Pub Date : 2024-03-20DOI: 10.17148/ijarcce.2024.13326
P.R. Krishna Prasad, Naga Sai Ajay Kumar Abburi, Pavan Sai Ganesh Cherukuri, Dheeraj Kumar Bhattu, Jaswanth Gadde
{"title":"Unveiling Personality Traits through Social Media Language Analysis: A Novel Approach using Language Models","authors":"P.R. Krishna Prasad, Naga Sai Ajay Kumar Abburi, Pavan Sai Ganesh Cherukuri, Dheeraj Kumar Bhattu, Jaswanth Gadde","doi":"10.17148/ijarcce.2024.13326","DOIUrl":"https://doi.org/10.17148/ijarcce.2024.13326","url":null,"abstract":"","PeriodicalId":513159,"journal":{"name":"IJARCCE","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140226244","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}
Pub Date : 2024-02-20DOI: 10.17148/ijarcce.2024.13319
Mr. M. Kishore Babu, K. Jayasri, K. Saran, K. Adithya, K. Harsha
{"title":"An Approach for Cyberbullying Detection on Social Media","authors":"Mr. M. Kishore Babu, K. Jayasri, K. Saran, K. Adithya, K. Harsha","doi":"10.17148/ijarcce.2024.13319","DOIUrl":"https://doi.org/10.17148/ijarcce.2024.13319","url":null,"abstract":"","PeriodicalId":513159,"journal":{"name":"IJARCCE","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140445352","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}
Pub Date : 2024-02-20DOI: 10.17148/ijarcce.2024.13246
Sakthivel V, S. P
{"title":"Synchronization Techniques in Real-Time Operating Systems: Implementation and Evaluation on Arduino with FreeRTOS","authors":"Sakthivel V, S. P","doi":"10.17148/ijarcce.2024.13246","DOIUrl":"https://doi.org/10.17148/ijarcce.2024.13246","url":null,"abstract":"","PeriodicalId":513159,"journal":{"name":"IJARCCE","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140445727","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}
Pub Date : 2024-02-20DOI: 10.17148/ijarcce.2022.111015
Saurabh Suman Choudhuri
{"title":"THE IMPACT OF ARTIFICIAL INTELLIGENCE ON BUSINESS LEADERSHIP AND STRATEGY","authors":"Saurabh Suman Choudhuri","doi":"10.17148/ijarcce.2022.111015","DOIUrl":"https://doi.org/10.17148/ijarcce.2022.111015","url":null,"abstract":"","PeriodicalId":513159,"journal":{"name":"IJARCCE","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140447102","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}
Diabetic retinopathy is referred as diabetic eye disease. It causes damage to the retina of the light sensitive tissues at the rear portion of the eye. It mainly affects the working age population in the developing country. Right now, recognizing DR is a tedious and manual interaction that requires a prepared clinician to analyze and assess advanced shading fundus photos of the retina. The rate of diabetes is more in local populations and the detection of diabetic retinopathy is needed but there is a shortage of equipment because there are expensive. With persistent advancement of deep learning models we hope to increase the accuracy of the technique and extend it to glaucoma diagnostics. In Early days convolutional neural network were used it takes more time and gives the low accuracy rate. In this paper Regional convolutional neural network and resnet were used to increase the accuracy rate and reduce the time consumption. Convolutional neural network takes image as an input and process it in different ways and assigns important to that images and produces the output by the images. In convolutional neural network there are many layers mainly input layer, hidden layer and output layer. If this technique is implemented. Diabetic retinopathy can be detected at the early stage and we can reduce the number of blindness and increase the accuracy rate and time consumption. Keyword Diabetic retinopathy, Fundus photography, Deep learning and Data set I.INTRODUCTION Diabetic retinopathy (DR) is a common complication of diabetes associated with ret inal vascular damage caused by long standing diabetes. Furthermore, the diagnosis of DR mostly depends on the observation and evaluation to fundus photographs of which procedure can be time consuming even for experienced experts. Therefore computer aided automated diagnosis approaches have great potential in clinical to accurately detect DR in a short time which can further help to improve the screening rate of DR and reduce the number of blindness. For a deep learning model, the most important parts that should be focused on are data set, network architecture and training method. Before being used to train our model, fundus images data set obtained from public resources is pre processed and augmented. The model accepts two fundus images corresponding to the left eye and right eye as inputs and then transmits them into the Siamese like blocks. The information from two eyes is gathered into the fully connected layer and finally the model will output the diagnosis result of each eye respectively.
糖尿病视网膜病变被称为糖尿病眼病。它导致眼睛后部感光组织视网膜受损。它主要影响发展中国家的劳动适龄人口。目前，识别糖尿病眼病是一项繁琐的手工操作，需要有准备的临床医生分析和评估视网膜的高级阴影眼底照片。当地人口的糖尿病发病率较高，需要检测糖尿病视网膜病变，但由于设备昂贵，因此存在设备短缺的问题。随着深度学习模型的不断进步，我们希望提高该技术的准确性，并将其扩展到青光眼诊断中。早期使用的卷积神经网络耗时较长，准确率较低。本文使用区域卷积神经网络和 resnet 来提高准确率并减少时间消耗。卷积神经网络将图像作为输入，以不同的方式对其进行处理，并为图像分配重要信息，然后根据图像生成输出。卷积神经网络有很多层，主要是输入层、隐藏层和输出层。如果采用这种技术。糖尿病视网膜病变可以在早期阶段被检测出来，我们可以减少失明的人数，提高准确率，节省时间。关键词：糖尿病视网膜病变；眼底摄影；深度学习；数据集 I.INTRODUCTION 糖尿病视网膜病变（DR）是一种常见的糖尿病并发症，与长期糖尿病引起的视网膜血管损伤有关。此外，糖尿病视网膜病变的诊断主要依赖于对眼底照片的观察和评估，即使是经验丰富的专家，这一过程也很耗时。因此，计算机辅助自动诊断方法在临床上具有很大的潜力，可以在短时间内准确检测出 DR，从而进一步帮助提高 DR 的筛查率，减少失明人数。对于深度学习模型来说，最重要的部分是数据集、网络架构和训练方法。在训练我们的模型之前，先对从公共资源中获取的眼底图像数据集进行预处理和增强。该模型接受对应于左眼和右眼的两张眼底图像作为输入，然后将它们传输到类似连体块的网络中。两只眼睛的信息被收集到全连接层，最后模型将分别输出每只眼睛的诊断结果。
{"title":"Diabetic Retinopathy Detection using Deep Learning Techniques","authors":"Dr.V. Ramachandran, Akhila Patchala, Lakshmi Sowjanya Potla, Phinehas Prakash Jupudi, Rohith Sai Obilisetty","doi":"10.17148/ijarcce.2024.13252","DOIUrl":"https://doi.org/10.17148/ijarcce.2024.13252","url":null,"abstract":"Diabetic retinopathy is referred as diabetic eye disease. It causes damage to the retina of the light sensitive tissues at the rear portion of the eye. It mainly affects the working age population in the developing country. Right now, recognizing DR is a tedious and manual interaction that requires a prepared clinician to analyze and assess advanced shading fundus photos of the retina. The rate of diabetes is more in local populations and the detection of diabetic retinopathy is needed but there is a shortage of equipment because there are expensive. With persistent advancement of deep learning models we hope to increase the accuracy of the technique and extend it to glaucoma diagnostics. In Early days convolutional neural network were used it takes more time and gives the low accuracy rate. In this paper Regional convolutional neural network and resnet were used to increase the accuracy rate and reduce the time consumption. Convolutional neural network takes image as an input and process it in different ways and assigns important to that images and produces the output by the images. In convolutional neural network there are many layers mainly input layer, hidden layer and output layer. If this technique is implemented. Diabetic retinopathy can be detected at the early stage and we can reduce the number of blindness and increase the accuracy rate and time consumption. Keyword Diabetic retinopathy, Fundus photography, Deep learning and Data set I.INTRODUCTION Diabetic retinopathy (DR) is a common complication of diabetes associated with ret inal vascular damage caused by long standing diabetes. Furthermore, the diagnosis of DR mostly depends on the observation and evaluation to fundus photographs of which procedure can be time consuming even for experienced experts. Therefore computer aided automated diagnosis approaches have great potential in clinical to accurately detect DR in a short time which can further help to improve the screening rate of DR and reduce the number of blindness. For a deep learning model, the most important parts that should be focused on are data set, network architecture and training method. Before being used to train our model, fundus images data set obtained from public resources is pre processed and augmented. The model accepts two fundus images corresponding to the left eye and right eye as inputs and then transmits them into the Siamese like blocks. The information from two eyes is gathered into the fully connected layer and finally the model will output the diagnosis result of each eye respectively.","PeriodicalId":513159,"journal":{"name":"IJARCCE","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140448345","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}
Pub Date : 2024-02-20DOI: 10.17148/ijarcce.2024.13223
Dr. S Rajesh, P. B. Ahamed, M. Deepakkumar, T. Subramanian, G. R. Karna
{"title":"Predicting Air Quality by Particulate Matter Based on Neural Networks","authors":"Dr. S Rajesh, P. B. Ahamed, M. Deepakkumar, T. Subramanian, G. R. Karna","doi":"10.17148/ijarcce.2024.13223","DOIUrl":"https://doi.org/10.17148/ijarcce.2024.13223","url":null,"abstract":"","PeriodicalId":513159,"journal":{"name":"IJARCCE","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140449447","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}
Pub Date : 2024-02-20DOI: 10.17148/ijarcce.2024.13226
Punit Dewani, Efrain Rodriguez
{"title":"Enhancing Debugging Efficacy in U.S. Tech Enterprises: An Empirical Study of Smart Locker Integration and its Impact on Bug Resolution Volumes","authors":"Punit Dewani, Efrain Rodriguez","doi":"10.17148/ijarcce.2024.13226","DOIUrl":"https://doi.org/10.17148/ijarcce.2024.13226","url":null,"abstract":"","PeriodicalId":513159,"journal":{"name":"IJARCCE","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140445551","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}
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