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Crop Recommendation and Yield Estimation Using Machine Learning

Journal of Mobile Multimedia 2022年第3期18卷 861-884页

作者： Ashwitha, A. Latha, C.A. Dept. of ISE MSRIT Bangalore India Dept. of CSE AMCEC Bangalore India Affiliated to Visvesvaraya Technological University Karnataka Belagavi India

In most developing countries like India, Agriculture is seen as one of the most widely followed habitations and contributes majorly to the country’s economy. Agriculture provides the primary source of food, income, livelihood and employment to the majority of rural populations in India. Many crops are destroyed every year due to a lack of technical knowledge and unpredictable weather patterns such as temperature, rainfall, and other atmospheric parameters, which play a massive role in deciding the crop yield and profit. Therefore, choosing the right crop to grow and enhancing crop yield is an essential aspect of improving real-life farming scenarios. One of the motives is to collect and integrate the agricultural data from specific regions that may be used to analyse the optimal crop and estimate the crop yield. This script is novel by using simple crop, soil and weather parameters like crop, the area under cultivation, nitrogen, phosphorus and potassium content of the soil, season, average rainfall and temperature of a district in Karnataka, India. The user can predict the most suitable crop and its estimated yield for a chosen year. This model uses primary classification, techniques like the random forest, k-NN, logistic regression, decision tree, XGBoost, SVM and gradient boosting classifier for determining the most suitable crop and regression algorithms like Linear regression, k-NN, DBSCAN, Random Forest and ANN algorithm to estimate the yield of the most optimal crop identified earlier. The algorithm that has the least mean error is chosen for prediction and estimation and thus gives better results than the particular machine learning algorithm domain. There is a web interface for ease of use for end-users. Therefore, this project assists the farmers in choosing the suitable crop that can be grown in a particular region during a specific season or specific period and estimate its yield and predict if the recommended crop is profitable. Hence this project helps

关键词： Agriculture Classification algorithms Crop recommendation Machine learning regression algorithms Yield estimation

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Wind Power Modeling based on Data Augmentation and Stacking Integrated Learning 41

Wind Power Modeling based on Data Augmentation and Stacking ...

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第41届中国控制会议

作者： Wenting Zha Ye Jin Zhiyan Li Yalong Li School of Electrical Mechanical and Information EngineeringChina University of Mining and Technology (Beijing) China Airport Construction Group Co. Ltd.

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In order to improve the modeling accuracy,this paper investigates the problem of the wind power modeling method based on the data augmentation technology and stacking integrated learning ***,since the raw wind power data are scarce and unevenly distributed,various flexible data augmentation techniques including jittering,scaling,magnitude warping,and time warping are used to transform the original data to expand the ***,in order to build a stacking integrated model of wind power,the Long Short-Term Memory(LSTM),Gated Recurrent Unit(GRU),and Convolutional Neural Network(CNN)based on time domain are selected as primary learners,and a stable Support Vector regression(SVR) algorithm is used as a secondary ***,the ablation and comparative experiments are conducted according to the measured data of a wind farm in *** experimental results show that the data augmentation technique significantly improves the robustness of each learner,and the proposed stacking integrated model is more effective compared with the single learner model,whose modeling accuracy has been effectively improved.

关键词： Wind farm Wind power modeling Data augmentation Integrated learning regression algorithms

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Impact of Genealogical Features in Transthyretin Familial Amyloid Polyneuropathy Age of Onset Prediction 12th

Impact of Genealogical Features in Transthyretin Familial Am...

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12th International Conference on Practical Applications of Computational Biology and Bioinformatics (PACBB)

作者： Pedroto, Maria Jorge, Alipio Mendes-Moreira, Joao Coelho, Teresa Univ Porto 1 LIAAD INESC TEC Porto Portugal Ctr Hosp Porto CHP Unidade Corino Andrade UCA P-4099001 Porto Portugal

ISBN: (纸本)9783319987026;9783319987019

Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP) is a neurological genetic disease that propagates from one family generation to the next. The disease can have severe effects on the life of patients after the first symptoms (onset) appear. Accurate prediction of the age of onset for these patients can help the management of the impact. This is, however, a challenging problem since both familial and non-familial characteristics may or may not affect the age of onset. In this work, we assess the importance of sets of genealogical features used for Predicting the Age of Onset of TTR-FAP Patients. We study three sets of features engineered from clinical and genealogical data records obtained from Portuguese patients. These feature sets, referred to as Patient, First Level and Extended Level Features, represent sets of characteristics related to each patient's attributes and their familial relations. They were compiled by a Medical Research Center working with TTR-FAP patients. Our results show the importance of genealogical data when clinical records have no information related with the ancestor of the patient, namely its Gender and Age of Onset. This is suggested by the improvement of the estimated predictive error results after combining First and Extended Level with the Patients Features.

关键词： Genealogical data regression algorithms Relevancy estimation Feature construction

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Non-invasive modeling of heart rate and blood pressure from a photoplethysmography by using machine learning techniques

Non-invasive modeling of heart rate and blood pressure from ...

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International Conference on COMmunication Systems and NETworkS (COMSNETS)

作者： Nidigattu, Govinda Rao Mattela, Govardhan Jana, Sayan Indian Inst Technol IIT Hyderabad India Redpine Signals Inc San Jose CA 95131 USA

ISBN: (纸本)9781728131870

Cardiovascular diseases(CVD) is one of the major causes of deaths in the world, which may damage the endothelium cells which may lead to atherosclerosis and cardiac arrhythmias. Blood pressure is an important parameter and indicator in cardiovascular disease, patients with CVD who have multiple risk factors such as hypertension, stress, and obesity have been increasing. Therefore, it is important in the field of cardiovascular disease prevention to predict those at risk of cardiovascular diseases in the general population. Electrocardiogram is not suitable for wearable devices and PPG is a non-invasive, inexpensive, and convenient diagnostic tool for monitoring of heart and blood pressure. Here, we present a PPG (photoplethysmography) based non-invasive detection of heart rate and blood pressure, containing 1260 segments from 140 subjects an age range of 20 - 50 years. Data acquisition was carried out using the standard operating procedures. The present study investigates the photoplethysmography signal filtering of various noise removal, extraction of PPG morphological features and its derivatives which contain a blood circulatory system information, and finally derived forty five diagnostic engineered features. A novel signal processing technique was applied to extract salient pulse wave for heart rate and blood pressure detection. The subset of optimal features was extracted using feature extraction methods in relation to the physiology of heart rate and blood pressure processes. Prediction of heart rate, systolic blood pressure and diastolic blood pressure with the root mean squared error (RMSE) of 4.3 beats per minute, 5.7 mmHg and 5.5 mmHg between Sphygmomanometer and PPG from 10-fold cross-validation method.

关键词： Cardiovascular diseases photoplethysmography heart rate systolic blood pressure diastolic blood pressure signal filtering feature engineering machine learning regression algorithms

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An Analysis of Car Price Prediction using Machine Learning 24

An Analysis of Car Price Prediction using Machine Learning

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9th International Conference on Machine Learning Technologies (ICMLT)

作者： Bhatnagar, Parth Gururaj, H. L. Shreyas, J. Flammini, Francesco Gautam, Shivansh Manipal Acad Higher Educ Manipal Inst Technol Bengaluru Manipal India Univ Appl Sci & Arts Southern Switzerland IDSIA USI SUPSI Via Santa 1 CH-6962 Lugano Switzerland

ISBN: (纸本)9798400716379

This research paper explores machine learning techniques, such as voting regressors, gradient boosting regressors, random forest regressors, decision tree regressors, and support vector regressors, for car predicting the car price. Each machine learning technique has its own unique advantages and disadvantages, with the voting regressor exhibiting the best results. Methodologically, GridSearchCV is used to tune hyperparameters on a dataset of more than 200 automobiles, each with 26 parameters. The outcomes demonstrate the predictive power of regression and ensemble techniques, providing insightful information to practitioners in the business and academics alike. The training accuracies range from 16.87% (MAPE) for Linear regression, 96.78% for Decision Tree Regressor, 96.49% for Random Forest Regressor, 97.84% for Gradient Boosting Regressor,95.8% for Voting Regressor, 81.89% for Support Vector Regressor, notably the testing accuracies vary from 19.44% (MAPE) for Linear regression, 87.76% for Decision Tree Regressor, 89.75% for Random Forest Regressor, 88.67% for Gradient Boosting Regressor, 88.02% for Voting Regressor, 79.55% for Support Vector Regressor.

关键词： Car Price Machine Learning algorithms regression algorithms Car Names and Car Models

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kNN estimation of the unilateral dependency measure between random variables 5

kNN estimation of the unilateral dependency measure between ...

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IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

作者： Cataron, Angel Andonie, Razvan Chueh, Yvonne Transylvania Univ Brasov Elect & Comp Dept Brasov Romania Cent Washington Univ Dept Comp Sci Ellensburg WA USA Cent Washington Univ Dept Math Ellensburg WA USA

ISBN: (纸本)9781479945184

The informational energy (IE) can be interpreted as a measure of average certainty. In previous work, we have introduced a non-parametric asymptotically unbiased and consistent estimator of the IE. Our method was based on the kth nearest neighbor (kNN) method, and it can be applied to both continuous and discrete spaces, meaning that we can use it both in classification and regression algorithms. Based on the IE, we have introduced a unilateral dependency measure between random variables. In the present paper, we show how to estimate this unilateral dependency measure from an available sample set of discrete or continuous variables, using the kNN and the naive histogram estimators. We experimentally compare the two estimators. Then, in a real-world application, we apply the kNN and the histogram estimators to approximate the unilateral dependency between random variables which describe the temperatures of sensors placed in a refrigerating room.

关键词： pattern classification regression analysis IE classification algorithms informational energy kth nearest neighbor kNN estimation kNN method nai?ve histogram estimators random variables regression algorithms unilateral dependency Approximation methods Estimation Histograms Ice Probability density function Random variables Temperature sensors Random variable histograms Temperature Sensor Device Component Approximation method Pattern recognition Probability density function Ice Nearest neighbor Joint Research Centre regression analysis Sorting algorithm Unilateral

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Machine learning techniques for vector control of permanent magnet synchronous motor drives

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COGENT ENGINEERING 2024年第1期11卷

作者： Tom, Ashly Mary Daya, J. L. Febin Vellore Inst Technol Sch Elect Engn Chennai campus Chennai India Vellore Inst Technol Chennai Elect Vehicles Incubat Testing & Res Ctr Chennai 600127 Tamil Nadu India

In the conventional vector control technique for motor drive, Proportional-Integral (PI) controllers are being used, which are sensitive to parameter variations of the drive system. This article presents Machine Learning (ML)-based controllers for a surface permanent magnet synchronous motor (PMSM) drive system. In this work, ML-based regression algorithms such as linear regression, support vector machine regression and feedforward neural network are investigated for speed control application. The entire vector control scheme implementing the ML-based control algorithms is investigated theoretically and simulated under various dynamic operating conditions. Simulation results and performance metrics are compared with those of the conventional PI controller, and they validate the effectiveness of the proposed control algorithms for speed control applications. The proposed ML-based controllers have the ability to meet the speed tracking requirements in the closed-loop system, with performance metrics superior to those of the PI controller, by an average value of 20% for different test scenarios. The transient levels of the motor drive reduce by 0.02% while using the proposed controllers.

关键词： Controllers machine learning (ML) neural network permanent magnet synchronous motor (PMSM) regression algorithms vector control

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Data Mining Approach for Feature Based Parameter Tunning for Mixed-Integer Programming Solvers

Data Mining Approach for Feature Based Parameter Tunning for...

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International Conference on Computational Science (ICCS)

作者： Vilas Boas, Matheus G. Santos, Haroldo G. Martins, Rafael de S. O. Merschmann, Luiz H. C. Univ Fed Ouro Preto Dept Comp Ouro Preto Brazil Univ Fed Ouro Preto Dept Informat Syst & Comp Joao Monlevade Brazil Univ Fed Lavras Dept Comp Sci Lavras Brazil

Integer Programming (IP) is the most successful technique for solving hard combinatorial optimization problems. Modern IP solvers are very complex programs composed of many different procedures whose execution is embedded in the generic Branch & Bound framework. The activation of these procedures as well the definition of exploration strategies for the search tree can be done by setting different parameters. Since the success of these procedures and strategies in improving the performance of IP solvers varies widely depending on the problem being solved, the usual approach for discovering a good set of parameters considering average results is not ideal. In this work we propose a comprehensive approach for the automatic tuning of Integer Programming solvers where the characteristics of instances are considered. Computational experiments in a diverse set of 308 benchmark instances using the open source COIN-OR CBC solver were performed with different parameter sets and the results were processed by data mining algorithms. The results were encouraging: when trained with a portion of the database the algorithms were able to predict better parameters for the remaining instances in 84% of the cases. The selection of a single best parameter setting would provide an improvement in only 56% of instances, showing that great improvements can be obtained with our approach. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： feature based parameter tunning mixed-integer programming data mining regression algorithms coin-or branch and cut

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Analysis of the Dimensionality Issues in House Price Forecasting Modeling 5

Analysis of the Dimensionality Issues in House Price Forecas...

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5th IEEE International Conference of Women in Data Science (WiDS-PSU)

作者： Sakri, Sapiah Binti Ali, Zaiton Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh 11671 Saudi Arabia Prince Sultan Univ Dept Finance Coll Business Adm Riyadh Saudi Arabia

ISBN: (纸本)9781665408127

House price fluctuations in the real estate market occur due to the effects of other variables that are correlated to housing prices. Some prices cannot be controlled or it is not known when they will increase or decrease. These fluctuations accord with the hedonic pricing theory, which suggests that house pricing depends on the composite features. Our study explores this econometric concept using machine teaming algorithms in the effort to accurately forecast house prices. To build the forecasted models, we use the Ames housing dataset, which include 82 explanatory features and 2930 entries on housing sales in Ames, Iowa, USA. We use particle swarm optimization feature selection to address the dataset dimensionality issues for both classification-based models and regression-based models. With dimension reduction, the experimental results demonstrate that the house price prediction accuracy increased from 81.4% to 84.4% for classification-based models, and the price prediction error decreased from 13.3% to 6.9% for regression-based models. The experimental results demonstrate that there is a substantial difference between using and not using feature selection in terms of the performance in predicting housing prices. The main benefit of applying feature selection is that it facilitates the selection of the more relevant and correlated features to prevent overfitting of the model with too many irrelevant features.

关键词： Classification algorithms hedonic house pricing model house price prediction particle swarm optimization feature selection regression algorithms

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Improved Stock Price Prediction by Integrating Data Mining algorithms and Technical Indicators: A Case Study on Dhaka Stock Exchange 9th

Improved Stock Price Prediction by Integrating Data Mining A...

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9th International Conference on Computational Collective Intelligence (ICCCI)

作者： Hasan, Syeda Shabnam Rahman, Rashida Mannan, Noel Khan, Haymontee Moni, Jebun Nahar Rahman, Rashedur M. North South Univ Plot 15Block B Dhaka 1229 Bangladesh

ISBN: (纸本)9783319670744;9783319670737

This paper employs a number of machine learning algorithms to predict the future stock price of Dhaka Stock Exchange. The outcomes of the different machine learning algorithms are combined to form an ensemble to improve the prediction accuracy. In addition, two popular and widely used technical indicators are combined with the machine learning algorithms to further improve the prediction performance. To evaluate the proposed techniques, historical price and volume data over the past 15 months of three prominent stocks enlisted in Dhaka Stock Exchange are collected, which are used as training and test data for the algorithms to predict the 1-day, 1-week and 1-month-ahead prices of these stocks. The predictions are made both on training and test data sets and results are compared with other existing machine learning algorithms. The results indicate that the proposed ensemble approach as well as the combination of technical indicators with the machine learning algorithms can often provide better results, with reduced overall prediction error compared to many other existing prediction algorithms.

关键词： Stock prediction Machine learning regression algorithms Time series forecast Technical indicators

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