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2024 Nonlinear Photonics, NP 2024

作者： Siddiqui, Muhammad Zain Akosman, Ahmet E. Ordu, Mustafa UNAM - Institute of Materials Science and Nanotechnology Bilkent University Cankaya Ankara06800 Turkey School of Engineering Computing and Construction Management Roger Williams University BristolRI02809 United States

A novel negative curvature hollow-core fiber is numerically designed capable of filtering specific frequencies. The six-tube silica fiber strongly favors fundamental mode transmission over higher order modes despite u... 详细信息

关键词： Natural fibers

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Design and Development of an Augmented Reality E-Commerce 3D models 3

Design and Development of an Augmented Reality E-Commerce 3D...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Nandhini, P. Hariprabha, S. Abirami, S. Jaseenash, R. Kaviyaraj, R. Sri Shanmugha College of Engineering and Technology Department of Computer Science and Engineering salem India Srm Institute of Science and Technology School of Computing Chennai India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

The world is becoming more modernized with augmented reality, and this paper proposes an idea for a new way to improve the user experience of all e-commerce platforms. This strategy makes use of Transient Chaotic Neural Networks (TCNN) to develop a new marker-less visual tracking system that makes use of pictorial information. It is made up of point correspondences between image sequences. The purpose of this is to provide the easy visualization of the dimensional importance of an object in the perspective of the user's own near environment to facilitate the making of accurate purchase decisions. This is accomplished by using Vuforia to create a 3D representation of the target object, Unity3D and C# to serve as the application's framework, and Wikitude for tracking the surrounding environment. The outcomes of the experiments provide evidence of both the effectiveness and efficiency of the proposed model. © 2022 IEEE.

关键词： Augmented reality

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An Iterative Optimizing Framework for Radiology Report Summarization With ChatGPT

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4163-4175页

作者： Ma, Chong Wu, Zihao Wang, Jiaqi Xu, Shaochen Wei, Yaonai Liu, Zhengliang Zeng, Fang Jiang, Xi Guo, Lei Cai, Xiaoyan Zhang, Shu Zhang, Tuo Zhu, Dajiang Shen, Dinggang Liu, Tianming Li, Xiang Northwestern Polytechnical University School of Automation Xi'an710072 China University of Georgia School of Computing AthensGA30602 United States Northwestern Polytechnical University School of Computer Science Xi'an710072 China Massachusetts General Hospital Department of Radiology BostonMA02114 United States University of Electronic Science and Technology of China Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology Chengdu611731 China The University of Texas at Arlington Department of Computer Science and Engineering ArlingtonVA76019 United States ShanghaiTech University School of Biomedical Engineering Shanghai201210 China Shanghai United Imaging Intelligence Company Ltd. Department of Research and Development Shanghai200030 China Shanghai Clinical Research and Trial Center Shanghai201210 China

The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians. Typically written by radiologists, this part is derived from the 'Findings' section, which can be laborious and error-prone. Although deep-learning-based models, such as bidirectional encoder representation from transformers (BERT), have achieved promising results in automatic impression generation (AIG), such models often require substantial amounts of medical data and have poor generalization performance. Recently, large language models (LLMs) like Chat Generative Pre-trained Transformer (ChatGPT) have shown strong generalization capabilities and performance, but their performance in specific domains, such as radiology, remains under-investigated and potentially limited. To address this limitation, we propose ImpressionGPT, leveraging the contextual learning capabilities of LLMs through our dynamic prompt and iterative optimization algorithm to accomplish the AIG task. ImpressionGPT initially employs a small amount of domain-specific data to create a dynamic prompt, extracting contextual semantic information closely related to the test data. Subsequently, the iterative optimization algorithm automatically evaluates the output of LLMs and provides optimization suggestions, continuously refining the output results. The proposed ImpressionGPT model achieves superior performance of AIG task on both the Medical Information Mart for Intensive Care - Chest X-ray database (MIMIC-CXR) and Open Access Biomedical Image Search Engine (OpenI) datasets without requiring additional training data or fine-tuning the LLMs. This work presents a paradigm for localizing LLMs that can be applied in a wide range of similar application scenarios, bridging the gap between general-purpose LLMs and the specific language processing needs of various domains. © 2020 IEEE.

关键词： Semantics

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Conditional LS-GAN Based Skylight Polarization Image Restoration and Application in Meridian Localization 48

Conditional LS-GAN Based Skylight Polarization Image Restora...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yang, Tian Bo, Hongbo Yang, Xinyu Gao, Jun Shi, Zijian Hefei University of Technology School of Computer Science and Information Engineering Hefei China University of Bristol Department of Computer Science Bristol United Kingdom Lancaster University 3School of Computing and Communications Lancaster United Kingdom

ISBN: (纸本)9781728163277

Skylight polarization images (SPIs) contain crucial spatial information that can be used for navigation purposes. Under most circumstances, the quality of the images becomes a major concern, especially when there is blocking between the perception equipment and the sky. This paper introduces a deep learning-based methodology for restoring SPIs and utilizing the restored images for navigation. First, an adversarial training paradigm is adopted to restore the SPIs collected in a severe blocking environment. We utilize a self-developed simulation method that uses solar altitude and azimuth angle to generate ground truth, and no prior knowledge of the masking information for noise is used. Second, we show how to locate the meridian based on the restored SPIs using a residual neural network. In experiments, we demonstrate the superiority of the proposed model in restoring SPIs, and the enhanced meridian localization precision by using the restored SPIs. © 2023 IEEE.

关键词： Image reconstruction

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Machine Learning Based Approaches for Livestock Symptoms and Diseases Prediction and Classification

Machine Learning Based Approaches for Livestock Symptoms and...

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2024 International Conference on Communication, Computer sciences and engineering, IC3SE 2024

作者： Bhardwaj, Priya Kumar, S. J. K. Jagadeesh Kanna, G. Prabu Mithila, A. Tula's Institute Department Of Cse Dehradun India Kathir College Of Engineering Department Of Cse Coimbatore India School Of Computing Science And Engineering Vit Bhopal University Madhya Pradesh India Kathir College Of Engineering Department Of Ai & Ds Coimbatore India

ISBN: (纸本)9798350366846

Farm animals farmed for financial gain are referred to as livestock. The majority of livestock animals are found in isolated locations with little access to medical care. Rapid and precise disease diagnosis is generally a challenging task. To prevent the disease from spreading among the cattle, it is essential to identify the disease's consequence and take appropriate precautions. Therefore, a system that can assist in forecasting cattle diseases based on symptoms and recommend preventive actions in relation to the projected disease is required. In this work, we analyze a dataset of livestock animal health issues using automated monitoring systems using eight machine learning methods. The variables that the machine learning algorithms examine are the age, symptoms, variations in temperature, disease type. Using a dataset, we have implemented machine learning algorithms for the purpose of early diagnosis and prediction of disease in livestock animals. Machine learning techniques like logistic regression, XGBoost, Catboost, light gradient boost, Random Forests and Support Vector Machines were integrated with feature selection algorithms to predict diseases. After analyzing the data for each classifier, the Catboost and Random Forest classifiers had the greatest and most reliable accuracy of 83.7% and 83.5% respectively. © 2024 IEEE.

关键词： Forecasting

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TexCIL: Text-Guided Continual Learning of Disease with Vision-Language Model

TexCIL: Text-Guided Continual Learning of Disease with Visio...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Wentao Zhao, Defeng Zheng, Wei-Shi Wang, Ruixuan Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Peng Cheng Laboratory Shenzhen China Moe Key Laboratory of Machine Intelligence and Advanced Computing Guangzhou China

ISBN: (纸本)9798350386226

Current intelligent diagnostic systems often catastrophically forget old knowledge when learning new diseases only from the training dataset of the new diseases. Inspired by human learning of visual classes with the effective help of language, we propose a continual learning framework based on a pre-trained visual-language model (VLM) without storing any image of previously learned diseases. In this framework, textual prior knowledge of each new disease can be obtained by utilizing the frozen VLM's text encoder, and then used to guide the visual learning of the new disease. This framework innovatively utilizes the textual prior knowledge of all previously learned diseases as out-of-distribution (OOD) information to help differentiate currently being-learned diseases from others. Extensive empirical evaluations on both medical and natural image datasets confirm the superiority of the proposed method over existing state-of-the-art methods in continual learning of new visual classes. The source code is available at https://***/OpenMedIA/TexCIL. © 2024 IEEE.

关键词： Contrastive Learning

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Astronomical Image Clustering Using Spectral, K-Means and Mean Shift

Astronomical Image Clustering Using Spectral, K-Means and Me...

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computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Vaishnavi Shivaji Mohite Krishna Gayatri Patra CH Hari Sankar Sarada Jayan Department of Computer Science Amrita School of Computing Bengaluru Department of Mathematics Amrita School of Engineering Bengaluru

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

By analyzing vast amounts of observational data through astronomical image analysis, you can gain valuable information about the structure of the universe. This paper presents a new method for clustering astronomical images that uses both spectral and K-means methods and also incorporates linear algebra and optimization principles. We propose a method that combines feature extraction techniques such as the Fourier transform with clustering algorithms to classify celestial objects based on their characteristics. The research examines the effectiveness of spectral clustering and K-means clustering in organizing astronomical images into meaningful groups, taking into account factors such as computer efficiency, clustering accuracy and interpretability. Experimenting with different datasets, including telescope images, we demonstrate the effectiveness of our approach in automating the classification of astronomical objects. Our results reveal insights into the underlying structure of astronomical knowledge and offer promising avenues for future research in computational astronomy. In general, this research advances both the field of astronomy and the application of mathematical methods to image analysis and clustering.

关键词： Image analysis Fourier transforms Accuracy Heuristic algorithms Clustering algorithms Linear algebra Feature extraction Optimization Astronomy Tuning

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Analysis and Visualization of Eye movements in Patients with Glaucoma 7

Analysis and Visualization of Eye movements in Patients with...

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7th IEEE International Conference on Recent Advances and Innovations in engineering, ICRAIE 2022

作者： Sandhiya, B. Amudha, J. Krishnan, Sajitha Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9781665489102

Glaucoma is one of the foremost causes that result in irreversible blindness. World Health Organisation (WHO) has estimated that around 4.5 million people are blind due to glaucoma. This vast number is because more than 90% of glaucoma cases are undiagnosed. Early-stage glaucoma detection system helps to identify the disease and prevent it from further blindness. The proposed system analyzed and visualized the eye movement patterns of glaucoma-affected participants using machine learning algorithms. The model collected eye-gaze patterns using an eye-tracking sensor and visualized them based on different eye gaze features. The model classified glaucoma and normal based on eye gaze data. The proposed model has incorporated different visualization techniques to understand how eye gaze patterns of normal and glaucoma differ while performing screen-based tasks such as Free-Viewing Task and Image Searching Task. The proposed system has also incorporated an interactive dashboard using Microsoft PowerBI Software. © 2022 IEEE.

关键词： Eye movements

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Network Coding For Data Compression In Genomic Sequencing

Network Coding For Data Compression In Genomic Sequencing

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Disruptive Technologies (ICDT), International Conference on

作者： Sultan Alam Saksham Agarwal Anshumaan Sharma School of Computing Science and Engineering VIT Bhopal University Sehore M.P. India School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Sehore M.P. India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

The exponential growth of next-generation sequencing technologies has resulted in a massive influx of genomic data, posing significant challenges in storage, transmission, and processing. Conventional compression methods, such as gzip and bzip2, are not optimized for the unique structural patterns and redundancies of genomic sequences, leading to suboptimal performance. Addressing this gap, our research introduces a novel data compression framework that leverages network coding principles to enhance efficiency, scalability, and reliability. Network coding, widely used in communication systems, is adapted here to process and compress genomic data by employing finite field arithmetic and redundancy-aware encoding techniques. Our method achieves a compression ratio of up to 50% while maintaining a data integrity level of 99.99%, as validated through extensive mathematical analysis and experimental testing on diverse genomic datasets. Furthermore, the framework demonstrates linear scalability and robust error resilience, making it particularly suited for large-scale applications in clinical genomics and research. By bridging the gap between traditional compression algorithms and the growing demands of modern genomic data, this study lays the groundwork for more efficient and reliable data management solutions in bioinformatics.

关键词： Sequential analysis Scalability Data integrity Genomics Data compression Network coding Reliability Bioinformatics Resilience Testing

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Deep Learning for Plant Disease Detection and Crop Yield Prediction based on NPP-WPF Analysis in Smart Agriculture 7

Deep Learning for Plant Disease Detection and Crop Yield Pre...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Amudha, S. Bindu, G. Sankar, Sasi Rekha Poongothai, E. VijayaLakshmi, K. Sarveshwaran, Velliangiri Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai602203 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798350341485

Despite a sharp rise in population, agriculture still provides food for all people and acts as a backbone of the country. Nowadays, no proper guidance given to farmers and they are not able to analyze the nature of soil, disease of plant and its requirements. This leads to the depletion of farming in future. Crop diseases must be identified and prevented in order to increase the productivity. To overcome this, the nature of the soil is analyzed through various parameters like Nitrogen-Potassium-Phosphorus Content in soil, Humidity, Temperature, pH value of the soil, Rainfall using sensors, plants disease using cameras in real-time environment. These parameters are obtained using various IoT Sensors, cameras and existing Data, are analyzed through ML Algorithms. The proposed system consists of two modules in crop yield prediction system using the features NPP-WPF whereas NPP stands for Nitrogen-Potassium-Phosphorus and WPF stands for Water Content-Pesticide-Fertilizer, the Farmers can be guided with the details of the crop that is suitable for the soil and about Fertilizer, Pesticide, Water requirements. The second module called as automatic disease detection system uses CNN, ResNet, VGGNet, and Mobile net models to recognize and diagnose plant diseases from their leaves at the early stage. The proposed model is implemented with the dataset of 14 different species of plants and with the categorical classes of 38 classes. The result analysis shows that the suggested model provides accuracy of 98.2% when compared to the existing state-of-the-art methodologies. © 2023 IEEE.

关键词： Phosphorus

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