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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Sharma, Vansheta Rani, Rupa Pathak, Priya Chakraborty, Sudeshna Department of Computer Science & Engineering- Data Science ABES Institute of Technology U.P. Ghaziabad India Department of Computer Science & Engineering Ajay Kumar Garg Engineering College U.P. Ghaziabad India Depatment of Computer Science & Engineering Banasthali Vidyapith Rajasthan India Department School of Computer Application and Technology Galgotias University UP India

ISBN: (纸本)9798350374629

Among the most vital organs that protect the body of a human from the external environment is the skin. Early skin illness identification is essential for reducing mortality because it prevents skin cancer, and any other skin disease from developing. But because it resembles skin patches, insect bites etc in appearance, early detection that might lengthen life expectancy is a fantastic experiment. We would be working with different skin diseases namely ‘Acne’, ‘Eczema or Atopic Dermatitis’, ‘melanoma’. A working model that uses image analysis and machine learning to classify and identify skin conditions. A brand-new, cutting edge automated system that can swiftly and accurately identify skin lesions is needed to deal with biomedical pictures. There is growing curiosity concerning the application of deep learning to the diagnosis and management of different diseases. For the early detection of skin disease, we therefore suggested a deep learning model based on CNN and the merger of the U-Net and ResNet models is presented as a fusion model that has been used to perform encoding decoding of the disorders using skin scans. This model integrates image processing, image enhancement, machine learning and segmentation techniques. The model yielded an overall accuracy of 95.6%. Incorporate additional skin conditions in the future to strengthen our model's suitability for medical applications. ©2024 IEEE.

关键词： Image enhancement

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Implicitly Guided Design with PropEn: Match your data to Follow the Gradient 38

Implicitly Guided Design with PropEn: Match your Data to Fol...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Tagasovska, Nataša Gligorijević, Vladimir Cho, Kyunghyun Loukas, Andreas Prescient/MLDD Genentech Research and Early Development United States Department of Computer Science Center for Data Science New York University United States

Across scientific domains, generating new models or optimizing existing ones while meeting specific criteria is crucial. Traditional machine learning frameworks for guided design use a generative model and a surrogate model (discriminator), requiring large datasets. However, real-world scientific applications often have limited data and complex landscapes, making data-hungry models inefficient or impractical. We propose a new framework, PropEn, inspired by "matching", which enables implicit guidance without training a discriminator. By matching each sample with a similar one that has a better property value, we create a larger training dataset that inherently indicates the direction of improvement. Matching, combined with an encoder-decoder architecture, forms a domain-agnostic generative framework for property enhancement. We show that training with a matched dataset approximates the gradient of the property of interest while remaining within the data distribution, allowing efficient design optimization. Extensive evaluations in toy problems and scientific applications, such as therapeutic protein design and airfoil optimization, demonstrate PropEn's advantages over common baselines. Notably, the protein design results are validated with wet lab experiments, confirming the competitiveness and effectiveness of our approach. Our code is available at https://***/prescient-design/propen. © 2024 Neural information processing systems foundation. All rights reserved.

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E-Medical Administration: An Automated Healthcare Management System 4th

E-Medical Administration: An Automated Healthcare Management...

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4th International Conference on data, Engineering, and Applications, IDEA 2022

作者： Awotunde, Joseph Bamidele Misra, Sanjay Abdullahi, Thairu Olanrewaju Agrawal, Akshat Department of Computer Science University of Ilorin Ilorin Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway Amity University Haryana Manesar India

ISBN: (纸本)9789819724505

Almost any service may now be found on the Internet, thanks to advances in web technology. Every major industry is converting and building a digital face for all of its core activities to get closer to the rising digital market. A person or company using redundancy tactics in today's society will not gain from it. All well-run and well-managed businesses today need Internet access. Information digitization is critical in healthcare because of the need to move swiftly and effectively. It lays the groundwork for the creation of software that makes it easier to go from paper documents to electronic ones. Therefore, this paper proposes an automated hospital management system. E-Medical Administration is a web-based platform that removes the need for paper prescriptions in hospitals and offers E-Medical Management, which would enhance patient management, doctor scheduling, and universal access to patient data anywhere in the hospital. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electronic health record

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TOWARDS ROBUST OUT-OF-DISTRIBUTION GENERALIZATION BOUNDS VIA SHARPNESS 12

TOWARDS ROBUST OUT-OF-DISTRIBUTION GENERALIZATION BOUNDS VIA...

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12th International Conference on Learning Representations, ICLR 2024

作者： Zou, Yingtian Kawaguchi, Kenji Liu, Yingnan Liu, Jiashuo Lee, Mong-Li Hsu, Wynne School of Computing National University of Singapore Institute of Data Science National University of Singapore Singapore Department of Computer Science & Technology Tsinghua University China

Generalizing to out-of-distribution (OOD) data or unseen domain, termed OOD generalization, still lacks appropriate theoretical guarantees. Canonical OOD bounds focus on different distance measurements between source and target domains but fail to consider the optimization property of the learned model. As empirically shown in recent work, the sharpness of learned minima influences OOD generalization. To bridge this gap between optimization and OOD generalization, we study the effect of sharpness on how a model tolerates data change in domain shift which is usually captured by "robustness" in generalization. In this paper, we give a rigorous connection between sharpness and robustness, which gives better OOD guarantees for robust algorithms. It also provides a theoretical backing for "flat minima leads to better OOD generalization". Overall, we propose a sharpness-based OOD generalization bound by taking robustness into consideration, resulting in a tighter bound than non-robust guarantees. Our findings are supported by the experiments on a ridge regression model, as well as the experiments on deep learning classification tasks. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Regression analysis

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In the Eyes of the Bystander: Are the Stances on Different Conflicts Correlated?

In the Eyes of the Bystander: Are the Stances on Different C...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Tao, Yiyao Zhang, Hengyu Dey, Babli Tulga, Selenge Lyu, Hanjia Luo, Jiebo University of Rochester Goergen Institute for Data Science RochesterNY United States University of Rochester Department of Computer Science RochesterNY United States

ISBN: (纸本)9798350362480

Public opinion on international conflicts, such as the concurrent Russia-Ukraine and Israel-Palestine crises, often reflects a society's values, beliefs, and history. These simultaneous conflicts have sparked heated global online discussions, offering a unique opportunity to explore the dynamics of public opinion in multiple international crises. This study investigates how public opinions toward one conflict might influence or relate to another, a relatively unexplored area in contemporary research. Focusing on Chinese netizens, who represent a significant online population, this study examines their perspectives, which are increasingly influential in global discourse due to China's unique cultural and political landscape. The research finds a range of opinions, including an overall neutral stance towards both conflicts and a statistical correlation between attitudes towards each, indicating interconnected or mutually influenced viewpoints. The study also highlights the significant role of news media in impacting public opinion, particularly in China, where state policies and global politics shape conflict portrayal. © 2024 IEEE.

关键词： opinion mining social media stance detection

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Early Detection and Classification of Tomato Leaf Blight Disease Using Deep Neural Networks 2

Early Detection and Classification of Tomato Leaf Blight Dis...

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2nd IEEE International Conference on data science and Network Security, ICDSNS 2024

作者： Halder, Anangsha Guha Neogi, Subhrendu Dutta, Shibdas Khamaru, Koustov Bhattacharya, Arghya Roy, Shovan The Neotia University Department of Computer Science and Engineering Sarisha India Dcg Data-Core System India Department of Information Technology Kolkata India University of Engineering and Management Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350373110

The world population relies on agricultural plants for food, and illnesses reduce yield, but proper plant disease monitoring can help to resolve such issues. computer vision and machine learning methods can detect plant illnesses early, reducing their negative consequences and overcoming the drawbacks of continual human monitoring. After pre-processing of input images, the target region needs to be isolated. The proposed models analyzes images for detection and classification to identify edges, colors, and textures. This research has agricultural applications for early disease identification. This study examined ResNet, MobileNet, DenseNet, InceptionV3 and modified CNN model on the PlantVillage dataset to classify tomato illnesses. The F1 score, accuracy, sensitivity, and specificity can evaluate models. This paper evaluates these models using a diverse collection of tomato leaf pictures, contributing to automated disease detection systems and may be helpful for improving farm management and sustainability. © 2024 IEEE.

关键词： Fruits

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A Review on the Applications of Machine Learning and Deep Learning Techniques for Skin Cancer Detection 4

A Review on the Applications of Machine Learning and Deep Le...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Skin cancer is the most commonly reported type of cancer globally and one of the few cancers that can be effectively treated if detected in its early stages. Recent advancements in artificial intelligence (AI) have significantly improved skin cancer diagnosis through Machine Learning (ML) and Deep Learning (DL) models. This review explores ML and DL methodologies, including Random Forest, Convolutional Neural Networks (CNNs), Inception Networks, ResNets, and Support Vector Machines (SVMs), which have been widely used for automated diagnosis. However, existing AI-based systems face several challenges, including the need for balanced datasets, model interpretability issues, computational complexity, and difficulties in generalizing across diverse populations. Moreover, multimodal detection systems and Explainable AI (XAI) present additional challenges in making AI-driven diagnoses more reliable and transparent. This article discusses these challenges while also highlighting emerging techniques that integrate multiple data sources and individualized diagnostic tools to enhance precision. By addressing these challenges, this review aims to provide insights into the practical advancements and future directions of AI in skin cancer detection and treatment. © 2025 IEEE.

关键词： Adversarial machine learning

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An effective compressed image authentication scheme based on N-variant AMBTC

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Multimedia Tools and Applications 2024年第2期83卷 3801-3829页

作者： Su, Guo-Dong Chang, Chin-Chen Lin, Chia-Chen School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fuzhou350300 China Department of Information Engineering and Computer Science Feng Chia University 100 Wenhwa Road Seatwen Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung41170 Taiwan

Image authentication is one technique that provides integrity protection for digital images, making them sensitive to any slight modification. Recently, the detectability of absolute moment block truncation coding (AMBTC) compressed images has been attracting more attention due to its integrity authentication concerns. Thus, this paper proposes an effective N-variant AMBTC based image authentication scheme for AMBTC compressed images. First, a novel AMBTC compression code is derived using a variant AMBTC technique that can provide a better quality of compressed images. Then, each AMBTC compression code is further eliminated into a version requiring fewer bits using the proposed N-variant AMBTC technique, and those vacated N-bits are used to conceal authentication codes, which is generated by hashing the AMBTC compression code and location information. Experimental results confirm that the proposed scheme has superior stability and accuracy in tampering detection while providing better visual quality of the watermarked image. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Authentication

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A Tiny Efficient U-Net with Gated Linear Attention for Medical Image Segmentation

A Tiny Efficient U-Net with Gated Linear Attention for Medic...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ju, Sibo Chen, Zhaozhen Liao, Xiangwen Shen, Yiqing He, Junjun Su, Yanzhou Fuzhou University College of Computer and Data Science Fuzhou China Johns Hopkins University Department of Computer Science Baltimore United States Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798350386226

Medical image segmentation is crucial for diagnosis and treatment planning. While recent advancements in deep learning, particularly UNet variants, have improved segmentation performance, they often result in increased model complexity, which limits their real-time applicability on resource-constrained devices in clinical settings. To address this challenge, we present the Tiny Efficient U-Net (TE-UNet), a novel lightweight model balancing efficiency and accuracy. TE-UNet uses a U-shaped encoder-decoder framework with a gated linear attention mechanism to process low-level and high-level features, preserving details and reducing complexity. It employs depth-wise separable convolutions for higher-level processing, enhancing efficiency without losing performance. Additionally, skip connections improve multi-scale feature extraction and information flow. Experiments on two public datasets across different modalities demonstrate that TE-UNet outperforms ten state-of-the-art methods, maintaining a parameter size under 40KB and low computational cost. TE-UNet makes real-time segmentation more accessible for various clinical applications. © 2024 IEEE.

关键词： Deep learning

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Improving AI-Based Skin Disease Classification with StyleGAN3 for Minority Skin Tone Generation 16

Improving AI-Based Skin Disease Classification with StyleGAN...

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16th Biomedical Engineering International Conference, BMEiCON 2024

作者： Tanantong, Tanatorn La-Or-On, Pangon Srijiranon, Krittakom Thammasat University Research Thammasat University Unit in Data Innovation and Artificial Intelligence Faculty of Science and Technology Department of Computer Science Pathum Thani Thailand

ISBN: (纸本)9798331505431

Skin diseases are prevalent in Thailand due to the hot temperatures, humid climate, and increasing diversity of skin tones, which complicates diagnosis. Artificial intelligence (AI) has improved skin disease detection through deep learning models, aiding faster diagnoses and reducing the workload on dermatologists. However, AI models often struggle with underrepresentation of darker skin tones in training datasets, affecting performance. To address this imbalance, data augmentation techniques like generative models are recommended. This research enhances skin disease classification using StyleGAN3 for data augmentation to balance minority skin tone classes. The Fitzpatrick17k dataset focusing on Acne, Psoriasis, and Vitiligo is used. The Fitzpatrick Skin Type Scale is reduced from six to three levels due to image availability. VGG16 is applied to classify skin tone classes. Two additional datasets, the original and traditionally augmented datasets, are used for comparison against the StyleGAN3-augmented dataset. Results show that the StyleGAN3-augmented dataset achieves the best performance with F1-scores of 0.7193, 0.7446, and 0.7543 and accuracy scores of 0.7200, 0.7368, and 0.7419 for Acne, Psoriasis, and Vitiligo respectively. The obtained results demonstrate that utilizing the StyleGAN3-augmented dataset for skin disease classification in minority skin tone classes yields the highest performance, with F1-scores increasing by 19.25%, 19.77%, and 25.94 % for Acne, Psoriasis, and Vitiligo, respectively. These findings highlight the effectiveness of StyleGAN3 in improving model performance for underrepresented skin tones in skin disease classification. © 2024 IEEE.

关键词： Dermatology

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