The future smart grid would help to benefit both the users and the electricity providing companies from smart pricing techniques. In addition, smart pricing can be used to achieve social objectives and would in turn fluctuate wholesale market into demand side. Collecting abundant information regarding the users electricity consumption pattern is a challenging task for utility providing companies. That is, users may not be willing to expose their indigenous information without any incentive. In this paper an Optimal Energy Consumption Scheduling (OECS) mechanism is proposed to tackle this problem. An agent-based forecasting method is designed, which is capable of predicting energy consumption of each consumer with a lead-time of one hour. This forecasting is exploited to estimate the cost of buying required amount of energy from multiple suppliers. Consequently, based on the estimated required energy and cost, an auction mechanism is proposed to optimize the energy traded between consumers and multiple suppliers within a smart grid. The objectives include increased efficiency and cost reduction of electricity usage by the end users. The results and properties of the proposed OECS mechanism are studied, and it is shown that the auction technique is budget balanced for distribution of electrical energy among consumers from diverse renewable generation resources. Extensive numerical simulations are also conducted to show and prove the beneficial properties of OECS mechanism.

Image matching is a fundamental step in several computer vision applications where the requirement is fast, accurate, and robust matching of images in the presence of different transformations. Detection and more importantly description of low-level image features proved to be a more appropriate choice for this purpose, such as edges, corners, or blobs. Modern descriptors use binary values to store neighbourhood information of feature points for matching because binary descriptors are fast to compute and match. This paper proposes a descriptor called Fast Angular Binary (FAB) descriptor that illustrates the neighbourhood of a corner point using a binary vector. It is different from conventional descriptors because of selecting only the useful neighbourhood of corner point instead of the whole circular area of specific radius. The descriptor uses the angle of corner points to reduce the search space and increase the probability of finding an accurate match using binary descriptor. Experiments show that FAB descriptor's performance is good, but the calculation and matching time is significantly less than BRIEF, the best known binary descriptor, and AMIE, a descriptor that uses entropy and average intensities of informative part of a corner point for the description.

This study explores the use of several non-parametric statistical tests for evaluating the performances of computer vision algorithms, specifically corner detectors, as a more reliable alternative to the graphical approaches that have been commonly employed to date. Using synthetic images carrying corners of different internal angles and orientations and a carefully designed testing framework, a ranking of the performances of corner detectors was established. It was found that Harris & Stephens and SUSAN out-performed more modern detectors. These are one of the few examples where evaluation of vision operators independent of the application has predicted performance in a real-world problem. A similar exercise on real images of the same patterns produced similar results and the findings of a real-world application that uses corners to identify signage were also consistent. Together, all of the tests considered essentially perform pairwise comparisons of performance, so when many algorithms are involved it is important to take account of the potential for type I statistical errors. Several approaches were evaluated and none were found to affect the conclusions. 2 Background Performance characterisation provides supporting evidence as to the effectiveness of a newly-developed algorithm or an enhancement of an existing one. Therefore, most of the reputed journals require the use of some appropriate statistical tests to justify the efficiency of new algorithms proposed in the research articles. Statistical methods such as precision-recall curves [3], Fmeasure, accuracy [2], receiver operating curves and relatively less common are sensitivity-specificity [8] graphs are most commonly

This study explores the potential for early detection of Metabolic Syndrome (MetS) using Machine Learning (ML) techniques. Dissection of prognostic components inciting the syndrome could help patients take cautious steps to prevent it in the early stages. MetS outnumbered diabetics by three to one, a 2020 report found that one billion people worldwide were affected. Patients with MetS typically have no symptoms or signs of the condition and are left undiagnosed. These conditions include extensive circulatory tension, high glucose levels, muscular overload around the abdomen, and unusual levels of cholesterol or fat. Supervised ML techniques like Naïve Bayes, Support Vector Machines, Random Forest, Logistic Regression, C4.5, Cart, etc. are widely used for predictions and diagnoses in various fields. It has been extensively used in medical sciences as well. ML is used for the prediction of the progression of certain diseases and analysis of important parameters in the medical domain. This research uses the aforementioned algorithms for the prediction of MetS using the patients’ dataset. The results were analyzed using precision-recall and Area Under the Curve (AUC) of Receiver Operating characteristic Curve (ROC). The results showed that Naïve Bayes predicted MetS more accurately showing 94.1% accuracy than the rest of the algorithms, while Random Forest surpassed the other tree-based algorithm. According to the results of this research, the prognosis factors for MetS identification are hyperglycemia, dyslipidemia, or a combination of high-density lipoprotein (HDL) dyslipidemia, hypertension, and obesity. Monitoring these factors reduces the risk of MetS occurrence, assists in the prevention, and provides important information for treatment in the early stages.