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  • Open Access

    ARTICLE

    Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

    Umar Rashid1,2,*, Arfan Jaffar1,2, Muhammad Rashid3, Mohammed S. Alshuhri4, Sheeraz Akram1,4,5

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3377-3390, 2024, DOI:10.32604/cmc.2024.046320

    Abstract Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and… More >

  • Open Access

    ARTICLE

    Video Summarization Approach Based on Binary Robust Invariant Scalable Keypoints and Bisecting K-Means

    Sameh Zarif1,2,*, Eman Morad1, Khalid Amin1, Abdullah Alharbi3, Wail S. Elkilani4, Shouze Tang5

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3565-3583, 2024, DOI:10.32604/cmc.2024.046185

    Abstract Due to the exponential growth of video data, aided by rapid advancements in multimedia technologies. It became difficult for the user to obtain information from a large video series. The process of providing an abstract of the entire video that includes the most representative frames is known as static video summarization. This method resulted in rapid exploration, indexing, and retrieval of massive video libraries. We propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint (BRISK) and bisecting K-means clustering algorithm. The current method effectively recognizes relevant frames using BRISK by extracting keypoints and the… More >

  • Open Access

    ARTICLE

    PREDICTION OF BINARY MIXTURE BOILING HEAT TRANSFER IN SYSTEMS WITH STRONG MARANGONI EFFECTS

    Kenneth M. Armijo, Van P. Carey*

    Frontiers in Heat and Mass Transfer, Vol.1, No.2, pp. 1-6, 2010, DOI:10.5098/hmt.v1.2.3003

    Abstract This paper investigates the impact of Marangoni phenomena for low concentrations of 2-propanol/water and methanol/water mixtures. In real systems the addition of small levels of surface-active contaminants can affect the surface tension of the liquid-vapor interface and thermodynamic conditions in this region. Analysis was performed for three widely accepted binary mixture correlations to predict heat flux and superheat values for subatmospheric experimental data using bulk fluid and film thermodynamic properties. Due to the non-ideal nature of these alcohol/water mixtures, this study also employs an average pseudo single-component (PSC) coefficient in place of an ideal heat transfer coefficient (HTC) to improve… More >

  • Open Access

    ARTICLE

    Binary Program Vulnerability Mining Based on Neural Network

    Zhenhui Li1, Shuangping Xing1, Lin Yu1, Huiping Li1, Fan Zhou1, Guangqiang Yin1, Xikai Tang2, Zhiguo Wang1,*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1861-1879, 2024, DOI:10.32604/cmc.2023.046595

    Abstract Software security analysts typically only have access to the executable program and cannot directly access the source code of the program. This poses significant challenges to security analysis. While it is crucial to identify vulnerabilities in such non-source code programs, there exists a limited set of generalized tools due to the low versatility of current vulnerability mining methods. However, these tools suffer from some shortcomings. In terms of targeted fuzzing, the path searching for target points is not streamlined enough, and the completely random testing leads to an excessively large search space. Additionally, when it comes to code similarity analysis,… More >

  • Open Access

    ARTICLE

    A Strengthened Dominance Relation NSGA-III Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem

    Liang Zeng1,2, Junyang Shi1, Yanyan Li1, Shanshan Wang1,2,*, Weigang Li3

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 375-392, 2024, DOI:10.32604/cmc.2023.045803

    Abstract The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems. It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives. The Non-dominated Sorting Genetic Algorithm III (NSGA-III) is an effective approach for solving the multi-objective job shop scheduling problem. Nevertheless, it has some limitations in solving scheduling problems, including inadequate global search capability, susceptibility to premature convergence, and challenges in balancing convergence and diversity. To enhance its performance, this paper introduces a strengthened dominance relation NSGA-III algorithm based on differential evolution… More >

  • Open Access

    ARTICLE

    Deep Convolutional Neural Networks for Accurate Classification of Gastrointestinal Tract Syndromes

    Zahid Farooq Khan1, Muhammad Ramzan1,*, Mudassar Raza1, Muhammad Attique Khan2,3, Khalid Iqbal4, Taerang Kim5, Jae-Hyuk Cha5

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1207-1225, 2024, DOI:10.32604/cmc.2023.045491

    Abstract Accurate detection and classification of artifacts within the gastrointestinal (GI) tract frames remain a significant challenge in medical image processing. Medical science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous diseases. Key to this is the development of robust algorithms for image classification and detection, crucial in designing sophisticated systems for diagnosis and treatment. This study makes a small contribution to endoscopic image classification. The proposed approach involves multiple operations, including extracting deep features from endoscopy images using pre-trained neural networks such as Darknet-53 and Xception. Additionally, feature optimization utilizes the binary dragonfly algorithm… More >

  • Open Access

    ARTICLE

    Binary Archimedes Optimization Algorithm for Computing Dominant Metric Dimension Problem

    Basma Mohamed1,*, Linda Mohaisen2, Mohammed Amin1

    Intelligent Automation & Soft Computing, Vol.38, No.1, pp. 19-34, 2023, DOI:10.32604/iasc.2023.031947

    Abstract In this paper, we consider the NP-hard problem of finding the minimum dominant resolving set of graphs. A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distances to the vertices in B. A resolving set is dominating if every vertex of G that does not belong to B is a neighbor to some vertices in B. The dominant metric dimension of G is the cardinality number of the minimum dominant resolving set. The dominant metric dimension is computed by a binary version of the Archimedes optimization… More >

  • Open Access

    ARTICLE

    A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized Database

    Nilkanth Mukund Deshpande1,2, Shilpa Gite3,4,*, Biswajeet Pradhan5,6, Abdullah Alamri7, Chang-Wook Lee8,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 593-631, 2024, DOI:10.32604/cmes.2023.030704

    Abstract Infection of leukemia in humans causes many complications in its later stages. It impairs bone marrow’s ability to produce blood. Morphological diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this case. The binary classification is employed to distinguish between normal and leukemia-infected cells. In addition, various subtypes of leukemia require different treatments. These sub-classes must also be detected to obtain an accurate diagnosis of the type of leukemia. This entails using multi-class classification to determine the leukemia subtype. This is usually done using a microscopic examination of these blood cells. Due to the requirement… More > Graphic Abstract

    A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized Database

  • Open Access

    ARTICLE

    Functional Pattern-Related Anomaly Detection Approach Collaborating Binary Segmentation with Finite State Machine

    Ming Wan1, Minglei Hao1, Jiawei Li1, Jiangyuan Yao2,*, Yan Song3

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3573-3592, 2023, DOI:10.32604/cmc.2023.044857

    Abstract The process control-oriented threat, which can exploit OT (Operational Technology) vulnerabilities to forcibly insert abnormal control commands or status information, has become one of the most devastating cyber attacks in industrial automation control. To effectively detect this threat, this paper proposes one functional pattern-related anomaly detection approach, which skillfully collaborates the BinSeg (Binary Segmentation) algorithm with FSM (Finite State Machine) to identify anomalies between measuring data and control data. By detecting the change points of measuring data, the BinSeg algorithm is introduced to generate some initial sequence segments, which can be further classified and merged into different functional patterns due… More >

  • Open Access

    ARTICLE

    THERMAL CONDUCTIVITY OF BINARY MIXTURES OF GASES

    Etim S. Udoetok*

    Frontiers in Heat and Mass Transfer, Vol.4, No.2, pp. 1-5, 2013, DOI:10.5098/hmt.v4.2.3008

    Abstract A model for the coefficient of thermal conductivity of binary mixtures of gases has been derived. The theory presented is based on the assumption of random fluctuations between two possible extreme arrangements of a binary gas mixture. The results obtained from the new model compared favorably with published experimental results. The proposed new model provides a simple approach without sacrificing much accuracy compared to previous models. It is applicable to any binary mixture of gases which includes monoatomic gas mixture, polyatomic gas mixtures and mixtures involving rare gases. The new model can be very useful in analysis like combustion where… More >

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