This paper presents an epilepsy detection method based on discrete wavelet transform (DWT) with Machine learning classifiers. Here DWT has been used for feature extraction as it provides a better decomposition of the signals in different frequency bands. At first, DWT has been applied to the EEG signal to extract the detail and approximate coefficients or different sub-bands. After the extraction of the coefficients, principal component analysis (PCA) has been applied on different sub-bands and then a feature level fusion technique is used to extract the main features in low dimensional feature space. Three classifiers name: Support Vector Machine (SVM) classifier, K-Nearest-Neighbor (KNN) classifier, and Naive Bayes (NB) classifier have been used in the proposed work for classifying the EEG signals. The raised method is tested over Bonn databases and provides a maximum of 100% recognition accuracy for KNN, SVM, NB classifiers. Keyword: Electroencephalography (EEG), Discrete wavelet transform (DWT), Principal Component Analysis (PCA), Machine learning classifiers.

Electrical utilities depend on short-term demand forecasting to proactively adjust production and distribution in anticipation of major variations. This systematic review analyzes 240 works published in scholarly journals between 2000 and 2019 that focus on applying Artificial Intelligence (AI), statistical, and hybrid models to short-term load forecasting (STLF). This work represents the most comprehensive review of works on this subject to date. A complete analysis of the literature is conducted to identify the most popular and accurate techniques as well as existing gaps. The findings show that although Artificial Neural Networks (ANN) continue to be the most commonly used standalone technique, researchers have been exceedingly opting for hybrid combinations of different techniques to leverage the combined advantages of individual methods. The review demonstrates that it is commonly possible with these hybrid combinations to achieve prediction accuracy exceeding 99%. The most successful duration for short-term forecasting has been identified as prediction for a duration of one day at an hourly interval. The review has identified a deficiency in access to datasets needed for training of the models. A significant gap has been identified in researching regions other than Asia, Europe, North America, and Australia.

Support vector machine (SVM) is a powerful classification method that has achieved great success in many fields. Since its performance can be seriously impaired by redundant covariates, model selection techniques are widely used for SVM with high dimensional covariates. As an alternative to model selection, significant progress has been made in the area of model averaging in the past decades. Yet no frequentist model averaging method was considered for SVM. This work aims to fill the gap and to propose a frequentist model averaging procedure for SVM which selects the optimal weight by cross validation. Even when the number of covariates diverges at an exponential rate of the sample size, we show asymptotic optimality of the proposed method in the sense that the ratio of its hinge loss to the lowest possible loss converges to one. We also derive the convergence rate which provides more insights to model averaging. Compared to model selection methods of SVM which require a tedious but critical task of tuning parameter selection, the model averaging method avoids the task and shows promising performances in the empirical studies.

Data Gathering: Any machine learning problem requires a lot of data for training/testing purposes. Identifying the right data sources and gathering data from these data sources is the key. Data could be found from databases, external agencies, the internet etc. Data Preprocessing: Before starting training the models, it is of utmost importance to prepare data appropriately. As part of data preprocessing, some of the following is done: Data cleaning: Data cleaning requires one to identify attributes having not enough data or attributes which are not having variance.

As a result, AI excels at handling large volumes and complex data, and identifying characteristic from the data, which the human brain cannot recognize. Although AI has been rapidly incorporated into oncologic research, the development of AI solutions is still in its infancy. Only a few AI-based applications have been approved for use in practice, e.g.

Support vector machines (SVMs) are supervised machine learning models. They are the most prominent member of the class of kernel methods. SVMs can be used both for classification and regression. The original SVM proposed in 1963 is a simple binary linear classifier. Special to SVMs is that they use not any hyperplane but the one that maximizes the distance between itself and the two sets of datapoints.

Standard contrastive learning approaches usually require a large number of negatives for effective unsupervised learning and often exhibit slow convergence. We suspect this behavior is due to the suboptimal selection of negatives used for offering contrast to the positives. We counter this difficulty by taking inspiration from support vector machines (SVMs) to present max-margin contrastive learning (MMCL). Our approach selects negatives as the sparse support vectors obtained via a quadratic optimization problem, and contrastiveness is enforced by maximizing the decision margin. As SVM optimization can be computationally demanding, especially in an end-to-end setting, we present simplifications that alleviate the computational burden. We validate our approach on standard vision benchmark datasets, demonstrating better performance in unsupervised representation learning over state-of-the-art, while having better empirical convergence properties.

The mental disorder of online users is determined using social media posts. The major challenge in this domain is to avail the ethical clearance for using the user generated text on social media platforms. Academic re searchers identified the problem of insufficient and unlabeled data for mental health classification. To handle this issue, we have studied the effect of data augmentation techniques on domain specific user generated text for mental health classification. Among the existing well established data augmentation techniques, we have identified Easy Data Augmentation (EDA), conditional BERT, and Back Translation (BT) as the potential techniques for generating additional text to improve the performance of classifiers. Further, three different classifiers Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR) are employed for analyzing the impact of data augmentation on two publicly available social media datasets. The experiments mental results show significant improvements in classifiers performance when trained on the augmented data.

Every machine learning algorithm has three components: Representation: how to represent knowledge. Examples include decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles and others. Machine Learning is a key to develop intelligent systems and analyze data in science and engineering. It has emerged as one of the most valuable and time investing domains in the current century. This course is designed for all the learners interested in starting their journey with Machine Learning. The course explains all the important concepts in machine learning.

The goal of this tutorial is to apply PCA to hyperspectral data. After reducing the dimensionality of the data using PCA, classify the data by applying the Support Vector Machine(SVM) to classify the different materials in the image. We are using the Hyperspectral Gulfport Dataset in this tutorial. The MUUFL Gulfport data contains the pixel-based ground truth map which was provided by manually labeling the pixels in the scene. The following classes were labeled in the scene trees, mostly grass, ground surface, mixed ground surface, dirt and sand, road, water, buildings, the shadow of buildings, sidewalk, yellow curb, cloth panels (targets), and unlabeled points.