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Co-Authors
- C. Sherin Shibi
- M. Aswinrani
- M. Sowmiya
- P. Ramamoorthy
- M. Balaanand
- Prasad K. Bhaskaran
- Felix Jose
- V. Balaji
- V. Vishnu Priya
- M. K. Vijaymeena
- K. Gowri Sankar
- R. R. Nadhaan
- A. Muthu Kumar
- Keserla Bhavani
- Rumana Khatija
- R. Sundara Ganapathy
- C. Kiran Kumar
- S. Thirukumaran
- P. T. Kalaivaani
- S. Chandra Sekaran
- S. Ramasamy
- T. Gobinath
- A. Muthumari
- R.S.V. Rama Swathi
Journals
- Current Science
- Programmable Device Circuits and Systems
- Fuzzy Systems
- Digital Image Processing
- Artificial Intelligent Systems and Machine Learning
- Research Journal of Pharmacy and Technology
- International Journal of Emerging Trends in Science & Technology
- ICTACT Journal on Image and Video Processing
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Gayathri, R.
- A Methodology for Unsupervised Feature Learning in Hyperspectral Imagery Using Deep Belief Network
Abstract Views :328 |
PDF Views:118
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Sri Venkateswara College of Engineering, Sriperumbudur 602 117, IN
1 Department of Electronics and Communication Engineering, Sri Venkateswara College of Engineering, Sriperumbudur 602 117, IN
Source
Current Science, Vol 120, No 11 (2021), Pagination: 1705-1711Abstract
Deep learning approaches have received major interest in the field of remote sensing. Hyperspectral imaging has rich data that are distributed in multi-dimensions. It is challenging to apply deep learning algorithms due to the limited amount of labelled data. So, unsupervised feature extraction approaches are used to overcome this limitation. In this study, we propose an unsupervised feature learning approach using deep belief network (DBN). In the proposed framework, the input hyperspectral image is segmented using entropy rate superpixel segmentation and filtered by domain transform recursive filter which extracts spatial and spectral information effectively. Then the features are learned by improved DBN. In the traditional methods, DBN is stacked with restricted Boltzmann machine (RBM) which is suitable for only binary value data. In the proposed framework, we used Gaussian–Bernoulli RBM which was constructed for real value data such as images. The experiments were carried out using Pavia University dataset. The results show that the proposed network has good performance in terms of classification accuracy and computation time.Keywords
Deep Belief Network, Hyperspectral Image, Remote Sensing, Spatial–Spectral Classification, Superpixel Segmentation.Full Text
References
- Khan, M. J., Khan, H. S., Yousaf, A., Khurshid, K. and Abbas, A., Modern trends in hyperspectral image analysis: a review. IEEE Access, 2018, 6, 14118–14129.
- Massoudifar, P., Rangarajan, A. and Gader, P., Superpixel estimation for hyperspectral imagery. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2014, pp. 287–292; doi:10.1109/CVPRW.2014.51.
- Felzenszwalb, P. F. and Huttenlocher, D. P., Efficient graph-based image segmentation. Int. J. Comput. Vis., 2004, 59, 167–181.
- Levinshtein, A. et al., TurboPixels: fast superpixels using geometric flows. IEEE Trans. Pattern Anal. Mach. Intell., 2009, 31, 2290– 2297.
- Achanta, R., Shaji, A., Smith, K. and Lucchi, A., SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, 2274–2282.
- Liu, M. Y., Tuzel, O., Ramalingam, S. and Chellappa, R., Entropy rate superpixel segmentation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011, pp. 2097–2104; doi:10.1109/CVPR.2011.5995323.
- Xie, F., Lei, C., Yang, J. and Jin, C., An effective classification scheme for hyperspectral image based on superpixel and discontinuity preserving relaxation. Remote Sensing, 2019, 11, 1149.
- Hughes, G. F., On the mean accuracy of statistical pattern recognizers. IEEE Trans. Inf. Theory, 1968, 14, 55–63.
- Prasad, S. and Bruce, L. M., Limitations of principal components analysis for hyperspectral target recognition. IEEE Geosci. Remote Sensing Lett., 2008, 5, 625–629.
- Villa, A., Benediktsson, J. A., Chanussot, J. and Jutten, C., Hyperspectral image classification with independent component discriminant analysis. IEEE Trans. Geosci. Remote Sensing, 2011, 49, 4865–4876.
- Liao, W., Pižurica, A., Scheunders, P., Philips, W. and Pi, Y., Semisupervised local discriminant analysis for feature extraction in hyperspectral images. IEEE Trans. Geosci. Remote Sensing, 2013, 51, 184–198.
- Chen, Z., Jiang, J., Jiang, X., Fang, X. and Cai, Z., Spectral– spatial feature extraction of hyperspectral images based on propagation filter. Sensors (Switzerland), 2018, 18, 1–15.
- Tu, B., Yang, X., Li, N., Ou, X. and He, W., Hyperspectral image classification via superpixel correlation coefficient representation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing, 2018, 11, 4113–4127.
- Kang, X., Li, S. and Benediktsson, J. A., Feature extraction of hyperspectral images with image fusion and recursive filtering. IEEE Trans. Geosci. Remote Sensing, 2014, 52, 3742–3752.
- Chen, Z., Jiang, J., Zhou, C., Fu, S. and Cai, Z., SuperBF: superpixelbased bilateral filtering algorithm and its application in feature extraction of hyperspectral images. IEEE Access, 2019, 7, 147796–147807.
- Melgani, F. and Bruzzone, L., Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci Remote Sensing, 2004, 42, 1778–1790.
- Kang, X., Member, S., Li, S. and Benediktsson, J. A., Spectral – spatial hyperspectral image classification with edge-preserving filtering. IEEE Trans. Geosci. Remote Sensing, 2014, 52, 2666–2677.
- Chen, Y., Jiang, H., Li, C., Jia, X. and Ghamisi, P., Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans. Geosci. Remote Sensing, 2016, 54, 6232–6251.
- Chen, Y., Zhao, X. and Jia, X., Spectral–spatial classification of hyperspectral data based on deep belief network. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing, 2015, 8, 2381–2392.
- Duan, W., Li, S. and Fang, L., Spectral-spatial hyperspectral image classification using superpixel and extreme learning machines. Commun. Comput. Inf. Sci., 2014, 483, 159–167.
- Gastal, E. S. L. and Oliveira, M. M., Domain transform for edgeaware image and video processing. ACM Trans. Graph., 2011, 30, 1–12.
- Hinton, G. E., Training products of experts by minimizing contrastive divergence. Neural Comput., 2002, 14, 1771–1800.
- Cho, K. H., Raiko, T. and Ilin, A., Gaussian–Bernoulli deep Boltzmann machine. In Proceedings of the International Joint Conference on Neural Networks, 2013; doi:10.1109/IJCNN.2013.6706831.
- Liu, P., Zhang, H. and Eom, K. B., Active deep learning for classification of hyperspectral images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing, 2017, 10, 712–724.
- Hyperspectral remote sensing scenes; http://www.ehu.eus/ ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes (accessed on 6 June 2018).
- Recognizing the Sensor Data in Cyber-Physical Systems
Abstract Views :196 |
PDF Views:4
Authors
Affiliations
1 Department of CSE, V.R.S.CET, IN
1 Department of CSE, V.R.S.CET, IN
Source
Programmable Device Circuits and Systems, Vol 7, No 1 (2015), Pagination: 7-11Abstract
A Cyber-Physical System (CPS) coordinates physical gadgets (i.e., sensors) with digital (i.e., enlightening) parts to structure a setting touchy framework that reacts adroitly to element changes in genuine circumstances. Such a framework has wide applications in the situations of activity control, front line observation, natural observing, et cetera. A center component of CPS is the accumulation and evaluation of data from loud, dynamic, and indeterminate physical situations incorporated with numerous sorts of the internet assets. The capability of this incorporation is unbounded. To attain this potential the crude information gained from the physical world must be changed into useable information continuously. In this way, CPS brings another measurement to learning revelation due to the rising synergism of the physical and the digital. The different properties of the physical world must be tended to in data administration and information revelation. This paper talks about the issues of mining sensor information in CPS: With an extensive number of remote sensors sent in an assigned region, the errand is continuous recognition of interlopers that enter the zone focused around proarious sensor information. The system of Intrumine is acquainted with find interlopers from dishonest sensor information. Intrumine first breaks down the dependability of sensor information, then identifies the interlopers' areas, and confirms the identifications focused around a diagram model of the connections in the middle of sensors and interlopers.
Keywords
Digital Physical Framework, Sensor System, Information Dependability.
- Enabling Volunteered Geographic Service Sharing for Emergency
Abstract Views :235 |
PDF Views:3
Authors
Source
Fuzzy Systems, Vol 8, No 4 (2016), Pagination: 95-97Abstract
We provide techniques that enable the users to get a solution regarding issue based on their location and time they consume based on so-called Volunteered Geographic Services system. With this system user can clear their issue or doubts with the help of other user who are near and willing to answer user’s query. System needs user’s location to achieve this; so that other users can view the user’s query. And a user can remove the query from the system once they got solution regarding it. User can extend the area with time if they didn’t get a proper or satisfied solution. User can block the responding user if they found the as a fake. To achieve this we develop a admin module, who monitor the process and can remove the fake user from the system. Then query users need only notify the system when they exit their current safe zone. Existing safe-zone models fall short in the papers setting. The paper covers underlying concepts, properties, and algorithms, and it covers applications in VGS tracking and presents findings of empirical performance studies. User need to get the permission from the admin to get into the system. Timer will be set once the user upload a query and is passed to the entire user who is available in a covered area and the willing user can answer the query. The covered area will be enlarged for no response so that some other volunteer can answer for user’s query and time also increased. Once the response is received from the volunteer user can communicate with that particular user. If user found any fake responses from volunteer or volunteer found any fake responses from the user can block the respective person.
Keywords
Volunteered Geographic Services, Location Tracking, Emergency Services, Service Sharing, VGS Tracking System.- Performance Evaluation of Multimodal Biometric System Using Fusion of Iris and Face
Abstract Views :215 |
PDF Views:2
Authors
Affiliations
1 Vel Tech Dr. RR & Dr. SR Technical University, Chennai, IN
2 Sri Shakthi Engineering College, IN
1 Vel Tech Dr. RR & Dr. SR Technical University, Chennai, IN
2 Sri Shakthi Engineering College, IN
Source
Digital Image Processing, Vol 3, No 7 (2011), Pagination: 401-405Abstract
Unimodal biometric systems have to contend with a variety of problems such as noisy data, intraclass variations, restricted degrees of freedom, non-universality, spoof attacks, and unacceptable error rates. Some of these limitations can be addressed by deploying multimodal biometric systems that integrate the evidence presented by multiple sources of information. Fusion of multiple biometrics for human authentication performance improvement has received considerable attention. This paper presents a novel multimodal biometric authentication method integrating face and iris based on score level fusion. For score level fusion, support vector machine (SVM) based fusion rule is applied to combine two matching scores, respectively from Laplacian face based face verifier and phase information based iris verifier, to generate a single scalar score which is used to make the final decision. Experimental results show that the performance of the proposed method can bring obvious improvement comparing to the unimodal biometric identification methods and the previous fused face-iris methods. This paper discusses the various scenarios that are possible to improve the performance of multimodal biometric systems using the combined characteristics such as iris and face, the level of fusion (score level fusion) is applied to that are possible and the integration strategies that can be adopted in order to increase the overall system performance.Keywords
Biometric, Multimodal, Score Level Fusion.- Shared Disk Big Data Analytics using Apache Hadoop
Abstract Views :212 |
PDF Views:4
Authors
R. Gayathri
1,
M. Balaanand
2
Affiliations
1 Computer Science and Engineering, University of Trichy, IN
2 V.R.S. College of Engineering and Technology, Arasur, Villupuram, IN
1 Computer Science and Engineering, University of Trichy, IN
2 V.R.S. College of Engineering and Technology, Arasur, Villupuram, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 7, No 10 (2015), Pagination: 285-289Abstract
Big Data is a term connected to information sets whose size is past the capacity of customary programming advancements to catch, store, oversee and prepare inside a passable slipped by time. The well known supposition around Huge Data examination is that it requires web scale adaptability: over many figure hubs with connected capacity. In this paper, we wrangle on the need of an enormously adaptable disseminated registering stage for Enormous Data examination in customary organizations. For associations which needn't bother with a flat, web request adaptability in their investigation stage, Big Data examination can be based on top of a customary POSIX Group File Systems utilizing a mutual stockpiling model. In this study, we looked at a broadly utilized bunched record framework: (SF-CFS) with Hadoop Distributed File System (HDFS) utilizing mainstream Guide diminish. In our investigations VxCFS couldn't just match the execution of HDFS, yet, additionally beat much of the time. Along these lines, endeavors can satisfy their Big Data examination need with a customary and existing shared stockpiling model without relocating to an alternate stockpiling model in their information focuses. This likewise incorporates different advantages like soundness and vigor, a rich arrangement of elements and similarity with customary examination application.Keywords
BigData, Hadoop, Clustered File Systems, Investigation, Cloud.- Moving Object Detection and Counting Using Fuzzy Color Histogram Features
Abstract Views :204 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Science and Engineering, V.R.S College of Engineering and Technology, Arasur, IN
1 Department of Computer Science and Engineering, V.R.S College of Engineering and Technology, Arasur, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 6, No 9 (2014), Pagination: 317-320Abstract
Object detection and counting from video stream is very important for many real-life applications. Existing detection and counting were based on Bayesian regression. We present a efficient object detection and counting based on background subtraction using fuzzy color histogram (FCH), which used effectively for removal of unwanted pixel from the background and capacity of extraordinarily weakening shade varieties created by foundation movements while as of now highlighting moving articles for effective individuals tallying. First, video is converted into frames for processing it to still images to detect objects. Fuzzy C means (FCM) technique used for data grouping, applying along with membership values for clustering with color planes [1]. Foreground and background classified with FCH features by applying threshold value ranges from 0 to 1. Then detected object proceed with morphological process and component analysis for smoothing. Finally, object is counted and we present number of objects in full video.
Keywords
Fuzzy C Means, Fuzzy Color Histogram, Membership Matrix, Object Detection, Connected Component Analysis.- Coastal Inundation Research:An overview of the Process
Abstract Views :384 |
PDF Views:150
Authors
Affiliations
1 Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology, Kharagpur 721 302, IN
2 Department of Marine and Ecological Sciences, Florida Gulf Coast University, Fort Myers, FL 33965, US
1 Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology, Kharagpur 721 302, IN
2 Department of Marine and Ecological Sciences, Florida Gulf Coast University, Fort Myers, FL 33965, US
Source
Current Science, Vol 112, No 02 (2017), Pagination: 267-278Abstract
Coastal inundation is the flooding of coastal zone resulting from increased river discharge, spring tides, severe storms, or generation of powerful waves from tectonic activity (tsunami). This article discusses the critical factors that contribute to coastal inundation. Among the probable factors that cause coastal flooding and destruction, storm surge is the most frequent, and hence this article provides a detailed evaluation of the progress made in storm inundation research. Recent advances in coastal inundation modelling include efforts to understand the nonlinear dynamic interaction of near-shore waves, wind and atmospheric pressure with still water sea level and coastal currents, and their combined effects on storm surge along the coast and interaction with coastal morphology. An advanced storm-surge model comprises different modules, viz. an atmospheric component, and two ocean components for surge and wave simulations; these modules are coupled with each other. The nesting of regional coastal model with an ocean-wide model captures the far-field boundary forcing of extreme events that usually originate from the warm open ocean. Even though significant advancements reported on the efficiency and accuracy of storm surge and inundation prediction, further studies are required to understand the nonlinear interaction of storm surge with coastal landforms and their vegetation (land cover). In the context of rising sea level, increased tropical cyclone activity and rapid shoreline change, it is pertinent to evaluate the future flooding risk associated with landfall of tropical cyclones in densely populated coastal cities.Keywords
Coastal Inundation, Coupled Models, Storm Surge, Tropical Cyclones.Full Text
- Awareness of Risk Factors for Obesity among College Students in Tamil Nadu:A Questionnaire Based Study
Abstract Views :278 |
PDF Views:1
Authors
Affiliations
1 Saveetha Dental College and Hospitals, Chennai-600 077, IN
2 Department of Biochemistry, Saveetha Dental College and Hospitals, Chennai-600 077, IN
1 Saveetha Dental College and Hospitals, Chennai-600 077, IN
2 Department of Biochemistry, Saveetha Dental College and Hospitals, Chennai-600 077, IN
Source
Research Journal of Pharmacy and Technology, Vol 10, No 5 (2017), Pagination: 1367-1369Abstract
Background: Obesity the most prevalent form of malnutrition in both developed and developing countries and affecting children as well as adults is replacing the more traditional public health concerns. Obesity and overweight are the fifth leading cause of deaths worldwide. As obesity is the key risk factor in natural history of other chronic non-communicable diseases, obesity prevention strategies offer a cost-effective approach in preventing other chronic non-communicable diseases. Awareness level is the basic necessity to effect a change in behaviour, more so in case of medical students as they can be the health educators of the community. Objective: To create awareness about risk factor of obesity among College students. Result: Among 100 students, 64 were men and 36 were women. It was found that 57.4% men and 72.7% women were not aware of its risk factors, respectively. Conclusion: The prevalence of overweight and obesity was higher compared to other studies and the awareness level was satisfactory.Keywords
Overweight, Obesity, Awareness, Risk Factors.- Detecting Criminal Attacks Using Rowhammer Technology
Abstract Views :330 |
PDF Views:0
Authors
Affiliations
1 CSE, M.Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
2 M. Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
1 CSE, M.Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
2 M. Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
Source
International Journal of Emerging Trends in Science & Technology, Vol 4, No 1 (2018), Pagination: 5-7Abstract
As an evolution of modern era, people are interlinked with social networks and data are stored in phones. All the information are not completely safe. A mass information gathering and connection structure in light of those data that spilled from the remote gadgets that individuals convey. This project is used to find the criminals and anonymous network users by the data. The device named Snoopy is composed in Python. Capable of working in an appropriated manner. Snoopy apparatus can draw particular and abnormal state conclusions and about people details in light of their computerized remote signals from the remote gadgets that individuals convey. The Rowhammer hardware bug allows an attacker to modify memory without accessing it, simply by repeatedly accessing, that is hammering”, a given physical memory location.Keywords
Android, Criminal, Rowhammer.References
- E. Ferrara, S. Catanese, P. D. Meo, and G. Fiumara, “Detecting criminal organizations in mobile phone networks”, Expert Systems with Applications, vol. 41, no. 13, pp. 5733-5750, October 2014.
- E. Dondyk, and C. C. Zou, “Denial of convenience attack to smart phones using a fake Wi-Fi access point,” University of Central Florida: Presentation transcript, 2012.
- V. V. D. Veen, Y. Fratantonio, M. Lindorfer, “Drammer: Deterministic row hammer attack on mobile platforms,” Proceedings of the ACM SIGSAC Conference on Computer and Communications Security, pp. 1675-1689, 2016.
- N. Mahendran, and T. Mekala, “A survey: Sentiment analysis using machine learning techniques for social media analytics,” Journal of Pure and Applied Mathematics, vol. 118, no. 8, pp. 419-422, 2018.
- C. Selvarathi, and A. Selvi, “Face tracking algorithm for tracking target in WSN,” International Journal of Pure and Applied Mathematics, vol. 118, no.8, pp. 579-584, 2018.
- Clinical Assessment on Knowledge of Garments Dust Induced Bronchial Asthma among Kongu Nadu Textile Workers
Abstract Views :219 |
PDF Views:0
Authors
Affiliations
1 Department of Pharmacology, Krupanidhi College of Pharmacy, Bangalore -35, Karnataka, IN
1 Department of Pharmacology, Krupanidhi College of Pharmacy, Bangalore -35, Karnataka, IN
Source
Research Journal of Pharmacy and Technology, Vol 12, No 6 (2019), Pagination: 2721-2723Abstract
One of the major factors that influence proper management of asthma is patient’s education. Prior knowledge of etiology, usage of medication regarding asthma is a necessity for better patient compliance. Evaluation of knowledge of diseases and attitude is very important for the patient’s wellbeing, which hinders asthma complication and also synergises health improvement. Data regarding disease knowledge and drug adherence for occupational asthma is negligible in India. Hence the evaluation of knowledge of garment induced bronchial asthma amongst textile workers which was conducted in a few textile industries adds in to the data. In reference to this regard, patient required information had been taken from 857 patients with a response rate of 96%. Majority of the participants were between 30-60 years of age. Ratio of male and female was found to be 39.08% (334) and 61.02 % (523) respectively. The participants received a score value of 20 and 15 for disease knowledge and attitude towards asthma respectively. Therefore present clinical study concluded that patients lack the knowledge and medication adherence which also induces misunderstanding in management of disease condition precisely in patients suffering from occupational asthma.Keywords
Kongunadu Textile Workers, Work Related Asthma, Knowledge of Asthma, Drug Adherence, Asthmagens.References
- Mahendra Kumar, Jimmy Jose, Kumarswamy M, Naveen Mr Assessing the knowledge, attitude and medication adherence among asthma patients in a rural population. Asian journal of pharmaceutical and clinical research. 2011: 4; 937.
- Maheshwari P, Ravichandiran V, Kumar KB, Sreelekha KV, Baig TS, Shahel SN. Prescribing patterns of antibiotics in paediatrics for respiratory tract infections/disorders in tertiary care hospital. Asian J Pharm Clin Res 2015: 8(4); 259-61.
- Rand Cs, Wise Ra. Johns Hopkins. Measuring adherence to asthma medication regimens asthma and allergy center. J Medline. 2012:12(3); 7
- Romano C, Sulotto F, Pavan I.A new case of occupational asthma from reactive dyes with severe anaphylactic response to the specific challenge Am J Ind Med. 1992: 21; 209-216.
- Muthukumar. A and SundaraGanapathy.R. Role Of Assorted Industrial Asthmagens: Hypotheses On Contemporary Approach To Target Disease Knowledge And Medication Adherence And Treat Occupation Induced Asthma', International Journal of Current Advanced Research. 2017: 06(05); 3872-3875.
- P. Maheshwari, I. Somasundaram. Health Related Quality of Life Measurement in Asthma and Chronic Obstructive Pulmonary Disease. Research J. Pharm. and Tech. 2016: 9(5); 518-520.
- Gajanan, JyothiHattiholi, And Alisha Chaudhury Role of health education and self-action plan in improving the drug compliance in bronchial asthma. J Family Med Prim Care. 2014:3(1); 33-8. doi: 10.4103/2249-4863.130269.
- Ajesh Kumar T. K. Assessment and Pathophysiology of Asthma. Int. J. Nur. Edu. and Research. 2014: 2(2); 117-120.
- Animesh Jain, H. Vinod Bhat and Das Acharya. Prevalence of bronchial asthma in rural Indian children a cross sectional study from south India. Indian j pediatr. 2010: 77 (1); 31-5.
- Global Initiative for Asthma (GINA). Global Strategy for Asthma Management and Prevention; 2008. Available from: http://www.ginasthma.org. [Last updated on 2009 Dec; Last accessed on 2010 May].
- Punita R Maurya, Yadunath M Joshi, Vilasrao J Kadam. A Review on Bronchial asthma. Research J. Pharmacology and Pharmacodynamics. 2013: 5(4); 257-265.
- Michael C. Sokol, Md, Ms, Kimberly A. Mcguigan, Phd, Robert R. Verbrugge, Phd, And Robert S. Epstein, Md, Ms. Impact of medication adherence on hospitalization risk and healthcare cost. Medical care. 2005:43(6); 521–30.
- A. Annalakshmi. Effectiveness of Breathing Exercise on Patients with Bronchial Asthma in Out Patient Department of P. S. G. Hospital, Coimbatore. Asian J. Nur. Edu. and Research. 2011: 1(4); 103-104.
- Pavithra H. Dave, Preetha. Pathogenesis and Novel Drug for Treatment of Asthma – A Review. Research J. Pharm. and Tech. 2016: 9(9); 1519-1523.
- Morisky DE, Green LW, Levine DM. Concurrent and predictive validity of a self-reported measure of medication adherence. Med Care 1986; 24(1):67-74.
- Rajinder Singh Bedi. Bedi clinic and nursing home. Patient education programme for asthmatics: Indian perspective. Indian J Chest Disease Allied Science. 2007:49(2); 93-8
- Ajay R Fugate, A M Kadamand M S Ganachari. Prospective study of medication adherence pattern in chronic obstructive pulmonary disease and asthma patients in tertiary care teaching hospital. Indian journal of pharmacy practice. 2015; 8(2); 79-83.
- Bestley Joe S, Gomathi T, Maflin Shaby S, Pandian R. Self-Assistance Devices for Asthma Patients using Android Application Research J. Pharm. and Tech 2018: 11(5); 1945-1950.
- Janet J, K.R. Biju. A Qualitative Study to Assess the Needs and Problems of High School Children with Asthma and Epilepsy. Int. J. Adv. Nur. Management. 2015: 3(1); 01-06
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- Muthukumar A and Sundaraganapathy R. A prospective clinical study on disease knowledge and medication adherence pattern among asthmatic in tertiary care hospital in a tirupur population. Asian Journal of Pharmaceutical and clinical research.2017:10(10); 388-91.
- Janet J, K.R. Biju. A Qualitative Study to Assess the Needs and Problems of High School Children with Asthma and Epilepsy. Int. J. Adv. Nur. Management. 2015: 3(1); 01-06
- Chetna A. Shamkuwar, NaliniKumari, Sushant H. Meshram, Ganesh N. Dakhaleand Vijay M. Motghare Evaluation of knowledge, attitude and medication adherence among asthmatics outpatients in teaching hospital-a questionnaire based study. J Young Pharm. 2016: 8(1); 39-43.
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- Gajanan S. Gaude. Factors affecting nonadherence in bronchial asthma and impact of health education. Indian j allergy asthma immunol. 2011: 25(1); 1-8.
- Lingaraju.CM, Santosh kumar SK, Munirathnamma. A Study to assess the knowledge on prevention of asthma among farmers in selected settings Mysuru. Int. J. Adv. Nur. Management. 2016: 4(4); 404-406.
- Mohamed HA, Al-Jaber MM, Al-Hamadani Z, Khmour HY, Al-Lenjawi BA, Schlogl JM. Prevalence of postnatal depression and associated risk factors among south Asian mothers living in a newly developing country. Asian J Pharm Clin Res 2016:9(6); 57-61.
- Efficient Non-Local Averaging Algorithm For Medical Images For Improved Visual Quality
Abstract Views :215 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Communication Engineering, Sona College of Technology, IN
1 Department of Electronics and Communication Engineering, Sona College of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 11, No 2 (2020), Pagination: 2306-2309Abstract
Image can be distorted by various ways including sensor inadequacy, transmission error, different noise factors and motion blurring. For controlling and maintaining the visual quality level of the image to be very high, it is very important to improve the image acquisition, image storage and image transmission, etc. Achieving high Peak Signal to Noise Ratio (PSNR) is essential goal of image restoration. This involves removing noises present in the image. Non-Local Means algorithm combined with Laplacian of Gaussian filter finds better results and produces good PSNR against impulse noise as well as Gaussian noise. Generally the effect of noise can be reduced using smooth filters for better results. Here, Laplacian of Gaussian (LoG) filter is applied for categorizing the edge and noisy pixels. Before that it is mandatory to obtain local smoothing of pixels. Finally the system performance is improved by averaging the non-local parameters. This is applicable to medical images also for removing impulse noise as well as Gaussian noise. The algorithm has been tested with MRI images and CT images efficiently. Better results are obtained in comparison with the previous methods with respect to better visual quality, PSNR and SSIM.Keywords
Non-Local Means Filtering, Image Denoising, Impulse Detector, Impulse Noise, LoG Filter.- Improving Medical Image Processing Using an Enhanced Deep Learning Algorithm
Abstract Views :148 |
PDF Views:1
Authors
Affiliations
1 Department of Data Science, Codecraft Technologies, Bangalore, Karnataka, IN
2 Electronics and Communication Engineering, Rajalakshmi Engineering College, IN
3 Department of Data Science, School of Science, Jain University, IN
4 Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, IN
1 Department of Data Science, Codecraft Technologies, Bangalore, Karnataka, IN
2 Electronics and Communication Engineering, Rajalakshmi Engineering College, IN
3 Department of Data Science, School of Science, Jain University, IN
4 Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 2 (2022), Pagination: 2811-2816Abstract
The use of ML methods with the objective of selecting wheat varieties that have a higher level of rust resistance encoded in their genomes is referred to as rust selection. In addition to that, the categorization of wheat illnesses by means of machine learning It has been attempted to classify wheat diseases by making use of a wide variety of machine learning techniques. In this paper, we develop an enhanced deep learning model to classify the disease present in the wheat plant. The study uses an improved convolutional neural network to classify the plant disease using a series of layers. The simulation is conducted in terms of the accuracy, precision, recall and f-measure. The results show that the proposed method achieves higher rate of accuracy than its predecessor.Keywords
ML, Wheat Varieties, Rust Resistance, Disease.References
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- An Improvised Ensemble CNN Algorithm for Detectting Video Stream in MultimediaAn Improvised Ensemble CNN Algorithm for Detectting Video Stream in Multimedia
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Authors
Affiliations
1 Data Science, Codecraft Technologies, Bangalore, IN
2 Department of Computer Science and Engineering, PSV College of Engineering and Technology, IN
3 Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, IN
4 Department of Computer Science and Engineering, Hindusthan Institute of Technology, IN
1 Data Science, Codecraft Technologies, Bangalore, IN
2 Department of Computer Science and Engineering, PSV College of Engineering and Technology, IN
3 Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, IN
4 Department of Computer Science and Engineering, Hindusthan Institute of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 2 (2022), Pagination: 2860-2864Abstract
The only criteria that are used to evaluate the various neural network-based object identification models that are currently in use are the inference times and accuracy levels. The issue is that in order to put these new classes and situations to use in smart cities, we need to train on them in real time. We were not successful in locating any research or comparisons that were centered on the length of time necessary to train these models. As a direct consequence of this, the initial reaction times of these object identification models will consistently be quite slow (maybe in days). As a consequence of this, we believe that models that put an emphasis on the speed of training rather than accuracy alone are in significant demand. Users are able to gather photos for use in training in the present by utilizing concept names in online data collection toolkits; however, these images are iconic and do not have bounding boundaries. Under these conditions, the implementation of semi-supervised or unsupervised models in a variety of smart city applications might be able to contribute to an improvement in the precision of data derived from IoMT. In this study, we categorize the video clips into their appropriate classes using an improved ensemble classification model.Keywords
CNN, Ensemble, Video Stream, IoT.References
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- J. Bethencourt, D. Song and B. Waters, “New Techniques for Private Stream Searching”, ACM Transactions on Information and System Security, Vol. 12, No. 3, pp. 1-32, 2009.
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- J. Hu and Z. Qin, “Detecting Compressed Deepfake Videos in Social Networks using Frame-Temporality Two-Stream Convolutional Network”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 32, No. 3, pp. 1089-1102, 2021.
- Shih-Hao Wang, Shih-Hsin Tai and Tihao Chiang, “A LowPower and Bandwidth-Efficient Motion Estimation IP Core Design using Binary Search”, IEEE Transactions on Circuits and System for Video Technology, Vol. 19, No. 5, pp. 760-765, 2009.
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- B. Zawali, S. Furnell and A. A-Dhaqm, “Realising a Push Button Modality for Video-Based Forensics”, Infrastructures, Vol. 6, No. 4, pp. 54-62, 2021.[1] J. Bethencourt, D. Song and B. Waters, “New Techniques for Private Stream Searching”, ACM Transactions on Information and System Security, Vol. 12, No. 3, pp. 1-32, 2009.
- David Money Harris, and Sarah L. Harris, “Digital Design and Computer Architecture”, Morgan Kaufmann, 2007.
- J. Bethencourt, D. Song and B. Waters, “New Techniques for Private Stream Searching”, ACM Transactions on Information and System Security, Vol. 12, No. 3, pp. 1-32, 2009.
- X.V. Nguyen and N.N. Dao, “Intelligent Augmented Video Streaming Services Using Lightweight QR Code Scanner”, Proceedings of IEEE International Conference on Communication, Networks and Satellite, pp. 103-107, 2021.
- D. Nagothu, R. Xu and A. Aved, “DeFake: Decentralized ENF-Consensus Based DeepFake Detection in Video Conferencing”, Proceedings of IEEE International Workshop on Multimedia Signal Processing, pp. 1-6, 2021.
- Caroline Fontaine and Fabien Galand, “A Survey of Homomorphic Encryption for Nonspecialists”, Journal of Information Security, Vol. 1, pp. 41-50, 2009.
- X. Jin and J. Xu, “Towards General Object-Based Video Forgery Detection via Dual-Stream Networks and Depth Information Embedding”, Multimedia Tools and Applications, Vol. 81, No. 25, pp. 35733-35749, 2022.
- S.M. Kulkarni, D.S. Bormane and S.L. Nalbalwar, “Coding of Video Sequences using Three Step Search Algorithm”, Proceedings of International Conference on Advance in Computing, Communication and Control, pp. 34-42, 2015.
- K. Leela Bhavani and R. Trinadh, “Architecture for Adaptive Rood Pattern Search Algorithm for Motion Estimation”, International Journal of Engineering Research and Technology, Vol. 1, No. 8, pp. 1-6, 2012
- R. Sudhakar and S. Letitia, “Motion Estimation Scheme for Video Coding using Hybrid Discrete Cosine Transform and Modified Unsymmetrical-Cross Multi Hexagon-Grid Search Algorithm”, Middle-East Journal of Scientific Research, Vol. 23, No. 5, pp. 848-855, 2015.
- J. Hu and Z. Qin, “Detecting Compressed Deepfake Videos in Social Networks using Frame-Temporality Two-Stream Convolutional Network”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 32, No. 3, pp. 1089-1102, 2021.
- Shih-Hao Wang, Shih-Hsin Tai and Tihao Chiang, “A LowPower and Bandwidth-Efficient Motion Estimation IP Core Design using Binary Search”, IEEE Transactions on Circuits and System for Video Technology, Vol. 19, No. 5, pp. 760-765, 2009.
- D. Nagothu and A. Aved, “Detecting Compromised Edge Smart Cameras using Lightweight Environmental Fingerprint Consensus”, Proceedings of ACM Conference on Embedded Networked Sensor Systems, pp. 505-510, 2021.
- B. Zawali, S. Furnell and A. A-Dhaqm, “Realising a Push Button Modality for Video-Based Forensics”, Infrastructures, Vol. 6, No. 4, pp. 54-62, 2021.
- Enhanced AI Based Feature Extraction Technique in Multimedia Image Retrieval
Abstract Views :174 |
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, IN
2 Department of Computer Science and Engineering, Chettinad College of Engineering and Technology, IN
3 Department of Computer Science and Engineering, University College of Engineering, IN
4 Department of Master of Business Administration, Koneru Lakshmaiah Education Foundation, IN
1 Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, IN
2 Department of Computer Science and Engineering, Chettinad College of Engineering and Technology, IN
3 Department of Computer Science and Engineering, University College of Engineering, IN
4 Department of Master of Business Administration, Koneru Lakshmaiah Education Foundation, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 4 (2023), Pagination: 3021-3027Abstract
In the era of rapid technological advancements, the demand for efficient and accurate identification and retrieval of information from multimedia images has seen a substantial increase. To meet this growing demand, artificial intelligence (AI)-based technologies, particularly feature extraction techniques, have gained significant popularity. Feature extraction involves the extraction of salient features from multimedia images, such as edges, lines, curves, textures, and colors, with the aim of representing the data in a more suitable format for analysis. This paper presents an enhanced AI-based feature extraction technique for multimedia image retrieval. The proposed method introduces a novel approach that combines the power of deep learning and evolutionary algorithms in a neuro-symbolic computation framework. Specifically, the renowned VGG16 deep learning algorithm is employed as the initial feature extractor. VGG16 is a state-of-the-art deep convolutional neural network that has demonstrated exceptional performance in various computer vision tasks, including image classification and feature extraction. The primary idea behind this approach is to leverage the capabilities of AI to extract the most discriminative features from the source images using VGG16. These features are then further refined using evolutionary algorithms, which employ a search and optimization process inspired by natural evolution. By iteratively improving the extracted features through the evolutionary algorithms, the method aims to enhance the discriminative power and representational quality of the extracted features. To evaluate the performance of the proposed approach, extensive experiments were conducted. The results demonstrate that the method achieves superior performance in terms of precision, recall, and F-measure when compared to conventional feature extraction techniques. Furthermore, a comprehensive comparison with state-of-the-art AI-based feature extraction techniques further highlights the potential and effectiveness of the proposed approach in multimedia image retrieval applications.Keywords
Information Retrieval, Feature Extraction, Multimedia, Images.References
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- Ramesh, G., Logeshwaran, J., Gowri, J., & Ajay Mathew (2022). The management and reduction of digital noise in video image processing by using transmission based noise elimination scheme. ICTACT Journal on image and video processing, 13(1), 2797-2801
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