Open Access
ARTICLE
Jiakai Li, Jianpeng Hu*, Geng Zhang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.050005
Abstract In the process of constructing domain-specific knowledge graphs, the task of relational triple extraction plays a critical role in transforming unstructured text into structured information. Existing relational triple extraction models face multiple challenges when processing domain-specific data, including insufficient utilization of semantic interaction information between entities and relations, difficulties in handling challenging samples, and the scarcity of domain-specific datasets. To address these issues, our study introduces three innovative components: Relation semantic enhancement, data augmentation, and a voting strategy, all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks. We first propose an innovative attention interaction… More >
Open Access
ARTICLE
Ahmed Alhussen1, Arshiya S. Ansari2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049260
Abstract Traffic in today’s cities is a serious problem that increases travel times, negatively affects the environment, and drains financial resources. This study presents an Artificial Intelligence (AI) augmented Mobile Ad Hoc Networks (MANETs) based real-time prediction paradigm for urban traffic challenges. MANETs are wireless networks that are based on mobile devices and may self-organize. The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts. This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network (CSFPNN) technique to assess real-time data acquired from various sources within… More >
Open Access
REVIEW
Qiong Yang1, Li Ma1,2,*, Shanshan Tu1, Sami Ullah3, Muhammad Waqas4,5, Hisham Alasmary6
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049970
Abstract Border Gateway Protocol (BGP) is a standard inter-domain routing protocol for the Internet that conveys network layer reachability information and establishes routes to different destinations. The BGP protocol exhibits security design defects, such as an unconditional trust mechanism and the default acceptance of BGP route announcements from peers by BGP neighboring nodes, easily triggering prefix hijacking, path forgery, route leakage, and other BGP security threats. Meanwhile, the traditional BGP security mechanism, relying on a public key infrastructure, faces issues like a single point of failure and a single point of trust. The decentralization, anti-tampering, and traceability advantages of blockchain offer… More >
Open Access
REVIEW
Meenakshi Aggarwal1, Vikas Khullar1, Sunita Rani2, Thomas André Prola3,4,5, Shyama Barna Bhattacharjee6, Sarowar Morshed Shawon7, Nitin Goyal8,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049846
Abstract The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation. However, FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios. The paper systematically reviewed the available literature using the PRISMA guiding principle. The study aims to provide a detailed overview of the increasing use of FL in IoT networks, including the architecture and challenges. A systematic review approach is used to collect, categorize and analyze FL-IoT-based articles. A search was performed in… More >
Open Access
ARTICLE
Zakir Hussain Ahmed1,*, Habibollah Haron2, Abdullah Al-Tameem3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049704
Abstract Genetic algorithms (GAs) are very good metaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems. A simple GA begins with a set of solutions represented by a population of chromosomes and then uses the idea of survival of the fittest in the selection process to select some fitter chromosomes. It uses a crossover operator to create better offspring chromosomes and thus, converges the population. Also, it uses a mutation operator to explore the unexplored areas by the crossover operator, and thus, diversifies the GA search space. A combination of crossover and mutation operators makes the GA search strong… More >
Open Access
ARTICLE
Mahmood Ul Haq1, Muhammad Athar Javed Sethi1, Najib Ben Aoun2,3, Ala Saleh Alluhaidan4,*, Sadique Ahmad5,6, Zahid farid7
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049645
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security, authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neural networks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since they do not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networks as a more robust design capable of retaining pose information and spatial correlations to recognize objects more like the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, and so on, which are routed to higher-level capsules via… More >
Open Access
ARTICLE
Di Wang, Yuefei Zhu, Jinlong Fei*, Maohua Guo
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049504
Abstract Website fingerprinting, also known as WF, is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination, even when using the Tor anonymity network. While advanced attacks based on deep neural network (DNN) can perform feature engineering and attain accuracy rates of over 98%, research has demonstrated that DNN is vulnerable to adversarial samples. As a result, many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable success. However, these methods suffer from high bandwidth overhead or require access to the target model, which is unrealistic. This… More >
Open Access
ARTICLE
Yasir Maqsood1, Syed Muhammad Usman1,*, Musaed Alhussein2, Khursheed Aurangzeb2,*, Shehzad Khalid3, Muhammad Zubair4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049410
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Wheat is a critical crop, extensively consumed worldwide, and its production enhancement is essential to meet escalating demand. The presence of diseases like stem rust, leaf rust, yellow rust, and tan spot significantly diminishes wheat yield, making the early and precise identification of these diseases vital for effective disease management. With advancements in deep learning algorithms, researchers have proposed many methods for the automated detection of disease pathogens; however, accurately detecting multiple disease pathogens simultaneously remains a challenge. This challenge arises due to the scarcity of RGB images for multiple diseases, class imbalance in existing public datasets, and the difficulty… More >
Open Access
ARTICLE
Kasidit Kokkhunthod1, Khomdet Phapatanaburi2, Wongsathon Pathonsuwan1, Talit Jumphoo1, Patikorn Anchuen3, Porntip Nimkuntod4, Monthippa Uthansakul1, Peerapong Uthansakul1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049276
Abstract Monitoring blood pressure is a critical aspect of safeguarding an individual’s health, as early detection of abnormal blood pressure levels facilitates timely medical intervention, ultimately leading to a reduction in mortality rates associated with cardiovascular diseases. Consequently, the development of a robust and continuous blood pressure monitoring system holds paramount significance. In the context of this research paper, we introduce an innovative deep learning regression model that harnesses phonocardiogram (PCG) data to achieve precise blood pressure estimation. Our novel approach incorporates a convolutional neural network (CNN)-based regression model, which not only enhances its adaptability to spatial variations but also empowers… More >
Open Access
ARTICLE
Norah Abdullah Al-Johany1,*, Sanaa Abdullah Sharaf1,2, Fathy Elbouraey Eassa1,2, Reem Abdulaziz Alnanih1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047392
Abstract The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memory systems. However, MPI implementations can contain defects that impact the reliability and performance of parallel applications. Detecting and correcting these defects is crucial, yet there is a lack of published models specifically designed for correcting MPI defects. To address this, we propose a model for detecting and correcting MPI defects (DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blocking point-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defects addressed by the DC_MPI model include illegal… More >
Open Access
ARTICLE
Khaldon Azzam Kordi1, Mardeni Roslee1,*, Mohamad Yusoff Alias1, Abdulraqeb Alhammadi2, Athar Waseem3, Anwar Faizd Osman4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.044890
Abstract This study comprehensively examines the current state of deep learning (DL) usage in indoor positioning. It emphasizes the significance and efficiency of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Unlike prior studies focused on single sensor modalities like Wi-Fi or Bluetooth, this research explores the integration of multiple sensor modalities (e.g., Wi-Fi, Bluetooth, Ultra-Wideband, ZigBee) to expand indoor localization methods, particularly in obstructed environments. It addresses the challenge of precise object localization, introducing a novel hybrid DL approach using received signal information (RSI), Received Signal Strength (RSS), and Channel State Information (CSI) data to enhance accuracy and stability.… More >
Open Access
ARTICLE
Sujithra Sankar1,*, S. Sathyalakshmi2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048408
（This article belongs to the Special Issue: Intelligent Detection Methods for AI-Powered Healthcare and Enhanced Medical Insights)
Abstract In the era of advanced machine learning techniques, the development of accurate predictive models for complex medical conditions, such as thyroid cancer, has shown remarkable progress. Accurate predictive models for thyroid cancer enhance early detection, improve resource allocation, and reduce overtreatment. However, the widespread adoption of these models in clinical practice demands predictive performance along with interpretability and transparency. This paper proposes a novel association-rule based feature-integrated machine learning model which shows better classification and prediction accuracy than present state-of-the-art models. Our study also focuses on the application of SHapley Additive exPlanations (SHAP) values as a powerful tool for explaining… More >
Open Access
ARTICLE
Chuyuan Wei*, Jinzhe Li, Zhiyuan Wang, Shanshan Wan, Maozu Guo
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047811
Abstract Deep neural network-based relational extraction research has made significant progress in recent years, and it provides data support for many natural language processing downstream tasks such as building knowledge graph, sentiment analysis and question-answering systems. However, previous studies ignored much unused structural information in sentences that could enhance the performance of the relation extraction task. Moreover, most existing dependency-based models utilize self-attention to distinguish the importance of context, which hardly deals with multiple-structure information. To efficiently leverage multiple structure information, this paper proposes a dynamic structure attention mechanism model based on textual structure information, which deeply integrates word embedding, named… More >
Open Access
ARTICLE
Moshe Dayan Sirapangi1, S. Gopikrishnan1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047438
Abstract Medical Internet of Things (IoT) devices are becoming more and more common in healthcare. This has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of multimodal data to find potential health risks early and help individuals in a personalized way. Existing methods, while useful, have limitations in predictive accuracy, delay, personalization, and user interpretability, requiring a more comprehensive and efficient approach to harness modern medical IoT devices. MAIPFE is a multimodal approach integrating pre-emptive analysis, personalized feature selection, and explainable AI for real-time health monitoring and disease detection. By… More >
Open Access
ARTICLE
Shuyi Li, Hongchao Hu*, Xiaohan Yang, Guozhen Cheng, Wenyan Liu, Wei Guo
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047275
Abstract Adversarial distillation (AD) has emerged as a potential solution to tackle the challenging optimization problem of loss with hard labels in adversarial training. However, fixed sample-agnostic and student-egocentric attack strategies are unsuitable for distillation. Additionally, the reliability of guidance from static teachers diminishes as target models become more robust. This paper proposes an AD method called Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation (LDAS&ET-AD). Firstly, a learnable distillation attack strategies generating mechanism is developed to automatically generate sample-dependent attack strategies tailored for distillation. A strategy model is introduced to produce attack strategies that enable adversarial examples (AEs) to… More >
Open Access
ARTICLE
Ruchun Jia1, Jianwei Zhang1,*, Lin Yi1, Yunxiang Han1, Feike Yang2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047543
（This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
Abstract In order to enhance the accuracy of Air Traffic Control (ATC) cybersecurity attack detection, in this paper, a
new clustering detection method is designed for air traffic control network security attacks. The feature set for
ATC cybersecurity attacks is constructed by setting the feature states, adding recursive features, and determining
the feature criticality. The expected information gain and entropy of the feature data are computed to determine
the information gain of the feature data and reduce the interference of similar feature data. An autoencoder is
introduced into the AI (artificial intelligence) algorithm to encode and decode the characteristics of ATC… More >
Open Access
ARTICLE
Halil Ibrahim Okur1,2,*, Kadir Tohma1, Ahmet Sertbas2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.050585
Abstract Text classification, by automatically categorizing texts, is one of the foundational elements of natural language processing applications. This study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata (Wikipedia database) database and BERT-based pre-trained Named Entity Recognition (NER) models. Focusing on a significant challenge in the field of natural language processing (NLP), the research evaluates the potential of using entity and relational information to extract deeper meaning from texts. The adopted methodology encompasses a comprehensive approach that includes text preprocessing, entity detection, and the integration of relational information. Experiments conducted on… More >
Open Access
ARTICLE
Hao Sun*, Xiubo Chen, Kaiguo Yuan
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049159
（This article belongs to the Special Issue: Security, Privacy, and Robustness for Trustworthy AI Systems)
Abstract Federated learning ensures data privacy and security by sharing models among multiple computing nodes instead of plaintext data. However, there is still a potential risk of privacy leakage, for example, attackers can obtain the original data through model inference attacks. Therefore, safeguarding the privacy of model parameters becomes crucial. One proposed solution involves incorporating homomorphic encryption algorithms into the federated learning process. However, the existing federated learning privacy protection scheme based on homomorphic encryption will greatly reduce the efficiency and robustness when there are performance differences between parties or abnormal nodes. To solve the above problems, this paper proposes a… More >
Open Access
ARTICLE
Danyang Zheng, Jinchen Xv, Feng Yue, Qiming Du, Zhiheng Wang, Zheng Shan*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048956
Abstract As cloud quantum computing gains broader acceptance, a growing quantity of researchers are directing their focus towards this domain. Nevertheless, the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity, which in turn hampers users from achieving optimal satisfaction. Therefore, cloud quantum computing service providers require a unified analysis and scheduling framework for their quantum resources and user jobs to meet the ever-growing usage demands. This paper introduces a new multi-programming scheduling framework for quantum computing in a cloud environment. The framework addresses the issue of limited quantum computing resources in cloud environments and ensures… More >
Open Access
ARTICLE
Shuai Zhou1, Dazhi Wang1,*, Yongliang Ni2, Keling Song2, Yanming Li2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048859
（This article belongs to the Special Issue: Intelligent Computing Techniques and Their Real Life Applications)
Abstract In the process of identifying parameters for a permanent magnet synchronous motor, the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration, resulting in low parameter accuracy. This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function. This approach addresses the topic of particle swarm optimization in parameter identification from two perspectives. Firstly, the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness function, making the extreme point… More >
Open Access
ARTICLE
Husam Ahmad Al Hamad*, Mohammad Shehab*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048527
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract Recognizing handwritten characters remains a critical and formidable challenge within the realm of computer vision. Although considerable strides have been made in enhancing English handwritten character recognition through various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexity arises from the diverse array of writing styles among individuals, coupled with the various shapes that a single character can take when positioned differently within document images, rendering the task more perplexing. In this study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locate the local minima of the vertical and diagonal word image densities… More >
Open Access
ARTICLE
Dmitry Gura1,2, Bo Dong3,*, Duaa Mehiar4, Nidal Al Said5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048238
Abstract The motivation for this study is that the quality of deep fakes is constantly improving, which leads to the need to develop new methods for their detection. The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection, which is then used as input to the CNN. The customized Convolutional Neural Network method is the date augmented-based CNN model to generate ‘fake data’ or ‘fake images’. This study was carried out using Python and its libraries. We used 242 films from the dataset gathered by the Deep Fake Detection Challenge, of which 199… More >
Open Access
ARTICLE
Chunhua Wang1,2, Wenqian Shang1,2,*, Tong Yi3,*, Haibin Zhu4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048135
（This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
Abstract The advent of self-attention mechanisms within Transformer models has significantly propelled the advancement of deep learning algorithms, yielding outstanding achievements across diverse domains. Nonetheless, self-attention mechanisms falter when applied to datasets with intricate semantic content and extensive dependency structures. In response, this paper introduces a Diffusion Sampling and Label-Driven Co-attention Neural Network (DSLD), which adopts a diffusion sampling method to capture more comprehensive semantic information of the data. Additionally, the model leverages the joint correlation information of labels and data to introduce the computation of text representation, correcting semantic representation biases in the data, and increasing the accuracy of semantic… More >
Open Access
ARTICLE
Jun Li1,2, Minqing Zhang1,2,*, Ke Niu1, Yingnan Zhang1, Xiaoyuan Yang1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048095
（This article belongs to the Special Issue: Multimedia Encryption and Information Security)
Abstract Among steganalysis techniques, detection against MV (motion vector) domain-based video steganography in the HEVC (High Efficiency Video Coding) standard remains a challenging issue. For the purpose of improving the detection performance, this paper proposes a steganalysis method that can perfectly detect MV-based steganography in HEVC. Firstly, we define the local optimality of MVP (Motion Vector Prediction) based on the technology of AMVP (Advanced Motion Vector Prediction). Secondly, we analyze that in HEVC video, message embedding either using MVP index or MVD (Motion Vector Difference) may destroy the above optimality of MVP. And then, we define the optimal rate of MVP… More >
Open Access
ARTICLE
Maher Alharby*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.050425
Abstract Cloud computing has emerged as a viable alternative to traditional computing infrastructures, offering various
benefits. However, the adoption of cloud storage poses significant risks to data secrecy and integrity. This article
presents an effective mechanism to preserve the secrecy and integrity of data stored on the public cloud by
leveraging blockchain technology, smart contracts, and cryptographic primitives. The proposed approach utilizes
a Solidity-based smart contract as an auditor for maintaining and verifying the integrity of outsourced data. To
preserve data secrecy, symmetric encryption systems are employed to encrypt user data before outsourcing it. An
extensive performance analysis is conducted to… More >
Open Access
ARTICLE
Farah Mohammad1,*, Saad Al-Ahmadi2, Jalal Al-Muhtadi1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.042873
（This article belongs to the Special Issue: Trends in Machine Learning and Internet of Things for Industrial Applications)
Abstract Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity
users. It hinders the economic growth of utility companies, poses electrical risks, and impacts the high energy
costs borne by consumers. The development of smart grids is crucial for the identification of power theft since
these systems create enormous amounts of data, including information on client consumption, which may be
used to identify electricity theft using machine learning and deep learning techniques. Moreover, there also exist
different solutions such as hardware-based solutions to detect electricity theft that may require human resources
and… More >
Open Access
ARTICLE
Xiaoqin Ma1,2, Jun Wang1, Wenchang Yu1, Qinli Zhang1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049147
Abstract The presence of numerous uncertainties in hybrid decision information systems (HDISs) renders attribute reduction a formidable task. Currently available attribute reduction algorithms, including those based on Pawlak attribute importance, Skowron discernibility matrix, and information entropy, struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values, and attributes with fuzzy boundaries and abnormal values. In order to address the aforementioned issues, this paper delves into the study of attribute reduction within HDISs. First of all, a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring… More >
Open Access
ARTICLE
Chuandong Qin1,2, Yu Cao1,*, Liqun Meng1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049228
（This article belongs to the Special Issue: Advanced Machine Learning and Optimization for Practical Solutions in Complex Real-world Systems)
Abstract Brain tumors come in various types, each with distinct characteristics and treatment approaches, making manual detection a time-consuming and potentially ambiguous process. Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes. Machine learning models have become key players in automating brain tumor detection. Gradient descent methods are the mainstream algorithms for solving machine learning models. In this paper, we propose a novel distributed proximal stochastic gradient descent approach to solve the L1-Smooth Support Vector Machine (SVM) classifier for brain tumor detection. Firstly, the smooth hinge loss is introduced to be used… More >
Open Access
ARTICLE
Amal Hkiri1,*, Hamzah Faraj2, Omar Ben Bahri2, Mouna Karmani1, Sami Alqurashi2, Mohsen Machhout1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049207
（This article belongs to the Special Issue: Advanced Machine Learning and Optimization for Practical Solutions in Complex Real-world Systems)
Abstract The Internet of Things (IoT) has witnessed a significant surge in adoption, particularly through the utilization of Wireless Sensor Networks (WSNs), which comprise small internet-connected devices. These deployments span various environments and offer a multitude of benefits. However, the widespread use of battery-powered devices introduces challenges due to their limited hardware resources and communication capabilities. In response to this, the Internet Engineering Task Force (IETF) has developed the IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) to address the unique requirements of such networks. Recognizing the critical role of RPL in maintaining high performance, this paper proposes a novel… More >
Open Access
ARTICLE
Zhijun Guo1, Yun Sun2,*, Ying Wang1, Chaoqi Fu3, Jilong Zhong4,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048112
（This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems)
Abstract Due to the time-varying topology and possible disturbances in a conflict environment, it is still challenging to maintain the mission performance of flying Ad hoc networks (FANET), which limits the application of Unmanned Aerial Vehicle (UAV) swarms in harsh environments. This paper proposes an intelligent framework to quickly recover the cooperative coverage mission by aggregating the historical spatio-temporal network with the attention mechanism. The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model. A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction… More >
Open Access
ARTICLE
Zhigao Zeng1,2, Aoting Tang1,2, Shengqiu Yi1,2, Xinpan Yuan1,2, Yanhui Zhu1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047989
（This article belongs to the Special Issue: Recent Advances in Ensemble Framework of Meta-heuristics and Machine Learning: Methods and Applications)
Abstract Radiomics is a non-invasive method for extracting quantitative and higher-dimensional features from medical images for diagnosis. It has received great attention due to its huge application prospects in recent years. We can know that the number of features selected by the existing radiomics feature selection methods is basically about ten. In this paper, a heuristic feature selection method based on frequency iteration and multiple supervised training mode is proposed. Based on the combination between features, it decomposes all features layer by layer to select the optimal features for each layer, then fuses the optimal features to form a local optimal… More >
Open Access
ARTICLE
K. Umapathi1,*, S. Shobana1, Anand Nayyar2, Judith Justin3, R. Vanithamani3, Miguel Villagómez Galindo4, Mushtaq Ahmad Ansari5, Hitesh Panchal6,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047961
Abstract Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effective treatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breast cancer from ultrasound images. The primary challenge is accurately distinguishing between malignant and benign tumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentation and classification. The main objective of the research paper is to develop an advanced methodology for breast ultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, and machine learning-based classification. A unique approach is introduced that combines Enhanced… More >
Open Access
ARTICLE
Han Zhou1,2, Hongtao Xu1,2, Xinyue Chang1,2, Wei Zhang1,2, Heng Dong1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047754
Abstract Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes. However, these methods often lack constraint information and overlook semantic consistency, limiting their performance. To address these issues, we present a novel approach for medical image registration called the Dual-VoxelMorph, featuring a dual-channel cross-constraint network. This innovative network utilizes both intensity and segmentation images, which share identical semantic information and feature representations. Two encoder-decoder structures calculate deformation fields for intensity and segmentation images, as generated by the dual-channel cross-constraint network. This design facilitates bidirectional communication between grayscale and segmentation information, enabling the… More >
Open Access
ARTICLE
J. Sheril Angel1, A. Diana Andrushia1,*, T. Mary Neebha1, Oussama Accouche2, Louai Saker2, N. Anand3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047326
Abstract Facial emotion recognition (FER) has become a focal point of research due to its widespread applications, ranging from human-computer interaction to affective computing. While traditional FER techniques have relied on handcrafted features and classification models trained on image or video datasets, recent strides in artificial intelligence and deep learning (DL) have ushered in more sophisticated approaches. The research aims to develop a FER system using a Faster Region Convolutional Neural Network (FRCNN) and design a specialized FRCNN architecture tailored for facial emotion recognition, leveraging its ability to capture spatial hierarchies within localized regions of facial features. The proposed work enhances… More >
Open Access
ARTICLE
Yaming Kang1,*, Peishun Ye1, Yuxiu Bai1, Shi Qiu2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049360
（This article belongs to the Special Issue: Advances and Applications in Signal, Image and Video Processing)
Abstract Hyperspectral imagery encompasses spectral and spatial dimensions, reflecting the material properties of objects. Its application proves crucial in search and rescue, concealed target identification, and crop growth analysis. Clustering is an important method of hyperspectral analysis. The vast data volume of hyperspectral imagery, coupled with redundant information, poses significant challenges in swiftly and accurately extracting features for subsequent analysis. The current hyperspectral feature clustering methods, which are mostly studied from space or spectrum, do not have strong interpretability, resulting in poor comprehensibility of the algorithm. So, this research introduces a feature clustering algorithm for hyperspectral imagery from an interpretability perspective.… More >
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