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Chondrosarcoma of the Pelvis recognition Based on MRI Radiomics 3rd

Chondrosarcoma of the Pelvis Recognition Based on MRI Radiom...

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3rd international conference on machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Xue, Suqi Ye, Zhaoming Hu, Kexin Zhou, Xingzhi Yao, Zhaonong Hu, Dinghan Cao, Jiuwen Li, Hengyuan Artificial Intelligence Institute Hangzhou Dianzi University Hangzhou China Department of Orthopedic Surgery The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China

ISBN: (纸本)9789819624676

Chondrosarcoma of the Pelvis is one of the most challenging tumors for the orthopaedic surgeon. Clinically, imaging modalities represented by Magnetic resonance imaging (MRI) are the regular approach for diagnosing Chondrosarcoma. In this paper, to further improve the automation of diagnosis and mine the complementary clinical information from high-dimensional features, we propose a pelvic chondrosarcoma identification model based on MRI radiomics features. Specifically, radiomic features, such as first-order, shape, and texture, are initially extracted, and then a subset of these features are selected based on the least absolute shrinkage sum selection operator (LASSO) analysis. Subsequently, a recursive feature elimination (RFE) algorithm is utilized to select the most significant features. The prediction results are finally obtained through the constructed multivariate logistic regression model. Experiments conducted on clinical data achieved the area under curve (AUC) of 98.1% on testing set, demonstrating the effectiveness of the proposed MRI-based model for identifying pelvic chondrosarcoma. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Logistic regression

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A Spot Welding Spatter Monitoring System Based on machine Vision 3

A Spot Welding Spatter Monitoring System Based on Machine Vi...

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3rd international conference on Electrical Engineering and Mechatronics Technology, ICEEMT 2023

作者： Wang, Xiaodong Zhu, Zhengqiang Peng, Hongyu Wang, Yongqiang Tangshan University College of Artificial Intelligence Hebei Tangshan China Nanchang University School of Advanced Manufacturing Jiangxi Nanchang China

ISBN: (纸本)9798350303698

A new vision monitoring method for resistance spot welding with high speed industrial camera is presented, and by this method the dynamic procedure of spot welding electrodes touching can be inspected. The pressure and current influence for resistance spot welding spatter can be distinguished by a serial of continuous images with millisecond time interval. The other spatter phenomena caused by welding pole wearing or fixing obliquely can also be distinguished by vision images. By a series of experiments, this machine vision system is be built by C++ language, which can detect the resistance spot welding spatter occurring and distinguish the spattering possible reasons. In conclusion, the machine vision monitoring system built in this paper can capture thousands of frames at one welding duty cycle, and then find the key frame with significantly welding spatter features, subsequently calculate the amount of spatter and distinguish the classification. The running testing of this system shows that this machine vision system run stably and can timely find the spot welding spatter problems in time. © 2023 IEEE.

关键词： Spot welding

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Research Trends in Perceived Behavioral Control from a Bibliometric Perspective 3

Research Trends in Perceived Behavioral Control from a Bibli...

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3rd international conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Purnama, Suryari Sunarjo, Richard Andre Rakhmansyah, Mohamad Rizky, Agung Esa Unggul University Faculty of Economies and Business Tangerang Indonesia Universitas of Raharja Faculty of Economies and Business Tangerang Indonesia University of Raharja Faculty Sains of Technology Tangerang Indonesia Pandawan Sejahtera Indonesia Faculty of Digital Business Tangerang Indonesia

ISBN: (纸本)9798350367492

Perceived Behavioral Control (PBC), a central element of the Theory of Planned Behavior, is essential for understanding and predicting human behavior across diverse fields such as psychology, business, and education. PBC refers to an individual's perceived ease or difficulty in executing a particular behavior. This study employs the Dimensions database and advanced bibliometric tools, leveraging artificial intelligence and machine learning algorithms to investigate the global influence and utilization of PBC. The analysis identifies key trends in PBC research, leading contributors, prominent institutions, and its interdisciplinary scope, particularly focusing on the intersection of PBC with technology adoption and human-computer interaction. Data from 68 countries highlight major contributions from China and Indonesia. Noteworthy institutions include Universiti Malaysia Kelantan, University of Malaya, and Binus University, with influential researchers such as Meng Bo and Arkorful. Leading journals such as Sustainability and the international Journal of Environmental Research and Public Health reflect PBC's interdisciplinary significance, particularly in the context of technological advancements and digital behavior analysis. Key-word co-occurrence analysis reveals PBC's frequent associations with attitudes, intentions, and subjective norms, which are crucial to the Theory of Planned Behavior and its application in technology acceptance models. This study underscores PBC's global significance in behavior prediction, showcasing its widespread application and the international collaboration fueling current research. By incorporating advanced data analytics and machine learning techniques, this research lays the groundwork for future studies to delve deeper into PBC's implications and applications in the realm of technology and digital interactions, making significant contributions to the IEEE community. © 2024 IEEE.

关键词： international cooperation

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Solving the problem of classifying forest cover types based on soil characteristics 3

Solving the problem of classifying forest cover types based ...

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3rd international conference on Optics, Computer Applications, and Materials Science, CMSD 2023

作者： Muhamediyeva, D.T. Safarova, L.U. Nabiyeva, S.S. Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan Samarkand State University of Veterinary Medicine Livestock and Biotechnologies Samarkand Uzbekistan Research Institute for the Development of Digital Technologies and Artificial Intelligence Uzbekistan

ISBN: (纸本)9781510674486

The " covtype " dataset in scikit-learn represents forest cover information and includes a variety of soil characteristics for seven different forest cover types. The proposed work solves the classification problem, where the goal is to accurately determine the type of forest cover based on given soil characteristics. The study uses various machine learning methods such as decision trees and naive Bayes classifier. Models are trained on an extensive training set and then evaluated on test data to determine their ability to accurately predict forest cover types. The classification results are analyzed, including metrics of accuracy, recall, F1-measures, as well as ROC curves are constructed and the areas under them (AUC) are calculated. The results and metrics obtained allow us to compare the effectiveness of different models in solving a given classification problem. The knowledge gained can be useful for the application of machine learning algorithms in ecology and forest resource management. © 2024 SPIE. All rights reserved.

关键词： Decision trees

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Detecting Islanding by Ensemble Classifier for pattern recognition Using HOG Features 3

Detecting Islanding by Ensemble Classifier for Pattern Recog...

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3rd IEEE international conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2024

作者： Pal, Kumaresh Goswami, Subhadip Debbarman, Sangeeta Manna, Pranendu Kumbhakar, Shyamal Bhuyan, Anshuman National Institute of Technology Department of Electrical Engineering Jamshedpur India National Institute of Technology Department of Electrical Engineering Arunachal Pradesh India Techno India University Department of Electrical Engineering Kolkata India Gandhi Institute for Education & Technology Department of Electrical Engineering Bhubaneswar India

ISBN: (纸本)9798350387193

The ever-increasing need for power encourages the growth of Distributed Generation (DG). Almost all DG sources are sustainably renewable. Islanding is one of the key challenges associated with excessive penetration of DG sources. The islanding could cause harm to the clients and their equipment. The islanding will be detected in two seconds, according to the IEEE 1547 DG interconnection rules, and the DG must be turned off. In this paper, a novel approach for detecting islanding based upon image categorisation using ensemble Classifier is proposed. Images are used to derive the histogram of oriented gradient (HOG) characteristics so as to identify non-islanding as well as islanding events. In the suggestive method, spectrogram images are obtained from the time-series signal which are derived from the point of common coupling. These images are utilised to extract the features using histogram of oriented gradient, which is then utilised as a feature vector input for the Ensemble classifier for training as well as testing. It uses rate of change of negative sequence voltage parameter for deriving spectrogram image. The ensemble classifier's effectiveness is assessed with 5, 10, 20 and 25-fold cross-validations. The results from classification demonstrates that detection of islanding using ensemble classifier and histogram of oriented gradient feature being used for image classification achieved excellent results with 97.45% accuracy and validation error of 0.188. © 2024 IEEE.

关键词： Spectrographs

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Improved smoking behavior detection algorithm based on YOLOv5s 3

Improved smoking behavior detection algorithm based on YOLOv...

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3rd international conference on machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Qiu, Quanyuan Sang, Qingbing School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China

ISBN: (纸本)9781510681880

Due to the limitations of monitoring equipment in actual non-smoking places and the complexity of the background in public places, the pictures taken often have problems such as too small detection target scale, too low resolution, and too many background interference factors. Mainstream detectors are prone to miss detection and false detection in practical applications, resulting in poor detection results. To solve the above problems, this paper proposes an improved SMYOLOv5s based on YOLOv5s. In the feature extraction stage, the convolutional attention module (CBAM) is integrated to enhance the expression of small object features. In the feature fusion stage, Swin Transformer Block module and BiFPN structure are introduced to effectively improve the model missing detection problem. On top of this, an additional small-size detection head is added to further strengthen the fusion of shallow information of small object and improve the detection accuracy. Experimental results show that the improved algorithm has a detection accuracy of 90.9% for smoking behavior. Compared with the pre-improved algorithm, the mAP increased by 3.8%. The detection effect is significantly improved and has high application value. © 2024 SPIE.

关键词： Photons

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The Impact of Artificial intelligence as Advanced Technologies on Bitcoin Industries 3

The Impact of Artificial Intelligence as Advanced Technologi...

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3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Srinivas, D. Ambore, Bhagyashree Chauhan, Sumit Tarawade, Abhijit Lourens, Melanie Tiwari, Mohit School of Business Sr University Telangana Warangal India Rns Institute of Technology Information Science and Engineering India Management Education & Research Institute New Delhi India Inha University in Tashkent Tashkent Uzbekistan Durban University of Technology Faculty of Management Sciences South Africa Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi A-4 Rohtak Road Paschim Vihar Delhi India

ISBN: (纸本)9798350330724

Cryptocurrencies have become a leading kind of digital currency in recent years, and the financial system is also becoming a key factor. Some Artificial intelligence techniques are needed to lower investing risk and anticipate price, trend, portfolio construction, and fraud detection. The most well-known cryptocurrency, Bitcoin, is discussed along with recent research on AI approaches for cryptocurrencies. The goal of this study is to examine how well machine learning (ML) and artificial intelligence (AI) techniques forecast the value of cryptocurrencies like bitcoin. With regard to Bitcoin, the largest decentralized digital currency in terms of market capitalization, we investigate the possibility of offering unusual risk-changed rewards using Support Vector machines (SVM) and Artificial Neural Networks (ANN) assembled techniques. The outcomes demonstrate the way that trader can utilize SVM to create moderate profits from a risk changed premise, even subsequent to thinking about exchange costs. The exploration likewise suggests that ANN can outflank even buy and-hold procedures during delayed bull patterns by using transient educational shortcomings to create abnormal benefits. © 2023 IEEE.

关键词： Support vector machines

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An Efficient Graph Transformer Network for Video-Based Human Mesh Reconstruction 3rd

An Efficient Graph Transformer Network for Video-Based Human...

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3rd CAAI international conference on Artificial intelligence (CICAI)

作者： Tang, Tao You, Yingxuan Wang, Ti Liu, Hong Peking Univ Shenzhen Grad Sch Key Lab Machine Percept Shenzhen Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China

ISBN: (纸本)9789819988495;9789819988501

Although existing image-based methods for 3D human mesh reconstruction have achieved remarkable accuracy, effectively capturing smooth human motion from monocular video remains a significant challenge. Recently, video-based methods for human mesh reconstruction tend to build more complex networks to capture temporal information of human motion, resulting in a large number of parameters and limiting their practical applications. To address this issue, we propose an Efficient Graph Transformer network to Reconstruct 3D human mesh from monocular video, named EGTR. Specifically, we present a temporal redundancy removal module that uses 1D convolution to eliminate redundant information among video frames and a spatial-temporal fusion module that combinesModulated GCN with transformer framework to capture human motion. Our method achieves better accuracy than the state-of-the-art video-based method TCMR on 3DPW, Human3.6M andMPI-INF-3DHP datasets while only using 8.7% of the parameters, indicating the effectiveness of our method for practical applications.

关键词： Transformer Graph Convolutional Network Temporal Redundancy Removal Video-based 3D Human Mesh Reconstruction

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BREAST AI: Low Cost, Explainable Artificial intelligence Based App for Efficient Diagnosis of Breast Cancer in Developing Areas 3

BREAST AI: Low Cost, Explainable Artificial Intelligence Bas...

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3rd IEEE international conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2023

作者： Addala, Vibha Liberal Arts and Science Academy Computer Science Austin United States

ISBN: (纸本)9798350333862

Breast cancer is the most common cancer among women. Accurate diagnosis of breast cancer is crucial for successful treatment. However, many women in developing areas do not have access to similar medical resources as those in more developed areas, resulting in poor outcomes. Disparities range from a lack of mammograms in rural settings, as they are solely applicable to the breast, to a lack of trained radiologists to diagnose breast cancer. The goal of this research was to develop an artificial intelligence-based app for the accurate diagnosis of breast cancer via ultrasound scans without the need for an internet connection. The artificial intelligence model developed was a fine-tuned MobileNet-V2 convolutional neural network, trained on 624 ultrasound scans and tested on 156 scans from the Baheya Hospital in Egypt. The testing accuracy was 87.50%. To understand how the algorithm was making predictions (Explainable AI), code was created to generate heat maps to highlight parts of the breast ultrasound that were most significant in helping the algorithm make its decision. The AI model was downloaded as a TensorFlow Lite file, which allows for on-device machine learning, eliminating the need for an internet connection to operate, and developed into a user-friendly app. Further work, such as using a more diverse dataset, trying different convolutional neural network architectures, building a multimodal model, and implementing denoising algorithms can be done to improve this research. © 2023 IEEE.

关键词： Diagnosis

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EntityLayout: Entity-Level Pre-training Language Model for Semantic Entity recognition and Relation Extraction 18th

EntityLayout: Entity-Level Pre-training Language Model for S...

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18th international conference on Document Analysis and recognition (ICDAR)

作者： Xu, Chun-Bo Chen, Yi-Ming Liu, Cheng-Lin ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201203 Peoples R China Hundsun Technol Inc Hundsun Ctr 1888 Binxing Rd Hangzhou Peoples R China Chinese Acad Sci State Key Lab Multimodal Artificial Intelligence Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (纸本)9783031705328;9783031705335

Semantic entity recognition (SER) and relation extraction (RE) are the core tasks of information extraction from visually-rich documents (Vrds). Although self-supervised pre-training models have advanced the performance of these tasks, existing methods are insufficient in fusing the token features and modeling SER and RE jointly. In this paper, we propose an entity-level language model, named Entity-Layout, and use a graph-based approach for efficient entity labeling and linking jointly on a continual pre-training model. In EntityLayout, a Token Fusion Module (TFM) is proposed to fuse the token feature and learn an entity-level representation. Then, we use the entity-level representation to build a graph and propose a Graph Pruning Module (GPM) to effectively prune the invalid links. Finally, SER and RE are accomplished simultaneously by a joint information extraction Task Module. Experimental results on public datasets FUNSD and COrd demonstrate that the proposed EntityLayout achieves competitive performance in SER and state-of-the-art performance in RE, i.e., SER F1 scores of 0.9108 and 0.9650, respectively, RE F1 scores of 0.8212 and 0.9898, respectively.

关键词： Visual Information Extraction Semantic Entity recognition Relation Extraction Graph Pruning Graph Neural Network

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