Optical Memory and Neural Networks最新文献

Polarization Singularity Index and Orbital Angular Momentum of Vector Light Fields

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Optical Memory and Neural Networks Pub Date : 2025-04-16 DOI: 10.3103/S1060992X24602100

V. V. Kotlyar, A. A. Kovalev, S. S. Stafeev

引用次数: 0

WHO Based K-Means Segmentation Algorithm and Hybrid VGG19-SVM Model for Identifying COVID-19 Patients in Chest X-Ray

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Optical Memory and Neural Networks Pub Date : 2025-04-16 DOI: 10.3103/S1060992X24700905

Ranjana Kumari, Rajesh Kumar Upadhyay, Javed Wasim

{"title":"WHO Based K-Means Segmentation Algorithm and Hybrid VGG19-SVM Model for Identifying COVID-19 Patients in Chest X-Ray","authors":"Ranjana Kumari, Rajesh Kumar Upadhyay, Javed Wasim","doi":"10.3103/S1060992X24700905","DOIUrl":"10.3103/S1060992X24700905","url":null,"abstract":"COVID-19 was thought to be the most lethal and devastating disease for humans caused by the novel coronavirus currently. Accurate diagnosis may lead to earlier COVID-19 discovery and lower patient mortality, especially in instances without evident symptoms. The majority of the time, chest X-ray (CXR) images are used to diagnose this illness. Patients who are infected with coronavirus exhibit symptoms that were very similar to those of pneumonia, and the virus targets body’s respiratory organs, making breathing difficult. This paper presented a hybrid VGG19-SVM model for identifying COVID-19 patients in CXR based on wild horse optimizer (WHO) based K-means segmentation to address these problems. The proposed segmentation algorithm comprises four phases such as data gathering, pre-processing, segmentation and COVID-19 detection. CXR data were gathered from medical Internet of Things (IoT) devices. Image pre-processing was performed with the assistance of image resizing, Markov random field (MRF) and adaptive gamma correction (AGC). Then, the proposed WHO based K-clustering is used to segment the affected portion of lung CXR effectively. The hybrid classification approach is introduced based on the combination of VGG19 and SVM, which is employed to classify if the patient is in normal condition either COVID-19, pneumonia or tuberculosis. Thus, various existing methods such as VGG19, AlexNet, VGG16 and GoogleNet are taken in this analysis. The proposed VGG19-SVM attained 0.96 of F1_score, 0.97 of NPV, 0.07 FNR and 0.008 of FPR, when compared to the existing methods obtained better findings using DL techniques. This shows the effectiveness of the proposed WHO based K-means clustering algorithm and hybrid VGG19-SVM model which can be useful for segment the CXR images.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 1","pages":"95 - 114"},"PeriodicalIF":1.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143840394","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

MRFO Based LU-Net Approach and Sparsity-Assisted Signal Smoothing for ECG Signal Denoising

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Optical Memory and Neural Networks Pub Date : 2025-04-16 DOI: 10.3103/S1060992X24601337

Bulty Chakrabarty, Imteyaz Ahmad

{"title":"MRFO Based LU-Net Approach and Sparsity-Assisted Signal Smoothing for ECG Signal Denoising","authors":"Bulty Chakrabarty,  Imteyaz Ahmad","doi":"10.3103/S1060992X24601337","DOIUrl":"10.3103/S1060992X24601337","url":null,"abstract":"Electrocardiographic (ECG) signals are vital for identifying and assessing cardiac problems. However, a variety of noises can contaminate ECG data, which affects the utility of ECG signals in application. Errors may be induced by patient movements, electromagnetic noise in surrounding devices, or muscle contraction artifacts. Traditional methods have often struggled with balancing effective noise reduction while preserving critical signal details, leading to compromised diagnostic accuracy. Various methods like adaptive filtering, wavelet methods, and EMD are used to denoise ECG signals to prevent noisy inference, but they may suffer with non-stationary noise or complex interference patterns. To address the aforementioned difficulties, an optimized deep learning approach and smoothing filter is designed for effectively increase the quality and reduce noise in the ECG signal. Initially, noisy ECG signals are obtained from the ECG heartbeat categorization dataset. The collected ECG raw signal is decomposed by the Multivariate dynamic mode decomposition (MDMD) technique for obtaining both high-frequency and low-frequency components of multivariate time-series data. Then, noise existing in both high frequency components is effectively removed by applying the LU-Net technique. Manta ray foreign optimization (MRFO) approach is utilized to select the learning rate and batch size of the LU-Net classifier in an optimal manner. The Integrate-and-Fire Time Encoding Machine (IF-TEM) method is used to reconstruct the denoised ECG signal. Signal sparsity assisted signal smoothing (SASS) approach is used to denoise and enhance the quality of ECG signal. The proposed MDLUTESS denoising method is compared with existing methods and its effectiveness is assessed using performance metrices like SNR, PSNR, MSE were 42, 53 dB, and 0.0017. Thus the proposed method successfully eliminates noise from the ECG signals.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 1","pages":"77 - 94"},"PeriodicalIF":1.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143840452","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

Intelligent Control of the Synthesis of Luminescent Carbon Dots with the Desired Photoluminescence Quantum Yield Using Machine Learning

IF 1

Optical Memory and Neural Networks Pub Date : 2025-04-16 DOI: 10.3103/S1060992X24700887

S. A. Dolenko, K. A. Laptinskiy, A. A. Korepanova, S. A. Burikov, T. A. Dolenko

{"title":"Intelligent Control of the Synthesis of Luminescent Carbon Dots with the Desired Photoluminescence Quantum Yield Using Machine Learning","authors":"S. A. Dolenko, K. A. Laptinskiy, A. A. Korepanova, S. A. Burikov, T. A. Dolenko","doi":"10.3103/S1060992X24700887","DOIUrl":"10.3103/S1060992X24700887","url":null,"abstract":"In this study, the results of solving a “synthesis–properties” type problem using artificial neural networks have been presented. The purpose of the study has been to determine the optimal conditions for synthesis of carbon dots to obtain nanoparticles with a given luminescence quantum yield (QY). Carbon dots were synthesized by hydrothermal synthesis from citric acid and ethylenediamine at various conditions. A multilayer perceptron (MLP) type artificial neural network was used to approximate the dependence of the target variable (luminescence QY) on the synthesis parameters. The neural network approach was successfully applied to the spectral data of a set of carbon dots of 343 samples to determine the optimal conditions for their hydrothermal synthesis from citric acid and ethylenediamine while varying the precursor ratio, temperature and reaction time over wide ranges to obtain nanoparticles with a given luminescence QY. Optimal carbon dots synthesis parameters to maximize the luminescence QY at 350 nm have been determined. Testing of the proposed neural network approach on an independent database of spectral data specially synthesized for this purpose showed good agreement between the results obtained using MLP and the experimentally measured values of the QY (the root-mean-squared error of the QY prediction was 2.14%).","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 1","pages":"18 - 29"},"PeriodicalIF":1.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143840395","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

Early Detection of Red Palm Weevil in Agricultural Environment Using Deep Learning 利用深度学习早期检测农业环境中的红掌象鼻虫

IF 1

Optical Memory and Neural Networks Pub Date : 2025-04-16 DOI: 10.3103/S1060992X24700899

Gehad Ismail Sayed, Samar Ibrahim, Aboul Ella Hassanien

{"title":"Early Detection of Red Palm Weevil in Agricultural Environment Using Deep Learning","authors":"Gehad Ismail Sayed, Samar Ibrahim, Aboul Ella Hassanien","doi":"10.3103/S1060992X24700899","DOIUrl":"10.3103/S1060992X24700899","url":null,"abstract":"The red palm weevil (RPW) represents a significant danger to palm trees farms all over the world, which will result in considerable financial losses. The absence of apparent signs until the death of the palm tree makes it difficult to identify RPW infections at an early stage. The prompt detection of RPW diseases is further complicated by large-scale farms. In order to accomplish early detection of RPW using image analysis, this paper proposed a RPW classification model based on the proposed modified ResNet-34 deep learning architecture. A dataset of 483 images is used to assess the model’s performance. For the assessment, two different dataset settings are used. In the initial dataset setup, images are divided into three groups: adults, eggs, and Pupae. Four additional categories are added to the classification in the second dataset setup: female adults, male adults, eggs, and pupae. Experimental findings show the usefulness of the proposed model, with a remarkable total accuracy of 98% for both dataset setups. These results highlight the value of using the modified ResNet-34 architecture for the early detection of RPW. Moreover, the findings demonstrated that the proposed model offers great potential for decreasing the negative effects of RPW on palm tree farms and preventing financial losses in the agriculture sector.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 1","pages":"63 - 76"},"PeriodicalIF":1.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143840451","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

Consumer Behavior Analysis in Social Networking Big Data Using Correlated Extreme Learning

IF 1

Optical Memory and Neural Networks Pub Date : 2025-04-16 DOI: 10.3103/S1060992X24700875

M. Arumugam, C. Jayanthi

{"title":"Consumer Behavior Analysis in Social Networking Big Data Using Correlated Extreme Learning","authors":"M. Arumugam, C. Jayanthi","doi":"10.3103/S1060992X24700875","DOIUrl":"10.3103/S1060992X24700875","url":null,"abstract":"Scrutiny of consumer tweets posted on social media is found to be indispensable for numerous business applications. In this manner, the model of big data analytics is applied in processing data and analyzes it to predict consumer behavioral patterns on social media. Different machine learning algorithms have gathered consumer data to analysis consumer behavior. Conventional methods are unable to discover extreme hidden patterns and require to be enhanced to produce more accurate behavioral patterns. In this work a hybrid method called, proposed Bouldin Correlation Clustering and Gradient Extreme Learning Machine (BCC-GELM) method to perform the consumer behavior analysis in social network with big data. The BCC-GELM method in hybrid model split into two modules. At first, Davis-Bouldin Index-based Correlation Clustering selects clusters with most edges within clusters as positive (i.e., similar information) while most edges between clusters as negative (i.e., dissimilar information), therefore minimizing the error rate. Consumer previous behavioral characteristics and twitter messages are analyzed by means of focal points (i.e., cluster center) via Davis-Bouldin Index. Subsequently, Stochastic Gradient Descent Extreme Learning Machine yields good results by considering distribution of tweets, therefore paving way for predicting consumer behavioral patterns in an optimal manner. The performance of BCC-GELM method is evaluated using experimental analysis and comparison is also made with traditional consumer behavioral pattern methods. The findings demonstrate that BCC-GELM method performs well than the traditional consumer behavioral pattern methods in terms of 9% of clustering accuracy, 45 and 54% of clustering time using without and with preprocessing (percent), 23% of clustering overhead and 46% of error rate.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 1","pages":"1 - 17"},"PeriodicalIF":1.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143840428","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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Monte Carlo Model for Describing Photon Interactions with Biological Tissue in New Approaches of Polarization-Sensitive Optical Coherence Tomography

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Optical Memory and Neural Networks Pub Date : 2025-04-16 DOI: 10.3103/S1060992X24602045

O. V. Angelsky, C. Yu. Zenkova, D. I. Ivanskyi, Yu. Ursuliak

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Magnetic Field-Controlled Phase Transitions in Antiferromagnetic Structures

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Optical Memory and Neural Networks Pub Date : 2025-02-03 DOI: 10.3103/S1060992X24700486

V. I. Egorov, B. V. Kryzhanovsky

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Enhanced Personality Prediction Using Knowledge Distillation with BERT: A Focus on MBTI

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Optical Memory and Neural Networks Pub Date : 2025-02-03 DOI: 10.3103/S1060992X2470084X

Suman A. Patil, Shivleela Patil, Vijayalaxmi V. Tadkal

{"title":"Enhanced Personality Prediction Using Knowledge Distillation with BERT: A Focus on MBTI","authors":"Suman A. Patil, Shivleela Patil, Vijayalaxmi V. Tadkal","doi":"10.3103/S1060992X2470084X","DOIUrl":"10.3103/S1060992X2470084X","url":null,"abstract":"A person’s personality comprises a range of behaviours, attitudes, and emotional patterns that shift throughout time due to ecological and biological influences. Personality prediction from the MBTI dataset poses computational efficiency, memory utilisation, and class imbalance challenges. This study proposes a novel approach leveraging Knowledge Distillation-based BERT to address these challenges. The process involves three stages: pre-processing, feature extraction, and classification. Initially, data is cleaned by removing irrelevant characters and URLs, followed by tokenisation and conversion to lowercase for consistency. The padding ensures uniform input size for DistilBERT, with attention masks aiding focus on relevant tokens. DistilBERT extracts contextual embeddings, enhanced by segment and positional embeddings, capturing semantic meaning via multi-head self-attention. A fully connected layer with GELU activation and batch normalisation mitigates overfitting, followed by a classification layer with Sparsemax activation, addressing the class imbalance. Fine-tuning pre-trained DistilBERT maximises detection accuracy while excluding irrelevant learning objectives. Dynamic masking during inference replaces static masking, and the Radam optimiser optimises hyperparameters for improved convergence. Our approach offers a robust solution that achieves 93% accuracy and 95% F1-score for accurate personality prediction while mitigating computational complexities and class imbalance issues.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 4","pages":"455 - 465"},"PeriodicalIF":1.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143108211","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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Optimized Jordan Neural Network and Bandwidth Aware Routing Protocol for Congestion Prediction and Avoidance in IOT for Effective Communication

IF 1

Optical Memory and Neural Networks Pub Date : 2025-02-03 DOI: 10.3103/S1060992X24700838

Mallavalli Raghavendra Suma, Bhosale Rajkumar Shankarrao, Adapa Gopi, Nilesh U. Sambhe, Laxmikant Umate

{"title":"Optimized Jordan Neural Network and Bandwidth Aware Routing Protocol for Congestion Prediction and Avoidance in IOT for Effective Communication","authors":"Mallavalli Raghavendra Suma, Bhosale Rajkumar Shankarrao, Adapa Gopi, Nilesh U. Sambhe, Laxmikant Umate","doi":"10.3103/S1060992X24700838","DOIUrl":"10.3103/S1060992X24700838","url":null,"abstract":"Development of 5G internet in today’s trend leads to the evaluation of many IOT devices. The information is transmitted by a network in IOT to store the data in the cloud. Due to the wide usage of IOT devices by people, congestion may occurs in IOT networks, which delays the information or sometimes resulting in data loss despite the implementation of congestion control methods. So many machine learning and congestion control protocols are used to predict and avoid congestion in IOT network. But these existing systems consist of drawbacks such as accuracy drop for prediction, packet loss and time delay. Hence, the Bandwidth Aware Routing Strategy (BARS) protocol using Jordan Neural Network (JNN) was developed to predict and avoid congestion in the network. Initially, the IOT nodes are deployed and the data are collected and preprocessed using a sigmoidal function and Extreme Learning machine to improve the quality of the original data. Then extract the features from the pre-processed data using Locality Preserving Projection (LPP). After that, Jordan Neural Network is used for congestion prediction and pine cone optimization is used to tune the hyper parameters such as learning rate and batch size which is utilized to improve the classifier performance. Then, BARS protocol is used to avoid the congestion present in the IOT network. According to the experimental approach, the proposed techniques achieves 95.45% of Accuracy, 95.71% of Precision, 95.39% of F1-Scorce and 95.02 of specificity. Thus, the congestion and avoidance of Information in the IOT network is processed in high efficiency by using this proposed approach.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 4","pages":"429 - 446"},"PeriodicalIF":1.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143108221","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