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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Raja, S.R. Suganthi, P. Vivekanandan, Munian Vanitha, N. Vivekanandan, G. Venu, Nookala Centre for Open and Digital Education Chennai India Sri Sai Ram Institute of Technology Department of Computer Science and Engineering Chennai India Rajalakshmi Institute of Technology Artificial Intelligence and Data Science Chennai India Sri Sairam Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai 44 India Sri Sairam Institute of Technology Department Computer Science and Engineering Chennai India Madhya Pradesh Gwalior474 005 India

ISBN: (纸本)9798350384277

In this ground breaking project, we delve into the fascinating realm of ornithology through an extensive dataset comprising 507 distinct bird breeds and a staggering 61,250 high-resolution images. Employing cutting-edge machine learning techniques, our aim is to develop a robust classification model capable of accurately identifying bird species based on visual attributes. The dataset, meticulously curated for diversity, spans a myriad of habitats, climates, and avian characteristics. Through this expansive collection, our model not only encapsulates the beauty of various feathered species but also ensures a broad spectrum of challenges in classification, pushing the boundaries of current bird recognition systems. Our approach transcends conventional methods by leveraging state-of-the-art neural networks, harnessing the power of deep learning to discern subtle nuances in plumage, size, and distinctive markings. The model's ability to adapt and learn from this vast dataset promises breakthroughs in bird breed classification, offering insights into the intricate world of avian biodiversity. This project stands as a testament to the synergy between ornithology and artificial intelligence, fostering a deeper understanding of avian ecosystems. By avoiding plagiarism and embracing creativity, our abstract articulates the uniqueness of our endeavour, propelling the field of bird breed classification into uncharted territories. © 2024 IEEE.

关键词： Birds

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A New Approach for Iris Segmentation Based on U-Net 6

A New Approach for Iris Segmentation Based on U-Net

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6th International Conference on computer Network, Electronic and Automation, ICCNEA 2023

作者： Yang, Yan Wang, Jianguo Xi'an Technological University School of Computer Science and Engineering Xi'an China Xi'an Technological University Research Institute of Artificial Intelligence and Data Science Xi'an China

ISBN: (纸本)9798350305388

The concept of iris segmentation was created to increase the accuracy of iris recognition. Past recognition methods used the entire eye image directly for recognition classification, which led to poor recognition results. For the sake of sovling this problem, this paper proposes the ResU-Net (RU-Net) model, which can guide the network to learn more features that distinguish between iris and non-iris pixels. First, based on the U-Net, the backbone network model is changed to ResNet50 in this paper. This has the advantage of reducing the number of parameters and network complexity, and improving the learning capability. For the sake of solving the problem of sample imbalance between iris region and background region, this paper introduces the Focal Loss loss function. focal loss can effectively deal with the case of sample category imbalance and make the network focus more on the pixels that are difficult to classify. In this paper, the proposed RU-Net model is experimentally evaluated on the CASIA-Iris-Thousand dataset. The experimental results demonstrate that RU-Net achieves significant improvements on NIR iris images, reaching 96.22% MIoU and 98.19% MPA. This indicates that the RU-Net method outperforms other representative iris segmentation methods and has better segmentation capability. © 2023 IEEE.

关键词： Pixels

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Brain Tumor Classification Using UNet Deep Neural Networks from 3D MRI Images 1

Brain Tumor Classification Using UNet Deep Neural Networks f...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Shaik, Mohammad Khaja Kalpana, D. Sesadri, U. Mukherjee, Saptarshi Dastagiraiah, C. Reddy, Pundru Chandra Shaker St. Ann's College of Engineering and Technology Electronics and Communication Engineering Andhra Pradesh Chirala India Departmet of CSE-Data Science India Vardhaman College of Engineering Computer Science and Engineering Telanagana Hyderabad India Maulana Abul Kalam Azad University of Technology M.Tech-Bioinformatics West Bengal India Anurag University Computer Science and Engineering Telangana Hyderabad India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

ISBN: (纸本)9798350371802

A patient's therapy options and prognosis are both improved by detecting and diagnosing a brain tumor early. Brain tumors can be located and diagnosed with the help of magnetic resonance imaging (MRI). In real practice, however, medical personnel rely entirely on their own time and expertise when manually identifying brain tumors from a vast number of MRI scans. These days, medical diagnosis and treatment recommendation systems that use computer-aided expertise are all the rage. Brain tumor detection makes use of a plethora of frameworks based on machine-learning(ML) &deep-learning(DL). The purpose of this research is to present a DL-based approach for effective brain tumor segmentation and classification. The research uses convolutional-neural-networks(CNNs) for tumor classification after volumetric segmentation of MRI-images using the 3D-UNet model. To demonstrate the models' validity, loss and precision plots are provided. We compare the outcomes of several methods described in the literature with the measured performance of the models we offer. When compared to the most recent cutting-edge methods, the proposed work comes out on top. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Deep Learning-Driven Black Box Approach For Image Segmentation 1

Deep Learning-Driven Black Box Approach For Image Segmentati...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Sudha, Nuthalapati Kasetty, Sai Bhargav Madhuri, K. Sai Nikitha, Jajala Margret, Issac Neha Rajakumar, K. Mahindra University School of Engineering Telangana Hyderabad India V.I.T - University School of Computer Science and Engineering Tamilnadu Vellore India BVRIT Narsapur Dept. of Computer Science and Data Science Telangana Hyderabad India VNR VIET Dept. of CSE - AIML and IoT Telangana Hyderabad India

ISBN: (纸本)9798350356816

The diagnosis of a range of eye disorders needs to categorize the retinal vessels. computerized implementation of this process is becoming increasingly essential for automated screening systems for retinal diseases. To achieve a more accurate extraction of the retinal vessels, a new pre-processing step is proposed. These proposed pre-processes are also compared to other algorithms to assess their impact. The proposed pre-processing process consists of two phases. The first phase is the implementation and validation of the pre-processing modules, and the second phase is the implementation of these pre-processes onto the retinal vessels that were to be extracted. To achieve a significantly improved segmented vessel image, the proposed pre-process phase employs a common image-processing technique. In recent years, there has been a great deal of focus on retinal vessel identification studies, and the importance of assessing and confirming the findings of retinal vessel segmentation. © 2024 IEEE.

关键词： Image segmentation

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Enhanced Building Prediction for Smart City Surveillance 1

Enhanced Building Prediction for Smart City Surveillance

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Natarajan, Yuvaraj Wadhwa, Gitanjali Saravanan, Lekshmipriya Natarajan, Mathivathani KPR Institute of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore641407 India KPR Institute of Engineering and Technology Dept. of Artificial Intelligence and Data Science Coimbatore641407 India

ISBN: (纸本)9798350348057

Nearly three-quarters of the total power used in the world is consumed by the built environment sector. Therefore, looking for viable methods to reduce building energy needs and decrease negative environmental repercussions is vital. By incorporating intelligence into applications, everyone in the current situation has become intelligent, which lessens the load of constant interruption or human control. By using Machine to Machine interactions, intelligence enables the ability to connect more real-time parameters and make moral judgments in perilous circumstances. The current study examined the framework, approach, and performance of currently available AI-based techniques. According to the research, choosing the best machine and deep learning model for a given task can be difficult. Among the primary areas of study in relation to the vast amounts of data collected is the viability in the practice of the used algorithms, both standard machine learning and the more recent branch known as deep learning. Present a quantitative analysis contrasting the effectiveness of conventional deep learning and machine learning strategies using the direction of human movement in detecting the applications based on the analogue pyroelectric infrared sensor (PIR) outputs. Here, Random Forest is used as the machine learning method because of its great prediction accuracy. Over deeper datasets, deeper CNNs outperform shallow models as a deep learning method. The study demonstrates that traditional Machine Learning performs with detecting values within the first few seconds, improving real-time detection. Additionally, it might be used on more smart buildings without upgrades. © 2023 IEEE.

关键词： Intelligent buildings

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On-Line System of Garbage Image-Orientated Intelligent Classification, Submission and Examination 20

On-Line System of Garbage Image-Orientated Intelligent Class...

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20th IEEE International Conference on e-Business engineering, ICEBE 2024

作者： Tian, Jiayin Wang, Yaozhi Liu, Jiaxin Chen, Yan School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China Xi'an Jiaotong University Shaanxi Key Lab of Big Data Knowledge Engineering Shaanxi Xi'an China School of Computer Science and Technology Xi'an Jiaotong University Xi'an Jiaotong University Shaanxi Key Lab of Big Data Knowledge Engineering Shaanxi Xi'an China

ISBN: (纸本)9798350365856

In a world brimming with new products continually, novel waste types are ubiquitous. This makes current image-based garbage classification systems difficult to perform well due to the long-tailed effects of distribution of garbage types, and necessitates an urgent and efficient garbage classification with abilities of detecting new and rare wastes and class-incremental learning for environmental sustainability. Therefore, we propose a framework of Online System of Garbage Image-Oriented Intelligent Classification, Submission, and Examination, facilitating the incremental garbage classification efforts. In which, to identify novel garbage effectively, we also introduced few-shot object detection method with two key algorithms: Two-Stage Object Detection Learning Algorithm and Dynamic Query-based Incremental Few-shot Learning Algorithm. Our experiment results show that Both outperform the current existing ones in dataset, MS COCO. Then, a strategy of Class-Incremental learning based Residual Network is proposed to meet the need of new waste class-incremental learning. The experimental results support our strategy. Finally, a prototype system employed the above algorithms and the strategy is described. © 2024 IEEE.

关键词： Zero-shot learning

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Learning Medical Subject Headings in PubMed Articles to Enhance Deep Predictions 11

Learning Medical Subject Headings in PubMed Articles to Enha...

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11th IEEE International Conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2024

作者： Dashdorj, Zolzaya Jargalsaikhan, Zoljargal Grigorev, Stanislav Trufanov, Andrey Kang, Tae-Koo Altangerel, Erdenebaatar Mongolian University of Science and Technology Computer Science Ulaanbaatar Mongolia School of Information Technology and Data Science Irkutsk National Research Technical University Irkutsk Russia Sangmyung University Department of Human Intelligence and Robot Engineering Seoul Korea Republic of

ISBN: (纸本)9798350350142

MeSH is a vocabulary of terms used in the life sciences and biomedical fields. A large amount of Medical Subject Headings (MeSH) occur in PubMed articles with different weights. In this research, we analyze a disease-symptom network exploiting MeSH metadata co-occurring in 7 million PubMed articles and configure a deep model in the context of biomedical and health care data. Optimized and efficient data networks are important to compile machine learning models. We optimize the deep model by reducing important features. Our result highlights that the degree of the severity of symptoms associated with a disease improves the accuracy of disease prediction models. The network was well optimized by reducing the number of symptoms. This finding may have important implications in healthcare: healthcare chatbots, clinical significance, practical implications. © 2024 IEEE.

关键词： data Analytics

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Cloud-Based Oxygen Management System with Seamless HTTP Protocol Integration 4

Cloud-Based Oxygen Management System with Seamless HTTP Prot...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Hemalatha, S. Ramasamy, Jayabharathi Selvi, K. Anandan, P. Meenakshi, R. Maniraj, S.P. Tagore Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India S. A. Engineering College Faculty of Management Studies Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350307757

In this study, an efficient system to improve the oxygen supply in hospitals and other healthcare institutions is proposed. It uses Internet of things (IoT) devices and cloud based data transmission and processing system. The controller unit collects the oxygen sensor data for the analysis and uses HTTP protocols to send and receive data with cloud computing in HTTP website server. The cloud is data storage, processes and real-time monitoring, analysis and control system. It has a user interface with viewed administrators and medical professionals. The interface provides real-time data regarding oxygen supply, consumption patterns, and possible concerns. Authorized workers can access and manage the system via the display, allowing for timely adjustments to oxygen supply settings, optimizations to refill schedules, and responses to emergency circumstances. It's adaptable, flexible, and resilient, which are benefits. There will be less need to worry about oxygen shortages and interruptions, and healthcare institutions of all sizes can benefit from this. The cloud's remote access and control capabilities provide continuous functioning and rapid reaction times. It's an effective solution for hospitals and other healthcare facilities that need to control their oxygen supplies. With its cloud-based design, the solution increases productivity, improves patient safety, and simplifies oxygen management. © 2024 IEEE.

关键词： HTTP

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Efficient Resource Allocation in Computing Power Networks Considering Similar Task Merging: A Lyapunov Optimization-Based DRL Approach

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IEEE Internet of Things Journal 2025年第13期12卷 23118-23138页

作者： Jia, Zhonghai Xue, Junxiao Shi, Lei Li, Jie He, Mengyang Zhejiang Lab Research Center for Space Computing System Hangzhou310000 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou450002 China SongShan Laboratory Zhengzhou450040 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Key Laboratory of Trusted Data Circulation and Governance Web3 Shanghai China

The cloud-edge-terminal architecture relies on hierarchy for resource allocation but lacks global optimization. The computing power network (CPN) introduces a new distributed computing paradigm, integrating cross-domain, heterogeneous resources for global scheduling. However, most CPN research focuses on task optimization during resource allocation, while neglecting the similarity of random tasks before the allocation stage. Additionally, fragmented CPN resources and complex task demands pose challenges to global load balancing. This paper proposes a deep reinforcement learning framework with task merging and congestion avoidance for on-demand resource allocation. Specifically, a low-complexity similar task merging algorithm reduces redundant resource consumption during task preprocessing. In task offloading, the principal neighborhood aggregated graph neural network captures CPN's intricate features. Lyapunov optimization, integrated into a multi-threaded training framework, minimizes resource backlog congestion. A carefully designed reward function balances multiple objectives, enhancing computing resource utilization efficiency and ensuring system stability. Theoretical analysis shows that with control parameter V, the trade-off between resource utilization efficiency and system stability follows the relationship O(1/V), O(V). Extensive experiments demonstrate a 33.5% improvement in resource utilization efficiency and a 62.7% increase in task offloading success rates with respect to those in state-of-the-art algorithms. The proposed algorithm exhibits robustness and effectiveness, particularly in high-load and real network topologies. © 2014 IEEE.

关键词： Computing power

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A Framework for Automated Cardiovascular Magnetic Resonance Image Quality Scoring based on EuroCMR Registry Criteria 13

A Framework for Automated Cardiovascular Magnetic Resonance ...

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Nabavi, Shahabedin Moghaddam, Mohsen Ebrahimi Abin, Ahmad Ali Frangi, Alejandro F. Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran The University of Manchester Division of Informatics Imaging and Data Sciences Schools of Computer Science and Health Sciences Manchester United Kingdom

ISBN: (纸本)9798350330151

Cardiovascular magnetic resonance (CMR) imaging is a radiation-free modality widely used for functional and structural evaluation of the cardiovascular system. Achieving an accurate diagnosis requires having good-quality images. Subjective CMR image quality assessment is a tedious, time-consuming and costly process. This paper presents an automated scoring framework for CMR image quality assessment that uses deep learning models to evaluate left ventricular coverage and CMR imaging artefacts. The quality scoring in the proposed framework is an attempt to automate some of the subjective quality control criteria of the EuroCMR registry for the short-axis cine steady-state free precession (SSFP) CMR images. The scores given by a radiologist and a cardiologist with experience in CMR imaging for the images of 50 subjects from the UK Biobank were used to validate the proposed framework. The Pearson correlation coefficient (PCC) and the Spearman rank-order correlation coefficient (SRCC) calculated for the experts' quality scores versus ones obtained from the proposed framework are 0.908 and 0.806 on average. The results show that the quality scoring by the proposed framework is highly correlated with the experts' opinions. The proposed framework can be used for post-imaging quality assessment of short-axis cine SSFP CMR images and quality control of large population studies such as the UK Biobank. © 2023 IEEE.

关键词： Quality control

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