Advances in Computational Intelligence in Materials Science最新文献_第2页

A Comprehensive Exploration of Neural Networks for Dental Caries Detection 神经网络龋病检测的综合探索

Advances in Computational Intelligence in Materials Science Pub Date : 2023-06-07 DOI: 10.53759/acims/978-9914-9946-9-8_22

V. G, Kandukuru Swaroop Krishna, Mallempati Uday Kiran, Shaik Nihal, K. V, U. Ramachandraiah

{"title":"A Comprehensive Exploration of Neural Networks for Dental Caries Detection","authors":"V. G, Kandukuru Swaroop Krishna, Mallempati Uday Kiran, Shaik Nihal, K. V, U. Ramachandraiah","doi":"10.53759/acims/978-9914-9946-9-8_22","DOIUrl":"https://doi.org/10.53759/acims/978-9914-9946-9-8_22","url":null,"abstract":"Dental caries, an illness due to bacteria that worsens with time, is the most common cause of tooth loss. This occurs as an outcome of least oral hygiene, which in addition contributes to a variety of dental disorders. Children's dental health will benefit considerably if caries can be detected at an early stage via tele-dentistry technology. Because severe caries causes disease and discomfort, tooth extraction may be necessary. As a result, early detection and diagnosis of these caries are the researchers' priority priorities. Soft computing techniques are commonly employed in dentistry to simplify diagnosis and reduce screening time. The goal of this study is to employ x-ray scanned images to detect dental cavities early on so that treatment can be completed promptly and effectively. As a tele-informatic oral health care system, this classification also applies to tele-dental care. We used a convolution neural network (CNN) deep learning model in the suggested work. We trained several CNN deep learning models. Training and testing were performed on a binary dataset with and without caries photos. The classification precision of CNN models is noted.","PeriodicalId":261928,"journal":{"name":"Advances in Computational Intelligence in Materials Science","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130115432","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

Analytical Study on Shear Response of Hollow Core Slab Subjected to Elevated Temperature using Extended Finite Element Method 高温作用下空心芯板剪切响应的扩展有限元分析研究

Advances in Computational Intelligence in Materials Science Pub Date : 2023-06-07 DOI: 10.53759/acims/978-9914-9946-9-8_17

Jeyashree T M, Varunram C

{"title":"Analytical Study on Shear Response of Hollow Core Slab Subjected to Elevated Temperature using Extended Finite Element Method","authors":"Jeyashree T M, Varunram C","doi":"10.53759/acims/978-9914-9946-9-8_17","DOIUrl":"https://doi.org/10.53759/acims/978-9914-9946-9-8_17","url":null,"abstract":"Prestressed hollow core slabs are members without transverse reinforcement and are often exposed to shear failure, especially in elevated temperatures. The study of shear response in the precast pre-stressed hollow core slab is essential to study the tension-compression damage of the flexural member. The hollow core slab is subjected to typical shear failure loading conditions and the loading condition is simulated through the finite element model in ABAQUS. The 3D model depicting the actual shear behaviour of the hollow core slab is developed with the simple concrete damage plasticity model. Extended Finite Element Method (XFEM) analysis is used to study the propagation of cracks, from which displacement and cracking patterns are obtained for the slab with the varying depth of 200 mm, 250 mm, and 300 mm. Effect of varying depth on the shear behaviour of hollow core slab under elevated temperature are determined and the results obtained from the finite element analysis are validated for the accuracy with the ACI equation for shear behaviour and it is observed that there is good agreement in the ultimate load values obtained. The real-time behaviour of the hollow core slab under the combined effect of shear and elevated temperature is depicted with the help of crack propagation analysis. Further, the developed finite element model can be used for crack propagation study of hollow core slabs under shear failure.","PeriodicalId":261928,"journal":{"name":"Advances in Computational Intelligence in Materials Science","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124633528","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

Detection of Poikilocytosis using Segmentation based Approach 基于分割的异型红细胞增多症检测方法

Advances in Computational Intelligence in Materials Science Pub Date : 2023-06-07 DOI: 10.53759/acims/978-9914-9946-9-8_25

G. N, A. S, D. S, Akilan R, Fayaz A

引用次数: 0

Data Integration and Modelling using Talend 使用Talend的数据集成和建模

Advances in Computational Intelligence in Materials Science Pub Date : 2023-06-07 DOI: 10.53759/acims/978-9914-9946-9-8_1

Avanthika S, Aniruthram K P, Anubama G, Sreemathy J

引用次数: 0

Operationalization of Design Thinking in Business Intelligence and Analysis 设计思维在商业智能与分析中的可操作性

Advances in Computational Intelligence in Materials Science Pub Date : 2023-06-07 DOI: 10.53759/acims/978-9914-9946-9-8_6

Akshaya V.S., K. S, Philomina M, Pradeepa N R, Purnimah S V

{"title":"Operationalization of Design Thinking in Business Intelligence and Analysis","authors":"Akshaya V.S., K. S, Philomina M, Pradeepa N R, Purnimah S V","doi":"10.53759/acims/978-9914-9946-9-8_6","DOIUrl":"https://doi.org/10.53759/acims/978-9914-9946-9-8_6","url":null,"abstract":"In Information Systems research, the majority of early studies concentrate on applying the specific toolkit to produceDesigningproductsandsystemstosolvestrategies, managerial, and operational challenges. Design Thinking has been effectively implemented in numerous domains. There is a minimal study on how Design Thinking may be supported in the context of business intelligence (BI) and also the analytics of business and integrated into the IS design- oriented aim to facilitate research (BA). How effective is Design Thinking facilitated in comparison to BI/BA to learn in a classroom setting, particularly in the proof-of-concept stage? How can it be integrated into the learning processes? Along with a guideline for the team involved in design thinking and the lessons acquired from each step of Design Thinking, a practical perspective on integrating the mentality and toolkit. As a case-based educational experience, it offers a design-thinking approach to the learning process. The findings of this study demonstrate that students may use Design Thinking techniques to frame their works and that these techniques are important alternatives for developing curriculum for teaching and learning.","PeriodicalId":261928,"journal":{"name":"Advances in Computational Intelligence in Materials Science","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131704902","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

AES Algorithm Based Data Possession in Cloud Using Blockchain 基于AES算法的区块链云数据占有

Advances in Computational Intelligence in Materials Science Pub Date : 2023-06-07 DOI: 10.53759/acims/978-9914-9946-9-8_11

M. B, Reddy Prashanthi M, N. S, Prathiksha M

引用次数: 0

Recommendation of Music Based on Facial Emotion using Machine Learning Technique 基于面部情感的机器学习音乐推荐

Advances in Computational Intelligence in Materials Science Pub Date : 2023-06-07 DOI: 10.53759/acims/978-9914-9946-9-8_16

S. G, Evangelin Blessy A, Jeya Aravinth S, Vignesh Prabhu M, VijayaSarathy R

引用次数: 0

Hybrid Approach of Big Data File Classification Based on Threat Analysis for Enhancing Security 基于威胁分析的大数据文件分类混合方法提高安全性

Advances in Computational Intelligence in Materials Science Pub Date : 2023-06-07 DOI: 10.53759/acims/978-9914-9946-9-8_24

S. N

{"title":"Hybrid Approach of Big Data File Classification Based on Threat Analysis for Enhancing Security","authors":"S. N","doi":"10.53759/acims/978-9914-9946-9-8_24","DOIUrl":"https://doi.org/10.53759/acims/978-9914-9946-9-8_24","url":null,"abstract":"Big Data is rapidly growing domain across various real time areas like Banking, Finance, Indusrty, Medicine, Trading and so on. Due to its diversified application, handling the big data for security during data transmission or management is highly risky. Most of the researchers try to handle big data classification based on the domain of interest for increasing productivity or customer satisfaction in decision making. Whereas, this paper focuses on the classification of big data file to enhance security during the data transmission over network and management.Most of the big data applications contains valuable and confidential data. The existing data security approaches are not sufficient on handling the security for data based on the threat level. Therefore, this paper proposes a hybrid approach to classify the big data based on the threat level of the contents associated with the data under consideration into open and close. To ensure the security of big data files, they are transmitted into the Hadoop Distributed File System along with relevant information to assess the level of threat they pose. The Threat Impact Level (TIL) is then calculated as a metric to determine the threshold level required for their protection.","PeriodicalId":261928,"journal":{"name":"Advances in Computational Intelligence in Materials Science","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126548632","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 Survey on Facial Emotion Identification using Deep Learning Models 基于深度学习模型的面部情绪识别研究综述

Advances in Computational Intelligence in Materials Science Pub Date : 2023-06-07 DOI: 10.53759/acims/978-9914-9946-9-8_3

Subha R, Suchithra, PendelaSatya Sudesh, MidhunReddy G, P. K, Mohammed Fadhil S

{"title":"A Survey on Facial Emotion Identification using Deep Learning Models","authors":"Subha R, Suchithra, PendelaSatya Sudesh, MidhunReddy G, P. K, Mohammed Fadhil S","doi":"10.53759/acims/978-9914-9946-9-8_3","DOIUrl":"https://doi.org/10.53759/acims/978-9914-9946-9-8_3","url":null,"abstract":"Facial expression detection has become a part of the current industry scenario. The face detection techniques implementation range from convolutional neural network to residual network. This paper tries to take up a survey on different scenarios, to understand the efficient implementation and also try to suggest an efficient strategy usage.In this paper, a set of data is taken up for training & testing, which helps the model in the identification of facial expressions. Computer vision trains and tests the machines for identifying the object. Computer Vision but it has to do much with cameras. The data and algorithms are the retinas, optic nerves and a visual cortex of any model. Computer Vision is applied with a system model, the model may be implemented using any of the artificial intelligence algorithms. A CNN with a help of machine learning or deep learning model takes up a “look” with a breakon images which splits it up into a pixel. The pixel is added with tags or labels. Usually, the convolution model is used for predictions; the mathematical operation on two function provides a third function with an efficient outcome. The result is then the recognition of the images about what is “seen”, as such of a human. The resultant accuracy is evaluated in a series of predictions.","PeriodicalId":261928,"journal":{"name":"Advances in Computational Intelligence in Materials Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128785237","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

Multi-Access Filtering using FOG-Enivronment 使用fog环境的多址过滤

Advances in Computational Intelligence in Materials Science Pub Date : 2023-06-07 DOI: 10.53759/acims/978-9914-9946-9-8_14

G. S, Mohammed Abdullah S, A. J, Nawin Ram Shanker I, Selwin Isac Neilsingh J

引用次数: 0