This paper aims to develop a writing robot by recognizing the speech signal from the user. The robot arm constructed mainly for the disabled people who can’ t perform writing on their own. Here, dynamic time warping (DTW) algorithm is used to recognize the speech signal from the user. The action performed by the robot arm in the environment is done by reducing the redundancy which frequently faced by the robot arm with high accuracy in both velocity and position in its own trajectory.

This paper presents a fault location technique for transmission lines with minimum current measurement. This algorithm investigates proper current ratios for fault location problem based on thevenin theory in faulty power networks and calculation of short circuit currents in each branch. These current ratios are extracted regarding lowest sensitivity on thevenin impedance variations of the network structure. Proposed algorithm compares current ratios from offline calculations with corresponding values achieved from measurements with a look-up table system. Best solution based on Dynamic Time Warping (DTW) algorithm is introduced as an output (location of the fault) which includes the line and the distance. Among many current ratios to form look-up table system, the minimum number of them will be extracted by a multi-objective optimization technique using Bees Algorithm (BA). This extraction is based on lowest possible number of buses for instruments installation and required current measurements, estimation accuracy and sensitivity degree from thevenin impedances changes. Accuracy of proposed algorithm is evaluated in a widely used multi-machine network of Western Systems Coordinating Council (WSCC).

Today, the use of data mining techniques such as classification, clustering, discover repetitive pattern and discover outliers in different domains including production, medicine, social, meteorology, stock exchange, sales, customer service and other areas are increasing. Data mining techniques are specifically designed for static data. Therefore, their use for time series data requires some modifications to their respective algorithms. One of these changes is the selection of the appropriate similarity measurement method, because similarity measurement methods are used in all data mining techniques. Therefore, in this research, we will evaluate and compare the effect of two commonly used and efficient methods of time series similarity measurement in data mining. This evaluation is done in relation to the effectiveness of these methods in achieving better results. These methods are the Longest Common Sub Sequence (LCSS) method and the Dynamic time Warping (DTW) method. The main purpose of this research is to compare the performance of these methods in time series data mining. The data mining techniques that used in this research are the nearest-neighbor technique and k-medoids clustering algorithm. The performance evaluation process is described in the text. This process uses the nearest-neighbor technique to calculate the accuracy of detection of right time series class, and uses the k-medoids clustering technique to calculate the clustering accuracy, the ability to correctly determine the number of clusters, and the ability to determine the better cluster representative. For this purpose, we use 63 time series data sets by random from a world-renowned database that named UCR collection. The results show that the effect of LCSS method is significantly better than the effect of DTW method on the correct detection accuracy of time series class and clustering accuracy by 99% and 92. 5% confidence, respectively, but there is no significant difference between them in terms of their effect in determining the number of clusters and cluster representatives. The results of this research help to use these methods in appropriate data mining techniques in issues such as customer segmentation, workshop scheduling and the like more accurately.

With the increasing growth of positioning technologies and the use of navigation systems, a large volume of moving point object data, such as people, cars, ships, and animals, is available. However, the lack of integrity and incompleteness of these data for systemic, human, and environmental reasons challenges the analysis of trajectories and their effective application in various fields. Therefore, the reconstruction of missing data plays an important role in maximizing the capacity of movement data, particularly in navigation and track tracking. In this study, using the similarity measurement of trajectories approach, trajectories containing gaps are reconstructed. In this regard, the context-based dynamic time warping (CDTW) method, along with speed and direction movement parameters, are used to measure the similarity and reconstruct the trajectories of vessels in two regions of the Atlantic and Pacific Oceans. Two mechanisms, a constant number of trajectories and a specified threshold, are considered for reconstruction. The results show that using a constant number of trajectories in comparison with the specified threshold reduces the root mean square error (RMSE) and mean absolute error (MAE) from 1. 5 and 1. 4 to 0. 5 and 0. 4, respectively. In addition, increasing the length of the trajectories improves the RMSE and MAE values from 0. 5 to 0. 1 in the case of a constant number of trajectories and 1. 5 to 0. 3 in the case of the specified threshold.

This paper proposes a novel 3D action recognition technique which uses the skeletal information extracted from depth image sequences. First, each action is represented by a multidimensional time series where each dimension represents the position variation of one skeleton joint over time. The time series is then mapped into the kernel Hilbert space using a metric defined by Dynamic Time Warping distance. Afterwards, regularized Fisher strategy is used to remap the kernel space into a discriminative one. This incorporates the correlation-distinctiveness relationship of the sequences into the recognition process and also mitigates the curse of dimensionality effect in the kernel space.  Unlike traditional kernel functions, the time warping used in the mapping strategy makes the kernel space robust to the temporal shift variations of the motion sequences. Moreover, our method eliminates the need for a complex design method for extracting the static and dynamic information of a motion sequence. A set of extensive experiments on three publically available databases; TST, UTKinect, and UCFKinect demonstrates the superiority of our method compared to a set of baseline algorithms.

Purpose: The increasing complexity of omnichannel retailing has necessitated retailers to redesign processes and forecasting methods and accept new approaches based on machine learning and artificial intelligence. Improving the accuracy of demand forecasting and managing customer needs from different channels due to reducing demand uncertainty are the most important challenges in omnichannel retailing that retailers should deal with. A better understanding of consumer behaviour patterns leads to more accurate demand forecasting, which in turn helps gain insight into transportation flows, improves distribution management, and enables better planning and execution of supply chain operations. This study aims to reduce the uncertainty of demand in omnichannel retailing by improving the accuracy of demand forecasting by considering customers buying behaviour through using machine learning methods. Design/methodology/approach: In this study to forecast future sales based on customers buying behaviour, a cosmetics retailer’s historical data on the monthly sales from February 2020 to June 2022 is used. The ID of eight products has been selected to analyze the performance of proposed methods and the method that the company applied to forecast demand. Clustering has been implemented using the dynamic time-warping algorithm due to the unequal length of the products’ time series. Initially, the nonlinear autoregressive neural network (NAR) has been applied to the time series in each cluster and later, the nonlinear autoregressive neural network with exogenous input (NARX) has been applied to the time series. The performance of the methods has been evaluated by testing R-squared and all R-squared coefficients and root mean square error (RMSE) to analyze the accuracy measure. Findings: The forecasting methods comparison, moving average (MA), the nonlinear autoregressive neural network (NAR), and the nonlinear autoregressive neural network with exogenous input (NARX) concerning testing R-squared coefficient, and also all R-squared and RMSE indicated that the nonlinear autoregressive neural network with exogenous input presented a good performance for all the products, so it confirmed that the application of the clustering to identification customers buying behaviour through the sales history of the products, integrated with artificial neural networks, to conduct demand forecasting, could be considered a good method for forecasting demand of omnichannel retailing supply chain products. Practical implications: The proposed method of this study leads to uncertainty reduction in omnichannel retailing by understanding the buying behaviour of customers, identifying patterns and using its analysis in the processes and operations, and its integration with machine learning methods improves distribution management and provides better planning and implementation of supply chain operations. Managers can use the proposed method to accurately predict complex demand patterns in the retailing industry. Using business data in demand planning provides an extra advantage to managers to include important variables based on their judgments. Social implications: Knowing the factors affecting the sale of a specific category of a product helps to effectively design promotions, advertising campaigns, the optimal combination of category displays and optimization of shelf space in retail stores. Also, accurate demand forecasts lead to better ordering policies, thus minimizing the cost of inventory management and optimal distribution and logistics planning to meet future demand. Originality/value: The proposed method presents a predictive approach for an omnichannel retailing supply chain that leads to uncertainty reduction in omnichannel retailing by understanding the buying behaviour of customers, identifying patterns, and using its analysis in the processes and operations and its integration with machine learning methods to improve distribution management, and provides better planning and implementation of supply chain operations.

The diagnosis of neurodevelopmental diseases, such as attention deficit/hyperactivity disorder (ADHD), has gained great attention in clinical studies due to its effect on the quality of human life. This disorder is caused by genetic factors, and anatomical and functional brain abnormalities, which can lead to timing deficits, working memory impairments, and inattention. Since the investigation of symmetry between activities of different brain regions may play an important role in the early diagnosis of this disorder, similarity quantification between brain signals is one of the existing challenges in the field of ADHD detection. The goal of this study is to compute symmetry between certain cortical areas from inter-hemispheric or intra-hemispheric channel pairs. For this purpose, a new algorithm based on dynamic time warping as a bivariate feature extraction step and support vector machine (SVM) classifier has been proposed. The proposed method's ability in distinct brain regions has also been explored.The proposed methods have been evaluated on electroencephalogram (EEG) recordings of 14 ADHD children and 19 age-matched healthy controls performing a time-reproduction task. It has achieved high average accuracy rates of 94.38±0.007 in discriminating between healthy controls and patients with ADHD. The experimental results have also demonstrated the superior performance of the proposed method in comparison with previous ADHD detection methods using EEG signals.

The most important issue with trajectory analysis is calculating similarity between trajectories. In this paper a novel method for measuring similarity between trajectories based on the cost to match a set of trajectories segments was introduced. The similarity between two trajectories is defined as a minimum cost to match a trajectory to the other one. For this purpose, the segment based distance was introduced to as a cost of matching two trajectories segments. In addition, the dynamic programming technique is used to implement the time warp method. We performed some experiments to compare the proposed similarity measure with the similar approaches in the application of trajectory classification. The empirical quality of the proposed similarity measure was evaluated on 1-nearest neighbor (1-NN) classification task using 13 publicly available data sets. Compared to the other well-known similarity measures, the proposed method proved to be effective in the considered experiments based on the accuracy of classification.