Smart grids (SGs) have emerged in the United States as part of the effort to modernize the nation’s aging electricity infrastructure. While existing cybersecurity tools are generally effective at detecting and preventing known threats, they fall short in addressing the growing complexity of advanced cyberattacks. Ensuring robust protection for the U.S. smart grid requires a comprehensive approach that integrates advanced technologies and proactive processes. Leveraging big data analytics, artificial intelligence (AI), and machine learning (ML) enables a more adaptive and holistic security framework. These technologies allow for the identification of new, previously undetected threats by analyzing behavioral patterns and predicting anomalies through intelligent systems. By combining known and unknown threat data, predictive analytics can significantly enhance the cybersecurity posture of critical energy infrastructure. This paper explores current trends and key challenges in safeguarding U.S. smart grids using big data and AI-driven approaches. It includes an overview of smart grid architecture and functionality, highlights technological advancements, and outlines a qualitative risk assessment method. Additionally, it discusses major contributions to improving the reliability, safety, and efficiency of the electric grid, proposes layered security measures, and evaluates cybersecurity risk management strategies for supervisory control and data acquisition (SCADA) systems.

Background: Autism Spectrum Disorder (ASD) presents challenges in early diagnosis and personalized treatment due to the subjectivity of traditional screening methods and the lack of tailored therapeutic approaches. Artificial Intelligence (AI) and Big Data Analytics offer innovative solutions for enhancing diagnostic accuracy and optimizing treatment pathways. Objective: This study explores the application of AI in ASD diagnosis and predictive modeling for personalized stem cell therapy, aiming to improve early detection and treatment effectiveness. Methods: A dataset of 704 individuals was analyzed using machine learning models, including LightGBM, Random Forest, Neural Networks, XGBoost, and Stacking Ensemble. Performance was assessed using ROC-AUC, Accuracy, Precision, Recall, and F1-score, while AI models were further applied to predict therapy response patterns for stem cell treatment optimization.Results: The Stacking Ensemble model (ROC-AUC = 0.9989, F1 = 0.9125) demonstrated superior performance in ASD classification. Neural Networks exhibited the highest recall (95.76%), making them ideal for early screening. AI-driven insights facilitated the identification of key ASD biomarkers, enabling personalized treatment strategies.Conclusion: AI significantly enhances ASD detection and treatment planning, providing a data-driven, personalized approach. Future research should focus on real-world validation, integrating genetic biomarkers, and ensuring ethical AI deployment in clinical settings. These advancements pave the way for precision medicine in neurodevelopmental disorders.

The digital marketing for food delivery business is the focus of this study, which investigates the use of ensemble machine learning (ML) approaches. The study's overarching goal is to pave the way for artificial intelligence (AI)-based recommendations by analyzing consumer data with the hope of discovering consumer preferences and predicting behavior. In order to improve the accuracy of predictions, the ensemble method combines the results of decision trees, naïve Bayes, and k-nearest neighbor algorithms. Both the decision tree and nearest neighbor algorithms were able to obtain perfect predictions with zero error and 100% accuracy, as seen in the accuracy matrix charts. On the other hand, the naïve Bayes method was able to accurately identify labels in all classes with a minimal error rate of 0.028 and a high accuracy of 97.175%. With a success rate of over 90%, the majority vote method allows models to be integrated using less than 50% of the randomized data, which minimizes customer dissatisfaction. When taken as a whole, these ML algorithms greatly improve the efficiency and efficacy of food delivery business digital marketing campaigns by cutting down on wasted time and money.

There was a significant lack of empirical data on the measure of the effect that Artificial Intelligence (AI) has on the efficiency of operations in the U.S. municipal governments. Robust research providing evidence on pre and post-implementation measures were non-existent despite the high level of acknowledgement of the theoretical potential. The present study had the positive influence of AI integration on the indicators of public service delivery as an object of investigation. The main research question was to assess any efficiency improvement in its main key operation dimensions before and after adoption of AI. A cross-sectional quantitative methodology was used accompanied by a before and after comparative design. Results were drawn through surveys of 152 Chief Information Officers of municipalities with a city size of greater than 100,000. Descriptive statistics and t-tests were performed on seventeen efficiency indicators-response time, cost, citizen satisfaction and transparency. The outcome showed statistically significant results on all of the dimensions (p < 0.001). Mean efficiency in service delivery shifted up by 43 percent, 3.12 to 4.46. The process automation reflected a maximum mean preference (+ 1.44), second is response time (+ 1.32) and environmental impact (+ 1.30). The cost of providing a service per transaction was reduced severely (mean improvement of + 1.21 on an efficiency scale) as were the satisfaction levels of citizens with a mean increase of 1.27 points. The results are strong empirical evidence that AI implementation can be a lever of introducing significant efficiency improvement, which municipal leaders and policymakers should consider when evaluating the possibility of using technology to make the most out of the available resource and achieve significant improvements in terms of outcomes for the population.

In the United States, early detection of diseases is critical to ensuring timely and effective treatment, as many conditions, if not diagnosed promptly, can become untreatable or even fatal. As a result, there is a growing reliance on advanced technologies to analyze complex medical data, reports, and images with both speed and precision. In many cases, subtle abnormalities in medical imaging may go unnoticed by the human eye, which is where machine learning (ML) has become indispensable. ML techniques are increasingly used in healthcare for data driven decision making, uncovering hidden patterns and anomalies that traditional methods might miss. Although developing such algorithms is complex, the greater challenge lies in optimizing them for higher accuracy while reducing processing time. Over the years, the integration of ML into biomedical research has significantly advanced the field, paving the way for innovations like precision medicine, which customizes treatments based on a patient’s genetic profile. Today, machine learning supports nearly every stage of delivery, from extracting critical information from electronic health records to diagnosing diseases through medical image analysis. Its role extends to patient management, resource optimization, and treatment development. Particularly, deep learning, powered by modern high-performance computing, has shown remarkable accuracy and reliability in these applications. It is now evident that in the U.S. healthcare system, computational biology and clinical decision making are deeply intertwined with machine learning, making it a core component of artificial intelligence in medicine. In this paper, the aim is to explore the current applications, challenges, and potential of machine learning in supporting healthcare decision-making in the United States, with a focus on diagnosis, medical imaging, and personalized treatment strategies.