## 9 Applications of Machine Learning  
Machine learning comes with a plethora of applications. However, there are advantages and disadvantages of different algorithms working on the same problem. This behavior is due to the nature of the problem and dataset quality. Table 3 presents a holistic view of various machine learning algorithms along with their advantages and disadvantage. Also, Table 4 shows the different types of applications of machine learning in various domains. \begin{table}
\begin{tabular}{c c c} \hline \hline  
* Energy Sector: As we discussed earlier in this article machine learning has been heavily used in the energy sector. In [126] a comparative study using random forest, support vector, naive Bayes, decision tree, and AdaBoost was performed to predict false data injection attacks in power systems. The experiment performed showed random forest yielded the most accurate results with and without feature selection and is very effective in such types of problems. However, feature selection can affect the performance of machine learning models to a great extent. A similar study was performed in [127] to detect power system faults using a decision tree, K-Nearest Neighbour and SVM. The results showed that the SVM outperformed DT and KNN algorithms with an accuracy of 91.6%. Machine learning is a very handy tool for anomaly detection in different domains, the study performed by the authors of [128] presented a study on autoencoder LSTM, Facebook prophet (time-series forecasting tool), and isolation forest predict anomaly in solar power plants. This study showed AE-LSTM can detect anomalies with high accuracy and can differentiate between anomalous signals and healthy signals.The demand for energy is increasing which is driving more households to install off-grid solar power plants. A study performed in [129] proposed a machine learning framework to maximize the consumption of solar energy using a random forest algorithm. Smart grids are vulnerable to cyberattacks. A detailed survey on various cyberattacks and their defense mechanism has been discussed in [130; 131]. Machine learning may be used to identify those attacks. This includes the fake data injection attack, which can have a significant effect on AI-based smart grids [132; 133]. An autoregressive integrated moving average(ARIMA) has been proposed in [134] to predict the state of charge for unknown charge and discharge rate. Also in [135] multi-layer perceptron and long short-term based model was proposed to predict battery state of charge. This work shows that the mean squared error for MLP is higher than LSTM model. * Healthcare: Both the Naive Bayes (NB) classifier and the KNN algorithm can be used for classification problems. In this analysis [136], the authors compared the performance of KNN and Naive Bayes in predicting breast cancer. The correctness of their performance was analyzed using a cross-validation technique. According to the findings, KNN provides the highest degree of accuracy of 97.51% and had the lowest error rate compared to the NB classifier with an accuracy of 96.19%. While in [137] GAN was used to generate synthetic skin lesions. chest x-rays and renal cell carcinoma. The model was trained using 10,000 real images to generate similar synthetic images, and the performance of the models was assessed by training the model on synthetic images and another training using synthetic and real images. In [138] the study was performed on 8066 patients to predict the factors for breast cancer survivors using machine learning. In this study, random forest yielded the most accurate results and showed that the cancer stage classification, tumor size, total auxiliary lymph nodes removal, positive lymph nodes, primary treatment type, and method of diagnosis were the key factors. A comparative study to predict heart disease using logistic regression, KNN, neural network, SVM, naive Bayes, and the decision was performed in [139]. The training was performed on 297 patients sample with 13 attributes.