In this research, an advanced framework is presented which combines Explainable Artificial Intelligence (XAI), machine learning algorithms and knowledge representation techniques to improve personalized treatment recommendations in healthcare. Random Forest, XGBoost and Deep Neural Networks (DNN) are used in this study to predict optimal treatment plans; thereby, SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) provides means of explaining models. A method is implemented, which uses knowledge graphs and SNOMED CT and UMLS ontologies for structuring patient data and disease-treatment relationships. Thus, the proposed framework is trained and tested on MIMIC-III and eICU Collaborative Research Database, utilizing over 50,000 patient records to assess its performance. The model performance is evaluated using accuracy, F1-score, AUC-ROC and SHAP scores to measure the model explain ability. Results show a 25% improvement in interpretability ratings of healthcare professionals and a 17.6% improvement in predictive accuracy from traditional AI models to state-of-the-art AI models. This study bridges representation gaps of AI driven recommendations and brings it closer to aid in clinical decision-making, improving transparency and trust in AI assisted healthcare. While integrating knowledge graphs and explainable AI techniques can help improve model performance and clinician adoption, using limited human insights to train AIs can perpetuate biased practices and institutions from linear AI. We will continue future research with real world clinical trials and further expand the framework to also utilize multi-institutional datasets for wider application.

The construction sector is responsible for damaging the environment, using lots of energy and emitting copious greenhouse gases. As ecological issues grow, projects that use bio-based and recycled materials in buildings are becoming more popular. It gives us better ways to build by lowering the environmental impact and supporting reuse of resources. Using a Life Cycle Sustainability Assessment, this study assesses how these materials perform with regard to environmental, economic and social factors. The industry and process input-output models are included in the framework’s LCSA method, which adds Life Cycle Assessment Life Cycle Costing, and Social Life Cycle Assessment . Existing case studies, EPDs and reports from companies were all sources of data used in this study. The process of economic and social assessment drew from stakeholder surveys along with market data and social hotspot databases. The research demonstrates that using bio-based and recycled materials results in less carbon emissions, less energy being used and less loss of important resources than traditional ones. Bio-based materials tend to be more expensive at first, but they give positive economic and health results over the long run. These materials encourage new job opportunities and are good for the community. The results support the idea that LCSA is essential for making environmentally friendly choices in construction and encourage using circular economy ideas in the building process.

Big data analytics’ incorporation into customized cancer care is a revolutionary development in medical science. Using knowledge from genomic sequencing, electronic health records (EHRs), clinical trial databases, and social determinants of health, this systematic review investigates the relationship between artificial intelligence (AI), machine learning, and big data analysis in customizing cancer treatments for each patient. The study highlights how AI‐driven prediction models might improve treatment outcomes, reduce side effects, and increase patient safety by synthesizing insights from the last 10 years of academic research. A systematic literature review that follows PRISMA guidelines and includes both quantitative and qualitative evaluations of academic publications is among the key approaches. The findings show that multiomics approaches, which combine transcriptomics, proteomics, metabolomics, and genomics, are becoming increasingly important for customized medicine. Real‐time data analytics and wearable technology are also noted as promising resources for prompt responses. Notwithstanding obstacles, including data heterogeneity, moral dilemmas, and validation problems, the results highlight how crucial AI is to treating tumor complexity and developing precision medicine. In order to improve regulatory frameworks, promote interdisciplinary collaboration, and optimize AI applications in cancer treatment planning, the study concludes by suggesting future research areas. For scientists, physicians, and legislators hoping to transform cancer treatment using big data analytics–driven tailored medicine, this review provides insightful information.

Modern-day AI infrastructure development requires more urgent need for reliable and efficient energy resources. Renewable energy obtains increasing attention, but coal-based energy generates substantial power in the worldwide energy consumption alongside emerging markets. The need for innovation becomes essential to optimize coal utilization because coal production contains efficiency problems alongside environmental challenges. The analysis draws data from multiple high-demand coal plant regions through their production logs with IoT sensors and their connected SCADA systems. Predictive models with machine learning algorithms, evaluate operational trends and breakdown patterns and environmental compliance performance. The implementation of BDA in AI-supported energy infrastructures is studied through case-based research that proves how better decisions, and reduced costs accompany balanced power distribution. Analysis of big data has proven to enhance coal-based energy operations its compatibility with AI-driven systems, which delivers better process efficiency and sustained energy production capabilities. The coal energy, artificial intelligence and big data analytics form a practical method to achieve smarter and more responsible energy operations in a data-centered environment. The study recommends political and energy sector investments in data infrastructure along with qualified personnel to bring out the complete advantages of these benefits.

A global pandemic of COVID-19 is underway. The world now thinks coronaviruses cause all illnesses, including this pandemic. The world is also seeing fast viral spread. COVID-19 spreads swiftly by respiratory droplets and contact, according to the WHO. Big data analytics technologies are essential for developing the information needed to make choices and take preventative action. The large volume of COVID-19 data available by multiple sources necessitates, however, a review of big data analysis's roles in containing the virus, highlighting the primary obstacles and future directions in COVID-19 data analysis, and offering a framework for relevant current applications and studies to aid in COVID-19 analysis research in the future. The aim of ML, DL methods in COVID-19 research and applications is the focus of this study. This endeavor use machine learning to evaluate and predict COVID-19 pandemic behavior to help control a pandemic. A study uses the Israeli Department of Health dataset to predict COVID-19 results using ML and big data analytics. The test results were analyzed using CNN-LSTM and Decision Tree classification algorithms. Model efficacy was measured by F1-score, recall, accuracy, and precision. CNN-LSTM was the most accurate, with 96.34% accuracy and strong predictive ability. Also, for the model explainability used LIME model. The findings may be used by various government agencies to implement remedial actions. It may be simpler to fight COVID-19 if methods for infectious illness forecasting were readily available.

The research explores how business analytics supports sustainable management by aligning organizational objectives with sustainable development goals. It highlights the importance of environmental and social responsibilities for organizations. Business analytics provides valuable insights by turning collected data into actionable value. The study uses online survey data and case study analysis to analyze secondary data from industries like manufacturing, retailing, and energy. The research shows that business analytics helps organizations understand process weaknesses and improve operational sustainability by eliminating or minimizing waste. By bridging the gap between data and actionable value generation, business analytics helps organizations meet international sustainability standards and contribute to sustainable management.