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
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
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
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
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
Smart waste management system
India is the second most populated country in this world. Waste produced daily is huge due to this reason waste management has been an unsolved problem for many years in our country. As we step into digitalization and urbanization, we need to take a smart step to curb waste in our country. The system aims at contributing to the smart city by better managing the waste generated. It is one of the many solutions in dealing with the waste generated on daily basis using modern technologies. Major issues of waste management in our city are overfilled garbage bins, improper disposal of waste, and delayed cleaning process. The system addresses these issues using various sensors and other components controlled by RASPBERRY PI 3B+ which helps in detecting and notifying the authorities once the waste is discarded into the garbage bin. With the help of the developed system overfilled garbage bins can be resolved by real-time monitoring of the bins. It also specializes in dealing with improper waste disposal. Through continuous monitoring, the system updates the data to the cloud which can be accessed by the respective authorities and the private sector companies which are collaborated. The system majorly aims at managing waste by implementing modern technologies like Internet of Things and Embedded Systems. The model is a culmination of components like sensors, microcontrollers, motors, etc. The developed system also caters to global problems like pollution, land fillings, and foul smell. Finally, this paper finds a complete survey of solution for waste management problems and helps in keeping our city clean and provides hygienic environment to citizens.
Published by: Ankitha A., Ramya S M, Geetha B V, Supriya K V
Author: Ankitha A.
Paper ID: V7I4-1702
Paper Status: published
Published: August 10, 2021
