2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)最新文献_第8页

Experimental Implementation of Shor's Quantum Algorithm to Break RSA Shor量子算法破解RSA的实验实现

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI: 10.1109/CICN56167.2022.10008287

Aminah Albuainain, Jana Alansari, Samiyah Alrashidi, Wasmiyah Alqahtani, Jana Alshaya, Naya Nagy

引用次数: 0

Low-End Hand Held Communication Devices in a Post-Disaster Scenario 灾后场景下的低端手持通信设备

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI: 10.1109/CICN56167.2022.10008328

R. R. Sarkar, Amitabha Chakrabarty, Mohammad Zahidur Rahman

引用次数: 0

Analysis of Threshold Effect of Dual-tone Frequency Estimation Based on Method of Interval Errors 基于区间误差法的双音频率估计阈值效应分析

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI: 10.1109/CICN56167.2022.10008251

En Yuan, Peng Liu, Bing Xu, Wenyu Zhang, Yongfen Wu, Yanqin Tang

引用次数: 0

Enhancing Monocular Depth Estimation via Image Pre-processing Techniques 通过图像预处理技术增强单目深度估计

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI: 10.1109/CICN56167.2022.10008288

M. Syed, Abdulrahman Javaid, Asaad A. Alduais, M. H. Shullar, U. Baroudi, Mustafa Alnasser

{"title":"Enhancing Monocular Depth Estimation via Image Pre-processing Techniques","authors":"M. Syed, Abdulrahman Javaid, Asaad A. Alduais, M. H. Shullar, U. Baroudi, Mustafa Alnasser","doi":"10.1109/CICN56167.2022.10008288","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008288","url":null,"abstract":"Robots and drones are getting popular in many applications nowadays. Autonomous operations of drones and robots are highly desirable to minimize human interventions and enhance operation efficiency. However, there are several challenges that need to be overcome before robots and drones can be automated with minimum hardware requirements. Currently, robotics industry employs costly sensors such as Lidar to estimate distance between a vehicle and objects. Recent advancement in Artificial Intelligence (AI) encouraged researcher to investigate techniques to estimate the distance between vehicle and objects using monocular camera and AI. However, distance (depth) estimation using monocular camera still suffers from low accuracy rate in depth estimation. This paper aims to improve the depth estimation values through applying several image pre-processing techniques such as Nonuniform Illumination Removal, Local Adaptive Thresholding, Histogram Equalization, Adaptive Histogram Equalization, White Balance, and Homo- morphic filtering techniques.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124434712","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

Load Frequency Control of Interconnected Power System Using Particle Swarm Optimization Based Disturbance Observer-Enhanced PI Controller 基于扰动观测器增强PI控制器的粒子群优化互联电力系统负荷频率控制

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI: 10.1109/CICN56167.2022.10008353

Edgar T. Zapanta, R. Santiago

{"title":"Load Frequency Control of Interconnected Power System Using Particle Swarm Optimization Based Disturbance Observer-Enhanced PI Controller","authors":"Edgar T. Zapanta, R. Santiago","doi":"10.1109/CICN56167.2022.10008353","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008353","url":null,"abstract":"Load frequency control is a vital control problem in power system operation and control. The primary purpose of load frequency control is to keep power system frequency at a nominal value and control net power interchange between power system tie-lines at predetermined limits during load variations or disturbances. One common proposed control method for load frequency control is the linear quadratic regulator (LQR) feedback controller. However, all the state variables may not always be available and measurable, limiting the application of the LQR controller. Therefore, A disturbance observer-based controller is proposed to estimate the state variables using the available measurements. The observer-controller gains are optimized by particle swarm optimization. The proposed controller has been validated in a well-known and widely used power system test case, Kundur's two-area 4-machine 11-bus power system and 10-machine IEEE 39-bus power system implemented in Matlab® and Simulink®, under different faulted conditions, and load disturbances. Furthermore, simulation results produced faster frequency control loop stabilization with minimal frequency nadir, which proved the effectiveness of the proposed controller in an interconnected power system.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122542535","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

Seismic Structures Classification Using Novel Features from Seismic Images 基于地震图像新特征的地震结构分类

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI: 10.1109/CICN56167.2022.10008257

Ghadah Alhabib, Ghazanfar Latif, J. Alghazo, G. B. Brahim

{"title":"Seismic Structures Classification Using Novel Features from Seismic Images","authors":"Ghadah Alhabib, Ghazanfar Latif, J. Alghazo, G. B. Brahim","doi":"10.1109/CICN56167.2022.10008257","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008257","url":null,"abstract":"Seismic facies can be used as novel features to classify different classes of seismic structures. Classification of seismic structure is beneficial for mineralogy, grain size approximation, the permeability of deposition units, and the identification of areas of interest. To extract features of seismic images, the following extraction methods were used: Discrete Wavelet Transform Features, Discrete Cosine Transform Features, Discrete Fourier Transform Features, and Gabor Features. The classification methods being considered are Support Vector Machine (SVM), Random Forest (RF), Fast Decision Trees (FDT), and Naïve Bayes (NB). The proposed study uses the LANDMASS database, composed of two datasets, LANDMASS-1, with 17,667 images, and LANDMASS-2, with 4,000 images. The datasets contain seismic images of four different classes of seismic structures; Chaotic, Fault, Horizon, and Salt Dome. The outcome of this study proves that the combination of Forest Tree classification method and the Discrete Cosine Transform Features extraction method achieved the highest accuracy, which was around 94.17% - higher than that achieved considering similar methods reported in the extant literature.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131577989","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

Detecting Open Banking API Security Threats Using Bayesian Attack Graphs 利用贝叶斯攻击图检测开放银行API安全威胁

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI: 10.1109/CICN56167.2022.10008365

Dawood Behbehani, M. Rajarajan, N. Komninos, Khalid Al–Begain

引用次数: 0

An Empirical Study on Fake News Prediction with Machine Learning Methods 基于机器学习方法的假新闻预测实证研究

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI: 10.1109/CICN56167.2022.10008333

Manisha Aluri, Divya sree Panchumarthi, Bhargav Boddupalli, M. Enduri, Sumana G Sree, Satish Anamalamudi

引用次数: 0

Text Mining on Hospital Stay Durations and Management of Sickle Cell Disease Patients 镰状细胞病患者住院时间和管理的文本挖掘

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI: 10.1109/CICN56167.2022.10008265

Mohammed Gollapalli, Latifa Alabdullatif, Farah Alsuwayeh, Moodhi Aljouali, Alhanoof Alhunief, Zaina Batook

{"title":"Text Mining on Hospital Stay Durations and Management of Sickle Cell Disease Patients","authors":"Mohammed Gollapalli, Latifa Alabdullatif, Farah Alsuwayeh, Moodhi Aljouali, Alhanoof Alhunief, Zaina Batook","doi":"10.1109/CICN56167.2022.10008265","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008265","url":null,"abstract":"Sickle cell disease (SCD) is a genetic blood disorder characterized by clumping of red blood cells, preventing blood and oxygen from reaching all parts of the body. SCD disease is very common in Sub-Saharan Africa, the Mediterranean basin, and the eastern regions of Saudi Arabia due to high consanguineous marriage practices. Patients are frequently admitted due to the prevalence of multiple organ damage among SCD patients as a result of repeated vascular occlusion, resulting in a large amount of medical notes recorded by doctors and nurses during each clinical trial. In this study, 12 years of SCD patient de-identified data (2018–2020) were obtained officially from the hospital and experimented with in relation to SCD patient medical notes. We used a text mining framework to analyze and predict the length of stay (LoS) of SCD patients using three machine learning (ML) models: XGBoost, Decision Tree, and KNN. The most frequently occurring words were extracted from 62,847 SCD medical screening records using text mining. Furthermore, feature models were created to investigate the effect of increasing or decreasing the number of terms on model performance. The XGBoost algorithm produced the best results, with 94.3% accuracy, while the other algorithms produced results of 93.5% for Decision Tree and 90.7% for KNN. The findings suggest that predicting the length of stay of SCD patients is highly feasible, allowing for better utilization of medical personnel and resources.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127679153","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: 2

Quantum Image Classification on NISQ Devices NISQ器件上的量子图像分类

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI: 10.1109/CICN56167.2022.10008259

Shuroog Al-Ogbi, Abdulrahman Ashour, Muhamad Felemban

{"title":"Quantum Image Classification on NISQ Devices","authors":"Shuroog Al-Ogbi, Abdulrahman Ashour, Muhamad Felemban","doi":"10.1109/CICN56167.2022.10008259","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008259","url":null,"abstract":"Quantum computing is an emerging computing field that is expected to make a huge impact on several scopes of science and technology. In this paper, we investigate the role of quantum computing in image classification, as an important branch of machine learning with widely used applications in healthcare, military, and IR4.0. In particular, we systemically compare the performance of two well-known classical image classification systems, i.e., Support Vector Machine (SVM) and Convolutions Neural Network (CNN), with equivalent quantum image classification algorithms, i.e., Quantum Support Vector Machine (Q-SVM) and Quantum Convolutional Neural Network (Q-CNN). Both quantum and classical algorithms are implemented on available Noisy-Intermediate Scale Quantum (NISQ) devices using MNIST dataset. Performance of models were compared regarding accuracy and training time. The results show that classical algorithms outperform the quantum algorithms for the given tasks. However, we observe that large-scale fault-tolerant quantum computing can effectively perform image classification tasks in the future.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125870489","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