Aspect Level Songs Rating Based Upon Reviews in English

Qureshi, Muhammad Aasim; Asif, Muhammad; Anwar, Sohel; Shaukat, Umar; Atta-ur-Rahman, _; Khan, Muhammad Adnan; Mosavi, Amir

doi:10.32604/cmc.2023.032173

Cited by 7 publications

(9 citation statements)

References 44 publications

(46 reference statements)

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“…Decision Trees are simple, interpretable, and can work well in practice, especially for small to medium-sized datasets. But it requires careful tuning and validation to prevent overfitting and instability, and its performance may be limited by its bias toward certain features [34].…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

Birthweight Range Prediction and Classification: A Machine Learning-Based Sustainable Approach

Alabbad,

Ajibi,

Alotaibi

et al. 2024

MAKE

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An accurate prediction of fetal birth weight is crucial in ensuring safe delivery without health complications for the mother and baby. The uncertainty surrounding the fetus’s birth situation, including its weight range, can lead to significant risks for both mother and baby. As there is a standard birth weight range, if the fetus exceeds or falls below this range, it can result in considerable health problems. Although ultrasound imaging is commonly used to predict fetal weight, it does not always provide accurate readings, which may lead to unnecessary decisions such as early delivery and cesarian section. Besides that, no supporting system is available to predict the weight range in Saudi Arabia. Therefore, leveraging the available technologies to build a system that can serve as a second opinion for doctors and health professionals is essential. Machine learning (ML) offers significant advantages to numerous fields and can address various issues. As such, this study aims to utilize ML techniques to build a predictive model to predict the birthweight range of infants into low, normal, or high. For this purpose, two datasets were used: one from King Fahd University Hospital (KFHU), Saudi Arabia, and another publicly available dataset from the Institute of Electrical and Electronics Engineers (IEEE) data port. KFUH’s best result was obtained with the Extra Trees model, achieving an accuracy, precision, recall, and F1-score of 98%, with a specificity of 99%. On the other hand, using the Random Forest model, the IEEE dataset attained an accuracy, precision, recall, and F1-score of 96%, respectively, with a specificity of 98%. These results suggest that the proposed ML system can provide reliable predictions, which could be of significant value for doctors and health professionals in Saudi Arabia.

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Section: Decision Tree (Dt)mentioning

confidence: 99%

Birthweight Range Prediction and Classification: A Machine Learning-Based Sustainable Approach

Alabbad,

Ajibi,

Alotaibi

et al. 2024

MAKE

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“…Figure 2 describes the evolution of the EHR in blockchain technology [40]. It explains that the data collected from the patient is stored in the EHR on his/her proper visit to his/her doctor/practitioner and further customized by user access.…”

Section: Data Sharingmentioning

confidence: 99%

“…In their system, medical records management is carried out using a digital archive that has access control rights to its owner's information. This is processed by setting up cloud encryption under the chain.Figure2describes the evolution of the EHR in blockchain technology[40]. It explains that the data collected from the patient is stored in the EHR on his/her proper visit to his/her doctor/practitioner and further customized by user access.…”

mentioning

confidence: 99%

Blockchain for Secure Healthcare: Opportunities, Challenges and Solutions

Alissa¹

2022

MMEP

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With the tremendous advancement in information and communication technologies (ICT), gradually human lives have been relying on them by means of healthcare, communication, and many other aspects. However, ICT has a major issue in terms of security which is one of the hottest areas of research at present. Among the numerous modern technologies that help improving the security and privacy, Blockchain has grabbed its place and remarkably enhanced many industries. Furthermore, Blockchain is executed within or on top of the cloud computing paradigm that allows the users to obtain the essential data with more security. With that, the inherent security and privacy related issues in the cloud computing paradigm are radically mitigated. Currently, among several industrial systems, healthcare systems are maintained through cloudbased data centers to facilitate diverse stakeholders like patients, practices, labs, pharmacies, and other healthcare entities, in terms of data processing and its ubiquity. However, that is not adequate in terms security and privacy thus vulnerable to several breaches. As a potential solution, Blockchain-based Cloud Computing offers security and availability for these records which is what Healthcare systems are mainly lacking. In this paper, we will state how Blockchain is affecting the Healthcare field, and what enhancements could happen if medical systems use Blockchain. Moreover, opportunities for Blockchain for secure healthcare systems, potential challenges and their solutions are discussed.

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“…Husain and Uzuner Applied several Arabic offensive datasets in the AraBERT model. They found that combining data from multiple datasets lowers the model's performance compared to testing it on individual datasets [32].…”

Section: Deep Learning and Bert Based Modelsmentioning

confidence: 99%

Spam and Sentiment Detection in Arabic Tweets Using MARBERT Model

Alotaibi¹,

Rahman²,

Alhaza³

et al. 2022

MMEP

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Saudi Telecom Company (STC) is among the most popular companies in Saudi Arabia, with many customers. Yet, there is still a big room for improvement in users' satisfaction. Social media is the most robust platform to gauge users' satisfaction and determine their sentiments and critics. Twitter is among the most popular social media platform in this regard. STC customers prefer to use Twitter to write their feedback because it's a fast way to get responses due to the STC customer services account. One way to achieve customer demands and improve customer service is using the Sentiment Analysis tool. Sentiment Analysis on Twitter is highly used because of the significant number of tweets and the different opinions. Likewise, Deep learning is the best existing Sentiment Analysis method, and it has diverse models. Bidirectional Encoder Representations from Transformers (BERT) model is one of the deep learning models which have achieved excellent results in Sentiment Analysis for Natural Language Processing (NLP). NLP is mainly investigated in the English language. However, for Arabic, there is a significant gap to be filled. This study trained the proposed model using MARBERT and measured the performance using f1-score, precision, and recall metrics. We trained the model with an Arabic dataset of 24,513 tweets, including 1,437 positive, 13,828 negative, 5,694 neutral, 1,221 sarcasm, and 2,297 indeterminate tweets. The main goal is to analyze the tweets and get the sentiment to improve STC customer service. The proposed scheme is promising in terms of accuracy in contrast to existing techniques in the literature.

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Aspect Level Songs Rating Based Upon Reviews in English

Cited by 7 publications

References 44 publications

Birthweight Range Prediction and Classification: A Machine Learning-Based Sustainable Approach

Birthweight Range Prediction and Classification: A Machine Learning-Based Sustainable Approach

Blockchain for Secure Healthcare: Opportunities, Challenges and Solutions

Spam and Sentiment Detection in Arabic Tweets Using MARBERT Model

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