Optical Memory and Neural Networks最新文献_第10页

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

IF 1

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}

引用次数: 0

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

IF 0.9

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

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

引用次数: 0

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

IF 0.9

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

S. Teknowijoyo, A. Gulian

引用次数: 0

Mirrorless Lasing: A Theoretical Perspective 无反光镜蚀刻：理论视角

IF 0.9

Optical Memory and Neural Networks Pub Date : 2024-01-30 DOI: 10.3103/s1060992x23070172

A. Ramaswamy, J. Chathanathil, D. Kanta, E. Klinger, A. Papoyan, S. Shmavonyan, A. Khanbekyan, A. Wickenbrock, D. Budker, S. A. Malinovskaya

引用次数: 0

Sensing: Equation One 传感：等式一

IF 0.9

Optical Memory and Neural Networks Pub Date : 2024-01-30 DOI: 10.3103/s1060992x23070056

D. Budker, M. G. Kozlov

引用次数: 0

Effect of Bloch-Band Dispersion on the Quantized Transport in a Topological Thouless Pump 布洛赫带色散对无拓扑泵中量化传输的影响

IF 0.9

Optical Memory and Neural Networks Pub Date : 2024-01-30 DOI: 10.3103/s1060992x23070226

R. G. Unanyan, M. Fleischhauer

引用次数: 0

Adiabatic States and Suppression of Dissipative Processes 绝热状态与耗散过程的抑制

IF 0.9

Optical Memory and Neural Networks Pub Date : 2024-01-30 DOI: 10.3103/s1060992x2307007x

E. A. Gazazyan, G. G. Grigoryan

引用次数: 0

Prospects for Using Thermoelectric Single-Photon Detectors in Quantum Information Systems and Astrophysics 在量子信息系统和天体物理学中使用热电单光子探测器的前景

IF 0.9

Optical Memory and Neural Networks Pub Date : 2024-01-30 DOI: 10.3103/s1060992x23070111

A. A. Kuzanyan, A. S. Kuzanyan, V. R. Nikoghosyan

{"title":"Prospects for Using Thermoelectric Single-Photon Detectors in Quantum Information Systems and Astrophysics","authors":"A. A. Kuzanyan, A. S. Kuzanyan, V. R. Nikoghosyan","doi":"10.3103/s1060992x23070111","DOIUrl":"https://doi.org/10.3103/s1060992x23070111","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3>In this paper, we propose the design of detection pixels for single-photon detectors, consisting of absorber and heat sink (Bi-2223), thermoelectric sensors (CeB6), and an antireflection layer (SiO2) located on a dielectric substrate (Al2O3). We employ modeling and simulation to study the heat propagation processes in multi-layer detection pixels following the absorption of photons with energy ranging from 0.8 eV to 1 keV. Calculations are performed using the heat transfer equation within a limited volume, employing the three-dimensional matrix method. We calculate the temperature temporal variation in different areas of the detection pixels, as well as the voltage generated on the sensor, for various thicknesses and surfaces of the detection pixel layers. We determine the maximum signal value, time at which the maximum signal is reached, signal decay time, and the detector’s count rate. We derive equations for Phonon and Johnson noise in the three-layer detection pixel and calculate the total noise. Based on the data obtained, we propose ways to improve the signal-to-noise ratio.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"29 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140886342","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

Long-Wavelength Optics of a Pair-Interacting Electron Gas in a Lens-Shaped Quantum Dot: Two-Dimensional Moshinsky Model 透镜形量子点中成对相互作用电子气体的长波光学：二维莫辛斯基模型

IF 0.9

Optical Memory and Neural Networks Pub Date : 2024-01-30 DOI: 10.3103/s1060992x23070123

M. A. Mkrtchyan, E. M. Kazaryan, H. A. Sarkisyan, M. Y. Vinnichenko, D. A. Firsov

引用次数: 0

Programmable Quantum Gate Operations Using Qutrit Quantum Dots 使用 Qutrit 量子点的可编程量子门操作

IF 0.9

Optical Memory and Neural Networks Pub Date : 2024-01-30 DOI: 10.3103/s1060992x23070160

E. A. Pogosyan, E. A. Gazazyan

引用次数: 0