2021 22nd International Arab Conference on Information Technology (ACIT)最新文献_第2页

Utilization of Robotics During the Covid-19 Pandemic Covid-19大流行期间机器人技术的应用

2021 22nd International Arab Conference on Information Technology (ACIT) Pub Date : 2021-12-21 DOI: 10.1109/acit53391.2021.9677152

N. Mirza, Adnan Ali

引用次数: 0

Prediction of Electrical Power Consumption in Jordan 约旦电力消耗预测

2021 22nd International Arab Conference on Information Technology (ACIT) Pub Date : 2021-12-21 DOI: 10.1109/acit53391.2021.9677382

Khalid Mansour, Mohammed Al-Sadeeg Al-Hussban, Yaseen Y. Al-Husban, Yaser A. Al-Lahham

{"title":"Prediction of Electrical Power Consumption in Jordan","authors":"Khalid Mansour, Mohammed Al-Sadeeg Al-Hussban, Yaseen Y. Al-Husban, Yaser A. Al-Lahham","doi":"10.1109/acit53391.2021.9677382","DOIUrl":"https://doi.org/10.1109/acit53391.2021.9677382","url":null,"abstract":"Modeling and predicting electricity power consumption is a crucial task in managing energy performance in present and future. For successful planning, it is important for decision-makers to have a precise idea about the needed electrical power in any period of time in the future. This paper tackles this task of predicting future power consumption in Jordan using a number of machine learning algorithms. Neural network, support vector machine (SVM), and random forest are used to build our prediction models. Two datasets are use: one small and another large. The results show that the random forest performed best when trained on the large data with and without data normalization. After normalizing the data, the performance of the neural networks becomes similar to that of random forest. The performance of the support vector machine was high before and after normalizing the data. Regarding the small dataset and before normalizing the data, the performance of the random forest was better than the other two algorithms. However, after normalizing the data, the neural network performed the best and the random forest comes next. Finally, in terms of feature importance, the experimental results show that the price feature was the most important feature in the large dataset. The price and the renewal energy projects were the most important features in the small dataset.","PeriodicalId":302120,"journal":{"name":"2021 22nd International Arab Conference on Information Technology (ACIT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128955902","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

Ascendant Hierarchical Clustering for Instance Matching 实例匹配的上升层次聚类

2021 22nd International Arab Conference on Information Technology (ACIT) Pub Date : 2021-12-21 DOI: 10.1109/acit53391.2021.9677377

S. Amrouch, S. Mostefai

{"title":"Ascendant Hierarchical Clustering for Instance Matching","authors":"S. Amrouch, S. Mostefai","doi":"10.1109/acit53391.2021.9677377","DOIUrl":"https://doi.org/10.1109/acit53391.2021.9677377","url":null,"abstract":"With the rapid advancement of semantic web, especially of the web of data, a growing number of independently designed and structured datasets represented by ontologies, are built and need to be integrated in the Linked Open Data (LOD) cloud. In this context, instance matching is presented as a fundamental solution for ontological data sharing and integration. It aims to link co-referent instances (instances that refer to the same real world objects) from various datasets to allow them to complement each other. Traditional systems depend a lot on the quality of schema-level mappings and especially on property mappings, which are not always obvious for the LOD paradigm. In this paper, we propose a schema-free instance matching approach that is independent from property matching results. We transform the instance matching problem into a clustering problem and we solve it by Ascendant Hierarchical Clustering algorithm. Furthermore, we employ some structural information to filter-out obtained results and eliminate wrong mappings. We evaluate our approach on instance matching track from Ontology Alignment Evaluation Initiative (OAEI) benchmark. The experiments show that our approach gets prominent results compared to several participating systems in OAEI’2019 and OAEI’2020.","PeriodicalId":302120,"journal":{"name":"2021 22nd International Arab Conference on Information Technology (ACIT)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132435874","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

Investigating the Challenges Facing Composable/ Disaggregated Infrastructure Implementation: A Literature Review 研究可组合/分解基础设施实施面临的挑战:文献综述

2021 22nd International Arab Conference on Information Technology (ACIT) Pub Date : 2021-12-21 DOI: 10.1109/acit53391.2021.9677094

Abir S. Al-Harrasi, Saqib Ali

{"title":"Investigating the Challenges Facing Composable/ Disaggregated Infrastructure Implementation: A Literature Review","authors":"Abir S. Al-Harrasi, Saqib Ali","doi":"10.1109/acit53391.2021.9677094","DOIUrl":"https://doi.org/10.1109/acit53391.2021.9677094","url":null,"abstract":"There has been an exponential growth of IT technologies in the past few decades. Many technologies target the improvement of IT infrastructure as it plays a primary concern of any IT management. Composable / Disaggregated Infrastructure (CDI) is one of the newest technologies introducing a new concept to manage the IT infrastructure. However, many challenges arise at the beginning stages of any technology life cycle. Hence, this study aims to investigate the challenges involved in migrating or implementing CDI for an oil and gas organization in Oman. To achieve this aim, the research employs a systematic literature review for the studies conducted by academics and industrial institutions. To link the results with an existing organization, an interview was conducted with an IT governance expert from a leading oil and gas organization in Oman. This study provides a holistic view of the highlighted challenges faced with CDI technology. The results highlighted five main challenges, which are controlling end-to-end latency, supporting higher bandwidth and bandwidth density, scaling of the network, orchestration of resources, and power consumption. Key challenges such as end-to-end latency, supporting higher bandwidth, and density can be addressed by using Silicon Photonic (SiP) transceivers. For the oil and gas sector, if the large-sized organizations decide to proceed with CDI migration or implementation, the organizations are prone to face two main challenges: scaling the network and orchestrating resources, thus these challenges still need to be addressed.","PeriodicalId":302120,"journal":{"name":"2021 22nd International Arab Conference on Information Technology (ACIT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123804836","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

Machine Learning Approach for the Design of an Assessment Outcomes Recommendation System 评估结果推荐系统设计的机器学习方法

2021 22nd International Arab Conference on Information Technology (ACIT) Pub Date : 2021-12-21 DOI: 10.1109/acit53391.2021.9677281

Fatime Al-Zahra, S. Mounir, Lamees Mohammad Dalbah, R. A. Zitar

引用次数: 0

[1] Energy consumption clustering using machine learning: K-means approach [1]基于机器学习的能耗聚类:K-means方法

2021 22nd International Arab Conference on Information Technology (ACIT) Pub Date : 2021-12-21 DOI: 10.1109/acit53391.2021.9677130

Aghyad Al Skaif, M. Ayache, H. Kanaan

{"title":"[1] Energy consumption clustering using machine learning: K-means approach","authors":"Aghyad Al Skaif, M. Ayache, H. Kanaan","doi":"10.1109/acit53391.2021.9677130","DOIUrl":"https://doi.org/10.1109/acit53391.2021.9677130","url":null,"abstract":"Nowadays, the accurate analysis of energy consumption has become vital for the development of efficient energy projects as well as, for demonstrating the consumptive behavior of the energy consumers in the system. The importance of this analysis comes from many reasons, one of them is that it leads to a better understanding of the system components. This paper presents a clustering algorithm for residential energy consumption using the K-Means algorithm in two different approaches. The dataset utilized in this article contains energy consumption features selected from 25 houses over a period of two years. Firstly, data cleaning has been used to remove and eliminate the inconsistent data, secondly the Elbow method has been applied to determine the optimal number of clusters before using the K-means approach for the purpose of clustering. In K-means, the data have been clustered into two different approaches. The first one is clustering the daily mean consumption in each season in each year. The second one is clustering the monthly mean consumption over the two years. Finally, data visualization has been applied in order to present the result of our proposed method. The paper finds that the households have different consumption behaviors in different seasons, days, and months and that it is due to the change of the average temperature in each season as well as the different appliances and consumptive patters of each house. The results are representative and match the aim of the paper. Further, they are significant for the further development of the energy system and efficient for tracking the consumption of the houses. Finally, the results of this paper are going to be used after running the algorithm again with a different number of clusters to compare the results and find new insights in the data that might affect the decision.","PeriodicalId":302120,"journal":{"name":"2021 22nd International Arab Conference on Information Technology (ACIT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131824971","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

Model based Approach for Automotive Embedded Systems 基于模型的汽车嵌入式系统设计方法

2021 22nd International Arab Conference on Information Technology (ACIT) Pub Date : 2021-12-21 DOI: 10.1109/acit53391.2021.9677298

A. Shaout, Shanmukha Pattela

引用次数: 1

Encryption for End-User Privacy: A Cyber-Secure Smart Energy Management System 终端用户隐私加密:网络安全智能能源管理系统

2021 22nd International Arab Conference on Information Technology (ACIT) Pub Date : 2021-12-21 DOI: 10.1109/acit53391.2021.9677341

Irshad Hussain, G. Samara, Ibrar Ullah, Naeem Khan

引用次数: 8

Redefining Knowledge Management in the Era of COVID-19: A Concept Analysis Approach in the Context of Higher Education 重新定义新冠肺炎时代的知识管理:高等教育背景下的概念分析方法

2021 22nd International Arab Conference on Information Technology (ACIT) Pub Date : 2021-12-21 DOI: 10.1109/acit53391.2021.9677262

Ahmad Ghandour

引用次数: 0

Stability and Accuracy of Feature Selection Methods on Datasets of Varying Data Complexity 不同数据复杂度数据集特征选择方法的稳定性和准确性

2021 22nd International Arab Conference on Information Technology (ACIT) Pub Date : 2021-12-21 DOI: 10.1109/acit53391.2021.9677329

Omaimah Al Hosni, A. Starkey

{"title":"Stability and Accuracy of Feature Selection Methods on Datasets of Varying Data Complexity","authors":"Omaimah Al Hosni, A. Starkey","doi":"10.1109/acit53391.2021.9677329","DOIUrl":"https://doi.org/10.1109/acit53391.2021.9677329","url":null,"abstract":"One widespread criterion used to evaluate feature selection techniques is the classifier performance of the selected features. Another criterion that has recently drawn attention in the feature selection community is the stability of feature selection techniques. Our study indicates that using feature selection techniques with different data characteristics may generate different subsets of features under variations to the training data. Our study motivation is that there are significant contributions in the research community from examining the effect of complex data characteristics such as class overlap on classification algorithms performance; however, relatively few studies have investigated the stability and the accuracy of feature selection methods with complex data characteristics. Accordingly, this study aims to conduct empirical study to measure the interactive effects of the class overlap with different data characteristics so we will provide meaningful insights into the root causes for feature selection methods misdiagnosing the relevant features among different data challenges associated with real world data in which will guide the practitioners and researchers to choose the correct feature selection methods that are more appropriate for particular dataset. Also, in this study we will provide a survey on the current state of research in the feature selection stability context.","PeriodicalId":302120,"journal":{"name":"2021 22nd International Arab Conference on Information Technology (ACIT)","volume":"173 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113980523","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