Hala AlShamlan^*, Halah AlMazrua^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 675-694, 2024, DOI:10.32604/cmc.2024.048146

Abstract In this study, our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization (GWO) with Harris Hawks Optimization (HHO) for feature selection. The motivation for utilizing GWO and HHO stems from their bio-inspired nature and their demonstrated success in optimization problems. We aim to leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification. We selected leave-one-out cross-validation (LOOCV) to evaluate the performance of both two widely used classifiers, k-nearest neighbors (KNN) and support vector machine (SVM), on high-dimensional cancer microarray… More >

Mark Ariel Levin^*

Journal of Quantum Computing, Vol.6, pp. 15-24, 2024, DOI:10.32604/jqc.2024.047423

Abstract Quantum algorithms for unstructured search problems rely on the preparation of a uniform superposition, traditionally achieved through Hadamard gates. However, this incidentally creates an auxiliary search space consisting of nonsensical answers that do not belong in the search space and reduce the efficiency of the algorithm due to the need to neglect, un-compute, or destructively interfere with them. Previous approaches to removing this auxiliary search space yielded large circuit depth and required the use of ancillary qubits. We have developed an optimized general solver for a circuit that prepares a uniform superposition of any N states while minimizing depth and… More >

Qinyue Wu, Hui Xu^*, Mengran Liu

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4091-4107, 2024, DOI:10.32604/cmc.2024.048461

Abstract Network traffic identification is critical for maintaining network security and further meeting various demands of network applications. However, network traffic data typically possesses high dimensionality and complexity, leading to practical problems in traffic identification data analytics. Since the original Dung Beetle Optimizer (DBO) algorithm, Grey Wolf Optimization (GWO) algorithm, Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO) algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution, an Improved Dung Beetle Optimizer (IDBO) algorithm is proposed for network traffic identification. Firstly, the Sobol sequence is utilized to initialize the dung beetle population, laying the… More >

Qazi Mazhar ul Haq¹, Fahim Arif^2,3, Khursheed Aurangzeb⁴, Noor ul Ain³, Javed Ali Khan⁵, Saddaf Rubab⁶, Muhammad Shahid Anwar^7,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4379-4397, 2024, DOI:10.32604/cmc.2024.047172

Abstract Software project outcomes heavily depend on natural language requirements, often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements. Researchers are exploring machine learning to predict software bugs, but a more precise and general approach is needed. Accurate bug prediction is crucial for software evolution and user training, prompting an investigation into deep and ensemble learning methods. However, these studies are not generalized and efficient when extended to other datasets. Therefore, this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems. The methods involved feature selection, which is used to… More >

Rashmi Sharma¹, Ashok Pal¹, Nitin Mittal², Lalit Kumar², Sreypov Van³, Yunyoung Nam^3,*, Mohamed Abouhawwash^4,5

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3489-3510, 2024, DOI:10.32604/cmc.2024.046606

Abstract This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm (MOALO) which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm (ALO) and the Genetic Algorithm (GA). MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions. The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO. A first-time hybrid of these algorithms is employed to solve… More >

Chenlin Zhu¹, Yan Zhao¹, Zhitao Jiang¹, Jiafeng Xie³, Lifang Zeng^2,*, Lijuan Qian^1,*

Frontiers in Heat and Mass Transfer, Vol.22, No.1, pp. 107-127, 2024, DOI:10.32604/fhmt.2023.044433

Abstract Efficient and secure refueling within the vehicle refueling systems exhibits a close correlation with the issues concerning fuel backflow and gasoline evaporation. This paper investigates the transient flow behavior in fuel hose refilling and simplified tank fuel replenishment using the volume of fluid method. The numerical simulation is validated with the simplified hose refilling experiment and the evaporation simulation of Stefan tube. The effects of injection flow rate and injection directions have been discussed in the fuel hose refilling part. For both the experiment and simulation, the pressure at the end of the refueling pipe in the lower located nozzle… More >

Yifan Huang¹, Zikang Zhou^1,2, Mingyu Li¹, Xuedong Luo^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3147-3165, 2024, DOI:10.32604/cmes.2024.045947

Abstract Accurately estimating blasting vibration during rock blasting is the foundation of blasting vibration management. In this study, Tuna Swarm Optimization (TSO), Whale Optimization Algorithm (WOA), and Cuckoo Search (CS) were used to optimize two hyperparameters in support vector regression (SVR). Based on these methods, three hybrid models to predict peak particle velocity (PPV) for bench blasting were developed. Eighty-eight samples were collected to establish the PPV database, eight initial blasting parameters were chosen as input parameters for the prediction model, and the PPV was the output parameter. As predictive performance evaluation indicators, the coefficient of determination (R²), root mean square… More >

R. Sujatha, K. Nimala^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1669-1686, 2024, DOI:10.32604/cmc.2023.046963

Abstract Sentence classification is the process of categorizing a sentence based on the context of the sentence. Sentence categorization requires more semantic highlights than other tasks, such as dependence parsing, which requires more syntactic elements. Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence, recognizing the progress and comparing impacts. An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus. The conversational sentences are classified into four categories: information, question, directive, and commission. These classification label sequences are for analyzing the conversation progress and… More >

Liya Yue¹, Pei Hu², Shu-Chuan Chu³, Jeng-Shyang Pan^3,4,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1957-1975, 2024, DOI:10.32604/cmc.2024.046962

Abstract Speech emotion recognition (SER) uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions. The number of features acquired with acoustic analysis is extremely high, so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition system. The proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum accuracy. First, we use the information gain and Fisher Score to sort the features extracted from signals. Then, we employ a multi-objective ranking method to evaluate these features and… More >

Bayi Xu¹, Lei Sun^2,*, Xiuqing Mao², Chengwei Liu³, Zhiyi Ding²

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1995-2022, 2024, DOI:10.32604/cmc.2023.046478

Abstract In recent years, frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security. This paper presents a novel intrusion detection system consisting of a data preprocessing stage and a deep learning model for accurately identifying network attacks. We have proposed four deep neural network models, which are constructed using architectures such as Convolutional Neural Networks (CNN), Bi-directional Long Short-Term Memory (BiLSTM), Bidirectional Gate Recurrent Unit (BiGRU), and Attention mechanism. These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the models, we apply various preprocessing… More >