An Improved Particle Swarm Optimization Algorithm with Chi-Square Mutation Strategy

Bangyal, Waqas Haider; Tayyab, Hafiz; Batool, Hafsa; Abdullah, Saad Mohammad; Ahmed, Jamil; Pervaiz, Sobia

doi:10.14569/ijacsa.2019.0100362

Cited by 10 publications

(5 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Social media content generated by social media platform users is an important source of data for hospitals, industry, scientists, policy-making, and much more. UGC (User Generated Content) on different review platforms or sites holds diverse information in the form of text that is extracted after applying opinion extraction algorithms and sentiment analysis techniques [ 9 ] These algorithms provide better performance in the feature extraction phase of text classification as well [ 10 – 24 ].…”

Section: Introductionmentioning

confidence: 99%

A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification

Qasim

Bangyal

Alqarni

et al. 2022

Journal of Healthcare Engineering

Self Cite

View full text Add to dashboard Cite

Text Classification problem has been thoroughly studied in information retrieval problems and data mining tasks. It is beneficial in multiple tasks including medical diagnose health and care department, targeted marketing, entertainment industry, and group filtering processes. A recent innovation in both data mining and natural language processing gained the attention of researchers from all over the world to develop automated systems for text classification. NLP allows categorizing documents containing different texts. A huge amount of data is generated on social media sites through social media users. Three datasets have been used for experimental purposes including the COVID-19 fake news dataset, COVID-19 English tweet dataset, and extremist-non-extremist dataset which contain news blogs, posts, and tweets related to coronavirus and hate speech. Transfer learning approaches do not experiment on COVID-19 fake news and extremist-non-extremist datasets. Therefore, the proposed work applied transfer learning classification models on both these datasets to check the performance of transfer learning models. Models are trained and evaluated on the accuracy, precision, recall, and F1-score. Heat maps are also generated for every model. In the end, future directions are proposed.

show abstract

Section: Introductionmentioning

confidence: 99%

A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification

Qasim

Bangyal

Alqarni

et al. 2022

Journal of Healthcare Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many machine learning (ML) classifiers have been used in the diagnosis of brain diseases. Particle swarm optimization (PSO) is perceived as the most probable population-based stochastic algorithm and, as such, it is used to tackle global optimization problems [5][6][7][8][9][10][11]; BA is also widely used in such fields to find out the optimal answers to problems [12,13]. Alzheimer's disease (AD) is recognised as a neuron-degenerative disease.…”

Section: Introductionmentioning

confidence: 99%

Constructing Domain Ontology for Alzheimer Disease Using Deep Learning Based Approach

Bangyal¹,

Rehman

Nawaz

et al. 2022

Electronics

Self Cite

View full text Add to dashboard Cite

Facts can be exchanged in multiple fields with the help of disease-specific ontologies. A range of diverse values can be produced by mining ontological approaches for demonstrating disease mechanisms. Alzheimer’s disease (AD) is an incurable neurological brain illness. An early diagnosis of AD can be helpful for better treatment and the prevention of brain tissue destruction. Researchers have used machine learning techniques to predict the early detection of AD. However, Alzheimer’s disorders are still underexplored in the knowledge domain. In the biomedical field, the illustration of terminologies and notions is essential. Multiple methods are adopted to represent these notions, but ontologies are the most frequent and accurate. Ontology construction is a complex and time-consuming process. The designed ontology relies on Disease Ontology (DO), which is considered the benchmark in medical practice. Ontology reasoning mechanisms can be adopted for AD identification. In this paper, a deep convolutional neural network-based approach is proposed to diagnose Alzheimer’s disease, using an AD dataset acquired from Kaggle. Machine learning-based approaches (logistic regression, gradient boosting, XGB, SGD, MLP, SVM, KNN, random forest) are also used for a fair comparison. The simulation results are generated using three strategies (default parameters, 10-cross validation, and grid search), and MLP provides superior results on a default parameter strategy with an accuracy of 92.12%. Furthermore, the deep learning-based approach convolutional neural network (CNN) achieved an accuracy of 94.61%. The experimental results indicate that the construction of ontology, with the help of deep learning knowledge, can produce better results where the robustness and scalability can be enhanced. In comparisons to other methods, the CNN results are excellent and encouraging.

show abstract

“…ML Algorithms [27][28][29][30][31][32] can assist us with plotting exact visual portrayals of such customer conduct.…”

Section: Introductionmentioning

confidence: 99%

A Review on Consumer Behavior Towards Online Shopping using Machine Learning

Bangyal¹,

Ashraf

Shakir

et al. 2022

IJEMD-CSAI

View full text Add to dashboard Cite

Study of consumer behavior in online shopping, as a rule, manages identification of consumers and their purchasing behavior. The purpose of such studies is to verify who purchases where, what, when, and how. The analysis of such consumer behavior is useful to get the buyer's prerequisites and requirements for their future aims towards the product. Through this review, E-commerce organizations can follow the utilization and sentiments appended to their items and adopt suitable promoting strategies to give a customized shopping experience to their buyers, consequently expanding their hierarchical benefit. This paper purpose to utilize information-driven promoting models, for example, information perception, natural language processing, and AI models that help in getting the demographics of an association. Additionally, make recommender frameworks through cooperative filtering, sentiment analysis, and neural networks.

show abstract

An Improved Particle Swarm Optimization Algorithm with Chi-Square Mutation Strategy

Cited by 10 publications

References 28 publications

A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification

A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification

Constructing Domain Ontology for Alzheimer Disease Using Deep Learning Based Approach

A Review on Consumer Behavior Towards Online Shopping using Machine Learning

Contact Info

Product

Resources

About