Rank Values Research Articles

Evaluasi Perbandingan Informasi Citra MRI Brain Sekuen Diffusion Weighted Imaging Irisan Axial Pada Variasi Nilai SENSE di Instalasi Radiologi RSUD Provinsi NTB

Magnetic Resonance is a radiodiagnostic examination tool in the field of radiology, especially in the medical world. Diffusion Weighted Imaging is a method to assess molecular function and microarchitecture of the human body. DWI signal contrast can be measured with visible diffusion coefficient maps and serves as a tool for treatment response evaluation and disease progression assessment. Parallel imaging techniques involve phased array coils to process signals from a slice, then combine these slices to form an image, reducing scan time by reducing the acquired K-space lines. The formation of Sensitivity Encoding (SENSE) can shorten the insertion of K-space. This study aims to test variations of SENSE values 2.0 and 2.5 in MRI brain examinations with axial DWI sequences that are capable of visualizing anatomical information with fast scan times without compromising image quality, resulting in optimal images. This research is quantitative with an experimental approach. The sample consists of 10 patients using voluntary sampling of MRI Brain with axial DWI sequence and varying SENSE values from July to August 2023. Data were collected through questionnaires filled out by three radiology specialists at the Radiology Installation of the Provincial Hospital of West Nusa Tenggara (RSUD Provinsi NTB). The results of the Wilcoxon test showed a significant difference in anatomical information in MRI brain with axial DWI sequences between SENSE values of 2 and 2.5, with a p-value of 0.012. The mean rank value results indicated a sum of ranks of 280.00 for the SENSE 2 value, demonstrating greater optimality compared to SENSE 2.5. This study concludes that there is a significant difference in anatomical information in MRI brain with axial DWI sequences between SENSE values of 2 and 2.5. SENSE 2 tends to provide more optimal information.

Sistem Pendukung Keputusan Persetujuan Pengajuan Kredit Mobil di PT Clipan Finance Indonesia Dengan Metode Simple Additive Weighting (SAW)

Abstract
 PT Clipan Finance Indonesia is a car loan financing leasing company. This company has a standard in determining whether or not a customer is eligible for car loan financing. In choosing clients who deserve for car loan financing, it must be done properly to avoid client credit congestion, so it is necessary to select client data analysis. In selecting this client data, a decision support system information was designed that can help determine the priority of clients who are eligible for car loan financing, by using the Simple Additive Weighting (SAW) method as a method of supporting decision making. The SAW method is used to support decision making to evaluate the feasibility value of clients in selecting data for clients who apply for car loans based on the criteria and weight of decision making. Through this system, it is hoped that it can help determine the feasibility of applying for a client's car loan more effectively and efficiently by making the ranking value obtained as a reference for determining client creditworthiness approval.
 Keywords: Car Loan Financing, Decision Support System, Simple Additive Weighting

A Rank Estimator Approach to Modeling Default Frequencies

This study introduces a non-parametric methodology for estimating expected frequencies of defaults and other credit events. The methodology allows for an independent estimation of a credit-quality variable, referred to as a default rank variable. In a subsequent step, the relationship between the rank variable and the expected default frequency is established. This analysis can be achieved by initially determining the functional dependence between the rank variable and the expected tail default frequencies representing the average default frequencies of entities ranked lower than a given rank value. The expected default frequency can then be derived from a simple linear integral equation. We propose a prototype model for public corporations which establishes generalized logistic function dependencies between the distance-to-default rank variable and the expected default frequencies in the log–log space. This relationship applies to public corporations across different credit rating categories.

MARKET INTEGRATION OF SWEET LIME BETWEEN DOMESTIC AND INTERNATIONAL MARKETS

The study focused on the integration of domestic and international markets for sweet limes during 2021. The co-integration of commodity prices was considered to be necessary for the implementation of a successful marketing reform for which both domestic and international market prices are required. Due to the lack of information regarding international prices, export price information was gathered from thewebsite of the Agricultural and Processed Food Products Export Development Authority (APEDA), and secondary data regarding domestic prices of sweet lime was gathered from the website of the Agricultural Marketing Information Network (AGMARKNET). The annual average prices data for the chosen markets were gathered, and the Pearson correlation coefficient and Johansen’s co-integration analysis approaches were used to analyse the relationship between the chosen market pair. Both markets were found to be correlated through correlation analysis, and the findings of the Johansen Co-integration rank, trace test, and eigen value statistics test were used to analyse the integration among the markets. The study’s findings on market integration reveal how closely related the selected market places’ pricing are to one another. Additionally, this research demonstrated that the model’s variables exhibited an equilibrium longrun co-movement with the market price series.

Sistem Pendukung Keputusan Pemilihan Staf Karyawan Bpn Terbaik Di Wilayah Kota Medan Dengan Mengunakan Metode Moora Dengan Pembobotan Roc

Decision Support System (DSS) is a decision support system that can help in handling or solving problems in a company or in the daily environment, in determining a problem we need data that we will take in a company or in the environment. In writing this is an example of a problem that is currently happening with the case study of the Selection of the Best BPN Employees in the City of Medan, therefore the author uses a method that will be able to find the best employee data, therefore the author creates a decision support system that will be able to determine the best BPN employee assessment process in the city of Medan. Where this system is a system that has a very important role in decision making. The system is DSS, where DSS is one of the systems that can assist in solving each problem by requiring data and methods contained in the DSS in order to obtain more relevant information and support in making each decision. In completing this research, the writer has to find the weight value of the criterion data where the writer uses the ROC method to produce the weight value and after that the writer also searches for the ranking value using the MOORA method. And where to produce ranking values in the MOORA method, the author must work according to predetermined steps in order to obtain precise and accurate reference values. The ROC and MOORA methods really help writers in generating values accurately.This decision support system was made using the MOORA method with ROC weighting where this method can be used or can be used as a reference in determining a problem because it aims to find out how many employees are Top Talent (Best Employee), after testing it is given a weight value on criteria and can be modified which aims to find out how many criteria will be added in each criterion which has its own weight value and after that a ranking is made in which the ranking can already determine the best BPN employee in the city of Medan, namely (T3) Anggi Alfarissi HSB with a score of 1,157. So because of this it was confirmed or very easy for the team to select in decision-making and were selected as the best BPN employees in the city of Medan.

A Population Diversity-Based Artificial Bee Colony Algorithm for Assembly Hybrid Flow Shop Scheduling with Energy Consumption

Assembly shop scheduling and energy-efficient scheduling have attracted much attention in the past decades; however, energy consumption is often ignored in assembly hybrid flow shop scheduling. Neglecting energy consumption will greatly diminish the progress of sustainable manufacturing. In this study, an assembly hybrid flow shop scheduling problem considering energy consumption (EAHFSP) is investigated, and a population diversity-based artificial bee colony algorithm (DABC) is proposed to minimize the makespan and total energy consumption (TEC) simultaneously. Diversified search strategies based on rank value are introduced to the employed bee phase; a novel probability selection method in the onlooker bee phase is designed to control the selection pressure; moreover, a diversity control strategy is applied to improve the diversity of food sources and avoid falling into stagnation. A number of experiments based on 44 extended benchmark instances from the literature and a real case are conducted to test the performance of the DABC algorithm. The statistical results show that the DABC algorithm is superior to the other four state-of-the-art algorithms on over 70% of the instances corresponding to metrics IGD and c, which means that the DABC algorithm is effective and competitive in solving the considered EAHFSP.

GuaRD: Guaranteed robustness of image retrieval system under data distortion turbulence.

Image search systems could be endangered by adversarial attacks and data perturbations. The image retrieval system can be compromised either by distorting the query or hacking the ranking system. However, existing literature primarily discusses attack methods, whereas the research on countermeasures to defend against such adversarial attacks is rare. As a defense mechanism against the intrusions, quality assessment can complement existing image retrieval systems. "GuaRD" is proposed as an end-to-end framework that uses the quality metric as a weighted-regularization term. Proper utilization and balance of the two features could lead to reliable and robust ranking; the original image is assigned a higher rank while the distorted image is assigned a relatively lower rank. Meanwhile, the primary goal of the image retrieval system is to prioritize searching the relevant images. Therefore, the use of leveraged features should not compromise the accuracy of the system. To evaluate the generality of the framework, we conducted three experiments on two image quality assessment(IQA) benchmarks (Waterloo and PieAPP). For the first two tests, GuaRD achieved enhanced performance than the existing model: the mean reciprocal rank(mRR) value of the original image predictions increased by 61%, and the predictions for the distorted input query decreased by 18%. The third experiment was conducted to analyze the mean average precision (mAP) score of the system to verify the accuracy of the retrieval system. The results indicated little deviation in performance between the tested methods, and the score was not effected or slightly decreased by 0.9% after the GuaRD was applied. Therefore, GuaRD is a novel and robust framework that can act as a defense mechanism for data distortions.

Exploring collocational complexity in L2 Russian: A corpus-driven contrastive analysis

Objective: The objective of this article is to discuss the pedagogical and practical need for automated assessment tools that enable teachers, researchers, and other language practitioners to relatively quickly and automatically assess the general proficiency of second language (L2) speakers according to a number of different linguistic parameters, specifically the use of collocations. Introduction: The Introduction discusses existing approaches to the concept of collocation and its role in L2 corpus studies. Methods: In the main part, Toward the automatic detection of collocational strength, we describe an approach to assessing collocations in Russian L2 written production, which is based on the corpus-driven contrastive analysis. In brief, this approach proposes ranking L2 collocations in accordance with the “collocational scores” of bigrams extracted from a reference (L1) corpus, with the assumption that the greater number of native-like bigrams in an L2 text and the higher collocational scores associated with them, the more proficient is the learner who created the text. To test this hypothesis, we conducted a series of experiments based on an L1 reference corpus and split the L2 datasets into four proficiency groups (Beginner, Intermediate, Advanced, and Superior). Results and evaluation: The findings, presented in the section Results and evaluation of the methods, established an empirical minimum and maximum of mean reciprocal rank (MRR) values, and the variation of the MRR across the four proficiency levels. In the last sections, we discuss shortcomings of the methods and propose further steps for overcoming them. We believe that the proposed method for assessing the collocational skill of Russian L2 writers can be successfully integrated into a bigger system capable of multifaceted automatic assessment of Russian learner texts.

Kualitas Informasi Anatomi Radiograf pada Pemeriksaan Sternum dengan Variasi Penyudutan Arah Sinar

Background: The sternum's radiographic examination involves the use of Right Anterior Oblique (RAO) and Lateral projections. However, if a patient cannot undergo RAO projection, Left Posterior Oblique (LPO) projection in the supine position and adjusting the central ray angle are alternatives. On another note, deviations from perpendicular alignment among the central ray, image receptor, and object can lead to distortions in shape, impacting image quality. Hence, a study is conducted to investigate the impact of beam angle variations on the quality of anatomical information in sternum radiographs to identify the optimal angle that yields the highest quality anatomical information in sternum radiographs. Methods: This research was quantitative research employing an experimental methodology. Data collection involved employing a single-body phantom, which underwent three exposures at each of the specified angle variations Results: Based on the results of the Kruskal Wallis test, there were significant differences in the quality of radiographic anatomic information (p value 0.05). The angle with the most optimal quality of radiographic anatomical information was an angle of 25° to the left with a mean rank value of 11.00. Conclusions: There was a significant difference in the quality of radiographic anatomic information (p value 0.05). The angle with the most optimal quality of radiographic anatomic information was the angle with the highest mean rank value, which was an angle of 25° to the left.

Informasi Anatomi Mri Knee Joint Sekuen T2 Space FS dan PD Space FS pada Potongan Sagital Dengan Klinis Ruptur Anterior Cruciate Ligament (ACL)

Background: The T2 and PD sequences are the most sensitive sequences in evaluating injury to the ACL and surrounding soft tissue. Based on observations at the Radiology Installation of Dr. Sardjito Hospital Yogyakarta, the anatomical information generated in the MRI knee joint examination of the PD SPACE FS sequence is almost the same as the T2 SPACE FS sequence. The purpose of this study is to determine the differences in anatomical information and optimal sequences on MRI examination of the knee joint sagittal section sequences of PD SPACE FS and T2 SPACE FS in clinical ACL rupture.Method: This research was a quantitative study with an experimental approach, carried out at the Radiology Installation of Dr. Sardjito Hospital Yogyakarta on 10 samples with the application of different sequences for each sample, with the anatomy assessed by the informant namely ACL, meniscus, articular cartilage, and bone marrow. Further data analysis was carried out using the Wilcoxon test. Result: There was a difference in anatomical information between PD SPACE FS sequences and T2 SPACE FS sequences on MRI examination of the sagittal knee joint with clinical ACL rupture, with the ρ-value of the Wilcoxon test 0.001 or ρ-value0.05. This was due to differences in the use of TR and TE parameters in each sequence. The PD SPACE FS sequence was more optimal in visualizing anatomical information than the T2 SPACE FS sequence with an anatomical mean rank of meniscus 3.50, articular cartilage 3.50, and bone marrow 5.50, whereas in ACL anatomy the mean rank value was the same between the two sequences, which is 2.00. Conclusion: It was because the PD SPACE FS sequence had a more heterogeneous contrast difference than the T2 SPACE FS sequence so each anatomy could be well evaluated in the PD SPACE FS sequence.

Efektifitas Guided Imagery dan Terapi Musik Klasik Mozart terhadap Tingkat Kecemasan Ibu Hamil Trimester III di Puskesmas Penyengat Olak Muaro Jambi

According to WHO, 13% of pregnant women experience anxiety disorders, especially depression. In fact, it could reach 19.8% in developing countries. In Indonesia alone, 28.7% of 373 million pregnant women experience anxiety, and 52.3% of 679,765 occur on the island of Sumatra. Seeing the above phenomena, the purpose of this study was to determine the effectiveness of and Mozart classical music therapy on reducing the anxiety level of third trimester pregnant women at the Penyengat Olak Health Center in 2021. The method in this study is a quasi-experimental method, where measurements were carried out twice using a two-group pretest-posttest design. The population and sample in this study were 60 pregnant women in the third trimester at Penyengat Olak Health Center, Muaro Jambi Regency, using total sampling technique, 30 people in the group and 30 people in the classical Mozart music therapy group who met the inclusion criteria. Data analysis in this study used univariate and bivariate tests. The results of this study indicate that the level of anxiety before being given reached an average of 22.03% and decreased to an average of 17.63% after being given the treatment. Meanwhile, the level of fear before being given Mozart classical music therapy reached an average of 23.07% and decreased to an average of 10.60% after being given Mozart classical music therapy. With the mean rank value in reached 41.93. Meanwhile, Mozart's classical music therapy is 19.07. From the results obtained, it can be said that media is more effective than Mozart classical music therapy media. Thus, media can be used for further intervention for Puskesmas Penyengat Olak

The Effect of Slice Thickness Variation on The Anatomical Information of CT Scan Paranasal Sinus Coronal Section in Clinical Rhinosinusitis

CT Scan is an excellent technique in assessing anatomical structures, anatomical abnormalities, and visualising the presence or absence of pathological tissue in the four sinuses and their extensions. One of the important parameters on CT Scan is slice thickness. Slice thickness is a thickness of the slice that can be adjusted according to clinical requirements with a variation of values from 1 to 10 mm. The purpose of this study is to determine the effect of slice thickness variation on anatomical information of CT Scan paranasal sinus coronal section in rhinosinusitis and slice thickness variation that produces the most optimal anatomical information. This study is an analytical quantitative study with an experimental approach conducted on 10 samples who performed CT scan examination of paranasal sinus in rhinosinusitis using slice thickness variations of 0.6 mm, 1 mm, 1.5 mm and 2 mm. Based on the results of Friedman test and Wilcoxon Post Hoc test, the p value is 0.0001 (p<0.05) which means there is an effect of using slice thickness variation on anatomical information. The mean rank value in the Friedman test for slice thickness variations of 0.6 mm is 3.70, 1 mm is 3.13, 1.5 mm is 1.85, and 2 mm is 1.32 so based on the test results, slice thickness variation that shows the most optimal anatomical information is 0.6 mm.

A machine learning approach to predict the most and the least feed–efficient groups in beef cattle

The present study evaluated three strategies to find the optimum subset of DNA markers from the 50 K Illumina Bovine panel to classify beef cattle into the most and the least feed-efficient groups without using individual feed intake and performance measures. Residual feed intake (RFI) and 50 K single nucleotide polymorphisms (SNPs) genotype data of 4,057 beef animals from research and commercial herds were included. Initially, all cattle were ranked based on their phenotypic RFI values. Then different datasets were created by selecting animals from the 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, and 45% range of top and bottom of the ranked RFI values. SNP subsets were selected based on the top-ranked SNPs contributing to the variance of RFI (first strategy), selecting SNPs from the SNP subsets created in the first strategy (strategy 2), and extracting SNPs from 50k SNPs (strategy 3). Then eleven ML algorithms were employed to classify the most and the least feed-efficient groups using 260 datasets generated by combinations of ten RFI phenotype percentage groups and 6, 18, and 2 SNP subsets in the first, second and third strategies, respectively. There was a high degree of accuracy (>69%) for classifying animals in the range of 1% for all ML algorithms under the three strategies and different SNP subsets. Implementing the linear Support Vector Machine algorithm for 15 K SNPs obtained in the first strategy predicted the 1% of the most and the least feed-efficient animals with an accuracy of 84%. In the second strategy, selecting 524 SNPs from the 15 K SNPs subset outperformed the other strategies with an accuracy of 81% for 1% of the population using the Naive Bayes algorithm. It was concluded that a smaller number of SNPs (524) could be used to predict the most and the least feed-efficient animals with an acceptable accuracy to reduce the cost of selection for RFI using genomic information.

A Multi-Strategy Differential Evolution Algorithm with Adaptive Similarity Selection Rule

The differential evolution (DE) algorithm is a simple and efficient population-based evolutionary algorithm. In DE, the mutation strategy and the control parameter play important roles in performance enhancement. However, single strategy and fixed parameter are not universally applicable to problems and evolution stages with diverse characteristics; besides, the weakness of the advanced DE optimization framework, called selective-candidate framework with similarity selection rule (SCSS), is found by focusing on its single strategy and fixed parameter greedy degree (GD) setting. To address these problems, we mainly combine the multiple candidates generation with multi-strategy (MCG-MS) and the adaptive similarity selection (ASS) rule. On the one hand, in MCG-MS, two symmetrical mutation strategies, “DE/current-to-pbest-w/1” and designed “DE/current-to-cbest-w/1”, are utilized to build the multi-strategy to produce two candidate individuals, which prevents the over-approximation of the candidate in SCSS. On the other hand, the ASS rule provides the individual selection mechanism for multi-strategy to determine the offspring from two candidates, where parameter GD is designed to increase linearly with evolution to maintain diversity at the early evolution stage and accelerate convergence at the later evolution stage. Based on the advanced algorithm jSO, replacing its offspring generation strategy with the combination of MCG-MS and ASS rule, this paper proposes multi-strategy differential evolution algorithm with adaptive similarity selection rule (MSDE-ASS). It combines the advantages of two symmetric strategies and has an efficient individual selection mechanism without parameter adjustment. MSDE-ASS is verified under the Congress on Evolutionary Computation (CEC) 2017 competition test suite on real-parameter single-objective numerical optimization, and the results indicate that, of the 174 cases in total, it wins in 81 cases and loses in 30 cases, and it has the smallest performance ranking value, of 3.05. Therefore, MSDE-ASS stands out compared to the other state-of-the-art DEs.

The Effect of Rebozo Technique on the Length of Time for Active Phase I Labor in the Noemuti Health Center Area in 2023

There are still numerous problems in Indonesia, including prolonged labor that can murder mothers and infants. The rebozo technique is a physiological endeavor to prevent prolonged labor that can accelerate or shorten the active phase of labor. In 2023, the objective is to ascertain the impact of the rebozo technique on the duration of active phase I labor in the Noemuti Health Center Region.Research technique The research design is a Quasi-Experiment employing a One-Group Pretest-Posttest Design with a control. 30 respondents in the population, 15 in the experimental group and 15 in the control group as the cohort. The investigation lasted one month, from April 2022 to May 2023. The rebozo technique intervention group had a rank value of 3.8 and a standard deviation of 2.0, and the p value was greater than 0.005, so it can be concluded that there was a significant difference between the intervention group and the control group. With a p value of 0.003 0.005, mothers who were intervened by the rebozo technique were able to reduce labor time to 6 hours and reduce pain. The conclusion The rebozo technique should be communicated to mothers and families at term gestation regarding the intervention of the rebozo technique if done regularly, it is hoped that when the mother enters the inpartu period, it can accelerate the opening of the cervix and the decrease in the fetal head in laboring mothers during the active phase I, thereby shortening the duration of labor.

Efficacy of intermittent versus daily vitamin D supplementation on improving circulating 25(OH)D concentration: a Bayesian network meta-analysis of randomized controlled trials.

Vitamin D deficiency is a widespread issue globally, resulting in increased use of vitamin D supplements. However, it is unclear whether intermittent (weekly or monthly) vitamin D supplementation is as effective as daily supplementation in improving circulating 25-hydroxyvitamin D [25(OH)D] levels. Three databases including Medline, EMBASE, and the Cochrane Library were systematically searched up to 10 November 2020. The risk of bias was evaluated according to Cochrane Collaboration's tool for rating methodological quality assessment. Direct and indirect comparisons between interventions and controls were performed by a Bayesian network meta-analysis (NMA), where the mean difference (MD) and its 95% confidence interval (CI) were used to indicate the efficacy. This NMA analysis included 116 RCTs with a total of 11,376 participants. Generally, we observed that 25(OH)D concentrations were significantly elevated regardless of vitamin D supplementation frequency. Although the findings of SUCRA indicated that daily vitamin D supplementation had a higher rank value than intermittent supplementation when the supplement dosage was similar, no statistically significant pooled mean differences of 25(OH)D concentration were noted between the daily supplementation group and intermittent supplementation group. Additionally, weekly supplementation with a total of 600,000 IU vitamin D supplementation during 3 months had the best efficacy in elevating 25(OH)D concentration (pooled MD = 63 nmol/L, 95%CI: 49-77). To achieve optimal 25(OH)D concentration (>75 nmol/L), we recommend 60,000 IU vitamin D supplementation monthly (~2,000 IU/day). The efficacy of intermittent vitamin D supplementation was similar to daily supplementation. Coupled with its convenience, the frequency and dosage of intermittent vitamin D supplements were recommended to reach the optimal 25(OH)D level.Systematic review registration:https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=257257, PROSPERO CRD42021257257.

Evaluation of Online Teaching Effect of Vocational College Teachers Based on TOPSIS Technology and the Hierarchical Chi-Square Model

Internet teaching space requires teachers to be skilled in new technological platforms and tools and use new teaching interaction modes and evaluation means. Moreover, most front-line teachers have no online teaching experience before and bear the great pressure of online teaching. Hence, the evaluation of online teaching is challenge that has to be solved. A total of 169 teachers from six vocational colleges in Zhejiang Province were chosen as the research subjects in this study to improve the poor accuracy of the simple linear evaluation results of traditional classroom teaching. The relative efficacy of the online teaching quality of 169 teachers was estimated through the technique for order of preference by similarity to ideal solution (TOPSIS) technology. Next, the causal relationship between excellent achievement in online teaching training and the teaching effect of teachers with different titles was analyzed using the hierarchical chi-square model. Research results demonstrated that 12 level-2 indexes used in this study could depict the online teaching effect of university teachers comprehensively. The TOPSIS method calculated the scientific and reasonable ranking and evaluation values of 169 teachers. The Cochran–Mantel– Haenszel conditional independence test of the chi-square test presented significance. Our study of the instruments used to evaluate teacher success in online teaching shows that significant differences exist between evaluation grades for online teaching training and excellent achievement in online teaching training. This study can provide important references to comprehensively guide teachers to understand the value orientation of the new online teaching mode.

A clustering-based method for large-scale group decision making in the hesitant fuzzy set environment

The hesitant fuzzy set (HFS) has received wide attention because it can neatly describe the fuzziness and hesitancy of decision information with several possible values. Hence, this paper proposes a clustering-based method for resolving large-scale group decision making problems with HFSs. Considering the different risk attitudes of decision makers (DMs), a new score function of hesitant fuzzy elements (HFEs) is defined by introducing weight factors of all possible values. According to the scores of attribute values, a neighborhood-based approach is put forward to detect and process abnormal evaluations that may influence decision results. Afterwards, a two-stage clustering approach along with three algorithms is presented to cluster DMs into sub-groups. The first stage clusters DMs with finite possible cluster numbers based on the supporters of each DM and the separations between DMs. In the second stage, according to intra and inter-differences among DMs, an index is introduced to evaluate clustering results and select the best cluster number from the finite ones given in the first stage. Finally, by fusing the information densities and cluster sizes, cluster weights are determined and ranking values of alternatives are generated by aggregating evaluation information of the alternatives. At length, a case study is provided to illustrate the applications of the proposed method. Furthermore, the experiments and comparative analyses are conducted to show the effectiveness and superiority of the proposed method.

Antlion Optimizer Algorithm Modification for Initial Centroid Determination in K-means Algorithm

Clustering is a grouping of data used in data mining processing. K-means is one of the popular clustering algorithms, is easy to use, and is fast in clustering data. The K-means method groups the data based on k distances and randomly determines the initial centroid as a reference for processing. Careless selection of centroids can result in poor clustering processes and local optima. One of the improvements in determining the initial centroid on the k-means method is to use the optimization method to determine the initial centroid. The modified Antlion Optimizer (ALO) method is used to improve poor clustering in the initial centroid determination and as an alternative to determining the initial centroid in the k-means method for better clustering results. The results of the research on the use of the proposed method for determining the initial centroid provide an increase in clustering compared to the usual k-means and k-means++ methods. This is evidenced by the evaluation of the sum of intragroup distance (SICD) with UCI datasets, namely iris, wine, glass, ecoli, and cancer, in each method, the best SICD value was obtained in the proposed method. Then measuring the best SICD value for each method and dataset is measured by providing a ranking proving that the proposed method on the iris, wine, and cancer datasets gets the first rank, and on the ecoli and glass datasets the proposed method and the k-means++ method both get the first rank. From the average ranking value, the proposed method is ranked first, which provides evidence that the proposed method can improve the clustering results and can be an alternative method for determining the initial center of a cluster using the k-means method.

Analysis of COVID-19 Pandemic Effect on Ship Dismantling and Recycling Industry: An Assessment on Türkiye

_ The average age of the global merchant fleet has been declining with the increasing number of new ship constructions and dismantled ships. Although a noteworthy part of ship dismantling and recycling (SDR) facilities have been performed in Asian countries, such as India, Pakistan, and Bangladesh, SDR facilities have also been increasing in Türkiye. SDR facilities provide substantial economic income and employment opportunities. However, SDR facilities should have been performed in a certain systematic framework by taking necessary precautions since toxic substances from dismantled ships affect the environment and employees’ health. That’s why maritime authorities make an effort to sustain the SDR facilities in the best way in the sense of safety, health, and environment through the European Union (EU) Ship Recycling Regulation (SRR), Basel Convention, and Hong Kong Convention (HKC). In this paper, the global SDR facilities have been evaluated in terms of ship types and their gross tonnages (GTs) over the past 5 years among the leading countries. The number, type, economic life span, steel weight, and light displacement tonnage (LDT) of dismantled ships have been analyzed in Türkiye, especially throughout the COVID-19 pandemic process. To determine the most effective year in terms of SDR, the weighted aggregated sum product assessment (WASPAS) method has been utilized based on scrap steel price, LDT, economic life span, number of dismantled ships, and steel structure weight coefficient of the ship by maritime experts. The year 2017 has been highlighted as the most effective year with a .909 rank value in the last 5 years. In addition to analysis results, the largest amount of scrap steel has been acquired from dry cargo ships, which is the most dismantled ship type under data getting from the SDR facilities in Türkiye. Temporary bans on cruises due to the COVID-19 pandemic have caused more than triple increments in the number of passenger ships (PS) dismantling. This paper will be a quite notable resource for experts, academics, and industry stakeholders in order to explore and compare the SDR process. Keywords ship dismantling; ship recycling; COVID-19; scrap steel; passenger ships