In order to use our Yosnalab shopping services, you need to read carefully about our terms and conditions. If You disagree any part of terms then you cannot access our service.
I agree to pay the rental rate for the period we used the product and in transit and further agrees to promptly return the products at the end of the rental period in the same condition as received.
I will give alert to Yosnalab if my contact information changes.
I also agree to pay for any damages to, or loss of, the rented merchandise occurring during their time of possession or because of loss and or damage of the products. Upon return and inspection if any and all repairs necessary and or accessories missing that were itemized will be charged at our current rates and billed to you.
A full day rental is charged, even for a partial day use. There are absolutely no refunds for early returns.
The products can be used upto 4 weeks only, Cost will be calculated as per day of usage. We are not responsible for the damage of materials or other liability of any kind resulting from the use or malfunction of the equipment. We will not return the initial deposit of money until the product is returned back.
I am responsible for keeping track of my due dates. I understand that any notices sent out by Yosnalab are a courtesy only and failure to receive them does not excuse me from any charges.
A copy of both sides of their valid institution identification card need to be present. Depositing the money can be done only in the form of cash, not through credit/debit cards.
If you have questions or suggestions, please contact us.
A methodology for reducing error rate using fuzzy logic in the bigdata environment. Fuzzy geospatial models or the missing data results in the spread or even the increase of errors. To deal with the quality storage of fuzzy geospatial big data, we found the methodology that allows us to store consistent (correct) fuzzy geospatial data compliant with multi-database systems. The geographical map data are stored in a database. Sometimes, the data may be reduced and destroyed. So, we are using fuzzy logic to reduce the error rate/confusion matrix in the bigdata environment. Currently, we are witnessing a growing trend in the study and application of problems in the framework of Big Data. This is mainly due to the great advantages which come from the knowledge extraction from a high volume of information. For this reason, we observe a migration of the standard Data Mining systems towards a new functional paradigm that allows at working with Big Data. Employing the MapReduce model and its different extensions, scalability can be successfully addressed, while maintaining a good fault tolerance during the execution of the algorithms. Among the different approaches used in Data Mining, those models based on fuzzy systems stand out for many applications. Among their advantages, we must stress the use of a representation close to the natural language. Additionally, they use an inference model that allows a good adaptation to different scenarios, especially those with a given degree of uncertainty. Despite the success of this type of systems, their migration to the Big Data environment in the different learning areas is at a preliminary stage yet. In this project, we will carry out an overview of the main existing proposals on the topic, analyzing the design of these models. Additionally, we will discuss those problems related to the data distribution and parallelization of the current algorithms, and also its relationship with the fuzzy representation of the information. Finally, we will provide our view on the expectations for the future in this framework according to the design of those methods based on fuzzy sets, as well as the open challenges on the topic.
Why: Problem statement
The data gets duplicated while storing a large amount of data in the database. In a normal data storage technique, they do not have enough memory for deploying more memory tasks. So, some geographical map data get destroyed in a big data environment.
How: Solution description
We found the solution by adding flexibility and scalability of the NoSQL system using map reduced programming model and fuzzy logic to reduced confusion matrix in the bigdata environment. In this project, we will first introduce some concepts on Big Data. Then, we present an overview of the MapReduce programming model that supports scalability in data processing. Finally, we will describe two novel programming frameworks developed as an alternative to the standard MapReduce, under the premise of solving the shortcomings of this model in certain scenarios.
How is it different from competition
Existing system: Fuzzy geospatial models or the missing data results in the spread, or even the increase of errors.
Proposed system: The flexibility and scalability of the NoSQL system. Using map reduced programming model and fuzzy logic, we reduced the calculation simulink in the bigdata environment.
Who are your customers
Common people use geographical map data. It is useful to find the path and reach the destination.
Project Phases and Schedule
Project Phase 1: Find the project of existing system problems and solutions.
Project Phase 2: Implement the project and solved the problem and developed.