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Pure Mathematics for Theoretical Computer Science | 2023

On Some Networks with Mobile Gateway Improving the performance of data sets systems

Nowadays, Automatic Meter Reading (AMR) systems are applied in many technologically advanced countries. Many researchers proved that Wireless Sensor Networks (WSN) is one of the most overwhelming technologies for meters information collection systems. In this research, we proposed a network design for the WSN to collect the electric meters information in Jableh city-Syria using mobile gateway. We improved the performance of IEEE 802.15.4 protocol using Guaranteed Time slots (GTS) according to our application need. WE also deduced the mathematical equations that describe the protocol and thus we could get pre-simulation results. We used NS 2.35 and SUMO 0.32.0, and we found that by choosing the appropriate values for the protocol parameters according to our network design and method of collection, we could achieve the reliable collection of electric meters for a gateway speed of up to 70 Km/h which is considered an improvement of the protocol performance compared to other literature studies.

groups
Sandy Montajab Hazzouri mail
link https://doi.org/10.54216/PMTCS.020201

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new
Pure Mathematics for Theoretical Computer Science | 2023

On Finding Cauchy – Pompeiu's Formula in The Octal Unit Disk

In this paper, we find the formula of Cauchy – Pompeiu's integral in the octal of unit disk  of the complex plain, by using the reflection method to determine the Integral's Cauchy – Pompeiu's operator. Also, we give many related examples about the novel formula.

groups
Rashel Abu Hakmeh mail
link https://doi.org/10.54216/PMTCS.020202

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new
Fusion: Practice and Applications | 2023

Examining the potential of machine learning for predicting academic achievement: A systematic review

Predicting student academic performance is a critical area of education research. Machine learning (ML) algorithms have gained significant popularity in recent years. The capability to forecast student performance empowers universities to devise an intervention strategy either at the beginning of a program or during a semester, which allows them to tackle any issues that may arise proactively. This systematic literature review provides an overview of the present state of the field under investigation, including the most commonly employed ML techniques, the variables predictive of academic performance, and the limitations and challenges of using ML to predict academic success. Our review of 60 studies published between January 2019 to March 2023 reveals that ML algorithms can be highly effective in predicting student academic performance. ML models can analyse various variables, including demographics, socioeconomic status, and academic history, to identify patterns and relationships that can predict academic performance. However, several limitations need to be addressed, such as the inconsistency in the variables used, small sample sizes, and the failure to consider external factors that may impact academic performance. Future research needs to address these limitations to develop more robust prediction models. Machine learning can fuse data from various sources like test scores like Coursera, edX & Open edX, Udemy, linkedin learning, learn words, and hacker’s rank platform etc, attendance, and online activity to help educators better understand student needs and improve teaching, can use for better decision.   In conclusion, ML has emerged as a promising approach for predicting student academic performance in online learning environments. Despite the current limitations, the continued refinement of ML techniques, the use of additional variables, and the incorporation of external factors will lead to more robust models and greater accuracy in predicting academic performance.

groups
M. Nazir mail -
A. Noraziah mail -
M. Rahmah mail -
Aditi Sharma mail
link https://doi.org/10.54216/FPA.130207

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new
Journal of Neutrosophic and Fuzzy Systems | 2023

Modern Elementary School: Neutrosophic and Education

Elementary school is a stage of primary education, it's the most critical stage of education in general, and teaching during the elementary stage is one of the most influential periods for children. In this stage,  pupils can develop their basic skills and information through elementary education. In this study, we hope to insert the concept of neutrosophic logic in elementary school curriculum to clearly define the logic for pupils using shapes, figures, games, graphs, or mathematics problems that contain some indeterminacy. These neutrosophic examples include some indeterminacy which is the basic compound for neutrosophic logic. The basic difference from classical logic, further to other compounds (truth, falsehood).

groups
Kawther F. Hamza Alhasan mail
link https://doi.org/10.54216/JNFS.070104

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new
Galoitica: Journal of Mathematical Structures and Applications | 2023

Improving the Performance Quality Parameters of Routing Protocol for MANET Networks

Routing and mobility in the network is a major in the field of wireless networks MNAET research and contributes greatly to the improvement of the performance of these networks, and achieving the best advantage of the available package which increases the yield and reduces network congestion. The importance of research is that it seeks to provide solutions for routing in MANET networks effectively and with high reliability and that the solutions offered are adapted to changes in the network topology. In addition to the above, it highlights the quest to find a solution to the two types of malfunctions resulting from roaming contracts, which has become necessary to ensure the best performance for this type of network, where you get these malfunctions in the most current routing algorithms.

groups
Sandy Montajab Hazzouri mail
link https://doi.org/10.54216/GJMSA.080201

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new
Galoitica: Journal of Mathematical Structures and Applications | 2023

The Applications of Digital Signal Processing Techniques for Enhancing the Performance of High Speed Optical Communication Systems

This paper presents Improving performance of 112 Gbit/s optical coherent communications systems using digital signal processing techniques to compensate linear impairments that are exposed to the signal during its propagation in optical fiber. In this paper, different multi-level modulation formats (DP-QPSK, DP 8PSK, DP- 16PSK and DP-16-QAM) were compared for the same data rate at different distances from the transmitter without amplification. the comparison was done by measuring bit error rate value. We used digital signal processing techniques for linear effects compensation. Where chromatic dispersion was compensated using a simple digital filter, and Polarization mod dispersion was realized by applying the constant-modulus algorithm (CMA). The phase and frequency mismatch between the transmitter and the local oscillator in the receiver was compensated using the modified Viterbi-Viterbi algorithm. We used the Optical simulator Opti-system , which was linked with MATLAB R2011 in order to implementation DSP algorithms and analyze obtained results.

groups
Sandy Montajab Hazzouri mail
link https://doi.org/10.54216/GJMSA.080202

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new
Galoitica: Journal of Mathematical Structures and Applications | 2023

Stationary Factor of Non-Stationary Random Process Based on Differential Transformation

This research studies the deviation of the output signal from stationary state by calculating stationary factor in differential filter with constant and non-stochastic coefficients, a stationary process is applied on its input. We show that this deviation is related to the degree of transformation the study range length and the form of a correlation function of the process applied on the input and the special solution of the equation LY=X and its correlation or un-correlation with that process.

groups
Rashel Abu Hakmeh mail
link https://doi.org/10.54216/GJMSA.080203

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new
Galoitica: Journal of Mathematical Structures and Applications | 2023

Deep Learning for Super Resolution and Applications

High-resolution technologies are aimed at obtaining a high-resolution image from a low-resolution image, and the importance of this field has increased due to the emergence of the need to have high-resolution images in many important applications such as medical, security, and other images. Methods for obtaining ultra-high-resolution images have developed after the advent of Deep Learning Technologies, which have shown good results in this task, Due to the importance of the field of ultra-high-resolution images and deep learning, In this article we will explain one of the deep learning models used to obtain a high-resolution image from a low-resolution image and how to build and train it based on one of the famous deep learning offices and using one of the google platforms used in training, namely Google Laboratory

groups
Zahraa Hasan mail
link https://doi.org/10.54216/GJMSA.080204

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new
Journal of Artificial Intelligence and Metaheuristics | 2023

Monthly Solar Prediction Using Machine Learning: Diyala Governorate, Iraq as a Case Study

Solar radiation constitutes the Earth’s primary energy source and is critical in regulating surface radiation equilibrium, vegetation photosynthesis, hydrological cycles, and extreme atmospheric. On the other hand, the depletion of global fossil fuel reserves mandates the power sector to adopt renewable energy-based sources, including photovoltaic and wind energy conversion systems. Therefore, the precise solar radiation prediction is imperative for climate research and the solar industry. This paper illustrates the use of two machine-learning approaches: random forest (RF) and support vector machine (SVM), to predict surface solar radiation in the Diyala governorate of Iraq for one step ahead, utilizing only lagged monthly time series data of the factor as input predictors. The findings were evaluated using three performance measures: coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The results showed that using 10 monthly lags time series as input predictors leads to the best prediction performance. Furthermore, in terms of the RMSE, the prediction performance of the RF algorithm was better than that of the SVM algorithm (RF's RMSE, MAE, and R2 were 181.398, 129.522, and 0.979, while for SVM were 240.149, 184.802, and 0.978, respectively).

groups
Noor Razzaq Abbas mail -
Hussein Alkattan mail -
Hamidreza Rabiei-Dastjerdi mail -
Mohamed Saber mail -
Marwa M. Eid mail
link https://doi.org/10.54216/JAIM.050204

Volume & Issue

Vol. Volume 5 / Iss. Issue 2

Details open_in_new