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检索条件"主题词=Classification Algorithms"

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Analysis of nursing-care freestyle japanese text classification using ga-based term selection

Analysis of nursing-care freestyle japanese text classificat...

引用

Proceedings of the Biannual World Automation Congress

作者： Manabu Nii Takafumi Yamaguchi Yutaka Takahashi Reiko Sakashita Atsuko Uchinuno WPI Immunology Frontier Research Center Osaka University Japan Graduate School of Engineering University of Hyogo Himeji Hyogo Japan College of Nursing Art & Science University of Hyogo Akashi Hyogo Japan

In this paper, classification performance of a term selection based on GA is analyzed. In the term selection based on GA, two objectives which are maximizing correctly classified texts and minimizing selected terms are optimized. An objective function based on the classification per-formance of the SVM with 10-fold cross validation is used for evaluating each individual in GA. Therefore, GA-based term selection is performed aiming at the improvement in classification per-formance on testing text sets. This causes the performance deterioration over unseen texts in actual use by GA-based term selection because terms are deleted excessively even when such terms have important role for the classification. In this paper, relation between the terms deleted by the term se-lection based on GA and the terms which appears in unseen texts is clarified by numerical simulation results.

关键词： Gallium Support vector machines Pain Text categorization classification algorithms Software

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Facial image classification based on age and gender

Facial image classification based on age and gender

引用

International Conference on Advances in ICT for Emerging Regions

作者： Thakshila R. Kalansuriya Anuja T. Dharmaratne University of Colombo School of Computing University of Colombo Colombo 7

Automatic face identification and verification from facial images attain good accuracy with large sets of training data while face attribute recognition from facial images still remain challengeable. We propose a methodology for automatic age and gender classification based on feature extraction from facial, images, namely, primary and secondary features. Our methodology · includes three main iterations: Preprocessing, Feature extraction and classification. Our solution is able to classify images in different lighting conditions and different illumination conditions. classification is done using Artificial Neural Networks according to the different shape and texture variations of wrinkles on face images.

关键词： Face Feature extraction Nose classification algorithms Accuracy Mouth Image color analysis

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Nonlinearly assembling method and its application in large-scale text classification

Nonlinearly assembling method and its application in large-s...

引用

IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Zhong-bao Liu Jing Zhang School of Software North University of China Taiyuan Shanxi CN School of Software North University of China Taiyuan China

Support Vector Machine (SVM) is one of widely-used text classification method. Although SVM performs well in practice, SVM encounters two problems: the data distribution is not taken into consideration in the process of classification and its performance is greatly influenced by noises. In view of this, Fuzzy Support Vector Machine based on Manifold Discriminant Analysis (FSVM-MDA) is proposed and Web text classification system is constructed based on Manifold Discriminant Analysis (MDA) and the fuzzy technology. The advantages of the proposed method are (1) it takes both the global and local characteristics into consideration; (2) it has the ability of noise-resistance. Comparative experiments on the authentic datasets show that the proposed method performs better than traditional method SVM.

关键词： Support vector machines Text categorization Manifolds classification algorithms Kernel Training Artificial neural networks

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Application of Deep Learning Convolutional Neural Network for Melanoma Skin Cancer classification

Application of Deep Learning Convolutional Neural Network fo...

引用

Information Technology and Digital Innovation (ISITDI), International Symposium on

作者： Hamdi Syukron Mustakim Information Systems Department Puzzle Research Data and Technology (Predatech) Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (数字)9798350389241

ISBN: (纸本)9798350389258

Melanoma skin cancer is a global health concern due to its life-threatening nature and visual similarity to benign skin conditions. Accurate melanoma identification requires specialized expertise, which is often scarce and expensive. This scarcity highlights the critical need for accessible and cost-effective diagnostic solutions. Leveraging deep learning, an Artificial Intelligence (AI) approach rooted in computer vision, offers a promising avenue for melanoma detection. This study utilizes Convolutional Neural Network (CNN) algorithms, particularly DenseNet121 and VGG19 architecture, to develop robust melanoma classification models. Through rigorous evaluation, DenseNet121 emerges as the optimal architecture, achieving an impressive accuracy of 0.9385 and a minimal loss of 0.1497. Furthermore, we demonstrate practical implementation by deploying the DenseNet121 model in a web-based application using the Flask framework. Our findings underscore the efficacy of deep learning in melanoma detection and highlight the feasibility of translating such advancements into user-friendly diagnostic tools, addressing the urgent need for accessible healthcare solutions in melanoma management.

关键词： Deep learning Computer vision Accuracy Computational modeling Melanoma Computer architecture classification algorithms Convolutional neural networks Medical diagnosis Artificial intelligence

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Real-Time Vehicle classification Using CNN

Real-Time Vehicle Classification Using CNN

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Nusrat Jahan Saiful Islam Md. Ferdouse Ahmed Foysal CSE Daffodil International University Dhaka Bangladesh

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Convolutional Neural Network (CNN) is a model of artificial neural networks that has grown to be most well known in computer vision assignment. In this paper work, we presented convolutional neural network for classifying four types of common vehicle in our country. Vehicle classification plays a vital role of various application such as surveillance security system, traffic control system. We addressed these issues and fixed an aim to find a solution to reduce road accident due to traffic related cases. The greatest challenge of computer vision is to achieve effective results to implement a system due to variation in shapes and colors of data. To classify the vehicle we used two methods feature extraction and classification. These two methods can straightforwardly performed by convolutional neural network. The method shows quite good performance on real-time standard dataset. Our mentioned method able to reach 97% accuracy in case of vehicle classification.

关键词： Road accidents Feature extraction Convolutional neural networks classification algorithms Real-time systems Neurons Deep learning

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EMG classification by using Swarm Intelligence for Myoelectric Prosthetic Hand

EMG Classification by using Swarm Intelligence for Myoelectr...

引用

IEEE International Conference on Intelligence and Safety for Robotics (ISR)

作者： Yuki Kuroda Shunta Togo Yinlai Jiang Hiroshi Yokoi Department of Mechanical Engineering and Intelligent Systems the University of Electro-Communications Tokyo Japan Brain Science Inspired Life Support Research Center the University of Electro-Communications Tokyo Japan

ISBN: (纸本)9781538655481

In recent years, myoelectric prosthetic hand (MPH) has been extensively studied due to the spread of 3D printers. However, it cannot do precisely movement now because it is difficult to identify electromyogram (EMG) by using existing method. The reasons for this are as follows; Hand movement is too complicated to use it as label for supervised learning method, EMG change its characteristics gently with time. Accordingly, we need to develop a new method adapted to MPH. In this study we developed an identification method using swarm intelligence which was optimized to the characteristic of EMG. To verify the function of the method, experiments were conducted. For some subjects, identification rates were high. Moreover, we discussed how to improve the method and conducted some experiments to verify it. It has been considered effective to investigate the optimization method of particle swarms.

关键词： Electromyography Training data Particle swarm optimization Prosthetic hand Neural networks classification algorithms

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A comparison study of multi-class sentiment classification for Chinese reviews

A comparison study of multi-class sentiment classification f...

引用

International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Dongmei Zhang Shengen Li Cuiling Zhu Xiaofei Niu Ling Song School of Computer Science & Technology Shandong Jianzhu University Jinan China School of Computer Science & Technology Shandong University Jinan China

Most of the previous researches on sentiment analysis concentrate on the binary distinction of positive vs. negative. This paper presents the multi-class sentiment classification problem that attempt to mine the implied rating information from reviews. We use four machine learning methods and two feature selection methods to find out whether or not the multi-class sentiment classification problem is the same to the binary sentiment classification problem, and whether it is equal to the traditional multi-class classification problem. Experiments show that multi-class sentiment classification problem is difficult than that of only determining the polarity of a review and that it is different from traditional multi-class classification problem, thus traditional multi-class classification method can not be directly used to deal with this problem.

关键词： Semantics Support vector machines Machine learning Learning systems classification algorithms Motion pictures Data mining

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classification Method of Navigational Aids in Inland Waters Based on Big Data

Classification Method of Navigational Aids in Inland Waters ...

引用

Japan-China Joint Workshop on Frontier of Computer Science and Technology (FCST)

作者： Xiaocai Zhang Lining Zhao Haibao Wang Haijiang Li Navigation College Dalian Maritime University Dalian China

ISBN: (纸本)9781467392969

Remote Monitoring and Controlling System for navigational aids belongs to a significant subsystem of ITS in water transport. The area of implementation is becoming wider and wider. The work covers clustering and classification of navigational aids based on Big Data. Purpose of the work is to explore a more scientific classification method in order to avoid man-made subjective arbitrary interferences. Besides, simulation experiment has shown the effectiveness, feasibility and practicability of classification of navigational aids. In addition, mode of hierarchical management of navigational aids is designed.

关键词： Navigation classification algorithms Clustering algorithms Big data Filling Standards Data mining

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A Review of Recent Advances in classification and Prediction of Brain Tumor using Deep Learning

A Review of Recent Advances in Classification and Prediction...

引用

International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： D L Kavya Reddy D R Soumya Abhayendra Nagar Abha Kiran Rajpoot School of Engineering and Technology Sharda University Greater Noida

ISBN: (纸本)9781665462013

The latest developments in deep learning to identify brain tumors are reviewed in this article. The aim of this bibliographic review is to provide a thorough understanding of the most current breakthroughs to the researchers that decide to work with deep learning and artificial neural networks for cancer diagnostics. Brain tumors are categorized into two terms malignant (cancerous) and benign (non-cancerous), just like other tumors based on the critical stage. While benign tumors expand slower and rarely spread, malignant tumors grow considerably quicker than benign tumors and frequently do so into the surrounding brain tissue. Only malignant tumors are regarded as cancerous, however benign brain tumors can still pose a threat because of how their growth may affect nearby brain tissue. It may cause death if not treated in the initial phases. Although there have been many substantial efforts and promising results in this field, precise segmentation and classification remain difficult tasks. Because of the differences in tumor location, shape, and size, detecting brain tumors is extremely difficult. The use of AI in medical research has improved the precision and accuracy of brain disease prediction and detection. In this article, we’ve analyzed methods such as CNN, FCN, U-Net, and others, based on the metrics of accuracy, sensitivity, specificity, precision, and F1-score, which aid in assessing the effectiveness of the algorithm used.

关键词： Deep learning Brain Image segmentation Shape Malignant tumors Benign tumors classification algorithms

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Enhanced Product classification Using Learned Prompt Ensembling and Dual Interpolation with CLIP-Based Model

Enhanced Product Classification Using Learned Prompt Ensembl...

引用

IEEE Workshop on Multimedia Signal Processing

作者： Takahisa Yamamoto Koichiro Niinuma László A. Jeni Converging Technologies Laboratory Fujitsu Research of America Pittsburgh PA USA Robotics Institute Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798350387254

ISBN: (纸本)9798350387261

Retail product classification is a crucial technology due to its market size and potential. Several algorithms have been proposed; however, these approaches are not suitable due to their inability to accommodate new data without retraining or their insufficient performance caused by class names reflecting product-specific names. In this study, we adopt a CLIP-based model for retail product classification to overcome the challenges associated with model retraining for new products and the issue of unique class names. Our approach, learned prompt ensembling and dual interpolation (LPEDI), combines prompt learning and its ensembling with encoder fine-tuning, and employs dual interpolation for coefficient adjustment. The method outperforms existing solutions on two retail product datasets, achieving a 5.9% improvement for in-distribution data and a 4.5% gain for out-of-distribution data. These results establish LPEDI as a practical and effective solution for retail product classification.

关键词： Training Interpolation Accuracy Multimedia systems Conferences Pipelines Signal processing algorithms Signal processing Vectors classification algorithms

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