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  • Open Access

    ARTICLE

    Bioconvective Hybrid Flow with Microorganisms Migration and Buongiorno’s Model under Convective Condition

    Azad Hussain1, Saira Raiz1, Ali Hassan1,2,*, Mohamed R. Ali3, Abdulkafi Mohammed Saeed4

    Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 433-453, 2024, DOI:10.32604/fhmt.2024.044121

    Abstract Heat transfer improves significantly when the working fluid has high thermal conductivity. Heat transfer can be found in fields such as food processing, solar through collectors, and drug delivery. Considering this notable fact, this work is focused on investigating the bio-convection-enhanced heat transfer in the existence of convective boundary conditions in the flow of hybrid nanofluid across a stretching surface. Buongiorno fluid model with hybrid nanoparticles has been employed along the swimming microorganisms to investigate the mixture base working fluid. The developed nonlinear flow governing equations have been tackled numerically with the help of the… More > Graphic Abstract

    Bioconvective Hybrid Flow with Microorganisms Migration and Buongiorno’s Model under Convective Condition

  • Open Access

    ARTICLE

    A Hybrid Manufacturing Process Monitoring Method Using Stacked Gated Recurrent Unit and Random Forest

    Chao-Lung Yang1,*, Atinkut Atinafu Yilma1,2, Bereket Haile Woldegiorgis2, Hendrik Tampubolon3,4, Hendri Sutrisno5

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 233-254, 2024, DOI:10.32604/iasc.2024.043091

    Abstract This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations. Since real-time production process monitoring is critical in today’s smart manufacturing. The more robust the monitoring model, the more reliable a process is to be under control. In the past, many researchers have developed real-time monitoring methods to detect process shifts early. However, these methods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties. In this paper, a robust monitoring model combining Gated Recurrent Unit (GRU) and Random… More >

  • Open Access

    ARTICLE

    Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling

    Muchang Rao, Hang Qin*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2647-2672, 2024, DOI:10.32604/cmc.2024.050380

    Abstract More devices in the Intelligent Internet of Things (AIoT) result in an increased number of tasks that require low latency and real-time responsiveness, leading to an increased demand for computational resources. Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension. However, the effective allocation of resources for task execution within fog environments, characterized by limitations and heterogeneity in computational resources, remains a formidable challenge. To tackle this challenge, in this study, we integrate fog computing and cloud computing. We begin by establishing a fog-cloud environment… More >

  • Open Access

    ARTICLE

    A Novel Approach to Energy Optimization: Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

    Muhammad Salman Qamar1,*, Ihsan ul Haq1, Amil Daraz2, Atif M. Alamri3, Salman A. AlQahtani4, Muhammad Fahad Munir1

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2945-2970, 2024, DOI:10.32604/cmc.2024.050168

    Abstract In pursuit of enhancing the Wireless Sensor Networks (WSNs) energy efficiency and operational lifespan, this paper delves into the domain of energy-efficient routing protocols. In WSNs, the limited energy resources of Sensor Nodes (SNs) are a big challenge for ensuring their efficient and reliable operation. WSN data gathering involves the utilization of a mobile sink (MS) to mitigate the energy consumption problem through periodic network traversal. The mobile sink (MS) strategy minimizes energy consumption and latency by visiting the fewest nodes or pre-determined locations called rendezvous points (RPs) instead of all cluster heads (CHs). CHs… More >

  • Open Access

    ARTICLE

    Posture Detection of Heart Disease Using Multi-Head Attention Vision Hybrid (MHAVH) Model

    Hina Naz1, Zuping Zhang1,*, Mohammed Al-Habib1, Fuad A. Awwad2, Emad A. A. Ismail2, Zaid Ali Khan3

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2673-2696, 2024, DOI:10.32604/cmc.2024.049186

    Abstract Cardiovascular disease is the leading cause of death globally. This disease causes loss of heart muscles and is also responsible for the death of heart cells, sometimes damaging their functionality. A person’s life may depend on receiving timely assistance as soon as possible. Thus, minimizing the death ratio can be achieved by early detection of heart attack (HA) symptoms. In the United States alone, an estimated 610,000 people die from heart attacks each year, accounting for one in every four fatalities. However, by identifying and reporting heart attack symptoms early on, it is possible to… More >

  • Open Access

    ARTICLE

    Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

    Xiaoqin Ma1,2, Jun Wang1, Wenchang Yu1, Qinli Zhang1,2,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2063-2083, 2024, DOI:10.32604/cmc.2024.049147

    Abstract The presence of numerous uncertainties in hybrid decision information systems (HDISs) renders attribute reduction a formidable task. Currently available attribute reduction algorithms, including those based on Pawlak attribute importance, Skowron discernibility matrix, and information entropy, struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values, and attributes with fuzzy boundaries and abnormal values. In order to address the aforementioned issues, this paper delves into the study of attribute reduction within HDISs. First of all, a novel metric based on the decision attribute is introduced to solve… More >

  • Open Access

    ARTICLE

    A Novel Hybrid Ensemble Learning Approach for Enhancing Accuracy and Sustainability in Wind Power Forecasting

    Farhan Ullah1, Xuexia Zhang1,*, Mansoor Khan2, Muhammad Abid3,*, Abdullah Mohamed4

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3373-3395, 2024, DOI:10.32604/cmc.2024.048656

    Abstract Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance grows. Traditional approaches frequently struggle with complex data and non-linear connections. This article presents a novel approach for hybrid ensemble learning that is based on rigorous requirements engineering concepts. The approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-Era Retrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms using in-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model, while a temporal convolutional network handles time-series complexities and data… More >

  • Open Access

    ARTICLE

    A Novel Approach to Breast Tumor Detection: Enhanced Speckle Reduction and Hybrid Classification in Ultrasound Imaging

    K. Umapathi1,*, S. Shobana1, Anand Nayyar2, Judith Justin3, R. Vanithamani3, Miguel Villagómez Galindo4, Mushtaq Ahmad Ansari5, Hitesh Panchal6,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1875-1901, 2024, DOI:10.32604/cmc.2024.047961

    Abstract Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effective treatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breast cancer from ultrasound images. The primary challenge is accurately distinguishing between malignant and benign tumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentation and classification. The main objective of the research paper is to develop an advanced methodology for breast ultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, and machine learning-based classification. A unique approach… More >

  • Open Access

    ARTICLE

    RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids

    Farah Mohammad1,*, Saad Al-Ahmadi2, Jalal Al-Muhtadi1,2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3175-3192, 2024, DOI:10.32604/cmc.2023.042873

    Abstract Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users. It hinders the economic growth of utility companies, poses electrical risks, and impacts the high energy costs borne by consumers. The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data, including information on client consumption, which may be used to identify electricity theft using machine learning and deep learning techniques. Moreover, there also exist different solutions such as hardware-based solutions to detect electricity theft that… More >

  • Open Access

    ARTICLE

    An Elite-Class Teaching-Learning-Based Optimization for Reentrant Hybrid Flow Shop Scheduling with Bottleneck Stage

    Deming Lei, Surui Duan, Mingbo Li*, Jing Wang

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 47-63, 2024, DOI:10.32604/cmc.2024.049481

    Abstract Bottleneck stage and reentrance often exist in real-life manufacturing processes; however, the previous research rarely addresses these two processing conditions in a scheduling problem. In this study, a reentrant hybrid flow shop scheduling problem (RHFSP) with a bottleneck stage is considered, and an elite-class teaching-learning-based optimization (ETLBO) algorithm is proposed to minimize maximum completion time. To produce high-quality solutions, teachers are divided into formal ones and substitute ones, and multiple classes are formed. The teacher phase is composed of teacher competition and teacher teaching. The learner phase is replaced with a reinforcement search of the More >

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