Open Access

ARTICLE

Tianzhe Jiao, Xiaoyue Feng, Chaopeng Guo, Dongqi Wang, Jie Song^*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3585-3603, 2023, DOI:10.32604/cmc.2023.040068
Abstract Mobile-edge computing (MEC) is a promising technology for the fifth-generation (5G) and sixth-generation (6G) architectures, which provides resourceful computing capabilities for Internet of Things (IoT) devices, such as virtual reality, mobile devices, and smart cities. In general, these IoT applications always bring higher energy consumption than traditional applications, which are usually energy-constrained. To provide persistent energy, many references have studied the offloading problem to save energy consumption. However, the dynamic environment dramatically increases the optimization difficulty of the offloading decision. In this paper, we aim to minimize the energy consumption of the entire MEC system under the latency constraint by… More >

View

280

Download

238

Open Access

ARTICLE

Abdulgbar A. R. Farea¹, Gehad Abdullah Amran^2,*, Ebraheem Farea³, Amerah Alabrah^4,*, Ahmed A. Abdulraheem⁵, Muhammad Mursil⁶, Mohammed A. A. Al-qaness⁷
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3605-3622, 2023, DOI:10.32604/cmc.2023.040121
（This article belongs to the Special Issue: AI-driven Cybersecurity in Cyber Physical Systems enabled Healthcare, Current Challenges, Requirements and Future research Foresights)
Abstract E-commerce, online ticketing, online banking, and other web-based applications that handle sensitive data, such as passwords, payment information, and financial information, are widely used. Various web developers may have varying levels of understanding when it comes to securing an online application. Structured Query language SQL injection and cross-site scripting are the two vulnerabilities defined by the Open Web Application Security Project (OWASP) for its 2017 Top Ten List Cross Site Scripting (XSS). An attacker can exploit these two flaws and launch malicious web-based actions as a result of these flaws. Many published articles focused on these attacks’ binary classification. This… More >

View

288

Download

218

Open Access

ARTICLE

Yang Yang¹, Yuhan Long¹, Yijing Lin², Zhipeng Gao¹, Lanlan Rui¹, Peng Yu^1,3,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3623-3651, 2023, DOI:10.32604/cmc.2023.040250
Abstract With the rapid development of the Internet of Things (IoT), the automation of edge-side equipment has emerged as a significant trend. The existing fault diagnosis methods have the characteristics of heavy computing and storage load, and most of them have computational redundancy, which is not suitable for deployment on edge devices with limited resources and capabilities. This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation. First, we offer a clustering-based self-knowledge distillation approach (Cluster KD), which takes the mean value of the sample diagnosis results, clusters them, and takes the clustering results as the… More >

View

242

Download

147

Open Access

ARTICLE

Muhammad Fezan Afzal¹, Imran Khan¹, Javed Rashid^1,2,3, Mubbashar Saddique^4,*, Heba G. Mohamed⁵
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3653-3670, 2023, DOI:10.32604/cmc.2023.041627
（This article belongs to the Special Issue: The Next Generation of Artificial Intelligence and the Intelligent Internet of Things)
Abstract Software Product Line (SPL) is a group of software-intensive systems that share common and variable resources for developing a particular system. The feature model is a tree-type structure used to manage SPL’s common and variable features with their different relations and problem of Crosstree Constraints (CTC). CTC problems exist in groups of common and variable features among the sub-tree of feature models more diverse in Internet of Things (IoT) devices because different Internet devices and protocols are communicated. Therefore, managing the CTC problem to achieve valid product configuration in IoT-based SPL is more complex, time-consuming, and hard. However, the CTC… More >

View

962

Download

339

Open Access

ARTICLE

Saima Yasmeen¹, Muhammad Usman Yaseen^1,*, Syed Sohaib Ali², Moustafa M. Nasralla³, Sohaib Bin Altaf Khattak³
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3671-3689, 2023, DOI:10.32604/cmc.2023.041195
Abstract SAR images commonly suffer from speckle noise, posing a significant challenge in their analysis and interpretation. Existing convolutional neural network (CNN) based despeckling methods have shown great performance in removing speckle noise. However, these CNN-based methods have a few limitations. They do not decouple complex background information in a multi-resolution manner. Moreover, they have deep network structures that may result in many parameters, limiting their applicability to mobile devices. Furthermore, extracting key speckle information in the presence of complex background is also a major problem with SAR. The proposed study addresses these limitations by introducing a lightweight pyramid and attention-based… More >

View

405

Download

144

Open Access

ARTICLE

Yan Li¹, Xiaoguang Zhang^1,*, Tianyu Gong¹, Qi Dong¹, Hailong Zhu¹, Tianqiang Zhang¹, Yanji Jiang^2,3
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3691-3705, 2023, DOI:10.32604/cmc.2023.040492
Abstract Automatic text summarization (ATS) plays a significant role in Natural Language Processing (NLP). Abstractive summarization produces summaries by identifying and compressing the most important information in a document. However, there are only relatively several comprehensively evaluated abstractive summarization models that work well for specific types of reports due to their unstructured and oral language text characteristics. In particular, Chinese complaint reports, generated by urban complainers and collected by government employees, describe existing resident problems in daily life. Meanwhile, the reflected problems are required to respond speedily. Therefore, automatic summarization tasks for these reports have been developed. However, similar to traditional… More >

View

251

Download

183

Open Access

ARTICLE

Asifa Mehmood Qureshi¹, Nouf Abdullah Almujally², Saud S. Alotaibi³, Mohammed Hamad Alatiyyah⁴, Jeongmin Park^5,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3707-3725, 2023, DOI:10.32604/cmc.2023.040738
Abstract Road congestion, air pollution, and accident rates have all increased as a result of rising traffic density and worldwide population growth. Over the past ten years, the total number of automobiles has increased significantly over the world. In this paper, a novel method for intelligent traffic surveillance is presented. The proposed model is based on multilabel semantic segmentation using a random forest classifier which classifies the images into five classes. To improve the results, mean-shift clustering was applied to the segmented images. Afterward, the pixels given the label for the vehicle were extracted and blob detection was applied to mark… More >

View

259

Download

158

Open Access

ARTICLE

Muhammad Naeem Akram¹, Muhammad Usman Yaseen¹, Muhammad Waqar¹, Muhammad Imran^1,*, Aftab Hussain²
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3727-3744, 2023, DOI:10.32604/cmc.2023.041855
Abstract This study presents a deep learning model for efficient intracranial hemorrhage (ICH) detection and subtype classification on non-contrast head computed tomography (CT) images. ICH refers to bleeding in the skull, leading to the most critical life-threatening health condition requiring rapid and accurate diagnosis. It is classified as intra-axial hemorrhage (intraventricular, intraparenchymal) and extra-axial hemorrhage (subdural, epidural, subarachnoid) based on the bleeding location inside the skull. Many computer-aided diagnoses (CAD)-based schemes have been proposed for ICH detection and classification at both slice and scan levels. However, these approaches perform only binary classification and suffer from a large number of parameters, which… More >

View

372

Download

185

Open Access

ARTICLE

Batyrkhan Omarov^1,*, Meirzhan Baikuvekov¹, Zeinel Momynkulov², Aray Kassenkhan³, Saltanat Nuralykyzy³, Mereilim Iglikova³
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3745-3761, 2023, DOI:10.32604/cmc.2023.042627
Abstract Heart disease is a leading cause of mortality worldwide. Electrocardiograms (ECG) play a crucial role in diagnosing heart disease. However, interpreting ECG signals necessitates specialized knowledge and training. The development of automated methods for ECG analysis has the potential to enhance the accuracy and efficiency of heart disease diagnosis. This research paper proposes a 3D Convolutional Long Short-Term Memory (Conv-LSTM) model for detecting heart disease using ECG signals. The proposed model combines the advantages of both convolutional neural networks (CNN) and long short-term memory (LSTM) networks. By considering both the spatial and temporal dependencies of ECG, the 3D Conv-LSTM model… More >

View

444

Download

270

Like

2

Open Access

ARTICLE

Sayyid Kamran Hussain¹, Ali Haider Khan^2,*, Malek Alrashidi³, Sajid Iqbal⁴, Qazi Mudassar Ilyas⁴, Kamran Shah⁵
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3763-3781, 2023, DOI:10.32604/cmc.2023.041722
（This article belongs to the Special Issue: Recent Advances in Ophthalmic Diseases Diagnosis using AI)
Abstract As ocular computer-aided diagnostic (CAD) tools become more widely accessible, many researchers are developing deep learning (DL) methods to aid in ocular disease (OHD) diagnosis. Common eye diseases like cataracts (CATR), glaucoma (GLU), and age-related macular degeneration (AMD) are the focus of this study, which uses DL to examine their identification. Data imbalance and outliers are widespread in fundus images, which can make it difficult to apply many DL algorithms to accomplish this analytical assignment. The creation of effcient and reliable DL algorithms is seen to be the key to further enhancing detection performance. Using the analysis of images of… More >

View

691

Download

213

Like

2

Open Access

ARTICLE

Jing Deng¹, Xiaofei Xing¹, Guoqiang Deng^2,*, Ning Hu³, Shen Su³, Le Wang³, Md Zakirul Alam Bhuiyan⁴
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3783-3795, 2023, DOI:10.32604/cmc.2023.034116
Abstract As one of the major threats to the current DeFi (Decentralized Finance) ecosystem, reentrant attack induces data inconsistency of the victim smart contract, enabling attackers to steal on-chain assets from DeFi projects, which could terribly do harm to the confidence of the blockchain investors. However, protecting DeFi projects from the reentrant attack is very difficult, since generating a call loop within the highly automatic DeFi ecosystem could be very practicable. Existing researchers mainly focus on the detection of the reentrant vulnerabilities in the code testing, and no method could promise the non-existent of reentrant vulnerabilities. In this paper, we introduce… More >

View

221

Download

148

Open Access

ARTICLE

Xiaoliang Lei¹, Xiaosheng Yu^2,*, Hao Wu³, Chengdong Wu^2,*, Jingsi Zhang²
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3797-3811, 2023, DOI:10.32604/cmc.2023.042069
Abstract Accurate tumor segmentation from brain tissues in Magnetic Resonance Imaging (MRI) imaging is crucial in the pre-surgical planning of brain tumor malignancy. MRI images’ heterogeneous intensity and fuzzy boundaries make brain tumor segmentation challenging. Furthermore, recent studies have yet to fully employ MRI sequences’ considerable and supplementary information, which offers critical a priori knowledge. This paper proposes a clinical knowledge-based hybrid Swin Transformer multimodal brain tumor segmentation algorithm based on how experts identify malignancies from MRI images. During the encoder phase, a dual backbone network with a Swin Transformer backbone to capture long dependencies from 3D MR images and a… More >

View

253

Download

255

Open Access

ARTICLE

Haitao Hu¹, Hongmei Ma², Shuli Mei^1,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3813-3832, 2023, DOI:10.32604/cmc.2023.041416
Abstract Biological slices are an effective tool for studying the physiological structure and evolution mechanism of biological systems. However, due to the complexity of preparation technology and the presence of many uncontrollable factors during the preparation processing, leads to problems such as difficulty in preparing slice images and breakage of slice images. Therefore, we proposed a biological slice image small-scale corruption inpainting algorithm with interpretability based on multi-layer deep sparse representation, achieving the high-fidelity reconstruction of slice images. We further discussed the relationship between deep convolutional neural networks and sparse representation, ensuring the high-fidelity characteristic of the algorithm first. A novel… More >

View

271

Download

170

Open Access

ARTICLE

Wen-Tsai Sung¹, Hao-Wei Kang¹, Sung-Jung Hsiao^2,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3833-3858, 2023, DOI:10.32604/cmc.2023.040024
Abstract In the speech recognition system, the acoustic model is an important underlying model, and its accuracy directly affects the performance of the entire system. This paper introduces the construction and training process of the acoustic model in detail and studies the Connectionist temporal classification (CTC) algorithm, which plays an important role in the end-to-end framework, established a convolutional neural network (CNN) combined with an acoustic model of Connectionist temporal classification to improve the accuracy of speech recognition. This study uses a sound sensor, ReSpeaker Mic Array v2.0.1, to convert the collected speech signals into text or corresponding speech signals to… More >

View

234

Download

141

Open Access

ARTICLE

Weizheng Wang^1,3, Xiangqi Wang^2,*, Xianmin Pan¹, Xingxing Gong³, Jian Liang³, Pradip Kumar Sharma⁴, Osama Alfarraj⁵, Wael Said⁶
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3859-3876, 2023, DOI:10.32604/cmc.2023.041346
Abstract Image-denoising techniques are widely used to defend against Adversarial Examples (AEs). However, denoising alone cannot completely eliminate adversarial perturbations. The remaining perturbations tend to amplify as they propagate through deeper layers of the network, leading to misclassifications. Moreover, image denoising compromises the classification accuracy of original examples. To address these challenges in AE defense through image denoising, this paper proposes a novel AE detection technique. The proposed technique combines multiple traditional image-denoising algorithms and Convolutional Neural Network (CNN) network structures. The used detector model integrates the classification results of different models as the input to the detector and calculates the… More >

View

302

Download

193

Open Access

ARTICLE

Naveed Khan¹, Jianbiao Zhang^1,*, Ghulam Ali Mallah², Shehzad Ashraf Chaudhry³
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3877-3896, 2023, DOI:10.32604/cmc.2023.032553
（This article belongs to the Special Issue: AI-driven Cybersecurity in Cyber Physical Systems enabled Healthcare, Current Challenges, Requirements and Future research Foresights)
Abstract The mobile cellular network provides internet connectivity for heterogeneous Internet of Things (IoT) devices. The cellular network consists of several towers installed at appropriate locations within a smart city. These cellular towers can be utilized for various tasks, such as e-healthcare systems, smart city surveillance, traffic monitoring, infrastructure surveillance, or sidewalk checking. Security is a primary concern in data broadcasting, particularly authentication, because the strength of a cellular network’s signal is much higher frequency than the associated one, and their frequencies can sometimes be aligned, posing a significant challenge. As a result, that requires attention, and without information authentication, such… More >

View

426

Download

171

Open Access

ARTICLE

Javed Rashid^1,2, Imran Khan¹, Irshad Ahmed Abbasi³, Muhammad Rizwan Saeed⁴, Mubbashar Saddique^5,*, Mohamed Abbas^6,7
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3897-3920, 2023, DOI:10.32604/cmc.2023.040356
（This article belongs to the Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
Abstract Many plant species have a startling degree of morphological similarity, making it difficult to split and categorize them reliably. Unknown plant species can be challenging to classify and segment using deep learning. While using deep learning architectures has helped improve classification accuracy, the resulting models often need to be more flexible and require a large dataset to train. For the sake of taxonomy, this research proposes a hybrid method for categorizing guava, potato, and java plum leaves. Two new approaches are used to form the hybrid model suggested here. The guava, potato, and java plum plant species have been successfully… More >

View

371

Download

217

Open Access

ARTICLE

Fang Yu¹, Wenbin Bi², Ning Cao^3,*, Jun Luo⁴, Diantang An⁵, Liqiang Ding⁴, Russell Higgs⁶
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3921-3931, 2023, DOI:10.32604/cmc.2023.028330
Abstract The purpose of introducing blockchain into electronic archives sharing and utilization is to break the information barrier between electronic archives sharing departments by relying on technologies such as smart contract and asymmetric encryption. Aiming at the problem of dynamic permission management in common access control methods, a new access control method based on smart contract under blockchain is proposed, which improves the intelligence level under blockchain technology. Firstly, the Internet attribute access control model based on smart contract is established. For the dynamic access of heterogeneous devices, the management contract, permission judgment contract and access control contract are designed; Secondly,… More >

View

268

Download

146

Like

1

Open Access

ARTICLE

S M Hasan Mahmud^1,2, Md Mamun Ali³, Mohammad Fahim Shahriar¹, Fahad Ahmed Al-Zahrani⁴, Kawsar Ahmed^5,6,*, Dip Nandi¹, Francis M. Bui⁵
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3933-3948, 2023, DOI:10.32604/cmc.2023.039020
（This article belongs to the Special Issue: IoMT and Smart Healthcare)
Abstract Alzheimer’s disease (AD) is a neurodevelopmental impairment that results in a person’s behavior, thinking, and memory loss. The most common symptoms of AD are losing memory and early aging. In addition to these, there are several serious impacts of AD. However, the impact of AD can be mitigated by early-stage detection though it cannot be cured permanently. Early-stage detection is the most challenging task for controlling and mitigating the impact of AD. The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the issue. To build… More >

View

308

Download

209

Open Access

ARTICLE

Lei Yu¹, Yucong Duan^2,*
CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3949-3965, 2023, DOI:10.32604/cmc.2023.038970
（This article belongs to the Special Issue: AI Powered Human-centric Computing with Cloud and Edge)
Abstract Cloud computing and edge computing brought more software, which also brought a new danger of malicious software attacks. Data synchronization mechanisms of software can further help reverse data modifications. Based on the mechanisms, attackers can cover themselves behind the network and modify data undetected. Related knowledge of software reverse engineering can be organized as rules to accelerate the attacks, when attackers intrude cloud server to access the source or binary codes. Therefore, we proposed a novel method to resist this kind of reverse engineering by breaking these rules. Our method is based on software obfuscations and encryptions to enhance the… More >

View

250

Download

161