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Balasubramanian, S.
- Analysis of Spatio-temporal Disease Pattern Using Spatial Auto Correlation Methods: Case of Acute Gastroenteritis in Coimbatore District, Tamilnadu, India
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Authors
Affiliations
1 Dept. of Zoology, Nirmala College for Women (Autonomous), Redfields, Coimbatore, Tamilnadu, IN
2 Department of Environmental Management, Bharathidasan University, Tiruchirapalli, Tamilnadu, IN
3 National Institute of Epidemiology, III Avenue, Ayapakkam, Ambattur, Chennai, IN
4 JSS Medical University, Srivratheeswarar Nagar, Mysore, IN
1 Dept. of Zoology, Nirmala College for Women (Autonomous), Redfields, Coimbatore, Tamilnadu, IN
2 Department of Environmental Management, Bharathidasan University, Tiruchirapalli, Tamilnadu, IN
3 National Institute of Epidemiology, III Avenue, Ayapakkam, Ambattur, Chennai, IN
4 JSS Medical University, Srivratheeswarar Nagar, Mysore, IN
Source
Indian Journal of Public Health Research & Development, Vol 5, No 3 (2014), Pagination: 296-301Abstract
Background and Objectives: In epidemiology many of the infectious disease events do not occur randomly in geographical context but occur in clusters. Geographical or spatial analysis comes into play due to the existence of spatial dependence in data. The main objective was to examine the variation in the prevalence of Acute Gastroenteritis using space time autocorrelation methods and to explore possible factors that might have influenced these variations in the study area.
Method: To identify the spatial similarity between the estimated Acute gastroenteritis values in the study region spatial autocorrelation was attempted to study the aggregated data of disease incidences. Different methods for measuring seasonal variations was adopted - Simple Averages, Ratio to Trend, Ratio to Moving Average and Link-Relative Methods. Results: The results show that a high incidence is recorded in the I and IV quarters, in all the taluks of Coimbatore district. It is observed that the incidence of Acute gastroenteritis is high in the months of January, February, March, April and November for all the taluks of Coimbatore district. The seasonal trend observations displayed a more comprehensive pattern of disease movement rather than the monthly pattern.
Interpretation and conclusion: It is evident that Acute Gastroenteritis is a seasonally dependent disease with more number of cases increasing in the winter season and less cases in the summer season. This methodology will improve the accuracy of public health forecasting and will help in developing mechanisms to combat large seasonal surges of infectious diseases.
Keywords
Link-relative, Ratio to Trend, Ratio to Moving Average, Seasonal Oscillations, Simple Averages, Spatial Autocorrelation
- Co-Curing Noisy Annotations for Facial Expression Recognition
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Authors
Affiliations
1 Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, IN
1 Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, IN
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 1 (2021), Pagination: 2508-2516Abstract
Driven by the advancement in technology that can facilitate implementation of deep neural networks (DNNs), and due to the availability of large scale datasets, automatic recognition performance of the machines has increased by leaps and bounds. This is also true with regard to facial expression recognition (FER) wherein the machine automatically classifies a given facial image in to one of the basic expressions. However, annotations of large scale datasets in FER suffer from noise due to various factors like crowd sourcing, automatic labelling based on key word search etc. Such noisy annotations impede the performance of FER due to the memorization ability of DNNs. To address it, this paper proposes a learning algorithm called Co-curing: peer training of two joint networks using a supervision loss and a mimicry loss that are balanced dynamically, and supplemented with a relabeling module to correct the noisy annotations. Specifically, peer networks are trained independently using supervision loss during early part of the training. As training progresses, mimicry loss is given higher weightage to bring consensus between the two networks. Our Co-curing does not need to know the noise rate. Samples with wrong annotations are relabeled based on the agreement of peer networks. Experiments on synthetic as well real world noisy datasets validate the effectiveness of our method. State-of-the-art (SOTA) results on benchmark in-the-wild FER datasets like RAF-DB (89.70%), FERPlus (89.6%) and AffectNet (61.7%) are reported.Keywords
Noisy Annotations, Facial Expression Recognition, Co-Curing, Mimicry Loss, Peer LearningReferences
- C. Darwin and P. Prodger, “The Expression of the Emotions in Man and Animals”, Oxford University Press, 1998.
- S. Li, W. Deng, “Deep Facial Expression Recognition: A Survey”, IEEE Transactions on Affective Computing, Early Access, 2020.
- P. Ekman and W.V. Friesen, “Constants across Cultures in the Face and Emotion”, Journal of Personality and Social Psychology, Vol. 17, No. 2, pp. 124-129, 1971
- P. Ekman, “Strong Evidence for Universals in Facial Expressions: A Reply to Russell’s Mistaken Critique”, Psychological Bulletin, Vol. 115, No. 2, pp. 268-287, 1994.
- D. Matsumoto, “More Evidence for the Universality of a Contempt Expression”, Motivation and Emotion, Vol. 16, No. 4, pp. 363-368, 1992.
- X. Fan, Z. Deng, K. Wang, X. Peng and Y. Qiao, “Learning Discriminative Representation for Facial Expression Recognition from Uncertainties”, Proceedings of IEEE International Conference on Image Processing, pp. 903-907, 2020.
- J. MA, “Facial Expression Recognition using Hybrid Texture Features based Ensemble Classifier”, International Journal of Advanced Computer Science and Applications, Vol. 6, pp. 1-13, 2017.
- C. Shan, S. Gong and P.W. Mcwoan, “Facial Expression Recognition based on Local Binary Patterns: A Comprehensive Study”, Image and Vision Computing, Vol. 27, No. 6, pp. 803-816, 2009.
- P. Hu, D. Cai, S. Wang, A. Yao and Y. Chen, “Learning Supervised Scoring Ensemble for Emotion Recognition in the Wild”, Proceedings of ACM International Conference on Multimodal Interaction, pp. 553-560, 2017.
- H. Chun Lo and R. Chung, “Facial Expression Recognition Approach for Performance Animation”, Proceedings of IEEE International Workshop on Digital and Computational Video, pp. 613-622, 2001.
- T. Kanade, J.F. Cohn and Y. Tian, “Comprehensive Database for Facial Expression Analysis”, Proceedings of 4th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 46-53, 2000.
- P. Lucey, J.F. Cohn, T. Kanade, J. Saragih, Z. Ambadar and I. Matthews, “The Extended Cohn-Kanade Dataset (ck+): A Complete Dataset for Action Unit and Emotion-Specified Expression”, Proceedings of IEEE International Workshops on Computer Vision and Pattern Recognition, pp. 94-101, 2010.
- G. Zhao, X. Huang, M. Taini, S.Z. Li and M. Pietikainen, “Facial Expression Recognition from Near-Infrared Videos”, Proceedings of IEEE International Conference on Image and Vision Computing, pp. 607-619, 2011.
- F.Y. Shih, C.F. Chuang and P.S.P. Wang, “Performance Comparisons of Facial Expression Recognition in Jaffe Database”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 22, No. 3, pp. 445-459, 2008.
- A. Mollahosseini, B. Hasani and M.H. Mahoor, “A Database for Facial Expression, Valence, and Arousal Computing in the Wild”, IEEE Transactions on Affective Computing, Vol. 10, No. 1, pp.18-31, 2017.
- E. Barsoum, C. Zhang, C.C. Ferrer and Z. Zhang, “Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution”, Proceedings of 18th ACM International Conference on Multimodal Interaction, pp. 279-283, 2016.
- S. Li and W. Deng, “Reliable Crowdsourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition”, IEEE Transactions on Image Processing, Vol. 28, No. 1, pp. 356-370, 2018.
- S. Li, W. Deng and J. Du, “Reliable Crowdsourcing and Deep Locality Preserving Learning for Expression Recognition in the Wild”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 28522-2861, 2017.
- K. Wang, X. Peng, J. Yang, S. Lu and Y. Qiao, “Suppressing Uncertainties for Large-Scale Facial Expression Recognition”, Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6897-6906, 2020.
- D. Arpit, S. Jastrz, N. Ballas, D. Krueger, E. Bengio, M.S. Kanwal, T. Maharaj, A. Fischer, A. Courville and Y. Bengio, “A Closer Look at Memorization in Deep Networks”, Proceedings of International Conference on Machine Learning, pp. 233-242, 2017.
- C. Zhang, S. Bengio, M. Hardt, B. Recht and O. Vinyals, “Understanding Deep Learning requires Rethinking Generalization”, Proceedings of International Conference on Machine Learning, pp. 1-13, 2017.
- B. Frenay and M. Verleysen, “Classification in the Presence of Label Noise: A Survey”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 25, No. 5, pp. 845-869, 2013.
- J. Goldberger and E. Ben-Reuven, “Training Deep Neural-Networks using A Noise Adaptation Layer”, Proceedings of International Conference on Machine Learning, pp. 1-5, 2016.
- G. Patrini, A. Rozza, A. Menon, R. Nock and L. Qu, “Making Neural Networks Robust to Label Noise: A Loss Correction Approach”, Proceedings of International Conference on Machine Learning, pp. 1-9, 2016.
- B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I. Tsang and M. Sugiyama, “Coteaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels”, Proceedings of International Conference on Machine Learning, pp. 1-13, 2018.
- Darshan Gera and S. Balasubramanian, “Landmark Guidance Independent Spatio-Channel Attention and Complementary Context Information based Facial Expression Recognition”, Pattern Recognition Letters, Vol:145, pp. 58-66, 2021.
- Samuli Laine and Timo Aila, “Temporal Ensembling for Semisupervised Learning”, Proceedings of International Conference on Neural and Evolutionary Computing, pp. 1-13, 2016.
- X. Yu, B. Han, J. Yao, G. Niu, I. Tsang, M. Sugiyama, “How does Disagreement Help Generalization Against Label Corruption?”, Proceedings of International Conference on Machine Learning, pp. 7164-7173, 2019.
- X. Wang, Y. Hua, E. Kodirov and N.M. Robertson, “Image for Noise-Robust Learning: Mean Absolute Error does not Treat Examples Equally and Gradient Magnitude’s Variance Matters”, Proceedings of International Conference on Machine Learning, pp. 1-14, 2019.
- Y. Wang, X. Ma, Z. Chen, Y. Luo, J. Yi and J. Bailey, “Symmetric Cross Entropy for Robust Learning with Noisy Labels”, Proceedings of IEEE/CVF International Conference on Computer Vision, pp. 322-330, 2019.
- Ying Zhang, Tao Xiang, Timothy M. Hospedales and Huchuan Lu, “Deep Mutual Learning”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4320-4328, 2018.
- Zhilu Zhang and Mert Sabuncu, “Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels”, Proceedings of IEEE Conference on Neural Information Processing Systems, pp. 8778-8788, 2018.
- Scott Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan and Andrew Rabinovich, “Training Deep Neural Networks on Noisy Labels with Bootstrapping”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3320-3328, 2015.
- H. Siqueira, S. Magg and S. Wermter, “Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks”, Proceedings of AAAI Conference on Artificial Intelligence, pp. 5800-5809, 2020.
- P. Jiang, B. Wan, Q. Wang and J. Wu, “Fast and Efficient Facial Expression Recognition using a Gabor Convolutional Network”, IEEE Signal Processing Letters, Vol. 27, pp. 1954-1958, 2020.
- P. Ding and R. Chellappa, “Occlusion-Adaptive Deep Network for Robust Facial Expression Recognition”, Proceedings of IEEE International Joint Conference on Biometrics, pp. 1-9, 2020.
- Y. Li, J. Zeng, S. Shan and X. Chen, “Occlusion Aware Facial Expression Recognition using CNN with Attention Mechanism”, IEEE Transactions on Image Processing, Vol. 28, No. 5, pp. 243902450, 2018.
- E. Malach and S. Shalev-Shwartz, “Decoupling” when to Update” from” How to Update”, Proceedings of IEEE International Conference on Advances in Neural Information Processing Systems, pp. 1-11, 2017.
- H. Wei, L. Feng, X. Chen and B. An, “Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization”, Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13726-13735, 2020.
- F. Sarfraz, E. Arani and B. Zonooz, “Noisy Concurrent Training for Efficient Learning under Label Noise”, Proceedings of IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 3159-3168, 2021.
- J. Zeng, S. Shan and X. Chen, “Facial Expression Recognition with Inconsistently Annotated Datasets”, Proceedings of European Conference on Computer Vision, pp. 222-237, 2018.
- K. Zhang, Z. Zhang, Z. Li and Y. Qiao, “Joint Face Detection and Alignment using Multitask Cascaded Convolutional Networks”, IEEE Signal Processing Letters, Vol. 23, No. 10, pp. 1499-1503, 2016.
- K. He, X. Zhang, S. Ren and J. Sun, “Deep Residual Learning for Image Recognition”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.
- Y. Guo, L. Zhang, Y. Hu, X. He and J. Gao, “Ms-Celeb-1m: A Dataset and Benchmark for Large-Scale Face Recognition”, Proceedings of European Conference on Computer Vision, pp. 87-102, 2016.
- K. Wang, X. Peng, J. Yang, D. Meng and Y. Qiao, “Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition”, IEEE Transactions on Image Processing, Vol. 29, pp. 4057-4069, 2020.
- S. Li and W. Deng, ‘Reliable Crowdsourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition”, IEEE Transactions on Image Processing, Vol. 28, No. 1, pp. 356-370, 2018.
- Fuzzy Logic Association Rules Integrating with Matlab for Effective Policing in Crime Analysis
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Authors
Affiliations
1 DRDO-BU CLS, Bharathiar University, Coimbatore-641046, IN
2 Department of MCA, Sri Ramakrishna Mission Vidyalaya, Coimbatore-641020, IN
3 Department of Environmental Science, JSS University, Mysore-500015, IN
1 DRDO-BU CLS, Bharathiar University, Coimbatore-641046, IN
2 Department of MCA, Sri Ramakrishna Mission Vidyalaya, Coimbatore-641020, IN
3 Department of Environmental Science, JSS University, Mysore-500015, IN
Source
Fuzzy Systems, Vol 4, No 1 (2012), Pagination: 23-29Abstract
Fuzzy logic systems are generally recovered for device, system identification, and pattern gratitude complications. Fuzzy logic has become a much conversed technique in mathematical, engineering literature; it has not yet found application in social science fields. Currently, this system is a nonlinear mapping from an input space to an output space that can be parameterized in various ways. Fuzzy logic is widely used in the current application and to solve social problems (for identification and management of law enforcement) and criminal justices when compared to classic logic. Fuzzy logic has the potential to add human-like subjective reasoning capabilities to machine intelligence.Keywords
Association Rules, Crime Data, Fuzzy Logic, Matlab, Map Object.- Exploratory Model Using Fuzzy Logic for Evaluation of Attitude and Aptitude
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Authors
Affiliations
1 Department of MCA, SRMVCAS, Coimbatore, IN
2 JSS University, Mysore, IN
1 Department of MCA, SRMVCAS, Coimbatore, IN
2 JSS University, Mysore, IN
Source
Data Mining and Knowledge Engineering, Vol 5, No 9 (2013), Pagination: 350-355Abstract
Human development depends on Physical health, Aptitude and Attitude. Physical health refers to Overweight, Normal and Underweight using Body Mass Index. Aptitude refers to intellectual ability or talent to solve the problem and Attitude refers to gunas which indicates the 'inherent energy or tendency' with which the mind functions. Attitude are categorized as Sattvic, Rajasic and Tamasic type. Aptitude are classified as high, medium and low. We developed statistical based method and Fuzzy Logic based method, to find the relationship between attitude, aptitude and physical. 535 males and 320 females were given questionnaire based on attitude, aptitude and physical. We are able to find each individual inherent tendency with intellectual ability and physical has no effect on it. Males are rajasic nature with low aptitude and females are rajasic nature with medium aptitude. The inherent tendency of 855 candidates are rajasic nature with low aptitude. Attitude plays dominant role in the development of aptitude.Keywords
Attitude, Aptitude, Fuzzy Logic, BMI.- Area Based Crime Analysis in Spatial Data Mining Approach for Association Rule in Geo-Referenced Data
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Authors
Affiliations
1 DRDO-BU CLS, Bharathiar University, Coimbatore-641046, IN
2 Sri Ramakrishna Mission Vidyalaya, Coimbatore-641020, IN
3 CJSS University, Mysore-500015, IN
1 DRDO-BU CLS, Bharathiar University, Coimbatore-641046, IN
2 Sri Ramakrishna Mission Vidyalaya, Coimbatore-641020, IN
3 CJSS University, Mysore-500015, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 2 (2012), Pagination: 59-63Abstract
In this paper which passion to integrate a large volume of data sets into useful information by adopting a various information techniques in the most modern technology world. The adopted approaches of Single variate Association Rule for Area to Crime based on the knowledge discovery techniques such as, clustering and association-rule mining. It reveals with an inherent of patterns of information into a fruitful exploratory tool for the discovery of spatio-temporal patterns. This tool is an autonomous pattern detector to reveal plausible cause-effect associations between layers of point and area data. We present VATA algorithm with an exploratory analysis for the effectively explore geo-referenced data. The present study of this paper was focuses through the real crime dataset by using algorithm. We demonstrate approach to a new type of analysis of the spatio-temporal dimensions of records of criminal events. We hope this will lead to new approaches in the exploration of large volumes of spatio-temporal data.Keywords
Algorithm, Clustering Association Rule, Crime Data, Data Mining, GIS, Spatio-Temporal Data.- Association Rule-Spatial Data Mining Approach for Geo-Referenced in Crime to Crime Analysis
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Authors
Affiliations
1 DRDO-BU CLS, Bharathiar University, Coimbatore-641046, IN
2 Department of MCA, Sri Ramakrishna Mission Vidyalaya, Coimbatore-641020, IN
3 Department of Environmental Science, JSS University, Mysore-500015, IN
1 DRDO-BU CLS, Bharathiar University, Coimbatore-641046, IN
2 Department of MCA, Sri Ramakrishna Mission Vidyalaya, Coimbatore-641020, IN
3 Department of Environmental Science, JSS University, Mysore-500015, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 1 (2012), Pagination: 31-36Abstract
Spatial data mining is a demanding field since huge amounts of spatial data that has been processed and turned into useful information by this paper. The increased crime rate and enormous amount of data being stored in crime databases by police personnel which has been collected from various jurisdiction of Coimbatore are gathered for the application of technologies which provides the means to turn data into information by data fusion and data mining. Data fusion organizes, combines and interprets information from multiple sources and it overcomes confusion from conflicting reports and cluttered or noisy backgrounds. Data mining is concerned with the automatic discovery of patterns and relationships with (crime to crime) in large databases. Technically, it is the process of finding correlations or patterns among dozens of fields in large relational databases using the tools of GIS. This paper provides a clear finding to prevent from crime with associated to another crime occurrence with the naked observation on correlation between one crime to another crime.Keywords
Algorithm, Association Rule, Data Mining, Crime Data, GIS.- Nano Particle a Reliable Source for Energy Efficient Eco Friendly Chilling for Fish Processing
Abstract Views :235 |
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Authors
Affiliations
1 Department of Basic Engineering, College of Fisheries Engineering, Tamil Nadu Fisheries University, Nagapattinam (T.N.), IN
2 Department of Aquacultural Engineering, College of Fisheries Engineering, Tamil Nadu Fisheries University, Nagapattinam (T.N.), IN
3 College of Fisheries Engineering, Tamil Nadu Fisheries University, Nagapattinam (T.N.), IN
1 Department of Basic Engineering, College of Fisheries Engineering, Tamil Nadu Fisheries University, Nagapattinam (T.N.), IN
2 Department of Aquacultural Engineering, College of Fisheries Engineering, Tamil Nadu Fisheries University, Nagapattinam (T.N.), IN
3 College of Fisheries Engineering, Tamil Nadu Fisheries University, Nagapattinam (T.N.), IN
Source
Rashtriya Krishi (English), Vol 12, No 2 (2017), Pagination: 159-159Abstract
Need: Cold chain management is the only art which is very tedious in this current scenario. To save the fish and agricultural products from decay and biological activities it is mandatory to keep them in sub zero temperature. Vapour compression refrigeration system provide a suitable and viable option to do so. But energy loss is inevitable. Hence the nano additives along with refrigerants were tried to be incorporated in the freezer technology for fish preservation and processing.- Gelatin Based Edible Coating:Preservation Technique for Seer (Scomberomorus guttatus) Fish Slices
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Authors
Affiliations
1 Department of Fish Process Engineering, College of Fisheries Engineering, Tamil Nadu Fisheries University, Nagapattinam-611001, Tamil Nadu, IN
2 College of Fisheries Engineering, Tamil Nadu Fisheries University, Nagapattinam-611001, Tamil Nadu, IN
3 Department of Basic Science, College of Fisheries Engineering, Tamil Nadu Fisheries University, Nagapattinam-611001, Tamil Nadu, IN
1 Department of Fish Process Engineering, College of Fisheries Engineering, Tamil Nadu Fisheries University, Nagapattinam-611001, Tamil Nadu, IN
2 College of Fisheries Engineering, Tamil Nadu Fisheries University, Nagapattinam-611001, Tamil Nadu, IN
3 Department of Basic Science, College of Fisheries Engineering, Tamil Nadu Fisheries University, Nagapattinam-611001, Tamil Nadu, IN
Source
The Indian Journal of Nutrition and Dietetics, Vol 55, No 2 (2018), Pagination: 177-186Abstract
The aim of this work is to examine the performance of gelatin based edible coating in preserving the seer fish slices. A composite edible coating solution was prepared with three different concentrations of gelatin, chitosan and vinegar. Coating experiments were performed in a laboratory scale edible coating tank which was fabricated for this purpose. The seer fish slices (13.65±2.50 mm thick and 43.89±6.82 g weight) were subjected to coating for three different soaking times (min). While coating performance of the solution was assessed through coating uptake (%), texture quality of the seer fish slices was assessed through weight loss (%) and microbial quality was assessed through Total Plate Count (CFU/g). Experiment was designed using Box-Behnken Design (BBD). The application of edible coating treatment to seer fish slices resulted in improvement of overall keeping quality of the seer fish slices (9 days) against the control (3 days) when stored under low temperature (7±1°C).Keywords
Edible Coating, Seer Fish, Texture, Weight Loss, Coating Uptake, Weight Loss.References
- Uchiyama, H. and Kato, N., Partial freezing as a means of preserving fish freshness, changes in amino acid, TMA-N, ATP and its related compounds, and nucleic acid during storage. Bull. Jap. Soc. Sci. Fish, 1974, 4, 1145.
- Farber, J.M., Microbiological aspects of modified-atmosphere packaging technology. J. Fd. Protect., 1991, 54, 58-70.
- Ozogul, F., Polat, A. and Ozogul, Y. The effect of modified atmosphere packaging and vacuum packaging on chemical, sensory and microbiological changes of sardines (Sardina pilchardus). Fd. Chem., 2004, 85, 49-57.
- Song, Y., Liu, L., Shen, H., You, J. and Luo, Y., Effect of sodium alginate-based edible coating containing different anti-oxidants on quality and shelf life of refrigerated bream (Megalobrama amblycephala). Fd. Control., 2011, 22, (3-4), 608-615.
- Chan, Y.O., Shwu, F.T. and Chieh, H.L., Using gelatin-based antimicrobial edible coating to prolong shelf-life of tilapia fillets. J. Fd. Quality., 2002, 25, 213-222.
- Fan, W.C. and Zhang, S., The use of tea polyphenol dip to extend the shelf life of silver carp (Hypophthalmicthys molitrix) during storage in ice. Fd. Chem., 2008, 108, 148-153.
- Kilincceker, O., Dogan, I.S. and Kucukoner, E., Effect of edible coatings on the quality of frozen fish fillets. LWT- Fd. Sci.Technol., 200, 42, 868-873.
- Soares, N.M., Mendes, T.S. and Antonio, A.V. Effect of chitosan-based solutions applied as edible coatings and water glazing on frozen salmon preservation–A pilot-scale study. J. Fd. Engg., 2013, 119, 316–323.
- Bag, S.K., Srivastav, P.P. and Mishra, H.N. Optimization of process parameters for foaming of bael (Aeglemarmelos L.) fruit pulp. Fd.Bio.Technol., 2011, 4, 1450-1458.
- Kaur, S., Sarkar, B.C., Sharma, H.K. and Singh, C., Optimization of enzymatic hydrolysis pre-treatment conditions for enhanced juice recovery from guava fruit using response surface methodology. Fd. Bio. Technol., 2009, 2, 1, 96-100.
- Rajesh, G.K., Thirupathi, V., Eyarkai Nambi, V. and Pandiselvam, R. Optimization of extrusion cooking process for rice-fish extrudates with an underutilized fish mince based on physical, functional and textural properties. Sci. J. Agr. Engg. 2014, 2, 9-21.
- AOAC. Official methods of analysis (17th ed.) Washington, DC: Association of Official Analytical Chemists, (2002).
- Tarun, P.S., Manish, K.C. and Jhari, S., Development of chitosan based edible films: process optimization using response surface methodology. J. Fd. Sci. Technol., 2015, 52, (5), 2530-2543, DOI 10.1007/s13197-014-1318-6.
- Dalgaard, P., Madsen, H.L., Samieian, N. and Emborg, J. Biogenic amine formation and microbial spoilage in chilled garfish (Belone belone) effect of modified atmosphere packaging and previous frozen storage. J. Appl. Microbiol., 2006, 101, 80-95. DOI: 10.1111/j.1365-2672.2006.02905.x
- Olafsdottir, G., Martinsdottir, E., Oehlenschlager, J., Dalgaard, P., Jensen, B., Undel, I., Mackie, I.M., Henehan, G., Nielsen, J. and Nilsen, H. Methods to evaluate fish freshness in research and industry. J. Fd. Sci. Technol., 1997, 8, 258-265.
- ICMSF, Microorganisms in Foods. Sampling for Microbiological Analysis: Principles and Specific Applications (2nd ed.), University of Toronto Press, NewYork. 1986.
- Statistical Analysis of M-Mode Echocardiographic Normal Reference Values in South Indian Adults
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Authors
Affiliations
1 Department of Water and Health, Faculty of Life Sciences, JSS Academy of Higher Education & Research, IN
2 Department of Community Medicine, JSS Medical College & Assistant Director Research, JSS Academy of Higher Education & Research, IN
3 Department of Water and Health, JSS Academy of Higher Education & Research, IN
4 Department of Cardiology, JSS Hospital, Mysuru, Karnataka, IN
1 Department of Water and Health, Faculty of Life Sciences, JSS Academy of Higher Education & Research, IN
2 Department of Community Medicine, JSS Medical College & Assistant Director Research, JSS Academy of Higher Education & Research, IN
3 Department of Water and Health, JSS Academy of Higher Education & Research, IN
4 Department of Cardiology, JSS Hospital, Mysuru, Karnataka, IN
Source
Indian Journal of Public Health Research & Development, Vol 10, No 12 (2019), Pagination: 24-29Abstract
Background: Normal values for echocardiographic measurements are derived from American Society of echocardiography and European Society of Cardiology/European Association of Cardio-Vascular Imaging and their publications. Indians have smaller cardiac chamber dimensions than the Europeans. However, it has been long felt that reference range from western data cannot be fully applied in our scenario.
Aim: The objective of this study was to develop age and gender-specific normative reference range values for echocardiography measurements.
Methodology: This was a retrospective cross-sectional single-center study conduct on 111 healthy Indian adults who were selected out of the 336 patients aged from 18– 76 years, who visited the cardiac medical check-up unit in a tertiary hospital. Echocardiograms were done in participants free of cardiovascular diseases, hypertension, diabetes, high blood pressure or other clinical evident disorders. M-mode Echocardiography in the left parasternal long-axis view was used to measure aorta, left atrium, left ventricle in systole and diastole,right ventricle, interventricular septum, and posterior wall thickness. The 95% reference range of the echocardiographic parameters calculated as a mean±2 “standard deviation for overall and genders specific”.
Results: This study provides a set of data with reference ranges for normal M-mode parameters according to age and gender. Around 58 males and 53 females were classified into the age group from 18-76 years as exclusive class intervals. Age group above of 38 years Indians had higher volume in all the echocardiographic measurements are observed in this study. On examining all our healthy participants, we found that the reference range of most echocardiographic parameters is a highly statistically significant difference as compared with those used in western studies.
Conclusion: The normal reference values for echocardiographic measurements derived from this study could be used for future reference in our local population.
Keywords
Echocardiography Normative Data, M-Mode Parameters, Normal Reference Values, South Indian Adults.- Iterative Collaborative Routing among Equivariant Capsules for Transformation-Robust Capsule Networks
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Authors
Affiliations
1 Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, IN
1 Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 2 (2022), Pagination: 2865-2873Abstract
Transformation-robustness is an important feature for machine learning models that perform image classification. Many methods aim to bestow this property to models by the use of data augmentation strategies, while more formal guarantees are obtained via the use of equivariant models. We recognise that compositional, or part-whole structure is also an important aspect of images that has to be considered for building transformation-robust models. Thus, we propose a capsule network model that is, at once, equivariant and compositionality aware. Equivariance of our capsule network model comes from the use of equivariant convolutions in a carefully-chosen novel architecture. The awareness of compositionality comes from the use of our proposed novel, iterative, graph-based routing algorithm, termed Iterative collaborative routing (ICR). ICR, the core of our contribution, weights the predictions made for capsules based on an iteratively averaged score of the degree-centralities of its nearest neighbours. Experiments on transformed image classification on FashionMNIST, CIFAR-10, and CIFAR-100 show that our model that uses ICR outperforms convolutional and capsule baselines to achieve state-of-the-art performance.Keywords
Equivariance, Transformation Robustness, Capsule Network, Image Classification, Deep Learning.References
- T. Cohen and M. Welling, “Group Equivariant Convolutional Networks”, Proceedings of International Conference on Machine Learning, pp. 2990-2999, 2016.
- T.S. Cohen and M. Welling, “Spherical CNNs”, Proceedings of International Conference on Learning Representations, pp. 1-6, 2018.
- S.R. Venkataraman, S. Balasubramanian and R.R. Sarma, “Building Deep Equivariant Capsule Networks”, Proceedings of International Conference on Learning Representations, pp. 1-6, 2020.
- M. Weiler and G. Cesa, “General E (2)-Equivariant Steerable CNNs”, Advances in Neural Information Processing Systems, Vol. 32, pp. 1-16, 2019.
- S. Batzner, J.P. Mailoa, M. Kornbluth and B. Kozinsky, “E (3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials”, Nature Communications, Vol. 13, No. 1, pp. 1-11, 2022.
- C. Esteves, K. Daniilidis and A. Makadia, “Cross-Domain 3D Equivariant Image Embeddings”, Proceedings of International Conference on Machine Learning, pp. 1812-1822, 2019.
- G.E. Hinton, A. Krizhevsky and S.D. Wang, “Transforming Auto-Encoders”, Proceedings of International Conference on Artificial Neural Networks, pp. 44-51, 2011.
- S. Sabour, N. Frosst and G.E. Hinton, “Dynamic Routing between Capsules”, Advances in Neural Information Processing Systems, Vol. 30, pp. 1-14, 2017.
- G.E. Hinton, S. Sabour and N. Frosst, “Matrix Capsules with EM Routing”, Proceedings of International Conference on Learning Representations, pp. 1-8, 2018.
- J. Rajasegaran, V. Jayasundara, S. Jayasekara and R. Rodrigo, “Deepcaps: Going Deeper with Capsule Networks”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 10725-10733, 2019.
- J. Choi, H. Seo, S. Im and M. Kang, “Attention Routing between Capsules”, Proceedings of International Conference on Computer Vision, pp. 1-5, 2019.
- J.E. Lenssen, M. Fey and P. Libuschewski, “Group Equivariant Capsule Networks”, Advances in Neural Information Processing Systems, Vol. 31, pp. 1-14, 2018.
- N. Garau, N. Bisagno and N. Conci, “DECA: Deep Viewpoint-Equivariant Human Pose Estimation using Capsule Autoencoders”, Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 11677-11686, 2021.
- B. Ozcan, F. Kinli and F. Kiraç, “Quaternion Capsule Networks”, Proceedings of International Conference on Pattern Recognition, pp. 6858-6865, 2021.
- M.D. Zeiler and R. Fergus, “Visualizing and Understanding Convolutional Networks”, Proceedings of International Conference on Computer Vision, pp. 818-833, 2014.
- K. Ahmed and L. Torresani, “Star-Caps: Capsule Networks with Straight-Through Attentive Routing”, Advances in Neural Information Processing Systems, Vol. 32, pp. 1-14, 2019.
- C. Pan and S. Velipasalar, “PT-CapsNet: a Novel Prediction-Tuning Capsule Network Suitable for Deeper Architectures”, Proceedings of International Conference on Computer Vision, pp. 11996-12005, 2021.
- A. Krizhevsky and G. Hinton, “Learning Multiple Layers of Features from Tiny Images”, Proceedings of International Conference on Computer Vision, pp. 1-12, 2009.
- K. He and J. Sun, “Deep Residual Learning for Image Recognition”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.
- I. Loshchilov and F. Hutter, “Decoupled Weight Decay Regularization”, Proceedings of International Conference on Learning Representations, pp. 1-15, 2018.
- L.N. Smith and N. Topin, “Super-Convergence: Very Fast Training of Neural Networks using Large Learning Rates”, Proceedings of International Conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, Vol. 11006, pp. 369-386, 2019.
- Shape Face Remove Guides, Available at http://sharenoesis.com/wp-content/uploads/2010/05/7ShapeFaceRemoveGuides.jpg, Accessed at 2010.
- Pixabay, Available at https://cdn.pixabay.com/photo/2016/11/29/11/57/dolphins-1869337_960_720.jpg, Accessed at 2016.
- Robustcaps: A Transformation-Robust Capsule Network For Image Classification
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Affiliations
1 Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, India., IN
1 Department of Mathematics and Computer Science, Sri Sathya Sai Institute of Higher Learning, India., IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 3 (2023), Pagination: 2883-2892Abstract
Geometric transformations of the training data as well as the test data present challenges to the use of deep neural networks to vision-based learning tasks. To address this issue, we present a deep neural network model that exhibits the desirable property of transformationrobustness. Our model, termed RobustCaps, uses group-equivariant convolutions in an improved capsule network model. RobustCaps uses a global context-normalised procedure in its routing algorithm to learn transformation-invariant part-whole relationships within image data. This learning of such relationships allows our model to outperform both capsule and convolutional neural network baselines on transformation-robust classification tasks. Specifically, RobustCaps achieves state-of-the-art accuracies on CIFAR-10, FashionMNIST, and CIFAR-100 when the images in these datasets are subjected to train and test-time rotations and translations.Keywords
Deep Learning, Capsule Networks, Transformation Robustness, Equivariance.References
- T. Cohen and M. Welling, “Group Equivariant Convolutional Networks”, Proceedings of International Conference on Machine Learning, pp. 2990-2999, 2016.
- M. Weiler and G. Cesa, “General E (2)-Equivariant Steerable CNNs”, Advances in Neural Information Processing Systems, Vol .32, pp. 1-15, 2019.
- T.S. Cohen and M. Welling, “Spherical CNNs”, Proceedings of International Conference on Learning Representations, pp. 1-7, 2018.
- G.E. Hinton, A. Krizhevsky and S.D. Wang, “Transforming Auto-Encoders”, Proceedings of International Conference on Artificial Neural Networks, pp. 44-51, 2011.
- S. Sabour and G.E. Hinton, “Dynamic Routing between Capsules”, Advances in Neural Information Processing Systems, Vol. 30, pp. 1-12, 2017.
- G.E. Hinton, S. Sabour and N. Frosst, “Matrix Capsules with EM Routing”, Proceedings of International Conference on Learning Representations, pp. 241-254, 2018.
- S.R. Venkataraman, S. Balasubramanian and R.R. Sarma, “Building Deep Equivariant Capsule Networks”, Proceedings of International Conference on Learning Representations, pp. 1-10, 2020.
- R. Pucci, C. Micheloni and N. Martinel, “Self-Attention Agreement Among Capsules”, Proceedings of International Conference on Computer Vision, pp. 272-280, 2021.
- J.E. Lenssen and P. Libuschewski, “Group Equivariant Capsule Networks”, Advances in Neural Information Processing Systems, Vol. 31, pp. 1-15, 2018.
- T.S. Cohen and M. Weiler, “A General Theory of Equivariant CNNs on Homogeneous Spaces”, Advances in Neural Information Processing Systems, Vol. 32, pp. 1-12, 2019.
- J. Rajasegaran, S. Seneviratne and R. Rodrigo, “Deepcaps: Going Deeper with Capsule Networks”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10725-10733, 2019.
- K. Ahmed and L. Torresani, “Star-Caps: Capsule Networks with Straight-Through Attentive Routing”, Advances in Neural Information Processing Systems, Vol. 32, pp. 167- 178, 2018.
- H. Xiao, K. Rasul and R. Vollgraf, “Fashion-Mnist: A Novel Image dataset for Benchmarking Machine Learning Algorithms”, Proceedings of International Conference on Computer Vision, pp. 1-8, 2017.
- A. Krizhevsky and G. Hinton, “Learning Multiple Layers of Features from Tiny Images”, Available at https://www.cs.toronto.edu/~kriz/learning-features-2009- TR.pdf, 2009.
- K. He and J. Sun, “Deep Residual Learning for Image Recognition”, Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.
- D. Romero and M. Hoogendoorn, “Attentive Group Equivariant Convolutional Networks”, Proceedings of the IEEE International Conference on Machine Learning, pp. 8188-8199, 2020.
- I. Loshchilov and F. Hutter, “Decoupled Weight Decay Regularization”, Proceedings of International Conference on Learning Representations, Vol. 32, pp. 89-97, 2018.
- L.N. Smith and N. Topin, “Super-Convergence: Very Fast Training of Neural Networks using Large Learning Rates”, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, Vol. 11006, pp. 369-386, 2019.