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An Elevator Button Recognition Method Combining YOLOv5 and OCR

Computers, Materials & Continua 2023年第4期75卷 117-131页

作者： Xinliang Tang Caixing Wang Jingfang Su Cecilia Taylor School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China Nankai-Birmingham Institute of Data Science Intelligence BirminghamB100ABBritain

Fast recognition of elevator buttons is a key step for service robots toride elevators automatically. Although there are some studies in this field, noneof them can achieve real-time application due to problems such as recognitionspeed and algorithm complexity. Elevator button recognition is a comprehensiveproblem. Not only does it need to detect the position of multiple buttonsat the same time, but also needs to accurately identify the characters on eachbutton. The latest version 5 of you only look once algorithm (YOLOv5) hasthe fastest reasoning speed and can be used for detecting multiple objects inreal-time. The advantages ofYOLOv5 make it an ideal choice for detecting theposition of multiple buttons in an elevator, but it’s not good at specific wordrecognition. Optical character recognition (OCR) is a well-known techniquefor character recognition. This paper innovatively improved the YOLOv5network, integrated OCR technology, and applied them to the elevator buttonrecognition process. First, we changed the detection scale in the YOLOv5network and only maintained the detection scales of 40 ∗ 40 and 80 ∗ 80, thusimproving the overall object detection speed. Then, we put a modified OCRbranch after the YOLOv5 network to identify the numbers on the ***, we verified this method on different datasets and compared it withother typical methods. The results show that the average recall and precisionof this method are 81.2% and 92.4%. Compared with others, the accuracyof this method has reached a very high level, but the recognition speed hasreached 0.056 s, which is far higher than other methods.

关键词： Button recognition deep learning multi-object detection

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JobViz:Skill-driven visual exploration of job advertisements

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Visual Informatics 2024年第3期8卷 18-28页

作者： Ran Wang Qianhe Chen Yong Wang Lewei Xiong Boyang Shen School of Journalism and Information Communication Huazhong University of Science and TechnologyWuhanChina School of Computer Science and Technology Huazhong University of Science and TechnologyWuhanChina College of Computing and Data Science Nanyang Technological UniversitySingaporeSingapore Wuhan National Laboratory for Optoelectronics WuhanChina Philosophy and Social Science Laboratory of Big Data and National Communication Strategy Ministry of EducationWuhanChina

Online job advertisements on various job portals or websites have become the most popular way for people to find potential career opportunities ***,the majority of these job sites are limited to offering fundamental filters such as job titles,keywords,and compensation *** often poses a challenge for job seekers in efficiently identifying relevant job advertisements that align with their unique skill sets amidst a vast sea of ***,we propose well-coordinated visualizations to provide job seekers with three levels of details of job information:a skill-job overview visualizes skill sets,employment posts as well as relationships between them with a hierarchical visualization design;a post exploration view leverages an augmented radar-chart glyph to represent job posts and further facilitates users’swift comprehension of the pertinent skills necessitated by respective positions;a post detail view lists the specifics of selected job posts for profound analysis and *** using a real-world recruitment advertisement dataset collected from 51Job,one of the largest job websites in China,we conducted two case studies and user interviews to evaluate *** results demonstrated the usefulness and effectiveness of our approach.

关键词： Visual exploration Job advertisements Skill-driven

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Revealing Security Flaws in Cross-Modal Retrieval Models through Video Poisoning

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 6184-6194页

作者： Jin, Ming Hu, Wenbo Hong, Richang Zhu, Lei Hefei University of Technology School of Computer and Information Hefei China Data Space Research Institute Hefei Comprehensive National Science Center China Tongji University School of Electronic and Information Engineering Shanghai China

Video-text cross-modal retrieval is widely studied to improve retrieval accuracy. However, the security of video-text cross-modal retrieval models receives little attention. If attackers exploit the security vulnerabilities in these models, it poses a significant threat to the retrieval models. Thus, identifying security flaws in video-text cross-modal retrieval models becomes the focus of our research. We are the first to design a video poisoning model to uncover security vulnerabilities in retrieval models. Existing poisoning models have certain limitations when it comes to exploiting vulnerabilities in retrieval models. These include failing to comprehensively embed malicious information into the original video and being unable to maintain visual consistency between the original and poisoned videos. These limitations can result in unsuccessful attacks on retrieval models and an inability to effectively identify security flaws within them. To address these shortcomings, we design an efficient poisoning model that embeds malicious information thoroughly into the original clean data to attack video-text cross-modal retrieval models. We are the first to use a poisoning model to attack retrieval models, thereby uncovering their security vulnerabilities. Second, we introduce a bi-level poisoning module to ensure that malicious information is thoroughly embedded into the original video, thereby enhancing the attack capability of the poisoning model. Finally, we design an adversarial module to improve visual consistency between the original and poisoned videos, thus enhancing the concealment of malicious information within the training data of retrieval models. Our poisoning model can identify security flaws in video-text cross-modal retrieval models, providing insights into improving the security of retrieval models. The effectiveness of our model is validated on the MSR-VTT, LSMDC, and MSVD datasets. © 1991-2012 IEEE.

关键词： Content based retrieval

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A novel lightweight deep learning approach for simultaneousoptic cup and optic disc segmentation in glaucoma detection

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Mathematical Biosciences and Engineering 2024年第4期21卷 5092-5117页

作者： Song, Yantao Zhang, Wenjie Zhang, Yue Institute of Big Data Science and Industry Shanxi University Taiyuan030006 China School of Computer and Information Technology Shanxi University Taiyuan030006 China

Glaucoma is a chronic neurodegenerative disease that can result in irreversible vision loss if not treated in its early ***-to-disc ratio is a key criterion for glaucoma screening and diagnosis, and it isdetermined by dividing the area of the optic cup (OC) by that of the optic disc (OD) in fundus images. Consequently, the automatic and accurate segmentation oftheOC and OD is a pivotal step in glaucoma detection. In recent years, numerous methods have resulted ingreat success on this task. However, most existing methods either have unsatisfactory segmentation accuracy orhigh time costs. In this paper, we propose a lightweight deep-learning architecture for the simultaneous segmentation oftheOC and OD, where we haveadoptedfuzzy learning andamulti-layer perceptron to simplify the learning complexity and improve segmentation accuracy. Experimental results demonstrate the superiority of our proposed method as compared tomost state-of-the-art approaches in terms of both training time and segmentation accuracy. © 2024the Author(s).

关键词： Multilayer neural networks

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AI-Driven Pattern Recognition in Medicinal Plants: A Comprehensive Review and Comparative Analysis

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Computers, Materials & Continua 2024年第11期81卷 2077-2131页

作者： Mohd Asif Hajam Tasleem Arif Akib Mohi Ud Din Khanday Mudasir Ahmad Wani Muhammad Asim Department of Information Technology Baba Ghulam Shah Badshah UniversityRajouriJammu and Kashmir185234India Department of Computer Science Samarkand International University of TechnologySamarkand141500Uzbekistan EIAS Data Science Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern *** the extensive history of medicinal plant usage,various plant parts,including flowers,leaves,and roots,have been acknowledged for their healing properties and employed in plant *** images,however,stand out as the preferred and easily accessible source of *** plant identification by plant taxonomists is intricate,time-consuming,and prone to errors,relying heavily on human *** intelligence(AI)techniques offer a solution by automating plant recognition *** study thoroughly examines cutting-edge AI approaches for leaf image-based plant identification,drawing insights from literature across renowned *** paper critically summarizes relevant literature based on AI algorithms,extracted features,and results ***,it analyzes extensively used datasets in automated plant classification *** also offers deep insights into implemented techniques and methods employed for medicinal plant ***,this rigorous review study discusses opportunities and challenges in employing these AI-based ***,in-depth statistical findings and lessons learned from this survey are highlighted with novel research areas with the aim of offering insights to the readers and motivating new research *** review is expected to serve as a foundational resource for future researchers in the field of AI-based identification of medicinal plants.

关键词： Pattern recognition artificial intelligence machine learning deep learning image processing plant leaf identification

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Stability and Generalization of Hypergraph Collaborative Networks

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Machine Intelligence Research 2024年第1期21卷 184-196页

作者： Michael K.Ng Hanrui Wu Andy Yip Institute of Data Science The University of Hong KongHong KongChina Department of Mathematics The University of Hong KongHong KongChina College of Information Science and Technology Jinan UniversityGuangzhou 510006China

Graph neural networks have been shown to be very effective in utilizing pairwise relationships across ***,there have been several successful proposals to generalize graph neural networks to hypergraph neural networks to exploit more com-plex *** particular,the hypergraph collaborative networks yield superior results compared to other hypergraph neural net-works for various semi-supervised learning *** collaborative network can provide high quality vertex embeddings and hyperedge embeddings together by formulating them as a joint optimization problem and by using their consistency in reconstructing the given *** this paper,we aim to establish the algorithmic stability of the core layer of the collaborative network and provide generaliz--ation *** analysis sheds light on the design of hypergraph filters in collaborative networks,for instance,how the data and hypergraph filters should be scaled to achieve uniform stability of the learning *** experimental results on real-world datasets are presented to illustrate the theory.

关键词： Hypergraphs vertices hyperedges collaborative networks graph convolutional neural networks(CNNs) stability generalization guarantees

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Intra-Patient and Inter-Patient Multi-Classification of Severe Cardiovascular Diseases Based on CResFormer

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Tsinghua science and technology 2023年第2期28卷 386-404页

作者： Dengao Li Changcheng Shi Jumin Zhao Yi Liu Chunxia Li College of Data Science Taiyuan University of TechnologyJinzhong 030600China College of Information and Computer Taiyuan University of TechnologyJinzhong 030600China Shanxi Bethune Hospital Taiyuan 030032China

Severe cardiovascular diseases can rapidly lead to *** present,most studies in the deep learning field using electrocardiogram(ECG)are performed on intra-patient experiments for the classification of coronary artery disease(CAD),myocardial infarction,and congestive heart failure(CHF).By contrast,actual conditions are inter-patient *** this study,we proposed a deep learning network,namely,CResFormer,with dual feature extraction to improve accuracy in classifying such ***,fixed segmentation of dual-lead ECG signals without preprocessing was used as input ***,one-dimensional convolutional layers performed moderate dimensionality reduction to accommodate subsequent feature ***,ResNet residual network block layers and transformer encoder layers sequentially performed feature extraction to obtain key associated abstract ***,the Softmax function was used for ***,the focal loss function is used when dealing with unbalanced *** average accuracy,sensitivity,positive predictive value,and specificity of four classifications of severe cardiovascular diseases are 99.84%,99.68%,99.71%,and 99.90%in intra-patient experiments,respectively,and 97.48%,93.54%,96.30%,and 97.89%in inter-patient experiments,*** addition,the model performs well in unbalanced datasets and shows good noise ***,the model has great application potential in diagnosing CAD,MI,and CHF in the actual clinical environment.

关键词： dual-lead ECG signals coronary artery disease myocardial infarction congestive heart failure interpatient experiments intra-patient experiments CResFormer

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Efficient Multi-Object Tracking Techniques using DAC Feature for Video Processing Applications 21

Efficient Multi-Object Tracking Techniques using DAC Feature...

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21st International Conference on ICT and Knowledge Engineering, ICT and KE 2023

作者： Boonchan, Wannadit Jongsawat, Nipat Tungkasthan, Anucha Data and Information Science Faculty of Science and Technology RMUTT Pathum Thani Thailand

ISBN: (纸本)9798350308716

Object Tracking (OT) is still a challenging area of research, especially Multi-Object Tracking Accuracy (MOTA) in complex scenes. In the past, most popular methods used the global bounding box feature to represent an object because these methods calculate very quickly. However, it is inefficient of accuracy when applied to multi-object tracking processes where objects in a scene have similar box characteristics, such as cars and people. Therefore, this paper presents a technique for increasing the efficiency of tracking multiple objects in video files by adding global low-level visual content to the global bounding box feature. The experimental results show that the proposed method can increase tracking accuracy and maintain reasonable processing performance. © 2023 IEEE.

关键词： Video signal processing

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Cost-Efficient Edge Caching for NOMA-Enabled IoT Services

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China Communications 2024年第8期21卷 182-191页

作者： Chen Ying Xing Hua Ma Zhuo Chen Xin Huang Jiwei School of Computer Science Beijing Information Science and Technology UniversityBeijing 100101China Beijing Key Laboratory of Petroleum Data Mining China University of PetroleumBeijing 102249China

Mobile edge computing(MEC)is a promising paradigm by deploying edge servers(nodes)with computation and storage capacity close to IoT *** Providers can cache data in edge servers and provide services for IoT devices,which effectively reduces the delay for acquiring *** the increasing number of IoT devices requesting for services,the spectrum resources are generally *** order to effectively meet the challenge of limited spectrum resources,the Non-Orthogonal Multiple Access(NOMA)is proposed to improve the transmission *** this paper,we consider the caching scenario in a NOMA-enabled MEC *** the devices compete for the limited resources and tend to minimize their own *** formulate the caching problem,and the goal is to minimize the delay cost for each individual device subject to resource *** reformulate the optimization as a non-cooperative game *** prove the existence of Nash equilibrium(NE)solution in the game ***,we design the Game-based Cost-Efficient Edge Caching Algorithm(GCECA)to solve the *** effectiveness of our GCECA algorithm is validated by both parameter analysis and comparison experiments.

关键词： caching cost Internet of Things mobile edge computing non-orthogonal multiple access

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