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A MobileNetV2-CBAM-based Model for Forest Fire Classification Using UAV Imagery

A MobileNetV2-CBAM-based Model for Forest Fire Classificatio...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Xinjie Deng Burhan Khan Chee Peng Lim Ming-Yu Liao Chongqing Jianzhu College Institute for Intelligent Systems Research and Innovation Deakin University Chongqing China Institute for Intelligent Systems Research and Innovation Deakin University Waurn Ponds VIC Australia Department of International Business National Kaohsiung University of Science and Technology Taiwan

Forest fires are one of the worldwide severe natural disasters causing human and environmental losses. Early and accurate fire detection is critical to minimize damages caused by forest fires and reduce human risks in firefighting efforts. Recently, Unmanned Aerial Vehicles (UAVs) mounted with cameras and edge computing devices with Artificial Intelligence (AI)-based algorithms have been employed to tackle forest fire detection problems in real time. Considering that edge computing is limited in resources and power consumption, the developed AI algorithms need to be lightweight. this paper proposes lightweight deep learning (DL) algorithms based on MobileNetV2 and attention mechanism to detect forest fires using UAV imagery. Based on several experiments, the proposed model yields an accuracy rate of 86.14% and an F1-score of 0.8719, outperforming other related methods in forest fire detection and classification.

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Employing machine learning algorithms to discover risk factors of glaucoma 4

Employing machine learning algorithms to discover risk facto...

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4th international conference on pattern recognition and Artificial Intelligence, PRAI 2021

作者： Al-Shamiri, Abdulkawi Yahya Radman School of Computer Science and Engineering Hodeidah University Department of Computer Science Al-Hodaidah Yemen

ISBN: (纸本)9781665413220

the statistics of the World Health Organization indicated that blindness or loss of vision in one of the eyes is one of the many threats and challenges in the world due to the increasing number of people infected annually. One of the many serious eye diseases is glaucoma, which leads to the loss of vision in the affected eye. Most studies on glaucoma are based on clinical trials and thus lack insights into the risk factors of glaucoma;these factors could be obtained from larger number of doctors and patients and analyzed sufficiently. With the recent development of technology, the need of utilizing machine learning algorithms in the medical field has increased to achieve a better and accurate health care system. thus, this study aims to effectively discover the hidden risk factors of glaucoma by machine learning algorithms using real data collected from doctors and patients. the author used one of decision tree algorithms so-called J48 and one of association rule algorithm socalled apriori to analyze the collected data. the study shows that some of the risk factors of glaucoma which have been proven by medical trials are also obtained by means of the two algorithms. therefore, machine learning algorithms can be used to effectively determine causes of any disease. the results not only provide a novel method to discover the risk factors of glaucoma but also reveal new risk factors of glaucoma which have not been discovered previously by studies based on medical trials such as discovery the relation chronic constipation and stomach upset with glaucoma as reasons leading to glaucoma. this study will contribute to avoiding blindness and preventing it by determining the recognition factors of glaucoma. © 2021 IEEE.

关键词： pattern recognition

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Mushroom Classification Based on Deep Residual Network

Mushroom Classification Based on Deep Residual Network

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Ju Feng Xufeng Ling Yubo Wang Jie Yang School of Artificial Intelligence Shanghai Normal University Tianhua College Shanghai China Shanghai Acoustics Laboratory Chinese Academy of Sciences Shanghai China Institute of Image Processing and Pattern Recognition and Institute of Medical Robotics Shanghai Jiaotong University Shanghai China

Due to the similarity in mushroom features and the difficulty in distinguishing between poisonous and nonpoisonous varieties, mushrooms pose a threat to human health. To address the challenge of mushroom classification and identification, this paper proposes a mushroom classification method based on residual networks. Firstly, a network architecture with multiple residual blocks is designed, and it is trained using an image dataset. then, a transfer learning strategy is employed to initialize the network parameters from a pre-trained model, followed by fine-tuning to adapt to the mushroom classification task. Finally, multiple testing experiments are conducted to evaluate the effectiveness of the proposed method. the experimental results demonstrate excellent performance of the proposed method in mushroom classification tasks. Compared to traditional feature extraction methods, it can better capture the details and texture features of mushrooms, thereby improving classification accuracy. In conclusion, the mushroom classification method based on residual networks exhibits high accuracy and generalization capability. this method has potential applications in the field of mushroom classification, aiding in the better identification and differentiation of poisonous mushrooms, thereby protecting human health.

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4th international Symposium on Intelligent Computing Systems, ISICS 2022

4th International Symposium on Intelligent Computing Systems...

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4th international Symposium on Intelligent Computing Systems, ISICS 2022

ISBN: (纸本)9783030984564

the proceedings contain 11 papers. the special focus in this conference is on Intelligent Computing Systems. the topics include: On the Performance of Preconditioned Methods to Solve Lp -Norm Phase Unwrapping;Fault Diagnosis of Combustion Engines in MTU 16VS4000-G81 Generator Sets Using Fuzzy Logic: An Approach to Normalize Specific Fuel Consumption;DDoS Attack Preventing and Detection with the Artificial Intelligence Approach;iris recognition Using Supervised learning Based on Matching Features;convolutional Neural Network for Segmentation of Single Cell Gel Electrophoresis Assay;Evaluation of Human SCD Test by Digital Image Analysis;machine Vision-Based Expert System for Automated Skin Cancer Detection;AI Used to Identify Car Accident Injury and Mortality Risk Factors in Dubai UAE;toward Automatic Water Pollution Analysis: A machine learning Approach for Water-Quality Monitoring through pattern Classification of Water Crystallization.

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Intelligent Customer Service System of Subway Based on Semantic Decision-Making Judgment

Intelligent Customer Service System of Subway Based on Seman...

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machine learning and Intelligent Systems Engineering (MLISE), international conference on

作者： Xinyu Liu School Of Urban Railway Transportation Shanghai University of Engineering Science China

ISBN: (数字)9798350375077

ISBN: (纸本)9798350375084

this study introduces an intelligent customer service system for urban rail transit, addressing inefficiencies and high labor costs in existing systems' problem classification and feedback mechanisms. Leveraging semantic understanding, the system integrates advanced speech recognition, intention classification, and entity extraction to automate the processing and intelligent classification of passenger hotline calls. Post-evaluation, the system demonstrated a minimum of 4.55% enhancement in data processing accuracy and F1 score, respectively, validating its efficacy in enhancing efficiency and precision. Furthermore, the research delves into the practical application of entity recognition, offering insights into the integration of natural language processing in intelligent customer service systems.

关键词： Rails Customer services Semantics Decision making Speech recognition machine learning data processing

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4th international conference on Recent Innovations in Computing, ICRIC 2021

4th International Conference on Recent Innovations in Comput...

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4th international conference on Recent Innovations in Computing, ICRIC 2021

ISBN: (纸本)9789811688911

the proceedings contain 45 papers. the special focus in this conference is on Recent Innovations in Computing. the topics include: learning-Based Techniques for Assessing Zero-Day Attacks and Vulnerabilities in IoT;a Review of Credit Card Fraud Detection Techniques;an Intensive Literature Review on machine Translation Using Neural Approach;cyber Security and IoT: Attacks and Security Countermeasures;scope and Challenges of Blockchain Technology;web Usage Mining—Process, Tools and Practices;e-Health Systems for Mother and Child Care Domain: A Systematic Review;hierarchical Clustering Based Characterization of Protein database Using Molecular Dynamic Simulation;model to Predict the Crop Cost Using machine learning and Read dataset;a Survey on Scalable Resource Allocation in Cloud Computing;computational Intelligence Techniques for Big data Analytics: A Contemplative Perspective;recent Techniques for Sentiment Analysis on Medical data to Predict Depression: A Review;deep learning in IoT and Edge/Fog Integrated Environments: A Review;challenges in recognition of Kashmiri Script;plantaeK: A Leaf database of Native Plants of Jammu and Kashmir;plummeting Makespan by Proficient Workflow Scheduling in Cloud;web Recommendation by Exploiting User Profile and Collaborative Filtering;Feature Extraction from Radiology Images for Visual Question Answering System Using CNN and BiLSTM Model;superpixels-Guided Background Modeling Approach for Foreground Detection;a Review on Leaf-Based Plant Disease Detection Systems Using machine learning;a Review on machine learning Techniques for the Diagnosis of Cancer;Usability Improvements in E-Governance Application with Simple & Usable Interface Using Citizen-Satisfaction-Model-of-Performance-Related-Pay (CSMPRP);License Plate Detection and recognition Using OpenCV–Python;corpus Creation for Anaphora Resolution in Punjabi Language.

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Metadata-P2T-Net: a skin lesion classification network integrating dermoscopic metadata and image data 4

Metadata-P2T-Net: a skin lesion classification network integ...

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4th international conference on Image Processing and Intelligent Control, IPIC 2024

作者： Liu, Weili Wang, Jingli Wang, Bo Software School Xinjiang University Urumqi830091 China Artificial Intelligence Institute North China University of Science and Technology Tangshan063000 China

ISBN: (纸本)9781510682313

Early detection of skin cancer significantly improves the five-year survival rate for patients. However, early-stage malignant tumors exhibit very subtle lesions in the skin, making their symptoms less noticeable. Professional doctors require multiple biopsies and tissue extraction to diagnose the type of lesion. Existing machine learning methods struggle to focus simultaneously on spatial detail and metadata semantic features, resulting in lower recognition accuracy in skin lesion images. To effectively represent spatial and metadata features and prevent issues such as misclassification of easily distinguishable images due to an overemphasis on detail, a new skin lesion classification network called Metadata-P2T-Net has been proposed. this method uses a P2T-based visual backbone network and incorporates a Multi-Head Attention Feature Fusion Module (MHAFFM), enhancing the model's capability to model global context and dynamically weight visual features with metadata information, highlighting metadata-related visual regions. Additionally, a Multi-Dimensional Fusion Residual Module (MFRM) has been introduced, which effectively integrates visual, fusion, and metadata features to improve the model's classification performance. Experiments conducted on the ISIC2019 dataset show that the Metadata-P2T-Net achieved a Matthews correlation coefficient (MCC) of 0.9086, an accuracy of 93.69%, a precision of 93.89%, a recall of 86.77%, and an F1-Score of 90.18%. Compared with other methods, these metrics significantly improved, demonstrating the effective extraction of detailed and metadata features and providing a new basis for the diagnosis of dermatoscopic images. © 2024 SPIE.

关键词： Deep learning

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4th international conference on Recent Innovations in Computing, ICRIC 2021

4th International Conference on Recent Innovations in Comput...

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4th international conference on Recent Innovations in Computing, ICRIC 2021

ISBN: (纸本)9789811682476

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Exploring the Consistency of Positive and Negative Images In Classification Networks 4

Exploring The Consistency of Positive and Negative Images In...

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4th IEEE international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2024

作者： Hu, Bibo Zuo, Wentao Liu, Zhongling Maalla, Allam Guangzhou College of Technology and Business School of Engineering Guangzhou510850 China

ISBN: (纸本)9798350363586

After converting the original sample pictures (positive pictures) to negative images, the retained objects' critical category information can be classified by humans. Still, the general neural network models are weak in recognizing negative images. In artificial intelligence, researchers are more interested in the performance of the models on the original images, and there is little research on negative image samples. To explore the effect of negative image features on model learning, using the visualization of feature maps and comparison of recognition ability in different scenes, three models trained by positive samples set, negative samples set, and mixed samples set are investigated for three image datasets: MNIST, CIFAR10, and ImageNet. the positive and negative samples were found to be consistent in the feature space of the model, enabling the network to fit both positive and negative images. From the accuracy point of view, the addition of negative samples adjusts the deep feature space of the network, resulting in uniform confidence output of positive and negative images for each class and a more compact distribution of samples within categories. From the perspective of adversarial robustness, models that learn positive and negative picture features also show symmetry in adversarial perturbations;moreover, models trained with negative samples can resist transfer attacks of general models to a certain extent. © 2024 IEEE.

关键词： Adversarial machine learning

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Automatic Inverse Synthetic Aperture Radar Ship Targets recognition using Simple Multiple Kernel learning Automatic ISAR Ship Targets recognition using Simple-MKL 24

Automatic Inverse Synthetic Aperture Radar Ship Targets Reco...

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4th international conference on Big data, Artificial Intelligence and Risk Management, ICBAR 2024

作者： Wu, Yuan Su, Fulin Li, Xue Zhu, Peipei Xie, Xunwei National Key Laboratory of Complex Aviation System Simulation Southwest China Institute of Electronic Technology Sichuan Chengdu China School of Electronics and Information Engineering Harbin Institute of Technology Heilongjiang Harbin China

ISBN: (纸本)9798400709753

In radar automatic target recognition (RATR), inverse synthetic aperture radar (ISAR) image recognition shows its advantages. Due to the limited sample size of ISAR images, support vector machine (SVM), known for its robustness in small sample classification, is often used for ISAR image recognition. For ISAR images of different targets, the single kernel SVM algorithm might lose its robustness. therefore, this paper applies multiple kernel learning (MKL) to ISAR ship target recognition. the process begins with the preprocessing of the ISAR images to suppress Gaussian white noise. then, principal component analysis (PCA) is employed to extract features from the ISAR images. Finally, the Simple-MKL method is used to recognize the samples. Experiments based on simulation data indicate that the method used in this paper improves the accuracy compared to other single-kernel SVM algorithms with different kernel functions. © 2024 Copyright held by the owner/author(s).

关键词： Inverse Synthetic Aperture Radar (ISAR) Radar Automatic Target recognition (RATR) Simple Multiple Kernel learning (SimpleMKL) Support Vector machine (SVM)

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