cmc.2022.022508 - 07 December 2021

    Abstract Depression has been a major global concern for a long time, with the disease affecting aspects of many people's daily lives, such as their moods, eating habits, and social interactions. In Arabic culture, there is a lack of awareness regarding the importance of facing and curing mental health diseases. However, people all over the world, including Arab citizens, tend to express their feelings openly on social media, especially Twitter, as it is a platform designed to enable the expression of emotions through short texts, pictures, or videos. Users are inclined to treat their Twitter accounts… More >

  • Open Access

    ARTICLE

    Arabic Fake News Detection Using Deep Learning

    Khaled M. Fouad1,3, Sahar F. Sabbeh1,2,*, Walaa Medhat1,3

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3647-3665, 2022, DOI:10.32604/cmc.2022.021449 - 07 December 2021

    Abstract Nowadays, an unprecedented number of users interact through social media platforms and generate a massive amount of content due to the explosion of online communication. However, because user-generated content is unregulated, it may contain offensive content such as fake news, insults, and harassment phrases. The identification of fake news and rumors and their dissemination on social media has become a critical requirement. They have adverse effects on users, businesses, enterprises, and even political regimes and governments. State of the art has tackled the English language for news and used feature-based algorithms. This paper proposes a… More >

  • Open Access

    ARTICLE

    LDSVM: Leukemia Cancer Classification Using Machine Learning

    Abdul Karim1, Azhari Azhari1,*, Mobeen Shahroz2, Samir Brahim Belhaouri3, Khabib Mustofa1

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3887-3903, 2022, DOI:10.32604/cmc.2022.021218 - 07 December 2021

    Abstract Leukemia is blood cancer, including bone marrow and lymphatic tissues, typically involving white blood cells. Leukemia produces an abnormal amount of white blood cells compared to normal blood. Deoxyribonucleic acid (DNA) microarrays provide reliable medical diagnostic services to help more patients find the proposed treatment for infections. DNA microarrays are also known as biochips that consist of microscopic DNA spots attached to a solid glass surface. Currently, it is difficult to classify cancers using microarray data. Nearly many data mining techniques have failed because of the small sample size, which has become more critical for… More >

  • Open Access

    ARTICLE

    Hybrid Renewable Energy Resources Management for Optimal Energy Operation in Nano-Grid

    Faiza Qayyum1, Faisal Jamil1, Shabir Ahmad2, Do-Hyeun Kim1,*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2091-2105, 2022, DOI:10.32604/cmc.2022.019898 - 07 December 2021

    Abstract Renewable energy resources are deemed a potential energy production source due to their cost efficiency and harmless reaction to the environment, unlike non-renewable energy resources. However, they often fail to meet energy requirements in unfavorable weather conditions. The concept of Hybrid renewable energy resources addresses this issue by integrating both renewable and non-renewable energy resources to meet the required energy load. In this paper, an intelligent cost optimization algorithm is proposed to maximize the use of renewable energy resources and minimum utilization of non-renewable energy resources to meet the energy requirement for a nanogrid infrastructure.… More >

  • Open Access

    ARTICLE

    A Novel Workload-Aware and Optimized Write Cycles in NVRAM

    J. P. Shri Tharanyaa1,*, D. Sharmila2, R. Saravana Kumar3

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2667-2681, 2022, DOI:10.32604/cmc.2022.019889 - 07 December 2021

    Abstract With the emergence of the Internet of things (IoT), embedded systems have now changed its dimensionality and it is applied in various domains such as healthcare, home automation and mainly Industry 4.0. These Embedded IoT devices are mostly battery-driven. It has been analyzed that usage of Dynamic Random-Access Memory (DRAM) centered core memory is considered the most significant source of high energy utility in Embedded IoT devices. For achieving the low power consumption in these devices, Non-volatile memory (NVM) devices such as Parameter Random Access Memory (PRAM) and Spin-Transfer Torque Magnetic Random-Access Memory (STT-RAM) are… More >

  • Open Access

    ARTICLE

    Machine Learning-Based Pruning Technique for Low Power Approximate Computing

    B. Sakthivel1,*, K. Jayaram2, N. Manikanda Devarajan3, S. Mahaboob Basha4, S. Rajapriya5

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 397-406, 2022, DOI:10.32604/csse.2022.021637 - 02 December 2021

    Abstract Approximate Computing is a low power achieving technique that offers an additional degree of freedom to design digital circuits. Pruning is one of the types of approximate circuit design technique which removes logic gates or wires in the circuit to reduce power consumption with minimal insertion of error. In this work, a novel machine learning (ML) -based pruning technique is introduced to design digital circuits. The machine-learning algorithm of the random forest decision tree is used to prune nodes selectively based on their input pattern. In addition, an error compensation value is added to the… More >

  • Open Access

    ARTICLE

    Hybridized Wrapper Filter Using Deep Neural Network for Intrusion Detection

    N. Venkateswaran1,*, K. Umadevi2

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 1-14, 2022, DOI:10.32604/csse.2022.021217 - 02 December 2021

    Abstract Huge data over the cloud computing and big data are processed over the network. The data may be stored, send, altered and communicated over the network between the source and destination. Once data send by source to destination, before reaching the destination data may be attacked by any intruders over the network. The network has numerous routers and devices to connect to internet. Intruders may attack any were in the network and breaks the original data, secrets. Detection of attack in the network became interesting task for many researchers. There are many intrusion detection feature… More >

  • Open Access

    ARTICLE

    Machine Learning Technique to Detect Radiations in the Brain

    E. Gothai1,*, A. Baseera2, P. Prabu3, K. Venkatachalam4, K. Saravanan5, S. SathishKumar6

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 149-163, 2022, DOI:10.32604/csse.2022.020619 - 02 December 2021

    Abstract The brain of humans and other organisms is affected in various ways through the electromagnetic field (EMF) radiations generated by mobile phones and cell phone towers. Morphological variations in the brain are caused by the neurological changes due to the revelation of EMF. Cellular level analysis is used to measure and detect the effect of mobile radiations, but its utilization seems very expensive, and it is a tedious process, where its analysis requires the preparation of cell suspension. In this regard, this research article proposes optimal broadcasting learning to detect changes in brain morphology due… More >

  • Open Access

    ARTICLE

    Hybridization of Differential Evolution and Adaptive-Network-Based Fuzzy Inference System in Estimation of Compression Coefficient of Plastic Clay Soil

    Manh Duc Nguyen1, Ha Nguyen Hai1, Nadhir Al-Ansari2,*, Mahdis Amiri3, Hai-Bang Ly4, Indra Prakash5, Binh Thai Pham4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 149-166, 2022, DOI:10.32604/cmes.2022.017355 - 29 November 2021

    Abstract One of the important geotechnical parameters required for designing of the civil engineering structure is the compressibility of the soil. In this study, the main purpose is to develop a novel hybrid Machine Learning (ML) model (ANFIS-DE), which used Differential Evolution (DE) algorithm to optimize the predictive capability of Adaptive-Network-based Fuzzy Inference System (ANFIS), for estimating soil Compression coefficient (Cc) from other geotechnical parameters namely Water Content, Void Ratio, Specific Gravity, Liquid Limit, Plastic Limit, Clay content and Depth of Soil Samples. Validation of the predictive capability of the novel model was carried out using… More >

  • Open Access

    REVIEW

    A Survey on Machine Learning in COVID-19 Diagnosis

    Xing Guo1,#, Yu-Dong Zhang2,#, Siyuan Lu2, Zhihai Lu1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 23-71, 2022, DOI:10.32604/cmes.2021.017679 - 29 November 2021

    Abstract Since Corona Virus Disease 2019 outbreak, many expert groups worldwide have studied the problem and proposed many diagnostic methods. This paper focuses on the research of Corona Virus Disease 2019 diagnosis. First, the procedure of the diagnosis based on machine learning is introduced in detail, which includes medical data collection, image preprocessing, feature extraction, and image classification. Then, we review seven methods in detail: transfer learning, ensemble learning, unsupervised learning and semi-supervised learning, convolutional neural networks, graph neural networks, explainable deep neural networks, and so on. What’s more, the advantages and limitations of different diagnosis More >

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