In recent years, there has been a significant shift in how Electronic Health Records (EHRs) are stored, with a new model that offers low operational costs, high flexibility, and availability. However, this shift also raises concerns about data privacy and network security for e-health systems, particularly when it comes to sharing EHRs among mobile users. Addressing this challenge requires a reliable approach that can ensure high levels of security while facilitating the sharing of EHRs. To that end, we propose a novel framework that combines blockchain and the decentralized interplanetary file system (IPFS), with a focus on developing a trustworthy access control mechanism that uses smart contracts to enable secure EHRs sharing among different patients and medical providers. The present project is focused on tackling security and scalability issues within the healthcare industry through the utilization of blockchain technology and storing purpose using IPFS. By adopting a decentralized and trustworthy approach to electronic health record sharing, data access can be streamlined for both patients and healthcare providers across multiple organizations. To ensure secure and efficient EHRs sharing, our proposed system employs a user access control framework that manages data access from various network entities. This framework effectively prevents unauthorized access to EHR resources, while allowing authorized entities to retrieve data quickly and easily. With the implementation of this control mechanism, we aim to establish a trusted platform for sharing EHRs, where only authorized users have access to modify and view data. The system is achieved to boost data security, integrity, and maintain trust between users and technology.

Keywords : Electronic Health Record(EHR), Interplanetary file system (IPFS), Blockchain Technology

Author : Hemanth Reddy.G, Dr. N. Praveena, Harsha Vardhan.V, Charan Kumar Reddy.K, Gopinath Reddy.M

Title : CHAINGUARD: A BLOCKCHAIN-BASED SOLUTION FOR SECURING E-HEALTH RECORDS

Volume/Issue : 2023;05(05)

Page No : 1- 11

As we know, the problem of the rapidly increasing number of vehicles around the world exacerbates the issue of the unavailability number of parking spots. The problem of unprecedented growth in the private vehicle segment has created a major problem with parking spaces. Not only is it frustrating, but it's also time-consuming. the average time a driver will park is approximately 8-10 minutes. In addition, it also puts pressure on the driver's pocket as today's parking facilities are getting more expensive every day. According to industry statistics, 30% of traffic jams exist due to drivers trying to find free parking spaces. The proposed intelligent parking system offers additional convenience and automation to both companies and clients. This paper also evaluates the benefits and shortcomings of the suggested approaches. This paper finally proposes a software-based solution for managing the parking spaces and recommending the slots based on users’ location.

Keywords : Metaheuristic optimization algorithm, Parking Recommendation system (PGS), Real-time occupancy information, Smart parking system, UPI, Parking availability status, Google Maps API, Shared parking, Integrated Parking Management System(IPMS), Parking navigation system

Author : Geetha L S, A Indhu Chowdary, Akshay M S, Lalana Nagaraj, Manish Athreya

Title : PARKING SPACE RECOMMENDATION SYSTEM

Volume/Issue : 2023;05(05)

Page No : 12 - 19

Today's social media platforms have a large user base, giving companies several options to engage with their audience. Social media can help hotels grow their customer base and establish a solid online presence. since 97% of visitors post images from their travels on social media. In other words, with the hotel's social media marketing plan, hoteliers can promote their hotels and draw in more visitors. Online social networks are one of the most successful methods to utilize the Internet to advertise your company. It is the most recent method of connecting with both the Internet generation and general users. Social networking as a modern phenomenon has passed its prime nowadays. This is the modern strategy that many network marketers are using today, and it ensures success by properly identifying and catering to the target market's needs. Social networking platforms like Twitter, MySpace, and Facebook offer useful tools that hotel companies may employ to make the most of their stay there. In this research paper, the impact of social media on hotel marketing strategies has been studied

Keywords : Social Media Platforms, Hotel Marketing Strategies, Social Media Marketing Strategy, Hotel Business

Author : Asmita Pawar

Title : THE STUDY OF THE IMPACT OF SOCIAL MEDIA ON HOTEL MARKETING STRATEGIES

Volume/Issue : 2023;05(05)

Page No : 20 - 25

Bayesian Deep Learning has emerged as a powerful framework for modelling uncertainty in deep neural networks. In traditional deep learning, models are often treated as deterministic, providing point estimates for predictions. However, in many real-world applications, it is crucial to quantify uncertainty, especially when dealing with limited data, noisy measurements, or safety-critical systems. This paper provides an overview of Bayesian Deep Learning and its applications for uncertainty estimation. We explore the foundational concepts, methodologies, and practical techniques for incorporating Bayesian principles into deep neural networks. Key topics covered include probabilistic modelling, Bayesian neural networks, variational inference, and Monte Carlo dropout. We discuss how Bayesian Deep Learning can be applied to various domains, including computer vision, natural language processing, reinforcement learning, and autonomous systems. The advantages and challenges of uncertainty estimation in these applications are highlighted. Furthermore, we review recent developments and open research directions in Bayesian Deep Learning, such as scalable Bayesian models, uncertainty-aware active learning, and model compression. These advancements are driving the integration of Bayesian principles into the mainstream of machine learning, enabling more robust and reliable decision-making in AI systems. Overall, this paper serves as a comprehensive introduction to Bayesian Deep Learning, emphasizing its significance in addressing uncertainty in modern machine learning, and it provides a roadmap for researchers and practitioners interested in harnessing the power of uncertaintyaware AI systems.

Keywords : Bayesian Deep Learning, Uncertainty Estimation, Probabilistic Modeling, Bayesian Neural Networks, Variational Inference, Monte Carlo Dropout, Deep Learning, Machine Learning, Uncertainty-Aware AI, Computer Vision, Natural Language Processing, Autonomous Systems, Safety-Critical Applications, Scalable Bayesian Models, Active Learning, Model Compression, Probabilistic Inference, Neural Network Uncertainty, Bayesian Statistics, Decision-Making Under Uncertainty

Author : Mr. Ramu V, Dr. M.Vinoth Kumar

Title : Probabilistic Deep Learning: Harnessing Bayesian Techniques for Uncertainty Estimation

Volume/Issue : 2023;05(05)

Page No : 27 - 33

Graph theory is a foundational discipline in mathematics with numerous practical applications in computer science, operations research, and network design. Among its fundamental problems, vertex coloring, and the determination of chromatic numbers are central topics of study. This paper introduces innovative approaches and novel insights into these areas, offering advanced methods for solving long-standing problems.The vertex colouring problem seeks to assign colors to the vertices of a graph in such a way that no adjacent vertices share the same color. Finding the minimum number of colours required, known as the chromatic number, is a classic problem. Traditional algorithms and heuristics have limitations in finding precise chromatic numbers for complex graphs. In this work, we propose a refined heuristic algorithm that leverages the power of machine learning to predict an improved initial colouring, allowing for better convergence to the optimal chromatic number. Additionally, we present groundbreaking results in establishing upper bounds on chromatic numbers. Conventional methods for bounding chromatic numbers are often based on the so-called Lovász ϑ function and semidefinite programming. We introduce an original approach that combines spectral techniques with semidefinite relaxation, leading to tighter and more accurate upper bounds for the chromatic number.

Keywords : Graph theory, Vertex coloring, Chromatic number, Heuristic algorithms, Spectral techniques, Semidefinite programming, Lovász ϑ function, Optimization, Machine learning.

Author : Ms. B. Elezabath Rani, DR.S.Subramaniyan

Title : Innovative Strategies for Node Colouring and Novel Upper Bounds on Chromatic Numbers

Volume/Issue : 2023;05(05)

Page No : 34 - 45