Ranking Method Research Articles

PHIGrader: Evaluating the effectiveness of Manifest file components in Android malware detection using Multi Criteria Decision Making techniques

The popularity of the Android operating system has itself become a reason for privacy concerns. To deal with such malware threats, researchers have proposed various detection approaches using static and dynamic features. Static analysis approaches are the most convenient for practical detection. However, several patterns of feature usage were found to be similar in the normal and malware datasets. Such high similarity in both datasets’ feature patterns motivates us to rank and select only the distinguishing set of features. Hence, in this study, we present a novel Android malware detection system, termed as PHIGrader for ranking and evaluating the efficiency of the three most commonly used static features, namely permissions, intents, and hardware components, when used for Android malware detection. To meet our goals, we individually rank the three feature types using frequency-based Multi-Criteria Decision Making (MCDM) techniques, namely TOPSIS and EDAS. Then, the system applies a novel detection algorithm to the rankings involving machine learning and deep learning classifiers to present the best set of features and feature type with higher detection accuracy as an output. The experimental results highlight that our proposed approach can effectively detect Android malware with 99.10% detection accuracy, achieved with the top 46 intents when ranked using TOPSIS, which is better than permissions, hardware components, or even the case where other popular MCDM techniques are used. Furthermore, our experiments demonstrate that the proposed system with frequency-based MCDM rankings is better than other statistical tests such as mutual information, Pearson correlation coefficient, and t-test. In addition, our proposed model outperforms various popularly used feature ranking methods such as Chi-square, Principal Component Analysis (PCA), Entropy-based Category Coverage Difference (ECCD), and other state-of-the-art Android malware detection techniques in terms of detection accuracy.

Turmeric Cultivation in Erode District: An Analysis of Farmer Constraints

Turmeric farming is a key agricultural activity in the Erode District of Tamil Nadu, contributing significantly to the region's economy. However, farmers in this area face a myriad of constraints that hinder their productivity and economic stability. This study investigates the specific challenges encountered by turmeric growers, focusing on technical, economic, marketing, storage, and general constraints. In terms of production, consumption, and exports, India leads the world in turmeric usage. India is estimated to produce 11.7 lakh metric tonnes of turmeric in 2022–2023. Telangana, Maharashtra, Karnataka, Tamil Nadu, Andhra Pradesh, Madhya Pradesh, and West Bengal are the main states in India that produce turmeric. In Tamil Nadu, 96,000 metric tons of turmeric are grown around 20,000 hectares. An ex-post facto research design was employed, with Kodumudi Block selected due to its prominence in turmeric cultivation in Erode district. A proportionate random sampling technique was used to select 120 respondents from five villages: Vengampur, Punjai Kollanali, Unjalur, Kollathupalayam, and Kodumudi. The inclusion of an interview schedule as the data collection tool adds depth to the study, as it allows for a structured approach to gathering information directly from the respondents, ensuring the reliability and validity of the findings. The data that were gathered was compiled and subjected to suitable statistical analysis like cumulative frequency method, percentage analysis and ranking method. The findings reveal that the most critical issues include high fluctuations in market prices, pest and disease outbreaks, inadequate storage facilities, and a lack of technical guidance. Economic challenges such as high input costs, difficulty in securing loans, and insufficient subsidies further exacerbate the situation. Marketing constraints, including poor transportation infrastructure and the absence of regulated markets, force farmers into distress sales. General issues like labor shortages and inadequate irrigation facilities add to the farmer burdens. To address these challenges, the study suggests implementing efficient irrigation systems, promoting integrated pest management (IPM) programs, establishing community-based storage facilities, and strengthening farmer cooperatives to enhance their bargaining power. Additionally, introducing government-backed procurement systems with minimum support prices (MSP) and improving access to financial services are recommended as the solutions. By tackling these constraints, the study aims to enhance the sustainability and profitability of turmeric farming in Erode District, ensuring a stable livelihood for the farmers.

PPIA, HRPT1, and PGK1 genes as the appropriate combination for RT-qPCR normalization in alveolar and femoral bone remodeling in olanzapine-treated rats.

Reliable gene expression analysis in bone remodeling studies requires an appropriate selection of internal controls, i.e. stable reference genes for the normalization of quantitative real-time PCR (RT-qPCR), the most common method used for quantifying gene expression measurements. Even the most widely used reference genes can have variable expression under different experimental conditions, or in different tissue types or treatment regimes, so selecting appropriate controls is a key step in ensuring reliable results. The aim of this research was to identify the most stable reference gene(s) for the study of olanzapine modulated bone remodeling in rats. RNA was isolated from the maxillary alveolar and femoral bones of olanzapine or placebo-treated Wistar rats and transcribed to cDNA. The expression of 12 candidate reference genes was assessed by RT-qPCR. Their expressions were analysed using GeNorm, NormFinder, BestKeeper and delta Ct algorithms, and by the comprehensive ranking method. PPIA, HRPT1 and PGK1 were the most stably expres sed reference genes and the combination of the three genes was optimal for normalization. This study is the first to identify the optimal reference genes for research in olanzapine-exposed rats, which serve as a pivotal benchmark for enhancing the accuracy and reliability of future RT-qPCR expression in bone studies.

Challenges in Fintech Adoption among Members of Farmer Producer Organisations – An Analysis

Aim: The purpose of this study is to determine the personal, technological, and trust-related barriers that prevent farmers in Tamil Nadu from adopting fintech. The research aims to provide insights for enhancing the utilization of fintech in the agricultural sector by analyzing these obstacles. Study Design: The limitations that limit farmers' use of fintech were investigated using an ex-post facto research design. Place and Duration of Study: From March to July 2023, the research was carried out in the Tiruvallur district of Tamil Nadu, India. Due to its high concentration of banking establishments, this area was chosen for fintech adoption research. Methodology: From four Farmer Producer Organizations (FPOs) affiliated with Krishi Vigyan Kendra (KVK), Tiruvallur, 120 farmers, 86 men and 34 women, aged 18 to 75, were chosen at random. A structured interview schedule was used to collect the data, which were divided into personal, technological, and trust-related constraints. The farmers' perspectives were used to evaluate the severity of these constraints using the Garrett ranking method. Results: Personal constraints, such as risk aversion (average score = 65.14) and a preference for cash transactions (average score = 56.68), were found to be the most significant obstacles to the adoption of fintech. Usage was further hampered by technological issues such as restricted access to technical support (64.79) and complicated user interfaces (52.95). Concerns about trust, particularly concerns about scams and fraud (65.83), were the most significant obstacles, highlighting the need for robust security measures. Conclusion: The results emphasize the need for targeted interventions to remove the major obstacles to the adoption of fintech, particularly by improving user-friendly designs, technical support, and security measures.

Economic crimes as a threat to Russia's socio-economic security

The security of the State, society and the individual is a fundamental task in ensuring the stability and prosperity of the country. This task implies the definition of rules and norms in relation to various social groups that correspond to the current institutional environment in Russia. The purpose of this study is to analyze the impact of economic crimes on the social security of citizens of the Russian Federation and ensuring economic growth in the country.To reveal the research topic, the paper considers the main basic indicators of Russia's socio-economic security. The methodological basis is general scientific methods: analysis, synthesis, comparison, generalization, ranking, etc. The main causes of the emergence and spread of economic crimes are presented in a systematic way. A retrospective analysis of economic crimes was carried out, and a trend line was drawn up. Using the ranking method, a rating of the leading regions in terms of socio-economic development was formed. It has been revealed that the scale of the spread of economic crimes has a fairly strong correlation with the institutional characteristics of the regions. The methods of combating economic offenses through the use of information technology are proposed.

Selection of Genotypes for Enhancing Tef Productivity Through Farmers’ Participation in Potential Environments of Ethiopia

Tef, <i>Eragrostis tef</i> (Zucc.) Trotter is the main crop grown in Ethiopia. However, its productivity remains low compared to its potential yield. A multi-location trial was conducted to evaluate selected best-performing lines from previous trials in terms of stability and yield coupled with farmers’ opinions and preferences, aiming to identify superior lines. A total of twenty tef genotypes, including a standard and a local check were field evaluated using RCBD with four replications. The lines were grown in 4 m² plots across eight different locations in Ethiopia during the 2018/19 and 2019/20 cropping seasons. Additionally, an on-farm participatory variety evaluation involving 198 participants, comprising farmers and agricultural experts, was conducted during the 2019 cropping season. Phenological and agromorphological traits were collected and subjected to statistical analysis to identify the best genotypes. The pooled analysis of variance revealed significant variation (at the 0.01% level) among genotypes, locations, and years for all traits except grain yield and days to maturity. While some genotypes produced comparable grain yields, none surpassed the standard check variety Negus. Additionally, Participatory variety selection was conducted during the crop maturity stage using the direct-matrix ranking method. Farmers sets their own selection criteria, these are crop stand, tillering capacity, panicle weight, lodging tolerance, culm strength, and pest infestation or infection. Based on their evaluation, the genotypes DZ-01-974 X GA-10-3 RIL 51, DZ-01-974 X GA-10-3 RIL 47 and DZ-01-974 X GA-10-3 RIL 68 were identified as farmers preferred varieties from direct matrix ranking evaluations and chosen for their performance in the field. Thus, this study highlights the feasibility of participatory variety selection in gaining insights into farmer’s perceptions, preferences, strengths and weaknesses of tef genotypes.

A methodology of informatics: the role of methodology in improvement of theory

The article has a propounding character. Based on methodological literature, the essence of the concept "methodology" is revealed, explaining the role of scientific methods in knowledge and the role of methodology in improving theory and research work in some fields of knowledge. The results of the literature analysis are considered a starting point for investigating the methodology of informatics. Currently, there is no developed system of philosophical, scientific, and general-theoretical principles that determine the program and methods for investigating the theory and methodology of informatics. Additionally, there is no established theory of method and scientific approach to the objective evaluation of practice. The absence of methodology is a major cause and effect of the crisis in contemporary information theory. We can only discuss the methods used in research work in the field of informatics. Special methods in informatics—such as statistical analysis of scientific documents, statistical analysis of references, and the method of ranking—are characterized. The main laws of informatics are discovered using these methods. A definition of the methodology of informatics and its main aims is put forward for discussion.

Optimized search for a binary black hole merger population in LIGO-Virgo O3 data

Maximizing the number of detections in matched filter searches for compact binary coalescence (CBC) gravitational wave signals requires a model of the source population distribution. In previous searches using the framework, sensitivity to the population of binary black hole (BBH) mergers was improved by restricting the range of filter template mass ratios and use of a simple one-dimensional population model. However, this approach does not make use of our full knowledge of the population and cannot be extended to a full parameter space search. Here, we introduce a new ranking method, based on kernel density estimation with adaptive bandwidth, to accurately model the probability distributions of binary source parameters over a template bank, both for signals and for noise events. We demonstrate this ranking method by conducting a search over LIGO-Virgo O3 data for BBHs with unrestricted mass ratio, using a signal model derived from previous significant detected events. We achieve over 10% increase in sensitive volume for a simple power-law simulated signal population, compared to the previous BBH search. Correspondingly, with the new ranking, eight additional candidate events above an inverse false alarm rate threshold 0.5 yr are identified. Published by the American Physical Society 2024

Evaluation of Hand-Crafted Feature Extraction for Fault Diagnosis in Rotating Machinery: A Survey.

This article presents a comprehensive collection of formulas and calculations for hand-crafted feature extraction of condition monitoring signals. The documented features include 123 for the time domain and 46 for the frequency domain. Furthermore, a machine learning-based methodology is presented to evaluate the performance of features in fault classification tasks using seven data sets of different rotating machines. The evaluation methodology involves using seven ranking methods to select the best ten hand-crafted features per method for each database, to be subsequently evaluated by three types of classifiers. This process is applied exhaustively by evaluation groups, combining our databases with an external benchmark. A summary table of the performance results of the classifiers is also presented, including the percentage of classification and the number of features required to achieve that value. Through graphic resources, it has been possible to show the prevalence of certain features over others, how they are associated with the database, and the order of importance assigned by the ranking methods. In the same way, finding which features have the highest appearance percentages for each database in all experiments has been possible. The results suggest that hand-crafted feature extraction is an effective technique with low computational cost and high interpretability for fault identification and diagnosis.

The geography of wild American ginseng in North Carolina, USA: a comparative study of four capability analysis methods

Wild American ginseng is the root component of a family of plants called Panax, and it grows mainly in the understory of the eastern deciduous forests in North America. The growing market for wild American ginseng in Asia along with the scarcity of this plant in the wild due to rising illegal harvest have been simultaneously pushing up the price of American ginseng in the market, increasing incentives further for illegal harvest of this soon-to-be-extinct species. This paper studies the geography of wild American ginseng to identify the spatial distribution of the suitable habitat of this plant in the wild and provides advice on what conditions are required and what regions are suitable for cultivating it in farms, introducing an alternative to the wild American ginseng to decrease the harvesting pressure on its wild counterpart. By employing four popular geospatial methods, including the Binary Screening method, the Ordinal Ranking method (both Graduated Screening and Addition-of-Factors methods), and the Weighted Linear Combination method, this paper carries out a capability analysis and mapping for wild American ginseng in North Carolina (NC). After building the models, they are validated by using the data of the known locations of wild American ginseng plants in NC. Developing these models helps in gaining a more comprehensive understanding of where the plant naturally grows and sheds light on how government agencies can more effectively and more efficiently plan for law enforcement activities to better protect this plant from illegal harvest. The general pattern of our results suggests that western NC counties such as Jackson, Haywood, Transylvania, Henderson, Cherokee, and Ash are some of the most suitable geographical areas for wild American ginseng to grow. The results of our model validation analysis, along with the comparison of our models' predictions to the observed occurrences of wild American ginseng in nature, indicate that the Binary Screening method’s predictions align with almost 96% of the reported observations in nature. This suggests that the influential natural factors necessary for wild American ginseng are more of complementary factors to each other than being substitutes, meaning that they all must exist in the environment for it to grow, and it could not be that the lack of one natural factor can be compensated by the abundance of another natural factor.

Factors Affecting the Innovation of Crop-Livestock Integration System (SITT) Adoption in North Minahasa Regency

The adoption of beef cattle crop-livestock integration system (SITT) innovations in North Minahasa Regency still faces various obstacles. As a result, beef cattle development tends to experience a decline in population, productivity, and competitiveness. The purpose of the study was to determine the factors influencing farmers' attitudes and adoption of SITT technology innovations for beef cattle in North Minahasa Regency. Mixed-method research was conducted from March to May 2024, where 25 extension agents and cattle farmers were selected purposively. The variables used were farmer characteristics, extension worker performance, nature of innovation, communication channels, attitudes, and farmer adoption of SITT. Data analysis used the Spearman rank method. The study revealed a strong correlation between extension worker performance and the nature of innovation on farmers' attitudes and SITT adoption. In contrast, communication behavior exhibited a negligible impact. These findings underscore the critical role of extension agents in facilitating technology adoption. To maximize the effectiveness of extension programs, efforts should focus on enhancing extension worker capabilities and creating innovative solutions tailored to farmers' needs.

Comparison of Various Ranking Methods with Pareto Distribution Methods in Imprecise Data

The Pareto distribution has seen wide application in many different fields recently. Nowadays, the data we collect may be imprecise and contain uncertainties. This means that the values we gather cannot necessarily be considered a single, definitive value. In this paper, it is explored how various estimation methods like the method of moments, maximum likelihood estimation, and least squares estimation are used to determine the parameters of a Pareto distribution when the data is fuzzy or uncertain in nature. When making decisions, ranking methods are often heavily relied upon. For this reason, the application of several different ranking techniques to triangular fuzzy numbers (TFNs) is considered. The ranking methods for TFNs are found to tend to produce estimated parameters that are quite similar or even identical. The estimating parameters of a Pareto distribution can be solved for when dealing with fuzziness by the use of ranking methods such as alpha-cut, Yeager’s method, sub-interval method, Pascal method, magnitude method, and centroid method on the TFNs. By comparing the results of these various ranking techniques, an analysis is conducted to whether the estimated parameters of the Pareto distribution are consistent across the different ranking methods for TFNs get the uniqueness of the solution or differ. The decision making done by among the various ranking techniques, which method preferable for future comparison with extended of new ranking techniques. The conclusion is that the simulation results match up well with actual application data, serving as a good example for explaining this overall strategy.

Weather Research and Forecasting Model (WRF) Sensitivity to Choice of Parameterization Options over Ethiopia

Downscaling seasonal climate forecasts using regional climate models (RCMs) became an emerging area during the last decade owing to RCMs’ more comprehensive representation of the important physical processes at a finer resolution. However, it is crucial to test RCMs for the most appropriate model setup for a particular purpose over a given region through numerical experiments. Thus, this sensitivity study was aimed at identifying an optimum configuration in the Weather, Research, and Forecasting (WRF) model over Ethiopia. A total of 35 WRF simulations with different combinations of parameterization schemes for cumulus (CU), planetary boundary layer (PBL), cloud microphysics (MP), longwave (LW), and shortwave (SW) radiation were tested during the summer (June to August, JJA) season of 2002. The WRF simulations used a two-domain configuration with a 12 km nested domain covering Ethiopia. The initial and boundary forcing data for WRF were from the Climate Forecast System Reanalysis (CFSR). The simulations were compared with station and gridded observations to evaluate their ability to reproduce different aspects of JJA rainfall. An objective ranking method using an aggregate score of several statistics was used to select the best-performing model configuration. The JJA rainfall was found to be most sensitive to the choice of cumulus parameterization and least sensitive to cloud microphysics. All the simulations captured the spatial distribution of JJA rainfall with the pattern correlation coefficient (PCC) ranging from 0.89 to 0.94. However, all the simulations overestimated the JJA rainfall amount and the number of rainy days. Out of the 35 simulations, one that used the Grell CU, ACM2 PBL, LIN MP, RRTM LW, and Dudhia SW schemes performed the best in reproducing the amount and spatio-temporal distribution of JJA rainfall and was selected for downscaling the CFSv2 operational forecast.

An Empirical Study on the Impact of Investor Gambling Factors on Stock Cross-Sectional Returns

This study explores how investors' gambling preferences affect the cross-sectional returns of stocks in China's A-share market through behavioral finance factors. The portfolio ranking method and Fama-Macbeth regression analysis show how gambling behavior affects stock returns. On this basis, a multi-factor model (CHG model) incorporating gambling factor innovation was constructed to capture market dynamics more accurately. Research shows that going long on a low gaming index stock portfolio and short on a high gaming index stock portfolio can achieve significant average monthly returns. After adjustment by the Fama-French three-factor model, the returns of this long and short portfolio are significant at the 1% level. Significantly. In the CHG model, the coefficient of the gambling index Gamble is -0.0241, and the t-statistic is -6.1858, which is significant at the 1% level, further confirming the negative impact of gambling preferences on stock returns. In addition, the CHG model's ability to explain anomalies is also better than the CH-4 model. This study provides a new perspective on understanding behavioral finance factors in the Chinese stock market, and the results highlight the importance of considering behavioral finance factors in asset pricing and investment decisions in a highly irrational market environment. Future research can further explore the performance of behavioral financial factors in different market environments, as well as the differences and similarities of behavioral factors in different markets.

A comparative evaluation of multi-criteria decision-making framework for armed unmanned aerial vehicle

PurposeThis study focuses on the selection of armed unmanned aerial vehicles (AUAV), which have recently taken an important place on the world agenda, are used effectively in the defense industry and change the war systems of countries. This study aims to select the most suitable armed AUAV by using and comparing multi-criteria decision-making (MCDM) methods.Design/methodology/approachThere are various types of (unmanned aerial vehicles) UAVs, and some of them are Armed UAVs. This study used the criteria obtained from the market and previous UAV studies and ranked/selected various AUAVs produced in line with the determined criteria. The AHP method was used to prioritize the criteria, and the PROMETHEE method, a powerful ranking method, was used to rank/select the alternatives.FindingsBy the expert judgments, the payload capacity (28.2%) criteria took first rank by far as the most important criteria. The AUAV alternatives are listed as 1-6-5-2-7-3-4, respectively.Practical implicationsUAVs around the world have been showing significant and rapid developments recently, and those concerned closely follow developments in this field. Depending on the development of the aviation industry and technology, UAVs provide services to individuals or institutions in various ways for civil or military use.Originality/valueThe difference from similar studies is the research of Armed UAVs. Sensitivity analysis was performed and alternatives were analyzed by their weights. Comparisons were made using the MEREC, LOPCOW, and ELECTRE methods.

POSITIONING OF RUSSIAN REGIONS IN LONG-TERM AND MEDIUM-TERM ECONOMIC GROWTH RATINGS

In the article the author creates ratings of Russian regions according to criteria — ​long-term and medium-term real economic growth. An indicator of economic growth is the index of the physical volume of gross regional product. By the long-term period we mean the period from 1997 to 2021, by the medium-term period — ​the period from 2016 to 2021. The purpose of the study is to rank the regions according to these periods. Objectives of the study are following: to conduct a comparative analysis of the results of building two ratings; to show the spatial location of regions from the standpoint of long-term economic growth; to show the specialization of the leading regions. Specific research methods are the ranking method, comparative and correlation analyses, and the graphical illustration method. The leaders of both long-term and medium-term economic growth are three regions. These are St. Petersburg, the Moscow region and the Republic of Adygea. The absolute outsider according to both ratings is the Komi Republic. The industry specificity of the leading regions was obtained: all regions are characterized by specialization in manufacturing. Regions leading long-term and medium-term growth also specialize in non-production activities — ​wholesale and retail trade, real estate operations. The leading regions of medium-term growth specialize both in the extractive industry and in the fields of transport and communications.

Computer aided diagnostic system with reduced electrode set for depression detection using spatio-temporal attention mechanism

Computer-aided diagnostic systems (CAD) have been extensively used in medical applications to identify neurological abnormalities. It enhances diagnostic accuracy, decreases misdiagnosis rates, and provides reliable decision support. The proposed manuscript has introduced an automated CAD system that uses EEG signals to detect depression. The proposed automated CAD system is designed to work with a reduced set of electrodes. This manuscript introduces a method for electrode ranking based on the Fisher score. Electrodes are selected based on the threshold criteria. EEG is spatio-temporal data. The spatial and temporal self-attention mechanism is used to extract spatial and temporal features in the spatial and temporal domain. The proposed automated CAD system reduces the requirements of electrodes by an order of magnitude without compromising the performance of the model. The proposed CAD system has a higher degree of automation. It reduces the cost of the scan, experimental time, and complexity of the system.

Research on Momentum Evaluation and Turning Point Prediction in Tennis Matches Based on RSR and GSRF

In the men's singles final of the 2023 Wimbledon Open, rising star Carlos Alcaraz defeated veteran Novak Djokovic, an astonishing turnaround and result that sparked an in-depth exploration of the sports phenomenon of "momentum". When one side of a match gains momentum, its technical level and adaptability will be greatly utilized, which will directly affect the victory or defeat of the match. Most of the past researches focus on verifying the authenticity of momentum in sports games and the factors affecting the change of momentum. In this paper, taking the above match as the research object, we use the rank and ratio comprehensive evaluation method (RSR) to mine the match data, and propose a new prediction model GSRF by combining the random forest model and the lattice search algorithm, and based on the independently established momentum model and the momentum difference model, we predicted the turning point of the momentum in the match and the result of the match. The difference effect size of Cohen's d-value of 0.035 and accuracy R² of 0.826 was obtained, which verified the higher accuracy and generalizability of the model. The study helps coaches and instructors optimize training programs, develop game strategies, and provide new ideas and approaches for coaches and instructors.

Surface roughness analysis of side-polished fiber based on the importance of texture features

In order to explore the use of side-polished fibre (SPF) for microprobe-type "lab-on-fibre", this study presents an analysis of the surface roughness in side-polished fiber (SPF) using the gray level co-occurrence matrix (GLCM) texture feature analysis method. Experimental results show that the flat areas of the SPF polished surface exhibit texture characteristics with high mean values in contrast and entropy, and low mean values in the angular second moment (ASM), homogeneity, and correlation. Employing the random forest (RF) feature importance ranking method based on the Gini coefficient and out-of-bag (OOB) error estimation, this study assesses the sensitivity of various GLCM texture parameters in classifying different roughness levels of the SPF polished surfaces. A feature subset comprising variance, ASM, entropy, and contrast is identified as optimal. Utilizing this subset, the paper conducts an RF classification validation experiment on the roughness of the SPF polished surfaces, with results showing an RF classification accuracy of 95.65%.This research provides evidence for exploring the impact of rough polished surfaces in SPF optic sensors on the light coupling mechanism with environmental materials and its influence on sensor sensitivity. It lays the foundation for exploring the precise identification of high-sensitivity areas on SPF polished surfaces.

Assessing the Impact of Criterion Weights on the Ranking of the Top Ten Universities in Vietnam

This study focuses on evaluating the influence of criterion weights on the ranking of the top ten universities in Vietnam. Criteria weights were determined using four different methods, including the equal weight method, the weights of the Vietnam University Rankings (VNUR) system, the entropy weight method, and the Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) weight method. Four university ranking methods were applied: Proximity Indexed Value (PIV), Ranking of Alternatives with Weights of Criterion (RAWEC), Root Assessment Method (RAM), and Simple Ranking Process (SRP). The results indicate that the use of different weight calculation methods does not significantly affect university rankings. The four leading universities in Vietnam consistently maintain their position in the rankings, regardless of the weight calculation or ranking methods used.