In this note, we present a component-wise algorithm combining several recent ideas from signal processing for simultaneous piecewise constants trend, seasonality, outliers, and noise decomposition of dynamical time series. Our approach is entirely based on convex optimisation, and our decomposition is guaranteed to be a global optimiser. We demonstrate the efficiency of the approach via simulations results and real data analysis.

This paper uses Artificial Intelligence (AI) based algorithm analysis to classify breast cancer Deoxyribonucleic (DNA). Main idea is to focus on application of machine and deep learning techniques. Furthermore, a genetic algorithm is used to diagnose gene expression to reduce the number of misclassified cancers. After patients' genetic data are entered, processing operations that require filling the missing values using different techniques are used. The best data for the classification process are chosen by combining each technique using the genetic algorithm and comparing them  in terms of accuracy.

Use the objective values to determine fitness values Selection:Select the fittest individuals for reproduction Reproduction:Create new individuals from the mating pool by crossover and mutation Initial Population:Create an initial population of random individual www.intechopen.com Variants of Hybrid Genetic Algorithms for Optimizing Likelihood ARMA Model Function and Many of Problems 221 extension of the results of section, in which are representative of the classes of unimodel , and multimodal function. In which competition is raised. DefinitionsDefinition 2.1 (Objective Function) An objective function f: with is a mathematical function which is subject to optimization. The co-domain of an objective function as well as its range must be a subset of the real numbers . The domain of f is called problem space and can represent any type of element like numbers, lists, construction plans, and so on. It is chosen according to the problem to be solved with the optimization process. Objective functions are not necessarily mere mathematical expressions, but can be complex algorithms that, for example , involve multiple simulations. Global optimization comprises all techniques that can be used to find the best element with respect to such criteria . Definition 2.2 (local Maximum) A local maximum x of one (objective) function : is an input element for all x neighbouring x . If , we can write::

Fuzzy C-means (FCM) is a clustering method used for collecting similar data elements within the group according to specific measurements. Tabu is a heuristic algorithm. In this paper, Probabilistic Tabu Search for FCM implemented to find a global clustering based on the minimum value of the Fuzzy objective function. The experiments designed for different networks, and cluster’s number the results show the best performance based on the comparison that is done between the values of the objective function in the case of using standard FCM and Tabu-FCM, for the average of ten runs.

Performance issues could be appearing from anywhere in a computer system, finding the root cause of those issues is a troublesome issue due to the complexity of the modern systems and applications. Microsoft builds multiple mechanisms to make their engineers understand what is happening inside All Windows versions including Windows 10 Home and the behavior of any application working on it whether Microsoft services or even third-party applications, one of those mechanisms is the Event Tracing for Windows (ETW) which is the core of logging and tracing in Windows operating system to trace the internal events of the system and its applications. This study goes deep into internal process activities to investigate root cause analysis on Windows 10 Home 20H2, core i5 processor with 4 cores and 8GB of RAM. After simulating workload to get a performance issue, that makes the system and application get unresponsive, then using Windows Performance Toolkit WPT to trace and analyze the event log for root cause investigation. Our results demonstrate analysis works using WPT for decision making such as the reasons of underutilization on CPU and disk, labeling the highlighted patterns, the unbalanced use of system calls in memory, and deciding that the usage preview is the best way to get an idea about applications behavior inside Windows systems resources. Overall improving resources utilization usage and identifying the cause of slowing memory allocation, inefficient disk usage, and throughput.