Optical Memory and Neural Networks最新文献_第2页

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

IF 0.9

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

Ebisa Gemechu, G. R. Kanagachidambaresan

引用次数: 0

Optimized Lung Nodule Prediction Model for Lung Cancer Using Contour Features Extraction 基于轮廓特征提取的肺癌肺结节预测模型优化

IF 0.9

Optical Memory and Neural Networks Pub Date : 2023-06-23 DOI: 10.3103/S1060992X23020091

Faiyaz Ahmad, U. Hariharan, S. Karthick, Vaibhav Eknath Pawar, S. Sharon Priya

{"title":"Optimized Lung Nodule Prediction Model for Lung Cancer Using Contour Features Extraction","authors":"Faiyaz Ahmad, U. Hariharan, S. Karthick, Vaibhav Eknath Pawar,  S. Sharon Priya","doi":"10.3103/S1060992X23020091","DOIUrl":"10.3103/S1060992X23020091","url":null,"abstract":"Lung cancer is one of the most complicated diseases in human assessment. It affects the lives of humans critically. For the treatment of the lungs and prevention of the disease, it is important for an accurate and timely diagnosis of the disease. In most aspects, it was not as successful as the traditional method, which uses past medical history. Classification techniques are effective and reliable in categorizing affected lungs and people with normal lungs. But unfortunately failed to provide proper nodule location as this became a tedious task to find it. In the proposed method, Hybrid Particle Swarm Optimization-Lung Nodule Candidate (HPSO-LNDC) Detection depends on the diagnostic system using disease image data set for lung segmentation assessment. Initially, the input Data images are pre-processed to reduce the computational complexity of the system. Then, those segmented images are subject to the proposed HPSO-LNDC model. To solve the problem of accuracy, so many researchers have used slicing techniques, but most of these techniques are stuck in the local minima and suffer from premature convergence. Therefore the HPSO-LNDC model is integrated with the Hybrid PSO to reduce loss function occurring in the lung nodule detection architecture. Such a combination helps the HPSO-LNDC to avoid low similarity values. The proposed algorithm is simulated using the MATLAB tool and tested experimentally. It is defined as accuracy, detection level and Dice Similarity Coefficient (DSC). Results show that HPSO-LNDC with 85% accuracy and DSC of 90% was better than conventional methods.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"32 2","pages":"126 - 136"},"PeriodicalIF":0.9,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4895882","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

Recognition of Mentally Pronounced Russian Phonemes Using Convolutional Neural Networks and Electroencephalography Data 利用卷积神经网络和脑电图数据识别俄语语音

IF 0.9

Optical Memory and Neural Networks Pub Date : 2023-06-23 DOI: 10.3103/S1060992X23020066

L. E. Seleznyev, A. A. Chupakhin, V. A. Kostenko, A. O. Shevchenko, A. V. Vartanov

引用次数: 1

Design and Simulation of a Reconfigurable Multifunctional Optical Sensor 可重构多功能光学传感器的设计与仿真

IF 0.9

Optical Memory and Neural Networks Pub Date : 2023-06-23 DOI: 10.3103/S1060992X2302008X

Shaher Dwik, G. Sasikala, S. Natarajan

{"title":"Design and Simulation of a Reconfigurable Multifunctional Optical Sensor","authors":"Shaher Dwik, G. Sasikala, S. Natarajan","doi":"10.3103/S1060992X2302008X","DOIUrl":"10.3103/S1060992X2302008X","url":null,"abstract":"Generally, in Visible Light Communication (VLC) systems, Position Sensitive Device (PSD) sensor is used only for tracking purposes. However, the tracking process functions until both transmitter and receiver align, thus the PSD sensor remains idle for a long time. This interval can be exploited to achieve other functions by modifying its architecture. In this paper, a modification of the PSD structure has been achieved to make it able to perform energy harvesting and data acquisition as well as tracking. As the PSD is mainly formed of an array of photodiodes, our main idea is to use transistors to switch between the two modes of operation of the photodiodes (photoconductive and photovoltaic). Furthermore, switching between the output pins could be achieved depending on the desired function. The proposed sensor can extend the battery lifetime, increase the integration and the functionality, and reduce the physical size of the system. Simulation using MATALB was performed to validate the concept of the proposed structure and its operation. The results showed that the proposed sensor works successfully and it can be considered for further manufacturing levels. The presented sensor might be used in Free Space Optical (FSO) communication like cube satellite, or even in Underwater Wireless Optical Communication (UWOC).","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"32 2","pages":"147 - 157"},"PeriodicalIF":0.9,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4898691","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

Density Function of Weighted Sum of Chi-Square Variables with Trigonometric Weights 三角加权卡方变量和的密度函数

IF 0.9

Optical Memory and Neural Networks Pub Date : 2023-04-02 DOI: 10.3103/S1060992X23010071

V. I. Egorov, B. V. Kryzhanovsky

引用次数: 0

Lidar Measurement of the Raman Differential Cross Section by Hydrogen Molecules 氢分子拉曼微分截面的激光雷达测量

IF 0.9

Optical Memory and Neural Networks Pub Date : 2023-04-02 DOI: 10.3103/S1060992X23010034

V. E. Privalov, V. G. Shemanin

引用次数: 0

Applications of Fluorescence Spectroscopy and Machine Learning Methods for Monitoring of Elimination of Carbon Nanoagents from the Body 荧光光谱和机器学习方法在监测体内碳纳米剂消除中的应用

IF 0.9

Optical Memory and Neural Networks Pub Date : 2023-04-02 DOI: 10.3103/S1060992X23010046

O. E. Sarmanova, A. D. Kudryashov, K. A. Laptinskiy, S. A. Burikov, M. Yu. Khmeleva, A. A. Fedyanina, S. A. Dolenko, P. V. Golubtsov, T. A. Dolenko

{"title":"Applications of Fluorescence Spectroscopy and Machine Learning Methods for Monitoring of Elimination of Carbon Nanoagents from the Body","authors":"O. E. Sarmanova, A. D. Kudryashov, K. A. Laptinskiy, S. A. Burikov, M. Yu. Khmeleva, A. A. Fedyanina, S. A. Dolenko, P. V. Golubtsov, T. A. Dolenko","doi":"10.3103/S1060992X23010046","DOIUrl":"10.3103/S1060992X23010046","url":null,"abstract":"The study considers the application of artificial neural networks to solve the inverse problem of fluorescence (FL) spectroscopy for monitoring the elimination of carbon nanocomplexes’ components from the body – the problem of determining the concentrations of carbon dots (CD) and the anticancer drug doxorubicin (Dox) excreted from the body with urine. The problem was solved in three ways using three sets of FL spectroscopy data: spectral data obtained by exciting FL of urine together with CD and Dox with radiation of 405 nm, 532 nm wavelength as well as spectra obtained by combining these data. Multilayer perceptrons (MLP) were applied to the obtained spectral data, which enabled the determination of the concentrations of CD and Dox in urine. To increase the accuracy of monitoring the excretion of CD and Dox with urine, principal component analysis and autoencoders were additionally used. The conducted studies showed that the best results of solving this problem are provided by the application of a MLP to spectral data compressed using an autoencoder. This approach allows us to determine the concentration of CD in urine with MAE of 39 ng/mL (3.3% of the upper limit of the concentration range) and the concentration of Dox with MAE of 27 ng/mL (2.8% of the upper limit of the concentration range). The proposed approach shows results comparable with analogues, however it lacks several significant drawbacks such as rigid fixation of the CD concentration in the suspension, and it can be used for simultaneous rapid monitoring of a number of substances.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"32 1","pages":"20 - 33"},"PeriodicalIF":0.9,"publicationDate":"2023-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4067219","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 and Prediction of Rice Leaf Disease Using a Hybrid CNN-SVM Model 基于CNN-SVM混合模型的水稻叶片病害检测与预测

IF 0.9

Optical Memory and Neural Networks Pub Date : 2023-04-02 DOI: 10.3103/S1060992X2301006X

Devchand J. Chaudhari, K. Malathi

{"title":"Detection and Prediction of Rice Leaf Disease Using a Hybrid CNN-SVM Model","authors":"Devchand J. Chaudhari, K. Malathi","doi":"10.3103/S1060992X2301006X","DOIUrl":"10.3103/S1060992X2301006X","url":null,"abstract":"Agriculture is one of India’s greatest money makers and a measure of financial growth. Rice is one of India’s most widely grown crops as a staple diet. Rice crops have been shown to be heavily afflicted by illnesses, resulting in significant losses in agriculture. Rice leaf diseases not only cause a loss of revenue for farmers, but they also decrease the quality of their final output. External appearances of diseased rice leaves can be subjected to image processing processes. On the other hand, disease sickness may vary depending on the different leaves. Each disease has its own distinct features, some of leaves have the same colour but various shapes, while others have different colours but the same shapes. Farmers are sometimes confused and unable to make an accurate judgement when it comes to pesticide choosing. To solve this problem, a hybrid CNN (Inception-ResNet)-SVM model for detecting and treating damaged rice leaves has been developed. In this designed model, the images are collected and gathered by capturing rice leaf using camera at the agricultural field. These images are refined to improve image quality and visibility for reliable estimation, and then segregated using Grab-Cut algorithm to eliminate undesired sections of image. Features of the segmented images are extracted and classified using hybrid CNN (Inception-Resnet V2)-SVM algorithm. The developed model’s study results are analysed and discussed to recent techniques. The suggested model achieved accuracy, precision, recall, and error values of 0.97, 0.93 and 0.03 accordingly. As a conclusion, suggested model outperforms revious methodologies.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"32 1","pages":"39 - 57"},"PeriodicalIF":0.9,"publicationDate":"2023-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4067227","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

Masked Face Recognition Using Generative Adversarial Networks by Restoring the Face Closed Part 利用生成对抗网络恢复人脸封闭部分的蒙面人脸识别

IF 0.9

Optical Memory and Neural Networks Pub Date : 2023-04-02 DOI: 10.3103/S1060992X23010022

Chaoxiang Chen, I. Kurnosov, Guangdi Ma, Yang Weichen, S. Ablameyko

引用次数: 2

Introductory Review on All-Optical Machine Learning Leap in Photonic Integrated Circuits 光子集成电路全光机器学习飞跃综述

IF 0.9

Optical Memory and Neural Networks Pub Date : 2023-01-10 DOI: 10.3103/S1060992X22040075

Ankur Saharia, Kamalkishor Choure, Nitesh Mudgal, Ravi Kumar Maddila, Manish Tiwari, Ghanshyam Singh

引用次数: 0