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Research Paper

Measurement of service quality in the banking system -A case study of State Bank of India (SBI)

The rapid growth in population and technology in India has led to rapid development in the service industry in the country. The aim of this study is to establish the aspects of the customer’s view on the service quality offered at the State Bank of India (SBI). To place customer’s needs into the quality dimension, a Survey questionnaire is developed to obtain customers' responses on the level of quality of services offered. Then, Factor analysis (FA) was carried out on the customer responses obtained through a questionnaire survey. Analytic Hierarchy Process (AHP) basically helps to establish the weights of the banking service quality dimensions. Technique for Order Preference Similarity to Ideal Solution (TOPSIS) is employed to obtain the ranking of different branches basing on the weights of quality dimension obtain from AHP.

Published by: Levi Ptang Uny Cheworei

Author: Levi Ptang Uny Cheworei

Paper ID: V7I4-1727

Paper Status: published

Published: August 11, 2021

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Research Paper

Heart disease clustering using K-Mean analysis

Clustering is a method of grouping records in a database based on certain criteria. One method of clustering is K-Means Clustering. K-Means Clustering divides data into multiple data sets and can accept data inputs without class labels. This research uses the K-Means Clustering method and implements it on the Heart disease dataset. In this paper, the risk factors that cause heart disease are considered and predicted using the K-means algorithm and the analysis is carried out using publicly available data for heart disease. The dataset holds 209 records with 8 attributes such as age, chest pain type, blood pressure, blood glucose level, ECG in rest, heart rate, and four types of chest pain. To predict heart disease, a K-means clustering algorithm is used along with data analytics and visualization tool. The paper discusses the pre-processing methods, classifier performances, and metrics. In the result section, the visualized data shows that the prediction is accurate.

Published by: Gomanth D Reddy

Author: Gomanth D Reddy

Paper ID: V7I4-1730

Paper Status: published

Published: August 11, 2021

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Research Paper

Modeling and CFD simulation of double pipe heat exchanger with different mass flow rates

In this study, the efficiency of a double pipe heat exchanger with the plain tube as well as coil insert tube is investigated by using water and various nanofluids mixed with water. Silicon Oxide and silver nanofluid are the nanofluids mixed with water at volume fractions of 0.35 percent. The properties of nanofluids are determined by theoretical calculations, which are then used as inputs for analysis. CATIA parametric software is used to build a 3D model of the double pipe heat exchanger (plain and coil insert tube). At different mass flow rates of 0.32, 0.52, and 0.72 kg/sec, CFD analysis is performed on the double pipe heat exchanger with water, silicon oxide, and silver nanoparticle. Furthermore, theoretical calculations on the double pipe heat exchanger were performed.

Published by: Koduri Nithesh, Vasili Srinivas

Author: Koduri Nithesh

Paper ID: V7I4-1743

Paper Status: published

Published: August 11, 2021

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Research Paper

Crime Prediction using Naïve Bayes Algorithm

This paper presents the detection of the crimes happening in India. The criminal offenses lead to certain punishment according to the Indian Penal Code (IPC). For particular crimes, particular sections are assigned to punish the criminal or convicts with jail terms and fines. On these pre-processed data sets, by applying the Naïve Bayesian algorithm we create a predictive model which analyzes the data and helps to predict the crime type in a near future. We are using a dataset to apply the Naïve Bayes algorithm to predict crimes in India.

Published by: Mohammad Tahir, Md Asif Anwar, Ruma Afsha Sultana, Manjunath S. R., Fardeen Khan

Author: Mohammad Tahir

Paper ID: V7I4-1713

Paper Status: published

Published: August 10, 2021

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Research Paper

Analysis on cost-effective cloud server provisioning for the predictable performance of big data analytics

Because of server over-supply, cloud data centers are underused. Cloud providers are offering consumers the option of run king workloads like BID analysis of under-used resources as cheap yet revokable transition servers to increase their use of the data center (e.g., EC2 spot instances, GCE preemptible instances). Although at very low pricing, large data analysis can drastically impact work performance on unreliable cloud transient servers due to instance revocations. This study offers a cost-effective transient server delivery mechanism, iSpot, to address this issue by focusing on Spark as a model of the large-format data analysis system (DAG)-style Directed Acyclic Graph (DAG). First of all, a precise long-term short-term memory (LSTM) pricing prediction approach detects the stable cloud transient servers during the workflow execution. By employing automated work step profiling, Spark's DAG data acquisitions may create the iSpot Supply Strategy to ensure task performance on steady transient servers and develop an analytical model and provide Spark with a lightweight crucial data control mechanism. Extensive EC2- and GCE-instance prototype studies reveal that while saving workplace costs up to 83:8% as compared to state-of-the-art server supply policies, iSpot is able to ensure the performance of large-data analytics running on cloud transient servers. The overhead overtime is still acceptable.

Published by: R. Sumathi, U. Dhanunjaya

Author: R. Sumathi

Paper ID: V7I4-1734

Paper Status: published

Published: August 10, 2021

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Research Paper

Emotion-based media player

Currently, people will overall dynamically have more pressing factors since of the awful economy, high regular expenses, etc Paying thoughtfulness regarding tune is a key development that assists with diminishing pressing factors. Regardless, it may be silly if the music doesn't actually gauge up for the current sensation of the crowd. Additionally, there is no music player which can pick tunes subject to the customer's feelings. To deal with this issue, this endeavor proposes an inclination-based music player, which can suggest songs reliant upon the customer's sentiments; hopeless, happy, fair-minded, and irate. The application gets a facial picture from a web camera. It then uses the request methodology to recognize the customer's inclination. Then, the application returns tunes that have a comparative perspective as the customer's inclination. The test outcomes show that the proposed approach can precisely mastermind the happy inclination considering the way that the beat extent of this inclination is wide.

Published by: Dr. Rajesh K. S., Heba Farheen B., Hemalatha K., Indhu V. S., Sadiya Banu

Author: Dr. Rajesh K. S.

Paper ID: V7I4-1744

Paper Status: published

Published: August 10, 2021

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