Pub Date : 2022-10-13DOI: 10.1109/ICECAA55415.2022.9936527
Ouyang Jing, Xiao Ying, Li Xue
The "three-integration" type of cultural self-confidence education for college students integrates cultural self-confidence into students' moral cultivation, academic improvement and ability reserve, guides students' development direction through correct cultural values, and enriches students' cultural self-confidence awareness through rich practical activities. Four kinds of cluster scheduling structures under the background of big data are introduced respectively: centralized structure, double-layer structure, distributed structure and hybrid structure, and the reasons, applicable scenarios, advantages and disadvantages of each structure are introduced. Put forward scientific, practical and multi-dimensional suggestions and countermeasures. Provide intellectual support for the government to cultivate industrial clusters, improve regional influence, and formulate effective policies.
{"title":"Interpretation of Students' Cultural Confidence and Design of Online Evaluation System from the Perspective of Cluster Big Data","authors":"Ouyang Jing, Xiao Ying, Li Xue","doi":"10.1109/ICECAA55415.2022.9936527","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936527","url":null,"abstract":"The \"three-integration\" type of cultural self-confidence education for college students integrates cultural self-confidence into students' moral cultivation, academic improvement and ability reserve, guides students' development direction through correct cultural values, and enriches students' cultural self-confidence awareness through rich practical activities. Four kinds of cluster scheduling structures under the background of big data are introduced respectively: centralized structure, double-layer structure, distributed structure and hybrid structure, and the reasons, applicable scenarios, advantages and disadvantages of each structure are introduced. Put forward scientific, practical and multi-dimensional suggestions and countermeasures. Provide intellectual support for the government to cultivate industrial clusters, improve regional influence, and formulate effective policies.","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":"131732531","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/ICECAA55415.2022.9936185
M. Nirmal, Pramod E Jadhav, N. Kadu
Pomegranate is a fruit with a good yield that grows in several Asian countries and is the most profitable one. However, due to a variety of factors, the plants become affected by a wide range of illnesses, resulting in the full destruction of the plant and a drastically reduced yield. Preventing decreases in agricultural production is possible with the early detection of plant diseases. Pomegranate leaf diseases are extremely tough to keep track on manually. As a result, pomegranate plant diseases are detected using Deep Learning (DL). Automating the disease detection system for pomegranates using leaf images is the goal of this study. Image gathering, processing, classification, and deployment are all part of the disease detection system process. Pomegranate leaf health and disease images are built using Mendeley data. The raw image is then processed further. Two DL models, AlexNet and VGG-16, are employed for classification. Accuracy and loss metrics are used to identify the optimal model. The metrics analysis shows that AlexNet is efficient in detecting leaf disease. A mobile app utilizing the AlexNet approach is then created to assist farmers in the detection of pomegranate disease without the assistance of specialists.
{"title":"Farmer Friendly Smart App for Pomegranate Disease Identification","authors":"M. Nirmal, Pramod E Jadhav, N. Kadu","doi":"10.1109/ICECAA55415.2022.9936185","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936185","url":null,"abstract":"Pomegranate is a fruit with a good yield that grows in several Asian countries and is the most profitable one. However, due to a variety of factors, the plants become affected by a wide range of illnesses, resulting in the full destruction of the plant and a drastically reduced yield. Preventing decreases in agricultural production is possible with the early detection of plant diseases. Pomegranate leaf diseases are extremely tough to keep track on manually. As a result, pomegranate plant diseases are detected using Deep Learning (DL). Automating the disease detection system for pomegranates using leaf images is the goal of this study. Image gathering, processing, classification, and deployment are all part of the disease detection system process. Pomegranate leaf health and disease images are built using Mendeley data. The raw image is then processed further. Two DL models, AlexNet and VGG-16, are employed for classification. Accuracy and loss metrics are used to identify the optimal model. The metrics analysis shows that AlexNet is efficient in detecting leaf disease. A mobile app utilizing the AlexNet approach is then created to assist farmers in the detection of pomegranate disease without the assistance of specialists.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"68 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":"127562297","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/ICECAA55415.2022.9936293
J. Gopinath, A. Elsden Christober, K. L. Ravindrananth, K. Malathi
Nowadays, plants are unable to acquire nutrients and water for survival when the availability of water in the root zone falls below a threshold level. As a result, giving high-quality water at the root zone before reaching as far as possible becomes crucial. This edge limit relies on sorts of plants, soil, and climate. Because, as far as is feasible, diverse types of plants are different. The proper amount of water must be applied at the proper time to the proper area of the plant according to scientific scheduling. This necessitates ongoing soil moisture monitoring. Depending on the type of plant, its growth, the soil, and the surrounding conditions, start irrigation at the root zone according to a pre-programmed timetable. In order to schedule irrigation, the signals generated and recognized by soil moisture sensors must be processed in a microcontroller as per pre-determined program using LoRA WAN communication for long range communication. The microcontroller should also be changed to send the signal to a remote site where siphoning and water system control instruments are installed. The microprocessor also manages the output from these sensors in accordance with a pre-established programme to turn off the water system depending on the type of plant, its developmental stage, the soil, and the weather.
{"title":"LoRa WAN Communication using Wireless Sensor Network","authors":"J. Gopinath, A. Elsden Christober, K. L. Ravindrananth, K. Malathi","doi":"10.1109/ICECAA55415.2022.9936293","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936293","url":null,"abstract":"Nowadays, plants are unable to acquire nutrients and water for survival when the availability of water in the root zone falls below a threshold level. As a result, giving high-quality water at the root zone before reaching as far as possible becomes crucial. This edge limit relies on sorts of plants, soil, and climate. Because, as far as is feasible, diverse types of plants are different. The proper amount of water must be applied at the proper time to the proper area of the plant according to scientific scheduling. This necessitates ongoing soil moisture monitoring. Depending on the type of plant, its growth, the soil, and the surrounding conditions, start irrigation at the root zone according to a pre-programmed timetable. In order to schedule irrigation, the signals generated and recognized by soil moisture sensors must be processed in a microcontroller as per pre-determined program using LoRA WAN communication for long range communication. The microcontroller should also be changed to send the signal to a remote site where siphoning and water system control instruments are installed. The microprocessor also manages the output from these sensors in accordance with a pre-established programme to turn off the water system depending on the type of plant, its developmental stage, the soil, and the weather.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"21 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":"128928581","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/ICECAA55415.2022.9936151
V. VishwaPriya
A Deep learning method has been presented to identify the risk factors for Pre-term Birth (PTB). Premature birth is one of the most important factors that affect the death of the infant. The existing method analyzes the Very low birth weight and pre-term infants more than 1500 grams is a high risk of developing intraventricular bleeding, which is a major cause of brain damage in premature infants. The previous method shows time complexity, and feature selection is being provided the highest error rate taken. To overcome the issues in this work proposed the method, Adaptive Deep Belief Neural Networks (ADBNNs) algorithm analysis to using the Softmax Late-Onset Sepsis (SLOS) function for utilizing the risk factors. Initially, the Pre-processing for non-redundant data from data begins to function using the Dynamic Ensemble Selection (DES) algorithm, which reduces the relevant values of the dataset. The proposed method Adaptive Deep Belief Neural Networks (ADBNNs) algorithm, was used to classify results based on the feature extracting information contained in the original set of features. The classification results show the Neonatal Apnea Level Classification should be calculated and combined with the Risk factors analysis based on the Softmax activation function classified the hidden layer function called Autoencoders Deep Belief Network. Hidden layers or invisible layers are not connected and are conditionally independent. Experimental results show that to perform a defect classification with the proposed method, an ADBNNs would isolate the optimal features of the individual with minimal network training time, and ultimately, the individual in the prediction and reducing the error rate, time complexity, and time complexity improving the accuracy.
提出了一种深度学习方法来识别早产(PTB)的危险因素。早产是影响婴儿死亡的最重要因素之一。现有方法分析极低出生体重和超过1500克的早产儿发生脑室内出血的风险很高，这是早产儿脑损伤的主要原因。前一种方法显示了时间复杂度，并且提供了最高的错误率。针对上述问题，本文提出了采用自适应深度信念神经网络(ADBNNs)算法分析的方法，利用Softmax迟发性脓毒症(SLOS)函数对危险因素进行利用。首先，对数据中非冗余数据的预处理开始使用动态集成选择(DES)算法，该算法降低了数据集的相关值。该方法采用自适应深度信念神经网络(ADBNNs)算法，根据原始特征集中包含的特征提取信息对结果进行分类。分类结果表明，基于Softmax激活函数分类的隐层函数Autoencoders Deep Belief Network应计算新生儿呼吸暂停水平分类并结合风险因素分析。隐藏层或不可见层不连接，并且是条件独立的。实验结果表明，采用该方法进行缺陷分类时，adbnn可以在最短的网络训练时间内分离出个体的最优特征，最终使个体在预测过程中减少错误率，降低时间复杂度，提高准确率。
{"title":"Adaptive Deep Belief Neural Networks for Pre-Term Birth Clinical Record to Sense Neonatal Apnea Level Classification","authors":"V. VishwaPriya","doi":"10.1109/ICECAA55415.2022.9936151","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936151","url":null,"abstract":"A Deep learning method has been presented to identify the risk factors for Pre-term Birth (PTB). Premature birth is one of the most important factors that affect the death of the infant. The existing method analyzes the Very low birth weight and pre-term infants more than 1500 grams is a high risk of developing intraventricular bleeding, which is a major cause of brain damage in premature infants. The previous method shows time complexity, and feature selection is being provided the highest error rate taken. To overcome the issues in this work proposed the method, Adaptive Deep Belief Neural Networks (ADBNNs) algorithm analysis to using the Softmax Late-Onset Sepsis (SLOS) function for utilizing the risk factors. Initially, the Pre-processing for non-redundant data from data begins to function using the Dynamic Ensemble Selection (DES) algorithm, which reduces the relevant values of the dataset. The proposed method Adaptive Deep Belief Neural Networks (ADBNNs) algorithm, was used to classify results based on the feature extracting information contained in the original set of features. The classification results show the Neonatal Apnea Level Classification should be calculated and combined with the Risk factors analysis based on the Softmax activation function classified the hidden layer function called Autoencoders Deep Belief Network. Hidden layers or invisible layers are not connected and are conditionally independent. Experimental results show that to perform a defect classification with the proposed method, an ADBNNs would isolate the optimal features of the individual with minimal network training time, and ultimately, the individual in the prediction and reducing the error rate, time complexity, and time complexity improving the accuracy.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"5 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":"133294549","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/ICECAA55415.2022.9936489
Pranab Kumar Goswami, Sunandan Baruah, L. Thakuria
For a long time, security and privacy have been a source of concern for the general population. Nonetheless, rapid technological advances, the rapid growth of the internet and electronic commerce, and the development of more contemporary methods for collecting, investigating, and using private information have elevated privacy to the forefront of public and government concerns. Because of the ease with which data can be collected and stored on PC systems, the area of data mining is growing insignificance. Data mining techniques, while allowing individuals to extract hidden information, on the one hand, offer a variety of privacy risks on the other. This article provides an overview of the various data mining methods that may be utilized in cyber security to identify intrusions. This article provides an overview of data mining in the context of cyber security.
{"title":"Cyber Security and Data Mining Techniques","authors":"Pranab Kumar Goswami, Sunandan Baruah, L. Thakuria","doi":"10.1109/ICECAA55415.2022.9936489","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936489","url":null,"abstract":"For a long time, security and privacy have been a source of concern for the general population. Nonetheless, rapid technological advances, the rapid growth of the internet and electronic commerce, and the development of more contemporary methods for collecting, investigating, and using private information have elevated privacy to the forefront of public and government concerns. Because of the ease with which data can be collected and stored on PC systems, the area of data mining is growing insignificance. Data mining techniques, while allowing individuals to extract hidden information, on the one hand, offer a variety of privacy risks on the other. This article provides an overview of the various data mining methods that may be utilized in cyber security to identify intrusions. This article provides an overview of data mining in the context of cyber security.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"98 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":"131878257","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/ICECAA55415.2022.9936386
Sinduja Natesan Subramanian, Chandrasekharan Nataraj, Shankar Duraikannan, S. Selvaperumal, Raed M. T. Abdulla
This article presents the design and simulation of a unique multiband microstrip patch antenna, using the star hexagon fractal concept. The significant contribution in this work is the logical modifications in the existing fractal design geometry with a rectangular notch. The configuration of the proposed design has a substrate of FR-4 epoxy having a thickness of 1.6 mm, a dielectric constant of 4.4, and a loss tangent of 0.02. Simulated using High-Frequency Structure Simulator (HFSS) software, the parameters of the proposed antenna were well optimized so it would be suitable to be operated in the frequency range from 24.9 GHz to 28.1 GHz with an overall bandwidth of 3.2 GHz. The designed antenna is remarkably compact, having the size of just 15 mm X 15 mm, which produces a peak gain of 4.688 dB at 28 GHz. Having resonant frequencies at 26/28 GHz, this antenna finds application in fifth-generation (5G) mobile communication millimeter wave (mmWave) band roll out in Malaysia and Local Multipoint Distribution Service (LMDS).
本文利用星形六边形分形的概念，设计并仿真了一种独特的多波段微带贴片天线。这项工作的重要贡献是对现有的矩形缺口分形设计几何图形进行了逻辑修改。所提出设计的结构具有FR-4环氧基板，厚度为1.6 mm，介电常数为4.4，损耗正切为0.02。利用高频结构模拟器(HFSS)软件进行仿真，对天线参数进行了优化，使其工作在24.9 ~ 28.1 GHz的频率范围内，总带宽为3.2 GHz。设计的天线非常紧凑，尺寸仅为15 mm X 15 mm，在28 GHz时产生4.688 dB的峰值增益。该天线谐振频率为26/28 GHz，适用于在马来西亚推出的第五代(5G)移动通信毫米波(mmWave)频段和本地多点分布服务(LMDS)。
{"title":"Multiband Fractal Antenna for 26/28 GHz Millimeter Wave Band","authors":"Sinduja Natesan Subramanian, Chandrasekharan Nataraj, Shankar Duraikannan, S. Selvaperumal, Raed M. T. Abdulla","doi":"10.1109/ICECAA55415.2022.9936386","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936386","url":null,"abstract":"This article presents the design and simulation of a unique multiband microstrip patch antenna, using the star hexagon fractal concept. The significant contribution in this work is the logical modifications in the existing fractal design geometry with a rectangular notch. The configuration of the proposed design has a substrate of FR-4 epoxy having a thickness of 1.6 mm, a dielectric constant of 4.4, and a loss tangent of 0.02. Simulated using High-Frequency Structure Simulator (HFSS) software, the parameters of the proposed antenna were well optimized so it would be suitable to be operated in the frequency range from 24.9 GHz to 28.1 GHz with an overall bandwidth of 3.2 GHz. The designed antenna is remarkably compact, having the size of just 15 mm X 15 mm, which produces a peak gain of 4.688 dB at 28 GHz. Having resonant frequencies at 26/28 GHz, this antenna finds application in fifth-generation (5G) mobile communication millimeter wave (mmWave) band roll out in Malaysia and Local Multipoint Distribution Service (LMDS).","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"171 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":"117288767","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/ICECAA55415.2022.9936589
Shu Li
This requires integrating the elements of the rule of law into the campus and building a campus culture of the rule of law that promotes the spirit of the socialist rule of law. This article starts from the basic connotation, and explores a reasonable optimization path through in-depth analysis of the problems and reasons existing in the construction of legal spirit and culture in colleges and universities. This paper studies the smart intervention methods and implementation strategies of the open and interoperable multimedia teaching environment, and builds a "3 points, 5 dimensions and 7S" smart intervention model, trying to provide sustainable, evaluable, and decision-making ideas for the teaching wisdom management system from application to service.
{"title":"Intelligent Scene Design for Deepening Legal Education in Colleges based on Computer Multimedia Immersive Display of Intelligent Environment","authors":"Shu Li","doi":"10.1109/ICECAA55415.2022.9936589","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936589","url":null,"abstract":"This requires integrating the elements of the rule of law into the campus and building a campus culture of the rule of law that promotes the spirit of the socialist rule of law. This article starts from the basic connotation, and explores a reasonable optimization path through in-depth analysis of the problems and reasons existing in the construction of legal spirit and culture in colleges and universities. This paper studies the smart intervention methods and implementation strategies of the open and interoperable multimedia teaching environment, and builds a \"3 points, 5 dimensions and 7S\" smart intervention model, trying to provide sustainable, evaluable, and decision-making ideas for the teaching wisdom management system from application to service.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"146 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":"116465070","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/ICECAA55415.2022.9936177
Juan Yan
Smart financial real-time control system implementation based on artificial intelligence and data mining is studied in the paper. Innovation and reform are important objectives for the operation and development of enterprises. With the construction, reform and innovation of intelligent financial management system, financial staff not only need to do a good job in budget management, cost control, system income data analysis, but also need to pay attention to the upgrading and maintenance of the financial management system functions. Enterprises can use the management model to adapt to the current dynamic and diversified market economic environment to ensure that smart financial management can create more economic value for the enterprise. The data mining and the AI models are integrated to construct the efficient model. It can be reflected that the designed model is intelligent and efficient.
{"title":"Smart Financial Real-Time Control System Implementation based on Artificial Intelligence and Data Mining","authors":"Juan Yan","doi":"10.1109/ICECAA55415.2022.9936177","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936177","url":null,"abstract":"Smart financial real-time control system implementation based on artificial intelligence and data mining is studied in the paper. Innovation and reform are important objectives for the operation and development of enterprises. With the construction, reform and innovation of intelligent financial management system, financial staff not only need to do a good job in budget management, cost control, system income data analysis, but also need to pay attention to the upgrading and maintenance of the financial management system functions. Enterprises can use the management model to adapt to the current dynamic and diversified market economic environment to ensure that smart financial management can create more economic value for the enterprise. The data mining and the AI models are integrated to construct the efficient model. It can be reflected that the designed model is intelligent and efficient.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"63 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":"123620475","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/ICECAA55415.2022.9936371
Mohammad Baig Mohammad, N. Bhuvaneswari, Ch. Pooja Koteswari, V. Priya
Forests being called as lungs of earth play a very important role in maintaining a sustainable climate on the earth. They are instrumental in maintaining a quality eco-system by filtering the air, preventing soil erosion and help to maintain diverse life on the earth. Forest fires are a matter of concern in terms of economic growth and ecological damage and damage to animals and human life. Forest fires contribute to global warming and imbalances the climate on the earth making the lives harder. Early detection of forest fire can prevent the damage by a great extent. Sensor based and Image processing-based methods have been widely used followed by machine learning techniques to process the sensor data and detect the occurrence of forest fires. These methods are costly and difficult to install at different locations in the forest. As the dimensions of the forest area increases, the complexity of the system also increases. Deep Learning techniques such as variations of convolutional neural networks process image data and can provide an early warning about the occurrence of the fire. In the proposed system different pre trained deep neural network architectures such as Resnet 50, InceptionV3, GoogleNet, AlexNet, MobileNet have been employed using transfer learning approaches on two very important datasets namely Mendely dataset and Kaggle Datasets. The best performing architecture i.e Alexnet has been deployed on to Raspberry PI embedded hardware to work as a standalone module. The trained models have demonstrated a good accuracy of 99.45% on Mendely and 99.42 on Kaggle Datasets for Fire detection.
{"title":"Hardware Implementation of Forest Fire Detection System using Deep Learning Architectures","authors":"Mohammad Baig Mohammad, N. Bhuvaneswari, Ch. Pooja Koteswari, V. Priya","doi":"10.1109/ICECAA55415.2022.9936371","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936371","url":null,"abstract":"Forests being called as lungs of earth play a very important role in maintaining a sustainable climate on the earth. They are instrumental in maintaining a quality eco-system by filtering the air, preventing soil erosion and help to maintain diverse life on the earth. Forest fires are a matter of concern in terms of economic growth and ecological damage and damage to animals and human life. Forest fires contribute to global warming and imbalances the climate on the earth making the lives harder. Early detection of forest fire can prevent the damage by a great extent. Sensor based and Image processing-based methods have been widely used followed by machine learning techniques to process the sensor data and detect the occurrence of forest fires. These methods are costly and difficult to install at different locations in the forest. As the dimensions of the forest area increases, the complexity of the system also increases. Deep Learning techniques such as variations of convolutional neural networks process image data and can provide an early warning about the occurrence of the fire. In the proposed system different pre trained deep neural network architectures such as Resnet 50, InceptionV3, GoogleNet, AlexNet, MobileNet have been employed using transfer learning approaches on two very important datasets namely Mendely dataset and Kaggle Datasets. The best performing architecture i.e Alexnet has been deployed on to Raspberry PI embedded hardware to work as a standalone module. The trained models have demonstrated a good accuracy of 99.45% on Mendely and 99.42 on Kaggle Datasets for Fire detection.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"89 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":"123664827","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/ICECAA55415.2022.9936402
Yan Yu-rao
The existing web page parsing technologies are analyzed and compared, and the method based on XQuery template is adopted to achieve accurate extraction of web page text metadata. 51 educational informatization policy documents are taken as the research object, and keyword research, word frequency research, text analysis and other methods have been used. 6 six keywords of China’s education informatization policy are put forward, and the picture of China’s education informatization policy is outlined. Introducing big data technology into education management practice and building an information-based education management model can better improve the level of education management in higher vocational colleges, play the role of education management as a guarantee, and cultivate students' professional quality.
{"title":"Hotspots and Policy Keyword Technology of Higher Vocational Education Reform based on Network Text Intelligent Analysis Algorithm","authors":"Yan Yu-rao","doi":"10.1109/ICECAA55415.2022.9936402","DOIUrl":"https://doi.org/10.1109/ICECAA55415.2022.9936402","url":null,"abstract":"The existing web page parsing technologies are analyzed and compared, and the method based on XQuery template is adopted to achieve accurate extraction of web page text metadata. 51 educational informatization policy documents are taken as the research object, and keyword research, word frequency research, text analysis and other methods have been used. 6 six keywords of China’s education informatization policy are put forward, and the picture of China’s education informatization policy is outlined. Introducing big data technology into education management practice and building an information-based education management model can better improve the level of education management in higher vocational colleges, play the role of education management as a guarantee, and cultivate students' professional quality.","PeriodicalId":273850,"journal":{"name":"2022 International Conference on Edge Computing and Applications (ICECAA)","volume":"273 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":"122153036","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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