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  • Open Access

    ARTICLE

    Optimization of the Active Composition of the Wind Farm Using Genetic Algorithms

    Nataliya Shakhovska1,*, Mykola Medykovskyy2, Roman Melnyk2, Nataliya Kryvinska3

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3065-3078, 2021, DOI:10.32604/cmc.2021.018761

    Abstract The article presents the results of research on the possibilities of using genetic algorithms for solving the multicriteria optimization problem of determining the active components of a wind farm. Optimization is carried out on two parameters: efficiency factor of wind farm use (integrated parameter calculated on the basis of 6 parameters of each of the wind farm), average power deviation level (average difference between the load power and energy generation capabilities of the active wind farm). That was done an analysis of publications on the use of genetic algorithms to solve multicriteria optimization problems. Computer simulations were performed, which allowed… More >

  • Open Access

    ARTICLE

    Fruit Ripeness Prediction Based on DNN Feature Induction from Sparse Dataset

    Wan Hyun Cho1, Sang Kyoon Kim2, Myung Hwan Na1, In Seop Na3,*

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4003-4024, 2021, DOI:10.32604/cmc.2021.018758

    Abstract Fruit processing devices, that automatically detect the freshness and ripening stages of fruits are very important in precision agriculture. Recently, based on deep learning, many attempts have been made in computer image processing, to monitor the ripening stage of fruits. However, it is time-consuming to acquire images of the various ripening stages to be used for training, and it is difficult to measure the ripening stages of fruits accurately with a small number of images. In this paper, we propose a prediction system that can automatically determine the ripening stage of fruit by a combination of deep neural networks (DNNs)… More >

  • Open Access

    ARTICLE

    Recurrent Convolutional Neural Network MSER-Based Approach for Payable Document Processing

    Suliman Aladhadh1, Hidayat Ur Rehman2, Ali Mustafa Qamar3,4,*, Rehan Ullah Khan1

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3399-3411, 2021, DOI:10.32604/cmc.2021.018724

    Abstract A tremendous amount of vendor invoices is generated in the corporate sector. To automate the manual data entry in payable documents, highly accurate Optical Character Recognition (OCR) is required. This paper proposes an end-to-end OCR system that does both localization and recognition and serves as a single unit to automate payable document processing such as cheques and cash disbursement. For text localization, the maximally stable extremal region is used, which extracts a word or digit chunk from an invoice. This chunk is later passed to the deep learning model, which performs text recognition. The deep learning model utilizes both convolution… More >

  • Open Access

    ARTICLE

    Local Features-Based Watermarking for Image Security in Social Media

    Shady Y. El-mashad1, Amani M. Yassen1, Abdulwahab K. Alsammak1, Basem M. Elhalawany2,*

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3857-3870, 2021, DOI:10.32604/cmc.2021.018660

    Abstract The last decade shows an explosion of using social media, which raises several challenges related to the security of personal files including images. These challenges include modifying, illegal copying, identity fraud, copyright protection and ownership of images. Traditional digital watermarking techniques embed digital information inside another digital information without affecting the visual quality for security purposes. In this paper, we propose a hybrid digital watermarking and image processing approach to improve the image security level. Specifically, variants of the widely used Least-Significant Bit (LSB) watermarking technique are merged with a blob detection algorithm to embed information into the boundary pixels… More >

  • Open Access

    ARTICLE

    Task Scheduling Optimization in Cloud Computing Based on Genetic Algorithms

    Ahmed Y. Hamed1,*, Monagi H. Alkinani2

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3289-3301, 2021, DOI:10.32604/cmc.2021.018658

    Abstract Task scheduling is the main problem in cloud computing that reduces system performance; it is an important way to arrange user needs and perform multiple goals. Cloud computing is the most popular technology nowadays and has many research potential in various areas like resource allocation, task scheduling, security, privacy, etc. To improve system performance, an efficient task-scheduling algorithm is required. Existing task-scheduling algorithms focus on task-resource requirements, CPU memory, execution time, and execution cost. In this paper, a task scheduling algorithm based on a Genetic Algorithm (GA) has been presented for assigning and executing different tasks. The proposed algorithm aims… More >

  • Open Access

    ARTICLE

    Centralized QoS Routing Model for Delay/Loss Sensitive Flows at the SDN-IoT Infrastructure

    Mykola Beshley1, Natalia Kryvinska2,*, Halyna Beshley1, Mykhailo Medvetskyi1, Leonard Barolli3

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3727-3748, 2021, DOI:10.32604/cmc.2021.018625

    Abstract The rapidly increasing number of Internet of Things (IoT) devices and Quality of Service (QoS) requirements have made the provisioning of network solutions to meet this demand a major research topic. Providing fast and reliable routing paths based on the QoS requirements of IoT devices is very important task for Industry 4.0. The software-defined network is one of the most current interesting research developments, offering an efficient and effective solution for centralized control and network intelligence. A new SDN-IoT paradigm has been proposed to improve network QoS, taking advantage of SDN architecture in IoT networks. At the present time, most… More >

  • Open Access

    ARTICLE

    Cloud Data Center Selection Using a Modified Differential Evolution

    Yousef Sanjalawe1,2, Mohammed Anbar1,*, Salam Al-E’mari1, Rosni Abdullah1, Iznan Hasbullah1, Mohammed Aladaileh1

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3179-3204, 2021, DOI:10.32604/cmc.2021.018546

    Abstract The interest in selecting an appropriate cloud data center is exponentially increasing due to the popularity and continuous growth of the cloud computing sector. Cloud data center selection challenges are compounded by ever-increasing users’ requests and the number of data centers required to execute these requests. Cloud service broker policy defines cloud data center’s selection, which is a case of an NP-hard problem that needs a precise solution for an efficient and superior solution. Differential evolution algorithm is a metaheuristic algorithm characterized by its speed and robustness, and it is well suited for selecting an appropriate cloud data center. This… More >

  • Open Access

    ARTICLE

    CNR: A Cluster-Based Solution for Connectivity Restoration for Mobile WSNs

    Mahmood ul Hassan1,*, Amin Al-Awady1, Khalid Mahmood2, Shahzad Ali3, Ibrahim Algamdi1, Muhammad Kashif Saeed4, Safdar Zaman5

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3413-3427, 2021, DOI:10.32604/cmc.2021.018544

    Abstract Wireless Sensor Networks (WSNs) are an integral part of the Internet of Things (IoT) and are widely used in a plethora of applications. Typically, sensor networks operate in harsh environments where human intervention is often restricted, which makes battery replacement for sensor nodes impractical. Node failure due to battery drainage or harsh environmental conditions poses serious challenges to the connectivity of the network. Without a connectivity restoration mechanism, node failures ultimately lead to a network partition, which affects the basic function of the sensor network. Therefore, the research community actively concentrates on addressing and solving the challenges associated with connectivity… More >

  • Open Access

    ARTICLE

    Convolutional Neural Network for Histopathological Osteosarcoma Image Classification

    Imran Ahmed1,*, Humaira Sardar1, Hanan Aljuaid2, Fakhri Alam Khan1, Muhammad Nawaz1, Adnan Awais1

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3365-3381, 2021, DOI:10.32604/cmc.2021.018486

    Abstract Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality rate. Early diagnosis may increase the chances of treatment and survival however the process is time-consuming (reliability and complexity involved to extract the hand-crafted features) and largely depends on pathologists’ experience. Convolutional Neural Network (CNN—an end-to-end model) is known to be an alternative to overcome the aforesaid problems. Therefore, this work proposes a compact CNN architecture that has been rigorously explored on a Small Osteosarcoma histology Image Dataaseet (a high-class imbalanced dataset). Though, during training, class-imbalanced data can negatively affect the performance of… More >

  • Open Access

    ARTICLE

    An AW-HARIS Based Automated Segmentation of Human Liver Using CT Images

    P. Naga Srinivasu1, Shakeel Ahmed2,*, Abdulaziz Alhumam2, Akash Bhoi Kumar3, Muhammad Fazal Ijaz4

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3303-3319, 2021, DOI:10.32604/cmc.2021.018472

    Abstract In the digestion of amino acids, carbohydrates, and lipids, as well as protein synthesis from the consumed food, the liver has many diverse responsibilities and functions that are to be performed. Liver disease may impact the hormonal and nutritional balance in the human body. The earlier diagnosis of such critical conditions may help to treat the patient effectively. A computationally efficient AW-HARIS algorithm is used in this paper to perform automated segmentation of CT scan images to identify abnormalities in the human liver. The proposed approach can recognize the abnormalities with better accuracy without training, unlike in supervisory procedures requiring… More >

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