This paper proposes a novel neural network (NN) training method that employs the hybrid exponential smoothing method and the Levenberg-Marquardt (LM) algorithm, which aims to improve the generalizatio...
This paper proposes a novel congestion‐aware HTTP adaptive streaming (HAS) system to improve video quality of clients over software‐defined network (SDN)–enabled Wi‐Fi network. The proposed system emp...
This paper presents extensions to the triage method for addressing continuous decision problems. These provide decision makers more tools with which to address situations where alternatives present th...
This paper presents a new, holistic, Internet-based quality control approach. In recent years, a rapidly changing business environment driven by fierce international and domestic competitions has push...
This paper presents a high-frequency strategy based on Deep Neural Networks (DNNs). The DNN was trained on current time (hour and minute), and \( n \)-lagged one-minute pseudo-returns, price standard ...
This paper focuses on fusion of statistical and machine learning models for improving the accuracy of time series prediction. Statistical model like integration of auto regressive (AR) and moving aver...
This paper explores the use of Multiple Linear Regression techniques in order to estimate sections of missing line reactance data sometimes found in the data received from synchrophasor measurement un...
This paper discusses a neural network development approach based on an exponential smoothing method which aims at enhancing previously used neural networks for traffic flow forecasting. The approach u...
This paper describes the powerful statistical technique Savitzky-Golay that can be used in many engineering applications and presents its application in selected technical experiment. The approach is ...
This paper approaches some main objectives of the analysis of trends in discrete time series. A major aspect of this analysis is to identify a mathematical model that describes the persistent long-ter...
This chapter represents the logical continuation of Chap. 5. Assuming that any set of reproducible/repeatable measurements can be considered as a quasi-periodic process, it is possible to derive the f...
This chapter is devoted to forecasting in the non-classical setting where the state space of the time series is finite, necessitating the use of discrete-valued time series models. The field of discre...
This chapter contains an overview of some methods that can be used for modeling the evolution of hydro-meteorological time series, methods employed in the next chapters. We distinguish among them: dec...
This chapter classifies the different machine learning algorithms into domains and provides a formal definition of machine learning. In addition, the chapter describes briefly a common set of the clas...
This article proposes a nearest neighbors—differential evolution (NNDE) short-term forecasting technique. The values for the parameters time delay $$\tau $$τ, embedding dimension m, and neighborhood s...
This article discusses multi-input intervention analysis to investigate the effect of interventions which may come from internal and/or external factors in time series data. The objective of this rese...
This article considers methods of machine learning, which are introduced into the master’s educational program under the direction of “Organization and management of knowledge-intensive industries”. T...
Thermal storage systems were originally designed to shift on-peak cooling production to off-peak cooling production in order to reduce on-peak electricity demand. Recently, however, the reduction of b...
There is a substantial number of telerobotics and teleoperation applications ranging from space operations, ground/aerial robotics, drive-by-wire systems to medical interventions. Major obstacles for ...
The use of two-photon microscopy allows for imaging of deep neural tissue in vivo. This paper examines frequency-based analysis to two-photon calcium fluorescence images with the goal of deriving smoo...