2022 IEEE World Conference on Applied Intelligence and Computing (AIC)最新文献_第4页

Detection of Two-Wheeler Traffic Rule Violation Using Deep Learning 基于深度学习的两轮车交通规则违规检测

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848979

M. Arshad, P. Kumar

引用次数: 3

ResStorage - Blockchain Based Decentralized Resume Storage Application ResStorage -基于区块链的分散简历存储应用程序

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848983

Pranav A. Gangurde, Melita Japhet, Clafacio Lobo, N. Patil, P. Patil

引用次数: 0

Assessing The Spatial Variability of Soil Nutrients Prediction Using GIS-based Interpolation Techniques 基于gis插值技术评估土壤养分预测的空间变异性

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848951

C. Singha, K. Swain

{"title":"Assessing The Spatial Variability of Soil Nutrients Prediction Using GIS-based Interpolation Techniques","authors":"C. Singha, K. Swain","doi":"10.1109/AIC55036.2022.9848951","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848951","url":null,"abstract":"GIS-based spatial interpolation techniques are utilized in soil sciences to analyze and predict the soil quality in precession agriculture. This research involved the analysis of seventy soil samples at depths of up to 40 centimeters from randomly selected farm plots surrounded by an area of 300ha from the Tarakeswar block in Hooghly region, West Bengal, India during the post-harvest period 2019-2020. Current work assigns the five spatial interpolation techniques namely IDW, RBF, LPI, OK, and EBK for the prediction of soil nutrients on a local scale with the site-specific soil management through GPS-aided Geographical Information System. The accuracy of different interpolation techniques examines by the coefficient of determination (R2) and root mean square error (RMSE) through the cross-validation method. Exponential models were appropriate to the experimental semivariograms for the soil OC, Zn, pH, EC, K, Sand, Silt, and Clay while P and N were best suited to the Gaussian model. The outcomes demonstrate that LPI and EBK is the most preferred method with the highest R2 and smallest RMSE value for interpolation of spatial variability of soil nutrients parameters distribution. It is widely believed that soil variability is an instrument that can help improve the management of land and reduce conflicts within the rural community.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132865596","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 Review on Swarm intelligence & Evolutionary Algorithms based Approaches for Diabetic Retinopathy Detection 基于群体智能和进化算法的糖尿病视网膜病变检测方法综述

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848841

Sachin Bhandari, Sunil Pathak, Sonal Amit Jain, Varun Deshmukh

{"title":"A Review on Swarm intelligence & Evolutionary Algorithms based Approaches for Diabetic Retinopathy Detection","authors":"Sachin Bhandari, Sunil Pathak, Sonal Amit Jain, Varun Deshmukh","doi":"10.1109/AIC55036.2022.9848841","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848841","url":null,"abstract":"Diabetic retinopathy has overtaken cataracts as the primary cause of new blindness globally. Diabetics are more likely to develop cataracts, visual loss, glaucoma, excessive intraocular pressure, and, most importantly, diabetic retinopathy (DR). If blood vessels in the retina are compromised, vision loss is irreversible. The patient may not exhibit any symptoms early on, and by the time they do, the damage has already been done. Early diabetes treatment helps to retain vision and permits a patient to see. Diabetic retinopathy is a worldwide health problem. To address the medical community’s requests for early identification of diabetes and other illnesses, several professionals have advocated a computer assisted diagnosis technique. In this work, image processing techniques and image classifiers that sort images based on the status of the disease will be used to describe automated ways to look at retinal images for important signs of diabetic retinopathy. There are compelling motivations to create retinopathy risk reduction models and strategies that can be used widely. The difficulty of acquiring accurate diabetic retinopathy at a reasonable cost needs a major investment in creating and testing computer-assisted diagnosis (CAD). This study looks at the different stages, traits, and types of models that may be used to reduce the risk of diabetic retinopathy and detect it early using Evolutionary computing and Swarm optimization.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133171959","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

Feature Selection Technique-Based Network Intrusion System Using Machine Learning 基于特征选择技术的机器学习网络入侵系统

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848861

Mahsa Mirlashari, S. Rizvi

{"title":"Feature Selection Technique-Based Network Intrusion System Using Machine Learning","authors":"Mahsa Mirlashari, S. Rizvi","doi":"10.1109/AIC55036.2022.9848861","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848861","url":null,"abstract":"Internet is a global public network, and as internet traffic has grown, so has the demand for security mechanisms. There are both harmful and harmless users on the Internet, and both have access to the same information. Malicious users get access to any organization's systems and cause significant damage. As a result, the necessity for the organization's private resources security has increased dramatically. Firewalls were installed by every corporation to protect their networks, although no network can be completely secure. Firewalls are topped with intrusion detection systems (IDS). The firewall defends the company against malicious attacks, and the IDS detects and generates an alert if someone attempts to intrude the firewall and has access to the system. In this paper, an IDS based on Machine Learning (ML) is proposed. The K-Nearest Neighbour (KNN), Naive Bayes (NB), Random Farest (RF), and Decision Tree (DT) ML technique are applied for NSL-KDD dataset. Besides, a Recursive Feature Elimination (RFE) is used for feature selection technique to enhance the performance, accuracy, and processing time of the model.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131769146","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 real-time model for COVID19 face-mask identification with “YOLOv4” 基于“YOLOv4”的新型冠状病毒口罩实时识别模型

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848864

Hema Shekhawat, Pooja Raj Verma

{"title":"A real-time model for COVID19 face-mask identification with “YOLOv4”","authors":"Hema Shekhawat, Pooja Raj Verma","doi":"10.1109/AIC55036.2022.9848864","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848864","url":null,"abstract":"At the beginning of 2020 WHO declared COVID19 as an epidemic; healthcare industries experts and academicians from worldwide are working in the directions to surveillance the daily behaviors of the citizens to combat the COVID-19 cases. In India, we thank the government for performing its outperformed active measures and spontaneous compliance to follow the policy of wearing masks when moving out to any public places; it entails active real-time monitoring to supervise the citizens by governments. In this process, real-time face-mask identification is a very challenging task of computer vision. And the absence of accurate datasets for this problem is a critical hard problem to solve. To address this bottleneck, we are proposing our real-time deep learning face-mask identification technique with annotated class labels with bounding boxes which have its real-time application to assist the governments to control and prevent the spread of these epidemics in its supervision. Our model is very robust and effective to classify the real-time images and videos for face mask detection with accuracy and average precision. The proposed model substitutes the manual surveillance with the object detection method using YOLOv4 supported on a deep learning approach to monitor the crowd accurately even if they change their respective locations. The experiment identify or classify the object within any dataset to distinguish the images or videos with two class labels such as “with-mask” and “without-mask” with approximately 98.26% accuracy, mAP of 68.28%, recall of 77%, and precision of 57%.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123879851","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

Deep Residual Learning based Discriminator for Identifying Deepfakes with Cut-Out Regularization 基于深度残差学习的切出正则化深度假货识别方法

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848964

Sai Dheeraj Gummadi, Anirban Ghosh

{"title":"Deep Residual Learning based Discriminator for Identifying Deepfakes with Cut-Out Regularization","authors":"Sai Dheeraj Gummadi, Anirban Ghosh","doi":"10.1109/AIC55036.2022.9848964","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848964","url":null,"abstract":"The recent development of Generative Adversarial Networks (GANs) have greatly eased the generation of deepfake images which are indistinguishable from real images. As a downside of such advancement, it is now easy to impersonate a person leading to identity theft and other malicious outcomes. In such a scenario it becomes imperative to have a robust algorithm in place which can segregate real images from the fake ones. In this study, we suggest a residual connection based convolutional neural network (CNN) architecture for detecting deepfake images and compare the results with the existing transfer learning algorithms for identifying the deepfakes. The data set used in this study is the combination of the Flickr-Faces-HQ (FFHQ) data set (Nvidia) and the deepfakes generated by the Style GAN, which is proposed by Nvidia. The data set consisting of 1,20,000 images is used for training and validating the network, while a separate set of 20,000 real world images are used for testing the performance of the model. In this current work, we test the robustness of three different algorithms - Inception Resnet V2, VGGFace2, and our customized Residual CNN with and without cut-out regularization in identifying real images. The residual architecture-based implementation in combination with cut-out architecture produces the lowest false positives rate at 0.0043% while the Inception Resnet V2 in combination with cut - regularization produces the best accuracy at 99.05%.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127796678","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

Conference Committees: AIC 2022 会议委员会:AIC 2022

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/aic55036.2022.9848988

引用次数: 0

Development of an Integrated Image Acquisition Setup for Assigning Selling Price of Rough and Milled Rice (Oryza Sativa L.) in the Supply Chain 用于在供应链中分配粗米和精米销售价格的集成图像采集设置的开发

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848907

John Gulshan Kullu, B. Panda, S. L. Shrivastava, Kanishka Bhunia, A. Datta

{"title":"Development of an Integrated Image Acquisition Setup for Assigning Selling Price of Rough and Milled Rice (Oryza Sativa L.) in the Supply Chain","authors":"John Gulshan Kullu, B. Panda, S. L. Shrivastava, Kanishka Bhunia, A. Datta","doi":"10.1109/AIC55036.2022.9848907","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848907","url":null,"abstract":"In agro-processing industries, cereal grains acquire a significant place but, many times the consistency in grain quality gets compromised during the supply chain, owing to improper and/or inefficient supervision. In the current work, an integrated computer vision setup, aided with the gravimetric principle, has been developed for assigning the best price for rough and milled rice in the supply chain hierarchy. Three local paddy varieties were classified based on physical and color features extraction, following established image processing techniques. Two unique features i.e., dry mass identifier (DMI) and head rice equivalent (HRE) were introduced for assessing inter-varietal paddy admixture, grain moisture content, and broken fractions at several size segments to achieve consistent pricing. Both features have shown a good correlation with the manual classification which is although accurate but lengthy and tedious. The devised setup can be adopted as an inexpensive, non-invasive, and non-destructive tool in rice trading.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"04 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128481004","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

Health Care Chatbot using Natural Language Processing with SGD and ADAM Optimizer Parameter Optimization 使用SGD和ADAM优化器进行自然语言处理的医疗保健聊天机器人参数优化

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI: 10.1109/AIC55036.2022.9848955

K. C. Bandhu, B. K. Mishra, Mohit Patel, Narottam Choyal, Priya Koushal, Prakhar Varathe

{"title":"Health Care Chatbot using Natural Language Processing with SGD and ADAM Optimizer Parameter Optimization","authors":"K. C. Bandhu, B. K. Mishra, Mohit Patel, Narottam Choyal, Priya Koushal, Prakhar Varathe","doi":"10.1109/AIC55036.2022.9848955","DOIUrl":"https://doi.org/10.1109/AIC55036.2022.9848955","url":null,"abstract":"In today’s world, everyone is not quite sure about the medicine that the users used in a similar situation or critical situation where any medical emergency has come and as all know that the ratio of patients and doctors are very high so, there is a requirement of such kind of applications to help in case of emergency. This paper proposed a novel approach for medical needs, as well as the suggested chatbot that will be useful in the pandemic circumstances. Natural Language Processing (NLP) based applications are proposed to provide help to the patient. In some situations, the patient home member just used it to type their query and if the patient situation is not so serious, so they get proper medicinal information from this application. The proposed methodology takes an input sentence then its tokenization, removal of stop words, feature extraction, and word corpus are used to find the sentence similarity, and the chatbot predicts the accurate sentence. In this work, the Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (ADAM) optimizer optimized parameter values are determined with 86 and 93 percent accuracy respectively. The optimized Lr_value 0.0099 and Decay value 1e-10 for SGD and optimized Learning_rate 0.0099 for ADAM are obtained.","PeriodicalId":433590,"journal":{"name":"2022 IEEE World Conference on Applied Intelligence and Computing (AIC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126388224","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