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A Survey on Blockchain-Based Certificate Authentication System: From Traditional to Digital

A Survey on Blockchain-Based Certificate Authentication Syst...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： R Vijaya Saraswathi Meda Venkata Vasavi Swathi Sakshi Manoj Punwatkar Shreya Konduru Mohammed Abdul Sami Dept. Of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

This research study pertains to the essential problems of certificate authentication within academic organizations. It concentrates on the major issues of forgery and inefficiencies as well as administrative bottlenecks that arise in conventional systems. The usual ways, which are based on one authority organization, are likely to be compromised, move slowly on verification, and have increased overhead costs. Blockchain technology is a distributed way of doing things without a middleman, and thus it is able to improve security, transparency, and the immutability of educational records. The article identifies the shortcomings of the present blockchain-based implementations with the focus on scalability, interoperability with legacy infrastructure, and high transaction costs. In the paper, an experiment on Ethereum-based smart contracts as well as the Secure Hash Algorithm (SHA-256) and InterPlanetary File System (IPFS) technologies shows the potential of these technologies to a certificate validation system by providing cryptographic integrity and, therefore, data storage that cannot be altered. The proposed work introduces a decentralized architecture that uses smart contracts for certificate issuing and verification to some extent. By integrating cryptographic hashing mechanisms, the system provides real-time, verifiable, and immutable credential records, addressing both fraud prevention and operational inefficiencies.

关键词： Data privacy Costs Scalability Smart contracts Authentication Ubiquitous computing Blockchains Fraud InterPlanetary File System Artificial intelligence

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Emergency Response Optimization System with Dynamic Traffic Signal Control: (Life Saver)

Emergency Response Optimization System with Dynamic Traffic ...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Rajeshwari B.S Vinay Y Venu Gopal V Tauksik Anil Kumar Viraj Chowdarapu Dept. of Computer Science Engineering B.M.S College of Engineering Bangalore India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

The traditional emergency response systems often face significant problems that can delay their services. These systems usually struggle with heavy traffic in cities, outdated ways to communicate, and old-fashioned methods for finding the best routes. These issues can be serious, especially in emergencies where every second matters. Also, the inability of these systems to adapt quickly to changing situations makes it even harder for emergency vehicles to get to where they need to be on time. To overcome these challenges, new technologies are being developed to improve how emergency responses are managed. One of these innovations is the Emergency Response Optimization System with Dynamic Traffic Signal Control. This system uses advanced tracking technologies to provide ambulance tracking for patients and dynamic traffic signal control. This paper looks at the problems with traditional emergency services and how new technologies can make a big difference, especially focusing on the advantages of dynamic traffic control systems.

关键词： Technological innovation Web services Urban areas Transportation Transforms Traffic control Emergency services Real-time systems Vehicle dynamics Optimization

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Intervention Analysis for Predicting Stock Market using Deep Learning Algorithms and Time Series Analysis

Intervention Analysis for Predicting Stock Market using Deep...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Krish Valecha Yash Pandloskar Sowmya Dadheech Pradnya Saval Dept. of Computer Science and Engineering (Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Stock market being intricate and volatile pose challenges for classical Time Series Analysis techniques in terms of generalization and computation on real-world data. In order to improve prediction capacities, new methods like intervention analysis and Deep Learning (DL) are required. The research seeks to unravel the complexities of stock price prediction modelling by comparing Time Series Analysis with Deep Learning augmented by intervention analysis. Upon evaluation on different datasets, the Deep Learning models, specifically the Recurrent Neural Network (RNN) performed better than the other models considered. RNN outperformed other models on Mean Squared Error (MSE) for Tesla and IBM data, with values 3.874 before intervention and 3.593 after intervention and 46.52 before intervention and 42.97 after intervention respectively, thus highlighting the advantages of Deep Learning models for predictions. The goal of investigating intervention analysis methods incorporated into predictive models is to create processes that can predict overall market trends and efficiently identify and address sudden deviations.

关键词： Deep learning Analytical models Recurrent neural networks Accuracy Computational modeling Time series analysis Predictive models Market research Data models Stock markets

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An Experimental Study on No-Reference Image Quality Assessment of Image Datasets 7

An Experimental Study on No-Reference Image Quality Assessme...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Magotra, Varun Rohil, Mukesh Kumar Bhartiya Vidya Bhavans Sardar Patel Institute of Technology Dept. of Computer Engineering Mumbai India Birla Institute of Technology and Science Dept. of Computer Science Rajasthan Pilani India

ISBN: (纸本)9781665421683

Image quality assessment algorithms are of various types depending upon the the method they are employing. Over the years, these algorithms have found various applications like image compression, image restoration, image transmission etc. Image quality assessment algorithms have played a significant in these fields by appropriately evaluating the quality of the images. Based on the structure of these algorithms, in this paper, we further explored the application of Image Quality Assessment algorithm especially Non-Reference Algorithm in the world of AI. In this paper, we have showed how the Non-reference IQA algorithms can be used to assess the quality of the images in the data that are being used in for training the deep learning models. In our study, we specially focused on the datasets that are being used in the training of the deep learning models that are used in the healthcare sector. We have showed how non-reference algorithms can be used to create a more quality data so that the efficiency of the automated systems which heavily relies on the data quality can be increased. © 2022 IEEE.

关键词： Image compression

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Global COVID-19 Related Waste Challenges, Response Strategies, and Potential Opportunities

Global COVID-19 Related Waste Challenges, Response Strategie...

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World Environmental and Water Resources Congress 2022: Adaptive Planning and Design in an Age of Risk and Uncertainty

作者： Smith-Lambert, Leigh Ferguson, Izaria Albert, Lynal Dept. of Engineering and Computer Science Tarleton State Univ. StephenvilleTX United States

ISBN: (数字)9780784484258

ISBN: (纸本)9780784484258

As the world navigates the COVID-19 pandemic, the surge in waste generation, the varied composition of waste, and its subsequent management has presented several challenges to many communities. Waste management techniques and cultivating an awareness of the environmental and health impacts of mismanaged wastes have varied with location, economics, and priorities of the region. Risk mitigation efforts including the wearing of masks, lockdowns, and stay at home orders intended to contain the spread of the virus have resulted in several unintended environmental implications. With the rise in consumer plastic consumption, single use PPE, take away food orders, and online shopping, many households across the globe have reported an increase in waste. While developed countries have been able to approach the surge by utilizing and expanding current municipal waste management programs, countries lacking this existing infrastructure battle an increased risk of continued infection and magnified environmental issues through open landfills, groundwater contamination, and limited community awareness. Adopting appropriate methods of risk communication and encouraging participation from the public to manage waste issues is critical to protecting the environment by limiting contamination, improving community involvement in reducing landfill waste, and encouraging recycling. It is imperative to develop waste management solutions while continuing the promotion of sustainable habits, combating the current pandemic situation, and preparing for future episodic events. Our study focuses on analyzing episodic waste generation during the pandemic, management strategies adopted globally, and identifying areas of possible improvement and alternate strategies that can be implemented. © ASCE.

关键词： Groundwater

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Data-Driven Assessment of Contaminant Vulnerability in River Ganga Coastal Aquifers

Data-Driven Assessment of Contaminant Vulnerability in River...

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OCEANS 2024 - Halifax, OCEANS 2024

作者： Fauziya, Farheen Das, Kousik Majumder, Prakrity Sen, Somdipta Banerjee, Champa Gupta, Ananya Sen Mukherjee, Abhijit Electrical and Computer Engineering University of Iowa Iowa CityIA United States Srm University-AP Department of Env. Science & Engineering Andhra Pradesh India Iit Kharagpur Dept. of Geology and Geophysics West Bengal Kharagpur India School of Environmental Science and Engineering Indian Institute of Technology Kharagpur West Bengal Kharagpur India

ISBN: (纸本)9798331540081

Groundwater of coastal aquifers have strong hy-draulic connections with the coastal hydrodynamics in a various spatiotemporal scale. But the along with the sub daily tidal influence the Tropical cyclone dependent acute groundwater level response has rarely been studied. These acute and instantaneous groundwater level (GWL) fluctuations during tropical cyclones (atmospheric depression) have a direct impact on groundwater flow dynamics and have an impact on instantaneous solute and contaminant mobilization. These groundwater dynamics include influx of sweater and submarine groundwater discharge over the period each atmospheric low-pressure events simultaneously. The dynamic state of GWL fluctuations get stabilized along with decay of atmospheric low-pressure events but the change in solute concentration may stay for a month to seasonal recharge of groundwater and is proportional to the level of acute GWL fluctuations. Thus, this monitoring and prediction of atmospheric low- pressure event dependent GWL fluctuations is a possible indicator of groundwater vulnerability, especially in coastal aquifers. This study conducts a comparative analysis of machine learning and deep learning models to predict groundwater level fluctuations. The models include a Fully Connected Neural Network (FCNN), Artificial Neural Network (ANN), MLPRe-gressor, Support Vector Regressor (SVR), and Random Forest Regressor (RFR). Each model is evaluated using metrics such as Mean Squared Error (MSE), R2 score, Normalized Root Mean Squared Error (NRMSE), Root Mean Squared Error (RMSE), and standard deviation. The results underscore the capability of deep learning techniques to capture nonlinear features in hydrological data, thereby enhancing the understanding and prediction of groundwater levels. Such predictions are crucial for assessing seawater infiltration, which is a potential threat to the available drinking water sources and nutrient and contaminant fluxes within coastal aquifers. Submarine

关键词： Groundwater pollution

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An Efficient CNN-LSTM Approach for Smartphone Sensor-Based Human Activity Recognition System 5

An Efficient CNN-LSTM Approach for Smartphone Sensor-Based H...

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5th International Conference on Computational Intelligence and Networks, CINE 2022

作者： Choudhury, Nurul Amin Soni, Badal National Institute of Technology Silchar Dept. of Computer Science & Engineering Assam Silchar788010 India

ISBN: (纸本)9781665464659

Human Activity Recognition - HAR is one of the most popular area in the filed of sensor technology and smart learning algorithms. Deep learning algorithms are immensely exploited in HAR systems as it eliminates the need of manual feature engineering. Researchers use normal and hybrid deep learning schemes for training and comparing the models. This paper proposes an efficient CNN-LSTM model for recognising daily human activities using smartphone sensor data. A contemporary CNN-LSTM model is created using time distributed feature extraction layers as it can efficiently handle hierarchical features and can selects the relevant features easily using LSTM memorization scheme. The proposed CNN-LSTM model is compared with two other models - DNN and LSTM in terms of accuracy, precision, recall, F1- score, training loss and computational times. The proposed model managed to outperform other models optimally in all the evaluation metrics. Using holdout training and testing split, the model managed to achieve an average accuracy of 97.609% and 98.69% with relu activation function and 100 training iteration. On validating the different models, the hybrid models takes less computational time and managed to achieve an computational efficiency of (76.23 ± 140.76)% from other models. © 2022 IEEE.

关键词： Wearable sensors

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An Efficient Use of Toffoli Quantum Gate for Boosting the Embedding Capacity in DNA Sequence

An Efficient Use of Toffoli Quantum Gate for Boosting the Em...

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2022 IEEE Asia-Pacific Conference on computer science and Data engineering, CSDE 2022

作者： Nahar, Mahbubun Kamal, A.H.M. Jatiya Kabi Kazi Nazrul Islam University Dept. of Computer Science and Engineering Trishal Bangladesh

ISBN: (纸本)9781665453059

While hiding data in DNA sequence, the steganograpy schemes try to increase the embedding payload, enhance the security of implanted data, protect the stego expansion and preserve the biological characteristics of DNA sequence. In this research, we have proposed a new data hiding method where the scheme implants secrets in DNA sequence using a classical Toffoli quantum gate mechanism. The proposed scheme implants 4-bits of information per nucleotide. Experimental results demonstrate that the proposed scheme achieves massive improvement over the others which is 4 to 12-times higher than the competing schemes. © 2022 IEEE.

关键词： Steganography

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Adversarial Machine Learning Attack on Machine Learning-based Controller for Solar Inverters

Adversarial Machine Learning Attack on Machine Learning-base...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Joaquin Massa Kyoung-Tak Kim Taesic Kim BoHyun Ahn Kumar Venayagamoorthy Jianwu Zeng Dept. of Electrical Engineering and Computer Science Texas A&M University-Kingsville Kingsville USA Dept. of Electrical Engineering Soongsil University Seoul Republic of Korea Dept. of Electrical Engineering and Computer Science University of Missour-Columbia Columbia USA Dept. of Electrical and Computer Engineering Clemson University Clemson USA Dept. of Electrical and Computer Engineering and Technology Minnesota State University Mankato Mankato USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

Adversarial machine learning (ML) attacks are stealthy attacks designed to mislead the ML model results. This paper explores adversarial ML attacks that generate adversarial noisy input data in an ML-based controller in a solar inverter. Three types of ML models, long short-term memory (LSTM), gated recurrent unit (GRU)), and bidirectional-LSTM (Bi-LSTM), are designed to replace proportional-integral (PI) controller-based vector control for a solar inverter and two white-box adversarial ML attacks (Basic Iterative Method (BIM) attack and Fast Sign-Gradient Method (FGSM)) are applied to the ML controllers. It is observed that the adversary ML attacks designed in stealthy way do not affect the PI controller, while significantly degrading performance of the ML-based controllers. Moreover, the BIM attack is more effective than FGSM and Bi-LSTM-based controller is relatively robust to the attacks compared to peer.

关键词： PI control Inverters Adversarial machine learning Hardware Robustness Real-time systems Noise measurement Long short term memory Machine vector control Microprogramming

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Optimization of New Radio Physical Uplink Shared Channel Resources Allocation with Efficient Demodulated Referenced Signals and PT-RE Deployment

Optimization of New Radio Physical Uplink Shared Channel Res...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Deepak Upadhyay Mridul Gupta Kunj Bihari Sharma Abhay Upadhyay Satya Prakash Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

This paper explores the resource allocation optimisation of New Radio Physical Uplink Shared Channels with an emphasis on the effective implementation of Phase Tracking Reference Signals and Demodulation Reference Signals in 5G networks. Understanding how important these reference signals are to dependable communication, we created a Python framework to model and display their distribution on a time-frequency grid. By carefully modifying important factors including mapping type, PRB allocation, and symbol allocation, our method greatly increased resource efficiency, decreased overhead, and improved signal quality. The results show how sophisticated resource allocation techniques may be used to maximise system performance, providing important information for the creation of more reliable and effective 5G NR communication systems

关键词： Wireless communication Time-frequency analysis System performance Symbols New Radio Resource management Reliability Uplink Optimization Demodulation

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