2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)最新文献

Developing Artificial Neural Networks Model for Concrete Mix Design 混凝土配合比设计的人工神经网络模型研究

2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) Pub Date : 2022-11-06 DOI: 10.1109/EICEEAI56378.2022.10050473

A. Shaqadan, Imad Alshalout, Mohammad Abojaradeh, R. Al-kasasbeh, Abdullah Al-Khatib

引用次数: 0

Investigating The Effect of Latency in A 5G Infrastructure for Applications Requiring Ultra-Low Latency 研究5G基础设施中延迟对需要超低延迟的应用的影响

2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) Pub Date : 2022-11-06 DOI: 10.1109/EICEEAI56378.2022.10050470

Omid Akbarzadeh Pivehzhani, Hani H. Attar

引用次数: 0

Local Walsh-Hadamard spectra in video sequence image classifiers 视频序列图像分类器中的局部Walsh-Hadamard谱

2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) Pub Date : 2022-11-06 DOI: 10.1109/EICEEAI56378.2022.10050467

R. Al-kasasbeh, Tokmakova Rimma Alexandrovna, S. Filist, Reutov Dmitry Konstantinovich, A. Shaqadan, N. Korenevskiy, Osama, O., M. Al-Habahbeh

引用次数: 0

Improving Fraud Detection in An Imbalanced Class Distribution Using Different Oversampling Techniques 利用不同过采样技术改进非平衡类分布中的欺诈检测

2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) Pub Date : 2022-11-06 DOI: 10.1109/EICEEAI56378.2022.10050500

R. Qaddoura, Mariam M. Biltawi

{"title":"Improving Fraud Detection in An Imbalanced Class Distribution Using Different Oversampling Techniques","authors":"R. Qaddoura, Mariam M. Biltawi","doi":"10.1109/EICEEAI56378.2022.10050500","DOIUrl":"https://doi.org/10.1109/EICEEAI56378.2022.10050500","url":null,"abstract":"Credit card fraud detection is essential for financial institutions to avoid charging customers for items they did not purchase. Fraud detection can be implemented through ML by building a model trained on a dataset containing transactions with fraud and non-fraud classes. The dataset available for this task is usually highly imbalanced. Therefore, the goal of this paper is to conduct a comprehensive comparison between five oversampling techniques. The oversampling techniques are the Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), borderline1 SMOTE, borderline2 SMOTE, and Support Vector Machine SMOTE (SVM SMOTE) to generate an enhanced model which can solve the imbalanced problem. The comparison is conducted by computing the geometric mean, recall, precision, and F1-score of six machine learning models with and without applying oversampling. The ML models experimented with are logistic regression, random forest, K-nearest neighbor, naive Bayes, support vector machine, and decision tree. Experimental results show that the oversampling techniques have improved the models' performance.","PeriodicalId":426838,"journal":{"name":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","volume":"240 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114714452","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

The Design of The Logarithmic Detector in The Front-End Board of Sonography Systems 超声系统前端板中对数检测器的设计

2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) Pub Date : 2022-11-06 DOI: 10.1109/EICEEAI56378.2022.10050492

Yasaman Moravej, R. Boostani, Saleh B. Abusuilik, I. Mansour

引用次数: 0

A decision-making system for medical diagnosis based on iterative nearest component analysis and optimized learning 基于迭代最近邻分析和优化学习的医疗诊断决策系统

2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) Pub Date : 2022-11-06 DOI: 10.1109/EICEEAI56378.2022.10050487

K. Rezaee, Mohammad Hossein Khosravi, Hani Attar, Mohammed Alghanim

{"title":"A decision-making system for medical diagnosis based on iterative nearest component analysis and optimized learning","authors":"K. Rezaee, Mohammad Hossein Khosravi, Hani Attar, Mohammed Alghanim","doi":"10.1109/EICEEAI56378.2022.10050487","DOIUrl":"https://doi.org/10.1109/EICEEAI56378.2022.10050487","url":null,"abstract":"A silent disease is a chronic illness without clear clinical symptoms, and is diagnosed at an advanced stage when there is irreversible damage. Increasingly, automatic machine methods are used for early diagnosis to reduce complications associated with diseases. The performance of previous automated methods, however, was plagued by uncertainty, lack of generalizability, and unreliability. Using the feature aggregation approach and optimized learning, this paper proposes a hybrid model incorporating iterative neighborhood component analysis (iNCA). Using the support vector machine (SVM) algorithm, which has been optimized from the water cycle algorithm (WCA) in the direction of classification, the best results have been reported from the classification of several types of diseases. A key feature of the WCA algorithm is its ability to find the global optimum. When selecting features, the method works rapidly and selects the subset of features that has the lowest error level. In this research, accuracy of diagnostics will be improved and the effects of overfitting will be reduced. We obtained the desired medical data from the UCI database, which contains diseases such as hepatitis, diabetes, kidney failure, and breast cancer datasets. As compared to similar methods that have been published in the last few years for automatic detection of silent diseases, it can be predicted that the results will be better.","PeriodicalId":426838,"journal":{"name":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128191844","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 Data Science Approach for Predicting Crowdfunding Success 预测众筹成功的数据科学方法

2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) Pub Date : 2022-11-06 DOI: 10.1109/EICEEAI56378.2022.10050465

Ahmed Banimustafa, S. Almatarneh, Olla Bulkrock, G. Samara, Mohammad Aljaidi

{"title":"A Data Science Approach for Predicting Crowdfunding Success","authors":"Ahmed Banimustafa, S. Almatarneh, Olla Bulkrock, G. Samara, Mohammad Aljaidi","doi":"10.1109/EICEEAI56378.2022.10050465","DOIUrl":"https://doi.org/10.1109/EICEEAI56378.2022.10050465","url":null,"abstract":"Crowdfunding is important for backing innovative projects and new startup businesses. However, success in achieving the target fundraising is a big challenge, and it depends on many complex factors. This work uses data science to predict the success of crowdfunding pledges using a historical dataset that was scrapped from the Kickstarter website. The dataset was subject to intensive data wrangling, exploration, and engineering procedures. Three machine learning models were constructed in this study using: (1) Random Forests (RF), (3) K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms. The models were trained using a separate portion representing two-thirds of the dataset, while the remaining third was used for evaluation. The KNN model achieved the best performance with a classification accuracy of 97.9% and an AUC of 98.3%. Random Forests was the second-best model, with a classification accuracy of 94.9% and an AUC of 98.9%. The Precision, Recall, F1, and AUC metrics also confirmed the validity of the reported results, while the confusion matrix and the calibration curve confirmed the robustness of the constructed models.","PeriodicalId":426838,"journal":{"name":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115546039","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

Improving Organizations Security Using Visual Cryptography Based on XOR and Chaotic-Based Key 基于异或和混沌密钥的可视化密码提高组织安全性

2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) Pub Date : 2022-11-06 DOI: 10.1109/EICEEAI56378.2022.10050448

Maryam Tahmasbi, R. Boostani, Mohammad Aljaidi, Hani Attar

{"title":"Improving Organizations Security Using Visual Cryptography Based on XOR and Chaotic-Based Key","authors":"Maryam Tahmasbi, R. Boostani, Mohammad Aljaidi, Hani Attar","doi":"10.1109/EICEEAI56378.2022.10050448","DOIUrl":"https://doi.org/10.1109/EICEEAI56378.2022.10050448","url":null,"abstract":"Since data security is an important branch of the wide concept of security, using simple and interpretable data security methods is deemed necessary. A considerable volume of data that is transferred through the internet is in the form of image. Therefore, several methods have focused on encrypting and decrypting images but some of the conventional algorithms are complex and time consuming. On the other hand, denial method or steganography has attracted the researchers' attention leading to more security for transferring images. This is because attackers are not aware of encryption on images and therefore they do not try to decrypt them. Here, one of the most effective and simplest operators (XOR) is employed. The received shares in destination only with XOR operation can recover original images. Users are not necessary to be familiar with computer programing, data coding and the execution time is lesser compared to chaos-based methods or coding table. Nevertheless, for designing the key when we have messy images, we use chaotic functions. Here, in addition to use the XOR operation, eliminating the pixel expansion and meaningfulness of the shared images is of interest. This method is simple and efficient and use both encryption and steganography; therefore, it can guarantee the security of transferred images.","PeriodicalId":426838,"journal":{"name":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116414561","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

Trends of Electricity Consumption In Jordan 约旦电力消费趋势

2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) Pub Date : 2022-11-06 DOI: 10.1109/EICEEAI56378.2022.10050498

Ahmed Banimustafa, Zakaria A. M. Al-Omari

{"title":"Trends of Electricity Consumption In Jordan","authors":"Ahmed Banimustafa, Zakaria A. M. Al-Omari","doi":"10.1109/EICEEAI56378.2022.10050498","DOIUrl":"https://doi.org/10.1109/EICEEAI56378.2022.10050498","url":null,"abstract":"Electricity plays a crucial role in modern civilization. However, despite technological development in electricity generation, storage in large grids is still limited, which necessitates optimizing the electricity generation and distribution to meet demand and reduce waste which can help reduce costs and cut carbon emissions. Predicting the actual electricity consumption can be decisive in achieving these endeavors. This paper aims to investigate the trends of electricity consumption in Jordan based on a historical dataset covering one year. The analysis covers the temporal analysis of electricity consumption over hours, days, weeks, months, and seasons. It also examines the electricity consumption trends in different weather conditions and temperature levels. The dataset used in the analysis was processed using sophisticated data science steps, which involved (1) Data Wrangling, (2) Data Processing, (3) Features Engineering, (4)Trends Analysis, and (5) Results in Evaluation. The trend analysis results achieved in this study were very promising, as it confirms the validity and potential of the data to carry out more predictive forecasting analysis using time series, regression, and machine learning algorithms.","PeriodicalId":426838,"journal":{"name":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130611986","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

Analytical Analysis of The Efficiency of Wave Energy Converter 波浪能转换器效率的解析分析

2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) Pub Date : 2022-11-06 DOI: 10.1109/EICEEAI56378.2022.10050463

Sebastian Sorek, W. Sulisz

引用次数: 0