- Subhajit Das
- Abhijit Mondal
- Nil Kamal Basak
- Souvik Naskar
- Subhadip Bhattacharya
- Surajit Biswas
- Sanket Dan
- Pritam Ghosh
- Subhranil Mustafi
- Kunal Roy
- Kaushik Mukherjee
- Dilip Kumar Hajra
- Santanu Banik
- Deepsubhra Sarkar
- Pushkal Guha Choudhur
- Abhisek Dutta
- Sourish Sarkar
- Arnab Nanda Goswami
- Shubhajyoti Das
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
Mandal, Satyendra Nath
- Reduction of Environmental Parameters Using Principal Component and Factor Analysis
Authors
1 Department of Information Technology, Kalyani Government Engineering College, Kalyani- 741235, Dist. Nadia, West Bengal, IN
Source
Reason-A Technical Journal (Formerly Reason-A Technical Magazine), Vol 11 (2012), Pagination: 21-28Abstract
The output of any physical problem is likely to be dependent on huge number of parameters. But, many of them are not significant and some are highly correlated with other parameters. Same result can be produced by fewer parameters in stead of considering all parameters. In this paper, an algorithm has been proposed to reduce parameters based on principal component analysis and factor analysis. The algorithm has been applied to reduce the environment parameters which are needed for the healthy growth of mustard plant. It has been observed that the growth of mustard plant has not been disturbed if the significant environment parameters have been supplied sufficiently.Keywords
Physical Problem, Environmental Parameters, Principal Component Analysis, Factor Analysis, Significant Parameters, Plant Growth.- A Novel Approach in Symmetric Key Image Encryption Using Genetic Algorithm
Authors
1 Nayagram Bani Bidyapith, Nayagram, IN
2 Kalyani Government Engineering College, Dept.of Information Tech., Kalyani, Nadia, IN
Source
Reason-A Technical Journal (Formerly Reason-A Technical Magazine), Vol 14 (2015), Pagination: 91-98Abstract
In this paper, a symmetric key image encryption algorithm has been proposed based on genetic algorithm. The algorithm has three steps, generation of random sequence, diffused image and image encryption. Key generation is based on a new integer sequence generation and a mixing process. The random integer sequence has been generated from 64 bits key and mixing. The input image has been diffused by genetic algorithm and parents have been selected from image folding. The encrypted image is formed after performing logical operation between diffused image and random sequenence. The effectiveness of the algorithm has been measured by applying number of statistical tests between plain and encrypted image.It has been observed that the proposed algorithm is giving satisfactory result in all cases.Keywords
Symmetric Key, Genetic Algorithm, Horizontal and Vertical Folding, Remainder Set and Security Level.References
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- Srikanth, V., Asati, U., Natarajan, V., Kumar, T.P., Mullapudi, T. and Iyengar, N.Ch.S.N., Bit-Level Encryption of Images using Genetic Algorithm, TECHNIA International Journal of Computing Science and Communication Technologies, Vol. 3, No.1, pp.546-550, 2010.
- Kumar, J. and Nirmala, S., Encryption of Images Based on Genetic Algorithm– A New Approach, Advances in Computer Science, Eng. & Appl., AISC 167, Springer-Verlag, Berlin, Heidelberg, pp.783-791, 2012.
- Liu, H., Wang, X. and Kadir, A., Image encryption using DNA complementary rule and chaotic maps. Appl Soft Computing, Vol. 12, pp.1457–1466, 2012.
- Das, S., Mondal, S.N. and Ghosal, N., An Innovative Approach in Image Encryption, ACEEE Proceedings of the Int. Conf. on Recent Trends in Information, Telecommunication and Computing, ITC 2014, pp.158-166, 2014.
- Jolfaei, A. and Mirghadri, A., A novel image encryption scheme using pixel shuffler and A5/1, Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence, pp.369-373, 2010.
- Sabouri, N., Javadi, H.H.S. and Asoudeh, T.Z., A Comparative Study on the Effect of Used Crossover Operator on Performance of GA as a Web Page Classifier, International Journal of Computer Applications, Vol. 71, No.23, pp.32-37, 2013.
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- Wu, S. and Zhang, Y., A Novel Encryption Algorithm Based on Shifting and Exchanging Rule of Bi-column Bi-row Circular Queue, Proceedings of the International Conference on Computer Science and Software Engineering, pp.841-844, 2008.
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- New Key Generation Technique in RSA Algorithm
Authors
1 Department of IT, Kalyani Government Engineering College, IN
Source
Reason-A Technical Journal (Formerly Reason-A Technical Magazine), Vol 8 (2008), Pagination: 1-11Abstract
The RSA cryptosystem, invented by Ron Rivest, Adi Shamir and Ten Adleman was first publicized in the August 1977 issue of Scientific American. The cryptosystem is most commonly used for providing privacy and ensuring authenticity of digital data. These days RSA is deployed in many commercial systems. It is used by web servers and browsers to secure web traffic, it is used to ensure privacy and authenticity of e-mail, it is used to secure remote login sessions, and it is at the heart of electronic credit-card payment systems. In short, RSA is frequently used in applications where security of digital data is a concern. Symmetric key distribution problem is solved by it.
Goal of this work is to derive a new prime generation method with the help of current system time. That is why the method generates a unique prime every time the program is executed.
- A Technique for Detection and Recognition of Optical Character in Digital Images
Authors
1 Kalyani Government Engineering College, Kalyani, Nadia, West Bengal, IN
2 Dept. of I.T., Kalyani Government Engineering College, Kalyani, Nadia, West Bengal, IN
Source
Reason-A Technical Journal (Formerly Reason-A Technical Magazine), Vol 9 (2010), Pagination: 17-22Abstract
Human eye have the ability to detect and recognize a character of some language, which they know, by the power of their brain as they are accustomed to that shape of the character font. But this thing is typically challenging for computers as it has no ability to read and learn automatically. All it needs to work is to provide it an artificial technique by human beings to make its artificial brain to read and understand from a character image, an optical character image. There are lots of algorithms available for the optical character recognition. Although no such technique yet exists, which can guarantee that its steps are 100%? In this paper, a new technique has been proposed to detect optical characters from digital images. Here the basic algorithm for character contour tracing algorithm using Fourier descriptors is used and then the proposed algorithm is used to recognize the optical character so that it can help the basic recognition technique more and more accurate.Keywords
Digital Image, Character Recognition, Fourier Descriptors, Contour Tracing, Shape Representation, Shape Similarity Measure, Feature Extraction.- History of Cryptography
Authors
1 Department of IT, Kalyani Government Engineering College, IN
Source
Reason-A Technical Journal (Formerly Reason-A Technical Magazine), Vol 8 (2008), Pagination: 29-33Abstract
Cryptography is an art of science to secure the confidential data. In the present paper, a brief history of the development of cryptography and its recent trends are outlined.- Identification of Goat Breeds by Digital Image using Convolution Neural Network
Authors
1 Kalyani Government Engineering College, Kalyani, Nadia, West Bengal 741235, IN
2 Department of Agronomy, UBKV, Pundibari, Cooch Behar, West Bengal 736165, IN
3 ICAR-National Research Centre on Pig, Rani, Guwahati, Assam 781131, IN
Source
Reason-A Technical Journal (Formerly Reason-A Technical Magazine), Vol 18 (2019), Pagination: 72-82Abstract
Diversity in domestic animals in most of the species is depicted in the form of breeds. Phenotypic and genotypic characterizations are the tools for breed identification of livestock species. Variation within breed or similar looking breeds make it difficult to confirm breed identity of individual animal. An experiment was conducted with the aim of identification of breed of an individual goat by the help of its image using Inception model v3; a convolutional neural network. More than 500 digital images of individual goat captured in restricted (to get similar image-background) and unrestricted (natural) environment without imposing stress to animals. Six different purebred goats (Blackbengal, Beetal, Jamunapari, Barbari, Jakhrana and Sirohi) which have been reared and maintained by reputed government research organizations in India were used for training and testing the model. 10% of the captured images were used for testing the trained model. Breed confirmation was made by seeing the value (probability) in output terminals corresponding to six different breeds under study which best described an input image. 56 images out of the 60 images used in the test were successfully interpreted for breed identity by the trained model and thus the model was 93.33% accurate. Output probability of more than or equal to 0.95 was taken as minimum confidence limit for determination of breed. Value less than 0.95 was considered as unsuccessful test. Upon testing with images from breeds for which the model was not trained on, the output values could not provide confirmatory result. Therefore, the technique has great potential to solve confusion on breed identity. It would also be useful in implementation of Global Plan ofAction for animal genetic resource (AnGR).Keywords
Livestock, Goat Breed Identification, Deep Learning, Convolutional Neural Network, Confidence Level.References
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- Food and Agriculture Organization of the United Nations, Molecular Genetic Characterization of Animal Genetic Resources, http://www.fao.org/3/i2413e/i2413e00.htm, Date of access: 24/06/2020.
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- ICAR- National Bureau of Animal Genetic Resources, REGISTERED BREEDS OF GOAT, http://www.nbagr.res.in/reggoat. html, Date of access: 24/06/2020.
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- A Brief Journey of Convolutional Neural Networks from 2012 to 2017
Authors
1 Department of Information Technology, Kalyani Government Engineering College, Kalyani- 741235, IN
Source
Reason-A Technical Journal (Formerly Reason-A Technical Magazine), Vol 20 (2021), Pagination: 84-96Abstract
With the recent advancements in the field of Deep Learning, largely due to the increase in computational power, the long-standing problem of large-scale image classification has been solved up to a considerable threshold. Since the advent of AlexNet in 2012, there has been an increase in the research and development of various Convolutional Neural Network based model to solve problems in the field of Computer Vision. This may be primarily attributed to the success of AlexNet in successfully classifying 1000 class subset of ImageNet with considerable accuracy. Consequently, many more Convolutional Neural Networks were introduced including VGG Net, Inception Net, ResNet, DenseNet, etc. each with better performance than its predecessor. This paper serves as a brief guide to the architecture of ResNet and some of its predecessors. It will also discuss DenseNet and ResNeXt as improvements of ResNet architecture.Keywords
Computer Vision, Convolutional Neural Network, Deep learning. Image classification, ResNet, AlexNet.References
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- Tomato Leaf Disease Detection using Convolution Neural Network
Authors
1 Department of Information Technology, Kalyani Government Engineering College, Kalyani- 741235., IN
Source
Reason-A Technical Journal (Formerly Reason-A Technical Magazine), Vol 21 (2022), Pagination: 19-28Abstract
Tomato grains are an essential significant, abundant product in the Indian market with high commercial value. Diseases are detrimental to the plant's health, affecting its growth. It is crucial to monitor the condition of the crop for a sustainable farming system. There are many types of tomato diseases that affect the leaves of the crop at an alarming rate. This paper slightly modifies the evolutionary neural network model called lnceptionV3 to detect and classify disease on tomato leaves. The main goal of the proposed work is to find solutions to the problem of tomato leaf disease detection using simple methods while using minimal computing resources to achieve results comparable to the latest technology. Neural network models employ automated feature extraction to classify the input image into the corresponding disease class. This proposed system has achieved an average accuracy of 94-95%, which indicates the feasibility of the neural network approach even in adverse conditions.Keywords
leaf disease detection, neural network, convolution, inceptionV3References
- Prajwala, T. M., Alia, P., Ashritha, K. S.: Chittaragi, N. B., Koolagudi, S. G., Tomato Leaf Disease Detection using Convolutional Neural Networks, Proc. Eleventh International Conference on Contemporary Computing (IC3), 2-4 August, 2018, Noida, India.
- Bhakat, A., Nandakumar, N. and Rajkumar, S., An enhanced approach for Plant Leaf Disease Detection, Algorithms, Computing and Mathematics Conference, August 19- 20,2021, Chennai, India.
- Zhao, S., Peng, Y, Liu, J. and Wu, S., Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module, Agriculture, Vol. 11, No. 7,p.651,2021.
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- Agarwal, M., Singh, A., Arjaria, S., Sinha, A. and Gupta, S., ToLeD: Tomato Leaf Disease Detection using Convolution Neural Network, International Conference on Computational Intelligence and Data Science (ICCIDS 2019).
- Wang, Q., Qi, R, Sun, M., Qu, J. and Xue, J., Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques, Computational Intelligence and Neuroscience, Vol. 2019, Article ID 9142753.
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- Nagamani, H. S. and Sarojadevi, H., Tomato Leaf Disease Detection using Deep Learning Techniques, International Journal of Advanced Computer Science and Applications, Vol. 13, No.1,2022.
- Gadade, H.D. and Kirange, D.K., Machine Learning Approach towards Tomato Leaf Disease Classification, International Journal of Advanced Trends in Computer Science and Engineering, Vol. 9, No. 1, pp. 490-495,2020.
- Wu, Y, Xu, L. and Goodman, E. D., Tomato Leaf Disease Identification and Detection Based on Deep Convolutional Neural Network, Intelligent Automation & Soft Computing, Vol. 28, No. 2,2021.
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