scholarly journals Crop Yield and Rainfall Prediction in Tumakuru District using Machine Learning

2019 ◽  
Author(s):  
Girish L
Keyword(s):  
Machine Learning ◽  
Call Center ◽  
Market Price ◽  
Predictive Analysis ◽  
Learning Technology ◽  
Rainfall Prediction ◽  
Analysis Technique ◽  
The Future ◽  
The Government ◽  
Smart Agriculture

Smart Agriculture is a development that emphasizes the use of information technology in the farming. Mostof the population in India depending on agriculture. This situation is one of the reason, that hindering the developmentof country. Nowadays, even though farmers get more yield for their crop but the market price for that crop will be less,in that case farmers get loss for their product and vice versa. Particularly, when growing new crops, farmers face therisks of both market price and production problems. To overcome these problems, a machine learning technology isused. Predictive analysis is a branch of data mining which predicts the future probabilities and trends. The predictionwill help the farmers to choose whether the particular crop is suitable for specific rainfall and crop price values. Thisapproach is to increase the net yield rate of the crop, based on rainfall. Prediction can be carried out by using variousmachine learning algorithms like linear regression, SVM, K NN method and decision tree algorithm out of which SVMis giving the highest efficiency. The predictive analysis technique can be implemented in several government sectors likeAPMC, kissan call center etc., by which the government and farmers can get the information of the future rainfall, cropyield and the market price.

Swarm Intelligence Optimization: An Exploration and Application of Machine Learning Technology

2021 ◽  
Vol 30 (1) ◽  
pp. 460-469
Author(s):  
Yinying Cai ◽  
Amit Sharma
Keyword(s):  
Machine Learning ◽  
Swarm Intelligence ◽  
Research Result ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
Data Set ◽  
Rice Pests ◽  
Machine Leaning ◽  
Smart Agriculture ◽  
Swarm Intelligence Optimization

Abstract In the agriculture development and growth, the efficient machinery and equipment plays an important role. Various research studies are involved in the implementation of the research and patents to aid the smart agriculture and authors and reviewers that machine leaning technologies are providing the best support for this growth. To explore machine learning technology and machine learning algorithms, the most of the applications are studied based on the swarm intelligence optimization. An optimized V3CFOA-RF model is built through V3CFOA. The algorithm is tested in the data set collected concerning rice pests, later analyzed and compared in detail with other existing algorithms. The research result shows that the model and algorithm proposed are not only more accurate in recognition and prediction, but also solve the time lagging problem to a degree. The model and algorithm helped realize a higher accuracy in crop pest prediction, which ensures a more stable and higher output of rice. Thus they can be employed as an important decision-making instrument in the agricultural production sector.

scholarly journals Prediction Model of Football World Cup Championship Based on Machine Learning and Mobile Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yanyang Bai ◽  
Xuesheng Zhang
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Clustering Algorithm ◽  
Technological Development ◽  
Rapid Development ◽  
Evidence Theory ◽  
Predictive Analysis ◽  
Learning Technology ◽  
World Cup ◽  
Python Programming

With the technological development and change of the times in the current era, with the rapid development of science and technology and information technology, there is a gradual replacement in the traditional way of cognition. Effective data analysis is of great help to all societies, thereby drive the development of better interests. How to expand the development of the overall information resources in the process of utilization, establish a mathematical analysis–oriented evidence theory system model, improve the effective utilization of the machine, and achieve the goal of comprehensively predicting the target behavior? The main goal of this article is to use machine learning technology; this article defines the main prediction model by python programming language, analyzes and forecasts the data of previous World Cup, and establishes the analysis and prediction model of football field by K-mean and DPC clustering algorithm. Python programming is used to implement the algorithm. The data of the previous World Cup football matches are selected, and the built model is used for the predictive analysis on the Python platform; the calculation method based on the DPC-K-means algorithm is used to determine the accuracy and probability of the variables through the calculation results, which develops results in specific competitions. Research shows how the machine wins and learns the efficiency of the production process, and the machine learning process, the reliability, and accuracy of the prediction results are improved by more than 55%, which proves that mobile algorithm technology has a high level of predictive analysis on the World Cup football stadium.

scholarly journals Share Market Prediction using Deep Neural Network

2019 ◽  
Vol 8 (3) ◽  
pp. 8619-8622
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Stock Market ◽  
Deep Neural Network ◽  
Learning Technology ◽  
Financial Investment ◽  
The Future ◽  
Future Prediction

People, due to their complexity and volatile actions, are constantly faced with challenges in understanding the situation in the market share and the forecast for the future. For any financial investment, the stock market is a very important aspect. It is necessary to study while understanding the price fluctuations of the stock market. In this paper, the stock market prediction model using the Recurrent Digital natural Network (RDNN) is described. The model is designed using two important machine learning concepts: the recurrent neural network (RNN), multilayer perceptron (MLP) and reinforcement learning (RL). Deep learning is used to automatically extract important functions of the stock market; reinforcement learning of these functions will be useful for future prediction of the stock market, the system uses historical stock market data to understand the dynamic market behavior when you make decisions in an unknown environment. In this paper, the understanding of the dynamic stock market and the deep learning technology for predicting the price of the future stock market are described.

scholarly journals A Comprehensive Framework for Epidemic Response in Smart cities through Machine Learning and NFA

2020 ◽  
Vol 9 (3) ◽  
pp. 2896-2901
Keyword(s):  
Machine Learning ◽  
Smart Cities ◽  
Finite Automata ◽  
Accurate Information ◽  
Learning Technology ◽  
Time Data ◽  
Health Security ◽  
Emergency Situations ◽  
The Government ◽  
Comprehensive Framework

Expertise in early detection against intimidating devastation using preventive procedure is an extremely challenging mission of this modern civilization. Sudden increases in the number of cases of disease threaten public health security. Guiding the public for the period of emergency situations plays a crucial role and this procedure saves countless lives. In this paper, we have developed a predictive model. When Epidemic predicted earlier on a proper and speedy response, the losses can be mitigated with the help of learning technology in AI. We proposed a comprehensive framework for the Epidemic management system which employs cumulated knowledge base construction through Machine Learning and Non-Deterministic Finite Automata technique. In this investigation, Real-time data acquisition, sharing accurate information and precise decision making are greatly augmented. This proposed model will enhance the government agencies and responders to act upon at all outbreaks.

Government Needs to Get Its Own House in Order

2021 ◽  
pp. 91-102
Author(s):  
Richard A. Clarke
Keyword(s):  
Machine Learning ◽  
National Security ◽  
Rapid Review ◽  
Learning Technology ◽  
Sensitive Material ◽  
Sensitive Data ◽  
Field Investigations ◽  
Highly Sensitive ◽  
The Media ◽  
The Government

To address leaks of classified information, the government should focus not on the media but on improving its monitoring of employees with access to sensitive data and changing how it handles “authorized” leaks. National security agencies should adopt “machine learning” technology to monitor activities of those in government with access to highly sensitive material. Polygraphs, field investigations, and additional screenings that the government currently uses are generally not efficacious. Machine learning technology would be far more effective and would be ethical because it is limited in scope and those who choose the relevant positions would be aware of the monitoring. To address “authorized” leaks, which are not actually authorized, the government should consider creating a senior interagency mechanism for rapid review of declassification requests from senior officials. This would clarify that a failure to adhere to policy-related disclosure procedures is a clear, serious violation, rather than “just something everybody does.”

Exploration and Application of Machine Learning Technology Based on Swarm Intelligence Optimization

2019 ◽  
Vol 13 ◽  
Author(s):  
Rui-rui Cai
Keyword(s):  
Machine Learning ◽  
Swarm Intelligence ◽  
Research Result ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
Data Set ◽  
Rice Pests ◽  
Machine Leaning ◽  
Smart Agriculture ◽  
Swarm Intelligence Optimization

Background: In the agriculture development and growth, the intelligent machinery and equipment plays an important role. Various researchers are involved for implementing the research and patents to aid the smart agriculture and author reviewer that machine leaning technologies are providing the best support for this growth. Method: To explore machine learning technology and machine learning algorithms, mostly based on swarm intelligence optimization, and their applications are studied. An optimized V3CFOA-RF model is built through V3CFOA. The algorithm is tested in the data set collected concerning rice pests, later analysed and compared in detail with other existing algorithms. Results: The research result shows that the model and algorithm proposed are not only more accurate in recognition and prediction, but also solve the time lagging problem to a degree. Conclusion: The model and algorithm helped realise a higher accuracy in crop pest prediction, which ensures a more stable and higher output of rice. Thus they can be employed as an important decision-making instrument in the agricultural production sector.

scholarly journals An Efficient Suicide based Dataset using Machine Learning Algorithms

2019 ◽  
Vol 8 (12S) ◽  
pp. 1267-1271
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Indian Government ◽  
The Future ◽  
Death State ◽  
The Government ◽  
Government Records ◽  
Major Factors ◽  
Year 2000

A study is presented on analyzing the major factors that affect the number of suicides in different parts of India from year 2000 to 2012 and using them to predict the number of suicides in the future. By analyzing the data and predicting the major causes of suicides it can help government to know which part of population is most affected, so that the government can provide required steps to avoid suicides. The Indian government records the database of each suicide occurs in India. Along with the age-group, cause of death, state of victim, this data was made public by crime branch bureau of the data analytics purpose. Relationship will be made between the different features of suicide so that a linear relationship can be formed with the help of linear regression and other machine learning algorithms will be used to develop a model for the prediction of number of suicides in the future. It has been found that the results obtained by machine learning algorithms are more accurate when compared with the traditional algorithms.

scholarly journals Analisis Sistem Penanggulangan Gawat Darurat Terpadu Melalui Implementasi Jember Safety Center Di Kabupaten Jember

2020 ◽  
Vol 3 (2) ◽  
pp. 61
Author(s):  
Desy Iswari Amalia ◽  
Isa Ma’rufi ◽  
Dewi Rokhmah
Keyword(s):  
Human Resources ◽  
Call Center ◽  
Service Management ◽  
Analysis Model ◽  
District Health ◽  
The Public ◽  
Study Approach ◽  
Analysis Technique ◽  
Collection Data ◽  
The Government

Some incidence of emergency in Jember District tends to increase every year. The Jember Safety Center (JSC) is an alternative solution to problems carried out by the Jember District in dealing with the high number of emergency cases in Jember District. Based on the results of the preliminary survey, researchers find several aspects, tasks, and functions of the JSC that have problems in its implementation. These problems indicate the need for more in-depth research related to the input and process aspects of the implementation of the JSC in Jember District. This study aims to analyze an integrated emergency response system through the implementation of the Jember Safety Center in Jember District. This type of research is qualitative research with a case study approach. The data analysis technique used the Miles & Huberman analysis model. They are data collection, data reduction, data presentation, and conclusions. This research is taken place at the location of the JSC call center, the Jember District Health Office, the Public Health Center in Jember District, the JSC user home, and the Soebandi Jember Hospital. The results of the study show that the need for human resources in organizing the Public Safety Center is guided by the Minister of Health Regulation Number 19 of 2016. However, the quality of human resources needs to be improved. The current budget given by the government is still focused on the physical budget and JSC equipment. Information and emergency calls from the community are still lacking, this is due to inadequate socialization, and not all services via the village ambulance are recorded through the JSC Call Center. It is necessary to improve the service management of JSC as the center for the Integrated Emergency Management System (SPGDT) in Jember District. Improvements are made by taking into account the input aspects consisting of Men, Money, Materials, Machines, and Methods in the Implementation of the Jember Safety Center in Jember District to improve services JSC to the community in Jember District Keywords: Jember Safety Center, Emergency, AKI and AKB

scholarly journals Stock Market Prediction Using Machine Learning Techniques

2021 ◽  
Author(s):  
Reshma R ◽  
Usha Naidu S ◽  
Sathiyavathi V ◽  
SaiRamesh L
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Market Price ◽  
Future Trend ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Future Market ◽  
Learning Techniques ◽  
The Future ◽  
Rbf Kernel

Predicting the future in all the areas using machine learning techniques was the recent research in the current scenario. Stock market is one among them which needs the prediction future market to invest in the new enterprise or to sell their existing shares to get profit. This need the efficient prediction technique which studies the previous exchanges of stock market and gives the future prediction based on that. This article proposed the prediction system of stock market price based on the exchange takes place in previous scenario. The system studies the diversing effect of market price of product in a particular time gap and analyze its future trend whether it’s loss or gain. During the system of thinking about diverse strategies and variables that should be taken into account, we observed out that strategies like random forest, Support vector machine and regression algorithm. Support vector regression is a beneficial and effective gadget gaining knowledge of approach to apprehend sample of time collection dataset. The data collected for the four years duration which was accumulated to get the expecting prices of the share of the firm. It can produce true prediction end result if the fee of essential parameters may be decided properly. It has been located that the guide vector regression version with RBF kernel indicates higher overall performance while in comparison with different models.

Corona virus-related Disease Pandemic: A Review on Machine Learning Approaches and Treatment Trials on Diagnosed Population for Future Clinical Decision Support

2021 ◽  
Vol 17 ◽  
Author(s):  
Reyana A ◽  
Sandeep Kautish
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Clinical Trials ◽  
Clinical Decision ◽  
Learning Technology ◽  
Learning Approaches ◽  
Related Disease ◽  
Treatment Trials ◽  
Corona Virus ◽  
The Government

Objective: Corona virus-related disease, a deadly illness, has raised public health issues worldwide. The majority of individuals infected are multiplying. The government takes aggressive steps to quarantine people, people exposed to infection, and clinical trials for treatment. Subsequently recommends critical care for the aged, children, and health-care personnel. While machine learning methods have been previously used to augment clinical decisions, there is now a demand for "Emergency ML." With rapidly growing datasets, there also remain important considerations when developing and validating ML models. Methods: This paper reviews the recent study that applies machine-learning technology addressing Corona virus-related disease issues' challenges in different perspectives. The report also discusses various treatment trials and procedures on Corona virus-related disease infected patients providing insights to physicians and the public on the current treatment challenges. Results: The paper provides the individual with insights into certain precautions to prevent and control the spread of this deadly disease. Conclusion: This review highlights the utility of evidence-based machine learning prediction tools in several clinical settings, and how similar models can be deployed during the Corona virus-related disease pandemic to guide hospital frontlines and health-care administrators to make informed decisions about patient care and managing hospital volume. Further, the clinical trials conducted so far for infected patients with Corona virus-related disease addresses their results to improve community alertness from the viewpoint of a well-known saying, “prevention is always better."

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