Comparing word embedding models for Arabic aspect category detection using a deep learning-based approach

Aspect category detection (ACD) is a task of aspect-based sentiment analysis (ABSA) that aims to identify the discussed category in a given review or sentence from a predefined list of categories. ABSA tasks were widely studied in English; however, studies in other low-resource languages such as Arabic are still limited. Moreover, most of the existing Arabic ABSA work is based on rule-based or feature-based machine learning models, which require a tedious task of feature-engineering and the use of external resources like lexicons. Therefore, the aim of this paper is to overcome these shortcomings by handling the ACD task using a deep learning method based on a bidirectional gated recurrent unit model. Additionally, we examine the impact of using different vector representation models on the performance of the proposed model. The experimental results show that our model outperforms the baseline and related work models significantly by achieving an enhanced F1-score of more than 7%.

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A Novel Feature Representation for Prediction of Global Horizontal Irradiance Using a Bidirectional Model

Machine Learning and Knowledge Extraction ◽

10.3390/make3040047 ◽

2021 ◽

Vol 3 (4) ◽

pp. 946-965

Author(s):

Sourav Malakar ◽

Saptarsi Goswami ◽

Bhaswati Ganguli ◽

Amlan Chakrabarti ◽

Sugata Sen Roy ◽

...

Keyword(s):

Deep Learning ◽

Solar Energy ◽

Weather Conditions ◽

Feature Representation ◽

Energy Prediction ◽

Pv Systems ◽

Proposed Model ◽

Energy Domain ◽

Learning Architectures ◽

Gated Recurrent Unit

Complex weather conditions—in particular clouds—leads to uncertainty in photovoltaic (PV) systems, which makes solar energy prediction very difficult. Currently, in the renewable energy domain, deep-learning-based sequence models have reported better results compared to state-of-the-art machine-learning models. There are quite a few choices of deep-learning architectures, among which Bidirectional Gated Recurrent Unit (BGRU) has apparently not been used earlier in the solar energy domain. In this paper, BGRU was used with a new augmented and bidirectional feature representation. The used BGRU network is more generalized as it can handle unequal lengths of forward and backward context. The proposed model produced 59.21%, 37.47%, and 76.80% better prediction accuracy compared to traditional sequence-based, bidirectional models, and some of the established states-of-the-art models. The testbed considered for evaluation of the model is far more comprehensive and reliable considering the variability in the climatic zones and seasons, as compared to some of the recent studies in India.

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Online Public Shaming Approach using Deep Learning Techniques

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst12675 ◽

2021 ◽

Vol 23 (3) ◽

Author(s):

Mehdi Surani ◽

◽

Ramchandra Mangrulkar ◽

Keyword(s):

Deep Learning ◽

Hate Speech ◽

Severe Depression ◽

Learning System ◽

Online Bullying ◽

Digital World ◽

Learning Techniques ◽

Proposed Model ◽

Social Media Platforms ◽

The Impact

Public shaming on social media platforms like Twitter / Instagram / Facebook etc. have recently increased from the past years. This results in affecting an individual’s social, political, mental and ﬁnancial life. The impact can range from mild bullying to severe depression. With the growing leniency on these social platforms, many people have started misusing the opportunity by turning to online bullying and hate speech. When something is posted online, it stays there forever and it becomes extremely hard taking something out of the digital world. Manually locating and categorizing such comments is a lengthy procedure and just cannot be relied upon. To solve this challenge, automation was performed to identify and classify the shamers. This has been done using the classic SVM model which worked on a given quantity of data. To identify the negative content being posted and discussed online, this paper further explores the deep learning system which can successfully classify these content pieces into proper labels. The text-based Convolution Neural Network (CNN) is the proposed model in this paper for this analysis.

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Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray

Sensors ◽

10.3390/s22020669 ◽

2022 ◽

Vol 22 (2) ◽

pp. 669

Author(s):

Irfan Ullah Khan ◽

Nida Aslam ◽

Talha Anwar ◽

Hind S. Alsaif ◽

Sara Mhd. Bachar Chrouf ◽

...

Keyword(s):

Deep Learning ◽

Clinical Data ◽

Disease Diagnosis ◽

University Hospital ◽

X Ray ◽

Clinical Patient ◽

Proposed Model ◽

Chest X Ray ◽

The Impact ◽

Deep Learning Model

The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising results achieved by integrating machine learning (ML), particularly deep learning (DL), in automating the multiple disease diagnosis process. In the current study, a model based on deep learning was proposed for the automated diagnosis of COVID-19 using chest X-ray images (CXR) and clinical data of the patient. The aim of this study is to investigate the effects of integrating clinical patient data with the CXR for automated COVID-19 diagnosis. The proposed model used data collected from King Fahad University Hospital, Dammam, KSA, which consists of 270 patient records. The experiments were carried out first with clinical data, second with the CXR, and finally with clinical data and CXR. The fusion technique was used to combine the clinical features and features extracted from images. The study found that integrating clinical data with the CXR improves diagnostic accuracy. Using the clinical data and the CXR, the model achieved an accuracy of 0.970, a recall of 0.986, a precision of 0.978, and an F-score of 0.982. Further validation was performed by comparing the performance of the proposed system with the diagnosis of an expert. Additionally, the results have shown that the proposed system can be used as a tool that can help the doctors in COVID-19 diagnosis.

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A Real-Time Trajectory Prediction Method of Small-Scale Quadrotors Based on GPS Data and Neural Network

Sensors ◽

10.3390/s20247061 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7061

Author(s):

Zhao Yang ◽

Rong Tang ◽

Jie Bao ◽

Jiahuan Lu ◽

Zhijie Zhang

Keyword(s):

Real Time ◽

Stable State ◽

Prediction Method ◽

Small Scale ◽

Trajectory Data ◽

Trajectory Prediction ◽

Unit Model ◽

Proposed Model ◽

Historical Trajectory ◽

Gated Recurrent Unit

This paper proposes a real-time trajectory prediction method for quadrotors based on a bidirectional gated recurrent unit model. Historical trajectory data of ten types of quadrotors were obtained. The bidirectional gated recurrent units were constructed and utilized to learn the historic data. The prediction results were compared with the traditional gated recurrent unit method to test its prediction performance. The efficiency of the proposed algorithm was investigated by comparing the training loss and training time. The results over the testing datasets showed that the proposed model produced better prediction results than the baseline models for all scenarios of the testing datasets. It was also found that the proposed model can converge to a stable state faster than the traditional gated recurrent unit model. Moreover, various types of training samples were applied and compared. With the same randomly selected test datasets, the performance of the prediction model can be improved by selecting the historical trajectory samples of the quadrotors close to the weight or volume of the target quadrotor for training. In addition, the performance of stable trajectory samples is significantly better than that with unstable trajectory segments with a frequent change of speed and direction with large angles.

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The Future of PHM Could be Tiny under Cloud: Exploring Potential Application Patterns of TinyML in PHM Scenarios

Annual Conference of the PHM Society ◽

10.36001/phmconf.2021.v13i1.3054 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Xingyu Zhou ◽

Zhuangwei Kang ◽

Robert Canady ◽

Shunxing Bao ◽

Daniel Allen Balasubramanian ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Remaining Useful Life ◽

Data Driven ◽

Learning Models ◽

Level Data ◽

Data Source ◽

Single Data ◽

The Impact ◽

Machine Learning Models

Deep learning has shown impressive performance acrosshealth management and prognostics applications. Nowadays, an emerging trend of machine learning deployment on resource constraint hardware devices like micro-controllers(MCU) has aroused much attention. Given the distributed andresource constraint nature of many PHM applications, using tiny machine learning models close to data source sensors for on-device inferences would be beneficial to save both time andadditional hardware resources. Even though there has beenpast works that bring TinyML on MCUs for some PHM ap-plications, they are mainly targeting single data source usage without higher-level data incorporation with cloud computing.We study the impact of potential cooperation patterns betweenTinyML on edge and more powerful computation resources oncloud and how this would make an impact on the application patterns in data-driven prognostics. We introduce potential ap-plications where sensor readings are utilized for system health status prediction including status classification and remaining useful life regression. We find that MCUs and cloud com-puting can be adaptive to different kinds of machine learning models and combined in flexible ways for diverse requirement.Our work also shows limitations of current MCU-based deep learning in data-driven prognostics And we hope our work can

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Deep Learning Sign Language Recognition System Based on Wi-Fi CSI

International Journal of Intelligent Systems and Applications ◽

10.5815/ijisa.2020.06.03 ◽

2020 ◽

Vol 12 (6) ◽

pp. 33-45

Author(s):

Marwa R. M. Bastwesy ◽

◽

Nada M. ElShennawy ◽

Mohamed T. Faheem Saidahmed

Keyword(s):

Deep Learning ◽

Sign Language ◽

Performance Metrics ◽

Recognition System ◽

Language Recognition ◽

Sign Language Recognition ◽

Proposed Model ◽

Complete Study ◽

Commercial Off The Shelf ◽

The Impact

Many sensing gesture recognition systems based on Wi-Fi signals are introduced because of the commercial off-the-shelf Wi-Fi devices without any need for additional equipment. In this paper, a deep learning-based sign language recognition system is proposed. Wi-Fi CSI amplitude and phase information is used as input to the proposed model. The proposed model uses three types of deep learning: CNN, LSTM, and ABLSTM with a complete study of the impact of optimizers, the use of amplitude and phase of CSI, and preprocessing phase. Accuracy, F-score, Precision, and recall are used as performance metrics to evaluate the proposed model. The proposed model achieves 99.855%, 99.674%, 99.734%, and 93.84% average recognition accuracy for the lab, home, lab + home, and 5 different users in a lab environment, respectively. Experimental results show that the proposed model can effectively detect sign gestures in complex environments compared with some deep learning recognition models.

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Weibo Text Sentiment Analysis Based on BERT and Deep Learning

Applied Sciences ◽

10.3390/app112210774 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10774

Author(s):

Hongchan Li ◽

Yu Ma ◽

Zishuai Ma ◽

Haodong Zhu

Keyword(s):

Deep Learning ◽

Public Opinion ◽

Sentiment Analysis ◽

Monitoring Network ◽

Feature Representation ◽

Vector Representation ◽

Text Data ◽

Proposed Model ◽

Sentiment Dictionary ◽

Text Sentiment Analysis

With the rapid increase of public opinion data, the technology of Weibo text sentiment analysis plays a more and more significant role in monitoring network public opinion. Due to the sparseness and high-dimensionality of text data and the complex semantics of natural language, sentiment analysis tasks face tremendous challenges. To solve the above problems, this paper proposes a new model based on BERT and deep learning for Weibo text sentiment analysis. Specifically, first using BERT to represent the text with dynamic word vectors and using the processed sentiment dictionary to enhance the sentiment features of the vectors; then adopting the BiLSTM to extract the contextual features of the text, the processed vector representation is weighted by the attention mechanism. After weighting, using the CNN to extract the important local sentiment features in the text, finally the processed sentiment feature representation is classified. A comparative experiment was conducted on the Weibo text dataset collected during the COVID-19 epidemic; the results showed that the performance of the proposed model was significantly improved compared with other similar models.

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A New Hybrid CNN-LSTM Model with Non-SoftMax Functions for Face Spoof Detection

10.21203/rs.3.rs-837209/v1 ◽

2021 ◽

Author(s):

S Lokesh Kumar ◽

Yamani Sai Asish ◽

Sannasi Ganapathy

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Hybrid Approach ◽

Mobile Payments ◽

Hybrid Techniques ◽

Video Frames ◽

Learning Techniques ◽

Proposed Model ◽

Full Picture ◽

The Impact

Abstract Recently, the emerging applications such as banking, mobile payments, face recognition technology are gradually booming and also increases the users count around the world. The extensive deployment of facial recognition systems has drawn close attention to the dependability of facial biometrics in the fight against spoof attacks, in which a picture, video or 3D mask of a real user's face may be used to access facilities or services illegitimately. While a number of anti-spoofing or liveness detection approaches (which identify whether a face is live or spoof when captured) were suggested, the problem is still unresolved because of the difficulty in discovering discriminatory and computer-cost characteristics and techniques for spoof assaults. Existing methods also utilise a full picture or video to determine luminosity. Often though, some facial areas (video frames) are redundant or relate to the confusion of the picture (video). In this paper, we propose a new hybrid deep learning technique called Hybrid Convolutional Neural Network (CNN) based architecture with Long Short-Term Memory (LSTM) units to study the impact in classification. In this technique is applied a non-softmax function for making effective decision on classification. The hybrid approach is implemented followed by a comparative analysis with existing conventional and hybrid techniques used for spoof detection. The proposed model is proved as better than the existing deep learning techniques and other hybrid models in terms of precision, recall, f-measure and accuracy.

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An Ensemble Deep Learning Model for Automatic Modulation Classification in 5G and Beyond IoT Networks

Computational Intelligence and Neuroscience ◽

10.1155/2021/5047355 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Chirag Roy ◽

Satyendra Singh Yadav ◽

Vipin Pal ◽

Mangal Singh ◽

Sarat Kumar Patra ◽

...

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Signal To Noise Ratio ◽

Confusion Matrix ◽

Wireless Systems ◽

Modulation Classification ◽

Automatic Modulation Classification ◽

Proposed Model ◽

Gated Recurrent Unit ◽

Deep Learning Model

With rapid advancement in artificial intelligence (AI) and machine learning (ML), automatic modulation classification (AMC) using deep learning (DL) techniques has become very popular. This is even more relevant for Internet of things (IoT)-assisted wireless systems. This paper presents a lightweight, ensemble model with convolution, long short term memory (LSTM), and gated recurrent unit (GRU) layers. The proposed model is termed as deep recurrent convoluted network with additional gated layer (DRCaG). It has been tested on a dataset derived from the RadioML2016(b) and comprises of 8 different modulation types named as BPSK, QPSK, 8-PSK, 16-QAM, 4-PAM, CPFSK, GFSK, and WBFM. The performance of the proposed model has been presented through extensive simulation in terms of training loss, accuracy, and confusion matrix with variable signal to noise ratio (SNR) ranging from −20 dB to +20 dB and it demonstrates the superiority of DRCaG vis-a-vis existing ones.

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An Optimized Hybrid Deep Learning Model to Detect COVID-19 Misleading Information

Computational Intelligence and Neuroscience ◽

10.1155/2021/9615034 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Bader Alouffi ◽

Abdullah Alharbi ◽

Radhya Sahal ◽

Hager Saleh

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Short Term Memory ◽

Learning Model ◽

Dense Layer ◽

Fake News ◽

Learning Models ◽

Proposed Model ◽

Deep Learning Model ◽

Machine Learning Models

Fake news is challenging to detect due to mixing accurate and inaccurate information from reliable and unreliable sources. Social media is a data source that is not trustworthy all the time, especially in the COVID-19 outbreak. During the COVID-19 epidemic, fake news is widely spread. The best way to deal with this is early detection. Accordingly, in this work, we have proposed a hybrid deep learning model that uses convolutional neural network (CNN) and long short-term memory (LSTM) to detect COVID-19 fake news. The proposed model consists of some layers: an embedding layer, a convolutional layer, a pooling layer, an LSTM layer, a flatten layer, a dense layer, and an output layer. For experimental results, three COVID-19 fake news datasets are used to evaluate six machine learning models, two deep learning models, and our proposed model. The machine learning models are DT, KNN, LR, RF, SVM, and NB, while the deep learning models are CNN and LSTM. Also, four matrices are used to validate the results: accuracy, precision, recall, and F1-measure. The conducted experiments show that the proposed model outperforms the six machine learning models and the two deep learning models. Consequently, the proposed system is capable of detecting the fake news of COVID-19 significantly.

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