2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)最新文献_第7页

(NMRNN-LSTM) - Novel Modified RNN with Long and Short-Term Memory Unit in Healthcare and Big Data Applications (NMRNN-LSTM) -具有长短期记忆单元的新型改进RNN在医疗保健和大数据中的应用

2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) Pub Date : 2022-11-19 DOI: 10.1109/ASSIC55218.2022.10088322

N. Deepa, S. Prabakeran, D. T

{"title":"(NMRNN-LSTM) - Novel Modified RNN with Long and Short-Term Memory Unit in Healthcare and Big Data Applications","authors":"N. Deepa, S. Prabakeran, D. T","doi":"10.1109/ASSIC55218.2022.10088322","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088322","url":null,"abstract":"In the modern world, people's expectations and needs are automatically supportive and easy to use such as voice messages, playing music, or movies automatically which may reduce the manual operations mostly. In past decades technological advances such as machine learning and its application over many data like structured and unstructured are very much tedious. Whereas the operations based on non-categorical data, and categorical data are working rapidly using Natural Language Processing (NLP) comparatively, existing ones were not very productive. Each process on the internet is carrying an enormous amount of information which can lag in storage as well as performance. When any CRUD operations such as create, modify, update and delete are being analyzed one at a time, complex data such as unstructured and structured data are used in any field. In such a way the location analysis, social media data, health organization information, etc are categorized in natural language processing (NLP). The proposed work is organized as i) managing the huge amount of data in healthcare and log files created due to electronic health record management(EHR), ii) Unstructured data that are generated from all electronic equipment such as monitoring heartbeat, brain waves, etc that can be interpreted to classify using machine learning algorithms. To overcome the complications and medical records access inefficiency due to the complex structure of the dataset, Natural language processing uses the recurrent neural network along with the novel modified long and short-term memory unit (NMRNN-LSTM). Using the big data types such as structured, unstructured, and reinforcement kind of databases which handle images such as CTs, X-rays, MRI, raw texts, video streaming medical history to have effective systems and clinical records for enhancing the technological Medical care.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116431523","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

Diabetic Retinopathy Detection Using Deep Learning 基于深度学习的糖尿病视网膜病变检测

2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) Pub Date : 2022-11-19 DOI: 10.1109/ASSIC55218.2022.10088331

K. Swathi, E. S. N. Joshua, B. Reddy, N. Rao

引用次数: 0

Employing Machine Learning Techniques to Categorize users in a Fitness Application 使用机器学习技术对健身应用程序中的用户进行分类

2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) Pub Date : 2022-11-19 DOI: 10.1109/ASSIC55218.2022.10088294

Shyamali Das, Pamela Chaudhury, Hrudaya Kumar Tripathy

引用次数: 0

Novel Trio-Neural Network towards Detecting Fake News on Social Media 基于三神经网络的社交媒体虚假新闻检测

2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) Pub Date : 2022-11-19 DOI: 10.1109/ASSIC55218.2022.10088401

T. Devi, K. Jaisharma, N. Deepa

{"title":"Novel Trio-Neural Network towards Detecting Fake News on Social Media","authors":"T. Devi, K. Jaisharma, N. Deepa","doi":"10.1109/ASSIC55218.2022.10088401","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088401","url":null,"abstract":"In recent days most people are using the internet to know the latest news faster, parallel false information also spreads for many reasons. The fake news is artificially manipulated and elongated by the true information, this creates negativity and diverse the users in particular opinions. Fake news detection is a more complicated and labor-consuming process because the data has kept on growing as big data. The detection of fake news using a single parameter has become less reliable and so there is a need to use multiple parameters to improve the reliability of the model. The parameters such as text, audio, video, and time were traditionally for fake news detection. In this article, the proposed model is designed to work with three parameters namely geolocation, text feed, and image data of the user in their handy smart mobile phone. The proposed Novel Trio-Neural Network has a binary classifier to detect fake or real news, the location spoofing is avoided by checking the movement probability of the user using Bayesian Geolocation Timestamp, the text feed posted by the users is analyzed by using BERT Fact Checker, and image shared by the user on the internet are mapped to text with similarity checker extracted feature from the image using VGG16 Similarity Mapping. The integrated Novel Trio-Neural Network was trained, tested, and validated with the FakeNewsNet dataset. The proposed model reached the F1-Score of 82.31%, and the performance of the model has significantly improved by 4.01% from the existing model.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127458518","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

Interplay of Artificial Intelligence and Ecofeminism: A Reassessment of Automated Agroecology and Biased Gender in the Tea Plantations 人工智能与生态女性主义的相互作用:对茶园自动化农业生态与性别偏见的再评估

2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) Pub Date : 2022-11-19 DOI: 10.1109/ASSIC55218.2022.10088342

Subhashree Rout, S. Samantaray

{"title":"Interplay of Artificial Intelligence and Ecofeminism: A Reassessment of Automated Agroecology and Biased Gender in the Tea Plantations","authors":"Subhashree Rout, S. Samantaray","doi":"10.1109/ASSIC55218.2022.10088342","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088342","url":null,"abstract":"Technology and agriculture are correlated where they share societal values and expertise. The development of the agricultural sector has been largely dependent on labour-saving agricultural technologies where innovation may operate with advanced AI solutions that is less labour-intensive, more reasonable and adaptable. The paper chronicles the benefits of implementing Artificial Intelligence, Cyborg Technologies, Robotics and Internet of Things in Agroecology to ensure the welfare of women and environment. Further, it accentuates on the various automation and precision, while stressing on agriculture related technical advancements like smart farms, automated weeding and spraying machines, molecular detection, smart eye software, and smart irrigation. The progressive strategies to uplift women labourers in the field of tea plantation, especially in India, is also highlighted. The paper brings out the benefits of implementing technological advancement that affects the relationship between gender and ecology and looks at the situation of women workers through the lens of eco-feministic perspective.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128126650","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

Line Reconstruction and Segmentation of Words and Characters using Measures of Central Tendency and Measures of Dispersion 用集中趋势和离散度量方法重建和分割文字

2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) Pub Date : 2022-11-19 DOI: 10.1109/ASSIC55218.2022.10088316

Aradhana Kar, S. Pradhan

引用次数: 0

Real-time Pothole Detection using YOLOv5 基于YOLOv5的实时坑洞检测

2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) Pub Date : 2022-11-19 DOI: 10.1109/ASSIC55218.2022.10088290

S. Ajmera, C. Kumar, P. Yakaiah, B. Kumar, K. Chowdary

{"title":"Real-time Pothole Detection using YOLOv5","authors":"S. Ajmera, C. Kumar, P. Yakaiah, B. Kumar, K. Chowdary","doi":"10.1109/ASSIC55218.2022.10088290","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088290","url":null,"abstract":"the worldwide is advancing towards a self sufficient surrounding at a remarkable pace, and it has to turn out to be a want of an hour, especially, at some point of the present day pandemic situation. Numerous industries have been hampered by the epidemic, with road maintenance and improvement being one among them. Creating a secure running surrounding for employees is a prime problem of street preservation at some point of such tough times. This may be carried out to a degree with the assist of a self-sufficient gadget as a way to goal at decreasing human dependency. The suggested machine uses a Deep Learning based absolutely set of regulations YOLO (You Only Look Once) for the detection of pothole. Further, a picture processing primarily based totally triangular similarity degree is used for pothole size estimation. The proposed gadget affords moderately correct effects of each pothole detection and size estimation. The proposed gadget additionally allows in decreasing the time required for street preservation. The gadget makes use of a custom-made dataset along with pix of water-logged and dry potholes of diverse shapes and sizes. Detailed real-time overall performance evaluation of modernday deep mastering fashions and item detection frameworks (YOLOv1, YOLOv2, YOLOv3, YOLOv4, Tiny-YOLOv4 YOLOv5, and SSD-mobilenetv2) for detecting the pothole is included.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128450810","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 Revolutionary Machine-Learning based approach for identifying Ayurvedic Medicinal Plants 一种革命性的基于机器学习的方法来识别阿育吠陀药用植物

2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) Pub Date : 2022-11-19 DOI: 10.1109/ASSIC55218.2022.10088298

Subhashree Darshana, Kasturi Soumyakanta

引用次数: 0

Breast Cancer Detection by Using Radient Based Algorithm on Mammogram Images 基于梯度的乳房x线图像乳腺癌检测

2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) Pub Date : 2022-11-19 DOI: 10.1109/ASSIC55218.2022.10088376

V. N. Reddy, N. Shaik, P. Rao, S. Nyamatulla

{"title":"Breast Cancer Detection by Using Radient Based Algorithm on Mammogram Images","authors":"V. N. Reddy, N. Shaik, P. Rao, S. Nyamatulla","doi":"10.1109/ASSIC55218.2022.10088376","DOIUrl":"https://doi.org/10.1109/ASSIC55218.2022.10088376","url":null,"abstract":"One of the most common cancers, particularly among women, is breast cancer. Cancer that originates in the breast tissue is called breast cancer. Indications of bosom disease could remember a protuberance for the bosom. Fluid emerges from the nipple by changing shape and dimpling the skin. When cells in the breast begin to grow out of control, breast cancer develops. Through screening and precise identification of masses, microcalcifications, and structural bends, mammography is the most effective and reliable method for the early detection of breasttumors. Breast disease is the leading cause of death for women worldwide. It is evident that recognizing danger early can aid in the investigation of a woman's infection and significantly increase the likelihood of survival. To find an abnormality in mammogram images, this novel segmentation technique, which is based on Iterative algorithms like the Markov random field (MRF) model, is proposed here. This algorithm processes the label with the lowest energy for all iterations. A label and boundary MRF can have a highly compressed relation thanks to this approach.","PeriodicalId":441406,"journal":{"name":"2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115171599","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

Automatically detection of multi-class Alzheimer disease using Deep Siamese Convolutional Neural Network 基于深度连体卷积神经网络的多类别阿尔茨海默病自动检测

2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) Pub Date : 2022-11-19 DOI: 10.1109/ASSIC55218.2022.10088299

A. Vashishtha, A. Acharya, Sujata Swain

引用次数: 0