Solar energy has been developed as a better alternative to fossil fuels in the past few years. It is a renewable and infinite source of energy which does not have a bad impact on the environment. It is also cheap and easily accessible, making it a better alternative for both personal and commercial purposes. Solar Arrays are made when PV modules used in solar panels are connected together. Energy is produced when sunlight falls on Solar Panels which can be used instead of Fossil fuel's produced energy. For execution of a PV system under different situations, estimating the parameters in a PV model plays an important role because it enables us to optimise the design and performance of the system which leads to increased energy production and improved performance. If a PV system is not performing as expected, then identification of parameters of the PV model helps identify the root cause of the problem. This could be due to factors such as shading, module mismatch, or degradation over time. By accurately estimating the parameters, we can determine the best method to improve its performance.

The journey of AI started in the 1950s with the pioneering work of Alan Turing, who proposed the Turing Test to determine if a machine could mimic human intelligence. In the 1960s, AI research gained momentum with the development of the first AI programming language, LISP, by John McCarthy. Early AI systems focused on symbolic reasoning and rule-based systems, which led to the development of expert systems in the 1970s and 1980s. The 1990s witnessed a shift in focus towards machine learning and data-driven approaches, driven by the increased availability of digital data and advancements in computing power. This period saw the rise of neural networks and the development of support vector machines, which allowed AI systems to learn from data, leading to better performance and adaptability.

This paper considers distributed optimization algorithms, with application in binary classification via distributed support-vector-machines (D-SVM) over multi-agent networks subject to some link nonlinearities. The agents solve a consensus-constraint distributed optimization cooperatively via continuous-time dynamics, while the links are subject to strongly sign-preserving odd nonlinear conditions. Logarithmic quantization and clipping (saturation) are two examples of such nonlinearities. In contrast to existing literature that mostly considers ideal links and perfect information exchange over linear channels, we show how general sector-bounded models affect the convergence to the optimizer (i.e., the SVM classifier) over dynamic balanced directed networks. In general, any odd sector-bounded nonlinear mapping can be applied to our dynamics. The main challenge is to show that the proposed system dynamics always have one zero eigenvalue (associated with the consensus) and the other eigenvalues all have negative real parts. This is done by recalling arguments from matrix perturbation theory. Then, the solution is shown to converge to the agreement state under certain conditions. For example, the gradient tracking (GT) step size is tighter than the linear case by factors related to the upper/lower sector bounds. To the best of our knowledge, no existing work in distributed optimization and learning literature considers non-ideal link conditions.

Data augmentation forms the cornerstone of many modern machine learning training pipelines; yet, the mechanisms by which it works are not clearly understood. Much of the research on data augmentation (DA) has focused on improving existing techniques, examining its regularization effects in the context of neural network over-fitting, or investigating its impact on features. Here, we undertake a holistic examination of the effect of DA on three different classifiers, convolutional neural networks, support vector machines, and logistic regression models, which are commonly used in supervised classification of imbalanced data. We support our examination with testing on three image and five tabular datasets. Our research indicates that DA, when applied to imbalanced data, produces substantial changes in model weights, support vectors and feature selection; even though it may only yield relatively modest changes to global metrics, such as balanced accuracy or F1 measure. We hypothesize that DA works by facilitating variances in data, so that machine learning models can associate changes in the data with labels. By diversifying the range of feature amplitudes that a model must recognize to predict a label, DA improves a model's capacity to generalize when learning with imbalanced data.

Full-field traffic state information (i.e., flow, speed, and density) is critical for the successful operation of Intelligent Transportation Systems (ITS) on freeways. However, incomplete traffic information tends to be directly collected from traffic detectors that are insufficiently installed in most areas, which is a major obstacle to the popularization of ITS. To tackle this issue, this paper introduces an innovative traffic state estimation (TSE) framework that hybrid regression machine learning techniques (e.g., artificial neural network (ANN), random forest (RF), and support vector machine (SVM)) with a traffic physics model (e.g., second-order macroscopic traffic flow model) using limited information from traffic sensors as inputs to construct accurate and full-field estimated traffic state for freeway systems. To examine the effectiveness of the proposed TSE framework, this paper conducted empirical studies on a real-world data set collected from a stretch of I-15 freeway in Salt Lake City, Utah. Experimental results show that the proposed method has been proved to estimate full-field traffic information accurately. Hence, the proposed method could provide accurate and full-field traffic information, thus providing the basis for the popularization of ITS.

I have observed two extreme approaches when it comes to aspiring data scientists attempting to learn machine learning algorithms. The first approach involves learning all the intricacies of the algorithms and implementing them from scratch to gain true mastery. The second approach, on the other hand, assumes that the computer will "learn" on its own, rendering the need for the individual to learn the algorithms unnecessary. This leads some to only rely on tools such as the package lazypredict. It is realistic to take an approach between the two extremes when learning machine learning algorithms. However, the question remains, where to start? In this article, I will categorize machine learning algorithms into three categories and provide my humble opinion on what to begin with and what can be skipped. Starting out in machine learning can be overwhelming due to the multitude of available algorithms. Linear regression, support vector machines (SVM), gradient descent, gradient boosting, decision trees, LASSO, ridge, grid search, and many more are some of the algorithms that come to mind when posed with the question.

Abstract: With the rapid growth of the use of social media websites, obtaining the users' feedback automatically became a crucial task to evaluate their tendencies and behaviors online. Despite this great availability of information, and the increasing number of Arabic users only few research has managed to treat Arabic dialects. The purpose of this paper is to study the opinion and emotion expressed in real Moroccan texts precisely in the YouTube comments using some well-known and commonly used methods for sentiment analysis. In this paper, we present our work of Moroccan dialect comments classification using Machine Learning (ML) models and based on our collected and manually annotated YouTube Moroccan dialect dataset. By employing many text preprocessing and data representation techniques we aim to compare our classification results utilizing the most commonly used supervised classifiers: k-nearest neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), and deep learning (DL) classifiers such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LTSM).

A new regression technique based on Vapnik's concept of support vectors is introduced. We compare support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space. On the basis of these experiments, it is expected that SVR will have advantages in high dimensionality space because SVR optimization does not depend on the dimensionality of the input space.

Support Vector Learning Machines (SVM) are finding application in pattern recognition, regression estimation, and operator inver(cid:173) sion for ill-posed problems. Against this very general backdrop, any methods for improving the generalization performance, or for improving the speed in test phase, of SVMs are of increasing in(cid:173) terest. In this paper we combine two such techniques on a pattern recognition problem. The method for improving generalization per(cid:173) formance (the "virtual support vector" method) does so by incor(cid:173) porating known invariances of the problem. This method achieves a drop in the error rate on 10,000 NIST test digit images of 1.4% to 1.0%.

We derive the correspondence between regularization operators used in Regularization Networks and Hilbert Schmidt Kernels appearing in Sup(cid:173) port Vector Machines. More specifica1ly, we prove that the Green's Func(cid:173) tions associated with regularization operators are suitable Support Vector Kernels with equivalent regularization properties. As a by-product we show that a large number of Radial Basis Functions namely condition(cid:173) ally positive definite functions may be used as Support Vector kernels.