Agentic AI, an emerging paradigm in artificial intelligence, refers to autonomous systems designed to pursue complex goals with minimal human intervention. Unlike traditional AI, which depends on structured instructions and close oversight, Agentic AI demonstrates adaptability, advanced decision-making capabilities and self-sufficiency, enabling it to operate dynamically in evolving environments. This survey thoroughly explores the foundational concepts, unique characteristics, and core methodologies driving the development of Agentic AI. We examine its current and potential applications across various fields, including healthcare, finance, and adaptive software systems, emphasizing the advantages of deploying agentic systems in real-world scenarios. The paper also addresses the ethical challenges posed by Agentic AI, proposing solutions for goal alignment, resource constraints, and environmental adaptability.We outline a framework for safely and effectively integrating Agentic AI into society, highlighting the need for further research on ethical considerations to ensure beneficial societal impacts. This survey serves as a comprehensive introduction to Agentic AI, guiding researchers, developers, and policymakers in engaging with its transformative potential responsibly and creatively.

This paper introduces a framework that integrates fairness and transparency into advanced machine learning models, specifically LightGBM and XGBoost, applied to loan approval and house price prediction datasets. The key contribution is using fairness-focused techniques, such as Calibrated Equalized Odds and Intersectional Fairness, which are not widely studied in financial and real estate contexts. To improve model transparency, SHAP (SHapley Additive exPlanations) is utilized along with a novel fairness-based interpretability method to measure both model fairness and the importance of individual features. Through comprehensive experiments, we show that LightGBM delivers high accuracy while balancing fairness and performance effectively. The broader relevance of this work is discussed in the context of governance and regulatory requirements, highlighting the importance of responsible practices in high-stakes financial decision-making processes. This research highlights the importance of fairness and transparency in real-world applications, promoting equity, trust, and adherence to evolving legal standards, and provides practical insights for data scientists, machine learning researchers, and professionals in the real estate and financial sectors.

This paper provides a comprehensive survey on the applications, challenges, and future directions of Artificial Intelligence (AI) in the insurance and real estate sectors. We explore key AI-driven solutions, such as advanced risk assessment, predictive analytics for fraud detection, and smart building management, highlighting their impact on enhancing operational efficiency and decision-making processes. The survey covers a wide range of AI techniques, including machine learning, deep learning, and natural language processing, and discusses their specific applications within these industries. We address critical challenges, such as data quality issues, the need for model interpretability, regulatory compliance, and integration with existing systems. Additionally, we identify emerging trends, such as the adoption of reinforcement learning for dynamic pricing and the use of AI for personalized insurance products. The paper concludes by outlining significant research gaps and proposing a roadmap for future work, aimed at guiding the development of more robust, explainable, and ethically sound AI applications in insurance and real estate.

Introduction Meningioma is the most common brain tumor in adults. Magnetic resonance imaging (MRI) is the preferred imaging modality for assessing tumor outcomes. Radiomics, an advanced imaging technique, assesses tumor heterogeneity and identifies predictive markers, offering a non-invasive alternative to biopsies. Machine learning (ML) based radiomics models enhances diagnostic and prognostic accuracy of tumors. Comprehensive review on ML-based radiomics models for predicting meningioma recurrence and survival are lacking. Hence, the aim of the study is to summarize the performance measures of ML based radiomics models in the prediction of outcomes such as progression/recurrence (P/R) and overall survival analysis of meningioma. Methods Data bases such as Scopus, Web of Science, PubMed, and Embase were used to conduct a literature search in order to find pertinent original articles that concentrated on meningioma outcome prediction. PRISMA (Preferred reporting items for systematic reviews and meta-analysis) recommendations were used to extract data from selected studies. Results Eight articles were included in the study. MRI Radiomics-based models combined with clinical and pathological data showed strong predictive performance for meningioma recurrence. A decision tree model achieved 90% accuracy, outperforming an apparent diffusion coefficient (ADC) based model (83%). A support vector machine (SVM) model reached an area under curve (AUC) of 0.80 with radiomic features, improving to 0.88 with ADC integration. A combined clinico-pathological radiomics model (CPRM) achieved an AUC of 0.88 in testing. Key predictors of recurrence include ADC values, radiomic scores, ki-67 index, and Simpson grading. For predicting overall survival analysis of meningioma, the combined clinicopathological and radiomic features achieved an AUC of 0.78. Conclusion Integrating radiomics with clinical and pathological data through ML models greatly improved the outcome prediction for meningioma. These ML models surpass conventional MRI in predicting meningioma recurrence and aggressiveness, providing crucial insights for personalized treatment and surgical planning.

Colorectal polyps, which are associated with colorectal cancer, can be detected using a colonoscopy. Using the data from colonoscopy images to segment polyps is crucial in medical practice because it provides critical data for identification and surgery. However, precise segmentation of polyps is difficult due to the following factors: the polyp-bordering mucosa boundary is not sharp, and polyps of the same type differ in texture, size, and color. We propose to use the DeepLabV3+ architecture for image segmentation for medical purposes by examining its segmentation results on colonoscopy images from the datasets Kvasir and CVC-ClinicDB. DeepLabV3+ generates an F1-score of 0.865 for CVC-ClinicDB on an NVIDIA A100 class Cloud-Based GPU. The model is divided into the following parts: an encoder that performs separable convolution on the input map and a decoder that up-samples the data provided by the encoder using transpose convolution. Our approach significantly enhances segmentation accuracy and offers a number of benefits with respect to generality and real-time segmentation efficiency, according to evaluations done quantitatively and qualitatively on the two datasets.