Nuclear Engineering International最新文献

A Review of Cybersecurity and Biometric Authentication in Cloud Network 云网络中的网络安全和生物特征认证研究综述

IF 0.6 4区工程技术

Nuclear Engineering International Pub Date : 2022-05-30 DOI: 10.18034/ei.v10i1.652

M. Reddy, Nur Mohammad Ali Chisty, Anusha Bodepudi

引用次数: 0

Network Security Framework, Techniques, and Design for Hybrid Cloud 混合云的网络安全框架、技术和设计

IF 0.6 4区工程技术

Nuclear Engineering International Pub Date : 2021-12-31 DOI: 10.18034/ei.v9i2.642

Sandesh Achar

引用次数: 0

Wave Structures for Nonlinear Schrodinger Types Fractional Partial Differential Equations Arise in Physical Sciences 非线性薛定谔型分数阶偏微分方程的波结构在物理科学中出现

IF 0.6 4区工程技术

Nuclear Engineering International Pub Date : 2021-07-30 DOI: 10.18034/EI.V9I2.560

Mst. Nasrin Nahar, M. Islam, Diganta Broto Kar

引用次数: 0

Significant of Gradient Boosting Algorithm in Data Management System 梯度增强算法在数据管理系统中的意义

IF 0.6 4区工程技术

Nuclear Engineering International Pub Date : 2021-07-20 DOI: 10.18034/EI.V9I2.559

S. Hosen, Ruhul Amin

{"title":"Significant of Gradient Boosting Algorithm in Data Management System","authors":"S. Hosen, Ruhul Amin","doi":"10.18034/EI.V9I2.559","DOIUrl":"https://doi.org/10.18034/EI.V9I2.559","url":null,"abstract":"Gradient boosting machines, the learning process successively fits fresh prototypes to offer a more precise approximation of the response parameter. The principle notion associated with this algorithm is that a fresh base-learner construct to be extremely correlated with the “negative gradient of the loss function” related to the entire ensemble. The loss function's usefulness can be random, nonetheless, for a clearer understanding of this subject, if the “error function is the model squared-error loss”, then the learning process would end up in sequential error-fitting. This study is aimed at delineating the significance of the gradient boosting algorithm in data management systems. The article will dwell much the significance of gradient boosting algorithm in text classification as well as the limitations of this model. The basic methodology as well as the basic-learning algorithm of the gradient boosting algorithms originally formulated by Friedman, is presented in this study. This may serve as an introduction to gradient boosting algorithms. This article has displayed the approach of gradient boosting algorithms. Both the hypothetical system and the plan choices were depicted and outlined. We have examined all the basic stages of planning a specific demonstration for one’s experimental needs. Elucidation issues have been tended to and displayed as a basic portion of the investigation. The capabilities of the gradient boosting algorithms were examined on a set of real-world down-to-earth applications such as text classification.","PeriodicalId":49736,"journal":{"name":"Nuclear Engineering International","volume":"72 2 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87742899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network 基于精细调节深度神经网络的手写体孟加拉数字识别

IF 0.6 4区工程技术

Nuclear Engineering International Pub Date : 2021-07-01 DOI: 10.18034/EI.V9I2.551

Md. Shahadat Hossain, Md Anwar Hossain, A. Abadin, M. Ahmed

{"title":"Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network","authors":"Md. Shahadat Hossain, Md Anwar Hossain, A. Abadin, M. Ahmed","doi":"10.18034/EI.V9I2.551","DOIUrl":"https://doi.org/10.18034/EI.V9I2.551","url":null,"abstract":"The recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition problems. This paper proposed a fine regulated deep neural network (FRDNN) for the handwritten numeric character recognition problem that uses convolutional neural network (CNN) models with regularization parameters which makes the model generalized by preventing the overfitting. This paper applied Traditional Deep Neural Network (TDNN) and Fine regulated deep neural network (FRDNN) models with a similar layer experienced on BanglaLekha-Isolated databases and the classification accuracies for the two models were 96.25% and 96.99%, respectively over 100 epochs. The network performance of the FRDNN model on the BanglaLekha-Isolated digit dataset was more robust and accurate than the TDNN model and depend on experimentation. Our proposed method is obtained a good recognition accuracy compared with other existing available methods.","PeriodicalId":49736,"journal":{"name":"Nuclear Engineering International","volume":"12 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80876543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Quantum Computing in High Frequency Trading and Fraud Detection 量子计算在高频交易和欺诈检测中的应用

IF 0.6 4区工程技术

Nuclear Engineering International Pub Date : 2021-07-01 DOI: 10.18034/EI.V9I2.549

Apoorva Ganapathy

引用次数: 5

A Survey of the Parameters of the Friction Stir Welding Process of Aluminum Alloys 6xxx Series 6xxx系列铝合金搅拌摩擦焊工艺参数研究

IF 0.6 4区工程技术

Nuclear Engineering International Pub Date : 2021-06-01 DOI: 10.18034/EI.V9I1.548

M. Essa, Fahad Salem Alhajri

引用次数: 0

Diagnosing Epidermal basal Squamous Cell Carcinoma in High-resolution, and Poorly Labeled Histopathological Imaging 在高分辨率和低标记的组织病理成像中诊断表皮基底鳞状细胞癌

IF 0.6 4区工程技术

Nuclear Engineering International Pub Date : 2020-12-31 DOI: 10.18034/ei.v8i2.574

Mani Manavalan

{"title":"Diagnosing Epidermal basal Squamous Cell Carcinoma in High-resolution, and Poorly Labeled Histopathological Imaging","authors":"Mani Manavalan","doi":"10.18034/ei.v8i2.574","DOIUrl":"https://doi.org/10.18034/ei.v8i2.574","url":null,"abstract":"The most appropriate method to uncover patterns from clinical records for each patient record is to create a bag with a variety of examples in the form of symptoms. The goal of medical diagnosis is to find useful ones first and then map them to one or more diseases. Patients are often represented as vectors in some aspect. Pathologists and dermatopathologists diagnose basal cell carcinomas (BCC), one of the most frequent cutaneous cancers in humans, on a regular basis. Improving histological diagnosis by producing diagnosis ideas, i.e. computer-assisted diagnoses, is a hotly debated research topic aimed at improving safety, quality, and efficiency. Due to their improved performance, machine learning approaches are rapidly being used. Typical images obtained by scanning histological sections, on the other hand, frequently have a resolution insufficient for today's state-of-the-art neural networks. Furthermore, weak labels hamper network training because just a small portion of the image signals the disease class, while the majority of the image is strikingly comparable to the non-disease class. The goal of this work is to see if attention-based deep learning models can detect basal cell carcinomas in histological sections and overcome the ultra-high resolution and poor labeling of full slide images. With an AUC of 0.99, we show that attention-based models can achieve nearly flawless classification performance.","PeriodicalId":49736,"journal":{"name":"Nuclear Engineering International","volume":"9 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75529868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Application of Convolution Neural Network for Digital Image Processing 卷积神经网络在数字图像处理中的应用

IF 0.6 4区工程技术

Nuclear Engineering International Pub Date : 2020-12-31 DOI: 10.18034/ei.v8i2.592

Venkata Naga Satya Surendra Chimakurthi

{"title":"Application of Convolution Neural Network for Digital Image Processing","authors":"Venkata Naga Satya Surendra Chimakurthi","doi":"10.18034/ei.v8i2.592","DOIUrl":"https://doi.org/10.18034/ei.v8i2.592","url":null,"abstract":"In order to train neural network algorithms for multiple machine learning tasks, like the division of distinct categories of objects, various deep learning approaches employ data. Convolutional neural networks deep learning algorithms are quite strong when it comes to image processing. With the recent development of multi-layer convolutional neural networks for high-level tasks like object recognition, object acquisition, and recent semantic classification, the field has seen great success in this approach. The two-phase approach is frequently employed in semantic segregation. In the second step of becoming a standard global graphical model, communication networks are educated to deliver good local intelligence with a pixel. Convolutional Neural Networks (CNN or ConvNet) are complicated neural server networks in the field of artificial intelligence. Because of their great accuracy, convolutional neural networks (CNNs) are frequently utilized in picture categorization and recognition. In the late 1990s, Yann LeCun, a computer scientist, was based on the human notion of cognition and came up with the idea. When constructing a network, CNN uses a hierarchical model that eventually results in a convolution layer in which all neurons are linked and output is processed. Using an example of an image processing application, this article demonstrates how the CNN architecture is implemented in its entirety. You can utilize this to better comprehend the advantages of this current photography website. \u0000 ","PeriodicalId":49736,"journal":{"name":"Nuclear Engineering International","volume":"47 3 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83396546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Maximizing the Potential of Artificial Intelligence to Perform Evaluations in Ungauged Washbowls 最大限度地发挥人工智能在未计量的洗衣机中进行评估的潜力

IF 0.6 4区工程技术

Nuclear Engineering International Pub Date : 2020-12-31 DOI: 10.18034/ei.v8i2.636

Sandesh Achar

引用次数: 2