This work is part of a research project carried out during the COVID-19 pandemic, involving the design and realization of an autonomous mobile hospital robot. Many real-world robotic tasks suffer from the critical characteristics: Noisy sensing, imperfect control, and environment changes. The Markov decision process MDP and its variants provide a mathematically based framework for modeling and solving robot decision and control tasks under uncertainty. This paper presents a review of Markov Decision Processes (MDPs) and their variants in the Control of Robotic systems. We begin by introducing the basic concepts of MDPs and their algorithms for solving completely observable decision problems, including value iteration and policy iteration. We then discuss the challenges associated with partially observable decision problems POMDPs. We review various approaches for solving POMDPs, including belief-state planning and Monte Carlo tree search. Finally, we discuss the concept of POMDP augmentation, which involves incorporating additional information into the decision-making process to improve performance. We present several examples of POMDP augmentation techniques, including the use of deep neural networks and transfer learning.
The research introduces a data sharing and retrieval method within the blockchain framework tailored for smart healthcare systems. This method addresses key challenges in data management, security, and accessibility. Two main components are presented: a distributed, parallel data-sharing scheme and an innovative data retrieval algorithm using the Merkle-Patricia Trie (MPT) and Bloom filters. The proposed healthcare blockchain system showcased improved performance metrics with notable efficiency in upload and download times, latency, delay, and response times. This adaptable approach promises enhanced patient care, medical research, and data analytics, emphasising effective, secure, and patient-centric healthcare data management.
Deep learning object recognition models, which are widely used in computer vision, may provide an opportunity to accurately recognize fruit trees. This is essential for fast data collection, selection and reducing human operational errors. This paper proposes a YOLOv5-based detection model for fruit tree detection in the farm plantation using UAV-collected data. This proposed model detects individual fruit trees from the agriculture field and also provide counts how many trees are detected. An image dataset was created from the publicly available UAV captured data which contains total 36 images. Among them 27 were used for training and 9 for testing the proposed model. Four different YOLOv5 scales for object recognition (YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) were selected for training, validation, and testing on image datasets.
The recent surge in monkeypox (mpox) cases across various nations has escalated into a significant public health challenge, underscoring the imperative for timely detection and diagnosis. In light of this, our research focused on identifying the most efficient deep learning model tailored for mpox detection. Clinically, the progression of monkeypox is characterized by four distinct stages: macular, papular, vesicular, and pustular. In this study, we explore the viability of leveraging a Deep Convolutional Generative Adversarial Network (DCGAN) in conjunction with a conditional vector to strengthen the diagnostic precision of monkeypox. By utilising the remarkable capabilities of GANs, we generate synthetic images mirroring monkeypox skin lesions, thereby amplifying our limited dataset. This approach of utilizing a conditional DCGAN, when paired with stage-specific conditions, results in a significant boost in classification accuracy, elevating it from 0.7532 to 0.8734, complemented by precision 0.91, recall 0.878 F1-score 0.8936. These promising results underscore the advantages of deploying GANs for data augmentation in biomedical image classification tasks, when compared with well known classification models. These models assures the potential of deep learning-assisted diagnostics in dermatology. Especially in therapeutic contexts, where real data may be sparse, the addition of synthetic images can act as a valuable resource, paving the way for enhanced diagnostic instruments.
This paper addresses the labor-intensive and wasteful nature of the traditional sugarcane bud cutting method. To overcome these challenges, the proposed approach leverages YOLOv5 technology for sugarcane bud identification. The machine-learning model is trained with diverse data samples, enabling it to accurately distinguish between sugarcane buds and other elements in the images. The implementation yields the best of 79
