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Proceedings of the 2023 2nd International conference on Frontiers of Artificial Intelligence and Machine Learning, FAIML 2023

Proceedings of the 2023 2nd International Conference on Fron...

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2nd International conference on Frontiers of Artificial Intelligence and Machine Learning, FAIML 2023

ISBN: (纸本)9798400707544

The proceedings contain 55 papers. The topics discussed include: inferring age from linguistic and verbal cues in celebrity interviews;text recognition based on weakly supervised learning;cardiovascular disease detection based on multi-modal data fusion and multi-branch residual network;BSDIB: balanced self-distillation information bottleneck for long-tailed visual recognition;re-parameterizable residual multiple convolutions network for efficient single image super-resolution;multi-perspective semantic enhanced model for heterogeneous graph embedding with contrastive learning;wireless mesh system design for photovoltaic power station based on LoRa;automatic program repair with improved transformer;vibration recognition based on feature extraction by deep autoencoder;K-means optimization method based on adaptive parallel hierarchical clustering;and design of intelligent search system for online education based on knowledge graph.

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A Review of RGB-D image Classification Methods 1

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2nd International conference on Advances in data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Yadav, Nand Kishor Saraswat, Mukesh Jaypee Institute of Information Technology Uttar Pradesh Noida India

ISBN: (数字)9789819995318

ISBN: (纸本)9789819995301

Depth image popularity is continuously increasing in a few decades with successful implementation in various robotics applications. The use of depth RGB images improves the accuracy of classification methodologies. However, there are many challenges associated with depth RGB image classification methods. A systematic review of the use of depth RGB images in classification tasks may provide the researchers the insight into the existing methods and their limitations which may be useful for further development. Therefore, this paper explores the various existing approaches for image vector representation from depth RGB images in classification tasks. The existing depth RGB image classification approaches may be divided into hand-crafted, learning-based, and middle-level methods. As compared to hand-crafted and learning-based methods, middle-level feature-based methods are computationally inexpensive and are carried out with insufficient availability of high computing resources. Therefore, further study is accomplished in the view of approaches associated with this category. In addition to these methods, this review covers available indoor RGB-D datasets and different quantitative evaluation parameters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image classification

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A Novel Deep Learning and image Processing Based Technique for Lumpy Skin Disease Detection 2

A Novel Deep Learning and Image Processing Based Technique f...

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2nd International conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Singh, Jay Prakash Ghosh, Debolina Singh, Jagannath Manipal University Jaipur Department of Comp. Sci. and Engg. Jaipur India Manipal University Jaipur Department of Information Technology Jaipur India School of Comp.Engg. Kiit Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798350370188

Lumpy skin disease is a very infectious disease in cattle. Every year many cattle die because of lumpy skin disease. Recently, Indian states especially Rajasthan, Haryana, Punjab have witnessed the death of many cows due to lumpy skin infection. This disease becomes very dangerous and spreads at a high rate if not treated well at the time of infection. A proper technique is required to detect the lumpy skin infection in cattle so that proper diagnosis can be adopted to stop the infection from spreading and to save the life of the infected cattle. With the emergence of machine learning and deep learning approaches it seems that detection can be done at the inception of infection in the cattle. Many researchers have suggested various ways to detect the lumpy skin disease but there is need of an approach which can guarantee the high accuracy, precision, recall and f1 score for the lumpy skin infection. We have used some of the advanced pre-trained deep learning models and also proposed a modified convolution neural network model for lumpy skin infection detection. We collected dataset from the online platform. We collected total 936 cattle images including healthy and lumpy skin infected cattle. Out of 936 cattle images 421 were lumpy affected and rest 515 were images of the healthy cattle. We split the image in training and testing dataset. We used 80 percent of images for training the models and 20 % of images for testing the performance of the models. We trained InceptionV3, VGG16, ResNet50V2 and modified CNN model under similar condition by properly tuning the hyper parameters. We have observed that proposed modified CNN model outperformed all the reset three models. On training data, modified CNN yield an accuracy of 99.01 %, InceptionV3 furnished an accuracy of 98.85 %, Resnet50V2 has an accuracy of 92.09% and VGG16 guaranteed an accuracy of 92%. On testing data, proposed modified CNN, InceptionV3, ResNet50V2 and VGG 16 has accuracy 97.87 %, 97.34%, 93.09% and 93

关键词： Statistical tests

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Wheat Leaf Disease Synthetic image Generation from Limited dataset Using GAN 2nd

Wheat Leaf Disease Synthetic Image Generation from Limited ...

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2nd International conference on Human-Centric Smart Computing, ICHCSC 2023

作者： Ramadan, Syed Taha Yeasin Sakib, Tanjim Haque, Md. Mizba Ul Sharmin, Nusrat Rahman, Md. Mahbubur Dhaka1216 Bangladesh

ISBN: (纸本)9789819977109

For deep learning models to be trained effectively, a sufficiently sizable and varied dataset must be available. data augmentation, which produces identical images from a small number of original training samples, has proven to be an effective strategy for addressing the problem of deep convolutional neural networks (DCNNs) missing sufficient training data. However, getting a good dataset is frequently challenging, particularly in the context of identifying plant diseases. In this article, we offer a distinctive approach for generating images of wheat leaf disease from a smaller dataset using generative adversarial networks (GANs). We investigate two well-known GAN architectures, DCGAN and CycleGAN, for producing wheat leaf disease images from a constrained number of real-world images. Our findings demonstrate that in terms of image quality and resemblance to real-world images, the CycleGAN architecture outperforms the DCGAN architecture. In different applications, such as the identification of plant diseases, our study shows the possibility of employing GANs to produce realistic images from fewer datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Generative adversarial networks

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Application of Magnetic Resin based on Grey Clustering Algorithm in Advanced Treatment of Industrial Wastewater 2

Application of Magnetic Resin based on Grey Clustering Algor...

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2nd International conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Mustare, Narendra B. Suguna, R. Saritha, P.S. Abraham, Arockia Raj Jain, Sanjiv Kumar Raja, Ch. Department of Electronics and Instrumentation Engineering CVR College Of Engineering Hyderabad India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Dhanalakshmi Srinivasan College Of Engineering Navakkarai Post Coimbatore India Department of CSE- Data Science Madanapalle Institute of Technology and Science Madanapalle Chittoor India Department of Electrical Engineering Medi-Caps University Indore AB M.P. Indore452001 India Department of ECE Mahatma Gandhi Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350305418

With the rapid development of industry, the types and quantities of wastewater have increased rapidly, and the pollution to water bodies has become increasingly widespread and serious. As far as water system is concerned, the information of water quality system is both known and unknown because of incomplete data information and great uncertainty of systematic observation information, such as data error, data quality and quantity influence, etc. Therefore, judging from the clarity of system information, water quality pollution assessment is actually a problem with definite extension but uncertain connotation. In this paper, a water quality detection method based on grey clustering algorithm is proposed to evaluate the treatment effect of industrial wastewater by magnetic resin adsorption, and the effectiveness of the proposed method is verified by comparative experiments. The results show that grey clustering algorithm can deal with the measured data of turbidity, dissolved oxygen and conductivity of water quality parameters without changing the basic trend of the measured data, and at the same time, it can remove some noise signals to get a smoother data curve. Through the analysis of practical data and the comparison of traditional water quality evaluation models, the prediction algorithm predicts the change trend of water turbidity more effectively, and improves the accuracy of water turbidity control in the process of water treatment. © 2023 IEEE.

关键词： Grey clustering algorithm Industrial wastewater Magnetic resin Testing water quality

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Enhanced three-dimensional reconstruction by photometric stereo 5

Enhanced three-dimensional reconstruction by photometric ste...

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5th International conference on Pattern Analysis and Intelligent Systems, PAIS 2023

作者： Abada, Lyes Hannachi, Islam Laallam, Mohamed Wail Aouat, Saliha Bab-Ezzouar Artificial Intelligence and Data Science Department Algiers Algeria

ISBN: (纸本)9798350381450

Computer vision is a very wide field, although, research is advancing rapidly. But there are still many incomplete areas of research. For this reason we are interested on 3D reconstruction field, which consists of creating a 3D object from one or more 2D images. In this paper, we propose two fully convolutional deep networks. These architectures are a combination between the PS-FCN model and the residual neural network architecture. Our two proposed architectures are improvements of an existing work (PS-FCN). We take as input an arbitrary number of 2D images of an object captured from the same camera position and each image is taken with a different light source position. As output they predict the needle map of the object. According to our study on datasets with different training parameters we show that our proposal outperforms the PS-FCN approach which is considered as a very powerful existing technique and gives effective results to solve the photometric stereo problem. © 2023 IEEE.

关键词： Singular value decomposition

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Process Construction of VR Technology in Interior Design: from image Feature Analysis to Global Texture Modelling 2

Process Construction of VR Technology in Interior Design: Fr...

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2nd International conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Yi, Fu Chunjiang, Yu Shenyang Jianzhu University Shenyang110168 China

ISBN: (纸本)9781665400527

Process construction of VR technology in interior design from image feature analysis to global texture modelling is studied in this paper. According to the driving method of the virtual finger, the pose data of the operator's palm is converted and mapped to the pose data of the virtual palm, and then the entity translation and rotation functions are called to change the pose of the virtual palm in real time. Therefore, this article introduces general human-computer interaction technology to enable users to select important areas to ensure that they can be more protected when zooming in and reduce the probability of distortion in the area. With this target, the VR system is enhanced based on the image global feature analysis. For testing the results, the novel data sets are collected and the proposed model is compared with the other approaches. © 2022 IEEE.

关键词： Architectural design

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Research on the Interpretability of Neural Networks Applied in image Classification 2

Research on the Interpretability of Neural Networks Applied ...

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2nd International conference on Electronic Information Engineering, Big data, and Computer Technology, EIBDCT 2023

作者： Liu, Binglin Liu, Haitian Du, Junchao Hu, Qianchao Wang, Feng Institute of Systems Engineering Academy of Military Sciences Beijing China

ISBN: (纸本)9781510664968

Artificial intelligence technology is a strategic technology leading a new era of the revolution of science and technology along with the industry transformation. However, with the progress of technology and the boosting complexity of model, the problem of lacking of interpretability of artificial intelligence has become extremely acute. from theoretical research to practical application, the obstacle caused by its poor interpretability has become a major weakness of AI at present. This paper proposes to evaluate the interpretability via robustness and induced metrics. And then, this paper gives some existing image-classification networks a comparison and an evaluation of robustness and interpretability, and get the interpretation of models shown in heatmap. © 2023 SPIE.

关键词： image classification

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BIIR: Blind Inpainting based image Reconstructon for Texture Defect Detection 2

BIIR: Blind Inpainting based Image Reconstructon for Texture...

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2nd International conference on Big data and Artificial Intelligence and Software Engineering (ICBASE)

作者： Qiu, Yuanhong Zhou, Cong Zhang, Zefeng Li, Bin Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan Peoples R China

ISBN: (纸本)9781665427098

image reconstruction is an important method for texture defect detection, and the existing image reconstruction algorithms based on Autoencoder and GAN cannot suppress the reconstruction of defect information, which affects the detection accuracy. To solve this problem, this paper proposes a novel image reconstruction algorithm based on image inpainting, which includes two modules of defect estimation network and defect inpainting network. Firstly, the defect estimation network used the pre-training model to extract the deep features of the defect image and applied the Gaussian distance to estimate the background area. and then the image inpainting network applied the contextual attention mechanism to repair the nonbackground area of the defect image. Through the experimental analysis which compared with other state-of-the-art image reconstruction algorithms on the public Mvtec texture dataset, the superiority of the proposed algorithm is effectively verified.

关键词： defect detection image inpainting contextual attention mechanism

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Fusion of image and Pointcloud data for 3D Object Detection with 4D Imaging Radar 2

Fusion of Image and Pointcloud Data for 3D Object Detection ...

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2nd IEEE International conference on Signal, Information and data Processing, ICSIDP 2024

作者： Li, Xiaozhi Pang, Zhaochen Li, Jian Ministry of Education Beijing China Beijing Institute of Technology School of Computer Science and Technology Beijing China Beijing Institute of Technology Radar Technology Research Institute School of Information and Electronics Beijing China

ISBN: (纸本)9798331515669

3D object detection aims to extract the spatial positions of objects from sensor data, thereby improving environmental awareness and comprehension. With the advancement of radar technology, the 4D imaging radar offers greater robustness in adverse weather conditions compared to LiDAR sensors, thus expanding its potential applications. However, due to the relative scarcity of 4D imaging radar datasets and the inherent sparsity of the radar point clouds, traditional point cloud-based 3D detection models perform suboptimally when applied directly. This paper focuses on the 3D object detection task with multimodal sensors, exploring the integration of RGB image features and 4D imaging radar point cloud features. We propose an innovative multi-modal detection fusion algorithm that markedly enhances detection accuracy on the VoD dataset. © 2024 IEEE.

关键词： Radar imaging

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