2023 IEEE 8th International Conference for Convergence in Technology (I2CT)最新文献_第9页

American Sign Language Fingerspelling Recognition using Attention Model 基于注意模型的美国手语拼写识别

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126277

Amruta E Kabade, P. Desai, S. C, Shankar G

{"title":"American Sign Language Fingerspelling Recognition using Attention Model","authors":"Amruta E Kabade, P. Desai, S. C, Shankar G","doi":"10.1109/I2CT57861.2023.10126277","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126277","url":null,"abstract":"Sign Language Recognition(SLR) is a complex gesture recognition problem because of the quick and highly coarticulated motion involved in gestures. This research work focuses on Fingerspelling recognition task, which constitutes 35% of the American Sign Language (ASL). Fingerspelling identifies the word letter by letter. Fingerspelling is used for signing the words which do not have designated ASL signs such as technical terms, content words and proper nouns. In our proposed work for ASL Fingerspelling recognition, we consider ChicagoFSWild dataset which consists of occlusions and images captured in varying illuminations, lighting conditions (in the wild environments). The optical flow is obtained from Lucas-Kanade algorithm, prior is generated, images are resized and cropped with face-roi technique to get the region of interest (ROI). The visual attention mechanism attends to the ROI iteratively. ResNet, pretrained on Imagenet is used for the extraction of spatial features. The Bi-LSTM network with Connectionist Temporal Classification (CTC) is used to predict the sign. It provides the accuracy of 57% on ChicagoFSWild dataset for Fingerspelling recognition task.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125002887","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 Convolution Neural Network-Based Classification and Diagnosis of Heart Disease using ElectroCardioGram (ECG) Images 基于深度卷积神经网络的心电图像心脏病分类与诊断

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126473

Thanu Kurian, T. S

引用次数: 0

License Plate Recognition for Detecting Stolen Vehicle Using Deep Learning 利用深度学习检测被盗车辆的车牌识别

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126393

Atul B. Kathole, Ajim Shikalgar, Nitish Supe, Tejasha Patil

{"title":"License Plate Recognition for Detecting Stolen Vehicle Using Deep Learning","authors":"Atul B. Kathole, Ajim Shikalgar, Nitish Supe, Tejasha Patil","doi":"10.1109/I2CT57861.2023.10126393","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126393","url":null,"abstract":"India is anticipated to overtake China as the third-largest vehicle market in the near future. Vehicle theft, according to data, has increased yearly. But the proportion of cases that the police really resolve is still quite small. It is challenging for police to locate stolen vehicles since they are sometimes carried to locations distant from the scene of the theft. Therefore, a need for an automated system to assist in tracking such cars arises. These issues are what our project tries to fix. The police will receive a tonne of information from this system that they may utilise to solve theft cases. Using the YOLO V3 algorithm and Canny Edge Detection, the identification system will automatically recognize automobile license plate numbers. After a license plate is identified, the following actions are taken: 1. to photograph the license plate. 2. to recognize and divide characters. 3. The time and date are then recorded in a database together with the identifying license plate for further use. 4. In the event that a stolen vehicle is discovered, a thorough report detailing the location and the time the vehicle first appeared is prepared, and police are notified that a match has been made. The method may be applied to increase security and accuracy.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125123463","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

Survey on Smartphone Sensors and User Intent in Smartphone Usage 智能手机传感器与用户使用智能手机意图的调查

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126192

Priyanka Bhatele, Dr Mangesh Bedekar

{"title":"Survey on Smartphone Sensors and User Intent in Smartphone Usage","authors":"Priyanka Bhatele, Dr Mangesh Bedekar","doi":"10.1109/I2CT57861.2023.10126192","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126192","url":null,"abstract":"Smartphone/Tablet users are approximately 3 million all over the world. It is likely to increase by several 100 million in the next few years. Around 40% of these users read online. Explicit means of feedback system is strongly based. It provides the most accuracy when rating an online learning application. Increase in the availability of content over the web and high user engagements, has led to the demand of the means that implicitly provide feedback. Implicit feedback relies on understanding the quality of the content based on the user activities performed over the web applications. Less accuracy is the limitation. It needs to stand with a support to provide as strong base as the explicit model does. Clipboard copy operations on the webpage provide an implicit insight to the user intentions. Screen activities like scrolling and pinch to zoom further can statistically be proven the positive indicators of user interest. Smartphone sensors like Gyroscope and Accelerometer silently sense human screen activities and mobile gestures. This review paper is based on the understanding of smartphone sensors and the inferences of user intent through it. The dig is based on various implicit indicators like mobile gestures, smartphone sensors and clipboard copy operations.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114583812","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

Thyroid Disease Prediction Model on Boosting-based Stacking Ensemble Approach 基于boosting叠加集成方法的甲状腺疾病预测模型

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126389

Subhash Mondal, Souptik Dutta, Soumadip Ghosh, Sarbartha Gupta, Dhrubajit Kakati, A. Nag

{"title":"Thyroid Disease Prediction Model on Boosting-based Stacking Ensemble Approach","authors":"Subhash Mondal, Souptik Dutta, Soumadip Ghosh, Sarbartha Gupta, Dhrubajit Kakati, A. Nag","doi":"10.1109/I2CT57861.2023.10126389","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126389","url":null,"abstract":"The thyroid gland plays a significant role in the human body's metabolism, growth, and development. Though it is not a life-threatening disease, a person suffering from thyroid faces many complications in their daily life. Recent trends have shown that women suffer more from thyroid-related diseases than men. The many contributing factors that lead to thyroid disease may be controlled upon early diagnosis stages. Machine learning prediction models help healthcare professionals diagnose thyroid diseases at an initial stage and take measures accordingly. This study deployed initial Sixteen ML models, including six boosting algorithms, on a dataset of 9172 instances with related features. The model performances have been judged through various standard performance metrics. The boosting algorithms showed exceptional results, and Cat Boost (CB) model produced the best accuracy of 95.75%. The hyperparameter tuning performed on boosting models by implementing Randomized Search CV increased the accuracy to 96.19% for CB. The stacking ensemble approach was applied on top of the six boosting tuned models with the CB classifier as the meta-learner. At the same time, the other boosting algorithms were kept as a base learner for the final model prediction. The accuracy of the stack model was impressive, with 95.32% compared with default models, the ROC-AUC at 0.95, and the other results were also promising. The model’s standard deviation was significantly less at 0.57, implying the model’s stability and robustness, and the False Negative (FN) rate reached 1.8%.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123849999","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

Design and Development of A Brain Computer Interface Controlled Wheelchair Prototype 脑机接口控制轮椅样机的设计与研制

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126472

Janani T, Nandhini Jagadeesan, Shivangi Pandey, Divya B

引用次数: 0

Security in LP-WAN Technologies: Challenges and Solutions LP-WAN技术的安全性:挑战与解决方案

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126493

Richa Tengshe, Eisha Akanksha

引用次数: 1

Electrical Characterization of XLPE Embodied With Copper Oxide Nanoparticles 氧化铜纳米颗粒掺杂交联聚乙烯的电学表征

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126315

Arghadeep Pal, A. Banerjee, A. Lahiri

引用次数: 0

High sensitivity strain sensor based on Polymer Fiber Bragg Grating 基于聚合物光纤光栅的高灵敏度应变传感器

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126215

Tony Alwin

引用次数: 0

Intelligent Medicine Box for COVID like Pandemic 抗疫智能药箱

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126248

J. Baikerikar, Nilesh Ghavate, Vaishali Kavathekar, Allen Kodiyan

引用次数: 0