An Evaluation of ARIMA and Persistence Models in IoT-Driven Smart Homes

Ahmed; Ahmed Mohamed; Marwa Adel

doi:https://doi.org/10.54216/JAIM.060201

American Journal of Business and Operations Research | 2024

Quantifying the Impact of Sustainable Practices on Business Operations

Based on the business context, resilience and sustainability seem to have multiple dimensions and connections. Administrative sustainability strategies can help a company develop and become more resilient. With the use of a sustainability maturation index (SMI), this study attempts to analyze how the financial success of a business is affected by its approach to sustainable development. As resilience abilities are closely linked to the SMI, this study proposes to explore the initial integration of both sustainable development and resilience criteria into a single framework. To determine whether there could be an interaction between the SMI and economic performance indices, planned conversations were used to gather data from 35 different firms. The investigation disproves widely circulated claims, demonstrating that there is no meaningful correlation between profitability and sustained business operations. It's noteworthy to point out that market emphasis, organizational size, and firm place of origin do not significantly correlate with SMI. One could argue that to evaluate the effects of environmentally friendly procedures, a company's multi-dimensional performance, which includes both financial and non-financial measurements, should be considered. In addition, more research is required to identify the nonfinancial metrics of success that businesses use to measure resilience and sustainable development to create a cohesive framework that facilitates trade-off evaluation.

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Ilknur Ozturk mail -

Festus Victor Bekun mail

link https://doi.org/10.54216/AJBOR.110107

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

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American Journal of Business and Operations Research | 2024

Intelligent Stock Price Fusion in Mobile Industries

In the tough cell phone business, guessing phone prices right is a key but hard job for new companies. Joining different types of info to look at stock prices may help, but we need strong ways to see how phone things and their costs tie together. This study wants to make stock price checking better in the cell phone business by using ways to join info. The work looks for strong ties between many phone things like memory, camera details, and screen size and how they affect the price. To fix this, very careful work was done to clean and fix the info. The Quadratic Discriminant Analysis rule was then used, along with top classifiers, for saying what will happen. Our findings demonstrate the QDA model's ability to detect subtle patterns and nonlinear correlations in the mobile phone data set. The model's resilience and predictive ability are demonstrated through visualizations such as ROC AUC and Precision-Recall curves. Comparative analyses with current approaches highlight the higher performance of the suggested data fusion approach. The use of QDA in data fusion models demonstrates its versatility in capturing complicated interactions, resulting in nuanced insights into mobile phone price factors. This study adds an improved prediction framework for mobile phone price analysis, which is critical for new enterprises looking to gain a competitive advantage in the volatile mobile industry.

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Muddassar Sarfraz mail -

Sana Ullah mail

link https://doi.org/10.54216/AJBOR.110108

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

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International Journal of BIM and Engineering Science |

BARRIERS TO ADOPT LEED IN SYRIA

This study investigates the barriers to Leadership in Energy and Environmental Design (LEED) adoption in Syria, focusing on four barriers: Localizing LEED, Economic Considerations, Regulatory Framework, and Capacity Building. Analyzing data from a sample of 72 respondents, descriptive statistics reveal substantial challenges across all dimensions. Additionally, Pearson correlation coefficients demonstrate significant positive relationships between Economic Considerations and Regulatory Framework. These findings underscore the intricate interplay of economic factors and regulatory frameworks in shaping sustainable construction practices. The study contributes insights crucial for policy adjustments and targeted interventions to overcome barriers, fostering LEED integration in Syria's construction industry and potentially informing global efforts to enhance sustainable building practices.

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International Journal of BIM and Engineering Science |

BARRIERS TO ADOPT LEED IN SYRIA

This study investigates the barriers to Leadership in Energy and Environmental Design (LEED) adoption in Syria, focusing on four barriers: Localizing LEED, Economic Considerations, Regulatory Framework, and Capacity Building. Analyzing data from a sample of 72 respondents, descriptive statistics reveal substantial challenges across all dimensions. Additionally, Pearson correlation coefficients demonstrate significant positive relationships between Economic Considerations and Regulatory Framework. These findings underscore the intricate interplay of economic factors and regulatory frameworks in shaping sustainable construction practices. The study contributes insights crucial for policy adjustments and targeted interventions to overcome barriers, fostering LEED integration in Syria's construction industry and potentially informing global efforts to enhance sustainable building practices.

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International Journal of BIM and Engineering Science |

BARRIERS TO ADOPT LEED IN SYRIA

This study investigates the barriers to Leadership in Energy and Environmental Design (LEED) adoption in Syria, focusing on four barriers: Localizing LEED, Economic Considerations, Regulatory Framework, and Capacity Building. Analyzing data from a sample of 72 respondents, descriptive statistics reveal substantial challenges across all dimensions. Additionally, Pearson correlation coefficients demonstrate significant positive relationships between Economic Considerations and Regulatory Framework. These findings underscore the intricate interplay of economic factors and regulatory frameworks in shaping sustainable construction practices. The study contributes insights crucial for policy adjustments and targeted interventions to overcome barriers, fostering LEED integration in Syria's construction industry and potentially informing global efforts to enhance sustainable building practices.

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American Journal of Business and Operations Research | 2024

Data-Driven Decision Support Systems for Business Process Improvement

The accessibility of data is altering how businesses make decisions at different levels. Scholars and professionals are investigating the ways in which Business Process suppliers can profit from the availability and application of data, particularly in relation to decision-making concerning service provision. Business Process Improvement is one of the applications that is anticipated to gain the most from the accessibility of information. Suppliers of services can avoid failures by making prompt and well-informed decisions based on the evaluation of the resource's health state. Despite this, providing data-driven BPI services is not simple, and providers must set up their systems to correctly gather, process, and utilize past and current data. This study introduces a data-driven business intelligence framework to provide use full insights for improving business process activities. This framework offers a set of visualization tools that help interpret the relation between different factors that can improve the management of different business processes. Moreover, our framework provides successful integration of random forests to allow predictive modeling of sales, profits, and discounts across different regions.

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Betul Aktas mail

link https://doi.org/10.54216/AJBOR.110109

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

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American Journal of Business and Operations Research | 2022

Evaluating the Role of Artificial Intelligence in Operational Decision-Making

In today’s paced and data centric world the integration of Artificial Intelligence (AI) technologies has become a game changer, in industries. However effectively utilizing AI to make informed decisions is still a task due to the complexities of datasets and the need for predictive models. This study aims to explore and evaluate Machine Learning (ML) classifiers such as Gradient Boosting, Light Gradient Boosting Machine (LightGBM) Extreme Gradient Boosting (XGBoost) and stacking classifiers within decision making scenarios. The objective is to assess their effectiveness in handling datasets and gain insights into their performance metrics for improving decision making processes. Comparative analysis of these classifiers reveals strengths and capabilities when applied in decision making contexts. The experimental findings highlight the potential of classifiers Gradient Boosting, in optimizing decision making even in complex situations.

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Abedallah Z. Abualkishik mail -

Rasha Almajed mail

link https://doi.org/10.54216/AJBOR.080106

Volume & Issue

Vol. Volume 8 / Iss. Issue 1

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Journal of Sustainable Development and Green Technology | 2023

Simulating Market Dynamics: Agent-Based Modeling in Operations Research

In the field of Operations Research, the growing popularity of fruits, avocados, in the United States has sparked a need for thorough market analysis. This study aims to use Agent Based Modeling (ABM) principles to understand and predict sales volumes. By using intelligence techniques, the Extra Trees Regressor (ETR) we strive to identify the various factors that influence avocado sales. Our approach involves modeling data within ABM to provide an assessment and comparison, with classifiers. The results clearly demonstrate that ETR outperforms classifiers when it comes to predicting sales volume. Through plots and error prediction curves we can see how this model effectively captures sales patterns in a dynamic market environment. The predictive prowess of the proposed solution is validated through visual evaluation tools including residual plots as well as prediction curves, which prove its adeptness in predicting operational sales patterns within a dynamic market. The findings of our experiments study put emphasis on role of intelligence-based Agent-Based Modeling within Operations Research, exemplified by the Extra Trees Regressor, which offer a reliable tool for elucidating and projecting intricate market trends.

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Abedallah Z. Abualkishik mail -

Rasha Almajed mail

link https://doi.org/10.54216/JSDGT.030204

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

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American Journal of Business and Operations Research | 2024

Predictive Analytics and Machine Learning in Direct Marketing for Anticipating Bank Term Deposit Subscriptions

Direct marketing strategies in the banking sector have undergone evolution with the integration of predictive analytics and machine learning techniques. The focus of this study is on the utilization of these technologies to foresee bank term deposit subscriptions. The methodology encompasses data exploration, visualization, and the implementation of machine learning models. Datasets from Kaggle are employed, relationships within the data are explored through crosstabulations and heat maps, and feature engineering and preprocessing techniques are applied. The study individually implements models such as SGD Classifier, k-nearest neighbor Classifier, and Random Forest Classifier. The results indicate that the best performance among the evaluated models was exhibited by the Random Forest Classifier, achieving an accuracy of 87.5%, a negative predictive value (NPV) of 92.9972%, and a positive predictive value (PPV) of 87.8307%. These findings provide valuable insights for banks seeking to optimize their marketing strategies within the dynamic landscape of the financial industry.

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Ahmed Mohamed Zaki mail -

Nima Khodadadi mail -

Wei Hong Lim mail -

S. K. Towfek mail

link https://doi.org/10.54216/AJBOR.110110

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

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Journal of Artificial Intelligence and Metaheuristics | 2023

An Evaluation of ARIMA and Persistence Models in IoT-Driven Smart Homes

Commencing with the transformative fusion of Smart Home and Internet of Things (IoT) technologies, this study scrutinizes the efficacy of predictive modeling approaches, specifically the autoregressive integrated moving average (ARIMA) and persistence algorithms. The primary focus lies in their potential for forecasting and optimizing energy consumption dynamics within the intricate framework of smart homes. The investigation reveals a nuanced comparison between the proposed ARIMA and conventional Persistence models. Smart Home, emblematic of innovative living, integrates seamlessly with IoT, promising an intelligent and interconnected domestic ecosystem. To enhance energy efficiency, this study explores the ARIMA model's capabilities alongside the persistence algorithm. Notably, the proposed ARIMA model showcases exceptional prowess in forecasting, substantiated by a significantly lower  compared to the Persistence model. The ARIMA model, with an Root Mean Square Error value of 0.03378, outshines the Persistence model with a higher Root Mean Square Error value of 0.158 when evaluated on the test dataset. This substantial reduction in  emphasizes the superior performance of the ARIMA model, making it a compelling choice for time series forecasting tasks. Beyond quantitative metrics, the precision of the ARIMA model holds transformative potential, promising cost-effective energy consumption, proactive maintenance, and an elevated quality of life within smart homes. This research establishes a robust foundation for integrating advanced predictive modeling, particularly the ARIMA model, to enhance the efficiency, sustainability, and inhabitant satisfaction of smart homes in the era of IoT.

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Ahmed Abdelmgeed mail -

Ahmed Mohamed Zaki mail -

Marwa Adel Soliman mail

link https://doi.org/10.54216/JAIM.060201

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new