An Intelligent Business Model for Product Price Prediction Using Machine Learning Approach

Mahoto, Naeem Ahmed; Iftikhar, Rabia; Shaikh, Asadullah; Asiri, Yousef; Alghamdi, Abdullah; Rajab, Khairan

doi:10.32604/iasc.2021.018944

Cited by 13 publications

(7 citation statements)

References 22 publications

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“…The AE classifies the best outcome among these models, it is concluded. In the near future, we will explore some more technique [18][19][20][21][22][23][24][25][26][27] of security to detect and classify the fraud detection in credit card.…”

Section: Discussionmentioning

confidence: 99%

Detecting Credit Card Fraud using Machine Learning

Almuteer

Aloufi

Alrashidi

et al. 2021

Int. J. Interact. Mob. Technol.

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Credit card is getting increasingly more famous in budgetary exchanges, simultaneously frauds are likewise expanding. Customary techniques use rule-based master frameworks to identify fraud practices, ignoring assorted circumstances, the outrageous lopsidedness of positive and negative examples. In this paper, we propose a CNN-based fraud detection system, to catch the natural examples of fraud practices gained from named information. Bountiful exchange information is spoken to by an element lattice, on which a convolutional neural organization is applied to recognize a bunch of idle examples for each example. Trials on true monstrous exchanges of a significant business bank show its boss presentation contrasted and some best-in-class strategies. The aim of this paper is to merge between Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and Auto-encoder (AE) to increase credit card fraud detection and enhance the performance of the previous models. By using these four models; CNN, AE, LSTM, and AE&LSTM. each of these models is trained by different parameter values highest accuracy has been achieved where the AE model has accuracy =0.99, the CNN model has accuracy =0.85, the accuracy of the LSTM model is 0.85, and finally, the AE&LSTM model obtained an accuracy of 0.32 by 400 epoch. It is concluded that the AE classifies the best result between these models.

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Section: Discussionmentioning

confidence: 99%

Detecting Credit Card Fraud using Machine Learning

Almuteer

Aloufi

Alrashidi

et al. 2021

Int. J. Interact. Mob. Technol.

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“…Machine learning algorithms (Random Forest and Logistic regression) mat be used to enable store staff to set product prices and discounts based on consumer behavior (Mahoto et al 2021). This model gave excellent results in predicting product prices.…”

Section: Machine Learning Prediction On Productivitymentioning

confidence: 99%

Predicting the Productivity of Municipality Workers: A Comparison of Six Machine Learning Algorithms

Bijalwan,

Gupta,

Mendiratta

et al. 2024

Economies

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One of the most significant areas of local government in the world is the municipality sector. It provides various services to the residents and businesses in their areas, such as water supply, sewage disposal, healthcare, education, housing, and transport. Municipalities also promote social and economic development and ensure democratic and accountable governance. It also helps in encouraging the involvement of communities in local matters. Workers of Municipalities need to maintain their services regularly to the public. The productivity of the employees is just one of the main important factors that influence the overall organizational performance. This article compares various machine learning algorithms such as XG Boost, Random Forest (RF), Histogram Gradient Boosting Regressor, LGBM Regressor, Ada Boost Regressor, and Gradient Boosting Regressor on the dataset of municipality workers. The study aims to propose a machine learning approach to predict and evaluate the productivity of municipality workers. The evaluation of the overall targeted and actual productivity of each department shows that out of 12 different departments, only 5 departments were able to meet their targeted productivity. A 3D Scatter plot visually displays the incentive given by the department to each worker based on their productivity. The results show that XG Boost performs best in comparison with the other five algorithms, as the value of R Squared is 0.71 and MSE (Mean Squared Error) is 0.01.

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“…In [25], a sophisticated business model was employed to forecast product pricing. A multistage decision model was constructed utilizing machine learning algorithms, specifically random forests and multiclass logistic regression.…”

Section: Machine Learning In Hr Departmentmentioning

confidence: 99%

New Recruitment Approach Based on Logistic Regression Model

Hatif Abd Almajed,

Nassreddine,

Younis

2024

Mesopotamian Journal of Computer Science

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Artificial intelligence (AI) is a pivotal technological advancement developed by humans with the aim of enhancing the quality of human existence. It signifies the capacity of a computerized machine resembling a robot to execute tasks typically performed by humans and replicate human behavior. Machine learning (ML), a subfield of AI, involves the construction of systems that acquire the ability to make predictions about new output values by leveraging existing data, without the need for human interaction. Currently, ML has been incorporated into various fields, including but not limited to medical diagnosis, image processing, prediction, classification, learning association, commerce, finance, and natural language processing. This project aims to employ ML techniques within the human resource (HR) department. The implemented model will enable the human resources department to effectively identify the most appropriate candidates for a job opening throughout the recruitment process, utilizing a comprehensive dataset and considering many criteria, all without the need for manual intervention. The construction of the model involves the utilization of an authentic dataset comprising recruitment tasks. Initially, the dataset undergoes a process of selecting the most pertinent elements from both pre-existing and extracted factors. These selected factors include educational level, age, and past experience. Furthermore, taking into consideration these aforementioned factors, a decision system is constructed utilizing the Binary classification approach. The logistic regression classifier is utilized in this investigation. Subsequently, the dataset is partitioned into two distinct subsets, namely the training subset and the testing subset. The effectiveness of the model is demonstrated by the utilization of various evaluation metrics, including the confusion matrix, recall, precision, accuracy, and F-measure values.

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An Intelligent Business Model for Product Price Prediction Using Machine Learning Approach

Cited by 13 publications

References 22 publications

Detecting Credit Card Fraud using Machine Learning

Detecting Credit Card Fraud using Machine Learning

Predicting the Productivity of Municipality Workers: A Comparison of Six Machine Learning Algorithms

New Recruitment Approach Based on Logistic Regression Model

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