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  • Open Access

    ARTICLE

    A New Hybrid Feature Selection Sequence for Predicting Breast Cancer Survivability Using Clinical Datasets

    E. Jenifer Sweetlin*, S. Saudia

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 343-367, 2023, DOI:10.32604/iasc.2023.036742

    Abstract This paper proposes a hybrid feature selection sequence complemented with filter and wrapper concepts to improve the accuracy of Machine Learning (ML) based supervised classifiers for classifying the survivability of breast cancer patients into classes, living and deceased using METABRIC and Surveillance, Epidemiology and End Results (SEER) datasets. The ML-based classifiers used in the analysis are: Multiple Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine and Multilayer Perceptron. The workflow of the proposed ML algorithm sequence comprises the following stages: data cleaning, data balancing, feature selection via a filter and wrapper sequence, More >

  • Open Access

    ARTICLE

    Energy Efficient Hyperparameter Tuned Deep Neural Network to Improve Accuracy of Near-Threshold Processor

    K. Chanthirasekaran, Raghu Gundaala*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 471-489, 2023, DOI:10.32604/iasc.2023.036130

    Abstract When it comes to decreasing margins and increasing energy efficiency in near-threshold and sub-threshold processors, timing error resilience may be viewed as a potentially lucrative alternative to examine. On the other hand, the currently employed approaches have certain restrictions, including high levels of design complexity, severe time constraints on error consolidation and propagation, and uncontaminated architectural registers (ARs). The design of near-threshold circuits, often known as NT circuits, is becoming the approach of choice for the construction of energy-efficient digital circuits. As a result of the exponentially decreased driving current, there was a reduction in… More >

  • Open Access

    ARTICLE

    Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

    Saeed Mohsen1,2,*, Sherif S. M. Ghoneim3, Mohammed S. Alzaidi3, Abdullah Alzahrani3, Ashraf Mohamed Ali Hassan4

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5271-5286, 2023, DOI:10.32604/cmc.2023.038758

    Abstract Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and… More >

  • Open Access

    ARTICLE

    Improved HardNet and Stricter Outlier Filtering to Guide Reliable Matching

    Meng Xu1, Chen Shen2, Jun Zhang2, Zhipeng Wang3, Zhiwei Ruan2, Stefan Poslad1, Pengfei Xu2,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4785-4803, 2023, DOI:10.32604/cmc.2023.034053

    Abstract As the fundamental problem in the computer vision area, image matching has wide applications in pose estimation, 3D reconstruction, image retrieval, etc. Suffering from the influence of external factors, the process of image matching using classical local detectors, e.g., scale-invariant feature transform (SIFT), and the outlier filtering approaches, e.g., Random sample consensus (RANSAC), show high computation speed and pool robustness under changing illumination and viewpoints conditions, while image matching approaches with deep learning strategy (such as HardNet, OANet) display reliable achievements in large-scale datasets with challenging scenes. However, the past learning-based approaches are limited to… More >

  • Open Access

    ARTICLE

    PREDICTION MODEL OF LIQUID HOLDUP BASED ON SOA-BPNN ALGORITHM

    Qi Zhuanga,* , Dong Liub, Bo Liuc, Mei Liua

    Frontiers in Heat and Mass Transfer, Vol.20, pp. 1-6, 2023, DOI:10.5098/hmt.20.13

    Abstract In the actual operation of wet gas pipeline, liquid accumulation is easy to form in the low-lying and uphill sections of the pipeline, which leads to a series of problems such as reduced pipeline transportation efficiency, increased pipeline pressure drop, hydrate formation, slug flow and intensified corrosion in the pipeline. Accurate calculation of liquid holdup is of great significance to the research of flow pattern identification, pipeline corrosion evaluation and prediction, and gas pipeline transportation efficiency calculation. Based on the experimental data of liquid holdup in horizontal pipeline, a commonly used BP neural network (BPNN)… More >

  • Open Access

    ARTICLE

    Numerical Stability and Accuracy of Contact Angle Schemes in Pseudopotential Lattice Boltzmann Model for Simulating Static Wetting and Dynamic Wetting

    Dongmin Wang1,2,*, Gaoshuai Lin1

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 299-318, 2023, DOI:10.32604/cmes.2023.027280

    Abstract There are five most widely used contact angle schemes in the pseudopotential lattice Boltzmann (LB) model for simulating the wetting phenomenon: The pseudopotential-based scheme (PB scheme), the improved virtual-density scheme (IVD scheme), the modified pseudopotential-based scheme with a ghost fluid layer constructed by using the fluid layer density above the wall (MPB-C scheme), the modified pseudopotential-based scheme with a ghost fluid layer constructed by using the weighted average density of surrounding fluid nodes (MPB-W scheme) and the geometric formulation scheme (GF scheme). But the numerical stability and accuracy of the schemes for wetting simulation remain… More >

  • Open Access

    ARTICLE

    Adaptive Learning Video Streaming with QoE in Multi-Home Heterogeneous Networks

    S. Vijayashaarathi1,*, S. NithyaKalyani2

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2881-2897, 2023, DOI:10.32604/csse.2023.036864

    Abstract In recent years, real-time video streaming has grown in popularity. The growing popularity of the Internet of Things (IoT) and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience (QoE) and performance objectives. Most researchers focused on Forward Error Correction (FEC) techniques when attempting to strike a balance between QoE and performance. However, as network capacity increases, the performance degrades, impacting the live visual experience. Recently, Deep Learning (DL) algorithms have been successfully integrated with FEC to stream videos across multiple… More >

  • Open Access

    ARTICLE

    A Secure and Effective Energy-Aware Fixed-Point Quantization Scheme for Asynchronous Federated Learning

    Zerui Zhen1, Zihao Wu2, Lei Feng1,*, Wenjing Li1, Feng Qi1, Shixuan Guo1

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2939-2955, 2023, DOI:10.32604/cmc.2023.036505

    Abstract Asynchronous federated learning (AsynFL) can effectively mitigate the impact of heterogeneity of edge nodes on joint training while satisfying participant user privacy protection and data security. However, the frequent exchange of massive data can lead to excess communication overhead between edge and central nodes regardless of whether the federated learning (FL) algorithm uses synchronous or asynchronous aggregation. Therefore, there is an urgent need for a method that can simultaneously take into account device heterogeneity and edge node energy consumption reduction. This paper proposes a novel Fixed-point Asynchronous Federated Learning (FixedAsynFL) algorithm, which could mitigate the… More >

  • Open Access

    ARTICLE

    Output Linearization of Single-Input Single-Output Fuzzy System to Improve Accuracy and Performance

    Salah-ud-din Khokhar1,2,*, QinKe Peng1, Muhammad Yasir Noor3

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2413-2427, 2023, DOI:10.32604/cmc.2023.036148

    Abstract For fuzzy systems to be implemented effectively, the fuzzy membership function (MF) is essential. A fuzzy system (FS) that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output (SISO) FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output. Utilizing a variety of non-linear techniques, a SISO FS is simulated. The results of FS experiments conducted in comparable conditions are then compared. The simulated results and the results of the experimental setup agree fairly well. The findings of the suggested More >

  • Open Access

    ARTICLE

    Floyd-Warshall Algorithm Based on Picture Fuzzy Information

    Shaista Habib1, Aqsa Majeed1, Muhammad Akram2,*, Mohammed M. Ali Al-Shamiri3,4

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2873-2894, 2023, DOI:10.32604/cmes.2023.026294

    Abstract The Floyd-Warshall algorithm is frequently used to determine the shortest path between any pair of nodes. It works well for crisp weights, but the problem arises when weights are vague and uncertain. Let us take an example of computer networks, where the chosen path might no longer be appropriate due to rapid changes in network conditions. The optimal path from among all possible courses is chosen in computer networks based on a variety of parameters. In this paper, we design a new variant of the Floyd-Warshall algorithm that identifies an All-Pair Shortest Path (APSP) in More > Graphic Abstract

    Floyd-Warshall Algorithm Based on Picture Fuzzy Information

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