scholarly journals Distracted Driving Detection with Machine Learning Methods by CNN Based Feature Extraction

2021 ◽  
Vol 9 (4) ◽  
pp. 116-121
Author(s):  
Shafeeq Kanaan Shakir AL-DOORI ◽  
Yavuz Selim TASPINAR ◽  
Murat KOKLU
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Distracted Driving ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Modulation Classification of Underwater Communication with Deep Learning Network

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yan Wang ◽  
Hao Zhang ◽  
Zhanliang Sang ◽  
Lingwei Xu ◽  
Conghui Cao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Language Processing ◽  
Communication Process ◽  
Underwater Acoustic Communication ◽  
Learning Method ◽  
Modulation Recognition ◽  
Learning Methods ◽  
Machine Learning Methods

Automatic modulation recognition has successfully used various machine learning methods and achieved certain results. As a subarea of machine learning, deep learning has made great progress in recent years and has made remarkable progress in the field of image and language processing. Deep learning requires a large amount of data support. As a communication field with a large amount of data, there is an inherent advantage of applying deep learning. However, the extensive application of deep learning in the field of communication has not yet been fully developed, especially in underwater acoustic communication. In this paper, we mainly discuss the modulation recognition process which is an important part of communication process by using the deep learning method. Different from the common machine learning methods that require feature extraction, the deep learning method does not require feature extraction and obtains more effects than common machine learning.

scholarly journals Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7620
Author(s):  
Zhenyi Ye ◽  
Yuan Liu ◽  
Qiliang Li
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Gas Sensor ◽  
Electronic Nose ◽  
Recent Progress ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Sensor Drift ◽  
Drift Compensation

Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.

scholarly journals Noise Removal and Feature Extraction in Airborne Radar Sounding Data of Ice Sheets

2022 ◽  
Vol 14 (2) ◽  
pp. 399
Author(s):  
Xueyuan Tang ◽  
Sheng Dong ◽  
Kun Luo ◽  
Jingxue Guo ◽  
Lin Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Ice Sheets ◽  
Ice Sheet ◽  
Noise Removal ◽  
Airborne Radar ◽  
Learning Methods ◽  
Ice Flow ◽  
Machine Learning Methods ◽  
Internal Structures

The airborne ice-penetrating radar (IPR) is an effective method used for ice sheet exploration and is widely applied for detecting the internal structures of ice sheets and for understanding the mechanism of ice flow and the characteristics of the bottom of ice sheets. However, because of the ambient influence and the limitations of the instruments, IPR data are frequently overlaid with noise and interference, which further impedes the extraction of layer features and the interpretation of the physical characteristics of the ice sheet. In this paper, we first applied conventional filtering methods to remove the feature noise and interference in IPR data. Furthermore, machine learning methods were introduced in IPR data processing for noise removal and feature extraction. Inspired by a comparison of the filtering methods and machine learning methods, we propose a fusion method combining both filtering methods and machine-learning-based methods to optimize the feature extraction in IPR data. Field data tests indicated that, under different conditions of IPR data, the application of different methods and strategies can improve the layer feature extraction.

scholarly journals Classification of Pulmonary CT Images by Using Hybrid 3D-Deep Convolutional Neural Network Architecture

2019 ◽  
Vol 9 (5) ◽  
pp. 940 ◽  
Author(s):  
Huseyin Polat ◽  
Homay Danaei Mehr
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Lung Cancer ◽  
Feature Extraction ◽  
Extraction Methods ◽  
Ct Images ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Diagnosis Of Lung Cancer ◽  
3D Cnn

Lung cancer is the most common cause of cancer-related deaths worldwide. Hence, the survival rate of patients can be increased by early diagnosis. Recently, machine learning methods on Computed Tomography (CT) images have been used in the diagnosis of lung cancer to accelerate the diagnosis process and assist physicians. However, in conventional machine learning techniques, using handcrafted feature extraction methods on CT images are complicated processes. Hence, deep learning as an effective area of machine learning methods by using automatic feature extraction methods could minimize the process of feature extraction. In this study, two Convolutional Neural Network (CNN)-based models were proposed as deep learning methods to diagnose lung cancer on lung CT images. To investigate the performance of the two proposed models (Straight 3D-CNN with conventional softmax and hybrid 3D-CNN with Radial Basis Function (RBF)-based SVM), the altered models of two-well known CNN architectures (3D-AlexNet and 3D-GoogleNet) were considered. Experimental results showed that the performance of the two proposed models surpassed 3D-AlexNet and 3D-GoogleNet. Furthermore, the proposed hybrid 3D-CNN with SVM achieved more satisfying results (91.81%, 88.53% and 91.91% for accuracy rate, sensitivity and precision respectively) compared to straight 3D-CNN with softmax in the diagnosis of lung cancer.

scholarly journals FastFeatGen: Faster parallel feature extraction from genome sequences and efficient prediction of DNA N6-methyladenine sites

2019 ◽  
Author(s):  
Md. Khaledur Rahman
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Shared Memory ◽  
Genome Sequences ◽  
Learning Methods ◽  
Feature Selection Technique ◽  
Source Codes ◽  
Machine Learning Methods ◽  
Tree Classifier ◽  
Parallel Feature

AbstractN6-methyladenine is widely found in both prokaryotes and eukaryotes. It is responsible for many biological processes including prokaryotic defense system and human diseases. So, it is important to know its correct location in genome which may play a significant role in different biological functions. Few computational tools exist to serve this purpose but they are computationally expensive and still there is scope to improve accuracy. An informative feature extraction pipeline from genome sequences is the heart of these tools as well as for many other bioinformatics tools. But it becomes reasonably expensive for sequential approaches when the size of data is large. Hence, a scalable parallel approach is highly desirable. In this paper, we have developed a new tool, called FastFeatGen, emphasizing both developing a parallel feature extraction technique and improving accuracy using machine learning methods. We have implemented our feature extraction approach using shared memory parallelism which achieves around 10× speed over the sequential one. Then we have employed an exploratory feature selection technique which helps to find more relevant features that can be fed to machine learning methods. We have employed Extra-Tree Classifier (ETC) in FastFeatGen and performed experiments on rice and mouse genomes. Our experimental results achieve accuracy of 85.57% and 96.64%, respectively, which are better or competitive to current state-of-the-art methods. Our shared memory based tool can also serve queries much faster than sequential technique. All source codes and datasets are available at https://github.com/khaled-rahman/FastFeatGen.

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