Stroke is a sudden interruption in the blood flow to a portion of the brain that can result in the loss of capacity to move certain parts of the body. In this work, all elements that impact this disease are studied, their influences are extracted using exploratory data analysis, and a model is constructed to forecast the disease's likely existence in a patient. Using under sampling and over sampling strategies, several data balancing techniques are used to increase model performance. As a consequence of the findings, a 98 percent accuracy rate has been achieved compared to earlier research. While the class distribution is severely skewed, there are relatively few examples of a class. This study reveals that over-sampling approaches yield better results than under-sampling ones. When sampling-based techniques are used instead of sampling-based approaches, the outcomes improve by 46%. 

Analysis of Uneven Stroke Prediction Dataset using Machine Learning

There has recently been increasing interest in postural stability aimed at gaining a better understanding of the human postural system. This system controls human balance in quiet standing and during locomotion. Parkinson’s disease (PD) is the most common degenerative movement disorder that affects human stability and causes falls and injuries. This paper proposes a novel methodology to differentiate between healthy individuals and those with PD through the empirical mode decomposition (EMD) method. EMD enables the breaking down of a complex signal into several elementary signals called intrinsic mode functions (IMFs). Three temporal parameters and three spectral parameters are extracted from each stabilometric signal as well as from its IMFs. Next, the best five features are selected using the feature selection method. The classification task is carried out using four known machine-learning methods, KNN, decision tree, Random Forest and SVM classifiers, over 10-fold cross validation. The used dataset consists of 28 healthy subjects (14 young adults and 14 old adults) and 32 PD patients (12 young adults and 20 old adults). The SVM method has a performance of 92% and the Dempster–Sahfer formalism method has an accuracy of 96.51%.

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EMD-Based Method for Supervised Classification of Parkinson’s Disease Patients Using Balance Control Data

Chaos plays a prominent role in nonlinear systems like energy, finance, and weather. In these systems, a changing parameter to time yields a chaotic time series that contains a lot of system information. This information helps to analyze and understand the behavior of the chaotic system. However, traditional methods of investigating these chaotic time series have low prediction accuracy. Recently, various researchers have got interested in applying machine learning models for these time series. Accordingly, this study has developed two machine learning models using LSTM and NAR neural networks to forecast monthly smoothed sunspot number time series. Further, Adam and Levenberg-Marquardt algorithms are utilized for LSTM and NAR neural networks during the training process. Besides, mean square error and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{R}^{2}$</tex> are used to assess the proposed models' performance. The simulation results demonstrated that the developed prediction models have successfully predicted the sunspot number. 

Time Series Forecasting Model for Sunspot Number

Chaos plays a prominent role in nonlinear systems like energy, finance, and weather. In these systems, a changing parameter to time yields a chaotic time series that contains a lot of system information. This information helps to analyze and understand the behavior of the chaotic system. However, traditional methods of investigating these chaotic time series have low prediction accuracy. Recently, various researchers have got interested in applying machine learning models for these time series. Accordingly, this study has developed two machine learning models using LSTM and NAR neural networks to forecast monthly smoothed sunspot number time series. Further, Adam and Levenberg-Marquardt algorithms are utilized for LSTM and NAR neural networks during the training process. Besides, mean square error and $\mathrm{R}^{2}$ are used to assess the proposed models' performance. The simulation results demonstrated that the developed prediction models have successfully predicted the sunspot number.


 This work presents a machine-learning (ML) algorithm for maximum power point tracking (MPPT) of an isolated photovoltaic (PV) system. Due to the dynamic nature of weather conditions, the energy generation of PV systems is non-linear. Since there is no specific method for effectively dealing with the non-linear data, the use of ML methods to operate the PV system at its maximum power point (MPP) is desirable. A strategy based on the decision-tree (DT) regression ML algorithm is proposed in this work to determine the MPP of a PV system. The data were gleaned from the technical specifications of the PV module and were used to train and test the DT. These algorithms predict the maximum power available and the associated voltage of the module for a defined amount of irradiance and temperature. The boost converter duty cycle was determined using predicted values. The simulation was carried out for a 10-W solar panel with a short-circuit current of 0.62 A and an open-circuit voltage of 21.50 V at 1000 W/m2 irradiance and a temperature of 25°C. The simulation findings demonstrate that the proposed method compelled the PV panel to work at the MPP predicted by DTs compared to the existing topologies such as β-MPPT, cuckoo search and artificial neural network results. From the proposed algorithm, efficiency has been improved by >93.93% in the steady state despite erratic irradiance and temperatures.

Maximum power point tracking using decision-tree machine-learning algorithm for photovoltaic systems

This paper focuses on developing a prediction model to predict heart stroke using the parameters, namely, age, hypertension, previous heart disease status, average body glucose level, BMI, and smoking status. The prediction model is developed using a support vector machine (SVM) algorithm. Further, the SVM algorithm with various decision boundaries like linear, quadratic, and cubic are also produced. The performance prediction results show that the linear and quadratic SVM has performed better in predicting the heart stoke with greater accuracy values. This is true for both the male and female databases during training and testing.

Prediction of Heart Stroke Using Support Vector Machine Algorithm

This paper focuses on developing a weather prediction model to predict temperature and humidity. Further, a classification model is also extended to predict the weather condition using the expected model’s output. The proposed hybrid model can predict the temperature and humidity and forecast future weather conditions. The prediction and classification models are created using neural networks and k-nearest neighbors, respectively. The prediction model’s results have shown the best ability for both the output variables (temperature and humidity) with R2 values close to one and MSE values close to zero. Further, the classification model’s results also showed better execution in classifying the weather conditions with the highest accuracy values.

Weather Prediction and Classification Using Neural Networks and k-Nearest Neighbors

The principal focus of this paper is to develop a prediction model to predict the turbidity of beach waves. The prediction model is developed using a nonlinear autoregressive neural network model using three input parameters: water temperature, wave height, and wave period. The beach wave turbidity is predicted without installing any additional sensors. The performance of the developed model is evaluated on three beaches in Chicago Park’s district. The proposed model performance showed better tracking ability for all the three considered beaches. The R2 and mean square errors MSE also confirm the best prediction model’s performance for both training and testing.

Kulkarni Rakshit Raghavendra

Papers

Prediction of Heart Stroke Using Support Vector Machine Algorithm

Weather Prediction and Classification Using Neural Networks and k-Nearest Neighbors

Prediction of Turbidity in Beach Waves Using Nonlinear Autoregressive Neural Networks