Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470771
Meenakshi Dheer, Adlin Jebakumari S, Shweta Singh
This paper analyzes hyperspectral satellite tv for pc snap shots for land cover mapping with multivariate statistical analysis (MSA). It describes the method of mapping land cowl and how the components containing the extraordinary land cowl lessons are recognized via MSA. The analysis of the facts considers the visible, near-infrared, and shortwave infrared spectra of the Landsat image facts. The diverse MSA techniques which might be used for identifying land cover kinds, such as significant thing evaluation, unbiased aspect analysis, linear discriminant analysis, multi-dimensional scaling, cluster evaluation, and correlation analysis, are explained in detail. The advantages of using MSA over conventional techniques also are mentioned. Eventually, the results are compared with the overall performance of MSA on particular land cowl sorts. It's miles concluded that MSA is a dependable technique to land cover mapping with hyperspectral satellite tv for pc pics. Multivariate statistical evaluation on hyperspectral satellite pictures offers an expansion of possibilities to categorize land cowl and resources in mapping numerous capabilities on the Earth. Such techniques consist of linear discriminant evaluation, fundamental aspect evaluation, independent component evaluation, Multivariate selection timber, Kernel Discriminant evaluation, and extra. Those fashions extract extensive statistical features from the pics, permitting more accuracy in detecting functions or classes of land cowl. Many of these techniques can also be integrated with different techniques and tree-primarily based classifiers to refine the land cover type further. Furthermore, these methods may be used along with remotely sensed data, including topographic maps, to provide extra insight into land cover's spatial and temporal characteristics. In precis, hyperspectral satellite tv for pc imagery offers a powerful device for knowledge of the Earth's surface, and multivariate statistical methods substantially enhance the accuracy of land cover mapping efforts.
本文分析了利用多变量统计分析（MSA）绘制土地覆被图的高光谱卫星电视（Satellite TV for PC）快照。它介绍了绘制土地覆盖图的方法，以及如何通过 MSA 识别包含特殊土地覆盖信息的组件。事实分析考虑了大地遥感卫星图像事实的可见光、近红外和短波红外光谱。详细介绍了可用于识别土地覆被类型的各种 MSA 技术，如重要事物评价、无偏见方面分析、线性判别分析、多维缩放、聚类分析和相关分析。还提到了使用 MSA 相对于传统技术的优势。最后，比较了 MSA 在特定地表类型上的总体性能。最后得出结论，MSA 是利用高光谱卫星电视进行土地覆被绘图的可靠技术。高光谱卫星图片的多变量统计评估为绘制地球上的多种能力地图提供了更多对土地覆盖和资源进行分类的可能性。这些技术包括线性判别评估、基本面评估、独立分量评估、多变量选择木、核判别评估等。这些方法可以从图片中提取大量的统计特征，从而更准确地检测土地覆盖层的功能或类别。其中许多技术还可以与其他技术和基于树的分类器相结合，进一步完善土地覆被类型。此外，这些方法还可与遥感数据（包括地形图）一起使用，以进一步了解土地覆被的时空特征。简而言之，高光谱卫星电视电脑图像为了解地球表面提供了一个强大的工具，而多元统计方法则大大提高了土地覆被绘图工作的准确性。
{"title":"Multivariate Statistical Analysis on Hyper Spectral Satellite Images for Land Cover Mapping","authors":"Meenakshi Dheer, Adlin Jebakumari S, Shweta Singh","doi":"10.1109/ICOCWC60930.2024.10470771","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470771","url":null,"abstract":"This paper analyzes hyperspectral satellite tv for pc snap shots for land cover mapping with multivariate statistical analysis (MSA). It describes the method of mapping land cowl and how the components containing the extraordinary land cowl lessons are recognized via MSA. The analysis of the facts considers the visible, near-infrared, and shortwave infrared spectra of the Landsat image facts. The diverse MSA techniques which might be used for identifying land cover kinds, such as significant thing evaluation, unbiased aspect analysis, linear discriminant analysis, multi-dimensional scaling, cluster evaluation, and correlation analysis, are explained in detail. The advantages of using MSA over conventional techniques also are mentioned. Eventually, the results are compared with the overall performance of MSA on particular land cowl sorts. It's miles concluded that MSA is a dependable technique to land cover mapping with hyperspectral satellite tv for pc pics. Multivariate statistical evaluation on hyperspectral satellite pictures offers an expansion of possibilities to categorize land cowl and resources in mapping numerous capabilities on the Earth. Such techniques consist of linear discriminant evaluation, fundamental aspect evaluation, independent component evaluation, Multivariate selection timber, Kernel Discriminant evaluation, and extra. Those fashions extract extensive statistical features from the pics, permitting more accuracy in detecting functions or classes of land cowl. Many of these techniques can also be integrated with different techniques and tree-primarily based classifiers to refine the land cover type further. Furthermore, these methods may be used along with remotely sensed data, including topographic maps, to provide extra insight into land cover's spatial and temporal characteristics. In precis, hyperspectral satellite tv for pc imagery offers a powerful device for knowledge of the Earth's surface, and multivariate statistical methods substantially enhance the accuracy of land cover mapping efforts.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"101 ","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":"140529958","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/icocwc60930.2024.10470597
{"title":"Speaker","authors":"","doi":"10.1109/icocwc60930.2024.10470597","DOIUrl":"https://doi.org/10.1109/icocwc60930.2024.10470597","url":null,"abstract":"","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"72 3-4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529879","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/ICOCWC60930.2024.10470657
K. R, Chandra Kant Gautam, Pradeep Kumar Vera
Wireless mesh networks (WMNs) are becoming increasingly famous because of their easy deployment with minimum infrastructure and occasional operational prices. As a result, routing protocols are a crucial thing of WMNs, and performance metrics, including throughput, throughput equity, and packet shipping ratio, have been extensively used to compare routing protocols. In this technical abstract, we evaluate the overall performance comparison of routing protocols for cell WMNs in terms of the metrics above. The overall performance of routing protocols for cellular WMNs can vary greatly depending on the environment and mobility sample of the nodes. Several routing protocols have been proposed inside the literature, together with advert hoc on-call for distance vector (AODV), dynamic supply routing (DSR), and distance vector routing (DVR). Network simulations are essential. Some works have investigated the performance of AODV, DSR, and DVR in cell WMNs. The consequences of this research display that DSR and AODV have better throughput and throughput fairness than DVR in cellular WMNs. However, the AODV packet delivery ratio is higher than DSR in situations with nodes exhibiting random mobility. In eventualities with mild node mobility, AODV additionally has a fine packet delivery ratio. Additionally, simulations.
{"title":"Performance Comparison of Routing Protocols for Mobile Wireless Mesh Networks","authors":"K. R, Chandra Kant Gautam, Pradeep Kumar Vera","doi":"10.1109/ICOCWC60930.2024.10470657","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470657","url":null,"abstract":"Wireless mesh networks (WMNs) are becoming increasingly famous because of their easy deployment with minimum infrastructure and occasional operational prices. As a result, routing protocols are a crucial thing of WMNs, and performance metrics, including throughput, throughput equity, and packet shipping ratio, have been extensively used to compare routing protocols. In this technical abstract, we evaluate the overall performance comparison of routing protocols for cell WMNs in terms of the metrics above. The overall performance of routing protocols for cellular WMNs can vary greatly depending on the environment and mobility sample of the nodes. Several routing protocols have been proposed inside the literature, together with advert hoc on-call for distance vector (AODV), dynamic supply routing (DSR), and distance vector routing (DVR). Network simulations are essential. Some works have investigated the performance of AODV, DSR, and DVR in cell WMNs. The consequences of this research display that DSR and AODV have better throughput and throughput fairness than DVR in cellular WMNs. However, the AODV packet delivery ratio is higher than DSR in situations with nodes exhibiting random mobility. In eventualities with mild node mobility, AODV additionally has a fine packet delivery ratio. Additionally, simulations.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"73 36","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":"140529584","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/ICOCWC60930.2024.10470560
Haripriya, Sachin Gupta, Sunil Kumar Gaur
Mobile social networks possess vastly more specific architectures than conventional networks due to the particular hardware on which they run and the precise demands mobile customers place on them. As such, it is vital to undertake the evaluation of those architectures to understand how quality to fulfill user necessities. This abstract will talk about the levels of analyzing modern-day mobile social community architectures, with emphasis on determining which platform will best take care of users' energetic cellular life and heavy facts needs. Analyzing cellular social networks consists of several tiers. First, the application's architecture and center functionality need to be analyzed to determine what requirements and restrictions the utility imposes on the cell device. Moreover, user and privacy concerns should be taken into account. Subsequently, overall hardware performance and memory utilization must be evaluated. It could decide whether the platform provides value-powerful scalability for the software. Finally, the cell network infrastructure must be considered. This step is, in particular, critical, as unexpected network delays can negatively affect user enjoyment.
{"title":"Analyzing the Current Architecture of Mobile Social Networks","authors":"Haripriya, Sachin Gupta, Sunil Kumar Gaur","doi":"10.1109/ICOCWC60930.2024.10470560","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470560","url":null,"abstract":"Mobile social networks possess vastly more specific architectures than conventional networks due to the particular hardware on which they run and the precise demands mobile customers place on them. As such, it is vital to undertake the evaluation of those architectures to understand how quality to fulfill user necessities. This abstract will talk about the levels of analyzing modern-day mobile social community architectures, with emphasis on determining which platform will best take care of users' energetic cellular life and heavy facts needs. Analyzing cellular social networks consists of several tiers. First, the application's architecture and center functionality need to be analyzed to determine what requirements and restrictions the utility imposes on the cell device. Moreover, user and privacy concerns should be taken into account. Subsequently, overall hardware performance and memory utilization must be evaluated. It could decide whether the platform provides value-powerful scalability for the software. Finally, the cell network infrastructure must be considered. This step is, in particular, critical, as unexpected network delays can negatively affect user enjoyment.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"43 3","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":"140529656","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/ICOCWC60930.2024.10470626
Qing Dai, Wende Zhuang, Jun Yang
The research of operation indicators plays an important role in intelligent monitoring of small and micro enterprises, but there is a problem of inaccurate monitoring. The traditional regression algorithm cannot solve the research problem of monitoring operation indicators in intelligent monitoring of small and micro enterprises, and the detection effect is not satisfactory. Therefore, this paper proposes a research on monitoring the operation indicators of small and micro enterprises based on electrical data monitoring, and analyzes the research on the operation indicators of small and micro enterprises. Firstly, the power system theory is used to locate the influencing factors, and the indicators is divided according to the requirements of the research of operation indicators, so as to reduce the interference factors in the research of operation indicators. Then, the power system theory is used to form a research scheme for monitoring the operation index of electrical data, and the research results of the operation index is comprehensively analyzed. The MATLAB simulation results show that under certain evaluation standards, electrical data monitoring is superior to the traditional regression method in terms of research accuracy of operation indicators and research influencing factor time of operation indicators.
{"title":"Research on Monitoring the Operation Indicators of Small and Micro Enterprises Based on Electricity Consumption Data","authors":"Qing Dai, Wende Zhuang, Jun Yang","doi":"10.1109/ICOCWC60930.2024.10470626","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470626","url":null,"abstract":"The research of operation indicators plays an important role in intelligent monitoring of small and micro enterprises, but there is a problem of inaccurate monitoring. The traditional regression algorithm cannot solve the research problem of monitoring operation indicators in intelligent monitoring of small and micro enterprises, and the detection effect is not satisfactory. Therefore, this paper proposes a research on monitoring the operation indicators of small and micro enterprises based on electrical data monitoring, and analyzes the research on the operation indicators of small and micro enterprises. Firstly, the power system theory is used to locate the influencing factors, and the indicators is divided according to the requirements of the research of operation indicators, so as to reduce the interference factors in the research of operation indicators. Then, the power system theory is used to form a research scheme for monitoring the operation index of electrical data, and the research results of the operation index is comprehensively analyzed. The MATLAB simulation results show that under certain evaluation standards, electrical data monitoring is superior to the traditional regression method in terms of research accuracy of operation indicators and research influencing factor time of operation indicators.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"10 5","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":"140529788","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/ICOCWC60930.2024.10470548
Ananya Saha, Rakesh Kumar Yadav, Abhinav
the compact multiband planar published monopole antenna is a sort of antenna that is designed to operate over more than one frequency bands in a compact size. Its miles usually fabricated the use of printed circuit board (PCB) technology, making it appropriate for integration into compact gadgets including cell phones, capsules, and other wireless gadgets. The design of this antenna includes selecting a particular geometry and configuration of the antenna factors to obtain the desired frequency bands. The antenna usually includes a main radiating detail, along with a monopole, and further parasitic factors that help to create the preferred frequency reaction. One approach to reaching multiband operation is by using the usage of an unmarried radiating element with more than one parasitic factor placed at specific locations alongside the antenna. Those parasitic elements act as directors or reflectors, changing the radiation pattern and resonant frequency of the antenna. By means of optimizing the scale, spacing, and configuration of these parasitic factors, the antenna can be made to function over a wide frequency variety. Some other technique is to apply multiple radiating factors which might be every designed to function at a one of a kind frequency band. These factors are normally coupled together to shape a compact structure and also can be mixed with parasitic factors to enhance performance.
{"title":"Design of a Compact Multiband Planar Printed Monopole Antenna","authors":"Ananya Saha, Rakesh Kumar Yadav, Abhinav","doi":"10.1109/ICOCWC60930.2024.10470548","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470548","url":null,"abstract":"the compact multiband planar published monopole antenna is a sort of antenna that is designed to operate over more than one frequency bands in a compact size. Its miles usually fabricated the use of printed circuit board (PCB) technology, making it appropriate for integration into compact gadgets including cell phones, capsules, and other wireless gadgets. The design of this antenna includes selecting a particular geometry and configuration of the antenna factors to obtain the desired frequency bands. The antenna usually includes a main radiating detail, along with a monopole, and further parasitic factors that help to create the preferred frequency reaction. One approach to reaching multiband operation is by using the usage of an unmarried radiating element with more than one parasitic factor placed at specific locations alongside the antenna. Those parasitic elements act as directors or reflectors, changing the radiation pattern and resonant frequency of the antenna. By means of optimizing the scale, spacing, and configuration of these parasitic factors, the antenna can be made to function over a wide frequency variety. Some other technique is to apply multiple radiating factors which might be every designed to function at a one of a kind frequency band. These factors are normally coupled together to shape a compact structure and also can be mixed with parasitic factors to enhance performance.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"61 41","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":"140529757","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/ICOCWC60930.2024.10470905
Amit Kumar Sharma, Manju Bargavi, Akhilendra Pratap Singh
Object-based image analysis of Hyperspectral Imagery using Semantic Segmentation strategies is a singular approach for analyzing far-off sensing statistics. This method leverages the energy of a superior system gaining knowledge of (ML) and computer vision algorithms to analyze multidimensional hyperspectral image datasets. The goal is to robustly organize pixels into clusters in step with their spectral and spatial traits. Those clusters are then used to give meaningful records approximately the content material of the photo., the enter pix are pre-processed to reduce noise and boom contrast. A semantic segmentation algorithm is then used to generate excessive-degree masks of the items of interest. The outcomes of those masks are mixed with the input hyperspectral statistics to create several feature vectors describing the spectral and texture homes of every cluster. Sooner or later, a device-mastering algorithm categorizes the gadgets consistent with their traits, presenting precise information about the items in the picture.
{"title":"Object-Based Image Analysis of Hyper Spectral Imagery Using Semantic Segmentation Techniques","authors":"Amit Kumar Sharma, Manju Bargavi, Akhilendra Pratap Singh","doi":"10.1109/ICOCWC60930.2024.10470905","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470905","url":null,"abstract":"Object-based image analysis of Hyperspectral Imagery using Semantic Segmentation strategies is a singular approach for analyzing far-off sensing statistics. This method leverages the energy of a superior system gaining knowledge of (ML) and computer vision algorithms to analyze multidimensional hyperspectral image datasets. The goal is to robustly organize pixels into clusters in step with their spectral and spatial traits. Those clusters are then used to give meaningful records approximately the content material of the photo., the enter pix are pre-processed to reduce noise and boom contrast. A semantic segmentation algorithm is then used to generate excessive-degree masks of the items of interest. The outcomes of those masks are mixed with the input hyperspectral statistics to create several feature vectors describing the spectral and texture homes of every cluster. Sooner or later, a device-mastering algorithm categorizes the gadgets consistent with their traits, presenting precise information about the items in the picture.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"18 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":"140529632","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/ICOCWC60930.2024.10470879
R. S. V. Durai, R. Vijayakumar, S. Lakshmisridevi, Shaik Thasleem Bhanu, U. Arunkumar
Precision agriculture is a cutting-edge farming strategy that maximizes harvests by using cutting-edge technology and data-driven decision-making. Optical sensors and other Internet of Things (IoT) devices have great promise to revolutionize farming operations in this setting. Sensor networks and Machine Learning (ML) based tracking devices are in great demand because of the precise data extraction and analysis they give. This research was undertaken with the goal of reducing agricultural hazards and promoting smart farming practices. Diseases caused by insects and other diseases may reduce crop yields if not addressed quickly. Thus, in this study, we provide a unique artificial swarm fish optimized naive bayes (ASFONB) method for keeping an eye on the health of the soil and preventing diseases from manifesting in cotton plants' leaves. In this research, numerous important indicators of crop growth and health were monitored using Internet of Things (IoT) devices equipped with optical sensors. The environmental factors like as temperature, humidity, light intensity, and chlorophyll content are recorded by these sensors. The proposed method involves sending the collected data to a central server for processing and analysis via wireless transmission. Once the disease has been detected, the information will be sent to the farmers via Android app. The Android app can show the chemical concentration in a container with soil factors like humidity, temperature, and wetness. Using an Android app, you may control the relay and hence the power supply and chemical sprinkler system. The experimental findings demonstrate that the proposed solution outperforms the status quo in disease identification.
{"title":"IoT Based Optical Sensor Network For Precision Agriculture","authors":"R. S. V. Durai, R. Vijayakumar, S. Lakshmisridevi, Shaik Thasleem Bhanu, U. Arunkumar","doi":"10.1109/ICOCWC60930.2024.10470879","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470879","url":null,"abstract":"Precision agriculture is a cutting-edge farming strategy that maximizes harvests by using cutting-edge technology and data-driven decision-making. Optical sensors and other Internet of Things (IoT) devices have great promise to revolutionize farming operations in this setting. Sensor networks and Machine Learning (ML) based tracking devices are in great demand because of the precise data extraction and analysis they give. This research was undertaken with the goal of reducing agricultural hazards and promoting smart farming practices. Diseases caused by insects and other diseases may reduce crop yields if not addressed quickly. Thus, in this study, we provide a unique artificial swarm fish optimized naive bayes (ASFONB) method for keeping an eye on the health of the soil and preventing diseases from manifesting in cotton plants' leaves. In this research, numerous important indicators of crop growth and health were monitored using Internet of Things (IoT) devices equipped with optical sensors. The environmental factors like as temperature, humidity, light intensity, and chlorophyll content are recorded by these sensors. The proposed method involves sending the collected data to a central server for processing and analysis via wireless transmission. Once the disease has been detected, the information will be sent to the farmers via Android app. The Android app can show the chemical concentration in a container with soil factors like humidity, temperature, and wetness. Using an Android app, you may control the relay and hence the power supply and chemical sprinkler system. The experimental findings demonstrate that the proposed solution outperforms the status quo in disease identification.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"37 6","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":"140529999","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/ICOCWC60930.2024.10470606
M. N. Nachappa, Chetan Chaudhary, Shiv Shankar Sharma
Item detection strategies and deep mastering are used to become aware of and classify items in a given image. However, the accuracy of the object detection performance is regularly restricted by the presence of complex or ambiguous instances, which can be difficult to classify correctly. To in addition enhance the accuracy of such methods, the latest procedures use adverse networks which act as an adversary in object detection. This paper gives an innovation to improve accuracy using adversarial Networks in the item detection era. The proposed method utilizes an adverse network as a further factor in the item detection device that's liable for thinking about the context of the encircling gadgets for you to classify the ambiguous cases better. The proposed method is examined on diverse benchmark datasets, which reveal improvement in accuracy over the existing techniques. The results also show that the proposed approach can substantially enhance object detection accuracy in complex and ambiguous cases. The proposed method highlights the ability to use antagonistic networks in aggregate with existing object detection methods to noticeably enhance the accuracy of object detection. Adversarial networks have received enormous attention for improving the accuracy of object detection responsibilities. Current work has shown that the capacity of a generative adverse community (GAN) to distinguish actual from generated information can be used to improve the detection of objects in pix. GANs can be skilled in locating objects using a classified dataset of snapshots. The GAN takes the input records and tries to hit upon the gadgets present inside the photos with the help of opposed mastering. In antagonistic gaining knowledge, two networks are skilled concurrently, one to generate the preferred output representation and the other to distinguish this artificial illustration from the floor reality statistics. The GAN is again and again up to date till both networks converge to a state wherein they can efficiently hit upon the objects present within the pics. As soon as trained, the GAN is used to generate a representation of the desired item in the entered records, enhancing object detection accuracy.
{"title":"An Innovation Object Detection to Improve the Accuracy Using Adversarial Networks","authors":"M. N. Nachappa, Chetan Chaudhary, Shiv Shankar Sharma","doi":"10.1109/ICOCWC60930.2024.10470606","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470606","url":null,"abstract":"Item detection strategies and deep mastering are used to become aware of and classify items in a given image. However, the accuracy of the object detection performance is regularly restricted by the presence of complex or ambiguous instances, which can be difficult to classify correctly. To in addition enhance the accuracy of such methods, the latest procedures use adverse networks which act as an adversary in object detection. This paper gives an innovation to improve accuracy using adversarial Networks in the item detection era. The proposed method utilizes an adverse network as a further factor in the item detection device that's liable for thinking about the context of the encircling gadgets for you to classify the ambiguous cases better. The proposed method is examined on diverse benchmark datasets, which reveal improvement in accuracy over the existing techniques. The results also show that the proposed approach can substantially enhance object detection accuracy in complex and ambiguous cases. The proposed method highlights the ability to use antagonistic networks in aggregate with existing object detection methods to noticeably enhance the accuracy of object detection. Adversarial networks have received enormous attention for improving the accuracy of object detection responsibilities. Current work has shown that the capacity of a generative adverse community (GAN) to distinguish actual from generated information can be used to improve the detection of objects in pix. GANs can be skilled in locating objects using a classified dataset of snapshots. The GAN takes the input records and tries to hit upon the gadgets present inside the photos with the help of opposed mastering. In antagonistic gaining knowledge, two networks are skilled concurrently, one to generate the preferred output representation and the other to distinguish this artificial illustration from the floor reality statistics. The GAN is again and again up to date till both networks converge to a state wherein they can efficiently hit upon the objects present within the pics. As soon as trained, the GAN is used to generate a representation of the desired item in the entered records, enhancing object detection accuracy.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"76 ","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":"140529917","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/ICOCWC60930.2024.10470508
Swati Singh, Namit Gupta, Febin Prakash
this paper examines using data augmentation strategies in the ensemble, getting to know medical photo segmentation with transfer learning. Various transfer-gaining knowledge of techniques, namely pretrained models, unsupervised function mastering, and multitasking studying, are explored. Pre-skilled models are skilled in one area and further high-quality-tuned using information from any other area to enhance segmentation overall performance. Unsupervised characteristic learning creates a common characteristic space that encodes the shared styles between numerous datasets. Multitask mastering combines challenge-particular multitasking getting to know, and feature-particular studying into a single, more accurate version. Records augmentation strategies unique to scientific photos, such as random cropping, random flipping, random rotation, and affine transformation, are mentioned. The effectiveness of different records augmentation strategies is evaluated on several scientific datasets, such as liver and lung datasets. Effects show combining statistics augmentation techniques with ensemble learning can drastically enhance segmentation accuracy. The look presents further evidence that information augmentation strategies can correctly be used for the clinical image segmentation venture.
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