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This paper presents a column generation-based modelling and solution approach for a teaching assistant workload scheduling problem that arises at academic institutions. A typical weekly workload schedule involves teaching deficiency classes, instructing problem-solving tutorial sessions, and allocating help-hours for students. For this purpose, a mixed-integer programming model that selects valid combinations of weekly schedules from the set of all feasible schedules is formulated. Due to the overwhelming number of variables in this model, an effective column generation procedure is developed. To illustrate the proof-of-concept along with modelling and algorithmic constructs, a case study related to the Department of Mathematics at Kuwait University is addressed. Computational results based on real data indicate that the generated schedules using the proposed model and solution procedure yield improved weekly workloads for teaching assistants in terms of fairness, and achieve enhanced satisfaction levels among assistants, as compared to schedules obtained using ad-hoc manual approaches.

Fuzzy sets can be used in many old-fashioned aspects of our lives in order to reach better performance and make fairer judgments. Evaluation through examination is typically conducted by educational centers, and multiple choice question (MCQ) exams are widely applied to score the examinees. Since scoring is potentially a difficult process to judge, we propose to evaluate examinees by fuzzy evaluation method. This method can overcome the main shortcoming of the classical MCQs, i.e. the random selection of the choices. The evaluation of the proposed fuzzy MCQ is more accurate and its ranking of examinees is fairer than classical MCQ.

World has become a global village after introduction of social media and social networks. However, it extensively increased the demand for network resources, particularly multimedia traffic like images, videos and audio. The medium for this extensive traffic is always public networks such as internet or cellular networks. But the open nature of such network like internet always creates security threats for data during transmission. Due to many intrinsic features and higher correlation in multimedia traffic, existing encryption algorithms are not very convincing to perform well under critical scenarios. Therefore, many people in the research community are still working to propose new encryption schemes which can address these issues and handle multimedia traffic effectively on public networks. In this paper, we explore the weaknesses of existing encryption schemes, which compromise in many scenarios due to high correlation of multimedia traffic. To tackle this issue we proposed certain enhancements in an existing scheme. Our enhanced modification includes addition of bitwise XORed operation using non-linear chaotic algorithm. Performance of enhanced scheme is tested against state of the art security parameters. Efficiency of the proposed scheme is also validated via entropy, correlation, peak signal to noise ratio, unified average change intensity and number of pixels change rate tests.

In this paper, one method for training the Support Vector Regression (SVR) machine in the complex data field is presented, which takes into account all the information of both the real and imaginary parts simultaneously. Comparing to the existing methods, it not only considers the geometric information of the complex-valued data, but also can be trained with the same amount of computation as the original SVR in the real data field. The accuracy of the proposed method is analysed by the simulation experiments. This also can be applied to the field of anti-interference for satellite navigation successfully, which shows its effectiveness in practical application.

In this paper, with respect to how to express the complex fuzzy information, we proposed the concept of interval-valued linguistic intuitionistic fuzzy numbers (IVLIFNs), whose membership and non-membership are represented by interval-valued linguistic terms, then the Hamming distance is defined, further, we also proposed the interval-valued linguistic intuitionistic fuzzy entropy. Considering that the VIKOR method can achieve the maximum “group utility” and minimum of “individual regret”, we extended the VIKOR method to process the interval-valued linguistic intuitionistic fuzzy information (IVLIFI), and proposed an extended VIKOR method for the multiple attribute decision making (MADM) problems with IVLIFI. And an illustrative example shows the effectiveness of the proposed approach.

Information systems contain a lot of data regarding business process execution history. Use of this data, in the form of an event log, can greatly support business process management. The paper presents an approach to construct Bayesian belief network from an event log that could facilitate decision support in business process execution. The approach is evaluated against multiple event logs by inferring data probabilities occurring in the business processes. The results show that the approach is suitable for the task and could be used in decision support with future research focused on prediction and simulation of business processes.

Scalar quantizer selection for processing a signal with a unit variance is a difficult problem, while both selection and quantizer design for the range of variances is even tougher and to the authors’ best knowledge, it is not theoretically solved. Furthermore, performance estimation of various image processing algorithms is unjustifiably neglected and there are only a few analytical models that follow experimental analysis. In this paper, we analyse application of piecewise uniform quantizer with Golomb-Rice coding in modified block truncation coding algorithm for grayscale image compression, propose design improvements and provide a novel analytical model for performance analysis. Besides the nature of input signal, required compression rate and processing delay of the observed system have a strong influence on quantizer design. Consequently, the impact of quantizer range choice is analysed using a discrete designing variance and it was exploited to improve overall quantizer performance, whereas variable-length coding is applied in order to reduce quantizer’s fixed bit-rate. The analytical model for performance analysis is proposed by introducing Inverse Gaussian distribution and it is obtained by discussing a number of images, providing general closed-form solutions for peak-signal-to-noise ratio and the total average bit-rate estimation. The proposed quantizer design ensures better performance in comparison to the other similar methods for grayscale image compression, including linear prediction of pixel intensity and edge-based adaptation, whereas analytical model for performance analysis provides matching with the experimental results within the range of 1 dB for PSQNR and 0.2 bpp for the total average bit-rate.

Compared to fuzzy numbers, intuitionistic fuzzy numbers provide greater opportunities for solving complex decision-making problems, especially when they are related to ambiguities, uncertainties and vagueness. However, their use is more complex, especially when it comes to ordinary users. Therefore, in this paper an approach adopted for evaluating alternatives on the basis of a smaller number of some more complex evaluation criteria is proposed. The approach is based on the use of linguistic variables, triangular intuitionistic fuzzy numbers, and the Hamming distance. At the end, a case study of hotels’ websites evaluation is given to demonstrate the practicality and effectiveness of the proposed approach, together with its limitations and weaknesses. Additionally, a new procedure for ranking intuitionistic fuzzy numbers is proposed and its use is verified.

The aim is to develop simple for industrial use neuro-fuzzy (NF) predictive controllers (NFPCs) that improve the system performance and stability compensating the nonlinear plant inertia and time delay. A NF plant predictor is trained from real time plant control data and validated to supply a main model-free fuzzy logic controller with predicted plant information. A proper prediction horizon is determined via simulation investigations. The NFPC closed loop system stability is validated based on a parallel distributed compensation (PDC) approximation of the NFPC. The PDC can easily be embedded in industrial controllers. The proposed approach is applied for the real time air temperature control in a laboratory dryer. The improvements are reduced overshoot and settling time.