Open Access
ARTICLE
Wonbyung Lee, Jang Hyun Kim*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.065413
Abstract Predicting player performance in sports is a critical challenge with significant implications for team success, fan engagement, and financial outcomes. Although, in Major League Baseball (MLB), statistical methodologies such as sabermetrics have been widely used, the dynamic nature of sports makes accurate performance prediction a difficult task. Enhanced forecasts can provide immense value to team managers by aiding strategic player contract and acquisition decisions. This study addresses this challenge by employing the temporal fusion transformer (TFT), an advanced and cutting-edge deep learning model for complex data, to predict pitchers’ earned run average (ERA), a key More >
Open Access
ARTICLE
Junbin He1,2, Wuxia Zhang3, Xianyi Liu1, Jinping Liu2,*, Guangyi Yang4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.064402
（This article belongs to the Special Issue: Big Data and Artificial Intelligence in Control and Information System)
Abstract The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System (ICPS), enhancing intelligence and autonomy. However, this transition also expands the attack surface, introducing critical security vulnerabilities. To address these challenges, this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection. Specifically, an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering (IVB-NCA-NLKF) method is developed to model nonlinear system dynamics, enabling optimal state estimation in multi-sensor ICPS environments. Intrusions within the physical sensing system are identified by More >
Open Access
ARTICLE
Tajinder Kumar1, Sarbjit Kaur2, Purushottam Sharma3,*, Ankita Chhikara4, Xiaochun Cheng5,*, Sachin Lalar6, Vikram Verma7
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062010
Abstract During its growth stage, the plant is exposed to various diseases. Detection and early detection of crop diseases is a major challenge in the horticulture industry. Crop infections can harm total crop yield and reduce farmers’ income if not identified early. Today’s approved method involves a professional plant pathologist to diagnose the disease by visual inspection of the afflicted plant leaves. This is an excellent use case for Community Assessment and Treatment Services (CATS) due to the lengthy manual disease diagnosis process and the accuracy of identification is directly proportional to the skills of pathologists.… More >
Open Access
ARTICLE
Peiying Zhang1,2,*, Yihong Yu1,2, Jing Liu3, Chong Lv1,2, Lizhuang Tan4,5, Yulin Zhang6,7,8
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.064654
（This article belongs to the Special Issue: Big Data and Artificial Intelligence in Control and Information System)
Abstract As Internet of Things (IoT) technologies continue to evolve at an unprecedented pace, intelligent big data control and information systems have become critical enablers for organizational digital transformation, facilitating data-driven decision making, fostering innovation ecosystems, and maintaining operational stability. In this study, we propose an advanced deployment algorithm for Service Function Chaining (SFC) that leverages an enhanced Practical Byzantine Fault Tolerance (PBFT) mechanism. The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings. By integrating blockchain technology and Deep Reinforcement Learning (DRL), our algorithm not… More >
Open Access
ARTICLE
Van-Ha Hoang1, Jong Weon Lee1, Chun-Su Park2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063468
Abstract Fire can cause significant damage to the environment, economy, and human lives. If fire can be detected early, the damage can be minimized. Advances in technology, particularly in computer vision powered by deep learning, have enabled automated fire detection in images and videos. Several deep learning models have been developed for object detection, including applications in fire and smoke detection. This study focuses on optimizing the training hyperparameters of YOLOv8 and YOLOv10 models using Bayesian Tuning (BT). Experimental results on the large-scale D-Fire dataset demonstrate that this approach enhances detection performance. Specifically, the proposed approach… More >
Open Access
ARTICLE
Mingen Zhong1, Kaibo Yang1,*, Ziji Xiao1, Jiawei Tan2, Kang Fan2, Zhiying Deng1, Mengli Zhou1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063383
Abstract With the rapid urbanization and exponential population growth in China, two-wheeled vehicles have become a popular mode of transportation, particularly for short-distance travel. However, due to a lack of safety awareness, traffic violations by two-wheeled vehicle riders have become a widespread concern, contributing to urban traffic risks. Currently, significant human and material resources are being allocated to monitor and intercept non-compliant riders to ensure safe driving behavior. To enhance the safety, efficiency, and cost-effectiveness of traffic monitoring, automated detection systems based on image processing algorithms can be employed to identify traffic violations from eye-level video… More >
Open Access
ARTICLE
Junfeng Lin1, Jialin Ma1,*, Wei Chen1,2, Hao Wang1, Weiguo Ding1, Mingyao Tang1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062895
Abstract The cross-modal person re-identification task aims to match visible and infrared images of the same individual. The main challenges in this field arise from significant modality differences between individuals and the lack of high-quality cross-modal correspondence methods. Existing approaches often attempt to establish modality correspondence by extracting shared features across different modalities. However, these methods tend to focus on local information extraction and fail to fully leverage the global identity information in the cross-modal features, resulting in limited correspondence accuracy and suboptimal matching performance. To address this issue, we propose a quadratic graph matching method… More >
Open Access
ARTICLE
Ronglei Hu1, Chuce He1,2, Sihui Liu1, Dong Yao1, Xiuying Li1, Xiaoyi Duan1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062542
Abstract Ciphertext data retrieval in cloud databases suffers from some critical limitations, such as inadequate security measures, disorganized key management practices, and insufficient retrieval access control capabilities. To address these problems, this paper proposes an enhanced Fully Homomorphic Encryption (FHE) algorithm based on an improved DGHV algorithm, coupled with an optimized ciphertext retrieval scheme. Our specific contributions are outlined as follows: First, we employ an authorization code to verify the user’s retrieval authority and perform hierarchical access control on cloud storage data. Second, a triple-key encryption mechanism, which separates the data encryption key, retrieval authorization key, More >
Open Access
ARTICLE
Louyi Jiang1,#, Sulei Wang1,#, Jiang Xie1, Haiya Wang2, Wei Shao3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061759
（This article belongs to the Special Issue: Emerging Trends and Applications of Deep Learning for Biomedical Signal and Image Processing)
Abstract Carotid artery plaques represent a major contributor to the morbidity and mortality associated with cerebrovascular disease, and their clinical significance is largely determined by the risk linked to plaque vulnerability. Therefore, classifying plaque risk constitutes one of the most critical tasks in the clinical management of this condition. While classification models derived from individual medical centers have been extensively investigated, these single-center models often fail to generalize well to multi-center data due to variations in ultrasound images caused by differences in physician expertise and equipment. To address this limitation, a Dual-Classifier Label Correction Network model… More >
Open Access
ARTICLE
Trong Hieu Luu1, Phan Nguyen Ky Phuc2, Quang Hieu Ngo1,*, Thanh Tam Nguyen3, Huu Cuong Nguyen1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.064007
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract This study presents a drone-based aerial imaging method for automated rice seedling detection and counting in paddy fields. Utilizing a drone equipped with a high-resolution camera, images are captured 14 days post-sowing at a consistent altitude of six meters, employing autonomous flight for uniform data acquisition. The approach effectively addresses the distinct growth patterns of both single and clustered rice seedlings at this early stage. The methodology follows a two-step process: first, the GoogleNet deep learning network identifies the location and center points of rice plants. Then, the U-Net deep learning network performs classification and… More >
Open Access
ARTICLE
Chen Wang1,2, Tiezheng Guo1, Qingwen Yang1, Yanyi Liu1, Jiawei Tang1, Yingyou Wen1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063676
（This article belongs to the Special Issue: Neural Architecture Search: Optimization, Efficiency and Application)
Abstract Primary light chain amyloidosis is a rare hematologic disease with multi-organ involvement. Nearly one-third of patients with amyloidosis experience five or more consultations before diagnosis, which may lead to a poor prognosis due to delayed diagnosis. Early risk prediction based on artificial intelligence is valuable for clinical diagnosis and treatment of amyloidosis. For this disease, we propose an Evolutionary Neural Architecture Searching (ENAS) based risk prediction model, which achieves high-precision early risk prediction using physical examination data as a reference factor. To further enhance the value of clinic application, we designed a natural language-based interpretable… More >
Open Access
ARTICLE
Rahim Khan1, Ihsan Rabbi1, Umar Farooq1, Jawad Khan2,*, Fahad Alturise3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063303
（This article belongs to the Special Issue: Deep Neural Networks-based Convergence Technology and Applications)
Abstract Leaf disease identification is one of the most promising applications of convolutional neural networks (CNNs). This method represents a significant step towards revolutionizing agriculture by enabling the quick and accurate assessment of plant health. In this study, a CNN model was specifically designed and tested to detect and categorize diseases on fig tree leaves. The researchers utilized a dataset of 3422 images, divided into four classes: healthy, fig rust, fig mosaic, and anthracnose. These diseases can significantly reduce the yield and quality of fig tree fruit. The objective of this research is to develop a… More >
Open Access
ARTICLE
Dang Hung Tran, Van Tinh Nguyen*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063228
（This article belongs to the Special Issue: Advances in Deep Learning and Neural Networks: Architectures, Applications, and Challenges)
Abstract It is known that long non-coding RNAs (lncRNAs) play vital roles in biological processes and contribute to the progression, development, and treatment of various diseases. Obviously, understanding associations between diseases and lncRNAs significantly enhances our ability to interpret disease mechanisms. Nevertheless, the process of determining lncRNA-disease associations is costly, labor-intensive, and time-consuming. Hence, it is expected to foster computational strategies to uncover lncRNA-disease relationships for further verification to save time and resources. In this study, a collaborative filtering and graph attention network-based LncRNA-Disease Association (CFGANLDA) method was nominated to expose potential lncRNA-disease associations. First, it… More >
Open Access
ARTICLE
Thien-Tan Cao, Huu-Thanh Duong, Viet-Tuan Le, Hau Nguyen Trung, Vinh Truong Hoang, Kiet Tran-Trung*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061583
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract Kinship verification is a key biometric recognition task that determines biological relationships based on physical features. Traditional methods predominantly use facial recognition, leveraging established techniques and extensive datasets. However, recent research has highlighted ear recognition as a promising alternative, offering advantages in robustness against variations in facial expressions, aging, and occlusions. Despite its potential, a significant challenge in ear-based kinship verification is the lack of large-scale datasets necessary for training deep learning models effectively. To address this challenge, we introduce the EarKinshipVN dataset, a novel and extensive collection of ear images designed specifically for kinship… More >
Open Access
ARTICLE
Shaowei He, Wenchao Cui*, Gang Li, Hairun Xu, Xiang Chen, Yu Tai
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063979
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract The Virtual Power Plant (VPP), as an innovative power management architecture, achieves flexible dispatch and resource optimization of power systems by integrating distributed energy resources. However, due to significant differences in operational costs and flexibility of various types of generation resources, as well as the volatility and uncertainty of renewable energy sources (such as wind and solar power) and the complex variability of load demand, the scheduling optimization of virtual power plants has become a critical issue that needs to be addressed. To solve this, this paper proposes an intelligent scheduling method for virtual power… More >
Open Access
ARTICLE
Vidivelli Soundararajan*, Manikandan Ramachandran*, Srivatsan Vinodh Kumar
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063059
Abstract Many applications, including security systems, medical diagnostics, and human-computer interfaces, depend on eye gaze recognition. However, due to factors including individual variations, occlusions, and shifting illumination conditions, real-world scenarios continue to provide difficulties for accurate and consistent eye gaze recognition. This work is aimed at investigating the potential benefits of employing transfer learning to improve eye gaze detection ability and efficiency. Transfer learning is the process of fine-tuning pre-trained models on smaller, domain-specific datasets after they have been trained on larger datasets. We study several transfer learning algorithms and evaluate their effectiveness on eye gaze… More >
Open Access
ARTICLE
Zhenfu Zhang1, Haiyan Yin2, Liudong Zuo3, Pan Lai1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062980
（This article belongs to the Special Issue: The Next-generation Deep Learning Approaches to Emerging Real-world Applications)
Abstract The knapsack problem is a classical combinatorial optimization problem widely encountered in areas such as logistics, resource allocation, and portfolio optimization. Traditional methods, including dynamic programming (DP) and greedy algorithms, have been effective in solving small problem instances but often struggle with scalability and efficiency as the problem size increases. DP, for instance, has exponential time complexity and can become computationally prohibitive for large problem instances. On the other hand, greedy algorithms offer faster solutions but may not always yield the optimal results, especially when the problem involves complex constraints or large numbers of items.… More >
Open Access
ARTICLE
Arshad Mehmmod1,#, Komal Batool1,#, Ahthsham Sajid2,3, Muhammad Mansoor Alam2,3, Mazliham MohD Su’ud3,*, Inam Ullah Khan3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062971
（This article belongs to the Special Issue: Advances in IoT Security: Challenges, Solutions, and Future Applications)
Abstract Traditional rule-based Intrusion Detection Systems (IDS) are commonly employed owing to their simple design and ability to detect known threats. Nevertheless, as dynamic network traffic and a new degree of threats exist in IoT environments, these systems do not perform well and have elevated false positive rates—consequently decreasing detection accuracy. In this study, we try to overcome these restrictions by employing fuzzy logic and machine learning to develop an Enhanced Rule-Based Model (ERBM) to classify the packets better and identify intrusions. The ERBM developed for this approach improves data preprocessing and feature selections by utilizing… More >
Open Access
ARTICLE
Pengfei Zhang1, Rui Xin1, Xing Xu1, Yuzhen Wang1, Xiaodong Li2, Xiao Zhang2, Meina Song2, Zhonghong Ou3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062618
Abstract Session-based recommendation systems (SBR) are pivotal in suggesting items by analyzing anonymized sequences of user interactions. Traditional methods, while competent, often fall short in two critical areas: they fail to address potential inter-session item transitions, which are behavioral dependencies that extend beyond individual session boundaries, and they rely on monolithic item aggregation to construct session representations. This approach does not capture the multi-scale and heterogeneous nature of user intent, leading to a decrease in modeling accuracy. To overcome these limitations, a novel approach called HMGS has been introduced. This system incorporates dual graph architectures to… More >
Open Access
ARTICLE
Faheem Shaukat1, Naveed Ejaz1,2, Rashid Kamal3,4, Tamim Alkhalifah5,*, Sheraz Aslam6,7,*, Mu Mu4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061702
Abstract Automated and accurate movie genre classification is crucial for content organization, recommendation systems, and audience targeting in the film industry. Although most existing approaches focus on audiovisual features such as trailers and posters, the text-based classification remains underexplored despite its accessibility and semantic richness. This paper introduces the Genre Attention Model (GAM), a deep learning architecture that integrates transformer models with a hierarchical attention mechanism to extract and leverage contextual information from movie plots for multi-label genre classification. In order to assess its effectiveness, we assess multiple transformer-based models, including Bidirectional Encoder Representations from Transformers… More >
Open Access
ARTICLE
Hongliang Tian, Meiruo Li*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060297
（This article belongs to the Special Issue: Security and Privacy for Blockchain-empowered Internet of Things)
Abstract The accelerated advancement of the Internet of Things (IoT) has generated substantial data, including sensitive and private information. Consequently, it is imperative to guarantee the security of data sharing. While facilitating fine-grained access control, Ciphertext Policy Attribute-Based Encryption (CP-ABE) can effectively ensure the confidentiality of shared data. Nevertheless, the conventional centralized CP-ABE scheme is plagued by the issues of key misuse, key escrow, and large computation, which will result in security risks. This paper suggests a lightweight IoT data security sharing scheme that integrates blockchain technology and CP-ABE to address the abovementioned issues. The integrity… More >
Open Access
ARTICLE
Yogendra P. S. Maravi*, Nishchol Mishra
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063964
Abstract The growing demand for international travel has highlighted the critical need for reliable tools to verify travelers’ healthcare status and meet entry requirements. Personal health passports, while essential, face significant challenges related to data silos, privacy protection, and forgery risks in global sharing. To address these issues, this study proposes a blockchain-based solution designed for the secure storage, sharing, and verification of personal health passports. This innovative approach combines on-chain and off-chain storage, leveraging searchable encryption to enhance data security and optimize blockchain storage efficiency. By reducing the storage burden on the blockchain, the system… More >
Open Access
ARTICLE
Sulaiman Al Amro*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063734
（This article belongs to the Special Issue: Empowered Connected Futures of AI, IoT, and Cloud Computing in the Development of Cognitive Cities)
Abstract The rapid proliferation of Internet of Things (IoT) devices has heightened security concerns, making intrusion detection a pivotal challenge in safeguarding these networks. Traditional centralized Intrusion Detection Systems (IDS) often fail to meet the privacy requirements and scalability demands of large-scale IoT ecosystems. To address these challenges, we propose an innovative privacy-preserving approach leveraging Federated Learning (FL) for distributed intrusion detection. Our model eliminates the need for aggregating sensitive data on a central server by training locally on IoT devices and sharing only encrypted model updates, ensuring enhanced privacy and scalability without compromising detection accuracy.… More >
Open Access
ARTICLE
Yong Zheng*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063498
Abstract A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors. Collaborative filtering, a popular technique within recommender systems, predicts user interests by analyzing patterns in interactions and similarities between users, leveraging past behavior data to make personalized recommendations. Despite its popularity, collaborative filtering faces notable challenges, and one of them is the issue of grey-sheep users who have unusual tastes in the system. Surprisingly, existing research has not extensively explored outlier detection techniques to address the grey-sheep problem. To fill this research gap, this study conducts… More >
Open Access
ARTICLE
Hao Huang1, Kongyu Yang2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063311
（This article belongs to the Special Issue: Advanced Bio-Inspired Optimization Algorithms and Applications)
Abstract Many existing immune detection algorithms rely on a large volume of labeled self-training samples, which are often difficult to obtain in practical scenarios, thus limiting the training of detection models. Furthermore, noise inherent in the samples can substantially degrade the detection accuracy of these algorithms. To overcome these challenges, we propose an immune generation algorithm that leverages clustering and a rebound mechanism for label propagation (LP-CRI). The dataset is randomly partitioned into multiple subsets, each of which undergoes clustering followed by label propagation and evaluation. The rebound mechanism assesses the model’s performance after propagation and More >
Open Access
ARTICLE
Ya-Jie Sun1, Li-Wei Qiao1, Sai Ji1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062950
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract Vehicle re-identification involves matching images of vehicles across varying camera views. The diversity of camera locations along different roadways leads to significant intra-class variation and only minimal inter-class similarity in the collected vehicle images, which increases the complexity of re-identification tasks. To tackle these challenges, this study proposes AG-GCN (Attention-Guided Graph Convolutional Network), a novel framework integrating several pivotal components. Initially, AG-GCN embeds a lightweight attention module within the ResNet-50 structure to learn feature weights automatically, thereby improving the representation of vehicle features globally by highlighting salient features and suppressing extraneous ones. Moreover, AG-GCN adopts More >
Open Access
ARTICLE
Milinda Priyankara Bandara Gamawelagedara1, Mian Usman Sattar1, Raza Hasan2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062826
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract Fuel station drive-offs, wherein the drivers simply drive off without paying, are a major issue in the UK (United Kingdom) due to rising fuel costs and financial hardships. The phenomenon has increased greatly over the last few years, with reports indicating a substantial increase in such events in the major cities. Traditional prevention measures such as Avutec and Driveoffalert rely primarily on expensive infrastructure and blacklisted databases. Such systems typically involve costly camera installation and maintenance and are consequently out of the budget of small fuel stations. These conventional approaches also fall short regarding real-time… More >
Open Access
ARTICLE
Shaoming Qiu, Xinchen Huang*, Liangyu Liu, Bicong E, Jingfeng Ye
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062482
Abstract Most existing knowledge graph relationship prediction methods are unable to capture the complex information of multi-relational knowledge graphs, thus overlooking key details contained in different entity pairs and making it difficult to aggregate more complex relational features. Moreover, the insufficient capture of multi-hop relational information limits the processing capability of the global structure of the graph and reduces the accuracy of the knowledge graph completion task. This paper uses graph neural networks to construct new message functions for different relations, which can be defined as the rotation from the source entity to the target entity… More >
Open Access
ARTICLE
Shuqin Zhang1, Zihao Wang1,*, Xinyu Su2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062080
Abstract The methods of network attacks have become increasingly sophisticated, rendering traditional cybersecurity defense mechanisms insufficient to address novel and complex threats effectively. In recent years, artificial intelligence has achieved significant progress in the field of network security. However, many challenges and issues remain, particularly regarding the interpretability of deep learning and ensemble learning algorithms. To address the challenge of enhancing the interpretability of network attack prediction models, this paper proposes a method that combines Light Gradient Boosting Machine (LGBM) and SHapley Additive exPlanations (SHAP). LGBM is employed to model anomalous fluctuations in various network indicators,… More >
Open Access
ARTICLE
Rajendren Subramaniam1, Saaidal Razalli Azzuhri2,*, Teh Ying Wah1, Atif Mahmood3, Vimala Balakrishnan1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061560
Abstract Counterfeiting is still a pervasive global issue, affecting multiple industries and hindering industrial innovation, while causing substantial financial losses, reputational damage, and risks to consumer safety. From luxury goods and pharmaceuticals to electronics and automotive parts, counterfeit products infiltrate supply chains, leading to a loss of revenue for legitimate businesses and undermining consumer trust. Traditional anti-counterfeiting measures, such as holograms, serial numbers, and barcodes, have proven to be insufficient as counterfeiters continuously develop more sophisticated replication techniques. As a result, there is a growing need for more advanced, secure, and reliable methods to prevent counterfeiting.… More >
Open Access
ARTICLE
Song Gao, Shixin Liu*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.058334
（This article belongs to the Special Issue: Recent Advances in Ensemble Framework of Meta-heuristics and Machine Learning: Methods and Applications)
Abstract With the development of economic globalization, distributed manufacturing is becoming more and more prevalent. Recently, integrated scheduling of distributed production and assembly has captured much concern. This research studies a distributed flexible job shop scheduling problem with assembly operations. Firstly, a mixed integer programming model is formulated to minimize the maximum completion time. Secondly, a Q-learning-assisted co-evolutionary algorithm is presented to solve the model: (1) Multiple populations are developed to seek required decisions simultaneously; (2) An encoding and decoding method based on problem features is applied to represent individuals; (3) A hybrid approach of heuristic… More >
Open Access
ARTICLE
Veena Dillshad1, Muhammad Attique Khan2,*, Muhammad Nazir1, Jawad Ahmad2, Dina Abdulaziz AlHammadi3, Taha Houda2, Hee-Chan Cho4, Byoungchol Chang5,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063851
（This article belongs to the Special Issue: Deep Learning and IoT for Smart Healthcare)
Abstract Globally, skin cancer is a prevalent form of malignancy, and its early and accurate diagnosis is critical for patient survival. Clinical evaluation of skin lesions is essential, but several challenges, such as long waiting times and subjective interpretations, make this task difficult. The recent advancement of deep learning in healthcare has shown much success in diagnosing and classifying skin cancer and has assisted dermatologists in clinics. Deep learning improves the speed and precision of skin cancer diagnosis, leading to earlier prediction and treatment. In this work, we proposed a novel deep architecture for skin cancer… More >
Open Access
ARTICLE
Ahmet Emre Ergün, Aytuğ Onan*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063826
Abstract Large Language Models (LLMs) have significantly advanced human-computer interaction by improving natural language understanding and generation. However, their vulnerability to adversarial prompts–carefully designed inputs that manipulate model outputs–presents substantial challenges. This paper introduces a classification-based approach to detect adversarial prompts by utilizing both prompt features and prompt response features. Eleven machine learning models were evaluated based on key metrics such as accuracy, precision, recall, and F1-score. The results show that the Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) cascade model delivers the best performance, especially when using prompt features, achieving an accuracy of over 97% in… More >
Open Access
ARTICLE
Tehreem Fatima1, Kewen Xia1,*, Wenbiao Yang2, Qurat Ul Ain1, Poornima Lankani Perera1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063686
Abstract The rising prevalence of diabetes in modern society underscores the urgent need for precise and efficient diagnostic tools to support early intervention and treatment. However, the inherent limitations of existing datasets, including significant class imbalances and inadequate sample diversity, pose challenges to the accurate prediction and classification of diabetes. Addressing these issues, this study proposes an innovative diabetes prediction framework that integrates a hybrid Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) model for classification with Adaptive Synthetic Sampling (ADASYN) for data augmentation. ADASYN was employed to generate synthetic yet representative data samples, effectively mitigating class… More >
Open Access
REVIEW
Hayder Faeq Alhashimi1, Mhd Nour Hindia1, Kaharudin Dimyati1,*, Effariza Binti Hanafi1, Feras Zen Alden2, Faizan Qamar3, Quang Ngoc Nguyen4,5,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062867
（This article belongs to the Special Issue: AI and Advanced High-Tech Research and Development)
Abstract The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time networks. Artificial Intelligence (AI) advancements have contributed to the development of several innovative technologies by providing sophisticated specific AI mathematical models such as machine learning models, deep learning models, and hybrid models. Furthermore, intelligent resource management allows for self-configuration and autonomous decision-making capabilities of AI methods, which in turn improves the performance of 6G networks. Hence, 6G networks rely substantially on AI methods to manage resources. This paper comprehensively surveys the recent… More >
Open Access
ARTICLE
Praveen Kumar Sekharamantry1,2,*, Farid Melgani1, Roberto Delfiore3, Stefano Lusardi3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062686
（This article belongs to the Special Issue: New Trends in Image Processing)
Abstract Recent advances in computer vision and artificial intelligence (AI) have made real-time people counting systems extremely reliable, with experts in crowd control, occupancy supervision, and security. To improve the accuracy of people counting at entry and exit points, the current study proposes a deep learning model that combines You Only Look Once (YOLOv8) for object detection, ByteTrack for multi-object tracking, and a unique method for vector-based movement analysis. The system determines if a person has entered or exited by analyzing their movement concerning a predetermined boundary line. Two different logical strategies are used to record… More >
Open Access
ARTICLE
Muhammad Hameed Siddiqi1,*, Menwa Alshammeri1, Jawad Khan2,*, Muhammad Faheem Khan3, Asfandyar Khan4, Madallah Alruwaili1, Yousef Alhwaiti1, Saad Alanazi1, Irshad Ahmad5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062340
（This article belongs to the Special Issue: Advancements in Natural Language Processing (NLP) and Fuzzy Logic)
Abstract As legal cases grow in complexity and volume worldwide, integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus. This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain. The proposed framework comprises three core modules: legal feature extraction, semantic similarity assessment, and verdict recommendation. For legal feature extraction, a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts. Semantic similarity between cases is evaluated using a hybrid method that… More >
Open Access
ARTICLE
Zhipeng Qin1,2,*, Hanbing Yan3, Biyang Zhang2, Peng Wang2, Yitao Li3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063308
Abstract With the widespread adoption of encrypted Domain Name System (DNS) technologies such as DNS over Hyper Text Transfer Protocol Secure (HTTPS), traditional port and protocol-based traffic analysis methods have become ineffective. Although encrypted DNS enhances user privacy protection, it also provides concealed communication channels for malicious software, compelling detection technologies to shift towards statistical feature-based and machine learning approaches. However, these methods still face challenges in real-time performance and privacy protection. This paper proposes a real-time identification technology for encrypted DNS traffic with privacy protection. Firstly, a hierarchical architecture of cloud-edge-end collaboration is designed, incorporating More >
Open Access
ARTICLE
Chunhao Zhang1,2, Bin Xie2,3,*, Zhibin Huo1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063151
Abstract Time series anomaly detection is crucial in finance, healthcare, and industrial monitoring. However, traditional methods often face challenges when handling time series data, such as limited feature extraction capability, poor temporal dependency handling, and suboptimal real-time performance, sometimes even neglecting the temporal relationships between data. To address these issues and improve anomaly detection performance by better capturing temporal dependencies, we propose an unsupervised time series anomaly detection method, VLT-Anomaly. First, we enhance the Variational Autoencoder (VAE) module by redesigning its network structure to better suit anomaly detection through data reconstruction. We introduce hyperparameters to control… More >
Open Access
ARTICLE
Zeyang Zhou1,*, Zachary James Ryan1, Utkarsh Sharma2, Tran Tien Anh3, Shashi Mehrotra4, Angelo Greco5, Jason West6, Mukesh Prasad1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060291
Abstract Accurate capacity and State of Charge (SOC) estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles. This study examines ten machine learning architectures, Including Deep Belief Network (DBN), Bidirectional Recurrent Neural Network (BiDirRNN), Gated Recurrent Unit (GRU), and others using the NASA B0005 dataset of 591,458 instances. Results indicate that DBN excels in capacity estimation, achieving orders-of-magnitude lower error values and explaining over 99.97% of the predicted variable’s variance. When computational efficiency is paramount, the Deep Neural Network (DNN) offers a strong alternative, delivering near-competitive accuracy with significantly reduced… More >
Open Access
ARTICLE
Gaoshang Lu#, Fa Fu#,*, Zixiang Tang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062556
Abstract In the data transaction process within a data asset trading platform, quantifying the trustworthiness of data source nodes is challenging due to their numerous attributes and complex structures. To address this issue, a distributed data source trust assessment management framework, a trust quantification model, and a dynamic adjustment mechanism are proposed. The model integrates the Analytic Hierarchy Process (AHP) and Dempster-Shafer (D-S) evidence theory to determine attribute weights and calculate direct trust values, while the PageRank algorithm is employed to derive indirect trust values. The direct and indirect trust values are then combined to compute More >
Open Access
ARTICLE
Jiling Wan, Lifeng Cao*, Jinlong Bai, Jinhui Li, Xuehui Du
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061964
Abstract Blockchain platforms with the unique characteristics of anonymity, decentralization, and transparency of their transactions, which are faced with abnormal activities such as money laundering, phishing scams, and fraudulent behavior, posing a serious threat to account asset security. For these potential security risks, this paper proposes a hybrid neural network detection method (HNND) that learns multiple types of account features and enhances fusion information among them to effectively detect abnormal transaction behaviors in the blockchain. In HNND, the Temporal Transaction Graph Attention Network (T2GAT) is first designed to learn biased aggregation representation of multi-attribute transactions among More >
Open Access
ARTICLE
Huiru Cao1, Shaoxin Li2, Xiaomin Li3,*, Yongxin Liu4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061954
Abstract Efficient flight path design for unmanned aerial vehicles (UAVs) in urban environmental event monitoring remains a critical challenge, particularly in prioritizing high-risk zones within complex urban landscapes. Current UAV path planning methodologies often inadequately account for environmental risk factors and exhibit limitations in balancing global and local optimization efficiency. To address these gaps, this study proposes a hybrid path planning framework integrating an improved Ant Colony Optimization (ACO) algorithm with an Orthogonal Jump Point Search (OJPS) algorithm. Firstly, a two-dimensional grid model is constructed to simulate urban environments, with key monitoring nodes selected based on… More >
Open Access
ARTICLE
Zhengdao Yang1, Xuewei Wang2, Yuling Chen1,*, Hui Dou1, Haiwei Sang3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.060564
（This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)
Abstract Anomaly detection (AD) in time series data is widely applied across various industries for monitoring and security applications, emerging as a key research focus within the field of deep learning. While many methods based on different normality assumptions perform well in specific scenarios, they often neglected the overall normality issue. Some feature extraction methods incorporate pre-training processes but they may not be suitable for time series anomaly detection, leading to decreased performance. Additionally, real-world time series samples are rarely free from noise, making them susceptible to outliers, which further impacts detection accuracy. To address these More >
Open Access
ARTICLE
He Su, Jianwei Gao, Kang Kong*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063355
Abstract Accurate and robust navigation in complex surgical environments is crucial for bronchoscopic surgeries. This study purposes a bronchoscopic lumen feature matching network (BLFM-Net) based on deep learning to address the challenges of image noise, anatomical complexity, and the stringent real-time requirements. The BLFM-Net enhances bronchoscopic image processing by integrating several functional modules. The FFA-Net preprocessing module mitigates image fogging and improves visual clarity for subsequent processing. The feature extraction module derives multi-dimensional features, such as centroids, area, and shape descriptors, from dehazed images. The Faster R-CNN Object detection module detects bronchial regions of interest and… More >
Open Access
ARTICLE
Huansha Wang*, Ruiyang Huang*, Qinrang Liu, Xinghao Wang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061902
Abstract Multi-modal Named Entity Recognition (MNER) aims to better identify meaningful textual entities by integrating information from images. Previous work has focused on extracting visual semantics at a fine-grained level, or obtaining entity related external knowledge from knowledge bases or Large Language Models (LLMs). However, these approaches ignore the poor semantic correlation between visual and textual modalities in MNER datasets and do not explore different multi-modal fusion approaches. In this paper, we present MMAVK, a multi-modal named entity recognition model with auxiliary visual knowledge and word-level fusion, which aims to leverage the Multi-modal Large Language Model… More >
Open Access
ARTICLE
Wenli Lei1,2,*, Xinghao Wu1,2, Kun Jia1,2, Jinping Han1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061268
Abstract Aiming to address the limitations of the standard Chimp Optimization Algorithm (ChOA), such as inadequate search ability and susceptibility to local optima in Unmanned Aerial Vehicle (UAV) path planning, this paper proposes a three-dimensional path planning method for UAVs based on the Improved Chimp Optimization Algorithm (IChOA). First, this paper models the terrain and obstacle environments spatially and formulates the total UAV flight cost function according to the constraints, transforming the path planning problem into an optimization problem with multiple constraints. Second, this paper enhances the diversity of the chimpanzee population by applying the Sine… More >
Open Access
ARTICLE
Lang Qin1, Zhengrui Jiang1, Xueshu Xing2, Xiao Wang1, Yaohua Yin2, Yuhui Zhou2, Zhiqin He1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063358
（This article belongs to the Special Issue: Intelligent Manufacturing, Robotics and Control Engineering)
Abstract In permanent magnet synchronous motor (PMSM) control, the jitter problem affects the system performance, so a novel reaching law is proposed to construct a non-singular fast terminal sliding mode controller (NFTSMC) to reduce the jitter. To enhance the immunity of the system, a disturbance observer is designed to observe and compensate for the disturbance to the sliding mode controller. In addition, considering that the controller parameters are difficult to adjust, and the traditional zebra optimization algorithm (ZOA) is prone to converge prematurely and fall into local optimum when solving the optimal solution, the improved zebra… More >
Open Access
ARTICLE
Hongliang Tian, Zhong Fan*, Zhiyang Ruan, Aomen Zhao
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063274
（This article belongs to the Special Issue: Distributed Computing with Applications to IoT and BlockChain)
Abstract With the continuous growth of exponential data in IoT, it is usually chosen to outsource data to the cloud server. However, cloud servers are usually provided by third parties, and there is a risk of privacy leakage. Encrypting data can ensure its security, but at the same time, it loses the retrieval function of IoT data. Searchable Encryption (SE) can achieve direct retrieval based on ciphertext data. The traditional searchable encryption scheme has the problems of imperfect function, low retrieval efficiency, inaccurate retrieval results, and centralized cloud servers being vulnerable and untrustworthy. This paper proposes… More >
Open Access
ARTICLE
Yuling Luo1,2, Xiaoguang Lin1,2, Junxiu Liu1,2,*, Qiang Fu1,2, Sheng Qin1,2, Zhen Min1,2, Tinghua Hu1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063860
（This article belongs to the Special Issue: Distributed Computing with Applications to IoT and BlockChain)
Abstract The majors accredited by the Engineering Education Accreditation (EEA) reflect the accreditation agency’s recognition of the school’s engineering programs. Excellent accreditation management holds significant importance for the advancement of engineering education programs. However, the traditional engineering education system framework suffers from the opacity of raw education data and the difficulty for accreditation bodies to forensically examine the self-assessment reports. To solve these issues, an EEA framework based on Hyperledger Fabric blockchain technology is proposed in this work. Firstly, all relevant stakeholders and information interactions occur within the blockchain network, ensuring the authenticity of educational data More >
Open Access
ARTICLE
Jing He1, Haonan Zhu2, Chenhao Zhao1, Minrui Zhao3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062437
Abstract Self-supervised monocular depth estimation has emerged as a major research focus in recent years, primarily due to the elimination of ground-truth depth dependence. However, the prevailing architectures in this domain suffer from inherent limitations: existing pose network branches infer camera ego-motion exclusively under static-scene and Lambertian-surface assumptions. These assumptions are often violated in real-world scenarios due to dynamic objects, non-Lambertian reflectance, and unstructured background elements, leading to pervasive artifacts such as depth discontinuities (“holes”), structural collapse, and ambiguous reconstruction. To address these challenges, we propose a novel framework that integrates scene dynamic pose estimation into… More >
Open Access
ARTICLE
Xianghong Cao, Chenxu Li*, Haoting Zhai
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061823
（This article belongs to the Special Issue: Research on Deep Learning-based Object Detection and Its Derivative Key Technologies)
Abstract The behavior safety testing of more and more elderly people living alone has become a hot research topic along with the arrival of an aging society. A YOLO-Abnormal Behaviour (YOLO-AB) algorithm for fusion detection of falling and smoking behaviors of elderly people living alone has been proposed in this paper, which can fully utilize the potential of the YOLOv8 algorithm on object detection and deeply explore the characteristics of different types of behaviors among the elderly, to solve the problems of single detection type, low fusion detection accuracy, and high missed detection rate. Firstly, datasets… More >
Open Access
ARTICLE
Zijun Gao*, Zheyi Li, Chunqi Zhang, Ying Wang, Jingwen Su
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061743
Abstract Pest detection techniques are helpful in reducing the frequency and scale of pest outbreaks; however, their application in the actual agricultural production process is still challenging owing to the problems of inter-species similarity, multi-scale, and background complexity of pests. To address these problems, this study proposes an FD-YOLO pest target detection model. The FD-YOLO model uses a Fully Connected Feature Pyramid Network (FC-FPN) instead of a PANet in the neck, which can adaptively fuse multi-scale information so that the model can retain small-scale target features in the deep layer, enhance large-scale target features in the… More >
Open Access
ARTICLE
Junjie Cao1,2, Zhiyong Yu2,*, Xiaotao Xu1, Baohong Zhu3, Jian Yang2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062371
（This article belongs to the Special Issue: Privacy-Preserving Deep Learning and its Advanced Applications)
Abstract This paper introduces a quantum-enhanced edge computing framework that synergizes quantum-inspired algorithms with advanced machine learning techniques to optimize real-time task offloading in edge computing environments. This innovative approach not only significantly improves the system’s real-time responsiveness and resource utilization efficiency but also addresses critical challenges in Internet of Things (IoT) ecosystems—such as high demand variability, resource allocation uncertainties, and data privacy concerns—through practical solutions. Initially, the framework employs an adaptive adjustment mechanism to dynamically manage task and resource states, complemented by online learning models for precise predictive analytics. Secondly, it accelerates the search for… More >
Open Access
ARTICLE
Ziyang Wang, Yuanzhen Feng*, Zhengxin Wang, Cong Zheng
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063179
Abstract Continuous control protocols are extensively utilized in traditional MASs, in which information needs to be transmitted among agents consecutively, therefore resulting in excessive consumption of limited resources. To decrease the control cost, based on ISC, several LFC problems are investigated for second-order MASs without and with time delay, respectively. Firstly, an intermittent sampled controller is designed, and a sufficient and necessary condition is derived, under which state errors between the leader and all the followers approach zero asymptotically. Considering that time delay is inevitable, a new protocol is proposed to deal with the time-delay situation.… More >
Open Access
ARTICLE
Jingcheng Yang1, Lili Fan2, Hongmei Liu1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062963
Abstract With the advancement of deep learning in the automotive domain, more and more researchers are focusing on autonomous driving. Among these tasks, free space detection is particularly crucial. Currently, many model-based approaches have achieved autonomous driving on well-structured urban roads, but these efforts primarily focus on urban road environments. In contrast, there are fewer deep learning methods specifically designed for off-road traversable area detection, and their effectiveness is not yet satisfactory. This is because detecting traversable areas in complex outdoor environments poses significant challenges, and current methods often rely on single-image inputs, which do not… More >
Open Access
REVIEW
Peicheng Shi1,*, Li Yang1, Xinlong Dong1, Heng Qi2, Aixi Yang3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063205
（This article belongs to the Special Issue: Advances in Object Detection: Methods and Applications)
Abstract As the number and complexity of sensors in autonomous vehicles continue to rise, multimodal fusion-based object detection algorithms are increasingly being used to detect 3D environmental information, significantly advancing the development of perception technology in autonomous driving. To further promote the development of fusion algorithms and improve detection performance, this paper discusses the advantages and recent advancements of multimodal fusion-based object detection algorithms. Starting from single-modal sensor detection, the paper provides a detailed overview of typical sensors used in autonomous driving and introduces object detection methods based on images and point clouds. For image-based detection… More >
Open Access
REVIEW
Chuan Li, Xuanlin Wen*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063047
Abstract Spark performs excellently in large-scale data-parallel computing and iterative processing. However, with the increase in data size and program complexity, the default scheduling strategy has difficulty meeting the demands of resource utilization and performance optimization. Scheduling strategy optimization, as a key direction for improving Spark’s execution efficiency, has attracted widespread attention. This paper first introduces the basic theories of Spark, compares several default scheduling strategies, and discusses common scheduling performance evaluation indicators and factors affecting scheduling efficiency. Subsequently, existing scheduling optimization schemes are summarized based on three scheduling modes: load characteristics, cluster characteristics, and matching More >
Open Access
ARTICLE
Jixiang Wang1, Jing Wei2, Siqi Chen1, Haiyang Yu1,3,4, Yilong Ren1,3,4,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062503
Abstract The real-time path optimization for heterogeneous vehicle fleets in large-scale road networks presents significant challenges due to conflicting traffic demands and imbalanced resource allocation. While existing vehicle-to-infrastructure coordination frameworks partially address congestion mitigation, they often neglect priority-aware optimization and exhibit algorithmic bias toward dominant vehicle classes—critical limitations in mixed-priority scenarios involving emergency vehicles. To bridge this gap, this study proposes a preference game-theoretic coordination framework with adaptive strategy transfer protocol, explicitly balancing system-wide efficiency (measured by network throughput) with priority vehicle rights protection (quantified via time-sensitive utility functions). The approach innovatively combines (1) a multi-vehicle… More >
Open Access
ARTICLE
Honglin Wang1, Yaohua Xu2,*, Cheng Zhu3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061848
（This article belongs to the Special Issue: Novel Methods for Image Classification, Object Detection, and Segmentation)
Abstract Medical image classification is crucial in disease diagnosis, treatment planning, and clinical decision-making. We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Augmentation (BSDA) with a Vision Mamba-based model for medical image classification (MedMamba), enhanced by residual connection blocks, we named the model BSDA-Mamba. BSDA augments medical image data semantically, enhancing the model’s generalization ability and classification performance. MedMamba, a deep learning-based state space model, excels in capturing long-range dependencies in medical images. By incorporating residual connections, BSDA-Mamba further improves feature extraction capabilities. Through comprehensive experiments on eight medical image More >
Open Access
ARTICLE
Qichang Li1,2,*, Bing Bu1, Junyi Zhao1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061525
（This article belongs to the Special Issue: Advanced Communication and Networking Technologies for Internet of Things and Internet of Vehicles)
Abstract With the integration of informatization and intelligence into the Communication-Based Train Control (CBTC) systems, the system is facing an increasing number of information security threats. As an important method of characterizing the system security status, the security situation assessment is used to analyze the system security situation. However, existing situation assessment methods fail to integrate the coupling relationship between the physical layer and the information layer of the CBTC systems, and cannot dynamically characterize the real-time security situation changes under cyber attacks. In this paper, a hierarchical security situation assessment approach is proposed to address… More >
Open Access
ARTICLE
Xin Xu1,*, Zhen Yang2, Yongfeng Huang1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061046
Abstract Attribute-Based Signature (ABS) is a powerful cryptographic primitive that enables fine-grained access control in distributed systems. However, its high computational cost makes it unsuitable for resource-constrained environments, and traditional monotonic access structures are inadequate for handling increasingly complex access policies. In this paper, we propose a novel smart contract-assisted ABS (SC-ABS) algorithm that supports non-monotonic access structures, aiming to reduce client computing overhead while providing more expressive and flexible access control. The SC-ABS scheme extends the monotonic access structure by introducing the concept of negative attributes, allowing for more complex and dynamic access policies. By… More >
Open Access
ARTICLE
Xiongwei Cui, Yunchao Wang, Qiang Wei*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063592
（This article belongs to the Special Issue: Security and Privacy in IoT and Smart City: Current Challenges and Future Directions)
Abstract The development of the Internet of Things (IoT) has brought convenience to people’s lives, but it also introduces significant security risks. Due to the limitations of IoT devices themselves and the challenges of re-hosting technology, existing fuzzing for IoT devices is mainly conducted through black-box methods, which lack effective execution feedback and are blind. Meanwhile, the existing static methods mainly rely on taint analysis, which has high overhead and high false alarm rates. We propose a new directed fuzz testing method for detecting bugs in web service programs of IoT devices, which can test IoT… More >
Open Access
ARTICLE
Chunming Tang*, Yu Wang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062328
Abstract Image dehazing aims to generate clear images critical for subsequent visual tasks. CNNs have made significant progress in the field of image dehazing. However, due to the inherent limitations of convolution operations, it is challenging to effectively model global context and long-range spatial dependencies effectively. Although the Transformer can address this issue, it faces the challenge of excessive computational requirements. Therefore, we propose the FS-MSFormer network, an asymmetric encoder-decoder architecture that combines the advantages of CNNs and Transformers to improve dehazing performance. Specifically, the encoding process employs two branches for multi-scale feature extraction. One branch… More >
Open Access
ARTICLE
Shaoming Qiu, Jingjie He, Yan Wang*, Bicong E
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061532
Abstract Software defect prediction (SDP) aims to find a reliable method to predict defects in specific software projects and help software engineers allocate limited resources to release high-quality software products. Software defect prediction can be effectively performed using traditional features, but there are some redundant or irrelevant features in them (the presence or absence of this feature has little effect on the prediction results). These problems can be solved using feature selection. However, existing feature selection methods have shortcomings such as insignificant dimensionality reduction effect and low classification accuracy of the selected optimal feature subset. In… More >
Open Access
ARTICLE
Rongsen Wu1, Jie Xu1, Yuhang Zhang1, Changming Zhao2,*, Yiweng Xie3, Zelei Wu1, Yunji Li2, Jinhong Guo4, Shiyang Tang5,6
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061396
Abstract With the rapid development of artificial intelligence and Internet of Things technologies, video action recognition technology is widely applied in various scenarios, such as personal life and industrial production. However, while enjoying the convenience brought by this technology, it is crucial to effectively protect the privacy of users’ video data. Therefore, this paper proposes a video action recognition method based on personalized federated learning and spatiotemporal features. Under the framework of federated learning, a video action recognition method leveraging spatiotemporal features is designed. For the local spatiotemporal features of the video, a new differential information… More >
Open Access
ARTICLE
Tao Zhou1,2, Yaxing Wang1,2,*, Huiling Lu3, Wenwen Chai1,2, Yunfeng Pan1,2, Zhe Zhang1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062400
Abstract The instance segmentation of impacted teeth in the oral panoramic X-ray images is hotly researched. However, due to the complex structure, low contrast, and complex background of teeth in panoramic X-ray images, the task of instance segmentation is technically tricky. In this study, the contrast between impacted Teeth and periodontal tissues such as gingiva, periodontal membrane, and alveolar bone is low, resulting in fuzzy boundaries of impacted teeth. A model based on Teeth YOLACT is proposed to provide a more efficient and accurate solution for the segmentation of impacted teeth in oral panoramic X-ray films.… More >
Open Access
ARTICLE
Qing Xie*, Ruiyun Yu
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063345
（This article belongs to the Special Issue: Neural Architecture Search: Optimization, Efficiency and Application)
Abstract This study presents a groundbreaking method named Expo-GAN (Exposition-Generative Adversarial Network) for style transfer in exhibition hall design, using a refined version of the Cycle Generative Adversarial Network (CycleGAN). The primary goal is to enhance the transformation of image styles while maintaining visual consistency, an area where current CycleGAN models often fall short. These traditional models typically face difficulties in accurately capturing expansive features as well as the intricate stylistic details necessary for high-quality image transformation. To address these limitations, the research introduces several key modifications to the CycleGAN architecture. Enhancements to the generator involve… More >
Open Access
ARTICLE
Qin Wang1, Xiaofeng Wang2,*, Jianghua Li2, Ruidong Han2, Zinian Liu1, Mingtao Guo3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063141
Abstract The majority of current deepfake detection methods are constrained to identifying one or two specific types of counterfeit images, which limits their ability to keep pace with the rapid advancements in deepfake technology. Therefore, in this study, we propose a novel algorithm, Stereo Mixture Density Network (SMNDNet), which can detect multiple types of deepfake face manipulations using a single network framework. SMNDNet is an end-to-end CNN-based network specially designed for detecting various manipulation types of deepfake face images. First, we design a Subtle Distinguishable Feature Enhancement Module to emphasize the differentiation between authentic and forged… More >
Open Access
ARTICLE
Yingyong Zou*, Xingkui Zhang, Tao Liu, Yu Zhang, Long Li, Wenzhuo Zhao
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062625
（This article belongs to the Special Issue: Advancements in Machine Fault Diagnosis and Prognosis: Data-Driven Approaches and Autonomous Systems)
Abstract High-speed train engine rolling bearings play a crucial role in maintaining engine health and minimizing operational losses during train operation. To solve the problems of low accuracy of the diagnostic model and unstable model due to the influence of noise during fault detection, a rolling bearing fault diagnosis model based on cross-attention fusion of WDCNN and BILSTM is proposed. The first layer of the wide convolutional kernel deep convolutional neural network (WDCNN) is used to extract the local features of the signal and suppress the high-frequency noise. A Bidirectional Long Short-Term Memory Network (BILSTM) is… More >
Open Access
ARTICLE
Haocheng Sun, Ping Li, Ying Li*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063029
（This article belongs to the Special Issue: Deep Neural Networks-based Convergence Technology and Applications)
Abstract Traffic flow prediction is a key component of intelligent transportation systems, particularly in data-scarce regions where traditional models relying on complete datasets often fail to provide accurate forecasts. These regions are characterized by limited sensor coverage and sparse data collection, pose significant challenges for existing prediction methods. To address this, we propose a novel transfer learning framework called transfer learning with deep knowledge distillation (TL-DKD), which combines graph neural network (GNN) with deep knowledge distillation to enable effective knowledge transfer from data-rich to data-scarce domains. Our contributions are three-fold: (1) We introduce, for the first… More >
Open Access
ARTICLE
Huiling Yu1, Xibei Jia2, Yongfeng Niu1, Yizhuo Zhang1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.061882
Abstract The counterflow burner is a combustion device used for research on combustion. By utilizing deep convolutional models to identify the combustion state of a counterflow burner through visible flame images, it facilitates the optimization of the combustion process and enhances combustion efficiency. Among existing deep convolutional models, InceptionNeXt is a deep learning architecture that integrates the ideas of the Inception series and ConvNeXt. It has garnered significant attention for its computational efficiency, remarkable model accuracy, and exceptional feature extraction capabilities. However, since this model still has limitations in the combustion state recognition task, we propose… More >
Open Access
ARTICLE
Huyong Yan1, Huidong Zhou2,*, Jian Zheng1, Zhaozhe Zhou1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062807
（This article belongs to the Special Issue: AI and Data Security for the Industrial Internet)
Abstract As smart manufacturing and Industry 4.0 continue to evolve, fault diagnosis of mechanical equipment has become crucial for ensuring production safety and optimizing equipment utilization. To address the challenge of cross-domain adaptation in intelligent diagnostic models under varying operational conditions, this paper introduces the CNN-1D-KAN model, which combines a 1D Convolutional Neural Network (1D-CNN) with a Kolmogorov–Arnold Network (KAN). The novelty of this approach lies in replacing the traditional 1D-CNN’s final fully connected layer with a KANLinear layer, leveraging KAN’s advanced nonlinear processing and function approximation capabilities while maintaining the simplicity of linear transformations. Experimental… More >
Open Access
ARTICLE
Qiming Li1, Mengcheng Wu1, Daozheng Chen1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062969
（This article belongs to the Special Issue: The Latest Deep Learning Architectures for Artificial Intelligence Applications)
Abstract Image style transfer is a research hotspot in the field of computer vision. For this job, many approaches have been put forth. These techniques do, however, still have some drawbacks, such as high computing complexity and content distortion caused by inadequate stylization. To address these problems, PhotoGAN, a new Generative Adversarial Network (GAN) model is proposed in this paper. A deeper feature extraction network has been designed to capture global information and local details better. Introducing multi-scale attention modules helps the generator focus on important feature areas at different scales, further enhancing the effectiveness of More >
Open Access
ARTICLE
Hui Xu, Jiahui Chen*, Zhonghao Hu
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.062189
（This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)
Abstract Nowadays, abnormal traffic detection for Software-Defined Networking (SDN) faces the challenges of large data volume and high dimensionality. Since traditional machine learning-based detection methods have the problem of data redundancy, the Metaheuristic Algorithm (MA) is introduced to select features before machine learning to reduce the dimensionality of data. Since a Tyrannosaurus Optimization Algorithm (TROA) has the advantages of few parameters, simple implementation, and fast convergence, and it shows better results in feature selection, TROA can be applied to abnormal traffic detection for SDN. However, TROA suffers from insufficient global search capability, is easily trapped in… More >
Open Access
ARTICLE
Changlong Wang1, Jiawei Jiang1, Chong Han1,2,*, Hengyi Ren3, Lijuan Sun1,2, Jian Guo1,2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.063295
Abstract Existing through-wall human activity recognition methods often rely on Doppler information or reflective signal characteristics of the human body. However, static individuals, lacking prominent motion features, do not generate Doppler information. Moreover, radar signals experience significant attenuation due to absorption and scattering effects as they penetrate walls, limiting recognition performance. To address these challenges, this study proposes a novel through-wall human activity recognition method based on MIMO radar. Utilizing a MIMO radar operating at 1–2 GHz, we capture activity data of individuals through walls and process it into range-angle maps to represent activity features. To… More >
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