Recognition and Digitization of Handwritten Text using Histogram of Gradients and Artificial Neural Network

Handwriting recognition is one of the most persuasive and interesting projects as it is required in many real-life applications such as bank-check processing, postal-code recognition, handwritten notes or question paper digitization etc. Machine learning and deep learning methods are being used by developers to make computers more intelligent. A person learns how to execute a task by learning and repeating it over and over before it memorises the steps. The neurons in his brain will then be able to easily execute the task that he has mastered. This is also very close to machine learning. It employs a variety of architectures to solve various problems. Handwritten text recognition systems are models that capture and interpret handwritten numeric and character data from sources such as paper documents and photographs. For this application, a variety of machine learning algorithms were used. However, several limitations have been found, such as a large number of iterations, high training costs, and so on. Even though the other models have given impressive accuracy, it still has some drawbacks. In an unsupervised way, the Artificial Neural Network is used to learn effective data coding. For recognising real-world data, we built a model using Histogram of Oriented Gradients (HOG) and Artificial Neural Networks (ANN).

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An Assessment of Machine Learning Techniques for Predicting Turbine Airfoil Component Temperatures, Using FEA Simulations for Training Data

Volume 5A: Heat Transfer ◽

10.1115/gt2019-91004 ◽

2019 ◽

Author(s):

James A. Tallman ◽

Michal Osusky ◽

Nick Magina ◽

Evan Sewall

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Surrogate Model ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Artificial Neural

Abstract This paper provides an assessment of three different machine learning techniques for accurately reproducing a distributed temperature prediction of a high-pressure turbine airfoil. A three-dimensional Finite Element Analysis thermal model of a cooled turbine airfoil was solved repeatedly (200 instances) for various operating point settings of the corresponding gas turbine engine. The response surface created by the repeated solutions was fed into three machine learning algorithms and surrogate model representations of the FEA model’s response were generated. The machine learning algorithms investigated were a Gaussian Process, a Boosted Decision Tree, and an Artificial Neural Network. Additionally, a simple Linear Regression surrogate model was created for comparative purposes. The Artificial Neural Network model proved to be the most successful at reproducing the FEA model over the range of operating points. The mean and standard deviation differences between the FEA and the Neural Network models were 15% and 14% of a desired accuracy threshold, respectively. The Digital Thread for Design (DT4D) was used to expedite all model execution and machine learning training. A description of DT4D is also provided.

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Machine learning model development for predicting road transport GHG emissions in Canada

WSB Journal of Business and Finance ◽

10.2478/wsbjbf-2019-0022 ◽

2019 ◽

Vol 53 (2) ◽

pp. 55-72

Author(s):

Mohd Jawad Ur Rehman Khan ◽

Anjali Awasthi

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Multilayer Perceptron ◽

Ghg Emissions ◽

Predictive Performance ◽

Road Transport ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Artificial Neural

Abstract Prediction of greenhouse gas (GHG) emissions is important to minimise their negative impact on climate change and global warming. In this article, we propose new models based on data mining and supervised machine learning algorithms (regression and classification) for predicting GHG emissions arising from passenger and freight road transport in Canada. Four models are investigated, namely, artificial neural network multilayer perceptron, multiple linear regression, multinomial logistic regression and decision tree models. From the results, it was found that artificial neural network multilayer perceptron model showed better predictive performance over other models. Ensemble technique (Bagging & Boosting) was applied on the developed multilayer perceptron model, which significantly improved the model’s predictive performance.

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Machine learning and artificial neural network prediction of interfacial thermal resistance between graphene and hexagonal boron nitride

Nanoscale ◽

10.1039/c8nr05703f ◽

2018 ◽

Vol 10 (40) ◽

pp. 19092-19099 ◽

Cited By ~ 21

Author(s):

Hong Yang ◽

Zhongtao Zhang ◽

Jingchao Zhang ◽

Xiao Cheng Zeng

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Boron Nitride ◽

Thermal Resistance ◽

Hexagonal Boron Nitride ◽

Machine Learning Algorithms ◽

Single Layer Graphene ◽

Interfacial Thermal Resistance ◽

Artificial Neural

Several machine learning algorithms and artificial neural network structures are used to predict the interfacial thermal resistance between single layer graphene and hexagonal boron nitride with only the knowledge of the system temperature, inter-layer coupling strength, and in-plane tensile strain.

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Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia

Scientific Reports ◽

10.1038/s41598-021-96872-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Marwah Sattar Hanoon ◽

Ali Najah Ahmed ◽

Nur’atiah Zaini ◽

Arif Razzaq ◽

Pavitra Kumar ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Air Temperature ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Unseen Data ◽

Temperature And Humidity ◽

Artificial Neural ◽

Artificial Neural Network Ann

AbstractAccurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neural network (ANN) architectures (multi-layered perceptron, radial basis function) for prediction of such as air temperature (T) and relative humidity (Rh). Daily data over 24 years for Kula Terengganu station were obtained from the Malaysia Meteorological Department. Results showed that MLP-NN performs well among the others in predicting daily T and Rh with R of 0.7132 and 0.633, respectively. However, in monthly prediction T also MLP-NN model provided closer standards deviation to actual value and can be used to predict monthly T with R 0.8462. Whereas in prediction monthly Rh, the RBF-NN model's efficiency was higher than other models with R of 0.7113. To validate the performance of the trained both artificial neural network (ANN) architectures MLP-NN and RBF-NN, both were applied to an unseen data set from observation data in the region. The results indicated that on either architecture of ANN, there is good potential to predict daily and monthly T and Rh values with an acceptable range of accuracy.

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Crop Recommendation System by Artificial Neural Network

10.21203/rs.3.rs-874525/v1 ◽

2021 ◽

Author(s):

Bangaru Kamatchi S ◽

R. Parvathi

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Recommendation System ◽

Climatic Factors ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Classification Model ◽

Crop Type ◽

Artificial Neural

Abstract The agriculture yield mostly depends on climate factors. Any information associated with climatic factors will help farmers in foreordained farming. Choosing a right crop at right time is most important to get proper yield. To help the farmers in decision making process a classification model is built by considering the agro climatic parameters of a crop like temperature, relative humidity, type of soil, soil pH and crop duration and a recommendation system is built based on three factors namely crop, type of crop and the districts. Predicting the districts is the novel approach in which crop pattern of 33 districts of Tamilnadu is marked and based on that classification model is built. Thorough analysis of machine learning algorithms incorporating pre-processing, data augmentation and comparison of optimizers and activation function of ANN. Log loss metric is used to validate the models. The results shows that artificial neural network is the best predictive model for classification of crops crop type and district based on agrometeorological climatic condition. The accuracy of artificial neural network model is compared with five different machine learning algorithms to analyse the performance.

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Development of an Artificial Neural Network Model and Comparison with Nomogram for Prediction of Pathological Complete Response After Neoadjuvant Chemotherapy in Breast Cancer

10.21203/rs.3.rs-609628/v1 ◽

2021 ◽

Author(s):

Ji-Jung Jung ◽

Eunyoung Kang ◽

Eun-Kyu Kim ◽

Jee Hyun Kim ◽

Se Hyun Kim ◽

...

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Neoadjuvant Chemotherapy ◽

Pathological Complete Response ◽

Complete Response ◽

Machine Learning Algorithms ◽

Ann Model ◽

Artificial Neural

Abstract Identifying breast cancer patients who may benefit from neoadjuvant chemotherapy will facilitate personalized treatment regarding chemotherapy and surgery. In our work, we developed two predictive models, nomogram and a machine learning model based on artificial neural network (ANN), to anticipate pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer. We demonstrated that high level of estrogen receptor (ER) positivity, positive human epidermal growth factor receptor 2 (HER2) status, complete response on magnetic resonance imaging (MRI), abnormal CEA level after NAC, and abnormal CA15-3 level after NAC were significant predictors of pCR. A nomogram and ANN model trained to predict pCR were developed using these five predictors. The performance of the two models were tested using a fully independent test set. Validation test showed the area under the receiver operating characteristic curve (AUC) of 0.789 (95% confidence interval (CI), 0.707-0.871) for the nomogram and 0.876 (95% CI, 0.808-0.943) for the ANN model. Both models showed excellent performance, but the ANN model performed better in terms of accuracy and discrimination. Machine-learning algorithms hold promise in medical application and provide better prediction than nomogram.

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Comparison between the different Artificial Neural Network (ANN) accuracy in diagnosis of asthma: مقارنة بين اختلاف دقة الشبكات العصبية الاصطناعية في تشخيص مرض الربو

Journal of engineering sciences and information technology - مجلة العلوم الهندسية و تكنولوجيا المعلومات ◽

10.26389/ajsrp.n260421 ◽

2021 ◽

Vol 5 (4) ◽

pp. 172-165

Author(s):

Hanein Omar Mohamed, Basma.F.Idris Hanein Omar Mohamed, Basma.F.Idris

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Learning Algorithms ◽

High Accuracy ◽

Machine Learning Algorithms ◽

Support Vector ◽

Data Mining Algorithms ◽

Artificial Neural

Asthma is a chronic disease that is caused by inflammation of airways. Diagnosis, predication and classification of asthmatic are one of the major attractive areas of research for decades by using different and recent techniques, however the main problem of asthma is misdiagnosis. This paper simplifies and compare between different Artificial Neural Network techniques used to solve this problem by using different algorithms to getting a high level of accuracyin diagnosis, prediction, and classification of asthma like: (data mining algorithms, machine learning algorithms, deep machine learning algorithms), depending and passing through three stages: data acquisition, feature extracting, data classification. According to the comparison of different techniques the high accuracy achieved by ANN was (98.85%), and the low accuracy of it was (80%), despite of the accuracy achieved by Support Vector Machine (SVM) was (86%) when used Mel Frequency Cepstral Coefficient MFCC for feature extraction, while the accuracy was (99.34%) when used Relief for extracting feature. Based in our comparison we recommend that if the researchers used the same techniques they should to return to previous studies it to get high accuracy.

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Artificial neural network models for coronary artery disease

Current Bioinformatics ◽

10.2174/1574893615666200214102837 ◽

2020 ◽

Vol 15 ◽

Author(s):

Elham Shamsara ◽

Sara Saffar Soflaei ◽

Mohammad Tajfard ◽

Ivan Yamshchikov ◽

Habibollah Esmaili ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Coronary Artery Disease ◽

Pattern Recognition ◽

Artificial Neural Network ◽

Coronary Artery ◽

Diagnostic Model ◽

Early Prediction ◽

Artificial Neural ◽

Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among diﬀerent machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Diﬀerent methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant diﬀerence among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among diﬀerent methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

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Automatically Determining Lumbar Load during Physically Demanding Work: A Validation Study

Sensors ◽

10.3390/s21072476 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2476

Author(s):

Charlotte Christina Roossien ◽

Christian Theodoor Maria Baten ◽

Mitchel Willem Pieter van der Waard ◽

Michiel Felix Reneman ◽

Gijsbertus Jacob Verkerke

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Discriminant Validity ◽

Real Life ◽

Working Environment ◽

Physically Active ◽

Perceived Workload ◽

Artificial Neural ◽

Lumbar Load ◽

Physically Demanding Work

A sensor-based system using inertial magnetic measurement units and surface electromyography is suitable for objectively and automatically monitoring the lumbar load during physically demanding work. The validity and usability of this system in the uncontrolled real-life working environment of physically active workers are still unknown. The objective of this study was to test the discriminant validity of an artificial neural network-based method for load assessment during actual work. Nine physically active workers performed work-related tasks while wearing the sensor system. The main measure representing lumbar load was the net moment around the L5/S1 intervertebral body, estimated using a method that was based on artificial neural network and perceived workload. The mean differences (MDs) were tested using a paired t-test. During heavy tasks, the net moment (MD = 64.3 ± 13.5%, p = 0.028) and the perceived workload (MD = 5.1 ± 2.1, p < 0.001) observed were significantly higher than during the light tasks. The lumbar load had significantly higher variances during the dynamic tasks (MD = 33.5 ± 36.8%, p = 0.026) and the perceived workload was significantly higher (MD = 2.2 ± 1.5, p = 0.002) than during static tasks. It was concluded that the validity of this sensor-based system was supported because the differences in the lumbar load were consistent with the perceived intensity levels and character of the work tasks.

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Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network

Journal of Marine Science and Engineering ◽

10.3390/jmse8100766 ◽

2020 ◽

Vol 8 (10) ◽

pp. 766

Author(s):

Dohan Oh ◽

Julia Race ◽

Selda Oterkus ◽

Bonguk Koo

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Network Model ◽

Neural Network Model ◽

Deep Neural Network ◽

Machine Learning Techniques ◽

Burst Pressure ◽

Comparison Results ◽

Artificial Neural

Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neural network, which is on the basis of artificial neural network algorithm and is a method amongst various machine learning methods, is applied in this study. The applicability of machine learning techniques such as deep neural network for the prediction of burst pressure has been investigated for dented API 5L X-grade pipelines. To this end, supervised learning is employed, and the deep neural network model has four layers with three hidden layers, and the neural network uses the fully connected layer. The burst pressure computed by deep neural network model has been compared with the results of finite element analysis based parametric study, and the burst pressure calculated by the experimental results. According to the comparison results, it showed good agreement. Therefore, it is concluded that deep neural networks can be another solution for predicting the burst pressure of API 5L X-grade dented pipelines.

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