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  • Open Access

    ARTICLE

    Employing a Backpropagation Neural Network for Predicting Fear of Cancer Recurrence among Non-Small Cell Lung Cancer Patients

    Man Liu1, Zhuoheng Lv1,#, Hongjing Wang2,*, Lu Liu1,*

    Psycho-Oncologie, Vol.18, No.4, pp. 305-316, 2024, DOI:10.32604/po.2024.054098 - 04 December 2024

    Abstract Objective: Non-small cell lung cancer (NSCLC) patients often experience significant fear of recurrence. To facilitate precise identification and appropriate management of this fear, this study aimed to compare the efficacy and accuracy of a Backpropagation Neural Network (BPNN) against logistic regression in modeling fear of cancer recurrence prediction. Methods: Data from 596 NSCLC patients, collected between September 2023 and December 2023 at the Cancer Hospital of the Chinese Academy of Medical Sciences, were analyzed. Nine clinically and statistically significant variables, identified via univariate logistic regression, were inputted into both BPNN and logistic regression models developed… More >

  • Open Access

    REVIEW

    AI-Powered Innovations in High-Tech Research and Development: From Theory to Practice

    Mitra Madanchian1,*, Hamed Taherdoost1,2,3,4

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2133-2159, 2024, DOI:10.32604/cmc.2024.057094 - 18 November 2024

    Abstract This comparative review explores the dynamic and evolving landscape of artificial intelligence (AI)-powered innovations within high-tech research and development (R&D). It delves into both theoretical models and practical applications across a broad range of industries, including biotechnology, automotive, aerospace, and telecommunications. By examining critical advancements in AI algorithms, machine learning, deep learning models, simulations, and predictive analytics, the review underscores the transformative role AI has played in advancing theoretical research and shaping cutting-edge technologies. The review integrates both qualitative and quantitative data derived from academic studies, industry reports, and real-world case studies to showcase the… More >

  • Open Access

    REVIEW

    Embracing the Future: AI and ML Transforming Urban Environments in Smart Cities

    Gagan Deep*, Jyoti Verma

    Journal on Artificial Intelligence, Vol.5, pp. 57-73, 2023, DOI:10.32604/jai.2023.043329 - 22 September 2023

    Abstract This research explores the increasing importance of Artificial Intelligence (AI) and Machine Learning (ML) with relation to smart cities. It discusses the AI and ML’s ability to revolutionize various aspects of urban environments, including infrastructure, governance, public safety, and sustainability. The research presents the definition and characteristics of smart cities, highlighting the key components and technologies driving initiatives for smart cities. The methodology employed in this study involved a comprehensive review of relevant literature, research papers, and reports on the subject of AI and ML in smart cities. Various sources were consulted to gather information… More >

  • Open Access

    ARTICLE

    Reinforcing Artificial Neural Networks through Traditional Machine Learning Algorithms for Robust Classification of Cancer

    Muhammad Hammad Waseem1, Malik Sajjad Ahmed Nadeem1,*, Ishtiaq Rasool Khan2, Seong-O-Shim3, Wajid Aziz1, Usman Habib4

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4293-4315, 2023, DOI:10.32604/cmc.2023.036710 - 31 March 2023

    Abstract Machine Learning (ML)-based prediction and classification systems employ data and learning algorithms to forecast target values. However, improving predictive accuracy is a crucial step for informed decision-making. In the healthcare domain, data are available in the form of genetic profiles and clinical characteristics to build prediction models for complex tasks like cancer detection or diagnosis. Among ML algorithms, Artificial Neural Networks (ANNs) are considered the most suitable framework for many classification tasks. The network weights and the activation functions are the two crucial elements in the learning process of an ANN. These weights affect the… More >

  • Open Access

    ARTICLE

    Forecasting Stock Volatility Using Wavelet-based Exponential Generalized Autoregressive Conditional Heteroscedasticity Methods

    Tariq T. Alshammari1, Mohd Tahir Ismail1, Nawaf N. Hamadneh3,*, S. Al Wadi2, Jamil J. Jaber2, Nawa Alshammari3, Mohammad H. Saleh2

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2589-2601, 2023, DOI:10.32604/iasc.2023.024001 - 17 August 2022

    Abstract In this study, we proposed a new model to improve the accuracy of forecasting the stock market volatility pattern. The hypothesized model was validated empirically using a data set collected from the Saudi Arabia stock Exchange (Tadawul). The data is the daily closed price index data from August 2011 to December 2019 with 2027 observations. The proposed forecasting model combines the best maximum overlapping discrete wavelet transform (MODWT) function (Bl14) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model. The results show the model's ability to analyze stock market data, highlight important events that contain the More >

  • Open Access

    ARTICLE

    Data-Driven Load Forecasting Using Machine Learning and Meteorological Data

    Aishah Alrashidi, Ali Mustafa Qamar*

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 1973-1988, 2023, DOI:10.32604/csse.2023.024633 - 01 August 2022

    Abstract Electrical load forecasting is very crucial for electrical power systems’ planning and operation. Both electrical buildings’ load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load forecasting. The meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh, Saudi Arabia. The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017. These data are provided by King Abdullah City for… More >

  • Open Access

    ARTICLE

    Germination Quality Prognosis: Classifying Spectroscopic Images of the Seed Samples

    Saud S. Alotaibi*

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1815-1829, 2023, DOI:10.32604/iasc.2023.029446 - 19 July 2022

    Abstract One of the most critical objectives of precision farming is to assess the germination quality of seeds. Modern models contribute to this field primarily through the use of artificial intelligence techniques such as machine learning, which present difficulties in feature extraction and optimization, which are critical factors in predicting accuracy with few false alarms, and another significant difficulty is assessing germination quality. Additionally, the majority of these contributions make use of benchmark classification methods that are either inept or too complex to train with the supplied features. This manuscript addressed these issues by introducing a More >

  • Open Access

    ARTICLE

    Short-Term Wind Energy Forecasting Using Deep Learning-Based Predictive Analytics

    Noman Shabbir1, Lauri Kütt1, Muhammad Jawad2, Oleksandr Husev1, Ateeq Ur Rehman3, Akber Abid Gardezi4, Muhammad Shafiq5, Jin-Ghoo Choi5,*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1017-1033, 2022, DOI:10.32604/cmc.2022.024576 - 24 February 2022

    Abstract Wind energy is featured by instability due to a number of factors, such as weather, season, time of the day, climatic area and so on. Furthermore, instability in the generation of wind energy brings new challenges to electric power grids, such as reliability, flexibility, and power quality. This transition requires a plethora of advanced techniques for accurate forecasting of wind energy. In this context, wind energy forecasting is closely tied to machine learning (ML) and deep learning (DL) as emerging technologies to create an intelligent energy management paradigm. This article attempts to address the short-term… More >

  • Open Access

    ARTICLE

    Smart Healthcare Using Data-Driven Prediction of Immunization Defaulters in Expanded Program on Immunization (EPI)

    Sadaf Qazi1, Muhammad Usman1, Azhar Mahmood1, Aaqif Afzaal Abbasi2, Muhammad Attique3, Yunyoung Nam4,*

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 589-602, 2021, DOI:10.32604/cmc.2020.012507 - 30 October 2020

    Abstract Immunization is a noteworthy and proven tool for eliminating lifethreating infectious diseases, child mortality and morbidity. Expanded Program on Immunization (EPI) is a nation-wide program in Pakistan to implement immunization activities, however the coverage is quite low despite the accessibility of free vaccination. This study proposes a defaulter prediction model for accurate identification of defaulters. Our proposed framework classifies defaulters at five different stages: defaulter, partially high, partially medium, partially low, and unvaccinated to reinforce targeted interventions by accurately predicting children at high risk of defaulting from the immunization schedule. Different machine learning algorithms are… More >

  • Open Access

    ARTICLE

    Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity

    Xiangao Jiang1, Megan Coffee2, 3, *, Anasse Bari4, *, Junzhang Wang4, Xinyue Jiang5, Jianping Huang1, Jichan Shi1, Jianyi Dai1, Jing Cai1, Tianxiao Zhang6, Zhengxing Wu1, Guiqing He1, Yitong Huang7

    CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 537-551, 2020, DOI:10.32604/cmc.2020.010691 - 30 March 2020

    Abstract The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a pandemic and has spread to every inhabited continent. Given the increasing caseload, there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness. We present a first step towards building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. COVID-19 has presented a pressing need as a) clinicians are still developing clinical acumen to this novel… More >

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