Application of semantic analysis and LSTM-GRU in developing a personalized course recommendation system

The selection of elective courses based on an individual's domain interest is a challenging and critical activity for students at the start of their curriculum. Effective and proper recommendation may result in building a strong expertise in the domain of interest, which in turn improves the outcomes of the students getting better placements, and enrolling into higher studies of their interest, etc. In this paper, an effective course recommendation system is proposed to help the students in facilitating proper course selection based on an individual's domain interest. To achieve this, the core courses in the curriculum are mapped with the predefined domain suggested by the domain experts. These core course contents mapped with the domain are trained semantically using deep learning models to classify the elective courses into domains, and the same are recommended based on the student's domain expertise. The recommendation is validated by analyzing the number of elective course credits completed and the grades scored by a student who utilized the elective course recommendation system, with the grades scored by the student who was subjected to the assessment without elective course recommendations. It was also observed that after the recommendation, the students have registered for a greater number of credits for elective courses on their domain of expertise, which in-turn enables them to have a better learning experience and improved course completion probability.Web of Science1221art. no. 1079

Study on a strong and weak n-connected total perfect k-dominating set in fuzzy graphs

In this paper, the concept of a strong n-Connected Total Perfect k-connected total perfect k-dominating set and a weak n-connected total perfect k-dominating set in fuzzy graphs is introduced. In the current work, the triple-connected total perfect dominating set is modified to an n-connected total perfect k-dominating set n(ctpkD)(G) and number gamma n(ctpkD)(G). New definitions are compared with old ones. Strong and weak n-connected total perfect k-dominating set and number of fuzzy graphs are obtained. The results of those fuzzy sets are discussed with the definitions of spanning fuzzy graphs, strong and weak arcs, dominating sets, perfect dominating sets, generalization of triple-connected total perfect dominating sets of fuzzy graphs, complete, connected, bipartite, cut node, tree, bridge and some other new notions of fuzzy graphs which are analyzed with a strong and weak n(ctpkD)(G) set of fuzzy graphs. The order and size of the strong and weak n(ctpkD)(G) fuzzy set are studied. Additionally, a few related theorems and statements are analyzed.Web of Science1017art. no. 317

Effect of inflation and permitted three-slot payment on two-warehouse inventory system with stock-dependent demand and partial backlogging

This study examines the effect of monetary inflation for a two-warehouse single-product inventory system, in which items are stored in a limited capacity Own Warehouse (OW) and an unlimited capacity Rental Warehouse (RW). Demand for an item is considered stock dependent. Items may deteriorate at a different constant rate in both warehouses. Shortages are allowed in the stock-out period and are partially backlogged and satisfied in the next replenishment point. The supplier permits flexible payment options for the retailer to pay the amount in three equal payments at different time points. The retailers' preferred payment option is as follows: the first payment is prior to the replenishment point with some discount; the second payment is one-third of the total purchasing cost, which is paid at the time of the replenishment epoch; and the third payment is after the replenishment point and before the start of the next cycle, with some penalty. The influence of inflation on the cost calculation is considered, and an analytic expression for optimal minimal cost is explicitly derived from this. We performed arrived sensitivity analysis to discern the effects of the inflation and backlogging rates, as well as the effects of the discount rate on purchasing cost, and the effects of penalties upon the late payment of purchasing costs in optimizing the total cost.Web of Science1021art. no. 394

Tungaloy Ceramic Cutting Tools at Interrupted Machninig

The paper deals with testing of ceramic cutting tools with an interrupted machining. Tests have been performed on fixture – interrupted cut simulator constructed at Department of Machining and Assembly, FME, VŠB-TU Ostrava within the scope of the Czech Science Foundation project. The criterion of tool wear is either destruction of cutting tool or 6000 shocks. Testing cutting tool material used in this research is ceramic cutting tool produced by Tungaloy Company. Tested machined materials are 13MoCrV6 and C45 steels. The cutting speeds (408 and 580 m/min) and cutting feeds (0.15; 0.2; 0.25 and 0.3 mm) are investigated with variable parameters whereas the cutting depth is a constant parameter. The pictures of index-able inserts and graphs of dependence at variable cutting speed and cutting feed are shown as a result of a conducted investigation

Analysis of the planar point identification accuracy in CMM measurements

The paper presents the results of the investigations on the direction-dependent accuracy of the point identification during contact probe measurements with a coordinate measuring machine (CMM). Considering the contact point identified by an orthogonal to the surface probe movement, the transformation of coordinates was made in order to calculate the displacement of the measured point. As a result, the positioning accuracy was estimated in three axes. The experiments demonstrated a strong dependence of the displacement on the declination angle. Moreover, it was found that the directional surface texture which provided different roughness in perpendicular directions, had an impact on the positioning accuracy.Web of Science2218art. no. 700

Microstructure, mechanical and wear behaviour of deep cryogenically treated EN 52 Silchrome valve steel

This study has compared the performance of cryogenically processed EN 52 Silchrome valve steel with untreated material. After completing the standard heat treatment process, EN 52 steel material specimens are subjected to a deep cryogenic process with varying soaking temperatures. The parameters of the deep cryogenic procedure were changed to find the best wear qualities. The key features of valve steel, such as microstructure, mechanical, and wear behaviour are evaluated by conducting a test study. The evolution of wear mechanisms after enhancing qualities of EN 52 steel is studied using scanning electron microscopy. The mechanical and wear behaviour improved due to factors such as fine carbide precipitation, conversion of residual austenite, and carbide refining formed after cryogenic treatment. With a maximum reduction in wear rate of up to 45%, the deep cryogenic treatment of EN 52 steel with a soaking temperature of -140 degrees C was the ideal parameter. All three cryo-treated samples had better properties than the untreated EN 52 valve steel.Web of Science1516art. no. 548

Measure of similarity between GMMs by embedding of the parameter space that preserves KL divergence

In this work, we deliver a novel measure of similarity between Gaussian mixture models (GMMs) by neighborhood preserving embedding (NPE) of the parameter space, that projects components of GMMs, which by our assumption lie close to lower dimensional manifold. By doing so, we obtain a transformation from the original high-dimensional parameter space, into a much lower-dimensional resulting parameter space. Therefore, resolving the distance between two GMMs is reduced to (taking the account of the corresponding weights) calculating the distance between sets of lower-dimensional Euclidean vectors. Much better trade-off between the recognition accuracy and the computational complexity is achieved in comparison to measures utilizing distances between Gaussian components evaluated in the original parameter space. The proposed measure is much more efficient in machine learning tasks that operate on large data sets, as in such tasks, the required number of overall Gaussian components is always large. Artificial, as well as real-world experiments are conducted, showing much better trade-off between recognition accuracy and computational complexity of the proposed measure, in comparison to all baseline measures of similarity between GMMs tested in this paper.Web of Science99art. no. 95

Measurement system analyses - gauge repeatability and reproducibility methods

The submitted article focuses on a detailed explanation of the average and range method (Automotive Industry Action Group, Measurement System Analysis approach) and of the honest Gauge Repeatability and Reproducibility method (Evaluating the Measurement Process approach). The measured data (thickness of plastic parts) were evaluated by both methods and their results were compared on the basis of numerical evaluation. Both methods were additionally compared and their advantages and disadvantages were discussed. One difference between both methods is the calculation of variation components. The AIAG method calculates the variation components based on standard deviation (then a sum of variation components does not give 100 %) and the honest GRR study calculates the variation components based on variance, where the sum of all variation components (part to part variation, EV & AV) gives the total variation of 100 %. Acceptance of both methods among the professional society, future use, and acceptance by manufacturing industry were also discussed. Nowadays, the AIAG is the leading method in the industry.Web of Science181272

On grill Sβ-open set in grill topological spaces

In this article we originate a new class of Grill Set, namely GS(beta)-Open Set, which is parallel to the beta Open Set in Grill Topological Space (X, theta, G). In addition, we entitle GS(beta)-continuous and GS(beta)-open functions by applying a GS(beta)-Open Set and we review some of its important properties. Many examples are given to explain the concept lucidly. The properties of GS(beta) open sets are investigated and studied. The theorems based on the arbitrary union and finite intersections are discussed with counter examples. Moreover, some operators like GS(beta) - closure and GS(beta) - interior are introduced and investigated. The concept of GS(beta) - continuous functions are compared with the idea of G - Semi Continuous function. The theorems based on GS(beta) - continunity have been proved.Web of Science1023art. no. 462

A comparative analysis of machine learning models in prediction of mortar compressive strength

Predicting the mechanical properties of cement-based mortars is essential in understanding the life and functioning of structures. Machine learning (ML) algorithms in this regard can be especially useful in prediction scenarios. In this paper, a comprehensive comparison of nine ML algorithms, i.e., linear regression (LR), random forest regression (RFR), support vector regression (SVR), AdaBoost regression (ABR), multi-layer perceptron (MLP), gradient boosting regression (GBR), decision tree regression (DT), hist gradient boosting regression (hGBR) and XGBoost regression (XGB), is carried out. A multi-attribute decision making method called TOPSIS (technique for order of preference by similarity to ideal solution) is used to select the best ML metamodel. A large dataset on cement-based mortars consisting of 424 sample points is used. The compressive strength of cement-based mortars is predicted based on six input parameters, i.e., the age of specimen (AS), the cement grade (CG), the metakaolin-to-total-binder ratio (MK/B), the water-to-binder ratio (W/B), the superplasticizer-to-binder ratio (SP) and the binder-to-sand ratio (B/S). XGBoost regression is found to be the best ML metamodel while simple metamodels like linear regression (LR) are found to be insufficient in handling the non-linearity in the process. This mapping of the compressive strength of mortars using ML techniques will be helpful for practitioners and researchers in identifying suitable mortar mixes.Web of Science107art. no. 138