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Seg-CycleGAN: An Improved CycleGAN for Abstract Painting Generation 4

Seg-CycleGAN: An Improved CycleGAN for Abstract Painting Gen...

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4th international conference on Computer Vision, Image and Deep Learning, CVIDL 2023

作者： Zhang, Haowei School of Software Engineering Sichuan University Chengdu China

ISBN: (纸本)9798350326444

In modern art, the creation of abstract painting has become a prominent artistic expression with significant aesthetic merit. With the development of artificial intelligence, the abstract paintings generated by AI has gradually been recognized by the public. this paper proposes an abstract painting creation method based on an improved CycleGAN network (Seg-CycleGAN). Our method starts by obtaining a collection of color blocks through clustering and color block reorganization based on the original input image which is then fed into the CycleGAN network for style transfer. In addition, we add a clustering layer on one of the generator of CycleGAN, which can effectively accelerate the convergence speed of the generator and improve the training efficiency and quality of results. this work provides a novel method for AI generation of abstract paintings and demonstrates its effectiveness in experiments. © 2023 IEEE.

关键词： Color

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Efficient Channel Attention for Speech Emotion Recognition with Asymmetric Convolution Module 4

Efficient Channel Attention for Speech Emotion Recognition w...

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4th IEEE international conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Wu, Tianqi Wang, Liejun Cheng, Shuli Zhang, Jiang Xinjiang University Xinjiang Key Laboratory of Signal Detection and Processing School of Information Science and Engineering Urumqi China

ISBN: (纸本)9798350324303

Speech emotion recognition (SER) is a challenging task due to the diversity and complexity of emotions. there are some unresolved issues, such as too much redundant information in the extracted features, the problems of hard-To-classify samples and category imbalance. To address these issues, we propose asymmetric convolution module (ACM) capture the spatial information of feature maps by improving CNNs structure and efficient channel attention (ECA) to establish the dependency relationship between channels. the combination of both extract more useful emotion information in the feature map and improve classification accuracy. In this paper, our proposed model achieves state-of-the-art results on IEMOCAP improvised data, with 70.99% and 89.78% unweighted accuracy (UA) on the full IEMOCAP and EMO-DB corpora. © 2023 IEEE.

关键词： Convolution

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Skin Cancer Detection and Classification using Deep Learning 4

Skin Cancer Detection and Classification using Deep Learning

引用

4th international conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Jain, Sweta Dhakate, Srushti Gujar, Prathamesh Rajbhoj, Prathamesh Sathawane, Utkarsh Shri Ramdeobaba College of Engineering and Management Department of Computer Science Nagpur India

ISBN: (纸本)9798350349856

Skin cancer is among the most common types of cancer, and quick identification considerably enhances the odds of survival. the purpose of this work is to develop cutting-edge deep learning models that can classify images of skin cells and accurately detect cases of skin cancer. the strength of deep learning algorithms is utilized in this research, which uses a cloud-based architecture. these algorithms, which make up the system's core, allow for the creation of models that significantly improve skin cancer prediction accuracy. the publication provides a thorough explanation of the model construction procedure and how it is used to categorize dermal cell images. Using the ISIC2018 dataset, the convolution neural network (CNN) deep learning technique was employed in this study to identify the two main categories of cancers, malignant and benign. this dataset includes 3297 benign and malignant skin lesions. the deep learning models developed for this study are carefully assessed using validated datasets. Precision, Recall, F1-Score, Support and the area under the curve, all measures, showed remarkable performance at a rate of 96.02. this study presents a model-driven architecture that practitioners can use to quickly diagnose skin cancer using state of the art deep learning architecture. © 2024 IEEE.

关键词： Diseases

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DWT-Net: A Medical Image Segmentation Model Incorporating Frequency Domain Information 4

DWT-Net: A Medical Image Segmentation Model Incorporating Fr...

引用

4th international conference on Communication Technology and Information Technology, ICCTIT 2024

作者： Chen, Lixian Wu, Xueyan Ma, Jiayi Li, Shichu Shi, Yang Huang, Mingxuan Guangdong University of Technology School of Management Guangzhou China Shenzhen University College of Physics and Optoelectronic Engineering Shenzhen China Guangdong University of Technology School of Computer Science and Technology Guangzhou China Sun Yat-sen University Zhongshan School of Medicine Guangzhou China

ISBN: (纸本)9798331528973

Numerous medical image segmentation frameworks have been proposed by researchers;however, the incorporation of frequency domain information has been largely overlooked in previous studies. By incorporating frequency information, we avoid the issue where traditional downsampling reduces computational load and communication bandwidth but also removes redundant and important information, leading to accuracy loss. this paper introduces a novel supervised convolutional neural network architecture, termed "DWT-Net,"which incorporates discrete wavelet convolution and a custom residual downsampling mechanism. through wavelet transformation, the model not only achieves a near-global receptive field but also significantly enhances its responsiveness to low-frequency information. To bolster the model's performance, data augmentation techniques were employed to enrich the dataset. Our model has been applied to the public datasets Kvasir-SEG and CVCClinicDB. Compared to other frameworks, it has shown significant improvements in display, achieving the state-of-the-art accuracy. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Enhancing the Compressed Video Quality through a Neural Network Approach 4

Enhancing the Compressed Video Quality through a Neural Netw...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Panneerselvam, Karthick Rajalakshmi, K. Sornalatha, P. Kpr Institute of Engineering and Technology Department of Computer Science & Engineering Tamil Nadu Uthupalayam641407 India Cmr Institute of Technology Department of Mca Karnataka Bengaluru560037 India Alagappa University Department of Computer Applications Tamil Nadu Karaikudi630003 India

ISBN: (纸本)9798331523923

Video compression mainly focuses on reducing the file size of videos with little loss of quality, but most video compression algorithms on the market do not focus on clarity. Data compression is increasingly being considered as a necessary issue in larger engineering communities. Data compression using neural networks has been the main topic of some related research. the main objective of this study would be to apply the video in an amazing way with the help of an Enhanced Generative Adversarial Network (EGAN). EGAN was better to manage and produced better consistent results with fewer artifacts than other Neural Networks (NN). Deep neural networks (DNNs), recurrent neural networks (RNNs), generational adversarial networks (GANs), and various autoencoder (AE) variants have all been used for various compression methods. It would be a potential option for incredible movies. Based on significantly reduced sample input films, the proposed neural network was able to maintain high-frequency information and generate pleasurable sensory videos. A Residual Dense Block (RDB) is the sort of block utilized in the design. In comparison to previous state-of-the-art approaches, the comprehensive Mean-Opinion-Score (MOS) test, and the Video Multi-Method Assessment Fusion (VMMAF) test, reveal extremely substantial increases in terms of human experiential accuracy. the researchers are also proposing a new data pre-processing method based on the Deep Recurrent Neural Network (DRNN) that has been combined with two leading-edge coding standards, and the Adaptable Video Coding (AVC). the results show consistent encoding benefits across all sequences examined at multiple spatial frequencies, with an average bit rate savings of 4.0 percent relative to AVC. © 2025 IEEE.

关键词： Image coding

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A Survey on: Design, Implementation, and Evaluation of a Secure and Anonymous Communication Platform Utilizing the TOR Network for Enhanced Privacy and Data Protection 4

A Survey on: Design, Implementation, and Evaluation of a Sec...

引用

4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Biradar, Santoshkumar Parsewar, Parth Suresh Mishra, Nilamadhab Galakatu, Ankita Atul Gandewar, Sarthak Santosh G H Raisoni College of Engineering & Management Pune Department Of Cyber Security & Data Science Pune India

ISBN: (纸本)9798331518981

this study presents an advanced anonymous messaging platform that integrates Tor network, VPN, and dynamic IP rotation to provide strong privacy protection for users. Tor network onion routing masks user identities with multilayer encryption between distributed nodes, while a VPN adds an encrypted tunnel, improving security by hiding the user's real IP address. the main innovation is the automatic rotation of the IP address of both the sender and the receiver every 10 seconds, which prevents backtracking and significantly complicates traffic analysis. this dynamic mechanism ensures that even if a user's communication is intercepted, it becomes impractical to trace it back to its original source or destination. By addressing weaknesses such as Tor network's potential exit node tracking and risks of VPN One Content Failure, the platform effectively reduces timing attacks and traffic correlation. the design balances latency and performance, ensuring minimal disruption while maintaining high scalability and resilience against adversaries. the combination of these technologies provides a state of the art solution for secure and private anonymous communication. Keywords: anonymous communication, Tor network, VPN, dynamic IP rotation, onion routing © 2024 IEEE.

关键词： Anonymity

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A Comprehensive Review of Flower Classification Techniques Using Deep Learning 4

A Comprehensive Review of Flower Classification Techniques U...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Varshney, Abhilasha Varshney, Sudeep Chaudhary, Ankur Varshney, Abhilasha School of Engineering and Technology Sharda University Department of Computer Science Sharda Greater Noida India DS Group Noida India ABES Engieering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350306118

this paper analyzes deep learning techniques for categorizing flowers, covering new advances, issues, and advancements in this emerging field. As the significance of automated flower identification continues to grow across many domains, deep learning algorithms have emerged as powerful tools for attaining accurate and effective categorization. By combining a variety of papers, methods, and datasets, this study offers a structured overview of the state-of-the-art in deep learning-based flower categorization. the main concepts, architectures, pre-processing techniques, transfer learning methodology, and performance evaluations employed in diverse research are covered in the article. By examining the benefits, drawbacks, and contrasting assessments of different methods, this research provides helpful insights for academics, practitioners, and enthusiasts interested in employing deep learning for flower categorization. © 2023 IEEE.

关键词： Computer vision

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Advanced Architectures and Management Strategies for Energy Efficiency in IoT Systems 4

Advanced Architectures and Management Strategies for Energy ...

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4th international conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Singh, Anupam George, Remya P. Ahmad, Nazia Hussain, Sajithunisa Khan, Rubina Liyakat Quraishi, Suhail Javed Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun India Applied College Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Mriirs Haryana Faridabad India

ISBN: (纸本)9798350387490

Internet of things (IoT) systems penetration has brought tremendous innovations in different areas from homes to industries. But, the upsurge in the usage of IOT devices to can lead to a massive demand for energy consumptions hence call for new solutions on how this energy may be used efficiently. In the current study, the authors focus on the state of the art solutions and approaches to the energy management in IoT context. It offers an understanding of how power optimisations in hardware designs are done, including in low-power SoC solutions and in more potent STT-MRAM architectures that decrease power usage at the macro component level. Pertaining to conservation, the study also reviews the energy-savvy architectures of the network, a new system of access point, and routing protocols that employ the least energy. In addition, the methods of use of renewable energy resources for IoT operation and energy harvesting techniques are also proposed. On the management front, the paper presents overall frameworks on the methods for developing EMS, utilization of big data technology for energy use measurement and control in real-time. It also looks into techniques of demand response management and load managing that reduces fluctuations in energy demand. Discussed here are some of the simple yet effective solutions for controlling and reducing energy consumption in buildings where actual performance evaluations are made by showing marked enhancement in the systems and substantial energy saving. the following are the research propositions and avenues for future research, and the emerging technologies that could be applied in the future to influence the energy efficiency of IoT systems. © 2024 IEEE.

关键词： MRAM devices

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SFFNet: Multi-Scale Sparse Focus Feature Cue-Aware for Efficient Facial Expression Recognition 4

SFFNet: Multi-Scale Sparse Focus Feature Cue-Aware for Effic...

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4th international conference on Electronic Information engineering and Computer Communication, EIECC 2024

作者： Deng, Liqian Liu, Tingting Wang, Minhong Liu, Hai Zhang, Zhaoli Li, Youfu Central China Normal University National Engineering Research Center of E-Learning Wuhan China The University of Hong Kong Faculty of Education Hong Kong City University of Hong Kong Department of Mechanical Engineering Hong Kong

ISBN: (纸本)9798331534622

Facial expression recognition is an important subtask in the field of computer vision. Accurate recognition of facial expressions plays a critical role in applications such as human-computer interaction, emotion analysis, and intelligent surveillance. In this work, we propose a novel approach for learning multi-scale sparse focus features by combining CNN and Swin Transformer architectures, which enables the effective capture of both local and global patterns in facial expression images. Additionally, we introduce two key modules: Adaptive Hierarchical Feature Construction (AHFC) and Dual Contextual Attention Synthesis Unit (DCASU). Specifically, the AHFC module leverages Swin Transformer to extract multi-scale facial expression features while mining the relationships among sparse focus features. the DCASU module, on the other hand, extracts local features and utilizes attention mechanisms to focus on crucial regions of the face. Extensive experiments demonstrate that our method achieves state-of-the-art performance on the RAF-DB (89.18%) and KDEF (95.81%) datasets, validating the effectiveness and superiority of our approach. © 2024 IEEE.

关键词： Human computer interaction

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Deep Learning-Enabled Intensity Level Classification of Corn Rust Disease 4

Deep Learning-Enabled Intensity Level Classification of Corn...

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4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kaur, Arshleen Kukreja, Vinay Gopal, Lisa Verma, Garima Sharma, Rishabh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350343670

Classifying plant diseases quickly and accurately is crucial to reducing their negative effect on agricultural output. In this research, we introduce a deep learning-based strategy for disease severity classification in maize using a multi-layer perceptron (MLP) model. Corn rust, which is brought on by Puccinia fungal infections, is a significant danger to harvest success. the proposed model makes use of a 25,000-image dataset that was painstakingly curated to capture various infection phases and then preprocessed to improve consistency and quality. the MLP framework is used to analyze the complex patterns that characterize fluctuating disease severity. Accuracy, precision, recall, and F1-score are used to assess the model's efficacy across a range of intensities. Understanding the subtleties of classification requires conducting a thorough confusion matrix study. the results of the model are also compared graphically to those of state-of-the-art methods. the results suggest that the model is capable of accurately classifying 96.74% of all intensity levels. Both the confusion matrix and the precision-recall metrics shed light on the strengths of individual classes. the suggested model's competitive position in the landscape of disease categorization algorithms is highlighted by the visual comparison. the ramifications of this study go beyond maize rust and contribute to the development of automated disease identification in agriculture. the combination of deep learning and agricultural knowledge provides a promising path toward better disease management. this study highlights the potential to strengthen food security through data-driven interventions as technology continues to reshape the agricultural landscape. © 2024 IEEE.

关键词： Corn disease Deep Learning Disease Classification Multi-layer Perceptron

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