2022 International Conference on Engineering and Emerging Technologies (ICEET)最新文献_第2页

A powerful Transfer learning technique for multiclass classification of lung cancer CT images 肺癌CT图像多类别分类的迁移学习技术

2022 International Conference on Engineering and Emerging Technologies (ICEET) Pub Date : 2022-10-27 DOI: 10.1109/ICEET56468.2022.10007294

A. Bhattacharjee, K. Shankar, R. Murugan, Tripti Goel

引用次数: 1

Optimization of photovoltaic plant capacity with battery storage for injection into main network: Case of Illoulofin solar plant in Benin 光伏电站容量优化与蓄电池储能并网——以贝宁伊洛芬太阳能电站为例

2022 International Conference on Engineering and Emerging Technologies (ICEET) Pub Date : 2022-10-27 DOI: 10.1109/ICEET56468.2022.10007307

Kévin Elisée Sonon, V. Zogbochi, P. Chetangny, G. Barbier, S. Houndedako, A. Vianou, J. Aredjodoun, D. Chamagne

{"title":"Optimization of photovoltaic plant capacity with battery storage for injection into main network: Case of Illoulofin solar plant in Benin","authors":"Kévin Elisée Sonon, V. Zogbochi, P. Chetangny, G. Barbier, S. Houndedako, A. Vianou, J. Aredjodoun, D. Chamagne","doi":"10.1109/ICEET56468.2022.10007307","DOIUrl":"https://doi.org/10.1109/ICEET56468.2022.10007307","url":null,"abstract":"population growth in developing countries is not in accordance with energy infrastructure development. To meet the growing demand for electrical energy, Benin has opted to integrate green energy to increase its energy capacity. Thus, a 25 MWp solar photovoltaic power plant has been set up and whose energy will be injected directly into the conventional grid without storage. This intermittent character of the plant causes a certain deficit in the production at certain times of the day with respect to the load. It is therefore necessary to add storage devices to suplly power when there is lack. This work aimed at minimizing the percentage of deficit and maximizing the storage capacity of the plant. In order to supply power at the maximum energy consumption time, we consider using lithium batteries for the considered zone We employ Particule Swarm optimization method (PSO) to solve for the expansion coefficient and maximum stage capacity of the bank. The results show that in order to reduce the deficit, an extension of the photovoltaic field should be made in addition to the energy storage system to be integrated. This also increases the amount of generated energy injected into the grid as opposed to that received from the grid.","PeriodicalId":241355,"journal":{"name":"2022 International Conference on Engineering and Emerging Technologies (ICEET)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127784772","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

Simple Machine Learning Approach for Liquid Concentration Estimation Employing Fiber Optic Tip Sensor 利用光纤尖端传感器估算液体浓度的简单机器学习方法

2022 International Conference on Engineering and Emerging Technologies (ICEET) Pub Date : 2022-10-27 DOI: 10.1109/ICEET56468.2022.10007308

N. M. Razali, N. Saris, Nurul Ashikin Daud

引用次数: 0

Authors List Country Wise 作者名单

2022 International Conference on Engineering and Emerging Technologies (ICEET) Pub Date : 2022-10-27 DOI: 10.1109/iceet56468.2022.10007368

引用次数: 0

Introducing a smart monitoring system (PHLIP) for integrated pest management in commercial orchards 引入智能监测系统(PHLIP)，在商业果园进行害虫综合治理

2022 International Conference on Engineering and Emerging Technologies (ICEET) Pub Date : 2022-10-27 DOI: 10.1109/ICEET56468.2022.10007399

M. Zare, M. Pflanz, M. Schirrmann

{"title":"Introducing a smart monitoring system (PHLIP) for integrated pest management in commercial orchards","authors":"M. Zare, M. Pflanz, M. Schirrmann","doi":"10.1109/ICEET56468.2022.10007399","DOIUrl":"https://doi.org/10.1109/ICEET56468.2022.10007399","url":null,"abstract":"In this study a developed modularized mobile system has been introduced in the framework of the research project PHLIP that enables spatiotemporally high-resolution population monitoring of insects (pests) in orchards, using deep learning (DL) object detection, which can be used as the basis for implementing a site-specific application of insecticides. As a case study, an image annotation database was built with images taken from yellow sticky traps and annotated cherry fruit flies. A faster Region-based Convolutional Neural Network (R-CNN) DL model was applied. The results showed average precision of 0.88 which, indicates that the DL model can perform as a component of an automated system for assessing pest insects in orchards. An important outcome of PHLIP will be the creation of application maps for site-specific insecticide application. Therefore, decreasing the amount of insecticides applied in orchards - which are critically assessed in terms of their environmental impact - should be possible, while the yield efficiency would not be changed. The spatial monitoring will create desirable conditions for a sustainable pest management in horticultural management.","PeriodicalId":241355,"journal":{"name":"2022 International Conference on Engineering and Emerging Technologies (ICEET)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130462007","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

Improved Automated Essay Grading System Via Natural Language Processing and Deep Learning 基于自然语言处理和深度学习的论文自动评分系统

2022 International Conference on Engineering and Emerging Technologies (ICEET) Pub Date : 2022-10-27 DOI: 10.1109/ICEET56468.2022.10007407

Samira Said Ibrahim, Essameldean F. Elfakharany, E. Hamed

{"title":"Improved Automated Essay Grading System Via Natural Language Processing and Deep Learning","authors":"Samira Said Ibrahim, Essameldean F. Elfakharany, E. Hamed","doi":"10.1109/ICEET56468.2022.10007407","DOIUrl":"https://doi.org/10.1109/ICEET56468.2022.10007407","url":null,"abstract":"In order to evaluate students’ total skills, educators repeatedly utilize questions based on free text to evaluate students’ total skills. Yet, when correction is done manually errors occur in addition to long time periods used, hard work, high costs and different opinions as to how to correct papers, a single paper is corrected by multiple people to avoid partiality. Thus, the smart system for automatic grading can solve the problem. Here, we present a very advanced system for grading essays automatically. This is based on Natural Language Processing and Deep Learning technologies. Thus, we need a system to automatically grade essays with low costs, less time and more accurate scores. We need thus an inelegant system for correcting essay questions on an automatic basis. We introduced a method which encodes essays in the form of sequential embeddings. We then use a long Short Term Memory Network (LSTM) working in two directions in order to register semantic information. This method also focuses concentration on each essay in order to be taught how to focus on those materials which are authentic in articles. We can also thus get a good proof of the result of prediction. This BI LSTM may be utilized also to produce neural networks which have the sequence information in the two directions: from the future to the past (backwards) or vice versa, which is called Bidirectional Long Short-Term Memory (BI-LSTM) (past to future). In order to train and test, we utilized the popular set of essays presented in the Automated Student Assessment Prize by Kaggle. The smart system used for automatic grading, in our research, predicts grades in an up-to-date manner. Moreover, the smart system for autograding we have proposed has the ability to highlight important words and sentences, evaluate the logical relationships in meaning in a sentence and gives us in advance grades that can be explained.","PeriodicalId":241355,"journal":{"name":"2022 International Conference on Engineering and Emerging Technologies (ICEET)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131435181","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

Image De-noising and Edge Segmentation using Bilateral Filtering and Gabor-cut for Edge Representation of a Breast Tumor 乳腺肿瘤边缘表示的双侧滤波与Gabor-cut图像去噪与边缘分割

2022 International Conference on Engineering and Emerging Technologies (ICEET) Pub Date : 2022-10-27 DOI: 10.1109/ICEET56468.2022.10007228

D. Saranyaraj

引用次数: 1

Analysis of urban area extracted from NDBI and classification approach by using satellite data NDBI提取的城市面积分析及卫星数据分类方法

2022 International Conference on Engineering and Emerging Technologies (ICEET) Pub Date : 2022-10-27 DOI: 10.1109/ICEET56468.2022.10007219

Ankush Agarwal, Shikha Verma

引用次数: 0

A Fractal based Machine Learning Method for Automatic Detection of Epileptic Seizures using EEG 基于分形的脑电癫痫发作自动检测机器学习方法

2022 International Conference on Engineering and Emerging Technologies (ICEET) Pub Date : 2022-10-27 DOI: 10.1109/ICEET56468.2022.10007280

G. Sharma, A. Joshi

{"title":"A Fractal based Machine Learning Method for Automatic Detection of Epileptic Seizures using EEG","authors":"G. Sharma, A. Joshi","doi":"10.1109/ICEET56468.2022.10007280","DOIUrl":"https://doi.org/10.1109/ICEET56468.2022.10007280","url":null,"abstract":"Epilepsy is one of the most common and chronic brain disorders that causes recurrent seizures. In this paper, a machine learning method utilizing non-linear features and a Support vector machine (SVM) classifier is proposed for the automatic detection of epileptic seizures using electroencephalographic (EEG) signals. In this proposed model, Hurst exponent and logarithmic Higuchi fractal dimension (HFD) non-linear features are extracted from EEG signals which are then classified using SVM and K-nearest neighbor (KNN) classifiers. For the model’s implementation and performance evaluation, a publicly available CHB-MIT EEG dataset is used. The proposed model uses the Hurst component, logarithmic HFD, and SVM classifier, resulting in an average accuracy of 99.81%, Recall 100%, and TNR 0.99. Similarly, the proposed model utilizing the Hurst component, logarithmic HFT, and KNN classifier resulted in an accuracy of 93.21%, recall 92.56%, and Tnr 0.92. This automated and highly accurate model can be implemented in remote-based applications using the Internet of Medical Things (IoMT) framework.","PeriodicalId":241355,"journal":{"name":"2022 International Conference on Engineering and Emerging Technologies (ICEET)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122169496","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

Adoption of Augmented Reality To Enhance Durban University of Technology's Learning Management System 采用增强现实技术提升德班理工大学学习管理系统

2022 International Conference on Engineering and Emerging Technologies (ICEET) Pub Date : 2022-10-27 DOI: 10.1109/ICEET56468.2022.10007142

Wappi Francis Djumo, Shaolin Lee Govender, Sanele Raphael Matha, Timothy T. Adeliyi

引用次数: 0