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Global Color-Aware Arbitrary Style Transfer with Discrete Wavelet Transform 20th

Global Color-Aware Arbitrary Style Transfer with Discrete W...

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Chen, Bochao Tan, Junhao Li, Xiaoqiang Pei, Songwen School of Computer Engineering and Science Shanghai University Shanghai200444 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9789819628292

The purpose of image arbitrary style transfer is to apply a given artistic or photorealistic style to a target content image. While existing methods can effectively transfer style information, the variability in color brightness features between the style and content images can significantly impact the quality of the style transfer. Specifically, traditional arbitrary style transfer relies on an encoder-decoder architecture of spatial-aware neural networks operating in the RGB domain, which typically preserves only local color brightness information and lacks a global color perception compared to the wavelet frequency domain. In this paper, we address the issue of insufficient global color perception in images from the perspective of image enhancement. We propose Global Color-aware Arbitrary Style Transfer with Discrete Wavelet Transform (GCAST), which achieves a balance between the global color characteristics of style images and the local details of content images. Our approach involves an encoder that integrates preliminary feature encoding through cross-layer attention fusion, while the decoder modifies frequency features using a wavelet-based adjustment block. To maintain a balance between content and style in the stylized images, we design a comprehensive loss function that incorporates multiple restoration loss, double style loss, and noise regularization loss. Extensive experiments comparing our method with state-of-the-art techniques demonstrate that our proposed model generates stylized images with superior results in terms of global color perception and human perceptual studies. © IFIP International Federation for Information Processing 2025.

关键词： Decoding

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A Robust Malware Classification Approach Leveraging Explainable AI

A Robust Malware Classification Approach Leveraging Explaina...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Hafiz, Md. Ferdous Bin Khan, Niaz Ashraf Kamal, Zahid Hossain, Sohrab Barman, Shohag University of Liberal Arts Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh East Delta University Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798350375237

Malware detection is a critical aspect of cybersecurity, with the constant evolution of malicious software posing significant challenges to computer systems and networks. Traditional security measures often struggle to detect obfuscated malware, which employs sophisticated techniques to evade detection. This research explores the utilization of memory analysis techniques coupled with machine learning and deep learning algorithms, augmented by Explainable AI (XAI), to enhance malware detection and classification. Leveraging a well-balanced dataset specifically tailored for memory analysis, various algorithms including Random Forest, XGBoost, Logistic Regression, LSTM, GRU, ANN, and hybrid models were employed for multiclass classification. Ensemble methods were also implemented to improve classification accuracy. The results demonstrate the efficacy of ensemble methods like Random Forest and XGBoost in accurately detecting obfuscated malware, with Random Forest achieving an impressive accuracy of 87.30%. Furthermore, the integration of GRU and LSTM architectures in hybrid models showed promising results, highlighting the effectiveness of combining different neural network architectures for improved performance in multiclass classification tasks. Through Explainable AI techniques such as LIME, insights into the underlying features and decision criteria influencing classification outcomes were provided, enhancing the interpretability and transparency of the models. This research contributes to advancing the field of malware detection and combating evolving cyber threats, with practical implications for real-world cybersecurity applications. © 2024 IEEE.

关键词： Cybersecurity

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Double Auction Strategies for Peer-to-Peer Energy Trading in Smart Grids: A Game Theoretic and Blockchain Approach 6

Double Auction Strategies for Peer-to-Peer Energy Trading in...

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6th International Conference on Energy, Power and Environment, ICEPE 2024

作者： Naik, Madhusudan Pradhan, Nihar Ranjan Pratap Singh, Akhilendra National Institute of Technology Meghalaya Department of Computer Science and Engineering Shillong India VIT-AP University School of Computer Science and Engineering Amaravati India

ISBN: (纸本)9798350379297

The concept of using blockchain technology for peer-to-peer energy trading (P2PET) in smart grids, particularly employing a double auction-based game theoretic method, is a fascinating application of both blockchain and economic principles. This paper deals with a blockchain-enabled framework for P2PET in a smart grid by using a double auction-based game theory approach. The proposed permissioned blockchain network was developed using Hyperledger Fabric, Minifab and designed by configuring the *** file. Three smart contracts, or Chaincode namely Double Auction Order, CreateP2PEnergy, and Matching, have been written using Golang and deployed into the blockchain network, thereby creating automatic energy trading transactions between energy producers and consumers. The client communication, such as consumer queries, user input, and producer trading concepts, was designed using the Postman API. Additionally, the double auction framework's performance has been measured by Hyperledger Caliper. Analysis shows that when 1000 transactions were initiated in each round by clients using Hyperledger Caliper and REST API services, the Proof of Elapsed Time (PoET) mechanism successfully processed 946 transactions, in contrast to the 627 transactions managed by Proof of Work (PoW). The outcomes from multiple rounds of testing underscore the enhanced performance of this framework. In summary, the combination of blockchain technology, P2P energy trading, smart grids, and game theory offers a promising approach to revolutionizing the energy sector. By decentralizing energy trading, empowering consumers, and leveraging market mechanisms, this approach has the potential to enhance efficiency, promote renewable energy adoption, and foster a more sustainable energy ecosystem. © 2024 IEEE.

关键词： Blockchain

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Advanced EEG-Based Classification of Periodic Leg Movements and Bruxism Using Complexity and Entropy Features 2

Advanced EEG-Based Classification of Periodic Leg Movements ...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： S., Tiwari D., Arora V., Nagar Department of Computer Science and Engineering Amity University Lucknow Campus India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

Periodic leg movements (PLM) and bruxism are prevalent sleep disorders that significantly impact sleep quality and overall health. Accurate and timely detection of these disorders is pivotal for effective treatment and improved patient well-being. In this research paper, we present a novel approach for the identification of Periodic Leg Movements (PLM) and bruxism. Our methodology leverages sophisticated complexity and entropy features extracted from electroencephalogram (EEG) signals recorded across various sleep stages. The implementation involves a diverse set of artificial intelligence (AI) classifiers, encompassing K-nearest neighbours (KNN), artificial neural networks, support vector machines (SVM) with radial basis function, SVM with polynomial kernel, and SVM with sigmoid kernel. These classifiers are employed to categorize EEG signals into three distinct classes: PLM, bruxism, or normal sleep patterns. To underscore the efficacy and superiority of our proposed approach, we conduct a thorough comparison with existing publications in the field. Our approach consistently outperforms previous methods and attains remarkable accuracies of 98.74% for PLM and 94.74% for bruxism, particularly when utilizing the SVM Sigmoid classifier. Furthermore, our study highlights the potential of EEG signals in elucidating alterations in brain activity associated with PLM and bruxism, underscoring the reliability of complexity and entropy features as robust biomarkers for these sleep disorders. Our research significantly advances the contemporary pinnacle in EEG-based detection of Periodic Leg Movements (PLM) and bruxism signifies a significant advancement, holding potential for impactful clinical applications in the realm of sleep disorder diagnosis and treatment. © 2024 Elsevier B.V.. All rights reserved.

关键词： Support vector machines

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Incident Analysis of Decentralized Finance 6

Incident Analysis of Decentralized Finance

引用

6th IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2024

作者： Lin, Cheng-Chieh Tsai, Chia-Cheng Liao, Shih-Wei National Cheng Kung University Department of Computer Science and Information Engineering Taiwan National Taiwan University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350316742

Decentralized Finance (DeFi) has emerged as a transformative force in the financial landscape, bringing about challenges in ensuring blockchain security. This paper systematically examines prominent DeFi incidents from June 2022 to May 2023. Our findings underscore the significance of continuous vigilance in DeFi operations. © 2024 IEEE.

关键词： Decentralized finance

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Television Price Prediction Based on Features with Machine Learning 1st

Television Price Prediction Based on Features with Machine L...

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1st EAI International Conference on Intelligent Systems and Machine Learning, ICISML 2022

作者： Dheeraj, Marumoju Pathak, Manan Anil, G.R. Yoosuf, Mohamed Sirajudeen Computer Science and Engineering Vardhaman College of Engineering Hyderabad India School of Computer Science and Engineering VIT-AP University Amaravati India

ISBN: (纸本)9783031350771

Television is both a source of information and a means of communication, and it plays an important role in everyone's life. It broadcasts news, documentaries, sporting events, and other events, among other things. In the market, different models of televisions having different features are available based on the user requirement. This paper tries to develop a model that can offer a client with a fair pricing estimate based on a tradeoff between features and price. A four-step process is devised for this objective, which includes real-time data scraping from an eCommerce website and creation of a model using machine learning algorithms. The algorithms like Multi Linear Regression, SVM (Regressor), Decision Tree Regressor are used for price prediction. Decision Tree Regression was found to be more accurate in predicting television prices in this study. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Machine learning

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Blockchain architecture in upcoming digital world 4th

Blockchain architecture in upcoming digital world

引用

4th Annual International Conference on Data science, Machine Learning and Blockchain Technology, AICDMB 2023

作者： Promod Kumar, B.M. Manoj Gowda, K.S. Heemashree, S.S. Siddesh Kumar, N.M. Tejas, S. Haran, Pranadhi Vikas, C.M. Department of Computer Science and Engineering PES College of Engineering Mandya India Department of Information Science and Engineering PES College of Engineering Mandya India Department of Mechanical Engineering PES College of Engineering Mandya India

ISBN: (纸本)9781032426853

Blockchain architecture is a multi-layered system with various components that work together to ensure stability, security, and efficiency. The bottom layer, the Data Layer, holds transactions and data, while the Consensus Layer ensures accuracy and validates transactions. The Network Layer manages communication and data transmission, and the Application Layer provides user access. The Contract Layer executes smart contracts and defines rules, and the Security Layer protects the system from attacks. Blockchain architecture is widely used in various industries including finance, healthcare, supply chain, and voting systems, providing secure, transparent, and immutable data. The combination of blockchain and AI technology results in secure, decentralized and automated execution of agreements, transforming industries such as finance, supply chain and healthcare. © 2024 The Author(s).

关键词： Blockchain

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Soft Computing Paradigms for Load Balancing in Cloud Computing 3rd

Soft Computing Paradigms for Load Balancing in Cloud Computi...

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3rd International Conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Ghafir, Shabina Alam, M. Afshar Alankar, Bhavya Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (纸本)9789819719457

This paper provides a comprehensive comparison of load-balancing algorithms employed in cloud computing, spanning from the earliest to the most recent developments. Emphasizing the significance of load balancing for system performance, resource optimization, and equitable allocation among users, the paper offers insights into various techniques, their effectiveness across workloads, and associated limitations. With an overview of each algorithm, this study caters to practitioners and researchers in the dynamic field of cloud computing, aiming to contribute valuable perspectives for enhanced system efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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DeepCerviNet: Enhanced Cervical Cancer Classification Using Supervised Learning Model 3

DeepCerviNet: Enhanced Cervical Cancer Classification Using ...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Rahaman, Md Anikur Binte Rabbani Nuha, Maksura Tahsin, Md Hossain, Al Islam, Raihan Ul Rifat Ahmmad Rashid, Mohammad Reza, Ahmed Wasif Ripon, Shamim H. East West University Department of Computer Science and Engineering Dhaka Bangladesh International Islamic University Chittagong Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798331534400

Cervical cancer is a female-specific malignancy that poses a substantial threat worldwide, especially in less privileged and underdeveloped regions. Despite continuous progress and advances in medical science research, the rate of death from cervical cancer remains strikingly high among women. Therefore, accurate early-stage cervical cancer identification is imperative for effective diagnosis. It facilitates cost-effective and timely treatment to increase the survival rate of patients, leading to an optimal diagnosis improvement in disease classification. Deep learning approaches demonstrate superior accuracy in medical data classification, motivating us to consider these techniques in this research. In our study, we propose DeepCerviNet, a custom CNN model that achieves 99% precision, which exceeds the accuracy of other deep learning models, MobileNetV2 (97%), ResNET34 (95%), and GoogleNet (94%). Furthermore, to assess our model's generalizability, we use another Multicancer dataset where the proposed model classifies various cancers with 99% accuracy. The DeepCerviNet model's strong performance and adaptability can assist in identifying more aggressive or recurrent cervical cancer types, aiding the development of targeted therapeutic strategies. © 2024 IEEE.

关键词： Self-supervised learning

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An Overview of Artificial Intelligence Diagnostic Systems for Hepatitis B Virus 4

An Overview of Artificial Intelligence Diagnostic Systems fo...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Singh, Dalwinder Kaur, Harjeet School of Computer Science and Engineering Lovely Professional University Punjab India Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798350387490

A virus that can attack a human's liver and cause serious liver damage is primarily known as Hepatitis B Virus or HVB. It influences nearly all functionalities performed by the liver. It is basically a short term infection;however, it will become a chronic disorder and remains for a long term in some individuals. The severe effect of this illness is that it can cause chronic liver diseases, and with time it results in cancer of the liver. Hence, it puts a great risk to the life of a patient. The early detection of the virus is necessary, which assists in saving the patient's life and brings more treatment procedures that can adapt by the doctor for treatment. However, this life-threatening disease's therapy and diagnosis are both costly and can have dangerous adverse effects. As a result, developing a diagnostic system that lowers the cost of therapy and diagnosis is critical. In the medical field, several artificial intelligence technologies have been employed to assist doctors and experts in the diagnosing process. The significant aim of this paper is to thoroughly review all these diagnostic systems developed or proposed by various researchers. Later on, the systems are compared with one another to evaluate which system or model is best to use in solving the problem. The parameter that is taken into account to complete the comparison. As a result, it is noticed that the system developed by using a genetic neural network is accurate for the diagnosis of HBV and has an accuracy of 99.14%. © 2024 IEEE.

关键词： Artificial Intelligence Comparative Study Diagnostic Systems Early Detection Hepatitis B Virus Intelligent Systems Medical Expert Systems

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