Asian Journal of Convergence in Technology
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A Novel Technique To Access Sensitive Medical Data With Access Policies
Get PDFEnsuring the efficient and secure access of sensitive health records is one of the main issues challenging healthcare systems. This work offers a novel method that combines the Harmony Search Algorithm (HSA) with Attribute-Based Encryption (ABE) to provide strict data security, patient privacy, and robust access controls. Inspired by the evolution of musical harmony, the Harmony Search Algorithm successfully integrates ABE fundamentals to create and enhance controls on access that manage the retrieval of personal medical records. A dynamic framework is managed through this association, whereby HSA optimizes the development and growth of access controls and ABE presents a fine-grained, attribute-based method to encrypting and decrypting sensitive data.
This creative approach makes use of the HSA's ability to adapt access rules continuously to meet changing legal requirements and healthcare needs. The ABE algorithm offers local management of data access through making sure that only allowed entities with the necessary features can decode specific medical information, which enhances data security. With a primary focus on ensuring legal compliance, the framework's development was influenced by tight healthcare data laws, patient confidentiality, and ethical values. The recommended methodology offers an optimal combination of data security concepts and efficiency methods, representing an important progress in the domain of medical data management. This method integrates HSA and ABE to provide a framework that is safe, flexible and responsible for obtaining private medical information. This will maintain the security of patients and safety while expanding data useful for specified organizations
Vibration-Based Condition Monitoring of Shaft Bearing Systems Using Machine Learning Techniques
Get PDFA shaft-bearing system is an essential part of rotating machinery. To guarantee that a shaft bearing system operates safely and reliably, the bearings' condition must be monitored on a regular basis. Bearing and shaft failures are thought to be the leading reasons of failure in various revolving machines used in the industry at highre and lower speeds. The condition of the bearing changes throughout use, so do the vibrations, and their characteristics vary depending on the reason. As a result, the bearing's unique property makes it suited for vibration monitoring and other procedures. The vibration measurement approach may reliably anticipate the upcoming failure and life of a mechanism or component based on changes in vibration signals.
As a result, the bearing's unique property makes it suited for vibration monitoring and other procedures. The vibration measurement approach may reliably anticipate the future failure and life of a machine or component based on changes in vibration signals. As a result, the goal is to extend the machine's life by detecting faults early on, allowing for an effective maintenance program to be implemented to remedy the problem. Subsequently, this research uses machine learning methods to detect bearing problems, compare them to various faulty and standard models, and categorize the bearing type. In this research work, we use outer race fault data from the Bearing data set to extract the time domain features from the dataset using Various machine learning models, including Principal Component Analysis, K-NEAREST NEIGHBOURS (K-NN), SUPPORT VECTOR MACHINES (SVM), RANDOM FOREST CLASSIFIER, DESICION TREE, and LOGISTIC REGRESSION. As a consequence, we obtain the best model that performs optimally on the data set. Finally, the proposed methods of condition monitoring will be implemented in a real-world case study of the shaft bearing system. Thus, vibration testing is used to monitor the state of the shaft bearing system, allowing for the identification of problematic bearings and improved performance after they are replaced
High Performance Approximate Multiplier using reversible logic gates
Get PDFReversible logic has previously been shown to cause higher power consumption and a significant amount of dissipated energy because of information loss in standard design methods. This project describes the approximate multiplier using Reversible logic gates. In this design, the reversible logic gates replace the half adder and full adders in the multiplier. It uses two RG(Reversible Gate) in place of single reversible gate. So that it reduces the garbage value produced, which helps to decrease the overall delay and power consumption. The proposed Approximate Multiplier uses the product’s least significant half as a constant compensation term and the remaining half is precisely calculated. This can be a effective alternative for exact multipliers in practical error-resilient applications and Digital Image Processing
Time-Dependent Demand and Price Effects on Inventory Models: An Analytical Study
Get PDFThis analytical study investigates the impact of time-dependent demand and pricing on inventory models, focusing on deteriorating items. Inventory management plays a critical role in operational efficiency and cost minimization. Understanding how demand and price fluctuations over time affect inventory models is crucial for optimizing stock levels and reducing waste. By reviewing recent advancements and methodologies, this study highlights key findings, identifies research gaps, and suggests areas for further investigation. The analysis encompasses various models and approaches, providing a comprehensive overview of the current state of research in this domain
Prototype Development and Testing of a Low-Cost Off-Grid PV Inverter for Sustainable Energy Solutions in Remote Regions
Get PDFThis study details the process of creating, modeling, and testing a novel off-grid photovoltaic (PV) inverter system for use in distant, small-scale energy applications. Solar photovoltaic (PV) modules, a battery pack, a charge controller, and a low-power inverter make up the system's structure. A dependable source of electricity for electronics like lights and phone chargers, the inverter transforms direct current (DC) from solar panels into alternating current (AC). The design focuses on optimizing energy storage and conversion for off-grid systems, with a special emphasis on handling variable loads. The effective energy conversion and reliable power production are highlighted by the simulation results, which indicate the inverter's electrical performance. The inverter's practicality and efficacy for renewable energy applications off-grid were demonstrated by the development of a physical prototype, which served to verify these results. Research like this shows that even modest solar power systems have the ability to help find long-term answers to our energy problems. In order to facilitate the widespread use of renewable energy sources, future research may investigate ways to scale these systems and incorporate more sophisticated energy management techniques
Visualize4Learning: An Augmented Reality Framework for Earth Shapes, Mechanical Parts, and Furniture Placement
Get PDFIn contemporary education, a significant challenge persists as many students encounter obstacles in spatial visualization, leading to difficulties in comprehending complex concepts across various disciplines. Simultaneously, there is a concerning trend in the decline of creativity among students. This decline may stem from the traditional methodologies employed in education that often fail to engage students in dynamic and immersive learning experiences. Furthermore, inadequate conceptualization and visualization of subject matters might contribute to a loss of interest among students. A lack of robust visual aids and interactive learning tools can hinder students' ability to grasp and retain information effectively. Notably, this limitation extends to the critical domain of understanding the profound impacts of climate change. The absence of proper visualization tools constrains students' comprehension of the destructive consequences of climate change, impeding the urgency for action and environmental awareness.
The proposed solution advocates for the integration of augmented reality (AR) as a transformative solution in education. By leveraging AR technology, students can overcome spatial visualization challenges through immersive and interactive learning experiences. Moreover, AR offers a platform to revitalize creativity, fostering innovative thinking and problem-solving skills
Effectiveness and Limitations of existing techniques for privacy preservation data mining (PPDM) for Medical Data
Get PDFThis survey paper examines the application of Federated Learning (FL) and secure Multiparty Computing (MPC) considering medical data privacy, further provides an overview of FL and MPC techniques and discusses their strengths and weaknesses. This also covers other techniques like Homomorphic Encryption, data masking, differential privacy for its efficiency, and limitations
Influencing users for paper recycling using an E-commerce website
Get PDFCurrently, the most used promotional strategy for paper recycling includes Mail, Ads, Posters, Articles, etc. We are taking an E-commerce website that will be built upon the ideology of recycling as a candidate for being the best tool for the promotion of paper recycling among this generation among its potential impact on carbon footprint reduction. In this paper we will be comparing various statistics regarding the trends of searches and the current state of recycling in different regions of our country. We will also conduct a survey among colleagues for their habits and their current contributions. We will also be comparing different literature related to our topic. We have found that there is significant potential for a website and it has many supportive statistics with it. An E-commerce website with modern retention techniques can also bring awareness to the young masses in this cause. Big tech companies are currently competing with each other using computer science for the attention of the masses. Bringing this attention to the current most important scenarios can be so much impactful
IoT Intravenous Bag Monitoring and Alert System
Get PDFThe day-to-day monitoring of patients in a hospital is a challenging task under our existing medical care system. During Health Hazard times like Covid 19 physicians or nurses are too busy to keep track of every patient. This leads to numerous issues. Work relating to health should be completed correctly and accurately. Saline or intravenous (IV) fluid injections into patient veins are an example of this kind of activity in our hospitals. Inadequate drip system monitoring can result in issues like blood loss, fluid backflow, and other issues. We present a solution called the IoT Intravenous Bag Monitoring and Alert System in order to lessen the strain and resolve such a dire issue in the domain of an intravenous drip monitoring system. Healthcare workers found themselves overburdened at the height of the Covid-19 Epidemic due to the constant influx of new patients. Frontline staff members cannot directly monitor and care for every patient during such periods. A medical procedure called Intravenous treatment is used to inject nutrients, medicines, and fluids straight into a patient's vein. IV therapy is essential to aid a patient in recovering quickly because it is frequently used to rehydrate and supply nutrients. Nonetheless, IV drips require routine inspection and replacement. Depending on the patient and their condition, the fluid flow must also be measured. The Weight Sensor used by this IoT intravenous fluid monitoring system detects when the fluid level in the IV infusion bottle drops and broadcasts the information over IoT
Nakshatra-Drishti: A Supervised Learning Approach for Low Light Image Enhancement Using Convolutional Neural Networks
Get PDFImages captured under low-light conditions pose significant challenges for subsequent analysis due to degradation in quality, including noise, loss of scene content, inaccurate colour, and contrast information. In this paper, we propose a supervised learning-based convolutional neural network (CNN) model, Nakshatra-Drishti, specifically designed for enhancing low-light images, videos, and real-time camera feeds. The model is trained on paired datasets and extensively evaluated on various benchmarks, demonstrating remarkable results. We also introduce a user-friendly web-based software application that enhances image perception in poorly illuminated environments, facilitating more effective artificial intelligence analysis and decision-making processes