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  • Open Access

    ARTICLE

    Prairie Araneida Optimization Based Fused CNN Model for Intrusion Detection

    Nishit Patil, Shubhalaxmi Joshi*

    Computer Systems Science and Engineering, Vol.49, pp. 49-77, 2025, DOI:10.32604/csse.2024.057702 - 03 January 2025

    Abstract Intrusion detection (ID) is a cyber security practice that encompasses the process of monitoring network activities to identify unauthorized or malicious actions. This includes problems like the difficulties of existing intrusion detection models to identify emerging attacks, generating many false alarms, and their inability and difficulty to adapt themselves with time when it comes to threats, hence to overcome all those existing challenges in this research develop a Prairie Araneida optimization based fused Convolutional Neural Network model (PAO-CNN) for intrusion detection. The fused CNN (Convolutional Neural Netowrk) is a remarkable development since it combines statistical… More >

  • Open Access

    ARTICLE

    Robust Network Security: A Deep Learning Approach to Intrusion Detection in IoT

    Ammar Odeh*, Anas Abu Taleb

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4149-4169, 2024, DOI:10.32604/cmc.2024.058052 - 19 December 2024

    Abstract The proliferation of Internet of Things (IoT) technology has exponentially increased the number of devices interconnected over networks, thereby escalating the potential vectors for cybersecurity threats. In response, this study rigorously applies and evaluates deep learning models—namely Convolutional Neural Networks (CNN), Autoencoders, and Long Short-Term Memory (LSTM) networks—to engineer an advanced Intrusion Detection System (IDS) specifically designed for IoT environments. Utilizing the comprehensive UNSW-NB15 dataset, which encompasses 49 distinct features representing varied network traffic characteristics, our methodology focused on meticulous data preprocessing including cleaning, normalization, and strategic feature selection to enhance model performance. A robust… More >

  • Open Access

    ARTICLE

    Contrast Normalization Strategies in Brain Tumor Imaging: From Preprocessing to Classification

    Samar M. Alqhtani1, Toufique A. Soomro2,*, Faisal Bin Ubaid3, Ahmed Ali4, Muhammad Irfan5, Abdullah A. Asiri6

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1539-1562, 2024, DOI:10.32604/cmes.2024.051475 - 20 May 2024

    Abstract Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries. Magnetic resonance imaging (MRI) and computed tomography (CT) are utilized to capture brain images. MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders. Typically, manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention. However, early diagnosis of brain tumors is intricate, necessitating the use of computerized methods. This research introduces an innovative approach for… More > Graphic Abstract

    Contrast Normalization Strategies in Brain Tumor Imaging: From Preprocessing to Classification

  • Open Access

    ARTICLE

    Predicting 3D Radiotherapy Dose-Volume Based on Deep Learning

    Do Nang Toan1,*, Lam Thanh Hien2, Ha Manh Toan1, Nguyen Trong Vinh2, Pham Trung Hieu1

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 319-335, 2024, DOI:10.32604/iasc.2024.046925 - 21 May 2024

    Abstract Cancer is one of the most dangerous diseases with high mortality. One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians. In our study, we focused on the 3D dose prediction problem in radiotherapy by applying the deep-learning approach to computed tomography (CT) images of cancer patients. Medical image data has more complex characteristics than normal image data, and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the… More >

  • Open Access

    ARTICLE

    An Intelligent Sensor Data Preprocessing Method for OCT Fundus Image Watermarking Using an RCNN

    Jialun Lin1, Qiong Chen1,2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1549-1561, 2024, DOI:10.32604/cmes.2023.029631 - 17 November 2023

    Abstract Watermarks can provide reliable and secure copyright protection for optical coherence tomography (OCT) fundus images. The effective image segmentation is helpful for promoting OCT image watermarking. However, OCT images have a large amount of low-quality data, which seriously affects the performance of segmentation methods. Therefore, this paper proposes an effective segmentation method for OCT fundus image watermarking using a rough convolutional neural network (RCNN). First, the rough-set-based feature discretization module is designed to preprocess the input data. Second, a dual attention mechanism for feature channels and spatial regions in the CNN is added to enable… More >

  • Open Access

    REVIEW

    Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

    Baydaa Abdul Kareem1,2, Salah L. Zubaidi2,3, Nadhir Al-Ansari4,*, Yousif Raad Muhsen2,5

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 1-41, 2024, DOI:10.32604/cmes.2023.027954 - 22 September 2023

    Abstract Forecasting river flow is crucial for optimal planning, management, and sustainability using freshwater resources. Many machine learning (ML) approaches have been enhanced to improve streamflow prediction. Hybrid techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone approaches. Current researchers have also emphasised using hybrid models to improve forecast accuracy. Accordingly, this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years, summarising data preprocessing, univariate machine learning modelling strategy, advantages and disadvantages of standalone ML… More > Graphic Abstract

    Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

  • Open Access

    ARTICLE

    Fusion of Feature Ranking Methods for an Effective Intrusion Detection System

    Seshu Bhavani Mallampati1, Seetha Hari2,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1721-1744, 2023, DOI:10.32604/cmc.2023.040567 - 30 August 2023

    Abstract Expanding internet-connected services has increased cyberattacks, many of which have grave and disastrous repercussions. An Intrusion Detection System (IDS) plays an essential role in network security since it helps to protect the network from vulnerabilities and attacks. Although extensive research was reported in IDS, detecting novel intrusions with optimal features and reducing false alarm rates are still challenging. Therefore, we developed a novel fusion-based feature importance method to reduce the high dimensional feature space, which helps to identify attacks accurately with less false alarm rate. Initially, to improve training data quality, various preprocessing techniques are… More >

  • Open Access

    REVIEW

    A Systematic Review on the Internet of Medical Things: Techniques, Open Issues, and Future Directions

    Apurva Sonavane1, Aditya Khamparia2,*, Deepak Gupta3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1525-1550, 2023, DOI:10.32604/cmes.2023.028203 - 26 June 2023

    Abstract IoT usage in healthcare is one of the fastest growing domains all over the world which applies to every age group. Internet of Medical Things (IoMT) bridges the gap between the medical and IoT field where medical devices communicate with each other through a wireless communication network. Advancement in IoMT makes human lives easy and better. This paper provides a comprehensive detailed literature survey to investigate different IoMT-driven applications, methodologies, and techniques to ensure the sustainability of IoMT-driven systems. The limitations of existing IoMT frameworks are also analyzed concerning their applicability in real-time driven systems More >

  • Open Access

    ARTICLE

    Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

    José Escorcia-Gutierrez1,*, Margarita Gamarra1, Roosvel Soto-Diaz2, Safa Alsafari3, Ayman Yafoz4, Romany F. Mansour5

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5255-5270, 2023, DOI:10.32604/cmc.2023.033731 - 29 April 2023

    Abstract A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs. Chest X-ray (CXR) gained much interest after the COVID-19 outbreak thanks to its rapid imaging time, widespread availability, low cost, and portability. In radiological investigations, computer-aided diagnostic tools are implemented to reduce intra- and inter-observer variability. Using lately industrialized Artificial Intelligence (AI) algorithms and radiological techniques to diagnose and classify disease is advantageous. The current study develops an automatic identification and classification model for CXR pictures using Gaussian Filtering based Optimized Synergic Deep Learning using… More >

  • Open Access

    ARTICLE

    Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

    Fatma Taher1, Hamoud Alshammari2, Lobna Osman3, Mohamed Elhoseny4, Abdulaziz Shehab5,2,*, Eman Elayat6

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4485-4499, 2023, DOI:10.32604/cmc.2023.036118 - 31 March 2023

    Abstract Cardiac diseases are one of the greatest global health challenges. Due to the high annual mortality rates, cardiac diseases have attracted the attention of numerous researchers in recent years. This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases. The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms. An ensemble of classifiers is then applied to the fusion’s results. The proposed model classifies the arrhythmia dataset from the University of California, Irvine into normal/abnormal classes as well as 16 classes of arrhythmia.… More >

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