The Metaverse is an online universe that combines virtual reality and augmented reality, linked together via a network.It has generated novel experiences that are fully engaging and transpire in real time, facilitating interpersonal communication and dialogue. Virtual environments with 3D space and avatars can boost patient-facing platforms, operational utilisation, digital education, diagnostics, and treatment choices in medicine and ophthalmology. Globally, there is an increasing prevalence of chronic diseases, with an estimated 25 percent of individuals presently contending with multiple chronic health issues. The management of chronic diseases is currently being rethought in light of the development of technology known together as "Smart Healthcare." A prime example is state-of-the-art wearable technology that incentivizes people to embrace healthier lifestyles through the monitoring of physiological indicators and metabolic processes. With better data organisation and analysis, chronic disease patients may benefit from improved health, privacy, and quality of life. Through the examination of physiological data acquired from wearable devices on a patient, Artificial Intelligence (AI) has the capability to generate informed recommendations pertaining to the diagnosis and treatment of illness. These recommendations can be provided by AI. The adoption of blockchain technology (BC) has the potential to significantly advance healthcare in a variety of ways, including decentralised data sharing, user privacy, user empowerment, and dependability in data administration. The potential impact of Wearable Technologies (WT), Artificial Intelligence (AI), and Blockchain Technology (BC) on Chronic Disease Management (CDM) could be a transition in emphasis from the hospital to the patient. This article provides a patient-centered technical framework for controlling chronic diseases using artificial intelligence, blockchain, and wearable technologies. Our proposed architecture depends on Metaverse environment. In order to participate in the Metaverse, both patients and physicians need to sign up on the Blockchain network. After entering, customers will be accompanied by avatars throughout the experience. A comprehensive record of all information gathered during doctor-patient consultations, including text, videos, images, audio, and clinical data, will be compiled, uploaded to the blockchain, and stored in perpetuity. Explainable Artificial Intelligence (XAI) algorithms examine these particulars in order to diagnose and forecast the progression of diseases. We conclude with a discussion of the constraints of this novel paradigm and recommendations for future research.

Accurate demand forecasting has become extremely important, particularly in the food industry, because many products have a short shelf life, and improper inventory management can result in significant waste and loss for the company. Several machine learning and deep learning techniques recently showed substantial improvements when handling time-dependent data. This paper takes the ’Food Demand Forecasting’ dataset released by Genpact, compares the effect of various factors on demand, extracts the characteristic features with possible influence, and proposes a comparative study of seven regressors to forecast the number of orders. In this study, we used Random Forest Regressor, Gradient Boosting Regressor (GBR), Light Gradient Boosting Machine Regressor (LightGBM), Extreme Gradient Boosting Regressor (XGBoost), Cat Boost Regressor, Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) in particular. The results demonstrate the potential of deep learning models in forecasting and highlight the superiority of LSTM over other algorithms. The RMSLE, RMSE, MAPE, and MAE reach 0.28, 18.83, 6.56%, and 14.18, respectively.

With the voluminous information being produced by the Internet of Things (IoT) smart gadgets, the consumers with their countless service requests are also growing rapidly. As there is a huge distance between the IoT devices and the Cloud datacenter, some latency is incurred in the communication between the IoT devices and the Cloud datacenter. This latency can be reduced by introducing a Fog layer in between the Cloud and the IoT layer and therefore, it is paramount to offload those tremendous data to leverage the overloaded storage and computation to the Cloud-based systems and Fog-assisted nodes. Moreover, these heavy computations consume significant energy from the distributed Fog servers as well as Cloud datacenters. Therefore, this work addresses the task migration problem in a Fog-Cloud system and load balancing to reduce the latency rate, energy utilized and service time while increasing the resource utilization for latency-sensitive systems. This paper uses a Fuzzy logic algorithm for determining the target layers for offloading considering the resource heterogeneity and the system requirements (i.e., network bandwidth, task size, resource utilization and latency sensitivity). A Binary Linear-Weight JAYA (BLWJAYA) task scheduling algorithm has been proposed to map the incoming IoT requests to computation-rich Fog nodes/virtual machines (VMs). Numerous experimental simulations have been carried out to appraise the efficacy of the suggested method and it is evident that the suggested method outperforms other baselines with an approximate improvement of 26.2%, 12%, 7%, 8.63% and 6% for Resource utilization, Service rate, Latency rate, Energy consumption and Load balancing rate. The presented approach is generic and scalable concerning addressing the unpredictability of data and the associated latency due to the task offloading criteria within the Fog layer.