Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (339)
  • Open Access

    ARTICLE

    Test Case Prioritization in Unit and Integration Testing: A Shuffled-Frog-Leaping Approach

    Atulya Gupta*, Rajendra Prasad Mahapatra

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5369-5387, 2023, DOI:10.32604/cmc.2023.031261 - 28 December 2022

    Abstract Both unit and integration testing are incredibly crucial for almost any software application because each of them operates a distinct process to examine the product. Due to resource constraints, when software is subjected to modifications, the drastic increase in the count of test cases forces the testers to opt for a test optimization strategy. One such strategy is test case prioritization (TCP). Existing works have propounded various methodologies that re-order the system-level test cases intending to boost either the fault detection capabilities or the coverage efficacy at the earliest. Nonetheless, singularity in objective functions and… More >

  • Open Access

    ARTICLE

    Deep Neural Network Based Cardio Vascular Disease Prediction Using Binarized Butterfly Optimization

    S. Amutha*, J. Raja Sekar

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1863-1880, 2023, DOI:10.32604/iasc.2023.028903 - 05 January 2023

    Abstract In this digital era, Cardio Vascular Disease (CVD) has become the leading cause of death which has led to the mortality of 17.9 million lives each year. Earlier Diagnosis of the people who are at higher risk of CVDs helps them to receive proper treatment and helps prevent deaths. It becomes inevitable to propose a solution to predict the CVD with high accuracy. A system for predicting Cardio Vascular Disease using Deep Neural Network with Binarized Butterfly Optimization Algorithm (DNN–BBoA) is proposed. The BBoA is incorporated to select the best features. The optimal features are… More >

  • Open Access

    ARTICLE

    An Efficient Hybrid Optimization for Skin Cancer Detection Using PNN Classifier

    J. Jaculin Femil1,*, T. Jaya2

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2919-2934, 2023, DOI:10.32604/csse.2023.032935 - 21 December 2022

    Abstract The necessity of on-time cancer detection is extremely high in the recent days as it becomes a threat to human life. The skin cancer is considered as one of the dangerous diseases among other types of cancer since it causes severe health impacts on human beings and hence it is highly mandatory to detect the skin cancer in the early stage for providing adequate treatment. Therefore, an effective image processing approach is employed in this present study for the accurate detection of skin cancer. Initially, the dermoscopy images of skin lesions are retrieved and processed… More >

  • Open Access

    ARTICLE

    Method for Fault Diagnosis and Speed Control of PMSM

    Smarajit Ghosh*

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2391-2404, 2023, DOI:10.32604/csse.2023.028931 - 21 December 2022

    Abstract In the field of fault tolerance estimation, the increasing attention in electrical motors is the fault detection and diagnosis. The tasks performed by these machines are progressively complex and the enhancements are likewise looked for in the field of fault diagnosis. It has now turned out to be essential to diagnose faults at their very inception; as unscheduled machine downtime can upset deadlines and cause heavy financial burden. In this paper, fault diagnosis and speed control of permanent magnet synchronous motor (PMSM) is proposed. Elman Neural Network (ENN) is used to diagnose the fault of… More >

  • Open Access

    ARTICLE

    An Improved Farmland Fertility Algorithm with Hyper-Heuristic Approach for Solving Travelling Salesman Problem

    Farhad Soleimanian Gharehchopogh1,*, Benyamin Abdollahzadeh1, Bahman Arasteh2

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 1981-2006, 2023, DOI:10.32604/cmes.2023.024172 - 23 November 2022

    Abstract Travelling Salesman Problem (TSP) is a discrete hybrid optimization problem considered NP-hard. TSP aims to discover the shortest Hamilton route that visits each city precisely once and then returns to the starting point, making it the shortest route feasible. This paper employed a Farmland Fertility Algorithm (FFA) inspired by agricultural land fertility and a hyper-heuristic technique based on the Modified Choice Function (MCF). The neighborhood search operator can use this strategy to automatically select the best heuristic method for making the best decision. Lin-Kernighan (LK) local search has been incorporated to increase the efficiency and More >

  • Open Access

    ARTICLE

    Al-Biruni Earth Radius (BER) Metaheuristic Search Optimization Algorithm

    El-Sayed M. El-kenawy1,2, Abdelaziz A. Abdelhamid3,4, Abdelhameed Ibrahim5, Seyedali Mirjalili6,7, Nima Khodadad8, Mona A. Al duailij9, Amel Ali Alhussan9,*, Doaa Sami Khafaga9

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1917-1934, 2023, DOI:10.32604/csse.2023.032497 - 03 November 2022

    Abstract Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and fields. Several metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention, such as particle swarm optimization (PSO), whale optimization algorithm (WOA), grey wolf optimization algorithm (GWO), genetic algorithm (GA), and gravitational search algorithm (GSA). According to the literature, no one metaheuristic optimization algorithm can handle all present optimization problems. Hence novel optimization methodologies are still needed. The Al-Biruni earth radius (BER) search optimization algorithm is proposed in this paper. The More >

  • Open Access

    ARTICLE

    Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-Hop Routing Protocol

    Manar Ahmed Hamza1,*, Haya Mesfer Alshahrani2, Sami Dhahbi3, Mohamed K Nour4, Mesfer Al Duhayyim5, ElSayed M. Tag El Din6, Ishfaq Yaseen1, Abdelwahed Motwakel1

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1759-1773, 2023, DOI:10.32604/csse.2023.030581 - 03 November 2022

    Abstract Wireless Sensor Networks (WSN) has evolved into a key technology for ubiquitous living and the domain of interest has remained active in research owing to its extensive range of applications. In spite of this, it is challenging to design energy-efficient WSN. The routing approaches are leveraged to reduce the utilization of energy and prolonging the lifespan of network. In order to solve the restricted energy problem, it is essential to reduce the energy utilization of data, transmitted from the routing protocol and improve network development. In this background, the current study proposes a novel Differential… More >

  • Open Access

    ARTICLE

    Signal Conducting System with Effective Optimization Using Deep Learning for Schizophrenia Classification

    V. Divya1,*, S. Sendil Kumar2, V. Gokula Krishnan3, Manoj Kumar4

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1869-1886, 2023, DOI:10.32604/csse.2023.029762 - 03 November 2022

    Abstract Signal processing based research was adopted with Electroencephalogram (EEG) for predicting the abnormality and cerebral activities. The proposed research work is intended to provide an automatic diagnostic system to determine the EEG signal in order to classify the brain function which shows whether a person is affected with schizophrenia or not. Early detection and intervention are vital for better prognosis. However, the diagnosis of schizophrenia still depends on clinical observation to date. Without reliable biomarkers, schizophrenia is difficult to detect in its early phase and hence we have proposed this idea. In this work, the… More >

  • Open Access

    ARTICLE

    Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

    Doaa Sami Khafaga1, Faten Khalid Karim1,*, Abdelaziz A. Abdelhamid2,3, El-Sayed M. El-kenawy4, Hend K. Alkahtani1, Nima Khodadadi5, Mohammed Hadwan6, Abdelhameed Ibrahim7

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3183-3198, 2023, DOI:10.32604/cmc.2023.033513 - 31 October 2022

    Abstract Managing physical objects in the network’s periphery is made possible by the Internet of Things (IoT), revolutionizing human life. Open attacks and unauthorized access are possible with these IoT devices, which exchange data to enable remote access. These attacks are often detected using intrusion detection methodologies, although these systems’ effectiveness and accuracy are subpar. This paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic optimization. The employed metaheuristic optimizer is a new version of the whale optimization algorithm (WOA), which is guided by the dipper… More >

  • Open Access

    ARTICLE

    Enhanced Coyote Optimization with Deep Learning Based Cloud-Intrusion Detection System

    Abdullah M. Basahel1, Mohammad Yamin1, Sulafah M. Basahel2, E. Laxmi Lydia3,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4319-4336, 2023, DOI:10.32604/cmc.2023.033497 - 31 October 2022

    Abstract Cloud Computing (CC) is the preference of all information technology (IT) organizations as it offers pay-per-use based and flexible services to its users. But the privacy and security become the main hindrances in its achievement due to distributed and open architecture that is prone to intruders. Intrusion Detection System (IDS) refers to one of the commonly utilized system for detecting attacks on cloud. IDS proves to be an effective and promising technique, that identifies malicious activities and known threats by observing traffic data in computers, and warnings are given when such threats were identified. The… More >

Displaying 201-210 on page 21 of 339. Per Page