Ranking Technique Research Articles

Comparative Evaluation of Imbalanced Data Management Techniques for Solving Classification Problems on Imbalanced Datasets

Dealing with imbalanced data is crucial and challenging when developing effective machine-learning models for data classification purposes. It significantly impacts the classification model's performance without proper data management, leading to suboptimal results. Many methods for managing imbalanced data have been studied and developed to improve data balance. In this paper, we conduct a comparative study to assess the influence of a ranking technique on the evaluation of the effectiveness of 66 traditional methods for addressing imbalanced data. The three classification models, i.e., Decision Tree, Random Forest, and XGBoost, act as classification models. The experimental settings have been divided into two segments. The first part evaluates the performance of various imbalanced dataset handling methods, while the second part compares the performance of the top 4 oversampling methods. The study encompasses 50 separate datasets: 20 retrieved from the UCI repository and 30 sourced from the OpenML repository. The evaluation is based on F-Measure and statistical methods, including the Kruskal-Wallis test and Borda Count, to rank the data imbalance handling capabilities of the 66 methods. The SMOTE technique is the benchmark for comparison due to its popularity in handling imbalanced data. Based on the experimental results, the MCT, Polynom-fit-SMOTE, and CBSO methods were identified as the top three performers, demonstrating superior effectiveness in managing imbalanced datasets. This research could be beneficial and serve as a practical guide for practitioners to apply suitable techniques for data management.

Revisiting Code Smell Severity Prioritization using learning to rank techniques

Code Smell Severity Prioritization (CSSP) is crucial in helping software developers minimize software maintenance costs and enhance software quality, particularly when faced with limited refactoring resources. Traditional code smell prioritization methods rely heavily on manual and semi-automatic approaches based on developer experience, often demanding considerable time and effort from experienced experts. Leveraging automated machine learning techniques can effectively overcome these limitations. However, most existing machine learning-based CSSP works have only considered limited pointwise Learning To Rank (LTR) algorithms and have used inappropriate metrics (e.g., Accuracy, Spearman, and MAE) to assess the performance of models. To address these limitations, we make a comprehensive comparison of 41 pointwise, 4 pairwise, and 4 listwise LTR algorithms for CSSP on four code smell severity datasets. Furthermore, we propose the adoption of Severity@20% and Cumulative Lift Chart (CLC) as the primary evaluation metrics to assess CSSP models more effectively. The results show that: (1) The ordinal Bagging (O-Bagging) algorithm demonstrates the highest performance for CSSP, achieving superior results in Severity@20% and CLC. (2) The ordinal classification method can help the top-performing base classification algorithms Bagging and XGBoost achieve better performance for CSSP tasks. (3) A higher (lower) Accuracy, higher (lower) Spearman, and lower (higher) MAE do not reliably indicate better (worse) performance for CSSP. This further underscores that Accuracy, Spearman, and MAE are unsuitable metrics for evaluating CSSP models’ effectiveness. To summarize, our study suggest that developers employ the O-Bagging algorithm for CSSP, with Severity@20% and CLC serving as the primary evaluation metrics.

A novel TODIM-based multi-attribute decision making method under information described by Z-numbers for selecting online B&B

This study designs a new TODIM-based multi-attribute decision making (MADM) method under information described by Z-numbers for selecting online Bed and Breakfast (B&B), which includes extracting the evaluation attributes and obtaining the best online B&B. To do that, first the fine-tuned Bidirectional Encoder Representation from Transformers (BERT) model is used to analyze the sentiment of all online reviews from nine regions of Airbnb, which these reviews about online B&Bs are divided into positive and negative comments. And then Term Frequency-Inverse Document Frequency (TF-IDF) is used to extract attribute feature words of positive and negative comments. Their intersection is sought to determine the attributes of the tourists' online B&B selection. Next, the ranking technique of Z-number and the distance of Z-number are put forward. Finally, a novel TODIM-based MADM method under information described by Z-numbers is proposed to select the B&Bs by the online review of B&Bs obtained from Ctrip.com. The advantage of this method is that it not only considers the reliability of online reviews, but also reflects the psychological factors of tourists. The feasibility of the proposed method is illustrated by ranking the B&Bs, and the flexibility and superiority of the method are highlighted by the sensitivity analysis and comparative analysis.

Perceived Barriers to the Adoption of Climate Smart Adaptive Livelihood Technology by the Farmers in Flood Prone Areas of Assam

Flood is an annual major natural disaster of Assam. It causes a huge loss to human, animal, crop, infrastructures and natural resources. Since agriculture is the prime source of livelihood and majority of inhabitants resides in rural areas, the impact of flood has a vital role in the living conditions of Assam’s population. In the present study different kinds of barriers were identified which found to create resistance in the resilience building by the farmers in the studied area. Here the Garrett’s ranking technique was used to rank the barriers associated with the resilience building by the farmers in the study area. The problem of limited skills upon climate smart adaptive livelihood technology (CSALT) got the highest mean score of 65.55 and ranked 1st among all the identified problems. However, the last rank was given to the problem of limited resources with farmers to invest in CSALT having mean score 44.44. It was seen that after the extension problem the other key problems in descending order of value were economic problem such as unavailability of quality seeds and planting materials at right time, unavailability of labour in vital period of farming, in addition to which higher wage and lack of post harvest technologies and marketing facilities were also observed along with communication and information problems and socio-personal problem. Moreover reluctance to take up new project due to deprived risk taking ability, lack of awareness and skill on advanced farming technologies, lack of interest in cultivation due to poor return in short term with poor educational status of the farmers were some of the observed barriers.

Students’ interests and attitudes toward science, technology, engineering, and mathematics careers

<p>Science, technology, engineering, and mathematics (STEM) education teaches students critical thinking skills that they can use to solve problems even after they enter the workforce. This study aimed to i) determine student attitudes toward the STEM field in each school area; ii) determine student interest in the STEM field in each school area; and iii) determine the correlation between student attitudes toward the STEM field and school area and student interest in STEM. This was a quantitative study with a cross-sectional survey design. The sample for this study came from Boyolali Regency in Central Java, Indonesia, and was divided into three groups: rural schools (24%), suburban schools (36.2%), and urban schools (39%). The proportional stratified random sampling technique was used to select the research sample of students in grades 7–9. The Spearman Rank technique was used for data analysis. The results showed that there is a strong relationship between students’ attitudes toward STEM, school area, and interest in STEM careers. On the other hand, there is a weak correlation between students’ attitudes toward STEM, the school environment, and their interest in STEM careers.</p>

Prioritizing dark patterns in the e-commerce industry – an empirical investigation using analytic hierarchy process

PurposeE-commerce companies use different types of dark patterns to manipulate choices and earn higher revenues. This study aims to evaluate and prioritize dark patterns used by e-commerce companies to determine which dark patterns are the most profitable and risky.Design/methodology/approachThe analytic hierarchy process (AHP) prioritizes the observed categories of dark patterns based on the literature. Several corporate and academic specialists were consulted to create a comparison matrix to assess the elements of the detected dark pattern types.FindingsEconomic indicators are the most significant aspect of every business. Consequently, many companies use manipulative methods such as dark patterns to boost their revenue. The study revealed that the revenue generated by the types of dark patterns varies greatly. It was found that exigency, social proof, forced action and sneaking generate the highest revenues, whereas obstruction and misdirection create only marginal revenues for an e-commerce company.Research limitations/implicationsThe limitation of the AHP study is that the rating scale used in the analysis is conceptual. Consequentially, pairwise comparisons may induce bias in the results.Practical implicationsThis paper suggests methodical and operational techniques to choose the priority of dark patterns to drive profits with minimum tradeoffs. The dark pattern ranking technique might be carried out by companies once a year to understand the implications of any new dark patterns used.Originality/valueThe advantages of understanding the trade-offs of implementing dark patterns are massive. E-commerce companies can optimize their spent time and resources by implementing the most beneficial dark patterns and avoiding the ones that drive marginal profits and annoy consumers.

Mushroom Cultivation MOOC at Kerala Agricultural University: Garrett Ranking Analysis of Constraints to Enhance Online Agriculture Learning

Participants in this extensive Massive Open Online Course (MOOC) on mushroom cultivation will gain a wealth of knowledge and useful skills necessary for cultivating a wide variety of mushrooms. In this study, the Center for e-learning at Kerala Agriculture University will analyse the barriers to mushroom growing as part of a MOOC. 30 participants in the MOOC course were chosen at random to participate in the study as responses. Using the specified sampling size and data collection methodology, an ex post facto research design was employed for the study. The requirements in the MOOC course were prioritized using the Garrett ranking technique after the data were statistically analysed. Results revealed that 55.06 per cent of the respondents have the economic constraint that is the power/ electricity usage costs more to them, of the technological challenges encountered, interacting with the resource person less frequently comes in the rank first with 60.13 per cent, the biggest personal challenge is developing skill-oriented knowledge, which is difficult and it has been ranked first with 67 per cent, Personal commitments, such as employment, family duties, health concerns etc., ranks first with 62.53 in terms of situational limitations and 66 percent of the respondents rranked first that there is more number of theory classes with minimum hands-on experience in psychological challenges

Совершенствование принципов автоматизированного планирования многономенклатурных технологических процессов механообработки

The scientific principles of improving the system of automated planning of technical processes in the conditions of multiproduct machining industries are viewed. The study of modern approaches to automation of design and implementation of machining processes taking into account the production situation, is carried out. The use of the system of automated planning of multiproduct machining processes is justified, because its basic procedure includes a possibility of real-time interaction between subsystems design and implementation of technical processes, where the key place is occupied by a database on the technological capabilities of equipment and tooling. The interrelation of the system of automated planning of multiproduct machining processes with subsystems for assessing production manufacturability, monitoring technological equipment state, control and measuring procedures and assembly, is shown. The principle of designing multiproduct machining processes, generated for a given range of parts, taking into account the condition and capabilities of the production system, is presented. It is proposed to use the value of the total manufacturing time of a given range of parts as a generalized criterion for the efficiency of the production system. Ranking technique of design procedures according to the degree of influence of the decisions made on the generalized criterion of efficiency of the production system, is described. A model, based on genetic algorithms, is characterized, allowing the system being automatically adjusted to the conditions, changing during the design process. As a result, the efficiency of technological preparation of diversified production increases due to the rational distribution of manufacturing process for machining operation of parts to existing production facilities. The development of the work is aimed at increasing the level of design automation of machining processes and obtaining feedback on the current state of the production system.

Garret Ranking of Constraints Perceived by Silkworm Rearers in Sericulture Enterprise

The current investigation primarily delves into the challenges faced by sericulture growers in mulberry and cocoon production. Sericulture, a highly competitive and capital-intensive enterprise, is prevalent across rural and semi-urban areas in India. This industry not only contributes to household income but also generates employment opportunities for entire families, with women actively participating in various facets of sericulture activities. A multistage sampling design was employed to select districts, tehsils, villages, and sericulture growers for the study. Specifically, Beed, Parbhani, and Jalna districts were purposively chosen in the Marathwada region due to their significant sericulture production. Two tehsils from each district and four villages from each tehsil were then selected, resulting in a total of 24 villages with 240 sample respondents. The study utilized Garret's ranking technique to assess the constraints affecting the adoption of sericulture production. The study identified four key types of constraints in the examined area: economic, technical, marketing, and supply constraints. According to Garret's ranking, the primary economic constraint was the high cost of inputs, including FYM, planting material, and chemical fertilizers. In the realm of technical constraints, a lack of information about Uzi fly control emerged as the major hurdle. Marketing constraints were predominantly characterized by fluctuations in cocoon prices, ranking highest in prominence. Additionally, insufficient FYM availability was highlighted as a significant supply constraint.

Evaluating Agriculture Robots: Case Study and Analysis

The technical revolution paved the way for the development of robots that can now operate in a variety of areas. The smart farming technique called robotic agrifarming has the potential to alleviate the worldwide labor shortage while increasing agricultural productivity and food safety. From planting to harvesting, agribots perform field operations that enhance soil quality and ensure long-term growth. That's why it's essential to create a decision-support system that helps choose the best robot for an agricultural task. The goal of this study is to create an integrated decision system for assessing agriculture robots using the method based on removal effects of criteria (MEREC). Founded on a lot of alternatives like project viability, ecosystem aptness, community conditions, governmental embracing, and profit, the performance of seven field robots is evaluated. The advantage of the developed ranking technique is that it follows the additive ratio assessment (ARAS) method, which is indeed a specific technique used for multi-attribute decision-making (MADM), particularly in the field of environmental management and evaluation. It's commonly employed when there are multiple factors or criteria to consider in decision-making processes and prioritizing the alternatives.

Fusion and classification algorithm of octacalcium phosphate production based on XRD and FTIR data

The present manuscript tested an automated analysis sequence to provide a decision support system to track the OCP synthesis from α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document}-TCP over time. Initially, the XRD and FTIR signals from a hundredfold scaled-up hydrolysis of OCP from α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document}-TCP were fused and modeled by the curve fitting based on the significantly established maxima from the literature and nine features extracted from the fitted shapes. Afterward, the analysis sequence enclosed the machine learning techniques for feature ranking, spatial filtering, and dimensionality reduction to support the automatic recognition of the synthesis stages. The proposed analysis pipeline for OCP identification might be the foundation for a decision support system explicitly targeting OCP synthesis. Future projects will exploit the suggested methodology for pinpointing the OCP production over time (including the intermediary phases present in the OCP formation) and for evaluating whether biological variables might be merged with biomaterial properties to build a unified model of tissue response to the implant.

Analysis of earthquake resilience factors for buildings: the case of Istanbul high-rise buildings

PurposeThe objective of this study is to investigate the concept of “earthquake resistance” in high-rise buildings and assess the current level of structural strength in the areas where these structures are situated. The study aims to identify and implement necessary measures to enhance resilience in these areas. A comprehensive literature review was conducted to develop a conceptual framework focusing on earthquake resistance's meanings, stages and physical elements to achieve these goals.Design/methodology/approachThis study focuses on Istanbul, a city known for its high earthquake risk, specifically targeting the Atasehir district. The research utilizes the DEMATEL (Decision-Making Trial and Evaluation Laboratory) method to evaluate urban resilience parameters. Additionally, the Fuzzy TOPSIS (Preference Ranking Technique by Similarity) method is employed to analyze the location of five buildings in Atasehir, using criterion weights derived from this methodology.FindingsThe findings indicate that resilience varies depending on the distance of the buffers. Moreover, the amount and quality of urban equipment in the study areas have a significant impact on the earthquake resistance level of the surrounding areas where high-rise buildings are situated. Building upon this analysis, the study suggests the implementation of measures aimed at augmenting the quantity and quality of urban facilities in the study areas, consequently enhancing urban resilience.Originality/valueThe originality and value of this study lie in its examination of seismic resilience within the context of high-rise buildings and the identification of necessary measures to increase resilience in areas where these structures are prevalent. By focusing on Istanbul, a city with a high earthquake risk, and specifically selecting the Atasehir district as the study area, this research provides a comprehensive conceptual framework for understanding urban resilience and its physical components. Moreover, the study offers a fresh perspective on urban resilience by highlighting the influence of tall buildings on the surrounding areas. Ultimately, it provides practical recommendations for architects, urban planners and other stakeholders to improve regional earthquake resilience.

Perception of farmers on varietal traits and constraints in

Greengram (Vigna radiata) is one of India’s most valuable pulse crops. Greengram is a native of India and CentralAsia and has grown in these regions since prehistoric times.India contributes more than 70% of world’s greengram production,while its cultivation has spread to Africa and America relatively in the recent times. The present study was confined toDharwad and Gadag districts of North Karnataka during the agriculture year 2021-22. The study aimed to assess theconstraints faced by greengram growers in adoption of improved varieties of greengram. A multistage random samplingprocedure was used for the selection of samples and data collected through a personal interview method using a pre-testedschedule. Collected data were analyzed by using suitable statistical tools like Garrett’s ranking technique and descriptivestatistics. The study revealed that the major production and constraints faced by the greengram growing farmers. Nonavailabilityof improved variety seeds and scarcity of labour during peak periods were the major production constraints,whereas, high price fluctuations andlack of storage facilities in villages were major marketing constraints faced by greengramgrowing farmers in study area. All the adopted farmers opined that improved variety (DGGV-2) is ideal for mechanicalharvesting and gave more grain yield than the existing varieties of greengram hence it plays amajor role in increasing the farmincome of the greengram cultivars.

Analysis of Abstractive and Extractive Summarization Methods

This paper explains the existing approaches employed for (automatic) text summarization. The summarizing method is part of the natural language processing (NLP) field and is applied to the source document to produce a compact version that preserves its aggregate meaning and key concepts. On a broader scale, approaches for text-based summarization are categorized into two groups: abstractive and extractive. In abstractive summarization, the main contents of the input text are paraphrased, possibly using vocabulary that is not present in the source document, while in extractive summarization, the output summary is a subset of the input text and is generated by using the sentence ranking technique. In this paper, the main ideas behind the existing methods used for abstractive and extractive summarization are discussed broadly. A comparative study of these methods is also highlighted.

Comprehensive evaluation of classification: an empirical study on consequence prediction of construction accidents in China

Purpose Consequence prediction is an emerging topic in safety management concerning the severity outcome of accidents. In practical applications, it is usually implemented through supervised learning methods; however, the evaluation of classification results remains a challenge. The previous studies mostly adopted simplex evaluation based on empirical and quantitative assessment strategies. This paper aims to shed new light on the comprehensive evaluation and comparison of diverse classification methods through visualization, clustering and ranking techniques. Design/methodology/approach An empirical study is conducted using 9 state-of-the-art classification methods on a real-world data set of 653 construction accidents in China for predicting the consequence with respect to 39 carefully featured factors and accident type. The proposed comprehensive evaluation enriches the interpretation of classification results from different perspectives. Furthermore, the critical factors leading to severe construction accidents are identified by analyzing the coefficients of a logistic regression model. Findings This paper identifies the critical factors that significantly influence the consequence of construction accidents, which include accident type (particularly collapse), improper accident reporting and handling (E21), inadequate supervision engineers (O41), no special safety department (O11), delayed or low-quality drawings (T11), unqualified contractor (C21), schedule pressure (C11), multi-level subcontracting (C22), lacking safety examination (S22), improper operation of mechanical equipment (R11) and improper construction procedure arrangement (T21). The prediction models and findings of critical factors help make safety intervention measures in a targeted way and enhance the experience of safety professionals in the construction industry. Research limitations/implications The empirical study using some well-known classification methods for forecasting the consequences of construction accidents provides some evidence for the comprehensive evaluation of multiple classifiers. These techniques can be used jointly with other evaluation approaches for a comprehensive understanding of the classification algorithms. Despite the limitation of specific methods used in the study, the presented methodology can be configured with other classification methods and performance metrics and even applied to other decision-making problems such as clustering. Originality/value This study sheds new light on the comprehensive comparison and evaluation of classification results through visualization, clustering and ranking techniques using an empirical study of consequence prediction of construction accidents. The relevance of construction accident type is discussed with the severity of accidents. The critical factors influencing the accident consequence are identified for the sake of taking prevention measures for risk reduction. The proposed method can be applied to other decision-making tasks where the evaluation is involved as an important component.

A comparative study of original equipment manufacturer brake pads using tribological testing and hybrid ranking for enhanced decision making and cost effectiveness

Tribological test and hybrid ranking techniques are employed to evaluate brake pads from original equipment manufacturers for Indian passenger cars. The study was conducted for four brake pads designated as OE1, OE2, OE3, and OE4. The assessment integrates the analytical hierarchy process with various ranking methods including Extension Evaluation Method, Complex Proportional Assessment, Preference Selection Index, and Technique for Order of Preference by Similarity to Ideal Solution. Brake pad samples undergo testing according to the standard IS2742 part 1-4 of 1994, generating crucial performance parameters such as performance coefficient of friction, hot coefficient of friction, normal coefficient of friction, fade percentage, recovery percentage, variation in coefficient of friction, wear thickness loss percentage, wear mass loss percentage, and time taken to reach maximum temperature. These performance metrics are technically analyzed and ranked using hybrid ranking techniques to enhance the decision-making process. The results show that OE4, a budget-friendly brake pad ranked first, is the best performer. OE2, a more expensive alternative, is placed second, and OE1 and OE3, which are more affordable possibilities, are ranked third and fourth, respectively. This research serves as a valuable resource for selecting brake pads for Indian passenger cars, especially in scenarios where technical specifications and cost-effectiveness are crucial considerations.

Analyzing Factors Influencing Milk Marketing Channel Strategies in Punjab

Raising livestock has long been regarded as an important activity in the nation’s rural areas. The Indian dairy industry has undergone noteworthy changes with the tremendous increase in the production of milk. The provision of assured marketing channels is crucial for dairy farmers to reap the economic benefits of milk production. The present study aims to examine the drivers behind the dairy farmers’ decisions about milk marketing channels in Punjab. The primary data used in this study has been collected from 21 villages and 420 dairy farmers using a multistage sampling technique in 2019, belonging to three different zones of the state. Henry Garrett’s Ranking Technique was used to determine the significant factors influencing their choice of milk marketing channels. Higher milk price, with a mean value of 71.64, is found to be the main driver for choosing the milk marketing channel, followed by doorstep collection with a mean value of 56.70, and provision of advances with a mean value of 54.55. There is a need to strengthen the dairy cooperatives to reduce the involvement of the unorganized sector in the marketing of milk in the state of Punjab.

Impact of TikTok Application to the Second Language Acquisition of Grade 10 Students

This research explores the Impact of the TikTok Application on Second Language Acquisition among Grade 10 students at the Central Bicol State University of Agriculture – Sipocot Laboratory High School. Employing a descriptive correlation method, the study aimed to discern the perceived impact of TikTok on second language acquisition, assess its utilization concerning grammar, vocabulary, and pronunciation, and establish a significant relationship between TikTok use and language acquisition. Various statistical treatments were used, i.e., weighted mean and ranking technique and Pearson’s R. The data were collected from 40 Grade 10 students using a researcher-made survey questionnaire and Google Form. The results indicated a consistently high utilization of TikTok across grammar, vocabulary, and pronunciation domains, with an overall weighted mean of 3.29, interpreted as ‘always.’ Significantly, the relationship between TikTok usage and its perceived impact on second language acquisition was affirmed (grammar p-value = 0.000 <0.001, vocabulary p-value = 0.000 <0.001, pronunciation p-value = 0.000 <0.001), suggesting a strong correlation. This study concludes that TikTok positively influences language learning in the specified domains, as evidenced by students’ favorable attitudes. Lastly, this study recommends that language teachers utilize the TikTok application as a tool to improve students’ language skills in grammar, vocabulary, and pronunciation.

An Artificial Intelligence-Infused Trigonometric Fuzzy Model for Strategic Insights into Green Communication of Emerging Economies

Green communication is important for businesses to achieve customer satisfaction and gain a significant competitive advantage. Therefore, improving the indicators is very significant for increasing the green communication performance of businesses. However, these improvements cause cost increase for businesses. Hence, there is a significant need for a priority analysis on the variables that will affect the green communication performance of businesses to use the budget more effectively. The purpose of this study is to evaluate important indicators of effective green communication for the companies. For this purpose, a novel model is proposed that has mainly two different parts. In this process, the evaluations of three decision makers are taken into consideration. At the first stage, selected indicators are examined by using artificial intelligence-based sine trigonometric Pythagorean fuzzy decision-making trial and evaluation laboratory (DEMATEL). Secondly, emerging seven countries are ranked according to the performance of the green communication. In this context, artificial intelligence-based sine trigonometric Pythagorean fuzzy ranking technique by geometric mean of similarity ratio to optimal solution (RATGOS) technique is taken into consideration. Moreover, these countries are also ranked by using additive ratio assessment (ARAS) methodology to make a comparative evaluation. The main contribution of this study is that artificial intelligence methodology is integrated with the fuzzy decision-making model. Artificial intelligence methodology is considered to generate decision matrix. With the help of this situation, more appropriate calculations can be made. Proposing RATGOS methodology to the literature by the authors is another significant contribution of this proposed model. To overcome criticisms regarding the existing ranking decision-making techniques in the literature, RATGOS model is generated by making computations with geometrical mean. Owing to this issue, it can be possible to reach more effective solutions. The findings demonstrate that informativeness is the most crucial issue for the improvement of green communication performance of the companies. Meeting customer expectation is another important situation that should be taken into consideration in this manner. Considering these findings, it would be appropriate to establish sectoral standards and guidelines to provide information in green communication. Thanks to these standards, it is possible for companies to provide detailed and comprehensive information to their customers. The ranking results of both RATGOS and ARAS are the same that gives information about the consistency and coherency of the proposed model. The ranking results indicate that China and Russia are the most successful emerging countries with respect to the green communication performance.

A discrete extension of the Burr-Hatke distribution: Generalized hypergeometric functions, different inference techniques, simulation ranking with modeling and analysis of sustainable count data

<abstract><p>The intertwining relationship between sustainability and discrete probability distributions found its significance in decision-making processes and risk assessment frameworks. Count data modeling and its practical applications have gained attention in numerous research studies. This investigation focused on a particular discrete distribution characterized by a single parameter obtained through the survival discretization method. Statistical attributes of this distribution were accurately explicated using generalized hypergeometric functions. The unveiled characteristics highlighted its suitability for analyzing data displaying "right-skewed" asymmetry and possessing extended "heavy" tails. Its failure rate function effectively addressed scenarios marked by a consistent decrease in rates. Furthermore, it proved to be a valuable tool for probabilistic modeling of over-dispersed data. The study introduced various estimation methods such as maximum product of spacings, Anderson-Darling, right-tail Anderson-Darling, maximum likelihood, least-squares, weighted least-squares, percentile, and Cramer-Von-Mises, offering comprehensive explanations. A ranking simulation study was conducted to evaluate the performance of these estimators, employing ranking techniques to identify the most effective estimator across different sample sizes. Finally, real-world sustainability engineering and medical datasets were analyzed to demonstrate the significance and application of the newly introduced model.</p></abstract>