Pub Date : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936126
Sreyes K, Anushka Xavier K, Dona Davis, N. Jayapandian
The rapid advancement of cloud technology has resulted in the emergence of many cloud service providers. Microsoft Azure is one among them to provide a flexible cloud computing platform that can scale business to exceptional heights. It offers extensive cloud services and is compatible with a wide range of developer tools, databases, and operating systems. In this paper, a detailed analysis of Microsoft Azure in the cloud computing era is performed. For this reason, the three significant Azure services, namely, the Azure AI (Artificial Intelligence) and Machine Learning (ML) Service, Azure Analytics Service and Internet of Things (IoT) are investigated. The paper briefs on the Azure Cognitive Search and Face Service under AI and ML service and explores this service's architecture and security measures. The proposed study also surveys the Data Lake and Data factory Services under Azure Analytics Service. Subsequently, an overview of Azure IoT service, mainly IoT Hub and IoT Central, is discussed. Along with Microsoft Azure, other providers in the market are Google Compute Engine and Amazon Web Service. The paper compares and contrasts each cloud service provider based on their computing capability.
{"title":"Internet of Things and Cloud Computing Involvement Microsoft Azure Platform","authors":"Sreyes K, Anushka Xavier K, Dona Davis, N. Jayapandian","doi":"10.1109/ICECAA55415.2022.9936126","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936126","url":null,"abstract":"The rapid advancement of cloud technology has resulted in the emergence of many cloud service providers. Microsoft Azure is one among them to provide a flexible cloud computing platform that can scale business to exceptional heights. It offers extensive cloud services and is compatible with a wide range of developer tools, databases, and operating systems. In this paper, a detailed analysis of Microsoft Azure in the cloud computing era is performed. For this reason, the three significant Azure services, namely, the Azure AI (Artificial Intelligence) and Machine Learning (ML) Service, Azure Analytics Service and Internet of Things (IoT) are investigated. The paper briefs on the Azure Cognitive Search and Face Service under AI and ML service and explores this service's architecture and security measures. The proposed study also surveys the Data Lake and Data factory Services under Azure Analytics Service. Subsequently, an overview of Azure IoT service, mainly IoT Hub and IoT Central, is discussed. Along with Microsoft Azure, other providers in the market are Google Compute Engine and Amazon Web Service. The paper compares and contrasts each cloud service provider based on their computing capability.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115691429","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936382
Museboyina Sirisha, S. Sudha
There have been various object detection models developed recently that have enabled better results in object detection. Object detection has gradually increased in computer vision with further development of application areas. Unmanned aerial vehicle (UAV) images feature smaller and more fragmented objects, as opposed to landscape images where objects occupy more space. Furthermore, rotational and measuremental factors reduce object detection accuracy. In this paper, an improved object detection framework based on YOLOv5 is proposed in order to resolve these issues. As such, this study proposes SSAM-Darknet for the detection of UAV images based on objects. SSAM-Darknet and Bi-FPN are used to extract multiscale and multilevel features from the input images. Additionally, dilated convolution and the Ada-bound optimizer are employed to enhance the proposed model in detecting the objects from UAV images. This experiment evaluates the accuracy of object detection by using VisDrone-DET. AP (Average Precision) and AR (Average Recall) metrics are proposed as a quantitative way of evaluating detection performance. The proposed model achieves an average precision of 34.32 making an increase in detection accuracy by 10% compared to other detectors.
{"title":"Object Detection in Unmanned Aerial Vehicle (UAV) Images using YOLOv5 with Supervised Spatial Attention Module","authors":"Museboyina Sirisha, S. Sudha","doi":"10.1109/ICECAA55415.2022.9936382","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936382","url":null,"abstract":"There have been various object detection models developed recently that have enabled better results in object detection. Object detection has gradually increased in computer vision with further development of application areas. Unmanned aerial vehicle (UAV) images feature smaller and more fragmented objects, as opposed to landscape images where objects occupy more space. Furthermore, rotational and measuremental factors reduce object detection accuracy. In this paper, an improved object detection framework based on YOLOv5 is proposed in order to resolve these issues. As such, this study proposes SSAM-Darknet for the detection of UAV images based on objects. SSAM-Darknet and Bi-FPN are used to extract multiscale and multilevel features from the input images. Additionally, dilated convolution and the Ada-bound optimizer are employed to enhance the proposed model in detecting the objects from UAV images. This experiment evaluates the accuracy of object detection by using VisDrone-DET. AP (Average Precision) and AR (Average Recall) metrics are proposed as a quantitative way of evaluating detection performance. The proposed model achieves an average precision of 34.32 making an increase in detection accuracy by 10% compared to other detectors.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116713846","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936395
M. Sudharsan, G. Thailambal
The most important area of digital image processing is biomedical image processing, which combines the Artificial Intelligence empowered learning Has been included algorithms to rapidly detect diseases. History's philosophy of biomedical image processing has advanced significantly, and the combination of potent deep learning and classification approaches offers a wide range of opportunities for illness prediction. In order to pinpoint the most serious brain-related disease, Alzheimer's, this study will develop a revolutionary disease prediction technique. This illness has a significant negative effect on the human brain and causes affected people to lose their memories permanently along with other cognitive impairments. surrounding brain cell region. In this the protein named amyloid is the main cause of such diseases, in which it aggregates over the brain cell region to generate plaques. Another important protein called Tau, it also aggregates on the brain cell region to lead to Alzheimer disease. In this paper, a novel deep learning strategy is introduced to identify the Alzheimer Disease using deep learning strategy, which is called Deep polynomial network with many models (DPNMM). This suggested method, called DPNMM, detects Alzheimer's disease through neuro-imaging data that is obtained through the use of scanning tools like Magnetic Resonance Imaging (MRI). Morphological Image Processing Techniques which have been applied In this study, temporal MRI scans with regard to 150 patient records with ages ranging from 60 to 96 are used .The Data set is Contain 65 Attributes Like Pixel Values,Entrophy,Contrast etc.They are part of an open source dataset made accessible through Kaggle repository. In the methods portion of this work, a brief description of the dataset and its definition will be provided.Based on this dataset the overall functionality is moving around and the processing is carried forward through the following way including Image Preprocessing, Normalization, Feature Selection and Classification. The proposed system efficiency is proved in terms of graphical emulations over the resulting section of this paper. For all the proposed learning strategy called Deep polynomial network with many models provides sufficient efficiency to identify the Alzheimer disease in perfect ratio and the resulting section has a proper proof for that in clear manner.
数字图像处理最重要的领域是生物医学图像处理，它结合了人工智能授权的学习，包括快速检测疾病的算法。生物医学图像处理的历史哲学有了显著的进步，强大的深度学习和分类方法的结合为疾病预测提供了广泛的机会。为了精确定位最严重的脑相关疾病——阿尔茨海默病，本研究将开发一种革命性的疾病预测技术。这种疾病对人类大脑有显著的负面影响，导致患者永久失去记忆以及其他认知障碍。周围的脑细胞区域。在这种情况下，被称为淀粉样蛋白的蛋白质是导致这些疾病的主要原因，它聚集在脑细胞区域产生斑块。另一种重要的蛋白质叫做Tau，它也聚集在脑细胞区域，导致阿尔茨海默病。本文提出了一种新的深度学习策略，即多模型深度多项式网络(deep polynomial network with many models, DPNMM)来识别阿尔茨海默病。这种建议的方法被称为DPNMM，通过使用磁共振成像(MRI)等扫描工具获得的神经成像数据来检测阿尔茨海默病。形态学图像处理技术在本研究中得到了应用，使用了150例年龄从60岁到96岁的患者的颞MRI扫描。数据集包含65个属性，如像素值、熵、对比度等。它们是一个开源数据集的一部分，可以通过Kaggle存储库访问。在本工作的方法部分，将提供数据集及其定义的简要描述。在此数据集的基础上进行整体功能的移动，并通过图像预处理、归一化、特征选择和分类等方式进行处理。本文最后通过图形仿真验证了系统的有效性。所提出的学习策略称为多模型深度多项式网络，提供了足够的效率，以完美的比率识别阿尔茨海默病，所得截面对此有适当的证明。
{"title":"An Recognition of Alzheimer Disease using Brain MRI Images with DPNMM through Adaptive Model","authors":"M. Sudharsan, G. Thailambal","doi":"10.1109/ICECAA55415.2022.9936395","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936395","url":null,"abstract":"The most important area of digital image processing is biomedical image processing, which combines the Artificial Intelligence empowered learning Has been included algorithms to rapidly detect diseases. History's philosophy of biomedical image processing has advanced significantly, and the combination of potent deep learning and classification approaches offers a wide range of opportunities for illness prediction. In order to pinpoint the most serious brain-related disease, Alzheimer's, this study will develop a revolutionary disease prediction technique. This illness has a significant negative effect on the human brain and causes affected people to lose their memories permanently along with other cognitive impairments. surrounding brain cell region. In this the protein named amyloid is the main cause of such diseases, in which it aggregates over the brain cell region to generate plaques. Another important protein called Tau, it also aggregates on the brain cell region to lead to Alzheimer disease. In this paper, a novel deep learning strategy is introduced to identify the Alzheimer Disease using deep learning strategy, which is called Deep polynomial network with many models (DPNMM). This suggested method, called DPNMM, detects Alzheimer's disease through neuro-imaging data that is obtained through the use of scanning tools like Magnetic Resonance Imaging (MRI). Morphological Image Processing Techniques which have been applied In this study, temporal MRI scans with regard to 150 patient records with ages ranging from 60 to 96 are used .The Data set is Contain 65 Attributes Like Pixel Values,Entrophy,Contrast etc.They are part of an open source dataset made accessible through Kaggle repository. In the methods portion of this work, a brief description of the dataset and its definition will be provided.Based on this dataset the overall functionality is moving around and the processing is carried forward through the following way including Image Preprocessing, Normalization, Feature Selection and Classification. The proposed system efficiency is proved in terms of graphical emulations over the resulting section of this paper. For all the proposed learning strategy called Deep polynomial network with many models provides sufficient efficiency to identify the Alzheimer disease in perfect ratio and the resulting section has a proper proof for that in clear manner.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116791540","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936500
M. N. Sachane, S. A. Patil
Laryngeal Cancer (LCA) is one of the predominant cancers found commonly among people around world that affects the head and neck region of humans. The change in patient’s voice is the early symptom of LCA and diagnosis the LCA at the primary stages is necessary to decrease the morbidity rate. Usage of laryngeal endoscopic images for automatic laryngeal cancer detection is advantageous in additional evaluation of the tumor structures and its characteristics enable to improve the quality of treatment, like computed aided surgery. Though, only fewer methods exist for detecting laryngeal cancer automatically, but increasing the performance still results a major challenge. In order to detect the laryngeal cancer automatically, this research proposes an effectual model for laryngeal cancer classification using proposed Adaptive Spotted Hyena Optimizer-based Deep Quantum Neural Network (ASHO-based Deep QNN). Here, the pre-processing is effectively done using Gaussian filtering and features, such as Spider Local Image Feature (SLIF), Gradient Binary Pattern (GBP), and Histogram of Gradients (HOG) are refined efficiently to enhance the performance of the model. Finally, classification is accomplished with the Deep QNN, wherein the introduced ASHO is made use of to tune the network classifier. The ASHO is devised by inheriting the benefits of Adaptive concept with Spotted Hyena Optimizer (SHO). Meanwhile, the proposed ASHO-based Deep QNN has achieved maximum values of accuracy, sensitivity, as well as specificity at 0.948, 0.952, and 0.924, respectively.
喉癌(LCA)是世界上常见的影响人类头颈部的主要癌症之一。患者的声音变化是LCA的早期症状，早期诊断LCA是降低发病率的必要条件。使用喉内窥镜图像进行喉癌自动检测，有利于对肿瘤结构及其特征进行额外评估，从而提高治疗质量，如计算机辅助手术。虽然目前用于喉癌自动检测的方法较少，但提高其性能仍然是一个重大挑战。为了自动检测喉癌，本研究提出了一种基于自适应斑点鬣狗优化器的深度量子神经网络(ASHO-based Deep QNN)的有效喉癌分类模型。该模型采用高斯滤波进行有效预处理，并对蜘蛛局部图像特征(SLIF)、梯度二值模式(GBP)和梯度直方图(HOG)等特征进行有效细化，提高了模型的性能。最后，使用深度QNN完成分类，其中使用引入的ASHO来调整网络分类器。ASHO是继承了斑点鬣狗优化器(spot Hyena Optimizer, SHO)自适应概念的优点而设计的。同时，本文提出的基于asho的Deep QNN的准确率、灵敏度和特异性分别达到了最大值0.948、0.952和0.924。
{"title":"Adaptive Spotted Hyena Optimizer-enabled Deep QNN for Laryngeal Cancer Classification","authors":"M. N. Sachane, S. A. Patil","doi":"10.1109/ICECAA55415.2022.9936500","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936500","url":null,"abstract":"Laryngeal Cancer (LCA) is one of the predominant cancers found commonly among people around world that affects the head and neck region of humans. The change in patient’s voice is the early symptom of LCA and diagnosis the LCA at the primary stages is necessary to decrease the morbidity rate. Usage of laryngeal endoscopic images for automatic laryngeal cancer detection is advantageous in additional evaluation of the tumor structures and its characteristics enable to improve the quality of treatment, like computed aided surgery. Though, only fewer methods exist for detecting laryngeal cancer automatically, but increasing the performance still results a major challenge. In order to detect the laryngeal cancer automatically, this research proposes an effectual model for laryngeal cancer classification using proposed Adaptive Spotted Hyena Optimizer-based Deep Quantum Neural Network (ASHO-based Deep QNN). Here, the pre-processing is effectively done using Gaussian filtering and features, such as Spider Local Image Feature (SLIF), Gradient Binary Pattern (GBP), and Histogram of Gradients (HOG) are refined efficiently to enhance the performance of the model. Finally, classification is accomplished with the Deep QNN, wherein the introduced ASHO is made use of to tune the network classifier. The ASHO is devised by inheriting the benefits of Adaptive concept with Spotted Hyena Optimizer (SHO). Meanwhile, the proposed ASHO-based Deep QNN has achieved maximum values of accuracy, sensitivity, as well as specificity at 0.948, 0.952, and 0.924, respectively.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127249188","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936227
T. D. Prakash, Nagaraju V
The purpose of this study is to use machine learning approaches to improve the accuracy of modern road accident prediction systems like the K-Nearest Neighbour Algorithm and Artificial Neural Networks Algorithm. Materials and techniques used include the K-Nearest Neighbour technique and the Artificial Neural Networks algorithm with sample size N=10, iterated 20 times in parallel to test the accuracy of forecasting road accidents. p0.05 indicates the significance of the K-Nearest Neighbour method. When comparing the results of the two algorithms, it is discovered that the K-Nearest Neighbour approach (81.22%) outperforms the Artificial Neural Networks algorithm (69.22%) in terms of accuracy in forecasting road accidents.
{"title":"Comparative Analysis using K - Nearest Neighbour with Artificial Neural Network to Improve Accuracy for Predicting Road Accidents","authors":"T. D. Prakash, Nagaraju V","doi":"10.1109/ICECAA55415.2022.9936227","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936227","url":null,"abstract":"The purpose of this study is to use machine learning approaches to improve the accuracy of modern road accident prediction systems like the K-Nearest Neighbour Algorithm and Artificial Neural Networks Algorithm. Materials and techniques used include the K-Nearest Neighbour technique and the Artificial Neural Networks algorithm with sample size N=10, iterated 20 times in parallel to test the accuracy of forecasting road accidents. p0.05 indicates the significance of the K-Nearest Neighbour method. When comparing the results of the two algorithms, it is discovered that the K-Nearest Neighbour approach (81.22%) outperforms the Artificial Neural Networks algorithm (69.22%) in terms of accuracy in forecasting road accidents.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127513466","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936285
Chandu Jagan Sekhar Madala, G. H. K. Yadav, S. Sivakumar, R. Nithya, M. M., M. Deivakani
The fast rise of the Internet and advanced technologies causes an increase in network traffic, making network infrastructure increasingly complicated and varied. Mobile phones, wearable gadgets, and driverless cars are all instances of dispersed networks that create massive amounts of data every day. The processing capability of these devices has also increased steadily, necessitating the need to transport data, store data locally, and direct network calculations to edge devices. Intrusion detection systems are essential in guaranteeing the safety and confidentiality of such equipment. Deep Learning (DL) combined Intrusion Detection Systems (IDS) have gained prominence due to their excellent categorization accuracy. However, the requirement to store and communicate data to a centralized server may jeopardize privacy and security concerns. Federated learning (FL), on the other hand, fits in nicely as private information decentralized learning approach that does not transport data but instead trains algorithms locally and sends the parameters to a centralized server. This work targets to offer an extensive overview of the FL in intrusion detection systems.
{"title":"Federated Learning Approach for Tracking Malicious Activities in Cyber-Physical Systems","authors":"Chandu Jagan Sekhar Madala, G. H. K. Yadav, S. Sivakumar, R. Nithya, M. M., M. Deivakani","doi":"10.1109/ICECAA55415.2022.9936285","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936285","url":null,"abstract":"The fast rise of the Internet and advanced technologies causes an increase in network traffic, making network infrastructure increasingly complicated and varied. Mobile phones, wearable gadgets, and driverless cars are all instances of dispersed networks that create massive amounts of data every day. The processing capability of these devices has also increased steadily, necessitating the need to transport data, store data locally, and direct network calculations to edge devices. Intrusion detection systems are essential in guaranteeing the safety and confidentiality of such equipment. Deep Learning (DL) combined Intrusion Detection Systems (IDS) have gained prominence due to their excellent categorization accuracy. However, the requirement to store and communicate data to a centralized server may jeopardize privacy and security concerns. Federated learning (FL), on the other hand, fits in nicely as private information decentralized learning approach that does not transport data but instead trains algorithms locally and sends the parameters to a centralized server. This work targets to offer an extensive overview of the FL in intrusion detection systems.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123546860","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936424
Huanhuan Fan
Design and develop a ledger system based on the front-end and back-end separation architecture. The system adopts the design concept of mobile first, and deeply customizes the functions and user interface for smart phones. Based on this, this paper briefly introduces the definition of rural ecotourism, analyzes the current situation of rural ecotourism marketing in my country, and proposes the precision of rural ecotourism in the big data environment. Marketing strategy to help the development of rural ecotourism in my country. The regional differences and dispersion of WeChat marketing performance are large, and the online customer relationship is dominated by positive emotions. It aims to create social and economic value from rural social big data, implement big data-driven precision marketing innovation strategies, enhance the competitiveness of rural digital economy, and promote rural smart growth and regional coordination and sustainable development.
{"title":"Intelligent Framework of Rural Tourism Marketing Big Data Mining based on PHP Algorithm of Intelligent Ledger System","authors":"Huanhuan Fan","doi":"10.1109/ICECAA55415.2022.9936424","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936424","url":null,"abstract":"Design and develop a ledger system based on the front-end and back-end separation architecture. The system adopts the design concept of mobile first, and deeply customizes the functions and user interface for smart phones. Based on this, this paper briefly introduces the definition of rural ecotourism, analyzes the current situation of rural ecotourism marketing in my country, and proposes the precision of rural ecotourism in the big data environment. Marketing strategy to help the development of rural ecotourism in my country. The regional differences and dispersion of WeChat marketing performance are large, and the online customer relationship is dominated by positive emotions. It aims to create social and economic value from rural social big data, implement big data-driven precision marketing innovation strategies, enhance the competitiveness of rural digital economy, and promote rural smart growth and regional coordination and sustainable development.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123770457","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936156
P. V. Krishna, Venkata Durgarao Matta
Intrusion Detection System (IDS) is one of the applications to detect intrusions in the network. IDS aims to detect any malicious activities that protect the computer networks from unknown persons or users called attackers. Network security is one of the significant tasks that should provide secure data transfer. Virtualization of networks becomes more complex for IoT technology. Deep Learning (DL) is most widely used by many networks to detect the complex patterns. This is very suitable approaches for detecting the malicious nodes or attacks. Software-Defined Network (SDN) is the default virtualization computer network. Attackers are developing new technology to attack the networks. Many authors are trying to develop new technologies to attack the networks. To overcome these attacks new protocols are required to prevent these attacks. In this paper, a unique deep intrusion detection approach (UDIDA) is developed to detect the attacks in SDN. Performance shows that the proposed approach is achieved more accuracy than existing approaches.
{"title":"A Unique Deep Intrusion Detection Approach (UDIDA) for Detecting the Complex Attacks","authors":"P. V. Krishna, Venkata Durgarao Matta","doi":"10.1109/ICECAA55415.2022.9936156","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936156","url":null,"abstract":"Intrusion Detection System (IDS) is one of the applications to detect intrusions in the network. IDS aims to detect any malicious activities that protect the computer networks from unknown persons or users called attackers. Network security is one of the significant tasks that should provide secure data transfer. Virtualization of networks becomes more complex for IoT technology. Deep Learning (DL) is most widely used by many networks to detect the complex patterns. This is very suitable approaches for detecting the malicious nodes or attacks. Software-Defined Network (SDN) is the default virtualization computer network. Attackers are developing new technology to attack the networks. Many authors are trying to develop new technologies to attack the networks. To overcome these attacks new protocols are required to prevent these attacks. In this paper, a unique deep intrusion detection approach (UDIDA) is developed to detect the attacks in SDN. Performance shows that the proposed approach is achieved more accuracy than existing approaches.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125424778","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 : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936102
Someswari Perla, N. K, Srinidhi Potta
In crowded countries like India, traffic problems are a big issue because of the large population. So, autonomous driving is becoming increasingly common and has the potential to disrupt our transportation system. In addition, self-driving cars are on their way to becoming legal, but they are still not safe enough to be used in the real world due to a lack of trust. The purpose of this survey is to describe an em-pirical study on the implementation of autonomous vehicles using machine learning algorithms. Different algorithms are used in the implementation of self-driving cars. Accuracy is used as the evaluation metric. Road Lane Detection, Support Vector Machine(SVM) for anomalies detection, and Disparity Map was used as the algorithms. From the experimental analysis, this research study has observed that these machine learning models have taken less time for processing images autonomously with model accuracies of 97% for road lane detection, and SVM has shown 98% of accuracy for anomaly detection. The proposed models have outperformed baseline models with a significant difference.
{"title":"Implementation of Autonomous Cars using Machine Learning","authors":"Someswari Perla, N. K, Srinidhi Potta","doi":"10.1109/ICECAA55415.2022.9936102","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936102","url":null,"abstract":"In crowded countries like India, traffic problems are a big issue because of the large population. So, autonomous driving is becoming increasingly common and has the potential to disrupt our transportation system. In addition, self-driving cars are on their way to becoming legal, but they are still not safe enough to be used in the real world due to a lack of trust. The purpose of this survey is to describe an em-pirical study on the implementation of autonomous vehicles using machine learning algorithms. Different algorithms are used in the implementation of self-driving cars. Accuracy is used as the evaluation metric. Road Lane Detection, Support Vector Machine(SVM) for anomalies detection, and Disparity Map was used as the algorithms. From the experimental analysis, this research study has observed that these machine learning models have taken less time for processing images autonomously with model accuracies of 97% for road lane detection, and SVM has shown 98% of accuracy for anomaly detection. The proposed models have outperformed baseline models with a significant difference.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126649104","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}
Analyze the characteristics of the laboratory intelligent monitoring system and establish the use of AVR Atmegal28 as the main controller; carry out research on the sensor technology, and analyze the characteristics of the sensor and laboratory intelligent monitoring system. Through a simple method is the use of titanium dioxide nanowires, reduction oxidation A novel porphyrin-sensitized TiO2/rGO photocatalyst was synthesized from graphene and porphyrin. Determine the temperature, humidity, alcohol, carbon dioxide and other sensors used in the laboratory intelligent monitoring system; take the AVR Atmegal28 as the core, expand the peripheral circuits, and connect the temperature, humidity, alcohol, carbon dioxide and other sensor modules to design the laboratory intelligent monitoring system.
{"title":"Intelligent Laboratory Safety Monitoring System for TiO2 Preparation and Catalytic Degradation of Industrial Wastewater based on Multi-Camera Technology","authors":"Faqiang Liu, Peibei Xia, Ling Tian, Leia Huang, Mengyao Liu, Yongmei Wu","doi":"10.1109/ICECAA55415.2022.9936423","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936423","url":null,"abstract":"Analyze the characteristics of the laboratory intelligent monitoring system and establish the use of AVR Atmegal28 as the main controller; carry out research on the sensor technology, and analyze the characteristics of the sensor and laboratory intelligent monitoring system. Through a simple method is the use of titanium dioxide nanowires, reduction oxidation A novel porphyrin-sensitized TiO2/rGO photocatalyst was synthesized from graphene and porphyrin. Determine the temperature, humidity, alcohol, carbon dioxide and other sensors used in the laboratory intelligent monitoring system; take the AVR Atmegal28 as the core, expand the peripheral circuits, and connect the temperature, humidity, alcohol, carbon dioxide and other sensor modules to design the laboratory intelligent monitoring system.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"22 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116394862","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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