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Sabrol, Hiteshwari
- Fuzzy and Neural Network based Tomato Plant Disease Classification using Natural Outdoor Images
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Authors
Affiliations
1 Department of Computer Science and Applications, Panjab University, Chandigarh - 160014, Punjab, IN
2 Department of Computer Applications P.U. SSG Regional Centre (Panjab University, Chandigarh), Hoshiarpur - 146023, Punjab, IN
1 Department of Computer Science and Applications, Panjab University, Chandigarh - 160014, Punjab, IN
2 Department of Computer Applications P.U. SSG Regional Centre (Panjab University, Chandigarh), Hoshiarpur - 146023, Punjab, IN
Source
Indian Journal of Science and Technology, Vol 9, No 44 (2016), Pagination:Abstract
Objectives: The aim of the study is to automate the plant disease recognition and classification process by using image processing and soft computing techniques. Methods/Analysis: The proposed method examined the five types of tomato plant diseases using natural outdoor images in the study. The tomato plant images categorized into six categories including five disease infected that are bacterial leaf spot, fungal septoria leaf spot, bacterial canker, fungal lateblight, tomato leaf curl and one non-infected (healthy). The total 180 images of the dataset used for training and testing purpose. The total thirteen features computed by using CIE XYZ color space conversions that included color moments, histogram, and color coherence vector features. For classification, computed features are fed into three classifiers, i.e., “Fuzzy Inference System based on subtractive clustering”, “Adaptive neuro-fuzzy inference system using hybrid learning algorithm and multi-layer feed forward back propagation neural network” for classification of six injured and healthy tomato plant disease. Finding: The classification accuracy is best yielded with multi-layer feed forward back propagation classifier of 87.2%. Novelty/Improvement: Usually, in the studies the only one type of plant disease considered for the recognition and classification purpose. The current study considered five different types of tomato plant diseases including fungal, bacterial and viral. It indicates that the proposed algorithm could reliably classify the different types of plant diseases in digital images.Keywords
Color Space Conversion, Disease Recognition, Fuzzy, Tomato Plant Disease Classification, Natural Outdoor Images, Neural Network.Full Text
- Blockchain Technology : A Survey on Applications beyond Cryptocurrency
Abstract Views :155 |
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Authors
Affiliations
1 Student (MCA), IN
2 Student (MCA), Department of Computer Science & Application, DAV University, Jalandhar, IN
3 Associate Professor, Department of Computer Science & Application, DAV University, Jalandhar, IN
1 Student (MCA), IN
2 Student (MCA), Department of Computer Science & Application, DAV University, Jalandhar, IN
3 Associate Professor, Department of Computer Science & Application, DAV University, Jalandhar, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 55-61Abstract
In the hyper-connected world, data has been playing a significant role in earthing the system of every domain that one can think of. But, it is undeniable fact that today data has prone to serious threats as it was never before, which has questioned the reliability, integrity as well as security of data. So, the technology has emerged that can mitigate all these issues, and that technology is none other than the technology residing at the heart of Bitcoin, The Blockchain.
Blockchain is one of the powerful technologies that has brought a revolution in conventional trading techniques. Conceptually, it is an open and distributed ledger that maintains an irrefutable database of records of all transactions or values that have been executed and shared peer-to-peer directly obsoleting the need for trust-worthy intermediatory in a secure, verifiable, moreover, in a permanent way alleviating the risk of fraud.
The main objective of this paper is not only to make blockchain a common business trading language but also to present its compelling applications in contrasting domains that are beyond currency rides, specifically in healthcare, politics, and its strategic implications in bridging strong economy using smart contracts that can revolutionize our digital global village.
Keywords
Bitcoin, Blockchain, Open-Ledger, Miners, Healthcare, Supply Chain, Smart Contracts.Full Text
References
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- Study on Applications and Challenges in Natural Disaster Management Using Multimodal System
Abstract Views :139 |
PDF Views:0
Authors
Affiliations
1 Research Scholar, DAV University, Jalandhar, IN
2 Assistant Professor, DAV University, Jalandhar, IN
3 Assistant Professor, CMR Engineering College, Hyderabad, IN
1 Research Scholar, DAV University, Jalandhar, IN
2 Assistant Professor, DAV University, Jalandhar, IN
3 Assistant Professor, CMR Engineering College, Hyderabad, IN
Source
Research Cell: An International Journal of Engineering Sciences, Vol 35, No SP (2023), Pagination: 289-310Abstract
Organizations, which include government agencies, the military, and humanitarian groups, are responsible for providing the most vulnerable individuals with aid and protection during emergencies and disasters. Their rapid decisions are made possible through the information they gather. Their information needs vary depending on their specific roles and responsibilities. In times of crisis, they need factual and timely information, especially when there is a lack of reliable sources such as radio or TV. Due to the increasing number of people using social media platforms and mobile technologies, the general public has gained access to more effective and practical ways to share information. The term multimodal refers to the combination of various computational methods used to analyze the data collected from social media platforms. Some studies show how social media analytics can be used to summarize and curate information related to disasters. This paper discusses the research being conducted in the field of crisis informatics, which is an interdisciplinary discipline that combines the expertise of social science and computing to extract information related to disasters. Due to the availability of social media data, this field is heavily focused on developing effective strategies to use it.Keywords
Multimodal System, Natural Disaster Management, Social Media, Machine Learning.Full Text
References
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- Alam, F., Ofli, F. and Imran, M., 2020. Descriptive and visual summaries of disaster events using artificial intelligence techniques: case studies of Hurricanes Harvey, Irma, and Maria. Behavior & Information Technology, 39(3), pp.288-318.
- Kumar, P., Ofli, F., Imran, M. and Castillo, C., 2020. Detection of disaster-affected cultural heritage sites from social media images using deep learning techniques. Journal on Computing and Cultural Heritage (JOCCH), 13(3), pp.1-31.
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- Vernier, M., Farinosi, M., Foresti, A. and Foresti, G.L., 2023. Automatic Identification and Geo-Validation of Event-Related Images for Emergency Management. Information, 14(2), p.78.
- Chaudhuri, N. and Bose, I., 2019. Application of image analytics for disaster response in smart cities.Proceedings of the 52nd Hawaii International Conference on System Sciences.pp.3036-3045.
- Fan, C., Zhang, C., Yahja, A. and Mostafavi, A., 2021. Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management. International Journal of Information Management, 56, p.102049.
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