Framework for Implementation of Android Automation Tool in Agro Business Sector

William, P.; Shrivastava, Anurag; Aswal, Upendra Singh; Kumar, Indradeep; Gupta, Manish; Rao, A. Kakoli

doi:10.1109/iciem59379.2023.10167328

Cited by 21 publications

(2 citation statements)

References 38 publications

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“…Although climate scenarios are considered when developing large-scale spatial plans, this method is much too broad to be applied when developing smaller-scale plans. As a consequence, comprehensive land-unit development plans often disregard weather considerations or address issues at the planned site [11]. Given that land unit planning decisions may have a significant influence on urban space throughout the watershed, it is obvious that this strategy is insufficient to increase urban resilience to pluvial floods.…”

Section: Review Of Literaturementioning

confidence: 99%

Integrated Decision Support System for Flood Disaster Management with Sustainable Implementation

William,

Oyebode,

Sharma

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Emergency management of a flood catastrophe should involve not only an immediate reaction to the flooding but also the pre-flood disaster phase, the flood disaster phase, and the post-flood disaster phase. This is in addition to the immediate response to the flooding. This research investigates whether or not a decision support system (DSS) for flood emergency management is necessary and whether or not it is feasible. Previously, we presented a model for the development of modular disaster management decision support systems. The prior work that we did on the integration of DSS model decomposition-derived modular subroutines is expanded upon in this research. The act of merging many models into a single comprehensive logical model is referred to as “Observed and Forecasted.” After that, it offers a systematic framework for handling crises throughout the whole of the process, and it concludes with an in-depth analysis of the function architecture of each application subsystem. In this work, we create a disaster management metamodel with the goal of eventually building a disaster management (DM) language. It will function as a representational layer of DM data that, over time, will create a system for DM decision support that mixes and matches various DM actions in line with the evolution of the disaster as it develops. A decision support system that will contain an innovative metamodel as a core component will be developed in order to accomplish the aims of integrating, enabling, and accelerating access to DM information. These objectives will be reached via the creation of a decision support system.

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Section: Review Of Literaturementioning

confidence: 99%

Integrated Decision Support System for Flood Disaster Management with Sustainable Implementation

William,

Oyebode,

Sharma

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

Self Cite

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“…This adjusts with our utilization of LSTMs within the successive setting of picture captioning, where capturing conditions in picture highlights over time is significant. Kwon and Kim [22] and Kwon and Lee [23] investigated security viewpoints in picture captioning models. They examined antagonistic assaults, particularly centring on restricted-area and backdoor assaults for picture captioning models.…”

Section: Related Workmentioning

confidence: 99%

Recurrent Neural Networks for Image Captioning: A Case Study with LSTM

Devi K, Manish Sharma, R. N. Patil, Anurag Shrivastava

2024

jes

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This research investigates the viability of Long Short-Term Memory (LSTM) systems, a subtype of Recurrent Neural Networks (RNNs), for picture captioning. Leveraging the MS COCO dataset, the study compares the execution of LSTM-based RNNs with Vanilla RNN, Gated Recurrent Unit (GRU), consideration components, and transformer-based models. Experimental comes about to illustrate that the LSTM-based RNN shows competitive execution, accomplishing a BLEU-4 score of 0.72, a METEOR score of 0.68, and a CIDEr score of 2.1. The comparative investigation uncovers its prevalence over Vanilla RNN and GRU, highlighting its capability to capture long-range conditions inside successive picture information. Moreover, the study investigates the effect of consideration instruments and transformer designs, exhibiting their potential improvements in the context-aware caption era. The transformer-based show outflanks all other models, accomplishing a BLEU-4 score of 0.78, a METEOR score of 0.72, and a CIDEr score of 2.5. The findings give important bits of knowledge toward the creating scene of picture captioning strategy, which makes LSTM-based RNNs solid and productive approaches for capturing worldly groupings in visual substance. In achieving these, the study provides a framework for future developments in hybrid models and manufacturing processes that push boundaries of smart image perception and understanding

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