Optical Memory and Neural Networks最新文献_第7页

Divergence Parametric Smoothing in Image Compression Algorithms 图像压缩算法中的发散参数平滑法

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Optical Memory and Neural Networks Pub Date : 2024-07-04 DOI: 10.3103/S1060992X24700012

M. V. Gashnikov

引用次数: 0

Lasers and Modern Energy 激光与现代能源

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Optical Memory and Neural Networks Pub Date : 2024-03-25 DOI: 10.3103/S1060992X24010090

V. E. Privalov, V. G. Shemanin

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Q-Memory Task Routing to Prevent Deadlocks in Ethernet Control with Memory Crossbar Switching 利用 Q-Memory 任务路由防止以太网控制中的死锁与内存交叉条交换

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Optical Memory and Neural Networks Pub Date : 2024-03-25 DOI: 10.3103/S1060992X24010077

Smita Sudhakar Palnitkar, Sudhir Kanade

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Forecasting PM2.5 Concentration Using Gradient-Boosted Regression Tree with CNN Learning Model 利用梯度提升回归树和 CNN 学习模型预测 PM2.5 浓度

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Optical Memory and Neural Networks Pub Date : 2024-03-25 DOI: 10.3103/S1060992X24010107

A. Usha Ruby, J. George Chellin Chandran, Prasannavenkatesan Theerthagiri, Renuka Patil, B. N. Chaithanya, T. J. Swasthika Jain

{"title":"Forecasting PM2.5 Concentration Using Gradient-Boosted Regression Tree with CNN Learning Model","authors":"A. Usha Ruby, J. George Chellin Chandran, Prasannavenkatesan Theerthagiri, Renuka Patil, B. N. Chaithanya, T. J. Swasthika Jain","doi":"10.3103/S1060992X24010107","DOIUrl":"10.3103/S1060992X24010107","url":null,"abstract":"Air pollution imposed by particle matter (PM) made it a public health concern and hazard to humans and the environment. Reduced vision, allergic responses, pneumonia, asthma, cardiovascular disorders, lung cancer, and even mortality can result from prolonged exposure to the concentration of air’s small particulate matter. Air quality prediction can offer reliable information for future air pollution status to operate air pollution control effectively and make preventative plans. Tracking, predicting, and regulating emissions is crucial. Controlling PM2.5 is the key for enhancing air quality, and it can be accomplished by forecasting PM2.5 concentrations. This work develops a methodology for forecasting PM2.5 concentrations using a gradient-boosted regression tree with Convolutional Neural Network (CNN) and fuzzy K-nearest neighbour (fuzzy-KNN). The results of the proposed methodology have been comparatively analysed with multiple linear regression, stacked long short-term memory, bidirectional gated recurrent unit, and gradient-boosted regression tree. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are evaluated, and it shows that the gradient-boosted regression tree model produces a reduced error with improved accuracy in forecasting air quality.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 1","pages":"86 - 96"},"PeriodicalIF":1.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140300090","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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Multi-Modal Co-Attention Capsule Network for Fake News Detection 用于假新闻检测的多模式协同关注胶囊网络

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Optical Memory and Neural Networks Pub Date : 2024-03-25 DOI: 10.3103/S1060992X24010041

Chunyan Yin, Yongheng Chen

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Review on Improved Machine Learning Techniques for Predicting Chronic Diseases 关于预测慢性疾病的改进型机器学习技术的综述

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Optical Memory and Neural Networks Pub Date : 2024-03-25 DOI: 10.3103/S1060992X24010028

L. Abirami, J. Karthikeyan

{"title":"Review on Improved Machine Learning Techniques for Predicting Chronic Diseases","authors":"L. Abirami, J. Karthikeyan","doi":"10.3103/S1060992X24010028","DOIUrl":"10.3103/S1060992X24010028","url":null,"abstract":"Healthcare industry is a stage which is presented with tremendous innovative headways consistently. Parkinson disease (PD) has become a critical overall general clinical issue starting late. To provide the solution for this problem, in this paper, use fusion of machine learning and federated learning techniques for processing electronically collected patients’ health record (PD dataset) in accurate manner. The PD dataset are constantly gathered and sorted out to give a point by point history of patients, their sicknesses and determination plans. The medical PD dataset contains 43 400 electronic records of potential patients which includes normal, Ischemic and Hemorrhagic stroke. Cleaning, finding feature correlation and imputing missing values in the PD has to be performed by preprocessing & normalization approach. For further processing, using Random over sampling (ROS) methods the imbalanced PD dataset will be converted into balanced. From the balanced PD datasets the stroke prediction accuracy will be validated using Decision Tree, Logistic Regression, Random Forest and Improved LSTM (Imp-LSTM) machine learning algorithms. Using distinct experiments of executing performance measurements the accuracy rate from our prediction classifiers for the patient with smokes category will be 62.29, 71.36, 96.51 and 99.56% respectively as like the patient with never smoked category dataset the accuracy will be 70.49, 75.86, 96.49 and 99.58% respectively. The proposed Imp-LSTM algorithm in this research will effectively produce high overall accuracy in both the datasets, which means a successful decrease in the misdiagnosis rate for stroke prediction.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 1","pages":"28 - 46"},"PeriodicalIF":1.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140300452","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

Lateral Motion Control of a Maneuverable Aircraft Using Reinforcement Learning 利用强化学习实现可操控飞机的横向运动控制

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Optical Memory and Neural Networks Pub Date : 2024-03-25 DOI: 10.3103/S1060992X2401003X

Yu. V. Tiumentsev, R. A. Zarubin

{"title":"Lateral Motion Control of a Maneuverable Aircraft Using Reinforcement Learning","authors":"Yu. V. Tiumentsev, R. A. Zarubin","doi":"10.3103/S1060992X2401003X","DOIUrl":"10.3103/S1060992X2401003X","url":null,"abstract":"Machine learning is currently one of the most actively developing research areas. Considerable attention in the ongoing research is paid to problems related to dynamical systems. One of the areas in which the application of machine learning technologies is being actively explored is aircraft of various types and purposes. This state of the art is due to the complexity and variety of tasks that are assigned to aircraft. The complicating factor in this case is incomplete and inaccurate knowledge of the properties of the object under study and the conditions in which it operates. In particular, a variety of abnormal situations may occur during flight, such as equipment failures and structural damage, which must be counteracted by reconfiguring the aircraft’s control system and controls. The aircraft control system must be able to operate effectively under these conditions by promptly changing the parameters and/or structure of the control laws used. Adaptive control methods allow to satisfy this requirement. One of the ways to synthesize control laws for dynamic systems, widely used nowadays, is LQR approach. A significant limitation of this approach is the lack of adaptability of the resulting control law, which prevents its use in conditions of incomplete and inaccurate knowledge of the properties of the control object and the environment in which it operates. To overcome this limitation, it was proposed to modify the standard variant of LQR (Linear Quadratic Regulator) based on approximate dynamic programming, a special case of which is the adaptive critic design (ACD) method. For the ACD-LQR combination, the problem of controlling the lateral motion of a maneuvering aircraft is solved. The results obtained demonstrate the promising potential of this approach to controlling the airplane motion under uncertainty conditions.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 1","pages":"1 - 12"},"PeriodicalIF":1.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140299881","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

Assault Type Detection in WSN Based on Modified DBSCAN with Osprey Optimization Using Hybrid Classifier LSTM with XGBOOST for Military Sector 基于改进的 DBSCAN 和 Osprey 优化的 WSN 攻击类型检测，使用混合分类器 LSTM 和 XGBOOST，用于军事领域

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Optical Memory and Neural Networks Pub Date : 2024-03-25 DOI: 10.3103/S1060992X24010089

R. Preethi

{"title":"Assault Type Detection in WSN Based on Modified DBSCAN with Osprey Optimization Using Hybrid Classifier LSTM with XGBOOST for Military Sector","authors":"R. Preethi","doi":"10.3103/S1060992X24010089","DOIUrl":"10.3103/S1060992X24010089","url":null,"abstract":"Military tasks constitute the most important and significant applications of Wireless sensor networks (WSNs). In military, Sensor node deployment increases activities, efficient operation, saves loss of life, and protects national sovereignty. Usually, the main difficulties in military missions are energy consumption and security in the network. Another major security issues are hacking or masquerade attack. To overcome the limitations, the proposed method modified DBSCAN with OSPREY optimization Algorithm (OOA) using hybrid classifier Long Short-Term Memory (LSTM) with Extreme Gradient Boosting (XGBOOST) to detect attack types in the WSN military sector for enhancing security. First, nodes are deployed and modified DBSCAN algorithm is used to cluster the nodes to reduce energy consumption. To select the cluster head optimally by using the OSPREY optimization Algorithm (OOA) based on small distance and high energy for transfer data between the base station and nodes. Hybrid LSTM-XGBOOST classifier utilized to learn the parameter and predict the four assault types such as scheduling, flooding, blackhole and grayhole assault. Classification and network metrics including Packet Delivery Ratio (PDR), Throughput, Average Residual Energy (ARE), Packet Loss Ratio (PLR), Accuracy and F1_score are used to evaluate the performance of the model. Performance results show that PDR of 94.12%, 3.2 Mbps throughput at 100 nodes, ARE of 8.94J, PLR of 5.88%, accuracy of 96.14%, and F1_score of 95.04% are achieved. Hence, the designed model for assault prediction types in WSN based on modified DBSCAN clustering with a hybrid classifier yields better results.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 1","pages":"53 - 71"},"PeriodicalIF":1.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140299878","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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Some Remarks on Possible Superconductivity of Composition Pb9CuP6O25 关于 Pb9CuP6O25 成分可能具有超导性的一些评论

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Optical Memory and Neural Networks Pub Date : 2024-01-30 DOI: 10.3103/s1060992x23070020

P. Abramian, A. Kuzanyan, V. Nikoghosyan, S. Teknowijoyo, A. Gulian

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Superconducting Polycrystalline Rhenium Films Deposited at Room Temperature 室温沉积的超导多晶铼薄膜

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Optical Memory and Neural Networks Pub Date : 2024-01-30 DOI: 10.3103/s1060992x23070184

S. Teknowijoyo, A. Gulian

引用次数: 0