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The purpose of this paper is to propose a new linear-nonlinear data preprocessing-based hybrid model to achieve a more accurate result at a lower cost for wind power forecasting. For this purpose, a decomposed based series-parallel hybrid model (PKF-ARIMA-FMLP) is proposed which can model linear/nonlinear and certain/uncertain patterns in underlying data simultaneously.

To design the proposed model at first, underlying data are divided into two categories of linear and nonlinear patterns by the proposed Kalman filter (PKF) technique. Then, the linear patterns are modeled by the linear-fuzzy nonlinear series (LLFN) hybrid models to detect linearity/nonlinearity and certainty/uncertainty in underlying data simultaneously. This step is also repeated for nonlinear decomposed patterns. Therefore, the nonlinear patterns are modeled by the linear-fuzzy nonlinear series (NLFN) hybrid models. Finally, the weight of each component (e.g. KF, LLFN and NLFN) is calculated by the least square algorithm, and then the results are combined in a parallel structure. Then the linear and nonlinear patterns are modeled with the lowest cost and the highest accuracy.

The effectiveness and predictive capability of the proposed model are examined and compared with its components, based models, single models, series component combination based hybrid models, parallel component combination based hybrid models and decomposed-based single model. Numerical results show that the proposed linear-nonlinear data preprocessing-based hybrid models have been able to improve the performance of single, hybrid and single decomposed based prediction methods by approximately 66.29%, 52.10% and 38.13% for predicting wind power time series in the test data, respectively.

The combination of single linear and nonlinear models has expanded due to the theory of the existence of linear and nonlinear patterns simultaneously in real-world data. The main idea of the linear and nonlinear hybridization method is to combine the benefits of these models to identify the linear and nonlinear patterns in the data in series, parallel or series-parallel based models by reducing the limitations of the single model that leads to higher accuracy, more comprehensiveness and less risky predictions. Although the literature shows that the combination of linear and nonlinear models can improve the prediction results by detecting most of the linear and nonlinear patterns in underlying data, the investigation of linear and nonlinear patterns before entering linear and nonlinear models can improve the performance, which in no paper this separation of patterns into two classes of linear and nonlinear is considered. So by this new data preprocessing based method, the modeling error can be reduced and higher accuracy can be achieved at a lower cost.

The purpose of this paper is to propose a comprehensive version of a hybrid autoregressive integrated moving average (ARIMA), and artificial neural networks (ANNs) in order to yield a more general and more accurate hybrid model for exchange rates forecasting. For this purpose, the Kalman filter technique is used in the proposed model to preprocess and detect the trend of raw data. It is basically done to reduce the existing noise in the underlying data and better modeling, respectively.

In this paper, ARIMA models are applied to construct a new hybrid model to overcome the above-mentioned limitations of ANNs and to yield a more general and more accurate model than traditional hybrid ARIMA and ANNs models. In our proposed model, a time series is considered as a function of a linear and nonlinear component, so, in the first phase, an ARIMA model is first used to identify and magnify the existing linear structures in data. In the second phase, a multilayer perceptron is used as a nonlinear neural network to model the preprocessed data, in which the existing linear structures are identified and magnified by ARIMA and to predict the future value of time series.

In this paper, a new Kalman filter based hybrid artificial neural network and ARIMA model are proposed as an alternate forecasting technique to the traditional hybrid ARIMA/ANNs models. In the proposed model, similar to the traditional hybrid ARIMA/ANNs models, the unique strengths of ARIMA and ANN in linear and nonlinear modeling are jointly used, aiming to capture different forms of relationship in the data; especially, in complex problems that have both linear and nonlinear correlation structures. However, there are no aforementioned assumptions in the modeling process of the proposed model. Therefore, in the proposed model, in contrast to the traditional hybrid ARIMA/ANNs, it can be generally guaranteed that the performance of the proposed model will not be worse than either of their components used separately. In addition, empirical results in both weekly and daily exchange rate forecasting indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid ARIMA/ANNs models.

In the proposed model, in contrast to the traditional hybrid ARIMA/ANNs, it can be guaranteed that the performance of the proposed model will not be worse than either of the components used separately. In addition, empirical results in exchange rate forecasting indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid ARIMA/ANNs models. Therefore, it can be used as an appropriate alternate model for forecasting in exchange ratemarkets, especially when higher forecasting accuracy is needed.

The purpose of this paper, because of the complexity of demand time series and the need to construct a more accurate hybrid model that can model all relationships in data, is to propose a parallel-series hybridization of seasonal neural networks and statistical models for demand time series forecasting.

The main idea of proposed model is centered around combining parallel and series hybrid methodologies to use the benefit of unique advantages of both hybrid strategies as well as intelligent and classic seasonal time series models simultaneously for achieving results that are more accurate for the first time. In the proposed model, in contrast of traditional parallel and series hybrid strategies, it can be generally shown that the performance of the proposed model will not be worse than components.

Empirical results of forecasting two well-known seasonal time series data sets, including the total production value of the Taiwan machinery industry and the sales volume of soft drinks, indicate that the proposed model can effectively improve the forecasting accuracy achieved by either of their components used in isolation. In addition, the proposed model can achieve more accurate results than parallel and series hybrid model with same components. Therefore, the proposed model can be used as an appropriate alternative model for seasonal time series forecasting, especially when higher forecasting accuracy is needed.

To the best of the authors’ knowledge, the proposed model, for first time and in contrast of traditional parallel and series hybrid strategies, is developed.

This research aims to build a system that will continuously. This paper is an extended version of SECPRE 2021 paper and presents a research on the development and validation of a behavioral biometrics continuous authentication (BBCA) system that is based on users keystroke dynamics and touch gestures on mobile devices. This paper aims to build a system that will continuously authenticate the user of a smartphone.

Session authentication schemes establish the identity of the user only at the beginning of the session, so they are vulnerable to attacks that tamper with communications after the establishment of the authenticated session. Moreover, smartphones themselves are used as authentication means, especially in two-factor authentication schemes, which are often required by several services. Whether the smartphone is in the hands of the legitimate user constitutes a great concern and correspondingly whether the legitimate user is the one who uses the services. In response to these concerns, BBCA technologies have been proposed on a large corpus of literature. This paper presents a research on the development and validation of a BBCA system (named BioPrivacy), which is based on the user’s keystroke dynamics and touch gestures, using a multi-layer perceptron (MLP). Also, this paper introduces a new BB collection tool and proposes a methodology for the selection of an appropriate set of BB.

The system achieved the best results for keystroke dynamics which are 97.18% accuracy, 0.02% equal error rate, 97.2% true acceptance rate and 0.02% false acceptance rate.

This paper develops a new BB collection tool, named BioPrivacy, by which behavioral data of users on mobile devices can be collected. This paper proposes a methodology for the selection of an appropriate set of BB. This paper presents the development of a BBCA system based on MLP.

Manufacturing companies can competitively be recognized among the most advanced and influential companies in the world by successfully implementing Quality 4.0. However, its successful implementation poses one of the most relevant challenges to the Industry 4.0. According to recent surveys, 80%–87% of data science projects never make it to production. Regardless of the low deployment success rate, more than 75% of investors are maintaining or increasing their investments in artificial intelligence (AI). To help quality decision-makers improve the current situation, this paper aims to review Process Monitoring for Quality (PMQ), a Quality 4.0 initiative, along with its practical and managerial implications. Furthermore, a real case study is presented to demonstrate its application.

The proposed Quality 4.0 initiative improves conventional quality control methods by monitoring a process and detecting defective items in real time. Defect detection is formulated as a binary classification problem. Using the same path of Six Sigma define, measure, analyze, improve, control, Quality 4.0-based innovation is guided by Identify, Acsensorize, Discover, Learn, Predict, Redesign and Relearn (IADLPR²) – an ad hoc seven-step problem-solving approach.

The IADLPR² approach has the ability to identify and solve engineering intractable problems using AI. This is especially intriguing because numerous quality-driven manufacturing decision-makers consistently cite difficulties in developing a business vision for this technology.

From the proposed method, quality-driven decision-makers will learn how to launch a Quality 4.0 initiative, while quality-driven engineers will learn how to systematically solve intractable problems through AI.

An anthology of the own projects enables the presentation of a comprehensive Quality 4.0 initiative and reports the approach’s first case study IADLPR². Each of the steps is used to solve a real General Motors’ case study.

The natural control, feedback, stimuli and protection of these subsequent principles founded this project. Via properly conducted experiments, a multilayer computer rehabilitation system was created that integrated natural interaction assisted by electroencephalogram (EEG), which enabled the movements in the virtual environment and real wheelchair. For blind wheelchair operator patients, this paper involved of expounding the proper methodology. For educating the value of life and independence of blind wheelchair users, outcomes have proven that virtual reality (VR) with EEG signals has that potential.

Individuals face numerous challenges with many disorders, particularly when multiple dysfunctions are diagnosed and especially for visually effected wheelchair users. This scenario, in reality, creates in a degree of incapacity on the part of the wheelchair user in terms of performing simple activities. Based on their specific medical needs, confined patients are treated in a modified method. Independent navigation is secured for individuals with vision and motor disabilities. There is a necessity for communication which justifies the use of VR in this navigation situation. For the effective integration of locomotion besides, it must be under natural guidance. EEG, which uses random brain impulses, has made significant progress in the field of health. The custom of an automated audio announcement system modified to have the help of VR and EEG for the training of locomotion and individualized interaction of wheelchair users with visual disability is demonstrated in this study through an experiment. Enabling the patients who were otherwise deemed incapacitated to participate in social activities, as the aim was to have efficient connections.

To protect their life straightaway and to report all these disputes, the military system should have high speed, more precise portable prototype device for nursing the soldier health, recognition of solider location and report about health sharing system to the concerned system. Field programmable gate array (FPGA)-based soldier’s health observing and position gratitude system is proposed in this paper. Reliant on heart rate which is centered on EEG signals, the soldier’s health is observed on systematic bases. By emerging Verilog hardware description language (HDL) programming language and executing on Artix-7 development FPGA board of part name XC7ACSG100t the whole work is approved in a Vivado Design Suite. Classification of different abnormalities and cloud storage of EEG along with the type of abnormalities, artifact elimination, abnormalities identification based on feature extraction, exist in the segment of suggested architecture. Irregularity circumstances are noticed through developed prototype system and alert the physically challenged (PHC) individual via an audio announcement. An actual method for eradicating motion artifacts from EEG signals that have anomalies in the PHC person’s brain has been established, and the established system is a portable device that can deliver differences in brain signal variation intensity. Primarily the EEG signals can be taken and the undesirable artifact can be detached, later structures can be mined by discrete wavelet transform these are the two stages through which artifact deletion can be completed. The anomalies in signal can be noticed and recognized by using machine learning algorithms known as multirate support vector machine classifiers when the features have been extracted using a combination of hidden Markov model (HMM) and Gaussian mixture model (GMM). Intended for capable declaration about action taken by a blind person, these result signals are protected in storage devices and conveyed to the controller. Pretending daily motion schedules allows the pretentious EEG signals to be caught. Aimed at the validation of planned system, the database can be used and continued with numerous recorded signals of EEG. The projected strategy executes better in terms of re-storing theta, delta, alpha and beta complexes of the original EEG with less alteration and a higher signal to noise ratio (SNR) value of the EEG signal, which illustrates in the quantitative analysis. The projected method used Verilog HDL and MATLAB software for both formation and authorization of results to yield improved results. Since from the achieved results, it is initiated that 32% enhancement in SNR, 14% in mean squared error (MSE) and 65% enhancement in recognition of anomalies, hence design is effectively certified and proved for standard EEG signals data sets on FPGA.

The proposed system can be used in military applications as it is high speed and excellent precise in terms of identification of abnormality, the developed system is portable and very precise. FPGA-based soldier’s health observing and position gratitude system is proposed in this paper. Reliant on heart rate which is centered on EEG signals the soldier health is observed in systematic bases. The proposed system is developed using Verilog HDL programming language and executing on Artix-7 development FPGA board of part name XC7ACSG100t and synthesised using in Vivado Design Suite software tool.