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CDCEO 2022 - Proceedings of the 2nd Workshop on Complex data Challenges in Earth Observation, co-located with 31st International Joint conference on Artificial Intelligence and the 25th European conference on Artificial Intelligence, IJCAI-ECAI 2022

CDCEO 2022 - Proceedings of the 2nd Workshop on Complex Data...

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2nd Workshop on Complex data Challenges in Earth Observation, CDCEO 2022

The proceedings contain 16 papers. The topics discussed include: multispectral U-Net: a semantic segmentation model using multispectral bands fusion mechanism for landslide detection;a center-masked convolutional transformer for hyperspectral image classification;a deep feature retrieved network for bitemporal remote sensing image change detection;disaster detection from SAR images with different off-nadir angles using unsupervised image translation;knowledge-aware cross-modal text-image retrieval for remote sensing images;mapping slums with deep learning feature extraction;predicting flood inundation depth based-on machine learning and numerical simulation;radar-based volumetric precipitation nowcasting: a 3D convolutional neural network with U-Net architecture;and task-guided denoising network for adversarial defense of remote sensing scene classification.

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Multiscale Fusion Graph Convolutional Networks for Hyperspectral image Classification 2

Multiscale Fusion Graph Convolutional Networks for Hyperspec...

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2022 2nd International conference on Artificial Intelligence, Big data and Algorithms, CAIBDA 2022

作者： Wang, Lei Wang, Xili School of Computer Science Shaanxi Normal University Xi’an China School of Mathematics and Computer Application Shangluo University Shangluo China Engineering Research Center of Qinling Health Welfare Big Data Universities of Shaanxi Province Shangluo726000 China

ISBN: (纸本)9783800760268

Graph neural network (GNN) has shown remarkable performance in many fields, such as edge prediction, node classification, etc. However, the traditional graph neural network can only accept the full graph to train the model, which leads to a very high computational complexity. Besides, the lack of multi-scale mechanism limits the actual classification performance. In this paper, we propose a multiscale fusion model based on graph convolutional network (GCN) and convolutional neural network (CNN) for hyperspectral image (HSI) classification tasks. This model can first sample randomly from the full graph to subgraphs of different scales, then trains GCN on sub-graphs of different scales and extract features, and finally integrates the spatial-spectral features extracted by multi-scale CNN classifier and the structural features extracted by multi-scale GCN for end-to-end mini-batch joint training. Experiments on two real HSI data s demonstrate that the proposed method significantly improves the classification performance compared with other comparison methods. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Graph neural networks

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Machine Learning based Abnormal Human Behaviour Detection 2

Machine Learning based Abnormal Human Behaviour Detection

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2nd International conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Marichamy, D. Sankar, M. Sivaprakash, P. Chithambaramane, R. Charaan, R. M. Dilip Ithayan, J. Vimala Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

Due to technological advancements, numerous surveillance cameras has been installed in our everyday living spaces to enhance security measures. Assessing abnormalities within video recordings, particularly in crowded environments, presents a formidable challenge. Anomalous occurrences, arising from infrequent and uncommon behaviours, are characterized by deviations in nearby spatiotemporal positions. To bolster public safety, surveillance cameras are frequently deployed in crowded areas such as hospitals, banks, and shopping districts. The proposed system combines You Only Look Once (YOLO) and 2D convolution layer (CONV2d) to efficiently detect unconventional human activities and abnormalities in real-time video footage. Employing computer vision and machine learning techniques, it scrutinizes video frames to identify potential threats or risks through the detection of abnormal behaviours. YOLO facilitates instantaneous object detection, while CONV2d effectively processes and analyses image data. By leveraging these technologies, the system is capable of monitoring and identifying human behaviour, thus enabling the real-time detection of abnormalities and potential threats. However, challenges persist regarding the placement of security cameras and the insufficient number of cameras compared to human monitors. Identifying abnormal events, such as crimes, illegal activities, and traffic accidents, remains a paramount duty in video surveillance and our proposed system strives to achieve improved accuracy in real-time event identification.

关键词： YOLO Object detection CONV2d Abnormality detection Human behaviour detection Video surveillance

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Iterative Deep Learning for Muon Scattering Tomography

Iterative Deep Learning for Muon Scattering Tomography

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2023 IEEE International conference on Big data, Bigdata 2023

作者： Ruta, Dymitr Ruta, Robert Khalifa University Ebtic Abu Dhabi United Arab Emirates University of Warsaw Faculty of Physics Warsaw Poland

ISBN: (纸本)9798350324457

Muon scattering tomography (MST) allows to reconstruct internal structure of the matter based on imaging of muon rays, Coulomb scattering when passing near heavier nuclei of the penetrated matter. While point of closest approach (PoCA) and maximum-likelihood expectation maximization (MLEM) methods offer reasonable MST solutions, they require large number of muons or huge computational cost while still struggling to distinguish between similar materials. As an alternative we propose to utilize deep, fully convolutional neural networks with atrous convolutions designed to deliver robust semantic image segmentation (SIS). The information from muon rays is summarized as image pixels containing path length and momentum weighted statistics of per-voxel muon scattering angles and displacements encoded independently along image rgb channels. Reserving one channel for iterative self-improvement by feeding classification outputs back as inputs delivered significant material detection improvements with nearly perfect localization and improved material differentiation. The presented iterative SIS model has been tested within IEEE Bigdata 2023 Cup dedicated to muon scattering tomography and scored 2nd place significantly outperforming PoCA/MLEM and Catboost with 99.9% localization accuracy and IoU score in excess of 0.35, on the very challenging task of detecting nested material layers composed of a mixture of up to 22 elements and compounds. © 2023 IEEE.

关键词： catboost convolutional neural networks deep learning deeplabv3 gradient boosting MLEM muon scattering tomography POCA semantic image segmentation xception

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Medical image Classification for Disease Prediction with the AID of Machine Learning Approach 2

Medical Image Classification for Disease Prediction with the...

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2nd International conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Kumar, Illa Pavan Kumar, T. Senthil Bhardwaj, Abhay Prakash, Surya Adakane, Prashant K. Verma, Devvret SRM Institute of Science and Technology Ktr Campus Department of Computer Science and Engineering Chennai India Vnrvjiet Department of Information Technology Hyderabad India Kiet School of Pharmacy Kiet Group of Institutions Dr. A.P.J. Abdul Kalam Technical University Uttar Pradesh Lucknow India G H Raisoni University Department of Computer Science and Engineering Amravati India Graphic Era Deemed to Be University Department of Biotechnology Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

The need and requirements of machine learning are increasing day by day in the field of medical image classification. This research paper illustrates the relevance of machine learning in making successful detection of medical diseases by classifying the medical images in an organised manner. In this context, a brief evolution of machine learning has been provided here. Besides the growth of machine learning in the field of healthcare is presented neatly in this research paper. The literature part clearly describes the previous work in the respective field and the mechanism of machine learning. In parallel to it, a brief description of how the research has been conducted is also presented in this research paper. In this context, the survey conduction among 67 people is mentioned here. Besides analysis and interpretation of the data which are gathered from the survey is present here. The research paper also talks about the flaws of machine learning which have misguided several medical diagnosis processes in the recent past. The dynamics of ML and the relevant results were presented in the discussion and finding parts. At last, a conclusive summarisation has been presented here to provide an overall idea about the image classification process by integrating ML in the modern medical treatment process. © 2022 IEEE.

关键词： image classification

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Proceedings - 2nd International conference on Smart Technologies, Communication and Robotics 2022, STCR 2022

Proceedings - 2nd International Conference on Smart Technolo...

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2nd International conference on Smart Technologies, Communication and Robotics, STCR 2022

ISBN: (纸本)9781665460477

The proceedings contain 101 papers. The topics discussed include: performance analysis of least square linear regression with various classifiers for cardiovascular respiratory detection from capnography;artificial intelligence system for classification of diabetic retinopathy;machine learning system for recognition and classification of overlapped fingerprints;low-light color image enhancement based on dark channel prior with Retinex model;processing of Wisconsin breast cancer data using Ebola optimization algorithm with mixture kernel SVM;gaussian mixture model based hybrid machine learning for lung cancer classification using symptoms;an investigation on speech to sign language translator for hearing impaired (SSLT) using machine learning techniques;and PV array reconfiguration methods based on game theory to eliminate shading effect during non-uniform irradiations: experimental feasibility.

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Fostering Sustainable Poultry Farming: Chicken Disease Classification through Ensemble Techniques and Local Attention-Based Fused Descriptor Definition 2

Fostering Sustainable Poultry Farming: Chicken Disease Class...

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

作者： Ghosh, Sayantani Chakraborty, Anik Das, Rik Mitra, Indranil Chakraborty, Raktim Nandy, Prasun Data & Analytics Advanced Analytics PwC India

ISBN: (纸本)9798350305418

Chicken disease classification assumes a vital role in maintaining the health and productivity of poultry farms. By accurately identifying diseases through automated image analysis, the poultry industry can take prompt preventive measures, reduce economic losses, and improve flock management practices. This approach offers a cost-effective solution for the pharmaceutical industry by reducing the need for expensive manual diagnosis and enabling targeted drug development, ultimately increasing sales revenue. Deep learning-based feature extraction plays a crucial role in chicken disease classification from poultry fecal images. By employing advanced techniques such as vision transformers and EfficientNet-B0-B0, intricate features can be extracted from the images, allowing for more accurate disease classification. To the best of our knowledge, none of the previous work has evaluated vision transformer in capturing global contextual information for chicken disease classification from fecal images. On the other hand, EfficientNetB0-B0 offers efficient and scalable feature extraction. Therefore, both the techniques are vital for analyzing complex patterns within the images. Hence, the work has explored early and late ensembling techniques to investigate feature generalization as well as decision fusion of diverse classifiers for enhanced precision in classification results of the chicken diseases. The classification process is carried out using classical algorithms viz, Support Vector Machine (SVM), Logistic Regression(LR) and Random Forest(RF) instead of transfer learning since traditional classifiers offer greater flexibility in evaluating tailored approaches like feature fusion and decision fusion. The late ensembling with classifier decision fusion has outclassed all the other classification techniques in efficiently identifying chicken diseases from fecal images. © 2023 IEEE.

关键词： chicken disease classification classifier ensemble computer vision feature ensemble representation learning vision transformers

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Asymmetric Source-Free Unsupervised Domain Adaptation for Medical image Diagnosis 2

Asymmetric Source-Free Unsupervised Domain Adaptation for Me...

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2nd IEEE conference on Artificial Intelligence (CAI)

作者： Zhang, Yajie Huang, Zhi-An Wu, Jibin Tan, Kay Chen Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China City Univ Hong Kong Dongguan Res Off Dongguan Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (纸本)9798350354102;9798350354096

Existing source-free unsupervised domain adaptation (SFUDA) methods primarily focus on addressing the domain gap issue for single-modal data, overlooking two crucial aspects: 1) In medical scenarios, clinicians often rely on multi-modal information for disease diagnosis. Consequently, emphasizing single-modal (symmetric modality) SFUDA algorithms neglect the complementary information from other modalities (asymmetric modalities). 2) Restricting SFUDA to a single modality limits downstream institutions's ability to handle diverse modalities beyond that singular modality. To tackle these challenges, we propose an Asymmetric Source-Free Unsupervised Domain Adaptation (A-SFUDA) algorithm. This method leverages source model and unlabeled data from both symmetric and asymmetric modalities in the target domain for disease diagnosis. A-SFUDA adopts a two-stage training approach. In the first stage, A-SFUDA employs knowledge distillation (KD) to obtain two models capable of handling symmetric and asymmetric data in the target domain, facilitating preliminary diagnosis ability. In the second stage, A-SFUDA optimizes the target models through a pseudo-label correction mechanism based on multi-modal prediction correction and class-centered distance correction. Incorporating the two pseudo-label correction modules effectively mitigates noise within the training data, thereby facilitating the learning of the target models. We validate the performance of the proposed A-SFUDA algorithm on a large chest X-ray dataset, demonstrating its excellent performance for disease diagnosis in the target domain.

关键词： source-free unsupervised domain adaptation pseudo-labeling asymmetric modality

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Invasive Ductal Carcinoma Detection using Convolutional Neural Network Architecture from Breast Histopathology images 2

Invasive Ductal Carcinoma Detection using Convolutional Neur...

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2nd International conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Kaur, Gurjot Sharma, Neha Chitkara Univ Inst Engn & Technol Rajpura Punjab India

ISBN: (纸本)9798331540661;9798331540678

The purpose of the work is to create a robust convolutional neural network-based classification model to distinguish images of invasive ductal carcinoma (IDC) breast disease. This collection includes histopathological images from breast tissue samples some with IDC-positive and others IDC-negative. First in data preparation are resizing images to 50x50 pixels and arranging them for training and testing. The sample consists of 3,747 IDC-positive images and 21,031 IDC-negative images for 24,678 images overall. The model's CNN architecture consisted of many layers: convolutional layers, max-pooling layers, batch normalization, and dropout layers aimed at reducing overfitting. Trained with binary cross-entropy loss function and an Adam optimizer running at 0.0001 learning rate. After 40 iterations on the training set, the model's accuracy on the testing set was 94.73%, and on the training, set was 99.12%. The performance of the model was assessed using several parameters including accuracy, precision, recall, and F1-score. The categorization report shows that both IDC-positive and IDC-negative classes have excellent recall and accuracy. Moreover, confirming the ability of the model to classify IDC-positive and IDC-negative samples is the confusion matrix. This unique CNN-based classification model of breast histomorphology images seems to do remarkably well in identifying IDC, showing both excellent accuracy and the capacity to distinguish between positive and negative IDC samples. The model could enable pathologists to identify breast cancer, therefore enhancing the accuracy and effectiveness of breast cancer screening campaigns. Future directions of this work could concentrate on improving the performance of the model with larger datasets and investigating alternative image augmentation techniques that will generalize its capabilities on diverse input datasets.

关键词： Invasive Ductal Carcinoma (IDC) Breast Histopathology images Convolutional Neural Network (CNN) Medical image Classification

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image steganography in the IoMT and telemedicine: Residual and Enhanced Discriminative Network 2

Image steganography in the IoMT and telemedicine: Residual a...

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

作者： Balaji, N.V. Sharma, Sorabh Sahoo, Ashok Kumar Murali, M.J. Umakanth, S. Patil, Swati B. Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India School of Management - UG Management Karnataka Bangalore India Vishwakarma Institute of Technology Department of Information Technology Pune India

ISBN: (纸本)9798350388916

Through the use of technology such as computers, phones, and medical equipment, telemedicine and the Internet of Medical Things (IoMT) enable off-site interactions between medical professionals, patients, and experts. Patient privacy or consultation outcomes might be jeopardized, leading to potentially catastrophic medical events, if the information sent in IoMT is unlawfully steganographic, altered, or leaked throughout transmission and storage. There is an immediate need to safeguard the privacy and value that is the picture data included in electronic health records (EHRs). A possible option may be image steganography. Consequently, a StegaStamp-based steganographic model for pictures from medical records is created. Medical record photos are document images, not like natural photographs, and hence they are susceptible to image cropping assaults. When it comes to detecting illicitly transmitted medical pictures in IoMT and telemedicine, steganalysis plays a crucial role. To address the challenges of picture steganalysis in IoMT and telemedicine, we provide RGAN-Net, a Residual Generative Adversarial Discriminative Network. A deep residual network, a steganographic information discriminative mechanism, and an augmentation module for steganographic information make up RGAN-Net. In order to improve the illicit steganographic signal in highly detailed medical images with complex textural qualities, the RGAN-Net utilizes a steganographic information improvement module. In order to extract and compress steganographic features, a deep residual network is used. The deep residual network uses a steganographic data discriminative technique to correct the steganographic characteristics and remove high-frequency characteristics that are erroneously classified as steganographic information. The range of RGAN-Net steganalysis error PE was found to be 0.0732-0.0010 and 0.231-0.026, respectively, in experiments carried out on public and private datasets with data concealing payl

关键词： Telemedicine

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