Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (1,560)
  • Open Access

    ARTICLE

    Managing Delivery of Safeguarding Substances as a Mitigation Against Outbreaks of Pandemics

    Said Ali Hassan1, Khalid Alnowibet2,3, Prachi Agrawal4, Ali Wagdy Mohamed5,6,*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1161-1181, 2021, DOI:10.32604/cmc.2021.015494

    Abstract The optimum delivery of safeguarding substances is a major part of supply chain management and a crucial issue in the mitigation against the outbreak of pandemics. A problem arises for a decision maker who wants to optimally choose a subset of candidate consumers to maximize the distributed quantities of the needed safeguarding substances within a specific time period. A nonlinear binary mathematical programming model for the problem is formulated. The decision variables are binary ones that represent whether to choose a specific consumer, and design constraints are formulated to keep track of the chosen route. To better illustrate the problem,… More >

  • Open Access

    ARTICLE

    Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classification

    Ayesha Bin T. Tahir1, Muhamamd Attique Khan1, Majed Alhaisoni2, Junaid Ali Khan1, Yunyoung Nam3,*, Shui-Hua Wang4, Kashif Javed5

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1099-1116, 2021, DOI:10.32604/cmc.2021.015154

    Abstract Background: A brain tumor reflects abnormal cell growth. Challenges: Surgery, radiation therapy, and chemotherapy are used to treat brain tumors, but these procedures are painful and costly. Magnetic resonance imaging (MRI) is a non-invasive modality for diagnosing tumors, but scans must be interpretated by an expert radiologist. Methodology: We used deep learning and improved particle swarm optimization (IPSO) to automate brain tumor classification. MRI scan contrast is enhanced by ant colony optimization (ACO); the scans are then used to further train a pretrained deep learning model, via transfer learning (TL), and to extract features from two dense layers. We fused… More >

  • Open Access

    ARTICLE

    Nature-Inspired Level Set Segmentation Model for 3D-MRI Brain Tumor Detection

    Oday Ali Hassen1, Sarmad Omar Abter2, Ansam A. Abdulhussein3, Saad M. Darwish4,*, Yasmine M. Ibrahim4, Walaa Sheta5

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 961-981, 2021, DOI:10.32604/cmc.2021.014404

    Abstract Medical image segmentation has consistently been a significant topic of research and a prominent goal, particularly in computer vision. Brain tumor research plays a major role in medical imaging applications by providing a tremendous amount of anatomical and functional knowledge that enhances and allows easy diagnosis and disease therapy preparation. To prevent or minimize manual segmentation error, automated tumor segmentation, and detection became the most demanding process for radiologists and physicians as the tumor often has complex structures. Many methods for detection and segmentation presently exist, but all lack high accuracy. This paper’s key contribution focuses on evaluating machine learning… More >

  • Open Access

    ARTICLE

    IPv6 Cryptographically Generated Address: Analysis, Optimization and Protection

    Amjed Sid Ahmed1,*, Rosilah Hassan2, Faizan Qamar3, Mazhar Malik1

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 247-265, 2021, DOI:10.32604/cmc.2021.014233

    Abstract In networking, one major difficulty that nodes suffer from is the need for their addresses to be generated and verified without relying on a third party or public authorized servers. To resolve this issue, the use of self-certifying addresses have become a highly popular and standardized method, of which Cryptographically Generated Addresses (CGA) is a prime example. CGA was primarily designed to deter the theft of IPv6 addresses by binding the generated address to a public key to prove address ownership. Even though the CGA technique is highly effective, this method is still subject to several vulnerabilities with respect to… More >

  • Open Access

    ARTICLE

    Rock Hyraxes Swarm Optimization: A New Nature-Inspired Metaheuristic Optimization Algorithm

    Belal Al-Khateeb1,*, Kawther Ahmed2, Maha Mahmood1, Dac-Nhuong Le3,4

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 643-654, 2021, DOI:10.32604/cmc.2021.013648

    Abstract This paper presents a novel metaheuristic algorithm called Rock Hyraxes Swarm Optimization (RHSO) inspired by the behavior of rock hyraxes swarms in nature. The RHSO algorithm mimics the collective behavior of Rock Hyraxes to find their eating and their special way of looking at this food. Rock hyraxes live in colonies or groups where a dominant male watch over the colony carefully to ensure their safety leads the group. Forty-eight (22 unimodal and 26 multimodal) test functions commonly used in the optimization area are used as a testing benchmark for the RHSO algorithm. A comparative efficiency analysis also checks RHSO… More >

  • Open Access

    ARTICLE

    Paddy Leaf Disease Detection Using an Optimized Deep Neural Network

    Shankarnarayanan Nalini1,*, Nagappan Krishnaraj2, Thangaiyan Jayasankar3, Kalimuthu Vinothkumar4, Antony Sagai Francis Britto5, Kamalraj Subramaniam6, Chokkalingam Bharatiraja7

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1117-1128, 2021, DOI:10.32604/cmc.2021.012431

    Abstract Precision Agriculture is a concept of farm management which makes use of IoT and networking concepts to improve the crop. Plant diseases are one of the underlying causes in the decrease in the number of quantity and quality of the farming crops. Recognition of diseases from the plant images is an active research topic which makes use of machine learning (ML) approaches. A novel deep neural network (DNN) classification model is proposed for the identification of paddy leaf disease using plant image data. Classification errors were minimized by optimizing weights and biases in the DNN model using a crow search… More >

  • Open Access

    ARTICLE

    Selection and Optimization of Software Development Life Cycles Using a Genetic Algorithm

    Fatimah O. Albalawi, Mashael S. Maashi*

    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 39-52, 2021, DOI:10.32604/iasc.2021.015657

    Abstract In the software field, a large number of projects fail, and billions of dollars are spent on these failed projects. Many software projects are also produced with poor quality or they do not exactly meet customers’ expectations. Moreover, these projects may exceed project budget and/or time. The complexity of managing software development projects and the poor selection of software development life cycle (SDLC) models are among the top reasons for such failure. Various SDLC models are available, but no model is considered the best or worst. In this work, we propose a new methodology that solves the SDLC optimization problem… More >

  • Open Access

    ARTICLE

    Study on Optimization of Urban Rail Train Operation Control Curve Based on Improved Multi-Objective Genetic Algorithm

    Xiaokan Wang*, Qiong Wang

    Journal on Internet of Things, Vol.3, No.1, pp. 1-9, 2021, DOI:10.32604/jiot.2021.010228

    Abstract A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve. In the train control system, the conversion point of operating mode is the basic of gene encoding and the chromosome composed of multiple genes represents a control scheme, and the initial population can be formed by the way. The fitness function can be designed by the design requirements of the train control stop error, time error and energy consumption. the effectiveness of new individual can be ensured by checking the validity of the original individual when… More >

  • Open Access

    ARTICLE

    PTS-PAPR Reduction Technique for 5G Advanced Waveforms Using BFO Algorithm

    Arun Kumar1, Manoj Gupta1, Dac-Nhuong Le2,3,*, Ayman A. Aly4

    Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 713-722, 2021, DOI:10.32604/iasc.2021.015793

    Abstract Non-orthogonal multiple access (NOMA) will play an imperative part in an advanced 5G radio arrangement, owing to its numerous benefits such as improved spectrum adeptness, fast data rate, truncated spectrum leakage, and, so on. So far, NOMA undergoes from peak to average power ratio (PAPR) problem, which shrinks the throughput of the scheme. In this article, we propose a hybrid method, centered on the combination of advanced Partial transmission sequence (PTS), Selective mapping (SLM), and bacteria foraging optimization (BFO), known as PTS-BFO and SLM-PTS. PTS and SLM are utilized at the sender side and divide the NOMA into several sub-blocks.… More >

  • Open Access

    ARTICLE

    Reconstructing the Time-Dependent Thermal Coefficient in 2D Free Boundary Problems

    M. J. Huntul*

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3681-3699, 2021, DOI:10.32604/cmc.2021.016036

    Abstract The inverse problem of reconstructing the time-dependent thermal conductivity and free boundary coefficients along with the temperature in a two-dimensional parabolic equation with initial and boundary conditions and additional measurements is, for the first time, numerically investigated. This inverse problem appears extensively in the modelling of various phenomena in engineering and physics. For instance, steel annealing, vacuum-arc welding, fusion welding, continuous casting, metallurgy, aircraft, oil and gas production during drilling and operation of wells. From literature we already know that this inverse problem has a unique solution. However, the problem is still ill-posed by being unstable to noise in the… More >

Displaying 1171-1180 on page 118 of 1560. Per Page