Journal of Artificial intelligence and Machine Learning

Automated Label Detection and Recommendation System Using Deep Convolution Neural Networks Using ANOVA

Sudhakara Reddy Peram — 2025-12-15

An automated label identification and recommendation system using deep convolutional neural networks, understanding user perceptions regarding the reliability, accuracy, and interpretability of the computer system is essential for developing robust and explainable AI solutions. This study contributes to this need by exploring several performance dimensions, including accuracy, contextual relevance, clarity, dataset quality, sensitivity, specificity, recall rate, false positive rate, and explain ability. Using a cross-sectional survey design, data were collected from respondents via Google Forms in November-December 2025. One-way ANOVA and two-way ANOVA (p < 0.05) were used with IBM SPSS to explore the relationships between socio-demographic variables and perceived performance metrics. The findings indicate consistent perceptions across experience levels and model types, while the model output format significantly impacted label accuracy and dataset quality. The results emphasize the importance of output representation in improving automated labeling performance and reliability, suggesting directions for future system optimization and validation frameworks.

Time Series Forecasting Mean Sea Level in Ocean City, Maryland Using a Neural Network Autoregressive Model

Yeong Nain Chi — 2025-12-19

The accelerating rise in sea levels poses a significant challenge for coastal communities, necessitating accurate forecasting methods. This study evaluates the efficacy of various time series models in predicting long-term sea level changes, including ARIMA, ETS, NNETAR, THETAM, TBATS, STLM, and their hybrid combinations. Using monthly mean sea level data from Ocean City, Maryland, spanning August 2002 to February 2025, a comparative analysis was conducted. The NNAR(24,1,12)[12] model emerged as the most accurate, performing exceptionally well across all metrics, particularly with very low RMSE and MAE values among all tested models. These findings underscore the potential of neural network-based approaches in sea level forecasting and highlight the importance of integrated modeling techniques as decision-support tools for local mean sea level predictions. Understanding historical sea level trends is crucial for improving future projections, and this study contributes to that knowledge base. Continued research efforts leveraging these data-driven insights can significantly enhance our ability to refine predictions and develop effective strategies to mitigate the impacts of sea level rise.

AI-Based Analysis of Post-Surgical Patient Data for Risk Stratification After Total Knee Arthroplasty

Naidu Paila — 2025-12-15

Post-surgical care generates large volumes of heterogeneous data, but most health information systems rely on sparse measurements collected at scheduled intervals. As a result, early deviations in recovery trajectories are often difficult to detect. Here, a data analytics framework is described for analyzing longitudinal post-surgical data, using total knee arthroplasty (TKA) as a representative use case.

Systematically gathered electronic health record data, patient-reported outcomes, and activity assessments were utilized to delineate recovery trajectories and ascertain deviations correlated with unfavorable outcomes.Interpretable machine learning models were trained and evaluated on prospectively collected data from 1,000 patients to estimate the probability of outcome events based on temporal patterns observed across multiple data streams.

The model exhibiting the highest efficacy attained an area under the receiver operating characteristic curve quantified at 0.896, demonstrating consistent performance across various validation folds. An examination of the model's features revealed that longitudinal alterations had a more significant impact on predictive efficacy compared to discrete measurements, thereby highlighting the importance of temporal modeling.

Data Marketplace Implementation: A Cross-Industry Analysis of Critical Success Factors and Organizational Performance Outcomes

Praveen Kumar Kanumarlapudi — 2025-12-05

Data marketplace implementation leverages customer data through advanced AI/ML tools to create personalized marketing strategies, integrating diverse data sources for audience segmentation, campaign optimization, and continuous performance measurement to enhance engagement and ROI.This study addresses critical gaps in understanding how organizations overcome data marketplace implementation barriers, including unrealistic vendor expectations, post-implementation management challenges, and internal data utilization inefficiencies, providing strategic insights for improved marketing performance.Primary data from 624 respondents across IT/Software, Manufacturing, Healthcare, Finance/Banking, and Retail sectors was collected using structured questionnaires with five-point Likert scales. The study examined eight dependent variables—development cost suitability, real-time support, market performance improvement, technological uncertainty, data quality, portability, security, and accessibility across various organizational roles, experience levels, firm sizes, and data marketplace models including centralized, decentralized block Chain-based, federated, subscription-based, and pay-per-use systems.Statistical analysis revealed in the reliability analysis, the scale items were found to have acceptable internal consistency (Cronbach's alpha = 0.76), moderate mean perceptions (1.88-2.67), and significant positive correlations among variables, particularly between data quality and market performance improvement (r = 0.492), with all variables achieving statistical significance (p < 0.001).Organizations require comprehensive strategies addressing interconnected factors cost, security, quality, accessibility simultaneously. Decentralized block Chain-based and centralized models dominate adoption. Future research should examine longitudinal patterns and sector-specific success factors for optimizing data marketplace implementation and maximizing ROI.