2022 IEEE Symposium on Industrial Electronics & Applications (ISIEA)最新文献_第2页

Bengali Social Media Post Sentiment Analysis using Deep Learning and BERT Model 使用深度学习和BERT模型的孟加拉社交媒体帖子情绪分析

2022 IEEE Symposium on Industrial Electronics & Applications (ISIEA) Pub Date : 2022-07-16 DOI: 10.1109/ISIEA54517.2022.9873680

Samsul Islam, Md. Jahidul Islam, Md. Mahadi Hasan, S. M. Shahnewaz Mahmud Ayon, Syeda Shabnam Hasan

{"title":"Bengali Social Media Post Sentiment Analysis using Deep Learning and BERT Model","authors":"Samsul Islam, Md. Jahidul Islam, Md. Mahadi Hasan, S. M. Shahnewaz Mahmud Ayon, Syeda Shabnam Hasan","doi":"10.1109/ISIEA54517.2022.9873680","DOIUrl":"https://doi.org/10.1109/ISIEA54517.2022.9873680","url":null,"abstract":"Social media platforms such as Facebook, Twitter, and others are becoming incredibly popular for expressing sentiments and thoughts. People use these platforms to express not only their happy moments, but also their feelings when they are depressed. Using sentiment analysis in natural language processing to analyse these social media posts, one’s emotional state can be determined, such as happy, sad, or angry at a particular time. The majority of research in this topic is conducted in English, therefore sentiment analysis from Bengali is not very accurate. So, our goal is to work on this topic using Bengali datasets obtained from various social media posts to improve sentiment detection accuracy. This work can be used to help building a system in our country’s mental health sector. In this research, we first gathered social media data. Then we used a number of feature selection and extraction techniques like Word2Vec, GloVe etc. and applied a number of deep learning model, such as RNN, LSTM, GRU etc. We have also applied hybrid and transformer-based BERT models like CNN-BiLSTM Bangla-BERT, mBERT etc and finally got the highest accuracy of 88.59% for the CNN-BiLSTM hybrid model using the GloVe feature vector.","PeriodicalId":200043,"journal":{"name":"2022 IEEE Symposium on Industrial Electronics & Applications (ISIEA)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122719169","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

Enhancement of Iris Recognition System using Deep learning 利用深度学习增强虹膜识别系统

2022 IEEE Symposium on Industrial Electronics & Applications (ISIEA) Pub Date : 2022-07-16 DOI: 10.1109/ISIEA54517.2022.9873666

Ruaa Waleed Jalal, M. F. Ghanim

{"title":"Enhancement of Iris Recognition System using Deep learning","authors":"Ruaa Waleed Jalal, M. F. Ghanim","doi":"10.1109/ISIEA54517.2022.9873666","DOIUrl":"https://doi.org/10.1109/ISIEA54517.2022.9873666","url":null,"abstract":"With the growing demand for security and accurate personal authentication, as well as the new dimensions in security issues confronting the world today, a dependable and secure authentication system is essential. The iris has become a popular biometric technique because it is a highly protected internal organ of the body. Furthermore, it is very hard to surgically change it without causing significant damage to the iris. The total accuracy of the iris recognition system is determined by the iris segmentation process's performance. This paper presents a deep learning technique for improving iris segmentation performance. The proposed method employs an efficient deep learning technique (SegNet), which performs joint semantic segmentation of ocular qualities (iris and pupil) with greater accuracy in unconstrained scenarios. These difficult circumstances limit the performance and dependability of ocular segmentation structures. Segmentation begins by denoising the pristine image with a deep convolutional neural network to address these issues (DCNN). The semantic segmentation of the iris and pupil is then accomplished with the help of a densely connected fully convolutional encoder-decoder network. Finally, for feature extraction and classification, the proposed system is implemented using a pre-trained convolutional neural network (Alex Net). The iris recognition system's performance is evaluated using five public databases: IITD, iris databases CASIA-Iris-V1, CASIA-Iris-V2 device 1, CASIA-Iris-V2 device 2, and MMU iris database. The results show that the proposed system has a high accuracy rate of 94.08 percent, 84 percent, 97.31 percent, 100 percent, and 97.7 percent, respectively and has time of execution of less than or equal to 2 minutes","PeriodicalId":200043,"journal":{"name":"2022 IEEE Symposium on Industrial Electronics & Applications (ISIEA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125149178","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

Food Recognition based on Deep Learning Algorithms 基于深度学习算法的食物识别

2022 IEEE Symposium on Industrial Electronics & Applications (ISIEA) Pub Date : 2022-07-16 DOI: 10.1109/ISIEA54517.2022.9873669

Anis Nasuha Mohd Zulfikri, F. Rahman, Shahrani Shabuddin, R. Mohamad

{"title":"Food Recognition based on Deep Learning Algorithms","authors":"Anis Nasuha Mohd Zulfikri, F. Rahman, Shahrani Shabuddin, R. Mohamad","doi":"10.1109/ISIEA54517.2022.9873669","DOIUrl":"https://doi.org/10.1109/ISIEA54517.2022.9873669","url":null,"abstract":"Accurate methods can help the technology nowadays to keep improving and provide a reliable system for the people to use. In this paper, two different image classification systems; Convolutional Neural Network (CNN) and Residual Neural Network (ResNet) were proposed in order to recognize six food classes; Apple, Orange, Avocado, Milo, Vico and Koko based on color features. Then, the overall performance for both classifications were analyzed in the end of this paper. Datasets of food images were collected from various sources consisting 400 images for each food classes to test the robustness of each classification system. The data were split into 60% training data, 20% validation data and 20% testing data. The system that is proposed in this paper consist of 4 layers for Convolutional Neural Network (CNN) while Residual Neural Network (ResNet) consist of 50 layers. The color feature extraction that is involved for both classifications, RGB values (Red, Green, Blue) are highly considered in order to determine the category of the food. Overall, this experimental results on food recognition showed 100% training accuracy and 98.67% overall testing accuracy for CNN while 99.87% training accuracy and 96.67% overall testing accuracy for ResNet.","PeriodicalId":200043,"journal":{"name":"2022 IEEE Symposium on Industrial Electronics & Applications (ISIEA)","volume":"239 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114435068","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

Study of 3-nm Cylindrical GAAFETs with Variations in High-k Dielectric Gate-oxide Materials 高k介电栅-氧化物材料变化下3nm圆柱形gaafet的研究

2022 IEEE Symposium on Industrial Electronics & Applications (ISIEA) Pub Date : 2022-07-16 DOI: 10.1109/ISIEA54517.2022.9873651

K.M. Ashraful Hoque Fahim, Md. Jubair Hasan Khalid, Md. Zesun Ahmed Mia, M. Rasheduzzaman

{"title":"Study of 3-nm Cylindrical GAAFETs with Variations in High-k Dielectric Gate-oxide Materials","authors":"K.M. Ashraful Hoque Fahim, Md. Jubair Hasan Khalid, Md. Zesun Ahmed Mia, M. Rasheduzzaman","doi":"10.1109/ISIEA54517.2022.9873651","DOIUrl":"https://doi.org/10.1109/ISIEA54517.2022.9873651","url":null,"abstract":"Semiconductor devices using high k dielectric materials are widely adopted in memory and amplifier applications. Among the semiconductor devices gate all around-FET (GAAFET) is now the latest trend being used instead of other field effect transistors to serve the purpose of reducing the short channel effects (SCE). In this work, we examine the performance of a circular cross-section gate all around-field effect transistor (GAA-FET) with varying gate dielectric characteristics with high-k dielectric oxide materials (Al2O3, HfO2, HfSiO4, SiO2, Ta2O5, TiO2) across the 3-nm channel length. These simulations showed that even though the dielectric constant over the channel increases in value, both ION-IOFF ratio and transconductance upsurge. The obtained results indicated that raising the dielectric constant in a gate oxide reduces subthreshold slope (SS), increases amplification rate, and reduces threshold voltage (VTH) roll-off as well. The Silvaco TCAD ATLAS simulation was calibrated against experimental data from different works of literature. The higher the dielectric constant, the lower the SCEs. It is also found that TiO2 is dominating over the other materials selected for the simulation for a higher value of dielectric constant.","PeriodicalId":200043,"journal":{"name":"2022 IEEE Symposium on Industrial Electronics & Applications (ISIEA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115996861","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

Selective Harmonic Elimination using MFO for a Reduced Switch Multi-level Inverter topology 基于MFO的简化开关多电平逆变器拓扑结构的选择性谐波消除

2022 IEEE Symposium on Industrial Electronics & Applications (ISIEA) Pub Date : 2022-07-16 DOI: 10.1109/ISIEA54517.2022.9873761

Ibrahim Haruna Shanono, Nor Rul Hasma Abdullah, H. Daniyal, A. Muhammad

引用次数: 0

Design and Analysis of a Wideband Microstrip 3 dB Power Divider 宽带微带3db功率分配器的设计与分析

2022 IEEE Symposium on Industrial Electronics & Applications (ISIEA) Pub Date : 2022-07-16 DOI: 10.1109/ISIEA54517.2022.9873715

Sanjin Hadzimusic, Sarah Sarac, S. Imeci, A. Almisreb, N. Tahir

引用次数: 0

Abnormal Behavior Detection in Real-time for Advanced Driver Assistance System (ADAS) using YOLO 基于YOLO的高级驾驶辅助系统(ADAS)异常行为实时检测

2022 IEEE Symposium on Industrial Electronics & Applications (ISIEA) Pub Date : 2022-07-16 DOI: 10.1109/ISIEA54517.2022.9873672

May Thu Soe, Aung Zaw Min, Hayma Thida Kyaw, Myo Min Paing, Sai Myo Htet, Bawin Aye

引用次数: 1

Crack Segmentation Network using Tversky Loss Function with Variable Alpha and Beta 基于可变α和β的Tversky损失函数的裂纹分割网络

2022 IEEE Symposium on Industrial Electronics & Applications (ISIEA) Pub Date : 2022-07-16 DOI: 10.1109/ISIEA54517.2022.9873770

Raza Ali, Joon Huang Chuah, M. S. A. Talip, N. Mokhtar, M. Shoaib

{"title":"Crack Segmentation Network using Tversky Loss Function with Variable Alpha and Beta","authors":"Raza Ali, Joon Huang Chuah, M. S. A. Talip, N. Mokhtar, M. Shoaib","doi":"10.1109/ISIEA54517.2022.9873770","DOIUrl":"https://doi.org/10.1109/ISIEA54517.2022.9873770","url":null,"abstract":"Cracks pixel segmentation is one of the most important factors in the structural health monitoring system. Extracting crack regions from the image is a challenging task due to the imbalanced dataset, which is one of the highly imbalanced datasets and the variation between crack and non-crack pixels is very high and varies from image to image. The imbalanced pixels directly affect the learning process of network architecture and lead to undesired network biasness. Several loss functions have been proposed to deal with the imbalanced issue including weighted cross-entropy loss, dice loss, focal loss, and Tversky loss function. In this paper, a modified Tversky loss function with a variable magnitude of penalties i.e., α and β is proposed to accurately classify crack pixels. A crack segmentation network architecture is integrated with the Tversky loss function with variable α and ß to evaluate the architecture. Experimental result shows that the modified architecture has achieved better crack and non-crack pixel accuracy as compared to FCN, SegNet, and Deeplabv3+. Furthermore, the variable α and β have performed better than the fixed values of α and β by achieving 98.93% crack pixel accuracy and 95.39% non-crack pixel accuracy.","PeriodicalId":200043,"journal":{"name":"2022 IEEE Symposium on Industrial Electronics & Applications (ISIEA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126446987","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

Photovoltaic Module Defects Classification Analysis using DenseNet Architecture 基于DenseNet架构的光伏组件缺陷分类分析

2022 IEEE Symposium on Industrial Electronics & Applications (ISIEA) Pub Date : 2022-07-16 DOI: 10.1109/ISIEA54517.2022.9873681

Syed Farid Syed Azis, S. Shahbudin, M. Kassim, R. Mohamad, F. Rahman

{"title":"Photovoltaic Module Defects Classification Analysis using DenseNet Architecture","authors":"Syed Farid Syed Azis, S. Shahbudin, M. Kassim, R. Mohamad, F. Rahman","doi":"10.1109/ISIEA54517.2022.9873681","DOIUrl":"https://doi.org/10.1109/ISIEA54517.2022.9873681","url":null,"abstract":"A Photovoltaic (PV) module is a module that absorbs solar energy and converts it into electrical energy, which will be used for electrical equipment. In recent years, the PV capacity of PV technologies has increased. However, the efficiency of solar modules has been reduced because of their defections on it, making the classification method required. There are many techniques that have been used in PV defect classification. One of the techniques is ResNet architecture which had been chosen as a suitable technique for the classifiers however there is a flaw in the architecture that affect the result of accuracy in classification. The flaw in the ResNet architecture is the identity shortcut that has the disadvantage of causing the collapsing domain problem, which decreases the network’s learning capacity. Therefore, the DenseNet-201 architecture is applied for classifying the types of PV defect modules. For comparison purposes, other CNN architectures namely, Deep CNN (DCNN) and VGG-16 Architecture are used in analysis performance. The result shows that DenseNet-201 gives the highest performance with achieved 94.70% of accuracy. In addition, DenseNet-201 also outperform in term of F1-Score (94.65%), Sensitivity (92.76%), Specificity (98.43%) and Precision (96.82%) when compared to other CNN architectures. Thus, the analysis will help in improving the PV module defects classification.","PeriodicalId":200043,"journal":{"name":"2022 IEEE Symposium on Industrial Electronics & Applications (ISIEA)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124988959","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: 3

Energy Saving of Electrical Appliances Through Mobile Application 透过流动应用程式节约电器能源

2022 IEEE Symposium on Industrial Electronics & Applications (ISIEA) Pub Date : 2022-07-16 DOI: 10.1109/ISIEA54517.2022.9873690

Wan Izzat Haikal Wan Izudi, S. B. Kutty, Murizah Kassim, S. Saaidin

引用次数: 0