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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

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Dual attention mechanism networks for fabric image retrieval 3

Dual attention mechanism networks for fabric image retrieval

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3rd international conference on computer Science and Communication Technology, ICCSCT 2022

作者： Wei, Hao Wang, Zhixiang School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Wuxi214000 China

ISBN: (纸本)9781510661240

In recent years, with the booming development of deep learning, content using images has become a mainstream research hotspot for retrieval. Fabric image retrieval, as a specific application scenario for retrieval, has potential value in many areas such as textile product design, e-commerce and inventory management. However, in fabric retrieval, fabric image has unique striped texture features, and it is still a challenging task to better extract its features for retrieval. To address these problems, this paper proposes a method for fabric retrieval based on a dual attention mechanism. The method inserts a dual attention mechanism module into the convolutional neural network, firstly strip attention module, which connects long and distant image information to capture narrow feature regions, and secondly the channel attention module, which adaptively recalibrates the channel feature responses and selects the prominent and effective feature maps. Experimental results show that the proposed method performs superiorly in fabric image retrieval, and DAMNet is proposed to solve the problem of feature extraction and retrieval of such fabric images. © 2022 SPIE.

关键词： Deep learning

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A new model of internet addiction tendency based on machine learning 3

A new model of internet addiction tendency based on machine ...

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3rd international conference on computer Science and Communication Technology, ICCSCT 2022

作者： Guo, Zhijun Zhang, Laigang College of Information Science and Technology Hunan Normal University Hunan Changsha410081 China School of Mechanical & Automotive Engineering Liaocheng University Shandong Liaocheng252000 China

ISBN: (纸本)9781510661240

This paper proposes a new model to analysis multi factors that affect internet addiction. Firstly, based on the latest research status and achievements of machine learning, this paper constructs a multi-factor weighted analysis model of Internet addiction tendency. Secondly, in order to analyze the determinants of internet addiction more effectively, this paper designs a deep belief network structure based on the method of machine. Through the training of this network, we can get more accurate weight distribution of various factors in the network structure, so as to extract more effective convolution features of multi-factors. Finally, it gives the compared experimental results which show that this new model can make the internet addiction tendency analysis method be more accurate and effective. © 2022 SPIE.

关键词： Convolution

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application of artificial intelligence technology in unconventional natural gas production forecasting 3

Application of artificial intelligence technology in unconve...

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3rd international conference on computer Science and Communication Technology, ICCSCT 2022

作者： Gao, Qichao Liao, Lulu Yang, Shunhui SINOPEC Research Institute of Petroleum Engineering Co. Ltd. Beijing China College of Petroleum Engineering China University of Petroleum Beijing China

ISBN: (纸本)9781510661240

Improving the speed and accuracy of unconventional natural gas production forecasts is the key to scientific and efficient development of unconventional resources. The existing prediction methods based on the transmission mechanism make assumptions and simplifications on the model, and it is difficult to comprehensively and accurately evaluate the main control factors of production capacity, resulting in large production prediction errors. This paper proposes a productivity prediction method for unconventional natural gas wells based on artificial intelligence (AI) and data mining technologies. We use the Pearson correlation coefficient and grey relational analysis to screen out the main control factors, and select the best yield intelligent forecasting model by training and comparing a variety of commonly used machine learning methods. This paper takes the Alberta tight gas field in Canada as an example to illustrate the effectiveness and practicability of this method. © 2022 SPIE.

关键词： Natural gas wells

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3rd international Workshop of Advances in Simplifying Medical Ultrasound, ASMUS 2022, held in Conjunction with 25th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2022

3rd International Workshop of Advances in Simplifying Medica...

引用

3rd international Workshop of Advances in Simplifying Medical Ultrasound, ASMUS 2022, held in Conjunction with 25th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2022

ISBN: (纸本)9783031169014

The proceedings contain 18 papers. The special focus in this conference is on Advances in Simplifying Medical Ultrasound. The topics include: Adnexal Mass Segmentation with Ultrasound Data Synthesis;self-Knowledge Distillation for First Trimester Ultrasound Saliency Prediction;a Universal End-to-End Description of Pulse-Echo Ultrasound Image Reconstruction;learning Generalized Non-rigid Multimodal Biomedical Image Registration from Generic Point Set Data;contact Force Prediction for a Robotic Transesophageal Ultrasound Probe via Operating Torque Sensing;meta-registration: learning Test-Time Optimization for Single-Pair Image Registration;automatic Quality Assessment of First Trimester Crown-Rump-Length Ultrasound Images;towards Multi-modal Self-supervised Video and Ultrasound Pose Estimation for Laparoscopic Liver Surgery;Spatio-Temporal Model for EUS Video Detection of Pancreatic Anatomy Structures;RL Based Unsupervised Video Summarization Framework for Ultrasound Imaging;prediction of Kidney Transplant Function with machine learning from Computational Ultrasound Features;differential learning from Sparse and Noisy Labels for Robust Detection of Clinical Landmarks in Echo Cine Series;end-to-End Myocardial Infarction Classification from Echocardiographic Scans;view Classification of Color Doppler Echocardiography via Automatic Alignment Between Doppler and B-Mode Imaging;AI-Enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography;efficient Pix2Vox++ for 3D Cardiac Reconstruction from 2D Echo Views.

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Fair machine learning-An Analytical Study Based on CiteSpace

Fair Machine Learning-An Analytical Study Based on CiteSpace

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Consumer Electronics and computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Xiangxi Luo Jianfeng Cui Shuai Ma College of Computer and Information Engineering Xiamen University of Technology Xiamen China School of Software Engineering Xiamen University of Technology Xiamen China

With the development of machine learning, fair machine learning has started to receive gradual attention. How to mitigate or eliminate the possible unfair decision results of machine learning has become a popular research topic in this field. At present, the research on fair machine learning is still in its initial stage. In this paper, we analyzed the research and articles related to fair machine learning (January 2011 to December 2022) using CiteSpace visualization software, explored research collaboration networks (authors, institutions, and countries), keyword co-occurrence and clustering networks, and literature co-citation and clustering networks, and analyzed and constructed knowledge graphs. To understand the research foundation, related research progress, the latest research directions, and the research methods receiving attention in the field of fair machine learning through the analysis of the knowledge graph. Relevant key articles are discussed, and future research directions are envisioned.

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application of Efficient Emotional Arousal in the "Internet +" Smart-classroom Teaching Environment 22

Application of Efficient Emotional Arousal in the "Internet ...

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3rd international conference on Education Development and Studies, ICEDS 2022

作者： Han, Ying Zhang, Yuqing Institute Of Psychology Institute Of Psychology Chinese Academy Of Sciences China Institute Of Psychology University Of Chinese Academy Of Sciences China

ISBN: (纸本)9781450396271

In the context of artificial intelligence, the application of "Internet +"smart-classroom teaching environment is of great value to modern teaching practice. In education environment, teachers can control the teaching environment and adopt emotional awakening as the main penetration principle, which can effectively promote students' learning efficiency. This study is based on the ecological valence theory (EVT), Combining Thomas and Chess PTQ as well as the odor preference questionnaire, 339 Chinese children aged 3-7 were randomly selected as subjects, The results show that 1, olfactory sensitivity can be used as an important environmental science application element in the smart-classroom of children aged 3-7;2, it can effectively improve Chinese children's mood and persistence that the intelligent ecosystem in the environment through the linkage application;3, it contributes to the accurate dissemination of odor categories in smart classrooms that establishment of child trait big data analysis;4, uses different sensing devices and man-machine interaction modules to effectively increase the control of the teaching space, enhance students' sense of self-efficacy. © 2022 Owner/Author.

关键词： Students

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Pituitary Tumor Classification using machine learning 3

Pituitary Tumor Classification using Machine Learning

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3rd international conference on Communication, Computing and Industry 4.0, C2I4 2022

作者： Sreeja, S.P. Asha, V. Saju, Binju Preethi, T.S. Prajwal Master of Computer Applications New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9781665474979

One of the more extreme problems that influences the two, kids and grown-ups is a cerebrum cancer. 85 to 90 percent of all essential Focal Sensory system malignancies are mind cancers. Around 11,700 patients are given a mind cancer finding every year. For the individuals who have a harmful cerebrum or Central Nervous System growth, the 5-year endurance rate is generally 34% for guys and 36% for ladies or women. In this paper, it is used with the classifiers to detect the tumor in human they are CNN, KNN, Logistic regression and SVM algorithms. Benign, malignant, pituitary, and other types of brain tumors are all classified. KNN-Classifier is used to categories tumors, and cross-validating accuracy score and hyperparameter tuning are used. The cross validation includes the experimental simulation with the best average score for K. Hyper parameters of the KNN Classifier state the best score when approaching the prediction with the highest accuracy score. Convolutional neural networks (CNNs) are used in our method to investigate discriminative information while integrating multi-layer dictionary learning. From the comparisons it is been analyzed that CNN Classifier has the best performance and achieved with 85.24 % accuracy. © 2022 IEEE.

关键词： Tumors

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A study of stock price analysis algorithms for new energy companies based on random forest and genetic algorithms 3

A study of stock price analysis algorithms for new energy co...

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3rd international conference on Artificial Intelligence and computer Engineering, ICAICE 2022

作者： Song, Shiyang Gao, Junhan Zhao, Anran Li, Yu Li, Chengyifan Zhu, Yuhang Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China College of Global Talents Beijing Institute of Technology Zhuhai China

ISBN: (纸本)9781510663473

China is the world's largest emerging energy market, but due to the rapid development of China's new energy industry special, the value of new energy companies are more susceptible to sharp increases and decreases brought about by external factors in the market compared to other *** this paper, we propose an algorithm that uses an effective cluster learning strategy (Random Forest) contrasted with a genetic algorithm. A new financial forecasting model GSRF is constructed by parameter optimization of random forest model through grid search algorithm,and the model is applied to short-term stock forecasting. This paper builds a stock price trend forecasting model based on both algorithms and uses the model to determine whether the cost of a stock will be higher than its cost at a given date. The experimental results show that the model built using GSRF stochastic forest has the highest return and the lowest risk compared to the return based on the traditional genetic algorithm trading strategy. © 2023 SPIE.

关键词： Genetic algorithms

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Sports Game Classification and Detection Using ResNet50 Model Through machine learning Techniques Using Artificial Intelligence

Sports Game Classification and Detection Using ResNet50 Mode...

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Innovation in Technology (INOCON), IEEE international conference for

作者： Kanwarpartap Singh Gill Vatsala Anand Sonal Malhotra Swati Devliyal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science & Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

The intention of artificial intelligence (AI) sports analysis is to use AI and machine learning to get useful insights and enhance sports performance. Data from sporting events, players, and welfare equipment are processed and analysed using AI methods. machine learning, computer vision, natural language processing, and other forms of artificial intelligence analysis are used to improve every facet of sports, from player efficiency to audience participation. It's a fast-paced, ever-changing industry that's revolutionising the way athletes, coaches, and fans interact with the sporting world. The ResNet-50 model and other machine learning techniques are at the bleeding edge of research into sports analysis and performance enhancement. The deep learning model ResNet-50 is used to analyse video and stills from sporting events. application areas include player monitoring, injury prediction, and game result predicting, all of which are made possible by the use of transfer learning to collect subtle details in sports data. This approach ushers in a new age of data-driven decision making in the sports industry while also revolutionising sports analytics, providing access to invaluable information for coaches, players, and spectators. The primary objective of this research was to categorise nine distinct sports-related picture classifications and predict their expected accuracy, which came in at 93 percent. Future researchers in this field of study will benefit from the high level of accuracy shown in this work.

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Design of a Deep learning-based Detection System for Criminal Activities 3

Design of a Deep Learning-based Detection System for Crimina...

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3rd international Informatics and Software Engineering conference, IISEC 2022

作者： Nojor, Verlyn V. Austria, Jarod Augustus C. Galit, Adrian A. Guevarra, Joaquin Tyrone B. Jogno, Kathleen Q. Venal, Maria Cecilia A. Somao-I, Mary Jane F. Technological Institute of the Philippines Computer Engineering Department Quezon City Philippines

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

In the last six years, Manila crime rate improvement was significantly low compare to other ASEAN cities. The Philippines' crime rate & statistics was recorded 8.93 in 2017 which has a percentage of 23.55% decline from 2016, then the Philippines' crime rate & statistics for 2018 was 6.47, a 22.96% decline from 2017. In 2019, there were more than 1,200 deliberate shooting deaths and more than 7000 cases of assault injuries in the nation. The project aims to design a deep learning-based detection system for criminal activities such as shooting and assault. Also, to design a web application that includes features such as a dashboard showing the number of confirmed detections, a notification page, and a live feed page for streaming the footage from the CCTV camera. The model is used for the system to work together with the Python framework. The system obtained an accuracy of 95% percentage of accuracy for crime detection along with a 100% accuracy percentage for the other project objectives. The design of the deep learning-based detection system has shown a significant potential to introduce a more active response to crime by supporting police officers in preventing violence and upholding a safer environment for all. © 2022 IEEE.

关键词： Crime

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