2023 3rd International Conference on Intelligent Technologies (CONIT)最新文献_第9页

Design of Directive Microstrip Slot Feed Patch Antenna for Medical Applications 医用指令微带槽馈电贴片天线的设计

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205820

N. Borkar, P. Parlewar

引用次数: 0

SOGI-SF Filter based Three Phase Grid Tied Solar Energy Conversion System 基于SOGI-SF滤波器的三相并网太阳能转换系统

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205746

P. Chakraborty, Tripurari Nath Gupta, Nitesh Kumar

引用次数: 0

Investigating the Influential Factors of Learner Performance in Online Education using Learning Analytics Approach 运用学习分析方法研究在线教育中学习者绩效的影响因素

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205849

Shabnam Ara S.J, R. Tanuja

{"title":"Investigating the Influential Factors of Learner Performance in Online Education using Learning Analytics Approach","authors":"Shabnam Ara S.J, R. Tanuja","doi":"10.1109/CONIT59222.2023.10205849","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205849","url":null,"abstract":"Online learning portals have become a crucial component of education systems worldwide. Despite offering benefits such as flexibility, accessibility, and convenience, virtual education also poses obstacles that contribute to a high dropout rate among online learners. These factors include poor time management, lack of motivation, difficulty adjusting to the virtual learning environment, and limited interaction with instructors and peers. To mitigate this issue we have to explore the factors that affect learners’ performance in online learning portals which will help in better design of these systems. This paper attempts to identify impact of geographical area, medium of study, sleep duration, login frequency, forum activity, and time spent on viewing online lectures and completing assignments on the performance of the technical students. The hypothesis were developed based on Transactional Distance Theory (TDT) and previous research to identify the relationships between various factors and academic outcome. To analyze our data and test our hypotheses, we utilized statistical techniques including two-sample t-tests and one-way ANOVA. Our results indicate that factors such as geographical area, medium of study, login frequency, forum activity, and time spent on viewing online lectures had a positive impact on learners’ performance, whereas sleep duration did not have a significant effect on performance. This study may be beneficial in enhancing learner performance in technical education by identifying the most critical factors that influence academic outcomes.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125110883","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

Stroktor-A System to Predict Ischemic Brain Stroke using Learning Techniques 使用学习技术预测缺血性脑卒中的stroktor系统

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205858

Rachna A. Karnavat, Eshwari B. Patole, Apurva S. Parkhi, Manasi A. Muluk, Surabhi J. More

{"title":"Stroktor-A System to Predict Ischemic Brain Stroke using Learning Techniques","authors":"Rachna A. Karnavat, Eshwari B. Patole, Apurva S. Parkhi, Manasi A. Muluk, Surabhi J. More","doi":"10.1109/CONIT59222.2023.10205858","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205858","url":null,"abstract":"According to WHO, over 15 million people suffer from a stroke which causes 5 million deaths and approximately 30% of survivors are facing serious disability. CDC (Centers for Disease Control and Prevention) has identified stroke as the fifth-leading cause of death globally. More than 70% of strokes are first events, hence making primary stroke prevention is particularly an important aspect. With the availability of a system to detect the brain stroke with the early symptoms occurring in patients would lead to early diagnosis and prevent severe consequences. This research work is dedicated to build a system that would detect brain stroke with premature symptoms and generate accurate results using neural networks and Computer Vision. Considering the severity of stroke, it is necessary to immediately consult medical practitioners to prevent the consequences which is a tough task. This state of art focuses on obtaining stroke possibility based on change in facial features as prominent symptoms and providing immediate precautionary measures. Along with this, providing an interactive platform for users to connect with available doctors using video calling for immediate consultation.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126097475","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 Novel Framework Approach for Diabetic Retinopathy Detection 一种新的糖尿病视网膜病变检测框架方法

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205570

Rahul Nijhawan, Alpana Dass, Ayush Dhankhar, Neha Mendirtta, Sanyam Kumar, Ashima Yadav

{"title":"A Novel Framework Approach for Diabetic Retinopathy Detection","authors":"Rahul Nijhawan, Alpana Dass, Ayush Dhankhar, Neha Mendirtta, Sanyam Kumar, Ashima Yadav","doi":"10.1109/CONIT59222.2023.10205570","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205570","url":null,"abstract":"The retinal veins are impacted by a disorder known as diabetic retinopathy (DR). (DR) is an eye disorder that damages the retina's small veins and is defined by the presence of different kinds of damage in the affected area. Among the diagnostic devices employed in the field of DR are the indirect ophthalmoscope, slit lamp examination, color photograph, and optical coherence tomography (OCT). If DR isn't diagnosed in the early stages, it might impair vision and even try to cause blindness. DR is divided into five stages or categories: typical, mild, severe, extreme, and PDR (Proliferative Diabetic Retinopathy). Usually, highly trained specialists examine the shaded fundus images to assess this deadly infection. Laser photocoagulation is still the standard treatment for people with proliferative diabetic retinopathy (PDR), even though it is an inherently bad idea. To naturally recognize DR and its various stages from retina images, several PC vision-based techniques have been developed. However, these methods are unable to decode the confusing hidden inclusions, and they have very poor precision when describing the many stages of DR, particularly the initial stages. The vast majority of the complexities of DR can be avoided with proper blood glucose management and treatment. Setting Diabetic retinopathy (DR) is the main cause of visual impairment in working-class Americans. There are several fresh DR interventions.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129414764","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

Effects of Image Augmentation Techniques for Rice Leaf Disease Detection 图像增强技术在水稻叶病检测中的应用

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205782

Trusha Talati, Akshath S Bhat, D. Kalbande

{"title":"Effects of Image Augmentation Techniques for Rice Leaf Disease Detection","authors":"Trusha Talati, Akshath S Bhat, D. Kalbande","doi":"10.1109/CONIT59222.2023.10205782","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205782","url":null,"abstract":"Rice leaf diseases substantially reduce crop yield, resulting in food shortages and financial losses. Early identification and control of these diseases can be aided and enhanced by automated computer vision-based detection systems. However, existing techniques suffer from low accuracy and inconsistency due to several issues. To improve model resilience against corrupted inputs and adversarial cases, this study examines the effects of image augmentation techniques on three transfer learning models for diagnosing rice leaf diseases. A consolidated dataset, which includes rice leaf images of five different classes, was used to train these models. The VGG-16 model trained on images augmented using the Random Flip technique achieves a maximum accuracy of 99.47%. However, we present the lightweight EfficientNet-B0 model, trained on MixUp augmented images, with an accuracy of 98.01%, as an alternative model that is more robust and suitable for deployment in mobile/web applications. Our results demonstrate that image augmentation techniques can enhance the model’s robustness against synthetically altered images without affecting its ability to detect and predict rice leaf diseases.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124639293","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

Random Fourier Features based approach for Covid-19 Twitter Sentiment Classification using Machine Learning and Deep Learning 使用机器学习和深度学习的基于随机傅立叶特征的Covid-19 Twitter情绪分类方法

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205735

E. Vignesh, Sachin Kumar S, N. Mohan, Kritik Soman

{"title":"Random Fourier Features based approach for Covid-19 Twitter Sentiment Classification using Machine Learning and Deep Learning","authors":"E. Vignesh, Sachin Kumar S, N. Mohan, Kritik Soman","doi":"10.1109/CONIT59222.2023.10205735","DOIUrl":"https://doi.org/10.1109/CONIT59222.2023.10205735","url":null,"abstract":"The outbreak of Corona has significantly impacted the daily lives of thousands of people. Many individuals turned to social media for guidance and information during this pandemic. However, while social media can be a valuable source of information, it also presents certain drawbacks such as the spread of misinformation. Despite this, social media played an essential role in sharing accurate health information and providing support for individuals struggling with mental health during the pandemic. Social networking sites like Twitter provided a means for individuals to connect and share their experiences during this difficult time. To evaluate the impact of COVID-19, we propose a strategy that utilizes sentiment analysis of tweets from Twitter users. This analysis can help identify the emotions that people are feeling towards COVID-19, such as hope, pessimism, fear, annoyance, sadness, or nervousness. We utilized feature engineering techniques for sentiment analysis to categorize tweets into positive, negative, or neutral categories. We also utilized machine learning models to evaluate the effectiveness of various feature extraction and engineering techniques. In addition, we utilized class imbalance strategies to address the imbalance of emotion classes. Our study compares different feature extraction methods for text data, including statistical methods, word embedding-based methods, kernel feature maps, and hybrid methods. We achieved superior accuracy, precision, f1-score, and recall compared to previous studies when applied to the Covid Senti - A, Covid Senti -B, and Covid Senti -C datasets. Our findings suggest the assessment of public sentiment towards COVID-19 through the analysis of social media data can be a useful resource.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124984375","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

Automatic Detection Method of Operation Error of Smart Electricity Meter Based on Machine Vision 基于机器视觉的智能电表运行误差自动检测方法

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205619

Jueyu Chen, Zhou Yang, Zhenglei Zhou

引用次数: 0

Solar Power Prediction Based on Recurrent Neural Networks Using LSTM and Dense Layer With ReLU Activation Function 基于LSTM和带ReLU激活函数的密集层递归神经网络的太阳能发电预测

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205605

Deepanshu Gupta, V. V. Ramana

引用次数: 0

Testing Significance of Layout Dependent Impacts on Silicon Chips Performance 布局依赖性对硅片性能影响的测试意义

2023 3rd International Conference on Intelligent Technologies (CONIT) Pub Date : 2023-06-23 DOI: 10.1109/CONIT59222.2023.10205910

Sandeep Kakde, Nadeem Khan

引用次数: 0