2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)最新文献_第6页

Detecting Deepfakes Using Deep Learning 使用深度学习检测深度伪造

2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Pub Date : 2021-08-27 DOI: 10.1109/RTEICT52294.2021.9573740

J. Dheeraj, Krutant Nandakumar, A. Aditya, B. S. Chethan, G. Kartheek

{"title":"Detecting Deepfakes Using Deep Learning","authors":"J. Dheeraj, Krutant Nandakumar, A. Aditya, B. S. Chethan, G. Kartheek","doi":"10.1109/RTEICT52294.2021.9573740","DOIUrl":"https://doi.org/10.1109/RTEICT52294.2021.9573740","url":null,"abstract":"Images play an important role in defining human perception, and the power to manipulate such images gives immense power to malicious users. The new advancement in Artificial Intelligence, has altogether worked on the quality and productivity in creating counterfeit face pictures; for instance, the face manipulated by GANs is sensible to such an extent that it is hard to recognize the validness, either by the computer or by people. To improve the accuracy of recognizing facial pictures created by AI from genuine facial ones, an enhanced model has been proposed in this paper which is dependent on profound learnings like Deep Learning, Convolutional Neural Network (CNN), and Error Level Analysis (ELA). Our findings push the boundaries of understanding DeepFake detection and our solution to detect these images is based on the concepts of image error level and Deep learning. Our model uses the Convolutional Neural Network (CNN) architecture that utilizes error level analysis (ELA) to pre-process the images. We have utilized a dataset comprising on 24,000 images with equal split of real and deepfake images to traing and test our model. We were able to achieve an accuracy of 99%. The proposed model has a shorter training time and higher efficiency than most other methods for DeepFake detection.","PeriodicalId":191410,"journal":{"name":"2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127873212","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: 3

Non-Invasive Devices for Early Diagnosis of Oral Potentially Malignant Disorders: A Comparative Analysis 早期诊断口腔潜在恶性疾病的无创设备:比较分析

2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Pub Date : 2021-08-27 DOI: 10.1109/RTEICT52294.2021.9573637

Kratharth Hegde, K. Shenoy, T. Krishna, Kavitha Sooda, Abhilash

引用次数: 0

Breast Mass Classification with Deep Transfer Feature Extractor Model and Random Forest Classifier 基于深度转移特征提取模型和随机森林分类器的乳腺肿块分类

2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Pub Date : 2021-08-27 DOI: 10.1109/RTEICT52294.2021.9573909

Aarti Bokade, Ankit Shah

{"title":"Breast Mass Classification with Deep Transfer Feature Extractor Model and Random Forest Classifier","authors":"Aarti Bokade, Ankit Shah","doi":"10.1109/RTEICT52294.2021.9573909","DOIUrl":"https://doi.org/10.1109/RTEICT52294.2021.9573909","url":null,"abstract":"Breast Cancer is the most common type of cancer & leading cause of deaths in women worldwide. Early diagnosis of breast cancer and proper treatment play a vital role in death rate reduction. The success of Deep Convolutional Neural Networks (CNN) models in image classification tasks with state of art level accuracy has always attracted researchers to use them for disease diagnosis in the field of medical imaging. The proposed method uses CNN based fixed feature extraction technique, a type of deep transfer learning approach to perform binary classification of breast masses using Mammography Images. Mammography images are obtained from three publicly available datasets namely Mammographic Image Analysis Society (MIAS), Digital Database for Screening Mammography (DDSM) and Inbreast. The pretrained CNN models: VGG16, VGG19 & Resnet-50 performs feature extraction from the mammography images. The extracted features from CNN models are then classified into malignant & benign masses using Random Forest machine learning classifier. The models performances have been summarized with performance matrices (Sensitivity, Specificity, F1-score, and Accuracy), onfusion matrices and ROC (Receiver Operating Characteristics) curves. The combination of pretrained models, VGG16/VGG19/Resnet-50 & RF classifier gave model accuracies of: 0.81%, 0.80%,0.83% for the MIAS dataset, 0.994%, 0.986%, 0.996% for DDSM Dataset and 0.83%, 0.81%,0.87% for Inbreast dataset respectively. Automated classification of breast mass from the mammography images can be used by the doctors as a quick and efficient method for breast cancer screening.","PeriodicalId":191410,"journal":{"name":"2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130620553","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: 1

Navigation Systems Using A* 导航系统使用A*

2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Pub Date : 2021-08-27 DOI: 10.1109/RTEICT52294.2021.9573801

Nithin Sameer Yerramilli, N. Johnson, Omsri Sainadh Y Reddy, S. Prajwal

{"title":"Navigation Systems Using A*","authors":"Nithin Sameer Yerramilli, N. Johnson, Omsri Sainadh Y Reddy, S. Prajwal","doi":"10.1109/RTEICT52294.2021.9573801","DOIUrl":"https://doi.org/10.1109/RTEICT52294.2021.9573801","url":null,"abstract":"Shortest path searching is very important in some special cases such as medical emergencies, fire brigade, etc. An optimal path may be defined on a number of factors such as least distance/time or traffic density in stochastic road networks. Hence, there is no proper definition of an optimal path within the underlying constraints. One approach taken as an answer to this question is to find a path with the minimum expected travel time. For this, the approach taken is to construct a map of a city/town. This map is created using graphs where the nodes and edges represent the landmarks/locations and roads respectively. Traversal of the graph denotes traversal among the city/town. There are multiple graph traversal algorithms such as Best First Search, Bellman-Ford, and Dijkstra; but an optimal algorithm that finds the shortest path with the least time complexity should be chosen and implemented. Hence the A* algorithm is ideal. The benefit of using the A* algorithm is that it provides the optimum path while adhering to the underlying constraints. While A* algorithm is an optimum path-finding algorithm, it works ideally when the graph created has accurate measurements. Scaling is also not an issue as space complexity increases with the size of the graph. A small part of The city of Bengaluru is taken as the map consisting of 83 locations in which 24 locations consist of the possible destinations and an ambulance is considered as the vehicle of traversal to eliminate underlying constraints such as traffic density and variable speed.","PeriodicalId":191410,"journal":{"name":"2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116808419","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

Array Factor Code Development of Fractal Array Antenna using Python: A Mini-Study on Free and Open Source Software for Antennas 用Python开发分形阵列天线的阵列因子代码——天线免费开源软件的一个小研究

2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Pub Date : 2021-08-27 DOI: 10.1109/RTEICT52294.2021.9573911

V. A. S. Ponnapalli, Abburu Venkata sai Manish, P. Ramu, Sanyasi Sudhiksha, Maduri Greeshma

引用次数: 0

Analysis on Texture Feature Extraction Methods for Face Recognition in New Born 新生儿面部识别的纹理特征提取方法分析

2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Pub Date : 2021-08-27 DOI: 10.1109/RTEICT52294.2021.9573777

U. Rahamathunnisa, K. Sudhakar

{"title":"Analysis on Texture Feature Extraction Methods for Face Recognition in New Born","authors":"U. Rahamathunnisa, K. Sudhakar","doi":"10.1109/RTEICT52294.2021.9573777","DOIUrl":"https://doi.org/10.1109/RTEICT52294.2021.9573777","url":null,"abstract":"Biometric methods such as fingerprint, palm print, iris, face and retina are used to detect the persons who involved in the forgery. The face biometric is the most important and widely used in many applications area such as supermarket, railway station, airport, hospitals and other application areas to monitor and control the forgeries. In the present scenario, face recognition gained its importance due to increase in forgeries. To avoid such crimes, face recognition system is given most importance. Feature extraction is an important step for further analysis in face recognition systems. There are many feature extraction algorithms for face recognition systems in the literature. The challenge is to provide better accuracy in face recognition system. While choosing the feature extraction algorithm, we have to consider the parameters which provides better accuracy and less computational time. The features extracted from an image form the basis for classification and the extracted features are used for training and testing purposes. This paper analyses various feature extraction methods such as Local Binary Pattern (LBP), Principal Component Analysis (PCA) and Gray Level Co-occurrence Matrix (GLCM). The Local Binary Pattern generates LBP descriptors. Eigen face and Eigenvectors are computed by Principal Component Analysis and Gray Level Co-occurrence Matrix generates the second order statistical features. These methods are applied on the new born baby images with different expression. The extracted features are given as an input for the Support Vector Machine for classification. The experimental results have shown that the Principal component analysis method provides an accuracy of 91 % and it provided better recognition rate and less computation time when compared with the other feature extraction methods.","PeriodicalId":191410,"journal":{"name":"2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132942907","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: 1

A Low-Voltage Ride-Through strategy using fuzzy based controller for 3phase grid connected PV system 基于模糊控制器的三相并网光伏系统低压穿越策略

2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Pub Date : 2021-08-27 DOI: 10.1109/RTEICT52294.2021.9573643

D. Roopashree, N. Venugopal

引用次数: 1

An Analysis for the Prediction of Prefetched Content on Social Media 社交媒体预取内容预测分析

2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Pub Date : 2021-08-27 DOI: 10.1109/RTEICT52294.2021.9573858

S. Saichandana, Kavitha Sooda

{"title":"An Analysis for the Prediction of Prefetched Content on Social Media","authors":"S. Saichandana, Kavitha Sooda","doi":"10.1109/RTEICT52294.2021.9573858","DOIUrl":"https://doi.org/10.1109/RTEICT52294.2021.9573858","url":null,"abstract":"In Recently, social media networks are popularly emerging through world. This has been a great platform for information sharing through network among people. Being ubiquitous in nature, social media are accessible anywhere and at any point of time. To provide Quality of Experience support, a learning-based model for social media is been proposed. This is mainly used to improve user's usage satisfaction and to reduce access delay in Online Social Networks (OSN). For this, an analysis of Twitter traces for over fourteen months is conducted. Over 2,800 users Twitter data is collected using Twitter API for the analysis of social media friendship. And a cluster-based analysis for these set of user friends is made for the prefetch prediction. A mechanism using XGBoost algorithm and Decision Tree Regressor algorithm, the performance of this framework is been determined. The performance of this framework using trace-drive emulations on the social media network has been evaluated. Evaluation results could do superior performance with XG Boost algorithm of around 85.1% accuracy of access delay reduction.","PeriodicalId":191410,"journal":{"name":"2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"15 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113976631","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

IoT and Deep Learning based Smart Greenhouse Disease Prediction 基于物联网和深度学习的智能温室疾病预测

2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Pub Date : 2021-08-27 DOI: 10.1109/RTEICT52294.2021.9573794

Karunakar Pothuganti, Balne Sridevi, Phaneendra Seshabattar

{"title":"IoT and Deep Learning based Smart Greenhouse Disease Prediction","authors":"Karunakar Pothuganti, Balne Sridevi, Phaneendra Seshabattar","doi":"10.1109/RTEICT52294.2021.9573794","DOIUrl":"https://doi.org/10.1109/RTEICT52294.2021.9573794","url":null,"abstract":"Globally, rapid industrialization and urbanization have resulted in a reduction in agricultural acreage and production. As a result of this, and the growing desire for chemical-free organic veggies among educated urban families, greenhouses are rapidly gaining popularity for their specific benefits, particularly in regions with severe weather. They provide the perfect conditions for longer and more productive growth seasons, as well as lucrative harvests. The current article proposes and shows a full IoT -based Smart Greenhouse system that combines monitoring, alerting, cloud storage, automation, and disease prediction into a single, easily deployed package. It constantly monitors environmental variables like as temperature, humidity, and soil moisture to guarantee a better crop production and quick correction in the event of aberrant circumstances. A built-in automated irrigation management system is also included. Finally, for disease detection using leaf pictures, it uses the most efficient deep learning model. Furthermore, using cloud storage to optimise memory and storage, a city dweller may construct a greenhouse and watch it from his home, allowing him to take corrective action as needed.","PeriodicalId":191410,"journal":{"name":"2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122630563","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: 6

IoT Based Secluded HRV System for Monitoring Hypertensive Patients 基于物联网的高血压患者隔离HRV监测系统

2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) Pub Date : 2021-08-27 DOI: 10.1109/RTEICT52294.2021.9573768

M. Yadav, M. Basha, B. Kumar, V. A. Goud

{"title":"IoT Based Secluded HRV System for Monitoring Hypertensive Patients","authors":"M. Yadav, M. Basha, B. Kumar, V. A. Goud","doi":"10.1109/RTEICT52294.2021.9573768","DOIUrl":"https://doi.org/10.1109/RTEICT52294.2021.9573768","url":null,"abstract":"In this paper, smooth and low-price remote heart rate variability (HRV) monitoring system is proposed which is based on the Internet of Things (IoT) environment for intermediate hypertensive sufferers. Heart Rate Variability (HRV) is a measure of variation in the time interval between consecutive heartbeats. HRV analysis is highly sensitive for risks linked with cardiovascular disease. In this framework, HRV parameters are derived with the usage of a pulse sensor. Here, the Arduino microcontroller transmits the affected person records to the server by the use of the message queue telemetry transport (MQTT) protocol. In case of an emergency, based on the records from HRV, information will be forwarded to the health practitioner via Short Message Service (SMS) for clinical help. The HRV evaluation structure indicates the instances of excessive danger for hypertensive sufferers in conjunction with the resource of a faraway health practitioner. So, the proposed framework goals at reaching the same, and it efficaciously fulfils all the suitable developments of secluded health monitoring system using low-price, lengthy range, security, promptness, and smooth-to-use that serves in saving lives. This method thus has an ample scope to be utilized in accurate and robust HRV detection by automation in clinical settings.","PeriodicalId":191410,"journal":{"name":"2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128706366","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: 1