Fusion: Practice and Applications最新文献_第7页

Interpretable Machine Learning Fusion and Data Analytics Models for Anomaly Detection 用于异常检测的可解释机器学习融合和数据分析模型

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.030104

A. Abdelmonem, N. N. Mostafa

{"title":"Interpretable Machine Learning Fusion and Data Analytics Models for Anomaly Detection","authors":"A. Abdelmonem, N. N. Mostafa","doi":"10.54216/fpa.030104","DOIUrl":"https://doi.org/10.54216/fpa.030104","url":null,"abstract":"Explainable artificial intelligence received great research attention in the past few years during the widespread of Black-Box techniques in sensitive fields such as medical care, self-driving cars, etc. Artificial intelligence needs explainable methods to discover model biases. Explainable artificial intelligence will lead to obtaining fairness and Transparency in the model. Making artificial intelligence models explainable and interpretable is challenging when implementing black-box models. Because of the inherent limitations of collecting data in its raw form, data fusion has become a popular method for dealing with such data and acquiring more trustworthy, helpful, and precise insights. Compared to other, more traditional-based data fusion methods, machine learning's capacity to automatically learn from experience with nonexplicit programming significantly improves fusion's computational and predictive power. This paper comprehensively studies the most explainable artificial intelligent methods based on anomaly detection. We proposed the required criteria of the transparency model to measure the data fusion analytics techniques. Also, define the different used evaluation metrics in explainable artificial intelligence. We provide some applications for explainable artificial intelligence. We provide a case study of anomaly detection with the fusion of machine learning. Finally, we discuss the key challenges and future directions in explainable artificial intelligence.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"18 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":"130703876","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 Novel Fuzzy Clustering with Metaheuristic based Resource Provisioning Technique in Cloud Environment 云环境下基于元启发式的模糊聚类资源分配技术

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.060102

A. N. Al-Masri, Manal M. Nasir

{"title":"A Novel Fuzzy Clustering with Metaheuristic based Resource Provisioning Technique in Cloud Environment","authors":"A. N. Al-Masri, Manal M. Nasir","doi":"10.54216/fpa.060102","DOIUrl":"https://doi.org/10.54216/fpa.060102","url":null,"abstract":"Cloud Computing (CC) becomes a commonly available tool to enable quick, on-demand services from a shared pool of configurable computing resources which can be allocated and utilized. Resource provisioning is a major issue in CC environment which ensures guaranteed outcomes on the applications related to CC. This study introduces an efficient fuzzy c-means clustering (FCM) with hybrid grey wolf optimization (GWO) and differential evolution (DE) algorithm, called FCM-GWODE for resource provisioning in cloud environment. The aim of the FCM-GWODE technique is to allocate the resources in such a way that the resource utilization can be accomplished. In addition, the FCM technique with metaheuristics is applied to partition the resources and scalable searching process can be minimized. Moreover, the GWODE algorithm is derived by resolving the local optima issue of the GWO and improve the population diversity using DE. A comprehensive simulation process takes place using CloudSim tool and the results are inspected interms of several evaluation metrics. The simulation results highlighted the supremacy of the FCM-GWODE technique over the other methods.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"60 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":"131170658","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

Ensemble Learning for Facial Expression Recognition 面部表情识别的集成学习

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.020104

A. Admin, Monika Gupta

引用次数: 2

Machine Learning Fusion and Data Analytics Models for Demand Forecasting in the Automotive Industry: A Comparative Study 汽车行业需求预测的机器学习融合和数据分析模型:比较研究

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.120102

Esraa Kamal, Amal F. Abdel Abdel-Gawad, Basem Ibraheem, S. Zaki

{"title":"Machine Learning Fusion and Data Analytics Models for Demand Forecasting in the Automotive Industry: A Comparative Study","authors":"Esraa Kamal, Amal F. Abdel Abdel-Gawad, Basem Ibraheem, S. Zaki","doi":"10.54216/fpa.120102","DOIUrl":"https://doi.org/10.54216/fpa.120102","url":null,"abstract":"Demand forecasting is a crucial aspect of managing the supply chain, as it helps companies optimize inventory levels and minimize expenses related to inventory shortages. In recent years, machine learning (ML) algorithms have gained popularity for demand forecasting, as they can handle large and complex datasets and provide accurate predictions. Precise demand prediction for car brands is vital for companies to minimize costs and prevent inventory shortages. The demand for distributing cars is a critical component of inventory management. However, estimating demand for new car sales is difficult due to its continuous nature. To address this challenge, a study was conducted to train, test, and compare the performance of five machine learning algorithms (Random Forest, Multiple Linear Regression, k-Nearest Neighbors, Extreme Gradient Boosting, and Support Vector Machine) using a benchmark dataset. Among all the experiments, the Support Vector Machine algorithm achieved the highest accuracy score of 71.42%. Moreover, Multiple Linear Regression performed well, with an accuracy score of 66.66%. On the other hand, the Extreme Gradient Boosting algorithm had the lowest accuracy score of 42.85%. All experiments used a train-test split of 7525.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"44 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":"116329053","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

E-College : an aid for E-Learning systems 电子学院:电子学习系统的辅助工具

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.030202

Vanita Jain, Monu Gupta, Neeraj Joshi, Anubhav Mishra, Vishakha Bansal

{"title":"E-College : an aid for E-Learning systems","authors":"Vanita Jain, Monu Gupta, Neeraj Joshi, Anubhav Mishra, Vishakha Bansal","doi":"10.54216/fpa.030202","DOIUrl":"https://doi.org/10.54216/fpa.030202","url":null,"abstract":"The use of Android apps has significantly increased over the past few years, making android the most accepted and trusted operating system for smart devices. According to a survey, in 2020, over 30.5 billion Android mobile apps were downloaded compared to merely 6 billion in 2016, which is quite noteworthy. People worldwide are also becoming habitual to the use of android apps such that they want everything to be available on their mobile devices. The authors have developed ‘E-College’ - an android-based mobile learning application that helps users grasp various programming languages like C++, Java, and Python and help them with their undergraduate computer engineering course curriculum-related resources. The proposed application allows the learner to access a particular programming language's tutorials and explains it with an example and the required code snippet. Using this application, users can also assess themselves by utilizing the provided quiz section, which has various questions. The proposed mobile application also has a recommendation system section that uses machine learning techniques to intelligently serve the most relevant and suitable content for each user. It considers the user's previous learnings on the application and suggests new and relevant learning content for that particular user. During the ongoing global pandemic situation, E-college application is the most effective way to learn and acquire technical skills without stepping out of their home and fraee of cost.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"299 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":"114576887","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

Building a New Semantic Social Network Using Semantic Web-Based Techniques 基于语义web技术构建新的语义社会网络

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.030201

A. Admin, K. .., Ehab R. Mohamed

{"title":"Building a New Semantic Social Network Using Semantic Web-Based Techniques","authors":"A. Admin, K. .., Ehab R. Mohamed","doi":"10.54216/fpa.030201","DOIUrl":"https://doi.org/10.54216/fpa.030201","url":null,"abstract":"Most people are more or less related to the web by participating in a kind of social networking site. Semantic Web technology plays a crucial role in these sites as they contain an enormous amount of data about ‎persons, pages, events, places, corporations, etc. This research is a Semantic Web application designed to create a new ‎semantic social community called Socialpedia. It links the already existing social public information to the newly ‎public ones. This information is linked with different information on the web to construct a new immense ‎data container. The resulting data container can be processed using a variety of Semantic Web techniques to produce ‎machine-understandable content. This content shows the promise of using integrated data to improve Web search and ‎Web-scale data analysis, unlike conventional search engines or social ones. This community involves obtaining data ‎from traditional users known as contributors or participants, linking data from existing social networks, extracting ‎structured data in triples using predefined ontologies, and finally querying and inferring such data to obtain ‎meaningful pieces of information. Socailpedia supports all popular functionalities of social networking websites ‎besides the enhanced features of the Semantic Web, providing advanced semantic search that acts as a semantic ‎search engine.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"35 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":"115071453","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

Benchmarking Machine Learning for Sentimental Analysis of Climate Change Tweets in Social Internet of Things 基于机器学习的社交物联网气候变化推文情感分析

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.100102

I. Pustokhina, D. A. Pustokhin

{"title":"Benchmarking Machine Learning for Sentimental Analysis of Climate Change Tweets in Social Internet of Things","authors":"I. Pustokhina, D. A. Pustokhin","doi":"10.54216/fpa.100102","DOIUrl":"https://doi.org/10.54216/fpa.100102","url":null,"abstract":"Climate change has become one of the most critical problems threatening our world, gaining an increased attention in either academia or industry. Climate change has been demonstrated as the major barrier in the way of sustainable development strategy in 2030 Agenda. Nowadays, Social Internet of Things (SIoT) have paved new ways for public deliberations and have transformed the communication of global issues such as climate change. Thus, sentiment analysis of SIoT media streams can offer a great help for improving the mitigation and adaption to climate changes. Machine learning (ML) is a demonstrating great success in wide range of SIoT applications. However, training ML algorithms for sentimental analysis of climate change is notoriously hard as it suffers from feature engineering issues, information squashing, unbalancing, curse-of-dimensionality, which bounds their possible power for modeling social awareness of climate change. Besides, the absence of standard benchmark with reasonable and dependable experimentations brings a practically intractable difficulty to the evaluation of the efficiency of new solutions. In this regard, this study introduces the first reasonable and reproducible benchmark devoted to evaluating the potentials of ML algorithms in identifying the users’ opinions about climate change. Moreover, a novel taxonomy is presented for categorizing the existing ML algorithms, exploring their optimal hyperparameter, and unifying their elementary settings. Inclusive experiments are then performed on real twitter data with different families of ML algorithms. To promote further study, a detailed analysis is provided for the state of the field to uncover the open research challenges and promising future directions.","PeriodicalId":269527,"journal":{"name":"Fusion: Practice and Applications","volume":"65 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":"134191978","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

Computer Network Design of the office area of the telecommunications company SERTOD 电信公司SERTOD办公区域计算机网络设计

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.060101

Oswald Aguilar Dowins, O. Cornelio

引用次数: 0

Fusion System for Blockchain Asset Securitization Risk Control Using Adaptive Deep Learning-Based Framework 基于自适应深度学习框架的区块链资产证券化风险控制融合系统

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.110206

Raed .., Omar Saad Ahmed, T. Al-sharify, Wasfi Hameed, Riyam K. Marjan

引用次数: 0

Intelligent Data Fusion Model for Electrocardiogram Classification for Efficient Decision Making in the Healthcare Sector 用于医疗保健部门高效决策的心电图分类智能数据融合模型

Fusion: Practice and Applications Pub Date : 1900-01-01 DOI: 10.54216/fpa.090104

Mahmoud A. Zaher, N. M. Eldakhly

{"title":"Intelligent Data Fusion Model for Electrocardiogram Classification for Efficient Decision Making in the Healthcare Sector","authors":"Mahmoud A. Zaher, N. M. Eldakhly","doi":"10.54216/fpa.090104","DOIUrl":"https://doi.org/10.54216/fpa.090104","url":null,"abstract":"Automatic classification of biomedical signals helps to perform decision making in the healthcare sector. Electrocardiogram (ECG) is a commonly employed 1-dimensional biomedical signal that can be utilized for the detection and classification of cardiovascular diseases. The recently developed deep learning (DL) models find useful for the detection and classification of ECG signals for cardiovascular diseases. With this motivation, this study develops an intelligent electrocardiogram classification using sailfish optimization algorithm with gated recurrent unit (SFOA-GRU) technique. The goal of the SFOA-GRU model is to detect the existence of cardiovascular disease by the classification of ECG signals. The SFOA-GRU model initially undergoes data pre-processing step to transform the actual values into useful format. Besides, GRU model is applied for the detection and classification of ECG signals. For improving the classification outcomes of the GRU model, the SFOA has been utilized to optimally adjust the hyper parameters involved in it. A wide-ranging experimental analysis is carried out to demonstrate the enhanced outcomes of the SFOA-GRU model. A comprehensive comparative study highlighted the promising performance of the SFOA-GRU model over the other recent approaches using different measures parameters.","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":"124835533","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