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Web 3.0-Based Crypto Wallet for Securing Assets and Blockchain Transactions

Web 3.0-Based Crypto Wallet for Securing Assets and Blockcha...

引用

International Conference on Recent Developments in Cyber Security, ReDCySec 2023

作者： Vaibhav Arora, Deepak Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India

ISBN: (纸本)9789819998104

The advent of Web 3.0 has opened up new possibilities for the development of secure, decentralized digital solutions. Having a safe and user-friendly cryptocurrency wallet is essential in this new era where digital assets and cryptocurrencies are growing in popularity among users. Customers may safely and easily manage, store, and transact with their digital assets using a crypto wallet. But developing a Web 3.0-based crypto wallet presents a variety of challenges, including those pertaining to user experience, scalability, and security. To address these issues, we utilized cutting-edge software such as ***, ***, and ThirdWeb. Using ***, ***, and ThirdWeb, a Web 3.0-based crypto wallet was created that offers a strong method for handling digital assets securely and carrying out blockchain transactions. We can develop a secure, scalable, and user-friendly crypto wallet that caters to the needs of today’s owners of digital assets by integrating the capabilities of these technologies. An appropriate solution for the administration of digital assets in the Web 3.0 era, crypto wallets are developed using Web 3.0 technologies like ***, ***, and ThirdWeb to guarantee that they are safe, scalable, and user-friendly. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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Deep Learning Model for Weather Prediction 1

Deep Learning Model for Weather Prediction

引用

1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Singh, Shubhanshi Tyagi, Neha Amity School of Engineering and Technology Dept. Computer Science and Engineering Noida India

ISBN: (纸本)9798350353648

Weather forecasting predicts atmospheric conditions at a particular time and place. It is an important task with a wide range of applications, including agriculture, transportation, and disaster prevention. Traditional weather forecasting methods rely on physical models of the atmosphere, which can be complex and computationally expensive. In recent years, deep learning techniques are effective for weather forecasting. Deep learning models can learn complex patterns from data, which can be used to predict future weather conditions accurately. This project aims to create a deep-learning model for forecasting weather. A huge dataset of historical meteorological data, including radar, satellite, and surface measurements, will be used to train the model. Numerous meteorological factors, such as temperature, precipitation, and wind speed, will be predicted by the model. We will compare the deep learning model's performance with conventional weather forecasting techniques. Creating a deep learning model that is more accurate and effective than current approaches is the project's main objective. The initiative may result in improved weather forecasts that are prompt and accurate, better agricultural, transportation, and disaster prevention decision-making, and less money lost because of weather-related incidents. Possible obstacles for the project are gathering and purifying a sizable dataset of meteorological data, using this data to train a deep learning model, and then comparing the model's output with more conventional techniques. © 2024 IEEE.

关键词： Wind forecasting

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Human Posture Identification and Recognition Using Deep Learning Techniques 11th

Human Posture Identification and Recognition Using Deep Lear...

引用

11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Grover, Anuj Arora, Deepak Grover, Anant Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Lucknow Campus Lucknow India

ISBN: (纸本)9789819717231

Human Pose Estimation is the task of estimating the human joints in a 2D or 3D coordinate plane from an input RGB image. The 2D HPE are of two types: top-down and bottom-up. The 3D Human Pose Estimation gives better understanding of the relative position of the joints of the person and is helpful to understand the positions of close joints. The evaluation metrics and the architectures for 2D HPE are discussed in brief in the paper. The paper focuses on 3D Human Pose Estimation because it provides more information about the depth of the joint and gives better spatial understanding of the joint’s position. The evaluation metrics and various 3D HPE architectures: Simple Baseline, Lifting from the Deep, Camera Distance Aware approach, and BlazePose. A custom in-the-wild dataset was built and was used for evaluating the 3D Human Pose Estimation architectures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer vision

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SkinSage - Lesion Diagnosis Using Deep Learning Techniques 14

SkinSage - Lesion Diagnosis Using Deep Learning Techniques

引用

14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Geetika Bhardwaj, Paarth Nimish Bhargava, Vaibhav Singh, Shivendra Amity School of Engineering and Technology Department of Computer Science and Engineering Noida201301 India

ISBN: (纸本)9798350344837

The alarming rise in instances of skin cancer in recent years, one of the most prevalent malignancies worldwide, emphasises how important early and accurate identification is. The SkinSage project, which combines the precision of deep learning models and technologies. Earlier, dermatologists relied on eye exams to spot skin lesions. But with datasets like HAM10000 and digital photography, it became easy to record a variety of skin conditions. SkinSage uses cutting-edge neural architectures like CNN, ResNet, DenseNet, and InceptionV3 to recognise patterns and nuances that are much above the capabilities of the human eye. In addition to being a computational marvel, the SkinSage project also exemplifies accessibility by making its analytical capabilities available via a smartphone application in future. High-quality skin lesion diagnostics will be accessible to everyone thanks to this platform, regardless of location or degree of training. With the confluence of these developments, SkinSage becomes more than just a tool for diagnosing skin cancer;it signals a paradigm shift that has the potential to democratise early detection and save countless lives in the future. © 2024 IEEE.

关键词： Deep learning

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A Survey of Text-to-Image Diffusion Models in Generative AI 14

A Survey of Text-to-Image Diffusion Models in Generative AI

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Kandwal, Siddharth Nehra, Vibha Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350344837

From dreamscapes to photorealistic portraits, text-to-image generation pushes the boundaries of AI creativity. This survey navigates diverse techniques, such as GANs, VAEs, and Diffusion models, uncovering their potential for transforming textual descriptions into captivating visuals. These models have significantly advanced the field, but they are not without their limitations. One notable issue is data bias, which could potentially lead to a deficiency in variety and cultural awareness in the produced visuals. Furthermore, recognising the significance of mitigating data bias in generative models, this report offers insights and strategies to address this pressing issue. It explores approaches that leverage inclusive datasets, fairness-aware training techniques, and ethical considerations. These methods aim to bridge the gap between the technological advancements in image generation and the imperative need for inclusivity and cultural sensitivity. © 2024 IEEE.

关键词： Diffusion

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Software Reliability Evaluation using a Hybrid Soft Computing Approach for Battery Management System 4

Software Reliability Evaluation using a Hybrid Soft Computin...

引用

4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Sagar, Shrddha Kumar, Avadhesh Shukla, Garima Singh, Sofia Raskar, Dipak School of Computer Science and Engineering Galgotias University Uttar Pradesh India Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Maharashtra Mumbai India Amity School of Engineering and Technology Amity University Department of Artificial Intelligence Uttar Pradesh Noida India

ISBN: (纸本)9798350383997

Predicting faults in a software system can be performed using software reliability models. Reliability is a real- world aspect that is related to several real-life issues which depends on several factors. Different algorithms and methodologies are present to predict software reliability, still the exploration of hybrid soft computing approaches is required to predict software reliability inefficient way. The reason is soft computing algorithms are able to deal with uncertainties and real time issues. The objective of this study is to propose a reliability assessment model using hybrid approach of GeneticAlgorithm and Self-Organizing Map (SOM) for Component Based Software System (CBSS). The reason for selecting CBSS is the reusability aspects of components, which makes software development process cheaper and faster. This paper is an attempt to identify most reliable components based on three reliability factors (cohesion, coupling, and component dependency) from a software project that can be reused for developing other applications efficiently. Simulation of Battery Management System software package components is completed by the use of MATLAB which were having 37 components out of which 6 most reliable components are selected using proposed method. © 2024 IEEE.

关键词： MATLAB

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Using Swin Transformer and SIFT Algorithm to Detect Bone Abnormalities 11

Using Swin Transformer and SIFT Algorithm to Detect Bone Abn...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Makwane, Simran Kiran, Anjali Bhardwaj, Shweta Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350350357

This research paper investigates the combination of cutting-edge deep learning architectures, specifically the Swin Transformer, with conventional feature extraction methods, such as the Scale-Invariant Feature Transform (SIFT) algorithm, for the identification and classification of fractures. We seek to improve the accuracy and robustness of fracture identification from medical pictures by integrating characteristics acquired from SIFT with the capabilities of Swin Transformer during a 16- week period, we show the efficacy of our hybrid technique through thorough experimentation and evaluation, providing insights into its performance in comparison to traditional methods. Our research advances fracture diagnosis techniques, which may lead to more precise and effective clinical decision-making in hospital settings. © 2024 IEEE.

关键词： Fracture

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Enhanced Cyclone Intensity Estimation Through CNN Analysis of INSAT-3D IR Imagery 12th

Enhanced Cyclone Intensity Estimation Through CNN Analysis o...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Kumar, Divvela Vishnu Sai Arora, Deepak Tiwari, Shivam Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Lucknow Campus Lucknow India Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9789819788354

Cyclone forecasting using satellite pictures involves anticipating the cyclone’s intensity in advance of its arrival. The results of this study can inform people’s preparations for the cyclone. In order to save lives and reduce damage, cyclone strength forecast is crucial. As a result of the many dangers they pose, these cyclones are among the most expensive natural disasters that have ever occurred. Five separate CNN (Convolutional Neural Network) models are presented by the authors to assess the cyclone’s severity using INSAT-3D infrared pictures. While many methods exist for making such an assessment, the authors’ top model yields an RMSE of 10.02 kts for intensity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Analysis of Big Data in Cloud Computing Technologies 11

Analysis of Big Data in Cloud Computing Technologies

引用

11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Thakur, Sanjeev Verma, Lalit Amity School of Engineering & Technology Computer Science & Engineeering Noida India

ISBN: (纸本)9798350350357

The abstract provides a brief summary of the research paper, highlighting the key topics covered. It explains that the paper will give a brief description of the various technologies and tools used for Big Data in cloud computing environments, as well as the advantages and difficulties posed by this technology. This paper reviews the research challenges and issues associated with big data analysis in situations using cloud computing. It discusses both the traits and benefits of big data analytics in cloud computing, and highlights the major research challenges such as scalability, security, privacy and resource management. The paper also presents some failure research directions and potential solutions to address these challenges. © 2024 IEEE.

关键词： Big data

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Probabilistic Forecasting Prediction Model for Anticipating Days with Elevated Air Pollution 5th

Probabilistic Forecasting Prediction Model for Anticipating ...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Singh, Divya Singh, Shikha Hazela, Braham Singh, Vineet Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh Lucknow India

ISBN: (纸本)9789819725496

Public health and the environment are both threatened by air pollution. For prompt responses and public awareness, accurate air pollution forecasting is essential, especially on days with high ozone levels. With a focus on ground ozone concentrations, the objective of this study is to develop a probabilistic forecasting model in Python. Using machine-learning algorithms and meteorological information like wind speed and outdoor temperature, the possibility of an ozone day is predicted. Python packages like Pandas, NumPy, and Scikit-Learn are used for implementation and assessment. The results demonstrate the effectiveness of the developed probabilistic forecasting models, with significant improvements over baseline configurations. The most promising configuration is found, demonstrating the power of ensemble approaches and parameter fine-tuning. The Brier skill score of the gradient boosting method shows the best results. The ensemble model’s performance is evaluated using Brier skill scores and compared to a naive prediction model. According to the results the gradient-boosting ensemble model has the best BSS of 0.119546. The importance of probabilistic forecasting in handling the complexity of air pollution prediction is emphasized. This research develops probabilistic forecasting approaches for air pollution prediction, which improves both environmental management and public health. Despite the challenges faced by the complexity of air pollution, the findings help enhance air pollution prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Air pollution

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