IRAOI JOURNAL OF STATISTICAL SCIENCES

The study aimed to investigate the relationship between students\' entry mode into the University of Ilorin and their academic performance. Two faculties, Education and Physical Science, were selected, and a total of seven departments were chosen: four from the Faculty of Physical Science (Zoology, Biochemistry, Statistics, and Mathematics) and three from the Faculty of Education (Guidance & Counseling, Education & Management, and Agricultural Education). A total of 45 students\' results were randomly selected from each department, comprising 15 from Remedial/Pre-degree, 15 from Jamb/UTME, and 15 from Direct Entry. Six null hypotheses were formulated and tested using various statistical tests such as Box’s M test, Shapiro Wilk test , Mardia test, MANOVA, and Hotelling T square, all at a 0.05 level of significance. The analysis revealed that there was no significant difference in the performance of students based on their mode of entry in all selected departments and between the faculties. However, the paper shows that students admitted through Direct Entry had the highest mean performance, indicating that they performed better than students admitted through Jamb/UTME and Remedial/Pre-Degree modes. The recommendation is for Nigerian universities to prioritize students entering through the Direct Entry program for admissions. This is because these students tend to exhibit more mature thinking and behavior, which would result in less stress for lecturers in the classrooms.

In this paper, we compare three different main methods for selecting variables for linear regression models: Adaptive Lad Lasso, Split Regularized Regression (SRR) and DLasso (AIC, GIC, BIC, CGV). In a simulation study, we show the performance of the methods considering the median model error. The case where the number of candidate variables exceeds the number of observations is considered as well. Also, the simulation study is used in determining which methods are best in all of the linear regression scenarios.

Cloud computing infrastructures have moved to the very heart of global business operations, which also places them in prime position for numerous advanced cyber threat challengers. This makes traditional predefined rule-based and known signature-based intrusion detection systems (IDS) almost useless in this era of advanced threats, including zero-day attacks, APTs - Advanced Persistent Threats exploiting polymorphic malware. The paper introduces a revolutionary hybrid model which uses the power of Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) to evolve cloud-based Intrusion detection system. This hybrid method uses the RNN to encode and learn from time-series data, acquiring memory over temporal anomalies in information; besides this it makes extensive use of Convolutional neural networks for spatial feature extraction at high-throughput which becomes essential for detecting these patterns across multitudes that suggest intrusions. In well-defined cloud setting, The overall effectiveness of this model is assessed by testing it under numerous attack scenarios. The results indicate that this model not only outperforms standard IDS in terms of detectio. but also demonstrates outstanding resilience against zero-day and emergent threats. This increased detection efficiency is obviously necessary to ensure the security and reliability of cloud services, allowing more stringent defense mechanisms which remains essential in modern dynamically evolving cyber threat landscapes

The network must choose the best route from a list of options to support Quality of Service (QoS) for engaging real-time video applications like video conferencing and remote learning. Other network paths may connect the point of origin and the destination, but because of the network's intricate architecture and strong coupling, it is challenging to find a different route. Network topologies like Integrated Services (ISs) might not provide the best performance as long as they install the path determined by the routing protocol. This paper's main goal is to use Python programming language and VirtualBox Manager to develop numerous interactive video contributions while determining the quality of service. To choose the optimal route from a network-wide viewpoint, a novel work utilising Web Real-Time Interaction (WebRTC) technological advances with Software-Defined Networking (SDN). Additionally, it has illustrated the results of a setup and evaluates performance based on message complexity and network throughput metrics.

This paper will present the Goel distribution as the occurrence rate of the non-homogeneous Poisson process (NHPP) to improve its occurrence rate, it is proposed to be called the Generalized Goel Process (GGP). As for the estimation of parameters of this process, a number of methods were discussed, the maximum likelihood estimator (MLE) was suggested and after that – a modification to this method was necessary due to the fact that it was impossible to find estimators using it. An intelligent algorithm of the likelihood function was added with the parameter and was known as the Modified Maximum Likelihood Estimator (MMLE). MMLE was then compared with another intelligent method the Particle Swarm Optimization (PSO) in estimating occurrence rate of the proposed Goel process to determine the best estimator of the process. Besides, the paper contains the simulation of the mentioned process and an example of its practical usage. The simulation and application results showed that the MMLE approach gave higher accuracy estimates than the PSO algorithm for the majority of the studied sample sizes, especially for the larger sizes.

In this paper, it was proposed to create a new chart that represents the Symlets wavelet chart with orders of (1, 2, and 3) to obtain discrete wavelet transformation maximum overall coefficients, through which the threshold parameter is estimated using the universal method and then applied hard threshold rule to obtain de-noise data which will be relied upon in constructing the Cumulative Sum Chart using the Tabular method. The efficiency of the proposed chart was measured and compared to the classical chart by simulating several cases and real data and calculating the difference between the control limits (Difference), the standard deviation and the number of points outside the control limits to determine the sensitivity of the chart to minor changes that may occur in the production process, using an algorithm in the MATLAB program it was designed for this purpose. The research results revealed that the proposed charts are more efficient and sensitive to minor changes (that may event) in the production process than the classical charts.

Is this paper we study statistical properties of TLR .distribution. Bayesian technique is used to estimate the parameters of distribution under Sq and Elinex loss functions. The shape parameter of linex last function treated as random variable. And distributed b~N(µ, 1) The results applied in simulated data with(different sample sizes and parameters) and real data sets and different values of µ.The Elinex is the best estimator when the location parameter is negative except in the case of scale parameter equal (θ) 2.5 The estimators under sq loss function is the best when( µ >0) except θ =2.5 and the sample size n =25 .In real data set the Elinex estimator is the best when (µ=-2) .this result of real data is supportive to the results of simulated data

In this paper, a complete randomized design (CRD) was used in case the number of replicates of the experiment was equal and only one observation was recorded and on the assumption that the experimental error term follows a non-normal distribution, and the importance of distributions with heavy tails is because they are a generalization for all Non-normal distributions: It was assumed that the error term follows the extension hyperbola distribution (ehd) and Laplace distribution(Ld), and based on the traditional method represented by the maximum likelihood method, the design parameters were estimated when the mathematical model was fixed once and random again. We concluded that the estimates of the model parameters when the experimental error follows a Laplace distribution (Ld) are similar to the estimates of the model parameters when the error is normal. Given the difficulty of obtaining an agricultural experiment that follows the (ehd) and (Ld), an experimental experiment was used through the MATLAB program, through the mean square error criterion, a comparison was made between the fixed and random mathematical model for a completely random design under different values of additional and torsion parameters. Through the experimental results, it was shown that the values of the mean square error criterion for the fixed and random mathematical model decreased as the additional parameters values decrease and for (ehd).

Auxiliary information has been confirmed to enhance precision in the estimators of ratio, regression and product respectively. Many cases of improved mixed estimators in single phase sampling have been advanced and recommendations made using more than one auxiliary variable and correct factors for extreme values. This study takes a look at case of extreme value in both study and auxiliary variable where the proposed mixed estimator is not corrected for extreme values in both study and auxiliary variable. However, it is interesting to know that the developed mixed estimator is efficient over developed single estimators of ratio and regression with correction factors for extreme value.

Rehab Talal Ahmed1 & Omar Salim Ibrahim
rehab.22csp56@student.uomosul.edu.iq omarsalim85@uomosul.edu.iq
Department of Statistics and Informatics, College of Computer Science and Mathematics, University of Mosul , Iraq

Abstract: This study examined the use of ARFIMA models to forecast imported banana prices in the city of Mosul, based on data obtained from the Directorate of Agriculture in Nineveh for the period from 2018 to 2023. Several methods were used to estimate long memory and determine the fractional differencing parameter (d), including single-stage methods such as the maximum likelihood (EML) method used in this research, and two-stage methods such as the Geweke-Porter-Hudak (GPH) estimator, the dsprio (Smoothed periodogram estimation) method, the Fracdiff method, the Rescaled Range (R/S) method, and the Whittle estimator. The model was built by verifying the presence of long memory in the time series through several tests, and then estimating the fractional differencing parameters. The single-stage ARFIMA (1,-0.06275898,0) model outperformed the other methods based on criteria such as BIC, MSE, RMSE, and MAE. The model passed diagnostic tests and was used for forecasting banana prices, with the aim of clarifying the steps for constructing an appropriate model.
Keywords: Long memory, ARFIMA , Forecasting , Methods for estimating the fractional differential parameter d.

Using a binomial logistic regression model, through this paper we attempt to study the relationship between the state of financial health of municipalities in Algeria, based on the wealth index, in relation to a group of independent variables related to their revenues, the size of spending, and some variables that reflect aspects of this spending and their various specializations. Logistic regression is one of the classification models, and it is considered an alternative model to linear regression models, because this type of model has the property of predicting the probability of the occurrence or non-occurrence of the values of the nominal dependent variables based on a set of explanatory variables (independent variables), whether in their quantitative or qualitative type.

Forecasting accuracy of evaporation time series is an importance to control environmental impacts, damages, and risks affecting especially plant life and growth, and thus that impact on human and animal life. Evaporation data are considered from climate time series, which are characterized by its nature a non-linearity data, as they suffer from the problem of heterogeneity because they contain many seasonal and periodic components, and necessarily that complexity may lead to inaccurate forecasts. The time stratified method will be used in this study with the proposed forecasting methods to achieve greater homogeneity and less complex temporal behavior. Two forecasting methods will be used, represented by the regression tree (RT) method and the integrated autoregressive and moving average (ARIMA)model, and it is proposed to hybridize them with a method that combines both within the hybrid ARIMA-RT model as a way to improve forecasting results by dealing more accurately with the non-linearity data. The effect of wavelet transformations (WT) will also be tested with both the ARIMA model and the hybrid ARIMA-RT model, and whether it will have a role in improving forecasting results. A time series modeling structure will be adopted to determine the input structure of the RT model within the proposed hybrid approach by using multiplicative seasonal ARIMA. Also, the use of WT will be limited to filtering a random errors series (residuals), which the rest of its time lags depended on, represented by the moving average variables process. The forecasting results of the proposed methods might comparisons with the traditional forecasting method. This study was concerned with investigating various methods for forecasting evaporation time series for an agricultural meteorological station in the city of Mosul, Iraq for hot and cold seasons. The results of this study reflected the superiority of the hybrid method compared to the traditional ARIMA model. The results also included that forecasts were clearly affected by the use of WT. it can be concluded that the ARIMA-RT hybrid model has a clear role in improving the accuracy of forecast results through this study. Using WT leads to a slight improvement in the accuracy of forecasts, and it may vary according to the data and its nature and homogeneity.

Partial Least Squares Structural Equation Modeling (PLS-SEM) has gained popularity as a method for estimating (complex) path models with latent variables and their relationships. In a research study conducted in Abuja, SmartPLS 4 software was used to investigate the effects of selected factors on business performance and growth in four major markets: Garki Ma
rket, Wuse Market, Deidei Market, and Kado Fish Market. The study involved business owners engaged in retail, supplies, distribution, or wholesale in these markets. Questionnaires on factors affecting business performance and growth were distributed among the business owners, and their responses provided demographic data and information on latent variables. The analysis revealed that individuals, family, business environment, financial institutions, and government significantly influence the business performance and growth of business owners.

Background: Tuberculosis (TB) is a globally deadly infectious disease responsible for 10 million new cases and 1.5 million deaths annually. Shorter TB treatment regimens show promise in reducing this problem, but there is an improved treatment success rate in South Africa, while retreatment cases remain a concern. An important feature of time-to-event modelling is its ability to consider transition probabilities of heterogeneous subgroups with different risk profiles. Survival analysis is generally performed to accurately estimate the transition probabilities associated with the risk profiles. This study explored the application of a flexible parametric survival model for analysing censored time-to-event data among TB patients.
Methods: The data were obtained from East London Central Clinic-TB unit, Eastern Cape, South Africa. In total, 174 patients were included in the analysis. The goodness of fit of the models was explored using AIC. We estimated the hazard ratios and baseline cumulative hazards of our model, which are necessary to calculate individual transition probabilities, and compared the model with the Cox model and additive hazard model to determine the survival predictions of TB patients.
Result: The flexible parametric survival model produced hazard ratio and baseline cumulative hazard estimates that were similar to those obtained using the Cox proportional hazards model. The analysis revealed that sex (HR=0.49, 95% CI: 0.38, 0.62), antiretroviral therapy, ART (HR=0.53, 95% CI: 0.34, 0.78), and diabetes (HR=0.58, 95% CI: 0.41, 0.78) were all statistically significant factors associated with improved treatment survival in tuberculosis patients.
Conclusion: Flexible parametric survival models are a powerful tool for modelling time-to-event data and individual transition probabilities. It is of great importance to fit models by modelling the baseline, which makes it easier to make different types of predictions and allows for non-proportional hazards since it is an interaction.

Tuberculosis (TB) is a globally deadly infectious disease responsible for 10 million new cases and 1.5 million deaths annually. Shorter TB treatment regimens show promise in reducing this problem, but there is an improved treatment success rate in South Africa, while retreatment cases remain a concern. An important feature of time-to-event modelling is its ability to consider transition probabilities of heterogeneous subgroups with different risk profiles. Survival analysis is generally performed to accurately estimate the transition probabilities associated with the risk profiles. This study explored the application of a flexible parametric survival model for analysing censored time-to-event data among TB patients.
The data were obtained from East London Central Clinic-TB unit, Eastern Cape, South Africa. In total, 174 patients were included in the analysis. The goodness of fit of the models was explored using Akaike information criterion (AIC). We estimated the hazard ratios (HR) and baseline cumulative hazards of our model, which are necessary to calculate individual transition probabilities, and compared the model with the Cox model and additive hazard model to determine the survival predictions of TB patients.
The flexible parametric survival model produced hazard ratio and baseline cumulative hazard estimates that were similar to those obtained using the Cox proportional hazards model. The analysis revealed that sex (HR=0.49, 95% CI: 0.38, 0.62), antiretroviral therapy (ART), (HR=0.53, 95% CI: 0.34, 0.78), and diabetes (HR=0.58, 95% CI: 0.41, 0.78) were all statistically significant factors associated with improved treatment survival in tuberculosis patients.
Flexible parametric survival models are a powerful tool for modelling time-to-event data and individual transition probabilities. It is of great importance to fit models by modelling the baseline, which makes it easier to make different types of predictions and allows for non-proportional hazards since it is an interaction.

Requirements management and analysis principles for software engineering projects are of enormous importance in the development of software systems. This article concentrates on examining the literature that highlights the significance of requirements management and analysis principles in bolstering software engineering development. The requirements management process includes a set of important processes that focus on identifying, documenting, analysing, reviewing, and managing the requirements of the software system during the stages of its construction or development. These requirements are the cornerstone of the success of any software project for software companies, as effective and correct management leads to the development of products that meet the needs and expectations of users. This process includes requirements analysis, which transforms user or lab or market requirements into technical specifications for implementation in later stages of the system construction process. This process concentrates on precisely studying and analysing requirements to eliminate any conflict or ambiguity, and to confirm their validity and the extent of their implementation. In general, this literature review reviews the different methods used in managing and analysing requirements by the work team, including requirements collection techniques, requirements verification methods, and managing changes during the requirements collection stage. The review also addresses the challenges facing requirements management in software projects and how to deal with them to ensure the success of the project.