Enhancing Security Feature in Financial Transactions using Multichain Based Blockchain Technology

Raj, Arman; Kumar, Avneesh; Sharma, Vandana; Rani, Seema; Shanu, Ankit Kumar

doi:10.1109/iciem59379.2023.10166589

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Average Temperature (Degree Celsius), Average Precipitation (in mm), Average Atmospheric Pressure (in millibars), Average Wind speed (in kmph),Atmospheric carbon dioxide concentration(in ppm). We will identify the climate factors that have the most significant impact on sustainability [25][26]. We will collect data on average temperature (in degrees Celsius), average precipitation (in millimeters), average atmospheric pressure (in millibars), average wind speed (in kilometers per hour), and atmospheric carbon dioxide concentration (in parts per million).…”

Section: Implementation Results and Discussionmentioning

confidence: 99%

Sustainable Climatic Metrics Determination with Ensemble Predictive Analytics

Pattanaik,

Sharma,

Kunj

et al. 2023

2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM)

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Sustainability depends upon some major climate factors which play a major role in ensuring and assuring that sustainability would be maintained if the range of safe values of parameters are maintained. Linear regression and random forest are few among the machine learning models that were employed in order to determine the dependency of each factor on sustainability. The climate data from Delhi from 1971 to 2020 is utilized for the study considering the variables like temperature, precipitation, humidity and atmospheric carbon dioxide concentration which were collected from various authorized sources such as the Indian Meteorological Department and the Central Pollution control board. After studying various factors involved in determining climate sustainability we found out that temperature and atmospheric carbon dioxide concentration have the greatest impact with a percentage of 45% and 30% respectively. Sectors like agriculture, forestry, energy and water management are majorly dependent on these key deciding factors. The R square value was determined to be 0.86 and 0.82 respectively for machine learning models implemented. We found that the random forest model had a better score in comparison to the linear regression model. With this study we thus found out that, how machine learning models can be trained and tested in order to predict the future outcomes for sustainability. This study demonstrates the importance of climate monitoring for maintaining sustainability.

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Section: Implementation Results and Discussionmentioning

confidence: 99%

Sustainable Climatic Metrics Determination with Ensemble Predictive Analytics

Pattanaik,

Sharma,

Kunj

et al. 2023

2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM)

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“…This feature pyramid is designed to capture and incorporate information at multiple scales. This enables the model to effectively handle objects of varying sizes and achieve accurate segmentation results.This structure needs fewer parameters and is adaptable [20].…”

Section: 𝐿 ({𝑝 𝑝mentioning

confidence: 99%

Design and Implementation of an Optimized Mask RCNN Model for Liver Tumour Prediction and Segmentation

Thakur,

Volety,

Sharma

et al. 2023

2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM)

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Liver tumour segmentation is a challenging task due to the wide diversity in size, position, depth, and proximity to surrounding organs. This research uses the state-of-the-art model of Mask R-CNN model with the ResNet-50 architecture as the backbone. The suggested methodology leverages the Mask Region-Convolutional Neural Network approach to accurately identify liver tumors by identifying tumour location. To address variations of the liver and CT scan images with different parameters. The normalized CT images are then fed into the RESNET-50 model to extract relevant features. Subsequently, the liver tumor are segmented using the Mask R-CNN algorithm. The experimental dataset used in this study consists of one hundred and thirty CT scans obtained from various hospitals and nursing homes, which are freely accessible on the LiTS web page. The suggested algorithm is trained on transformed CT image slices. The results demonstrate that the proposed Mask RCNN system, with its innovative connections, surpasses state-of-the-art methods in identifying liver tumor, achieving a remarkable DSC value of 0.97%. This technique has the potential to significantly contribute to early and precise diagnosis of liver tumor in the field of biotechnology, potentially saving many patients' lives.

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