2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)最新文献_第3页

An Enhanced Image Loading Framework for Social Media Applications 一种用于社交媒体应用程序的增强图像加载框架

2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS) Pub Date : 2023-05-18 DOI: 10.1109/ACCESS57397.2023.10200278

Siji Rani S, Lekshmi S. Nair, Vaisakh M S

引用次数: 0

Pandemic Outbreak Prediction using Optimization-based Machine Learning Model 基于优化的机器学习模型的大流行爆发预测

2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS) Pub Date : 2023-05-18 DOI: 10.1109/ACCESS57397.2023.10199872

Soni Singh, S. Mittal

{"title":"Pandemic Outbreak Prediction using Optimization-based Machine Learning Model","authors":"Soni Singh, S. Mittal","doi":"10.1109/ACCESS57397.2023.10199872","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10199872","url":null,"abstract":"Many pandemic epidemics have a variety of effects on people. Disease modelling is important in order to predict and evaluate the effects of these pandemics. A variety of statistical and machine learning (ML) models are built to produce the forecast. The models employ several ML strategies, but due to the available dataset, they are unable to achieve higher accuracy. To avoid the problem and improve forecast accuracy, we suggested an ML-based prediction model. This study optimises the parameters of the already employed machine learning models using the proposed Ant Colony Optimization approach (ACO). A comparison of different machine learning (ML) approaches, such as Polynomial Regression (PR), Support Vector Machine (SVM), and Linear Regression, is provided to predict the pandemic outbreak (LR). For COVID-19 datasets, the accuracy and Root Mean Square Error (RMSE) score of the proposed model are used to assess its performance. The results show that, as assessed by the RMSE score, the suggested method delivers good accuracy for daily prediction. The outcome forecast shows that PR-ACO outperforms other ML strategies in terms of final results. According to the results of the predictions, the proposed ACO parameter optimising algorithm will increase the capacity of current ML techniques to anticipate outbreaks across diverse countries.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122332492","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

Comparitive Analysis of Deep Learning Techniques for Assistive Soft Wearable Robots 辅助软可穿戴机器人深度学习技术的比较分析

2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS) Pub Date : 2023-05-18 DOI: 10.1109/ACCESS57397.2023.10199699

Ranjeesh R Chandran, Sreedeep Krishnan, Y. Chakrapani, D. Dharmaraj

{"title":"Comparitive Analysis of Deep Learning Techniques for Assistive Soft Wearable Robots","authors":"Ranjeesh R Chandran, Sreedeep Krishnan, Y. Chakrapani, D. Dharmaraj","doi":"10.1109/ACCESS57397.2023.10199699","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10199699","url":null,"abstract":"A new innovation called wearable assistive robotics has the potential of assisting those with sensorimotor disabilities in doing routine tasks. A lot of research is done on soft robots because of its adaptability, deformability, and flexibility. In contrast to soft robots and rigid robots, face obstacles in control, calibration, and modelling because the properties of soft materials which result in complex behaviors due to hysteresis and non-linearity. The use of deep learning techniques in real-time poses additional challenges for researchers when it comes to accuracy and equipment cost. Recent research has used various deep learning algorithms to address these constraints. This paper gives deep insight and analysis of existing deep learning techniques in the area of assistive soft wearable robotics and classifies their applicability in various soft robotic applications. The current constraints in the study field, along with an analysis of various deep learning models with regard to various types of assistive soft wearable robot applications, are provided, followed by a description and implementation of the available deep learning techniques for assistive soft wearable robotics.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114963899","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

Heart Disease Prediction System using Machine Learning Model 基于机器学习模型的心脏病预测系统

2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS) Pub Date : 2023-05-18 DOI: 10.1109/ACCESS57397.2023.10200757

P. S. Tomar, Kalpana Rai, Mohd. Zuber

{"title":"Heart Disease Prediction System using Machine Learning Model","authors":"P. S. Tomar, Kalpana Rai, Mohd. Zuber","doi":"10.1109/ACCESS57397.2023.10200757","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10200757","url":null,"abstract":"Computer-based computation play a tremendous role over the last two decades, as the demand for computers and technology is growing day by day, Data science is a technique to handle the large amount of data which is generated from the computer and other computational-based devices, the data is playing very crucial role nowadays. Artificial intelligence techniques can handle a large amount of data in various filed, like computer-based vision, medical imaging, object detection and tracking, security surveillance, and other fields. The healthcare sector is one of the most promising and necessity-based sectors among the other sectors, therefore machine learning-based models provide all the required computation and solutions for the computer-aided disease diagnosis. In this work we present the comparative machine learning model to predict heart disease on basis of different features and, also improve the accuracy and some other performance parameters to detect the respective diseases. Our experimental result shows better accuracy and other performance parameters value than existing techniques.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133418210","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

Modeling of high-speed ISL links in Satellite Communication 卫星通信中高速ISL链路建模

2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS) Pub Date : 2023-05-18 DOI: 10.1109/ACCESS57397.2023.10199144

Meet Kumari

引用次数: 0

Implementation of Deep Learning Technique for Citrus Leaf Blotch Disease Severity Detection 柑橘叶斑病严重程度检测的深度学习技术实现

2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS) Pub Date : 2023-05-18 DOI: 10.1109/ACCESS57397.2023.10199890

Nutika, Rishabh Sharma, V. Kukreja, Prince Sood, Ankit Bansal

引用次数: 0

Experimental study of a novel technique of data hiding with high PSNR 一种新的高信噪比数据隐藏技术的实验研究

2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS) Pub Date : 2023-05-18 DOI: 10.1109/ACCESS57397.2023.10200743

S. Verdiyev, A. Naghiyeva, Ajay Kumar

引用次数: 0

Protecting Contactless Credit Card Payments from Fraud through Ambient Authentication and Machine Learning 通过环境认证和机器学习保护非接触式信用卡支付免受欺诈

2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS) Pub Date : 2023-05-18 DOI: 10.1109/ACCESS57397.2023.10200022

Divneet Singh

{"title":"Protecting Contactless Credit Card Payments from Fraud through Ambient Authentication and Machine Learning","authors":"Divneet Singh","doi":"10.1109/ACCESS57397.2023.10200022","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10200022","url":null,"abstract":"Credit card fraud (CCF) is a persistent issue in the financial sector with serious consequences. Data mining has proven to be extremely useful in detecting fraud in online transactions. However, detecting CCF through data mining is quite a difficult task because of two causes: constant changes in the profiles of normal and fraudulent behaviour, and the highly skewed nature of the data sets. The outcome of fraud detection in credit card transactions depends on the sampling approach, detection techniques, and variable selection. This work studies the performance of K-Nearest Neighbor, Naive Bayes, Logistic Regression and Random Forest algorithms on a highly skewed dataset. The dataset contains 2,84,807 transactions and has been collected from European cardholder transactions. A hybrid of under-sampling and oversampling techniques has been used on the skewed data. The four techniques were utilized on both data namely preprocessed and raw, and the results are evaluated using specificity, accuracy, sensitivity, and F1-score. The outcomes show that the optimal accuracy for Naive Bayes, Logistic Regression, K-Nearest Neighbor and Random Forest classifiers are 98.72%, 52.34%, 96.89%, 91.67%, respectively. The comparative results indicate that K-Nearest Neighbor performs better than Logistic Regression, Random Forest and Naive Bayes techniques.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117031935","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

Hybrid Model for Sentiment Analysis of Whatsapp Data Whatsapp数据情感分析的混合模型

2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS) Pub Date : 2023-05-18 DOI: 10.1109/ACCESS57397.2023.10200411

Royal Kaushal, Raman Chadha

{"title":"Hybrid Model for Sentiment Analysis of Whatsapp Data","authors":"Royal Kaushal, Raman Chadha","doi":"10.1109/ACCESS57397.2023.10200411","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10200411","url":null,"abstract":"Sentiment analysis, also called opinion mining (OM), is an approach to analyzing the sentiments expressed in data. This approach uses Natural Language Processing to classify the data based on emotions in different classes. Various Social media data are analyzed to determine sentiment, and Machine Learning (ML) techniques classify the data. This study utilizes ML models to analyze sentiment in WhatsApp data. The sentiment analysis process includes some steps, such as to pre-process the data, extract the features, and classify the data. The initial stage contributes to the clean-up of raw data and transforms it to make it suitable for analysis. Feature extraction is a stage to retrieve a relevant feature from the pre-processed data that contribute to determining sentiment. Finally, machine learning algorithms classify data to determine the sentiments expressed in the text. This work proposes a voting classifier which is hybrid architecture comprising SVM, KNN, and a Decision tree. Python is executed to simulate the suggested algorithm, and its performance is evaluated based on accuracy, precision, and recall metrics. These parameters are useful in measuring the efficiency of the algorithm in accurately classifying the sentiments existing in the data.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121493691","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

Development of an Innovative Optimal Route Selection Model Based on an Improved Multi-Objective Genetic Algorithm (IMOGA) Method in IoT Healthcare 基于改进多目标遗传算法(IMOGA)的物联网医疗创新最优路径选择模型

2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS) Pub Date : 2023-05-18 DOI: 10.1109/ACCESS57397.2023.10201024

Jeejo K P, Bobby Mathews C

{"title":"Development of an Innovative Optimal Route Selection Model Based on an Improved Multi-Objective Genetic Algorithm (IMOGA) Method in IoT Healthcare","authors":"Jeejo K P, Bobby Mathews C","doi":"10.1109/ACCESS57397.2023.10201024","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10201024","url":null,"abstract":"One of the most widely used IoT applications, healthcare aims to reduce the need for hospitalisation by monitoring patients' vital signs throughout the day for several weeks. Many sensors, including as vital and unstructured message sensors as well as environmental monitoring sensors, are deployed in healthcare systems to collect patient information and lower costs for the patients. Several issues along the transmission channel could result in the loss of data gathered by sensors integrated into medical equipment. In order to identify nearly optimal routes and create a cutting-edge optimal route selection model for IoT healthcare, this article employs the Improved Multi-Objective Genetic Algorithm (IMOGA) technique. For a variety of causes, data transmitted by sensors integrated into medical equipment may be lost. As a result, creating a safe communication method in IoT networks is crucial for the healthcare industry. As a result, the best path for medical data is chosen while taking energy, distance, and delay into account. The performances of the adopted work are then contrasted. According on experimental findings, Energy, distance, and delay have all been improved by the suggested strategy by 14%, 2%, and 5.6%, respectively.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"210 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114668213","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