Journal of Informatics Electrical and Electronics Engineering (JIEEE)

Securely Cloud Data Storage and Sharing

2023-04-26T05:10:58+0530

With the increasing adoption of cloud computing, data storage and sharing have be-come integral parts of our digital lives. However, ensuring the security and privacy of data stored in the cloud remains a significant challenge. This paper proposes a novel approach for securely storing and sharing data in the cloud, addressing the vulnerabilities associated with traditional cloud storage models. The proposed approach utilizes advanced cryptographic techniques, including hyperchaotic encryption and hash functions, to protect the confidentiality and integrity of data stored in the cloud. The hyperchaotic encryption algorithm provides a high level of security by introducing chaos-based dynamics into the encryption process, making it resistant to various attacks. Additionally, the hash function ensures the integrity of data by generating unique identifiers for each file stored in the cloud. To enhance data sharing security, the proposed approach employs access control mechanisms and user authentication protocols. Access control rules are enforced to restrict unauthorized access to data, while user authentication ensures that only legitimate users can access and modify the shared data.

Optimizing Cloud Resource Management Using PSO

2024-04-25T00:34:15+0530

This research explores how cloud resource management is changing in businesses, with a focus on Amazon Web Services (AWS) as the leader in cloud computing. It highlights how crucial excellent resource management is to attaining scalability, cost-effectiveness, and peak performance. The study explores on using Particle Swarm Optimization (PSO) as a cutting-edge optimization method in cloud computing settings. It talks about the difficulties brought on by fluctuating workloads and the requirement for clever resource allocation strategies. Additionally, the study assesses several optimization techniques using performance parameters including computing overhead, convergence time, and solution quality. These techniques include PSO, Genetic Algorithm (GA), and Firefly Algorithm (FA). In-depth simulations and case studies with organizations such as Siemens and Deloitte are used in the study to demonstrate how these algorithms work best in cloud environments to maximize resource usage, cut costs, and improve overall service quality. In the end, it emphasizes the continuous requirement for optimizing techniques to successfully handle the complexity of cloud computing ecosystems.

A Review on Fast Charging Methodologies of Electric Vehicles

2024-04-24T08:51:26+0530

The Li-Ion battery is charged using a constant voltage, constant current charging method. The solution, photovoltaic energy, is making its appearance in the EV charging infrastructure. By altering the transformer ratio, the inverter's voltage gain can be adjusted. Instantaneous thermal gradients are altered by fast charging. The grid and electricity quality may be impacted by EV charging.The goal of this study is to create a comprehensive, current overview of fast charging techniques for battery-electric vehicles (BEVs). The foundational ideas of single battery cell charging as well as existing and upcoming charging standards are covered in this paper. Globally, battery-powered electric vehicles are becoming more and more common. Numerous causes, such as the need to lessen noise and air pollution and our reliance on fossil fuels, are driving this trend. The primary disadvantage of modern electric vehicles is their short range and the length of time needed to charge their batteries. Through research and development, great strides have been achieved recently to use pulse charging, as opposed to continuous current and/or voltage delivery, to shorten the charging period of the batteries in electric vehicles. The portion that needs to be concentrated on estimating the electrical properties of the vehicle's battery is crucial for learning about the potential driving range.

Sorting Visualizer: A Visual Journey Through Sorting Algorithms

2024-04-24T09:45:02+0530

This paper, which is based on the importance of sorting algorithms, will carefully compare the features of various algorithms, beginning with their work effectiveness, algorithm execution, introductory concepts, sorting styles, and other aspects, and make conclusions in order to create more effective sorting algorithms. Searching techniques and sorting algorithms are not the same. Sorting is placing the provided list in a predetermined order, which can be either ascending or descending, whereas searching is predicated on the possibility of finding a specific item in the list. Only a section of the data is sorted, and the piece of data that's actually used to establish the sorted order is the key. The maturity of this data is being compared. Depending on the kind of data structure, there are several algorithms for doing the same set of duties and other conditioning, and each has pros and cons of its own. Numerous sorting algorithms have been analysed grounded on space and time complexity. The aim of this relative study is to identify the most effective sorting algorithms or styles. This relative study grounded on the same analysis allows the user to select the applicable sorting algorithm for the given situation.

Enhancing Location Accuracy by Minimizing RMS Using RSS-AMLE in WSN

2023-11-14T04:03:16+0530

Over the past decade, there has been significant growth in wireless sensor networks, particularly in the context of industrial applications. Mobile sensor networks have garnered research interest due to their ability to facilitate communication between various devices. Still, the mobility of these nodes gives rise to challenges such as network coverage and connectivity issues. Addressing these challenges necessitates accurate estimation of sensor node locations, a critical factor in network performance. Numerous methods, such as Angle of Arrival (AOA) and Time of Arrival (TOA), have been proposed for node localization. Still, these methods are plagued by localization errors and high implementation costs. To overcome these localization errors in wireless sensor networks, we present an adaptive approach based on the Received Signal Strength (RSS) model. This model views localization as a non-convex problem and employs an adaptive maximum likelihood estimation to minimize localization errors. An extensive simulation study is carried out to measure the performance of the intended approach in minimizing the localization error. The results unequivocally demonstrate that our localization scheme achieves higher accuracy in locating sensor nodes while reducing deployment costs. Comparative analysis against existing methods further underscores the significance of our approach.

Centralized Database and Automation: Key to Overcome the Challenge of Missing or Inaccurate Standard Settlement Instructions

2023-05-03T05:34:27+0530

To achieve effective and automated payment processes, straight-through processing (STP) has been implemented in the financial sector. The implementation of STP is, however, still hampered by the existence of incorrect or absent standing settlement instructions (SSIs). This research study explores the reasons of missing or incorrect SSIs in the banking sector, their effects, and potential fixes. The complexity of the situation is further increased by the examination of the variety of channels used by parties to transmit their SSIs. According to the report, incomplete or inaccurate SSIs are a significant cause of payment failures and unneeded expenses, hence a workable solution is required. The research recommends the implementation of a centralized SSI database that can be accessed by all parties involved in the payment process, as well as the automation of SSI updates to ensure accuracy and efficiency.

Multiclass Brain Tumor Classification Using Transfer Learning

2023-08-04T08:07:10+0530

Tumors are a collection of abnormal cells that multiply enormously than required which leads to cancer and divergent and also can be fatal, if not identified at an early stage. Usually, brain scan described as Magnetic resonance imaging (MRI) is deployed for high transparency and representation in different angles but causes huge delay in declaring the result of the test. In this project, images obtained from these tests are carefully observed and classified by implementing Deep Residual Network (RESNET) to classify the type of tumor. There are four types of tumors such as glioma, meningioma, pituitary, and no tumor. Brain tumor classification (Multi Label) – CNN dataset has been imported to train and test the model. This deep learning model is a sophisticated approach which is developed to classify the tumor based on the image, so that appropriate treatment can be given on time. The output determines the type of tumor if present, otherwise no tumor with accuracy of 87% using epochs.