Academic literature on the topic 'Keypoints detection,machine learning,random forest,convolutional neural network'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Keypoints detection,machine learning,random forest,convolutional neural network.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Keypoints detection,machine learning,random forest,convolutional neural network"
1
Wang, Yingying, Yibin Li, Yong Song, and Xuewen Rong. "Facial Expression Recognition Based on Random Forest and Convolutional Neural Network." Information 10, no. 12 (November 28, 2019): 375. http://dx.doi.org/10.3390/info10120375.
Full textAbstract:
As an important part of emotion research, facial expression recognition is a necessary requirement in human–machine interface. Generally, a face expression recognition system includes face detection, feature extraction, and feature classification. Although great success has been made by the traditional machine learning methods, most of them have complex computational problems and lack the ability to extract comprehensive and abstract features. Deep learning-based methods can realize a higher recognition rate for facial expressions, but a large number of training samples and tuning parameters are needed, and the hardware requirement is very high. For the above problems, this paper proposes a method combining features that extracted by the convolutional neural network (CNN) with the C4.5 classifier to recognize facial expressions, which not only can address the incompleteness of handcrafted features but also can avoid the high hardware configuration in the deep learning model. Considering some problems of overfitting and weak generalization ability of the single classifier, random forest is applied in this paper. Meanwhile, this paper makes some improvements for C4.5 classifier and the traditional random forest in the process of experiments. A large number of experiments have proved the effectiveness and feasibility of the proposed method.
2
Yan, Guobing, Qiang Sun, Jianying Huang, and Yonghong Chen. "Helmet Detection Based on Deep Learning and Random Forest on UAV for Power Construction Safety." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 1 (January 20, 2021): 40–49. http://dx.doi.org/10.20965/jaciii.2021.p0040.
Full textAbstract:
Image recognition is one of the key technologies for worker’s helmet detection using an unmanned aerial vehicle (UAV). By analyzing the image feature extraction method for workers’ helmet detection based on convolutional neural network (CNN), a double-channel convolutional neural network (DCNN) model is proposed to improve the traditional image processing methods. On the basis of AlexNet model, the image features of the worker can be extracted using two independent CNNs, and the essential image features can be better reflected considering the abstraction degree of the features. Combining a traditional machine learning method and random forest (RF), an intelligent recognition algorithm based on DCNN and RF is proposed for workers’ helmet detection. The experimental results show that deep learning (DL) is closely related to the traditional machine learning methods. Moreover, adding a DL module to the traditional machine learning framework can improve the recognition accuracy.
3
Mehbodniya, Abolfazl, Izhar Alam, Sagar Pande, Rahul Neware, Kantilal Pitambar Rane, Mohammad Shabaz, and Mangena Venu Madhavan. "Financial Fraud Detection in Healthcare Using Machine Learning and Deep Learning Techniques." Security and Communication Networks 2021 (September 9, 2021): 1–8. http://dx.doi.org/10.1155/2021/9293877.
Full textAbstract:
Healthcare sector is one of the prominent sectors in which a lot of data can be collected not only in terms of health but also in terms of finances. Major frauds happen in the healthcare sector due to the utilization of credit cards as the continuous enhancement of electronic payments, and credit card fraud monitoring has been a challenge in terms of financial condition to the different service providers. Hence, continuous enhancement is necessary for the system for detecting frauds. Various fraud scenarios happen continuously, which has a massive impact on financial losses. Many technologies such as phishing or virus-like Trojans are mostly used to collect sensitive information about credit cards and their owner details. Therefore, efficient technology should be there for identifying the different types of fraudulent conduct in credit cards. In this paper, various machine learning and deep learning approaches are used for detecting frauds in credit cards and different algorithms such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network are skewed for training the other standard and abnormal features of transactions for detecting the frauds in credit cards. For evaluating the accuracy of the model, publicly available data are used. The different algorithm results visualized the accuracy as 96.1%, 94.8%, 95.89%, 97.58%, and 92.3%, corresponding to various methodologies such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network, respectively. The comparative analysis visualized that the KNN algorithm generates better results than other approaches.
4
Gaifilina, Diana, and Igor Kotenko. "Analysis of deep learning models for network anomaly detection in Internet of Things." Information and Control Systems, no. 1 (March 3, 2021): 28–37. http://dx.doi.org/10.31799/1684-8853-2021-1-28-37.
Full textAbstract:
Introduction: The article discusses the problem of choosing deep learning models for detecting anomalies in Internet of Things (IoT) network traffic. This problem is associated with the necessity to analyze a large number of security events in order to identify the abnormal behavior of smart devices. A powerful technology for analyzing such data is machine learning and, in particular, deep learning. Purpose: Development of recommendations for the selection of deep learning models for anomaly detection in IoT network traffic. Results: The main results of the research are comparative analysis of deep learning models, and recommendations on the use of deep learning models for anomaly detection in IoT network traffic. Multilayer perceptron, convolutional neural network, recurrent neural network, long short-term memory, gated recurrent units, and combined convolutional-recurrent neural network were considered the basic deep learning models. Additionally, the authors analyzed the following traditional machine learning models: naive Bayesian classifier, support vector machines, logistic regression, k-nearest neighbors, boosting, and random forest. The following metrics were used as indicators of anomaly detection efficiency: accuracy, precision, recall, and F-measure, as well as the time spent on training the model. The constructed models demonstrated a higher accuracy rate for anomaly detection in large heterogeneous traffic typical for IoT, as compared to conventional machine learning methods. The authors found that with an increase in the number of neural network layers, the completeness of detecting anomalous connections rises. This has a positive effect on the recognition of unknown anomalies, but increases the number of false positives. In some cases, preparing traditional machine learning models takes less time. This is due to the fact that the application of deep learning methods requires more resources and computing power. Practical relevance: The results obtained can be used to build systems for network anomaly detection in Internet of Things traffic.
5
Akhtar, Shamila, Fawad Hussain, Fawad Riasat Raja, Muhammad Ehatisham-ul-haq, Naveed Khan Baloch, Farruh Ishmanov, and Yousaf Bin Zikria. "Improving Mispronunciation Detection of Arabic Words for Non-Native Learners Using Deep Convolutional Neural Network Features." Electronics 9, no. 6 (June 9, 2020): 963. http://dx.doi.org/10.3390/electronics9060963.
Full textAbstract:
Computer-Aided Language Learning (CALL) is growing nowadays because learning new languages is essential for communication with people of different linguistic backgrounds. Mispronunciation detection is an integral part of CALL, which is used for automatic pointing of errors for the non-native speaker. In this paper, we investigated the mispronunciation detection of Arabic words using deep Convolution Neural Network (CNN). For automated pronunciation error detection, we proposed CNN features-based model and extracted features from different layers of Alex Net (layers 6, 7, and 8) to train three machine learning classifiers; K-nearest neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). We also used a transfer learning-based model in which feature extraction and classification are performed automatically. To evaluate the performance of the proposed method, a comprehensive evaluation is provided on these methods with a traditional machine learning-based method using Mel Frequency Cepstral Coefficients (MFCC) features. We used the same three classifiers KNN, SVM, and RF in the baseline method for mispronunciation detection. Experimental results show that with handcrafted features, transfer learning-based method and classification based on deep features extracted from Alex Net achieved an average accuracy of 73.67, 85 and 93.20 on Arabic words, respectively. Moreover, these results reveal that the proposed method with feature selection achieved the best average accuracy of 93.20% than all other methods.
6
Huang, Shiyao, and Hao Wu. "Texture Recognition Based on Perception Data from a Bionic Tactile Sensor." Sensors 21, no. 15 (August 2, 2021): 5224. http://dx.doi.org/10.3390/s21155224.
Full textAbstract:
Texture recognition is important for robots to discern the characteristics of the object surface and adjust grasping and manipulation strategies accordingly. It is still challenging to develop texture classification approaches that are accurate and do not require high computational costs. In this work, we adopt a bionic tactile sensor to collect vibration data while sliding against materials of interest. Under a fixed contact pressure and speed, a total of 1000 sets of vibration data from ten different materials were collected. With the tactile perception data, four types of texture recognition algorithms are proposed. Three machine learning algorithms, including support vector machine, random forest, and K-nearest neighbor, are established for texture recognition. The test accuracy of those three methods are 95%, 94%, 94%, respectively. In the detection process of machine learning algorithms, the asamoto and polyester are easy to be confused with each other. A convolutional neural network is established to further increase the test accuracy to 98.5%. The three machine learning models and convolutional neural network demonstrate high accuracy and excellent robustness.
7
Ghorbanzadeh, Omid, Thomas Blaschke, Khalil Gholamnia, Sansar Meena, Dirk Tiede, and Jagannath Aryal. "Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection." Remote Sensing 11, no. 2 (January 20, 2019): 196. http://dx.doi.org/10.3390/rs11020196.
Full textAbstract:
There is a growing demand for detailed and accurate landslide maps and inventories around the globe, but particularly in hazard-prone regions such as the Himalayas. Most standard mapping methods require expert knowledge, supervision and fieldwork. In this study, we use optical data from the Rapid Eye satellite and topographic factors to analyze the potential of machine learning methods, i.e., artificial neural network (ANN), support vector machines (SVM) and random forest (RF), and different deep-learning convolution neural networks (CNNs) for landslide detection. We use two training zones and one test zone to independently evaluate the performance of different methods in the highly landslide-prone Rasuwa district in Nepal. Twenty different maps are created using ANN, SVM and RF and different CNN instantiations and are compared against the results of extensive fieldwork through a mean intersection-over-union (mIOU) and other common metrics. This accuracy assessment yields the best result of 78.26% mIOU for a small window size CNN, which uses spectral information only. The additional information from a 5 m digital elevation model helps to discriminate between human settlements and landslides but does not improve the overall classification accuracy. CNNs do not automatically outperform ANN, SVM and RF, although this is sometimes claimed. Rather, the performance of CNNs strongly depends on their design, i.e., layer depth, input window sizes and training strategies. Here, we conclude that the CNN method is still in its infancy as most researchers will either use predefined parameters in solutions like Google TensorFlow or will apply different settings in a trial-and-error manner. Nevertheless, deep-learning can improve landslide mapping in the future if the effects of the different designs are better understood, enough training samples exist, and the effects of augmentation strategies to artificially increase the number of existing samples are better understood.
8
Vishwanath, Manoj, Salar Jafarlou, Ikhwan Shin, Miranda M. Lim, Nikil Dutt, Amir M. Rahmani, and Hung Cao. "Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice." Sensors 20, no. 7 (April 4, 2020): 2027. http://dx.doi.org/10.3390/s20072027.
Full textAbstract:
Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today’s world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios.
9
Nagarajan, G., Dr A. Mahabub Basha, and R. Poornima. "Autism Spectrum Disorder Identification Using Polynomial Distribution based Convolutional Neural Network." NeuroQuantology 19, no. 2 (March 20, 2021): 19–30. http://dx.doi.org/10.14704/nq.2021.19.2.nq21013.
Full textAbstract:
One main psychiatric disorder found in humans is ASD (Autistic Spectrum Disorder). The disease manifests in a mental disorder that restricts humans from communications, language, speech in terms of their individual abilities. Even though its cure is complex and literally impossible, its early detection is required for mitigating its intensity. ASD does not have a pre-defined age for affecting humans. A system for effectively predicting ASD based on MLTs (Machine Learning Techniques) is proposed in this work. Hybrid APMs (Autism Prediction Models) combining multiple techniques like RF (Random Forest), CART (Classification and Regression Trees), RF-ID3 (RF-Iterative Dichotomiser 3) perform well, but face issues in memory usage, execution times and inadequate feature selections. Taking these issues into account, this work overcomes these hurdles in this proposed work with a hybrid technique that combines MCSO (Modified Chicken Swarm Optimization) and PDCNN (Polynomial Distribution based Convolution Neural Network) algorithms for its objective. The proposed scheme’s experimental results prove its higher levels of accuracy, precision, sensitivity, specificity, FPRs (False Positive Rates) and lowered time complexity when compared to other methods.
10
Jamil, Ramish, Imran Ashraf, Furqan Rustam, Eysha Saad, Arif Mehmood, and Gyu Sang Choi. "Detecting sarcasm in multi-domain datasets using convolutional neural networks and long short term memory network model." PeerJ Computer Science 7 (August 25, 2021): e645. http://dx.doi.org/10.7717/peerj-cs.645.
Full textAbstract:
Sarcasm emerges as a common phenomenon across social networking sites because people express their negative thoughts, hatred and opinions using positive vocabulary which makes it a challenging task to detect sarcasm. Although various studies have investigated the sarcasm detection on baseline datasets, this work is the first to detect sarcasm from a multi-domain dataset that is constructed by combining Twitter and News Headlines datasets. This study proposes a hybrid approach where the convolutional neural networks (CNN) are used for feature extraction while the long short-term memory (LSTM) is trained and tested on those features. For performance analysis, several machine learning algorithms such as random forest, support vector classifier, extra tree classifier and decision tree are used. The performance of both the proposed model and machine learning algorithms is analyzed using the term frequency-inverse document frequency, bag of words approach, and global vectors for word representations. Experimental results indicate that the proposed model surpasses the performance of the traditional machine learning algorithms with an accuracy of 91.60%. Several state-of-the-art approaches for sarcasm detection are compared with the proposed model and results suggest that the proposed model outperforms these approaches concerning the precision, recall and F1 scores. The proposed model is accurate, robust, and performs sarcasm detection on a multi-domain dataset.
Dissertations / Theses on the topic "Keypoints detection,machine learning,random forest,convolutional neural network"
1
Avigni, Andrea. "Learning to detect good image features." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/12856/.
Full textAbstract:
State-of-the-art keypoint detection algorithms have been designed to extract specific structures from images and to achieve a high keypoint repeatability, which means that they should find the same points in images undergoing specific transformations. However, this criterion does not guarantee that the selected keypoints will be the optimal ones during the successive matching step. The approach that has been developed in this thesis work is aimed at extracting keypoints that maximize the matching performance according to a pre-selected image descriptor. In order to do that, a classifier has been trained on a set of “good” and “bad” descriptors extracted from training images that are affected by a set of pre-defined nuisances. The set of “good” keypoints used for the training is filled with those vectors that are related to the points that gave correct matches during an initial matching step. On the contrary, randomly chosen points that are far away from the positives are labeled as “bad” keypoints. Finally, the descriptors computed at the “good” and “bad” locations form the set of features used to train the classifier that will judge each pixel of an unseen input image as a good or bad candidate for driving the extraction of a set of keypoints. This approach requires, though, the descriptors to be computed at every pixel of the image and this leads to a high computational effort. Moreover, if a certain descriptor extractor is used during the training step, it must be used also during the testing. In order to overcome these problems, the last part of this thesis has been focused on the creation and training of a convolutional neural network (CNN) that uses as positive samples the patches centered at those locations that give correct correspondences during the matching step. Eventually, the results and the performances of the developed algorithm have compared to the state-of-the-art using a public benchmark.
Conference papers on the topic "Keypoints detection,machine learning,random forest,convolutional neural network"
1
Huang, Jiayu, Nurretin Sergin, Akshay Dua, Erfan Bank Tavakoli, Hao Yan, Fengbo Ren, and Feng Ju. "Edge Computing Accelerated Defect Classification Based on Deep Convolutional Neural Network With Application in Rolling Image Inspection." In ASME 2020 15th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/msec2020-8261.
Full textAbstract:
Abstract This paper develops a unified framework for training and deploying deep neural networks on the edge computing framework for image defect detection and classification. In the proposed framework, we combine the transfer learning and data augmentation with the improved accuracy given the small sample size. We further implement the edge computing framework to satisfy the real-time computational requirement. After the implement of the proposed model into a rolling manufacturing system, we conclude that deep learning approaches can perform around 30–40% better than some traditional machine learning algorithms such as random forest, decision tree, and SVM in terms of prediction accuracy. Furthermore, by deploying the CNNs in the edge computing framework, we can significantly reduce the computational time and satisfy the real-time computational requirement in the high-speed rolling and inspection system. Finally, the saliency map and embedding layer visualization techniques are used for a better understanding of proposed deep learning models.
2
Maia, Lucas Bezerra, Alan Carlos Lima, Pedro Thiago Cutrim Santos, Nigel da Silva Lima, João Dallyson Sousa De Almeida, and Anselmo Cardoso Paiva. "Evaluation of Melanoma Diagnosis using Imbalanced Learning." In XVIII Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/sbcas.2018.3680.
Full textAbstract:
Melanoma is the most lethal type of skin cancer when compared to others, but patients have high recovery rates if the disease is discovered in its early stages. Several approaches to automatic detection and diagnosis have been explored by different authors. Training models with the existing data sets has been a difficult task due to the problem of imbalanced data. This work aims to evaluate the performance of machine learning algorithms combined with imbalanced learning techniques, regarding the task of melanoma diagnosis. Preliminary results have shown that features extracted with ResNet Convolutional Neural Network, along with Random Forest, achieved an improvement of sensibility of approximately 21%, after balancing the training data with Synthetic Minority Oversampling TEchnique (SMOTE) and Edited Nearest Neighbor (ENN) rule.