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Using Additional Information to Improve Classification of Clothing Item 2

Using Additional Information to Improve Classification of Cl...

引用

2nd International conference on Computing and Machine Intelligence (ICMI)

作者： Ridaoui, Fatma Genc, Yakup Izmir Demokrasi Univ Comp Engn Izmir Turkey Gebze Tech Univ Comp Engn Gebze Turkey

ISBN: (纸本)9781665474849;9781665474832

Clothing classification can be a difficult task for clothing e-commerce sites. When it comes to distinguishing between two types of garments that look alike, discrimination of similar categories of clothing can be complicated. Even simply glancing at photos, categories like suit and tunic are difficult to interpret. Investigation of the effect of using additional information derived from photos shows that pose along with unsupervised latent space embedding (auto-encoders) improves the classification performance significantly. Validated on data obtained from a clothing e-commerce site, additional constraints provide better performance with similar model complexities. Especially human pose representing wearers gait (or advertisement attitude) increases the classification performance by 1% in accuracy.

关键词： Deep Learning image Classification Human Gait (Pose) Auto-encoders

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Advanced Malware Classification Using Convolutional Neural Networks and image Transformation Techniques 2

Advanced Malware Classification Using Convolutional Neural N...

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2nd IEEE International conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Porna, Shweta Bhattacharjee Hossain, Md Zakir Chowdhury, Abdullah Al Naseeh Raj, M Govind Uddin, Roise Elahi, Mukit Al Basit, Abdul Khan, Mohammad Aman Ullah Bhavani, Girigula Durga Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh Grand Canyon University Department of Data Science United States Sylhet Engineering College Sylhet Bangladesh Malla Reddy Engineering College Department of Ece Telangana Maisammaguda500100 India California State University San Bernardino United States Pacific states university Department of Cse Los AngelesCA United States Pacific states university Department of Cse 4651 Rosewood St United States School of Data and Sciences Brac University Department of Cse Dhaka1212 Bangladesh VIT-AP University School of Computer Science and Engineering Andhra Pradesh Amaravati522237 India

ISBN: (纸本)9798350390179

Malware seriously compromises cybersecurity and calls for sophisticated and effective categorization methods to find and lessen its effects. This research article offers a complete method based on image processing and deep learning to classify malware. ResNet18, VGG16, VGG19, AlexNet, and our custom-designed MalwareNet are among the several deep learning models we use to categorize malware photos from the nine different classes in the Big 2015 dataset-which shows a clear class imbalance. Using hyperband and bayesian optimization approaches, our strategy maximizes hyperparameters to provide the greatest performance. The hyperparameters for ResNet18, VGG16, VGG19, and AlexNet models comprise learning rate, kernel size, number of epochs, dropout rate, and dense layer size as well as particular parameters for MalwareNet including the number of neurons in each layer and the fully connected layer size. With MalwareNet obtaining especially remarkable results, the examination of our models shows great accuracy, precision, recall, and F1 scores. We examine the training accuracy, F1 score, and log loss measures in great detail;we then show the classification performance over all classes using confusion matrices. With notable increases in malware detection and categorization, the suggested model offers a strong and practical answer for uses in cybersecurity. Supported by diligent hyperparameter adjustment and performance evaluation, this work expands the field by providing a comprehensive comparison of many deep learning architectures and how effectively they classify malware. The results show how well deep learning models might improve malware detection systems and protect digital environments against hostile attacks. © 2024 IEEE.

关键词： Cybersecurity

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A Novel Point Cloud Coarse Registration Method Combining 2D image information 2

A Novel Point Cloud Coarse Registration Method Combining 2D ...

引用

2nd International conference on image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Dong, Chuanxiang Zhang, Hongri Sun, Lin Ma, Nan Shandong University of Science and Technology College of Geodesy and Geomatics Qingdao China China University of Petroleum School of Geoscience Qingdao China

ISBN: (纸本)9798350331417

Point cloud registration is one of the key technologies for point cloud data processing. Feature descriptor-based point cloud registration methods have been widely adopted for point cloud registration. However, these methods demand a high overlap between the point clouds to be registered, struggle to acquire matched points, and involve extensive computations. To address the above issues, a novel point cloud coarse registration method combining 2D image information is proposed in this paper. This method introduces a checkerboard and utilizes a 3D structured light depth camera to simultaneously acquire point cloud data and 2D images of the target scene. Based on camera imaging principles, corner points are extracted from 2D images and camera poses are estimated to obtain matched points for point clouds, thereby fitting a rigid transformation to achieve point cloud coarse registration. The experimental results demonstrate that the proposed method is minimally influenced by point cloud overlap rate, and can achieve accurate and efficient point cloud registration. © 2023 IEEE.

关键词： 2D images camera posese coarse registration corner points matched points

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A Deep Learning-Based image Segmentation Model for Extracting Migratory Birds and Insects Echoes Using Polarimetric Weather Radar data 2

A Deep Learning-Based Image Segmentation Model for Extractin...

引用

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

作者： Sun, Zhuoran Cui, Kai Wang, Rui Yan, Zujing Ding, Mingming Wu, Dongli Beijing Institute of Technology Radar Technology Research Institute School of Information and Electronics Beijing China China Meteorological Administration Meteorological Observation Center Beijing China

ISBN: (纸本)9798331515669

Weather radars are essential for monitoring airborne animals, including birds and insects. Despite the potential of dual-polarization radar moments for echo classification, extracting migration echoes from birds and insects at pixel-level remains challenging. In this study, six radar moments were rendered into two RGB images, and a deep learning-based image segmentation model was developed to extract bird and insect echoes at the pixel level. The model uses the typical encoder-decoder architecture. The ResNet-50 model was used to extract the features on different spatial scales, which was modified to accept six input channels, matching the six radar moments of polarimetric weather radar data during the encoding stages. The U-Net model was employed for multiscale feature fusion during the decoding stages. Training and validation on a manually labeled dataset demonstrated that the proposed method performs exceptionally well, achieving a mean intersection over union of 98%. The model's performance was further evaluated using two representative cases. © 2024 IEEE.

关键词： Birds

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image Acquisition and Electric Field Application in the Belousov-Zhabotinsky Reaction Using LabVIEW 1

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2nd International conference on Nonlinear Dynamics and Applications, ICndA 2024

作者： Sibeesh, Puthiyapurayil Shajahan, T.K. Department of Physics National Institute of Technology Karnataka Surathkal Mangalore575025 India

ISBN: (数字)9783031691461

ISBN: (纸本)9783031691454

This paper introduces LabVIEW-based software and hardware designed to simultaneously record and control chemical wave activity in the Belousov-Zhabotinsky (BZ) reaction. The chemical waves in the BZ reaction can be controlled by DC and polarized electrical stimuli. Our software can be used to study the interaction of DC or different types of polarized electric fields with chemical waves. The software allows the user to capture and save images for further analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Chemical waves data acquisition DC electric field Polarised electric field Triggering

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Industrial CT Truncated Projection data reconstruction System Based on Artificial Intelligence

Industrial CT Truncated Projection Data Reconstruction Syste...

引用

Signal Processing and Intelligent Computing (SPIC), International conference on

作者： Yang Yang Songjie Wu Liaoning Institute of Science and Technology Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

In industry, Computer Tomography (CT) is a key technology used for non-destructive testing and evaluation of internal structures, but significant challenges remain in the processing of truncated projection data. The aim of this study is to investigate an artificial intelligence-based reconstruction system for industrial CT truncated projection data. We investigate the common data truncation problem in industrial CT technology and propose a new data reconstruction method. The method uses the ideas of full variance minimization and segmentation constants to achieve accurate reconstruction of truncated projection data by means of parallel computation. Our study validates the effectiveness and superiority of the method through comparative experiments. Compared with conventional methods, our system excels in both accuracy and stability of reconstruction results, and is better able to handle the challenges posed by truncated data. In addition, we explore the practical application value of the system, demonstrating its potential use in industrial CT. from a single-core to a dual-core processor, the processing time is reduced from 120 to 60 seconds, showing a doubling of the processing speed, which indicates that tasks are efficiently distributed and executed among multiple cores. The research results in this paper will have a positive impact on the development and application of industrial CT technology and promote its wide application in industrial production and quality control.

关键词： Technological innovation image segmentation Accuracy TV Computed tomography Parallel processing Minimization Stability analysis image reconstruction Testing

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Satellite image Denoising Using Deep Learning: Autoencoder Decoder Application 11

Satellite Image Denoising Using Deep Learning: Autoencoder D...

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11th International conference on Electrical Engineering, Computer Science and Informatics, EECSI 2024

作者： Pula, Rolando Advincula Cruz, Sean Chris Kirk Patrick P. Ilagan, Lorena College of Engineering Architecture and Technology University of Perpetual Help System DALTA Las Pinas Philippines

ISBN: (纸本)9798350355314

Through deep learning Autoencoder Decoders, it is possible to clean noisy or damaged image data received from satellites. Two models with a PSNR of 25.6 dB and 25.54 dB were generated using a dataset consisting of 610 weather satellite images retrieved from the National Oceanic and Atmospheric Administration (NOAA) Weather (WX) satellites. These images were acquired via the ground station at the University of Perpetual Help System DALTA - Las Pinas campus. Initially, only a few alterations were made to the code in order to work with later Keras versions. However, this led to crashes during training as well as noticeable darkening in the predictions. The original code also used accuracy as the training metric, instead of PSNR. The rewrite rebuilt the model using updated Keras 3 API as well as introduced a PSNR metric. Doing so eliminated the darkening and crashes entirely. © 2024 IEEE.

关键词： Weather satellites

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A Network for Medical Segmentation of images with Multiple Centers That Preserves Privacy 2

A Network for Medical Segmentation of Images with Multiple C...

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2nd IEEE Global conference on Communications and Information Technologies, GCCIT 2024

作者： Jayaprakash, B. Bordoloi, Dibyahash Mehta, Pradnya Amudha, A. Marwaha, Pradeep Selvan, K. Senthamil School of Sciences Computer Science & It Karnataka Bangalore India Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India

ISBN: (纸本)9798350388916

An innovative system for safe medical image segmentation in multi-center collaborations, the Multi-Center Privacy-Preserving Network is introduced in this research. Reduce data transmission traffic while improving data privacy protection with our methodology's novel method for multi-center collaborative learning. Our solution relies on a single encrypted data transfer, as opposed to federated learning's round-by-round model data communication among the central server as well as local servers. The suggested CNN is a three-layer architecture that includes networks for encryption, segmentation, and decryption. Consequently, we provide a method for secure and adaptable cloud-based medical picture segmentation using a convolutional neural network. This research demonstrates that three cloud-based servers can train the algorithm using user-provided photos;nevertheless, they are unable to access comprehensive data on the model's variables and user input. The experiment verifies that the technique, when put into effect, can guarantee the system's security and efficiency while simultaneously reducing the user's compute and storage loads. We use an encryption network to transform the raw picture data into ciphertext, and then we present a refined U-Net to separate the encrypted image data. The last step is to use a decryption network to get the segmentation mask. Computable image encryption is made possible by this design, which allows for ciphertext-based picture segmentation. We test our method on three datasets, one of which is a CTPA dataset, and two of which are cardiac MRI datasets. We show that MP-Net may safely use data from many sources to build a more accurate and comprehensive segmentation model. © 2024 IEEE.

关键词： image segmentation

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Improving the Efficiency of Multimodal Approach for Chest X-Ray 2nd

Improving the Efficiency of Multimodal Approach for Chest...

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2nd International conference on Advances in Computing Research, ACR 2024

作者： Upadhya, Jiblal Vargas, Jorge Poudel, Khem Ranganathan, Jaishree Computational and Data Science Middle Tennessee State University MurfreesboroTN37132 United States Department of Engineering Technology Middle Tennessee State University MurfreesboroTN37132 United States Department of Computer Science Middle Tennessee State University MurfreesboroTN37132 United States

ISBN: (纸本)9783031569494

Medical data analysis with limited data has been a challenging problem in machine learning. Privacy regulations limit the sharing and use of patient data, making it challenging to access and aggregate large datasets for research purposes. To address this challenge, we propose a deep learning-based Multimodal network that incorporates data from multiple data sources: images, and text in the learning process. When faced with limited data of one type, incorporating data from multiple sources can enhance the model’s performance and generalization. Our approach integrates the text sub-model and image sub-model with deep network of Dense 512 using the transfer learning process. Specifically, the goal is to explore the impact of hyperparameter adjustments on the performance of the model with different learning rates. Evaluation of our proposed approach on the Indiana University Chest X-ray dataset that consists of textual (radiological reports) and visual (X-ray images) show that our approach significantly outperforms several baseline methods in classification accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Large datasets

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Urban Land Cover Classification Algorithms and Simulation 2

Urban Land Cover Classification Algorithms and Simulation

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2nd International conference on Algorithms, High Performance Computing and Artificial Intelligence, AHPCAI 2022

作者： Lu, Houhan Electrical Engineering Institution Sichuan University Sichuan610044 China

ISBN: (纸本)9798350334784

This paper proposes a novel integrated discriminant model using the UCI urban land cover dataset. Remote sensing image classification uses computers to perform feature processing and model classification of pictures obtained by satellite remote sensing. In order to achieve accurate land type classification, the current research mainly focuses on extracting features from pictures and how to train models to achieve classification based on features. This paper proposes a novel method for the latter, using Random Forest, SVM, Naïve Bayes, and KNN, these four supervised machine learning algorithms to train each set of data extracted from the different coarser scales of the original image and compare the prediction accuracy of algorithms. The algorithm that performs best at the corresponding scale data is used as the model at that scale. Finally, judging each model's prediction weights the results to obtain the final result. Experiments have shown that the model performs superiorly on all scales compared to existing methods. © 2022 IEEE.

关键词： image classification

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