2022 International Conference on Edge Computing and Applications (ICECAA)最新文献_第5页

Interpretation of Students' Cultural Confidence and Design of Online Evaluation System from the Perspective of Cluster Big Data 集群大数据视角下学生文化自信的解读与在线评价系统设计

2022 International Conference on Edge Computing and Applications (ICECAA) Pub Date : 2022-10-13 DOI: 10.1109/ICECAA55415.2022.9936527

Ouyang Jing, Xiao Ying, Li Xue

引用次数: 0

Farmer Friendly Smart App for Pomegranate Disease Identification 农民友好的智能应用程序石榴疾病识别

2022 International Conference on Edge Computing and Applications (ICECAA) Pub Date : 2022-10-13 DOI: 10.1109/ICECAA55415.2022.9936185

M. Nirmal, Pramod E Jadhav, N. Kadu

{"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}

引用次数: 1

LoRa WAN Communication using Wireless Sensor Network 利用无线传感器网络进行LoRa广域网通信

2022 International Conference on Edge Computing and Applications (ICECAA) Pub Date : 2022-10-13 DOI: 10.1109/ICECAA55415.2022.9936293

J. Gopinath, A. Elsden Christober, K. L. Ravindrananth, K. Malathi

{"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}

引用次数: 0

Adaptive Deep Belief Neural Networks for Pre-Term Birth Clinical Record to Sense Neonatal Apnea Level Classification 基于自适应深度信念神经网络的早产儿临床记录感知新生儿呼吸暂停水平分类

2022 International Conference on Edge Computing and Applications (ICECAA) Pub Date : 2022-10-13 DOI: 10.1109/ICECAA55415.2022.9936151

V. VishwaPriya

{"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}

引用次数: 0

Cyber Security and Data Mining Techniques 网络安全和数据挖掘技术

2022 International Conference on Edge Computing and Applications (ICECAA) Pub Date : 2022-10-13 DOI: 10.1109/ICECAA55415.2022.9936489

Pranab Kumar Goswami, Sunandan Baruah, L. Thakuria

引用次数: 0

Multiband Fractal Antenna for 26/28 GHz Millimeter Wave Band 26/28 GHz毫米波多波段分形天线

2022 International Conference on Edge Computing and Applications (ICECAA) Pub Date : 2022-10-13 DOI: 10.1109/ICECAA55415.2022.9936386

Sinduja Natesan Subramanian, Chandrasekharan Nataraj, Shankar Duraikannan, S. Selvaperumal, Raed M. T. Abdulla

引用次数: 0

Intelligent Scene Design for Deepening Legal Education in Colleges based on Computer Multimedia Immersive Display of Intelligent Environment 基于计算机多媒体沉浸式智能环境的高校法学教育深化智能场景设计

2022 International Conference on Edge Computing and Applications (ICECAA) Pub Date : 2022-10-13 DOI: 10.1109/ICECAA55415.2022.9936589

Shu Li

引用次数: 0

Smart Financial Real-Time Control System Implementation based on Artificial Intelligence and Data Mining 基于人工智能和数据挖掘的智能金融实时控制系统实现

2022 International Conference on Edge Computing and Applications (ICECAA) Pub Date : 2022-10-13 DOI: 10.1109/ICECAA55415.2022.9936177

Juan Yan

引用次数: 0

Hardware Implementation of Forest Fire Detection System using Deep Learning Architectures 基于深度学习架构的森林火灾探测系统硬件实现

2022 International Conference on Edge Computing and Applications (ICECAA) Pub Date : 2022-10-13 DOI: 10.1109/ICECAA55415.2022.9936371

Mohammad Baig Mohammad, N. Bhuvaneswari, Ch. Pooja Koteswari, V. Priya

{"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}

引用次数: 1

Hotspots and Policy Keyword Technology of Higher Vocational Education Reform based on Network Text Intelligent Analysis Algorithm 基于网络文本智能分析算法的高职教育改革热点与政策关键词技术

2022 International Conference on Edge Computing and Applications (ICECAA) Pub Date : 2022-10-13 DOI: 10.1109/ICECAA55415.2022.9936402

Yan Yu-rao

引用次数: 0