Proposed Prediction Algorithm Research Articles

The prediction of ship motion attitude in seaway based on BSO-VMD-GRU combination model

Short-term prediction of ship attitude holds the potential to provide decision support for precise control and optimal timing selection of ship operations at sea, thereby ensuring the safety and efficiency of offshore activities. However, the time series data representing ship motion attitude exhibits complex nonlinear and non-stationary characteristics, which pose challenges to achieving accurate predictions. To improve prediction accuracy, this study employs two sequence decomposition techniques: Variational Mode Decomposition (VMD) and Adaptive Noise Complete Ensemble Empirical Mode Decomposition (CEEMDAN). These decomposition methods are integrated with a Gated Recurrent Unit (GRU) to forecast ship motion attitude. Firstly, the decomposition techniques are applied to decompose the time series data of ship motion attitude into multiple intrinsic mode function (IMF) components. Subsequently, each IMF component is predicted using the GRU. Ultimately, the final prediction results are obtained by aggregating the GRU’s predictions for each individual component. To alleviate the impact of manual parameter configuration in the VMD algorithm, this study introduces a metaheuristic optimization algorithm called the Binary System Optimization Algorithm (BSO), which is empirically validated for its effectiveness. Moreover, real ship data from the vessel named “Yukun” is utilized to experimentally verify the proposed prediction algorithm. The experimental outcomes reveal that both decomposition algorithms can enhance the prediction accuracy of the GRU, with the VMD algorithm yielding superior prediction results compared to those obtained using CEEMDAN.

Trajectory prediction in pipeline form for intercepting hypersonic gliding vehicles based on LSTM

The interception problem of Hypersonic Gliding Vehicles (HGVs) has been an important aspect of missile defense systems. In order to provide interceptors with accurate information of target trajectory, a model based on an improved Long Short-Time Memory (LSTM) network for trajectory prediction pipeline is proposed for the interception of a skip gliding hypersonic target. Firstly, for trajectory prediction required by intercepting guidance laws, the altitude, velocity and velocity direction of the target are formulated in the form of analytic functions, consisting of linear decay terms and amplitude decay sinusoidal terms. Then, the dynamic characteristics of the model parameters are analyzed, and the target trajectory prediction pipeline is proposed with the prediction error considered. Finally, an improved LSTM network is designed to estimate parameters in a dynamically-updated manner, and estimation results are used for the calculation of the final trajectory prediction pipeline. The proposed prediction algorithm provides information on the velocity vector for midcourse guidance with the effect of prediction errors on interception taken into account. Simulation is conducted and the results show the high accuracy of the algorithm in HGVs’ trajectory prediction which is conducive to increasing the interception success rate.

Projecting Annual Rainfall Timeseries Using Machine Learning Techniques

Hydropower plays an essential role in Europe’s energy transition and can serve as an important factor in the stability of the electricity system. This is even more crucial in areas that rely strongly on renewable energy production, for instance, solar and wind power, as for example the Peloponnese and the Ionian islands in Greece. To safeguard hydropower’s contribution to total energy production, an accurate prediction of the annual precipitation is required. Valuable tools to obtain accurate predictions of future observations are firstly a series of sophisticated data preprocessing techniques and secondly the use of advanced machine learning algorithms. In the present paper, a complete procedure is proposed to obtain accurate predictions of meteorological data, such as precipitation. This procedure is applied to the Greek automated weather stations network, operated by the National Observatory of Athens, in the Peloponnese and the Ionian islands in Greece. The proposed prediction algorithm successfully identified the climatic zones based on their different geographic and climatic characteristics for most meteorological stations, resulting in realistic precipitation predictions. For some stations, the algorithm underestimated the annual total precipitation, a weakness also reported by other research works.

Possibility of Prediction Improvements for Atomic Clock Ensembles: Basis Selection in Undetectable Systems

To generate accurate time scales, such as national standard time in each country and network time in communication systems, a novel prediction algorithm is proposed for the atomic clock ensembles in which the system is unobservable. In the proposed prediction algorithm, to deal with unobservable state space, we consider a steady-state Kalman filter and an intuitive prediction algorithm to separately treat the observable and unobservable state spaces after Kalman canonical decomposition. It turns out that the basis selection plays an important role in affecting prediction performance. We compare the performance of the proposed algorithm with the conventional one and discuss the possibility of prediction improvements by altering the transformation matrix. A couple of numerical examples are presented to illustrate the efficacy of our results.

Prediction the number of hunting animal populations in the Yaroslavl region based on matrix verified models

Prediction the dynamics of the level and structure of regional hunting resources is relevant from the standpoint of their rational use. Matrix models are widely used to make predictions on the dynamics of hunting animal populations. The algorithm of the modified P. H. Leslie matrix model with a correction matrix is used. The accuracy of predictions on the dynamics of hunting animal populations based on matrix models is improved by their verification. In the proposed study, model verification is considered not only as a method for determining the correspondence of the model to the corresponding modeling object, but also as a tool for clarifying model parameters under conditions of possible uncertainty of information. A retrospective verification of models of the dynamics of prediction the number of hunting animal populations is considered, the results of which are verified by prospective verification. On the basis of retrospective verification under the conditions of incompleteness of the available information, the parameters of the models are clarified. The proposed prediction algorithm works well with a steady increase in the population of hunting animals. In practice, there are regressive and unstable scenarios of the dynamics of the number of hunting animal populations. In a regressive and stable scenario, the proposed algorithm for predicting the number of hunting animal populations works well, but regressive results give predictions for a decrease and even degradation of the population. In this case, the prediction tasks change. For example, for determining the percentage of production of a given type of hunting animals while maintaining the population size or its insignificant growth. As a result of the research, predictions were made on the dynamics of the populations of the main hunting animals in the Yaroslavl region (moose, bear, fox, white hare, grouse and capercaillie) based on verified matrix models.

Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data

This paper deals with fuel consumption prediction based on vehicle velocity, acceleration, and road slope time series inputs. Several data-driven models are considered for this purpose, including linear regression models and neural network-based ones. The emphasis is on accounting for the road slope impact when forming the model inputs, in order to improve the prediction accuracy. A particular focus is devoted to conversion of length-varying driving cycles into fixed dimension inputs suitable for neural networks. The proposed prediction algorithms are parameterized and tested based on GPS- and CAN-based tracking data recorded on a number of city buses during their regular operation. The test results demonstrate that a proposed neural network-based approach provides a favorable prediction accuracy and reasonable execution speed, thus making it suitable for various applications such as vehicle routing optimization, synthetic driving cycle validation, transport planning and similar.

Predicting Cardiovascular and Cerebrovascular Events Based on Instantaneous High-Order Singular Entropy and Deep Belief Network.

Automatically predicting cardiovascular and cerebrovascular events (CCEs) is a key technology that can prevent deaths and disabilities. Herein, we propose predicting CCE occurrences based on heart rate variability (HRV) analysis and a deep belief network (DBN). The proposed prediction algorithm uses eight novel HRV signal features, which are calculated based on the following steps. First, the instantaneous amplitude (IA), instantaneous frequency (IF), and instantaneous phase (IP) are calculated for the HRV signals. Second, the high-order cumulant is estimated for the HRV and its IA, IF, and IP. Third, a high-order singular entropy is calculated to measure the fluctuation in signals. Fourth, eight novel features are obtained and processed using a DBN classifier designed for CCE prediction. The DBN classification method, with the novel HRV features, outperformed existing methods in terms of accuracy. Thus, the scheme proposed herein provided a novel direction for predicting CCEs.

ПРОГНОЗИРОВАНИЕ ПРОФИЛЯ ФУНКЦИОНИРОВАНИЯ КОМПЬЮТЕРНОЙ СИСТЕМЫ НА ОСНОВЕ МНОГОЗНАЧНЫХ ЗАКОНОМЕРНОСТЕЙ

Purpose of work is to create a new algorithm for predicting anomalous states of computer systems (CS) using the mathematical apparatus of multivalued dependencies (Multivalued Dependencies Prognosus Algorithm, MDPA), which are categorical concepts. The research method is the analysis of historical data using the mathematical apparatus of multivalued dependencies. Objects of study are theoretical and practical issues of solving and visualizing information security problems. Results of the study. A methodology and algorithm for predicting the state of CS have been developed. The boundaries of the input parameters of the algorithm are derived and justified. The boundaries of the input parameters need to be pre-configured for the correct generation of the prognosis. A software implementation of the proposed prediction algorithm has been developed. The efficiency of the algorithm has been tested on real experimental data. A spatial analysis of the prediction results was carried out. The main disadvantage of the proposed algorithm is the need to fine-tune the input parameters for each set of “historical data”. Scientific significance. The scope of application of multivalued dependencies has been expanded; a new algorithm for predicting anomalous states of CS, which are categorical concepts, has been proposed. The developed prediction algorithm can be generalized to any subject area containing historical data of any type

A novel prediction algorithm for multivariate data sets

Regression analysis is a statistical technique that is most commonly used for forecasting. Data sets are becoming very large due to continuous transactions in today's high-paced world. The data is difficult to manage and interpret. All the independent variables can’t be considered for the prediction because it costs high for maintenance of the data set. A novel algorithm for prediction has been implemented in this paper. Its emphasis is on extraction of efficient independent variables from various variables of the data set. The selection of variables is based on Mean Square Errors (MSE) as well as on the coefficient of determination r2p, after that the final prediction equation for the algorithm is framed on the basis of deviation of actual mean. This is a statistical based prediction algorithm which is used to evaluate the prediction based on four parameters: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and residuals. This algorithm has been implemented for a multivariate data set with low maintenance costs, preprocessing costs, lower root mean square error and residuals. For one dimensional, two-dimensional, frequent stream data, time series data and continuous data, the proposed prediction algorithm can also be used. The impact of this algorithm is to enhance the accuracy rate of forecasting and minimized average error rate.

Extended-Range Prediction Model Using NSGA-III Optimized RNN-GRU-LSTM for Driver Stress and Drowsiness.

Road traffic accidents have been listed in the top 10 global causes of death for many decades. Traditional measures such as education and legislation have contributed to limited improvements in terms of reducing accidents due to people driving in undesirable statuses, such as when suffering from stress or drowsiness. Attention is drawn to predicting drivers’ future status so that precautions can be taken in advance as effective preventative measures. Common prediction algorithms include recurrent neural networks (RNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) networks. To benefit from the advantages of each algorithm, nondominated sorting genetic algorithm-III (NSGA-III) can be applied to merge the three algorithms. This is named NSGA-III-optimized RNN-GRU-LSTM. An analysis can be made to compare the proposed prediction algorithm with the individual RNN, GRU, and LSTM algorithms. Our proposed model improves the overall accuracy by 11.2–13.6% and 10.2–12.2% in driver stress prediction and driver drowsiness prediction, respectively. Likewise, it improves the overall accuracy by 6.9–12.7% and 6.9–8.9%, respectively, compared with boosting learning with multiple RNNs, multiple GRUs, and multiple LSTMs algorithms. Compared with existing works, this proposal offers to enhance performance by taking some key factors into account—namely, using a real-world driving dataset, a greater sample size, hybrid algorithms, and cross-validation. Future research directions have been suggested for further exploration and performance enhancement.

Online Adaptive Prediction of Human Motion Intention Based on sEMG.

Accurate and reliable motion intention perception and prediction are keys to the exoskeleton control system. In this paper, a motion intention prediction algorithm based on sEMG signal is proposed to predict joint angle and heel strike time in advance. To ensure the accuracy and reliability of the prediction algorithm, the proposed method designs the sEMG feature extraction network and the online adaptation network. The feature extraction utilizes the convolution autoencoder network combined with muscle synergy characteristics to get the high-compression sEMG feature to aid motion prediction. The adaptation network ensures the proposed prediction method can still maintain a certain prediction accuracy even the sEMG signals distribution changes by adjusting some parameters of the feature extraction network and the prediction network online. Ten subjects were recruited to collect surface EMG data from nine muscles on the treadmill. The proposed prediction algorithm can predict the knee angle 101.25 ms in advance with 2.36 degrees accuracy. The proposed prediction algorithm also can predict the occurrence time of initial contact ms in advance. Meanwhile, the proposed feature extraction method can achieve accuracy of sEMG reconstruction and can guarantee accuracy even when the distribution of sEMG is changed without any adjustment. The online adaptation network enhances the accuracy of sEMG reconstruction of CAE to and decreases the angle prediction error from to . The proposed method achieves effective motion prediction in advance and alleviates the influence caused by the non-stationary of sEMG.

Cloud Based Extensible Business Management Suite and Data Predictions

Introduction: The present research work was carried out using an enhanced market prediction algorithm in the cloud-based business, handing out the CRM system. The approach that is being predicted will handle and forecast the business with a higher degree of precision and constancy, the outcome solution of which is found to be compatible with any organization. Methods: The CRM framework was proposed in order to overcome the lack of multi-module integration and functionalities. The developed application is a complete tool for the purpose of managing the business over cloud. The business owner can host this application on any user interface PHP- and MySQL-enabled server. An optimized regression model was developed to predict the future of business and relationship with the customer. For the experimental test, a sample dataset was collected from the UCI machine learning repository. Results: The proposed prediction algorithm is an extension of the time series–based forecasting model. The projected system is tested with the income and expense cluster data of one year that were projected for analysis and the result shows better accuracy and constancy than the traditional opportunity creation driven model. Conclusion: It has been observed that the proposed Cloud-Based CRM Framework supports the organizations to achieve business predictions and maintain healthy relations with their clients and has worked best for the companies that have to maintain customer end relations. In addition, the customized CRM tool helps business managers and entrepreneurs to define innovative business strategies. The CRM application influences customer satisfaction on a large scale and is dominant for the supply chain system. Overall, the business model indicates that corporate organizations could grab business insights in a cost-efficient way with upcoming forecasting of sales. Discussion: Customer Relationship Management (CRM) is a technology used by the corporate sector to manage potential customers and their experiences in the view of the seller to consumer through purchase and post-purchase. In this era of advanced information technology, the sales departments of multi-product and service-end businesses are struggling hard in order to attain the signal view of the business. The key criterion for business-boosting decisions includes a single dashboard interface with a description of relevant information. The biggest challenge for businesses with umbrella views is to combine the data from various places and obtain market forecasts from the description. The deficiency in data collection and group management causes market loss or a lack of consumer interest in the product.

An Efficient Attribute Reduction and Fuzzy Logic Classifier for Heart Disease and Diabetes Prediction

Introduction: Over the past decade Heart and diabetes disease prediction are major research works in the past decade. For prediction of the Heart and Diabetes diseases, a model using an approach based on rough sets for reducing the attributes and for classification, fuzzy logic system is proposed in this paper. Methods: The overall process of prediction is split into two main steps, 1) Using rough set theory and hybrid firefly and BAT algorithms, feature reduction is done 2) Fuzzy logic system classifies the disease datasets. Reduction of attributes is carried out by rough sets and Hybrid BAT and Firefly optimization algorithm. Results and Discussion: Then the classification of datasets is carried out by the fuzzy system which is based on the membership function and fuzzy rules. The experimentation is performed on several heart disease datasets available in UCI Machine learning repository like datasets of Hungarian, Cleveland, and Switzerland and diabetes dataset collected from a hospital in India. The experimentation results show that the proposed prediction algorithm outperforms existing approaches by achieving better accuracy, specificity, and sensitivity.

Multi-hop Energy-Efficient Reliable Cluster-based Sectoring Scheme Using Markov Chain Model to Improve QoS Parameters in a WSN

The data dissemination toward the sink node in a wireless sensor network in one of three ways: single-hop data dissemination, multi-hop dissemination, and multi-hop dissemination, using the nearest cluster head or sector head (SH). This study focuses on third multi-hop data dissemination in which SHs are selected in the network, other nodes disseminate data toward these SHs, and then, the SHs transfer the collected data to the sink node. This paper proposes a novel approach to SH selection called Energy-Efficient Reliable Sectoring-Scheme (EERSS). It is supported by an optimization algorithm called Cat Swarm Optimization (CSO), which proves that the elected number of SH is optimal. This CSO based EERSS algorithm is implemented to rank quality of service parameters, such as network lifetime maximization, with reliability and to minimize energy consumption. A path reliability prediction-based Markov chain model is applied to the CSO-based-EERSS algorithm to achieve results that are superior to that of traditional models. After the construction of the proposed prediction algorithms CSO-based-EERSS (P-CSO-EERSS), its performance is compared with that of the traditional CSO-based-EERSS, EERSS, and Particle Swarm Optimization based EERSS, concerning packet delivery ratio(PDR), end-to-end delay, and energy consumption. The SH selection performance of the proposed P-CSO-EERSS is superior to that of the traditional algorithms based on low energy consumption and high PDR.

A Transaction Trade-Off Utility Function Approach for Predicting the End-Price of Online Auctions in IoT

To stimulate large-scale users to participate in the big data construction of IoT (internet of things), auction mechanisms based on game theory are used to select participants and calculate the corresponding reward in the process of crowdsensing data collection from IoT. In online auctions, bidders bid many times and increase their bid price. All the bidders want to maximize their utility in auctions. An effective incentive mechanism can maximize social welfare in online auctions. It is complicated for auction platforms to calculate social welfare and the utility of each bidder’s bidding items in online auctions. In this paper, a transaction trade-off utility incentive mechanism is introduced. Based on the transaction trade-off utility incentive mechanism, it can make the forecasting process consistent with bidding behaviors. Furthermore, an end-price dynamic forecasting agent is proposed for predicting end prices of online auctions. The agent develops a novel trade-off methodology for classifying online auctions by using the transaction trade-off utility function to measure the distance of auction items in KNN. Then, it predicts the end prices of online auctions by regression. The experimental results demonstrate that an online auction process considering the transaction utility is more consistent with the behaviors of bidders, and the proposed prediction algorithm can obtain higher prediction accuracy.

REFINED TAYLOR SERIES EXPANSION-BASED PREDICTION OF THE OVERALL SURVIVAL FOR NONSMALL CELL LUNG CANCER PATIENTS OF CLINICAL STAGE IIIA-N2 AFTER VARIOUS TREATMENTS: TAIWAN POPULATION-BASED STUDY OF 2655 CASES FROM 2010 TO 2017

The overall survival (OS) prediction for nonsmall cell lung carcinoma (NSCLC) patients of clinical IIIA-N2 stage undergone various treatments was investigated through a refined Taylor series expansion algorithm. The model was created according to a population-based study in Taiwan. The proposed prediction algorithm is based on the well-known hit and target model adopted for analyzing the cell death from the microscopic viewpoint. It also implies the application of the Taylor series expansion to the population-based survey dataset. In the proposed algorithm, the basic degradation of a patient’s health is represented via a specific function comprising a single exponential term exp([Formula: see text]). The refined algorithm successfully predicted NSCLC IIIA-N2 patients’ OS rate. A total of 127,301 patients were collected from 2010 to 2017. Then, 2655 patients were recognized as effective events and classified into eight classes according to various medical treatments, namely surgical operation, radiotherapy, and chemotherapy. For each class of patients, the average life was evaluated, according to Taylor’s expansion algorithm, and the average derived life range spread from 3.51 to 7.81 years. An index of life gain with specific treatment was defined according to the Taguchi optimization analysis. The life gains provided by the surgical operation, chemotherapy, and radiotherapy were 2.74, 1.18, and 0.48 years. The surgical operation was the most beneficial treatment, which is in concert with recommendations of European experts. A similar finding was also reflected in four out of eight classes, which included the surgical operation in the treatment plans of most Taiwanese hospitals.

A NOVEL QUOTIENT PREDICTION FOR FLOATING-POINT DIVISION

At present, floating-point operations are used as add-on functions in critical embedded systems, such as physics, aerospace system, nuclear simulation, image and digital signal processing, automatic control system and optimal control and financial, etc. However, floating-point division is slower than floating-point multiplication. To solve this problem, many existing works try to reduce the required number of iterations, which exploit large Look Up Table (LUT) resource to achieve approximate mantissa of a quotient. In this paper, we propose a novel prediction algorithm to achieve an optimal quotient by predicting certain bits in a dividend and a divisor, which reduces the required LUT resource. Therefore, the final quotient is achieved by accumulating all predicted quotients using our proposed prediction algorithm. The experimental results show that only 3 to 5 iterations are required to obtain the final quotient in a floating-point division computation. In addition, our proposed design takes up 0.84% to 3.28% (1732 LUTs to 6798 LUTs) and 5.04% to 10.08% (1916 (ALUT) to 3832 (ALUT)) when ported to Xilinx Virtex-5 and Altera Stratix-III FPGAs, respectively. Furthermore, our proposed design allows users to track remainders and to set customized thresholds of these remainders to be compatible with a specific application.

Driver intention prediction using model-added Bayesian network

The autonomous driving technology requires reliable detection and prediction of the surrounding environment. Predicting the lane change intention of the surrounding traffic is critical to evaluate the potential threat around the host vehicle. This paper develops a lane change maneuver prediction algorithm based on a newly proposed driver model combined with a Bayesian network. The innovation of the proposed algorithm is the utilization of the driver model while calibrating and executing the Bayesian network. The prediction algorithm can provide not only the driver’s intention but also the probability associated with the intention. The Next-Generation Simulation data sets are used to develop and validate the prediction model. In total, there are more than 2000 lane change events used in this paper. The result shows that the proposed prediction algorithm can provide an accurate prediction of the surrounding vehicle’s lane change maneuver.

OP Performance Prediction for Complex Mobile Multiuser Networks Based on Extreme Learning Machine

Due to the complex and variable environments of mobile communication, the mobile multiuser networks become a hot topic. To process active complex event in mobile multiuser networks, it is important to predict the system performance. In this work, the authors consider the multiuser networks which utilizes transmit antenna selection (TAS). We derive novel closed-form expressions for the outage probability (OP) in terms of the Meijer's G-function. Then, a extreme learning machine (ELM)-based OP performance prediction algorithm is proposed. We use the theoretical results to generate training data. We test back-propagation (BP) neural network, locally weighted linear regression (LWLR), wavelet neural network, ELM, and support vector machine (SVM) methods. Compared with wavelet neural network, SVM, BP neural network, and LWLR methods, the Monte-Carlo results shows that the proposed prediction algorithm can consistently achieve higher OP performance prediction results.

Hourly Heat Load Prediction Model Based on Temporal Convolutional Neural Network

Smart district heating system (SDHS) is an important way to realize green energy saving and comfortable heating in the future, which is conducive to improving energy utilization efficiency and reducing pollution emissions. The accurate prediction algorithm of heating load plays an important role in on-demand heat supply, however, the heating load prediction is a complicated nonlinear optimization problem, and the prediction accuracy is limited due to the poor nonlinear expression ability of the traditional prediction algorithms. This paper proposes a heating load prediction model based on temporal convolutional neural network (TCN), which implements the rapid extraction of complex data features due to the integration of both the parallel feature processing of convolution neural network (CNN) and the time-domain modeling capability of recurrent neural network (RNN). The engineering data of four heat exchange stations located in Anyang, China in the 2018 heating season is used to evaluate and verify the performance of proposed prediction algorithm based on TCN, and the comprehensive comparisons with state-of-the-art algorithms, such as RFR, ETR, GBR, SVR, NuSVR, SGD, Bagging, Boosting, MLP, RNN, LSTM, etc., were analyzed carefully. The experimental results shown that the proposed heat load prediction algorithm based on TCN has performance superiority.