Decoding Consumer Behaviour: Leveraging Big Data and Machine Learning for Personalized Digital Marketing DOI Creative Commons
Anber Abraheem Shlash Mohammad, Sulieman Ibraheem Shelash Al-Hawary,

Badrea Al Oraini

et al.

Data & Metadata, Journal Year: 2025, Volume and Issue: 4, P. 700 - 700

Published: Feb. 11, 2025

IntroductionBig data analytics and machine learning have transformed digital marketing by enabling data-driven insights for personalization. This study investigates the role of engagement metrics, sentiment analysis, consumer segmentation in enhancing effectiveness. Specifically, it examines how these technologies process interaction to uncover actionable insights, segment audiences, drive purchase conversions.MethodThe employed a mixed-methods approach, integrating big techniques. Descriptive statistics highlighted patterns, while k-means clustering segmented consumers based on behavioural emotional data. Sentiment conducted using Natural Language Processing (NLP), captured emotions as positive, neutral, or negative. Regression analysis evaluated influence social media activity, click-through rates, session duration, scores conversion rates.ResultsDescriptive revealed significant variability sentiment, with 37.5% expressing positive sentiment. Clustering identified three distinct segments, reflecting differences showed that had but statistically insignificant relationship conversions, other such rates exhibited minimal impact. The overall explanatory power regression model was low (R-squared = 0.001), indicating need additional factors understand behaviour.ConclusionThe findings emphasize potential analysis. However, their direct impact is limited without broader variables. A holistic, adaptive framework combining behavioural, emotional, contextual essential maximizing personalization driving outcomes dynamic environments.

Language: Английский

Evidence Detection in Cloud Forensics: Classifying Cyber-Attacks in IaaS Environments using machine learning DOI Creative Commons
Suhaila Abuowaida, Hamza Abu Owida, Sulieman Ibraheem Shelash Al-Hawary

et al.

Data & Metadata, Journal Year: 2025, Volume and Issue: 4, P. 699 - 699

Published: Feb. 10, 2025

Introduction: Cloud computing is considered a remarkable paradigm shift in Information Technology (IT), offering scalable and virtualized resources to end users at low cost terms of infrastructure maintenance. These offer an exceptional degree flexibility adhere established standards, formats, networking protocols while being managed by several management entities. However, the existence flaws vulnerabilities underlying technology outdated opens door for malicious network attacks.Methods: This study addresses these introducing method classifying attacks Infrastructure as Service (IaaS) cloud environments, utilizing machine learning methodologies within digital forensics framework. Various algorithms are employed automatically identify categorize cyber-attacks based on metrics related process performance. The dataset divided into three distinct categories—CPU usage, memory disk usage—to assess each category’s impact detection systems.Results: Decision Tree Neural Network models recommended analyzing disk-related features due their superior performance detecting with accuracy 90% 87.9%, respectively. deemed more suitable identifying CPU behavior, achieving 86.2%. For memory-related features, K-Nearest Neighbor (KNN) demonstrates best False Negative Rate (FNR) value 1.8%.Discussion: Our highlights significance customizing selection classifiers specific system feature intended focus detection. By tailoring particular activities IaaS environments can be enhanced, practical insights effective attack classification.

Language: Английский

Citations

0
Enhancing Metadata Management And Data-Driven Decision-Making In Sustainable Food Supply Chains Using Blockchain And AI Technologies DOI
Anber Abraheem Shlash Mohammad, Ammar Mohammad Al-Ramadan, Sulieman Ibraheem Shelash Al-Hawary

et al.

Data & Metadata, Journal Year: 2025, Volume and Issue: 4, P. 683 - 683

Published: Feb. 14, 2025

Sustainability in food supply chains is a critical global challenge, particularly resource-constrained regions like Jordan, where operational inefficiencies and environmental concerns are prevalent. This study explores the integration of blockchain artificial intelligence (AI) technologies to enhance metadata management, forecast sustainability metrics, support decision-making Jordan’s chains. Blockchain's ability improve accuracy, standardization, traceability, combined with AI’s predictive capabilities, offers powerful solution for addressing challenges.MethodsThe research employed mixed-methods approach, combining real-time data from transaction logs, AI-generated forecasts, stakeholder surveys. Blockchain platforms Hyperledger Fabric Ethereum provided insights into accuracy traceability. AI models were developed using machine learning techniques, such as linear regression, waste reduction, carbon footprint energy efficiency. Multi-Criteria Decision Analysis (MCDA), AHP TOPSIS, was applied evaluate trade-offs among goals.ResultsThe results revealed significant improvements (from 83% 96.66%) reductions traceability time 4.0 2.35 hours) following implementation. demonstrated high explaining 88%, 81%, 76% variance efficiency, respectively. ConclusionThis underscores transformative potential achieving goals. By fostering transparency, insights, data-driven decision-making, these innovations can address key challenges chains, offering actionable strategies stakeholders.

Language: Английский

Citations

0
Machine Learning Models for Predicting Employee Attrition: A Data Science Perspective DOI
Anber Abraheem Shlash Mohammad,

Zeyad Alkhazali,

Sulieman Ibraheem Shelash Al-Hawary

et al.

Data & Metadata, Journal Year: 2025, Volume and Issue: 4, P. 669 - 669

Published: Feb. 27, 2025

Introduction: Employee attrition poses significant challenges for organizations, impacting productivity and profitability. This study explores patterns using machine learning models, integrating predictive analytics with established human resource theories to identify key drivers of workforce turnover. Methods: The research analysed a dataset comprising demographic, job-related, engagement factors. Logistic Regression was employed as the baseline model interpret linear relationships, while Random Forest Decision Trees captured non-linear interactions. Performance metrics such accuracy, precision, recall, F1-score, AUC-ROC were used evaluate effectiveness, alongside feature importance analysis actionable insights. Results: Results revealed that job satisfaction, tenure, departmental dynamics, levels are critical predictors attrition. emerged most effective model, achieving an accuracy 92% 94%, highlighting its capability capture complex patterns. provided interpretable decision rules, offering practical thresholds HR interventions. complemented these models by insights into direct, relationships between Conclusion: finds improves identifying enabling proactive retention strategies. Predictive strengthens traditional theories, providing structured approach reducing employee Organizations can use enhance stability performance. Future could incorporate qualitative methods longitudinal studies refine strategies assess long-term impacts.

Language: Английский

Citations

0
Riding into the Future: Transforming Jordan’s Public Transportation with Predictive Analytics and Real-Time Data DOI
Anber Abraheem Shlash Mohammad, Sulieman Ibraheem Shelash Al-Hawary,

Khaleel Ibrahim Al‐ Daoud

et al.

Data & Metadata, Journal Year: 2025, Volume and Issue: 4, P. 887 - 887

Published: April 4, 2025

Introduction: This study explores how predictive analytics and real-time data integration can improve efficiency in Jordan’s public transportation network. By addressing scheduling, route optimization, congestion management, it responds to growing urban transit demands the region.Methods: Data were collected over three months from official ridership logs, GPS-enabled buses, traffic APIs. ARIMA-based time-series forecasting captured historical trends, while a Random Forest model incorporated index, average wait times, other operational variables. Metadata management protocols (JSON/XML) facilitated cross-agency sharing.Results: ARIMA proved effective for short-term passenger demand projections, although occasionally underpredicted sudden peaks. The approach yielded stronger overall accuracy, explaining roughly 85% of variation when combining with records. Real-time streams further supported dynamic scheduling adjustments.Conclusion: Combining models IoT-based enhance reliability user satisfaction system. Although limited by timeframe scope, findings underscore importance multi-agency collaboration ongoing policy support sustain data-driven innovations.

Language: Английский

Citations

0
Data-Driven Decision-Making for Employee Training and Development in Jordanian Public Institutions DOI

Nancy Shamaylah,

Sulieman Ibraheem Shelash Al-Hawary, Badrea Al Oraini

et al.

Data & Metadata, Journal Year: 2025, Volume and Issue: 4, P. 886 - 886

Published: April 4, 2025

Introduction: AI-driven training and HR analytics have revolutionized employee development by offering personalized learning experiences optimizing skill enhancement. Public institutions are increasingly leveraging AI-based recommendations adaptive algorithms to improve workforce training. However, the effectiveness challenges of these approaches in real-world applications require further investigation.Methods: This study employed a descriptive analytical research design, utilizing both quantitative qualitative methods. Data was collected from 385 employees Jordanian public using structured surveys sentiment analysis feedback. Statistical techniques, including regression analysis, ANOVA, correlation were applied assess impact data analytics, recommendations, personalization on effectiveness.Results: The findings indicate that significantly effectiveness. Skill emerged as strongest predictor success (β = 0.7282, p < 0.001). Sentiment revealed 82% responded positively training, while 10% expressed concerns about content relevance interactivity. ANOVA results confirmed no significant differences across job roles, indicating equitable experiences.Conclusion: AI-powered is widely accepted but requires refinement address engagement concerns. Organizations should adopt hybrid approach, integrating with instructor-led guidance. Future explore long-term impacts performance organizational enhance digital strategies.

Language: Английский

Citations

0
Customer Sentiment Analysis for Food and Beverage Development in Restaurants using AI in Jordan DOI
Anber Abraheem Shlash Mohammad, Ammar Mohammad Al-Ramadan, Sulieman Ibraheem Shelash Al-Hawary

et al.

Data & Metadata, Journal Year: 2025, Volume and Issue: 4, P. 922 - 922

Published: April 20, 2025

Introduction: customer sentiment analysis is a vital tool for understanding consumer preferences and enhancing service quality in the food beverage industry. Online reviews significantly influence decisions, making it essential businesses to analyze trends manage their digital reputation effectively. This study examines across different establishment types platforms Jordan, providing insights into patterns strategic implications.Method: dataset of 384 from various restaurants hotels was analyzed using rule-based classification approach. Sentiments were categorized as positive, neutral, or negative. To assess variations, an ANOVA test conducted compare types, Chi-Square performed examine differences platforms.Results: findings indicate that luxury fine dining establishments receive more positive sentiment, while budget fast chains experience higher negative sentiment. However, showed no statistically significant suggesting all mix categories. The confirmed platforms, with TripAdvisor attracting most reviews, Facebook Google Reviews showing balanced Twitter experiencing highest sentiment.Conclusion: these emphasize importance platform-specific management. Businesses should strategically engage customers on address complaints proactively, utilize AI-driven tools improve satisfaction. Future research explore AI-based predictive analytics monitoring hospitality

Language: Английский

Citations

0
Decoding Consumer Behaviour: Leveraging Big Data and Machine Learning for Personalized Digital Marketing DOI Creative Commons
Anber Abraheem Shlash Mohammad, Sulieman Ibraheem Shelash Al-Hawary,

Badrea Al Oraini

et al.

Data & Metadata, Journal Year: 2025, Volume and Issue: 4, P. 700 - 700

Published: Feb. 11, 2025

IntroductionBig data analytics and machine learning have transformed digital marketing by enabling data-driven insights for personalization. This study investigates the role of engagement metrics, sentiment analysis, consumer segmentation in enhancing effectiveness. Specifically, it examines how these technologies process interaction to uncover actionable insights, segment audiences, drive purchase conversions.MethodThe employed a mixed-methods approach, integrating big techniques. Descriptive statistics highlighted patterns, while k-means clustering segmented consumers based on behavioural emotional data. Sentiment conducted using Natural Language Processing (NLP), captured emotions as positive, neutral, or negative. Regression analysis evaluated influence social media activity, click-through rates, session duration, scores conversion rates.ResultsDescriptive revealed significant variability sentiment, with 37.5% expressing positive sentiment. Clustering identified three distinct segments, reflecting differences showed that had but statistically insignificant relationship conversions, other such rates exhibited minimal impact. The overall explanatory power regression model was low (R-squared = 0.001), indicating need additional factors understand behaviour.ConclusionThe findings emphasize potential analysis. However, their direct impact is limited without broader variables. A holistic, adaptive framework combining behavioural, emotional, contextual essential maximizing personalization driving outcomes dynamic environments.

Language: Английский

Citations

0