Capella University

Automated diagnosis of cancer using digital histopathological images is a critical frontier in modern medicine. The dataset of 25,000 histological images of lung and colon cancer (LC25000), which are categorized into five distinct and uniformly distributed classes, presents the multi-class classification as a difficult challenge: Lung Adenocarcinoma, Lung Benign Tissue, Lung Squamous Cell Carcinoma, Colon Adenocarcinoma, and Colon Benign Tissue. We propose a solution with Deep Transfer Learning in EfficientNetB0, with a custom classification head and trained for 10 epochs. The model did well in an independent 10% test set of 2,500 images, providing a true Test Accuracy and a perfect F1-score for every class. A Stratified Cross-Validation of 5-fold is performed for reliability and generalizability, with an average Validation accuracy of 99%. On top of that, while 9 of the popular CNN models were evaluated in an analysis, EfficientNetB0 was the best performer. Grad-CAM imaging tests validate the model's interpretability. This validated methodology, in contrast to the results obtained by ensemble or multi-scale attention models, is a highly accurate, stable, and interpretable automated diagnostic tool that demonstrates its utility for immediate clinical evaluation.

Detecting wheat diseases is crucial for global food security. Yellow rust, leaf rust, powdery mildew, etc. reduce the amount and quality of the crop. All these are fungal and foliar infections. Conventional visual inspection processes are inefficient, incorrect, and unsuitable for large-scale agricultural monitoring. To solve the issue, the present paper proposed an automated wheat disease classification that uses a deep transfer learning (DTL) with Support Vector Machine (SVM) Classifier and utilizes Binary Barber Optimization Algorithm (BBOA) for optimal features selection. The three state-of-the-art deep learning architectures i.e., EfficientNet (EN), ResNet (RN), and GoogLeNet (GN) were explored individually as well as in hybrid combination with BBOA metaheuristic algorithm. The experimental results indicate that BBOA integration allows better feature discrimination as well as a reduction in model overfitting. The accuracy of EN+BBOA is 98.53%, RN+BBOA is 98.01%, and GN+BBOA increased significantly from 92.82% to 97.88% As per maximum performance of Ensemble CNN + BBOA attained 99.33%, 99.39%, 99.31%, and 99.35% for accuracy, precision, recall, and F1-score respectively. The results show that combining CNN-based feature extraction with a metaheuristic optimizer creates a more robust, accurate and computationally efficient diagnostic strategy. The hybrid model proposed is an effective scalable approach for intelligent crop disease monitoring under varying environmental and biological conditions.

A multi-agent GenAI architecture is introduced to support the automation of ethical cloud security, solving the problem of scalability or adaptability, and compliance in dynamic cloud environments. This framework combines dedicated generative agents such as policy analysts, threat detectors, remediation organizers, and auditor agents which interact via common knowledge graph and can be explained by a decision log. The agents utilize context-based prompt generation, generation constraints, provenance management, and generation to generate security policies, anomaly detection, provide automated mitigation, and maintain human-in-the-loop control. Some of the ethical protections are bias audits, privacy-sensitive learning, least-privilege enforcement, and policy verifiability, to warrant the correctness of the decisions taken in compliance with regulatory or organizational limits. Testing with representative cloud work lines shows that there is shorter energy on discerning and correcting occurrences, elevated coverage of controls and signs when the automated actions are traced. The framework enables adjustable levels of trust and escalation measures to accommodate the autonomy versus governance. The method fosters usage of GenAI to deal with cloud security by offering modular agents, verifiable ethics controls, metrics-based assessment, hence fostering responsible automation that is also transparent, auditable, and considers changing threats.

The U.S. healthcare system is fundamentally more than just hospitals and prescriptions; it’s a complex network of interconnected industries with diverse lineages — including payers, providers, pharma, med device manufacturers, digital health platforms, and regulatory agencies— all revolving around the patient. This study proposes a complete beginner's introduction for how these all fit into the layers of the technical stack (a la the OSI model) including front-end systems, operations logic and programmatic representation, data integration, and analytics. Quality is the cornerstone of providing good health service, and includes six key dimensions such as safety, effectiveness, patient-centeredness, timeliness, efficiency and equity. However, quality principles can seem abstract and esoteric — until you see how they're brought to life with real-world examples: for instance, surgical checklists driving down infections 47 percent; using bar code medication administration to cut errors by 80 percent; or chronic disease registries enhancing care co-ordination. Using a closed loop risk prediction model to identify high-risk patients based on demographic, mental health and chronic disease determinants: how will it impact on our emergency department when we implement this approach? U.S. healthcare system spends more than $5 trillion a year but still struggles with affordability, accessibility and fragmentation. Understanding ecosystem complexity, prioritizing quality measures (eg, HEDIS, CMS STAR ratings), and adopting patient-centered care models will help transform fragmented care into coordinated value-based health that enhances outcomes for all members. Using the new indicators proposed in this paper, a Composite Healthcare Quality Index of 72.1 (out of 100) was obtained. The index of effectiveness was the highest and indicated the greatest potential for quality improvement for the index of timeliness and of equity. The emergency department (ED) utilization model developed for this study had an accuracy of 87% in its predictions, and the greatest number of instances of high ED use were for individuals with mental health conditions.

Dissertation students face numerous challenges, including narrowing down research topics, managing scholarly writing, and overcoming writer’s block. This article explores the creation and piloting of collaborative virtual spaces designed to support dissertation mentees in their academic journey. Through this project, three distinct virtual spaces emerged, each addressing specific student needs: emotional support, academic writing, and research alignment. The development of these spaces was influenced by theory as well as workplace cultural initiatives in an online program for dissertation mentoring. Creating these collaborative virtual spaces based on student needs led to transformative best practices in virtual learning environments. This method provides structured environments tailored to different aspects of the dissertation process. The emotional support space mitigates feelings of isolation and offers vital emotional support, fostering a supportive community for sharing experiences and building resilience. The writer's room enhances academic writing skills and helps mentees write in an academically honest fashion. The researcher's room focuses on the development and refinement of research methodologies, promoting the scientific merit of the study, and aiding in research alignment. By utilizing these virtual learning spaces, higher education institutions can address the multifaceted challenges faced by dissertation students. This pilot project highlights the potential benefits for both students and faculty, advocating for the implementation of these spaces as a best practice in higher education to promote a growth mindset. Recommendations for setting up these spaces and identifying outcomes and indicators of student success are presented.