Human-Centric Artificial Intelligence (HCAI) is rapidly emerging as a transformative paradigm, shifting the focus of AI development from mere algorithmic optimization to ethical alignment, user trust, and societal integration most notably within the critical domain of healthcare. This review offers a comprehensive examination of the principles, architecture, challenges, and future directions underpinning HCAI, with an emphasis on its applications in health-related contexts. We begin by exploring the conceptual foundations of human-centricity, including core values such as transparency, fairness, autonomy, and privacy, all of which are essential in sensitive environments like clinical decision-making and patient data management. The paper then surveys key enabling technologies such as Explainable AI (XAI), Human-in-the-Loop learning, affective computing, and multi-agent collaboration demonstrating how these approaches operationalize human alignment in real-world systems, especially in personalized healthcare delivery and diagnostic support. Societal implications are critically evaluated, encompassing trust, data sovereignty, algorithmic bias, regulatory compliance, and cross-cultural adaptability, which are particularly pronounced in global health systems. We highlight the limitations of existing benchmarks and propose a multi-metric, user-centered evaluation framework capable of assessing both technical robustness and alignment with human values in healthcare and beyond. Finally, we identify open research challenges and outline a strategic agenda that integrates cognitive science, ethical theory, and participatory design. This review aims to serve as both a foundational reference and a forward-looking roadmap for researchers, developers, and policymakers striving to build AI systems that are not only intelligent, but also responsible, inclusive, and aligned with human dignity, particularly in life-critical domains like healthcare

Cloud computing has emerged as a new paradigm for providing on-demand computing resources and outsourcing software and hardware infrastructures. Load balancing is one of the major concerns in cloud computing environment means how to distribute load efficiently among all the nodes. For solving such a problem, we need some load balancing algorithms, so in this paper we will compare the existing algorithms for web application.and based on results obtained we choose the best among them.

In this study, we address critical operational inefficiencies in emergency departments (EDs) by developing a hybrid predictive model that integrates long short-term memory (LSTM) networks with decision trees (DT). This model significantly enhances the prediction of patient volumes, a key factor in reducing wait times, optimizing resource allocation, and improving overall service quality in hospitals. By accurately forecasting the number of incoming patients, our model facilitates the efficient distribution of both human and material resources, tailored specifically to anticipated demand. Furthermore, this predictive accuracy ensures that EDs can maintain high service standards even during peak times, ultimately leading to better patient outcomes and more effective use of healthcare facilities. This paper demonstrates how advanced data analytics can be leveraged to solve some of the most pressing challenges faced by emergency medical services today.

Introduction Wireless Sensor Networks Wireless Sensor Networks (WSNs) consist of a number of physically disperse sensor nodes which use wireless communication to do sensing and retrieving information from the physical world. In a network of battery driven and energy limited nodes, the network lifetime is an important concern. Driven by the above mechanism, as a cluster of nodes is frustrated, the function of the entire network drops dramatically or even becomes inefficient. Low-Energy Adaptive Clustering Hierarchy (LEACH) LEACH is one of the most well-known energy-efficient clustering protocols for making wireless sensor networks more sustainable by grouping nodes and moderating the group head. However, in LEACH protocol, the operation of cluster head is selected randomly, which might lead to the waste of energy in the node. Here, this paper proposes an enhanced LEACH that integrates fuzzy logic-based decision-making with cluster head selection. The fuzzy-centric LEACH versions improve significantly in terms of energy consumption and network lifetime through the involvement of some parameters such as residual energy, distance to BS, and the density of sensor nodes. Simulation results confirms that the proposed fuzzy implementation of LEACH outperforms the existing classical variant and gives a feasible solution for WSN deployment environment.

Deep learning has revolutionized data-driven solutions across diverse domains. This paper explores advancements in deep learning applications in emerging fields such as healthcare, agriculture, autonomous systems, smart cities, and environmental monitoring. We examine innovative architectures, including convolutional neural networks (CNNs), transformers, graph neural networks (GNNs), and generative adversarial networks (GANs), and their impact on tasks like medical diagnostics, precision agriculture, autonomous navigation, urban planning, and climate modeling. We address challenges such as data scarcity, computational efficiency, model interpretability, and ethical considerations. Through extensive case studies, performance evaluations, and discussions on optimization techniques, we highlight the transformative potential of deep learning while identifying critical areas for future research.

This study aimed to address the increasing sophistication of cybersecurity threats by analyzing a substantial dataset to evaluate the efficacy of detection methodologies. Utilizing a dataset comprising 14,133 records and 16 variables, we investigated the prevalence of various cyber threats and assessed the performance of machine learning models in their detection. Our methodological approach centered on the application of attention-based neural networks, which facilitated a comprehensive analysis of complex threat scenarios. Results showed that unauthorized access and data breaches were the most prevalent threats, representing a significant portion of the dataset. Additionally, malware and phishing attacks emerged as notable threats, highlighting the necessity for enhanced detection strategies. The implementation of attention-based neural networks significantly improved threat detection capabilities, with these models effectively classifying diverse threat patterns. This methodological advancement underscores the potential of machine learning in augmenting cybersecurity measures. Our findings suggest a pressing need for targeted detection methods tailored to specific threat categories, thereby optimizing threat detection and response strategies. The identification of system vulnerabilities emphasizes the importance of proactive mitigation strategies to bolster cybersecurity resilience. These results indicate that integrating advanced machine learning models into cybersecurity frameworks can substantially enhance detection accuracy and efficiency. In conclusion, this research provides critical insights into the landscape of cybersecurity threats and the effectiveness of detection methodologies. By leveraging a large dataset, we have elucidated key patterns in threat prevalence and detection efficacy. Our findings reinforce the imperative for ongoing innovation in cybersecurity practices, with attention-based neural networks offering a promising avenue for future exploration.