Crop Recommendation System by Artificial Neural Network

Recommendation System ◽

Climatic Factors ◽

Learning Algorithms ◽

Classification Model ◽

Crop Type ◽

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.

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 ◽

Surrogate Model ◽

Learning Algorithms ◽

Training Data ◽

Machine Learning Techniques ◽

Learning Techniques ◽

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.

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 ◽

Air Temperature ◽

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.

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

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

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 ◽

Learning Algorithms ◽

High Accuracy ◽

Support Vector ◽

Data Mining Algorithms ◽

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.

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

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i6.5702 ◽

2021 ◽

Vol 12 (6) ◽

pp. 2555-2564

Author(s):

Dr.S.K.Nivetha Et al.

Keyword(s):

Neural Network ◽

Machine Learning ◽

Real Life ◽

Postal Code ◽

Training Costs ◽

Handwritten Text ◽

Handwritten Text Recognition ◽

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).

An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine

Energies ◽

10.3390/en14216928 ◽

2021 ◽

Vol 14 (21) ◽

pp. 6928

Author(s):

Łukasz Wojtecki ◽

Sebastian Iwaszenko ◽

Derek B. Apel ◽

Tomasz Cichy

Keyword(s):

Neural Network ◽

Machine Learning ◽

Rock Mass ◽

Decision Tree ◽

Risk Prediction ◽

Learning Algorithms ◽

Hard Coal ◽

Underground Excavations

Rockburst is a dynamic rock mass failure occurring during underground mining under unfavorable stress conditions. The rockburst phenomenon concerns openings in different rocks and is generally correlated with high stress in the rock mass. As a result of rockburst, underground excavations lose their functionality, the infrastructure is damaged, and the working conditions become unsafe. Assessing rockburst hazards in underground excavations becomes particularly important with the increasing mining depth and the mining-induced stresses. Nowadays, rockburst risk prediction is based mainly on various indicators. However, some attempts have been made to apply machine learning algorithms for this purpose. For this article, we employed an extensive range of machine learning algorithms, e.g., an artificial neural network, decision tree, random forest, and gradient boosting, to estimate the rockburst risk in galleries in one of the deep hard coal mines in the Upper Silesian Coal Basin, Poland. With the use of these algorithms, we proposed rockburst risk prediction models. Neural network and decision tree models were most effective in assessing whether a rockburst occurred in an analyzed case, taking into account the average value of the recall parameter. In three randomly selected datasets, the artificial neural network models were able to identify all of the rockbursts.

A new ML-based approach to enhance student engagement in online environment

PLoS ONE ◽

10.1371/journal.pone.0258788 ◽

2021 ◽

Vol 16 (11) ◽

pp. e0258788

Author(s):

Sarra Ayouni ◽

Fahima Hajjej ◽

Mohamed Maddeh ◽

Shaha Al-Otaibi

Keyword(s):

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Student Engagement ◽

Decision Tree ◽

Learning Algorithms ◽

Support Vector ◽

Online Environment

The educational research is increasingly emphasizing the potential of student engagement and its impact on performance, retention and persistence. This construct has emerged as an important paradigm in the higher education field for many decades. However, evaluating and predicting the student’s engagement level in an online environment remains a challenge. The purpose of this study is to suggest an intelligent predictive system that predicts the student’s engagement level and then provides the students with feedback to enhance their motivation and dedication. Three categories of students are defined depending on their engagement level (Not Engaged, Passively Engaged, and Actively Engaged). We applied three different machine-learning algorithms, namely Decision Tree, Support Vector Machine and Artificial Neural Network, to students’ activities recorded in Learning Management System reports. The results demonstrate that machine learning algorithms could predict the student’s engagement level. In addition, according to the performance metrics of the different algorithms, the Artificial Neural Network has a greater accuracy rate (85%) compared to the Support Vector Machine (80%) and Decision Tree (75%) classification techniques. Based on these results, the intelligent predictive system sends feedback to the students and alerts the instructor once a student’s engagement level decreases. The instructor can identify the students’ difficulties during the course and motivate them through e-mail reminders, course messages, or scheduling an online meeting.

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 ◽

Multilayer Perceptron ◽

Ghg Emissions ◽

Predictive Performance ◽

Road Transport ◽

Supervised Machine Learning ◽

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.

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 ◽

Boron Nitride ◽

Thermal Resistance ◽

Hexagonal Boron Nitride ◽

Single Layer Graphene ◽

Interfacial Thermal Resistance ◽

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.

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 ◽

Neoadjuvant Chemotherapy ◽

Pathological Complete Response ◽

Complete Response ◽

Ann Model ◽

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.

Teaching a neural network modeling socio-economic development of the region

Digital sociology ◽

10.26425/2658-347x-2019-2-34-40 ◽

2019 ◽

Vol 2 (2) ◽

pp. 34-40

Author(s):

S. V. Romanchukov ◽

O. G. Berestneva ◽

L. A. Petrova

Keyword(s):

Neural Network ◽

Machine Learning ◽

Information Technologies ◽

Research Team ◽

Learning Algorithms ◽

Third Party ◽

Neural Network Modeling ◽

Data Set ◽

The article is devoted to the formation of an array of data for the construction of an artificial neural network, designed to search for relationships between social and economic parameters of the development of regions of the Russian Federation. The relevance of research in this area is confirmed both by a large number of studies in the field of regional comparativistics and by the limited methods used in this kind of research, often limited to descriptive methods and basic techniques of parametric statistics. Under these conditions, the expansion of the mathematical apparatus and the more active introduction of information technologies (including in the area of Big Data analysis and the construction of predictive models based on artificial neural networks) can be viable. At the same time, however, it should be noted that the resources of an individual research team may be (and most likely will be) insufficient to create their own software solution for the implementation of machine learning algorithms from scratch. The use of third-party cloud-based software platforms (primarily IBM and Google infrastructures) allows to bypass the problem of the research team’s lack of expensive material and technical base, however they impose a number of limitations dictated by the requirements of the existing machine learning algorithms and the specific architecture provided platforms This puts the research team in front of the need to prepare the accumulated data set for processing: reducing the dimension, checking the data for compliance with the platform requirements and eliminating potential problem areas: “data leaks”, “learning distortions” and others. The paper was reported to the section “Sociology of Digital Society: Structures, Processes, Governance” of the International Conference Session “Public Administration and Development of Russia: National Goals and Institutions”.