Fusion: Practice and Applications最新文献_第8页

Enhanced Template Protection Algorithms Based on Fuzzy Vault and Cuckoo Hashing for Fingerprint Biometrics 基于模糊保险库和杜鹃哈希的指纹生物识别增强模板保护算法

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.100201

M. B. Akanbi, R. Jimoh, A. Imoize, J. B. Awotunde, ,. O. S. Isiaka, Shade B. Abdulrahaman

{"title":"Enhanced Template Protection Algorithms Based on Fuzzy Vault and Cuckoo Hashing for Fingerprint Biometrics","authors":"M. B. Akanbi, R. Jimoh, A. Imoize, J. B. Awotunde, ,. O. S. Isiaka, Shade B. Abdulrahaman","doi":"10.54216/fpa.100201","DOIUrl":"https://doi.org/10.54216/fpa.100201","url":null,"abstract":"Biometrics provides better authentication. Unprotected biometrics is open to attacks from intruders as stolen biometrics may not be revocable. Although there are several points where attacks can be launched on biometric systems, template databases are said to be the most frequently attacked. When a template database is attacked, attackers can add fresh templates, modify the existing ones, copy or steal templates and later construct a spoof from it or replay it back into the biometric system to impersonate a genuine user. Several template security systems have been presented in the literature to secure biometric templates. Fuzzy vault, as proposed by many researchers is, to some extent, one of the best algorithms to achieve template protection as it has good security. Fuzzy vault, however, lacks irreversibility, revocability, and diversity. To address these disadvantages and strengthen fuzzy vault, this study combines a noninvertible feature transformation template protection algorithm known as cuckoo hashing that possesses irreversibility, revocability, and diversity properties with a fuzzy vault for privacy. The study used fingerprint biometrics as it is widely used. The proposed algorithm was implemented in the MATLAB 2016a environment using FVC 2004 DB1 fingerprint public database. The proposed algorithm recorded a FAR of 0.01% and an FRR value of 0.09% with an EER of 0.05%.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"25 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":"125641289","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

Breast Cancer Detection Using Deep Learning and Feature Decision Level Fusion 基于深度学习和特征决策融合的乳腺癌检测

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.080105

S. .., J. Kumar, Gopal Chaudhary, Manju Khari

{"title":"Breast Cancer Detection Using Deep Learning and Feature Decision Level Fusion","authors":"S. .., J. Kumar, Gopal Chaudhary, Manju Khari","doi":"10.54216/fpa.080105","DOIUrl":"https://doi.org/10.54216/fpa.080105","url":null,"abstract":"Among women, breast cancer has a high incidence and high fatality rate. Due to a lack of early detection facilities and barriers to accessing technological improvements in battling this illness, mortality rates are disproportionately greater in underdeveloped countries. Biopsies done by trained pathologists are the only certain approach to diagnosing cancer. With the use of computer-aided diagnostic algorithms, pathologists may improve their efficiency, objectivity, and consistency in making diagnoses. A key goal of this research is to create an accurate automated system for diagnosing breast cancer that can function in the current clinical setting. In this work, we offer an algorithm for the identification of breast cancer that uses asymmetric analysis as the basic choice and decision-level fusion. Fusion of local nuclei features extracted using convolutional neural network (CNN) models pre-trained on the database constitutes the picture feature representation. The dataset is accessible for public use, and the results are evaluated by running 25 random trials with an 80%-20% split between train and test. Overall, the suggested framework was 86%. The proposed framework is shown to outperform numerous current methods and to provide results on par with the state-of-the-art techniques without requiring extensive computing resources. Breast cancer detection from histological pictures may be greatly aided by the use of this qualitative approach based on transfer learning.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"16 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":"121215482","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 Pelican Optimization Algorithm and Black Hole Algorithm for Kernel Semi-Parametric Fusion Modeling 核半参数融合建模的混合鹈鹕优化算法和黑洞算法

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.110104

F. AL-Taie, Z. Algamal, O. Qasim

引用次数: 0

Using a Fuzzy Logic Integrated Machine Learning Algorithm for Information Fusion in Smart Parking 基于模糊逻辑集成机器学习算法的智能停车信息融合

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.110109

M. A. J. Maktoof, Anwar Ja’afar M. .., Hasan M. Abd, Ahmed Husain, A. Majdi

{"title":"Using a Fuzzy Logic Integrated Machine Learning Algorithm for Information Fusion in Smart Parking","authors":"M. A. J. Maktoof, Anwar Ja’afar M. .., Hasan M. Abd, Ahmed Husain, A. Majdi","doi":"10.54216/fpa.110109","DOIUrl":"https://doi.org/10.54216/fpa.110109","url":null,"abstract":"The free flow of people and products within metropolitan areas depends on well-managed transportation systems. However, public parking places in smart cities are often limited by traffic, causing cars and residents to waste time, money, and fuel. To counteract this issue, today's automobile systems combine information fusion with intelligent parking solutions. In this research, we present a Fuzzy Logic Integrated Machine Learning Algorithm (FL-MLA) for use in smart parking and traffic management in a metropolis. The FL-MLA use fuzzy induction to distinguish between parked and moving vehicles while calculating traffic flow. The suggested technique efficiently resolves the problem of locating suitable parking places by avoiding incorrect configurations that govern traffic management difficulties. Therefore, the FL-MLA is used in traffic management systems to boost performance metrics like efficiency ratio (98.1%) and accident detection (98.1%) based on simulation results like reduced energy consumption (95.3%), more accurate traffic estimation (97.9%), higher average daily park occupancy (97.2%), and higher efficiency ratio (98.1%).","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"13 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":"129026696","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

An efficient extraction of information from Indian Government issued documents Aadhar and Pan Card 有效地提取了印度政府颁发的Aadhar和Pan卡文件的信息

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.040201

Rachna Tewani, Achin Jain, Eshika Agarwal, Disha Mittal, A. Dubey

引用次数: 0

Multi-Level Fusion Optimization in Cyber-Physical Systems Using Computer Vision-Based Fault Detection 基于计算机视觉故障检测的信息物理系统多级融合优化

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.110205

A. Admin, Anwar Ja’afar M. .., M. Jalil, Noor Sami, Z. Madhi

{"title":"Multi-Level Fusion Optimization in Cyber-Physical Systems Using Computer Vision-Based Fault Detection","authors":"A. Admin, Anwar Ja’afar M. .., M. Jalil, Noor Sami, Z. Madhi","doi":"10.54216/fpa.110205","DOIUrl":"https://doi.org/10.54216/fpa.110205","url":null,"abstract":"The healthcare sector's use of cyber-physical systems to provide high-quality patient treatment highlights the need for sophisticated security solutions due to the wide range of attack surfaces from medical and mobile devices, as well as body sensor nodes. Cyber-physical systems have various processing technologies to choose from, but these technical methods are as varied. Existing technologies are not well-suited for managing complex information about problem identification and diagnosis, which is distinct from technology. To address this issue, intelligent techniques for fusion processing, such as multi-sensor fusion system architectures and fusion optimization, can be used to improve fusion score and decision-making. Additionally, the use of deep learning models and multimedia data fusion applications can help to combine multiple models for intelligent systems and enhance machine learning for data fusion in E-Systems and cloud environments. Fuzzy approaches and optimization algorithms for data fusion can also be applied to robotics and other applications.. In this paper, a computer vision technology-based fault detection (CVT-FD) framework has been suggested for securely sharing healthcare data. When utilizing a trusted device like a mobile phone, end-users can rest assured that their data is secure. Cyber-attack behavior can be predicted using an artificial neural network (ANN), and the analysis of this data can assist healthcare professionals in making decisions. The experimental findings show that the model outperforms with current detection accuracy (98.3%), energy consumption (97.2%), attack prediction (96.6%), efficiency (97.9%), and delay ratios (35.6%) over existing approaches.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"20 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":"128758974","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

Using method of Nadaraya-Watson kernel regression to detection outliers in multivariate data fusion 利用Nadaraya-Watson核回归方法检测多元数据融合中的异常值

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.100207

Omar A. abd Alwahab

引用次数: 0

Blog Feedback Prediction based on Ensemble Machine Learning Regression Model: Towards Data Fusion Analysis 基于集成机器学习回归模型的博客反馈预测:迈向数据融合分析

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.090103

Hamzah A. Alsayadi, E. El-Kenawy, A. Ibrahim, M. Eid, Abdelaziz A. Abdelhamid

引用次数: 0

An effective Decision making model through Fusion Optimization and risk associated with flash flood hazards: A case study Asyut, Egypt 基于融合优化的有效决策模型和与山洪灾害相关的风险:以埃及Asyut为例

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.120105

N. AbdelAziz, Hassan H. Mohammed, Khalid A. Eldrandaly

{"title":"An effective Decision making model through Fusion Optimization and risk associated with flash flood hazards: A case study Asyut, Egypt","authors":"N. AbdelAziz, Hassan H. Mohammed, Khalid A. Eldrandaly","doi":"10.54216/fpa.120105","DOIUrl":"https://doi.org/10.54216/fpa.120105","url":null,"abstract":"One of the most dangerous natural disasters, which causes massive damage all over the world, is flash floods. Therefore, the assessment of flash floods disasters is considered increasingly urgent and important. The widely used techniques for studying and analyzing the causes and impact of natural hazards are multi-criteria techniques. Several researchers used traditional multi-criteria decision-making techniques in the estimation process of flash floods problems as the analytical hierarchy process, decision making trial and evaluation laboratory and analytic network process. The main disadvantage of these traditional models is the incapability of simulating and reflecting uncertain human thoughts. Since neutrosophic logic has a great ability for simulating human’s thoughts and increase the flexibility of expert's preferences in real world problems, we applied it in this study. There are different locations in Egypt that are at a serious risk of flooding, especially in Upper Egypt. Asyut has suffered from frequent flash floods, with some flood events that lead to mortality, damages, and economic losses in the last decades. The intensity of floods in Egypt varies from year to year, according to several climatic and hydrological variables. This study focuses on using a Neutrosophic Decision making trial and evaluation laboratory (N-DEMATEL) technique with remotely sensed data and geographical information system (GIS) for producing a flash floods hazard map. The N-DEMATEL technique is applied to determine the weights of various factors that related to flash flooding, including elevation, slope, topographic wetness index, distance from the stream, flow accumulation, aspect, flow direction, soil, land cover, watershed, curvature, drainage density , total population , population density and precipitation. The obtained weight of selected criteria used then to produce the flood hazard map (FHM) using a raster calculator tool in geographic information system.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"9 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":"121168153","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

Leukemia Cancer Detection Using Various Deep Learning Algorithms 利用各种深度学习算法检测白血病癌症

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.090106

Devanshu Joshi, Rishabh Tater, Priya Yaday, Tripti Jain, P. Nagrath

引用次数: 0