2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)最新文献_第4页

A Multi-Embeddings Approach Coupled with Deep Learning for Arabic Named Entity Recognition 结合深度学习的阿拉伯语命名实体识别多嵌入方法

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) Pub Date : 2020-10-24 DOI: 10.1109/NILES50944.2020.9257975

Abeer Youssef, M. Elattar, S. El-Beltagy

{"title":"A Multi-Embeddings Approach Coupled with Deep Learning for Arabic Named Entity Recognition","authors":"Abeer Youssef, M. Elattar, S. El-Beltagy","doi":"10.1109/NILES50944.2020.9257975","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257975","url":null,"abstract":"Named Entity Recognition (NER) is an important task in many natural language processing applications. There are several studies that have focused on NER for the English language. However, there are some limitations when applying the current methodologies directly on the Arabic language text. Recent studies have shown the effectiveness of pooled contextual embedding representations and significant improvements in English NER tasks. This work investigates the performance of pooled contextual embeddings and bidirectional encoder representations from Transformers (BERT) model when used for NER on the Arabic language while addressing Arabic specific issues. The proposed method is an end-to-end deep learning model that utilizes a combination of pre-trained word embeddings, pooled contextual embeddings, and BERT model. Embeddings are then fed into bidirectional long-short term memory networks with a conditional random field. Different types of classical and contextual embeddings were experimented to pool for the best model. The proposed method achieves an F1 score of 77.62% on the AQMAR dataset, outperforming all previously published results of deep learning, and non-deep learning models on the same dataset. The presented results also surpass those of the wining system for the same task on the same data in the Topcoder website competition.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126722217","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

Improved Semantic Segmentation of Low-Resolution 3D Point Clouds Using Supervised Domain Adaptation 基于监督域自适应的低分辨率三维点云改进语义分割

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) Pub Date : 2020-10-24 DOI: 10.1109/NILES50944.2020.9257903

Asmaa Elhadidy, Mohamed Afifi, Mohammed Hassoubah, Yara Ali, M. Elhelw

{"title":"Improved Semantic Segmentation of Low-Resolution 3D Point Clouds Using Supervised Domain Adaptation","authors":"Asmaa Elhadidy, Mohamed Afifi, Mohammed Hassoubah, Yara Ali, M. Elhelw","doi":"10.1109/NILES50944.2020.9257903","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257903","url":null,"abstract":"One of the key challenges in applying deep learning to solve real-life problems is the lack of large annotated datasets. Furthermore, for a deep learning model to perform well on the test set, all samples in the training and test sets should be independent and identically distributed (i.i.d.), which means that test samples should be similar to the samples that were used to train the model. In many cases, however, the underlying training and test set distributions are different. In such cases, it is common to adapt the test samples by transforming them to their equivalent counterparts in the domain of the training data before being processed by the deep learning model. In this paper, we perform domain adaptation of low-resolution 8, 16 and 32 channels LiDAR 3D point clouds projected on 2D spherical images in order to improve the quality of semantic segmentation tasks. To achieve this, the low-resolution 3D point clouds are transformed using an end-to-end supervised learning approach to spherical images that are very similar to those obtained by projecting high-resolution 64 channels LiDAR point clouds, without changing the underlying structure of the scene. The proposed framework is evaluated by training a semantic segmentation model on 64 channels LiDAR clouds from the Semantic KITTI dataset [1] and using this model to segment 8, 16 and 32 channel point clouds after adapting them using our framework. The results obtained from carried out experiments demonstrate the effectiveness of our framework where segmentation results surpassed those obtained with nearest neighbor interpolation methods.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125970120","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: 6

Optimizing SCAPS model for perovskite solar cell equivalent circuit with utilizing Matlab-based parasitic resistance estimator algorithm 利用基于matlab的寄生电阻估计算法优化钙钛矿太阳能电池等效电路的SCAPS模型

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) Pub Date : 2020-10-24 DOI: 10.1109/NILES50944.2020.9257929

Ahmed A. Eid, Zahraa S. Ismail, S. Abdellatif

引用次数: 11

Probabilistic Time Series Forecasting for Unconventional Oil and Gas Producing Wells 非常规油气生产井概率时间序列预测

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) Pub Date : 2020-10-24 DOI: 10.1109/NILES50944.2020.9257962

Hadeel Afifi, M. Elmahdy, M. E. Saban, Mervat Abu-Elkheir

引用次数: 3

An MTCMOS Subthreshold-Leakage Reduction Algorithm 一种MTCMOS亚阈值泄漏降低算法

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) Pub Date : 2020-10-24 DOI: 10.1109/NILES50944.2020.9257933

S. Sharroush

引用次数: 1

Comparing Machine Learning Models For Predicting Water Pipelines Condition 预测水管状况的机器学习模型比较

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) Pub Date : 2020-10-24 DOI: 10.1109/NILES50944.2020.9257945

N. Elshaboury, M. Marzouk

引用次数: 7

Dynamic Programming Applications: A Suvrvey 动态规划应用:调查

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) Pub Date : 2020-10-24 DOI: 10.1109/NILES50944.2020.9257968

A. Tarek, H. Elsayed, M. Rashad, Manar Hassan, Passant El-Kafrawy

引用次数: 2

Self-Organizing Maps to Assess Rehabilitation Progress of Post-Stroke Patients 自组织地图评估脑卒中后患者康复进展

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) Pub Date : 2020-10-24 DOI: 10.1109/NILES50944.2020.9257927

Hussein Sarwat, M. Awad, S. Maged, Hassan Sarwat

引用次数: 2

A Novel Optical Micro Ring Resonator Biosensor Design using Lithium Niobate on Insulator (LNOI) to Detect The Concentration of Glucose 利用绝缘体上铌酸锂(LNOI)检测葡萄糖浓度的新型光学微环谐振器生物传感器设计

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) Pub Date : 2020-10-24 DOI: 10.1109/NILES50944.2020.9257944

Md Ashif Uddin, M. Maswood, Uzzwal Kumar Dey, Abdullah G. Alharbi, Moriom Akter

{"title":"A Novel Optical Micro Ring Resonator Biosensor Design using Lithium Niobate on Insulator (LNOI) to Detect The Concentration of Glucose","authors":"Md Ashif Uddin, M. Maswood, Uzzwal Kumar Dey, Abdullah G. Alharbi, Moriom Akter","doi":"10.1109/NILES50944.2020.9257944","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257944","url":null,"abstract":"Sensing is not only essential but also unavoidable in the medical fields to analyze different types of biological samples for diagnostic purposes. Although, the conventional laboratory based sensing method provides high accuracy, sometimes, it is not suitable in terms of cost, sensing time, and amount of samples needed for sensing. In this work, we design a novel optical micro ring resonator biosensor utilizing the properties of lithium niobate (LiNbO3) on insulator (LNOI) to detect the concentration of glucose in blood and urine. Optical micro ring resonator attracts researchers in the biomedical field for their compactness, tenability, and low cost. Moreover, LNOI offers some special properties like favorable optical, mechanical, pieozoelectrical, photoelastic, photorefractive, and photovoltaic properties. First, various samples of devices were designed in COMSOL to perform the modal analysis. Then, these devices were implemented in Opti-FDTD to evaluate the performance of the sensor. By varying different parameters like rib height and width, we optimized the structure of the device where rib height, rib width, top layer width of LiNbO3, ring radius, and the distance between ring and waveguide are 0.56 µm, 0.5 µm, 0.16 µm, 15 µm, and approximately 70 to 80 nm, respectively. This optimized structure shows high quality (Q) factor, sharp resonance wavelength, and more distance between two resonance wavelengths of two different concentration of glucose. For sensing purpose, Gaussian modulated continuous wave of 1545 nm wavelength was used as input and best results in output were obtained at 1250 to 1280 nm wavelength.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125265924","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

Sign Language Interpreter System: An alternative system for machine learning 手语翻译系统:机器学习的替代系统

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) Pub Date : 2020-10-24 DOI: 10.1109/NILES50944.2020.9257958

Salma A. Essam El-Din, Mohamed A. Abd El-Ghany

{"title":"Sign Language Interpreter System: An alternative system for machine learning","authors":"Salma A. Essam El-Din, Mohamed A. Abd El-Ghany","doi":"10.1109/NILES50944.2020.9257958","DOIUrl":"https://doi.org/10.1109/NILES50944.2020.9257958","url":null,"abstract":"Losing the ability to speak exerts psychological and social impacts on the affected people due to the lack of proper communication. Thus, Sign Language (SL) is considered a boon to people with hearing and speech impairment. SL has developed as a handy mean of communication that form the core of local deaf cultures. It is a visual–spatial language based on positional and visual components, such as the shape of fingers and hands, their location and orientation as well as arm and body movements. The problem is that SL is not understood by everyone, forming a communication gap between the mute and the able people. Multiple and systematic scholarly interventions that vary according to context have been implemented to overcome disability-related difficulties. Sign language recognition (SLR) systems based on sensory gloves are significant innovations that aim to procure data on the shape or movement of the human hand to bridge this communication gap, as the proposed system. The proposed model is a glove equipped with five flex sensors, interfacing with a control unit fixed on the arm, translating American Sign Language (ASL) and Arabic Sign Language (ArSL) to both text and speech, displayed on a simple Graphical User Interface (GUI). The proposed system aims to provide an affordable and user friendly SL translator system, working on the basis of Machine Learning (ML). However, it adapts to each person’s hand instead of using a generic data set. The system achieved 95% recognition rate with static gestures and up to 88% with dynamic gestures.","PeriodicalId":253090,"journal":{"name":"2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123752912","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: 14