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  • Open Access

    ARTICLE

    A Neural Study of the Fractional Heroin Epidemic Model

    Wajaree Weera1, Thongchai Botmart1,*, Samina Zuhra2, Zulqurnain Sabir3, Muhammad Asif Zahoor Raja4, Salem Ben Said5

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4453-4467, 2023, DOI:10.32604/cmc.2023.033232

    Abstract This works intends to provide numerical solutions based on the nonlinear fractional order derivatives of the classical White and Comiskey model (NFD-WCM). The fractional order derivatives have provided authentic and accurate solutions for the NDF-WCM. The solutions of the fractional NFD-WCM are provided using the stochastic computing supervised algorithm named Levenberg-Marquard Backpropagation (LMB) based on neural networks (NNs). This regression approach combines gradient descent and Gauss-Newton iterative methods, which means finding a solution through the sequences of different calculations. WCM is used to demonstrate the heroin epidemics. Heroin has been on-growth world wide, mainly in Asia, Europe, and the USA.… More >

  • Open Access

    ARTICLE

    Load Balancing Based on Multi-Agent Framework to Enhance Cloud Environment

    Shrouk H. Hessen1,2,*, Hatem M. Abdul-kader1, Ayman E. Khedr3, Rashed K. Salem1

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3015-3028, 2023, DOI:10.32604/cmc.2023.033212

    Abstract According to the advances in users’ service requirements, physical hardware accessibility, and speed of resource delivery, Cloud Computing (CC) is an essential technology to be used in many fields. Moreover, the Internet of Things (IoT) is employed for more communication flexibility and richness that are required to obtain fruitful services. A multi-agent system might be a proper solution to control the load balancing of interaction and communication among agents. This paper proposes a multi-agent load balancing framework that consists of two phases to optimize the workload among different servers with large-scale CC power with various utilities and a significant number… More >

  • Open Access

    ARTICLE

    An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

    Hasanien K. Kuba1, Mustafa A. Azzawi2, Saad M. Darwish3,*, Oday A. Hassen4, Ansam A. Abdulhussein5

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4119-4133, 2023, DOI:10.32604/cmc.2023.033182

    Abstract It is crucial, while using healthcare data, to assess the advantages of data privacy against the possible drawbacks. Data from several sources must be combined for use in many data mining applications. The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures. Historically, numerous heuristics (e.g., greedy search) and metaheuristics-based techniques (e.g., evolutionary algorithm) have been created for the positive association rule in privacy preserving data mining (PPDM). When it comes to connecting seemingly unrelated diseases and drugs, negative association rules may be more informative… More >

  • Open Access

    ARTICLE

    Type-2 Neutrosophic Set and Their Applications in Medical Databases Deadlock Resolution

    Marwan H. Hassan1, Saad M. Darwish2,*, Saleh M. Elkaffas3

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4417-4434, 2023, DOI:10.32604/cmc.2023.033175

    Abstract Electronic patient data gives many advantages, but also new difficulties. Deadlocks may delay procedures like acquiring patient information. Distributed deadlock resolution solutions introduce uncertainty due to inaccurate transaction properties. Soft computing-based solutions have been developed to solve this challenge. In a single framework, ambiguous, vague, incomplete, and inconsistent transaction attribute information has received minimal attention. The work presented in this paper employed type-2 neutrosophic logic, an extension of type-1 neutrosophic logic, to handle uncertainty in real-time deadlock-resolving systems. The proposed method is structured to reflect multiple types of knowledge and relations among transactions’ features that include validation factor degree, slackness… More >

  • Open Access

    ARTICLE

    A Hyperparameter Optimization for Galaxy Classification

    Fatih Ahmet Şenel*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4587-4600, 2023, DOI:10.32604/cmc.2023.033155

    Abstract In this study, the morphological galaxy classification process was carried out with a hybrid approach. Since the Galaxy classification process may contain detailed information about the universe’s formation, it remains the current research topic. Researchers divided more than 100 billion galaxies into ten different classes. It is not always possible to understand which class the galaxy types belong. However, Artificial Intelligence (AI) can be used for successful classification. There are studies on the automatic classification of galaxies into a small number of classes. As the number of classes increases, the success of the used methods decreases. Based on the literature,… More >

  • Open Access

    ARTICLE

    Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

    Reem Alkanhel1,*, Doaa Sami Khafaga2, El-Sayed M. El-kenawy3, Abdelaziz A. Abdelhamid4,5, Abdelhameed Ibrahim6, Rashid Amin7, Mostafa Abotaleb8, B. M. El-den6

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2695-2709, 2023, DOI:10.32604/cmc.2023.033153

    Abstract The Internet of Things (IoT) is a modern approach that enables connection with a wide variety of devices remotely. Due to the resource constraints and open nature of IoT nodes, the routing protocol for low power and lossy (RPL) networks may be vulnerable to several routing attacks. That’s why a network intrusion detection system (NIDS) is needed to guard against routing assaults on RPL-based IoT networks. The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks. Therefore, we propose in this paper a novel approach to balance… More >

  • Open Access

    ARTICLE

    Log Anomaly Detection Based on Hierarchical Graph Neural Network and Label Contrastive Coding

    Yong Fang, Zhiying Zhao, Yijia Xu*, Zhonglin Liu

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4099-4118, 2023, DOI:10.32604/cmc.2023.033124

    Abstract System logs are essential for detecting anomalies, querying faults, and tracing attacks. Because of the time-consuming and labor-intensive nature of manual system troubleshooting and anomaly detection, it cannot meet the actual needs. The implementation of automated log anomaly detection is a topic that demands urgent research. However, the prior work on processing log data is mainly one-dimensional and cannot profoundly learn the complex associations in log data. Meanwhile, there is a lack of attention to the utilization of log labels and usually relies on a large number of labels for detection. This paper proposes a novel and practical detection model… More >

  • Open Access

    ARTICLE

    Adapted Speed System in a Road Bend Situation in VANET Environment

    Said Benkirane1, Azidine Guezzaz1, Mourade Azrour1, Akber Abid Gardezi2, Shafiq Ahmad3, Abdelaty Edrees Sayed3, Salman Naseer4, Muhammad Shafiq5,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3781-3794, 2023, DOI:10.32604/cmc.2023.033119

    Abstract Today, road safety remains a serious concern for governments around the world. In fact, approximately 1.35 million people die and 2–50 million are injured on public roads worldwide each year. Straight bends in road traffic are the main cause of many road accidents, and excessive and inappropriate speed in this very critical area can cause drivers to lose their vehicle stability. For these reasons, new solutions must be considered to stop this disaster and save lives. Therefore, it is necessary to study this topic very carefully and use new technologies such as Vehicle Ad Hoc Networks (VANET), Internet of Things… More >

  • Open Access

    ARTICLE

    Proposed Framework for Detection of Breast Tumors

    Mostafa Elbaz1,2,*, Haitham Elwahsh1, Ibrahim Mahmoud El-Henawy2

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2927-2944, 2023, DOI:10.32604/cmc.2023.033111

    Abstract Computer vision is one of the significant trends in computer science. It plays as a vital role in many applications, especially in the medical field. Early detection and segmentation of different tumors is a big challenge in the medical world. The proposed framework uses ultrasound images from Kaggle, applying five diverse models to denoise the images, using the best possible noise-free image as input to the U-Net model for segmentation of the tumor, and then using the Convolution Neural Network (CNN) model to classify whether the tumor is benign, malignant, or normal. The main challenge faced by the framework in… More >

  • Open Access

    ARTICLE

    Natural Language Processing with Optimal Deep Learning-Enabled Intelligent Image Captioning System

    Radwa Marzouk1, Eatedal Alabdulkreem2, Mohamed K. Nour3, Mesfer Al Duhayyim4,*, Mahmoud Othman5, Abu Sarwar Zamani6, Ishfaq Yaseen6, Abdelwahed Motwakel6

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4435-4451, 2023, DOI:10.32604/cmc.2023.033091

    Abstract The recent developments in Multimedia Internet of Things (MIoT) devices, empowered with Natural Language Processing (NLP) model, seem to be a promising future of smart devices. It plays an important role in industrial models such as speech understanding, emotion detection, home automation, and so on. If an image needs to be captioned, then the objects in that image, its actions and connections, and any silent feature that remains under-projected or missing from the images should be identified. The aim of the image captioning process is to generate a caption for image. In next step, the image should be provided with… More >

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