Brain Cancer Detection Using Advanced Deep Learning Algorithm

Brain Cancer Detection Using Advanced Deep Learning Algorithm

The disease needs to be treated cautiously because of the complexity of brain cancer. Since each patient’s brain tumor is unique in terms of dimension, shape, and localization, it is challenging to identify them in medical imaging. Magnetic Resonance Imaging (MRI) process is used to diagnose the brain tumor anatomy results in inaccurate detection of the tumor and it is very time consuming. Therefore, many classification techniques from Machine Learning (ML) and Deep Learning (DL) are utilized to rapidly recognize the tumor. This paper proposes segmenting and detecting the brain cancer using Deep Convolutional Neural Network (DCNN). An image is first pre processed using a median filter to minimize noise in the image.  Global thresholding is a simple image segmentation technique that uses a single threshold value to separate an image into distinct regions. After that, the feature extraction method of Histogram of Oriented Gradients (HOG) is employed to detect brain tumors with higher accuracy and lower computational complexity than other methods. This performance measure is applicable to quantifying the performance of the segmented tumor region. Additionally, DCNN classification algorithm has achieved high performance and accuracy. For the purpose of identifying the performance of the model, this proposed project produced a confusion matrix. With a classification accuracy of 91.23% and a precision of 90.39%, the experiment’s results demonstrated that the method is more successful than the current methods.

An Efficient Cervical Cancer Prediction Using Chicken Swarm Optimized ANN Classifier

Cervical cancer remains a significant global health challenge, ranking as a leading cause of cancer related deaths among women. This research addresses the need for improved detection methods by leveraging advanced image processing techniques. Implement Adaptive Median Filtering (AMF) for effective noise reduction while preserving critical details in cervical images, enhancing overall image quality. Utilizing K means clustering for segmentation, accurately isolate regions of interest, and Local Binary Patterns (LBP) for feature extraction enable improved differentiation between normal and cancerous tissues. Additionally, the integration of the Chicken Swarm Optimized algorithm with Artificial Neural Networks (CSOANN) significantly enhances classification accuracy, facilitating earlier detection of cervical cancer. These methodologies not only improve diagnostic accuracy but also contribute to better patient outcomes and more effective screening strategies, ultimately advancing cervical cancer management.

Crow Search Optimized Pi Control Approach for Photovoltaic Connected Grid System

This paper presents an advanced Photovoltaic (PV) grid connected system utilizing a Superlift Luo Converter (SLC) to enhance energy conversion efficiency and system performance. The proposed system integrates a Crow Search Algorithm (CSA) optimized Proportional Integral (PI) controller to regulate the output of SLC. The SLC is employed to boost the voltage from the PV panels, enhancing energy extraction. The VSI, controlled by the optimized PI controller, ensures stable and efficient power transfer to the grid. An LC filter is incorporated to mitigate harmonics and ensure compliance with grid standards. The use of CSA for PI controller optimization improves dynamic response and system robustness. This results accomplished using MATLAB demonstrate that the proposed system achieves high efficiency, improved stability, and superior grid compliance compared to conventional approaches. The comparative analysis presented in this work demonstrates that the proposed approach significantly outperforms traditional methods, achieving an impressive efficiency of 95.2%. This highlights the effectiveness of the new system in optimizing performance, making it a valuable alternative to established techniques. This approach highlights the effectiveness of combining advanced converter topologies and optimization techniques to enhance PV grid connected systems.

Intelligent MPPT for High Gain Zeta Converter for Standalone PV Application with Galvanic Isolation

 

In this paper, Standalone PV system is designed to operate without the need of electric utility grid which are commonly designed to supply specific DC electric loads. These type of PV system are generally powered using photovoltaic array and consist of solar charging modules, controllers and regulators. The objective of this work is to implement the Radial Basis Function Neural Network (RBFNN) based Maximum Power Point Tracking (MPPT) technique to attain constant DC link voltage of the ZETA converter. This method utilizes a ZETA converter to achieve better voltage gain and lower ripple in the output current and voltage. Using an Artificial Neural Network (ANN) controller to operate a high frequency converter, which improves power conversion efficiency. To ensure safety and prevent electrical faults, an isolation transformer provides galvanic isolation between the high frequency converter and the PV system.  Galvanic isolation avoids electrical faults from being transmitted and safeguards connected loads. The rectifier is used to supply the Direct Current (DC) load with the output from the isolation transformer. The rectifier changes the transformer’s Alternating Current (AC) output into DC current to power DC loads. An intelligent MPPT is employed to stabilize the converter’s output voltage and address the intermittent characteristics of PV, and also assists in sustaining the DC link voltage without alterations and ripples from the converter. The efficiency of this work is authenticated via MATLAB simulation 2021a.

Advanced Controller System with Hybrid Renewable Sources Enabling Vehicle-To-Grid and Grid-To-Vehicle Operations

Power demand is rising worldwide, forcing the search for an alternative source to solve the imminent power crisis, which is expected to be accomplished through the deployment of grid synchronized electric vehicles (EV). Wind and solar energy are incorporated into this integration to those concerns. One smart grid innovation that permits energy exchange between the EV and the grid is vehicle to grid (V2G) technology. For the purpose of charging EVs, this research proposes employing hybrid energy. Here, the neighboring household’s linear and non linear demands are powered by the excess electricity from the photovoltaics (PV) panel, wind turbine, and AC generator that is utilized to charge the EV battery. Because of its dependability and independence from the real system model, a proportional integral (PI) controller is used to maximize the DC voltage extracted from the PV panel. The improved DC voltage overall is a result of the SEPIC converter’s improved DC output. Concurrently, an AC generator is incorporated into the system to supply extra power to the grid. An artificial neural network (ANN) controller is used to convert the power generated by the PV panel’s Sepic conversion process into an improved output that is then converted to AC using a 3 phase Voltage Source Inverter (VSI).  Harmonic components in the VSI output are corrected with an LC filter to ensure grid compatibility and safe current injection into the grid. This paper discusses the usage of a bidirectional converter to adjust the EV battery’s voltage. MATLAB 2021a / Simulink software is used to simulate and validate the proposed approach.

HD-REGNET: Heart Disease Prediction Using REGNET in Image Processing

Abnormal functionality of the heart due to any cause is known as heart disease. It primarily affects older persons. Every year, almost six out of ten persons diagnosed with heart diseases are above the age of 65. In this paper a novel HD RegNet have been proposed for heart disease prediction using CXR images. Initial the input CXR images are pre processed using Adaptive Gaussian Star Filtering. Then the pre processed images are subjected into RegNet for extracting the features. The best features are selected using Tyrannosaurus optimization Algorithm. Finally the normal and abnormal classes of heart diseases classified utilizing the Deep Neural Network (DNN). The CHD dataset are utilized to evaluate the performance of the developed work interms of performance metrics such as accuracy, recall and precision. The proposed HD RegNet attains an accuracy rate of 99.53% in the normal class and 99.37% in the abnormal class. It achieves an overall accuracy rate 0.37%, 0.52% and 0.75% compared to the existing methods such as RFRF ILM [12], LU Net [17] and CNN & Bi LSTM [18] respectively.

Comprehensive Review of DC-DC Converters for PV Connected Grid System

Renewable Energy Systems (RES) harness natural sources like sunlight, wind, and water to generate sustainable power. They decrease dependency on fossil fuels and minimize greenhouse gas emissions, promoting environmental sustainability and energy security. Photovoltaic (PV) systems are a prominent application of renewable energy, converting sunlight directly into electricity. However, the efficiency of PV systems adversely affected by varying environmental conditions and climatic changes, leading to fluctuations in voltage output. To address these challenges and optimize energy extraction, the integration of DC-DC converters into PV system is essential. In this paper an exhaustive review of various DC-DC converters technologies used in PV connected grid systems, including SEPIC converter, Buck boost converter, Boost converter, and CUK converter. Every converter plays a crucial role in managing and stabilizing voltage levels by adjusting the output voltage to meet the needs of the grid or load. A comparative analysis of recent research highlights various methodologies for enhancing converter performance. The review includes studies on ultra gain converters, Fourth order boost DC-DC converter (FBDC), high step up interleaved converters, and Quasi Z source converters. Each approach presents unique benefits and challenges, reflecting ongoing efforts to optimize DC-DC conversion in PV systems. The paper concludes with insights into the future direction of converter technologies and their role in enhance the stability and efficiency of PV connected systems.

Revolutionizing Palm Print Recognition with Advanced Sift-Based Feature Analysis Techniques

Palm print recognition has emerged as a robust biometric authentication method due to its distinctiveness and reliability. This paper explores the revolution in palm print recognition driven by advanced feature analysis techniques, focusing on the integration of sophisticated preprocessing and feature extraction and classification methods. Effective palm print recognition relies heavily on preprocessing technique such as noise reduction, contrast enhancement, and image filtering. Thus, this study emphasize the role of Gabor filter excel in image processing by effectively capturing spatial frequency and orientation information. The paper highlights the use of Local Ternary Patterns (LTrP) and Local Modified Ternary Patterns (LMTrP) to enhance the accuracy and robustness of feature extraction in palm print matching and identification. Additionally, the Scale Invariant Feature Transform (SIFT) used to further strengthens the system by providing further enhancing the reliability and rotation. Moreover, this paper implemented by using python programming language.  Overall, this work provides insights into their combined impact on the accuracy and reliability of palm print recognition systems, marking a significant advancement in biometric authentication technology.

Bridge Crack Detection Using Multi-SVM Classifier

The Automatic crack detection system is a significant technology to detect crack in concrete images. Identification of the crack Intensity and severity is the main objective of the detection system. Detection of images containing unwanted noises, blurriness and interference is considerably difficult hence image pre processing is done using the adaptive filter by which the disturbances is removed. Images with non uniform light, poor contrast or sudden brightness changes is rectified in the segmentation process using the fuzzy c algorithm that uses clustering technique. Techniques like Gray scale processing do not provide accurate detection therefore Gray level co event matrices (GLCM) technique which extracts high level features needed in order to perform classification of images and Multi support vector machine (SVM) is used because they can handle multiple continuous and categorical variables. The proposed technique provides better outcomes for crack detection and treatment. The PYTHON software is utilized.

Enhanced Prediction of Diabetic Retinopathy Types Using Deep Convolutional Neural Networks

Diabetic retinopathy is an eye condition which leads to the loss of eye sight in the person who already has diabetics. There are several stages in detecting the diabetic retinopathy which includes manual medical methods or detected by automatic methods using image processing. In these cases, neural networks are used in detecting the defects caused in the retina of the diabetics. These neural network process as same as human brain, in this methodology Artificial Intelligence (AI) is implemented. In this methodology, the image is pre processed to improve the image clarity by Gaussian filtering method. The pre processed image undergoes segmentation using the Fuzzy C means (FCM) clustering method. By involving these methods, the complexity in detecting the diabetic retinopathy is reduced. The Deep Convolutional Neural Network (DCNN) method is implemented in which data is compared to find the defect. The pooling layer samples the image to speed up computation, and the fully connected layer provides the final prediction. In the Convolutional Neural Network, the data to the compared is already processed and stored. This paper provides excellent specificity and sensitivity for classifying images as having or without diabetic retinopathy. This paper is implemented using Python Google Colab software.