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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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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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Automatic SMS Spam Filtering for Disaster Response Using Classification Algorithms 4th

Automatic SMS Spam Filtering for Disaster Response Using Cla...

引用

4th international conference on Computing, Communications, and Cyber-Security, IC4S 2022

作者： Rao, K. Nitalaksheswara Amiripalli, Shanmuk Srinivas Rampay, Venkata Rao Jitendra, M.S.N.V. Andhra Pradesh Visakhapatnam530045 India Department of Computer Science and Engineering Wollega University Nekemte Ethiopia

ISBN: (纸本)9789819914784

Short Message Service (SMS) acts as one of the media of communication for the mobile device system due to rapid increase of mobile users. During disasters, the government provides rescue agencies for the people to deal with emergencies. Since there exist critical positions during the time of disasters, the rescue agencies cannot categorize the messages based on emergency. So, a hybrid system of the SMS spam filtering method is used to classify messages like spam, ham, alert1, alert2 and alert3 using machine learning algorithms such as Naive Bayesian Algorithm and Decision Tree Algorithm. Since it is a hybrid system significant messages are considered effectively by the rescue agencies and help the victims. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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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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A Comprehensive Study on Earth System Science using machine learning and Process mining

A Comprehensive Study on Earth System Science using Machine ...

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Communication, Computing and Industry 4.0 (C2I4), international conference on

作者： Vijayalakshmi V Gomathi thiyagarajan Department of DSBS SRM Institute of Science and Technology Kattankulathur Chennai Department of Computer Applications CMR Institute of Technology Bengaluru

machine learning has emerged as a powerful tool in Earth System Science offering new avenues for data analysis and predictive modeling. this paper deep dive into the application of machine learning techniques to understand and predict air quality a critical aspect of Earth System Science. Process mining techniques are incorporated to explore data patterns and relationships. Our work explores the use of machine learning and process mining techniques in Earth System Science, with a specific focus on air quality assessment. the study involves data collection, preprocessing and the development of machine learning models including Linear Regression, Decision Trees, Random Forest, XGBoost and CatBoost to predict Air Quality Index (AQI). Performance evaluation using Root Mean Squared Error (RMSE) and R-squared (R 2 ) metrics reveals that XGBoost and CatBoost demonstrate superior predictive accuracy. Process mining techniques are applied to datasets containing city-specific NOx (nitrogen oxides) measurements, resulting in the creation of heuristic process models. this research serves as a foundational exploration of the integration of machine learning models and process mining techniques in Earth System Science with a focus on air quality.

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A Review of the Various machine learning Algorithms for Cloud Computing 4

A Review of the Various Machine Learning Algorithms for Clou...

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4th international conference on Advanced Science and Engineering, ICOASE 2022

作者： Amanuel, Sandy Victor Ahmed, Ibrahim M. Technical College of Informatic Akre Duhok Polytechnic University Information Technology Department Duhok Iraq College of Science University of Mosul Mosul Iraq

ISBN: (纸本)9781665472227

Cloud computing (CC) provides network services on request, especially data storage and processing capacity, without users' specific and direct management. CC recently became a collection of public and private data centers that provide the customer with a shared Internet network. Edge Computing is an emerging computing and knowledge storage model that puts end-users closer together to increase reaction times and save communication power. However, CC and edge computing face protection issues, including customer risk and corporate recognition, that hinder the swift implementation of computing modelling. One solution to this problem, because of its complexity and severity, is machine learning (ML) which consists of researching computational algorithms and naturally advancing knowledge. the problem and solution issues are raised by the overview article that analyses CC safety risks, problems, and solutions that use one or more ML algorithms. Study various ML algorithms, such as controlled, unmonitored, semi-supervised, and enforced training, to solve cloud protection problems. the paper assesses each technique's efficiency based on its characteristics, advantages, and drawbacks. In addition, it will have potential study guidance on safeguarding CC usage and applications. © 2022 IEEE.

关键词： Cybersecurity

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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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Node Classification in CAD data: A Graph learning Approach for Machining Feature Localization 4

Node Classification in CAD Data: A Graph Learning Approach f...

引用

4th international conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Bohm, Stefan Andreas Neumayer, Martin Song, Rui Rib, Fabian Knoll, Alois Technical University of Munich Chair of Robotics Artificial Intelligence and Real-Time Systems Munich Germany Rosenheim Technical University of Applied Sciences Research and Development Department Rosenheim Germany Fraunhofer Institute for Transportation and Infrastructure Systems Ingolstadt Germany Rosenheim Technical University of Applied Sciences Faculty of Engineering Sciences Rosenheim Germany

ISBN: (纸本)9798350391183

pattern recognition is becoming increasingly important in many sectors, such as the manufacturing industry. With rising customer demands and advancing technology, product data, often represented as 3D meshes, is becoming highly complex. therefore, intelligent approaches are needed to recognize and localize features in 3D meshes to determine complex manufacturing processes. However, previous approaches solely work on voxel-, pixel-or. STEP-based formats and not directly on mesh-based formats such as STL data. To overcome these issues, we present a configurable data generator and customize graph neural network architectures for the problem of localizing machining features directly in 3D meshes. After comparing the graph neural networks against each other, the best resulting network is compared to an existing state-of-the-Art approach, i.e., SsdNet. Our results indicate that the best-performing graph neural network exhibits superior classification accuracies on meshes with few machining features compared to SsdNet with an average F1 score of 80%. While the accuracy slightly falls behind SsdNet with more features, the graph neural network shows substantially better runtime performance results, which are, on average, 70 times faster. © 2024 IEEE.

关键词： Graph neural networks

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Leveraging Sequential pattern Information for Active learning from Sequential data 25

Leveraging Sequential Pattern Information for Active Learnin...

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25th international conference on pattern recognition (ICPR)

作者： Fidalgo-Merino, Raul Gabrielli, Lorenzo Checchi, Enrico European Commiss Joint Res Ctr JRC Ispra Italy CNR Ist Sci & Tecnol Informaz I-56124 Pisa Italy

ISBN: (纸本)9781728188089

this paper presents a novel active learning technique aimed at the selection of sequences for manual annotation from a database of unlabelled sequences. Supervised machine learning algorithms can employ these sequences to build better models than those based on using random sequences for training. the main contribution of the proposed method is the use of sequential pattern information contained in the database to select representative and diverse sequences for annotation. these two characteristics ensure the proper coverage of the instance space of sequences and, at the same time, avoids over-fitting the trained model. the approach, called SPIAL (Sequential pattern Information for Active learning), uses sequential pattern mining algorithms to extract frequently occurring sub-sequences from the database and evaluates how representative and diverse each sequence is, based on this information. the output is a list of sequences for annotation sorted by representativeness and diversity. the algorithm is modular and, unlike most current techniques, it is independent from the sequential machine learning technique that builds the model as SPIAL does not need to know the set of the features taken into account by the training algorithm. Experiments done on well-known benchmarks involving sequential data show that the models trained using SPIAL increase their convergence speed while reducing manual effort by selecting small sets of very informative sequences for annotation. In addition, the computation cost using SPIAL is much lower than for the state-of-the-art algorithms evaluated.

关键词： database systems

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data mining Technology Based Targeted Funding AI System for the Poor College Students

Data Mining Technology Based Targeted Funding AI System for ...

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2022 international conference on Computation, Big-data and Engineering, ICCBE 2022

作者： Li, Yang Wenhua College Hubei province Wuhan China

ISBN: (数字)9781665470551

ISBN: (纸本)9781665470551

With the expansion of higher education in China and the resultant increasing number of college students, the number of students from poor areas and poor families has also increased. To enable students with financial difficulties to successfully complete their studies, China has been improving the financial aid system. However, the current financial aid for the students is mostly based on individual application and subjective determination, which is often unsatisfactory to achieve targeted funding. thus, we analyzed the advantages of big data in targeted funding and then designed an index system for the accurate identification of college funding needs. the construction principles of index system design was included in the basic process of data mining, logistic regression model, data flow map design, and the classification method. We proposed the basic framework of targeted funding with data mining technology for poor college students, which is conducive to the development and realization of related systems. © 2022 IEEE.

关键词： Students

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