Refine your search
Collections
Year
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
Kiran Kumar, C.
- Improving Medical Image Processing Using an Enhanced Deep Learning Algorithm
Abstract Views :91 |
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
- Z. Li and B. Wang, “Plant Disease Detection and Classification by Deep Learning-A Review”, IEEE Access, Vol. 9, pp. 56683-56698, 2021.
- B. Subramanian, V. Saravanan and S. Hariprasath, “Diabetic Retinopathy-Feature Extraction and Classification using Adaptive Super Pixel Algorithm”, International Journal of Engineering and Advanced Technology, Vol. 9, pp. 618-627, 2019.
- A. Abbas and S. Vankudothu, “Tomato Plant Disease Detection using Transfer Learning with C-GAN Synthetic Images”, Computers and Electronics in Agriculture, Vol. 187, pp. 106279-106287, 2021.
- R.K. Nayak, R. Tripathy and D.K. Anguraj, “A Novel Strategy for Prediction of Cellular Cholesterol Signature Motif from G Protein-Coupled Receptors based on Rough Set and FCM Algorithm”, Proceedings of 4th International Conference on Computing Methodologies and Communication, pp. 285-289, 2020.
- M. Zia Ur Rehman and I. Hussain, “Classification of Citrus Plant Diseases using Deep Transfer Learning”, Computers, Materials and Continua, Vol. 70, No. 1, pp. 1-12, 2021.
- R.D. Aruna and B. Debtera, “An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease Using Nanotechnology Sensors”, Computational Intelligence and Neuroscience, Vol. 2022, pp. 1-8, 2022.
- J. Annrose and D.G. Immanuel, “A Cloud-Based Platform for Soybean Plant Disease Classification using Archimedes Optimization based Hybrid Deep Learning Model”, Wireless Personal Communications, Vol. 122, No. 4, pp. 2995-3017, 2022.
- J. Schuler, H. Rashwan and D. Puig, “Color-Aware Two-Branch Dcnn for Efficient Plant Disease Classification”, Nature, Vol. 28, No. 1, pp. 55-62, 2022.
- E. Akanksha and K. Gulati, “OPNN: Optimized Probabilistic Neural Network based Automatic Detection of Maize Plant Disease Detection”, Proceedings of International Conference on Inventive Computation Technologies, pp. 1322-1328, 2021.
- Z. Chen, S. Chen, Z. Yuan and X. Zou, “Plant Disease Recognition Model based on Improved Yolov5”, Agronomy, Vol. 12, No. 2, pp. 365-373, 2022.
- An Improvised Ensemble CNN Algorithm for Detectting Video Stream in MultimediaAn Improvised Ensemble CNN Algorithm for Detectting Video Stream in Multimedia
Abstract Views :105 |
PDF Views:1
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
- 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.[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.