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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.

groups
Muddassar Sarfraz mail -
Sana Ullah mail
link https://doi.org/10.54216/AJBOR.110108

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new
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.

groups
Betul Aktas mail
link https://doi.org/10.54216/AJBOR.110109

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new
Journal of Neutrosophic and Fuzzy Systems | 2026

Persistent Choquet Fuzzy Evidence for Detecting and Attributing Distribution Drift in Data Streams

Data-stream drift is rarely one-dimensional: a changed stream may move in location, inflate in scale, alter its tail geometry, or differ globally even when no single moment changes decisively. This paper develops a fuzzy monitoring layer for a scalar stream zt ∈ R by comparing adjacent windows At and Bt through four robust evidences dj,t : median displacement, robust log-scale change, interquantile tail-shape change, and normalized one-dimensional transport. Stationary calibration maps each dj,t to a fuzzy grade uj,t ∈ [0,1]. A normalized 2-additive capacity then aggregates the evidence by qt = 4Σ j=1 mjuj,t +Σ j<k mjk min(uj,t ,uk,t ) ∈ [0,1], so pairwise reinforcement is modeled explicitly rather than hidden inside an arithmetic score. Persistence is separated from instantaneous evidence through At = [λAt−1 +qt −δ]+, and an alarm occurs when At ≥ h. The resulting Persistent Choquet Fuzzy Drift Monitor (PCFDM) also admits an exact component decomposition qt = Σj φj,t for drift attribution. A reproducible Monte Carlo study uses 120 independent stationary calibration streams and 220 test replications for each of seven scenarios. At matched stream-wise calibration, PCFDM detects mean, scale, mixed, and gradual drifts in 94.5%, 89.5%, 95.0%, and 92.3% of runs, with median delays 72, 88, 72, and 192 samples. Its transient-shock alarm rate is 40.5%, compared with 49.1% for fuzzy-mean evidence, 76.8% for maximum fuzzy evidence, and 65.9% for transport alone. Heavy-tail drift remains more difficult (48.2% detection), revealing a genuine trade-off between persistent multi-evidence confirmation and sensitivity to isolated shape changes. The contribution is therefore a mathematically decomposable fuzzy evidence mechanism for monitoring and explaining drift, not a claim of universal dominance over specialized change detectors.

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Rama Asad Nadweh mail
link https://doi.org/10.54216/JNFS.110202

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new
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

Details open_in_new
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.

groups
Abedallah Z. Abualkishik mail -
Rasha Almajed mail
link https://doi.org/10.54216/JSDGT.030204

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new
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

Details open_in_new
Journal of Neutrosophic and Fuzzy Systems | 2026

A Neutrosophic Layer for Fuzzy c-Means Clustering: Score Function Theory, Metric Properties, and Diagnostic-Ambiguity Quantification on Breast Cancer Data

Fuzzy c-means clustering assigns every data point a degree of membership to each cluster but offers no separate account of how ambiguous that assignment is. This paper builds a single-valued neutrosophic layer on top of the classical fuzzy c-means membership distribution, representing each point by a truth-membership Ti (its strongest cluster membership), an indeterminacy Ii (the normalized Shannon entropy of its full membership vector), and a falsity-membership Fi = 1−Ti. Four results are proved: the fuzzy c-means update equations are re-derived from the Lagrangian stationarity conditions of the underlying constrained optimization; the resulting (Ti, Ii,Fi) triplet is shown to be bounded and to attain its extremes exactly at crisp and maximally ambiguous membership distributions; a score function combining the three components is shown to be strictly monotone in each; the natural root-mean-square distance between two neutrosophic triplets is shown to satisfy the metric axioms; and, for the two-cluster case specifically, indeterminacy is proved to be an exact deterministic function of truth-membership, so that a third, genuinely independent source of information requires three or more clusters. Every result is checked numerically, including a direct verification of the two-cluster degeneracy result to floating-point precision. Applied to the Breast CancerWisconsin Diagnostic dataset (569 cases, 30 measured features), the clustering recovers the malignant/benign partition with 91.4% accuracy and an adjusted Rand index of 0.683, matching a hard k-means baseline on point accuracy; the neutrosophic layer nonetheless adds diagnostic information the hard baseline cannot provide, since indeterminacy is significantly higher for misclassified cases than for correctly classified ones (Mann–Whitney U-test, p < 10−18), correctly flagging the cases nearest the decision boundary as the ones most likely to be wrong.

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Takaaki Fujita mail
link https://doi.org/10.54216/JNFS.110104

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new
Journal of Neutrosophic and Fuzzy Systems | 2026

Dual-Scale Fuzzy Boundary Abstention for Selective Prototype Classification

A classifier can be accurate on average and still be unreliable near regions in which competing classes overlap. We study this problem as selective fuzzy classification: for x ∈ Rd, the decision is either a label by(x) ∈ {1, . . . ,K} or abstention ⊥. The proposed dual-scale fuzzy boundary index (DFBI) decomposes local ambiguity into a distributed term Bmass(x) and an extremal term Bpress(x). The former quantifies similarity-weighted contradictory neighborhood mass, whereas the latter compares the strongest opposing and supporting fuzzy relations. Their geometric fusion qDFBI(x) = {Bmass(x)Bpress(x)}1/2 ∈ [0,1] induces the selective map gθc (x) = 1{qDFBI(x) ≤ θc}, where the empirical validation quantile θc targets coverage c. The analysis is entirely numerical rather than graphical and uses repeated stratified splits, selective accuracy, macro-F1, error capture, relative risk reduction, AURC, AUGRC, paired bootstrap intervals, ablation, neighborhood sensitivity, and a nonlinear stress test. At nominal c = 0.90, the mean selective-accuracy vector is ¯a = (0.9354,0.9882,0.9847,0.9744) for Iris, Wine, Breast Cancer, and Digits, while the corresponding error-capture vector is ¯e=(0.5726,0.7528,0.8093,0.7937). Relative to membership-margin uncertainty, Δa = (0.0211,0.0022,0.0194,0.0446). Thus the gain is not obtained by changing the base classifier f ; it follows from a local fuzzy acceptance policy ( f ,gθc ) whose uncertainty ordering is informed by boundary geometry.

groups
Ajoy Kanti Das mail -
Suman Das mail
link https://doi.org/10.54216/JNFS.110201

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new
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
Financial Technology and Innovation | 2023

Time Series Forecasting of Cryptocurrency Prices with Long Short-Term Memory Networks

The rapid evolution of cryptocurrencies has brought transformative changes to the financial landscape. Cryptocurrency prices, characterized by their inherent volatility, pose challenges for precise forecasting. This study introduces a novel approach to cryptocurrency price forecasting, leveraging Long Short-Term Memory (LSTM) networks, known for discerning temporal dependencies within time series data. Motivated to enhance prediction accuracy, this research investigates the effectiveness of LSTM networks in capturing complexities inherent in cryptocurrency price movements. The proposed methodology involves meticulous data collection and preprocessing, utilizing an extensive dataset from Kaggle. This dataset forms the foundation for predictive modeling and facilitates an in-depth analysis of cryptocurrency price dynamics. Exploratory data analysis, including visualization techniques, and a dedicated Time Series Analysis precede the implementation of predictive models, such as LSTM networks. Results and evaluation showcase promising outcomes, emphasizing the models' precision, accuracy, and explanatory power. The Mean Absolute Error (MAE) of 0.0177 underscores the precision achieved in predicting cryptocurrency prices, while the Mean Squared Error (MSE) of 0.00066 and the R² Score of 0.9486 attest to our models' overall accuracy and explanatory power. This research significantly contributes to understanding cryptocurrency forecasting by incorporating LSTM networks, paving the way for advancements in this evolving domain.

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El-Sayed M. El-Kenawy mail -
Abdelaziz A. Abdelhamid mail -
Abdelhameed Ibrahim mail -
Marwa M. Eid mail -
Faris H. Rizk mail -
Ahmed Mohamed Zaki mail
link https://doi.org/10.54216/FinTech-I.020202

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new