IRJEAS

INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING & APPLIED SCIENCES

ISSN 2322-0821(E), ISSN 2394-9910(P)

An ISO 9001:2015 Certified Publication

Volume 10 Issue 4                         October-December 2022

Review Article

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A Review on Breast Cancer Prediction Using Machine Learning [pp 01-07]

DOI: https://doi.org/10.55083/irjeas.2022.v10i04001

Country- INDIA

 Swasti Goyal, Tanya Sharma, Anuj Kumar

PAPER ID: IRJEAS04V10I410221222001

Published: 15 October 2022

Journal: IRJEAS, Volume 10, Issue 4

Pages: 01-07

Keywords: Breast Cancer, KNN, Machine Learning, SVM, Tumor

Abstract: 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 [5]. 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 [1]. 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%.

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Live Memory Forensic for Windows [pp 08-17]

DOI: https://doi.org/10.55083/irjeas.2022.v10i04002

Country- USA

  Priya Parameswarappa

PAPER ID: IRJEAS04V10I410221222002

Published: 27 October 2022

Journal: IRJEAS, Volume 10, Issue 4

Pages: 08-17

Keywords: Cyber security, Memory analysis, Memory forensic, Windows

Abstract: 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.

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Machine Learning Based Efficient Protection Scheme for AC Microgrid [pp 18-23]

DOI: https://doi.org/10.55083/irjeas.2022.v10i04009

Country- INDIA

  Abhishek Dixit, Santosh Kumar 

PAPER ID: IRJEAS04V10I410221222009

Published: 25 December 2022

Journal: IRJEAS, Volume 10, Issue 4

Pages: 18-23

Keywords: Classification, Ensemble classifier, Fault detection and categorization, Machine Learning, Microgrid, PV Systems, Wavelet Transform

Abstract: 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.

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Fault Diagosis of CHB Inverter Using Machine Learning [pp 24-29]

DOI: https://doi.org/10.55083/irjeas.2022.v10i04007

Country- INDIA

  Satish Kumar Yadav, Santosh Kumar 

PAPER ID: IRJEAS04V10I410221222007

Published: 27 December 2022

Journal: IRJEAS, Volume 10, Issue 4

Pages: 24-29

Keywords: Decision Tree, Discrete Wavelet transform, Seven level Cascaded H-Bridge Inverter, Switch fault, fault diagnosis

Abstract: 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.

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Different Aspects of Nano-material and Biodegradable Polymers for Cancer Diagnosis and Treatment: A Review [pp 30-42]

DOI:  https://doi.org/10.55083/irjeas.2022.v10i04006

Country- INDIA

  Rashmi Pancholi, Dr. Santosh Jain 

PAPER ID: IRJEAS04V10I410221222006

Published: 30 December 2022

Journal: IRJEAS, Volume 10, Issue 4

Pages: 30-42

Keywords: Cellular targeting, Chemotherapy, Cryosurgery, Multidrug resistance, Nanoparticles

Abstract:

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.

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