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 this 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 this 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 this 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 this 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 this 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 >
Open Access
ARTICLE
Aodi Liu, Na Wang*, Xuehui Du, Dibin Shan, Xiangyu Wu, Wenjuan Wang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049011
（This article belongs to this Special Issue: Cybersecurity for Cyber-attacks in Critical Applications in Industry)
Abstract Big data resources are characterized by large scale, wide sources, and strong dynamics. Existing access control
mechanisms based on manual policy formulation by security experts suffer from drawbacks such as low policy
management efficiency and difficulty in accurately describing the access control policy. To overcome these
problems, this paper proposes a big data access control mechanism based on a two-layer permission decision
structure. This mechanism extends the attribute-based access control (ABAC) model. Business attributes are
introduced in the ABAC model as business constraints between entities. The proposed mechanism implements
a two-layer permission decision structure composed of the inherent attributes of… More >
Open Access
ARTICLE
Dingping Chen1, Zhiheng Zhu2, Jinyang Fu1,3, Jilin He1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049048
Abstract The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safety and performing routine tunnel maintenance. The automatic and accurate detection of cracks on the surface of road tunnels is the key to improving the maintenance efficiency of road tunnels. Machine vision technology combined with a deep neural network model is an effective means to realize the localization and identification of crack defects on the surface of road tunnels. We propose a complete set of automatic inspection methods for identifying cracks on the walls of road tunnels as a… More >
Open Access
ARTICLE
Yakui Liu1,2,3,*, Xing Jiang1, Ruikang Xu1, Yihao Cui1, Chenhui Yu1, Jingqi Yang1, Jishuai Zhou1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048864
Abstract The rapid pace of urban development has resulted in the widespread presence of construction equipment and increasingly complex conditions in transmission corridors. These conditions pose a serious threat to the safe operation of the power grid. Machine vision technology, particularly object recognition technology, has been widely employed to identify foreign objects in transmission line images. Despite its wide application, the technique faces limitations due to the complex environmental background and other auxiliary factors. To address these challenges, this study introduces an improved YOLOv8n. The traditional stepwise convolution and pooling layers are replaced with a spatial-depth convolution (SPD-Conv) module, aiming to… More >
Open Access
ARTICLE
Zhanfeng Wang1, Lisha Yao2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048304
Abstract Convolutional neural networks struggle to accurately handle changes in angles and twists in the direction of images, which affects their ability to recognize patterns based on internal feature levels. In contrast, CapsNet overcomes these limitations by vectorizing information through increased directionality and magnitude, ensuring that spatial information is not overlooked. Therefore, this study proposes a novel expression recognition technique called CAPSULE-VGG, which combines the strengths of CapsNet and convolutional neural networks. By refining and integrating features extracted by a convolutional neural network before introducing them into CapsNet, our model enhances facial recognition capabilities. Compared to traditional neural network models, our… More >
Open Access
ARTICLE
Maria Gul1, Imran Ali Khan1, Gohar Zaman2, Atta Rahman3,*, Jamaluddin Mir2, Sardar Asad Ali Biabani4,5, May Issa Aldossary6, Mustafa Youldash7, Ashraf Saadeldeen8, Maqsood Mahmud9, Asiya Abdus Salam6, Dania Alkhulaifi3, Abdullah AlTurkey3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048289
Abstract Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, many researchers proposed game theoretic models to avoid and remove obstacles for crowd evacuation. Game theoretical models aim to study and analyze the strategic behaviors of individuals within a crowd and their interactions during the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. These models consider a group of individuals as homogeneous objects with the same goals, involve complex mathematical formulation, and cannot model real-world scenarios such as panic, environmental information, crowds that move dynamically, etc. The proposed work presents… More >
Open Access
ARTICLE
Wael M. S. Yafooz*, Abdullah Alsaeedi
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048270
（This article belongs to this Special Issue: Optimization for Artificial Intelligence Application)
Abstract In the last decade, technical advancements and faster Internet speeds have also led to an increasing number of mobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobile apps. The use of these apps eases our daily lives, and all customers who need any type of service can access it easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digital services to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services, particularly during two large occasions, Umrah and Hajj. However, pilgrims… More >
Open Access
ARTICLE
Zezheng Meng, Zefeng Cai, Jie Feng*, Hanjie Ma, Haixiang Zhang, Shaohua Li
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048002
（This article belongs to this Special Issue: Metaheuristics, Soft Computing, and Machine Learning in Image Processing and Computer Vision)
Abstract Optical braille recognition methods typically employ existing target detection models or segmentation models for the direct detection and recognition of braille characters in original braille images. However, these methods need improvement in accuracy and generalizability, especially in densely dotted braille image environments. This paper presents a two-stage braille recognition framework. The first stage is a braille dot detection algorithm based on Gaussian diffusion, targeting Gaussian heatmaps generated by the convex dots in braille images. This is applied to the detection of convex dots in double-sided braille, achieving high accuracy in determining the central coordinates of the braille convex dots. The… More >
Open Access
ARTICLE
Fayez Alfayez*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.046443
（This article belongs to this Special Issue: Deep Learning in Medical Imaging-Disease Segmentation and Classification)
Abstract This paper emphasizes a faster digital processing time while presenting an accurate method for identifying spine fractures in X-ray pictures. The study focuses on efficiency by utilizing many methods that include picture segmentation, feature reduction, and image classification. Two important elements are investigated to reduce the classification time: Using feature reduction software and leveraging the capabilities of sophisticated digital processing hardware. The researchers use different algorithms for picture enhancement, including the Wiener and Kalman filters, and they look into two background correction techniques. The article presents a technique for extracting textural features and evaluates three picture segmentation algorithms and three… More >
Open Access
ARTICLE
Ankita Gangwar1, Vijaypal Singh Dhaka1, Geeta Rani2,*, Shrey Khandelwal1, Ester Zumpano3,4, Eugenio Vocaturo3,4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048119
Abstract A consumption of 46.9 million tons of processed tomatoes was reported in 2022 which is merely 20% of the
total consumption. An increase of 3.3% in consumption is predicted from 2024 to 2032. Tomatoes are also rich
in iron, potassium, antioxidant lycopene, vitamins A, C and K which are important for preventing cancer, and
maintaining blood pressure and glucose levels. Thus, tomatoes are globally important due to their widespread usage
and nutritional value. To face the high demand for tomatoes, it is mandatory to investigate the causes of crop loss
and minimize them. Diseases are one of the major causes… More >
Open Access
ARTICLE
Qiuyang Wang, Zhen Xu*, Lei Yang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047915
Abstract Wireless sensor networks (WSNs) have the trouble of limited battery power, and wireless charging provides a
promising solution to this problem, which is not easily affected by the external environment. In this paper, we study
the recharging of sensors in wireless rechargeable sensor networks (WRSNs) by scheduling two mobile chargers
(MCs) to collaboratively charge sensors. We first formulate a novel sensor charging scheduling problem with the
objective of maximizing the number of surviving sensors, and further propose a collaborative charging scheduling
algorithm (CCSA) for WRSNs. In the scheme, the sensors are divided into important sensors and ordinary sensors.
Two MCs… More >
Open Access
ARTICLE
Yuancong Chai, Yuefei Zhu*, Wei Lin, Ding Li
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049904
Abstract With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has become a core key technology in network supervision. In recent years, many different solutions have emerged in this field. Most methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-level features of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites, temporal features can exhibit significant variations due to changes in communication links and transmission quality. Additionally, partial spatial features can change due to reasons like data reordering and retransmission. Faced with these challenges, identifying… More >
Open Access
ARTICLE
Yuyuan Li1,2, Lang Li1,2,*, Yu Ou1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049882
Abstract The Inner Product Masking (IPM) scheme has been shown to provide higher theoretical security guarantees than the Boolean Masking (BM). This scheme aims to increase the algebraic complexity of the coding to achieve a higher level of security. Some previous work unfolds when certain (adversarial and implementation) conditions are met, and we seek to complement these investigations by understanding what happens when these conditions deviate from their expected behaviour. In this paper, we investigate the security characteristics of IPM under different conditions. In adversarial condition, the security properties of first-order IPMs obtained through parametric characterization are preserved in the face… More >
Open Access
ARTICLE
Lin Wang1, Juan Zhao2, Hu Song3, Xiaolong Xu4,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048688
（This article belongs to this Special Issue: Deep Learning based Object Detection and Tracking in Videos)
Abstract In smart classrooms, conducting multi-face expression recognition based on existing hardware devices to assess students’ group emotions can provide educators with more comprehensive and intuitive classroom effect analysis, thereby continuously promoting the improvement of teaching quality. However, most existing multi-face expression recognition methods adopt a multi-stage approach, with an overall complex process, poor real-time performance, and insufficient generalization ability. In addition, the existing facial expression datasets are mostly single face images, which are of low quality and lack specificity, also restricting the development of this research. This paper aims to propose an end-to-end high-performance multi-face expression recognition algorithm model suitable… More >
Open Access
ARTICLE
Jun Wang1,2, Linxi Zhang1,2, Hao Zhang1, Funan Peng1,*, Mohammed A. El-Meligy3, Mohamed Sharaf3, Qiang Fu1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048495
（This article belongs to this Special Issue: Recent Advances in Ensemble Framework of Meta-heuristics and Machine Learning: Methods and Applications)
Abstract The existing algorithms for solving multi-objective optimization problems fall into three main categories: Decomposition-based, dominance-based, and indicator-based. Traditional multi-objective optimization problems mainly focus on objectives, treating decision variables as a total variable to solve the problem without considering the critical role of decision variables in objective optimization. As seen, a variety of decision variable grouping algorithms have been proposed. However, these algorithms are relatively broad for the changes of most decision variables in the evolution process and are time-consuming in the process of finding the Pareto frontier. To solve these problems, a multi-objective optimization algorithm for grouping decision variables based… More >
Open Access
ARTICLE
Tianshu Wei1, Jinjie Huang1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048098
Abstract Zero-shot learning enables the recognition of new class samples by migrating models learned from semantic features and existing sample features to things that have never been seen before. The problems of consistency of different types of features and domain shift problems are two of the critical issues in zero-shot learning. To address both of these issues, this paper proposes a new modeling structure. The traditional approach mapped semantic features and visual features into the same feature space; based on this, a dual discriminator approach is used in the proposed model. This dual discriminator approach can further enhance the consistency between… More >
Open Access
ARTICLE
Sapiah Sakri1, Shakila Basheer1, Zuhaira Muhammad Zain1, Nurul Halimatul Asmak Ismail2,*, Dua’ Abdellatef Nassar1, Manal Abdullah Alohali1, Mais Ayman Alharaki1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048051
Abstract Background: Sepsis, a potentially fatal inflammatory disease triggered by infection, carries significant health implications worldwide. Timely detection is crucial as sepsis can rapidly escalate if left undetected. Recent advancements in deep learning (DL) offer powerful tools to address this challenge. Aim: Thus, this study proposed a hybrid CNNBDLSTM, a combination of a convolutional neural network (CNN) with a bi-directional long short-term memory (BDLSTM) model to predict sepsis onset. Implementing the proposed model provides a robust framework that capitalizes on the complementary strengths of both architectures, resulting in more accurate and timelier predictions. Method: The sepsis prediction method proposed here utilizes… More >
Open Access
ARTICLE
Emmanuel Oluwatobi Asani1,2,3, Aderemi Elisha Okeyinka4, Sunday Adeola Ajagbe5,6, Ayodele Ariyo Adebiyi1, Roseline Oluwaseun Ogundokun1,2,7,*, Temitope Samson Adekunle8, Pragasen Mudali5, Matthew Olusegun Adigun5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047898
Abstract The study presents the Half Max Insertion Heuristic (HMIH) as a novel approach to solving the Travelling Salesman Problem (TSP). The goal is to outperform existing techniques such as the Farthest Insertion Heuristic (FIH) and Nearest Neighbour Heuristic (NNH). The paper discusses the limitations of current construction tour heuristics, focusing particularly on the significant margin of error in FIH. It then proposes HMIH as an alternative that minimizes the increase in tour distance and includes more nodes. HMIH improves tour quality by starting with an initial tour consisting of a ‘minimum’ polygon and iteratively adding nodes using our novel Half… More >
Open Access
ARTICLE
Jinlin Xu1,2, Wansu Pan1,*, Longle Cheng1,2, Haibo Tan1,2, Munan Yuan1,*, Xiaofeng Li1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049622
Abstract The existing multipath routing in Software Defined Network (SDN) is relatively blind and inefficient, and there is a
lack of cooperation between the terminal and network sides, making it difficult to achieve dynamic adaptation
of service requirements and network resources. To address these issues, we propose a multi-constraint path
optimization scheme based on information fusion in SDN. The proposed scheme collects network topology and
network state information on the network side and computes disjoint paths between end hosts. It uses the Fuzzy
Analytic Hierarchy Process (FAHP) to calculate the weight coefficients of multiple constrained parameters and
constructs a composite quality… More >
Open Access
ARTICLE
Qixiang Tong, Zhipeng Zhu, Min Zhang, Kerui Cao, Haihua Xing*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049187
（This article belongs to this Special Issue: Advances and Applications in Signal, Image and Video Processing)
Abstract High-resolution remote sensing image segmentation is a challenging task. In urban remote sensing, the presence
of occlusions and shadows often results in blurred or invisible object boundaries, thereby increasing the difficulty
of segmentation. In this paper, an improved network with a cross-region self-attention mechanism for multi-scale
features based on DeepLabv3+is designed to address the difficulties of small object segmentation and blurred target
edge segmentation. First, we use CrossFormer as the backbone feature extraction network to achieve the interaction
between large- and small-scale features, and establish self-attention associations between features at both large and
small scales to capture global contextual feature… More >
Open Access
ARTICLE
Khaled Soliman1,*, Mohamed Sobh2, Ayman M. Bahaa-Eldin2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048883
Abstract The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leads
to wide losses for various organizations. These dangers have proven that signature-based approaches are insufficient
to prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious Executable
Detection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE)
files in hosts using Windows operating systems through collecting PE headers and applying machine learning
mechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031
benign files and 179,071 malware samples from diverse sources to ensure the efficiency… More >
Open Access
ARTICLE
Xiaohui Yang, Peiyin Zhao*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047666
Abstract Electronic medical records (EMR) facilitate the sharing of medical data, but existing sharing schemes suffer from privacy leakage and inefficiency. This article proposes a lightweight, searchable, and controllable EMR sharing scheme, which employs a large attribute domain and a linear secret sharing structure (LSSS), the computational overhead of encryption and decryption reaches a lightweight constant level, and supports keyword search and policy hiding, which improves the high efficiency of medical data sharing. The dynamic accumulator technology is utilized to enable data owners to flexibly authorize or revoke the access rights of data visitors to the data to achieve controllability of… More >
Open Access
ARTICLE
Syeda Shamaila Zareen1,*, Guangmin Sun1,*, Mahwish Kundi2, Syed Furqan Qadri3, Salman Qadri4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047418
（This article belongs to this Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Skin cancer diagnosis is difficult due to lesion presentation variability. Conventional methods struggle to manually extract features and capture lesions spatial and temporal variations. This study introduces a deep learning-based Convolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which used as the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extraction and temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesion photos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-Term Memory (LSTM) for temporal dependencies, the model achieves a high average recognition… More >
Open Access
ARTICLE
Le Zou1, Linsong Hu1, Yifan Wang1, Zhize Wu2, Xiaofeng Wang1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048889
Abstract Depth estimation is an important task in computer vision. Collecting data at scale for monocular depth estimation is challenging, as this task requires simultaneously capturing RGB images and depth information. Therefore, data augmentation is crucial for this task. Existing data augmentation methods often employ pixel-wise transformations, which may inadvertently disrupt edge features. In this paper, we propose a data augmentation method for monocular depth estimation, which we refer to as the Perpendicular-Cutdepth method. This method involves cutting real-world depth maps along perpendicular directions and pasting them onto input images, thereby diversifying the data without compromising edge features. To validate the… More >
Open Access
ARTICLE
Meiyan Zhang1, Boqi Zhao1, Jipu Li2, Qisong Wang1,*, Dan Liu1, Jinwei Sun1, Jingxiao Liao1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048643
（This article belongs to this Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems)
Abstract Driving fatigue is a physiological phenomenon that often occurs during driving. After the driver enters a fatigued state, the attention is lax, the response is slow, and the ability to deal with emergencies is significantly reduced, which can easily cause traffic accidents. Therefore, studying driver fatigue detection methods is significant in ensuring safe driving. However, the fatigue state of actual drivers is easily interfered with by the external environment (glasses and light), which leads to many problems, such as weak reliability of fatigue driving detection. Moreover, fatigue is a slow process, first manifested in physiological signals and then reflected in… More >
Open Access
ARTICLE
Kazim Jawad1, Muhammad Ahmad2, Majdah Alvi3, Muhammad Bux Alvi3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047466
（This article belongs to this Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
Abstract Sentiment analysis, the meta field of Natural Language Processing (NLP), attempts to analyze and identify the sentiments in the opinionated text data. People share their judgments, reactions, and feedback on the internet using various languages. Urdu is one of them, and it is frequently used worldwide. Urdu-speaking people prefer to communicate on social media in Roman Urdu (RU), an English scripting style with the Urdu language dialect. Researchers have developed versatile lexical resources for features-rich comprehensive languages, but limited linguistic resources are available to facilitate the sentiment classification of Roman Urdu. This effort encompasses extracting subjective expressions in Roman Urdu… More >
Open Access
ARTICLE
Yuhang Wang1,*, Zhiqin He1, Qinmu Wu1, Tingsheng Lu2, Yu Tang1, Maoyun Zhu1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047379
Abstract This paper addresses the common orthopedic trauma of spinal vertebral fractures and aims to enhance doctors’ diagnostic efficiency. Therefore, a deep-learning-based automated diagnostic system with multi-label segmentation is proposed to recognize the condition of vertebral fractures. The whole spine Computed Tomography (CT) image is segmented into the fracture, normal, and background using U-Net, and the fracture degree of each vertebra is evaluated (Genant semi-qualitative evaluation). The main work of this paper includes: First, based on the spatial configuration network (SCN) structure, U-Net is used instead of the SCN feature extraction network. The attention mechanism and the residual connection between the… More >
Open Access
REVIEW
Shafi Ullah1, Sibghat Ullah Bazai1,*, Mohammad Imran2, Qazi Mudassar Ilyas3,*, Abid Mehmood4, Muhammad Asim Saleem5, Muhmmad Aasim Rafique3, Arsalan Haider6, Ilyas Khan7, Sajid Iqbal3, Yonis Gulzar4, Kauser Hameed3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048796
（This article belongs to this Special Issue: Advances and Applications in Signal, Image and Video Processing)
Abstract Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)-based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices (MTCDs) regularly share extensive data without human intervention while making all types of decisions. These decisions may involve controlling sensitive ventilation systems maintaining uniform temperature, live heartbeat monitoring, and several different alert systems. Many of these devices simultaneously share data to form an automated system. The data shared between machine-type communication devices (MTCDs) is prone to risk due to limited computational power, internal memory, and energy capacity. Therefore, securing the data and devices becomes challenging… More >
Open Access
ARTICLE
Radwan Abu-Gdairi1, R. Mareay2,*, M. Badr3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048647
（This article belongs to this Special Issue: Emerging Trends in Fuzzy Logic)
Abstract Recently, much interest has been given to multi-granulation rough sets (MGRS), and various types of MGRS models have been developed from different viewpoints. In this paper, we introduce two techniques for the classification of MGRS. Firstly, we generate multi-topologies from multi-relations defined in the universe. Hence, a novel approximation space is established by leveraging the underlying topological structure. The characteristics of the newly proposed approximation space are discussed. We introduce an algorithm for the reduction of multi-relations. Secondly, a new approach for the classification of MGRS based on neighborhood concepts is introduced. Finally, a real-life application from medical records is… More >
Open Access
ARTICLE
Wuyan Liang1, Xiaolong Xu2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047861
（This article belongs to this Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract Recognition of human gesture actions is a challenging issue due to the complex patterns in both visual and skeletal features. Existing gesture action recognition (GAR) methods typically analyze visual and skeletal data, failing to meet the demands of various scenarios. Furthermore, multi-modal approaches lack the versatility to efficiently process both uniform and disparate input patterns. Thus, in this paper, an attention-enhanced pseudo-3D residual model is proposed to address the GAR problem, called HgaNets. This model comprises two independent components designed for modeling visual RGB (red, green and blue) images and 3D skeletal heatmaps, respectively. More specifically, each component consists of… More >
Open Access
ARTICLE
Shabana Ramzan1, Yazeed Yasin Ghadi2, Hanan Aljuaid3, Aqsa Mahmood1,*, Basharat Ali4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047603
（This article belongs to this Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
Abstract Traditional farming procedures are time-consuming and expensive as based on manual labor. Farmers have no proper knowledge to select which crop is suitable to grow according to the environmental factors and soil characteristics. This is the main reason for the low yield of crops and the economic crisis in the agricultural sector of the different countries. The use of modern technologies such as the Internet of Things (IoT), machine learning, and ensemble learning can facilitate farmers to observe different factors such as soil electrical conductivity (EC), and environmental factors like temperature to improve crop yield. These parameters play a vital… More >
Open Access
ARTICLE
Yutao Hu1, Yuntao Zhao1,*, Yongxin Feng2, Xiangyu Ma1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.042216
Abstract In the face of the increasingly severe Botnet problem on the Internet, how to effectively detect Botnet traffic in real-time has become a critical problem. Although the existing deep Q network (DQN) algorithm in Deep reinforcement learning can solve the problem of real-time updating, its prediction results are always higher than the actual results. In Botnet traffic detection, although it performs well in the training set, the accuracy rate of predicting traffic is as high as%; however, in the test set, its accuracy has declined, and it is impossible to adjust its prediction strategy on time based on new data… More >
Open Access
ARTICLE
Anas W. Abulfaraj*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047945
（This article belongs to this Special Issue: Intelligent Management and Machine Learning for Big Data in IoT-Enabled Pervasive Computing)
Abstract The utilization of visual attention enhances the performance of image classification tasks. Previous attentionbased models have demonstrated notable performance, but many of these models exhibit reduced accuracy when
confronted with inter-class and intra-class similarities and differences. Neural-Controlled Differential Equations
(N-CDE’s) and Neural Ordinary Differential Equations (NODE’s) are extensively utilized within this context. NCDE’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with
enhanced clarity. To this end, an attentive neural network has been proposed to generate attention maps, which
uses two different types of N-CDE’s, one for adopting hidden layers and the other to generate… More >
Open Access
ARTICLE
Jungpil Shin1,*, Md. Al Mehedi Hasan2, Md. Maniruzzaman1, Taiki Watanabe1, Issei Jozume1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.046954
Abstract Person identification is one of the most vital tasks for network security. People are more concerned about their
security due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprints
and faces have been widely used for person identification, which has the risk of information leakage as a result
of reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiable
pattern, which will not be reproducible falsely by capturing psychological and behavioral information of a person
using vision and sensor-based techniques. In existing studies, most of the researchers used very… More >
Open Access
ARTICLE
Yushan Yin1, Witold Pedrycz1,2, Zhiwu Li1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048787
Abstract Utilizing granular computing to enhance artificial neural network architecture, a new type of network emerges—the granular neural network (GNN). GNNs offer distinct advantages over their traditional counterparts: The ability to process both numerical and granular data, leading to improved interpretability. This paper proposes a novel design method for constructing GNNs, drawing inspiration from existing interval-valued neural networks built upon NNNs. However, unlike the proposed algorithm in this work, which employs interval values or triangular fuzzy numbers for connections, existing methods rely on a pre-defined numerical network. This new method utilizes a uniform distribution of information granularity to granulate connections with… More >
Open Access
ARTICLE
Mohammed Alatiyyah*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047790
（This article belongs to this Special Issue: Edge Computing in Advancing the Capabilities of Smart Cities)
Abstract In the contemporary era of technological advancement, smartphones have become an indispensable part of individuals’ daily lives, exerting a pervasive influence. This paper presents an innovative approach to passenger counting on buses through the analysis of Wi-Fi signals emanating from passengers’ mobile devices. The study seeks to scrutinize the reliability of digital Wi-Fi environments in predicting bus occupancy levels, thereby addressing a crucial aspect of public transportation. The proposed system comprises three crucial elements: Signal capture, data filtration, and the calculation and estimation of passenger numbers. The pivotal findings reveal that the system demonstrates commendable accuracy in estimating passenger counts… More >
Open Access
ARTICLE
Ahsan Wajahat1, Jingsha He1, Nafei Zhu1, Tariq Mahmood2,3, Tanzila Saba2, Amjad Rehman Khan2, Faten S. Alamri4,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047530
Abstract The growing usage of Android smartphones has led to a significant rise in incidents of Android malware and privacy breaches. This escalating security concern necessitates the development of advanced technologies capable of automatically detecting and mitigating malicious activities in Android applications (apps). Such technologies are crucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital world. Current methods employed to detect sensitive data leaks in Android apps are hampered by two major limitations they require substantial computational resources and are prone to a high frequency of false positives. This means that while attempting to… More >
Open Access
ARTICLE
Naglaa F. Soliman1, Fatma E. Fadl-Allah2, Walid El-Shafai3,4,*, Mahmoud I. Aly2, Maali Alabdulhafith1, Fathi E. Abd El-Samie1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.046757
Abstract The efficient transmission of images, which plays a large role in wireless communication systems, poses a significant challenge in the growth of multimedia technology. High-quality images require well-tuned communication standards. The Single Carrier Frequency Division Multiple Access (SC-FDMA) is adopted for broadband wireless communications, because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio (PAPR). Data transmission through open-channel networks requires much concentration on security, reliability, and integrity. The data need a space away from unauthorized access, modification, or deletion. These requirements are to be fulfilled by digital image watermarking and encryption. This paper is mainly… More >
Open Access
ARTICLE
Xue Zhao, Shaojun Tao, Hongying Tang, Jiang Wang*, Baoqing Li*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047996
Abstract In recent years, target tracking has been considered one of the most important applications of wireless sensor
network (WSN). Optimizing target tracking performance and prolonging network lifetime are two equally critical
objectives in this scenario. The existing mechanisms still have weaknesses in balancing the two demands. The
proposed heuristic multi-node collaborative scheduling mechanism (HMNCS) comprises cluster head (CH)
election, pre-selection, and task set selection mechanisms, where the latter two kinds of selections form a two-layer
selection mechanism. The CH election innovatively introduces the movement trend of the target and establishes
a scoring mechanism to determine the optimal CH, which can… More >
Open Access
ARTICLE
Aizaz Ali1, Maqbool Khan1,2, Khalil Khan3, Rehan Ullah Khan4, Abdulrahman Aloraini4,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048712
（This article belongs to this Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
Abstract Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understanding public opinion and user sentiment across diverse languages. While numerous scholars conduct sentiment analysis in widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grappling with resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language, characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu, Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguistic features, presents an additional hurdle due… More >
Open Access
ARTICLE
Yiming Tang1,2, Jianwei Gao1,*, Yifan Huang1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049341
（This article belongs to this Special Issue: Emerging Trends in Fuzzy Logic)
Abstract The α-universal triple I (α-UTI) method is a recognized scheme in the field of fuzzy reasoning, which was proposed
by our research group previously. The robustness of fuzzy reasoning determines the quality of reasoning algorithms
to a large extent, which is quantified by calculating the disparity between the output of fuzzy reasoning with
interference and the output without interference. Therefore, in this study, the interval robustness (embodied as
the interval stability) of the α-UTI method is explored in the interval-valued fuzzy environment. To begin with,
the stability of the α-UTI method is explored for the case of an individual rule,… More >
Open Access
ARTICLE
Jingyi Mao, Yuchen Zhou, Yifan Wang, Junyu Li, Ziqing Liu, Fanliang Bu*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048703
（This article belongs to this Special Issue: Multimodal Learning in Image Processing)
Abstract Voice portrait technology has explored and established the relationship between speakers’ voices and their facial features, aiming to generate corresponding facial characteristics by providing the voice of an unknown speaker. Due to its powerful advantages in image generation, Generative Adversarial Networks (GANs) have now been widely applied across various fields. The existing Voice2Face methods for voice portraits are primarily based on GANs trained on voice-face paired datasets. However, voice portrait models solely constructed on GANs face limitations in image generation quality and struggle to maintain facial similarity. Additionally, the training process is relatively unstable, thereby affecting the overall generative performance… More >
Open Access
ARTICLE
Kranthi Alluri, S. Gopikrishnan*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048621
（This article belongs to this Special Issue: Multimedia Encryption and Information Security)
Abstract Secure authentication and accurate localization among Internet of Things (IoT) sensors are pivotal for the functionality and integrity of IoT networks. IoT authentication and localization are intricate and symbiotic, impacting both the security and operational functionality of IoT systems. Hence, accurate localization and lightweight authentication on resource-constrained IoT devices pose several challenges. To overcome these challenges, recent approaches have used encryption techniques with well-known key infrastructures. However, these methods are inefficient due to the increasing number of data breaches in their localization approaches. This proposed research efficiently integrates authentication and localization processes in such a way that they complement each… More >
Open Access
ARTICLE
Meng Huang, Honglei Wei*, Xianyi Zhai
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048510
（This article belongs to this Special Issue: Neural Architecture Search: Optimization, Efficiency and Application)
Abstract In pursuit of cost-effective manufacturing, enterprises are increasingly adopting the practice of utilizing recycled semiconductor chips. To ensure consistent chip orientation during packaging, a circular marker on the front side is employed for pin alignment following successful functional testing. However, recycled chips often exhibit substantial surface wear, and the identification of the relatively small marker proves challenging. Moreover, the complexity of generic target detection algorithms hampers seamless deployment. Addressing these issues, this paper introduces a lightweight YOLOv8s-based network tailored for detecting markings on recycled chips, termed Van-YOLOv8. Initially, to alleviate the influence of diminutive, low-resolution markings on the precision of… More >
Open Access
ARTICLE
Chunming Wu1, Wukai Liu2,*, Xin Ma3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048136
（This article belongs to this Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract A novel image fusion network framework with an autonomous encoder and decoder is suggested to increase the visual impression of fused images by improving the quality of infrared and visible light picture fusion. The network comprises an encoder module, fusion layer, decoder module, and edge improvement module. The encoder module utilizes an enhanced Inception module for shallow feature extraction, then combines Res2Net and Transformer to achieve deep-level co-extraction of local and global features from the original picture. An edge enhancement module (EEM) is created to extract significant edge features. A modal maximum difference fusion strategy is introduced to enhance the… More >
Open Access
ARTICLE
Sharaf J. Malebary*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047917
（This article belongs to this Special Issue: Intelligent Management and Machine Learning for Big Data in IoT-Enabled Pervasive Computing)
Abstract Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates. Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitating the development of more precise and efficient methodologies. To address this formidable challenge, we propose an advanced approach for segmenting brain tumor Magnetic Resonance Imaging (MRI) images that harnesses the formidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methods have displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, marked by irregular shapes, varying sizes, uneven distribution, and limited available… More >
Open Access
REVIEW
Souhayla Dargaoui1, Mourade Azrour1,*, Ahmad El Allaoui1, Azidine Guezzaz2, Abdulatif Alabdulatif3, Abdullah Alnajim4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047625
Abstract Nowadays, devices are connected across all areas, from intelligent buildings and smart cities to Industry 4.0 and smart healthcare. With the exponential growth of Internet of Things usage in our world, IoT security is still the biggest challenge for its deployment. The main goal of IoT security is to ensure the accessibility of services provided by an IoT environment, protect privacy, and confidentiality, and guarantee the safety of IoT users, infrastructures, data, and devices. Authentication, as the first line of defense against security threats, becomes the priority of everyone. It can either grant or deny users access to resources according… More >
Open Access
ARTICLE
Jiajun Yan, Ying Zhou*, Anchen Dai, Tao Wang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048922
Abstract As an emerging network paradigm, the software-defined network (SDN) finds extensive application in areas such as smart grids, the Internet of Things (IoT), and edge computing. The forwarding layer in software-defined networks is susceptible to eavesdropping attacks. Route hopping is a moving target defense (MTD) technology that is frequently employed to resist eavesdropping attacks. In the traditional route hopping technology, both request and reply packets use the same hopping path. If an eavesdropping attacker monitors the nodes along this path, the risk of 100% data leakage becomes substantial. In this paper, we present an effective route hopping approach, called two-day… More >
Open Access
ARTICLE
Tsu-Yang Wu1,2, Haonan Li2, Saru Kumari3, Chien-Ming Chen1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048347
Abstract Hyperspectral image classification stands as a pivotal task within the field of remote sensing, yet achieving high-precision classification remains a significant challenge. In response to this challenge, a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm (AFLA-SCNN) is proposed. The Adaptive Fick’s Law Algorithm (AFLA) constitutes a novel metaheuristic algorithm introduced herein, encompassing three new strategies: Adaptive weight factor, Gaussian mutation, and probability update policy. With adaptive weight factor, the algorithm can adjust the weights according to the change in the number of iterations to improve the performance of the algorithm. Gaussian mutation helps the algorithm avoid… More >
Open Access
ARTICLE
Cheng Wan1, Jiani Zhao1, Xiangqian Hong2, Weihua Yang2,*, Shaochong Zhang2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048307
（This article belongs to this Special Issue: Deep Learning in Computer-Aided Diagnosis Based on Medical Image)
Abstract Age-related macular degeneration (AMD) ranks third among the most common causes of blindness. As the most conventional and direct method for identifying AMD, color fundus photography has become prominent owing to its consistency, ease of use, and good quality in extensive clinical practice. In this study, a convolutional neural network (CSPDarknet53) was combined with a transformer to construct a new hybrid model, HCSP-Net. This hybrid model was employed to tri-classify color fundus photography into the normal macula (NM), dry macular degeneration (DMD), and wet macular degeneration (WMD) based on clinical classification manifestations, thus identifying and resolving AMD as early as… More >
Open Access
ARTICLE
Amardeep Singh1, Hamad Ali Abosaq2, Saad Arif3, Zohaib Mushtaq4,*, Muhammad Irfan5, Ghulam Abbas6, Arshad Ali7, Alanoud Al Mazroa8
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048036
（This article belongs to this Special Issue: Multimedia Encryption and Information Security)
Abstract Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries, especially in light of the growing number of cybersecurity threats. A major and ever-present threat is Ransomware-as-a-Service (RaaS) assaults, which enable even individuals with minimal technical knowledge to conduct ransomware operations. This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models. For this purpose, the network intrusion detection dataset “UNSW-NB15” from the Intelligent Security Group of the University of New South Wales, Australia is analyzed. In the initial phase, the rectified linear… More >
Open Access
ARTICLE
Xiangyan Tang1,2, Chengchun Ruan1,2,*, Xiulai Li2,3, Binbin Li1,2, Cebin Fu1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047541
Abstract Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in the field of small object detection on unmanned aerial vehicles (UAVs). This task is challenging due to variations in UAV flight altitude, differences in object scales, as well as factors like flight speed and motion blur. To enhance the detection efficacy of small targets in drone aerial imagery, we propose an enhanced You Only Look Once version 7 (YOLOv7) algorithm based on multi-scale spatial context. We build the MSC-YOLO model, which incorporates an additional prediction head, denoted as P2, to improve adaptability for small objects.… More >
Open Access
ARTICLE
Yan Dong1,2, Kang Zhao1, Liang Gao1, Hao Li1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048870
（This article belongs to this Special Issue: Multiscale Computational Methods for Advanced Materials and Structures)
Abstract With the continuous advancement in topology optimization and additive manufacturing (AM) technology, the capability to fabricate functionally graded materials and intricate cellular structures with spatially varying microstructures has grown significantly. However, a critical challenge is encountered in the design of these structures–the absence of robust interface connections between adjacent microstructures, potentially resulting in diminished efficiency or macroscopic failure. A Hybrid Level Set Method (HLSM) is proposed, specifically designed to enhance connectivity among non-uniform microstructures, contributing to the design of functionally graded cellular structures. The HLSM introduces a pioneering algorithm for effectively blending heterogeneous microstructure interfaces. Initially, an interpolation algorithm is… More >
Open Access
ARTICLE
Hongle Li1, SeongKi Kim2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048402
（This article belongs to this Special Issue: Intelligent Computing Techniques and Their Real Life Applications)
Abstract Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetables and determines the stop and the start according to the demands. This study explores the optimization of dynamic vehicle scheduling and real-time route planning in urban public transportation systems, with a focus on bus services. It addresses the limitations of current shared mobility routing algorithms, which are primarily designed for simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. The research introduces an route planning algorithm designed to dynamically accommodate passenger travel needs and enable real-time route modifications.… More >
Open Access
ARTICLE
Siyuan Liu1,*, Jinying Huang2, Jiancheng Ma1, Licheng Jing2, Yuxuan Wang2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.049484
（This article belongs to this Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems)
Abstract Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems, such as relatively ideal speed conditions and sample conditions. In engineering practice, the rotational speed of the machine is often transient and time-varying, which makes the sample annotation increasingly expensive. Meanwhile, the number of samples collected from different health states is often unbalanced. To deal with the above challenges, a complementary-label (CL) adversarial domain adaptation fault diagnosis network (CLADAN) is proposed under time-varying rotational speed and weakly-supervised conditions. In the weakly supervised learning condition, machine prior information is used for sample annotation via cost-friendly complementary label learning.… More >
Open Access
ARTICLE
Kai Wei1, Song Yu2, Qingxian Pan1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048240
（This article belongs to this Special Issue: Edge Computing in Advancing the Capabilities of Smart Cities)
Abstract Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource allocation. While traditional methods for task allocation can help reduce costs and improve efficiency, they may encounter challenges when dealing with abnormal data flow nodes, leading to decreased allocation accuracy and efficiency. To address these issues, this study proposes a novel two-part invalid detection task allocation framework. In the first step, an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous data. Compared to the baseline method, the model achieves an approximately 4% increase in the F1 value on the public dataset. In… More >
Open Access
ARTICLE
Yuejiao Wang, Zhong Ma*, Chaojie Yang, Yu Yang, Lu Wei
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047108
（This article belongs to this Special Issue: Development and Industrial Application of AI Technologies)
Abstract The quantization algorithm compresses the original network by reducing the numerical bit width of the model,
which improves the computation speed. Because different layers have different redundancy and sensitivity to data
bit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determine
the optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantization
can effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In this
paper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bit
width is proposed,… More >
Open Access
ARTICLE
Yifan Gao*, Jieming Zhang, Zhanchen Chen, Xianchao Chen
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048615
（This article belongs to this Special Issue: AI and Data Security for the Industrial Internet)
Abstract In this paper, we propose a novel anomaly detection method for data centers based on a combination of graph structure and abnormal attention mechanism. The method leverages the sensor monitoring data from target power substations to construct multidimensional time series. These time series are subsequently transformed into graph structures, and corresponding adjacency matrices are obtained. By incorporating the adjacency matrices and additional weights associated with the graph structure, an aggregation matrix is derived. The aggregation matrix is then fed into a pre-trained graph convolutional neural network (GCN) to extract graph structure features. Moreover, both the multidimensional time series segments and… More >
Open Access
ARTICLE
Supeng Yu1, Fen Huang1,*, Chengcheng Fan2,3,4,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048608
Abstract Significant advancements have been achieved in road surface extraction based on high-resolution remote sensing image processing. Most current methods rely on fully supervised learning, which necessitates enormous human effort to label the image. Within this field, other research endeavors utilize weakly supervised methods. These approaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such as scribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised and edge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equipped with a distinct decoder module dedicated to road extraction tasks. One… More >
Open Access
ARTICLE
Luda Chen1, Kuangzhu Bao2, Ying Chen2, Jingang Hao2,*, Jianfeng He1,3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048507
（This article belongs to this Special Issue: Deep Learning in Computer-Aided Diagnosis Based on Medical Image)
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) is proposed, merging decoupled lesion… More >
Open Access
ARTICLE
Sardar Hasen Ali*, Maiwan Bahjat Abdulrazzaq
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048356
（This article belongs to this Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Handwritten character recognition (HCR) involves identifying characters in images, documents, and various sources such as forms surveys, questionnaires, and signatures, and transforming them into a machine-readable format for subsequent processing. Successfully recognizing complex and intricately shaped handwritten characters remains a significant obstacle. The use of convolutional neural network (CNN) in recent developments has notably advanced HCR, leveraging the ability to extract discriminative features from extensive sets of raw data. Because of the absence of pre-existing datasets in the Kurdish language, we created a Kurdish handwritten dataset called (KurdSet). The dataset consists of Kurdish characters, digits, texts, and symbols. The dataset… More >
Open Access
ARTICLE
Jiaxiang Wang1, Zhengyi Li1, Peng Shi1, Hongying Yu2, Dongbai Sun1,3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.046929
（This article belongs to this Special Issue: Advances and Applications in Signal, Image and Video Processing)
Abstract Scanning electron microscopy (SEM) is a crucial tool in the field of materials science, providing valuable insights
into the microstructural characteristics of materials. Unfortunately, SEM images often suffer from blurriness
caused by improper hardware calibration or imaging automation errors, which present challenges in analyzing
and interpreting material characteristics. Consequently, rectifying the blurring of these images assumes paramount
significance to enable subsequent analysis. To address this issue, we introduce a Material Images Deblurring
Network (MIDNet) built upon the foundation of the Nonlinear Activation Free Network (NAFNet). MIDNet
is meticulously tailored to address the blurring in images capturing the microstructure of materials.… More >
Open Access
ARTICLE
Yongjiang Zhao, Haoyi Zhong, Chang Cyoon Lim*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048771
（This article belongs to this Special Issue: Edge Computing in Advancing the Capabilities of Smart Cities)
Abstract This paper examines the difficulties of managing distributed power systems, notably due to the increasing use of renewable energy sources, and focuses on voltage control challenges exacerbated by their variable nature in modern power grids. To tackle the unique challenges of voltage control in distributed renewable energy networks, researchers are increasingly turning towards multi-agent reinforcement learning (MARL). However, MARL raises safety concerns due to the unpredictability in agent actions during their exploration phase. This unpredictability can lead to unsafe control measures. To mitigate these safety concerns in MARL-based voltage control, our study introduces a novel approach: Safety-Constrained Multi-Agent Reinforcement Learning… More >
Open Access
ARTICLE
Ananthakrishnan Balasundaram1,2, Ayesha Shaik1,2,*, Japmann Kaur Banga2, Aman Kumar Singh2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048362
（This article belongs to this Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract Industrial activities, through the human-induced release of Green House Gas (GHG) emissions, have been identified as the primary cause of global warming. Accurate and quantitative monitoring of these emissions is essential for a comprehensive understanding of their impact on the Earth’s climate and for effectively enforcing emission regulations at a large scale. This work examines the feasibility of detecting and quantifying industrial smoke plumes using freely accessible geo-satellite imagery. The existing system has so many lagging factors such as limitations in accuracy, robustness, and efficiency and these factors hinder the effectiveness in supporting timely response to industrial fires. In this… More >
Open Access
ARTICLE
Hala AlShamlan*, Halah AlMazrua*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048146
Abstract In this study, our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization (GWO) with Harris Hawks Optimization (HHO) for feature selection. The motivation for utilizing GWO and HHO stems from their bio-inspired nature and their demonstrated success in optimization problems. We aim to leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification. We selected leave-one-out cross-validation (LOOCV) to evaluate the performance of both two widely used classifiers, k-nearest neighbors (KNN) and support vector machine (SVM), on high-dimensional cancer microarray… More >
Open Access
ARTICLE
Ameni Ellouze1, Nesrine Kadri2, Alaa Alaerjan3,*, Mohamed Ksantini1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048061
（This article belongs to this Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
Abstract Recognizing human activity (HAR) from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases. Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not. Typically, smartphones and their associated sensing devices operate in distributed and unstable environments. Therefore, collecting their data and extracting useful information is a significant challenge. In this context, the aim of this paper is twofold: The first is to analyze human behavior based on the recognition of physical activities. Using the results of physical activity detection… More >
Open Access
ARTICLE
Muhammad Umar Nasir1, Omar Kassem Khalil2, Karamath Ateeq3, Bassam SaleemAllah Almogadwy4, M. A. Khan5, Khan Muhammad Adnan6,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047874
Abstract Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in the fourth position because of the leading death cause in its premature stages. The cervix which is the lower end of the vagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumor in the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approach uses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer to train the weights with varying neurons… More >
Open Access
ARTICLE
Yu Zhou1, Bosong Lin1, Siqi Hu2, Dandan Yu3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047836
（This article belongs to this Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
Abstract This paper addresses the problem of predicting population density leveraging cellular station data. As wireless communication devices are commonly used, cellular station data has become integral for estimating population figures and studying their movement, thereby implying significant contributions to urban planning. However, existing research grapples with issues pertinent to preprocessing base station data and the modeling of population prediction. To address this, we propose methodologies for preprocessing cellular station data to eliminate any irregular or redundant data. The preprocessing reveals a distinct cyclical characteristic and high-frequency variation in population shift. Further, we devise a multi-view enhancement model grounded on the… More >
Open Access
ARTICLE
Shahid Naseem1, Tariq Mahmood2,3, Amjad Rehman Khan2, Umer Farooq1, Samra Nawazish4, Faten S. Alamri5,*, Tanzila Saba2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047623
（This article belongs to this Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)
Abstract Recently, there have been several uses for digital image processing. Image fusion has become a prominent application in the domain of imaging processing. To create one final image that proves more informative and helpful compared to the original input images, image fusion merges two or more initial images of the same item. Image fusion aims to produce, enhance, and transform significant elements of the source images into combined images for the sake of human visual perception. Image fusion is commonly employed for feature extraction in smart robots, clinical imaging, audiovisual camera integration, manufacturing process monitoring, electronic circuit design, advanced device… More >
Open Access
ARTICLE
Yan Xiang1,2, Daofeng Li1,2,*, Xinyi Meng1,2, Chengfeng Dong1,2, Guanglin Qin1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047143
（This article belongs to this Special Issue: Security and Privacy for Blockchain-empowered Internet of Things)
Abstract The rapid expansion of Internet of Things (IoT) devices across various sectors is driven by steadily increasing demands for interconnected and smart technologies. Nevertheless, the surge in the number of IoT device has caught the attention of cyber hackers, as it provides them with expanded avenues to access valuable data. This has resulted in a myriad of security challenges, including information leakage, malware propagation, and financial loss, among others. Consequently, developing an intrusion detection system to identify both active and potential intrusion traffic in IoT networks is of paramount importance. In this paper, we propose ResNeSt-biGRU, a practical intrusion detection… More >
Open Access
ARTICLE
Fangfang Shan1,2,*, Huifang Sun1,2, Mengyi Wang1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.046202
Abstract As social networks become increasingly complex, contemporary fake news often includes textual descriptions of events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely to create a misleading perception among users. While early research primarily focused on text-based features for fake news detection mechanisms, there has been relatively limited exploration of learning shared representations in multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal model for detecting fake news, which relies on similarity reasoning and adversarial networks. The model employs Bidirectional Encoder Representation from Transformers (BERT) and Text Convolutional Neural… More >
Open Access
ARTICLE
Jiao Wang, Bin Wu*, Hongying Zhang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.046011
（This article belongs to this Special Issue: Development and Industrial Application of AI Technologies)
Abstract Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention due to its outstanding performance and nonlinear application. However, most existing methods neglect that view-private meaningless information or noise may interfere with the learning of self-expression, which may lead to the degeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistency and Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple views and fuses them based on their discrimination, so that it can effectively explore consistent and complementary information for achieving precise clustering. Specifically, the view-specific self-expression is learned by… More >
Open Access
ARTICLE
Fangjun Luan1,2,3, Xuewen Mu1,2,3, Shuai Yuan1,2,3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048502
Abstract Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.
However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. To
address these issues, we propose a novel approach for online signature verification, using a one-dimensional GhostACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolution
with a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residual
structure is introduced to leverage both self-attention and convolution mechanisms for capturing global feature
information and extracting local information, effectively complementing whole and local signature features and
mitigating… More >
Open Access
ARTICLE
Yiwei Liu1, Yingnan Zhao1,*, Yi Chen1, Zheng Hu1, Min Xia2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047901
（This article belongs to this Special Issue: Deep Learning based Object Detection and Tracking in Videos)
Abstract Scene text detection is an important task in computer vision. In this paper, we present YOLOv5 Scene Text
(YOLOv5ST), an optimized architecture based on YOLOv5 v6.0 tailored for fast scene text detection. Our primary
goal is to enhance inference speed without sacrificing significant detection accuracy, thereby enabling robust
performance on resource-constrained devices like drones, closed-circuit television cameras, and other embedded
systems. To achieve this, we propose key modifications to the network architecture to lighten the original backbone
and improve feature aggregation, including replacing standard convolution with depth-wise convolution, adopting
the C2 sequence module in place of C3, employing Spatial Pyramid… More >
Open Access
ARTICLE
Yasmine M. Ibrahim1,2, Reem Essameldin3, Saad M. Darwish1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047840
（This article belongs to this Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
Abstract Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due to
the complex nature of language used in such platforms. Currently, several methods exist for classifying hate
speech, but they still suffer from ambiguity when differentiating between hateful and offensive content and they
also lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm
(WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two Multi-Layer Perceptron (MLPs)
for neutrosophic sets classification. During the training process of the MLP, the WOA is employed to explore
and determine… More >
Open Access
ARTICLE
Qiang Fu1, Jun Wang1,*, Changfu Si1, Jiawei Liu2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.048117
（This article belongs to this Special Issue: Recent Advances in Ensemble Framework of Meta-heuristics and Machine Learning: Methods and Applications)
Abstract As industrialization and informatization become more deeply intertwined, industrial control networks have entered an era of intelligence. The connection between industrial control networks and the external internet is becoming increasingly close, which leads to frequent security accidents. This paper proposes a model for the industrial control network. It includes a malware containment strategy that integrates intrusion detection, quarantine, and monitoring. Based on this model, the role of key nodes in the spread of malware is studied, a comparison experiment is conducted to validate the impact of the containment strategy. In addition, the dynamic behavior of the model is analyzed, the… More >
Open Access
ARTICLE
Xiao Lu1,*, Chengling Jiang1, Zhoujun Ma1, Haitao Li2, Yuexin Liu2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047469
（This article belongs to this Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract Insulator defect detection plays a vital role in maintaining the secure operation of power systems. To address the issues of the difficulty of detecting small objects and missing objects due to the small scale, variable scale, and fuzzy edge morphology of insulator defects, we construct an insulator dataset with 1600 samples containing flashovers and breakages. Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed. Firstly, a high-resolution feature map is introduced and a small object prediction layer is added so that the model can detect tiny objects. Secondly, a simplified… More >
Open Access
ARTICLE
Yashan Feng1, Yafang Tian1, Yongxin Yang1, Yufang Zhang1, Haiwei Guo1, Jing’an Li2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047004
（This article belongs to this Special Issue: Application of Soft Computing in Techniques in Materials Development)
Abstract Electroactive anticorrosion coatings are specialized surface treatments that prevent or minimize corrosion. The study employs strategic thermodynamic equilibrium calculations to pioneer a novel factor in corrosion protection. A first-time proposal, the total acidity (TA) potential of the hydrogen (pH) concept significantly shapes medical magnesium alloys. These coatings are meticulously designed for robust corrosion resistance, blending theoretical insights and practical applications to enhance our grasp of corrosion prevention mechanisms and establish a systematic approach to coating design. The groundbreaking significance of this study lies in its innovative integration of the TA/pH concept, which encompasses the TA/pH ratio of the chemical environment.… More >
Open Access
ARTICLE
Xingfan Zhao1, Changgen Peng1,2,*, Weijie Tan2, Kun Niu1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047975
（This article belongs to this Special Issue: Security and Privacy for Blockchain-empowered Internet of Things)
Abstract Traditional blockchain key management schemes store private keys in the same location, which can easily lead to security issues such as a single point of failure. Therefore, decentralized threshold key management schemes have become a research focus for blockchain private key protection. The security of private keys for blockchain user wallet is highly related to user identity authentication and digital asset security. The threshold blockchain private key management schemes based on verifiable secret sharing have made some progress, but these schemes do not consider participants’ self-interested behavior, and require trusted nodes to keep private key fragments, resulting in a narrow… More >
Open Access
ARTICLE
Zenglun Guan1,2, Murad S. Alfarzaeai1,3,*, Eryi Hu1,3,*, Taqiaden Alshmeri4, Wang Peng3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.047159
（This article belongs to this Special Issue: Advanced Computer Technology for Materials Characterization, Properties Prediction, Design and Discovery)
Abstract In the coal mining industry, the gangue separation phase imposes a key challenge due to the high visual similarity between coal and gangue. Recently, separation methods have become more intelligent and efficient, using new technologies and applying different features for recognition. One such method exploits the difference in substance density, leading to excellent coal/gangue recognition. Therefore, this study uses density differences to distinguish coal from gangue by performing volume prediction on the samples. Our training samples maintain a record of 3-side images as input, volume, and weight as the ground truth for the classification. The prediction process relies on a… More >
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