Papers by Pantea Keikhosrokiani
Lecture notes on data engineering and communications technologies, 2024
Social sciences & humanities open, 2024
Studies in computational intelligence, Dec 31, 2022
Multimedia Tools and Applications
Lecture Notes in Computer Science, 2023
Social sciences & humanities open, 2023
2021 International Congress of Advanced Technology and Engineering (ICOTEN), Jul 4, 2021
Geographic Information Systems (GIS) is a system that gathers and analyzes data related to positi... more Geographic Information Systems (GIS) is a system that gathers and analyzes data related to positions on Earth’s surface. It can be used in different ways like mapping, navigation, tourism development, etc. The purpose of the study is to develop a GIS-based application to improve the tourism experience in Terengganu. A multi-step method was used to develop the GIS-based application in this study. The first step is planning the data field and selecting hardware and software. The next step is data acquisition which collects data for the area of interest and stores the data into a local database. Data validation is done to prevent system or result error. Then, the database is designed and analyzed to determine the most suitable database model and apply Dijkstra’s algorithm in the system. Finally, GIS is developed, and the analyzed data is overlaid on the map to give the tourist a clear picture or information on the tourists’ area of interest by displaying more detailed attributes.
Advances in marketing, customer relationship management, and e-services book series, Jun 24, 2022
The COVID-19 pandemic instigated thousands of companies' closures and affected offline retail... more The COVID-19 pandemic instigated thousands of companies' closures and affected offline retail shops. Thus, online B2C business models enable traditional offline stores to boost their sales. This study aims to explore the use of historical sales and behavioral data analytics to construct a recommendation model. A process model is proposed, which is the combination of recency, frequency, and monetary (RFM) analysis method and the k-means clustering algorithm. RFM analysis is used to segment customer levels in the company while the association rule theory and the apriori algorithm are utilized for completing the shopping basket analysis and recommending products based on the results. The proposed recommendation model provides a good marketing mix to improve sales and market responsiveness. In addition, it recommends specific products to new customers as well as specific groups of target customers. This study offered a practical business transformation case that can assist companies in a similar situation to transform their business model and improve their profits.
Applied Intelligence, Nov 4, 2020
The competitive society in the new era calls for more research to improve the well-being of worke... more The competitive society in the new era calls for more research to improve the well-being of workers as well as to improve their productivity. Knowledge workers face a high mental workload in terms of planning and coordination. One solution is to predict the mental workload of knowledge workers. Some machine learning models have been implemented for mental workload prediction, but deep learning models are yet to be introduced for this purpose. Deep learning models are superior to machine learning models because of their ability to correct inaccurate predictions if they ever occur. Therefore, this study aims to optimize the extreme learning adaptive neuro-fuzzy inference system (ELANFIS) by integrating particle swarm optimization into a microgenetic algorithm to predict the mental workload of knowledge workers. Although the adaptive neuro-fuzzy inference system (ANFIS) shows reasonable prediction performance, it also suffers from the curse of dimensionality and has a poor computation time. Thus, ELANFIS is introduced because its curse of dimensionality is less severe when solving problems with a high number of input dimensions. The integration of the advantages of a micro-genetic algorithm and particle swarm optimization is suggested to optimize the premise parameters of ELANFIS, as this can allow better solutions to be located at a faster rate. The proposed model yields promising prediction results, with improvements of 6.0665 in the Mean Squared Error(MSE) and 1.279 in the Root Mean Squared Error (RMSE) for regression; the proposed model even surpasses the prediction results of ELANFIS optimized with PSO alone, with improvements of 1.5369 in MSE and 0.4094 in RMSE for regression. The findings are expected to assist employers in determining an appropriate working lifestyle for their employees.
IEEE Access, 2020
Anomaly detection is becoming widely used in Manufacturing Industry to enhance product quality. A... more Anomaly detection is becoming widely used in Manufacturing Industry to enhance product quality. At the same time, it plays a great role in several other domains due to the fact that anomaly may reveal rare but represent an important phenomenon. The objective of this paper is to detect anomalies and identify the possible variables that caused these anomalies on historical assembly data for two series of products. Multiple anomaly detection techniques were performed; HBOS, IForest, KNN, CBLOF, OCSVM, LOF, and ABOD. Moreover, we used AUROC and Rank Power as performance metrics, followed by Boosting ensemble learning method to ensure the best anomaly detectors robustness. The techniques that gave the highest performance are KNN, ABOD for both product series datasets with 0.95 and 0.99 AUROC respectively. Finally, we applied a statistical root cause analysis on the detected anomalies with the use of Pareto chart to visualize the frequency of the possible causes and its cumulative occurrence. The results showed that there are seven rejection causes for both product series, whereas the first three causes are responsible for 85% of the rejection rates. Besides, assembly machines engineers reported a significant reduction in the rejection rates in both assembly machines after tuning the specification limits of the rejection causes identified by this research results.
Lecture Notes in Computer Science, 2023
IGI Global eBooks, Dec 30, 2022
Elsevier eBooks, 2020
Abstract The main goal of this chapter is to explain Mobile Medical Information System (mMIS) eva... more Abstract The main goal of this chapter is to explain Mobile Medical Information System (mMIS) evaluation, which is the last step of prototyping phase. mMIS assists the system analysts and managers to validate and find the applicability of the proposed system. Furthermore, it helps to find the strength and weakness of the system, which can be useful for its further improvement. Evaluation along with setting objectives and methods for mMIS evaluation are described in this chapter briefly. Afterward, conducting mMIS evaluation and its documentation are explained. Finally, the concept is applied for iHeart to create a clear understanding for real-time evaluation of mMIS.
Computers, materials & continua, 2022
The COVID-19 has brought us unprecedented difficulties and thousands of companies have closed dow... more The COVID-19 has brought us unprecedented difficulties and thousands of companies have closed down. The general public has responded to call of the government to stay at home. Offline retail stores have been severely affected. Therefore, in order to transform a traditional offline sales model to the B2C model and to improve the shopping experience, this study aims to utilize historical sales data for exploring, building sales prediction and recommendation models. A novel data science life-cycle and process model with Recency, Frequency, and Monetary (RFM) analysis method with the combination of various analytics algorithms are utilized in this study for sales prediction and product recommendation through user behavior analytics. RFM analysis method is utilized for segmenting customer levels in the company to identify the importance of each level. For the purchase prediction model, XGBoost and Random Forest machine learning algorithms are used to build prediction models and 5-fold Cross-Validation method is utilized to evaluate their. For the product recommendation model, the association rules theory and Apriori algorithm are used to complete basket analysis and recommend products according to the outcomes. Moreover, some suggestions are proposed for the marketing department according to the outcomes. Overall, the XGBoost model achieved better performance and better accuracy with F1-score around 0.789. The proposed recommendation model provides good recommendation results and sales combinations for improving sales and market responsiveness. Furthermore, it recommend specific products to new customers. This study offered a very practical and useful business transformation case that assists companies in similar situations to transform their business models.
Elsevier eBooks, 2020
Abstract The main goal of this chapter is to introduce the main required concept for developing a... more Abstract The main goal of this chapter is to introduce the main required concept for developing a Mobile Medical Information System (mMIS). Therefore, mMIS is defined along with System Development Life Cycle to provide instruction for its development. In addition, communication technologies which are required to develop mMIS are introduced followed by the concept of Internet of Things and the required security and privacy. The concept of big data in mMIS is discussed afterward. Policies and regulations and ontology modeling for mMIS are mentioned after the concept of big data. Finally, iHeart, which is used as an example of mMIS for better understanding of the concepts in each chapter, is explained.
IGI Global eBooks, Feb 18, 2022
Text mining is an important field of study that has proved beneficial for scholars of various dis... more Text mining is an important field of study that has proved beneficial for scholars of various disciplines. Literary scholars use text mining to examine the data produced by creative writers, literary readers, publishers, and distributing companies. The produced data are generally in unstructured form that cannot be used to extract useful information. Text mining can discover the unstructured data and convert it to interesting information through several processes. This chapter proposes a text mining technique by using topic modelling and sentiment analysis to retrieve information about the attitude of the user-readers toward the four volumes of KL Noir books on the Goodreads website. The main significance of this approach is to gain the trends by analyzing the book reviews written on Goodreads.
2023 11th International Conference on Information and Communication Technology (ICoICT)
Lecture notes on data engineering and communications technologies, 2022
IGI Global eBooks, Feb 18, 2022
The rapid advancements in data science techniques and approaches have influenced disciplines, suc... more The rapid advancements in data science techniques and approaches have influenced disciplines, such as literary studies, that are particularly engaged in qualitative text analysis. This chapter aims to apply natural language preprocessing (NLP) to identify the connection between theme and sentiment in a corpus of six life writings by or about Iraqi people. To do so, the study uses Latent Dirichlet Allocation (LDA) from topic modeling and the two models of Gensim and Mallet. It also implements TextBlob dictionary to calculate the polarity and subjectivity scores to measure the sentiment for detected themes. Nine topics are extracted from both models. The extracted themes point to the prevalence of traumatic events that the authors have personally endured. Gensim works better than Mallet as it has high coherence score and relevant terms. In sentiment analysis, most of the themes appeared as positive. The application of LDA using Gensim also revealed that the selected life writings are shaped and influenced by the authors' personal feelings. It is hoped that the analytical models can encourage future studies to improve existing qualitative methods in literary studies.
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Papers by Pantea Keikhosrokiani