2023 World Conference on Communication & Computing (WCONF)最新文献

An Analysis of Fabric Defect Detection Techniques for Textile Industry Quality Control 纺织工业质量控制中织物疵点检测技术分析

2023 World Conference on Communication & Computing (WCONF) Pub Date : 2023-07-14 DOI: 10.1109/WCONF58270.2023.10235154

Aarva Mehta, Reetu Jain

引用次数: 0

Intelligent Hoisting Method and System of Prefabricated Building Based on Machine Vision 基于机器视觉的装配式建筑智能吊装方法及系统

2023 World Conference on Communication & Computing (WCONF) Pub Date : 2023-07-14 DOI: 10.1109/WCONF58270.2023.10235149

Ming-Wei Huang, Shanshan Li, Jia Liu, Jinfeng Chen

引用次数: 0

Detection of Sleep Apnea and its Intensity in Adults 成人睡眠呼吸暂停及其强度的检测

2023 World Conference on Communication & Computing (WCONF) Pub Date : 2023-07-14 DOI: 10.1109/WCONF58270.2023.10235037

Bhupinder Singh Saini, Chirag Kaushik, Ayussh Vashishth, Lavi Tanwar

{"title":"Detection of Sleep Apnea and its Intensity in Adults","authors":"Bhupinder Singh Saini, Chirag Kaushik, Ayussh Vashishth, Lavi Tanwar","doi":"10.1109/WCONF58270.2023.10235037","DOIUrl":"https://doi.org/10.1109/WCONF58270.2023.10235037","url":null,"abstract":"The following paper introduces a new method for identifying Obstructive Sleep Apnea (OSA), a widespread sleep disorder that impacts a large number of people globally. OSA is characterized by breathing pauses lasting from a few seconds to a minute or more. Our proposed approach utilizes audio signals for OSA detection. Existing studies require the use of ECG or EEG signals, which entail bulky equipment, electrodes, and instruments attached to the patient, resulting in a time-consuming and inconvenient signal extraction process. Conversely, our study uses audio signals due to their accessibility and convenience. To accurately detect OSA, we convert audio signals to time and frequency domains using FFT and DWT. Features are then extracted and used in the ANN model to obtain high accuracy and specificity in OSA detection. The proposed approach achieves high accuracy and specificity in detecting OSA. With the ANN model, we achieved an accuracy of 94.1%, sensitivity of 98.5%, and specificity of 88.7%. This indicates the potential of using audio signals for OSA detection, serving as a non-invasive and cost-effective method for OSA diagnosis.","PeriodicalId":202864,"journal":{"name":"2023 World Conference on Communication & Computing (WCONF)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124994939","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

Design and Application of Computer Basic Education Network Platform Based on GABP Algorithm 基于GABP算法的计算机基础教育网络平台设计与应用

2023 World Conference on Communication & Computing (WCONF) Pub Date : 2023-07-14 DOI: 10.1109/WCONF58270.2023.10235208

Shanshan Cheng

{"title":"Design and Application of Computer Basic Education Network Platform Based on GABP Algorithm","authors":"Shanshan Cheng","doi":"10.1109/WCONF58270.2023.10235208","DOIUrl":"https://doi.org/10.1109/WCONF58270.2023.10235208","url":null,"abstract":"With the widespread application of computer and network technology in education, education has entered the fast lane of information technology development. Education network platforms provide various information services for education and teaching, playing an increasingly important role in current education informatization. The design and application of a computer basic education network platform based on the Gabp algorithm is an innovative method to improve students’ computer literacy. The Gabp algorithm represents gradient adaptive backpropagation and is a machine learning technique that has been used in various fields such as image recognition and speech processing. The platform aims to provide students with an interactive and engaging learning experience by integrating multimedia elements such as videos, animations, and interactive quizzes. The Gabp algorithm is used to personalize each student’s learning experience based on their personal progress and performance. The application of this platform may benefit schools, colleges, and universities where computer literacy is crucial. It can also be used in online learning environments to provide accessible education for individuals who may not be able to use traditional classroom environments. Overall, the design and application of a computer basic education network platform based on the Gabp algorithm may completely change the way we teach computer skills and improve students’ digital literacy.","PeriodicalId":202864,"journal":{"name":"2023 World Conference on Communication & Computing (WCONF)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126967656","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

Pre-trained Deep learning model for Monkeypox Prediction using Dermoscopy Images in Healthcare 医疗保健中使用皮肤镜图像进行猴痘预测的预训练深度学习模型

2023 World Conference on Communication & Computing (WCONF) Pub Date : 2023-07-14 DOI: 10.1109/WCONF58270.2023.10234989

Shikha Prasher, Leema Nelson, S. Gomathi

{"title":"Pre-trained Deep learning model for Monkeypox Prediction using Dermoscopy Images in Healthcare","authors":"Shikha Prasher, Leema Nelson, S. Gomathi","doi":"10.1109/WCONF58270.2023.10234989","DOIUrl":"https://doi.org/10.1109/WCONF58270.2023.10234989","url":null,"abstract":"Monkeypox is a medical skin problem that can be transferred from animals to humans and then from one person to other. Its species is Otho poxvirus. The manifestations of monkeypox and smallpox are virtually identical thus, antiviral medication developed to prevent the smallpox virus may be used for monkeypox despite the absence of effective therapy. Infected individuals, smallpox vaccination, prevention infection, and use of personal Protective Equipment (PPE) kits are all part of the control of monkey pox. In this study, deep learning-based convolution neural network (CNN) is used to detect monkeypoxes. In this research, three optimizers namely SGD, RMSProp and Adam are employed to predict monkeypox. From the three optimizers, the best optimizer is selected based on accuracy. The SGD optimizer achieves highest accuracy of 93.39% in 100 epochs. Other optimizers were RMSProp and Adam, with scores of 91.30% and 93.22%, respectively. Using a single image of an infected person, the CNN model easily predicts the monkeypox virus. This model can be used as second source of opinion for medical practitioners to identify the monkeypox.","PeriodicalId":202864,"journal":{"name":"2023 World Conference on Communication & Computing (WCONF)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115595558","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

Collaborative Intelligence in AgriTech: Federated Learning CNN for Bean Leaf Disease Classification 农业技术中的协同智能:用于豆叶病分类的联合学习CNN

2023 World Conference on Communication & Computing (WCONF) Pub Date : 2023-07-14 DOI: 10.1109/WCONF58270.2023.10235072

Shiva Mehta, V. Kukreja, A.M. Gupta

{"title":"Collaborative Intelligence in AgriTech: Federated Learning CNN for Bean Leaf Disease Classification","authors":"Shiva Mehta, V. Kukreja, A.M. Gupta","doi":"10.1109/WCONF58270.2023.10235072","DOIUrl":"https://doi.org/10.1109/WCONF58270.2023.10235072","url":null,"abstract":"This study introduces a convolutional neural network (CNN) technique based on federated learning to classify bean leaf diseases. The research allays data privacy by allowing users to train local models on their datasets without sharing raw data. Our method combines local models from four customers to produce a high-performing global model that can categorize bean leaf diseases into five groups. According to the findings, the local models for each customer performed well in terms of precision, recall, F1 score, and accuracy. The performance measures’ macro, weighted, and micro averages showed that the aggregated global model performed equally well across all customers. The global model’s weighted average precision, recall, F1-score, and accuracy were 92.61%, 89.72%, and 92.87%, respectively. This research demonstrates how the federated learning-based CNN technique can effectively use various data types from multiple clients while maintaining data privacy. The accuracy with which this method identified bean leaf illnesses demonstrates the potential of federated learning in the agricultural field and provides a viable strategy for further study and real-world applications.","PeriodicalId":202864,"journal":{"name":"2023 World Conference on Communication & Computing (WCONF)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114178023","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

Application of Active Learning Algorithm in Mobile Ad Hoc Network Intrusion Detection 主动学习算法在移动Ad Hoc网络入侵检测中的应用

2023 World Conference on Communication & Computing (WCONF) Pub Date : 2023-07-14 DOI: 10.1109/WCONF58270.2023.10234999

Ming Yin

{"title":"Application of Active Learning Algorithm in Mobile Ad Hoc Network Intrusion Detection","authors":"Ming Yin","doi":"10.1109/WCONF58270.2023.10234999","DOIUrl":"https://doi.org/10.1109/WCONF58270.2023.10234999","url":null,"abstract":"Network intrusion detection system is widely used in the security protection of network systems. It monitors network traffic, finds suspicious traffic, and actively responds. In recent years, the network intrusion detection system based on anomaly detection has attracted widespread attention because it can detect unknown attacks. Introduction Mobile ad hoc network is a wireless network, which is composed of mobile nodes. These nodes can communicate with each other without using fixed infrastructure. Communication between nodes is based on routing protocols such as distance vector protocol (DV), link state protocol (LSP) and hybrid protocol. Mobile ad hoc networks have applications in many applications, such as sensor networks, vehicle networks, wireless sensor networks, and so on. Due to the characteristics of open media, dynamic topology, interaction and limited resources, mobile ad hoc networks need more security than traditional networks. This paper will discuss the application of active learning algorithm in mobile ad hoc intrusion detection system. An integrated intrusion detection model is introduced. In this model, the classifier with supervised anomaly detection is based on support vector machine. At the same time, three pool-based active learning algorithms applied in the model are introduced. Compared with the traditional self-learning algorithm, the pool-based active learning algorithm can effectively reduce the dependence on training samples and reduce the impact of noise data on the performance of the intrusion detection system. It is suitable for the requirements of mobile ad hoc networks for high detection rate, high anti-noise ability and low computational delay of the intrusion detection system.","PeriodicalId":202864,"journal":{"name":"2023 World Conference on Communication & Computing (WCONF)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114262612","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

Growing the Seed of Future from Soil to Server: An Introductory Exploration of Current Tech in Farming Practices 从土壤到服务器种植未来的种子:当前农业实践技术的介绍性探索

2023 World Conference on Communication & Computing (WCONF) Pub Date : 2023-07-14 DOI: 10.1109/WCONF58270.2023.10235232

Vastav Nissan Swain, Aditya Tyagi, Jayanti Rani Mahata, Er. Simran

{"title":"Growing the Seed of Future from Soil to Server: An Introductory Exploration of Current Tech in Farming Practices","authors":"Vastav Nissan Swain, Aditya Tyagi, Jayanti Rani Mahata, Er. Simran","doi":"10.1109/WCONF58270.2023.10235232","DOIUrl":"https://doi.org/10.1109/WCONF58270.2023.10235232","url":null,"abstract":"Smart farming, also referred to as precision agriculture, is revolutionizing the agricultural industry by utilizing cutting-edge technologies like cloud computing, the Internet of Things (IoT), machine learning(ML), and Cloud Computing. Farmers can now collect information from a variety of sources, including soil monitors, weather systems, and drones, to make well-informed agricultural management choices thanks to the use of these technologies. Large amounts of data can now be processed and stored more easily thanks to the use of cloud computing, which makes it simpler for farms to analyze the data and use it to maximize agricultural yields and conserve resources. Additionally, agricultural development trends have been predicted using machine learning methods, which have also been used to optimize irrigation, fertilizer, and pesticide use. Precision agriculture has greatly benefited from the IoT, which enables farmers to virtually watch and manage various systems and devices. Data on soil wetness, temperature, and other variables are collected using IoT devices, which are then used to send the data to cloud-based platforms for analysis. With this knowledge, producers can decide when and how much to fertilize or irrigate their crops, saving water and preventing pesticide overflow. IoT devices can also assist in agricultural health monitoring, pest and disease detection, and alerting producers when a response is required. In general, precision agriculture has benefited greatly from the integration of cloud computing, IoT, and machine learning, including increased agricultural yields, enhanced resource economy, and greater environmental sustainability. Future agriculture could be transformed by smart farming, which would also help to secure sustenance for a rising world population. The advantages of incorporating cloud computing, IoT, and machine learning in farmland are highlighted in this paper’s description of smart farming.","PeriodicalId":202864,"journal":{"name":"2023 World Conference on Communication & Computing (WCONF)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122144792","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

Ensemble Model for Predicting the Best Fruit Crop based on Soil Chemical Composition and Environmental Variables 基于土壤化学成分和环境变量的最佳水果作物预测集成模型

2023 World Conference on Communication & Computing (WCONF) Pub Date : 2023-07-14 DOI: 10.1109/WCONF58270.2023.10235170

Shaik Imran Mohammad, K. Vani, Ganta Lokeshwar, K.S Vijaya Lakshmi

{"title":"Ensemble Model for Predicting the Best Fruit Crop based on Soil Chemical Composition and Environmental Variables","authors":"Shaik Imran Mohammad, K. Vani, Ganta Lokeshwar, K.S Vijaya Lakshmi","doi":"10.1109/WCONF58270.2023.10235170","DOIUrl":"https://doi.org/10.1109/WCONF58270.2023.10235170","url":null,"abstract":"For sustainable agriculture and food security, it is essential to choose crops that are suitable for the particular soil type and environmental circumstances. The goal of this study is to create a machine learning model that can forecast the best fruit crop based on a particular combination of environmental factors and soil chemical composition. The model outputs the most suitable fruit crops for those soil conditions and environmental variables based on input features like nitrogen (N), magnesium (Mg), phosphorus (P), potassium (K), calcium (Ca), zinc (Zn), potential of hydrogen (pH), temperature, rainfall, electrical conductivity, and levels of organic carbon (OC). Using a dataset of soil samples, environmental circumstances, and their related best fruit harvest, we compare the performance of several machine learning methods, such as Random forests (Rf), Support vector machines (SVM), Naive Bayes (NB), Logistic Regression, and K-Nearest Neighbours (KNN). To increase the accuracy of the less accurate models, feature selection strategies and hyperparameter tuning are then explored. Building an ensemble machine learning model by integrating these improved models with the Stacking classifier and Voting classifier. Based on the chemical makeup of the soil and other environmental parameters, our model can help farmers and agronomists make educated judgements about which fruit crops to produce. Farmers can choose the most suitable fruit crop by taking into account these variables, there by increasing agricultural production and maintaining food security.","PeriodicalId":202864,"journal":{"name":"2023 World Conference on Communication & Computing (WCONF)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129354679","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

A Neural Radiance Field-Based Architecture for Intelligent Multilayered View Synthesis 一种基于神经辐射场的多层视图智能合成体系结构

2023 World Conference on Communication & Computing (WCONF) Pub Date : 2023-07-14 DOI: 10.1109/WCONF58270.2023.10234976

D. Dhinakaran, S. M. U. Sankar, G. Elumalai, N. J. Kumar

{"title":"A Neural Radiance Field-Based Architecture for Intelligent Multilayered View Synthesis","authors":"D. Dhinakaran, S. M. U. Sankar, G. Elumalai, N. J. Kumar","doi":"10.1109/WCONF58270.2023.10234976","DOIUrl":"https://doi.org/10.1109/WCONF58270.2023.10234976","url":null,"abstract":"NeRF, or neural radiation field, is a technique for producing distinctive ways of complicated scenes by maximizing a continuous voxel scenery functional result with a constrained amount of input point of views. NeRF’s main goal is to train this neural network to forecast the radiance values at any given 3D point in the given 3D coordinates. Using multilayer perceptive weights, neural radiation fields (NRFs) replicate the color and volume of an object as a function of three-dimensional parameters. The current method for creating neural radiance fields includes improving the representation for each scene separately, which necessitates multiple calibrated views and a large amount of computation time. We begin to address these issues with a framework that completely convolutionally subjects a NeRF to picture inputs. NeRF is capable of modeling several common, everyday phenomena in restrained photos, such as fluctuating lighting or transitory obstruction, however it is ineffective while predicting pictures of immobile subjects that were shot in constrained environments. To resolve these problems and reconstructions from unstructured picture sets downloaded from the internet, we developed several NeRF enhancements. By examining online image collections from prominent websites, we show that our method creates fresh perspective renderings that are much more accurate than the beginning state of the art.","PeriodicalId":202864,"journal":{"name":"2023 World Conference on Communication & Computing (WCONF)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123970950","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