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Advanced Adversarial Techniques for Enhanced Grayscale Video Colorization Using Deep Learning 2

Advanced Adversarial Techniques for Enhanced Grayscale Video...

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2nd international conference on Intelligent data communication Technologies and Internet of Things, IDCIoT 2024

作者： Reddy, G.Banu Siva Teja Anudeep, Y. Kumar, P.Pavan Reddy, E.Chandrahasa Jansi, R. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Kattankulathur Tamil Nadu 603203 India College of Engineering and Technology SRM Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur Tamil Nadu 603203 India

ISBN: (纸本)9798350327533

This research uses deep learning to automatically colorize grayscale videos. Grayscale video frames are colored using the Generative Adversarial Network (GAN) architecture. A generator and a discriminator are both utilized in this architecture. Prior to being enhanced during adversarial training, the generator receives pre-Training, utilizing a U-Net architecture. The research colorizes grayscale videos using a GAN, a deep learning method. A U-Net generator is initially pre-Trained using L1 loss to enhance color predictions. The generator's predictions are then improved by using an adve rsarial training strategy and a discriminator that can tell the difference between real and fake color images. Realistic colorizations can be created using the generator more easily. GAN created colorized video frames with an average PSNR of *** average SSIM value attained was 0.90886. These results show that this technique can be useful in various applications like virtual reality, medical imaging etc. © 2024 IEEE.

关键词： Discriminator GenerativeAdversarial Network Generator Image colorization U-Net architecture

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AI-Powered Blockchain Systems for Real-Time Fraud Detection in Financial Services 2

AI-Powered Blockchain Systems for Real-Time Fraud Detection ...

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2nd IEEE international conference on IoT, communication and Automation technology, ICICAT 2024

作者： Fallah, Mohammed Hussein Siri, Dharmapuri Kumar, G Ravi Sheeba, G. Sharma, Himanshu Devendran, A. College of Technical Engineering Department of Computers Techniques Engineering The Islamic University Najaf Iraq GRIET Bachupally Department of CSE Telangana Hyderabad India CMR College of Engineering & Technology Department of CSE India New Prince Shri Bhavani College of Engineering and Technology Department of ECE Tamil Nadu Chennai600073 India GLA University Department of Computer Engineering & Applications Mathura India Woxsen University School of Business Hyderabad India

ISBN: (纸本)9798350368109

The AI based blockchain system proposed in this paper targets at providing real time fraud detection in the financial services overcoming the drawbacks of conventional fraud identification approaches. The proposed solution follows a permissioned blockchain architecture combined with machine learning to allow detection of the fraudulent activities. In real-time, the gradient boosting, and the recurrent neural network models deployed in blockchain smart contracts analyze the transaction data. Simulation experiments helped to confirm a high accuracy of fraud detection, which for the most cases does not exceed 2% while the experimental blockchain network demonstrated enough speed, with the number of transactions per second being up to 1200 while latency being low enough. AI and blockchain integration outlined in the study demonstrate the possibilities of their use in improving the tools for fraud detection, and create a basis for the advancement of secure financial technologies. © 2024 IEEE.

关键词： Access Control Cloud Storage Security data Privacy Homomorphic Encryption Machine Learning

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An Intelligent Gardening Advisor System Empowered by Wireless Sensor Networks and Cloud Computing 2

An Intelligent Gardening Advisor System Empowered by Wireles...

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2nd IEEE international conference on Device Intelligence, Computing and communication Technologies, DICCT 2024

作者： Vimal, S.P. Srividhya, V. Kumar, G. Hussain, Mohammed Jubair Meera Rajmohan, M. Sujatha, S. Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Meenakshi College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Kattankulathur Faculty of Management Studies Tamil Nadu Chennai India University of Technology and Applied Sciences- Al Mussanah Department of Engineering Oman Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350372847

The future development of gardening consulting services will be facilitated by the combination of wireless sensor networks (WSN) and Cloud Computing, representing a state-of-the-art technological advancement. The technique utilizes wireless sensors deployed in gardens to collect real-time data on the temperature of the soil, moisture, and sunlight. This data is then sent to a cloud-based framework for analysis and storage. The data may be accessed from any location with a connection to the internet. The system processes and analyzes the data, enabling an advanced gardening chatbot to provide personalized advice using the huge computational capabilities of the cloud. The user is provided with timely guidance tailored to the unique conditions of their garden, facilitating optimal plant maintenance, efficient pest control, and conformance to seasonal patterns. The integration of WSN with the cloud's computing capabilities enhances the adaptability of the system, allowing it to evolve in response to changing environmental dynamics. This novel approach transforms traditional gardening guidance into a data-centric and contextually aware encounter, facilitating the emergence of an era characterized by intelligent horticulture. © 2024 IEEE.

关键词： Horticulture

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Optimizing DDoS Attack Detection Using Machine Learning 2

Optimizing DDoS Attack Detection Using Machine Learning

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2nd international conference on Computational Intelligence, communication technology and Networking, CICTN 2025

作者： Aggarwal, Saransh Behera, Bhagrajyoti Singh, Murari Kumar Sharma, Ajeet Kumar School of Computing Science and Engineering Sharda University Greater Noida India School of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9798331530389

The increase in the Distributed Denial of Service attack (DDoS) leads to a significant threat to the network security. Inability to timely and accurately detect DDoS attacks disrupts services offered by companies and the government causing financial losses. Additionally, prolonged DDoS attacks can spoil user experience and degrade company reputation. The recent literature reveals various techniques for detecting DDoS attacks, including pre-trained machine learning and deep learning algorithms. This research paper addresses this challenge by tweaking random forest algorithms for maximum accuracy using CICDDoS2019 dataset. The methodology includes collecting of dataset, data processing, feature scaling and training multiple machine learning algorithms to identify maximum accuracy for detecting DDoS attacks. The result shows improvement of accuracy and precision of machine learning models used. Future work may include training the model for other types of attacks, real time implementation of the model developed, developing a hybrid approach for detection and protection from DDoS attack. © 2025 IEEE.

关键词： Network security

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A Hybrid Approach for Inventory Price Estimation Based on Sentiment and Technical Analysis 2

A Hybrid Approach for Inventory Price Estimation Based on Se...

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2nd international conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Sinha, Vivek Kulkarni, Vedang Suares, Calvin Vinoth Kumar, C. Vivek Menon, U. Baskar, Nivetha Selvaprabhu, Poongundran School of Electronics Engineering Vellore Institute of Technology Department of Communication Engineering Vellore India

ISBN: (纸本)9798350328202

Recent years have seen significant studies into applying machine learning techniques for stock price prediction. However, most existing work in this field focuses on examining historical stock price data for forecasting stock prices, ignoring the role of public sentiments and mood on the stock market. In this work, a new approach is introduced that considers the sentiment factor along with the historical stock price to increase the precision of stock price forecasting. The proposed system includes a deep neural network that takes historical stock price information and sentiment analysis of news headlines as input characteristics. The system evaluates the framework's performance using the dataset of news headlines and stock prices of six different industries. There currently needs to be more literature in the field of stock price prediction, which incorporates both historical stock price data and sentimental data. Hence, the proposed hybrid approach for stock price prediction using sentiment and technical analysis significantly contributes to estimating the stock price. © 2024 IEEE.

关键词： Sentiment analysis

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Modeling data Security and Privacy Threats for VANET using STRIDE and LIndDUN 2

Modeling Data Security and Privacy Threats for VANET using S...

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2nd international conference on Software engineering and Information technology, ICoSEIT 2024

作者： Agustina, Esti Rahmawati Hakim, Arif Rahman Ramli, Kalamullah Fakultas Teknik Universitas Indonesia Dept. of Electrical Engineering Depok Indonesia

ISBN: (纸本)9798350317503

Vehicular Ad Hoc Networks (VANETs) represent an innovative technology with the potential to revolutionize road safety, traffic management, and vehicular communication. Nevertheless, addressing substantial security concerns to ensure privacy and data integrity within the network is imperative for successfully deploying VANETs. This study conducts a comprehensive analysis of assets/threats target identification, threat modeling, and the development of mitigation strategies for the VANET environment using qualitative methods. We initiate the process by meticulously identifying and categorizing the assets vital to the operations of VANETs. These assets encompass various components based on the data Flow Diagram (DFD) elements. A thorough understanding of these assets is crucial for assessing the security landscape in VANETs. Subsequently, we employ the STRIDE and LIndDUN threat modeling methodology to identify potential data security and privacy threats. We have identified 11 assets/threat targets and 34 distinct threats and then proposed 11 customized mitigation strategies for VANETs. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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A Comprehensive Approach to Brain Tumor Detection through MRI-CNN Fusion for Accurate and Efficient Imaging Analysis 2

A Comprehensive Approach to Brain Tumor Detection through MR...

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2nd IEEE international conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Deepa, N. Dhona, Nikita Singh, Atul Faculty of Engineering & Technology Srm Institute of Science & Technology Department of Networking and Communication Chennai India Srm Institute of Science & Technology Department of Networking and Communication Chennai India

ISBN: (纸本)9798350384277

In the field of medical imaging, prompt and precise brain tumor identification is critical for efficient treatment planning and better patient outcomes. The goal of this project is to improve diagnostic skills by creating a novel hybrid method for analyzing Magnetic Resonance Imaging (MRI) data in real-time. Our proposed methodology integrates a cutting-edge machine learning algorithm, Convolution Neural Network with traditional image processing techniques to achieve a synergistic fusion of speed and accuracy in brain tumor identification. By harnessing the power of deep learning, our model leverages a diverse dataset to train and optimize its predictive capabilities, ensuring adaptability to a spectrum of tumor types and anatomical variations. © 2024 IEEE.

关键词： Convolutional neural networks

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Blockchain Stationed Managerial Framework for Logistics & Oblige Chain Direction for Reliable data Control 2

Blockchain Stationed Managerial Framework for Logistics & Ob...

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2nd international conference on Advanced Computing and communication Technologies, ICACCTech 2024

作者： Jain, Khushi Chirag Mittal, Harish Kumar Vasesi, Sonika Paramjeet Kumar, Jatin B.M Institute of Engineering and Technology Computer Science and Engineering Sonipat India

ISBN: (纸本)9798331519056

Blockchain ensures the distribution and immutability of data. In recent years, logistics and supply chain management have increasingly recognized the impact of blockchain technology. Businesses are exploring blockchain solutions to address the complexities of connected devices. Blockchain facilitates stable, cost-effective delivery, enhances product traceability, and improves collaboration among customers, partners, and financial entities. The core focus of this project is to combine the decentralized nature of blockchain with chain governance to ensure security and transparency. The framework is implemented using Ganache, Metamask, MySQL, PHP, NodeJS, Solidity, and JavaScript. Blockchain technology also helps to reduce process interruptions caused by man-in-the-middle attacks. It assists in eliminating counterfeit goods from the market, thereby ensuring the integrity and authenticity of products for both producers and consumers at all levels. With the implementation of this framework, logistics procedures become transparent and well-managed, providing important information regarding logistics and transportation processes, including transportation vehicles, cargo status, and personnel. © 2024 IEEE.

关键词： Blockchain

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Machine Learning Model Comparison for Flood Detection Application from Satellite Images 2

Machine Learning Model Comparison for Flood Detection Applic...

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2nd IEEE international conference on data Science and Network Security, ICDSNS 2024

作者： Sonavale, Amruta Chakkaravarthy, Midhun Rao, Surampudi Srinivasa Salleh, Hishamuddin Bin M. Jadhav, Jagannath Lincoln University College Department of Computer Science and Multimedia Malaysia Malla Reddy College of Engineering and Technology Department of Electronics and Communication Engineering Telangana India Kle College of Engineering and Technology Department of Electronics and Communication Engineering Chikodi India

ISBN: (纸本)9798350373110

Backgrounds: Flooding, a rapidly increasing and increasingly frequent natural disaster, requires effective management, even if unavoidable, and the development of an effective classifier for image detection is crucial for effective response. Methods: The study uses machine learning methods like closest K-nearest neighbors, logistic regression, support vector classification, and decision tree as classifiers. The accuracy of these algorithms varies. The dataset, consisting of ground and space-based sources, is used to determine their accuracy rates. The most accurate classifier is used for forecasting flooding in advance, allowing for faster evacuation and care for those in need. Result: In this case, we could anticipate the likelihood of flooding by analyzing historical rainfall data. The support vector machine produced an efficient accuracy of 93.92%. Conclusion: The accuracy of each classifier is shown, and detection is performed mostly with great precision. The outcomes of any image tests conducted are shown as well. © 2024 IEEE.

关键词： Adversarial machine learning

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Evaluating Self-Supervised Learning Models for Multi-Label Pathology Detection: A Benchmark Against Supervised Learning 2

Evaluating Self-Supervised Learning Models for Multi-Label P...

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2nd international conference on Computational Intelligence, communication technology and Networking, CICTN 2025

作者： Agarwal, Nidhi Pooja Kumar, Nitish Anushka Garg, Yashica Department of Computer Science Engineering Galgotias University Greater Noida India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Information Technology Bharati Vidyapeeth College of Engineering New Delhi India Department of Computer Science and Engineering Indira Gandhi Delhi Technical University for Women New Delhi India

ISBN: (纸本)9798331530389

The analysis and objective of the current work is to check the performance of SSL Models in multi-label classification on the available dataset of NIH Chest X-ray images which is a large-scale medical imaging dataset. The performance of SSL models, Dino_v_bl6, Dino with ResNet-50, and MoCo (Momentum Contrast) has been evaluated against the supervised model ResNet. These models are supposed to train to detect multiple pathologies using chest X-ray images. We also note that self-supervised embedding models outperform traditional supervised learning to a significant extent, achieving similar or higher performance on a number of quantitative metrics when compared to the softmax supervised ResNet model. This also draws attention to the possibilities of using SSL for biosignal-specific medical images. Furthermore, SSL approaches offer a promising solution in scenarios where there is an overabundance of annotated datasets available for training models such as deep neural networks. The effectiveness of SSL approaches in addressing the problem of data annotation is also emphasized by this study. © 2025 IEEE.

关键词： Self-supervised learning

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