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Sharmila, M.
- Predatory Efficiency and Developmental Attributes of Harmonia dimidiata (Fabricius) (Coleoptera:Coccinellidae) in Relation to Prey Density
Abstract Views :229 |
PDF Views:146
Authors
Affiliations
1 Aphid Research Laboratory, Department of Life Sciences, Manipur University, Canchipur 795 003, Manipur, IN
2 Department of Life Sciences, Tripura University, Agartala 799 130, Tripura, IN
1 Aphid Research Laboratory, Department of Life Sciences, Manipur University, Canchipur 795 003, Manipur, IN
2 Department of Life Sciences, Tripura University, Agartala 799 130, Tripura, IN
Source
Journal of Biological Control, Vol 24, No 3 (2010), Pagination: 218-221Abstract
The predatory efficiency and survival of immature stages of an aphidophagous ladybeetle, Harmonia dimidiata (Fabricius) were investigated at five different prey densities, viz., 25, 50, 75, 100 and 125 using Cervaphis quercus Takahashi as prey. A substantial influence of prey density on the rate of larval growth and development was observed. Increased prey density reduced the developmental period. The study also revealed that larval development could be completed at the lowest prey density of 25 prey aphids. The analysis revealed a positive correlation between survival of developmental stages and density of prey provided. A relative increase in weight was observed with increasing density of aphid prey, but only up to a prey density of 100. The functional response exhibited by fourth instar larva exemplified type II predatory response with optimum response at a prey density of 100.Keywords
Harmonia dimidiata, Cervaphis quercus, Predatory Efficiency, Prey Density, Immature Survival.Full Text
- Intelligent Heart Disease Prediction System Using Multi Atlas Segmentation
Abstract Views :132 |
PDF Views:5
Authors
Affiliations
1 Regional Center of Anna University, Madurai, IN
1 Regional Center of Anna University, Madurai, IN
Source
Biometrics and Bioinformatics, Vol 6, No 5 (2014), Pagination: 141-144Abstract
The evaluation of ventricular function is important for the diagnosis of cardiovascular diseases. It involves the measurement o f the left ventricular (LV) mass. In this paper, a multi atlas segmentation method is proposed for cardiac magnetic resonance (MR) image segmentation. The proposed method is novel in two aspects. First, it formulates a patch-based label fusion model in a Bayesian framework. Second, it improves Image registration accuracy by utilizing label information, which improves image segmentation accuracy. The proposed method was evaluated on a cardiac MR Images set of 28 subjects. The results show that the proposed method is able to provide accurate information for clinical diagnosis.
Keywords
Image Registration, Image Segmentation, Multi-Atlas Segmentation, Patch-Based Segmentation, Image Registration.
- Hybrid Cloud Based Deduplication Scheme Based on User’s Privileges
Abstract Views :153 |
PDF Views:0
Authors
Affiliations
1 School of Computing, SASTRA University, Thirumalaisamudram, Thanjavur - 613401, Tamilnadu, IN
1 School of Computing, SASTRA University, Thirumalaisamudram, Thanjavur - 613401, Tamilnadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 48 (2016), Pagination:Abstract
Background/Objectives: This research work reviews different techniques to protect the data and discussed about their merits and demerits which help to realize a secured cloud deduplication environment. Methods: Hybrid cloud based deduplication approach is proposedto avoid the data duplication and to ensure the confidentiality in the cloud. Further, the security investigations ensure that the proposed security framework enhances the data security with an insignificant additional overhead. Mention the data source accessed and keywords used; inclusion and exclusion criteria etc. Findings: Provide your findings. Applications: This approach makes us to understand the elements of good cloud deduplication framework which can be directly used by any cloud storage service provider to reduce their storage costs.Keywords
Cloud Computing, Data Deduplication, Hybrid Cloud, Private Cloud, Public Cloud.Full Text
- An Empirical Analysis of Different Machine Learning Algorithms for Predicting Lung Cancer
Abstract Views :234 |
PDF Views:0
Authors
Affiliations
1 Assistant Professor, Dept. of Information Technology, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
2 Professor and Head, Dept. of Information Technology, M.Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
1 Assistant Professor, Dept. of Information Technology, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
2 Professor and Head, Dept. of Information Technology, M.Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
Source
International Journal of Emerging Trends in Science & Technology, Vol 7, No 2 (2021), Pagination: 15-21Abstract
In the current scenario, cancer disease is substantial menace to human life globally. About 32 percent of people worldwide are affected by various types of cancer. But lung cancer depicts the highest ratio. Nowadays peoples are not having awareness about to detect the cancer in early stage. The survival rate of five year for lung cancer disease is 55 percent of the cases are affected most. However, only 14 percent of lung tumor cases are diagnosed at an early stage. For slight tumors the five-year survival rate is simply 3 percent. There are 4 stages in lung cancer. If we predict the disease in I and II stage, it is easy to cure by effortless operations. If it exceeds second stage, it may not be cured. So, diagnosing the cancer in earlier stage is the best solution to predict the patients from death. For that, the system uses the Decision Tree and K-Nearest Neighbor (KNN) Algorithms as preferred classification model. By using these algorithms, it becomes easier to diagnose the cancer in early stage. So, the survival rate of lung cancer patients becomes higher. This comparative analysis, calculates and compares the precision of Random Forest, Naive Bayes and KNN and the preliminary result reveals that ID3 furnish better precision for cancer dataset. The input has been accessed only in numeric format. The algorithms also maintain key stuffs of the dataset, which are predominant for extracting performance, and so it may warrant the correct defense and effective preservation. This leads to protection of any extracting works that depends on the sequence of distances between objects, such as Random Forest, Naive Bayes -search and classification, as well as many visualization techniques. In particular, it establishes a restricted isometric property, in which it is the tight leap on the shrinkage and enlargement of the original distances.Keywords
Machine Learning, Unsupervised Learning, Naive Bayes Classifiers, Decision Tree, Random Forest, Decision Support System, Neural NetworkReferences
- . Andrzej Skalski; Jacek Jakubowski; Tomasz Drewniak, “Lung tumor segmentation and detection on Computed Tomography data”, Imaging Systems and Techniques (IST), 2016 IEEE International Conference on 4-6 Oct. 2016
- . Aneesh kumar and. A. C. Jothi Venkateswaran, “Estimating the Surveillance of Lung Disorder using Classification Algorithms”, International Journal of Computer Applications (0975 – 8887), Volume 57– No.6, November 2012.
- . Bard HSSINA, Abdel Karim MERBOUHA,” A comparative study of decision tree ID3 and C4.5”, International Journal of Computer Applications Beni-Mellal, BP: 523
- . Bendi Venkata Ramana, Prof. M.Surendra Prasad Babu and Prof. N. B. Venkateswarlu, “A Critical Study of Selected Classification Algorithms for Lung Disease Diagnosis”, International Journal of Database Management Systems (IJDMS), Vol.3, No.2 (2011), PP.101-11.
- . Cybenko.G, “Approximation by super positions of a sigmoidal function”, Mathematics of Control, Signals, and Systems, Vol.2 (1989), PP. 303-314.
- . Emrana Kabir Hashi, Md. Shahid Uz Zaman and Md. Rokibul Hasan, “An Expert Clinical Decision Support System to Predict Disease Using Classification Techniques”, International Conference on Electrical, Computer and Communication Engineering (ECCE), February 16-18, 2017, Cox’s Bazar, Bangladesh (2017).
- . Han Sang Lee; Helen Hong; Junmo Kim, “Detection and segmentation of small renal masses in contrast-enhanced CT images using texture and context feature classification”, Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on 18-21 April 2017, ISSN: 1945-8452.
- . Isabelle Guyon and Andr´eElisseeff,” An Introduction to Variable and Feature Selection”, Journal of Machine Learning Research 3 (2013) 1157-1182.
- . Jankishran Pahariyavohra, Jagdeesh makhijani and sanjay patsariya, “Lung patient classification using intelligence techniques”, International journal of advanced research in computer science and software engineering, Volume 4, Issue 2, Pages 295-299,2013.
- . John C. Platt,” Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines”, Technical Report, April 21, 2010.
- . Rajeswari P and Sophia Reena.G, “Analysis of Lung Disorder Using Data Mining Algorithm”, Global Journal of Computer Science and Technology, Vol. 10 Issue 14 (Ver. 1.0) November 2010.
- Flight Delays Prediction using Supervised Learning Algorithm
Abstract Views :98 |
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.
- An Empirical Analysis of Different Machine Learning Algorithms for Predicting Lung Cancer
Abstract Views :185 |
PDF Views:0
Authors
Affiliations
1 Assistant Professor, Dept. of Information Technology, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
2 Professor and Head, Dept. of Information Technology, M.Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
1 Assistant Professor, Dept. of Information Technology, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
2 Professor and Head, Dept. of Information Technology, M.Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
Source
Journal of Entrepreneurship & Management, Vol 7, No 2 (2021), Pagination: 15-21Abstract
In the current scenario, cancer disease is substantial menace to human life globally. About 32 percent of people worldwide are affected by various types of cancer. But lung cancer depicts the highest ratio. Nowadays peoples are not having awareness about to detect the cancer in early stage. The survival rate of five year for lung cancer disease is 55 percent of the cases are affected most. However, only 14 percent of lung tumor cases are diagnosed at an early stage. For slight tumors the five-year survival rate is simply 3 percent. There are 4 stages in lung cancer. If we predict the disease in I and II stage, it is easy to cure by effortless operations. If it exceeds second stage, it may not be cured. So, diagnosing the cancer in earlier stage is the best solution to predict the patients from death. For that, the system uses the Decision Tree and K-Nearest Neighbor (KNN) Algorithms as preferred classification model. By using these algorithms, it becomes easier to diagnose the cancer in early stage. So, the survival rate of lung cancer patients becomes higher. This comparative analysis, calculates and compares the precision of Random Forest, Naive Bayes and KNN and the preliminary result reveals that ID3 furnish better precision for cancer dataset. The input has been accessed only in numeric format. The algorithms also maintain key stuffs of the dataset, which are predominant for extracting performance, and so it may warrant the correct defense and effective preservation. This leads to protection of any extracting works that depends on the sequence of distances between objects, such as Random Forest, Naive Bayes -search and classification, as well as many visualization techniques. In particular, it establishes a restricted isometric property, in which it is the tight leap on the shrinkage and enlargement of the original distances.Keywords
Machine Learning; Unsupervised Learning; Naive Bayes classifiers; Decision Tree; Random forest; Decision Support system; Neural network.References
- . Andrzej Skalski; Jacek Jakubowski; Tomasz Drewniak, “Lung tumor segmentation and detection on Computed Tomography data”, Imaging Systems and Techniques (IST), 2016 IEEE International Conference on 4-6 Oct. 2016
- . Aneesh kumar and. A. C. Jothi Venkateswaran, “Estimating the Surveillance of Lung Disorder using Classification Algorithms”, International Journal of Computer Applications (0975 – 8887), Volume 57– No.6, November 2012.
- . Bard HSSINA, Abdel Karim MERBOUHA,” A comparative study of decision tree ID3 and C4.5”, International Journal of Computer Applications Beni-Mellal, BP: 523
- . Bendi Venkata Ramana, Prof. M.Surendra Prasad Babu and Prof. N. B. Venkateswarlu, “A Critical Study of Selected Classification Algorithms for Lung Disease Diagnosis”, International Journal of Database Management Systems (IJDMS), Vol.3, No.2 (2011), PP.101-11.
- . Cybenko.G, “Approximation by super positions of a sigmoidal function”, Mathematics of Control, Signals, and Systems, Vol.2 (1989), PP. 303-314.
- . Emrana Kabir Hashi, Md. Shahid Uz Zaman and Md. Rokibul Hasan, “An Expert Clinical Decision Support System to Predict Disease Using Classification Techniques”, International Conference on Electrical, Computer and Communication Engineering (ECCE), February 16-18, 2017, Cox’s Bazar, Bangladesh (2017).
- . Han Sang Lee; Helen Hong; Junmo Kim, “Detection and segmentation of small renal masses in contrast-enhanced CT images using texture and context feature classification”, Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on 18-21 April 2017, ISSN: 1945- 8452.
- . Isabelle Guyon and Andr´eElisseeff,” An Introduction to Variable and Feature Selection”, Journal of Machine Learning Research 3 (2013) 1157-1182.
- . Jankishran Pahariyavohra, Jagdeesh makhijani and sanjay patsariya, “Lung patient classification using intelligence techniques”, International journal of advanced research in computer science and software engineering, Volume 4, Issue 2, Pages 295-299,2013.
- . John C. Platt,” Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines”, Technical Report, April 21, 2010.
- .Rajeswari P and Sophia Reena.G, “Analysis of Lung Disorder Using Data Mining Algorithm”, Global Journal of Computer Science and Technology, Vol. 10 Issue 14 (Ver. 1.0) November 2010.