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检索条件"主题词=Classification Algorithms"

共 26406 条记录，以下是541-550 订阅

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Study of Explainability Analysis Methods for the LAMDA Family algorithms in classification and Clustering Tasks

Study of Explainability Analysis Methods for the LAMDA Famil...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Carlos Quintero Gull Jose Aguilar Rodrigo García Dpto de Cs Aplicadas y Humanísticas Facultad Ingeniería Universidad de Los Andes Mérida Venezuela IMDEA Networks Institute Madrid Spain CEMISID Universidad de Los Andes Mérida Venezuela GIDITIC Universidad EAFIT Medellín Colombia Universidad del Sinú Monteria Colombia

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Explainability analysis is a very relevant topic today, due to the interest of allowing the interpretability of machine learning models. In this work, we carry out an in-depth study of explainability analysis for the algorithms of the LAMDA (Learning Algorithm for Multivariate Data Analysis) family that have been used in the context of supervised and unsupervised learning. In particular, for the case of classification the LAMDA-HAD algorithm, and for the case of clustering the LAMDA-RD algorithm. For the explainability analysis, two classic methods from the explainability area were considered, LIME (Local Interpretable Model-Agnostic Explanation) and Feature Importance, and another one developed by us for the LAMDA family. In particular, our explainability method for LAMDA allows measuring the importance of each characteristic in a general way, and for each cluster. In general, the results obtained in both cases (classification and clustering) are satisfactory, especially because our explainability method for LAMDA gives an explainability similar to the traditional ones, but in addition, it can be given by cluster.

关键词： Analytical models Machine learning algorithms Neural networks Clustering algorithms Machine learning Particle measurements classification algorithms Viruses (medical) Unsupervised learning Cancer

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Performance Assessment of Machine Learning algorithms for Yellow Rust Wheat Disease classification with UAV RGB Images

Performance Assessment of Machine Learning Algorithms for Ye...

引用

IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

作者： Fatemeh Khalesi Amin Talaeizadeh Aria Alasty Pasquale Daponte Luca De Vito Francesco Picariello Department of Engineering University of Sannio Benevento Italy Department of Mechanical Engineering Sharif University of Technology Tehran Iran

ISBN: (数字)9798350355444

ISBN: (纸本)9798350355451

This paper proposes a workflow for the detection of yellow rust winter wheat disease from RGB images captured by Unmanned Aerial Vehicles, together with a sensitivity analysis against several influence quantities to Machine Learning (ML) classification algorithms working on these images. The study utilizes various image processing tools to extract meaningful features, and one-way ANOVA is employed to identify and prioritize critical features for disease diagnosis. Furthermore, ML algorithms are applied to these features to classify vegetation sickness, and the sensitivity analysis of the final ML model is evaluated against uncertainty sources such as blurring, light conditions, and noise affecting the acquired images. The model exhibits greater robustness to speckle noise when compared to other noise types while showing higher sensitivity towards contrast and light conditions in comparison.

关键词： Machine learning algorithms Uncertainty Sensitivity analysis Noise Vegetation mapping Machine learning Feature extraction Autonomous aerial vehicles classification algorithms Diseases

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Feature Selection In Support Vector Machine And Random Forest algorithms For The classification Of Recipients Of The Smart Indonesia Program

Feature Selection In Support Vector Machine And Random Fores...

引用

Circuit, Systems and Communication (ICCSC), International Conference on

作者： Nanda Try Luchia Mustakim Mustakim Noviarni Noviarni Kelik Sussolaikah Teguh Arifianto Department of Information System Faculty of Science and Technology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Department of Mathematic Education Faculty of Education and Teaching Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Department of Informatic Engineering Faculty of Engineering Universitas PGRI Madiun Madiun Indonesia Department of Railway Electrical Technology Politeknik Perkeretaapian Indonesia Madiun Indonesia

ISBN: (数字)9798350365306

ISBN: (纸本)9798350365313

Education is very important for everyone in order to provide intelligence and build a good soul in themselves. Indonesia has a high dropout rate. This is because the education level of the Indonesian people is still in the low category. So the government runs a Smart Indonesia Program (PIP) to help improve the quality of education. As in SMAN 1 Tualang. This research classifies prospective PIP recipients using Feature Selection Chi Square and Information Gain with SVM and Random Forest algorithms. There are 1112 students with 26 selection attributes. This study concluded that the Random Forest algorithm with Feature Selection is better than the SVM algorithm with the highest accuracy value of 99%, while the SVM algorithm has the highest accuracy value of 98%. Feature selection that has a big influence in determining accuracy is Information Gain.

关键词： Support vector machines Accuracy Education Government Forestry Feature extraction classification algorithms

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Experimental study and comparison of medical methodology and machine learning models to enhance algorithms for morphological classification of clinical and hematologic syndromes

Experimental study and comparison of medical methodology and...

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Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Indira Uvaliyeva David Borozenets School of Digital Technology and Artificial Intelligence D. Serikbayev East Kazakhstan Technical University Ust-Kamenogorsk Kazakhstan

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

The dynamic evolution of modern medicine highlights the increasing significance of automating diagnostic procedures. This progression is not solely a matter of convenience but a vital step towards fully leveraging technological advancements, with the goal of elevating both research and clinical outcomes to unprecedented levels. Among the notable advancements in this arena, the emergence of diagnostic systems founded on morphological classification algorithms holds particular prominence. This study compares the performance of different methods, including both traditional medical approaches and modern machine learning techniques. Clinical and hematologic syndromes refer to a broad range of medical conditions characterized by specific sets of signs, symptoms, and laboratory findings. Experimental study and comparison of medical methodology and machine learning models allowed to determine the most effective approaches to morphological classification of clinical-hematologic syndromes, which may lead to improved diagnosis and treatment of patients.

关键词： Human computer interaction Adaptation models Machine learning algorithms Machine learning Forestry classification algorithms Medical diagnostic imaging

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classification of Malicious Financial Applications Using Naive Bayes and K-Nearest Neighbor algorithms Based on Android Permission

Classification of Malicious Financial Applications Using Nai...

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International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)

作者： Mst.Liya Akter Iffath Tanjim Moon Md Akash Akter Zhakia Tabessum Shafa Department of Computer Science & Engineering Pundra University of Science & Technology Bogura Bangladesh

ISBN: (数字)9798350388282

ISBN: (纸本)9798350388299

In this research paper, we have worked on some of the vulnerabilities of multiple Android apps and we have explored the vulnerability of financial apps and how these apps are acting as threats in our daily lives. Nowadays in this digital technology, mobile banking is a common way for people to make transactions, through which many people have to face financial loss. we have identified the vulnerability of Financial Apps by performing permission analysis to protect the privacy, security, and financial security of these customers. Our main contribution is to find out the vulnerabilities of Android apps by permission analysis to create high-quality datasets using static analysis and learning the dataset through the K-Nearest Neighbor algorithm and Naive Bayes algorithm to show whether it is malicious or not by using the ‘Permission Dataset’ and the ‘Appdroid’ dataset. To test the developed model, we have extracted 1438 combined attributes from these two datasets and took 12050 applications samples. According to the test findings, the Naive Bayes model can improve detection by roughly 82.07% For further testing it was 88.25% that the KNN model has the highest accuracy and efficiency. Our main objective is to identify multiple Financials app vulnerabilities and create a comprehensive dataset by permission analysis and by using a machine learning model, as a result, we have been able to determine the Financial Apps vulnerability and safeguard these financial security, privacy, and safety.

关键词： Accuracy Machine learning algorithms Machine learning Banking Feature extraction Malware classification algorithms

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Implementation of Synthetic Minority Over-Sampling Technique in the Anaemia classification Using the LSTM and Bi-LSTM algorithms

Implementation of Synthetic Minority Over-Sampling Technique...

引用

International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

作者： Yuri Pamungkas Ratri Dwi Indriani Zain Budi Syulthoni Medical Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Medicine Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

Anaemiais a health disorder characterized by a lack of red blood cells in a person's body. Anaemia sufferers will tire more quickly, and their faces look paler than normal people's. Anaemia can occur over a short or long period (depending on the severity). If anaemia continues to be ignored, complications can worsen a person's health condition. Considering the impact caused by anaemia, a solution is needed to detect anaemia precisely and accurately. One way can be taken is by utilizing an artificial intelligence-based system to detect or classify anaemia based on its symptoms. Therefore, we tried to analyze factors related to anaemia in this study and carry out anaemia classification based on the LSTM and Bi-LSTM algorithms. The dataset used in this study came from the Kaggle repository, which contains medical record information for 1421 patients (620 patients diagnosed with anaemia and 801 patients with non-anaemia). The patient's medical record information includes gender, blood haemoglobin level, MCH, MCHC, MCV, and diagnosis results. In the research dataset, we also applied SMOTE to balance the data classes for anaemia and non-anaemia sufferers and compare the classification results' performance. Based on the research results, the haemoglobin level factor has the highest correlation value of 0.8 compared to other factors such as gender (0.25), MCHC (0.05), MCH (0.03), and MCV (0.02). Meanwhile, the classification results show that the use of SMOTE can increase the specificity (100%), precision (100%), F1-score (98.93%), and accuracy (98.86%) of the LSTM algorithm during the classification process.

关键词： Electrical engineering Computer science Correlation Red blood cells Tires classification algorithms Informatics Long short term memory Medical diagnostic imaging Faces

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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 ...

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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...

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

Multiclass Classification Learning-based Hashing Algorithms ...

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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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