Business analytics has undergone significant transformation because of artificial intelligence and machine learning evolution, which now fulfills a critical function in economic expansion and organizational decision-making operations. Organization within the United States economy utilizes Management Information Systems to gain AI-powered insights, which enhance productivity and optimize resources and market prediction accuracy. The research evaluates how AI analytics drive economic growth because they enhance predictions while reducing possible hazards and generate strategic decisions through data-based approaches. The research uses both quantitative data methods together with qualitative case studies to investigate the subject. A combination of secondary market data, business reports, and economic statistics undergoes ML algorithm analysis, which reveals economic patterns and correlations because of their impact on performance. The research obtains practical information and usage barriers from both business managers and policymakers by conducting structured interviews regarding their implementation experiences with AI-based economic decision systems. The paper examines three significant AI methods, including predictive analytics with natural language processing and deep learning, which are applied to business intelligence. Voluntary business analytics, which run on artificial intelligence systems, boost decision-making by generating instant analytic information and simplifying complicated economic analysis tasks. Organizations that integrate AI and MIS systems to build data-based strategies boost operational performance while gaining competitive markets and ensuring durable economic expansion. Organizations should resolve the integration challenges together with data privacy concerns and ethical issues. The research findings demonstrate why organizations need to adopt AI-based analytics systems for developing business resilience and economic innovation in the United States.

The clinical use of artificial intelligence for precision medicine delivers transformative results in drug discovery through its combination of big data analytics and machine learning with genomic research. The traditional methods used in the drug discovery process are expensive to implement and take lengthy durations while failing to deliver customized therapy for patients. The advent of artificial intelligence now conducts fast genomic database analysis to find new drug aims and develop targeted medicine for individual patients. The application of ML algorithms enables researchers to make disease progression forecasts as well as identify drug effectiveness and potential adverse effects leading to improved outcomes for genomic drug discovery patients. The research discusses how deep learning and reinforcement learning models are applied to work with big genomic and biomedical information datasets. Data storage through cloud computing platforms together with high-performance computing systems allows precision medicine to scale and become more efficient. AI computational biology combined with clinical information enhances pharma research by optimally structuring drug reuse analysis and promoting new drug development processes. The use of artificial intelligence leads genomics-based drug discovery to a new direction through high efficiency and cost reduction and individual patient therapy delivery. Precision medicine based on AI and bioinformatics advancements push forward personal healthcare into the future despite ongoing obstacles such as data security problems and interpretation challenges and regulatory limitations.

This research examines how MIS frameworks strengthen energy infrastructure resilience through consolidated use of predictive models alongside data analytics and crisis management resources. There are multi factors, such as escalating natural disasters and elevated cyber threats with aging infrastructure systems, constantly push. The U.S. energy system toward declining resilience levels. The strategic decision-making and performance enhancement now depends heavily on Management Information Systems. This study uses qualitative research methods and relies on secondary data from energy reports alongside energy grid failure analysis and MIS implementation studies. This analysis reviews various MIS systems, such as SCADA and ERP, to identify how they could improve monitoring operations and evaluation procedures and quick response functionality. MIS development based on specific system needs leads to greater energy system surveillance capabilities and better resource management with improved recovery protocols. The research demonstrates how energy infrastructure protection improves when intelligent MIS combines real-time analysis along with predictive artificial intelligence technologies towards reaching national security goals for U.S. energy systems.

The Agile Project Management methodology has evolved in the face of fast-moving and changing information technology, focusing on adaptability, iterative development, and collaboration of teams. The road to maximizing the benefits, with agile methodologies increasingly supported by organizations through MIS on data-driven decisions, real-time communication, and smooth processing of projects. It reviews how MIS integrates into agile project management to drive better factors of organizational success like adaptability, resource management, and decision-making efficiency. The case studies and current literature on the various ways MIS supports agile projects were investigated, enabling IT teams to act quickly on changing project requirements to continuously improve the project outcomes. Also, the review discusses issues of MIS implementation in agile settings with regard to data security and integration problems. The aim of this research paper is to provide insight into how MIS tools can be utilized effectively by IT managers and professionals in achieving agile success that would lead to optimized project performance and maximization of satisfaction for stakeholders.

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.

Semantic communication has emerged as a viable paradigm for enhancing efficiency and reliability in next-generation edge-enabled IoT, as well as 6G networks. The paper presents a proposal for a lightweight secure semantic communication (LSSC) architecture, which combines semantic encoding and security validation at the edge. It has been shown that the scheme proposed has 0.91 semantic similarity at 5 dB SNR and semantic similarity of 0.86 at 0 dB, the experimental results being better than the non-secure semantic and conventional bit-level transmission. The results of reported performance improvements are achieved through the controlled edge-IoT in AWGN and Rayleigh fading channel with SNR of 0-20 dB employing publicly available sensing and short-text data. Findings as such are hotspot-based semantic inference performance under limited deployment constraints, as opposed to universal network behaviors. The results show that LSSC has the potential to provide secure and efficient semantic communication within edge-enhanced 6G environments.