Rapid climate change and global warming have accelerated the shift from traditional to alternative power sources. Solar Photovoltaic system (SPV) is most attractive among Renewable energy sources. In order to mitigate the intermittency and unreliability, Hybrid Renewable Power Generation (HRPG) is developed. Maximum Power Point Tracking (MPPT) is desirable for enhancing the performance of solar-based HRPG systems. As far as convenience and popularity are concerned, many solar-based hybrid power generation systems are employed. An MPPT algorithm is developed in solar hybrid power generation for enhancing the tracking efficiency. In this research, the suitability of the Incremental Conductance Algorithm (ICA) is inspected. Also, the performance parameters of overall solar hybrid power generation with the ICA MPPT algorithm are determined using MATLAB/Simulink environment.
The evolution of e-commerce accompanied with enormous amount of sentiment reviews drive practical difficulty for consumers to decide upon a purchase choice. Sentiment analysis assists in analysing the reviews for a consumer to decide a purchase choice and a manufacturer to follow up on their clients and manage their requirements. Aspect based sentiment analysis is a subtask of sentiment research that aims to identify and analyse sentiment towards specific aspects or features of a target entity within a given text. Aspect extraction from the text input reviews is another important function in sentiment analysis at aspect level. Identifying those aspects and selecting required aspects is very essential task in opinion analysis based on features. In the proposed analysis, we initiate a supervised machine learning technique-decision tree classifier for selecting the required aspects and sentiment research is conducted by utilizing various machine learning techniques. The proposed work gives an accuracy of 85 - 93%.
Worldwide, Type II Diabetes Mellitus (T2DM) is a major condition whose incidence is rising. It is a public health issue as well as the silent killer of humanity. Its diverse aetiology, which includes genetic predisposition and environmental influences, may offer a distinctive trait in the population that is useful for early diagnosis. As a result, researchers are seeking novel approaches to its early diagnosis and treatment. Now, manual methods are used to measure and compare the axial triradius (ATD) angles of both palms using a Goniometer for the early prediction of T2DM. The creation of datasets containing individuals' left and right palm prints is the primary objective of this project, which aims to develop a model that would assist in the early prediction of T2DM. The model would have the option to track down the distinction in the ATD points in the two palms and foresee assuming that the singular will be determined to have T2DM in the future. A total of 390 instances were collected out of which 130 are diabetic and 260 were non-diabetic patients. It consists of various image pre-processing techniques and delta-point detection methods. The model would be able to predict the results if the user simply uploaded images of both palms. The Image Thresholding and Segmentation Method was used to evaluate the best features and the crossing number method was used to automate the model, indicating that the model achieved an accuracy of 91.02% in predicting the ATD angle. Using biomarkers, this project can be implemented in the health sector for early detection of Type II Diabetes Mellitus.
The detection and analysis of cardiovascular diseases is a critical study field within the medical field. Cardiovascular diseases are one of the leading causes of mortality globally, and timely identification is vital to forego adverse effects and improve outcomes for patients with these conditions. The incorporation of machine learning technologies to forecast onset of cardiovascular diseases and analyzing their progression has shown promising results. This paper demonstrates the machine learning models that can provide near-accurate predictions and help identify the right data set based on accuracy. The choice of the best-fitting algorithm has been made by a comparative study and with the inclusion of K-fold cross-validation. We infer that the Random Forest algorithm is the best algorithm for detection and K-Nearest Neighbor Algorithm for the analysis of prominent heart diseases like Stable Angina, ST-elevated myocardial infarction, and non-ST elevated myocardial infarction which will serve as a direction to further diagnosis. Overall, the potential for machine learning to improve early detection and treatment of Cardiovascular Diseases, which could ultimately save lives and reduce healthcare costs, has been recognized. Index Terms—Artificial intelligence, Machine learning, Supervised learning, Unsupervised learning, Accuracy
Drones are a big advancement in precision agriculture and crop management based on GPS and big data as a method to boost crop productivity while addressing water and food shortages. According to studies, there has been an increased demand for systems which deal with accurate spraying of palms and on-board disease detection. This research presents the simultaneous localisation and mapping (SLAM) of palm plantations, and their geolocation using YOLOv4 detector. Autonomous Sequential Decision Process (SDP) is carried out to navigate using the Pixhawk’s inbuilt PX4 system that uses the palms detected through the camera as targets and navigates around them and successively to the next palm. Images captured by the drone are scanned for any signs of yellow leaf disease or rot by learning vector quantization (LVQ) and K-Means Clustering within the RGB channels. The key interest of this research is the trajectorial nozzle system which tracks the arecanut and actively adjusts the spray to match the arecanauts’ position.
