This page has only limited features, please log in for full access.

Ms. Wafa Shafqat
Jeju National University, South Korea

Basic Info

Basic Info is private.

Research Keywords & Expertise

0 Deep Learning
0 Natural Language Processing
0 Crowdfunding Platform
0 recommendation system
0 Machine Learning and Applications

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 20 April 2021 in Actuators
Reads 0
Downloads 0

In today’s world, smart buildings are considered an overarching system that automates a building’s complex operations and increases security while reducing environmental impact. One of the primary goals of building management systems is to promote sustainable and efficient use of energy, requiring coherent task management and execution of control commands for actuators. This paper proposes a predictive-learning framework based on contextual feature selection and optimal actuator control mechanism for minimizing energy consumption in smart buildings. We aim to assess multiple parameters and select the most relevant contextual features that would optimize energy consumption. We have implemented an artificial neural network-based particle swarm optimization (ANN-PSO) algorithm for predictive learning to train the framework on feature importance. Based on the relevance of attributes, our model was also capable of re-adding features. The extracted features are then applied as input parameters for the training of long short-term memory (LSTM) and optimal control module. We have proposed an objective function using a velocity boost-particle swarm optimization (VB-PSO) algorithm that reduces energy cost for optimal control. We then generated and defined the control tasks based on the fuzzy rule set and optimal values obtained from VB-PSO. We compared our model’s performance with and without feature selection using the root mean square error (RMSE) metric in the evaluation section. This paper also presents how optimal control can reduce energy cost and improve performance resulting from lesser learning cycles and decreased error rates.

ACS Style

Sehrish Malik; Wafa Shafqat; Kyu-Tae Lee; Do-Hyeun Kim. A Feature Selection-Based Predictive-Learning Framework for Optimal Actuator Control in Smart Homes. Actuators 2021, 10, 84 .

AMA Style

Sehrish Malik, Wafa Shafqat, Kyu-Tae Lee, Do-Hyeun Kim. A Feature Selection-Based Predictive-Learning Framework for Optimal Actuator Control in Smart Homes. Actuators. 2021; 10 (4):84.

Chicago/Turabian Style

Sehrish Malik; Wafa Shafqat; Kyu-Tae Lee; Do-Hyeun Kim. 2021. "A Feature Selection-Based Predictive-Learning Framework for Optimal Actuator Control in Smart Homes." Actuators 10, no. 4: 84.

Journal article
Published: 06 August 2020 in Applied Sciences
Reads 0
Downloads 0

The COVID-19 pandemic is swiftly changing our behaviors toward online channels across the globe. Cultural patterns of working, thinking, shopping, and use of technology are changing accordingly. Customers are seeking convenience in online shopping. It is the peak time to assist the digital marketplace with right kind of tools and technologies that uses the strategy of click and collect. Session-based recommendation systems have the potential to be equally useful for both the customers and the service providers. These frameworks can foresee customer's inclinations and interests, by investigating authentic information on their conduct and activities. Various methods exist and are pertinent in various situations. We propose a product recommendation system that uses a graph convolutional neural network (GCN)-based approach to recommend products to users by analyzing their previous interactions. Unlike other conventional techniques, GCN is not widely explored in recommendation systems. Therefore, we propose a variation of GCN that uses optimization strategy for better representation of graphs. Our model uses session-based data to generate patterns. The input patterns are encoded and passed to embedding layer. GCN uses the session graphs as input. The experiments on data show that the optimized GCN (OpGCN) was able to achieve higher prediction rate with around 93% accuracy as compared with simple GCN (around 88%).

ACS Style

Wafa Shafqat; Yung-Cheol Byun. Enabling “Untact” Culture via Online Product Recommendations: An Optimized Graph-CNN based Approach. Applied Sciences 2020, 10, 5445 .

AMA Style

Wafa Shafqat, Yung-Cheol Byun. Enabling “Untact” Culture via Online Product Recommendations: An Optimized Graph-CNN based Approach. Applied Sciences. 2020; 10 (16):5445.

Chicago/Turabian Style

Wafa Shafqat; Yung-Cheol Byun. 2020. "Enabling “Untact” Culture via Online Product Recommendations: An Optimized Graph-CNN based Approach." Applied Sciences 10, no. 16: 5445.

Journal article
Published: 18 May 2020 in Sustainability
Reads 0
Downloads 0

The significance of contextual data has been recognized by analysts and specialists in numerous disciplines such as customization, data recovery, ubiquitous and versatile processing, information mining, and management. While a generous research has just been performed in the zone of recommender frameworks, by far most of the existing approaches center on prescribing the most relevant items to customers. It usually neglects extra-contextual information, for example time, area, climate or the popularity of different locations. Therefore, we proposed a deep long-short term memory (LSTM) based context-enriched hierarchical model. This proposed model had two levels of hierarchy and each level comprised of a deep LSTM network. In each level, the task of the LSTM was different. At the first level, LSTM learned from user travel history and predicted the next location probabilities. A contextual learning unit was active between these two levels. This unit extracted maximum possible contexts related to a location, the user and its environment such as weather, climate and risks. This unit also estimated other effective parameters such as the popularity of a location. To avoid feature congestion, XGBoost was used to rank feature importance. The features with no importance were discarded. At the second level, another LSTM framework was used to learn these contextual features embedded with location probabilities and resulted into top ranked places. The performance of the proposed approach was elevated with an accuracy of 97.2%, followed by gated recurrent unit (GRU) (96.4%) and then Bidirectional LSTM (94.2%). We also performed experiments to find the optimal size of travel history for effective recommendations.

ACS Style

Wafa Shafqat; Yung-Cheol Byun. A Context-Aware Location Recommendation System for Tourists Using Hierarchical LSTM Model. Sustainability 2020, 12, 4107 .

AMA Style

Wafa Shafqat, Yung-Cheol Byun. A Context-Aware Location Recommendation System for Tourists Using Hierarchical LSTM Model. Sustainability. 2020; 12 (10):4107.

Chicago/Turabian Style

Wafa Shafqat; Yung-Cheol Byun. 2020. "A Context-Aware Location Recommendation System for Tourists Using Hierarchical LSTM Model." Sustainability 12, no. 10: 4107.

Journal article
Published: 31 December 2019 in Sustainability
Reads 0
Downloads 0

With rapid advancements in internet applications, the growth rate of recommendation systems for tourists has skyrocketed. This has generated an enormous amount of travel-based data in the form of reviews, blogs, and ratings. However, most recommendation systems only recommend the top-rated places. Along with the top-ranked places, we aim to discover places that are often ignored by tourists owing to lack of promotion or effective advertising, referred to as under-emphasized locations. In this study, we use all relevant data, such as travel blogs, ratings, and reviews, in order to obtain optimal recommendations. We also aim to discover the latent factors that need to be addressed, such as food, cleanliness, and opening hours, and recommend a tourist place based on user history data. In this study, we propose a cross mapping table approach based on the location’s popularity, ratings, latent topics, and sentiments. An objective function for recommendation optimization is formulated based on these mappings. The baseline algorithms are latent Dirichlet allocation (LDA) and support vector machine (SVM). Our results show that the combined features of LDA, SVM, ratings, and cross mappings are conducive to enhanced performance. The main motivation of this study was to help tourist industries to direct more attention towards designing effective promotional activities for under-emphasized locations.

ACS Style

Wafa Shafqat; Yung-Cheol Byun. A Recommendation Mechanism for Under-Emphasized Tourist Spots Using Topic Modeling and Sentiment Analysis. Sustainability 2019, 12, 320 .

AMA Style

Wafa Shafqat, Yung-Cheol Byun. A Recommendation Mechanism for Under-Emphasized Tourist Spots Using Topic Modeling and Sentiment Analysis. Sustainability. 2019; 12 (1):320.

Chicago/Turabian Style

Wafa Shafqat; Yung-Cheol Byun. 2019. "A Recommendation Mechanism for Under-Emphasized Tourist Spots Using Topic Modeling and Sentiment Analysis." Sustainability 12, no. 1: 320.

Journal article
Published: 13 December 2019 in Applied Sciences
Reads 0
Downloads 0

The accelerated growth rate of internet users and its applications, primarily e-business, has accustomed people to write their comments and reviews about the product they received. These reviews are remarkably competent to shape customers’ decisions. However, in crowdfunding, where investors finance innovative ideas in exchange for some rewards or products, the comments of investors are often ignored. These comments can play a markedly significant role in helping crowdfunding platforms to battle against the bitter challenge of fraudulent activities. We take advantage of the language modeling techniques and aim to merge them with neural networks to identify some hidden discussion patterns in the comments. Our objective is to design a language modeling based neural network architecture, where Recurrent Neural Networks (RNN) Long Short-Term Memory (LSTM) is used to predict discussion trends, i.e., either towards scam or non-scam. LSTM layers are fed with latent topic distribution learned from the pre-trained Latent Dirichlet Allocation (LDA) model. In order to optimize the recommendations, we used Particle Swarm Optimization (PSO) as a baseline algorithm. This module helps investors find secure projects to invest in (with the highest chances of delivery) within their preferred categories. We used prediction accuracy, an optimal number of identified topics, and the number of epochs, as metrics of performance evaluation for the proposed approach. We compared our results with simple Neural Networks (NNs) and NN-LDA based on these performance metrics. The strengths of both integrated models suggest that the proposed model can play a substantial role in a better understanding of crowdfunding comments.

ACS Style

Wafa Shafqat; Yung-Cheol Byun. Topic Predictions and Optimized Recommendation Mechanism Based on Integrated Topic Modeling and Deep Neural Networks in Crowdfunding Platforms. Applied Sciences 2019, 9, 5496 .

AMA Style

Wafa Shafqat, Yung-Cheol Byun. Topic Predictions and Optimized Recommendation Mechanism Based on Integrated Topic Modeling and Deep Neural Networks in Crowdfunding Platforms. Applied Sciences. 2019; 9 (24):5496.

Chicago/Turabian Style

Wafa Shafqat; Yung-Cheol Byun. 2019. "Topic Predictions and Optimized Recommendation Mechanism Based on Integrated Topic Modeling and Deep Neural Networks in Crowdfunding Platforms." Applied Sciences 9, no. 24: 5496.