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Intelligent Bioinformatics System Architecture for Water Borne Diseases Diagnosis and Monitoring 3

Intelligent Bioinformatics System Architecture for Water Bor...

引用

3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Barmola, Pareshwar Prasad Sharma, Sachin Bhadula, Shuchi Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350338607

Artificial intelligence (AI), is emerged as an edge to advancement which shows potential in the field of quality healthcare. The fields of AI and biological research are integrated for extraction and application of the information stored in live organisms to refine biomedical research. AI is now being used in various fields in health and medicine result into customized healthcare solutions and drug discovery. Water borne diseases seems to be crucial health hazardous as putting burden on population. Drinking water which is unfit to consume results into diseases like malaria, typhoid, cholera, dysentery and many more. Prevention of the water borne diseases before it spreads and become too severe to handle is a field requiring utmost investigation. Intelligent and diligent pathogen identification is therefore critical for the treatment and prevention of waterborne diseases. AI seems to be an emerging field that integrate with many recent advancements and revolutionize the field of disease prevention. This paper summarizes the role of computer vision and AI in water borne disease prevention. Various issues and challenges in AI based disease prevention systems are also discussed. © 2023 IEEE.

关键词： computer vision

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Distributed computer Solution Design of Tourism English Translation Strategy based on Cloud Real-Time Collaborative System 3

Distributed Computer Solution Design of Tourism English Tran...

引用

3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Liang, Chen Guilin University School of Language and Literature 541006 China

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Aiming at the subjectivity of user evaluation behavior and the sparsiness of data results in a problem that the score prediction result produces errors, a cloud model-based BP neural network score prediction algorithm is proposed. Tourism Foreign Xuan Material Translation Should take readers as a tourist or potential tourist identity;discusses the reader's expected vision, and compares the reader's directional expectations, distributed control system structure, and designed new distribution The overall structure of the computer interlock system. Including the design of various components and system networks in the system, implementing control functions, operation management concentration, structural redundancy, distributed intelligence, dispersion communication, distributed load. © 2022 IEEE.

关键词： Behavioral research

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Joat: to dynamically aggregate visual queries in transformer for visual question answering 3

Joat: to dynamically aggregate visual queries in transformer...

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3rd international conference on Machine vision, Automatic Identification, and Detection, MVAID 2024

作者： Wang, Mingben Yang, Juan Xue, Lixia Wang, Ronggui School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China

ISBN: (纸本)9781510681880

Attention mechanisms have shown impressive abilities in solving downstream multi-modal tasks. However, there exists a natural semantic gap between vision and language modalities that hinders conventional attention-based models in achieving effective cross-modal semantic alignment. In this paper, we present JoAt, a Joint Attention net, through which we investigate how to utilize the visual background information more directly in a query-adaptive manner to enrich querying semantics for each visual token, and how to more fully bridge the semantic gap to achieve cross-modal alignment between visual-grid and textual features. Specifically, our JoAt utilizes each query’s neighboring pixels, aggregates the visual query tokens from different receptive fields, and allows the model to dynamically select the most relevant neighboring tokens for each query, then obtains representations that are more semantically matched with the textual features to realize better interaction between visual and linguistic modalities. The experimental results show that our JoAt net can fully utilize different semantic-level signals from visual features at different receptive fields and effectively narrow the natural semantic differences between visual and language modalities. Our JoAt achieved an accuracy of 72.15% and 98.90% on the VQAv2.0 test-std and CLEVR benchmarks, respectively. © 2024 SPIE.

关键词： Aggregates

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A comparative study of machine vision-based rail foreign object intrusion detection models 3

A comparative study of machine vision-based rail foreign obj...

引用

3rd IEEE international conference on Power, Electronics and computer Applications, ICPECA 2023

作者： Cai, Xinnan Ding, Xuewen Tianjin University of Technology and Education Tianjin China

ISBN: (纸本)9781665472784

Due to the lack of track foreign body intrusion dataset, classical target detection models are rarely used in the field of foreign body intrusion on railway tracks, and model comparison experiments are also insufficient. Aiming at these problems, this paper makes a comparative study on the application of yolov5 and fast RCNN in railway foreign object intrusion detection. First, the train and test dataset was established by image preprocessing, data cleaning and data labeling of UAV aerial images. Second, the canny edge detection algorithm combined with Hough transform was used to extract the track features for delineating the detection area. Finally, Yolov5 and fast RCNN, two widely used models, were used to train and test respectively based on our dataset for comparative studies. Experiment results show that YOLOv5 has better comprehensive performance in detection rate and detection speed, and Faster RCNN model cannot meet the requirements of real-time detection of track foreign objects intrusion. © 2023 IEEE.

关键词： computer vision

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Estimating Commercial Fish Crop Potential Using Hyper Spectral Analysis 3

Estimating Commercial Fish Crop Potential Using Hyper Spectr...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Yadav, Surendra Jayaprakash, B. Shrivastav, Amit Kumar Vivekananda Global University Department of Computer Science & Application Jaipur India School of Sciences Department of Computer Science & It Karnataka Bangalore India Arka Jain University Office of Registrar Jharkhand Jamshedpur India

ISBN: (纸本)9798350381931

Fish cultivation is a critical part of global meal safety, supplying an environmentally sustainable aid of animal protein and other nutrients to tens of millions of people. The control of fish populations in aquaculture calls for effective land use planning and the most appropriate harvest to ensure that the greatest viable amount of fish may be produced within the maximum sustainable way feasible. Remote sensing gear which includes hyper spectral evaluation can offer guidance for aquaculture land use-making plans, as well as assist in estimating the capability of industrial fish vegetation in a certain area. This paper discusses the capacity of hyperspectral evaluation to degree the fraction of photo synthetically active flowers (FPAR) to be able to better understand the capability of fish manufacturing in an area, in addition to other useful packages of this generation. The implications of understanding the biophysical properties of aquatic flora for aquatic useful resource management also are discussed. © 2024 IEEE.

关键词： Fish

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Combining Autoencoder and Category-Based Low-Rank Domain Adaptation Method for Multi-Site ASD Identification 3

Combining Autoencoder and Category-Based Low-Rank Domain Ada...

引用

3rd international conference on computer vision and Pattern Analysis, ICCPA 2023

作者： Yu, Lei Wang, Li Cheng, Ming Xue, Minhao Wang, Lei School of Electrical Engineering and Control Science Nanjing Tech University Jiangsu Nanjing211816 China

ISBN: (纸本)9781510667563

With the development of deep learning in diagnosing autism spectrum disorder (ASD), multi-site resting-state functional magnetic resonance imaging (rs-fMRI) images have made excellent progress. However, there is a heterogeneous problem between the multi-site data caused by inconsistent data distribution. To address this problem, we propose a combining autoencoder and category-based low-rank domain adaptation (AECLR) method for multi-site ASD identification. The main idea is to extract non-linear features and alignment the distribution of these features. In the first stage, the unsupervised autoencoder is used to obtain the non-linear representation. In the second stage, the common structure between all domains was mined by the category-based low-rank constraints, which transform all source domain data and the target domain data into the common latent space and then the source domain data could be linearly represented by the target domain data. As a result, the ablation experiments of the AECLR method achieve independent performance and the AECLR method also obtain a satisfactory classification when compared with the state-of-the-art method. © 2023 SPIE.

关键词： Magnetic resonance imaging

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Visual Question Answering Model Based on Fusing Global-Local Feature 3

Visual Question Answering Model Based on Fusing Global-Local...

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3rd international conference on computer vision and Pattern Analysis, ICCPA 2023

作者： Yang, Guan Zhang, Ziming Liu, Xiaoming School of Computer Science Zhongyuan University of Technology Henan Zhengzhou China China Internet Public Opinion Research Center Henan Zhengzhou China

ISBN: (纸本)9781510667563

In order to solve the problem of insufficient learning ability of mutual representation of two modal information in existing visual question answer models, the Global-Local Attention Network (GLAN) approach to visual question answer model with global-local features is proposed by analyzing the reasons why the model cannot distinguish the relationship between similar targets in images and the reasons for the model's reliance on text information. To supplement the missing local information in the global features, the model is designed with a convolution guided attention module to enhance the image representation capability and increase the bias of image information in the model. Experiments are conducted on VQAv2.0 and GQA datasets, and the accuracy of the proposed model is improved by 0.60% and 0.38%, respectively, compared with Baseline, which can effectively improve the inference ability of the model. © 2023 SPIE.

关键词： Convolutional neural networks

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Bangladeshi Fresh-Rotten Fruit & Vegetable Detection Using Deep Learning Deployment in Effective Application 3

Bangladeshi Fresh-Rotten Fruit & Vegetable Detection Using D...

引用

3rd IEEE international conference on computer Communication and Artificial Intelligence, CCAI 2023

作者： Hamim, Md Abrar Tahseen, Jeba Hossain, Kazi Md. Istiyak Akter, Nasrin Asha, Umme Fatema Tuj Daffodil International University Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798350335262

Finding rotten fruits and vegetables has been important, especially in the agricultural industry. computer vision has significant applications in the automation of damaged, freshness detection of fruits and vegetables. In recent decades, the farming sector has discovered computer machine vision and image processing technology to be more and more beneficial, particularly for implementations in quality control by identifying rotten and freshness. Farmers cannot contribute effectively between fresh and rotten fruits, vegetables because this is mainly done by people. People tire out after performing the same task for several days, whereas robots do not. By identifying weaknesses in agricultural product, the study suggested a technique for minimizing human effort and worktime. Vegetables and fruits with defects might affect healthy fruits if they are not identified in time. As an outcome, we put up a methodology to stop rottenness from spreading. The suggested model detects between fresh and decaying fruits and vegetables depending on the input fruit and vegetable photos. In this work, we used six different types of fruits and vegetables like carrot, potato, calabash, cucumber, eggplant, and cauliflower, as well as fruits likes mango, banana, star fruit, jackfruit, guava, and papaya. This study discusses multiple image processing methods for rottenness categorization of fruits and vegetables. A Convolutional Neural Network (CNN), KNN, and SVM are used to gather the features from the data fruit and vegetable photos. On Google and Kaggle datasets, the efficiency of the suggested model is evaluated, and CNN model shows the greatest accuracy which is 95 percent. © 2023 IEEE.

关键词： Vegetables

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Light field structured light projection data generation with Blender 3

Light field structured light projection data generation with...

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3rd international conference on computer vision, Image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Zhu, Xinjun Zhang, Zhizhi Hou, Linpeng Song, Limei Wang, Hongyi Tiangong University School of Artificial Intelligence Tianjin China Tiangong University School of Control Science and Engineering Tianjin China

ISBN: (纸本)9781665459112

Light field structured light 3D measurement has gained popularity by merging the advantages of light field and structured light methods. Generating light field structured light dataset is necessary for studying light field 3D reconstruction algorithms, but it is time-consuming and expensive in a real sense, especially for ground truth data. This paper proposes a method to generate light field structured light projection data with Blender simulation. The proposed method allows for the modification of camera and projector settings and parameters, as well as rotating objects. The dataset generated by this method contains 107730 light field structured light images. The label data (ground truth data) including depth and disparity by the 9×9 light field camera array are provided for the performance evaluation of 3D reconstruction algorithms. To the best of our knowledge, it is the first public dataset in the light field structured light projection environment. Diverse 3D reconstruction methods, including deep learning methods, are used to evaluate the proposed data generation method and dataset. The dataset is available at https://***/sabaizzz/Light-field-structured-light-dataset. © 2022 IEEE.

关键词： Cameras

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Improved-GCN based Collaborative Filtering Recommendation Model 3

Improved-GCN based Collaborative Filtering Recommendation Mo...

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3rd international conference on computer vision and Pattern Analysis, ICCPA 2023

作者： Yang, Jitao Zhai, Zhengli Dong, Zhaohe School of information and control engineering Qingdao University of Technology QingDao266520 China

ISBN: (纸本)9781510667563

Machine learning (ML) graph convolution network (GCN) has been applied in various fields, including recommendation systems, and has achieved great success. The core goal of the recommendation system is to infer user preferences so as to actively provide users with valuable and targeted information. Collaborative Filtering (CF) is a classic and widely used recommendation method. However, GCN-based recommendation methods still have many problems. For example, GCN recursively merges messages from different order neighborhoods, it is difficult to distinguish between different node messages, which leads to training difficulties, and problems such as over-smoothing have great constraints on the recommendation model. Different from the current GCN-based method, in view of the above problems, it is proposed to use a simple GCN model to separately aggregate neighbor messages in different orders for collaborative filtering, and then aggregate them in a hierarchical manner without introducing other model parameters. After that, the dropout idea was transferred to the model, by generating multiple views of the same node from the three dimensions of nodes, edges and graphs to reduce the effect of over-smoothing, which prevented over-fitting and improved the performance of the model. The experimental results on three datasets demonstrate the effectiveness of the proposed model. © 2023 SPIE.

关键词： Convolution

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