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Rajesh, Sudha
- Prediction of Air Pollution using Supervised Machine Learning
Abstract Views :97 |
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Authors
Affiliations
1 Assistant Professor, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, IN
2 Master of Computer Application, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, IN
1 Assistant Professor, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, IN
2 Master of Computer Application, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, IN
Source
Journal of Applied Information Science, Vol 10, No 1 (2022), Pagination: 10-16Abstract
The prediction and estimation of the air pollution stands a necessary analysis area. The main abstract of this is to explore machine learning which is used for checking of air quality forecasting to predict the outcome in best accuracy. In supervised machine learning this analysis is done to measure information like identification of variable and analysis of univariate, bivariate and multivariate analysis and identify the values which are missed and used to analyze validation of data and cleaning of data and visualization of data is done by entire by dataset. This analysis is to provide complete guide to sensitive analysis of parameters in addition to forecasting of air pollution by accurate prediction. This analysis is to suggest framework to expect the index of air excellence by predicting the results to form good accuracy and used to compare supervise classification machine learning algorithms. In addition it is to discuss the algorithm from the dataset taken from traffic department with graphic user interface air quality prediction.Keywords
Air Pollution, Air Quality, Classification Method, GUI Results, Prediction, Python, Quality.
- Breast Cancer Detection by using Supervised Learning Algorithm
Abstract Views :108 |
PDF Views:0
Authors
B. Abarna
1,
Sudha Rajesh
2
Affiliations
1 Master of Computer Application, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, IN
2 Assistant Professor, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, IN
1 Master of Computer Application, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, IN
2 Assistant Professor, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, IN
Source
Journal of Applied Information Science, Vol 10, No 1 (2022), Pagination: 29-33Abstract
Breast cancer is one of the largest second causes of dead among women. So, computer aided diagnosis system will be working on mammograms. In early stage breast cancer can be identified by through CAD algorithms.But, this algorithms accuracy of existing system unsatisfied result. Later, they are using to detect this cancer like microscopic images, mammography to ultrasonography and MRI. This type of prediction gave only false prediction then it took more time and cost effects. In proposed system of in this project, it will be working on big data and machine learning based on nine features in breast cancer data set from UCI Irvine Machine Learning Repository database. Big data is used to pre-processing from various fields from datasets and used to accurate detection. Machine Learning can be used to implementing the supervised algorithm’s to detect cancer type based on benign and malignant breast masses. These algorithms will give us additional accurate results for detecting the breast cancer.Keywords
Big Data, Breast Cancer Detection, GUI, Machine LearningReferences
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- Y. Jiang, “Computer-aided diagnosis of breast cancer in mammography: Evidence and potential,” Technology in Cancer Research & Treatment, vol. 1, no. 3, pp. 211-216, Jun. 2002.
- A. C. Tan, and D. Gilbert, “Ensemble machine learning on gene expression data for cancer classification,” Applied Bioinformatics, vol. 2, no. sp.3, pp. S75-S83, 2003,
- D. Delen, G. Walker, and A. Kadam, “Predicting breast cancer survivability: A comparison of three data mining methods,” Artificial Intelligence in Medicine, vol. 34, no. 2, pp. 113-127, 2005.
- S. Destounis, and A. Santacroce, “Age to begin and intervals for breast cancer screening: Balancing benefits and harms,” American Journal of Roentgenology, vol. 210, no. 2, pp. 279-284, 2018.
- S. Sahran, A. Qasem, K. Omar, D. Albashih, A. Adam, ....., and A. Shukor, “Machine learning methods for breast cancer diagnostic,” Nov. 2018.
- M. Tahmooresi, A. Afshar, B. B. Rad, K. B. Nowshath, and M. Bamiah, “Early detection of breast cancer using machine learning techniques,” Journal of Telecommunication, Electronic and Computer Engineering , vol. 10, pp. 21-27, 2018.
- Q. Huqng, Y. Chen, L. Liu, D. Tao, and X. Li, “On combining biclustering mining and Adaboost breast tumour classification,” IEEE Transactions on Knowledge and Data Engineering, vol. 13, 2019.
- Short-Term Rainfall using Logistic Regression Algorithm
Abstract Views :85 |
PDF Views:0
Authors
P. Nishanthi
1,
Sudha Rajesh
2
Affiliations
1 Master of Computer Application, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, IN
2 Assistant Professor, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, IN
1 Master of Computer Application, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, IN
2 Assistant Professor, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, IN
Source
Journal of Applied Information Science, Vol 10, No 1 (2022), Pagination: 39-43Abstract
Farmers and all peoples are expecting for the good rainfall, for farming and for usage of daily water. But for the past decades, we didn’t get much rainfall and all peoples suffered a lot. So, a work is proposed to predict the rainfall. Rainfall data is collected from the hydrological data to predict the storm warning. This is study as an idea as it consumes the large amount of records from the different various distributed system. Spatial temporal characteristics using a Map Reduce Framework to manage the database. The workload is classified using Support Vector Machine (SVM) is classified for workload. It uses the reduction algorithm for dataset and use for feature selection. Different rainfall concept prediction is executed using the big rainfall data. The impact of the dataset parameters are classified into locally, hourly and overall rainfall storms. The proposed system is deals with hadoop tool for predicting a rainfall data from a large amount of data it has no limitation data in a structure manner. The result will be improve the performance of the proposed system it has more accuracy and efficiency..Keywords
Map Reduce Framework, Reduction Algorithm, Support Vector Machine (SVM)- Digital Crime Reporting System by using PHP
Abstract Views :90 |
PDF Views:0
Authors
Affiliations
1 Assistant Professor, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, IN
2 Master of Computer Application, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, IN
1 Assistant Professor, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, IN
2 Master of Computer Application, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, IN
Source
Journal of Applied Information Science, Vol 10, No 1 (2022), Pagination: 44-49Abstract
The “Digital Crime Reporting System” is a website based on php scripting language for online crime reporting and for managing crime records in a computerized way. Here in this website the registration of a person is earlier than login is must if he/she wants to report a complaint or to report an incident related to crime so that the authentication of the user login will be done by the administrator in the website to allow the user to file the complaint. Thus the police will received the complaint and they can reply a message about the complaint status to the user. Officers can use this system to manage a distinctive crime and to manage the manual works which is carried out in the police department. The police login id and password will receive directly from the admin. The modules like news feed, missing peoples, safety tips and the list of most wanted criminals are available in the website which can be seen without logging in of the user. So this system helps the officer to discover the issues in the town/city without actually the people coming into the police station. The digital crime reporting system will manage activities (like complaint registration, updating information, searching a particular crime report) all these activity will save time and manpower. So this system helps the officer to discover the issues in the town/city without actually the people coming into the police station.Keywords
Bootstrap, Crime Reporting System, FIR, PhpReferences
- X. Li, S. Karnan, and J. A. Chishti, “An empirical study of three PHP frameworks,” 2017 4th International Conference on Systems and Informatics (ICSAI), Hangzhou, China, 11-13 Nov. 2017.
- S. Sathyadevan, Devan M. S., and S. S. Gangadharan, “Crime analysis and prediction using data mining,” First International Conference on Networks & Soft Computing (ICNSC), 2014, doi: 10.1109/CNSC.2014.6906719.
- Aparna, N. Verma, and Himanshu, “A research paper on web application development using CMS (Xampp/PHP),” Journal of Web Engineering & Technology, vol. 6, no. 1, pp. 37-43, 2019.
- A. Jaiswal, N. Gunjal, P. Londhe, S. Singh, and R. Solanki, “Crime automation and reporting system,” International Journal of Science and Modern Engineering (IJISME), vol. 1, no. 11, pp. 5-6 Oct. 2013.
- P. Natalya, and B. Victoria, “Analysis and practical application of PHP frameworks in development of web information systems,” Procedia Computer Science, vol. 104, pp. 51-56, 2017.
- P. Mishra, Ghousiya B. N., Mohsina S., M. Sultana, and S. Singh, “Online criminal record management system,” International Journal of Engineering Science and Computing, vol. 9, no. 5, pp. 22359-22361, May 2019.
- Mohd. Shahnawaz, “Crime reporting and crime updates,” 3rd International Conference on System Modeling in Research Trends (SMART) College of Computer Science and Information Technology (CCSIT), Teerthanker Mahaveer University, Moradabad, 2014.
- K. Tabassum, H. Shaiba, S. Shamrani, and S. Otaibi, “e-Cops: An online crime reporting and management system for Riyadh city,” IEEE 2018 1st International Conference on Computer Applications & Information Security (ICCAIS), 2018, pp. 1-8.
- Flight Delays Prediction using Supervised Learning Algorithm
Abstract Views :87 |
PDF Views:0
Authors
M. Sharmila
1,
Sudha Rajesh
2
Affiliations
1 Master of Computer Application, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, IN
2 Assistant Professor, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, IN
1 Master of Computer Application, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, IN
2 Assistant Professor, Department of Computer Application, B. S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamil Nadu, IN
Source
Journal of Applied Information Science, Vol 10, No 1 (2022), Pagination: 55-59Abstract
The ceaseless development in the interest for air transportation surpasses the limit of existing foundation, generally prompting questionable flight plans, long flight delays and uncertainties in landing/takeoff and taxi times. In light of the multi-target streamlining, a heuristic calculation thinking about vulnerabilities in flight landing/takeoff time is intended to accomplish an improvement in airplane terminal throughput and a decrease in flight delay. We are analyzing the forecasts, timings to make these delays reduce by small amount. With our future proposal, we can make the datasets real-time and reduces flight delay by huge hunk of time. The supervised machine learning algorithm helps us to find the prediction with more accuracy.Keywords
Flight Delays Prediction, Hadoop, Takeoff TimeReferences
- M. Güvercin, N. Ferhatosmanoglu, and B. Gedik, “Forecasting flight delays using clustered models based on airport networks,” 2019.
- M. Hansen, and C. Y. Hsiao, “Going South? An econometric analysis of US airline flight delays from 2000 to 2004,” Presented at the 84th Annual Meeting of the Transportation Research Board (TRB), Washington D.C., 2005.
- S. S. Allan, J. A. Beesley, J. E. Evans, and S. G. Gaddy, “Analysis of delay causality at network international airport,” 2001.
- A. Rosen, “Flights delays on US airlines: The impact of congestion externalities in hub and spoke networks,” 2002.
- P. Chandraa, Prabakaran N., and Kannadasan R., “Airline delay predictions using supervised machine learning,” International Journal of Pure and Applied Mathematics, vol. 119, no. 7, pp. 329-337, 2018.
- S. Shaik, and K. P. Surya Teja, “Flight delay prediction using machine learning algorithm XGBoost,” Journal of Advanced Research in Dynamical and Control Systems, vol. 11, no. 5, pp. 379-388, 2019.
- A. Barrat, M. Barthelemy, R. Pastor-Satorras, and A. Vespignani, “The architecture of complex weighted networks,” PNAS, vol. 101, no. 11, pp. 3747-3752, 2004.
- S. Li, Y. Xu, M. Zhu, S. Ma, and H. Tang, “Remote sensing airport detection based on end-to-end deep transferable convolutional neural networks,” IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 10, pp. 1640-1644, Oct. 2019.
- U. Bhatia, D. Kumar, E. Kodra, and A. R. Ganguly, “Network science based quantification of resilience demonstrated on the Indian Railways network,” PLoS ONE, vol. 10, no. 11, e0141890, 2015.
- P. Fleurquin, J. J. Ramasco, and V. M. Eguiluz,“Systemic delay propagation in the US airport
- network,” Scientific Reports, vol. 3, 2013, Art. no. 1159.