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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Kanakia, Harshil T. Nair, Suraj Prakkash Sardar Patel Institute of Technology Master of Computer Application Department Mumbai India

ISBN: (纸本)9798350300093

This research paper presents a website that leverages the power of the Image GPT engine for image generation. The website allows users to input a textual prompt and generate a corresponding image using Image GPT's natural language processing and machine learning capabilities. The paper details the architecture of the website, the Image GPT API integration, and the algorithms used for image generation. Additionally, we present the results and evaluate its quality and discuss potential applications of this technology in various industries such as advertising, art and design. We also discuss how the performance of image generation can be potentially improved. Overall, the website demonstrates the potential of combining natural language processing and machine learning for image generation and opens up new avenues for future research in this field. © 2023 IEEE.

关键词： Engines

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Overcoming Spatial Constraints in VR:A Survey of Redirected Walking Techniques

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Journal of computer Science & technology 2024年第4期39卷 841-870页

作者： Jia-Hong Liu Yang-Fu Ren Qi Wen Gan Kui Huang Fiona Xiao Yu Chen Er-Xia Luo Khang Yeu Tang Yue-Yao Fu Cheng-Wei Fan Sen-Zhe Xu Song-Hai Zhang Academy of Arts and Design Tsinghua UniversityBeijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China College of Computer Technology and Application Qinghai UniversityXining 810016China Zhili College Tsinghua UniversityBeijing 100084China School of Computer Beijing Information Science and Technology UniversityBeijing 100192China Department of Physics Tsinghua UniversityBeijing 100084China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

As the virtual reality(VR)technology strives to provide immersive and natural user experiences,the challenge of aligning vast virtual environments with limited physical spaces remains *** survey comprehensively explores the advancements in redirected walking(RDW)techniques aimed at overcoming spatial constraints in *** addresses this by subtly manipulating users’physical movements to allow for seamless navigation within constrained *** survey delves into gain perception mechanisms,detailing how slight discrepancies between virtual and real-world movements can be utilized without user awareness,thus extending the effective navigable *** RDW control algorithms for gain-based RDW are analyzed,highlighting their implementation and effectiveness in maintaining immersion and minimizing perceptual ***,novel methods extending beyond traditional gain-based techniques are discussed,showcasing innovative approaches that further refine VR *** practical implications of RDW in enhancing safety and reducing physical collisions in VR environments are underscored,alongside its potential to improve user experience by aligning virtual exploration more closely with natural human behavior *** a thorough review of existing literature and recent advancements,this survey provides a systematic understanding for researchers,developers,and industry *** underscores the importance of RDW in the future of VR,emphasizing RDW's role in making VR more accessible and practical across various applications,from education and training to therapy and *** paper concludes with a forward-looking perspective on the continued evolution and potential of RDW in revolutionizing virtual reality experiences.

关键词： virtual reality(VR) redirected walking(RDW) redirection gain perception

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6G WIRTinX Subnetworks in context of ICN

6G WIRTinX Subnetworks in context of ICN

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2024 IEEE Calcutta Conference, CALCON 2024

作者： Saha, Kundan Kanti Ray, Sangram Dasgupta, Mou National Institute of Technology Sikkim Department of Computer Science and Engineering Sikkim Ravangla India National Institute of Technology Raipur Department of Computer Application Chhattisgarh Raipur India

ISBN: (纸本)9798331516666

This article provides a vision of combining Wireless Isochronous Real Time (WIRT) in-X Subnetworks with the Information Centric Networking framework. Here, the advantages of ICN over traditional IP-based networks are discussed in the context of WinXS implementation. A novel privacy preserving naming scheme along with an ICN packet structure and associated communication protocol are proposed for WinXS. The proposed scheme is tested on the AVISPA and NS3 simulation tools and it is found that the scheme satisfies the WinXS security and efficiency requirements, respectively © 2024 IEEE.

关键词： Internet protocols

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Fusion of IoT and Blockchain (BIoT): A Novel Architecture for Smart Agriculture 8th

Fusion of IoT and Blockchain (BIoT): A Novel Architecture...

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8th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Das, Priyanka Ray, Sangram Dasgupta, Mou Govil, Mahesh Chandra Department of Computer Science and Engineering National Institute of Technology Sikkim Sikkim Ravangla737139 India Department of Computer Application National Institute of Technology Raipur Chhattisgarh Raipur492010 India

ISBN: (纸本)9789819757855

Internet of Things (IoT) plays a crucial role in automating systems, offering effectual and reliable services with minimal human intervention. To be precise, IoT-enabled smart agriculture is a vital factor in the modern digital world which ensures to provide various functionalities such as—water level monitoring, temperature monitoring, moisture sensing, and plant growth tracking. These functionalities of smart agriculture significantly simplify the work of farmers. But on the contrary, several security and privacy-related concerns were identified such as critical agriculture field data storage and management. Therefore, the evolution of integration of the IoT and blockchain (BIoT) came into the limelight. BIoT-based smart agriculture can enhance efficiency, sustainability, etc., leading to increased productivity, improved resource management, and secure data storing management in a decentralized manner. In this paper, we propose an architecture for a smart agriculture ecosystem based on the fusion of both IoT and blockchain. Primarily, our proposed architecture consists of three stages, PEGASIS-based chain construction phase, mutual authentication and key agreement phase, and blockchain-based data storage in base-station phase. Finally, this paper highlights on security analysis of our proposed architecture and found that it is well-secured against broadly known security attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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A Survey on Data Pricing: Methods, Challenges, and Prospects 3

A Survey on Data Pricing: Methods, Challenges, and Prospects

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3rd International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2023

作者： Chi, Zangbo Zhao, Hua Fang, Zhi Zhang, Hong Ji, Liangliang Yu, Zengwen Beijing Institute of Computer Technology and Application Beijing China School of Computer Science and Technology Xidian University Xi'an710071 China

ISBN: (纸本)9781643684444

As organizations increasingly rely on data-driven decision-making, understanding the true value and potential of data becomes crucial. Data pricing, which aims to determine the financial value of information, plays a pivotal role in data circulation and transactions. However, the existing surveys on data pricing have limitations that need to be addressed. This paper presents a comprehensive survey of big data pricing methods from a data science perspective. It begins by providing an overview of the fundamental concepts underlying data pricing and the data market. Subsequently, it delves into the general principles and challenges associated with data pricing. The survey categorizes and summarizes various approaches and methods employed in data pricing, assessing their respective advantages and disadvantages. In conclusion, this paper identifies potential research directions to enhance our understanding of data pricing. By rectifying the deficiencies in existing surveys, this comprehensive study aims to contribute to the development of effective data pricing strategies and foster advancements in the field of data science. © 2023 The Authors.

关键词： Big data

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Deep Learning Techniques for Weather Prediction

Deep Learning Techniques for Weather Prediction

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Dhanalakshmi, R. Paul, Tushar School of Computer Science and Engineering Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India Masters of Computer Application Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331510022

Human activities are directly impacted by weather occurrences. Forest fires, high air temperatures that lead to drought, and extreme weather events caused by global warming all make life more difficult for humans. Precautions against such climatic calamities require weather forecast models that are both accurate and effective. As a result, creating models that accurately forecast the weather is crucial. The creation of effective models of deep learning-based weather forecasting was greatly aided by technological advancements. This study suggested a hybrid weather prediction model based on convolutional and recurrent neural networks, which had a decent success rate. The dataset named Jena, which comprises 14 parameters, extensive meteorological information that has not been used for predicting the weather, was subjected to the suggested hybrid model. The experimental findings were compared using well-known machine learning, deep learning, and statistical techniques such CNN, Integrated Moving Average with Auto-Regression, LSTM, RNN, Random Forest, Multilayer Perceptrons and SVM. These comparisons showed that the suggested hybrid model had the best prediction outcome across the board. For instance, the suggested hybrid model produced the following findings based on the weather forecast data for Jena. © 2024 IEEE.

关键词： Convolutional neural networks

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Food Genie, Recipe Search Algorithm Using Web Scraping 3

Food Genie, Recipe Search Algorithm Using Web Scraping

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3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Suwalka, Nikhil Shanbhag, Nishit Salmani, Sakina Raundale, Pooja Sardar Patel Institute of Technology Master of Computer Application Department Mumbai India

ISBN: (纸本)9798350302288

Coping up with our busy lifestyle and unhealthy eating habits, we are compelled to eat diet foods containing specific ingredients and recipes. It's not easy to ask the chef to make a range of recipes that contain the required ingredients. There are several recipe websites containing a wide range of recipes to choose from. This allows the chef to make new meals containing the specified calories and other nutritional values. The challenge is to find a list of all recipes containing the required ingredients along with the maximum calories allowed without having any data in-hand. This paper presents a method to get a list of all recipes along with its details like methods, ingredients, nutritional information which contains all or some of the mentioned ingredients. The method will allow users to search for recipes easily without worrying about the ingredients. Web scraping using Scrapy and Neo4j graph database is used to implement the algorithm. Web scraping takes the contents of a web page and saves them in a Neo4j graph database using the Python scrapy package. This database will be used to do more study on the food genie application in order to improve search results. Chefs may use the online app to prepare a variety of recipes using particular components to maintain a healthy lifestyle. © 2023 IEEE.

关键词： Websites

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Session-based Recommendation with Temporal Graph Neural Network and Contrastive Learning 3

Session-based Recommendation with Temporal Graph Neural Netw...

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3rd International Conference on Neural Networks, Information and Communication Engineering, NNICE 2023

作者： Li, Xiaolong Wang, Xiaoru Zhang, Haoxiang Zhang, Jiabin Beijing University of Posts and Telecommunications Computer Application Technology Center Beijing China

ISBN: (纸本)9798350335972

Session-based recommendation aims to predict the next clicked item using the current ongoing session of an anonymous user. Since the user's information is unknown, the available information is limited. In recent years, due to the excellent performance of graph neural networks in many applications, many works have applied graph neural networks to session-based recommendation. However, we found that the conversion of session data into graph-structured data is a lossy graph encoding method, which leads to the loss of item order information in the session. In addition, only one session can be used for recommendation in session-based recommendation. Compared with other recommendations, the problem of data sparsity is more serious. Self-supervised learning can discover ground-truth samples and has great potential in solving the data sparsity problem. Therefore, we propose a session-based recommendation model based on graph neural network and contrastive learning to solve the problem of information loss and data sparsity in graph encoding. Extensive experiments on three benchmark datasets demonstrate that our model outperforms other methods. © 2023 IEEE.

关键词： Graph neural networks

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Forest Fire Likelihood And Risk Modeling: A Machine Learning Approach 4

Forest Fire Likelihood And Risk Modeling: A Machine Learning...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Save, Payal M. Thakur, Pallavi Sardar Patel Institute of Technology Master of Computer Application Department Mumbai India

ISBN: (纸本)9798350300093

Forest fires significantly threaten natural environments and human life, necessitating accurate forest fire prediction and risk modeling. The aim of the paper is to investigate the forest fires in the forested areas of Assam, India. The threats to the biodiversity of Assam are multi-faceted and include habitat loss or destruction and fragmentation, unlawful wildlife trade, human-wildlife conflict, climate change, pollution, invasive species, and forest fires. Forest fires, in particular, pose a significant threat, leading to habitat destruction, depletion of vegetation cover, and eviction of flora and fauna. The distinctive ecosystems of Assam consisting of tropical rainforests, grasslands, and riverine habitats, are especially vulnerable including sanctuaries, national parks, and reserves. To address the challenges posed by forest fires, the integration of novel machine learning techniques offers promising solutions. Machine learning algorithms can leverage historical fire data, weather patterns, and other relevant variables to develop predictive models for forest fire likelihood. The paper aims to predict the probability or likelihood and risk level of forest fires in the Nameri National Park And Forest Reserve region of Assam using novel machine learning approaches. © 2023 IEEE.

关键词： Forecasting

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Research on identity authentication and labeling technology based on MR neural network

Research on identity authentication and labeling technology ...

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2022 International Conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2022

作者： Yang, Hao Tang, Chuanqian Department of Computer Technology and Application Qinghai University Qinghai Xining810016 China

ISBN: (纸本)9781510662988

Aiming at the problems of poor convenience, poor scalability, and low authentication rate in traditional authentication technology using physical contact authentication methods such as magnetic cards and passwords, this paper explores the accuracy and convenience of the practical application of MR neural network in personal identity authentication. In the MR wearable device, the neural network person identity authentication method is studied flexibly and quickly to detect and identify the person. The 3D information of the face is collected and preprocessed by the depth camera, and the MR identity authentication data set is established. The neural network Resnet model is used for face detection and face feature vector extraction, and the Euclidean method is used to compare the feature vectors and label the characters. The neural network authentication algorithm is mapped to the MR wearable device, and the deep face information in the scene is identified, matched, and labeled by using the unique spatial mapping of MR technology and the camera of the MR wearable device. It solves the problems of low flexibility, poor reliability of face information, and weak recognition stability in traditional identity authentication methods, enabling MR technology to provide a more intelligent identification and labeling method for person identity authentication. © 2023 SPIE. All rights reserved.

关键词： Face recognition

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