Predicting Students’ Performance In Basic Algorithms Programming In an E-Learning Environment Using Decision Tree Approach

Predicting the performance of students plays an important role in every institution to protect their students from failures and leverage their quality in higher education. Algorithm and Programming is a fundamental course for the students who start their studies in Informatics. Hence, the scope of this research is to identify the critical attributes which influence student performance in the E-learning Environment on Moodle LMS (Learning Management System) Platform and its accuracy. Data mining helps the process of preprocessing data in a dataset from raw data to quality data for advanced analysis. Dataset set is consisting of student academic performance such as grades of Quizzes, Mid exams, Final exams, and Final projects. Moreover, the dataset from LMS is considered as well in the process of modeling, in terms of constructing the decision tree, such as punctuality submission of Quizzes, Assignments, and Final Projects. Regarding the Basic Algorithm and Programming course, which is separated into two subjects in the first and second semester, thus the research will predict the student performance in the Basic Algorithm and programming course in the second semester based on the Introduction to programming course in the first semester. Decision Tree techniques are applied by using information gain in ID3 algorithm to get the important feature which is the PP index has the highest information gain with value 0.44, also the accuracy between ID3 and J48 algorithm that shows ID3 has the highest accuracy of modeling which is 84.80% compared to J48 82.34%.

A New Discrete Implicit Monte Carlo Scheme for Simulating Radiative Transfer Problems

We present a new algorithm for radiative transfer—based on a statistical Monte Carlo approach—that does not suffer from teleportation effects, on the one hand, and yields smooth results, on the other hand. Implicit Monte Carlo (IMC) techniques for modeling radiative transfer have existed from the 1970s. When they are used for optically thick problems, however, the basic algorithm suffers from “teleportation” errors, where the photons propagate faster than the exact physical behavior, due to the absorption-blackbody emission processes. One possible solution is to use semianalog Monte Carlo, in its new implicit form (ISMC), which uses two kinds of particles, photons and discrete material particles. This algorithm yields excellent teleportation-free results, but it also produces noisier solutions (relative to classic IMC), due to its discrete nature. Here, we derive a new Monte Carlo algorithm, Discrete Implicit Monte Carlo (DIMC), which also uses the idea of two kinds of discrete particles, and thus does not suffer from teleportation errors. DIMC implements the IMC discretization and creates new radiation photons for each time step, unlike ISMC. Using the continuous absorption technique, DIMC yields smooth results like classic IMC. One of the main elements of the algorithm is the avoidance of the explosion of the particle population, by using particle merging. We test the new algorithm on 1D and 2D cylindrical problems, and show that it yields smooth, teleportation-free results. We finish by demonstrating the power of the new algorithm on a classic radiative hydrodynamic problem—an opaque radiative shock wave. This demonstrates the power of the new algorithm for astrophysical scenarios.

Vitality Analysis Algorithm in the Study of Plant Individuals and Populations

Background: The article presents an algorithm of the vitality analysis of plant individuals in the populations that enables the assessment of the prospects for the existence of species within certain phytocenoses and provides important information on the conditions of their growth. There are three basic stages of the algorithm: the first stage is the selection of qualitative characters, which characterize the viability of individuals; the second stage is the assessment of the vitality of specific plant individuals included in the sampling; the third stage is an integral assessment of the population vitality structure. Objective: The goal of the study is to develop the basic algorithm for vitality analysis of populations based on the assessment of the vitality of plant individuals, as well as the authors’ algorithms for vitality analysis, considering the characteristic features of species, in particular, their different life strategies (C-type and R-type). The algorithm of the vitality analysis is demonstrated on the example of populations of the annual weed Persicaria scabra Moench (Polygonaceae), which grows in the pea crop planting (Sumy Region, Ukraine). Methods: The algorithm of vitality analysis is based on the method of Yu. A. Zlobin, which includes 3 main stages. The vitality analysis of populations is carried out on the basis of the assessment of the vitality of certain individuals. The assessment of the vitality structure of populations is the third stage of vitality analysis, where the population belonging to the prosperous, equilibrium, or depressive types is determined depending on the ratio of individuals of different vitality classes (a, b, c). The calculation of the vitality analysis provides for the transformation of absolute values into unit fractions. It ensures the equivalence of the contribution of each of the features used in the assessment of the vitality of individuals and populations as a whole. Results: The article presents a basic algorithm for vitality analysis of plant populations. It also shows the algorithm for vitality analysis considering some biological and ecological characters of the studied species, which may be used in special and relatively rare cases. Some examples of analyses with a well-defined primary strategy ‒ competitors (C-type) or explerents (R-type) have been presented in the article. To calculate the morphoparameters of plant individuals and populations, the most convenient is the statistical package “Statistics”, which provides for the possibility of calculation automation via the command line. The division of populations into three types according to vitality is of general nature. The method of assessing the population vitality is inherently comparative, and this feature is considered to be its advantage. Conclusion: Vitality analysis is useful in assessing the populations of rare plant species, meadow grasses, chemical contamination on the population of plants, identifying any changes in the status populations of forest herbs in the change of forest growth conditions, as well as a number of species of forest-forming tree species. The proposed variants of the algorithm to calculate the vitality of plant species and local populations are characterized by the high biological informative value and flexibility. The incorporated information on the vitality structure of populations in quantitative PVA models to predict their dynamics will significantly increase the reliability of forecasts regarding the prospects for the existence of phytopopulations of species in various plant communities.

A Personalized Recommendation Algorithm Based on Weighted Information Entropy and Particle Swarm Optimization

Similarity calculation is the most important basic algorithm in collaborative filtering recommendation. It plays an important role in calculating the similarity between users (items), finding nearest neighbors, and predicting scores. However, the existing similarity calculation is affected by over reliance on item scores and data sparsity, resulting in low accuracy of recommendation results. This paper proposes a personalized recommendation algorithm based on information entropy and particle swarm optimization, which takes into account the similarity of users’ score and preference characteristics. It uses random particle swarm optimization to optimize their weights to obtain the comprehensive similarity value. Experimental results on public data sets show that the proposed method can effectively improve the accuracy of recommendation results on the premise of ensuring recommendation coverage.

Constrained global optimization of multivariate polynomials using polynomial B-spline form and B-spline consistency prune approach

In this paper, we propose basic and improved algorithms based on polynomial B-spline form for constrained global optimization of multivariate polynomial functions. The proposed algorithms are based on a branch-and-bound framework. In improved algorithm we introduce several new ingredients, such as B-spline box consistency and B-spline hull consistency algorithm to prune the search regions and make the search more efficient. The performance of the basic and improved algorithm is tested and compared on set of test problems. The results of the tests show the superiority of the improved algorithm over the basic algorithm in terms of the chosen performance metrics. We compare optimal value of global minimum obtained using the proposed algorithms with CENSO, GloptiPoly and several state-of-the-art NLP solvers, on set of $11$ test problems. The results of the tests show the superiority of the proposed algorithm and CENSO solver (open source solver for global optimization of B-spline constrained problem) in that it always captures the global minimum to the user-specified accuracy.

Using the modified k-mean algorithm with an improved teaching-learning-based optimization algorithm for feedforward neural network training

In this paper we proposed a novel procedure for training a feedforward neural network. The accuracy of artificial neural network outputs after determining the proper structure for each problem depends on choosing the appropriate method for determining the best weights, which is the appropriate training algorithm. If the training algorithm starts from a good starting point, it is several steps closer to achieving global optimization. In this paper, we present an optimization strategy for selecting the initial population and determining the optimal weights with the aim of minimizing neural network error. Teaching-learning-based optimization (TLBO) is a less parametric algorithm rather than other evolutionary algorithms, so it is easier to implement. We have improved this algorithm to increase efficiency and balance between global and local search. The improved teaching-learning-based optimization (ITLBO) algorithm has added the concept of neighborhood to the basic algorithm, which improves the ability of global search. Using an initial population that includes the best cluster centers after clustering with the modified k-mean algorithm also helps the algorithm to achieve global optimum. The results are promising, close to optimal, and better than other approach which we compared our proposed algorithm with them.

PROSPECTIVE TRENDS OF HARMONIOUS POSSIBILITIES IN OPTIMIZATION OF PRODUCTION SYSTEMS OF CONSTRUCTION

Purpose. Scientific formation of algorithmic support of construction organization and management processes with the use of metaheuristic methods in solving practical problems of optimal control of nonlinear dynamic production systems. Methodology. Use of metaheuristic methods of optimization, system analysis and system substantiation, use of methods of systems theory, use of methods of modeling theory for the purpose of perspective management of production systems of building branch on the basis of the general laws and principles of harmony. Results. The scientific formation of the concept of harmonious optimization of production systems of construction by the metaheuristic method of the golden section is performed. The scheme of work of the method of golden section in optimization problems of construction production is developed and the detailed visualization of this method on levels of symmetry is executed. The ways of application of the principles of harmonious management in the optimization of construction production systems in the direction of sustainable and logical development are substantiated. The components of harmonious production in improving the interaction of different departments and accelerating the response to rapid change, which will accelerate the level of success of any organization. Originality. The concept of harmonious optimization of production systems of construction by metaheuristic method of golden section is offered. The scheme of work of the method of golden ratio in optimization problems of construction production is developed and the detailed visualization of this method on levels of symmetry is executed. Practical value. The use of optimization measures to increase the efficiency of construction production is proposed to be implemented according to the developed basic algorithm for optimizing the functioning of the construction production system according to the concept of the golden ratio method. Conclusions. The substantiation of ways of application of principles of harmonious management in optimization of building production systems in the direction of steady and logical development is executed. The components of harmonious production in improving the interaction of different departments and accelerating the response to rapid change, which will accelerate the level of success of any organization. The result is the creation of all conditions for the harmonious interaction of performers and equipment at all levels of construction management, which confirms the need to develop modern approaches to production on the principles of harmony.

Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design

The research presented in this manuscript proposes a novel Harris Hawks optimization algorithm with practical application for evolving convolutional neural network architecture to classify various grades of brain tumor using magnetic resonance imaging. The proposed improved Harris Hawks optimization method, which belongs to the group of swarm intelligence metaheuristics, further improves the exploration and exploitation abilities of the basic algorithm by incorporating a chaotic population initialization and local search, along with a replacement strategy based on the quasi-reflection-based learning procedure. The proposed method was first evaluated on 10 recent CEC2019 benchmarks and the achieved results are compared with the ones generated by the basic algorithm, as well as with results of other state-of-the-art approaches that were tested under the same experimental conditions. In subsequent empirical research, the proposed method was adapted and applied for a practical challenge of convolutional neural network design. The evolved network structures were validated against two datasets that contain images of a healthy brain and brain with tumors. The first dataset comprises well-known IXI and cancer imagining archive images, while the second dataset consists of axial T1-weighted brain tumor images, as proposed in one recently published study in the Q1 journal. After performing data augmentation, the first dataset encompasses 8.000 healthy and 8.000 brain tumor images with grades I, II, III, and IV and the second dataset includes 4.908 images with Glioma, Meningioma, and Pituitary, with 1.636 images belonging to each tumor class. The swarm intelligence-driven convolutional neural network approach was evaluated and compared to other, similar methods and achieved a superior performance. The obtained accuracy was over 95% in all conducted experiments. Based on the established results, it is reasonable to conclude that the proposed approach could be used to develop networks that can assist doctors in diagnostics and help in the early detection of brain tumors.

JUSTIFICATION OF THE FEASIBILITY OF INTRA-CITY RAILWAY COMMUNICATION ON THE EXAMPLE OF THE ST. PETERSBURG AGGLOMERATION

This article discusses the following issues: justification of the feasibility of intra-city railway communication on the example of St. Petersburg, creation of a basic algorithm for commissioning this project for this purpose, the analysis of existing methods used for the introduction of new types of transport was carried out, the main advantages and disadvantages of railway communication are given, the basic algorithm for the introduction of intra-city railway communication is given, the possibility of implementing the project in St. Petersburg is considered. The commissioning of the intra-city railway network allows: to reconsider the issue of the development of the transport network of the agglomeration, promotes the development of an off-street mode of transport that can compete with the metro, redistribute passenger flows coming from the Leningrad region and the suburbs of St. Petersburg, improve the environmental situation in the region by reducing congestion at the entrance to the city by transferring to the intra-city railway communication, which will also reduce the travel time of all traffic participants.

Fault Diagnosis of Rolling Bearing Based on Improved VMD and KNN

Variational modal decomposition (VMD) has the end effect, which makes it difficult to efficiently obtain fault eigenvalues from rolling bearing fault signals. Inspired by the mirror extension, an improved VMD is proposed. This method combines VMD and mirror extension. The mirror extension is a basic algorithm to inhibit the end effect. A comparison is made with empirical mode decomposition (EMD) for fault diagnosis. Experiments show that the improved VMD outperforms EMD in extracting the fault eigenvalues. The performance of the new algorithm is proven to be effective in real-life mechanical fault diagnosis. Furthermore, in this article, combining with singular value decomposition (SVD), fault eigenvalues are extracted. In this way, fault classification is realized by K-nearest neighbor (KNN). Compared with EMD, the proposed approach has advantages in the recognition rate, which can accurately identify fault types.