Pub Date : 2022-07-28DOI: 10.1109/IAICT55358.2022.9887421
Fauzan Azhima Tasa, Istiqomah, M. A. Murti, Ibnu Alinursafa
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.
{"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}
Pub Date : 2022-07-28DOI: 10.1109/IAICT55358.2022.9887528
P. Amruthavarshini, C. V. Raghu, G. Jagadanand
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.
{"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}
Pub Date : 2022-07-28DOI: 10.1109/IAICT55358.2022.9887390
Elmar Wings, Stefan Reck, Hendrik Boomgaarden, Farzaneh Nourmohammadi, Thomas Peetz
In the context of making shipping more environ-mentally friendly this paper provides a first concept for setting up a cost-effective overall system letting various subsystems and devices cooperate automatically. In the implementation a Flettner rotor or Torqeedo motor gets controlled by a Raspberry Pi 4B sending and receiving data via MQTT. The data of various subsystems is stored efficiently in a database for later optimisation purposes. The database is also implemented with a Raspberry Pi 4B. The concept of collecting data can also be interesting for similar projects.
在使航运更加环保的背景下，本文提出了建立一个具有成本效益的整体系统的第一个概念，让各个子系统和设备自动协作。在实现中，由树莓派4B通过MQTT发送和接收数据来控制Flettner转子或Torqeedo电机。各个子系统的数据有效地存储在数据库中，以便以后进行优化。该数据库也是用Raspberry Pi 4B实现的。收集数据的概念对于类似的项目也很有趣。
{"title":"Implementing a Low-Cost Control Unit Network focusing on Data Collection and Flettner Rotor Control","authors":"Elmar Wings, Stefan Reck, Hendrik Boomgaarden, Farzaneh Nourmohammadi, Thomas Peetz","doi":"10.1109/IAICT55358.2022.9887390","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887390","url":null,"abstract":"In the context of making shipping more environ-mentally friendly this paper provides a first concept for setting up a cost-effective overall system letting various subsystems and devices cooperate automatically. In the implementation a Flettner rotor or Torqeedo motor gets controlled by a Raspberry Pi 4B sending and receiving data via MQTT. The data of various subsystems is stored efficiently in a database for later optimisation purposes. The database is also implemented with a Raspberry Pi 4B. The concept of collecting data can also be interesting for similar projects.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"37 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":"123061491","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}
Pub Date : 2022-07-28DOI: 10.1109/IAICT55358.2022.9887521
P. Kartheek, E. P. Jayakumar
Demosaicing refers to the reconstruction of full color image by the incomplete color samples produced by the single-chip image sensor. So there is a need of interpolation to obtain the missing color pixels. In this work a hardware architecture has been proposed for the adaptive edge-directed interpolation algorithm which uses an edge estimator for the interpolation. The proposed hardware architecture is implemented in Verilog HDL (Hardware Description Language) and synthesized using Cadence Genus compiler with 90nm technology in typical mode. For the proposed architecture, the power dissipation is found to be 26 mW, delay is 7.2 ns and requires 2.3 mm2 area. The demosaiced images obtained using the proposed architecture is observed to have better image quality in terms of peak signal-to-noise ratio and structural similarity while comparing with existing architectures.
{"title":"Hardware Architecture for Adaptive Edge-Directed Interpolation Algorithm","authors":"P. Kartheek, E. P. Jayakumar","doi":"10.1109/IAICT55358.2022.9887521","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887521","url":null,"abstract":"Demosaicing refers to the reconstruction of full color image by the incomplete color samples produced by the single-chip image sensor. So there is a need of interpolation to obtain the missing color pixels. In this work a hardware architecture has been proposed for the adaptive edge-directed interpolation algorithm which uses an edge estimator for the interpolation. The proposed hardware architecture is implemented in Verilog HDL (Hardware Description Language) and synthesized using Cadence Genus compiler with 90nm technology in typical mode. For the proposed architecture, the power dissipation is found to be 26 mW, delay is 7.2 ns and requires 2.3 mm2 area. The demosaiced images obtained using the proposed architecture is observed to have better image quality in terms of peak signal-to-noise ratio and structural similarity while comparing with existing architectures.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"80 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":"117224295","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}
Pub Date : 2022-07-28DOI: 10.1109/IAICT55358.2022.9887460
V. V. Zunin, I. Romanova
In this paper, the use of systolic arrays for data processing in the training or executing neural networks is explored. Two types of systolic arrays were developed, and a comparison on spending resources (ALM) and result calculation time was made. The comparison was conducted with two variable parameters of the input matrices: the number of rows of the first matrix and the number of columns of the second matrix. It is shown that (depending on the available resources) one of the methods for calculating the result can be used to synthesize the systolic array module: 1) to generate a systolic array of a given size and multiply matrices in which the first of them does not exceed the array size; 2) to synthesize a systolic array of a limited size and perform the multiplication of two matrices using the “Divide-and-Conquer” algorithm.
{"title":"Parameterized Computing Module Generator Based on a Systolic Array","authors":"V. V. Zunin, I. Romanova","doi":"10.1109/IAICT55358.2022.9887460","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887460","url":null,"abstract":"In this paper, the use of systolic arrays for data processing in the training or executing neural networks is explored. Two types of systolic arrays were developed, and a comparison on spending resources (ALM) and result calculation time was made. The comparison was conducted with two variable parameters of the input matrices: the number of rows of the first matrix and the number of columns of the second matrix. It is shown that (depending on the available resources) one of the methods for calculating the result can be used to synthesize the systolic array module: 1) to generate a systolic array of a given size and multiply matrices in which the first of them does not exceed the array size; 2) to synthesize a systolic array of a limited size and perform the multiplication of two matrices using the “Divide-and-Conquer” algorithm.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"12 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":"126557451","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}
Pub Date : 2022-07-28DOI: 10.1109/IAICT55358.2022.9887471
Fatema Al Mukhaini, Shaikhah Al Abdoulie, Aisha Al Kharuosi, Amal El Ahmad, M. Aldwairi
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.
{"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}
Pub Date : 2022-07-28DOI: 10.1109/IAICT55358.2022.9887523
Fransisca Elisa Rahardjo, Favian Dewanta, S. Rizal
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).
{"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}
Pub Date : 2022-07-28DOI: 10.1109/IAICT55358.2022.9887389
I. Recto, Andrea Danelle P. Quilang, L. Vea
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.
本文研究了2020年3月至2021年4月，即2019冠状病毒病(COVID-19)大流行的一年，学生的情绪表现。我们的推文妥协了Taglish(塔加洛语-英语)文本，这是一种资源较少的代码转换语言。这些文本被清理干净并从塔利英语翻译成英语。使用WordNet Affect以Happy, Angry, Sad, Surprise, and Fear作为输出对文本进行注释。采用具有注意层的双向门控循环单元(Bidirectional Gated Recurrent unit, Bi-GRU)神经网络，并与常用的塔格英语情感识别算法Bernoulli Naïve Bayes (BNB)和支持向量机(SVM)进行比较。在训练前对数据进行100维GloVe词嵌入。增强方法对模型的性能没有负面影响;反而帮助Bi-GRU提高了它的性能。与其他三种算法相比，带有注意力的Bi-GRU在所有情绪上都有更高的f1分，但正如预期的那样，它需要大量的数据。结果显示，学生全年表现出的最主要情绪是惊讶，其次是悲伤和恐惧。这三者的价值相近。
{"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}
Pub Date : 2022-07-28DOI: 10.1109/IAICT55358.2022.9887447
Lyla B. Das, C. V. Raghu, G. Jagadanand, Ritu Ann Roy George, Priyamvada Yashasawi, N. Kumaran, Vinay Kumar Patnaik
The significance of technology has exponentially grown in this increasingly virtual world, making online learning and evaluation the new normal. In the evaluation of writing assignments, many existing automated methods either focus on semantics or machine-learned features alone. In our project, we incorporate content analysis with structural analysis to provide a complete grading system. Also, revision and feedback are essential aspects of the writing process, with the help of which, students may increase their writing quality. Here, Automated Essay Scoring (AES) systems can be very useful as they can provide the student with a score as well as a feedback within seconds. Below we present an automated scoring system, built using the concepts of Long Short Term Memory (LSTM) and Entity Detection, incorporating a User Interface to input an essay and obtain its score along with the breakdown analysis of the essay.
{"title":"FACToGRADE: Automated Essay Scoring System","authors":"Lyla B. Das, C. V. Raghu, G. Jagadanand, Ritu Ann Roy George, Priyamvada Yashasawi, N. Kumaran, Vinay Kumar Patnaik","doi":"10.1109/IAICT55358.2022.9887447","DOIUrl":"https://doi.org/10.1109/IAICT55358.2022.9887447","url":null,"abstract":"The significance of technology has exponentially grown in this increasingly virtual world, making online learning and evaluation the new normal. In the evaluation of writing assignments, many existing automated methods either focus on semantics or machine-learned features alone. In our project, we incorporate content analysis with structural analysis to provide a complete grading system. Also, revision and feedback are essential aspects of the writing process, with the help of which, students may increase their writing quality. Here, Automated Essay Scoring (AES) systems can be very useful as they can provide the student with a score as well as a feedback within seconds. Below we present an automated scoring system, built using the concepts of Long Short Term Memory (LSTM) and Entity Detection, incorporating a User Interface to input an essay and obtain its score along with the breakdown analysis of the essay.","PeriodicalId":154027,"journal":{"name":"2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"30 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":"124140848","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}
Pub Date : 2022-07-28DOI: 10.1109/iaict55358.2022.9887489
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