Deep attention based optimized Bi-LSTM for improving geospatial data ontology

Sambandam, Palaniappan; Yuvaraj, D.; Padmakumari, P.; Swaminathan, Subbiah

doi:10.1016/j.datak.2022.102123

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…In conclusion, deep learning techniques such as Faster R-CNN can be used to automate waste segregation processes. This can improve the efficiency of waste management systems, reduce manual labor, and promote sustainable waste disposal practices [23,24] In our proposed concept once the waste is placed in conveyor straight to camera, then the images will be taken by the camera and it will be processed by the PC to classify the waste. Once the waste is classified then the data will be sent to the Micro-controller using USB to TTL convertor whether the waste is bio-degradable or non biodegradable and the received data will be displayed in the LCD module [25,26].…”

Section: Figure 1 Block Diagram Of Proposed Systemmentioning

confidence: 99%

Advanced Waste Seclusion and Chucking System Using Deep Learning Techniques

S. Shanmuga Priya

2024

jes

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Any material that is undesirable or unusable is considered waste. Waste can be in any form (Liquid, solid or gas) but generally, waste is a solid form. There are various types of waste like paper, displeasing food, torn clothes, dried plants, kitchen waste, etc., Skin disease, diarrhea, tuberculosis, whooping cough, pneumonia etc.., are some other common diseases spread due to immoral waste management. Separating waste allows us to salvage more items, preventing their scraping in landfills. By reducing landfills disposal Segregating waste is important not only to reduce its impact on the environment but also to prevent health issues that can arise from the disposal of waste and toxins. Although the problem of waste segregation is a big challenge, there are several methods for automatic segregation, which eliminates the need for human hands. The proposed system utilizes Faster R-CNN algorithms to segregate waste into bio-degradable and non-biodegradable categories which, effectively eliminating the need for human involvement in the segregation process. This method employs artificial intelligence techniques to achieve waste segregation.

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Section: Figure 1 Block Diagram Of Proposed Systemmentioning

confidence: 99%

Advanced Waste Seclusion and Chucking System Using Deep Learning Techniques

S. Shanmuga Priya

2024

jes

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Performance evaluation of NLP and CNN models for disaster detection using social media data

Islam,

Rabbi,

Hossain

2024

Soc. Netw. Anal. Min.

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Ontology-Based Deep Learning Model for Object Detection and Image Classification in Smart City Concepts

Adegun,

Fonou-Dombeu,

Viriri

et al. 2024

Smart Cities

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Object detection in remotely sensed (RS) satellite imagery has gained significance in smart city concepts, which include urban planning, disaster management, and environmental monitoring. Deep learning techniques have shown promising outcomes in object detection and scene classification from RS satellite images, surpassing traditional methods that are reliant on hand-crafted features. However, these techniques lack the ability to provide in-depth comprehension of RS images and enhanced interpretation for analyzing intricate urban objects with functional structures and environmental contexts. To address this limitation, this study proposes a framework that integrates a deep learning-based object detection algorithm with ontology models for effective knowledge representation and analysis. The framework can automatically and accurately detect objects and classify scenes in remotely sensed satellite images and also perform semantic description and analysis of the classified scenes. The framework combines a knowledge-guided ontology reasoning module into a YOLOv8 objects detection model. This study demonstrates that the proposed framework can detect objects in varying environmental contexts captured using a remote sensing satellite device and incorporate efficient knowledge representation and inferences with a less-complex ontology model.

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Deep attention based optimized Bi-LSTM for improving geospatial data ontology

Cited by 3 publications

References 22 publications

Advanced Waste Seclusion and Chucking System Using Deep Learning Techniques

Advanced Waste Seclusion and Chucking System Using Deep Learning Techniques

Performance evaluation of NLP and CNN models for disaster detection using social media data

Ontology-Based Deep Learning Model for Object Detection and Image Classification in Smart City Concepts

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