Purpose: Managers encounter many decisions that require the simultaneous use of different types of data in their decision‐making process. A critical decision area for managers is the performance evaluation of personnel, whether individually or as a member of a team. Performance evaluation is critically essential for the effective management of the human resource of an organization and evaluation of staff that help develop individuals, improve organizational performance, and feed into business planning.Design/methodology/approach: Performance evaluations require and often involve disparate types of information that are vague, incomplete, objective, and subjective. This paper proposes a performance evaluation system of employees considering various performance evaluation criteria using fuzzy logic. The main task in the proposed approach involves determining the performance indices of employees considering their respective performance in various qualitative and quantitative evaluation criteria and then selecting the best employee who holds highest performance index comparing all the indices.Findings: A model is developed for any kind of organization where performance evaluation is significantly important for staff motivation, attitude and behavior development, communicating and aligning individual and organizational aims, and fostering positive relationships between management and staff. Fuzzy control is used to determine the overall performance index by combining results of the performance in selected criteria and provided it in numerical values which will undoubtedly ensure convenience of the concerned human resource personnel during performance rating calculation.Originality/value: This is the first time, a performance evaluation model is developed using fuzzy approach for any kind of organization where performance evaluation is significantly important for staff motivation, attitude and behavior development, communicating and aligning individual and organizational aims, and fostering positive relationships between management and staff.

Anomalies are data points or a cluster of data points that lie away from the neighboring points or clusters and are inconsistent with the overall pattern of the data. Anomaly detection techniques help distinguish the anomalous observations from the regular ones, and thus provide the basis for developing a standard performance guideline for process control. The process of identifying anomalies becomes complicated in the absence of labeled training data as in supervised learning. Moreover, Euclidean distance between two points is less likely able to reflect the intrinsic structural distance imposed by the underlying manifold structure. In this paper, the authors propose a minimum spanning tree (MST)-based anomaly detection method. The merit of the method is that an MST provides a new distance measure, capable of capturing the relative connectedness of data points/clusters in a complicated manifold, and could be a better (dis)similarity metric, than the simple Euclidean distance, to identify anomalies in unsupervised learning settings. The proposed method is compared with 13 popular anomaly detection methods on 20 benchmark data sets, demonstrating a considerable improvement in its ability of identifying anomalies. Furthermore, the MST-based anomaly detection is applied to the data set from a hydropower turbine and demonstrates remarkable detection competence.

In today’s competitive business, supplier selection problem plays a significant role as a strategic feature in company’s success. As variety of uncontrollable and unpredictable factors affect the evaluation and decision-making process at different levels, supplier selection problem is usually very complex and unstructured, Various decision making approaches have been proposed to tackle the problem as part of a general tendering process, particularly those of multi-criteria analysis which use both quantitative and qualitative data. The aim of this paper is to integrate Fuzzy Delphi method with fuzzy analytic hierarchy process (AHP) and Fuzzy TOPSIS based approach to select the best supplier providing the highest satisfaction for the criteria determined. Fuzzy Delphi method is used to identify the most important and significant criteria then Fuzzy AHP is used to obtain the relative importance of the evaluation criteria and finally Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is used to rank the suppliers with a view to select the best one from them. In order to demonstrate the applicability of proposed approach an illustrative example is presented and the result is analyzed at the end of this paper.

Dimensionality reduction is a crucial first step for many unsupervised learning tasks including anomaly detection and clustering. Autoencoder is a popular mechanism to accomplish dimensionality reduction. In order to make dimensionality reduction effective for high-dimensional data embedding nonlinear low-dimensional manifold, it is understood that some sort of geodesic distance metric should be used to discriminate the data samples. Inspired by the success of geodesic distance approximators such as ISOMAP, we propose to use a minimum spanning tree (MST), a graph-based algorithm, to approximate the local neighborhood structure and generate structure-preserving distances among data points. We use this MST-based distance metric to replace the euclidean distance metric in the embedding function of autoencoders and develop a new graph regularized autoencoder, which outperforms a wide range of alternative methods over 20 benchmark anomaly detection datasets. We further incorporate the MST regularizer into two generative adversarial networks and find that using the MST regularizer improves the performance of anomaly detection substantially for both generative adversarial networks. We also test our MST regularized autoencoder on two datasets in a clustering application and witness its superior performance as well.

In marine surveillance, distinguishing between normal and anomalous vessel movement patterns is critical for identifying potential threats in a timely manner. Once detected, it is important to monitor and track these vessels until a necessary intervention occurs. To achieve this, track association algorithms are used, which take sequential observations comprising the geological and motion parameters of the vessels and associate them with respective vessels. The spatial and temporal variations inherent in these sequential observations make the association task challenging for traditional multi-object tracking algorithms. Additionally, the presence of overlapping tracks and missing data can further complicate the trajectory tracking process. To address these challenges, in this study, we approach this tracking task as a multivariate time series problem and introduce a 1D CNN-LSTM architecture-based framework for track association. This special neural network architecture can capture the spatial patterns as well as the long-term temporal relations that exist among the sequential observations. During the training process, it learns and builds the trajectory for each of these underlying vessels. Once trained, the proposed framework takes the marine vessel’s location and motion data collected through the automatic identification system (AIS) as input and returns the most likely vessel track as output in real-time. To evaluate the performance of our approach, we utilize an AIS dataset containing observations from 327 vessels traveling in a specific geographic region. We measure the performance of our proposed framework using standard performance metrics such as accuracy, precision, recall, and F1 score. When compared with other competitive neural network architectures, our approach demonstrates a superior tracking performance.