Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470719
Ankita Agarwal, Rekha Devrani, A. Kannagi
This paper proposes a Recursive Neural framework for the clever mindset evaluation of network interference customers. Our technique builds on previous work achieved in sentiment analysis using extracting a person's man or woman mindset from complicated and incomplete statistics streams. The framework, to begin with, gets the sentiment layers based on consumer interactions from the datasets, after which it integrates this fact with various Recursive Neural networks to seize the sentiment of a single user. The community extracts capabilities associated with the user and learns to distinguish between the behaviors of two users inside the community. Once the community is educated on the datasets, it may classify the sentiment of users based on various contextual cues. We evaluated our framework through crowd-sourced sentiment annotation datasets from a web forum, and it confirmed superior overall performance than different present approaches. We proposed a Recursive Neural framework that utilizes contextual schemas and sentiment to analyze user attitudes and behaviors for community interference scenarios. It can open up promising new opportunities for observing consumer mindset and behavior in online networks. This paper offers a recursive neural framework for competent mindset evaluation of network interference customers. Recursive Neural Networks, broadly carried out in natural language processing responsibilities with sentiment analysis, combine word embeddings with a recursive architecture to gain a perception of the syntactic shape of sentences. On this, look at the Recursive Neural Network (RNN) architecture tailored to research the sentiment mindset of community interference users. The information amassed from Twitter, Weibo, and different open-supply platforms had been pre-processed using the frequency inverted report frequency technique before constructing an RNN for its modeling. Checks at the built community proved that the proposed model furnished pleasant consequences, reaching a median accuracy of 88.36%. In an evaluation with a conventional non-recursive network, the RNN version resulted in a 7.3% relative growth in classification accuracy, demonstrating its efficacy in sentiment evaluation. The outcomes produced by using this examination are promising and may be tremendous for protection practitioners in helping to higher recognize consumer sentiment for network interference.
{"title":"The Smart Attitude Analysis of Network Interference User using Recursive Neural Framework","authors":"Ankita Agarwal, Rekha Devrani, A. Kannagi","doi":"10.1109/ICOCWC60930.2024.10470719","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470719","url":null,"abstract":"This paper proposes a Recursive Neural framework for the clever mindset evaluation of network interference customers. Our technique builds on previous work achieved in sentiment analysis using extracting a person's man or woman mindset from complicated and incomplete statistics streams. The framework, to begin with, gets the sentiment layers based on consumer interactions from the datasets, after which it integrates this fact with various Recursive Neural networks to seize the sentiment of a single user. The community extracts capabilities associated with the user and learns to distinguish between the behaviors of two users inside the community. Once the community is educated on the datasets, it may classify the sentiment of users based on various contextual cues. We evaluated our framework through crowd-sourced sentiment annotation datasets from a web forum, and it confirmed superior overall performance than different present approaches. We proposed a Recursive Neural framework that utilizes contextual schemas and sentiment to analyze user attitudes and behaviors for community interference scenarios. It can open up promising new opportunities for observing consumer mindset and behavior in online networks. This paper offers a recursive neural framework for competent mindset evaluation of network interference customers. Recursive Neural Networks, broadly carried out in natural language processing responsibilities with sentiment analysis, combine word embeddings with a recursive architecture to gain a perception of the syntactic shape of sentences. On this, look at the Recursive Neural Network (RNN) architecture tailored to research the sentiment mindset of community interference users. The information amassed from Twitter, Weibo, and different open-supply platforms had been pre-processed using the frequency inverted report frequency technique before constructing an RNN for its modeling. Checks at the built community proved that the proposed model furnished pleasant consequences, reaching a median accuracy of 88.36%. In an evaluation with a conventional non-recursive network, the RNN version resulted in a 7.3% relative growth in classification accuracy, demonstrating its efficacy in sentiment evaluation. The outcomes produced by using this examination are promising and may be tremendous for protection practitioners in helping to higher recognize consumer sentiment for network interference.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"47 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470487
K. Hussain, S. Kaliappan, Arul Joseph Amalraj. M, Parvesh Saini, S. K. Nandha Kumar, J. Dhanraj
The ability of PMUs to provide precise, synchronized readings of voltage, current and frequency has made them valuable for the observation of microgrids. In some microgrids, PMU s are utilized without a current transformer and only measure voltage phasor values. This research outlines a procedure to use μPMU (or micro-PMU) voltage readings to ascertain electric loads or photovoltaic (PV) production through gauging power flow (PF). The results of a study conducted at the Federal University of Paraná's Polytechnic School (UFPR) in Brazil demonstrated that utilizing the power flow calculated by a “virtual CT” approach, as measured by a standard power meter and with a higher time resolution from a microPMU, is a reliable and efficient method for recognizing events, monitoring PV generation, and non-intrusively monitoring load (NILM).
{"title":"PV Generation Monitoring Using Calculated Power Flow from μPMUS","authors":"K. Hussain, S. Kaliappan, Arul Joseph Amalraj. M, Parvesh Saini, S. K. Nandha Kumar, J. Dhanraj","doi":"10.1109/ICOCWC60930.2024.10470487","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470487","url":null,"abstract":"The ability of PMUs to provide precise, synchronized readings of voltage, current and frequency has made them valuable for the observation of microgrids. In some microgrids, PMU s are utilized without a current transformer and only measure voltage phasor values. This research outlines a procedure to use μPMU (or micro-PMU) voltage readings to ascertain electric loads or photovoltaic (PV) production through gauging power flow (PF). The results of a study conducted at the Federal University of Paraná's Polytechnic School (UFPR) in Brazil demonstrated that utilizing the power flow calculated by a “virtual CT” approach, as measured by a standard power meter and with a higher time resolution from a microPMU, is a reliable and efficient method for recognizing events, monitoring PV generation, and non-intrusively monitoring load (NILM).","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"16 7","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, deep gaining knowledge has emerged as an effective device for medical photo segmentation. This paper proposes a unique model that mixes convolutional neural networks and recurrent neural networks with an attention mechanism to improve the accuracy of segments for medical pictures, including magnetic resonance images. The eye mechanism is used to weigh each pixel, focusing the model's interest on regions of a photo that might be more applicable to classifying the item being segmented. The version is examined on medical imaging datasets - the clinical Segmentation Decathlon and the medical Segmentation Benchmark. The effects demonstrate that using the attention-based recurrent neural networks model considerably outperforms convolutional neural networks and recurrent neural networks on my own, with a median increase in dice score of up to ten%. Those effects suggest that the proposed technique can improve the accuracy of medical photo segmentation and help further facilitate the improvement of deep gaining knowledge of-based medical photograph analysis applications
{"title":"Enhancing Medical Image Segmentation with Attention-Based Recurrent Neural Networks","authors":"Rakesh Kumar Dwivedi, Ananya Saha, Meenakshi Sharma","doi":"10.1109/ICOCWC60930.2024.10470617","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470617","url":null,"abstract":"In recent years, deep gaining knowledge has emerged as an effective device for medical photo segmentation. This paper proposes a unique model that mixes convolutional neural networks and recurrent neural networks with an attention mechanism to improve the accuracy of segments for medical pictures, including magnetic resonance images. The eye mechanism is used to weigh each pixel, focusing the model's interest on regions of a photo that might be more applicable to classifying the item being segmented. The version is examined on medical imaging datasets - the clinical Segmentation Decathlon and the medical Segmentation Benchmark. The effects demonstrate that using the attention-based recurrent neural networks model considerably outperforms convolutional neural networks and recurrent neural networks on my own, with a median increase in dice score of up to ten%. Those effects suggest that the proposed technique can improve the accuracy of medical photo segmentation and help further facilitate the improvement of deep gaining knowledge of-based medical photograph analysis applications","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"17 2","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470681
Liang Zhang, Yuan Fang, Yuexin Shen, Xiyin Wang
With the rapid development of information technology, the problem of network security has become increasingly prominent. Camouflage intrusion, as a common means of network attack, has strong concealment and destructiveness, which brings great security threats to enterprises and organizations. In order to effectively deal with camouflage intrusion, more and more researchers apply machine learning and data mining technology to the field of intrusion detection. Among them, Random Forest (RF) algorithm, as an ensemble learning algorithm, has the advantages of high accuracy and low complexity, and has been widely concerned. However, the traditional RF algorithm still has some problems when dealing with camouflage intrusion detection, such as single feature selection, strong correlation between base classifiers and so on
{"title":"Research on Smart Contract Vulnerability Detection Method of Power Equipment Based on Deep Learning Algorithm","authors":"Liang Zhang, Yuan Fang, Yuexin Shen, Xiyin Wang","doi":"10.1109/ICOCWC60930.2024.10470681","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470681","url":null,"abstract":"With the rapid development of information technology, the problem of network security has become increasingly prominent. Camouflage intrusion, as a common means of network attack, has strong concealment and destructiveness, which brings great security threats to enterprises and organizations. In order to effectively deal with camouflage intrusion, more and more researchers apply machine learning and data mining technology to the field of intrusion detection. Among them, Random Forest (RF) algorithm, as an ensemble learning algorithm, has the advantages of high accuracy and low complexity, and has been widely concerned. However, the traditional RF algorithm still has some problems when dealing with camouflage intrusion detection, such as single feature selection, strong correlation between base classifiers and so on","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"52 3","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470477
Deepak Kumar, Febin Prakash, Gaurav Shukla
this paper investigates the antenna-to-antenna spatial correlation of a multi-consumer millimeter-wave (mm Wave) system, considering the angular spread of every randomly located antenna inside the mobile. A signal-power-dependent correlation model based on the azimuth perspective domain is proposed. Furthermore, an iterative clustering set of rules for unmarried-cellular beam forming is advanced and analyzed to quantify the performance of multi-user mm Wave structures. Simulation outcomes show that after the angular spread exceeds 20°, the antenna-to-antenna correlation must be considered within the analysis. The beam forming overall performance with antenna correlation substantially progresses with a reduction in the number of antennas, and the benefit increases because the angular spread increases.
{"title":"Analysis of Antenna-to-Antenna Spatial Correlation in Multi-User Millimeter-Wave Systems","authors":"Deepak Kumar, Febin Prakash, Gaurav Shukla","doi":"10.1109/ICOCWC60930.2024.10470477","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470477","url":null,"abstract":"this paper investigates the antenna-to-antenna spatial correlation of a multi-consumer millimeter-wave (mm Wave) system, considering the angular spread of every randomly located antenna inside the mobile. A signal-power-dependent correlation model based on the azimuth perspective domain is proposed. Furthermore, an iterative clustering set of rules for unmarried-cellular beam forming is advanced and analyzed to quantify the performance of multi-user mm Wave structures. Simulation outcomes show that after the angular spread exceeds 20°, the antenna-to-antenna correlation must be considered within the analysis. The beam forming overall performance with antenna correlation substantially progresses with a reduction in the number of antennas, and the benefit increases because the angular spread increases.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"51 12","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470537
Rakhi Gupta, Gaurav Kumar Rajput, M. N. Nachappa
This paper provides a unique low electricity facts aggregation method utilizing the Extended Kalman Filtering (EKF) algorithm. Using time-collection evaluation on low energy facts streams, EKF can provide extra correct mixture values. This paper examines the system of characteristic extraction from low-strength records series streams and the underlying prolonged Kalman Filtering (EKF) model formula. The EKF version formula produces a correlated time-series representation of the low-strength records streams and estimates its parameters. Further, a case study of the real-world utility of this technique is supplied. The outcomes show that the proposed methodology can yield an advanced low-energy records aggregation method compared to standard strategies. The proposed EKF -based method holds the giant capacity for efficient strength, calling for forecasting in realistic settings. This paper examines prolonged Kalman Filtering (EKF) for low electricity information aggregation of time series evaluation. EKF is a recursive estimation technique primarily based on first principles and implements an optimally weighted linear aggregate of recursive estimates for nations and parameters. This look presents the analytical method of EKF implemented for the cause of time collection modeling and state estimation. A simulated case look at on-strength demand for a given length illustrates the gain of EKF for the low-strength data aggregation venture., a correct estimation is obtained from the time series information with a restrained range of samples and minimum computational attempt.
{"title":"Time Series Analysis for Low Energy Data Aggregation Using Extended Kalman Filtering","authors":"Rakhi Gupta, Gaurav Kumar Rajput, M. N. Nachappa","doi":"10.1109/ICOCWC60930.2024.10470537","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470537","url":null,"abstract":"This paper provides a unique low electricity facts aggregation method utilizing the Extended Kalman Filtering (EKF) algorithm. Using time-collection evaluation on low energy facts streams, EKF can provide extra correct mixture values. This paper examines the system of characteristic extraction from low-strength records series streams and the underlying prolonged Kalman Filtering (EKF) model formula. The EKF version formula produces a correlated time-series representation of the low-strength records streams and estimates its parameters. Further, a case study of the real-world utility of this technique is supplied. The outcomes show that the proposed methodology can yield an advanced low-energy records aggregation method compared to standard strategies. The proposed EKF -based method holds the giant capacity for efficient strength, calling for forecasting in realistic settings. This paper examines prolonged Kalman Filtering (EKF) for low electricity information aggregation of time series evaluation. EKF is a recursive estimation technique primarily based on first principles and implements an optimally weighted linear aggregate of recursive estimates for nations and parameters. This look presents the analytical method of EKF implemented for the cause of time collection modeling and state estimation. A simulated case look at on-strength demand for a given length illustrates the gain of EKF for the low-strength data aggregation venture., a correct estimation is obtained from the time series information with a restrained range of samples and minimum computational attempt.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"14 6","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470613
Associate Professor A Kannagi, Neeraj Das, Meenakshi Dheer
in excessive-performance computing environments, wherein huge amounts of data want to be processed quickly, the overall performance of statistics processing systems is crucial. Analyzing the performance of these structures is essential to become aware of bottlenecks and optimize their performance. This studies aims to increase statistical strategies for overall performance analysis of facts processing systems in high-performance computing environments. The evaluation technique is to gather overall performance facts from the goal device. This fact frequently consists of numerous measurements, making it challenging to draw meaningful insights. To cope with this difficulty, statistical strategies, transformation, outlier detection, and dimensionality discount can be implemented to clear out noise and pick out styles within the records. Regression evaluation may version the relationship among gadget parameters and overall performance metrics. It helps identify which device parameters have the most considerable effect on performance and may guide similarly optimization efforts. Moreover, cluster analysis can be used to institution systems with comparable performance traits, allowing comparison and identity of pinnacle-appearing systems.
{"title":"Statistical Methods for Performance Analysis of Data Processing Systems in High-Performance Computing Environments","authors":"Associate Professor A Kannagi, Neeraj Das, Meenakshi Dheer","doi":"10.1109/ICOCWC60930.2024.10470613","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470613","url":null,"abstract":"in excessive-performance computing environments, wherein huge amounts of data want to be processed quickly, the overall performance of statistics processing systems is crucial. Analyzing the performance of these structures is essential to become aware of bottlenecks and optimize their performance. This studies aims to increase statistical strategies for overall performance analysis of facts processing systems in high-performance computing environments. The evaluation technique is to gather overall performance facts from the goal device. This fact frequently consists of numerous measurements, making it challenging to draw meaningful insights. To cope with this difficulty, statistical strategies, transformation, outlier detection, and dimensionality discount can be implemented to clear out noise and pick out styles within the records. Regression evaluation may version the relationship among gadget parameters and overall performance metrics. It helps identify which device parameters have the most considerable effect on performance and may guide similarly optimization efforts. Moreover, cluster analysis can be used to institution systems with comparable performance traits, allowing comparison and identity of pinnacle-appearing systems.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"46 34","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470710
Surendra Yadav, Rakesh Kumar Dwivedi, Gobi N
this study appears to use self-supervised transfer mastering for sturdy scientific photo classes. Switch getting to know is a powerful approach for enhancing the accuracy of deep mastering fashions in scientific imaging. This paper investigates using self-supervised getting-to-know techniques for scientific picture classes within characteristic-based procedures. By leveraging self-supervised schooling strategies, consisting of contrastive mastering, distributed representations, clustering, pseudo-venture gaining knowledge of, and self-supervised multi-undertaking gaining knowledge of, the proposed technique can learn representations that are extra sturdy to the area shift of various clinical imaging datasets. Experiments performed on an extensive x-ray and ultrasound snapshots dataset reveal that the proposed approach affords extra improvement in type accuracy compared to traditional feature-primarily based techniques.
这项研究似乎将自监督转移掌握用于坚固的科学照片类。在科学成像中，转换获取知识是提高深度掌握方法准确性的有力方法。本文研究了在基于特征的程序中对科学图片类别使用自监督获取知识技术。通过利用自监督学习策略（包括对比掌握、分布式表示、聚类、伪探险获取知识和自监督多目标获取知识），所提出的技术可以学习到对各种临床成像数据集的区域变化更坚固的表示。在一个广泛的 X 射线和超声波快照数据集上进行的实验表明，与传统的基于特征的技术相比，所提出的方法能进一步提高类型准确性。
{"title":"Leveraging Self-Supervised Transfer Learning for Robust Medical Image Classification","authors":"Surendra Yadav, Rakesh Kumar Dwivedi, Gobi N","doi":"10.1109/ICOCWC60930.2024.10470710","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470710","url":null,"abstract":"this study appears to use self-supervised transfer mastering for sturdy scientific photo classes. Switch getting to know is a powerful approach for enhancing the accuracy of deep mastering fashions in scientific imaging. This paper investigates using self-supervised getting-to-know techniques for scientific picture classes within characteristic-based procedures. By leveraging self-supervised schooling strategies, consisting of contrastive mastering, distributed representations, clustering, pseudo-venture gaining knowledge of, and self-supervised multi-undertaking gaining knowledge of, the proposed technique can learn representations that are extra sturdy to the area shift of various clinical imaging datasets. Experiments performed on an extensive x-ray and ultrasound snapshots dataset reveal that the proposed approach affords extra improvement in type accuracy compared to traditional feature-primarily based techniques.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"20 13","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470883
Febin Prakash, Sachin Gupta, Garima Goswami
The objective of the modern-day work is to propose a characteristic extraction of the usage of canonical correlation analysis (CCA) mixed with different advanced strategies for the advanced recognition of items in hyperspectral data. CCA has come to be a famous tool for characteristic extraction as it permits nonlinear modeling of the information that's, in particular, helpful while we are exposing a hyperspectral photograph. CCA seeks to maximize the correlation between variable sets which is especially useful when the image consists of spurious noise, which might otherwise degrade the overall recognition performance. Additionally, CCA allows for retaining the spatial patterns inside the information. Other preprocessing and statistical techniques such as wavelet transforms, statistical covariance illustration, Kreskas-Wallis, and second Estimation strategies have been integrated into this work to improve the effects further. Experimental outcomes demonstrate that the proposed technique based totally on CCA, while combined with different techniques, improves the recognition rate of items and offers a better fitting of the information.
这项现代研究的目标是提出一种特征提取方法，利用典型相关分析（CCA）与不同的高级策略相结合，对高光谱数据中的项目进行高级识别。CCA 已成为特征提取的著名工具，因为它允许对信息进行非线性建模，这在我们曝光高光谱照片时尤其有用。CCA 致力于最大限度地提高变量集之间的相关性，这在图像包含杂散噪声时尤其有用，否则可能会降低整体识别性能。此外，CCA 还能保留信息中的空间模式。其他预处理和统计技术，如小波变换、统计协方差图解、Kreskas-Wallis 和二次估计策略，也被整合到这项工作中，以进一步提高效果。实验结果表明，所提出的完全基于 CCA 的技术与不同的技术相结合，提高了项目的识别率，并提供了更好的信息拟合。
{"title":"Feature Extraction Using Canonical Correlation Analysis for Improved Recognition of Objects in Hyper Spectral Data","authors":"Febin Prakash, Sachin Gupta, Garima Goswami","doi":"10.1109/ICOCWC60930.2024.10470883","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470883","url":null,"abstract":"The objective of the modern-day work is to propose a characteristic extraction of the usage of canonical correlation analysis (CCA) mixed with different advanced strategies for the advanced recognition of items in hyperspectral data. CCA has come to be a famous tool for characteristic extraction as it permits nonlinear modeling of the information that's, in particular, helpful while we are exposing a hyperspectral photograph. CCA seeks to maximize the correlation between variable sets which is especially useful when the image consists of spurious noise, which might otherwise degrade the overall recognition performance. Additionally, CCA allows for retaining the spatial patterns inside the information. Other preprocessing and statistical techniques such as wavelet transforms, statistical covariance illustration, Kreskas-Wallis, and second Estimation strategies have been integrated into this work to improve the effects further. Experimental outcomes demonstrate that the proposed technique based totally on CCA, while combined with different techniques, improves the recognition rate of items and offers a better fitting of the information.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"56 52","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470551
Feon Jaison, Viiay Kumar Pandey, Ashish Bishnoi
help vector machines (SVMs) have become increasingly famous in scientific photo analysis because of their capacity to model complex relationships among inputs and outputs. SVMs are exceptionally high-quality because of their advanced overall performance in excessive-dimensional information units and their ability to address non-linear information. In clinical image evaluation, SVMs are used for various packages, including detecting tumors in Magnetic Resonance Imaging (MRI) and classifying lesions in Computed Tomography (CT) scans. No matter its benefits, growing dependable SVMs for scientific photograph evaluation remains a venture because of the uncertainty associated with scientific pics that regularly require information preprocessing and feature extraction before education. This paper surveys current work on developing robust SVMs for medical photo analysis, from preprocessing to publish-processing, and affords a comprehensive evaluation of the cutting-edge state of the art. mainly; we discuss diverse preprocessing and function extraction strategies that can be employed to improve performance, in addition to publish-processing strategies that can be used to enhance the general accuracy of the version. We also talk about ability directions for future research in this field.
{"title":"Developing Support Vector Machines for Accurate Medical Image Analysis","authors":"Feon Jaison, Viiay Kumar Pandey, Ashish Bishnoi","doi":"10.1109/ICOCWC60930.2024.10470551","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470551","url":null,"abstract":"help vector machines (SVMs) have become increasingly famous in scientific photo analysis because of their capacity to model complex relationships among inputs and outputs. SVMs are exceptionally high-quality because of their advanced overall performance in excessive-dimensional information units and their ability to address non-linear information. In clinical image evaluation, SVMs are used for various packages, including detecting tumors in Magnetic Resonance Imaging (MRI) and classifying lesions in Computed Tomography (CT) scans. No matter its benefits, growing dependable SVMs for scientific photograph evaluation remains a venture because of the uncertainty associated with scientific pics that regularly require information preprocessing and feature extraction before education. This paper surveys current work on developing robust SVMs for medical photo analysis, from preprocessing to publish-processing, and affords a comprehensive evaluation of the cutting-edge state of the art. mainly; we discuss diverse preprocessing and function extraction strategies that can be employed to improve performance, in addition to publish-processing strategies that can be used to enhance the general accuracy of the version. We also talk about ability directions for future research in this field.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"25 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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