Development of a Novel Framework for Hazardous Materials Placard Recognition System to Conduct Commodity Flow Studies Using Artificial Intelligence AlexNet Convolutional Neural Network

2021 ◽  
pp. 036119812110266
Author(s):  
Sherif Gaweesh ◽  
Md Nasim Khan ◽  
Mohamed M. Ahmed
Keyword(s):  
Neural Network ◽  
Data Collection ◽  
Convolutional Neural Network ◽  
Road Traffic ◽  
Video Recording ◽  
Weather Conditions ◽  
Hazardous Materials ◽  
Geographic Area ◽  
Machine Learning Techniques ◽  
Commodity Flow

Conducting hazardous materials (HAZMAT) commodity flow studies (CFS) is crucial for emergency management agencies. Identifying the types and amounts of hazardous materials being transported through a specified geographic area will ensure timely response if a HAZMAT incident takes place. CFS are usually conducted using manual data collection methods, which may expose the personnel to some risks by them being subjected to road traffic and different weather conditions for several hours. On other hand, the quality and accuracy of the collected HAZMAT data are affected by the skill and alertness of the data collectors. This study introduces a framework to collect HAZMAT transportation data exploiting advanced image processing and machine learning techniques on video feed. A promising convolutional neural network (CNN), named AlexNet was used to develop and test the automatic HAZMAT placard recognition framework. A solar-powered mobile video recording system was developed using high-resolution infra-red (IR) cameras, connected to a network video recorder (NVR) mounted on a mobile trailer. The developed system was used as the continuous data collection system. Manual data collection was also conducted at the same locations to calibrate and validate the newly developed system. The results showed that the proposed framework could achieve an accuracy of 95% in identifying HAZMAT placard information. The developed system showed significant benefits in reducing the cost of conducting HAZMAT CFS, as well as eliminating the associated risks that data collection personnel could face.

scholarly journals bCNN-Methylpred: Feature-Based Prediction of RNA Sequence Modification Using Branch Convolutional Neural Network

Genes ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1155
Author(s):  
Naeem Islam ◽  
Jaebyung Park
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Domain Knowledge ◽  
Feature Space ◽  
Experimental Methods ◽  
Machine Learning Techniques ◽  
Rna Modification ◽  
Biological Processes ◽  
Rna Sequence ◽  
Functional Mechanisms

RNA modification is vital to various cellular and biological processes. Among the existing RNA modifications, N6-methyladenosine (m6A) is considered the most important modification owing to its involvement in many biological processes. The prediction of m6A sites is crucial because it can provide a better understanding of their functional mechanisms. In this regard, although experimental methods are useful, they are time consuming. Previously, researchers have attempted to predict m6A sites using computational methods to overcome the limitations of experimental methods. Some of these approaches are based on classical machine-learning techniques that rely on handcrafted features and require domain knowledge, whereas other methods are based on deep learning. However, both methods lack robustness and yield low accuracy. Hence, we develop a branch-based convolutional neural network and a novel RNA sequence representation. The proposed network automatically extracts features from each branch of the designated inputs. Subsequently, these features are concatenated in the feature space to predict the m6A sites. Finally, we conduct experiments using four different species. The proposed approach outperforms existing state-of-the-art methods, achieving accuracies of 94.91%, 94.28%, 88.46%, and 94.8% for the H. sapiens, M. musculus, S. cerevisiae, and A. thaliana datasets, respectively.

Spectral features based convolutional neural network for accurate and prompt identification of schizophrenic patients

2020 ◽  
pp. 095441192096693
Author(s):  
Kuldeep Singh ◽  
Sukhjeet Singh ◽  
Jyoteesh Malhotra
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Frequency Domain ◽  
Short Term Memory ◽  
Spectral Power ◽  
Machine Learning Techniques ◽  
Spectral Amplitude ◽  
Spectral Features ◽  
Eeg Signals ◽  
Schizophrenic Patients

Schizophrenia is a fatal mental disorder, which affects millions of people globally by the disturbance in their thinking, feeling and behaviour. In the age of the internet of things assisted with cloud computing and machine learning techniques, the computer-aided diagnosis of schizophrenia is essentially required to provide its patients with an opportunity to own a better quality of life. In this context, the present paper proposes a spectral features based convolutional neural network (CNN) model for accurate identification of schizophrenic patients using spectral analysis of multichannel EEG signals in real-time. This model processes acquired EEG signals with filtering, segmentation and conversion into frequency domain. Then, given frequency domain segments are divided into six distinct spectral bands like delta, theta-1, theta-2, alpha, beta and gamma. The spectral features including mean spectral amplitude, spectral power and Hjorth descriptors (Activity, Mobility and Complexity) are extracted from each band. These features are independently fed to the proposed spectral features-based CNN and long short-term memory network (LSTM) models for classification. This work also makes use of raw time-domain and frequency-domain EEG segments for classification using temporal CNN and spectral CNN models of same architectures respectively. The overall analysis of simulation results of all models exhibits that the proposed spectral features based CNN model is an efficient technique for accurate and prompt identification of schizophrenic patients among healthy individuals with average classification accuracies of 94.08% and 98.56% for two different datasets with optimally small classification time.

scholarly journals SF-CNN: Signal Filtering Convolutional Neural Network for Precipitation Intensity Estimation

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 551
Author(s):  
Chih-Wei Lin ◽  
Xiuping Huang ◽  
Mengxiang Lin ◽  
Sidi Hong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Weather Conditions ◽  
Critical Issue ◽  
Activation Function ◽  
Precipitation Intensity ◽  
The Self ◽  
Intensity Estimation ◽  
Signal Filtering ◽  
Complex Models

Precipitation intensity estimation is a critical issue in the analysis of weather conditions. Most existing approaches focus on building complex models to extract rain streaks. However, an efficient approach to estimate the precipitation intensity from surveillance cameras is still challenging. This study proposes a convolutional neural network known as the signal filtering convolutional neural network (SF-CNN) to handle precipitation intensity using surveillance-based images. The SF-CNN has two main blocks, the signal filtering block (SF block) and the gradually decreasing dimension block (GDD block), to extract features for the precipitation intensity estimation. The SF block with the filtering operation is constructed in different parts of the SF-CNN to remove the noise from the features containing rain streak information. The GDD block continuously takes the pair of the convolutional operation with the activation function to reduce the dimension of features. Our main contributions are (1) an SF block considering the signal filtering process and effectively removing the useless signals and (2) a procedure of gradually decreasing the dimension of the feature able to learn and reserve the information of features. Experiments on the self-collected dataset, consisting of 9394 raining images with six precipitation intensity levels, demonstrate the proposed approach’s effectiveness against the popular convolutional neural networks. To the best of our knowledge, the self-collected dataset is the largest dataset for monitoring infrared images of precipitation intensity.

scholarly journals A convolutional neural network for fast upsampling of undersampled tomograms in X-ray CT time-series using a representative highly sampled tomogram

2019 ◽  
Vol 26 (3) ◽  
pp. 839-853
Author(s):  
Dimitrios Bellos ◽  
Mark Basham ◽  
Tony Pridmore ◽  
Andrew P. French
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Data Collection ◽  
Convolutional Neural Network ◽  
Real World ◽  
Feature Detection ◽  
Salt Water ◽  
Frame Rate ◽  
High Rate ◽  
X Ray

X-ray computed tomography and, specifically,time-resolvedvolumetric tomography data collections (4D datasets) routinely produce terabytes of data, which need to be effectively processed after capture. This is often complicated due to the high rate of data collection required to capture at sufficient time-resolution events of interest in a time-series, compelling the researchers to perform data collection with a low number of projections for each tomogram in order to achieve the desired `frame rate'. It is common practice to collect a representative tomogram with many projections, after or before the time-critical portion of the experiment without detrimentally affecting the time-series to aid the analysis process. For this paper these highly sampled data are used to aid feature detection in the rapidly collected tomograms by assisting with the upsampling of their projections, which is equivalent to upscaling the θ-axis of the sinograms. In this paper, a super-resolution approach is proposed based on deep learning (termed an upscaling Deep Neural Network, or UDNN) that aims to upscale the sinogram space of individual tomograms in a 4D dataset of a sample. This is done using learned behaviour from a dataset containing a high number of projections, taken of the same sample and occurring at the beginning or the end of the data collection. The prior provided by the highly sampled tomogram allows the application of an upscaling process with better accuracy than existing interpolation techniques. This upscaling process subsequently permits an increase in the quality of the tomogram's reconstruction, especially in situations that require capture of only a limited number of projections, as is the case in high-frequency time-series capture. The increase in quality can prove very helpful for researchers, as downstream it enables easier segmentation of the tomograms in areas of interest, for example. The method itself comprises a convolutional neural network which through training learns an end-to-end mapping between sinograms with a low and a high number of projections. Since datasets can differ greatly between experiments, this approach specifically develops a lightweight network that can easily and quickly be retrained for different types of samples. As part of the evaluation of our technique, results with different hyperparameter settings are presented, and the method has been tested on both synthetic and real-world data. In addition, accompanying real-world experimental datasets have been released in the form of two 80 GB tomograms depicting a metallic pin that undergoes corruption from a droplet of salt water. Also a new engineering-based phantom dataset has been produced and released, inspired by the experimental datasets.

scholarly journals Convolutional Neural Network Computation for Autonomous Vehicle

2020 ◽  
pp. 368-372
Author(s):  
Aires Da Conceicao ◽  
Sheshang D. Degadwala
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Autonomous Vehicle ◽  
Human Interaction ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Robust Feature Extraction

Self-driving vehicle is a vehicle that can drive by itself it means without human interaction. This system shows how the computer can learn and the over the art of driving using machine learning techniques. This technique includes line lane tracker, robust feature extraction and convolutional neural network.

scholarly journals Corn Disease Classification Using Transfer Learning and Convolutional Neural Network

2021 ◽  
Vol 9 (2) ◽  
pp. 211
Author(s):  
Faisal Dharma Adhinata ◽  
Gita Fadila Fitriana ◽  
Aditya Wijayanto ◽  
Muhammad Pajar Kharisma Putra
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Early Detection Of Disease ◽  
Global Average ◽  
Learning Techniques ◽  
Corn Plants

Indonesia is an agricultural country with abundant agricultural products. One of the crops used as a staple food for Indonesians is corn. This corn plant must be protected from diseases so that the quality of corn harvest can be optimal. Early detection of disease in corn plants is needed so that farmers can provide treatment quickly and precisely. Previous research used machine learning techniques to solve this problem. The results of the previous research were not optimal because the amount of data used was slightly and less varied. Therefore, we propose a technique that can process lots and varied data, hoping that the resulting system is more accurate than the previous research. This research uses transfer learning techniques as feature extraction combined with Convolutional Neural Network as a classification. We analysed the combination of DenseNet201 with a Flatten or Global Average Pooling layer. The experimental results show that the accuracy produced by the combination of DenseNet201 with the Global Average Pooling layer is better than DenseNet201 with Flatten layer. The accuracy obtained is 93% which proves the proposed system is more accurate than previous studies.

scholarly journals Automated predictive analytics tool for rainfall forecasting

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maulin Raval ◽  
Pavithra Sivashanmugam ◽  
Vu Pham ◽  
Hardik Gohel ◽  
Ajeet Kaushik ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Predictive Analytics ◽  
Weather Conditions ◽  
Machine Learning Algorithms ◽  
Rainfall Data ◽  
Machine Learning Techniques ◽  
Rainfall Forecasting ◽  
Rainfall Prediction ◽  
Prediction Techniques

AbstractAustralia faces a dryness disaster whose impact may be mitigated by rainfall prediction. Being an incredibly challenging task, yet accurate prediction of rainfall plays an enormous role in policy making, decision making and organizing sustainable water resource systems. The ability to accurately predict rainfall patterns empowers civilizations. Though short-term rainfall predictions are provided by meteorological systems, long-term prediction of rainfall is challenging and has a lot of factors that lead to uncertainty. Historically, various researchers have experimented with several machine learning techniques in rainfall prediction with given weather conditions. However, in places like Australia where the climate is variable, finding the best method to model the complex rainfall process is a major challenge. The aim of this paper is to: (a) predict rainfall using machine learning algorithms and comparing the performance of different models. (b) Develop an optimized neural network and develop a prediction model using the neural network (c) to do a comparative study of new and existing prediction techniques using Australian rainfall data. In this paper, rainfall data collected over a span of ten years from 2007 to 2017, with the input from 26 geographically diverse locations have been used to develop the predictive models. The data was divided into training and testing sets for validation purposes. The results show that both traditional and neural network-based machine learning models can predict rainfall with more precision.

scholarly journals Pedestrian Detection at Night in Infrared Images Using an Attention-Guided Encoder-Decoder Convolutional Neural Network

2020 ◽  
Vol 10 (3) ◽  
pp. 809 ◽  
Author(s):  
Yunfan Chen ◽  
Hyunchul Shin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Pedestrian Detection ◽  
Weather Conditions ◽  
Superior Performance ◽  
Low Resolution ◽  
Feature Maps ◽  
Camera System ◽  
Ir Camera ◽  
Multi Scale

Pedestrian-related accidents are much more likely to occur during nighttime when visible (VI) cameras are much less effective. Unlike VI cameras, infrared (IR) cameras can work in total darkness. However, IR images have several drawbacks, such as low-resolution, noise, and thermal energy characteristics that can differ depending on the weather. To overcome these drawbacks, we propose an IR camera system to identify pedestrians at night that uses a novel attention-guided encoder-decoder convolutional neural network (AED-CNN). In AED-CNN, encoder-decoder modules are introduced to generate multi-scale features, in which new skip connection blocks are incorporated into the decoder to combine the feature maps from the encoder and decoder module. This new architecture increases context information which is helpful for extracting discriminative features from low-resolution and noisy IR images. Furthermore, we propose an attention module to re-weight the multi-scale features generated by the encoder-decoder module. The attention mechanism effectively highlights pedestrians while eliminating background interference, which helps to detect pedestrians under various weather conditions. Empirical experiments on two challenging datasets fully demonstrate that our method shows superior performance. Our approach significantly improves the precision of the state-of-the-art method by 5.1% and 23.78% on the Keimyung University (KMU) and Computer Vision Center (CVC)-09 pedestrian dataset, respectively.

scholarly journals Comparing bags of features, conventional convolutional neural network and AlexNet for fruit recognition

2019 ◽  
Vol 14 (1) ◽  
pp. 333 ◽  
Author(s):  
Nik Noor Akmal Abdul Hamid ◽  
Rabiatul Adawiya Razali ◽  
Zaidah Ibrahim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Operation Time ◽  
Machine Learning Techniques ◽  
Bag Of Features ◽  
Learning Techniques ◽  
Recognition Result ◽  
Different Types

This paper presents a comparative study between Bag of Features (BoF), Conventional Convolutional Neural Network (CNN) and Alexnet for fruit recognition.  Automatic fruit recognition can minimize human intervention in their fruit harvesting operations, operation time and harvesting cost.  On the other hand, this task is very challenging because of the similarities in shapes, colours and textures among various types of fruits. Thus, a robust technique that can produce good result is necessary. Due to the outstanding performance of deep learning like CNN and its pre-trained models like AlexNet in image recognition, this paper investigates the accuracy of conventional CNN, and Alexnet in recognizing thirty different types of fruits from a publicly available dataset.  Besides that, the recognition performance of BoF is also examined since it is one of the machine learning techniques that achieves good result in object recognition.   The experimental results indicate that all of these three techniques produce excellent recognition accuracy. Furthermore, conventional CNN achieves the fastest recognition result compared to BoF, and Alexnet.

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