2023 World Conference on Communication & Computing (WCONF)最新文献_第2页

Alzheimer Detection Using CNN and GAN Augmentation 基于CNN和GAN增强的阿尔茨海默病检测

2023 World Conference on Communication & Computing (WCONF) Pub Date : 2023-07-14 DOI: 10.1109/WCONF58270.2023.10235172

Sanchit Vashisht, Bhanu Sharma, Shweta Lamba

{"title":"Alzheimer Detection Using CNN and GAN Augmentation","authors":"Sanchit Vashisht, Bhanu Sharma, Shweta Lamba","doi":"10.1109/WCONF58270.2023.10235172","DOIUrl":"https://doi.org/10.1109/WCONF58270.2023.10235172","url":null,"abstract":"Alzheimer’s disease is a neurological condition which slowly weakens the memory, ability of thinking and reasoning along with the ability of performing day to day activities. The senior citizens are mostly affected with this disorder especially those in their sixties. The specific cause for this condition is still not clear but it can genetic, accidental or can be caused by some other circumstances considered as hypothesis at the moment. There are a few methods of detecting the disease but the MRI scans are the prominent ones among them. And for this research MRI scans are used in the form of scanned images. The disease has been classified into four categories for this research that are healthy, mild demented, very mild demented, and moderately demented. Deep learning algorithms are being used because of their efficient ways of working in the medical field. CNN the commonly used deep learning algorithm is kept as the base for the proposed model and the dataset is collected from Kaggle. The collected dataset is increased with the help of GAN augmentation to improve the accuracy of the model. The model gives accurate results up to 98.5% for detecting the disease and its categories. This model can help the medical workers in the form of a second opinion when combined with the present detecting techniques and can reduce their workloads.","PeriodicalId":202864,"journal":{"name":"2023 World Conference on Communication & Computing (WCONF)","volume":"63 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":"123188711","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

Combining CNN and LSTM for Precise Detection and Classification of Tomato Speck Disease 结合CNN和LSTM的番茄斑点病精确检测与分类

2023 World Conference on Communication & Computing (WCONF) Pub Date : 2023-07-14 DOI: 10.1109/WCONF58270.2023.10235126

Kushwant Kaur, Rishabh Sharma, A. Jain, Vikrant Sharma, V. Kukreja

{"title":"Combining CNN and LSTM for Precise Detection and Classification of Tomato Speck Disease","authors":"Kushwant Kaur, Rishabh Sharma, A. Jain, Vikrant Sharma, V. Kukreja","doi":"10.1109/WCONF58270.2023.10235126","DOIUrl":"https://doi.org/10.1109/WCONF58270.2023.10235126","url":null,"abstract":"In tomato crops, a fungus called tomato speck disease can result in considerable output losses. For the disease to be managed and controlled effectively, accurate and prompt diagnosis of the condition is essential. In this paper, we present a hybrid CNN and LSTM model for tomato speck disease detection and multi-classification based on 5 distinct severity levels. 10,000 tomato photos from a large dataset were used to train and test the model, which had a binary classification accuracy of 91.18% for determining whether the illness was present or not and an overall multi-classification accuracy of 94.45% for determining the disease severity level. The suggested method outperforms conventional DL approaches in terms of performance, and because to its high degree of accuracy and resilience, it is ideal for use in real-world applications. The results of this study might have a big impact on how tomato speck disease is identified and classified, which could improve the output and quality of tomato crops in the agricultural sector. Future study could involve enhancing it for usage on edge devices and expanding it to additional plant diseases and crops.","PeriodicalId":202864,"journal":{"name":"2023 World Conference on Communication & Computing (WCONF)","volume":"23 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":"123341974","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 Suitable Approach for Classifying Skin Disease Using Deep Convolutional Neural Network 基于深度卷积神经网络的皮肤病分类方法研究

2023 World Conference on Communication & Computing (WCONF) Pub Date : 2023-07-14 DOI: 10.1109/WCONF58270.2023.10235079

Gunjan Sharma, Vatsala Anand, Vijay Kumar

引用次数: 0

Blood stroke Classification using Proposed CNN Model 基于CNN模型的脑卒中分类

2023 World Conference on Communication & Computing (WCONF) Pub Date : 2023-07-14 DOI: 10.1109/WCONF58270.2023.10235028

Rahul Singh, N. Sharma, Himakshi Gupta

引用次数: 0

Low Code Backend As A Service Platform 低代码后端即服务平台

2023 World Conference on Communication & Computing (WCONF) Pub Date : 2023-07-14 DOI: 10.1109/WCONF58270.2023.10234969

Abijith Prasanthan, K. S. Anand, Bharath Prathap Nair, K. Gautham Santhosh, J. Swaminathan

引用次数: 0

Machine Learning-Based Sentiment Analysis of Movie Review 基于机器学习的电影评论情感分析

2023 World Conference on Communication & Computing (WCONF) Pub Date : 2023-07-14 DOI: 10.1109/WCONF58270.2023.10235239

Vrushabh Amrutiya, Disney Javiya, Hemang Thakar

{"title":"Machine Learning-Based Sentiment Analysis of Movie Review","authors":"Vrushabh Amrutiya, Disney Javiya, Hemang Thakar","doi":"10.1109/WCONF58270.2023.10235239","DOIUrl":"https://doi.org/10.1109/WCONF58270.2023.10235239","url":null,"abstract":"An important method for evaluating a film’s performance is through movie reviews. A collection of movie reviews is what provides us with a deeper qualitative insight on various aspects of the movie, whereas providing a movie with a numerical rating in the form of stars tells us about the success or failure of the movie quantitatively. We can learn about the movie’s strengths and weaknesses from a textual review, and a morein-depth analysis of a movie review can tell us if the movie overall meets the reviewer’s expectations. One of the most important areas of machine learning is sentiment analysis, which seeks to extract subjective information from written reviews. Natural language processing and text mining are closely related to sentiment analysis. It can be used to determine the reviewer’s perspective on a variety of subjects or the review’s overall polarity. Using sentiment analysis, we can determine whether the reviewer was ”positive,” ”negative,” and so on while providing their feedback. In this project, we want to use Sentiment Analysis on a set of movie reviews written by reviewers to figure out how they felt about the movie overall, such as whether they liked it or hated it. We want to use the relationships between the words in the review to predict the review’s overall polarity.","PeriodicalId":202864,"journal":{"name":"2023 World Conference on Communication & Computing (WCONF)","volume":"18 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":"126502813","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

Fake Product Review Detection and Elimination using Opinion Mining 基于意见挖掘的虚假产品评论检测与消除

2023 World Conference on Communication & Computing (WCONF) Pub Date : 2023-07-14 DOI: 10.1109/WCONF58270.2023.10234996

A. Thilagavathy, P. R. Therasa, J. Jasmine, M. Sneha, R. Shree Lakshmi, S. Yuvanthika

{"title":"Fake Product Review Detection and Elimination using Opinion Mining","authors":"A. Thilagavathy, P. R. Therasa, J. Jasmine, M. Sneha, R. Shree Lakshmi, S. Yuvanthika","doi":"10.1109/WCONF58270.2023.10234996","DOIUrl":"https://doi.org/10.1109/WCONF58270.2023.10234996","url":null,"abstract":"Identification and elimination of fake reviews and their removal from the dataset provided using the supervised machine learning algorithm and natural language processing techniques based on a vast variety of aspects. In this proposed paper, we trained the counterfeit review dataset by the process of using two independently developed machine learning algorithm models for assessing the extent to which the information being provided is real. The counterfeit product evaluations can be found on numerous online retailers are mostly influencing the customers to buy those products and profit for those products is probably dependent on the reviews of those products. Hence these counterfeit reviews must be noticed so that large E-commerce companies like Meesho, Amazon, Flipkart, Nykaa, etc. can address this issue so that fraudsters and fraudulent critics are taken out, sustaining users’ credibility in shopping sites. This approach may be utilized for websites and apps with relatively few consumers, estimating the authenticity of reviews so that online businesses can respond to them suitably. This model is developed using Naive Bayes, Support Vector Machine,and TF-IDF (term frequency-inverse document frequency)Vectorizer. To detect spam reviews on a website or application instantly, one can make use of these models. However, effectively countering spammers requires a sophisticated model that has to undergo training on a large dataset of millions of reviews. In this work” Reviews of 20 Hotels in Chicago hotel dataset” a limited dataset is utilized to train the models on a small scale, but it can be expanded to achieve greater accuracy and authenticity in the reviews.","PeriodicalId":202864,"journal":{"name":"2023 World Conference on Communication & Computing (WCONF)","volume":"14 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":"132052735","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

Edge Computing in 5G for Mobile AR/VR Data Prediction and Slicing Model 5G边缘计算用于移动AR/VR数据预测和切片模型

2023 World Conference on Communication & Computing (WCONF) Pub Date : 2023-07-14 DOI: 10.1109/WCONF58270.2023.10235144

P. J. Kumar, M. K. Kanth, B. Nikhil, D. H. Vardhana, Vithya Ganesan

引用次数: 0

HaarCascade and LBPH Algorithms in Face Recognition Analysis 人脸识别分析中的HaarCascade和LBPH算法

2023 World Conference on Communication & Computing (WCONF) Pub Date : 2023-07-14 DOI: 10.1109/WCONF58270.2023.10235019

M. Gupta, Karan Bisht, Abhay Sharma, Deepak Upadhyay

引用次数: 0

Operation and Maintenance Management Technology of Distribution Network Based on RFID Technology 基于RFID技术的配电网运维管理技术

2023 World Conference on Communication & Computing (WCONF) Pub Date : 2023-07-14 DOI: 10.1109/WCONF58270.2023.10235053

Liyun Dong

{"title":"Operation and Maintenance Management Technology of Distribution Network Based on RFID Technology","authors":"Liyun Dong","doi":"10.1109/WCONF58270.2023.10235053","DOIUrl":"https://doi.org/10.1109/WCONF58270.2023.10235053","url":null,"abstract":"In recent years, the research of distribution network operation and maintenance management technology based on RFID technology has received widespread attention. This technology provides an effective solution for improving the efficiency of distribution network by providing real-time monitoring, data collection and analysis. RFID technology can identify and track goods from production to delivery to ensure their safe and timely delivery. In addition, it helps to reduce human intervention in inventory management, thus reducing operating costs. The application of RFID technology in distribution network also helps to improve maintenance management by detecting faults and predicting potential faults. This allows timely repair or replacement before failure (which may cause major losses). In a word, the research of distribution network operation and maintenance management technology based on RFID is very important to improve the efficiency and effectiveness of distribution network. The implementation of this technology will improve productivity, reduce costs, increase profits, and ensure customer satisfaction through timely delivery. Improve the level of cable (channel) management and realize the accuracy, informatization, intelligence and efficiency of distribution asset management. The system provides a good guarantee for electric power enterprises to grasp and manage the operation status of equipment in real time. The application of intelligent patrol management system improves the core competitiveness of enterprises and greatly improves the modern management level of enterprises.","PeriodicalId":202864,"journal":{"name":"2023 World Conference on Communication & Computing (WCONF)","volume":"113 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":"130800183","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