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Emerging Science Journal
Published by Ital Publication
ISSN : 26109182     EISSN : -     DOI : -
Core Subject : Social,
Environmental Science
Emerging Science Journal is not limited to a specific aspect of science and engineering but is instead devoted to a wide range of subfields in the engineering and sciences. While it encourages a broad spectrum of contribution in the engineering and sciences. Articles of interdisciplinary nature are particularly welcome.
Arjuna Subject : -
Articles 903 Documents
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Advanced Genetic Programming vs. State-of-the-Art AutoML in Imbalanced Binary Classification Franz Frank; Fernando Bacao
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-021

Abstract

The objective of this article is to provide a comparative analysis of two novel genetic programming (GP) techniques, differentiable Cartesian genetic programming for artificial neural networks (DCGPANN) and geometric semantic genetic programming (GSGP), with state-of-the-art automated machine learning (AutoML) tools, namely Auto-Keras, Auto-PyTorch and Auto-Sklearn. While all these techniques are compared to several baseline algorithms upon their introduction, research still lacks direct comparisons between them, especially of the GP approaches with state-of-the-art AutoML. This study intends to fill this gap in order to analyze the true potential of GP for AutoML. The performances of the different tools are assessed by applying them to 20 benchmark datasets of the imbalanced binary classification field, thus an area that is a frequent and challenging problem. The tools are compared across the four categories average performance, maximum performance, standard deviation within performance, and generalization ability, whereby the metrics F1-score, G-mean, and AUC are used for evaluation. The analysis finds that the GP techniques, while unable to completely outperform state-of-the-art AutoML, are indeed already a very competitive alternative. Therefore, these advanced GP tools prove that they are able to provide a new and promising approach for practitioners developing machine learning (ML) models. Doi: 10.28991/ESJ-2023-07-04-021 Full Text: PDF
On the Locating Rainbow Connection Number of Trees and Regular Bipartite Graphs Ariestha W. Bustan; A. N. M. Salman; Pritta E. Putri; Zata Y. Awanis
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-016

Abstract

Locating the rainbow connection number of graphs is a new mathematical concept that combines the concepts of the rainbow vertex coloring and the partition dimension. In this research, we determine the lower and upper bounds of the locating rainbow connection number of a graph and provide the characterization of graphs with the locating rainbow connection number equal to its upper and lower bounds to restrict the upper and lower bounds of the locating rainbow connection number of a graph. We also found the locating rainbow connection number of trees and regular bipartite graphs. The method used in this study is a deductive method that begins with a literature study related to relevant previous research concepts and results, making hypotheses, conducting proofs, and drawing conclusions. This research concludes that only path graphs with orders 2, 3, 4, and complete graphs have a locating rainbow connection number equal to 2 and the order of graph G, respectively. We also showed that the locating rainbow connection number of bipartite regular graphs is in the range of r-⌊n/4⌋+2 to n/2+1, and the locating rainbow connection number of a tree is determined based on the maximum number of pendants or the maximum number of internal vertices. Doi: 10.28991/ESJ-2023-07-04-016 Full Text: PDF
Architectural Model and Modified Long Range Wide Area Network (LoRaWAN) for Boat Traffic Monitoring and Transport Detection Systems in Shallow Waters Diaz Saputra; Ford Lumban Gaol; Edi Abdurachman; Dana Indra Sensuse; Tokuro Matsuo
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-011

Abstract

Monitoring the movement of boats in shallow waters requires a real-time monitoring system. However, for small-size wooden boats, they are still monitored manually, and data is unavailable in real time, which makes it difficult to effectively monitor them. The integration of IoT platforms with the boat monitoring system is a challenging task, especially in the transport system. This paper has the objective of developing an architectural model of a modified LoRaWAN-based boat monitoring system that is connected to a GPS-based mobile device and base station. The proposed architectural model is an integration of Bluetooth Low Energy (BLE) and LoRaWAN networks, which are also tested in real time to solve the boat traffic monitoring issues. The field tests with parameters of signal transmission, location coordinates, and position of the boats are also presented. The analysis result shows the proposed model is suitable for waters with high noise levels, especially in shallow water and delta rivers. The signal noise can be reduced by extracting the real-time data. In addition, signal interference can be minimized. The performance of this system is also compared to the reference system in real conditions, which shows an adequate correlation result. This proof of concept forms an important basis for deploying it for large-scale applications and commercialization capabilities. Doi: 10.28991/ESJ-2023-07-04-011 Full Text: PDF
Effect of a Classroom-based Intervention on the Social Skills of Students with Learning Difficulties Emad M. Alghazo; Mahmoud Gharaibeh; Samer Abdel-Hadi
Emerging Science Journal Vol 7 (2023): Special Issue "Current Issues, Trends, and New Ideas in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-SIED2-011

Abstract

Children with learning difficulties often face challenges in social skills, hindering their ability to adjust and interact within society. The present study was designed to evaluate the effectiveness of a training program designed to enhance the social skills of individuals with disabilities. The quasi-experimental study involved 20 primary school students with learning difficulties exhibiting deficits in social skills in the United Arab Emirates. To evaluate the level of social skills of the sample children, a social skills assessment scale was employed, which was developed by the researchers. The assessment scale consisted of 24 statements that were organized into three dimensions based on previous research and theoretical frameworks. The results of the present study showed that the training program significantly and positively impacted the social skills of these children. There were statistically significant disparities between the mean ranks of the experimental group and the control group's scores on the social skills assessment scale after program completion. In conclusion, the study recommends integrating the developed training and similar programs into the public and private education curricula, including both government and private schools, to improve the social communication abilities of children with learning difficulties. Doi: 10.28991/ESJ-2023-SIED2-011 Full Text: PDF
The Partial L-Moment of the Four Kappa Distribution Pannarat Guayjarernpanishk; Piyapatr Bussababodhin; Monchaya Chiangpradit
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-06

Abstract

Statistical analysis of extreme events such as flood events is often carried out to predict large return period events. The behaviour of extreme events not only involves heavy-tailed distributions but also skewed distributions, similar to the four-parameter Kappa distribution (K4D). In general, this covers many extreme distributions such as the generalized logistic distribution (GLD), the generalized extreme value distribution (GEV), the generalized Pareto distribution (GPD), and so on. To utilize these distributions, we have to estimate parameters accurately. There are many parameter estimation methods, for example, Method of Moments, Maximum Likelihood Estimator, L-Moments, or partial L-Moments. Nowadays, no researchers have applied the partial L-Moments method to estimate the parameters of K4D. Therefore, the objective of this paper is to derive the partial L-Moments (PL-Moments) for K4D, namely the PL-Moments of the K4D in order to estimate hydrological extremes from censored data. The findings of this paper are formulas of parameter estimation for K4D based on the PL-Moments approach. We have derived the Partial Probability-Weighted Moments (PPWMs) of the K4D (β'r) and derive the estimation of parameters when separated by shape parameters (k,h) conditions i.e., case k>-1 and h>0, case k>-1 and h=0 and case -1<k<-1/h and h<0. Finally, we expect that the parameter estimate for K4D from this formula will help to make accurate forecasts. Doi: 10.28991/ESJ-2023-07-04-06 Full Text: PDF
A Unified Power-Delay Model for GDI Library Cell Created Using New Mux Based Signal Connectivity Algorithm Jebashini Ponnian; Senthil Pari; Uma Ramadass; Chee Pun Ooi
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-022

Abstract

The challenges of innovative IC technology typically come with various new design constraints in terms of circuit implementation, behaviour, scaling, and an accurate power-delay model to evaluate the circuit's performance. The circuit realization technique using GDI is gaining popularity because of its power and transistor utilization factors. Considering the core advantage of the GDI technique, this research presents the creation of new GDI library cells implemented using the MUX-based algorithm and its delay-power model. This research defines two goals; the former goal depicts the proposal of GDI library cells with full swing using a MUX-based signal connectivity model, and the later presents the mathematical delay-power model for the proposed GDI library cells. The number of attributes defined in the delay and power model incorporates minimum variables without sacrificing precision. It calculates the delay for simple RC networks and combinational circuits with multiple paths. The power model is given using the node activity factor and the power factor related to the internal node capacitances, wiring, and gate capacitances of the driving and receiving GDI nodes. The experimental results of this study, which conform to the specifications of the sub-micron library supported for the SilTerra 130 nm 6-metal layer fabricated for the CMOS n-well process, demonstrate that the proposed GDI library is indeed superior in terms of delay-transistor and power utilisation to PTL and CMOS technology. The simulation results reveal that there is 55 to 65 % improvement in terms of power and delay factor with the existing CMOS and PTL logic. The proposed delay model demonstrates that GDI cells require less logical effort than CMOS technology. The proposed power model shows that the node activity factor of the proposed GDI cells lies between 0.1 and 0.2, while in CMOS, it is between 0.1 and 0.3. Doi: 10.28991/ESJ-2023-07-04-022 Full Text: PDF
A Genetic Programming Based Heuristic to Simplify Rugged Landscapes Exploration Gloria Pietropolli; Giuliamaria Menara; Mauro Castelli
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-01

Abstract

Some optimization problems are difficult to solve due to a considerable number of local optima, which may result in premature convergence of the optimization process. To address this problem, we propose a novel heuristic method for constructing a smooth surrogate model of the original function. The surrogate function is easier to optimize but maintains a fundamental property of the original rugged fitness landscape: the location of the global optimum. To create such a surrogate model, we consider a linear genetic programming approach coupled with a self-tuning fitness function. More specifically, to evaluate the fitness of the produced surrogate functions, we employ Fuzzy Self-Tuning Particle Swarm Optimization, a setting-free version of particle swarm optimization. To assess the performance of the proposed method, we considered a set of benchmark functions characterized by high noise and ruggedness. Moreover, the method is evaluated over different problems’ dimensionalities. The proposed approach reveals its suitability for performing the proposed task. In particular, experimental results confirm its capability to find the global argminimum for all the considered benchmark problems and all the domain dimensions taken into account, thus providing an innovative and promising strategy for dealing with challenging optimization problems. Doi: 10.28991/ESJ-2023-07-04-01 Full Text: PDF
The Role of Self-Awareness in Predicting the Level of Emotional Regulation Difficulties among Faculty Members Samer A. Abdel Hadi; Mahmoud Gharaibeh
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-017

Abstract

University faculty members seek to regulate emotions to achieve professional and social goals in the work environment. The process of emotion regulation is influenced by self-awareness, as self-awareness is an important predictor of self-regulation, and the outcomes of the self-regulation process depend, in part, on the level of self-awareness. The purpose of the present paper was to examine whether or not self-awareness is used to predict emotional regulation difficulties among faculty members. The current quantitative study was designed using a survey research design. The participants comprised 172 faculty members from Philadelphia University in Jordan, the Arab Open University in Jordan, and Al Falah University in the U.A.E. Data were collected using the Self-Awareness Scale (SAS) and Emotional Regulation Difficulties Scale (ERDS). The researchers revealed that increasing the self-awareness subscale (self-critical) decreases the non-acceptance of emotional responses. The researchers also found that when there is an increase in the self-awareness subscale (desire for realistic awareness), there is a tendency toward lower levels of non-acceptance of emotional responses and difficulties engaging in goal-directed. Researchers also came to that an increase in the self-awareness subscale (self-reflection) decreases the non-acceptance of emotional responses, difficulties engaging in goal-directed and impulse control difficulties. The researchers concluded there is a need to work on university faculties' self-awareness and emotional regulation to balance realistic awareness and emotional responses related to task engagement and control difficulties. Based on the findings, it is concluded that it is necessary to pay attention to enhancing self-awareness and emotion regulation among faculty members in general and conduct more scientific studies on emotional regulation difficulties to examine their relationship with other variables. Doi: 10.28991/ESJ-2023-07-04-017 Full Text: PDF
Using PPO Models to Predict the Value of the BNB Cryptocurrency Dmitrii V. Firsov; Sergey N. Silvestrov; Nikolay V. Kuznetsov; Evgeny V. Zolotarev; Sergey A. Pobyvaev
Emerging Science Journal Vol 7, No 4 (2023): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-07-04-012

Abstract

This paper identifies hidden patterns between trading volumes and the market value of an asset. Based on open market data, we try to improve the existing corpus of research using new, innovative neural network training methods. Dividing into two independent models, we conducted a comparative analysis between two methods of training Proximal Policy Optimization (PPO) models. The primary difference between the two PPO models is the data. To showcase the drastic differences the PPO model makes in market conditions, one model uses historical data from Binance trading history as a data sample and the trading pair BNB/USDT as a predicted asset. Another model, apart from purely price fluctuations, also draws data on trading volume. That way, we can clearly illustrate what the difference can be if we add additional markers for model training. Using PPO models, the authors conduct a comparative analysis of prediction accuracy, taking the sequence of BNB token values and trading volumes on 15-minute candles as variables. The main research question of this paper is to identify an increase in the accuracy of the PPO model when adding additional variables. The primary research gap that we explore is whether PPO models specifically trained on highly volatile assets can be improved by adding additional markers that are closely linked. In our study, we identified the closest marker, which is a trading volume. The study results show that including additional parameters in the form of trading volume significantly reduces the model's accuracy. The scientific contribution of this research is that it shows in practice that the PPO model does not require additional parameters to form accurately predicting models within the framework of market forecasting. Doi: 10.28991/ESJ-2023-07-04-012 Full Text: PDF
Contributions of Neuroscience to Educational Praxis: A Systematic Review Evgenia Gkintoni; Ioannis Dimakos; Constantinos Halkiopoulos; Hera Antonopoulou
Emerging Science Journal Vol 7 (2023): Special Issue "Current Issues, Trends, and New Ideas in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2023-SIED2-012

Abstract

Objectives: In education, neuroscience is an interdisciplinary research field. It seeks to improve educational practice by applying brain research findings. Additional findings from the scientific fields of education, psychology, and neurophysiology aim to enhance the learning process and improve educational practices. The application of neuroscience to education involves neuroscientific and psychological knowledge. Methods/Analysis: In this systematic literature review, the final studies included in the analysis table are decided by searching databases according to predefined inclusion criteria. The PRISMA approach was utilized to study the relationship between neuroscience and the educational process and to optimize the educational process based on the relevant data. Findings: The review's findings emphasize the significance of integrating neuroscience into educational praxis and challenges and raise ethical concerns regarding its implementation in educational contexts. Novelty /Improvement: The discipline of educational neuroscience is associated with education, research, and the cognitive neuroscience of learning. Neuroscience can serve as the basis for education in a similar direction that biology serves as the basis for medicine, meaning that each field retains its innovation but cannot contravene the rules of the other. This study examines the relationship between neuroscience and educational praxis as well as how the educational community might bridge this gap to include prospective findings from neuroscientific research. Doi: 10.28991/ESJ-2023-SIED2-012 Full Text: PDF

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