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Research Paper

A speculation technique for the stock market using time series

Using ARIMA (Auto-Regressive Integrated Moving Average) which is a model, this research study presents a method for anticipating stock market values. To begin, historical stock price data is gathered and pre-processed. Pre-processing is a subcategory of Data Mining in which raw original data is processed into a specified format so that the model can use it. Pre-processing is done to fill in the missing values and arrange the data based on the qualities required for developing the prediction model because the raw data to be processed is partly partial or inconsistent with certain errors and missing values. After the data has been filtered and classified, it is standardized by transforming it into a common format that can be used to train the prediction model. The obtained data is then divided into two data - training and testing data, with the majority of the data being used to train the model. And using the variables linked with the stock price, the testing data is fed to the ARIMA model to predict the corresponding stock price on a particular date. The results are used to determine the model's accuracy.

Published by: Rajesh K. S., B. S. Darshith, Amaresh A. Channad, Amaresh R. Vasisth, G. Jayavardhan

Author: Rajesh K. S.

Paper ID: V7I4-1715

Paper Status: published

Published: August 9, 2021

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Thesis

Anomaly detection in pharmaceutical pills using image processing

There are various kinds of technologies available in the market for categorizing and packaging in the production line. Our project here discusses various image processing techniques and their shortcomings in the process. Our aim is to categorize the defective tablets based on their color shape missing tablets, color changes due to oxidation. The images of the tablet strip are captured using the camera. The features are extracted for a defect-less tablet strip. The input image is compared with the reference image present in the database, the amount of features that can be compared with the pre-loaded reference image is compared for a threshold value and is discarded if found to be less by separating them, and rest are allowed to pass for packaging.

Published by: Kaushik T. S., Chandan Prasad, Prajwal V., Dr. Manjula A. V.

Author: Kaushik T. S.

Paper ID: V7I4-1717

Paper Status: published

Published: August 9, 2021

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Others

Asthma prediction using Machine Learning

Patient telemonitoring brings about a conglomeration of huge measures of data about quiet illness direction. Notwithstanding, the possible utilization of this data for the early expectation of asthma in grown-ups has not been methodically assessed. The point of this examination was to investigate the information for building AI calculations that anticipate asthma before they happen. The investigation dataset involved 278847 records presented by grown-up asthma patients. Prescient displaying included readiness of preparing informational indexes, prescient component choice, and assessment of coming about classifiers. AI classifiers are utilized to foster these prescient models; including Random Forest, Logistic Regression, Decision Tree, and Naïve Bayes strategy. Of the multitude of classifiers carried out, strategic relapse classifier brought about the most elevated expectation precision. Our investigation showed that AI methods have huge potential in creating customized choice help for ongoing illness telemonitoring frameworks. Future examinations may profit with a far-reaching prescient system that consolidates information with different elements influencing the probability of creating asthma. Approaches carried out for cutting edge asthma expectations might be stretched out to early mediation of persistent ailments in patients

Published by: Manasa G. V. Kumar, Yogesh R, Soumya ranjan nayak, Vinod R, Shreyas M S

Author: Manasa G. V. Kumar

Paper ID: V7I4-1680

Paper Status: published

Published: August 9, 2021

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Research Paper

Electronic Voting Machine Based on IRIS Authentication

In every election, the commission is facing plenty of troubles and problems throughout the election. The foremost and familiar issue faced by the commission is inappropriate confirmation with relevance to the arrangement of casting the votes, duplication or illegal casting of votes. In this paper, a secure and new electoral system is developed to enhance the present legal system using iris recognition. Iris, one amongst the foremost secure biometric of person identification. The counting of vote is going to be immediately which makes voting process efficient, fast, and secure. There by can reduce the proxies drained the election system or electoral system and elect the right candidates as rulers. All details are updated to web server through IOT. The main goal of this article is to avoid duplication of casting votes.

Published by: M. K. Sivaranjani, S.Santhiya, S.Swathika, P.Ramya, G.Mohanavel

Author: M. K. Sivaranjani

Paper ID: V7I4-1700

Paper Status: published

Published: August 9, 2021

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Review Paper

Review on the effect of preparative parameters of spray pyrolysis technique on optical and electrical properties of Zinc Oxide thin film

The spray pyrolysis technique is one of the most widely used, simple, and inexpensive techniques to deposit thin films of various materials. The physio-chemical properties of these thin films are depending upon preparative parameters such as concentration of precursor solution, dopants, substrate temperature, spray rate, post-annealing treatments, etc. In this paper, an intensive review of the effect of preparative parameters on the optical and electrical properties of ZnO thin films deposited by the spray pyrolysis method has been presented. These properties of the thin film can be precisely controlled by optimizing the preparative parameters. ZnO due to its attractive optical and electrical properties plays an important role in various electronics, optoelectronics, biomedical and sensing applications.

Published by: Nikam S. V., B .T. Jadhav

Author: Nikam S. V.

Paper ID: V7I4-1682

Paper Status: published

Published: August 9, 2021

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Research Paper

Modeling and simulation of the electric vehicle coupled with battery and ultracapacitor

Electric vehicles boom in the market started after the improvement in the battery technology but the improvement needs to be enhanced further. An Electric Vehicle has zero greenhouse gas emissions compared to conventional ICE vehicles or Hybrid Electric Vehicles and hence is a better alternative. An Electric Vehicle has a longer charging time which is a drawback. Hence using Ultracapacitor for fast charging purposes can be coupled with a battery to overcome the drawback. Therefore, improving the transient power capabilities of the battery to satisfy the road load demand is critical. This research studies integration of Ultra-Capacitor (UC) to Battery. The objective is to analyze the effect of integrating UCs on the transient response of the BEV powertrain. UCs have higher power density which can overcome the slow dynamics of batteries. An energy management strategy utilizing a peak power-sharing strategy is implemented. The goal is to decrease the power load on batteries and operate Ultracapacitor in its most efficient region. A complete model to simulate the physical behavior of UC-Integrated BEV is developed using MATLAB/SIMULINK. This increases the life of the battery since its protected from overcurrent. which increases the health of the battery based on the number of charge/discharge cycles.

Published by: Yeshas Raghu Gowda

Author: Yeshas Raghu Gowda

Paper ID: V7I4-1718

Paper Status: published

Published: August 9, 2021

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