International Research Journal of Engineering & Applied Sciences
ISSN / EISSN: 23949910 / 23220821
Published by: Createcom Technologies
Total articles ≅ 36
Latest articles in this journal
Published: 31 December 2022
International Research Journal of Engineering & Applied Sciences, Volume 10, pp 18-23; https://doi.org/10.55083/irjeas.2022.v10i04009
Micro grids have become popular as a way to reduce carbon emissions and use nonrenewable energy sources to produce power. Microgrids allow users to generate and regulate energy as needed, reducing their reliance on the utility grid. They may also sell excess electricity to the grid and make money. Due to its simple design, fast installation, and easy maintenance, photovoltaic systems are a vital microgrid resource. Microgrids threaten the reliability and optimum functioning of major power grids. It's crucial to discover defects early and fix them before catastrophic system breakdown. This research proposes a unique method based on Discrete wavelet transform and ensemble of Decision tree classifier for detecting and classifying microgrid faults. Once the particular fault type is recognised and categorised, a suitable protective strategy may be used to address it early, enhancing the system's overall safety.
Published: 30 December 2022
International Research Journal of Engineering & Applied Sciences, Volume 10, pp 30-42; https://doi.org/10.55083/irjeas.2022.v10i04006
Cancer, one of the most prevalent causes of death and disease, has a convoluted pathophysiology. Chemotherapy, immunotherapy and radiation therapy are examples of traditional cancer treatments. However, lack of selectivity, restrictions such cytotoxicity, and Drug resistance is a significant barrier to successful cancer treatment. With the development of nanotechnology, the study of cancer treatment has undergone a revolution. For treatment of cancer Nanoparticles can be used because of their special advantages, less toxicity, more good stability, stronger permeability, and exact placement. There are several varieties of nanoparticles. The innovative nanoparticle based drug delivery system makes advantage of characteristics of the tumour and its surroundings. Nanoparticles overcomes the disadvantages of conventional treatment of cancer in addition to avoiding multiple drug resistance. As additional multidrug resistance mechanisms are found and examined, nanoparticle research is also being pursued actively. The therapy includes consequences of Nano formulation have provided fresh perspectives on cancer treatment. The biggest chunk of studies, however, is restricted to in vivo and in vitro experiments, and the number of authorized Nano drugs has not increased significantly over time. This study covers a wide range of nanoparticle kinds, targeting strategies, and authorized Nanotherapy includes use in the cancer treatment. We also provide a summary of the pros, disadvantages, and present state of clinical translation.
Published: 27 December 2022
International Research Journal of Engineering & Applied Sciences, Volume 10, pp 24-29; https://doi.org/10.55083/irjeas.2022.v10i04007
Renewable energy has gained popularity due to depleting natural resources and escalating fossil fuel and nuclear pollution. Power electronic engineers design grid-connected power conversion systems. MLIs provide more power and solutions. Cascaded H-Bridge (CHB) MLIs start with two or more 3L single-phase H-bridge inverters. Each H-bridge may produce three separate voltage levels. Combining the separated dc voltage sources produces a stepped output voltage with a step size equal to the magnitude of the connected sources. The present work develops a method for detecting and resolving switch failures in a three-phase CHB inverter, ensuring system dependability and allowing for system redundancy. The recommended approach uses Wavelet transform to extract features, then Decision Tree classifier to detect and characterise defects. Increased classification accuracy shows the DT-based fault diagnosis system's efficiency in identifying inverter switch faults.
Published: 27 October 2022
International Research Journal of Engineering & Applied Sciences, Volume 10, pp 08-17; https://doi.org/10.55083/irjeas.2022.v10i04002
This work describes a functional, generic, broad-scoped investigative methodology for Windows memory analysis. The methodology applies equally to functional and damaged, or corrupted memory images and relies on Volatility. It is based on the author’s various memory analysis case studies. Summing it up succinctly, the methodology aids the forensic practitioner in squeezing the maximum amount of possible evidence from a memory image. The proposed methodology is suitable for analysts at all levels of investigative capability. It provides guidance in extracting maximum evidence using simple, commonplace tools and techniques familiar to digital forensic practitioners. As with all methodologies, nothing is written in stone; the forensic practitioner must be flexible and agile in responding to ever-changing investigative requirements. To assess the performance of various tools for gaining, analysing, and improving criminal evidence from volatile memory. A comparison of several tools is offered in order to provide a better understanding of the tools used.
Published: 15 October 2022
International Research Journal of Engineering & Applied Sciences, Volume 10, pp 01-07; https://doi.org/10.55083/irjeas.2022.v10i04001
One of the most in demand research topic in today’s technology world is medical area and cancer is one of them. The second main cause of death in the world is cancer. In 2015 about 8.8 million people have died due to cancer . For the early detection of breast cancer several types of research have been done to start the treatment and increase the survivability. Breast cancer affects the women mentally as well as emotionally. The goal of this challenge is to provide a framework that uses a cancer medical dataset as input and then analyses the dataset to produce findings that help medical experts better understand the state of the disease. The majority of studies concentrate on mammography results. However, incorrect detection in mammography pictures can occasionally result, endangering the patient's health. Cancer that forms in the cells of breast is said to be breast cancer. The cancer should be cured if it is diagnosed in early stage. So, with the help of machine learning algorithm the cancer should be diagnosed early. Most of the women lives are affected by the breast cancer in all over the world. There are two types of tumors that can be found in breast cancer i.e. malignant or benign. If a person is having a cancer disease, then it will be categorized as malignant otherwise it is known as benign. In 2020, 685000 deaths and 2.3 million women diagnosed with breast cancer globally, somewhere in world in every 14 seconds, a woman is diagnosed with breast cancer . Patient life from the breast cancer can be saved only if it is found in early stage; if it is diagnosed later then the chances of survival are less. If the cancer is diagnosed early then the patient will get a better treatment. This study will concentrate on a few machine learning methods for identifying if a breast cancer is malignant or benign. The Wisconsin Breast Cancer Dataset, which was acquired via Kaggle, was used in this study. Our goal is to evaluate how accurately various machine learning algorithms can detect breast cancer. These include Random Forest Classifier, Decision Tree Classifier, Support Vector Machine, and K-Nearest Neighbors. All the experiments are conducted on a Jupiter platform. After the analyzing the accuracy of each algorithm the most suitable one is Support Vector Machine that gives the better accuracy among all i.e., 98%.
Published: 4 September 2022
International Research Journal of Engineering & Applied Sciences, Volume 10, pp 42-48; https://doi.org/10.55083/irjeas.2022.v10i03013
Model-based security metrics are an emerging topic of cyber security research that focuses on assessing an information system's risk exposure. We propose an end-to-end solution with the deployment of a zero-trust network utilising Artificial Intelligence in this article to understand the security posture of a system before it is rolled out and as it matures. The major part contains a discussion about the key methods and techniques which was utilized in the development process and simplified operation principles of each developed process. Some developed processes were tested practically to evaluate the problems in the processes. Modules for automatic processing and data analysis were also developed. These modules can be connected in case it is needed. The most important data collection methods were benchmarked to detect problematic situations in the operation in different realistic situations. With the perception from the benchmark test, the problematic parts of the data collection were discovered and proposals for the solution were made which could be developed and tested in the next iterations of the development process. Working Artificial intelligence-based detection and data enrichment methods were created. The results of the article allow multiple continuous research and development projects related to data collection and data analysis with statistical and artificial intelligence-based methods.
Published: 6 August 2022
International Research Journal of Engineering & Applied Sciences, Volume 10, pp 34-41; https://doi.org/10.55083/irjeas.2022.v10i03006
Nowadays e-commerce and online transaction is growing rapidly. For online and offline transaction most of the customer uses credit card. Credit card used globally for online transaction, buy goods, product, and payment. The rising use of credit card can increase the chances of fraud in credit card. Credit card system is at risk now. The effect of this fraudulent transaction is on the bank and institute causing a financial loss to them. For the detection of distinguish frauds, several machine learning models are utilized for better prediction. The major objective of this article is to identify the fraudulent transaction and outlier in credit card transaction. The dataset of credit card is unbalanced. There are various techniques by which fraudulent transaction can be detected and we have used these techniques such as isolation forest method, local outlier factor and support vector machine to determine fraud in credit card. We have used different matrices for enhancing the performance and accuracy. At last comparison analysis is done by using isolation forest, support vector machine, and local outlier which give the better result.
Published: 10 July 2022
International Research Journal of Engineering & Applied Sciences, Volume 10, pp 24-33; https://doi.org/10.55083/irjeas.2022.v10i03003
Internet-based provision of computer resources is known as cloud computing. It is possible to utilise data that is controlled by a third party or some other individual at a distant place through cloud computing. Service Level Agreements (SLAs) are used by the majority of Cloud providers to define the services they provide. As part of the SLA, the service provider promises a certain level of quality of the service. Computing and data clouds are two sorts of clouds in a cloud-based system. In cloud technology, task scheduling is critical to ensuring service quality and SLA. One of the most important aspects of cloud computing is a well-organized work schedule. In this article, we have designed an optimal task scheduling method using RAO approach. The RAO algorithm is simple, required number of parameters, and required no tuning of parameters as compared to the other algorithm. Further, a multi-objective function is designed based on RAO algorithm performs the optimal scheduling. The performance evaluation is done by considering number of tasks such as 50,100,200, 300,400, and 500. Further, number of performance metrics are determined for it. The outcomes represents that the presented technique provides lesser values of AWT, ATT, and make span over the existing method.
Published: 10 July 2022
International Research Journal of Engineering & Applied Sciences, Volume 10, pp 01-09; https://doi.org/10.55083/irjeas.2022.v10i03001
This research work, presents Fuzzy Logic Controller (FLC) based D-STATCOM for power quality (PQ) enhancement in power distribution network. In the power distribution system, PQ is the major issue that is occurring due to non-linearity and dynamic changes in the connected loads. The proposed work utilizes FLC for generating switching Pulses for IGBT switches in the D-STATCOM to enhance quality of power in distribution systems. This research work also shows superior performance over conventional PI controllers in mitigation of harmonics by using proposed FLC topology. The proposed system is simulated with Matlab/Simulink software to ensure effective realization.
Published: 10 July 2022
International Research Journal of Engineering & Applied Sciences, Volume 10, pp 10-23; https://doi.org/10.55083/irjeas.2022.v10i03002
Cloud computing is delivered as a storage service by third party. It gains wide acceptance from various Business organizations & Information Technology (IT) Industries. Cloud computing provides various services to users through the internet; those services are like Applications, computation, and storage etc. In spite of these advantages, cloud technology faces different types of privacy and security related issues. These issues become major barriers to adopt cloud technology into various organizations. This survey paper addresses the cloud architecture, various security and privacy issues, challenges and threats, attacks, and future research directions to overcome the security and privacy related problems in the cloud environment.