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  • Open Access

    ARTICLE

    Applying an Improved Dung Beetle Optimizer Algorithm to Network Traffic Identification

    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 >

  • Open Access

    ARTICLE

    Enhancing PDF Malware Detection through Logistic Model Trees

    Muhammad Binsawad*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3645-3663, 2024, DOI:10.32604/cmc.2024.048183

    Abstract Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity, and because of its complexity and evasiveness, it is challenging to identify using traditional signature-based detection approaches. The study article discusses the growing danger to cybersecurity that malware hidden in PDF files poses, highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial methodologies. The article presents a new method that improves PDF virus detection by using document analysis and a Logistic Model Tree. Using a dataset from the Canadian Institute for Cybersecurity, a comparative analysis is carried out with well-known machine learning… More >

  • Open Access

    ARTICLE

    Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

    Muhammad Armghan Latif1, Zohaib Mushtaq2, Saad Arif3, Sara Rehman4, Muhammad Farrukh Qureshi5, Nagwan Abdel Samee6, Maali Alabdulhafith6,*, Yeong Hyeon Gu7, Mohammed A. Al-masni7

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4225-4241, 2024, DOI:10.32604/cmc.2024.047621

    Abstract Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland. Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care. This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques. Sequential forward feature selection, sequential backward feature elimination, and bidirectional feature elimination are investigated in this study. In ensemble learning, random forest, adaptive boosting, and bagging classifiers are employed. The effectiveness of these techniques is evaluated using… More >

  • Open Access

    ARTICLE

    Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

    Sureka Sivavelu, Venkatesh Palanisamy*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3469-3487, 2024, DOI:10.32604/cmc.2024.047407

    Abstract The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric… More >

  • Open Access

    ARTICLE

    Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

    Qazi Mazhar ul Haq1, Fahim Arif2,3, Khursheed Aurangzeb4, Noor ul Ain3, Javed Ali Khan5, Saddaf Rubab6, Muhammad Shahid Anwar7,*

    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 >

  • Open Access

    ARTICLE

    An Artificial Intelligence-Based Framework for Fruits Disease Recognition Using Deep Learning

    Irfan Haider1, Muhammad Attique Khan1,*, Muhammad Nazir1, Taerang Kim2, Jae-Hyuk Cha2

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 529-554, 2024, DOI:10.32604/csse.2023.042080

    Abstract Fruit infections have an impact on both the yield and the quality of the crop. As a result, an automated recognition system for fruit leaf diseases is important. In artificial intelligence (AI) applications, especially in agriculture, deep learning shows promising disease detection and classification results. The recent AI-based techniques have a few challenges for fruit disease recognition, such as low-resolution images, small datasets for learning models, and irrelevant feature extraction. This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization. Three fruit types have been employed in this work for… More >

  • Open Access

    ARTICLE

    Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition

    Liya Yue1, Pei Hu2, Shu-Chuan Chu3, Jeng-Shyang Pan3,4,*

    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 >

  • Open Access

    ARTICLE

    Exploring Sequential Feature Selection in Deep Bi-LSTM Models for Speech Emotion Recognition

    Fatma Harby1, Mansor Alohali2, Adel Thaljaoui2,3,*, Amira Samy Talaat4

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2689-2719, 2024, DOI:10.32604/cmc.2024.046623

    Abstract Machine Learning (ML) algorithms play a pivotal role in Speech Emotion Recognition (SER), although they encounter a formidable obstacle in accurately discerning a speaker’s emotional state. The examination of the emotional states of speakers holds significant importance in a range of real-time applications, including but not limited to virtual reality, human-robot interaction, emergency centers, and human behavior assessment. Accurately identifying emotions in the SER process relies on extracting relevant information from audio inputs. Previous studies on SER have predominantly utilized short-time characteristics such as Mel Frequency Cepstral Coefficients (MFCCs) due to their ability to capture the periodic nature of audio… More >

  • Open Access

    ARTICLE

    Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

    Ch Avais Hanif1, Muhammad Ali Mughal1, Muhammad Attique Khan2,3,*, Nouf Abdullah Almujally4, Taerang Kim5, Jae-Hyuk Cha5

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 357-374, 2024, DOI:10.32604/cmc.2023.043061

    Abstract The demand for a non-contact biometric approach for candidate identification has grown over the past ten years. Based on the most important biometric application, human gait analysis is a significant research topic in computer vision. Researchers have paid a lot of attention to gait recognition, specifically the identification of people based on their walking patterns, due to its potential to correctly identify people far away. Gait recognition systems have been used in a variety of applications, including security, medical examinations, identity management, and access control. These systems require a complex combination of technical, operational, and definitional considerations. The employment of… More >

  • Open Access

    ARTICLE

    Stroke Risk Assessment Decision-Making Using a Machine Learning Model: Logistic-AdaBoost

    Congjun Rao1, Mengxi Li1, Tingting Huang2,*, Feiyu Li1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 699-724, 2024, DOI:10.32604/cmes.2023.044898

    Abstract Stroke is a chronic cerebrovascular disease that carries a high risk. Stroke risk assessment is of great significance in preventing, reversing and reducing the spread and the health hazards caused by stroke. Aiming to objectively predict and identify strokes, this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost (Logistic-AB) based on machine learning. First, the categorical boosting (CatBoost) method is used to perform feature selection for all features of stroke, and 8 main features are selected to form a new index evaluation system to predict the risk of stroke. Second, the borderline synthetic minority oversampling technique (SMOTE)… More >

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