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RISS 인기검색어
- 검색키워드
- 저자 : J. Suganthi (검색결과 3 건)
- 무료
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Hybrid CNN-SVM Based Seed Purity Identification and Classification System
Suganthi, M,Sathiaseelan, J.G.R. International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.10
Manual seed classification challenges can be overcome using a reliable and autonomous seed purity identification and classification technique. It is a highly practical and commercially important requirement of the agricultural industry. Researchers can create a new data mining method with improved accuracy using current machine learning and artificial intelligence approaches. Seed classification can help with quality making, seed quality controller, and impurity identification. Seeds have traditionally been classified based on characteristics such as colour, shape, and texture. Generally, this is done by experts by visually examining each model, which is a very time-consuming and tedious task. This approach is simple to automate, making seed sorting far more efficient than manually inspecting them. Computer vision technologies based on machine learning (ML), symmetry, and, more specifically, convolutional neural networks (CNNs) have been widely used in related fields, resulting in greater labour efficiency in many cases. To sort a sample of 3000 seeds, KNN, SVM, CNN and CNN-SVM hybrid classification algorithms were used. A model that uses advanced deep learning techniques to categorise some well-known seeds is included in the proposed hybrid system. In most cases, the CNN-SVM model outperformed the comparable SVM and CNN models, demonstrating the effectiveness of utilising CNN-SVM to evaluate data. The findings of this research revealed that CNN-SVM could be used to analyse data with promising results. Future study should look into more seed kinds to expand the use of CNN-SVMs in data processing.
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A Desirable Strategy for Resource Allocation using Virtual Machine in Cloud
B. Abinaya.,J. Suganthi,R. G. Suresh Kumar,T. Nalini 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.5
Cloud computing is a facsimile of legalizing ubiquitous, expedient, on-demand network access to a shared pool of configurable computing resources that can be rapidly furnished and released with negligible management effort. It relies on sharing computing resources rather than having local servers or personal devices to handle applications. The resource allocation, still lack on sustaining tools that enable developers to compare different resource allocation strategies in cloud computing. In this paper we initiate the concept of “skewness” to measure the bumpy utilization of a server. By minimizing skewness, we can improve the overall utilization of servers in the face of multidimensional resource constraints. Here we use skewness metric to combine VMs with different resource characteristics suitably so that the capacities of servers are well utilized.
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AI-driven drowned-detection system for rapid coastal rescue operations
Dileep P,M. Durairaj,Sharmila Subudhi,V V R Maheswara Rao,J. Jayanthi,D Suganthi 대한공간정보학회 2024 Spatial Information Research Vol.32 No.2
Recent observations indicate that nearly 50% of the public frequently visit coastal areas during weekends, seeking the health benefits of natural sunlight and fostering familial bonds. Notably, a significant portion of these visitors are unaware of swimming techniques or face other physical challenges, rendering them vulnerable to drowning, especially in areas lacking adequate lifeguard support or immediate medical emergency services. This study introduces an advanced drowneddetection device that employs a deep learning algorithm, grounded in artificial intelligence architecture, to swiftly detect and address potential drowning incidents. The system is particularly vigilant towards high-risk groups, such as children and the elderly. Upon detecting a threat, it autonomously deploys drones equipped with inflatable rescue tubes and notifies local authorities. Preliminary results suggest that our proposed model can effectively rescue a drowning individual in under 7 min, highlighting its prospective utility in curtailing swimming-related fatalities worldwide. This research underscores the need for technological intervention to enhance safety measures at coastal destinations and seeks to raise awareness about the importance of well-established lifeguard support.
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