The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting. The majority of currently available methods use either a generative adversarial network (GAN) or a recurrent neural network (RNN) to generate new handwriting styles. This is why these techniques frequently fall short of producing diverse and realistic text pictures, particularly for terms that are not commonly used. To resolve that, this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting styles. This network excels in generating conditional text by extracting style vectors from a series of style images. The model performs admirably on a range of handwriting synthesis tasks, including the production of text that is out-of-vocabulary. It works more effectively than previous approaches by displaying lower values on key Generative Adversarial Network evaluation metrics, such Geometric Score (GS) (3.21 × 10⁻⁵) and Fréchet Inception Distance (FID) (8.75), as well as text recognition metrics, like Character Error Rate (CER) and Word Error Rate (WER). A thorough component analysis revealed the steady improvement in image production quality, highlighting the importance of specific handwriting styles. Applicable fields include digital forensics, creative writing, and document security.

A new era of data access and management has begun with the use of cloud computing in the healthcare industry. Despite the efficiency and scalability that the cloud provides, the security of private patient data is still a major concern. Encryption, network security, and adherence to data protection laws are key to ensuring the confidentiality and integrity of healthcare data in the cloud. The computational overhead of encryption technologies could lead to delays in data access and processing rates. To address these challenges, we introduced the Enhanced Parallel Multi-Key Encryption Algorithm (EPM-KEA), aiming to bolster healthcare data security and facilitate the secure storage of critical patient records in the cloud. The data was gathered from two categories Authorization for Hospital Admission (AIH) and Authorization for High Complexity Operations. We use Z-score normalization for preprocessing. The primary goal of implementing encryption techniques is to secure and store massive amounts of data on the cloud. It is feasible that cloud storage alternatives for protecting healthcare data will become more widely available if security issues can be successfully fixed. As a result of our analysis using specific parameters including Execution time (42%), Encryption time (45%), Decryption time (40%), Security level (97%), and Energy consumption (53%), the system demonstrated favorable performance when compared to the traditional method. This suggests that by addressing these security concerns, there is the potential for broader accessibility to cloud storage solutions for safeguarding healthcare data.

Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetables and determines the stop and the start according to the demands. This study explores the optimization of dynamic vehicle scheduling and real-time route planning in urban public transportation systems, with a focus on bus services. It addresses the limitations of current shared mobility routing algorithms, which are primarily designed for simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. The research introduces an route planning algorithm designed to dynamically accommodate passenger travel needs and enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based, multi-objective heuristic A* approach, offering a solution to the inflexibility and limited coverage of suburban bus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based on Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, route length, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposed algorithm significantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existing models, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. The study concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalable solution for improving urban mobility.