Artificial Intelligence (AI) and Machine Learning (ML) have witnessed unprecedented growth and innovation in recent years, revolutionizing various sectors of society and industry. This article provides a comprehensive overview of the latest developments in AI and ML, highlighting key breakthroughs, emerging trends, and potential future directions. We examine advancements in deep learning architectures, natural language processing, computer vision, and reinforcement learning.

The current research seeks to analyze the use of artificial intelligence-based predictive maintenance techniques in HVAC systems, with particular emphasis on reducing equipment downtime. Work done in this paper shows that conventional maintenance techniques, such as reactive and preventive maintenance, contribute to rising operational costs and unanticipated system breakdowns.

Principal Component Analysis (PCA) is a powerful tool for understanding the underlying structure and relationships within multivariate datasets, often collected through extensive field surveys and monitoring programs. This study explores the best practices for performing PCA on incomplete datasets with missing values, with a focus on the significance of sophisticated imputation techniques or resilient missing data strategies to maintain the analytical value of ecological datasets.

Today, engineering design has been enhanced significantly by the integration of Artificial Intelligence (AI). By simplifying routine work related to parametric design and performance optimization, AI enables engineers to work on more advanced tasks. The fast processing of large amounts of data makes it possible for designers to consider a great number of design solutions within a considerably shorter time than it would take with traditional approaches. This has mainly resulted in shorter design cycle times as well as reductions in the amounts of material used and total costs of a project.

In the day and age of exponential data, not only has volume increased, but so has demand for meaningful aggregation, processing and representation of information. Data modelling can be recognised as a basic process for improving data preparedness for visualisation. In data modelling, data requirements of a specific domain are depicted based on a structure that can be implemented in a way that is neutral to the implementation scheme. Data modelling formalisms define the elements of the model that have to be developed.

This article explores the integration of DataOps principles into modern online signature verification systems to address challenges in data management, scalability, and cybersecurity. By leveraging distributed systems, hybrid machine learning (ML) frameworks, and cloud-native technologies, the proposed solution achieves seamless data orchestration, improving accuracy, fault tolerance, and real-time processing. A detailed evaluation highlights the transformative potential of DataOps in overcoming traditional bottlenecks, paving the way for robust, scalable, and efficient signature verification.

The widespread adoption of electric vehicles (EVs) is crucial for reducing carbon emissions and combating climate change. However, the efficiency of EV battery charging systems remains a critical challenge, with issues such as long charging times, energy inefficiency, and battery degradation. This paper explores the application of artificial intelligence (AI) and machine learning (ML) techniques to optimize EV battery charging processes. By leveraging predictive models, adaptive charging strategies, and intelligent energy management systems, AI can significantly enhance charging efficiency, reduce energy consumption, and improve battery lifespan.

The integration of robotics into the medical field has resulted in significant advancements, especially in minimally invasive surgery, rehabilitation, and diagnostics. Human-Robot Interaction (HRI) interfaces play a critical role in the success of these robotic systems, serving as the bridge between medical professionals and robots. Effective interface design is essential for ensuring the safety, efficiency, and comfort of medical practitioners, which directly impacts patient outcomes. This paper explores the theoretical foundations of HRI interfaces, focusing on cognitive and psychological factors, usability principles, and human factors engineering within the medical robotics domain. The design considerations for user types, interface modalities, customization, error prevention, and autonomy levels are also discussed.

PPML is a novel and rising interdisciplinary field that deals with the application of artificial intelligence to learn models while preserving data privacy. The pervasive and consequential use of data in diverse fields calls for providing security for information, particularly when used for ML purposes.