Optical Memory and Neural Networks最新文献_第9页

Machine Learning for Multiscale Video Coding 用于多尺度视频编码的机器学习

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Optical Memory and Neural Networks Pub Date : 2023-09-25 DOI: 10.3103/S1060992X23030037

M. V. Gashnikov

引用次数: 0

English-Afaan Oromo Machine Translation Using Deep Attention Neural Network 基于深度注意神经网络的英语阿法阿罗莫语机器翻译

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Optical Memory and Neural Networks Pub Date : 2023-09-25 DOI: 10.3103/S1060992X23030049

Ebisa A. Gemechu, G. R. Kanagachidambaresan

{"title":"English-Afaan Oromo Machine Translation Using Deep Attention Neural Network","authors":"Ebisa A. Gemechu, G. R. Kanagachidambaresan","doi":"10.3103/S1060992X23030049","DOIUrl":"10.3103/S1060992X23030049","url":null,"abstract":"Attention-based neural machine translation (attentional NMT), which jointly aligns and translates, has got much popularity in recent years. Besides, a language model needs an accurate and larger bilingual dataset_ from the source to the target, to boost translation performance. There are many such datasets publicly available for well-developed languages for model training. However, currently, there is no such dataset available for the English-Afaan Oromo pair to build NMT language models. To alleviate this problem, we manually prepared a 25K English-Afaan Oromo new dataset for our model. Language experts evaluate the prepared corpus for translation accuracy. We also used the publicly available English-French, and English-German datasets to see the translation performances among the three pairs. Further, we propose a deep attentional NMT model to train our models. Experimental results over the three language pairs demonstrate that the proposed system and our new dataset yield a significant gain. The result from the English-Afaan Oromo model achieved 1.19 BLEU points over the previous English-Afaan Oromo Machine Translation (MT) models. The result also indicated that the model could perform as closely as the other developed language pairs if supplied with a larger dataset. Our 25K new dataset also set a baseline for future researchers who have curiosity about English-Afaan Oromo machine translation.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"32 3","pages":"159 - 168"},"PeriodicalIF":0.9,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41079707","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

Skin Cancer Detection and Classification System by Applying Image Processing and Machine Learning Techniques 应用图像处理和机器学习技术的癌症皮肤检测与分类系统

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Optical Memory and Neural Networks Pub Date : 2023-09-25 DOI: 10.3103/S1060992X23030086

Dr. A. Rasmi, Dr. A. Jayanthiladevi

{"title":"Skin Cancer Detection and Classification System by Applying Image Processing and Machine Learning Techniques","authors":"Dr. A. Rasmi,  Dr. A. Jayanthiladevi","doi":"10.3103/S1060992X23030086","DOIUrl":"10.3103/S1060992X23030086","url":null,"abstract":"In these modern days, cancers like Skin cancers is the general type of cancer that alters the life style of millions of citizens in each time. Around three million people are identified with it in each and every year in the US alone. Skin cancer related to the irregular enlargement of cells. On account of malignancy characteristic skin type cancer is termed as melanoma. Melanoma seems on skins because of the contact to ultraviolet emission and hereditary reasons. Thus melanoma seems like brown and black colour, but also occurs anyplace of the patient. Mostly the skin type cancers could be treatable at the earliest phases of beginning. So a fast recognition of skin cancer could rescue the life of patient. However, identifying skin cancer in its starting phases may be difficult and moreover it is expensive. Thus in the paper, they try to cope with such types of problems by making a wise decision scheme for skin lesion identification like the starting phase, that should be set into a smart robot for physical condition monitoring in our present surroundings to support early on detection. The scheme is enhanced to classify benign and malignant skin lesions with different procedures, comprising of pre-processing for instance noise elimination, segmentation, and feature extraction from lesion sections, feature collection and labelling. Following the separation of lots of raw images, colour and texture characteristics from the lesion regions, is employed to categorize the largely prejudiced noteworthy subsets for fit and cancerous circumstances. In it SVM has been applied to carry out benign and malignant lesion detection.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"32 3","pages":"197 - 203"},"PeriodicalIF":0.9,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41079703","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}

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Comparison of the 2011 and 2020 Stratospheric Ozone Events at Arctic and Northern Eurasian Latitudes Using TEMIS and Aura MLS Data 利用TEMIS和Aura MLS数据对北极和欧亚北部纬度2011年和2020年平流层臭氧事件的比较

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Optical Memory and Neural Networks Pub Date : 2023-09-25 DOI: 10.3103/S1060992X23030025

O. E. Bazhenov

{"title":"Comparison of the 2011 and 2020 Stratospheric Ozone Events at Arctic and Northern Eurasian Latitudes Using TEMIS and Aura MLS Data","authors":"O. E. Bazhenov","doi":"10.3103/S1060992X23030025","DOIUrl":"10.3103/S1060992X23030025","url":null,"abstract":"Winters-springs 2019–2020 and 2010–2011 became the periods of the severest ozone events in the Arctic throughout the satellite era. They stemmed from extremely cold and persistent polar stratospheric cloud (PSC) seasons, conducive to record strong chemical ozone destruction. TEMIS observations indicate that the total ozone (TO) column diverged from long-term norm by 45 to 55% in 2020 and by 37 to 44% in 2011 at Arctic sites; and by 27 to 32% in 2020 and by 27 to 36% in 2011 at midlatitudes. Aura MLS profiles showed that the minimum temperature was 8–13% lower than norm over the Arctic in 2020 and 8–12% lower in 2011. The ozone mixing ratios were 4% of the long-term mean at height of 20 km on March 27, 2020 and 25% at height of 21 km on March 20, 2011 for Eureka; and 7% at 19 km on April 19, 2020 and 24% at 20 km on March 20, 2011 for Ny-Ålesund. The divergences in water vapor and ozone mixing ratios, water vapor mixing ratio and temperature, and ozone mixing ratio and temperature show stronger correlations in 2020 than 2011. The correlations weaken equatorward, until becoming almost insignificant at extra-vortex latitudes.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"32 3","pages":"182 - 188"},"PeriodicalIF":0.9,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41079700","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

Algorithm for Data Processing from Ozone Lidar Sensing in the Atmosphere 大气臭氧激光雷达遥感数据处理算法

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Optical Memory and Neural Networks Pub Date : 2023-09-25 DOI: 10.3103/S1060992X23030050

A. A. Nevzorov, A. V. Nevzorov, A. I. Nadeev, N. G. Zaitsev, Ya. O. Romanovskii

引用次数: 0

Application of Deep Neural Network Structures in Semantic Segmentation for Road Scene Understanding 深度神经网络结构在道路场景语义分割中的应用

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Optical Memory and Neural Networks Pub Date : 2023-06-23 DOI: 10.3103/S1060992X23020108

Qusay Sellat, Kanagachidambaresan Ramasubramanian

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Detection and Prediction of Breast Cancer Using Improved Faster Regional Convolutional Neural Network Based on Multilayer Perceptron’s Network 基于多层感知器网络的改进更快区域卷积神经网络乳腺癌检测与预测

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Optical Memory and Neural Networks Pub Date : 2023-06-23 DOI: 10.3103/S1060992X23020054

Poonam Rana, Pradeep Kumar Gupta, Vineet Sharma

{"title":"Detection and Prediction of Breast Cancer Using Improved Faster Regional Convolutional Neural Network Based on Multilayer Perceptron’s Network","authors":"Poonam Rana, Pradeep Kumar Gupta, Vineet Sharma","doi":"10.3103/S1060992X23020054","DOIUrl":"10.3103/S1060992X23020054","url":null,"abstract":"One of the most frequent causes of death for women worldwide is breast cancer. In most cases, breast cancer can be quickly identified if certain symptoms emerge. But many women with breast cancer don’t show any symptoms. So, it is very critical to detect this disease in early stage also numerous radiologists are needed to diagnose this disease which is quite expensive for the majority of cancer hospitals. To address these concerns, the proposed methodology creates a Faster-Regional Convolutional Neural Network (Faster-RCNN) for recognizing breast cancer. Ultrasound images are collected and pre-processed utilizing resizing, adaptive median filter, histogram global contrast enhancement and high boost filtering. Image resizing is utilized to change the image size without cutting anything out. Adaptive median filter is utilized to remove unwanted noise present in the resized image. Histogram global contrast enhancement is used to enhancing the contrast level of the image. High boost filtering is utilized to sharpening the edges present in the image. After that, pre-processed images are fetched as an input to Faster R-CNN, which extract the features and segment the accurate region of the tumour. These segmented regions are classified using Multilayer Perceptron’s for detecting whether the patients are affected by breast cancer or not. According to the experimental study, the proposed approach achieves 97.1% correctness, 0.03% error, 91% precision and 93% specificity. Therefore, the developed approach attains better performance compared to other existing approaches. This prediction model helps to detect breast cancer at early stage and improve patient’s living standard.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"32 2","pages":"86 - 100"},"PeriodicalIF":0.9,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4895873","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

Improving the Performance of Human Part Segmentation Based on Swin Transformer 改进基于Swin变压器的人体部位分割性能

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Optical Memory and Neural Networks Pub Date : 2023-06-23 DOI: 10.3103/S1060992X23020030

Juan Du, Tao Yang

{"title":"Improving the Performance of Human Part Segmentation Based on Swin Transformer","authors":"Juan Du,  Tao Yang","doi":"10.3103/S1060992X23020030","DOIUrl":"10.3103/S1060992X23020030","url":null,"abstract":"One of the current challenges in deep learning is semantic segmentation. Moreover, human part segmentation is a sub-task in image segmentation, which differs from traditional segmentation to understand the human body’s intrinsic connections. Convolutional Neural Network (CNN) has always been a standard feature extraction network in human part segmentation. Recently, the proposed Swin Transformer surpasses CNN for many image applications. However, few articles have explored the performance of Swin Transformer in human part segmentation compared to CNN. In this paper, we make a comparison experiment on this issue, and the experimental results prove that even in the area of human part segmentation and without any additional trick, the Swin Transformer has good results compared with CNN. At the same time, this paper also combines the Edge Perceiving Module (EPM) currently commonly used in CNN with Swin Transformer to prove that Swin Transformer can see the intrinsic connection of segmented parts. This research demonstrates the feasibility of applying Swin Transformer to the part segmentation of images, which is conducive to advancing image segmentation technology in the future.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"32 2","pages":"101 - 107"},"PeriodicalIF":0.9,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4898556","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}

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Filters Based on Defect Modes by 1D Star Waveguides Defective System 基于一维星形波导缺陷系统缺陷模式的滤波器

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Optical Memory and Neural Networks Pub Date : 2023-06-23 DOI: 10.3103/S1060992X23020029

Y. Ben-Ali, Y. Errouas, I. El Kadmiri, Z. Rahou, A. Hallaoui, D. Bria

{"title":"Filters Based on Defect Modes by 1D Star Waveguides Defective System","authors":"Y. Ben-Ali, Y. Errouas, I. El Kadmiri, Z. Rahou, A. Hallaoui, D. Bria","doi":"10.3103/S1060992X23020029","DOIUrl":"10.3103/S1060992X23020029","url":null,"abstract":"In this paper, we present a theoretical study of the properties of defect modes in one-dimensional defective photonic star waveguides (SWGs). Both symmetrically and asymmetrically stacked defective SWGs are considered in the analysis. The properties of the defect modes are studied through the calculation of the frequency-dependent transmittance. We have also explored the effects of defect length and permittivity, as well as the position of the defect and the number of resonators grafted onto the SWGs on the number of defect modes, their transmission, and their quality factor. The findings demonstrate that geometric defects result in two modes of maximum transmission with a high-quality factor (({{Q}_{1}} = 358) and ({{Q}_{2}} = 1550)). However, when there are both geometric and material defects, the quality factor of the second filter ({{Q}_{2}}) improved from 1550 to 2203, while the quality factor of the first filter ({{Q}_{1}})remains almost unchanged. Moreover, when only material defects are present, two electromagnetic filters with maximum transmission and high-quality factor (({{Q}_{1}} = 1708) and ({{Q}_{2}} = 9085)) can be obtained.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"32 2","pages":"108 - 125"},"PeriodicalIF":0.9,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4895869","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}

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Text-Text Neural Machine Translation: A Survey 文本-文本神经机器翻译研究综述

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Optical Memory and Neural Networks Pub Date : 2023-06-23 DOI: 10.3103/S1060992X23020042

Ebisa Gemechu, G. R. Kanagachidambaresan

引用次数: 0