Fusion: Practice and Applications最新文献_第4页

Improving Link Prediction in Network Representation Learning with Feature Fusion and Local Outlier Factor 利用特征融合和局部离群因子改进网络表示学习中的链路预测

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.120210

A. Al-furas, M. Alrahmawy, W. M. Al-Adrousy, S. Elmougy

{"title":"Improving Link Prediction in Network Representation Learning with Feature Fusion and Local Outlier Factor","authors":"A. Al-furas, M. Alrahmawy, W. M. Al-Adrousy, S. Elmougy","doi":"10.54216/fpa.120210","DOIUrl":"https://doi.org/10.54216/fpa.120210","url":null,"abstract":"Complex networks are a diverse set of networks found in various fields, such as social, technological, and biological networks. One important task in complex network analysis is link prediction, which involves detecting missing links or predicting future link formation. Many methods based on network structure analysis have been developed for link prediction, including network representation learning (NRL) models that represent nodes in a low-dimensional space. Fusion-based attributed NRL methods are particularly effective, as they capture both content and structure information. However, NRL models for link prediction are binary classification models, which face challenges in identifying negative links and prioritizing predicted links. To address these challenges, we propose a novel approach that treats link prediction as a novelty detection problem. Our approach uses the Local Outlier Factor (LOF) algorithm to quantify the novelty of non-existent links based on the representations of existing links. Our experimental results show that our proposed approach outperforms existing methods, particularly when used with fusion-based attributed NRL models","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"260 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122683932","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

An innovative multi-criteria decision-making framework for assessing India's airport operating efficiency 一个创新的多标准决策框架，用于评估印度机场的运营效率

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.040204

S. .., J. Kumar, Gopal Chaudhary

引用次数: 0

Reduce the Spread Risk of COVID-19 based on Clinical Fusion Data and Monitoring System in Wireless Sensor Network 基于临床融合数据和无线传感器网络监测系统降低新型冠状病毒传播风险

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.110102

Majed Hamed .., A. N. Rashid

{"title":"Reduce the Spread Risk of COVID-19 based on Clinical Fusion Data and Monitoring System in Wireless Sensor Network","authors":"Majed Hamed .., A. N. Rashid","doi":"10.54216/fpa.110102","DOIUrl":"https://doi.org/10.54216/fpa.110102","url":null,"abstract":"The expression “COVID-19” has been the fiercest but most trending Google search since it first appeared in November 2019. Due to advances in mobile technology and sensors, Healthcare systems based on the Internet of Things are conceivable. Instead of the traditional reactive healthcare systems, these new healthcare systems can be proactive and preventive. This paper suggested a framework for real-time suspect detection based on the Internet of Things. In the early phases of predicting COVID-19, the framework evaluates the existence of the virus by extracting health variables obtained in real-time from sensors and other IoT devices, in order to better understand the behavior of the virus by collecting symptom data of COVID-19, In this paper, four machine learning models (Random Forest, Decision Tree, K-Nearest Neural Network, and Artificial Neural Network) are proposed, these data and applied as a machine learning model to obtain high diagnostic accuracy, however, it is noted that there is a problem when collecting clinical fusion data that is scarce and unbalanced, so a dataset augmented by Generative Adversarial Network (GAN) was used. Several algorithms achieved high levels of accuracy (ACC), including Random Forest (99%), and Decision Tree (99%), K-Nearest Neighbour (98%), and Artificial Neural Network (99%). These results show the ability of GANs to generate data and their ability to provide relevant data to efficiently manage Covid-19 and reduce the risk of its spread through accurate diagnosis of patients and informing health authorities of suspected cases.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131306175","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

Comparison of Epilepsy Induced by Ischemic Hypoxic Brain Injury and Hypoglycemic Brain Injury using Multilevel Fusion of Data Features 基于数据特征多水平融合的缺血性缺氧脑损伤与低血糖脑损伤致癫痫的比较

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.100106

Sameer Kadem, Noor Sami, A. Elaraby, Shahad .., M. Jalil, M. Altaee, Muntather Almusawi, Ismaeel, A. Ghany, Ali Kamil Kareem, M. Kamalrudin, Adnan Allwi Ftaiet

{"title":"Comparison of Epilepsy Induced by Ischemic Hypoxic Brain Injury and Hypoglycemic Brain Injury using Multilevel Fusion of Data Features","authors":"Sameer Kadem, Noor Sami, A. Elaraby, Shahad .., M. Jalil, M. Altaee, Muntather Almusawi, Ismaeel, A. Ghany, Ali Kamil Kareem, M. Kamalrudin, Adnan Allwi Ftaiet","doi":"10.54216/fpa.100106","DOIUrl":"https://doi.org/10.54216/fpa.100106","url":null,"abstract":"The study aims to investigate the similarities and differences in the brain damage caused by Hypoxia-Ischemia (HI), Hypoglycemia, and Epilepsy. Hypoglycemia poses a significant challenge in improving glycemic regulation for insulin-treated patients, while HI brain disease in neonates is associated with low oxygen levels. The study examines the possibility of using a combination of medical data and Electroencephalography (EEG) measurements to predict outcomes over a two-year period. The study employs a multilevel fusion of data features to enhance the accuracy of the predictions. Therefore this paper suggests a hybridized classification model for Hypoxia-Ischemia and Hypoglycemia, Epilepsy brain injury (HCM-BI). A Support Vector Machine is applied with clinical details to define the Hypoxia-Ischemia outcomes of each infant. The newborn babies are assessed every two years again to know the neural development results. A selection of four attributes is derived from the Electroencephalography records, and SVM does not get conclusions regarding the classification of diseases. The final feature extraction of the EEG signal is optimized by the Bayesian Neural Network (BNN) to get the clear health condition of Hypoglycemia and Epilepsy patients. Through monitoring and assessing physical effects resulting from Electroencephalography, The Bayesian Neural Network (BNN) is used to extract the test samples with the most log data and to report hypoglycemia and epilepsy patients non-invasively. The experimental findings demonstrate that the suggested strategy improves accuracy by 95.05% and reduces the error rate to 0.41 when comparing diseases.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128665414","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

Experimental study of an automotive air conditioning system with alternative refrigerants 替代制冷剂汽车空调系统的实验研究

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.050104

M. Ismail, Abduallah Gamal

引用次数: 0

Multimedia Imaging System of Data Collection and Antenna Alignment for Unmanned Aerial Vehicles Based Internet of Things 基于物联网的无人机数据采集与天线对准多媒体成像系统

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.120202

Maysaloon Abed Qasim, Qusay Abboodi Ali, N. M. Sahab, R. A. Jaleel, M. M. A. Zahra

{"title":"Multimedia Imaging System of Data Collection and Antenna Alignment for Unmanned Aerial Vehicles Based Internet of Things","authors":"Maysaloon Abed Qasim, Qusay Abboodi Ali, N. M. Sahab, R. A. Jaleel, M. M. A. Zahra","doi":"10.54216/fpa.120202","DOIUrl":"https://doi.org/10.54216/fpa.120202","url":null,"abstract":"Because network of sensors gives a more accurate representation of remotely sensed environments, a network of wirelessly connected sensors is essential. Data packets must be routed to the base station hop by hop, which causes conventional network data collecting to use a lot of power. Unmanned aerial vehicles (UAV) were employed for hovering over the detected environment and gather data to solve this issue. The paper also aims to provide an automatic alignment for UAV antennas for tracking by utilising computer vision technologies. A directional antenna with high gain is used by a ground station that can operate by a pan-tilt to point towards the low-gain omnidirectional antenna carried by the UAV. To center the UAV's antenna's image in the frame, the antenna is equipped with a camera, and a computer detects the video and controls the pan-tilt. The antennas are aligned if there are no more than a few pixels between the UAV image center and the image center. The proposed imaging system exhibits fast data collection, thus attaining a high packet delivery rate and the minimum use of energy. With the suggested antenna auto-alignment approach, the antennas can be accurately aligned with an angle error of under one. UAVs must take the smoothest and shortest pathways possible to accommodate their motion and time constraints. As a result, the Traveling Sales Problem (TSP) is utilized to determine the shortest route, and Bezier curves are then employed to turn paths into a flyable path.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115996439","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

A Hybrid Approach for Neural Network in Pattern Storage 一种混合神经网络模式存储方法

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.060201

Kumud Sachdeva, S. Aggarwal

引用次数: 7

Improving Penalized-Based Clustering Model in Big Fusion Data by Hybrid Black Hole Algorithm 基于混合黑洞算法改进大融合数据中基于惩罚的聚类模型

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.110105

Sarah G. M. Al- Kababchee, Z. Algamal, O. Qasim

引用次数: 0

Watermarking System for Medical Images Using Optimization Algorithm 基于优化算法的医学图像水印系统

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.100105

Mohamed Saber, E. El-Kenawy, A. Ibrahim, M. Eid

{"title":"Watermarking System for Medical Images Using Optimization Algorithm","authors":"Mohamed Saber, E. El-Kenawy, A. Ibrahim, M. Eid","doi":"10.54216/fpa.100105","DOIUrl":"https://doi.org/10.54216/fpa.100105","url":null,"abstract":"One of the main methods used to provide security for medical records when exchanging these records through open networks is digital watermarking. In order to preserve the privacy of patients, this system also requires a means to secure images. In this paper, a watermarking based on discrete wavelet transform (DWT), and discrete and discrete cosine transform (DCT) in cascade provides more robustness and security. DCT divides the image into low and high-frequency regions, watermarking message can be embedded into low-frequency regions to prevent distortion of the original image. DWT splits the image into four frequency coefficients; horizontal, vertical, approximation, and detailed frequency component. The judgment factors for the strength of the watermark system are robustness, invisibility, and embedded message capacity. Invisibility means transparency of the watermark logo or data in the original or host image without any distortion. Capacity data payload means the size of the embedded image which is related to the amount of data or logo size that will be embedded in the host image. Robustness refers to the capability of the watermark to stand with the host image operations. In this paper, we propose an optimizer to trade-off between robustness, invisibility, and message capacity. Three metrics were employed to assess the results achieved by the proposed approach, namely, Peak Signal-to-Noise Ratio (PSNR), Normalized Cross Correlation (NCC), and Image Fidelity (IF). The achieved results confirmed the effectiveness and superiority of the proposed approach for real-world digital watermarking applications.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116549258","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

Accurate Recognition of Natural language Using Machine Learning and Feature Fusion Processing 基于机器学习和特征融合处理的自然语言准确识别

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.100108

H. M. Salman, Vian S. Al-Doori, Hayder Sharif, Wasfi Hameed4, Rusul S. Bader

{"title":"Accurate Recognition of Natural language Using Machine Learning and Feature Fusion Processing","authors":"H. M. Salman, Vian S. Al-Doori, Hayder Sharif, Wasfi Hameed4, Rusul S. Bader","doi":"10.54216/fpa.100108","DOIUrl":"https://doi.org/10.54216/fpa.100108","url":null,"abstract":"To enhance the performance of Chinese language pronunciation evaluation and speech recognition systems, researchers are focusing on developing intelligent techniques for multilevel fusion processing of data, features, and decisions using deep learning-based computer-aided systems. With a combination of score level, rank level, and hybrid level fusion, as well as fusion optimization and fusion score improvement, these systems can effectively combine multiple models and sensors to improve the accuracy of information fusion. Additionally, intelligent systems for information fusion, including those used in robotics and decision-making, can benefit from techniques such as multimedia data fusion and machine learning for data fusion. Furthermore, optimization algorithms and fuzzy approaches can be applied to data fusion applications in cloud environments and e-systems, while spatial data fusion can be used to enhance the quality of image and feature data In this paper, a new approach has been presented to identify the tonal language in continuous speech. This study proposes the Machine learning-assisted automatic speech recognition framework (ML-ASRF) for Chinese character and language prediction. Our focus is on extracting highly robust features and combining various speech signal sequences of deep models. The experimental results demonstrated that the machine learning neural network recognition rate is considerably higher than that of the conventional speech recognition algorithm, which performs more accurate human-computer interaction and increases the efficiency of determining Chinese language pronunciation accuracy.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131648701","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