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SepFE:Separable Fusion Enhanced Network for Retinal Vessel Segmentation

computer Modeling in Engineering & Sciences 2023年第9期136卷 2465-2485页

作者： Yun Wu Ge Jiao Jiahao Liu College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002China

The accurate and automatic segmentation of retinal vessels fromfundus images is critical for the early diagnosis and prevention ofmany eye diseases,such as diabetic retinopathy(DR).Existing retinal vessel segmentation approaches based on convolutional neural networks(CNNs)have achieved remarkable ***,we extend a retinal vessel segmentation model with low complexity and high performance based on U-Net,which is one of the most popular *** view of the excellent work of depth-wise separable convolution,we introduce it to replace the standard convolutional *** complexity of the proposed model is reduced by decreasing the number of parameters and calculations required for *** ensure performance while lowering redundant parameters,we integrate the pre-trained MobileNet V2 into the ***,a feature fusion residual module(FFRM)is designed to facilitate complementary strengths by enhancing the effective fusion between adjacent levels,which alleviates extraneous clutter introduced by direct ***,we provide detailed comparisons between the proposed SepFE and U-Net in three retinal image mainstream datasets(DRIVE,STARE,and CHASEDB1).The results show that the number of SepFE parameters is only 3%of U-Net,the Flops are only 8%of U-Net,and better segmentation performance is *** superiority of SepFE is further demonstrated through comparisons with other advanced methods.

关键词： Retinal vessel segmentation U-Net depth-wise separable convolution feature fusion

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DBST:a lightweight block cipher based on dynamic S-box

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Frontiers of computer Science 2023年第3期17卷 177-185页

作者： Liuyan YAN Lang LI Ying GUO College of Computer Science and Technology Hengyang Normal UniversityHengyang 421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang 421002China

IoT devices have been widely used with the advent of *** devices contain a large amount of private data during *** is primely important for ensuring their ***,we proposed a lightweight block cipher based on dynamic S-box named *** is introduced for devices with limited hardware resources and high throughput *** is a 128-bit block cipher supporting 64-bit key,which is based on a new generalized Feistel variant *** retains the consistency and significantly boosts the diffusion of the traditional Feistel *** SubColumns of round function is implemented by combining bit-slice technology with *** S-box is dynamically associated with the *** has been demonstrated that DBST has a good avalanche effect,low hardware area,and high *** S-box has been proven to have fewer differential features than RECTANGLE *** security analysis of DBST reveals that it can against impossible differential attack,differential attack,linear attack,and other types of attacks.

关键词： internet of things 5G dynamic S-box bit-slice technology lightweight block cipher

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Constructing Conditional Random Fields for Automated Land Cover Mapping from Hyper Spectral Images

Constructing Conditional Random Fields for Automated Land Co...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Sharma, Sunil Prabha, S. Singh, Vaishali Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

This paper proposes an automatic land cover mapping technique using Conditional Random Fields (CRFs) from hyper-spectral snapshots. It introduces a hierarchical feature vector method to encode the labels' spatial relationships. The paper opinion associated paintings on land cover mappings and introduces the idea of CRFs inside the context of the land cowl category. A description of the facts, pre-processing techniques, and the parameters used for schooling and checking out the model is supplied. The paper develops and evaluates the CRF model, evaluating the results of other land cowl class strategies. Finally, the authors discuss the capacity packages of this technique in the context of other land cowl mapping obligations. This painting is innovative in its method to land cowl mapping and offers a successful implementation of the CRF model for this hassle. It explores using Conditional Random Fields (CRFs) to facilitate automatic land cover mapping from hyperspectral photos (HSI). The CRF method was implemented to estimate the land cover labels in an unsupervised gaining knowledge of style. To this stop, the pixel-level land cowl information for a given area changed into modeled as a graphical version, permitting place-degree marginal probability computations for a dense prediction. Experimental outcomes validated that the proposed technique ought to obtain a sizeable development in the accuracy of land cover mapping for the tested records set compared to its baseline. The proposed CRF-primarily based land cowl mapping method exhibited a higher convergence price and better nearby minima convergence. Moreover, the proposed approach was located to be sturdy in phrases of sensitivity to preliminary land cowl labels. © 2024 IEEE.

关键词： category Conditional evaluating hyper spectral schooling

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Utilizing Deep Learning in Pre-Clinical and Clinical Diagnosis of Peripheral Neuropathies

Utilizing Deep Learning in Pre-Clinical and Clinical Diagnos...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Singh, Akhilendra Pratap Kharbas, Vikas Kumar Mahajan, Prerna Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Peripheral neuropathies are a group of problems that affect the peripheral frightened machine, often leading to a spread of symptoms and impairments. Diagnosis of these situations is frequently imprecise and time-consuming, leading to delays in remedy. Deep studying techniques provide the capacity to automate and enhance the analysis of peripheral neuropathies. Through deep gaining knowledge, clinicians can obtain more excellent correct diagnoses based on enter from MRI pics, photographs of nerve biopsies, or different imaging facts. Moreover, deep studying may be used to create characteristic vectors from different medical features and EEGs that may facilitate the popularity of signs and symptoms and diagnoses extra speedy and more appropriate than traditional methods. This paper explores the capacity of deep learning to accurately diagnose and stratify peripheral neuropathies in pre-scientific and medical settings. It gives a top-level view of contemporary studies on deep getting-to-know strategies for spotting signs and symptoms and diagnosing peripheral neuropathies. Similarly, the paper outlines capacity applications and blessings of deep studying for diagnosing peripheral neuropathies and discusses areas of destiny studies. The software of deep mastering in the pre-medical and clinical prognosis of peripheral neuropathies has been increasingly studied in latest years. Deep mastering fashions, consisting of convolutional neural networks, have shown promising effects in analyzing electromyography (EMG) alerts for the early detection, quantification, and class of peripheral neuropathies, complementing and, in a few cases surpassing conventional diagnostic methods. Moreover, those methods have been advanced as they should be detected and differentiated between myopathic and neuropathic abnormalities and muscle activity. Those algorithms can also distinguish between the diverse kinds of peripheral neuropathies and are potentially useful for early detection and reme

关键词： illnesses peripheral photographs prognosis styarchitectures ling

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A Robust Ensemble Learning Approach for Medical Image Segmentation with Transfer Learning

A Robust Ensemble Learning Approach for Medical Image Segmen...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Shree, Ritu Kavitha, R. Singh, Shweta Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

This paper proposes a robust ensemble mastering approach for clinical picture segmentation. The proposed technique combines a convolution neural community (CNN) with a switch studying-based totally ensemble model. The CNN is pre-educated with a dataset containing clinical photo modalities, particularly Magnetic Resonance Imaging (MRI) and computed tomography (CT)., a weight-averaged ensemble model is acquired between the two scientific photograph modalities. This version is an initial way to the clinical image segmentation problem. Eventually, the ensemble model is great-tuned with additional imaging statistics from an unmarried modality to improve the segmentation accuracy. The proposed technique is evaluated on a range of datasets, and the effects display that it achieves competitive performance compared to the country of the artwork. Moreover, the experiments reveal that combining the transfer gaining knowledge of-based totally ensemble mastering approach with extra imaging information enhances the accuracy of medical picture segmentation. © 2024 IEEE.

关键词： additional convolution learning state subsequently

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Improving Efficiency and Accuracy in Clinical Diagnostic Decision Making with Deep Learning

Improving Efficiency and Accuracy in Clinical Diagnostic Dec...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kharbas, Vikas Kumar Gobi, N. Kumar, Dinesh Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Deep learning is a modern form of synthetic intelligence (AI) based on large quantities of statistics and algorithms that allow computer systems to approximate features and solutions, ensuing in more excellent, efficient, and accurate selections. In the scientific field, deep gaining knowledge is increasingly used to help clinicians with diagnostic decision-making, decreasing the value and attempt associated with diagnostic approaches. This paper discusses the potential of deep learning to enhance the performance and accuracy of medical diagnostic selection-making. The paper provides an overview of the cutting-edge nation of the field, with a particular awareness of picture processing and its application to improve accuracy and decrease the price associated with scientific diagnoses. It also provides a few capacity challenges and pitfalls and outlines the issues essential to ensure a hit implementation and successful results. Eventually, it offers a few recommendations for similar studies to increase deep getting to know algorithms further. Deep gaining knowledge has been broadly explored in recent years as a powerful tool for enhancing the accuracy and efficiency of clinical diagnostic decision-making. Deep studying models can investigate patterns in medical datasets, deliberating the traits of affected person information and ailment signs to generate extra specific and reliable diagnoses. By incorporating extra facts such as transcripts from affected person-clinician interactions and medical imaging, those fashions may be trained to assist clinicians in identifying illnesses early and provide well-timed and correct analysis. Moreover, deep learning allows for incorporating case-primarily based reasoning and presents the capacity to combine scientific knowledge in real-time. By having extra sturdy and accurate models, clinicians can reduce the time required to diagnose a condition and make treatment choices. Deep gaining knowledge also lets in for the integration o

关键词： disorder enhance healthcare value

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Investigating the Integrity of Irrigation Systems Through Hyper Spectral Image Analysis

Investigating the Integrity of Irrigation Systems Through Hy...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Ahmed, Zahid Preethi, D. Yadav, Dhananjay Kumar Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

This research aims to use hyperspectral picture analysis to research the integrity of irrigation systems, hoping to reduce power and water utilization. Traditional methods for measuring irrigation integrity are timeingesting, luxurious, and unreliable. With the software of the advanced imaging era, this project seeks to create a detailed model of an irrigation system so one can discover and diagnose troubles quickly and appropriately. A huge-statistics technique utilizing spectral statistics mixed with professional gadget wisdom gives the potential irrigation structures control. analysis might be conducted using a combination of airborne imaging and computational strategies along with satellite tv for pc and in-area evaluation. Those techniques will assist in offering a value-effective monitoring machine for identifying and diagnosing irrigation troubles and detecting modifications that can suggest water misuse or inefficient layout. Using this era, we intend to enhance irrigation structures' overall performance and integrity. © 2024 IEEE.

关键词： combination diagnosing hyperspectral measuring research aims

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Prediction of Reliability of Data in Underwater Communication Network Using Fuzzy Logic

Prediction of Reliability of Data in Underwater Communicatio...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Yadav, Surendra Gobi, N. Yadav, Rakesh Kumar Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

This paper uses fuzzy logic to propose a reliability prediction set of rules for the underwater communication community. The set of rules uses fixed fuzzy rules to expect communication reliability among nodes in an underwater environment. Those fuzzy rules are derived from a reliability model based on the characteristics of an ordinary underwater conversation system, such as physical and environmental elements. The algorithm is then examined using accurate statistics from an underwater communication test. The effects imply that this approach is promising in predicting the reliability of the community. It has also been discovered that reliability prediction accuracy can be advanced if the bushy guidelines include extra factors, the sign channel's records rate, and the machine's performance parameters. This study offers a practical approach to improving the performance and reliability of underwater communique networks. © 2024 IEEE.

关键词： algorithm communication environmental fuzzy logic

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Correlating Time Series Analysis with Radiotherapy for the Early Detection of Hodkin Lymphoma

Correlating Time Series Analysis with Radiotherapy for the E...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Varun Somanna, M.N. Yadav, Rakesh Kumar Inzimam Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

This study aimed to quantify the temporal capabilities of radiotherapy facts from sufferers with Hodgkin lymphoma (HL) and correlate those functions with time collection evaluation (TSA) measures to perceive potential temporal relationships with early analysis of HL. Excessive-decision 3-dimensional computed tomography (CT) scans of HL sufferers have been analyzed using batch-clever TSA methods to derive the temporal collection that characterizes the radiotherapy facts. In the end, a set of temporal features from the measured temporal series have been extracted and correlated with HL's medical and laboratory analysis. The outcomes showed that the derived temporal capabilities had undoubtedly correlated with the analysis, indicating a capability for using radiotherapy information to diagnose HL early. The mean and general deviation of the temporal collection in the radiotherapy curves from HL sufferers has been notably one-of-a-kind from wholesome patients. Furthermore, the temporal series of radiotherapy statistics from HL sufferers showed notably better peaks in the temporal capabilities than healthful patients, indicating an elevated stage of radiotherapy for HL sufferers. The effects of this observation recommend that TSA may be a viable tool for early analysis of HL, presenting a more comprehensive view of the temporal traits of radiotherapy data for HL sufferers. © 2024 IEEE.

关键词： capabilities evaluation radiotherapy relationships tomography

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Presentation Analysis of Reasoning Radio-Based Routing Protocols in Wireless Mesh Networks

Presentation Analysis of Reasoning Radio-Based Routing Proto...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kumar, Ritesh Kharbas, Vikas Kumar Mehta, Deepak Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Reasoning radio-based total routing protocols present a promising technique for permitting powerful conversation in wi-fi mesh networks. By using the spectrum sensing capabilities of Reasoning radio, routers can intelligently pick out and pick out spectrum assets to ahead records, thereby allowing for improved network throughput and reliability. In this paper, we carry out a comprehensive Presentation evaluation of routing protocols primarily based on Reasoning radio in wi-fi mesh networks. Mainly, we Presentation metrics including packet shipping fee, latency, jitter, and energy Presentation. We examine various routing protocols from an empirical and theoretical angle and speak about the strengths and weaknesses of every technique. Subsequently, our consequences provide insights into the capability advantages and tradeoffs of Reasoning radio-based routing protocols in wireless mesh networks, offering practical steerage to designers and engineers within the subject. © 2024 IEEE.

关键词： Consequences Engineers Networks Practical Reasoning Routing Spectrum

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