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Single Image Dehazing Using A Tiramisu Auto- Encoder

Single Image Dehazing Using A Tiramisu Auto- Encoder

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2024 international conference on intelligent systems for Cybersecurity, ISCS 2024

作者： Ray, Ahan Sharanya, S. SRM Institute of Science and Technology Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9798350375237

This paper presents a comprehensive approach to address the challenge of dehazing on-road images by synthesising datasets and training advanced deep learning models. Leveraging Pix2Pix GAN and introducing a novel Tiramisu Autoencoder architecture, the research endeavours to overcome the scarcity of real-world data through data augmentation and synthesis. The Pix2Pix GAN is modified to generate realistic haze, while the Tiramisu Autoencoder is used to de-haze the images. Challenges in data collection, including the absence of on-road data and time-series data, are elucidated. The novel architecture demonstrates promising results on benchmark datasets. The research boasts advances in both data synthesis and image de-hazing. © 2024 ieee.

关键词： Roads and streets

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ST-Mamba: Spatial-Temporal Mamba for Traffic Flow Estimation Recovery using Limited data

ST-Mamba: Spatial-Temporal Mamba for Traffic Flow Estimation...

引用

ieee/CIC international conference on Communications in China (ICCC)

作者： Yuan, Doncheng Xue, Jianzhe Su, Jinshan Xu, Wenchao Zhou, Haibo Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Peoples R China Yili Normal Univ Key Lab Vibrat Signal Capture & Intelligent Proc Yining 835000 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

ISBN: (纸本)9798350378412

Traffic flow estimation (TFE) is crucial for urban intelligent traffic systems. While traditional on-road detectors are hindered by limited coverage and high costs, cloud computing and data mining of vehicular network data, such as driving speeds and GPS coordinates, present a promising and cost-effective alternative. Furthermore, minimizing data collection can significantly reduce overhead. However, limited data can lead to inaccuracies and instability in TFE. To address this, we introduce the spatial-temporal Mamba (ST-Mamba), a deep learning model combining a convolutional neural network (CNN) with a Mamba framework. ST-Mamba is designed to enhance TFE accuracy and stability by effectively capturing the spatial-temporal patterns within traffic flow. Our model aims to achieve results comparable to those from extensive data sets while only utilizing minimal data. Simulations using real-world datasets have validated our model's ability to deliver precise and stable TFE across an urban landscape based on limited data, establishing a cost-efficient solution for TFE.

关键词： ST-Mamba traffic flow estimation recovery method limited data

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Implementation of Cryptographic Algorithm for Cloud data Security

Implementation of Cryptographic Algorithm for Cloud Data Sec...

引用

2024 international conference on intelligent systems for Cybersecurity, ISCS 2024

作者： Atwal, Shikha Juneja, Sonam Anand, Monika Anupriva Bhati, Bhoopesh Singh Chandigarh University Department of Computer Science and Engineering Mohali140413 India

ISBN: (纸本)9798350375237

Cloud computing has a wide variety of applications, one of which is the storage of substantial amounts of data on remote servers. Numerous businesses take use of this feature, which is known as 'cloud storage.' Therefore, it may be necessary to archive the information, which may come in the form of text, audio, video, and many othermedia. on an effort to keep sensitive data safe on the cloud, researchers have built a number of unique algorithms that draw on a wide range of methods. As a result, a thorough examination of the related literature has beenconducted. In an effort to ensure the safety of statistical data, this research will make anattempt to highlight key algorithms. This study intends to make such attempt. © 2024 ieee.

关键词： Genetic algorithms

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Prediction of Hazardous Asteroids Using Machine Learning 4

Prediction of Hazardous Asteroids Using Machine Learning

引用

4th international conference on Emerging systems and intelligent computing, ESIC 2024

作者： Surekha, Nemmaluri Purna Nanjundan, Preethi Bashir, Shahid Christ University Pune Department of Data Science Lavasa India

ISBN: (纸本)9798350349856

As the need for early detection and mitigation of potential threats from near-Earth objects continues to grow, this study presents a comprehensive approach to predicting hazardous asteroids through the application of machine learning techniques. With the increasing interest in safeguarding our planet from potential impact events, the accurate classification and prediction of hazardous asteroids is of paramount importance. This research leverages a diverse dataset comprising a wide array of asteroid characteristics, including orbital parameters, physical properties, and historical impact data, to train and validate machine learning models. The study employs a combination of feature engineering, data preprocessing, and state-of-the-art machine learning algorithms to assess the risk posed by asteroids in near-Earth space. © 2024 ieee.

关键词： Forecasting

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A Graph-Based Model for Vehicle-Centric data Sharing Ecosystem 27

A Graph-Based Model for Vehicle-Centric Data Sharing Ecosyst...

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27th ieee international conference on intelligent Transportation systems, ITSC 2024

作者： Yuan, Haiyue Raza, Ali Matyunin, Nikolay Patra, Jibesh Li, Shujun School of Computing University of Kent United Kingdom Honda Research Institute Europe GmbH Germany

ISBN: (纸本)9798331505929

The development of technologies has prompted a paradigm shift in the automotive industry, with an increasing focus on connected services and autonomous driving capabilities. This transformation allows vehicles to collect and share vast amounts of vehicle-specific and personal data. While these technological advancements offer enhanced user experiences, they also raise privacy concerns. To understand the ecosystem of data collection and sharing in modern vehicles, we adopted the ontology 101 methodology to incorporate information extracted from different sources, including analysis of privacy policies using GPT-4, a small-scale systematic literature review, and an existing ontology, to develop a high-level conceptual graph-based model, aiming to get insights into how modern vehicles handle data exchange among different parties. This serves as a foundational model with the flexibility and scalability to further expand for modelling and analysing data sharing practices across diverse contexts. Two realistic examples were developed to demonstrate the usefulness and effectiveness of discovering insights into privacy regarding vehicle-related data sharing. We also recommend several future research directions, such as exploring advanced ontology languages for reasoning tasks, supporting topological analysis for discovering data privacy risks/concerns, and developing useful tools for comparative analysis, to strengthen the understanding of the vehicle-centric data sharing ecosystem. © 2024 ieee.

关键词： Differential privacy

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Efficient and Secure Spatial Range Query over Large-scale Encrypted data 43

Efficient and Secure Spatial Range Query over Large-scale En...

引用

43rd ieee international conference on Distributed computing systems (ICDCS)

作者： Miao, Yinbin Xu, Chao Zheng, Yifeng Liu, Ximeng Meng, Xiangdong Deng, Robert H. Xidian Univ Sch Cyber Engn Xian Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Shenzhen Peoples R China Fuzhou Univ Sch Math & Comp Sci Fuzhou Peoples R China Henan Key Lab Network Cryptog Technol Zhengzhou Peoples R China Singapore Management Univ Sch Informat Syst Singapore Singapore

ISBN: (纸本)9798350339864

Spatial range query enjoys widespread application scenarios due to the ever-growing geo-positioning technology in recent years. Huge amounts of encrypted geo-location data are being outsourced to cloud servers to alleviate local storage and computational overheads without leaking sensitive information. However, most existing Privacy-preserving Spatial Range Query (PSRQ) cannot achieve high efficiency while satisfying strong security over large-scale encrypted spatial data. To strike a best-possible balance between security and efficiency, we propose a novel efficient Privacy-preserving Spatial Range Query (ePSRQ) scheme in dual-cloud architecture over large-scale dataset. Specifically, we propose an efficient PSRQ scheme by designing a novel index structure based on Geohash algorithm, Circular Shift and Coalesce Zero-Sum Garbled Bloom Filter (CSC-ZGBF) and Symmetric Homomorphic Encryption (SHE), which makes the computational complexity of query process independent of dataset size. Formal security analysis proves that our scheme can achieve Indistinguishability against Chosen-Plaintext Attack (IND-CPA), and extensive experiments prove that our scheme is feasible in real-world applications.

关键词： Spatial range query privacy-preserving encrypted spatial data dual-cloud large-scale dataset

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Effective data Analysis Assistance with AI and Streamlit Integration with LangChain 4

Effective Data Analysis Assistance with AI and Streamlit Int...

引用

4th international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2024

作者： Reshini, S. Adarsh, Nelavalli Basava Subhash, Nelakurthi Reddy, Saribala Udaykiran Kumar Reddy, Reddem Sunil Kumar Sai, Devarapalli Hemanth Kalasalingam Academy of Research and Education Department of Computer Science and Education Tamilnadu Virudhnagar India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Virudhnagar India

ISBN: (纸本)9798331529635

In todays age of data resources and information overload concerns have surfaced regarding the crucial role of effective data analysis and management for both businesses and individuals alike. Here in this document is presented 'Enhanced Support for data Analysis through AI and Streamlit Integration with LangChain' a system crafted to streamline the process of data handling and analysis by incorporating cutting edge technologies. By employing Streamlit to power its web interface functionality the system provides a user engaging platform where individuals can seamlessly input their data sets, for modification and real time analysis purposes. The use of intelligence with the help of Hugging Face APIs and transformer models allows for advanced data analysis that delivers valuable insights, from user inputs effortlessly. Moreover, the inclusion of LangChain boosts the system's capacity to manage data requests and simplify intricate data processing activities. This fusion of Streamlit, AI technologies and LangChain not just enhances the efficiency of data analysis procedures. Also equips users with a robust tool to uncover significant findings from their datasets. The paper explains how the system is set up and the technologies used along with the workflow to show how well the platform helps with, in depth data analysis tasks. © 2024 ieee.

关键词： data assimilation

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Enhancement of Spectral Efficiency in intelligent Reflecting Surfaces (IRS's) over Distributed and Cloud-computing systems 2

Enhancement of Spectral Efficiency in Intelligent Reflecting...

引用

2nd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Abed, Ghassan A. Jaleel, Inas F. Engineering College Network Engineering Department Al-Iraqia University Iraq Ministry of Higher Education and Scientific Research Iraq

ISBN: (纸本)9798350397635

The majority of computer networks and distributed systems, where cloud storage is located, experience several problems with the processes of storing these types of data, whether they are text files, audio files, video files, or other types of data, in addition to a fundamental problem, which is the inability to send and receive such data and access it from multiple points. In-depth discussions of these difficulties and suggestions for utilizing intelligent reflecting surfaces (IRSs) to improve distributed systems' data storage capacity, access speed, and acquisition will be provided in this study. In this paper, a virtual network was designed using Matlab software, where this network contains a group of IRSs to improve the performance of cloud and distributed computing by improving the speed of data transmission between the sender and receiver in addition to improving storage capacity. IRSs were used with supporting network hardware once, and again without supporting network hardware. The proposed system was implemented, and the results were very good when using IRSs without supporting hardware devices, because the presence of hardware devices may cause a defect in the data transmission process due to the problems that these devices face in their work. © 2023 ieee.

关键词： MATLAB

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A Novel Estimation Method for Satellite Network Traffic data Based on GCN-HMM 6

A Novel Estimation Method for Satellite Network Traffic Data...

引用

6th ieee international conference on Power, intelligent computing and systems, ICPICS 2024

作者： Liu, Xiyuan Wang, Jiale Wang, Xinrui Bu, Le Pu, Jiajie Xidian University Xi'an China

ISBN: (纸本)9798350374315

Traffic measurements are essential to the maintenance and operation of satellite networks. The complexity and overhead of global measurement are particularly challenging because satellite networks are large-scale and highly dynamic. One potential approach is to estimate global network traffic data from partial traffic measurements. However, most existing estimation techniques focus on low-rank linear decomposition, which is not a precise estimator. It's unable to represent the complex nonlinear spatio-temporal relationship that exists in large-scale, highly dynamic traffic data. This paper proposes a novel estimation method for satellite network traffic data. We introduce the Graph-Clustering module to group nodes in the network adaptively. On different time slices, different groups were sampled or estimated according to the HMM and round-robin mechanism. Our model performs much better than baseline approaches, according to experiments conducted on small, medium, and large-scale satellite network datasets. With a 76.5% rise, it offers significant benefits in recovering error measurements, especially for larger networks. The methodology exhibits potential for implementation in real-world systems. © 2024 ieee.

关键词： Satellite communication systems

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JATAYU: A Large-scale Indian UAV dataset for Vehicle Detection and Tracking 10

JATAYU: A Large-scale Indian UAV Dataset for Vehicle Detecti...

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10th ieee international conference on Electronics, computing and Communication Technologies (ieee CONECCT)

作者： Maji, Abhijnan Preetham, Kummara Ghosh, Indrajit Indian Inst Technol Roorkee Dept Civil Engn Roorkee Uttar Pradesh India Indian Inst Technol Roorkee Mehta Family Sch Data Sci & Artificial Intelligen Roorkee Uttar Pradesh India

ISBN: (纸本)9798350385939;9798350385922

Advances in Unmanned Aerial Vehicle (UAV) technology require extensive vehicular datasets for a wide range of applications within intelligent Transportation systems. However, the existing datasets partially cover the real-world complexities and the representation of a wide variety of vehicle types prevalent in the traffic streams of developing countries. The "Joint Aerial dataset for deTection and trAcking of vehicles for road safetY and sUrveillance (JATAYU)" is introduced in this study to address these shortcomings. The dataset comprises varied vehicle classes across different Indian locales. The dataset categorizes vehicle types according to the Indian Highway Capacity Manual (Indo-HCM). The utility of this dataset spans the Indian subcontinent and Southeast Asian countries due to the presence of vehicles like auto-rickshaws, e-rickshaws, motorcycles, cycle-rickshaws, and tractor-trolleys in these regions. JATAYU contains 215,000 labeled objects with nearly 30,000 annotated images in ten vehicle classes. It includes day-time, night-time, and partially obscured vehicular aerial images.

关键词： UAV dataset Computer Vision Vehicle Detection Vehicle Tracking YOLOv8 ByteTrack ITS Mixed Traffic

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