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Few-shot sketch recognition for plotting system

Few-shot sketch recognition for plotting system

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2021 International Conference on Green Communication, Network, and Internet of Things, GCNIoT 2021

作者： Yuan, Zelin Dept. of Geography Graphics and Image Research and Development North China Institute of Computing Technology No.211 North Fourth Ring Road Beijing100084 China

ISBN: (数字)9781510650473

ISBN: (纸本)9781510650466

Plotting system refers to a software system that can draw a series of vector symbols, typesetting, saving, reading and so on. The traditional plotting system uses key and mouse operation to draw. With the development of electronic equipment touch screen in recent years, the traditional menu selection is not convenient enough, but the way of using hand-painted sketch and recognized by the system is more natural and fast. Traditional image recognition methods need huge amount of data for training, but the vector symbol set in the plotting system is not a common symbol set, so it is difficult to obtain a large amount of data. Therefore, this paper proposes a β-TCVAE-Attention hand-drawn plot recognition method which can obtain better recognition accuracy in the case of small samples. The model first uses the Image dataset to pre-Train βTCVAE, and then uses the small sample data of the plot sketch to input the β-TCVAE encoder to obtain the sketch features. After the sketch features are enhanced with the attention module, they are input into the Softmax classifier based on cosine similarity. The neural network is fine-Tuned according to the classification results of the classifier. Finally, when using network prediction, input the support set and query graph into the network for classification. In this paper, a 20-way-5-shot plotting data set is collected for experiment, and the experiment proves that the model has certain accuracy. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Touch screens

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research of Attention-Based Bi-GRU-CRF for Slot Filling

Research of Attention-Based Bi-GRU-CRF for Slot Filling

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2020 International Conference on Computer Big Data and Artificial Intelligence, ICCBDAI 2020

作者： Zhou, Luwang Huang, Zhimin Nie, Ying Dept. of Geography Graphics and Image Research and Development North China Institute of Computing Technology No.211 North Fourth Ring Road Beijing100084 China

Slot Filling (SF) is a critical part of spoken language understanding (SLU) which targets to capture semantic constituents from a specific utterance. It is considered as a sequence labeling issue. Currently, recurrent neural networks have shown promising effectiveness in this issue. Considering the effects of interrelated information within adjacent words and labels, we present that a novel approach consists of bi-directional gate recurrent unit (Bi-GRU), attention mechanism, and conditional random field (CRF). Our model can utilize interrelated information from words in the neighborhood, highlight key information, and exploit the dependencies within labels corresponding to surrounding words. The empirical experiments illustrate that our model significantly boosts the F1 score with around 1% and 5.1% relative enhancement on two public benchmark dataset ATIS and SNIPS separately. © Published under licence by IOP Publishing Ltd.

关键词： Recurrent neural networks

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Metabolite Profiling of Herbal Medicines using an Artificial Intelligence Approach-A Concise Review 4

Metabolite Profiling of Herbal Medicines using an Artificial...

引用

4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Shet, Ajay S. Somasundaram, R. Sravan Kumar, G. Kaliappan, S. Ramprasad, S.R. Srinivas, Maloth Canara Engineering College Department of Cse Karnataka Mangalore India Kongu Engineering College Management Studies Department TN Erode India Telangana Hyderabad India Lovely Professional University Division of Research and Development Punjab Jalandhar India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Dept. of Cse TN Chennai India Telangana Secunderabad India

ISBN: (纸本)9798331518981

Artificial intelligence (AI) is being developed extremely swiftly in medicine. A structured review should be conducted to comprehend the current status of AI models since they have been introduced in Traditional medicine to Investigate and classify the present applications of AI in TM. Following the technique suggested by the Joanna Briggs Institute, a thorough scoping review was conducted. To find papers on AI and CAM, three databases were searched: PubMed, Embase, and Cochrane Library. Studies published only in English since 2000 are taken into account. Those without mention of traditional medicine or AI methods were also disqualified, as were studies that were not peer-reviewed. All pertinent studies were located using a comprehensive search technique. This review demonstrated the potential use of AI models in TM;nevertheless, to develop AI in TM in the era of digital health, future research should concentrate on validating and verifying the models by carrying out a sizable clinical investigation. © 2024 IEEE.

关键词： Medicinal chemistry

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3-D AIR POLLUTION ESTIMATION USING A HYBRID SPATIAL MODEL: A CASE STUDY OF ZHUNAN-MIAOLI AREA, TAIWAN

3-D AIR POLLUTION ESTIMATION USING A HYBRID SPATIAL MODEL: A...

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2023 Philippine Geomatics Symposium, PhilGEOS 2023

作者： Hsu, C.W. Chern, Y.R. Su, J.J. Wijaya, C. Chen, Y.C. Lung, S.C. Hsiao, T.C. Teo, T.A. Shih, I.L. Wu, C.D. Dept. of Geomatics National Cheng Kung University Tainan701 Taiwan Agricultural Engineering Research Center Taoyuan320 Taiwan National Institute of Environmental Health Sciences National Health Research Institutes Miaoli350 Taiwan Research Center for Environmental Changes Academia Sinica Taipei115 Taiwan Department of Atmospheric Sciences National Taiwan University Taipei106 Taiwan Graduate Institute of Environmental Engineering National Taiwan University Taipei106 Taiwan Dept. of Civil Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan National Center for High-Performance Computing Hsinchu300 Taiwan Innovation and Development Center of Sustainable Agriculture National Chung Hsing University Taichung402 Taiwan

The rapid global urbanization has transformed cityscapes, giving rise to iconic skyscrapers that define modern cities. However, alongside this urban evolution, a pressing concern arises – the air quality within these towering urban environments. Fine particulate matter, known as PM2.5, poses a grave threat to human health and the environment. These tiny particles, measuring 2.5 micrometers or less, can penetrate deep into the human respiratory system, posing severe health risks. Due to the limitations of traditional land-use regression models in estimating the variation of air pollution with altitude, this study employs a novel hybrid spatial model to assess the three-dimensional distribution of PM2.5 in the atmosphere. We employ a comprehensive methodology, integrating diverse datasets and advanced modelling techniques, to uncover significant findings. Our analysis reveals the non-uniform nature of PM2.5 distribution, both horizontally and vertically. Variable selection identifies key factors influencing PM2.5 levels, including Broadleaf Forest, Carbon Monoxide (CO), and Height. Our ensemble model demonstrates robust performance, with Gradient Boosting Regression (GBR) and Random Forest Regression (RFR) exhibiting superior predictive capabilities. This study provides valuable insights into the complex interplay of environmental factors affecting PM2.5 concentrations in high-rise urban environments, emphasizing the need for targeted air quality management strategies considering both horizontal and vertical variations. © Author(s) 2024.

关键词： Air quality

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Discrimination of Cocoa Beans using Structural Image Features: An Experimental Analysis 1

Discrimination of Cocoa Beans using Structural Image Feature...

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1st IEEE IAS Global Conference on Emerging Technologies, GlobConET 2022

作者： Das, Samikshan Akuli, Amitava Biswas, SuparnaParua Dey, Aniruddha Ghosh, Ankush Shaw, RabindraNath Centre for Development of Advanced Computing Kolkata700 091 India Maulana Abul Kalam Azad University of Technology Dept. of Information Technology Nadia741 249 India Neotia University School of Engineering and Applied Science Kolkata India Bharath Institute of Higher Education and Research Chennai India Chandigarh University Punjab Mohali India

ISBN: (数字)9781665443579

ISBN: (纸本)9781665443579

This paper proposes a machine vision solution for classification of cocoa beans using structural characteristics like shape and size of the cocoa beans extracted from images using classic machine learning techniques. The features are extracted from images through a chain of image processing *** traditional machine learning techniques (KNN, SVM, Decision Tree and Random Forest) are employed to classify the cocoa beans in four classes e.g. large, medium, small and rejected. Comparative studies among different techniques are also performed. Prior to train the model, extracted features are optimized by two optimization techniques-Univariate Selection and Feature Importance. Trained models are evaluated using stratified K-fold cross validation and finally mean cross validation scores are calculated for performance analysis. The experimental results show that the Random Forest Classifier provides highest accuracy score of 0.75. © 2022 IEEE.

关键词： Decision trees

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Design and development of an IoT learning system for health-related applications

Design and development of an IoT learning system for health-...

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International Multidisciplinary Conference on Computer and Energy Science (SpliTech)

作者： Milovan Medojević Marko Vasiljević-Toskić Dubravko Ćulibrk Petar Šolić Toni Perković Milana Medojević IVI The Institute for Artificial Intelligence Research and Development of Serbia Novi Sad Serbia EnergyPulse DOO Novi Sad Serbia Dept. of Power Electronics and Telecommunication Engineering UNS University of Novi Sad Novi Sad Serbia Dept. of Industrial Engineering and Engineering Management UNS University of Novi Sad Novi Sad Serbia Dept. of Electronics and Computing FESB University of Split Split Croatia

This paper provides relevant outcomes regarding the design and development of an IoT learning system, aimed to stimulate learning by doing while offering IoT deployment in medical science and related practices toward health monitoring and diagnostics. The system is conceptualized as a through-hole module integration board that enables data acquisition regarding body temperature, heart rate, and the saturation of oxygen in the blood, accompanied by psychrometric parameters of the environment in which the measurement is performed. The paper provides an overview in terms of favoring the development of digital technologies curriculums due to the importance and influences it might have on future experts. Last but not least, technical specifications, schematics, and overall system appearance are enclosed in detail. Finally, possible applications in process engineering and thermo-energetics are discussed as well.

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Analytical Study of the Features and Parameters of Off-Angle Iris Image vs. Deep Learning Model

Analytical Study of the Features and Parameters of Off-Angle...

引用

Decision Aid Sciences and Application (DASA), International Conference on

作者： Aghiad Khedr Suleiman Darin Shafek Mahmoud Mahfuri Mohammad Khishe Ali Saeed Sameh Ghwanmeh Cybersecurity and Cloud Computing Technologies Engineering Dept. Alma'moon University College Baghdad iraq Computer Engineering Techniques Dept. Alma'moon University College Baghdad iraq Computer and information Technology Department International University for Science and Technology Innovation Center for Artificial Intelligence Applications Yuan Ze University Taiwan Jadara research center Jadara University Jordan Computer Center Northern Technical University Ninevah Iraq Computer Science Dept College of Computer Information Technology American University in the Emirates

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

This paper presents an analytical study and a technique for extracting the features of a common case of images of the iris called off-angle iris which was taken for persons identification systems. The main problem when using biological iris measurements to identify the persons is the difficulty of identifying and extracting features of the iris. This problem is increasing when dealing with off-angle iris and it leads to decreased system accuracy and increased system rate error. In return, all the transfer learning techniques face difficulties in the case of heavily degraded data and partially occluded, and off-angle. A new method has a whole new methodology to deal with the image as it is without transformation processes. A new algorithm has been included for extracting features of the iris through the pupil switching points. The most discriminating points of the iris depend on the biological human eye statistics and analytical study. It has been trained and tested on the common images of the off-angle iris database so-called: “CASIA Iris 1.0”. It has been implemented in the MATLAB environment. The results showed the efficiency of this technique, high precision and more importantly low failed acceptance rate. It emphasized that it is adaptive as well as efficiency improvement of the system.

关键词： Deep learning Analytical models Adaptation models Accuracy Transfer learning Feature extraction Cameras Mathematical models Pupils Iris recognition

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Metabolite Profiling of Herbal Medicines using an Artificial Intelligence Approach-A Concise Review

Metabolite Profiling of Herbal Medicines using an Artificial...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Ajay S Shet Somasundaram R Sravan Kumar G S. Kaliappan S.R. Ramprasad Maloth Srinivas Department of CSE Canara Engineering College Mangalore Karnataka India Management Studies Department Kongu Engineering College Erode TN India Dept. of CSE (Cyber Security) CVR College of Engineering Hyderabad Telangana India Division of Research and Development Lovely Professional University Jalandhar Punjab India Dept. of CSE School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai TN India Dept. of CSE (AIML) St. Martin's Engineering College Secunderabad Telangana India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Artificial intelligence (AI) is being developed extremely swiftly in medicine. A structured review should be conducted to comprehend the current status of AI models since they have been introduced in Traditional medicine to Investigate and classify the present applications of AI in TM. Following the technique suggested by the Joanna Briggs Institute, a thorough scoping review was conducted. To find papers on AI and CAM, three databases were searched: PubMed, Embase, and Cochrane Library. Studies published only in English since 2000 are taken into account. Those without mention of traditional medicine or AI methods were also disqualified, as were studies that were not peer-reviewed. All pertinent studies were located using a comprehensive search technique. This review demonstrated the potential use of AI models in TM; nevertheless, to develop AI in TM in the era of digital health, future research should concentrate on validating and verifying the models by carrying out a sizable clinical investigation.

关键词： Reviews Databases Machine learning Predictive models Prediction algorithms Knowledge discovery Data models Libraries Data mining Artificial intelligence

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A Hybrid Approach Towards Data optimization for cloud Based IoT Environment

A Hybrid Approach Towards Data optimization for Cloud Based ...

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International Conference on Emerging Trends in Networks and Computer Communications (ETNCC)

作者： Souvik Pal N. Z. Jhanjhi D. Akila Monir Abdullah Memona Humayun Sandeep Kumar Rahul Kumar Dept. of Management IS Saveetha College of Liberal Arts & Sciences Saveetha Institute of Medical & Tech. Sciences Chennai India Dept. of CS and Engineering Sister Nivedita University Kolkata India School of Computer Science SCS Taylor's University Subang Jaya Malaysia Dept. of Computer Applications Saveetha College of Liberal Arts & Sciences SIMATS Deemed University hennai India Department of CS and AI College of Computing & IT University of Bisha Bisha Saudi Arabia Department of Computing School of Arts Humanities & Social Sciences University of Roehampton London United Kingdom Division of Research and Development Lovely Professional University Phagwara-144411 India KKR and KSR Institute of Technology and Sciences Guntur Andhra Pradesh India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

An innovative method for data optimization in the Internet of Things (IoT) assister sensing devices is presented in this research. The many elements of the cloud platform with IoT assistance are addressed. A fresh design for IoT-aided sensor networks is introduced to monitor the environment. A Data Optimisation in cloud Internet of Things (DO-CIoT) is proposed in this paper to optimize the data stored in the cloud and presents methods to access the data in end devices. The suggested technique uses a membership function to improve data allocation while considering all node data. The suggested model is assessed regarding performance ratios, reliability percentage, coverage ratios, and sensing error and contrasted with two-stage efficiency enhancement. A mean performance ratio of 89%, reliability of 91%, coverage of 89

关键词： Performance evaluation cloud computing Computer network reliability Market research Sensors Internet of Things Reliability Resource management Optimization Monitoring

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An Enhanced Segmentation and Deep Learning Architecture for Early Diabetic Retinopathy Detection

An Enhanced Segmentation and Deep Learning Architecture for ...

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IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Renato R. Maaliw Zoren P. Mabunga Maria Rossana D. De Veluz Alvin S. Alon Ace C. Lagman Manuel B. Garcia Luisito Lolong Lacatan Rhowel M. Dellosa College of Engineering Southern Luzon State University Lucban Quezon Philippines Digital Transformation Center Batangas State University Batangas City Philippines Information Technology Dept. FEU Institute of Technology Manila Philippines College of Computing & Engineering Pamantasan ng Cabuyao Cabuyao Laguna Philippines Research and Development Office University of Northern Philippines Vigan City Philippines

Diabetic retinopathy is a serious complication needing prompt diagnosis and medication to avert vision loss. Lesions caused by the condition are difficult to track because they are hidden behind the eye's structure in small and subtle forms. To extract relevant features., we created a robust pipeline using multiple preprocessing techniques., image segmentation architecture (DR-UNet) with atrous spatial pyramid pooling., and an attention-aware deep learning convolutional network with different modules based on ResidualNet. Empirical results show that our framework has segmentation accuracies of 87.10% (intersection over union) and 84.50% (dice similarity coefficient). Moreover., classification performance of 99.20% provided better results than existing schemes., as reinforced by the smooth convergence of training/validation loss and accuracy. This study has the potential to supplement traditional diagnosis to identify better the ailment in its early and advanced stages.

关键词： Deep learning Image segmentation Retinopathy Pipelines Feature extraction Diabetes Lesions

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