Fog computing is one of the growing distributed computing platforms incorporated by Industries today as it performs real-time data analysis closer to the edge of the IoT network. It offers cloud capabilities at the edge of the fog networks through improved efficiency and flexibility. As the demands of Internet of Things (IoT) devices keep varying, it is important to rapidly modify the resource allocation policies to satisfy them. Constant fluctuation of the demands leads to over or under provisioning of resources. The computing capability of the fog nodes is small, and hence there is a necessity to develop resource provisioning policies that reduce the delay and bandwidth consumption. In this paper, a novel large language model (LLM)-guided Q-learning framework is designed and developed. The uncertainty in the fog environment in terms of delay incurred, bandwidth usage, and heterogeneity of fog nodes is represented using the LLM model. The reward shaping of a Q-learning agent is enriched by considering the heuristic value of the LLM model. The experimental results ensure that the proposed framework is good with respect to processing delay, energy consumption, load balancing, and service level agreement violation under a finite and infinite fog computing environment. The results are further validated through the expected value analysis statistical methodology.

Nowadays, more enterprises are rapidly transitioning to cloud computing as it has become an ideal platform to perform the development and deployment of software systems. Because of its growing popularity, around ninety percent of enterprise applications rely on cloud computing solutions. The inherent dynamic and uncertain nature of cloud computing makes it difficult to accurately measure the exact state of a system at any given point in time. Potential challenges arise with respect to task scheduling, load balancing, resource allocation, governance, compliance, migration, data loss, and lack of resources. Among all challenges, task scheduling is one of the main problems as it reduces system performance due to improper utilization of resources. State Action Reward Action (SARSA) learning, a policy variant of Q learning, which learns the value function based on the current policy action, has been utilized in task scheduling. But it lacks the ability to provide better heuristics for state and action pairs, resulting in biased solutions in a highly dynamic and uncertain computing environment like cloud. In this paper, the SARSA learning ability is enriched by the guidance of the Large Language Model (LLM), which uses LLM heuristics to formulate the optimal Q function. This integration of the LLM and SARSA for task scheduling provides better sampling efficiency and also reduces the bias in task allocation. The heuristic value generated by the LLM is capable of mitigating the performance bias and also ensuring the model is not susceptible to hallucination. This paper provides the mathematical modeling of the proposed LLM_SARSA for performance in terms of the rate of convergence, reward shaping, heuristic values, under-/overestimation on non-optimal actions, sampling efficiency, and unbiased performance. The implementation of the LLM_SARSA is carried out using the CloudSim express open-source simulator by considering the Google cloud dataset composed of eight different types of clusters. The performance is compared with recent techniques like reinforcement learning, optimization strategy, and metaheuristic strategy. The LLM_SARSA outperforms the existing works with respect to the makespan time, degree of imbalance, cost, and resource utilization. The experimental results validate the inference of mathematical modeling in terms of the convergence rate and better estimation of the heuristic value to optimize the value function of the SARSA learning algorithm.

Nowadays heart attack is becoming more common among adolescents due to several reasons like heavy work pressure, lack of physical activities, irregular food habits, obesity, eating high fat food, smoking, sedentary life style, heavy drinking and so on. It has been reported that above ten lakh adolescents die every year due to heart related diseases. In literature maximum number of works have been carried out to address heart attack problem among old age people, however there are very less works concentrating on the heart attack detection focusing only adolescents. Hence in this paper the focus is towards adolescents' heart attack risk detection and classification. This is achieved by proposing a novel architecture which detects chances of heart attack in early stages using soft set theory and fuzzy rules. The performance of the proposed architecture is found to be good with respect to several parameters like accuracy, delay and efficiency.

Digital Twin is a leading-edge technology used to build a virtual prototype of products. Starting from the design, a Digital Twin helps to analyse the data available, predicting the possible effects of the data on the model, thereby improving the performance of a device or a process. It uses technologies like: Internet of Things, Data Analysis, Machine Learning, Artificial Intelligence, Computer Vision, Augmented Reality/Virtual Reality, Cloud Computing and others. This paper addresses the comparison of IoT and IIoT with Digital Twin. Our research on this paper motivated us to develop a keen interest towards this technology. This paper aims at discussing some real-time problems that can be solved by using Digital Twin. In addition to this, our paper discusses some real-time applications of Digital Twin and helps the reader to find a Twin-model solution for a problem that he wants to address. Hence, our paper provides a complete insight about this amazing technology to the reader.

Nowadays women are prone to a variety of metabolic bone diseases like osteoporosis, osteopetrosis, osteomalacia, paget's disease etc. due to calcium deficiency. The existing works fail to dynamically learn generic features of bone from the X-ray image and precisely classify the metabolic bone diseases. So, in this paper a convoluted neural network architecture embedded with deep layers is proposed which significantly classifies the bone diseases with high precision and a lowered error value.