Judson University is a private evangelical university in Elgin, Illinois. It was founded in 1913. Judson was formed out of the liberal arts component of Northern Baptist Theological Seminary. When the seminary moved from Chicago to Lombard, Illinois, it was decided to make the college separate from the seminary. Originally known as Judson College, it was named after Adoniram Judson, the first American Baptist missionary to foreign shores. The university has campuses in Elgin and Rockford, Illinois, and a student body of approximately 1,300. Judson College became Judson University on August 28, 2007.
The problem regarding plastic waste requires ingenious solutions to make recycling more efficient and less environmentally harmful. Traditional methods of sorting plastic materials have several disadvantages; most rely on manual effort or low-degree automation regarding speed, accuracy, and scalability. Thus, this paper presents an innovative sorting system that employs IoT, generative AI, and machine learning to sort plastic waste in real-time. It manifests itself in a system where IoT sensors capture detailed waste characteristics, which later advanced AI algorithms sort into highly particularized categories of plastics. Generative AI models raise sorting accuracy by generating synthetic datasets to handle the variation within plastic waste forms, enhancing classification models’ robustness. Real-time data processing acts for immediate automated decision-making, thus considerably reducing the time taken in sorting and operational costs. The results indicated that the proposed system increases sorting efficiency by 30
The rapid growth of medical imaging data and shortage of expert clinicians have created a critical need for automated, accurate, and interpretable diagnostic systems. This study proposes a unified deep learning framework for early disease identification from multi-modal medical images, including X-rays, MRI, CT scans, and histopathological slides. The approach integrates a hybrid CNN–Transformer architecture to effectively capture both local spatial features and global contextual dependencies, enhancing diagnostic performance. To address the "black-box" limitation of deep learning models, explainable artificial intelligence techniques such as Grad-CAM and SHAP are incorporated to provide visual and interpretable insights into model predictions. Transfer learning and data augmentation strategies are employed to overcome limited annotated medical datasets and improve generalization. Experimental results demonstrate that the proposed framework achieves high accuracy, sensitivity, and specificity across multiple disease detection tasks, outperforming existing baseline models. Furthermore, the generated explainability maps enhance clinical trust by highlighting diagnostically relevant regions. The findings emphasize the potential of explainable deep learning systems as scalable, reliable, and ethically aligned solutions for next-generation computer-aided diagnosis.
This study addresses the growing need for proactive healthcare by leveraging artificial intelligence (AI) and machine learning (ML) for early disease detection and enhanced clinical decision support. Traditional diagnostic approaches are often reactive, identifying diseases only after symptoms appear, which limits treatment effectiveness. To overcome this limitation, the proposed framework integrates heterogeneous healthcare data—including electronic health records, medical imaging, genomic data, and real-time patient monitoring—into a unified predictive analytics pipeline. The methodology employs an ensemble learning approach combining Random Forest, XGBoost, and Deep Neural Networks to improve predictive performance and robustness. Experimental evaluation across cardiovascular diseases, oncology, and diabetes demonstrates superior performance, achieving 94.78% accuracy and an AUC-ROC of 0.951, significantly outperforming conventional models. Additionally, SHAP-based interpretability enhances transparency and clinician trust in AI-driven predictions. The study also highlights key challenges such as data heterogeneity, privacy concerns, and algorithmic bias, while proposing strategies for ethical and scalable deployment. Overall, the proposed system enables a shift from reactive to proactive, precision-based healthcare, improving early diagnosis, optimizing treatment decisions, and enhancing patient outcomes.
The future of sustainable business development is increasingly based on an organization understanding the mood of the customers and just being able to forecast the behavioral patterns within changing digital settings. The aforementioned opportunities and challenges of the data-driven decision-making can be attributed to the exponential growth of user-generated content in social media, e-commerce, and customer service systems. This article is a proposal of an AI-based system which combines sentiment analysis and behavioral modelling through deep learning and explainable decision support to deliver actionea and interpretable insights into customer preferences and engagement. The framework processes textual and transactional data of heterogeneous nature, it extracts emotional and behavioral dynamics and integrates these findings to anticipate churn, streamline marketing sales, and improve retaining customers. Experimental testing with benchmark databases and actual customer-interaction data shows that the proposed framework is better than the traditional sentiment and behavioral ones as it is more accurate, provides better predictive capabilities, and can yield tangible business-related gains such as higher retention, lower churn rates, and cost-effective engagement. The findings confirm the framework as a useful mechanism to facilitate sustainable, customer-focused business practices and transparency and confidence in AI-based decision-making.
Employee well-being has been always strategic priority for organizations due to its direct correlation with organizational performance and productive. However, hydration—despite its established relationship with cognitive performance, alertness, and fatigue—remains critically underrepresented in corporate wellness initiatives. Previous research has shown that mild dehydration is correlated with decreased focus, heightened fatigue, and lower self-reported productivity in workplace settings [1] [2] [3]. Because knowledge workers operate under sustained cognitive demands in desk-bound environments, unmanaged dehydration poses a meaningful but overlooked risk to employee health and performance.Smart hydration technologies—including IoT-enabled water bottles with sensor-driven fluid intake tracking, behavioral reminders, and real-time feedback loops— represent a scalable, technology-enabled pathway to address this performance gap [4]–[6]. However, whether these technologies can meaningfully encourage consistent water intake and reduce fatigue among corporate employees in real workplace conditions remains underexplored. This study directly addresses the existing gap by investigating whether the utilization of smart water bottles converts the recognized advantages of hydration into quantifiable productivity results within office environments.Employing Social Cognitive Theory (SCT) [7] as its theoretical framework, this investigation implemented a quantitative, two-group pre–post intervention design involving 50 full-time corporate employees. The participants were divided into an intervention group (n=25), which utilized a smart water bottle for a duration of four to six weeks, and a control group (n=25), which maintained its standard routines without the smart water bottle. Data were collected from two quantitative sources: objective IoT sensor data, which recorded fluid intake along with corresponding time stamps, and validated pre/post surveys designed to assess hydration awareness, perceived weariness, cognitive focus, and self-reported productivity. Descriptive statistics, paired-sample t- tests, and independent samples t-tests will be employed to analyze intra-group changes and inter-group differences regarding hydration behavior, weariness, and self-reported productivity results. This study will be conducted in accordance with Institutional Review Board (IRB) ethical guidelines, ensuring voluntary participation, informed consent, and data anonymization.This study presents three unique contributions: Theoretical — anchoring smart hydration research in Social Cognitive Theory to connect IoT behavioral feedback with self-efficacy and productivity results; Methodological — implementing a two-group quantitative design that integrates IoT sensor data with survey instruments; and Practical — offering evidence-based recommendations for HR decision-makers regarding the deployment of smart water bottles to mitigate fatigue and enhance workplace productivity. Future investigations should employ Structural Equation Modeling (SEM) with larger samples to evaluate the complete causal chain of Social Cognitive Theory (SCT).
