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Duplicate Detection on Cloud Data with Re-Encryption Based Data Sharing
Cloud data storage has gained immense popularity in recent years due to its scalability and accessibility. However, the efficient management of data in the cloud, especially with the ever-increasing volume of data, remains a significant challenge. Data deduplication is a crucial technique to reduce storage costs and enhance data management efficiency. It also concerns the secure cloud data storage and sharing. Here implement a novel approach to de duplication-based cloud data storage using chunk-based partitioning. In this approach, data is divided into fixed-size chunks, and a deduplication mechanism is applied to identify and eliminate redundant chunks. The process of duplicate detection leading to improved storage efficiency and reduced data redundancy. It not only conserves storage space but also enhances data retrieval performance by reducing the volume of data to be transferred over the network. Proposed system also enhance the security of cloud data storage by employing Base64 encoding for data representation and implementing re-encryption techniques based on the Advanced Encryption Standard (AES) for secure data sharing. The Base64 encoding methodology is employed to transform binary data into a text format, rendering it less susceptible to unintended manipulations during data transmission and storage. This encoding mechanism ensures data integrity and facilitates its secure storage in cloud environments. By utilizing AES encryption, data shared among users is re-encrypted with unique keys, ensuring that only authorized recipients can access and decrypt the shared information. The proposed approach significantly enhances data confidentiality and mitigates risks associated with unauthorized access, there by fortifying the security of cloud-based data sharing.
DIGI Attendance
The Digi Attendance project is a comprehensive and innovative system designed to transform attendance management in various settings, including educational institutions, workplaces, and events. This project report presents the development, implementation, and evaluation of the Digi Attendance system, highlighting its key features, benefits, and future potential. Digi Attendance system deal with the maintenance of the student's attendance details. It generates the attendance of the student on basis of presence in class. It is maintaining daily basis of attendance, only if the student presents the particular date, the attendance will be calculated.
A Block Chain-Based Access Control Framework with Privacy Protection in Cloud
Access control is a pivotal aspect of modern computing systems, ensuring that only authorized entities can interact with sensitive data and resources. Traditional access control methods, while effective, face challenges in terms of security, transparency, and scalability. Blockchain technology has emerged as a promising solution to enhance access control in cloud computing environments. Blockchain's inherent features, such as decentralization, immutability, and cryptographic security, offer a unique framework for addressing the limitations of traditional access control systems. In this paper, we explore the integration of blockchain technology with cloud computing to establish a more secure and transparent access control mechanism. By utilizing blockchain, access control policies and permissions can be stored in a tamper-proof and transparent ledger. Scalability is an issue since processing many access control transactions on the blockchain might cause network congestion and sluggish processing. Transactions take time to upload to the blockchain, which can delay real-time access choices. It takes skill to integrate and manage blockchain and cloud technologies together. Choosing the correct consensus mechanism affects system efficiency and security. Consider the costs of establishing and maintaining such a system and the difficulty of fixing faults owing to blockchain immutability. In conclusion, this paper underscores the significance of access control in cloud computing and the limitations of traditional approaches. By harnessing the power of blockchain technology, a more secure, transparent, and scalable access control framework can be established, revolutionizing the way we manage access to sensitive data and resources in complex digital ecosystems.
Emergency Fuel Ordering and Management System for Vehicles
In today’s fast paced world, monitoring systems are necessary to track the changes in the environment for better understanding of current scenarios and predictions thereof. The same is true for fuel tanks in vehicles as well. By keeping strict track of fuel intake and consumption, vehicles can be made more fuel and cost efficient. This can be done using remote monitoring and data collection systems deployed at the site of the fuel storage tank. When an agent starts filling petrol in your bike/car, the flow sensor is activated. Capacitance is used in the suggested IoT gadget to measure the amount of fuel. The driver receives a notification through mobile application when the vehicle's Fuel reaches a low level.
Big Data Visualisation of Heart Disease Prediction Using High Level Ada Boosting Algorithm and Gradient Boosting Algorithm
Machine learning and artificial intelligence have been found useful in various disciplines during the course of their development, especially in the enormous increasing data in recent years. It can be more reliable for making better and faster decisions for disease predictions. So, machine learning algorithms are increasingly finding their application to predict various diseases. Constructing a model can also help us visualize and analyze diseases to improve reporting consistency and accuracy. This article has investigated how to detect heart disease by applying various machine learning algorithms. The study in this article has shown a two-step process. The heart disease dataset is first prepared into a required format for running through machine learning algorithms. Medical records and other information about patients are gathered from the UCI repository. The heart disease dataset is then used to determine whether or not the patients have heart disease. Secondly, Many valuable results are shown in this article. The accuracy rate of the machine learning algorithms, such as Logistic Regression, Support vector machine, K-Nearest-Neighbours, Adaboost, and Gradient Boosting Classifier, are validated through the confusion matrix. Current findings suggest that the Logistic Regression algorithm gives a high accuracy rate of 95% compared to other algorithms. It also shows high accuracy for f 1-score, recall, and precision than the other four different algorithms. However, increasing the accuracy rates to approximately 97% to 100% of the machine learning algorithms is the future study and challenging part of this research.
Finger Print Voting System
The field of biometrics, or the identification of people based on physiological or behavioral characteristics [6], has recently received a significant amount of attention in current research as a result of its applicability in the fields of information technology and security. With the increased usage of PIN numbers and passwords in everyday life, the vulnerabilities of these two technologies are becoming more apparent (e.g. password-cracking). Biometric authorization’s strengths are that it requires the user to be present and that it eliminates the hassles of passwords and PIN’s (or can be used in parallel with these for added security). Specifically, online identity verification, ATM machines, and building entrance authorization, are all areas where it is very useful to implement an automatic identification system.
Energy-Efficient Framework for Green Cloud Computing in Dynamic Resource Allocation
Cloud Computing has been a trending technology for a few years supporting computational services over internet. But ever since its adoption, cloud’s consistent challenge is in its dynamic resource allocation. The existing cloud model details the online and offline algorithms used to decide the dynamic resource allocation. The goal is to have a dynamic resource allocation framework that aligns to cloud data management’s objective of maximizing revenue with minimum cost. This encourages both consumers and cloud providers not only with energy-efficient power usage but also high CPU utilization. This article discusses the impediments of migrating to Public Cloud, what is dynamic resource allocation, HPC workloads with complex communication path on cloud platform, and the benefits of bare metal platform for latency-sensitive applications. We shed light on trade- offs (compute balance) between Private and Public Cloud, how existing resources can be leveraged, Random forest (RF) solutions including a study on hybrid cloud computing capacity optimization framework. Understanding RL architecture, problem solving approach, learning structure and Hybrid Cloud Management Architecture framework are also explored. Also given are a few RL implemented gaming examples on how it makes an impact. Lastly, we shall do the comparisons of RL with other Machine Learning (ML) approaches.
Analyzing the States of Brain under Anesthesia Using ANES-metanet based on Deep Learning Models
Monitoring the depth of unconsciousness during anesthesia is beneficial in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time .Continuous monitoring of electroencephalogram (EEG) recordings in humans under general anesthesia (GA), local anesthesia (LA) and regional anesthesia (RA) has demonstrated that changes in EEG dynamics induced by an anesthetic drug are reliably associated with the altered arousal states caused by the drug. This observation suggests that an intelligent, closed-loop anesthesia delivery (CLAD) system operating in real-time could track EEG dynamics and control the infusion rate of a programmable pump to precisely maintain unconsciousness. In this process we propose a deep learning algorithm to analysis the anesthesia from EEG signal. So the deep learning algorithms are Convolution Neural Network, Long Short Term Neural Network and Artificial Neural Network will applied and analyze EEG signal and it generates the results in the form of metrics like accuracy, precision, recall and f1-score.
Land Registration System Using Blockchain
The current land registration system relies on paper-based methods, prone to fraud, errors, and security vulnerabilities. These shortcomings contribute to prolonged registration processes and an increase in fraudulent activities. To overcome these challenges, this paper proposes a blockchain-based land registration system. Blockchain technology provides an immutable and decentralized ledger, ensuring the security and reliability of land records. By eliminating intermediaries, the blockchain-based registry streamlines land ownership validation, reduces costs, and accelerates registration timelines. This paper explores the potential of blockchain in land registration, focusing on fraud prevention, secure ownership record storage, and facilitation of transfer through cryptographic algorithms and smart contracts. The paper discusses the limitations of traditional land registration systems, including security, transparency, and efficiency issues. It then outlines how blockchain technology addresses these challenges by offering a transparent and secure registration mechanism. Utilizing cryptographic algorithms and smart contracts, blockchain ensures tamper-proof record-keeping and enables swift and transparent ownership transfers.
Blockchain Based Placement Management System And Job Recommendation Using Content-Based Filtering
The integration of blockchain technology with placement management systems and job recommendation mechanisms presents a robust solution for enhancing the efficiency, transparency, and security of the recruitment process. This paper proposes a Blockchain-based Placement Management System (BPMS) coupled with Content-Based Filtering (CBF) techniques to streamline the job matching process for both job seekers and recruiters. The BPMS leverages the decentralized nature of blockchain to ensure immutable records of job listings, candidate profiles, and transactional data, thereby reducing the risk of fraudulent activities and ensuring data integrity. Smart contracts are utilized to automate various aspects of the recruitment process, such as job application submissions, candidate evaluations, and offer acceptance, facilitating trustless interactions between parties.In parallel, the CBF approach is employed to provide personalized job recommendations to candidates based on the analysis of their skills, qualifications, and preferences. By analyzing the content of job descriptions and candidate profiles, the system generates tailored recommendations that match the expertise and interests of the candidates, improving the likelihood of successful placements.The proposed system offers several advantages over traditional placement management systems, including increased transparency, reduced operational costs, minimized bias in job recommendations, and enhanced security of sensitive data. Through the synergy of blockchain technology and content-based filtering techniques, the BPMS aims to revolutionize the recruitment industry by fostering trust, efficiency, and fairness in the job market.
Improving Healthcare Data Security and Privacy With Blockchain technology using SHA-256 Algorithm
Block chain technology has the potential to completely transform healthcare management systems by enhancing data security, interoperability, and integrity because to its decentralized and unchangeable structure. Sensitive patient information is more difficult for bad actors to breach thanks to the SHA-256 algorithm, which improves data security. Interoperability between diverse healthcare systems is facilitated by smart contracts and standardized data formats. However, issues with user adoption, scalability, and regulatory compliance also face block chain implementation in the healthcare industry. This study offers important insights into the changing environment of healthcare data management by highlighting the possible advantages and challenges of integrating block chain technology in healthcare management systems.
Block Chain Based Agri-Food Traceability with SHA-256 Algorithm
Block chain technology has emerged as a promising solution for enhancing transparency and efficiency in various industries, including agriculture and food supply chains. This article examines the influence of block chain technology on agriculture, explores ongoing projects and initiatives, and assesses its potential implications, obstacles, and promises. Despite its potential to revolutionize the food supply chain by enabling transparent transactions among dispersed and untrustworthy parties without intermediaries like banks, several challenges hinder its widespread adoption. These challenges encompass technological limitations, educational gaps, and regulatory hurdles. This study provides insights into the current state of blockchain adoption in agriculture and offers critical perspectives on project maturity.
AI Enabled MED Drone for Healthcare Application
The project proposes the development of an AI-enabled MedDrone for medication delivery, introducing an innovative and efficient solution to enhance healthcare logistics. The MedDrone, equipped with artificial intelligence capabilities, aims to revolutionize the process of medication delivery by leveraging autonomous aerial technology. The system integrates advanced algorithms to optimize route planning, ensuring timely and accurate delivery of medications to designated locations. The AI component enables the drone to adapt to real-time variables such as weather conditions and traffic, enhancing the reliability and responsiveness of the delivery process. This project addresses the challenges of traditional medication distribution, offering a futuristic and intelligent approach to healthcare logistics through the deployment of AI-enabled autonomous drones for efficient and timely medication delivery. The proposed system integrates AI algorithms for real-time route optimization, obstacle avoidance, and decision-making, enabling the drone to autonomously navigate complex environments and deliver essential medical supplies such as first aid kits, defibrillators, and medications to the point of need. Through the utilization of machine learning techniques, the drone continuously learns from its interactions with the environment, enhancing its adaptability and performance over time. Additionally, the system incorporates advanced communication technologies to enable seamless coordination with emergency responders and healthcare professionals, ensuring timely delivery and proper utilization of resources. The efficacy of the proposed AI-based medical drone system is demonstrated through simulations and real-world trials, highlighting its potential to revolutionize emergency response efforts and improve healthcare accessibility, particularly in challenging or resource-constrained environments.
A Deep Learning Approach to Detect Rheumatoid Arthritis Using Medical Images and Blood Samples
Rheumatoid arthritis (RA) is a long-term autoimmune disease that mainly affects the joints. It happens when the immune system, which should aid in defending the body against illness and infection, unintentionally targets healthy tissues. This may result in joint pain, edema, stiffness, and loss of function. RA postures are a noteworthy clinical challenge due to their inveterate nature and potential for joint deformation and systemic complications. Conventional demonstrative strategies frequently drop briefly in giving exact and opportune discovery, requiring imaginative approaches. In this way of thinking, the developed novel system coordinates profound learning strategies with multi-modal information, counting blood biomarkers and restorative pictures of influenced joints of the hand, to revolutionize RA discovery and seriousness assessment. By leveraging a comprehensive dataset and considering indication length, the proposed approach outperforms the confinements of existing strategies, advertising prevalent symptomatic precision and early discovery capabilities. Through fastidious assessment, the system illustrates the diminishment of wrong positives and untrue negatives, hence progressing persistent results and diminishing long-term joint damage. Furthermore, this novel system contributes to progressing the understanding of RA pathophysiology and treatment viability by cultivating logical advances through the development of a comprehensive dataset. As part of the multi-modal framework integration process, the American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) 2010 criteria are taken into account, and the system is improved by drawing on existing systems or discovered techniques. Specifically, the investigation was done on automating RA seriousness assessment using Profound Learning computations associated with hand X-ray pictures. Notwithstanding these advances, difficulties persist in accurately identifying early indications of RA from common variations or other joint disorders, as well as in identifying subtle joint deviations from the norm. To improve demonstration accuracy and advance in the comprehension of care outcomes in Rheumatoid Joint pain determination and severity evaluation, machine learning methodologies and multi-modal information integration were used.
Unlocking the Potential of 3D Printing in Modern Manufacturing
The process of producing tangible items by layering on materials according to geometric representations is called digital fabrication technology, sometimes referred to as additive manufacturing or 3D printing. This innovative technique has gained popularity. Numerous industries, including healthcare, automotive, locomotive, and aviation, now make extensive use of the quickly expanding area of 3D printing technology. This page discusses the materials utilized in the production process, provides a summary of the many types of 3D printing technology, and illustrates the extensive variety of uses for them With the potential to create intricate structures and personalized patterns straight from computer-aided design (CAD) models, 3D printing technology is a potent instrument that has the potential to completely alter the face of contemporary production.
Real-Time Monitoring 3D Printer Device for Bio Models
IoT and embedded electronics is getting popular due to its versatile applications in various fields for domestic, commercial and industrial usage. However electronics or electrical systems that are primarily built using electronics, needs enclosures to protect it from the environment as well as for maintaining its privacy concerns. 3D printers are valuable to rapidly prototype/fabricate/build enclosures for custom applications. The steps, involved in rapid prototyping using 3D printers start from understanding the specification and measurement requirements e.g. colour, look, dimensions, and tolerances. Followed by Certainly! Cura is a popular slicing software used in 3D printing that converts your 3D models (STL files) into a format (G-code) that your 3D printer can understand. Integrating IoT (Internet of Things) with 3D printing using Modified OctoPrint. It can greatly enhance the functionality and accessibility of your 3D printing setup. OctoPrint is an open-source software that allows you to control and monitor your 3D printer remotely via a web interface. By adding IoT capabilities, you can further automate and monitor your printing processes, making them more efficient and comfort
Effect of Corrosion on R.C.Columns
Reinforced Cement Concrete is one of the most important structural materials used in the construction industry worldwide. It is expected that these structures will serve for a long period of time. But there are many instances of premature failure of RC structures, raising doubts about its durability. Most of these failures are attributed to the corrosion of reinforcement embedded in concrete. Generally, concrete provides an environment, which should ideally protect the embedded steel. This is true provided the quality of the concrete is good and sufficient cover is provided to the reinforcement. Concrete of low water-cement ratio and good compaction has low permeability that minimizes penetration of corrosive agents like oxygen, carbon-di-oxide, chloride ions etc. Corrosion of reinforcement is an international problem that causes extensive damages to various types of structures. The present project work deals mainly with corrosion of reinforcing steel in concrete columns in saline environment. The influence of concrete mix, concrete cover and reinforcing bar diameter on the rate of corrosion and the loss of bond due to corrosion level of reinforcing bars are also investigated.
Estimation of EIA by Semi-Automated Direct Method for Western Indian Urban Catchment Area
Urbanization impacts water resources, with impervious surfaces indicating the degree of disturbance. Effective impervious area (EIA) is a better measure than total impervious area (TIA) for urban runoff. EIA is the portion of TIA connected to the storm sewer system. Remote sensing data alone is insufficient for EIA determination. An alternative approach integrates remote sensing data with drainage networks to ensure connectivity of impervious surfaces. This approach uses GIS data including multi-sensor data. The modified literature algorithm was used in python to analyze the study area. It is observed that for western Indian urban catchment area, some empirical equation such as Sutherland’s and Alley-Veenhuis showed under-estimation and over-estimation at some watershed parts which were obtained using DEM delineation. Based on the results obtained from proposed method, a quadratic relationship was established between easily measured TIA and hydraulically relevant EIA for an urban catchment in western India. These outcomes should result in more effective and properly designed SCMs, with potential improvements in water quality and cost savings.
Simulation of Kirchoff's Law In Ni-Multisim Application to Determining the Current on a Circuit
The increasingly rapid development of technology has given rise to simulation methods used in circuit specifically in dynamic electricity. One of them is the NI-Multisim application which is useful for determining current and voltage values efficiently and effectively with a circuit design that is built first in the application. Research activities were carried out by simulating Khirchoff's law on NI Multisim by building a circuit scheme based on the circuit composition, namely resistance, voltage source and using voltage measuring instruments and current measuring instruments which are all arranged in such a way by referring to electrical circuit theory. The results of this research are that there is a difference in the total resistance value between the values in the circuit kit module practice and the use of Multisim. However, the difference is very small with an error of around 7.83% for the series-parallel circuit. The current value for each resistance which is connected in a mixed manner can be seen that the practical and theoretical values have an error value of 0% to 24.24% respectively located at R3 at the 6V voltage source and R2 at 12V. The current in I3 is equal to the sum of the current in I2 and the current in I1 for each specified voltage variation. The measurement values with the Khirchoff kit module are different from the NI Multisim simulation, but the differences are still relatively small, ranging from 6% to 28%.
Exploring User Addiction to Social Media Apps: The Role of Design Elements and User Behaviour
In the age of smartphones and social media, user engagement has reached unprecedented levels. However, this surge has also given rise to phone addiction, particularly as social media platforms increasingly adopt design elements that keep users hooked. This article explores the key design elements that drive user engagement and contribute to addiction. Focusing on patterns like infinite scroll, social validation, and personalised content, the article draws insights from popular social media apps like Instagram, TikTok, and Facebook. Additionally, it discusses the psychological underpinnings of these designs and examines the ethical concerns associated with their implementation.