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检索条件"主题词=Classification Algorithms"

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Cyberbullying detection and classification with improved IG and BiLSTM

Cyberbullying detection and classification with improved IG ...

引用

Electronics and Devices, Computational Science (ICEDCS), International Conference on

作者： Mengtian Xin Jiayu Shen Peifeng Hao Northeastern University Shenyang China

ISBN: (数字)9781665455411

ISBN: (纸本)9781665455428

Twitter is a microblogging and social networking service platform; users can post what they feel and think about to share with others. Although it facilitates users' social behaviour, a high degree of freedom of speech also leads to cyberbullying. The statement released by UNICEF showed that 36.5% of middle and high school students experienced cyberbullying, and 87% observed cyberbullying. Cyberbullying has greatly affected people's daily lives. We conduct this study to detect whether online comments contain cyberbullying behaviours and classify cyberbullying to alleviate this problem. This paper uses an improved information gain algorithm for feature selection, and the bidirectional LSTM neural network is used for classification. On the premise that the information gain threshold is limited to 0.0004, the precision on the test set can reach 95.15%.

关键词： Scientific computing Blogs Neural networks Cyberbullying Feature extraction classification algorithms

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Optimization of Traffic Sign Detection and classification Based on Faster R-CNN

Optimization of Traffic Sign Detection and Classification Ba...

引用

International Conference on Computer Technology, Electronics and Communication (ICCTEC)

作者： Kun Qiao Hanzhou Gu Jiaming Liu Pei Liu International School Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781538657850

Traffic sign detection and recognition is a key area of research on intelligent transportation, which has significant theoretical value and an expansive market application prospect. As a crucial part, the algorithm of traffic sign detection and classification has great impact on subsequent procedures. In this way, implementing a faster and robust algorithm is what most researchers are pursuing in this area. However, sometimes, such a great variety of signs are hard to be detected or classified especially if they are spoiled or the driving environment is complicated. Traditional methods are mostly based on extracting features like color or shape, which need higher quality of images and may sometimes lead to a poor precision and robustness. This paper provides an optimization based on Faster R-CNN combining with ZF and VGG network. This algorithm improves validation accuracy and robustness, which also reduces the requirements of quality of images and related computation.

关键词： Feature extraction classification algorithms Training Optimization Neural networks Proposals Object detection

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Taxonomy of tree based classification algorithm

Taxonomy of tree based classification algorithm

引用

International Conference on Computer and Communication Technology (ICCCT)

作者： Daya Gupta Dilpreet Singh Kohli Rajni Jindal Department of Computer Engineering Delhi Technological University New Delhi India

In this paper we are suggesting improvements over an existing C4.5 Algorithm. This is a very popular tree based classification algorithm, used to generate decision tree from a set of training examples. The heuristic function used in this algorithm is based on the concept of information entropy. We are proposing two new heuristic functions which are better than the one used by C4.5 Algorithm by some way or the other. First heuristic function is better in terms of execution time. Second heuristic function is more realistic, gives importance to realistic attributes and thus gives more accurate and reasonable results. So in this way we are proposing two new improvements over J48/C4.5 Algorithm. Throughout the paper we will be using two case studies (examples), one of weather and the other one of student classification for comparing the performance of algorithms.

关键词： Prediction algorithms classification algorithms Decision trees Heuristic algorithms Accuracy Training Computers

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Short-Term PV Power Prediction Method Based on Weather classification and TCN-Attention

Short-Term PV Power Prediction Method Based on Weather Class...

引用

IEEE International Conference on Smart Grid and Smart Cities (ICSGSC)

作者： Kuokuo Zhu Wenyi Li College of Electric Power Inner Mongolia University of Technology Hohhot China

ISBN: (数字)9798350377408

ISBN: (纸本)9798350377415

For the purpose of improving the prediction accuracy of short-term PV power under different weather conditions, a short-term PV power prediction method based on weather classification combined with temporal convolutional network-attention mechanism combination model is proposed. Firstly, Spearman and Kendall correlation coefficients are used to select the main meteorological factors affecting PV power, including total solar irradiance, direct irradiance, global horizontal irradiance, temperature and relative humidity. Then, the K-means++ algorithm was employed to classify the historical PV data into three types: sunny, cloudy and rainy days. Finally, based on the temporal feature extraction ability of time-convolutional network and the ability of attention mechanism to highlight the key features, the combined time-convolutional network-attention mechanism model is established. Simulation results show that the proposed method is able to improve the accuracy of short-term PV power prediction under different weather conditions with strong adaptability.

关键词： Analytical models Adaptation models Accuracy Meteorological factors Clouds Predictive models Prediction algorithms Feature extraction classification algorithms Meteorology

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ECG beat classification using Ant Colony Optimization for Continuous Domains

ECG beat classification using Ant Colony Optimization for Co...

引用

National Conference on Electrical, Electronics and Computer Engineering (ELECO)

作者： Berat Doğan Mehmet Korürek Elektronik ve Haberleşme Mühendisliği Bölümü İstanbul Teknik Üniversitesi Turkey

In this study, a naturally inspired optimization algorithm, Ant Colony Optimization for Continuous Domains (ACO R ), is used to classify six types of ECG beats including, Normal Beat (N), Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Artrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f). A radial basis function neural network is evolved for classification with the training set obtained from MIT-BIH arrhythmia database by using Ant Colony Optimization for Continuous Domains. Training set includes 50 feature vectors for each class. The results are then compared with the classical radial basis function training methods such as Orthogonal Least Square Algorithm and the K-Means algorithm. It is observed that the proposed method can classify ECG beats with a smaller size of network without making any concession on classification performance when compared to the classical methods.

关键词： classification algorithms Ant colony optimization Electrocardiography Particle swarm optimization Radial basis function networks Training Optimization

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Mobile-based Malware Detection and classification using Ensemble Artificial Intelligence

Mobile-based Malware Detection and Classification using Ense...

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Advancements in Computing (ICAC), 2019 International Conference on

作者： Suhashini Somasundaram Dharshana Kasthurirathna Lakmal Rupasinghe Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9781728141701

ISBN: (纸本)9781728141718

The Android operating system is one of the most used operating systems in the world and has become a target to malware authors. Traditional malware detection methods such as signatures find it impossible to deal with detecting complex and intelligent malware which are capable of obfuscating and repackaging to avoid being detected. There is therefore an increase in the need to have more efficient and intelligent forms of malware detection. Artificial intelligence has now been brought to the field of malware detection and classification. Due to its accuracy and intelligence it has become an ideal solution to bridge the gap between traditional classifiers and the intelligent malware. Currently, research is mainly being conducted using either machine learning or deep learning techniques to target all or a given malware family. This paper proposes a methodology which brings an ensemble solution between the Support Vector Machine algorithm and the Convolutional Neural Network to create a solution that provides a higher accuracy than available techniques.

关键词： Malware classification algorithms Tools Machine learning Androids Humanoid robots Support vector machines

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Unsupervised classification of the Earth's Surface Data

Unsupervised Classification of the Earth's Surface Data

引用

Russia and Pacific Conference on Computer Technology and Applications (RPC)

作者： V. Sidorova Institute of Computational Mathematics and Mathematical Geophysics of the Siberian Branch of the RAS Novosibirsk Russia

A fast hierarchical, divisible histogram clustering algorithm for multispectral remote sensing data is proposed. A dimensional of the spectral data space is considered as well. Examples are given.

ISBN: (纸本)9781538675328

A fast hierarchical, divisible histogram clustering algorithm for multispectral remote sensing data is proposed. A dimensional of the spectral data space is considered as well. Examples are given.

关键词： Clustering algorithms Histograms Quantization (signal) classification algorithms Remote sensing Eigenvalues and eigenfunctions Jacobian matrices

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Scalable classification over SQL databases

Scalable classification over SQL databases

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International Conference on Data Engineering

作者： S. Chaudhuri U. Fayyad J. Bernhardt Microsoft Research Redmond WA USA

We identify data-intensive operations that are common to classifiers and develop a middleware that decomposes and schedules these operations efficiently using a backend SQL database. Our approach has the added advantage of not requiring any specialized physical data organization. We demonstrate the scalability characteristics of our enhanced client with experiments on Microsoft SQL Server 7.0 by varying data size, number of attributes and characteristics of decision trees.

关键词： Databases Middleware Statistics classification tree analysis classification algorithms Decision trees Data mining Probability Predictive models File servers

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SVM spatio-temporal classification of HR satellite image time series using graph based kernel

SVM spatio-temporal classification of HR satellite image tim...

引用

International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Safa Réjichi Ferdaous Chaabane Communication signaux et images laboratory (COSIM) Carthage University Ecole Supérieure des Communications de Tunis (SUP'COM) Ariana Tunisia

Satellite Image Time Series (SITS) are a very useful source of information for geoscientists especially for land cover monitoring. In this paper a new multi-temporal classification approach for High Resolution (HR) SITS is proposed. It is mainly two stages original approach using two different kernels based SVM algorithms. The first step of this approach consists in applying multiband RBF kernel based SVM classification on individual images. Then, for each cartographic region of the first classified image, a graph characterizing its temporal evolution is built using texture features and radiometry for graph labeling. In the second stage, a graph kernel based SVM algorithm is used to analyze and classify the temporal behaviors of these regions that are modeled by different graphs aspects. The resulted temporal map discern between cartographic regions behaviors (stable, periodic, growing, etc.), which is very beneficial in many applications fields. The experimental results have been conducted on synthesized and real data proving the accuracy of the proposed approach.

关键词： Kernel Support vector machines Radiometry classification algorithms Feature extraction Standards Vectors

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Fault classification Using Machine Learning with Deep Learning-Based Scalogram Wavelet Feature Extraction and Metaheuristic Feature Selection

Fault Classification Using Machine Learning with Deep Learni...

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IEEE International Conference on Automatica (ICA-ACCA)

作者： Emilio Flores Jon Xabier Rementería Estefanía Planas Escuela de Ingeniería Informática Universidad de Valparaíso Valparaíso Chile Departamento de Tecnología Electrónica Universidad del País Vasco Bilbao España

ISBN: (数字)9798350378115

ISBN: (纸本)9798350378122

Electrical fault classification is one of the most complex tasks in electrical systems. In this paper, we propose a classification model based on scalograms using the Continuous Wavelet Transform (CWT) and feature extraction using the EfficientNetV2B3 backbone. Features are then selected using the hybrid metaheuristic algorithm GWO-WOA to maximize the multi-objective function of precision and recall for training a Quadratic Discriminant Analysis (QDA) model. The dataset was generated from a three-phase electrical model in Matlab/Simulink, with measurements of currents (Ia, Ib, Ic) and voltages (Va, Vb, Vc). CWT was used to obtain scalograms for each signal, producing a total of ${6, 4 8 0}$ RGB-type images. The results indicate that the hybrid GWO-WOA algorithm maximizes the performance of the QDA model trained with the selected features, achieving an accuracy of ${9 4 \%}$, a precision of ${9 4 \%}$, and a recall of ${9 4 \%}$. The results for each class indicate an F1-score above ${9 1 \%}$.

关键词： Continuous wavelet transforms Voltage measurement Machine learning algorithms Metaheuristics Feature extraction Mathematical models classification algorithms Reliability Integrated circuit modeling MATLAB

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