Digitization and automation have always had an immense impact on healthcare. It embraces every new and advanced technology. Recently the world has witnessed the prominence of the metaverse which is an emerging technology in digital space. The metaverse has huge potential to provide a plethora of health services seamlessly to patients and medical professionals with an immersive experience. This paper proposes the amalgamation of artificial intelligence and blockchain in the metaverse to provide better, faster, and more secure healthcare facilities in digital space with a realistic experience. Our proposed architecture can be summarized as follows. It consists of three environments, namely the doctor’s environment, the patient’s environment, and the metaverse environment. The doctors and patients interact in a metaverse environment assisted by blockchain technology which ensures the safety, security, and privacy of data. The metaverse environment is the main part of our proposed architecture. The doctors, patients, and nurses enter this environment by registering on the blockchain and they are represented by avatars in the metaverse environment. All the consultation activities between the doctor and the patient will be recorded and the data, i.e., images, speech, text, videos, clinical data, etc., will be gathered, transferred, and stored on the blockchain. These data are used for disease prediction and diagnosis by explainable artificial intelligence (XAI) models. The GradCAM and LIME approaches of XAI provide logical reasoning for the prediction of diseases and ensure trust, explainability, interpretability, and transparency regarding the diagnosis and prediction of diseases. Blockchain technology provides data security for patients while enabling transparency, traceability, and immutability regarding their data. These features of blockchain ensure trust among the patients regarding their data. Consequently, this proposed architecture ensures transparency and trust regarding both the diagnosis of diseases and the data security of the patient. We also explored the building block technologies of the metaverse. Furthermore, we also investigated the advantages and challenges of a metaverse in healthcare.

The natural phenomenon of harmful algae bloom (HAB) has a bad impact on the quality of pure and freshwater. It increases the risk to human health, water bodies and overall aquatic ecosystem. It is necessary to continuously monitor and perform proper action against HAB. The inspection of algae blooms by using conventional methods, like algae detection under microscopes, is a difficult, expensive, and time-consuming task, however, computer vision-based deep learning models play a vital role in identifying and detecting harmful algae growth in aquatic ecosystems and water reservoirs. Many studies have been conducted to address harmful algae growth by using a CNN based model, however, the YOLO model is considered more accurate in identifying the algae. This advanced deep learning method is extensively used to detect algae and classify them according to their corresponding category. In this study, we used various versions of the convolution neural network (CNN) based on the You Only Look Once (YOLO) model. Recently YOLOv5 has been getting more attention due to its performance in real-time object detection. We performed a series of experiments on our custom microscopic images dataset by using YOLOv3, YOLOv4, and YOLOv5 to detect and classify the harmful algae bloom (HAB) of four classes. We used pre-processing techniques to enhance the quantity of data. The mean average precision (mAP) of YOLOv3, YOLOv4, and YOLO v5 is 75.3%, 83.0%, and 91.0% respectively. For the monitoring of algae bloom in freshwater, computer-aided based systems are very helpful and effective. To the best of our knowledge, this work is pioneering in the AI community for applying the YOLO models to detect algae and classify from microscopic images.

Among pets, dogs are very famous in the whole world. The owners of dogs are very cautious about the well-being of their dogs. The well-being of dogs can be ensured by continuous monitoring of their activities. Studies related to activity detection have gained much popularity due to the significant progress in sensor technology during the last few years. Automatic monitoring of pet applications includes real-time monitoring systems and surveillance which detect the pets with high accuracy using the latest pet activity classification techniques. The revolution in the domain of technology has allowed us to obtain better results using latest techniques. Convolutional neural networks (CNNs) 1D recently become a cutting-edge approach for signal processing-based systems such as patient-individual ECG categorization, sensor-based health monitoring systems, and anomaly identification in manufacturing areas. Adaptive and compact 1D models have several advantages over their conventional 2D counterparts. A limited dataset is sufficient to train a 1D CNN efficiently while 2D CNNs require a plethora of data for training. Its architecture is not very complicated, so it is suitable for real-time detection of activities. The main goal of this study is to develop a state-of-the-art system that can detect and classify the activities based on sensors’ data (accelerometer, and gyroscope. We proposed a 1D CNN-based system for pet activity detection. The objective of this study was to recognize ten pet activities such as walking, sitting, down, staying, eating, sideway, jumping, running, shaking, and nose work respectively, using wearable sensor devices based on deep learning technique. The data collection procedure for this study was conducted with 10 dogs of different breeds, sex (male=7, female = 3), age (age = 4±3), and sizes (small, medium, large) in a healthy environment. After collecting the data, the following steps, namely data synchronization, and data preprocessing were considered to remove the irrelevant data from the dataset. To overcome imbalanced problems in the dataset we used the class-weight technique. Subsequently, we applied 1D CNN algorithm using the class-weight technique. The model with the class-weight technique showed 99.70% training accuracy and 96.85% validation accuracy. The 1D CNN approach will be helpful for real-time monitoring of activities and for tracing the behavior of dogs.

Multiple-instance learning has become popular over recent years due to its use in some special scenarios. It is basically a type of weakly supervised learning where the learning dataset contains bags of instances instead of a single feature vector. Each bag is associated with a single label. This type of learning is flexible and a natural fit for multiple real-world problems. MIL has been employed to deal with a number of challenges, including object detection and identification tasks, content-based image retrieval, and computer-aided diagnosis. Medical image analysis and drug activity prediction have been the main uses of MIL in biomedical research. Many Algorithms based on MIL have been put forth over the years. In this paper, we will discuss MIL, the background of MIL and its application in multiple domains, some MIL-based methods, challenges, and lastly, the conclusions and prospects.

Cervical spine fractures are a medical emergency that can cause permanent paralysis and even death. Traditional fracture detection techniques, such as manual radiography image interpretation, are time-consuming and prone to human error. Deep learning algorithms have shown promising results in various medical imaging applications i.e., disease diagnosis, including fracture detection of bones. In this study, we propose a two-stage approach for detecting cervical spine fractures. The first stage employs a convolutional neural network (CNN) model to determine the presence or absence of a fracture in the cervical spine, using a dataset of cervical spine Computed Tomography (CT) scan images as well as Grad-CAM for enhanced visualization and interpretation. In the second stage, our focus shifts to specific vertebrae within the cervical spine. To accomplish this task, we trained and evaluated the performance of the YOLOv5 and YOLOv8 models with 9170 images consisting of seven vertebrae. The detection results of both YOLO versions are compared and evaluated. The precision, recall, mAP50, and mAP50-90 were 0.900, 0.890, 0.935, 0.872, respectively. The results of this research demonstrate the potential of deep learning-based approaches for cervical spine fracture detection. By automating the detection process, these algorithms can assist radiologists and healthcare professionals in making accurate and timely diagnoses, leading to improved patient outcomes.