Search

Lung cancer is among the top causes of death globally, significantly impacting global health due to its high incidence and mortality rates. Breathing difficulties and reduced quality of life are common in people with respiratory illnesses such as asthma, interstitial lung disease, and chronic obstructive pulmonary disease (COPD). Doctors diagnose and establish the stages of cancer, which may not always be correct. The only way to increase the chances of human survival is to recognize them early. The average survival rate increased from 14% to 49% when lung cancer was diagnosed early. Although computed tomography (CT) is significantly more effective than radiography, a complete diagnosis requires a combination of imaging methods. Machine learning technology has been developed and tested for lung cancer detection using CT images. Image processing and machine learning techniques for lung cancer identification were used to a dataset of CT scans in order to categorize the presence of lung cancer. From kaggle, we obtained a lungs imaging dataset. Fuzzy C-means, its variants and K-means algorithms were considered for image segmentation. Aberrant images were segmented to concentrate on the tumor. SVM, KNN, RF, and CNN were employed to study the classification efficiency and to determine if the CT image of the patient is normal or abnormal. The study reveals that the EnFCM method of Segmentation followed by CNN showed 99% accuracy, EnFCM segmentation followed by classification in tumor detection and identification has a good impact. The accuracy obtained with this model is more efficient.

The recent world is being focused on deriving methods for using renewable energy-based systems to meet the energy demands. There are various research areas to be focused upon for making the output from these energy systems more reliable and efficient. The focus of this paper is on examining on such key component, a Unified power flow Controller (UPFC) into solar Photovoltaic (PV) structures to enhance green energy transmission and efficiency. The UPFC structure has the benefits combining the STATCOM and SSSC, which is found to be vital in regulating the active and reactive power flows within the network, ensuring stability amidst system fluctuations. The software MATLAB/SIMULINK is being used for making a 400.0-kW PV-UPFC farm, the study explores the effect of UPFC on enhancing power quality and stability, addressing real-world challenges like harmonics and voltage fluctuations like sags/swells. The work also delves into novel control strategies, consisting of using Proportional-integral (PI) controllers and advanced optimization techniques, to manage the power flow effectively. It can be concluded that the research highlights the UPFC’s capability in mitigating issues inherent in integrating solar energy into the grid, inclusive of voltage rise, reverse power flow, and system instability, by means of showcasing more desirable system voltage profiles and decreased energy oscillations in the designed system.

In making the Machines Intelligent, and enable them to work as human, Speech recognition is one of the most essential requirement. Human Language conveys various types of information such as the energy, pitch, loudness, rhythm etc., in the sound, the speech and its context such as gender, age and the emotion. Identifying the emotion from a speech pattern is a challenging task and the most useful solution especially in the era of widely developing speech recognition systems with digital assistants. Digital assistants like Bixby, Blackberry assistant are building products that consist of emotion identification and reply the user in step with user point of view. The objective of this work is to improve the accuracy of the speech emotion prediction using deep learning models. Our work experiments with the MLP and CNN classification models on three benchmark datasets with 5700 speech files of 7 emotion categories. The proposed model showed improved accuracy.