Novsheena Rasool^1,*, Javaid Iqbal Bhat¹, Najib Ben Aoun^2,3, Abdullah Alharthi⁴, Niyaz Ahmad Wani⁵, Vikram Chopra⁶, Muhammad Shahid Anwar^7,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 885-909, 2024, DOI:10.32604/cmc.2024.055900 - 15 October 2024

Abstract Gliomas are aggressive brain tumors known for their heterogeneity, unclear borders, and diverse locations on Magnetic Resonance Imaging (MRI) scans. These factors present significant challenges for MRI-based segmentation, a crucial step for effective treatment planning and monitoring of glioma progression. This study proposes a novel deep learning framework, ResNet Multi-Head Attention U-Net (ResMHA-Net), to address these challenges and enhance glioma segmentation accuracy. ResMHA-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention mechanisms. This powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture… More >

Mahesh Thyluru Ramakrishna¹, Kuppusamy Pothanaicker², Padma Selvaraj³, Surbhi Bhatia Khan^4,7,*, Vinoth Kumar Venkatesan⁵, Saeed Alzahrani⁶, Mohammad Alojail⁶

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 867-883, 2024, DOI:10.32604/cmc.2024.053563 - 15 October 2024

Abstract Brain tumor is a global issue due to which several people suffer, and its early diagnosis can help in the treatment in a more efficient manner. Identifying different types of brain tumors, including gliomas, meningiomas, pituitary tumors, as well as confirming the absence of tumors, poses a significant challenge using MRI images. Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification. These methods often rely on manual feature extraction and basic convolutional neural networks (CNNs). The limitations include inadequate accuracy, poor generalization of new data, and limited ability… More >

Cong Shen^1,*, Wei Zhang^1,2,*, Tanping Zhou^1,2, Yiming Zhang¹, Lingling Zhang³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4729-4748, 2024, DOI:10.32604/cmc.2024.054434 - 12 September 2024

Abstract With the increasing awareness of privacy protection and the improvement of relevant laws, federal learning has gradually become a new choice for cross-agency and cross-device machine learning. In order to solve the problems of privacy leakage, high computational overhead and high traffic in some federated learning schemes, this paper proposes a multiplicative double privacy mask algorithm which is convenient for homomorphic addition aggregation. The combination of homomorphic encryption and secret sharing ensures that the server cannot compromise user privacy from the private gradient uploaded by the participants. At the same time, the proposed TQRR (Top-Q-Random-R) More >

Huang Zeng, Mingtian Zhang, Tengfei Liu, Anjia Yang^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5125-5142, 2024, DOI:10.32604/cmc.2024.052846 - 20 June 2024

Abstract Federated learning is an important distributed model training technique in Internet of Things (IoT), in which participant selection is a key component that plays a role in improving training efficiency and model accuracy. This module enables a central server to select a subset of participants to perform model training based on data and device information. By doing so, selected participants are rewarded and actively perform model training, while participants that are detrimental to training efficiency and model accuracy are excluded. However, in practice, participants may suspect that the central server may have miscalculated and thus… More >

Lin Ma¹, Liyong Wang¹, Shuang Zeng¹, Yutong Zhao¹, Chang Liu¹, Heng Zhang¹, Qiong Wu^2,*, Hongbo Ren²

Energy Engineering, Vol.121, No.6, pp. 1473-1493, 2024, DOI:10.32604/ee.2024.047332 - 21 May 2024

Abstract Accurate load forecasting forms a crucial foundation for implementing household demand response plans and optimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations, a single prediction model is hard to capture temporal features effectively, resulting in diminished prediction accuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neural network (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), is proposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features from the original data, enhancing the quality of data… More >

Hela Elmannai^1,*, Monia Hamdi¹, Souham Meshoul¹, Amel Ali Alhussan², Manel Ayadi³, Amel Ksibi³

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1429-1457, 2024, DOI:10.32604/cmes.2024.048557 - 20 May 2024

Abstract Glaucoma disease causes irreversible damage to the optical nerve and it has the potential to cause permanent loss of vision. Glaucoma ranks as the second most prevalent cause of permanent blindness. Traditional glaucoma diagnosis requires a highly experienced specialist, costly equipment, and a lengthy wait time. For automatic glaucoma detection, state-of-the-art glaucoma detection methods include a segmentation-based method to calculate the cup-to-disc ratio. Other methods include multi-label segmentation networks and learning-based methods and rely on hand-crafted features. Localizing the optic disc (OD) is one of the key features in retinal images for detecting retinal diseases,… More >

Fatimah Mudhhi Alanazi^*, Bothina Abdelmeneem Elsobky, Shaimaa Aly Elmorsy

Computer Systems Science and Engineering, Vol.48, No.3, pp. 835-851, 2024, DOI:10.32604/csse.2024.042401 - 20 May 2024

Abstract Proliferation of technology, coupled with networking growth, has catapulted cybersecurity to the forefront of modern security concerns. In this landscape, the precise detection of cyberattacks and anomalies within networks is crucial, necessitating the development of efficient intrusion detection systems (IDS). This article introduces a framework utilizing the fusion of fuzzy sets with support vector machines (SVM), named FSVM. The core strategy of FSVM lies in calculating the significance of network features to determine their relative importance. Features with minimal significance are prudently disregarded, a method akin to feature selection. This process not only curtails the… More >

Luda Chen¹, Kuangzhu Bao², Ying Chen², Jingang Hao^2,*, Jianfeng He^1,3,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 409-427, 2024, DOI:10.32604/cmc.2024.048507 - 25 April 2024

Abstract Pancreatic diseases, including mass-forming chronic pancreatitis (MFCP) and pancreatic ductal adenocarcinoma (PDAC), present with similar imaging features, leading to diagnostic complexities. Deep Learning (DL) methods have been shown to perform well on diagnostic tasks. Existing DL pancreatic lesion diagnosis studies based on Magnetic Resonance Imaging (MRI) utilize the prior information to guide models to focus on the lesion region. However, over-reliance on prior information may ignore the background information that is helpful for diagnosis. This study verifies the diagnostic significance of the background information using a clinical dataset. Consequently, the Prior Difference Guidance Network (PDGNet)… More >

Saba Awan^1,*, Zahid Mehmood^2,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2535-2555, 2024, DOI:10.32604/cmc.2024.047337 - 27 February 2024

Abstract Highway safety researchers focus on crash injury severity, utilizing deep learning—specifically, deep neural networks (DNN), deep convolutional neural networks (D-CNN), and deep recurrent neural networks (D-RNN)—as the preferred method for modeling accident severity. Deep learning’s strength lies in handling intricate relationships within extensive datasets, making it popular for accident severity level (ASL) prediction and classification. Despite prior success, there is a need for an efficient system recognizing ASL in diverse road conditions. To address this, we present an innovative Accident Severity Level Prediction Deep Learning (ASLP-DL) framework, incorporating DNN, D-CNN, and D-RNN models fine-tuned through More >

Nadir Omer^1,*, Ahmed H. Samak², Ahmed I. Taloba^3,4, Rasha M. Abd El-Aziz^3,5

Computer Systems Science and Engineering, Vol.48, No.1, pp. 77-95, 2024, DOI:10.32604/csse.2023.039265 - 26 January 2024

Abstract Today’s world depends on the Internet to meet all its daily needs. The usage of the Internet is growing rapidly. The world is using the Internet more frequently than ever. The hazards of harmful attacks have also increased due to the growing reliance on the Internet. Hazards to cyber security are actions taken by someone with malicious intent to steal data, destroy computer systems, or disrupt them. Due to rising cyber security concerns, cyber security has emerged as the key component in the fight against all online threats, forgeries, and assaults. A device capable of… More >