2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)最新文献_第3页

Classification of Earthquake Vibrations Using the ANN (Artificial Neural Network) Algorithm 基于人工神经网络的地震振动分类

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887421

Fauzan Azhima Tasa, Istiqomah, M. A. Murti, Ibnu Alinursafa

{"title":"Classification of Earthquake Vibrations Using the ANN (Artificial Neural Network) Algorithm","authors":"Fauzan Azhima Tasa, Istiqomah, M. A. Murti, Ibnu Alinursafa","doi":"10.1109/IAICT55358.2022.9887421","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887421","url":null,"abstract":"The Indo-Australian Plate, the Eurasian Plate, and the Pacific Plate all converge where Indonesia is situated. As a result, Indonesia is a nation where earthquakes occur frequently. Some researchers have studied machine learning algorithms for categorizing earthquake vibrations. In this experiment, earthquake vibrations are categorized using the Artificial Neural Network method. We need appropriate datasets to obtain the maximum accuracy from the artificial neural network technique. The findings of this experiment show that feature extraction is required for the datasets to be trained to obtain a high accuracy value. The mean, median, maximum, minimum, skew, and kurtosis values are the feature that are extracted. In addition to employing feature extraction, it is crucial to modify the algorithm model. The experimental setup that uses “sigmoid” activation on the input layer, the three hidden layers, and the output layer yields the best accuracy when all feature are extracted, with training to test ratio of 90% to 10%. This is demonstrated by the exceptional training accuracy and testing accuracy values, which are 99.85 percent for training accuracy and 99.12 percent for validation accuracy. The mean value yields the highest accuracy result compared to employing just one feature extraction. Only 90.97 and 90.37 percent of training and validation accuracy are obtained when the mean is used alone for feature extraction.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129261187","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

Development Of An IoT Enabled Smart Projection System For Classroom Needs 基于物联网的教室智能投影系统的开发

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887528

P. Amruthavarshini, C. V. Raghu, G. Jagadanand

{"title":"Development Of An IoT Enabled Smart Projection System For Classroom Needs","authors":"P. Amruthavarshini, C. V. Raghu, G. Jagadanand","doi":"10.1109/IAICT55358.2022.9887528","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887528","url":null,"abstract":"Usually, an overhead projector along with a computer is used as a display system in college/school classrooms. The major drawback of such a system is that the bulky computers used in the system are of high cost and dissipate large power. Moreover, the faculty members need to carry their data in a pen-drive or laptop in order to use in this set up. Spreading of computer virus will happen very easily through pen-drives. Connecting laptop with projector using cable every time is troublesome and can cause damage to connectors and cables. In order to overcome these problems, usage of a Single Board Computer(SBC) in place of the bulky computer is proposed in this work. Faculty members can transfer their files to this SBC through college Local Area Network(LAN). A web-based administrator account is provided on SBC for management and control. In the classroom, an RF-based mini keyboard is used to navigate on the SBC desktop and display files. A wireless screen sharing mechanism from laptops is an added feature for this product. The set-up was tested in a real classroom, and it is found to be a very convenient and easy to use method.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129883282","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

Implementing a Low-Cost Control Unit Network focusing on Data Collection and Flettner Rotor Control 以数据采集和Flettner转子控制为核心的低成本控制单元网络实现

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887390

Elmar Wings, Stefan Reck, Hendrik Boomgaarden, Farzaneh Nourmohammadi, Thomas Peetz

引用次数: 0

Hardware Architecture for Adaptive Edge-Directed Interpolation Algorithm 自适应边缘插值算法的硬件结构

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887521

P. Kartheek, E. P. Jayakumar

引用次数: 1

Parameterized Computing Module Generator Based on a Systolic Array 基于收缩阵列的参数化计算模块生成器

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887460

V. V. Zunin, I. Romanova

引用次数: 2

FALSE: Fake News Automatic and Lightweight Solution 虚假:假新闻自动轻量级解决方案

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887471

Fatema Al Mukhaini, Shaikhah Al Abdoulie, Aisha Al Kharuosi, Amal El Ahmad, M. Aldwairi

{"title":"FALSE: Fake News Automatic and Lightweight Solution","authors":"Fatema Al Mukhaini, Shaikhah Al Abdoulie, Aisha Al Kharuosi, Amal El Ahmad, M. Aldwairi","doi":"10.1109/IAICT55358.2022.9887471","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887471","url":null,"abstract":"Fake news existed ever since there was news, from rumors to printed media then radio and television. Recently, the information age, with its communications and Internet breakthroughs, exacerbated the spread of fake news. Additionally, aside from e-Commerce, the current Internet economy is dependent on advertisements, views and clicks, which prompted many developers to bait the end users to click links or ads. Consequently, the wild spread of fake news through social media networks has impacted real world issues from elections to 5G adoption and the handling of the Covid-19 pandemic. Efforts to detect and thwart fake news has been there since the advent of fake news, from fact checkers to artificial intelligence-based detectors. Solutions are still evolving as more sophisticated techniques are employed by fake news propagators. In this paper, R code have been used to study and visualize a modern fake news dataset. We use clustering, classification, correlation and various plots to analyze and present the data. The experiments show high efficiency of classifiers in telling apart real from fake news.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115416841","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

Fault Tolerant Scheme in Steganographic Video Streaming Using n - Repetition Code 基于n重复码的隐写视频流容错方案

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887523

Fransisca Elisa Rahardjo, Favian Dewanta, S. Rizal

{"title":"Fault Tolerant Scheme in Steganographic Video Streaming Using n - Repetition Code","authors":"Fransisca Elisa Rahardjo, Favian Dewanta, S. Rizal","doi":"10.1109/IAICT55358.2022.9887523","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887523","url":null,"abstract":"The rapid exchange of information increases the need for information security, particularly for confidential data information. Confidential data can be secured using a steganography technique by inserting the data into a cover media, in this case, the cover media is in the form of video. This video becomes a medium for sending a message in real time, which is known as video streaming. However, video streaming has the potential for packet loss. This paper proposes a fault tolerant scheme in steganographic video streaming by using repetition code for ensuring the reception of hidden information in a noisy channel such as packet drop in video streaming. This idea comes from the simplest error correction that can minimize errors in the transmission process of data information with the aim of finding the best fault-tolerant value for video steganography. The method used in this study during video streaming is repetition code with n = odd and multiples of 3. This study describes the embedding and extraction process using the Discrete Wavelet Transform (DWT) method on the YUV color space - Luminance(Y) Chrominance (”U” and ”V”), especially Luminance (Y) channel. The measurement of packet loss effect is done by using Peak Signal to Noise Ratio (PSNR) calculation, in which the higher the PSNR value, the higher the quality of the reconstruction. The use of the DWT method which offers high resolution at low frequencies provides a PSNR value of 131.49 dB with the use of the H.265 codec when the packet drop is at a percentage of 15%, as well as message insertion and repetition in every odd frame (1, 3, 5, 7, …853).","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122573697","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

Emotion Recognition of Students’ Bilingual Tweets during COVID-19 Pandemic using Attention-based Bi-GRU 基于注意力的Bi-GRU在COVID-19大流行期间学生双语推文情感识别中的应用

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887389

I. Recto, Andrea Danelle P. Quilang, L. Vea

{"title":"Emotion Recognition of Students’ Bilingual Tweets during COVID-19 Pandemic using Attention-based Bi-GRU","authors":"I. Recto, Andrea Danelle P. Quilang, L. Vea","doi":"10.1109/IAICT55358.2022.9887389","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887389","url":null,"abstract":"This paper studied the emotions manifested by students from March 2020 to April 2021, a year of the Coronavirus Disease-2019 (COVID-19) pandemic. Our tweet compromises Taglish (Tagalog—English) texts, a low-resource code-switching language. The texts were cleaned and translated from Taglish to English. WordNet Affect was used to annotate the text with Happy, Angry, Sad, Surprise, and Fear as the output. A neural network, Bidirectional Gated Recurrent unit (Bi-GRU) with Attention layer, was used, and it was compared to Bernoulli Naïve Bayes (BNB) and Support Vector Machine (SVM), which are commonly used algorithms for Taglish emotion recognition. A 100-dimensional GloVe word embedding was applied to the data before training. The augmentation method does not affect the model’s performance negatively; instead has helped the Bi-GRU with Attention boost its performance. Bi-GRU with attention has a higher F1-score on all emotions compared to the other three algorithms but, as expected, requires a large amount of data. The results show that the most dominant emotion manifested by students throughout the year is surprise immediately followed by Sad and Fear. The three are close in values.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126553211","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

FACToGRADE: Automated Essay Scoring System FACToGRADE:自动作文评分系统

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/IAICT55358.2022.9887447

Lyla B. Das, C. V. Raghu, G. Jagadanand, Ritu Ann Roy George, Priyamvada Yashasawi, N. Kumaran, Vinay Kumar Patnaik

引用次数: 1

IAICT 2022 Cover Page iact2022封面页

2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Pub Date : 2022-07-28 DOI: 10.1109/iaict55358.2022.9887489

引用次数: 0