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检索条件"主题词=Classification Algorithms"

共 26420 条记录，以下是551-560 订阅

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A Review of Deep Learning Based Target Detection algorithms for Fish classification

A Review of Deep Learning Based Target Detection Algorithms ...

引用

International Conference on Advanced Computer Theory and Engineering, ICACTE

作者： Zhuo Fang Zhenglong Ding Zhigeng Pan Renan Ma School of Electronic and Information Engineering Anhui Jianzhu University Hefei Anhui China School of artificial Intelligence Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798331539887

ISBN: (纸本)9798331539894

Deep learning, with its excellent feature extraction and classification capabilities, has shown great potential in the field of fish classification and become a crucial part of fish classification research. The tasks, difficulties and typical algorithms for deep learning-based fish target detection classification are presented, as well as the current status analysis in terms of several common problems of fish target detection such as complex backgrounds, small targets and lightweighting. Finally, the dataset used for model training and the performance metrics for evaluating the model are presented, and the existing problems of deep learning-based fish classification and the prospects of fish classification algorithms in aquaculture and other fields are discussed, and the future research directions and challenges are pointed out.

关键词： Deep learning Training Measurement Reviews Object detection Fish Feature extraction classification algorithms Aquaculture

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Self Regulating classification of Healthy and AMD Class Retinal OCT Images with Deep-Learning algorithms

Self Regulating Classification of Healthy and AMD Class Reti...

引用

IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： Sivakumar Rajendran Dept. of Electrical and Electronics Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai INDIA

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

The Optical Coherence Tomography (OCT) images offer cross-sectional imaging of retina excluding the need of conventional method. It enables the diagnosis of various retinal diseases by non-invasive technique. Age-related Macular Degeneration (AMD) is among the major eye diseases that affect human vision. Early detection of AMD may help to prevent the vision loss. This work deals deep learning approach diagnosis of diseases based on classification of OCT images into healthy and AMD groups. The proposed model has three stages of operation: binary classification, feature extraction and image resizing. Various deep learning algorithms are tested for the selected images and EfficientNetV2S approach produces better response based on the parametric metrics with an accuracy of 0.95.

关键词： Deep learning Measurement Macular degeneration Accuracy Optical coherence tomography Computational modeling Imaging Retina Feature extraction classification algorithms

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Multiclass classification Learning-based Hashing algorithms based on “supervised discrete hashing”

Multiclass Classification Learning-based Hashing Algorithms ...

引用

Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Suraj Sharma Farhan RazaRizvi Ratnesh Kumar Dubey Pallavi Khatri Nidhi Dandotiya Shashi Kant Gupta Department of Computer Science and Engineering ITM University Gwalior MP India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The reason learning-based hashing algorithms are "warm subjects" is that they are significantly expanding the size at which existing techniques work. In this research, we introduce a novel knowledge-gathering method based on a fully hashing methodology, namely "supervised discrete hashing" (SDH). In typical minimal squares regression, the schooling model (or hash code) is regressed to the same grandeur labels. Rather than taking this route, the suggested learning-based total hashing method uses a very simple yet effective regression of the education sample's elegance designation to the same hash code in order to increase the collection of rules. To the best of our knowledge, hashing has not yet been done using this method. The elaboration in classical SDH is divided into three sub-divisions. Discrete optimization for binary hash codes is the most challenging issue, and it is solved by employing repeated discrete cyclic coordinate descent (DCC), which is exceedingly time-consuming and laborious. Nevertheless, the suggested learning-based total hashing has a closed-form solution and, despite its great strength, only requires a single hash code instead of repeatedly adjusting hash codes. Furthermore, our suggested learning-primarily based hashing is frequently faster than standard SDH since it is typically faster than SDH for setting the projection matrix for least squares regression.

关键词： Synchronous digital hierarchy Closed-form solutions Education Binary codes classification algorithms Object recognition Standards Optimization Videos

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Crack Image classification: Performance Comparison Among Different Machine Learning algorithms

Crack Image Classification: Performance Comparison Among Dif...

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International Conference on Image and Signal Processing and their Applications (ISPA)

作者： Benelmir Okba Dhiabi Fathi Electrical Engeneering Departement Faculty of Sciences and Technology University Mohamed Khider Biskra Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

classification of surface crack images is an ever-evolving area of research that aims to develop machine learning models capable of identifying and categorizing cracks in materials. This study concentrates on evaluating and comparing the effectiveness of various machine learning algorithms when employed in the classification of crack images. The objective is to identify the most suitable classification technique for this purpose. To conduct this comparative study, several machine learning algorithms are selected, such as k-nearest neighbors (KNN), decision trees, support vector machines (SVM), random forests and neural networks (NN). Each of these algorithms is trained and evaluated on a dataset of previously annotated crack images.

关键词： Support vector machines Machine learning algorithms Signal processing algorithms Prediction algorithms Vectors classification algorithms Data mining

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Ensemble-Based Botnet Attack Detection and classification Using Machine Learning algorithms on NBaIoT Dataset

Ensemble-Based Botnet Attack Detection and Classification Us...

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IEEE Region 10 Symposium

作者： Mamta Rawat Avneet Singh Bedi Balvinder Singh Sneha Gupta Gaurav Singal Preeti Kaur Department of Computer Science (NSUT) New Delhi India

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

Through botnet assaults, Mirai and BASHLITE present serious risks in the context of the Internet of Things (loT). A reliable detection and classification model is the need of the hour. This article presents a solution to the problem by suggesting an ensemble-based algorithm to detect and classify different categories of Mirai and BASHLITE attacks. We have used the NBaloT, a heterogeneous dataset, that purposefully com-promised on many devices, and exposes subtle attack categories. Interestingly, certain devices resist Mirai infections, improving our analysis. By utilizing important data types and various time-based window sizes, our approach seeks to create an effective model for identifying and classifying IoT botnet assaults to strengthen the IoT ecosystem against new attacks.

关键词： Machine learning algorithms Botnet Ecosystems Resists Data models classification algorithms Reliability Internet of Things IEEE Regions

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Automated Box classification in a Virtual Industrial Environment Using Machine Vision algorithms

Automated Box Classification in a Virtual Industrial Environ...

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International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Juan C. Guacheta-Alba Anny Astrid Espitia Cubillos Robinson Jimenez Moreno Mechanical Engineering Program Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil Industrial Engineering Program Universidad Militar Nueva Granada Bogotá Colombia Mechatronic Engineering Program Universidad Militar Nueva Granada Bogotá Colombia

ISBN: (数字)9798331517519

ISBN: (纸本)9798331517526

This article presents the development and evaluation of an automated system for box classification based on the detection of some ISO 780:2015 symbols, using a UR5 robotic manipulator and advanced machine vision techniques. The methodology integrates sensors, vision algorithms, and robotics to achieve precise classification. The UR5 manipulator, with six degrees of freedom, was kinematically modeled using Denavit-Hartenberg parameters, allowing for the calculation of the end-effector’s position and orientation. The system employs the Speeded-Up Robust Features (SURF) algorithm for the detection and classification of symbols on the boxes. Simulation results in CoppeliaSim, processed in MATLAB, reveal an average detection time of 55 milliseconds and a recognition rate exceeding 95%, even under varying lighting and orientation conditions. The system demonstrates high precision and flexibility in handling boxes of different sizes and arrangements, validating its efficiency in automated virtual industrial environments.

关键词： Accuracy Service robots Machine vision Heuristic algorithms Robot kinematics Symbols Lighting Robot sensing systems classification algorithms Trajectory

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classification of Stunting in Toddlers from Bandarharjo Using K-Nearest Neighbors and Random Forest algorithms

Classification of Stunting in Toddlers from Bandarharjo Usin...

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IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Ratin Kani Putu Harry Gunawan School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798350368086

ISBN: (纸本)9798350368093

Stunting is a major health priority for children in Indonesia, with a prevalence of $21.6 \%$ in 2022, prompting the government to aim for a reduction to $14 \%$ by 2024 in accordance with WHO standards. Stunting, caused by prolonged malnutrition, affects children’s physical and cognitive development. Machine learning is needed to develop predictive models that can identify stunting early, enabling timely interventions. This study aims to develop a machine learning-based predictive model for early intervention in stunting. It evaluates the performance of Random Forest (RF), K-Nearest Neighbors (KNN), and a proposed ensemble learning algorithm called Stacked RFKNN. Using a dataset from Bandarharjo Health Center that includes toddler measurements and growth indicators for training and testing, RF excels at handling large feature sets and identifying important predictors, KNN effectively recognizes local patterns, and Stacked RFKNN combines the strengths of both. Given the dataset’s imbalance, with only $4.1 \%$ of data related to stunting, oversampling was applied to the minority class. Results show the proposed Stacked RFKNN algorithm outperformed both RF and KNN, achieving a classification accuracy of $\mathbf{9 9. 2 1 \%}$ and an F1-score of $\mathbf{9 5. 4 2 \%}$. In comparison, RF achieved an accuracy of $\mathbf{9 9. 1 2 \%}$ and an F1-score of $\mathbf{9 5. 0 5 \%}$, while KNN attained an accuracy of $\mathbf{9 7. 1 9 \%}$ and an $\mathbf{F 1}$-score of $\mathbf{8 6. 1 4 \%}$. This approach offers an accurate predictive system for early stunting intervention, aiding the reduction of stunting rates in Indonesia.

关键词： Radio frequency Pediatrics Accuracy Machine learning algorithms Nearest neighbor methods Predictive models Prediction algorithms Data models classification algorithms Random forests

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Systematic Literature Review: Disease classification Modeling Using Deep Learning algorithms

Systematic Literature Review: Disease Classification Modelin...

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Technology Innovation and Its Applications (ICTIIA), International Conference on

作者： Wawan Gunawan Hindriyanto Purnomo Christine Dewi Ade Iriani Irwan Sembiring Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

This research focuses on models and algorithms used in studies of diseases such tuberculosis, malaria, diabetes, heart disease, and cancer. It also looks at long-term management, therapy, and prevention of chronic disease complications. The objective is to achieve a thorough understanding of the algorithms employed for various diseases, aiding in the development of more effective treatment strategies. Various algorithms, such as decision trees, SVM, Random Forest, Naive Bayes, and neural networks like RNN, LSTM, and CNN, are analyzed. The study finds that deep learning algorithms, when combined with data reduction techniques such as PCA and LDA, show significant results in disease modeling. These techniques enhance efficiency and accuracy in diagnosis, treatment, and prevention. This study uncovers how these algorithms can identify patterns, trends, and associations in large and complex health datasets. This contributes to new insights in disease research that deep learning algorithms show significant results in disease modeling with data reduction techniques such as PCA and LDA. This research also finds that deep learning algorithms can improve efficiency and accuracy in diagnosis, treatment, and disease prevention. The combination of PCA with deep learning algorithms such as LSTM, CNN, and GNN shows potential in improving predictive performance.s.

关键词： Deep learning Accuracy Prevention and mitigation Data science Prediction algorithms Data models classification algorithms Long short term memory Diseases Principal component analysis

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Detection & classification of Lung Diseases Using Hybrid Deep Learning algorithms

Detection & Classification of Lung Diseases Using Hybrid Dee...

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Engineering and Technology (PICET), Parul International Conference on

作者： Cherry Chopra Naman Ahuja Neha Tyagi Amity School of Engineering & Technology Amity University Noida Uttar Pradesh India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

Accurate and early diagnosis of lung diseases remains crucial for effective treatment and improved patient care. Conventional methods often lack precision and objectivity. Deep learning algorithms have emerged as promising tools for lung disease detection and classification, with Convolutional Neural Networks (CNNs) like VGG16, Xception and Inception architecture and transfer learning models like ResNet34 demonstrating remarkable performance. Hence, this paper explores the applicability of these models for enhanced lung disease detection, focusing primarily on three most widespread lung diseases - pneumonia, lung cancer and Covid-19. The Lung & Colon Cancer dataset, containing 15000 images and Lung X-Ray dataset containing more than 5500 images, both have been taken from Kaggle and utilized for model training and testing purposes. Inclusion of a user-friendly web application that integrates the three models into a single platform is also explored. The results shed light on the advantages and limitations of different deep learning architectures, offering valuable insights for further research and development of robust and reliable lung disease detection systems.

关键词： Deep learning Training Transfer learning Lung cancer Lung classification algorithms Medical diagnosis X-ray imaging Tumors Testing

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Exploring the Effectiveness of Transformer-Based Models and ML algorithms for Bengali Hate Speech classification

Exploring the Effectiveness of Transformer-Based Models and ...

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Power, Electrical, and Electronics and Industrial Applications (PEEIACON), IEEE International Conference on

作者： Shudipto Trafder Shawon Kumar Saha Haraprosad Biswas SWE Discipline Khulna University Banglasesh Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Banglasesh

ISBN: (数字)9798331517984

ISBN: (纸本)9798331517991

The number of internet users is growing every day. Social media is becoming more popular because the internet is accessible almost everywhere. People use social media to share their opinions and sometimes post offensive thoughts, known as hate speech, which can cause problems in society. There are many ways to detect hate speech in different languages, but there are few methods for Bengali comments and posts. In this study, we aim to identify various types of hate speech in Bengali social media posts and comments using transformer-based models. The transformer-based model 3cube-pune/bengali-bert achieved an accuracy of 75.99% and an f'1-score of 76.07%, which is higher than the other models we considered.

关键词： Accuracy Social networking (online) Hate speech Transformers classification algorithms

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