Open Access
ARTICLE
Ange Chen, Chengdong Wu*, Chuanjiang Leng
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.059284
Abstract Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly, meaning that skeleton structure information is not utilized and multi-view pose information is not completely fused. Moreover, existing graph convolutional operations do not consider the specificity of different joints and different views of pose information when processing skeleton graphs, making the correlation weights between nodes in the graph and their neighborhood nodes shared. Existing Graph Convolutional Networks (GCNs) cannot extract global and deep-level skeleton structure information and view… More >
Open Access
ARTICLE
Xin Tong1, Jingya Wang1,*, Ying Yang2, Tian Peng3, Hanming Zhai1, Guangming Ling4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.059018
Abstract With the widespread use of SMS (Short Message Service), the proliferation of malicious SMS has emerged as a pressing societal issue. While deep learning-based text classifiers offer promise, they often exhibit suboptimal performance in fine-grained detection tasks, primarily due to imbalanced datasets and insufficient model representation capabilities. To address this challenge, this paper proposes an LLMs-enhanced graph fusion dual-stream Transformer model for fine-grained Chinese malicious SMS detection. During the data processing stage, Large Language Models (LLMs) are employed for data augmentation, mitigating dataset imbalance. In the data input stage, both word-level and character-level features are More >
Open Access
ARTICLE
Wenju Wang1, Zehua Gu1,*, Bang Tang1, Sen Wang2, Jianfei Hao2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057392
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition, 2nd Edition)
Abstract Current spatio-temporal action detection methods lack sufficient capabilities in extracting and comprehending spatio-temporal information. This paper introduces an end-to-end Adaptive Cross-Scale Fusion Encoder-Decoder (ACSF-ED) network to predict the action and locate the object efficiently. In the Adaptive Cross-Scale Fusion Spatio-Temporal Encoder (ACSF ST-Encoder), the Asymptotic Cross-scale Feature-fusion Module (ACCFM) is designed to address the issue of information degradation caused by the propagation of high-level semantic information, thereby extracting high-quality multi-scale features to provide superior features for subsequent spatio-temporal information modeling. Within the Shared-Head Decoder structure, a shared classification and regression detection head is constructed. A More >
Open Access
ARTICLE
Yuanjun Qu, Xiameng Si*, Haiyan Kang, Hanlin Zhou
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057368
Abstract With the widespread use of blockchain technology for smart contracts and decentralized applications on the Ethereum platform, the blockchain has become a cornerstone of trust in the modern financial system. However, its anonymity has provided new ways for Ponzi schemes to commit fraud, posing significant risks to investors. Current research still has some limitations, for example, Ponzi schemes are difficult to detect in the early stages of smart contract deployment, and data imbalance is not considered. In addition, there is room for improving the detection accuracy. To address the above issues, this paper proposes LT-SPSD… More >
Open Access
ARTICLE
Xinchao Han1,2, Aojun Zhang1,2, Runchuan Li1,2,*, Shengya Shen3, Di Zhang1,2, Bo Jin1,2, Longfei Mao1,2, Linqi Yang1,2, Shuqin Zhang1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.059403
（This article belongs to the Special Issue: Emerging Trends and Applications of Deep Learning for Biomedical Signal and Image Processing)
Abstract Electrocardiogram (ECG) analysis is critical for detecting arrhythmias, but traditional methods struggle with large-scale Electrocardiogram data and rare arrhythmia events in imbalanced datasets. These methods fail to perform multi-perspective learning of temporal signals and Electrocardiogram images, nor can they fully extract the latent information within the data, falling short of the accuracy required by clinicians. Therefore, this paper proposes an innovative hybrid multimodal spatiotemporal neural network to address these challenges. The model employs a multimodal data augmentation framework integrating visual and signal-based features to enhance the classification performance of rare arrhythmias in imbalanced datasets. Additionally, More >
Open Access
ARTICLE
Ziqiang Tang1, Chao Han1, Hongwu Li1, Zhou Fan1, Ke Sun1, Yuntian Huang1, Yuhang Chen2, Chenxing Wang2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.059094
Abstract Accurate vector extraction from design drawings is required first to automatically create 3D models from pixel-level engineering design drawings. However, this task faces the challenges of complicated design shapes as well as cumbersome and cluttered annotations on drawings, which interfere with the vector extraction heavily. In this article, the transmission tower containing the most complex structure is taken as the research object, and a semantic segmentation network is constructed to first segment the shape masks from the pixel-level drawings. Preprocessing and postprocessing are also proposed to ensure the stability and accuracy of the shape mask… More >
Open Access
ARTICLE
Hailong Wang1, Junchao Shi1,2, Xinjie Li2,3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058277
Abstract In response to the limitations and low computational efficiency of one-dimensional water and sediment models in representing complex hydrological conditions, this paper proposes a dual branch convolution method based on deep learning. This method utilizes the ability of deep learning to extract data features and introduces a dual branch convolutional network to handle the non-stationary and nonlinear characteristics of noise and reservoir sediment transport data. This method combines permutation variant structure to preserve the original time series information, constructs a corresponding time series model, models and analyzes the changes in the outbound water and sediment… More >
Open Access
REVIEW
Hassan Saeed1, Imran Shafi1, Jamil Ahmad2, Adnan Ahmed Khan3, Tahir Khurshaid4,*, Imran Ashraf5,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057587
Abstract Software-related security aspects are a growing and legitimate concern, especially with 5G data available just at our palms. To conduct research in this field, periodic comparative analysis is needed with the new techniques coming up rapidly. The purpose of this study is to review the recent developments in the field of security integration in the software development lifecycle (SDLC) by analyzing the articles published in the last two decades and to propose a way forward. This review follows Kitchenham’s review protocol. The review has been divided into three main stages including planning, execution, and analysis.… More >
Open Access
ARTICLE
Gabriela Dobrița, Simona-Vasilica Oprea*, Adela Bâra
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058656
Abstract In the rapidly evolving landscape of television advertising, optimizing ad schedules to maximize viewer engagement and revenue has become significant. Traditional methods often operate in silos, limiting the potential insights gained from broader data analysis due to concerns over privacy and data sharing. This article introduces a novel approach that leverages Federated Learning (FL) to enhance TV ad schedule optimization, combining the strengths of local optimization techniques with the power of global Machine Learning (ML) models to uncover actionable insights without compromising data privacy. It combines linear programming for initial ads scheduling optimization with ML—specifically,… More >
Open Access
ARTICLE
Jiajia Liu1,*, Peng Xie2, Wei Li2, Bo Tang2, Jianhua Liu2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058810
（This article belongs to the Special Issue: Edge-based IoT Systems with Cross-Designs of Communication, Computing, and Control)
Abstract As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective… More >
Open Access
ARTICLE
Rahma Gantassi, Zaki Masood, Yonghoon Choi*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058143
（This article belongs to the Special Issue: From Nodes to Knowledge: Harnessing Wireless Sensor Networks)
Abstract Wireless sensor network (WSN) technologies have advanced significantly in recent years. Within WSNs, machine learning algorithms are crucial in selecting cluster heads (CHs) based on various quality of service (QoS) metrics. This paper proposes a new clustering routing protocol employing the Traveling Salesman Problem (TSP) to locate the optimal path traversed by the Mobile Data Collector (MDC), in terms of energy and QoS efficiency. To be more specific, to minimize energy consumption in the CH election stage, we have developed the M-T protocol using the K-Means and the grid clustering algorithms. In addition, to improve More >
Open Access
ARTICLE
Ali Alqarni*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.059238
Abstract Open networks and heterogeneous services in the Internet of Vehicles (IoV) can lead to security and privacy
challenges. One key requirement for such systems is the preservation of user privacy, ensuring a seamless experience
in driving, navigation, and communication. These privacy needs are influenced by various factors, such as data
collected at different intervals, trip durations, and user interactions. To address this, the paper proposes a Support
Vector Machine (SVM) model designed to process large amounts of aggregated data and recommend privacypreserving measures. The model analyzes data based on user demands and interactions with service More >
Open Access
ARTICLE
Ming Liu, Qichao Yang, Wenqing Wang, Shengli Liu*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.059417
Abstract The proliferation of internet traffic encryption has become a double-edged sword. While it significantly enhances user privacy, it also inadvertently shields cyber-attacks from detection, presenting a formidable challenge to cybersecurity. Traditional machine learning and deep learning techniques often fall short in identifying encrypted malicious traffic due to their inability to fully extract and utilize the implicit relational and positional information embedded within data packets. This limitation has led to an unresolved challenge in the cybersecurity community: how to effectively extract valuable insights from the complex patterns of traffic packet transmission. Consequently, this paper introduces the… More >
Open Access
ARTICLE
Jiewen Liu1, Bing Chen1,2,*, Baolu Xue1, Mengya Guo1, Yuntao Xu1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057814
Abstract Federated Graph Neural Networks (FedGNNs) have achieved significant success in representation learning for graph data, enabling collaborative training among multiple parties without sharing their raw graph data and solving the data isolation problem faced by centralized GNNs in data-sensitive scenarios. Despite the plethora of prior work on inference attacks against centralized GNNs, the vulnerability of FedGNNs to inference attacks has not yet been widely explored. It is still unclear whether the privacy leakage risks of centralized GNNs will also be introduced in FedGNNs. To bridge this gap, we present PIAFGNN, the first property inference attack… More >
Open Access
ARTICLE
Shengxi Jiao1, Fenghao Xu1, Haitao Guo2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057192
（This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies)
Abstract Underwater shipwreck identification technology, as a crucial technique in the field of marine surveying, plays a significant role in areas such as the search and rescue of maritime disaster shipwrecks. When facing the task of object detection in shipwreck side-scan sonar images, due to the complex seabed environment, it is difficult to extract object features, often leading to missed detections of shipwreck images and slow detection speed. To address these issues, this paper proposes an object detection algorithm, CSC-YOLO (Context Guided Block, Shared Conv_Group Normalization Detection, Cross Stage Partial with 2 Partial Convolution-You Only Look… More >
Open Access
ARTICLE
Ziyang Deng1, Weidong Min1,2,3,*, Qing Han1,2,3, Mengxue Liu1, Longfei Li1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057456
Abstract Dynamic sign language recognition holds significant importance, particularly with the application of deep learning to address its complexity. However, existing methods face several challenges. Firstly, recognizing dynamic sign language requires identifying keyframes that best represent the signs, and missing these keyframes reduces accuracy. Secondly, some methods do not focus enough on hand regions, which are small within the overall frame, leading to information loss. To address these challenges, we propose a novel Video Transformer Attention-based Network (VTAN) for dynamic sign language recognition. Our approach prioritizes informative frames and hand regions effectively. To tackle the first… More >
Open Access
REVIEW
Zhimao Lai1,2, Saad Arif3, Cong Feng4, Guangjun Liao5, Chuntao Wang6,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.059370
（This article belongs to the Special Issue: Challenges and Innovations in Multimedia Encryption and Information Security)
Abstract With the rapid advancement of visual generative models such as Generative Adversarial Networks (GANs) and stable Diffusion, the creation of highly realistic Deepfake through automated forgery has significantly progressed. This paper examines the advancements in Deepfake detection and defense technologies, emphasizing the shift from passive detection methods to proactive digital watermarking techniques. Passive detection methods, which involve extracting features from images or videos to identify forgeries, encounter challenges such as poor performance against unknown manipulation techniques and susceptibility to counter-forensic tactics. In contrast, proactive digital watermarking techniques embed specific markers into images or videos, facilitating More >
Open Access
ARTICLE
Murun Yang1,*, Fuxue Li2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058248
Abstract Preserving formal style in neural machine translation (NMT) is essential, yet often overlooked as an optimization objective of the training processes. This oversight can lead to translations that, though accurate, lack formality. In this paper, we propose how to improve NMT formality with large language models (LLMs), which combines the style transfer and evaluation capabilities of an LLM and the high-quality translation generation ability of NMT models to improve NMT formality. The proposed method (namely INMTF) encompasses two approaches. The first involves a revision approach using an LLM to revise the NMT-generated translation, ensuring a… More >
Open Access
ARTICLE
Shaobo Deng*, Meiru Xie, Bo Wang, Shuaikun Zhang, Sujie Guan, Min Li
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057874
Abstract In recent years, particle swarm optimization (PSO) has received widespread attention in feature selection due to its simplicity and potential for global search. However, in traditional PSO, particles primarily update based on two extreme values: personal best and global best, which limits the diversity of information. Ideally, particles should learn from multiple advantageous particles to enhance interactivity and optimization efficiency. Accordingly, this paper proposes a PSO that simulates the evolutionary dynamics of species survival in mountain peak ecology (PEPSO) for feature selection. Based on the pyramid topology, the algorithm simulates the features of mountain peak… More >
Open Access
ARTICLE
Ibrar Afzal1, Noor ul Amin1,*, Zulfiqar Ahmad1,*, Abdulmohsen Algarni2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057755
（This article belongs to the Special Issue: Practical Application and Services in Fog/Edge Computing System)
Abstract The deployment of the Internet of Things (IoT) with smart sensors has facilitated the emergence of fog computing as an important technology for delivering services to smart environments such as campuses, smart cities, and smart transportation systems. Fog computing tackles a range of challenges, including processing, storage, bandwidth, latency, and reliability, by locally distributing secure information through end nodes. Consisting of endpoints, fog nodes, and back-end cloud infrastructure, it provides advanced capabilities beyond traditional cloud computing. In smart environments, particularly within smart city transportation systems, the abundance of devices and nodes poses significant challenges related… More >
Open Access
ARTICLE
Song Liu1,2, Hongying Zhang1,*, Xue Li1, Xi Yang1,3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057662
（This article belongs to the Special Issue: Data and Image Processing in Intelligent Information Systems)
Abstract Enhancing low-light images with color distortion and uneven multi-light source distribution presents challenges. Most advanced methods for low-light image enhancement are based on the Retinex model using deep learning. Retinexformer introduces channel self-attention mechanisms in the IG-MSA. However, it fails to effectively capture long-range spatial dependencies, leaving room for improvement. Based on the Retinexformer deep learning framework, we designed the Retinexformer+ network. The “+” signifies our advancements in extracting long-range spatial dependencies. We introduced multi-scale dilated convolutions in illumination estimation to expand the receptive field. These convolutions effectively capture the weakening semantic dependency between pixels… More >
Open Access
ARTICLE
Hoon Ko1, Marek R. Ogiela2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057257
（This article belongs to the Special Issue: Innovative AI Applications in Pattern Recognition and Visual Data Processing and Hiding)
Abstract This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology. By combining the strengths of blockchain and generative AI, the research team aimed to address the timely challenge of safeguarding visual medical content. The participating researchers conducted a comprehensive analysis, examining the vulnerabilities of medical AI services, personal information protection issues, and overall security weaknesses. This multifaceted exploration led to an in-depth evaluation of the model’s performance and security. Notably, the correlation between accuracy, detection rate, and error rate was… More >
Open Access
ARTICLE
Wenkai Zhang1,2, Hao Zhang1,2, Xianming Liu1, Xiaoyu Guo1,2, Xinzhe Wang1, Shuiwang Li1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.059000
（This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)
Abstract Under low-illumination conditions, the quality of image signals deteriorates significantly, typically characterized by a peak signal-to-noise ratio (PSNR) below 10 dB, which severely limits the usability of the images. Supervised methods, which utilize paired high-low light images as training sets, can enhance the PSNR to around 20 dB, significantly improving image quality. However, such data is challenging to obtain. In recent years, unsupervised low-light image enhancement (LIE) methods based on the Retinex framework have been proposed, but they generally lag behind supervised methods by 5–10 dB in performance. In this paper, we introduce the Denoising-Distilled… More >
Open Access
ARTICLE
Hongxing Wang1, Xilai Ju2, Hua Zhu1,*, Huafeng Li1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058785
（This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems)
Abstract Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals, which has certain limitations. Conversely, deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency. Recently, utilizing the respective advantages of convolution neural network (CNN) and Transformer in local and global feature extraction, research on cooperating the two have demonstrated promise in the field of fault diagnosis. However, the cross-channel convolution mechanism in CNN and the self-attention calculations in… More >
Graphic Abstract
Open Access
ARTICLE
Yiqing Lu1, Liutao Zhao2,*, Qiankun Zhao3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058757
Abstract Ecological monitoring vehicles are equipped with a range of sensors and monitoring devices designed to gather data on ecological and environmental factors. These vehicles are crucial in various fields, including environmental science research, ecological and environmental monitoring projects, disaster response, and emergency management. A key method employed in these vehicles for achieving high-precision positioning is LiDAR (lightlaser detection and ranging)-Visual Simultaneous Localization and Mapping (SLAM). However, maintaining high-precision localization in complex scenarios, such as degraded environments or when dynamic objects are present, remains a significant challenge. To address this issue, we integrate both semantic and… More >
Open Access
ARTICLE
Ziji Xiao1, Kaibo Yang1, Mingen Zhong1,*, Kang Fan2, Jiawei Tan2, Zhiying Deng1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058345
Abstract The polyp dataset involves the confidentiality of medical records, so it might be difficult to obtain datasets with accurate annotations. This problem can be effectively solved by expanding the polyp data set with algorithms. The traditional polyp dataset expansion scheme usually requires the use of two models or traditional visual methods. These methods are both tedious and difficult to provide new polyp features for training data. Therefore, our research aims to efficiently generate high-quality polyp samples, so as to effectively expand the polyp dataset. In this study, we first added the attention mechanism to the… More >
Open Access
ARTICLE
Sofia Yousuf1, Muhammad Bilal Kadri2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.054587
Abstract Terrain Aided Navigation (TAN) technology has become increasingly important due to its effectiveness in environments where Global Positioning System (GPS) is unavailable. In recent years, TAN systems have been extensively researched for both aerial and underwater navigation applications. However, many TAN systems that rely on recursive Unmanned Aerial Vehicle (UAV) position estimation methods, such as Extended Kalman Filters (EKF), often face challenges with divergence and instability, particularly in highly non-linear systems. To address these issues, this paper proposes and investigates a hybrid two-stage TAN positioning system for UAVs that utilizes Particle Filter. To enhance the… More >
Open Access
ARTICLE
Yue Chen1, Xiaoman Liang1,2,*, Mugang Lin1,2, Fachao Zhang1, Huihuang Zhao1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.059530
Abstract The objective of image-based virtual try-on is to seamlessly integrate clothing onto a target image, generating a realistic representation of the character in the specified attire. However, existing virtual try-on methods frequently encounter challenges, including misalignment between the body and clothing, noticeable artifacts, and the loss of intricate garment details. To overcome these challenges, we introduce a two-stage high-resolution virtual try-on framework that integrates an attention mechanism, comprising a garment warping stage and an image generation stage. During the garment warping stage, we incorporate a channel attention mechanism to effectively retain the critical features of… More >
Open Access
ARTICLE
Honglin Wang1, Yangyang Zhang2,*, Cheng Zhu3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058932
（This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies)
Abstract Forest fires pose a serious threat to ecological balance, air quality, and the safety of both humans and wildlife. This paper presents an improved model based on You Only Look Once version 5 (YOLOv5), named YOLO Lightweight Fire Detector (YOLO-LFD), to address the limitations of traditional sensor-based fire detection methods in terms of real-time performance and accuracy. The proposed model is designed to enhance inference speed while maintaining high detection accuracy on resource-constrained devices such as drones and embedded systems. Firstly, we introduce Depthwise Separable Convolutions (DSConv) to reduce the complexity of the feature extraction… More >
Open Access
ARTICLE
Xin Fan1,2, Zhenlei Fu1,2,*, Jian Shu1,2, Zuxiong Shen1,2, Yun Ge1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057695
Abstract Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of… More >
Open Access
ARTICLE
Beike Yu1, Dafang Wang1,*, Xing Cui2, Bowen Yang1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.059594
Abstract Realistic urban scene generation has been extensively studied for the sake of the development of autonomous vehicles. However, the research has primarily focused on the synthesis of vehicles and pedestrians, while the generation of cyclists is rarely presented due to its complexity. This paper proposes a perspective-aware and realistic cyclist generation method via object retrieval. Images, semantic maps, and depth labels of objects are first collected from existing datasets, categorized by class and perspective, and calculated by an algorithm newly designed according to imaging principles. During scene generation, objects with the desired class and perspective… More >
Open Access
ARTICLE
Bolin Zhang, Ruichun Gu*, Haiying Liu
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.060331
（This article belongs to the Special Issue: Graph Neural Networks: Methods and Applications in Graph-related Problems)
Abstract Federated Graph Learning (FGL) enables model training without requiring each client to share local graph data, effectively breaking data silos by aggregating the training parameters from each terminal while safeguarding data privacy. Traditional FGL relies on a centralized server for model aggregation; however, this central server presents challenges such as a single point of failure and high communication overhead. Additionally, efficiently training a robust personalized local model for each client remains a significant objective in federated graph learning. To address these issues, we propose a decentralized Federated Graph Learning framework with efficient communication, termed Decentralized… More >
Open Access
ARTICLE
Yujiao Tang1, Yadong Wu1,*, Yuanmei He2, Jilin Liu1, Weihan Zhang1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.059115
Abstract Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion… More >
Open Access
ARTICLE
Fengju Zhang1, Kai Zhu2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058944
（This article belongs to the Special Issue: Advanced Communication and Networking Technologies for Internet of Things and Internet of Vehicles)
Abstract The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology plays a crucial role in vehicle localization and navigation. Traditional Simultaneous Localization and Mapping (SLAM) systems are designed for use in static environments, and they can result in poor performance in terms of accuracy and robustness when used in dynamic environments where objects are in constant movement. To address this issue, a new real-time visual SLAM system called MG-SLAM has been developed. Based on ORB-SLAM2,… More >
Open Access
ARTICLE
Binhui Wang, Hongfeng Wang*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058885
（This article belongs to the Special Issue: Recent Advances in Ensemble Framework of Meta-heuristics and Machine Learning: Methods and Applications)
Abstract The distributed permutation flow shop scheduling problem (DPFSP) has received increasing attention in recent years. The iterated greedy algorithm (IGA) serves as a powerful optimizer for addressing such a problem because of its straightforward, single-solution evolution framework. However, a potential draw-back of IGA is the lack of utilization of historical information, which could lead to an imbalance between exploration and exploitation, especially in large-scale DPFSPs. As a consequence, this paper develops an IGA with memory and learning mechanisms (MLIGA) to efficiently solve the DPFSP targeted at the mini-mal makespan. In MLIGA, we incorporate a memory… More >
Open Access
ARTICLE
Honglin Wang1, Zitong Shi2,*, Cheng Zhu3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058474
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition, 2nd Edition)
Abstract In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scale object detection algorithm based on an improved YOLOv8 has been proposed. Firstly, a lightweight attention mechanism, Triplet Attention, is introduced to enhance the algorithm’s ability to extract multi-dimensional and multi-scale features, thereby improving the receptive capability of the feature maps. Secondly, the Diverse Branch Block (DBB) is integrated into the CSP Bottleneck with two Convolutions (C2F) module to strengthen the fusion of semantic information across different… More >
Open Access
ARTICLE
Jeng-Shyang Pan1,2, Na Yu1, Shu-Chuan Chu1,*, An-Ning Zhang1, Bin Yan3, Junzo Watada4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058450
Abstract The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s… More >
Open Access
ARTICLE
Shiren Ye, Qi Meng, Shuo Zhang, Hui Wang*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058250
（This article belongs to the Special Issue: Medical Imaging Based Disease Diagnosis Using AI)
Abstract WCE (Wireless Capsule Endoscopy) is a new technology that combines computer vision and medicine, allowing doctors to visualize the conditions inside the intestines, achieving good diagnostic results. However, due to the complex intestinal environment and limited pixel resolution of WCE videos, lesions are not easily detectable, and it takes an experienced doctor 1–2 h to analyze a complete WCE video. The use of computer-aided diagnostic methods, assisting or even replacing manual WCE diagnosis, has significant application value. In response to the issue of intestinal lesion detection in WCE videos, this paper proposes a multi-scale feature… More >
Open Access
ARTICLE
Mengdi Yang, Feng Yue, Weilong Wang, Xiangdong Meng, Lixin Wang, Pengyu Han, Haoran He, Benzheng Yuan, Zhiqiang Fan, Chenhui Wang, Qiming Du, Danyang Zheng, Xuefei Feng, Zheng Shan*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058163
（This article belongs to the Special Issue: Cyber-Physical Systems: recent advances, challenges and opportunities)
Abstract Quantum circuit fidelity is a crucial metric for assessing the accuracy of quantum computation results and indicating the precision of quantum algorithm execution. The primary methods for assessing quantum circuit fidelity include direct fidelity estimation and mirror circuit fidelity estimation. The former is challenging to implement in practice, while the latter requires substantial classical computational resources and numerous experimental runs. In this paper, we propose a fidelity estimation method based on Layer Interleaved Randomized Benchmarking, which decomposes a complex quantum circuit into multiple sublayers. By independently evaluating the fidelity of each layer, one can comprehensively… More >
Open Access
ARTICLE
Gang Xu1,2,6, Xinyu Fan1, Xiu-Bo Chen2, Xin Liu4, Zongpeng Li5, Yanhui Mao6,7, Kejia Zhang3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057882
Abstract Blockchain-enabled Internet of Medical Things (BIoMT) has attracted significant attention from academia and healthcare organizations. However, the large amount of medical data involved in BIoMT has also raised concerns about data security and personal privacy protection. To alleviate these concerns, blind signature technology has emerged as an effective method to solve blindness and unforgeability. Unfortunately, most existing blind signature schemes suffer from the security risk of key leakage. In addition, traditional blind signature schemes are also vulnerable to quantum computing attacks. Therefore, it remains a crucial and ongoing challenge to explore the construction of key-secure,… More >
Open Access
ARTICLE
Libo Han1,2, Sha Tao1,2, Wen Xia3, Weixin Sun3, Li Yan3, Wanlin Gao1,2,3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057833
Abstract At present, salient object detection (SOD) has achieved considerable progress. However, the methods that perform well still face the issue of inadequate detection accuracy. For example, sometimes there are problems of missed and false detections. Effectively optimizing features to capture key information and better integrating different levels of features to enhance their complementarity are two significant challenges in the domain of SOD. In response to these challenges, this study proposes a novel SOD method based on multi-strategy feature optimization. We propose the multi-size feature extraction module (MSFEM), which uses the attention mechanism, the multi-level feature… More >
Open Access
ARTICLE
Fengyu Yang1,2,*, Fa Zhong2, Xiaohui Wei1, Guangdong Zeng2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057697
Abstract The primary goal of software defect prediction (SDP) is to pinpoint code modules that are likely to contain defects, thereby enabling software quality assurance teams to strategically allocate their resources and manpower. Within-project defect prediction (WPDP) is a widely used method in SDP. Despite various improvements, current methods still face challenges such as coarse-grained prediction and ineffective handling of data drift due to differences in project distribution. To address these issues, we propose a fine-grained SDP method called DIDP (drift-immune defect prediction), based on drift-immune graph neural networks (DI-GNN). DIDP converts source code into graph… More >
Open Access
ARTICLE
Xinlu Zong*, Fan Yu, Zhen Chen, Xue Xia
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057494
（This article belongs to the Special Issue: Graph Neural Networks: Methods and Applications in Graph-related Problems)
Abstract Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a More >
Open Access
ARTICLE
Binyong Li1,2,3, Liangming Deng1,*, Jie Zhang1, Xianhui Deng1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.056730
Abstract To solve the challenges of connecting and coordinating multiple platforms in the automotive industry and to enhance collaboration among different participants, this research focuses on addressing the complex supply relationships in the automotive market, improving data sharing and interactions across various platforms, and achieving more detailed integration of data and operations. We propose a trust evaluation permission delegation method based on the automotive industry chain. The proposed method combines smart contracts with trust evaluation mechanisms, dynamically calculating the trust value of users based on the historical behavior of the delegated entity, network environment, and other More >
Open Access
ARTICLE
Xinlai Xing, Junliang Chen*, Xiaochuan Zhang, Shuran Zhou, Runqing Zhang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.056877
（This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
Abstract In task-oriented dialogue systems, intent, emotion, and actions are crucial elements of user activity. Analyzing the relationships among these elements to control and manage task-oriented dialogue systems is a challenging task. However, previous work has primarily focused on the independent recognition of user intent and emotion, making it difficult to simultaneously track both aspects in the dialogue tracking module and to effectively utilize user emotions in subsequent dialogue strategies. We propose a Multi-Head Encoder Shared Model (MESM) that dynamically integrates features from emotion and intent encoders through a feature fusioner. Addressing the scarcity of datasets More >
Open Access
ARTICLE
Guorong Qi1, Jian Mao1,*, Kai Huang1, Zhengxian You2, Jinliang Lin2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058396
（This article belongs to the Special Issue: Fortifying the Foundations: Novel Approaches to Cyber-Physical Systems Intrusion Detection and Industrial 4.0 Security)
Abstract Abnormal network traffic, as a frequent security risk, requires a series of techniques to categorize and detect it. Existing network traffic anomaly detection still faces challenges: the inability to fully extract local and global features, as well as the lack of effective mechanisms to capture complex interactions between features; Additionally, when increasing the receptive field to obtain deeper feature representations, the reliance on increasing network depth leads to a significant increase in computational resource consumption, affecting the efficiency and performance of detection. Based on these issues, firstly, this paper proposes a network traffic anomaly detection… More >
Open Access
ARTICLE
Haitao He1, Bingjian Yan1, Ke Xu1,*, Lu Yu1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058779
（This article belongs to the Special Issue: Applications of Artificial Intelligence for Information Security)
Abstract Software defect prediction aims to use measurement data of code and historical defects to predict potential problems, optimize testing resources and defect management. However, current methods face challenges: (1) Coarse-grained file level detection cannot accurately locate specific defects. (2) Fine-grained line-level defect prediction methods rely solely on local information of a single line of code, failing to deeply analyze the semantic context of the code line and ignoring the heuristic impact of line-level context on the code line, making it difficult to capture the interaction between global and local information. Therefore, this paper proposes a… More >
Open Access
ARTICLE
Na Deng, Jiuan Zhang*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058103
Abstract Rapid and accurate identification of high-quality patents can accelerate the transformation process of scientific and technological achievements, optimize the management of intellectual property rights and enhance the vitality of innovation. Aiming at the shortcomings of the traditional high-value patent assessment method, which is relatively simple and seldom considers the influence of patentees, this paper proposes a high-quality patent method HMFM (High-Value Patent Multi-Feature Fusion Method) that fuses multi-dimensional features. A weighted node importance assessment method in complex network called GLE (Glob-Local-struEntropy) based on improved structural entropy is designed to calculate the influence of the patentee More >
Open Access
ARTICLE
Yong Liu1,4, Zhiyu Wang2,*, Shouling Ji3, Daofu Gong1,5, Lanxin Cheng1, Ruosi Cheng1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057859
（This article belongs to the Special Issue: Multimedia Security in Deep Learning)
Abstract In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by fusing lip images and audio signals. The main method used is lip-audio matching detection technology based on the Siamese neural network, combined with MFCC (Mel Frequency Cepstrum Coefficient) feature extraction of band-pass filters, an improved dual-branch Siamese network structure, and a two-stream network structure design. Firstly, the video stream is preprocessed to extract lip images, and the audio stream is preprocessed to extract MFCC… More >
Open Access
ARTICLE
Dawei Xu1,2,3, Min Wang1, Yue Lv1, Moxuan Fu2, Yi Wu4,5,*, Jian Zhao1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058178
Abstract Website fingerprinting (WF) attacks can reveal information about the websites users browse by de-anonymizing encrypted traffic. Traditional website fingerprinting attack models, focusing solely on a single spatial feature, are inefficient regarding training time. When confronted with the concept drift problem, they suffer from a sharp drop in attack accuracy within a short period due to their reliance on extensive, outdated training data. To address the above problems, this paper proposes a parallel website fingerprinting attack (APWF) that incorporates an attention mechanism, which consists of an attack model and a fine-tuning method. Among them, the APWF… More >
Open Access
ARTICLE
Chao-Hsien Hsieh1, Qingqing Yang2,*, Dehong Kong2, Fengya Xu2, Hongmei Wang2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058122
Abstract The Cyber-Physical Systems (CPS) supported by Wireless Sensor Networks (WSN) helps factories collect data and achieve seamless communication between physical and virtual components. Sensor nodes are energy-constrained devices. Their energy consumption is typically correlated with the amount of data collection. The purpose of data aggregation is to reduce data transmission, lower energy consumption, and reduce network congestion. For large-scale WSN, data aggregation can greatly improve network efficiency. However, as many heterogeneous data is poured into a specific area at the same time, it sometimes causes data loss and then results in incompleteness and irregularity of… More >
Open Access
ARTICLE
Yuting Zhu1,*, Wenyu Zhang1,2, Hainan Wang1, Junjie Hou1, Haining Wang1, Meng Wang1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057441
Abstract Addressing the complex issue of emergency resource distribution center site selection in uncertain environments, this study was conducted to comprehensively consider factors such as uncertainty parameters and the urgency of demand at disaster-affected sites. Firstly, urgency cost, economic cost, and transportation distance cost were identified as key objectives. The study applied fuzzy theory integration to construct a triangular fuzzy multi-objective site selection decision model. Next, the defuzzification theory transformed the fuzzy decision model into a precise one. Subsequently, an improved Chaotic Quantum Multi-Objective Harris Hawks Optimization (CQ-MOHHO) algorithm was proposed to solve the model. The… More >
Open Access
ARTICLE
Shaozheng Zhang, Qiuyu Zhang*, Jiahui Tang, Ruihua Xu
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057269
（This article belongs to the Special Issue: Emerging Trends and Applications of Deep Learning for Biomedical Signal and Image Processing)
Abstract Medical institutions frequently utilize cloud servers for storing digital medical imaging data, aiming to lower both storage expenses and computational expenses. Nevertheless, the reliability of cloud servers as third-party providers is not always guaranteed. To safeguard against the exposure and misuse of personal privacy information, and achieve secure and efficient retrieval, a secure medical image retrieval based on a multi-attention mechanism and triplet deep hashing is proposed in this paper (abbreviated as MATDH). Specifically, this method first utilizes the contrast-limited adaptive histogram equalization method applicable to color images to enhance chest X-ray images. Next, a… More >
Open Access
ARTICLE
Khalil Ullah1,*, Song Jian1, Muhammad Naeem Ul Hassan1, Suliman Khan2, Mohammad Babar3,*, Arshad Ahmad4, Shafiq Ahmad5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057328
（This article belongs to the Special Issue: Exploring the Metaverse: Advancements in Future Networks Communication and Computing Technologies for Enhanced Quality of Experience)
Abstract The emergence of next generation networks (NextG), including 5G and beyond, is reshaping the technological landscape of cellular and mobile networks. These networks are sufficiently scaled to interconnect billions of users and devices. Researchers in academia and industry are focusing on technological advancements to achieve high-speed transmission, cell planning, and latency reduction to facilitate emerging applications such as virtual reality, the metaverse, smart cities, smart health, and autonomous vehicles. NextG continuously improves its network functionality to support these applications. Multiple input multiple output (MIMO) technology offers spectral efficiency, dependability, and overall performance in conjunction with More >
Open Access
ARTICLE
Liang Mao1,2, Yue Li1,2, Linlin Wang1,*, Jie Li1, Jiajun Tan1, Yang Meng1, Cheng Xiong1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058409
Abstract This paper introduces an advanced and efficient method for distributed drone-based fruit recognition and localization, tailored to satisfy the precision and security requirements of autonomous agricultural operations. Our method incorporates depth information to ensure precise localization and utilizes a streamlined detection network centered on the RepVGG module. This module replaces the traditional C2f module, enhancing detection performance while maintaining speed. To bolster the detection of small, distant fruits in complex settings, we integrate Selective Kernel Attention (SKAttention) and a specialized small-target detection layer. This adaptation allows the system to manage difficult conditions, such as variable… More >
Open Access
ARTICLE
Xinyu Liu#, Longfei Wang#, Yuxin Ma, Peng Shao*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058294
（This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
Abstract Stereoscopic agriculture, as an advanced method of agricultural production, poses new challenges for multi-task trajectory planning of unmanned aerial vehicles (UAVs). To address the need for UAVs to perform multi-task trajectory planning in stereoscopic agriculture, a multi-task trajectory planning model and algorithm (IEP-AO) that synthesizes flight safety and flight efficiency is proposed. Based on the requirements of stereoscopic agricultural geomorphological features and operational characteristics, the multi-task trajectory planning model is ensured by constructing targeted constraints at five aspects, including the path, slope, altitude, corner, energy and obstacle threat, to improve the effectiveness of the trajectory… More >
Open Access
ARTICLE
Yitong Zhou, Tianliang Lu*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057554
Abstract Facial recognition systems have become increasingly significant in public security efforts. They play a crucial role in apprehending criminals and locating missing children and elderly individuals. Nevertheless, in practical applications, around 30% to 50% of images are obstructed to varied extents, for as by the presence of masks, glasses, or hats. Repairing the masked facial images will enhance face recognition accuracy by 10% to 20%. Simultaneously, market research indicates a rising demand for efficient facial recognition technology within the security and surveillance sectors, with projections suggesting that the global facial recognition market would exceed US$3… More >
Open Access
ARTICLE
Tianzhi Zhang1, Gang Zhou1,*, Shuang Zhang2, Shunhang Li1, Yepeng Sun1, Qiankun Pi1, Shuo Liu3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.055943
Abstract Joint Multimodal Aspect-based Sentiment Analysis (JMASA) is a significant task in the research of multimodal fine-grained sentiment analysis, which combines two subtasks: Multimodal Aspect Term Extraction (MATE) and Multimodal Aspect-oriented Sentiment Classification (MASC). Currently, most existing models for JMASA only perform text and image feature encoding from a basic level, but often neglect the in-depth analysis of unimodal intrinsic features, which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features. Given this problem, we propose a Text-Image Feature Fine-grained… More >
Open Access
ARTICLE
Xunguang Yan1,2, Wenrui Wang1, Fanglin Lu1, Hongyong Fan3, Bo Wu1, Jianfeng Yu1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057797
（This article belongs to the Special Issue: Advances in Object Detection: Methods and Applications)
Abstract To maintain the reliability of power systems, routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues. The increasing resolution of drone-captured images has posed a challenge for traditional target detection methods, especially in identifying small objects in high-resolution images. This study presents an enhanced object detection algorithm based on the Faster Region-based Convolutional Neural Network (Faster R-CNN) framework, specifically tailored for detecting small-scale electrical components like insulators, shock hammers, and screws in transmission line. The algorithm features an improved backbone network for Faster R-CNN, which significantly boosts the More >
Open Access
ARTICLE
Shijie Tang1,2, Yong Ding1,3,4,*, Huiyong Wang5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.059143
（This article belongs to the Special Issue: Security and Privacy for Blockchain-empowered Internet of Things)
Abstract As more and more devices in Cyber-Physical Systems (CPS) are connected to the Internet, physical components such as programmable logic controller (PLC), sensors, and actuators are facing greater risks of network attacks, and fast and accurate attack detection techniques are crucial. The key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time series. To address this issue, we propose an anomaly detection method based on distributed deep learning. Our method uses a bilateral filtering algorithm for sequential sequences to remove noise in the More >
Open Access
ARTICLE
Jiansheng Peng1,2,3,4,*, Wei Qian1, Hongyu Zhang1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058028
Abstract Dynamic visual SLAM (Simultaneous Localization and Mapping) is an important research area, but existing methods struggle to balance real-time performance and accuracy in removing dynamic feature points, especially when semantic information is missing. This paper presents a novel dynamic SLAM system that uses optical flow tracking and epipolar geometry to identify dynamic feature points and applies a regional dynamic probability method to improve removal accuracy. We developed two innovative algorithms for precise pruning of dynamic regions: first, using optical flow and epipolar geometry to identify and prune dynamic areas while preserving static regions on stationary… More >
Open Access
ARTICLE
Menglin Yin1, Yong Qin1,2,3,4,*, Jiansheng Peng1,2,3,4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057460
Abstract In dynamic scenarios, visual simultaneous localization and mapping (SLAM) algorithms often incorrectly incorporate dynamic points during camera pose computation, leading to reduced accuracy and robustness. This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability. Firstly, a parallel thread employs the YOLOX object detection model to gather 2D semantic information and compensate for missed detections. Next, an improved K-means++ clustering algorithm clusters bounding box regions, adaptively determining the threshold for extracting dynamic object contours as dynamic points change. This process divides the image into low dynamic, suspicious dynamic, and high More >
Open Access
ARTICLE
Xiaowei Wang, Haiyang Zhang, Jiasheng Zhang, Yingkai Ge, Kexin Cui, Zifu Peng, Zhengyi Li, Lihua Wang*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058559
（This article belongs to the Special Issue: Distributed Computing with Applications to IoT and BlockChain)
Abstract In response to the challenges presented by the unreliable identity of the master node, high communication overhead, and limited network support size within the Practical Byzantine Fault-Tolerant (PBFT) algorithm for consortium chains, we propose an improved PBFT algorithm based on XGBoost grouping called XG-PBFT in this paper. XG-PBFT constructs a dataset by training important parameters that affect node performance, which are used as classification indexes for nodes. The XGBoost algorithm then is employed to train the dataset, and nodes joining the system will be grouped according to the trained grouping model. Among them, the nodes… More >
Open Access
ARTICLE
Feifei Yu1, Yongxian Huang2,*, Guoyan Chen1, Xiaoqing Yang2, Canyi Du2,*, Yongkang Gong2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058051
Abstract To accurately diagnose misfire faults in automotive engines, we propose a Channel Attention Convolutional Model, specifically the Squeeze-and-Excitation Networks (SENET), for classifying engine vibration signals and precisely pinpointing misfire faults. In the experiment, we established a total of 11 distinct states, encompassing the engine’s normal state, single-cylinder misfire faults, and dual-cylinder misfire faults for different cylinders. Data collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,840 Hz. The collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and… More >
Open Access
ARTICLE
Youhe Wang, Yi Sun*, Yujie Li, Chuanqi Zhou
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057710
Abstract With the development of anti-virus technology, malicious documents have gradually become the main pathway of Advanced Persistent Threat (APT) attacks, therefore, the development of effective malicious document classifiers has become particularly urgent. Currently, detection methods based on document structure and behavioral features encounter challenges in feature engineering, these methods not only have limited accuracy, but also consume large resources, and usually can only detect documents in specific formats, which lacks versatility and adaptability. To address such problems, this paper proposes a novel malicious document detection method-visualizing documents as GGE images (Grayscale, Grayscale matrix, Entropy). The… More >
Open Access
ARTICLE
Shorouq Alshawabkeh, Li Wu*, Daojun Dong, Yao Cheng, Liping Li
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057213
（This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems)
Abstract Detecting pavement cracks is critical for road safety and infrastructure management. Traditional methods, relying on manual inspection and basic image processing, are time-consuming and prone to errors. Recent deep-learning (DL) methods automate crack detection, but many still struggle with variable crack patterns and environmental conditions. This study aims to address these limitations by introducing the MaskerTransformer, a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network (Mask R-CNN) with the global contextual awareness of Vision Transformer (ViT). The research focuses on leveraging the strengths of both architectures… More >
Open Access
ARTICLE
Zhe Li, Yun Liang, Jinyu Wang, Yang Gao*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057225
Abstract Iced transmission line galloping poses a significant threat to the safety and reliability of power systems, leading directly to line tripping, disconnections, and power outages. Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source, neglect of irregular time series, and lack of attention-based closed-loop feedback, resulting in high rates of missed and false alarms. To address these challenges, we propose an Internet of Things (IoT) empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather… More >
Open Access
ARTICLE
Kai Liu1, Meizhao Liu1, Ming Tang1, Chen Zhang2,*, Junwu Zhu2,3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057074
Abstract The prediction of power grid faults based on meteorological factors is of great significance to reduce economic losses caused by power grid faults. However, the existing methods fail to effectively extract key features and accurately predict fault types due to the complexity of meteorological factors and their nonlinear relationships. In response to these challenges, we propose the Feature-Enhanced XGBoost power grid fault prediction method (FE-XGBoost). Specifically, we first combine the gradient boosting decision tree and recursive feature elimination method to extract essential features from meteorological data. Then, we incorporate a piecewise linear chaotic map to More >
Open Access
ARTICLE
Hailong Wang*, Junchao Shi
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057706
（This article belongs to the Special Issue: Deep Learning and Computer Vision for Industry 4.0 and Emerging Technologies)
Abstract A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can bottom spray code number recognition. In the coding number detection stage, Differentiable Binarization Network is used as the backbone network, combined with the Attention and Dilation Convolutions Path Aggregation Network feature fusion structure to enhance the model detection effect. In terms of text recognition, using the Scene Visual Text Recognition coding number recognition network for end-to-end training can alleviate… More >
Open Access
ARTICLE
Shuqiao Liu1, Zhao Zhang2,*, Hongyan Zhou1, Xuebo Chen1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057319
Abstract This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection systems. Combining Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis data with Variation Autoencoder (VAE) and Generative Adversarial Network (GAN) the network framework model (SAE-GAN), is proposed for environmental data reconstruction. The model combines two popular generative models, GAN and VAE, to generate features conditional on categorical data embedding after SWOT Analysis. The model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample data. Reconstructed data is… More >
Open Access
ARTICLE
Zicheng Zhou1, Shaowen Sun2, Teng Liang3, Mengjuan Li4,*, Fengling Xia5,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.056880
Abstract Z-curve’s encoding and decoding algorithms are primely important in many Z-curve-based applications. The bit interleaving algorithm is the current state-of-the-art algorithm for encoding and decoding Z-curve. Although simple, its efficiency is hindered by the step-by-step coordinate shifting and bitwise operations. To tackle this problem, we first propose the efficient encoding algorithm LTFe and the corresponding decoding algorithm LTFd, which adopt two optimization methods to boost the algorithm’s efficiency: 1) we design efficient lookup tables (LT) that convert encoding and decoding operations into table-lookup operations; 2) we design a bit detection mechanism that skips partial order More >
Open Access
ARTICLE
Shi Li, Didi Sun*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057349
（This article belongs to the Special Issue: Next-Generation Techniques and Applications on Opinion Mining and Affective Computing)
Abstract With the rapid expansion of social media, analyzing emotions and their causes in texts has gained significant importance. Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text, facilitating a deeper understanding of expressed sentiments and their underlying reasons. This comprehension is crucial for making informed strategic decisions in various business and societal contexts. However, recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneously model extracted features and their interactions, or inconsistencies in label prediction between emotion-cause pair extraction… More >
Open Access
ARTICLE
Jun Wang1,2, Mingjie Wang1,2, Zijie Li1,2, Ken Chen1,2, Jiatian Mei1,2, Shu Zhang1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057224
Abstract In the field of intelligent education, the integration of artificial intelligence, especially deep learning technologies, has garnered significant attention. Knowledge tracing (KT) plays a pivotal role in this field by predicting students’ future performance through the analysis of historical interaction data, thereby assisting educators in evaluating knowledge mastery and tailoring instructional strategies. Traditional knowledge tracing methods, largely based on Recurrent Neural Networks (RNNs) and Transformer models, primarily focus on capturing long-term interaction patterns in sequential data. However, these models may neglect crucial short-term dynamics and other relevant features. This paper introduces a novel approach to… More >
Open Access
ARTICLE
Li Ma1, Cai Dai1,*, Xingsi Xue2, Cheng Peng3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057168
Abstract The multi-objective particle swarm optimization algorithm (MOPSO) is widely used to solve multi-objective optimization problems. In the article, a multi-objective particle swarm optimization algorithm based on decomposition and multi-selection strategy is proposed to improve the search efficiency. First, two update strategies based on decomposition are used to update the evolving population and external archive, respectively. Second, a multi-selection strategy is designed. The first strategy is for the subspace without a non-dominated solution. Among the neighbor particles, the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle… More >
Open Access
ARTICLE
Jiyu Zhang1,*, Kehe Wu1, Ruomeng Yan1, Zheng Tian2, Yizhen Sun2, Yuxi Wu2, Yaogong Guo3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.055243
Abstract Renewable Energy Systems (RES) provide a sustainable solution to climate warming and environmental pollution by enhancing stability and reliability through status acquisition and analysis on cloud platforms and intelligent processing on edge servers (ES). However, securely distributing encrypted data stored in the cloud to terminals that meet decryption requirements has become a prominent research topic. Additionally, managing attributes, including addition, deletion, and modification, is a crucial issue in the access control scheme for RES. To address these security concerns, a trust-based ciphertext-policy attribute-based encryption (CP-ABE) device access control scheme is proposed for RES (TB-CP-ABE). This… More >
Open Access
ARTICLE
Yicong Yu1,2, Kaixin Lin1, Jiajun Hong1, Rong-Guei Tsai3,*, Yuanzhi Huang1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058467
Abstract In recent years, the number of patients with colon disease has increased significantly. Colon polyps are the precursor lesions of colon cancer. If not diagnosed in time, they can easily develop into colon cancer, posing a serious threat to patients’ lives and health. A colonoscopy is an important means of detecting colon polyps. However, in polyp imaging, due to the large differences and diverse types of polyps in size, shape, color, etc., traditional detection methods face the problem of high false positive rates, which creates problems for doctors during the diagnosis process. In order to… More >
Open Access
ARTICLE
Jianxin Feng*, Jianhao Zhang, Ge Cao, Zhiguo Liu, Yuanming Ding
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058374
Abstract Multivariate time series forecasting is widely used in traffic planning, weather forecasting, and energy consumption. Series decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time series. However, the decomposition kernel of previous decomposition-based models is fixed, and these models have not considered the differences in frequency fluctuations between components. These problems make it difficult to analyze the intricate temporal variations of real-world time series. In this paper, we propose a series decomposition-based Mamba model, DecMamba, to obtain the intricate temporal dependencies and… More >
Open Access
ARTICLE
Yuewei Wu1, Ruiling Fu1, Tongtong Xing1, Fulian Yin1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057606
Abstract In the Internet era, recommendation systems play a crucial role in helping users find relevant information from large datasets. Class imbalance is known to severely affect data quality, and therefore reduce the performance of recommendation systems. Due to the imbalance, machine learning algorithms tend to classify inputs into the positive (majority) class every time to achieve high prediction accuracy. Imbalance can be categorized such as by features and classes, but most studies consider only class imbalance. In this paper, we propose a recommendation system that can integrate multiple networks to adapt to a large number… More >
Open Access
ARTICLE
Liuqiang Shu, Lei Yu*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.056077
（This article belongs to the Special Issue: Medical Imaging Based Disease Diagnosis Using AI)
Abstract Bone age assessment (BAA) aims to determine whether a child’s growth and development are normal concerning their chronological age. To predict bone age more accurately based on radiographs, and for the left-hand X-ray images of different races model can have better adaptability, we propose a neural network in parallel with the quantitative features from the left-hand bone measurements for BAA. In this study, a lightweight feature extractor (LFE) is designed to obtain the feature maps from radiographs, and a module called attention eraser module (AEM) is proposed to capture the fine-grained features. Meanwhile, the dimensional… More >
Open Access
ARTICLE
Yuxiang Zou1, Ning He2,*, Jiwu Sun1, Xunrui Huang1, Wenhua Wang1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.055732
Abstract In recent years, gait-based emotion recognition has been widely applied in the field of computer vision. However, existing gait emotion recognition methods typically rely on complete human skeleton data, and their accuracy significantly declines when the data is occluded. To enhance the accuracy of gait emotion recognition under occlusion, this paper proposes a Multi-scale Suppression Graph Convolutional Network (MS-GCN). The MS-GCN consists of three main components: Joint Interpolation Module (JI Moudle), Multi-scale Temporal Convolution Network (MS-TCN), and Suppression Graph Convolutional Network (SGCN). The JI Module completes the spatially occluded skeletal joints using the (K-Nearest Neighbors)… More >
Open Access
ARTICLE
Jianwei Zhang1,2,*, Zhishang Zhao3, Zengyu Cai3, Yuan Feng4, Liang Zhu3, Yahui Sun3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058438
Abstract The rapid development of short video platforms poses new challenges for traditional recommendation systems. Recommender systems typically depend on two types of user behavior feedback to construct user interest profiles: explicit feedback (interactive behavior), which significantly influences users’ short-term interests, and implicit feedback (viewing time), which substantially affects their long-term interests. However, the previous model fails to distinguish between these two feedback methods, leading it to predict only the overall preferences of users based on extensive historical behavior sequences. Consequently, it cannot differentiate between users’ long-term and short-term interests, resulting in low accuracy in describing… More >
Open Access
ARTICLE
Congcong Wang1, Chen Wang2,3,*, Wenying Zheng4,*, Wei Gu5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057975
（This article belongs to the Special Issue: Security, Privacy, and Robustness for Trustworthy AI Systems)
Abstract As smart grid technology rapidly advances, the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection. Current research emphasizes data security and user privacy concerns within smart grids. However, existing methods struggle with efficiency and security when processing large-scale data. Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge. This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities. The approach optimizes data preprocessing, More >
Open Access
ARTICLE
Jizhao Liu, Minghao Guo*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057660
Abstract The increasing popularity of the Internet and the widespread use of information technology have led to a rise in the number and sophistication of network attacks and security threats. Intrusion detection systems are crucial to network security, playing a pivotal role in safeguarding networks from potential threats. However, in the context of an evolving landscape of sophisticated and elusive attacks, existing intrusion detection methodologies often overlook critical aspects such as changes in network topology over time and interactions between hosts. To address these issues, this paper proposes a real-time network intrusion detection method based on… More >
Open Access
ARTICLE
Zhiguo Liu1,#, Yuqing Gui1,#, Lin Wang2,*, Yingru Jiang1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057353
（This article belongs to the Special Issue: Practical Application and Services in Fog/Edge Computing System)
Abstract Satellite edge computing has garnered significant attention from researchers; however, processing a large volume of tasks within multi-node satellite networks still poses considerable challenges. The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers, making it necessary to implement effective task offloading scheduling to enhance user experience. In this paper, we propose a priority-based task scheduling strategy based on a Software-Defined Network (SDN) framework for satellite-terrestrial integrated networks, which clarifies the execution order of tasks based on their priority. Subsequently, we More >
Open Access
ARTICLE
Hengyang Liu1, Yang Yuan1,*, Pengcheng Ren1, Chengyun Song1, Fen Luo2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.056478
（This article belongs to the Special Issue: Deep Learning in Computer-Aided Diagnosis Based on Medical Image)
Abstract Existing semi-supervised medical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch. However, current copy-paste methods have three limitations: (1) training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information; (2) low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data; (3) the segmentation performance in low-contrast and local regions is less than optimal. We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy (SADT), which enhances feature diversity and learns high-quality features to overcome these problems. To be more… More >
Open Access
ARTICLE
Adnan Ahmad, Zhao Li*, Irfan Tariq, Zhengran He
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.055768
Abstract Micro-expressions (ME) recognition is a complex task that requires advanced techniques to extract informative features from facial expressions. Numerous deep neural networks (DNNs) with convolutional structures have been proposed. However, unlike DNNs, shallow convolutional neural networks often outperform deeper models in mitigating overfitting, particularly with small datasets. Still, many of these methods rely on a single feature for recognition, resulting in an insufficient ability to extract highly effective features. To address this limitation, in this paper, an Improved Dual-stream Shallow Convolutional Neural Network based on an Extreme Gradient Boosting Algorithm (IDSSCNN-XgBoost) is introduced for ME… More >
Open Access
ARTICLE
Xueyan Ding1,2, Xiyu Chen1, Jiaxin Wang1, Jianxin Zhang1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057655
Abstract Underwater target detection is extensively applied in domains such as underwater search and rescue, environmental monitoring, and marine resource surveys. It is crucial in enabling autonomous underwater robot operations and promoting ocean exploration. Nevertheless, low imaging quality, harsh underwater environments, and obscured objects considerably increase the difficulty of detecting underwater targets, making it difficult for current detection methods to achieve optimal performance. In order to enhance underwater object perception and improve target detection precision, we propose a lightweight underwater target detection method using You Only Look Once (YOLO) v8 with multi-scale cross-channel attention (MSCCA), named… More >
Open Access
ARTICLE
Weijian Fan1,*, Chunhua Wang2, Xiao Han3, Chichen Lin4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.056930
Abstract The proliferation of robot accounts on social media platforms has posed a significant negative impact, necessitating robust measures to counter network anomalies and safeguard content integrity. Social robot detection has emerged as a pivotal yet intricate task, aimed at mitigating the dissemination of misleading information. While graph-based approaches have attained remarkable performance in this realm, they grapple with a fundamental limitation: the homogeneity assumption in graph convolution allows social robots to stealthily evade detection by mingling with genuine human profiles. To unravel this challenge and thwart the camouflage tactics, this work proposed an innovative social… More >
Open Access
ARTICLE
Jing Guo*, Ziying Wang, Yajuan Guo, Haitao Jiang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057045
（This article belongs to the Special Issue: Best Practices for Smart Grid SCADA Security Systems Using Artificial Intelligence (AI) Models)
Abstract The rapid proliferation of electric vehicle (EV) charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system. This study presents an innovative anomaly detection framework for EV charging stations, addressing the unique challenges posed by third-party aggregation platforms. Our approach integrates node equations-based on the parameter identification with a novel deep learning model, xDeepCIN, to detect abnormal data reporting indicative of aggregation attacks. We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation. The xDeepCIN model, incorporating a Compressed Interaction Network, has the ability… More >
Open Access
ARTICLE
Peng Li, Yanrui Wei, Lili Yin*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.056651
Abstract Stock price prediction is a typical complex time series prediction problem characterized by dynamics, nonlinearity, and complexity. This paper introduces a generative adversarial network model that incorporates an attention mechanism (GAN-LSTM-Attention) to improve the accuracy of stock price prediction. Firstly, the generator of this model combines the Long and Short-Term Memory Network (LSTM), the Attention Mechanism and, the Fully-Connected Layer, focusing on generating the predicted stock price. The discriminator combines the Convolutional Neural Network (CNN) and the Fully-Connected Layer to discriminate between real stock prices and generated stock prices. Secondly, to evaluate the practical application… More >
Open Access
ARTICLE
Chunhui Li1,2, Xiaoying Wang1,2,*, Qingjie Zhang1,2, Jiaye Liang1, Aijing Zhang1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058081
（This article belongs to the Special Issue: Security, Privacy, and Robustness for Trustworthy AI Systems)
Abstract Previous studies have shown that deep learning is very effective in detecting known attacks. However, when facing unknown attacks, models such as Deep Neural Networks (DNN) combined with Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) combined with LSTM, and so on are built by simple stacking, which has the problems of feature loss, low efficiency, and low accuracy. Therefore, this paper proposes an autonomous detection model for Distributed Denial of Service attacks, Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention (MSCNN-BiGRU-SHA), which is based on a Multi-strategy Integrated Zebra Optimization Algorithm (MI-ZOA). The… More >
Open Access
ARTICLE
Xiaoyun Chen1, Lanyao Zhang1, Xiaoling Chen1, Yigang Cen2, Linna Zhang1,*, Fugui Zhang1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.058063
Abstract Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules. In the production process, defect samples occur infrequently and exhibit random shapes and sizes, which makes it challenging to collect defective samples. Additionally, the complex surface background of polysilicon cell wafers complicates the accurate identification and localization of defective regions. This paper proposes a novel Lightweight Multi-scale Feature Fusion network (LMFF) to address these challenges. The network comprises a feature extraction network, a multi-scale feature fusion module (MFF), and a segmentation network. Specifically, a feature extraction network is proposed to obtain… More >
Open Access
ARTICLE
Ao Shen1, Zhiquan Lai1,*, Dongsheng Li1,*, Xiaoyu Hu2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2024.057491
Abstract Large-scale Language Models (LLMs) have achieved significant breakthroughs in Natural Language Processing (NLP), driven by the pre-training and fine-tuning paradigm. While this approach allows models to specialize in specific tasks with reduced training costs, the substantial memory requirements during fine-tuning present a barrier to broader deployment. Parameter-Efficient Fine-Tuning (PEFT) techniques, such as Low-Rank Adaptation (LoRA), and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency. Among these, QLoRA, which combines PEFT and quantization, has demonstrated notable success in reducing memory footprints during fine-tuning, prompting the development… More >
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