NDLI: Machine Learning Facilitated Rice Prediction in Bangladesh

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2014 Annual Global Online Conference on Information and Computer Technology

Table of contents

Chairman's Message

Committees

Reviewers

Machine Learning Facilitated Rice Prediction in Bangladesh

The PSO Algorithm and the Diagnosis of Multiple Sclerosis Using Artificial Neural Networks

A Performance Analysis of Cloud Computing Using the Balanced Scorecard Approach

Cloud Infrastructure for Higher Education: The Sullivan Experience

Linux Containers Simplify Engineering and Scientific Simulations in the Cloud

HTML Educational Node.js System (HENS): An Applied System for Web Development

Mining Bug Database for Detecting Potential Areas of Bug Occurrence

Impact of Affordable Care Act (ACA) on Health Informatics

An Empirical Study into Information Security Governance Focus Areas and their Effects on Risk Management

Management Wake-Up and Govern: The Era of the Cyber Security Governance

User Online Privacy and Identity Management Behaviors: A Comparative Study

An Energy Meter Reader with Load Control Capacity and Secure Switching Using a Password Based Relay Circuit

SYMBOL-BASED INTERFLOW NETWORK CODING FOR WIRELESS NETWORKS

A Fault Tolerant Workflow for Reproducible Research

A Reference Architectural Pattern: Component-Based Scheduling System for Heterogeneous Computing Environment

Finding Fault-Tolerant Solutions for Complete Bipartite Networks

HTML Architecture, a Novel Development System (HANDS): An Approach for Web Development

Identifying Effective IT Project Manager Personality Characteristics

Outsourcing Information and Computer Technology

Author index

Machine Learning Facilitated Rice Prediction in Bangladesh

Content Provider	IEEE Xplore Digital Library
Author	Rahman, M.M. Haq, N. Rahman, R.M.
Copyright Year	2014
Description	Author affiliation: Electr. & Comput. Eng. Dept., North South Univ., Dhaka, Bangladesh (Rahman, M.M.; Haq, N.; Rahman, R.M.)
Abstract	The climate of a region is often determined by its landscape and amount of vegetation present in it. Environment parameters such as rainfall, wind-speed and humidity are highly influenced by these alluvial features. Bangladesh, a country situated on the banks of the Himalaya, does not have a homogeneous topography. Human settlement over the course of centuries has led to pockets of micro-regions. Each of those regions has a different micro climate. An entrepreneur involved in the food industry therefore has to carefully choose regions of land that will give him/her the desirable production. In this study a research initiative has been taken to predict the yield of crops using machine learning models. The models were at first trained on the correlation between past environmental patterns and crop production rate. Then the models are compared to measure their effectiveness on unknown climatic variables.
Starting Page	1
Ending Page	4
File Size	393373
Page Count	4
File Format	PDF
ISBN	9781479983117
DOI	10.1109/GOCICT.2014.9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Linear regression Bangladesh climate Neural network Self organizing maps (SOM) Decision tree K-means clustering Generalized linear regression (GLM) Regression tree Bangladesh rice yield Production Vegetation Humidity Agriculture Ensemble learning Regression tree analysis
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Applying data mining techniques to predict annual yield of major crops and recommend planting different crops in different districts in Bangladesh

Integrated phenology and climate in rice yields prediction using machine learning methods

Crops yield prediction based on machine learning models: Case of West African countries

Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data

Clustering Properties of Hierarchical Self-Organizing Maps (1992)

Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART)

Studi Komparatif Penerapan Metode Hierarchical, K-means Dan Self Organizing Maps (som) Clustering Pada Basis Data

Segmental K-Means initialization for SOM-based speaker clustering

Predictive ability of machine learning methods for massive crop yield prediction

Machine Learning Facilitated Rice Prediction in Bangladesh

Similar Documents

Applying data mining techniques to predict annual yield of major crops and recommend planting different crops in different districts in Bangladesh

Integrated phenology and climate in rice yields prediction using machine learning methods

Crops yield prediction based on machine learning models: Case of West African countries

Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data

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Segmental K-Means initialization for SOM-based speaker clustering

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Machine Learning Facilitated Rice Prediction in Bangladesh