scholarly journals Smart Irrigation System for Urban Gardening using Logistic Regression algorithm and Raspberry Pi

2021 ◽  
Vol 2129 (1) ◽  
pp. 012044
Author(s):  
R Aminuddin ◽  
A S Sahrom ◽  
M H A Halim

Abstract People have shown an increasing interest in urban gardening. Irrigation is one of the common methods used to take care of the plant growth. However, the proper irrigation timing of plant is much unclear for most people. Moreover, the manual irrigation is impossible when people do not have physical access to the plant in a long period of time. Hence, a smart irrigation system using Raspberry Pi has been proposed to ease the irrigation. In this system, three different sensors, including moisture, humidity and temperature sensors are installed in the soil of the plant. The collected data from the sensors will be used to predict whether the plant need to be watered or not. This system implements a machine-learning algorithm called Binary Logistic Regression using Python library to test the accuracy of the system. The accuracy of the algorithm to predict the irrigation is 82%. The finding from this study is believed to be helpful as it may contribute to the development of better irrigation system.

Author(s):  
Abdul Karim ◽  
Azhari Azhari ◽  
Samir Brahim Belhaouri ◽  
Ali Adil Qureshi

The fact is quite transparent that almost everybody around the world is using android apps. Half of the population of this planet is associated with messaging, social media, gaming, and browsers. This online marketplace provides free and paid access to users. On the Google Play store, users are encouraged to download countless of applications belonging to predefined categories. In this research paper, we have scrapped thousands of users reviews and app ratings. We have scrapped 148 apps’ reviews from 14 categories. We have collected 506259 reviews from Google play store and subsequently checked the semantics of reviews about some applications form users to determine whether reviews are positive, negative, or neutral. We have evaluated the results by using different machine learning algorithms like Naïve Bayes, Random Forest, and Logistic Regression algorithm. we have calculated Term Frequency (TF) and Inverse Document Frequency (IDF) with different parameters like accuracy, precision, recall, and F1 and compared the statistical result of these algorithms. We have visualized these statistical results in the form of a bar chart. In this paper, the analysis of each algorithm is performed one by one, and the results have been compared. Eventually, We've discovered that Logistic Regression is the best algorithm for a review-analysis of all Google play store. We have proved that Logistic Regression gets the speed of precision, accuracy, recall, and F1 in both after preprocessing and data collection of this dataset.


2021 ◽  
Vol 11 (4) ◽  
pp. 2747-2757
Author(s):  
Y.S.V. Raman ◽  
P. Suresh ◽  
P. Jnana Sudheer ◽  
P. Prudhvi ◽  
S. Gopi ◽  
...  

Agriculture is still a major occupation in rural areas of India. Increase in technology creates many opportunities in different fields and attracts human resources from rural areas. Farmers are facing sever human and natural resource problems. Monitoring crops with low man power is the major problem. Smart crop monitoring and automation irrigation system deals with these problems by developing a mobile application which helps farmer to get detailed information about plant diseases and to use irrigation system efficiently. This model uses image processing techniques to identify the picture of the leaf and also provides information about temperature and moisture on field. The Raspberry Pi is the project's control unit, which controls and executes the entire system's operation. Pi camera is placed at the face of the moving vehicle to take the pictures of the leaves. These pictures are analyzed using convolution neural network which is an efficient machine learning algorithm. If the captured leaf image has a disease that is already in the given dataset then farmer will get output message which contains disease cause and pesticide or fertilizers we need to provide to eradicate the disease. Mobile application also sends the data which is sensed using sensors.


Author(s):  
Windyaning Ustyannie ◽  
S Suprapto

The class imbalance is a condition when one class has a higher percentage than the other then it can affect the accuracy. One method in data mining that can be used to classification is logistic regression method. The method used in this research is RWO-sampling method using random replicate approach for synthetic data generation on descrete attribute. The result of the research can handle the problem of class imbalance, RWO-sampling method with random replicate approach shows better accuracy than RWO-sampling method with roulette and ROS approach. The accuracy value for RWO-Sampling method with roulette and RWO-Sampling approach with random replicate approach has increased to an average of 15.55% of each dataset. As for comparithem with the ROS method has increased an average of 3.7% of each dataset. Furthermore, for testing the underfitting problem in logistic regression, the oversampling method is better than non-oversampling with an increase in accuracy value reaching an average of 2.3% of each dataset.


Author(s):  
Umniy Salamah

The predictions about the number of people with diabetes will be increased which leads to a reduced balanced ratio between the quality of the eye care service providers with the number of patients. The alternative to solve this problem is to provide early detection service for the last condition of eye health in the diabetic patients. To detect the damage of the retina can be done help machine learning algorithm of the logistics regression. The justification for selection the logistic regression algorithm for retina damage detection in this research is that it has been widely used in a variety of machine learning problems where LR can describe the response variables with one or more variables predictors well. The methodology of research contained five phases, including preparation, feature extraction, normalization, classification, evaluation for processing dataset of digital fundus image were provided by EyePACS using scikit-learn as machine learning library and the Python as programming language. As result, we found the accuracy of retina damage detection using logistic regression is 0.7392 with following by F1-score 0.6317, Recall 0.7392, Precision 0.6043 and Kappa 0.0051.


2017 ◽  
Vol 2 (2) ◽  

Background: Gestational diabetes mellitus is a condition that affects many pregnancies and ethnicity appears to be a risk factor. Data indicate that approximately 18% of Tamil women are diagnosed with gestational diabetes mellitus. Today, approximately 50,000 of Tamils live in Switzerland. To date, there is no official tool available in Switzerland that considers the eating and physical activity habits of this migrant Tamil population living in Switzerland, while offering a quick overview of gestational diabetes mellitus and standard dietetics management procedures. The NutriGeD project led by Bern University of Applied Sciences in Switzerland aimed at closing this gap. The aim of this present study was to evaluate the implementation potential of the tools developed in the project NutriGeD for dietetic counseling before their wide scale launch in Swiss hospitals, clinics and private practices. Method: An online survey was developed and distributed to 50 recruited healthcare professionals working in the German speaking region of Switzerland from October – December 2016 (31% response rate). The transcultural tools were sent to participants together with the link to the online survey. The evaluation outcome was analysed using binary logistic regression and cross tabulation analysis with IBM SPSS version 24.0, 2016. Results: 94% (N=47) respondents believed that the transcultural tools had good potential for implementation in hospitals and private practices in Switzerland. A binary logistic regression analysis revealed that the age of participants had a good correlation (42.1%) on recommending the implementation potential of the transcultural tool. The participants with age group 34- 54 years old where the highest group to recommend the implementation potential of the transcultural tool and this was found to be statistically significant (p=0.05). 74% (34 out of 50) of the respondents clearly acknowledged the need for transcultural competence knowledge in healthcare practices. 80% (N =40) of the respondents agreed that the information presented in the counseling display folder was important and helpful while 60% (N= 30) agreed to the contents being clinically applicable. 90% (N=45) participants recommended the availability of the evaluated transcultural tools in healthcare settings in Switzerland. Conclusion: The availability in healthcare practice of the evaluated transcultural tools was greatly encouraged by the Swiss healthcare practitioners participating in the survey. While they confirmed the need for these transcultural tools, feed-backs for minor adjustments were given to finalize the tools before their official launch in practice. The developed materials will be made available for clinical visits, in both hospitals and private practices in Switzerland. The Migmapp© transcultural tool can serve as a good approach in assisting healthcare professionals in all fields, especially professionals who practice in areas associated with diet - related diseases or disorders associated with populations at risk.


2019 ◽  
Vol 34 (Spring 2019) ◽  
pp. 157-173
Author(s):  
Kashif Siddique ◽  
Rubeena Zakar ◽  
Ra’ana Malik ◽  
Naveeda Farhat ◽  
Farah Deeba

The aim of this study is to find the association between Intimate Partner Violence (IPV) and contraceptive use among married women in Pakistan. The analysis was conducted by using cross sectional secondary data from every married women of reproductive age 15-49 years who responded to domestic violence module (N = 3687) of the 2012-13 Pakistan Demographic and Health Survey. The association between contraceptive use (outcome variable) and IPV was measured by calculating unadjusted odds ratios and adjusted odds ratios with 95% confidence intervals using simple binary logistic regression and multivariable binary logistic regression. The result showed that out of 3687 women, majority of women 2126 (57.7%) were using contraceptive in their marital relationship. Among total, 1154 (31.3%) women experienced emotional IPV, 1045 (28.3%) women experienced physical IPV and 1402 (38%) women experienced both physical and emotional IPV together respectively. All types of IPV was significantly associated with contraceptive use and women who reported emotional IPV (AOR 1.44; 95% CI 1.23, 1.67), physical IPV (AOR 1.41; 95% CI 1.20, 1.65) and both emotional and physical IPV together (AOR 1.49; 95% CI 1.24, 1.72) were more likely to use contraceptives respectively. The study revealed that women who were living in violent relationship were more likely to use contraceptive in Pakistan. Still there is a need for women reproductive health services and government should take initiatives to promote family planning services, awareness and access to contraceptive method options for women to reduce unintended or mistimed pregnancies that occurred in violent relationships.


2019 ◽  
Vol 70 (3) ◽  
pp. 184-192
Author(s):  
Toan Dao Thanh ◽  
Vo Thien Linh

In this article, a system to detect driver drowsiness and distraction based on image sensing technique is created. With a camera used to observe the face of driver, the image processing system embedded in the Raspberry Pi 3 Kit will generate a warning sound when the driver shows drowsiness based on the eye-closed state or a yawn. To detect the closed eye state, we use the ratio of the distance between the eyelids and the ratio of the distance between the upper lip and the lower lip when yawning. A trained data set to extract 68 facial features and “frontal face detectors” in Dlib are utilized to determine the eyes and mouth positions needed to carry out identification. Experimental data from the tests of the system on Vietnamese volunteers in our University laboratory show that the system can detect at realtime the common driver states of “Normal”, “Close eyes”, “Yawn” or “Distraction”


Author(s):  
Jeremy Freese

This article presents a method and program for identifying poorly fitting observations for maximum-likelihood regression models for categorical dependent variables. After estimating a model, the program leastlikely will list the observations that have the lowest predicted probabilities of observing the value of the outcome category that was actually observed. For example, when run after estimating a binary logistic regression model, leastlikely will list the observations with a positive outcome that had the lowest predicted probabilities of a positive outcome and the observations with a negative outcome that had the lowest predicted probabilities of a negative outcome. These can be considered the observations in which the outcome is most surprising given the values of the independent variables and the parameter estimates and, like observations with large residuals in ordinary least squares regression, may warrant individual inspection. Use of the program is illustrated with examples using binary and ordered logistic regression.


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