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Xinlong Diao, Xiao Wang^*, Junkang Qin, Qinmu Wu, Zhiqin He, Xinghong Fan
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2617-2665, 2024, DOI:10.32604/cmc.2024.047377
（This article belongs to the Special Issue: Deep Learning in Computer-Aided Diagnosis Based on Medical Image)
Abstract In recent years, Artificial Intelligence (AI) has revolutionized people’s lives. AI has long made breakthrough progress in the field of surgery. However, the research on the application of AI in orthopedics is still in the exploratory stage. The paper first introduces the background of AI and orthopedic diseases, addresses the shortcomings of traditional methods in the detection of fractures and orthopedic diseases, draws out the advantages of deep learning and machine learning in image detection, and reviews the latest results of deep learning and machine learning applied to orthopedic image detection in recent years, describing the contributions, strengths and weaknesses,… More >

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ARTICLE

Xiao Luo^1,3, Hao Zhu^1,2,*, Zhenli Zhang^1,2
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2667-2687, 2024, DOI:10.32604/cmc.2024.047988
（This article belongs to the Special Issue: Deep Learning based Object Detection and Tracking in Videos)
Abstract Road traffic safety can decrease when drivers drive in a low-visibility environment. The application of visual perception technology to detect vehicles and pedestrians in infrared images proves to be an effective means of reducing the risk of accidents. To tackle the challenges posed by the low recognition accuracy and the substantial computational burden associated with current infrared pedestrian-vehicle detection methods, an infrared pedestrian-vehicle detection method A proposal is presented, based on an enhanced version of You Only Look Once version 5 (YOLOv5). First, A head specifically designed for detecting small targets has been integrated into the model to make full… More >

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ARTICLE

Fatma Harby¹, Mansor Alohali², Adel Thaljaoui^2,3,*, Amira Samy Talaat⁴
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2689-2719, 2024, DOI:10.32604/cmc.2024.046623
Abstract Machine Learning (ML) algorithms play a pivotal role in Speech Emotion Recognition (SER), although they encounter a formidable obstacle in accurately discerning a speaker’s emotional state. The examination of the emotional states of speakers holds significant importance in a range of real-time applications, including but not limited to virtual reality, human-robot interaction, emergency centers, and human behavior assessment. Accurately identifying emotions in the SER process relies on extracting relevant information from audio inputs. Previous studies on SER have predominantly utilized short-time characteristics such as Mel Frequency Cepstral Coefficients (MFCCs) due to their ability to capture the periodic nature of audio… More >

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ARTICLE

Haipeng Sun^1,2, Xueyan Ding^1,2,*, Jian Sun^1,2, Hua Yu³, Jianxin Zhang^1,2,*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2721-2739, 2024, DOI:10.32604/cmc.2024.046449
Abstract Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition, we present a deep learning-based approach for Yi character detection and recognition. In the detection stage, an improved Differentiable Binarization Network (DBNet) framework is introduced to detect Yi characters, in which the Omni-dimensional Dynamic Convolution (ODConv) is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features, thereby improving the accuracy of Yi character detection. Then, the feature pyramid network fusion module is used to further extract Yi character image features, improving target recognition… More >

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ARTICLE

Yongliang Yang, Linghua Xu^*, Maolin Luo, Xiao Wang, Min Cao
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2741-2765, 2024, DOI:10.32604/cmc.2024.046768
Abstract Due to the complex environment of the university laboratory, personnel flow intensive, personnel irregular behavior is easy to cause security risks. Monitoring using mainstream detection algorithms suffers from low detection accuracy and slow speed. Therefore, the current management of personnel behavior mainly relies on institutional constraints, education and training, on-site supervision, etc., which is time-consuming and ineffective. Given the above situation, this paper proposes an improved You Only Look Once version 7 (YOLOv7) to achieve the purpose of quickly detecting irregular behaviors of laboratory personnel while ensuring high detection accuracy. First, to better capture the shape features of the target,… More >

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Asma Hassan Alshehri^*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2767-2786, 2024, DOI:10.32604/cmc.2023.046838
Abstract Online review platforms are becoming increasingly popular, encouraging dishonest merchants and service providers to deceive customers by creating fake reviews for their goods or services. Using Sybil accounts, bot farms, and real account purchases, immoral actors demonize rivals and advertise their goods. Most academic and industry efforts have been aimed at detecting fake/fraudulent product or service evaluations for years. The primary hurdle to identifying fraudulent reviews is the lack of a reliable means to distinguish fraudulent reviews from real ones. This paper adopts a semi-supervised machine learning method to detect fake reviews on any website, among other things. Online reviews… More >

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118

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ARTICLE

Ye Lu^1,*, Tao Feng¹, Chunyan Liu², Wenbo Zhang³
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2787-2811, 2024, DOI:10.32604/cmc.2023.046106
Abstract The Access control scheme is an effective method to protect user data privacy. The access control scheme based on blockchain and ciphertext policy attribute encryption (CP–ABE) can solve the problems of single—point of failure and lack of trust in the centralized system. However, it also brings new problems to the health information in the cloud storage environment, such as attribute leakage, low consensus efficiency, complex permission updates, and so on. This paper proposes an access control scheme with fine-grained attribute revocation, keyword search, and traceability of the attribute private key distribution process. Blockchain technology tracks the authorization of attribute private… More >

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ARTICLE

Yi-Bo Cao^1,*, Xiu-Bo Chen¹, Yun-Feng He², Lu-Xi Liu², Yin-Mei Che², Xiao Wang², Ke Xiao³, Gang Xu³, Si-Yi Chen¹
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2813-2827, 2024, DOI:10.32604/cmc.2024.046816
Abstract Due to the rapid advancements in network technology, blockchain is being employed for distributed data storage. In the Internet of Things (IoT) scenario, different participants manage multiple blockchains located in different trust domains, which has resulted in the extensive development of cross-domain authentication techniques. However, the emergence of many attackers equipped with quantum computers has the potential to launch quantum computing attacks against cross-domain authentication schemes based on traditional cryptography, posing a significant security threat. In response to the aforementioned challenges, our paper demonstrates a post-quantum cross-domain identity authentication scheme to negotiate the session key used in the cross-chain asset… More >

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111

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ARTICLE

Liqiu Ren, Zhanying Li^*, Xueyu He, Lingyan Kong, Yinghao Zhang
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2829-2845, 2024, DOI:10.32604/cmc.2024.047028
（This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)
Abstract For underwater robots in the process of performing target detection tasks, the color distortion and the uneven quality of underwater images lead to great difficulties in the feature extraction process of the model, which is prone to issues like error detection, omission detection, and poor accuracy. Therefore, this paper proposed the CER-YOLOv7(CBAM-EIOU-RepVGG-YOLOv7) underwater target detection algorithm. To improve the algorithm’s capability to retain valid features from both spatial and channel perspectives during the feature extraction phase, we have added a Convolutional Block Attention Module (CBAM) to the backbone network. The Reparameterization Visual Geometry Group (RepVGG) module is inserted into the… More >

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Xue Li, Hongying Zhang^*, Zixun Ye, Xiaoru Huang
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2847-2864, 2024, DOI:10.32604/cmc.2024.047093
（This article belongs to the Special Issue: Development and Industrial Application of AI Technologies)
Abstract Transformer-based stereo image super-resolution reconstruction (Stereo SR) methods have significantly improved image quality. However, existing methods have deficiencies in paying attention to detailed features and do not consider the offset of pixels along the epipolar lines in complementary views when integrating stereo information. To address these challenges, this paper introduces a novel epipolar line window attention stereo image super-resolution network (EWASSR). For detail feature restoration, we design a feature extractor based on Transformer and convolutional neural network (CNN), which consists of (shifted) window-based self-attention ((S)W-MSA) and feature distillation and enhancement blocks (FDEB). This combination effectively solves the problem of global… More >

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