scholarly journals Smart Agriculture Monitoring System using ML

2020 ◽  
Vol 9 (4) ◽  
pp. 2404-2407
Keyword(s):  
Machine Learning ◽  
Temperature Sensor ◽  
Agricultural Sector ◽  
Cost Effective ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Agriculture Sector ◽  
Learning Techniques ◽  
Effective Manner ◽  
Cloud Computation

Agriculture plays vital role in every individual’s life. As the technology improves, agricultural sector has been improving by the needs of people. Basically, the idea here deals with monitoring of weather, temperature, soil moisture and other agriculture related aspects. The objective of this paper is to upgrade -growth probability. So by making use of Advance technologies good and efficient crop can be yield. Cloud (Firebase) is typically used to store the pre-computed data (data sets) and the data from the efficiency of agriculture sector. This idea comprises of Machine Learning techniques, Cloud Computation [5] and IoT. Here we will use machine learning techniques for predicting crop sensors and comparison between these. IoT includes NPK sensors, temperature sensor, and humidity sensor. The mechanism goes like this- initially the data from humidity, temperature sensor will be noted and NPK sensors will be placed in the soil, the values from the sensors will be sent to cloud by making use of any communication technology (ZigBee, IoT gateway devices). In cloud comparison of pre-computed data and data from sensors happens by making use of machine learning. The outcome from cloud may be stored in the server (Admin) or directly be notified to authorized person of the land in the form for notification. By taking all these parameters into consideration, we can predict the best suitable crop that can be grown and farmers will earn profit in a cost-effective manner.

scholarly journals Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

2018 ◽  
Vol 11 (1) ◽  
pp. 105 ◽  
Author(s):  
Syed Abidi ◽  
Mushtaq Hussain ◽  
Yonglin Xu ◽  
Wu Zhang
Keyword(s):  
Machine Learning ◽  
Sustainable Development ◽  
Teaching And Learning ◽  
Performance Metrics ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Tutoring System ◽  
Learning Techniques

Incorporating substantial, sustainable development issues into teaching and learning is the ultimate task of Education for Sustainable Development (ESD). The purpose of our study was to identify the confused students who had failed to master the skill(s) given by the tutors as homework using the Intelligent Tutoring System (ITS). We have focused ASSISTments, an ITS in this study, and scrutinized the skill-builder data using machine learning techniques and methods. We used seven candidate models including: Naïve Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Trees (XGBoost). We trained, validated, and tested learning algorithms, performed stratified cross-validation, and measured the performance of the models through various performance metrics, i.e., ROC (Receiver Operating Characteristic), Accuracy, Precision, Recall, F-Measure, Sensitivity, and Specificity. We found RF, GLM, XGBoost, and DL were high accuracy-achieving classifiers. However, other perceptions such as detecting unexplored features that might be related to the forecasting of outputs can also boost the accuracy of the prediction model. Through machine learning methods, we identified the group of students that were confused when attempting the homework exercise, to help foster their knowledge and talent to play a vital role in environmental development.

Deep Learning Applications in Agriculture

2021 ◽  
pp. 325-345
Author(s):  
Hari Kishan Kondaveeti ◽  
Gonugunta Priyatham Brahma ◽  
Dandhibhotla Vijaya Sahithi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Rapid Growth ◽  
Agricultural Sector ◽  
The Other ◽  
Learning Methods ◽  
Agriculture Sector ◽  
Learning Techniques ◽  
Recent Trends

Deep learning (DL), a part of machine learning (ML), comprises a contemporary technique for processing the images and analyzing the big data with promising outcomes. Deep learning methods are successfully being used in various sectors to gain better results. Agriculture sector is one of the sectors that could be benefitted from the deep learning techniques since the current agriculture techniques cannot keep up with the rapid growth in population. In this chapter, the recent trends in the applications of deep learning techniques in the agricultural sector and the survey of the research efforts that employ deep learning techniques are going to be discussed. Also, the models that are implemented are going to be analyzed and compared with the other existing models.

scholarly journals Identification of Pneumonia Disease Applying an Intelligent Computational Framework Based on Deep Learning and Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yar Muhammad ◽  
Mohammad Dahman Alshehri ◽  
Wael Mohammed Alenazy ◽  
Truong Vinh Hoang ◽  
Ryan Alturki
Keyword(s):  
Machine Learning ◽  
Medical Image Analysis ◽  
Machine Learning Techniques ◽  
World Health ◽  
X Ray ◽  
Expert Radiologist ◽  
Learning Techniques ◽  
Effective Manner ◽  
The World ◽  
Chest X Ray

Pneumonia is a very common and fatal disease, which needs to be identified at the initial stages in order to prevent a patient having this disease from more damage and help him/her in saving his/her life. Various techniques are used for the diagnosis of pneumonia including chest X-ray, CT scan, blood culture, sputum culture, fluid sample, bronchoscopy, and pulse oximetry. Medical image analysis plays a vital role in the diagnosis of various diseases like MERS, COVID-19, pneumonia, etc. and is considered to be one of the auspicious research areas. To analyze chest X-ray images accurately, there is a need for an expert radiologist who possesses expertise and experience in the desired domain. According to the World Health Organization (WHO) report, about 2/3 people in the world still do not have access to the radiologist, in order to diagnose their disease. This study proposes a DL framework to diagnose pneumonia disease in an efficient and effective manner. Various Deep Convolutional Neural Network (DCNN) transfer learning techniques such as AlexNet, SqueezeNet, VGG16, VGG19, and Inception-V3 are utilized for extracting useful features from the chest X-ray images. In this study, several machine learning (ML) classifiers are utilized. The proposed system has been trained and tested on chest X-ray and CT images dataset. In order to examine the stability and effectiveness of the proposed system, different performance measures have been utilized. The proposed system is intended to be beneficial and supportive for medical doctors to accurately and efficiently diagnose pneumonia disease.

scholarly journals Developing an Open-Source Lightweight Game Engine with DNN Support

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1421
Author(s):  
Haechan Park ◽  
Nakhoon Baek
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Open Source ◽  
Programming Languages ◽  
Cost Effective ◽  
Machine Learning Techniques ◽  
Learning Technology ◽  
Game Engine ◽  
Learning Techniques ◽  
Technical Issues

With the growth of artificial intelligence and deep learning technology, we have many active research works to apply the related techniques in various fields. To test and apply the latest machine learning techniques in gaming, it will be very useful to have a light-weight game engine for quick prototyping. Our game engine is implemented in a cost-effective way, in comparison to well-known commercial proprietary game engines, by utilizing open source products. Due to its simple internal architecture, our game engine is especially beneficial for modifying and reviewing the new functions through quick and repetitive tests. In addition, the game engine has a DNN (deep neural network) module, with which the proposed game engine can apply deep learning techniques to the game features, through applying deep learning algorithms in real-time. Our DNN module uses a simple C++ function interface, rather than additional programming languages and/or scripts. This simplicity enables us to apply machine learning techniques more efficiently and casually to the game applications. We also found some technical issues during our development with open sources. These issues mostly occurred while integrating various open source products into a single game engine. We present details of these technical issues and our solutions.

Fake Review Detection Using Machine Learning Techniques

2020 ◽  
Vol 3 (2) ◽  
pp. 46-54
Author(s):  
Abhinandan V. ◽  
Aishwarya C. A. ◽  
Arshiya Sultana
Keyword(s):  
Machine Learning ◽  
Online Reviews ◽  
Vital Role ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Search Results ◽  
Learning Techniques ◽  
The World ◽  
Fake Reviews

Online reviews play a vital role in today's business and commerce. In the world of e-commerce, reviews are the best signs of success and failure. Businesses that have good reviews get a lot of free exposure on websites and pages that have good reviews show up at the top of the search results. Fake reviews are everywhere online. Online fake reviews are the reviews which are written by someone who has not actually used the product or the services. Because of the cut-throat competition, sellers are now willing to resort to unfair means to make their product stand out. This work introduces some supervised machine learning techniques to detect fake online reviews and also be able to block the malicious users who post such reviews.

From information- to knowledge-management: the role of rule induction and neural net machine learning techniques in knowledge generation

1989 ◽  
Vol 15 (4-5) ◽  
pp. 299-304 ◽  
Author(s):  
Nigel Ford
Keyword(s):  
Machine Learning ◽  
Cost Effective ◽  
Rule Induction ◽  
Machine Learning Techniques ◽  
Knowledge Generation ◽  
Neural Net ◽  
Knowledge Resources ◽  
Learning Techniques ◽  
Knowledge Manager

Developments in artificial intelligence mean that it is now increasingly possible to store not only information but also knowledge as an exploitable resource. Insofar as he or she is concerned with creating, organizing and monitoring knowledge resources to support effective decision making within an organization, the information manager is developing the role of knowledge manager. As well as its organization and dissemina tion, the generation of storable knowledge is very much on the agenda of the knowledge manager. The extent to which com puters can help in the process of knowledge generation is central to his or her concerns. Machine learning techniques have been developed which are capable of giving us an increasing amount of help in this process. The contributions of rule induction and artificial neural net systems are discussed. It is likely that such tech niques will prove to be useful tools both for the information/knowledge manager requiring practical working systems enabling the cost-effective exploitation of knowledge resources, and for the information/knowledge scientist requir ing advances in our more fundamental theoretical knowledge of the nature of information and ways of processing it.

scholarly journals Quantification of toxic metallic elements using machine learning techniques and spark emission spectroscopy

2019 ◽  
Author(s):  
Seyyed Ali Davari ◽  
Anthony S. Wexler
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Emission Spectroscopy ◽  
Cost Effective ◽  
The United States ◽  
Machine Learning Techniques ◽  
Metallic Elements ◽  
Learning Techniques ◽  
Single Feature ◽  
Us Epa

Abstract. The United States Environmental Protection Agency (US EPA) list of Hazardous Air Pollutants (HAPs) includes metal elements suspected or associated with development of cancer. Traditional techniques for detecting and quantifying toxic metallic elements in the atmosphere are either not real time, hindering identification of sources, or limited by instrument costs. Spark emission spectroscopy is a promising and cost effective technique that can be used for analyzing toxic metallic elements in real time. Here, we have developed a cost-effective spark emission spectroscopy system to quantify the concentration of toxic metallic elements targeted by US EPA. Specifically, Cr, Cu, Ni, and Pb solutions were diluted and deposited on the ground electrode of the spark emission system. Least Absolute Shrinkage and Selection Operator (LASSO) was optimized and employed to detect useful features from the spark-generated plasma emissions. The optimized model was able to detect atomic emission lines along with other features to build a regression model that predicts the concentration of toxic metallic elements from the observed spectra. The limits of detections (LOD) were estimated using the detected features and compared to the traditional single-feature approach. LASSO is capable of detecting highly sensitive features in the input spectrum; however for some elements the single-feature LOD marginally outperforms LASSO LOD. The combination of low cost instruments with advanced machine learning techniques for data analysis could pave the path forward for data driven solutions to costly measurements.

scholarly journals Quantification of toxic metals using machine learning techniques and spark emission spectroscopy

2020 ◽  
Vol 13 (10) ◽  
pp. 5369-5377
Author(s):  
Seyyed Ali Davari ◽  
Anthony S. Wexler
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Toxic Metals ◽  
Emission Spectroscopy ◽  
Cost Effective ◽  
The United States ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Single Feature ◽  
Us Epa

Abstract. The United States Environmental Protection Agency (US EPA) list of hazardous air pollutants (HAPs) includes toxic metal suspected or associated with development of cancer. Traditional techniques for detecting and quantifying toxic metals in the atmosphere are either not real time, hindering identification of sources, or limited by instrument costs. Spark emission spectroscopy is a promising and cost-effective technique that can be used for analyzing toxic metals in real time. Here, we have developed a cost-effective spark emission spectroscopy system to quantify the concentration of toxic metals targeted by the US EPA. Specifically, Cr, Cu, Ni, and Pb solutions were diluted and deposited on the ground electrode of the spark emission system. The least absolute shrinkage and selection operator (LASSO) was optimized and employed to detect useful features from the spark-generated plasma emissions. The optimized model was able to detect atomic emission lines along with other features to build a regression model that predicts the concentration of toxic metals from the observed spectra. The limits of detections (LODs) were estimated using the detected features and compared to the traditional single-feature approach. LASSO is capable of detecting highly sensitive features in the input spectrum; however, for some toxic metals the single-feature LOD marginally outperforms LASSO LOD. The combination of low-cost instruments with advanced machine learning techniques for data analysis could pave the path forward for data-driven solutions to costly measurements.

Machine learning techniques for liquid level estimation using FBG temperature sensor array

2021 ◽  
Vol 65 ◽  
pp. 102612
Author(s):  
Katiuski Pereira Nascimento ◽  
Anselmo Frizera-Neto ◽  
Carlos Marques ◽  
Arnaldo Gomes Leal-Junior
Keyword(s):  
Machine Learning ◽  
Temperature Sensor ◽  
Sensor Array ◽  
Liquid Level ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Machine Learning Approaches Reveal That the Number of Tests Do Not Matter to the Prediction of Global Confirmed COVID-19 Cases

2020 ◽  
Vol 3 ◽  
Author(s):  
Md Hasinur Rahaman Khan ◽  
Ahmed Hossain
Keyword(s):  
Machine Learning ◽  
Well Being ◽  
Vital Role ◽  
Health Care Facilities ◽  
Public Health Care ◽  
Machine Learning Techniques ◽  
Disease Spread ◽  
Cumulative Number ◽  
Learning Approaches ◽  
Learning Techniques

Coronavirus disease 2019 (COVID-19) has developed into a global pandemic, affecting every nation and territory in the world. Machine learning-based approaches are useful when trying to understand the complexity behind the spread of the disease and how to contain its spread effectively. The unsupervised learning method could be useful to evaluate the shortcomings of health facilities in areas of increased infection as well as what strategies are necessary to prevent disease spread within or outside of the country. To contribute toward the well-being of society, this paper focusses on the implementation of machine learning techniques for identifying common prevailing public health care facilities and concerns related to COVID-19 as well as attitudes to infection prevention strategies held by people from different countries concerning the current pandemic situation. Regression tree, random forest, cluster analysis and principal component machine learning techniques are used to analyze the global COVID-19 data of 133 countries obtained from the Worldometer website as of April 17, 2020. The analysis revealed that there are four major clusters among the countries. Eight countries having the highest cumulative infected cases and deaths, forming the first cluster. Seven countries, United States, Spain, Italy, France, Germany, United Kingdom, and Iran, play a vital role in explaining the 60% variation of the total variations by us of the first component characterized by all variables except for the rate variables. The remaining countries explain only 20% of the variation of the total variation by use of the second component characterized by only rate variables. Most strikingly, the analysis found that the variable number of tests by the country did not play a vital role in the prediction of the cumulative number of confirmed cases.

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