2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)最新文献_第4页

Multicast Optimization: Operational Research Theory and Applications 组播优化:运筹学理论与应用

2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) Pub Date : 2022-11-06 DOI: 10.1109/EICEEAI56378.2022.10050486

Omid Akbarzadeh Pivehzhani, Mostafa Karami, Nazanin Fasihihour, Mohammed Alghanim, Sahand Hamzehei

引用次数: 0

Image Captions and Hashtags Generation Using Deep Learning Approach 使用深度学习方法生成图像标题和标签

2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) Pub Date : 2022-11-06 DOI: 10.1109/EICEEAI56378.2022.10050455

Yahya Qusay AL-Sammarraie, Khaled E. Al-Qawasmi, M. Al-Mousa, Sameh F. Desouky

{"title":"Image Captions and Hashtags Generation Using Deep Learning Approach","authors":"Yahya Qusay AL-Sammarraie, Khaled E. Al-Qawasmi, M. Al-Mousa, Sameh F. Desouky","doi":"10.1109/EICEEAI56378.2022.10050455","DOIUrl":"https://doi.org/10.1109/EICEEAI56378.2022.10050455","url":null,"abstract":"social media are fantastic tools for public communication. Social media has become an integral part of our everyday lives, and an increasing number of individuals use it for marketing and communication. Social networking enables you to demonstrate your skills and knowledge without leaving home. Companies exert significant efforts to make social media more controlled and valuable while avoiding negative repercussions. They accomplish this with artificial intelligence (AI), which enables them to develop unique applications and algorithms. It can eliminate inappropriate information or spam automatically, for instance. The description and hashtags that grab the reader's attention are among the most critical aspects of a social media post's success. Typically, individuals generate multiple captions and hashtags before selecting the optimal content for a post. Occasionally, they employ content writers, which requires time, effort, and money. The suggested method makes correct captions and hashtags using conventional neural networks (CNN) trained on image datasets containing captions","PeriodicalId":426838,"journal":{"name":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","volume":"503 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":"116180748","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

Statistical Wind Energy Potential Assessment in Ras Munif, Jordan 约旦拉斯穆尼夫统计风能潜力评估

2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) Pub Date : 2022-11-06 DOI: 10.1109/EICEEAI56378.2022.10050447

Nour Khlaifat, Zakaria A. M. Al-Omari

引用次数: 1

An Efficient Classifier: Kernel SVM-LDA 一种高效分类器:核SVM-LDA

2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) Pub Date : 2022-11-06 DOI: 10.1109/EICEEAI56378.2022.10050472

Fahimeh Jamshidian Tehrani, B. Nasihatkon, Khaled E. Al-Qawasmi, M. Al-Mousa, R. Boostani

引用次数: 0

EEG-Based Discrimination Between Patients with MCI and Alzheimer 基于脑电图的轻度认知损伤与阿尔茨海默病患者的鉴别

2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) Pub Date : 2022-11-06 DOI: 10.1109/EICEEAI56378.2022.10050494

B. Kazimipour, R. Boostani, A. Borhani-Haghighi, S. Almatarneh, Mohammad Aljaidi

{"title":"EEG-Based Discrimination Between Patients with MCI and Alzheimer","authors":"B. Kazimipour, R. Boostani, A. Borhani-Haghighi, S. Almatarneh, Mohammad Aljaidi","doi":"10.1109/EICEEAI56378.2022.10050494","DOIUrl":"https://doi.org/10.1109/EICEEAI56378.2022.10050494","url":null,"abstract":"There is a high similarity between the signs and symptoms of patients with Alzheimer and those with mild cognitive impairment (MCI). Although several attempts have been made to differentiate these two groups of patients by decoding the fluctuation of their electroencephalogram (EEG), the achieved results are not yet promising. To increase the differentiation rate, in this study, 14 patients with Alzheimer from 13 patients with MCI have been voluntarily enrolled while their EEG signals are recorded in presence of visual stimuli. To suppress the disrupting artifacts and noises (e.g., eye-blink and movement artefact) from the recorded EEGs, independent component analysis is applied. Next, the visual evoke potential (VEP) patterns are extracted by synchronous averaging and then multi-linear principal component analysis (MPCA) is applied to elicit discriminative features from VEPs of the patients. After feature extraction by MPCA, the reduced feature vectors of both groups are applied to a nearest neighbor classifier, leading to 77.35% differentiation accuracy.","PeriodicalId":426838,"journal":{"name":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","volume":"25 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":"125953160","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

The Role of Smart Environment Initiatives on Environmental Degradation: Consolidating the Resilient Built Landscape 智能环境倡议在环境退化中的作用:巩固有弹性的建筑景观

2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) Pub Date : 2022-11-06 DOI: 10.1109/EICEEAI56378.2022.10050481

O. P. Agboola, Samuel Moveh, Khalid Yahya, Hani H. Attar, A. Amer

{"title":"The Role of Smart Environment Initiatives on Environmental Degradation: Consolidating the Resilient Built Landscape","authors":"O. P. Agboola, Samuel Moveh, Khalid Yahya, Hani H. Attar, A. Amer","doi":"10.1109/EICEEAI56378.2022.10050481","DOIUrl":"https://doi.org/10.1109/EICEEAI56378.2022.10050481","url":null,"abstract":"Primarily in industrialized and some developing nations, the adoption of the smart city approach as a sustainable approach to the management and implementation of infrastructure developments has been rising. Initiatives to build resilience are critical for successful cities in these developing nations, like Nigeria. This research explores the various advantages of building resilience in Nigeria's smart cities in light of this growth. Few scholars have examined the building and smart city efforts aimed at enhancing Nigeria's built environment's sustainability in the context of the current global environmental issues. Thus, this study closes the gaps by evaluating the various aspects of building and the city's resilience with a focus on Lagos, the most populated metropolis in Africa. The following issues are covered with reviews of the literature: (i) the current Lagos Smart City projects; (ii) Smart City and Building initiatives in Nigeria; and (iii) the goal of robust resilience. By succinctly describing the strategic planning for smart city development, in addition to the opportunities discovered in the smart city and building initiatives, this paper contributes to the conversation around smart cities. This will aid in the documentation, forecasting, and future decision-making processes for Nigeria's Smart Cities.","PeriodicalId":426838,"journal":{"name":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","volume":"52 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":"121501217","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: 2

Two-Layer SVM, Towards Deep Statistical Learning 面向深度统计学习的两层SVM

2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) Pub Date : 2022-11-06 DOI: 10.1109/EICEEAI56378.2022.10050469

Alireza Kazemi, R. Boostani, Mahmoud Odeh, M. Al-Mousa

{"title":"Two-Layer SVM, Towards Deep Statistical Learning","authors":"Alireza Kazemi, R. Boostani, Mahmoud Odeh, M. Al-Mousa","doi":"10.1109/EICEEAI56378.2022.10050469","DOIUrl":"https://doi.org/10.1109/EICEEAI56378.2022.10050469","url":null,"abstract":"Support Vector Machine (SVM) is originally a binary large-margin classifier emerged from the concept of structural risk minimization. Multiple solutions such as one-versus-one and one-versus-all have been proposed for creating multi-class SVM using elementary binary SVMs. Also multiple solutions have been proposed for SVM model selection, adjusting margin-parameter C and the Gaussian kernel variance. Here, an improved classifier named SVM-SVM is proposed for multi-class problems which increases accuracy and decreases dependency to margin-parameter selection. SVM-SVM adopts two K-class one-vs-one SVMs in a cascaded two-layer structure. In the first layer, input features are fed to one-vs-one SVM with non-linear kernels. We introduce this layer as a large-margin non-linear feature transform that maps input feature space to a discriminative K*(K-1)/2 dimensional space. To assess our hierarchical classifier, some datasets from the UCI repository are evaluated. Standard one-vs-one SVM and one-vs-one fuzzy SVM are used as reference classifiers in experiments. Results show significant improvements of our proposed method in terms of test accuracy and robustness to the model (margin and kernel) parameters in comparison with the reference classifiers. Our observations suggest that a multi-layer (deep) SVM structures can gain the same benefits as is seen in the deep neural nets (DNNs).","PeriodicalId":426838,"journal":{"name":"2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)","volume":"103 S6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132477453","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

Media Picture 媒体的图片

2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) Pub Date : 2022-11-06 DOI: 10.1109/eiceeai56378.2022.10050476

引用次数: 0

CUDA Parallelization of Teacher-Learner Optimization Algorithm CUDA并行化的师生优化算法

2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) Pub Date : 2022-11-06 DOI: 10.1109/EICEEAI56378.2022.10050483

Mahdi Abbasi, Mohammed Alghanim, Mohammad Sabri, Mohammad Hossein Khosravi

引用次数: 0

A Parameter Extraction Based on PSO for A Signal PV Module Using MATLAB 基于粒子群算法的信号光伏组件参数提取

2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) Pub Date : 2022-11-06 DOI: 10.1109/EICEEAI56378.2022.10050480

Hamsa Nashoor, Khalid Yahya, Mahmoud Aldababsa, A. Amer, Saleh B. Abusuilik

引用次数: 0