Stock exchange analysis using Hadoop user experience (Hue)

Sirisha, N; Kiran, K. V. D.

doi:10.1109/iss1.2017.8389363

Cited by 4 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…P. Lakshmi Prasanna, D. Rajeswara Rao [5] proved that the accuracy of PRO-RNN algorithm did increase opposing the loss of information due to memory inconsistency. N. Sirisha, K.V.D Kiran [6] concluded that with help of Hadoop derived from BigData can improve the performance of stock exchange analysis by learning through the previous results. Poonam Somani, Shreyas Talele, Suraj Sawant [7] opined that by using SVM, Hidden Markov Model , Neural Networks has given more accuracy than the traditional techniques.…”

Section: Deep Learning Techniques Like Long Short Termmentioning

confidence: 99%

Stock Price Prognosticator using Machine Learning Techniques

Nishitha

Bano

Reddy

et al. 2020

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)

View full text Add to dashboard Cite

S tock market price prediction is one of the favourite research topics under consideration for professionals from various fields like mathematics, statistics, history, finance, computer science engineering etc., as it requires a set of skills to predict variation of price of shares in a very volatile and challenging share market scenario. S hare market trading is mostly dependent on sentiments of investors and other factors like economic policies, political changes, natural disasters etc., Many theories were forwarded, mathematical and statistical applications in conjunction with probability, to simplify the complex process. After the advent of computers, it got further simplified but still challenging due to various external influential factors ruling the volatility of the market prices. Thus, AI and ML algorithms were being developed, but for only for next day using Linear Regression procedures.Our project aims to predict the prices of shares more precisely and accurately using special algorithms using RNN by improvising the back propagation, feedback routines to overcome the short-term memory loss involved in RNN thus providing efficiency in LS TM applications.Our project emphasizes how the LS TM applications perform with datasets of extreme, larger and minimal fluctuating data.

show abstract

Section: Deep Learning Techniques Like Long Short Termmentioning

confidence: 99%

Stock Price Prognosticator using Machine Learning Techniques

Nishitha

Bano

Reddy

et al. 2020

2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)

View full text Add to dashboard Cite

show abstract

“…Dimensional modelling allows writing good performance queries for customized reporting by joining different multi-dimensional structures to provide more intuitive queries on individual home energy consumption with respect to neighborhood [22]. The visualization results in the form of graphs, charts, and tables are rendered to a scale of one million homeowners and utility providers in real time through a web-based querying interface [23]. The querying engine for energy consumption visualization can be selected by the consumers and utility providers based on performance analysis results and recommendations.…”

Section: Introductionmentioning

confidence: 99%

“…dataset. An open-source visualization tool[23] on top of distributed file system was used for visualization. Eighteen queries are constructed to enable the user with graphical visualization on consumption per device, per day, per month, and per year, (queries 1-5 are consumer queries, queries 6-10 are for community utility provider,This work is licensed under a Creative Commons Attribution 4.0 License.…”

mentioning

confidence: 99%

Big Data Energy Management, Analytics and Visualization for Residential Areas

et al. 2020

View full text Add to dashboard Cite

With the rapid development of IoT based home appliances, it has become a possibility that home owners share with Utilities in the management of home appliances energy consumption. Thus, the proposed work empowers home owners to manage their home appliances energy consumption and allow them to compare their consumption with respect to their local community total consumption. This serves as a nudge in consumer's behavior to schedule their home appliances operation according to their local community consumption profile and trend. Utilizing the same common communication infrastructure, it also allows the utilities on different consumption levels (community, state , country) to monitor and visualize the energy consumption in their respective grid segments on daily, monthly, and yearly basis. A high-speed distributed computing cluster based on commodity hardware with efficient big data mathematical algorithm is employed in this work. To achieve this, two big data processing paradigms are evaluated with a set of qualitative and quantitative metrics with subsequent recommendations. One million smart meter data is simulated to access individual homes. With the utilization of distributed storage and computing cluster for handling energy big data, the utilities can perform consumer load analysis and visualization on a scale of one million consumers. This helps the utilities in providing consumers a more accurate representation of how much energy they are consuming with greater granularity and with respect to their local community. Consumer and Utility centric queries are developed to create a web-based real time energy consumption management system presented in terms of dashboard charts, graphs, and reports that can be accessed by the consumer and utility providers remotely.

show abstract