scholarly journals Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Wei Hao ◽  
Donglei Rong ◽  
Kefu Yi ◽  
Qiang Zeng ◽  
Zhibo Gao ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Short Term Memory ◽  
Road Traffic ◽  
Back Propagation ◽  
Memory Storage ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Continuous Training ◽  
System Sensitivity ◽  
Q Learning

With the exponential growth of traffic data and the complexity of traffic conditions, in order to effectively store and analyse data to feed back valid information, this paper proposed an urban road traffic status prediction model based on the optimized deep recurrent Q-Learning method. The model is based on the optimized Long Short-Term Memory (LSTM) algorithm to handle the explosive growth of Q-table data, which not only avoids the gradient explosion and disappearance but also has the efficient storage and analysis. The continuous training and memory storage of the training sets are used to improve the system sensitivity, and then, the test sets are predicted based on the accumulated experience pool to obtain high-precision prediction results. The traffic flow data from Wanjiali Road to Shuangtang Road in Changsha City are tested as a case. The research results show that the prediction of the traffic delay index is within a reasonable interval, and it is significantly better than traditional prediction methods such as the LSTM, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), exponential smoothing method, and Back Propagation (BP) neural network, which shows that the model proposed in this paper has the feasibility of application.

scholarly journals PlncRNA-HDeep: plant long noncoding RNA prediction using hybrid deep learning based on two encoding styles

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Jun Meng ◽  
Qiang Kang ◽  
Zheng Chang ◽  
Yushi Luan
Keyword(s):  
Deep Learning ◽  
Noncoding Rna ◽  
Nearest Neighbor ◽  
Short Term Memory ◽  
Biological Activities ◽  
Support Vector ◽  
Multiple Perspectives ◽  
K Nearest Neighbor ◽  
Rna Sequences ◽  
Deep Learning Model

Abstract Background Long noncoding RNAs (lncRNAs) play an important role in regulating biological activities and their prediction is significant for exploring biological processes. Long short-term memory (LSTM) and convolutional neural network (CNN) can automatically extract and learn the abstract information from the encoded RNA sequences to avoid complex feature engineering. An ensemble model learns the information from multiple perspectives and shows better performance than a single model. It is feasible and interesting that the RNA sequence is considered as sentence and image to train LSTM and CNN respectively, and then the trained models are hybridized to predict lncRNAs. Up to present, there are various predictors for lncRNAs, but few of them are proposed for plant. A reliable and powerful predictor for plant lncRNAs is necessary. Results To boost the performance of predicting lncRNAs, this paper proposes a hybrid deep learning model based on two encoding styles (PlncRNA-HDeep), which does not require prior knowledge and only uses RNA sequences to train the models for predicting plant lncRNAs. It not only learns the diversified information from RNA sequences encoded by p-nucleotide and one-hot encodings, but also takes advantages of lncRNA-LSTM proposed in our previous study and CNN. The parameters are adjusted and three hybrid strategies are tested to maximize its performance. Experiment results show that PlncRNA-HDeep is more effective than lncRNA-LSTM and CNN and obtains 97.9% sensitivity, 95.1% precision, 96.5% accuracy and 96.5% F1 score on Zea mays dataset which are better than those of several shallow machine learning methods (support vector machine, random forest, k-nearest neighbor, decision tree, naive Bayes and logistic regression) and some existing tools (CNCI, PLEK, CPC2, LncADeep and lncRNAnet). Conclusions PlncRNA-HDeep is feasible and obtains the credible predictive results. It may also provide valuable references for other related research.

scholarly journals Novel Mathematical Model of Breast Cancer Diagnostics Using an Associative Pattern Classification

Diagnostics ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 136 ◽  
Author(s):  
Raúl Santiago-Montero ◽  
Humberto Sossa ◽  
David A. Gutiérrez-Hernández ◽  
Víctor Zamudio ◽  
Ignacio Hernández-Bautista ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Back Propagation ◽  
Cancer Diagnostics ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Breast Cancer Death ◽  
Positron Emission ◽  
The Government

Breast cancer is a disease that has emerged as the second leading cause of cancer deaths in women worldwide. The annual mortality rate is estimated to continue growing. Cancer detection at an early stage could significantly reduce breast cancer death rates long-term. Many investigators have studied different breast diagnostic approaches, such as mammography, magnetic resonance imaging, ultrasound, computerized tomography, positron emission tomography and biopsy. However, these techniques have limitations, such as being expensive, time consuming and not suitable for women of all ages. Proposing techniques that support the effective medical diagnosis of this disease has undoubtedly become a priority for the government, for health institutions and for civil society in general. In this paper, an associative pattern classifier (APC) was used for the diagnosis of breast cancer. The rate of efficiency obtained on the Wisconsin breast cancer database was 97.31%. The APC’s performance was compared with the performance of a support vector machine (SVM) model, back-propagation neural networks, C4.5, naive Bayes, k-nearest neighbor (k-NN) and minimum distance classifiers. According to our results, the APC performed best. The algorithm of the APC was written and executed in a JAVA platform, as well as the experimental and comparativeness between algorithms.

Comparison of the Hybrid Credit Scoring Models Based on Various Classifiers

2012 ◽  
pp. 220-239
Author(s):  
Fei-Long Chen ◽  
Feng-Chia Li
Keyword(s):  
Nearest Neighbor ◽  
Credit Scoring ◽  
Back Propagation ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Hybrid Features ◽  
Credit Data ◽  
Learning Machine ◽  
Propagation Network

Credit scoring is an important topic for businesses and socio-economic establishments collecting huge amounts of data, with the intention of making the wrong decision obsolete. In this paper, the authors propose four approaches that combine four well-known classifiers, such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Back-Propagation Network (BPN) and Extreme Learning Machine (ELM). These classifiers are used to find a suitable hybrid classifier combination featuring selection that retains sufficient information for classification purposes. In this regard, different credit scoring combinations are constructed by selecting features with four approaches and classifiers than would otherwise be chosen. Two credit data sets from the University of California, Irvine (UCI), are chosen to evaluate the accuracy of the various hybrid features selection models. In this paper, the procedures that are part of the proposed approaches are described and then evaluated for their performances.

Comparison of the Hybrid Credit Scoring Models Based on Various Classifiers

2010 ◽  
Vol 6 (3) ◽  
pp. 56-74 ◽  
Author(s):  
Fei-Long Chen ◽  
Feng-Chia Li
Keyword(s):  
Nearest Neighbor ◽  
Credit Scoring ◽  
Back Propagation ◽  
Support Vector ◽  
Sufficient Information ◽  
Features Selection ◽  
K Nearest Neighbor ◽  
Hybrid Features ◽  
Wrong Decision ◽  
Propagation Network

Credit scoring is an important topic for businesses and socio-economic establishments collecting huge amounts of data, with the intention of making the wrong decision obsolete. In this paper, the authors propose four approaches that combine four well-known classifiers, such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Back-Propagation Network (BPN) and Extreme Learning Machine (ELM). These classifiers are used to find a suitable hybrid classifier combination featuring selection that retains sufficient information for classification purposes. In this regard, different credit scoring combinations are constructed by selecting features with four approaches and classifiers than would otherwise be chosen. Two credit data sets from the University of California, Irvine (UCI), are chosen to evaluate the accuracy of the various hybrid features selection models. In this paper, the procedures that are part of the proposed approaches are described and then evaluated for their performances.

scholarly journals Rapid and Nondestructive On-Site Classification Method for Consumer-Grade Plastics Based on Portable NIR Spectrometer and Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yinglin Yang ◽  
Xin Zhang ◽  
Jianwei Yin ◽  
Xiangyang Yu
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Diffuse Reflectance Spectrum ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Site Classification ◽  
Classification Models ◽  
K Nearest Neighbor

The classification of plastic waste before recycling is of great significance to achieve effective recycling. In order to achieve rapid, nondestructive, and on-site detection, a portable near-infrared spectrometer was used in this study to obtain the diffuse reflectance spectrum for both standard and commercial plastics made by ABS, PC, PE, PET, PP, PS, and PVC. After applying a series of pretreatments, the principal component analysis (PCA) was used to analyze the cluster trend. K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural network (BPNN) classification models were developed and evaluated, respectively. The result showed that different plastics could be well separated in top three principal components space after pretreatment, and the classification models performed excellent classification results and high generalization capability. This study indicated that the portable NIR spectrometer, integrated with chemometrics, could achieve excellent performance and has great potential in the field of commercial plastic identification.

scholarly journals Identification of Cherry Leaf Disease Infected by Podosphaera Pannosa via Convolutional Neural Network

2019 ◽  
Vol 10 (2) ◽  
pp. 98-110 ◽  
Author(s):  
Keke Zhang ◽  
Lei Zhang ◽  
Qiufeng Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Back Propagation ◽  
Support Vector ◽  
Automatic Identification ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Leaf Disease

The cherry leaves infected by Podosphaera pannosa will suffer powdery mildew, which is a serious disease threatening the cherry production industry. In order to identify the diseased cherry leaves in early stage, the authors formulate the cherry leaf disease infected identification as a classification problem and propose a fully automatic identification method based on convolutional neural network (CNN). The GoogLeNet is used as backbone of the CNN. Then, transferred learning techniques are applied to fine-tune the CNN from pre-trained GoogLeNet on ImageNet dataset. This article compares the proposed method against three traditional machine learning methods i.e., support vector machine (SVM), k-nearest neighbor (KNN) and back propagation (BP) neural network. Quantitative evaluations conducted on a data set of 1,200 images collected by smart phones, demonstrates that the CNN achieves best precise performance in identifying diseased cherry leaves, with the testing accuracy of 99.6%. Thus, a CNN can be used effectively in identifying the diseased cherry leaves.

Prospects of Machine Learning With Blockchain in Healthcare and Agriculture

2021 ◽  
pp. 178-208
Author(s):  
Pushpa Singh ◽  
Narendra Singh ◽  
Ganesh Chandra Deka
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Reinforcement Learning ◽  
Decision Tree ◽  
Basic Principle ◽  
Agricultural Sector ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Q Learning

Presently, machine learning (ML) techniques have gained considerable attention, with growing interest in various areas and applications. Healthcare, agriculture, and bioinformatics are the most identified areas to study with the help of ML. This chapter introduces about the basic principle of ML such as data, model, basic mathematical details of ML, and types of learning. The important aspect of ML is “how to teach a machine.” This chapter focuses on the types of learning: supervised, unsupervised, semi-supervised, and reinforcement learning. Some commonly used ML algorithms such as decision tree (DT), k-nearest neighbor (KNN), support vector machine (SVM), naïve Bayes, k-mean, q-learning, etc. are briefly discussed for understanding. Finally, the author offers the application of ML with blockchain that is reforming the traditional healthcare and agricultural sector to a more reliable means.

scholarly journals Pattern Recognition of DC Partial Discharge on XLPE Cable Based on ADAM-DBN

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4566 ◽  
Author(s):  
Zhe Li ◽  
Yongpeng Xu ◽  
Xiuchen Jiang
Keyword(s):  
Pattern Recognition ◽  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Partial Discharge ◽  
Back Propagation ◽  
Training Sample ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Set Size ◽  
Sample Set

Pattern recognition of DC partial discharge (PD) receives plenty of attention and recent researches mainly focus on the static characteristics of PD signals. In order to improve the recognition accuracy of DC cable and extract information from PD waveforms, a modified deep belief network (DBN) supervised fine-tuned by the adaptive moment estimation (ADAM) algorithm is proposed to recognize the four typical insulation defects of DC cable according to the PD pulse waveforms. Moreover, the effect of the training sample set size on recognition accuracy is analyzed. Compared with naive Bayes (NB), K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural networks (BPNN), the ADAM-DBN method has higher accuracy on four different defect types due to the excellent ability in terms of the feature extraction of PD pulse waveforms. Moreover, the increase of training sample set size would lead to the increase of recognition accuracy within a certain range.

scholarly journals Automatic Detection and Staging of Lung Tumors using Locational Features and Double-Staged Classifications

2019 ◽  
Vol 9 (11) ◽  
pp. 2329 ◽  
Author(s):  
May Phu Paing ◽  
Kazuhiko Hamamoto ◽  
Supan Tungjitkusolmun ◽  
Chuchart Pintavirooj
Keyword(s):  
Lung Cancer ◽  
Nearest Neighbor ◽  
Treatment Options ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Classification Performance ◽  
Clinical Staging ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Experimental Findings

Lung cancer is a life-threatening disease with the highest morbidity and mortality rates of any cancer worldwide. Clinical staging of lung cancer can significantly reduce the mortality rate, because effective treatment options strongly depend on the specific stage of cancer. Unfortunately, manual staging remains a challenge due to the intensive effort required. This paper presents a computer-aided diagnosis (CAD) method for detecting and staging lung cancer from computed tomography (CT) images. This CAD works in three fundamental phases: segmentation, detection, and staging. In the first phase, lung anatomical structures from the input tomography scans are segmented using gray-level thresholding. In the second, the tumor nodules inside the lungs are detected using some extracted features from the segmented tumor candidates. In the last phase, the clinical stages of the detected tumors are defined by extracting locational features. For accurate and robust predictions, our CAD applies a double-staged classification: the first is for the detection of tumors and the second is for staging. In both classification stages, five alternative classifiers, namely the Decision Tree (DT), K-nearest neighbor (KNN), Support Vector Machine (SVM), Ensemble Tree (ET), and Back Propagation Neural Network (BPNN), are applied and compared to ensure high classification performance. The average accuracy levels of 92.8% for detection and 90.6% for staging are achieved using BPNN. Experimental findings reveal that the proposed CAD method provides preferable results compared to previous methods; thus, it is applicable as a clinical diagnostic tool for lung cancer.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

Sign in / Sign up

Export Citation Format

Share Document