Similar Ranking Research Articles

GENDER DIFFERENCES IN THE PERCEPTIONS OF SPORT COACHING STANDARDS AMONG MALAYSIAN SPORT COACHES

This study examined the perception of Malaysian sport coaches on the sport coaching standards (SCS). A total of 384 coaches (age = 28.84+4.02 years; male = 67.7%, female = 32.3%) responded to the survey via an online google document. Almost 59% of the respondents were club level coaches with 42% having 1-3 years coaching experience. Majority (56.2%) of the coaches was involved in soccer, frisbee, dodgeball, and futsal. The adopted research instrument consisted of 40 coaching standards and were categorized into eight domains. The items were measured through an 11-point (0-10) Likert scale. The results suggested that Malaysian coaches perceived the 40 standards (minimum = 6.94 and maximum = 9.00) as knowledge and abilities that effective coaches must possess. As for the domains, Malaysian coaches ranked the top three domains to be ‘Growth and Development’, ‘Teaching and Communication’, and ‘Skills and Tactics’ respectively. Similar ranking of the domain was also reported according to gender. Even though coaching education in terms of sport specific and sport science programs is in place in Malaysia, it is imperative for Malaysian Sport Coaching Academy and National Sport Associations to incorporate some coaching standards in their relevant coaching education curriculum.

Feature disentangling and reciprocal learning with label-guided similarity for multi-label image retrieval

Image retrieval usually faces scale-variance issues as the amount of image data is rapidly increasing, which calls for more accurate retrieval technology. Besides, existing methods usually treat pair-image similarity as a binary value which indicates whether two images share either at least one common label or none of shared labels. However, such similarity definition cannot truly describe the similarity ranking for different numbers of common labels when handling the multi-label image retrieval problem. In this paper, a Feature Disentangling and Reciprocal Learning (FDRL) method is introduced with label-guided similarity to solve the above multi-label image retrieval problem. Multi-scale features are first extracted by BNInception network and then disentangled to the corresponding high- and low-correlation features under the guidance of estimated global correlations. After that, the disentangled features are combined through a reciprocal learning approach to enhance the feature representation ability. Final hash codes are learned based on the global features derived from BNInception network and the combined features generated by reciprocal learning. The whole network is optimized by the proposed label-guided similarity loss function which aims to simultaneously preserve absolute similarity for hard image pairs and relative similarity for soft image pairs. Experimental results on three public benchmark datasets demonstrate that the proposed method outperforms current state-of-the-art techniques. The code is online here: ‘https://github.com/Yong-DAI/FDRL’.

New Members Selection for the Expansion of Energy Communities

Energy communities are key enablers for end-users to actively participate in the energy transition in a more consumer-centric context. This paper focuses on the expansion of existing energy communities that may need to select new members among a pool of candidates. Selection is based on heuristic methods for better explainability and to promote a transparent selection process from end-users’ perspectives. The proposed methodology is further verified with an accurate optimization-based energy management strategy. The member selection is performed in an iterative process where the best potential candidate is added as a new member of the energy community before running the same procedure over successive iterations. Simulations were performed for a complete month with a real community of six houses and nine potential candidates. The proposed rule-based method achieves similar ranks among candidates for two investigated metrics and return the same results as the more accurate optimization. Furthermore, the results show a hint on how to identify the best location (i.e., member) to install new assets that can contribute best to the energy community since it can boost the value brought by the candidates to the community. In that sense, the proposed method also serves as an investment decision support tool as well as a selection strategy for inhabitants of an energy community.

Radiographic Evaluation of Distal Radius Fracture Healing by Time: Orthopedist versus Qualitative Assessment of Image Processing

Distal radius fractures are among the most prevalent long-bone fractures in the body. Fracture healing assessment is based on clinical evaluation and radiological examinations. A lack of consensus exists regarding the radiographic criteria for fracture union. Our work examined the commonly used criteria for the assessment of fracture healing. Thirty-two patients, conservatively treated for distal radius fracture, participated in a prospective study. Enrolled patients followed protocol for 26 weeks. Four orthopedic surgeons with similar ranks were asked to evaluate three parameters of radiographic measurements for each set of radiographs, including callus formation, the presence of a fracture line, and bridging of fracture sites or sites of fracture edges in 70 radiographs. Ten patients were eligible for the study. The degree of agreement among surgeons was “good” (Cronbach’s alpha): callus formation—0.8, bridging of fracture sites—0.775, blurring of fracture line gap—0.795. A timeline based on the specific week and grading system was made. Radiographic detection of callus formation was seen after the second film, between 6 and 9 weeks, and an agreement among surgeons was achieved for more than half of the patients for the blurring of the fracture gap. The radiographic healing progression of the distal radius can be detected after 6 and 9 weeks in all three parameters with good agreement between different surgeons. A timeline graph such as the one that was made in this model can be used for the follow-up of patients’ fracture healing or early detection of non-union.

Who Gets Hired at the Top? The Academic Caste System Theory in the Planning Academy

This study is the first to examine detailed faculty demographics and impacts of elite hiring networks in the planning academy. Institutional prestige significantly shapes faculty placements. Nearly half of planning faculty graduated from Berkeley, Massachusetts Institute of Technology (MIT), University of California, Los Angeles (UCLA), Cornell, and University of North Carolina (UNC)-Chapel Hill. Faculty are predominantly hired in similar or lower ranking programs with little upward mobility, after accounting for demographics and program factors. While race and gender did not have a significant relationship to placements, the findings demonstrate how status-based inequities are perpetuated through elite programs and constrain faculty representation.

Ranking Info Noise Contrastive Estimation: Boosting Contrastive Learning via Ranked Positives

This paper introduces Ranking Info Noise Contrastive Estimation (RINCE), a new member in the family of InfoNCE losses that preserves a ranked ordering of positive samples. In contrast to the standard InfoNCE loss, which requires a strict binary separation of the training pairs into similar and dissimilar samples, RINCE can exploit information about a similarity ranking for learning a corresponding embedding space. We show that the proposed loss function learns favorable embeddings compared to the standard InfoNCE whenever at least noisy ranking information can be obtained or when the definition of positives and negatives is blurry. We demonstrate this for a supervised classification task with additional superclass labels and noisy similarity scores. Furthermore, we show that RINCE can also be applied to unsupervised training with experiments on unsupervised representation learning from videos. In particular, the embedding yields higher classification accuracy, retrieval rates and performs better on out-of-distribution detection than the standard InfoNCE loss.

Personalized Recommendation Algorithm for Interactive Medical Image Using Deep Learning

Personalized interactive image recommendation has several issues, such as being slow or having poor recommendation quality. Therefore, we propose an image personalized recommendation algorithm (IPRA) using deep learning to improve the time and quality of personalized interactive image recommendations. First, the feature subimage is obtained and converted into a one-dimensional vector using the convolution neural network model. Single input and single output functional and dual input and single output generalized functional network model are integrated into the model to improve the learning ability of nonlinear mapping and avoid overfitting during the training process; second, a one-dimensional vector is clustered using the fuzzy k-means approach and then translated into hyperbolic space; Finally, the Poincare map model is used to map the updated vector, the transformed vector is mapped using the PM model, and the image information is fed back to the two-dimensional plane, and the image recommendation set is formed based on the ranking of similarity, and the visual recommendation is presented to the user. The results show that the size of the convolution kernel is 2 × 2, and the image one-dimensional vector clustering can be better completed. The optimal value of F1 is 0.92, and the optimal value of average time is 11 s. The image recommendation quality is better, and the image recommendation can be formed according to the photographic similarity, which has good application value.

Household income mobility in India, 1993–2011

AbstractUsing a nationally representative longitudinal survey and both absolute and relative measures of mobility, we examine the differences in income mobility across social groups in rural India over 1993–2004 and 2004–2011. We find significant differentials in income mobility that mirror the historical Indian caste system hierarchy. Conditional on having similar rankings in base‐period income distribution, the Forward Hindu Caste households have the highest (lowest) probability of upward (downward) income mobility, followed by the Other Backward Caste, Scheduled Caste, and Scheduled Tribe households. Moreover, these differentials in mobility are only partially explained by differences in household characteristics. We find similar but smaller differentials in mobility across social groups in Urban India. We also find that urban population witnessed a higher (lower) upward (downward) mobility during 2004–2011 compared with rural population. Furthermore, we find similar differentials in mobility using consumption as a measure of well‐being.

Adherence, Compliance, and Diet Quality Among Popular Diet Followers

Objectives1. To compare diet quality of vegan, vegetarian, paleo, and whole-food plant-based (WFPB) diets; 2. To examine how adherence varies by diet and relates to quality and diet duration; 3. To examine how compliance varies by diet and relates to quality, adherence, and duration. MethodsAnalysis was conducted on a subsample of ADAPT participants (n = 1392) who completed a demographics and food frequency questionnaire (FFQ; DHQ-II). After excluding implausible energy intake (600 ≤ KCAL < 5,000), the final sample was 1291 (vegan [n = 355]; vegetarian [n = 90]; WFPB [n = 710]; paleo [n = 136]). Diet quality was measured with HEI and AHEI. Duration was categorized as short [<2 years (n = 501)]; moderate [2 to <7 y (n = 200)]; and long-term [7–10 + y (n = 592)]. A self-report adherence score was derived from questions targeting commitment to diet, ranging from 0–32. Compliance score for each diet was derived from reported intakes and DHQ-II (i.e., diet-specific restrictions), and data were %-based standardized. Models were adjusted for age and sex; select models were adjusted for diet duration. ResultsMean (SD) HEI score was high in all plant-based diets, highest for WFPB 76.7(5.8), vegan 75.7 (6.5), vegetarian 72.6 (9.2), compared to paleo 62.5 (8.5); and with a similar ranking for AHEI. Vegans had highest mean adherence 23.5 (2.8) and paleos the lowest 20.0 (3.3). Vegetarians had highest mean compliance 71% (9) and WFPB lowest 53% (10). Compliance was positively associated with HEI score for WFPB and vegan with one percentage point of compliance associated with a 6.4-point increase in HEI for WFPB (SE ± 2, p < .004) and 17.1 for vegan (SE ± 4, p < .0001). For every point increase in compliance, adherence improved (5.4 ± 0.8, p < .0001) in the full sample, and by diet group. Diet duration was significantly associated with adherence in all but vegetarian. No relationship was observed between duration and compliance within diet. ConclusionsWFPB and vegan diets had the highest diet quality; all four had higher mean HEI than reported average in US adults (HEI = 59). Findings suggest that even imperfect compliance to plant-based diets is associated with greater diet quality. Diet adherence is a complex component of eating behavior, however reported adherence appears to predict higher compliance and longer maintenance. Funding SourcesUSDA Cooperative Agreements; NIFA National Needs Fellowship.

Sürdürülebilir Tedarikçi Seçimi için Aralık Tip-2 Bulanık Kural Tabanlı BWM Yaklaşımı

Fuzzy logic is a theory based on human-specific approximate reasoning. Therefore, fuzzy logic applications can bring simple and more effective solutions to situations that classical methods cannot overcome. The type-1 fuzzy set is a set, which has a continuous (crisp) membership degree to which a membership degree between 0 and 1 is assigned, and is characterised by membership functions. Type-2 fuzzy sets, which have the power to express uncertainty better, are expressed by membership functions, where the membership degrees of each element belonging to that set also specify a fuzzy set.Therefore, type-2 fuzzy sets allow us to include the membership functions uncertainty in fuzzy set theory. Using expert knowledge and using sensitivity of human to reflect the level of the decision maker influence is expressed as a fuzzy rule based system. Recently, it has been seen that fuzzy rules are frequently used together with multi-criteria decision making (MCDM) methods. Again, combining fuzzy rules with type-2 fuzzy numbers is also found. In this study, the Best Worst Method (BWM), one of the MCDM methods, has been integrated with fuzzy rules based interval type-2. The developed hybrid method was defined as Interval Type-2 Fuzzy Rule-Based BWM (IT2 FRB BWM). The proposed hybrid method has an important place when there are alternatives with similar ranking positions. Thus, even if there is a small difference in each alternative, it will show the difference better (more sensitively). This makes the proposed hybrid method forceful and unique.The proposed approach has been applied to a sustainable supplier selection problem comparatively with the BWM. The results show that the IT2 FRB BWM approach is more successful in ordering alternatives than the classical BWM method.

An Adaptive Voltage Control Using Local Voltage Profile Mode and Similarity Ranking

Emergency voltage control provides a real-time online response to maintain the long-term voltage stability of a power system. Searching for an optimal emergency control solution is a hard combinatorial optimization problem because of the highly dynamic and nonlinear characteristics of power systems with discrete control variables. Additionally, real-time response is required. A new mode-matching–based adaptive voltage control is proposed in this article to achieve a fast response by exploring local voltage profile modes and the technique of similarity ranking. First, some typical emergencies are studied in advance, and their control knowledge is collected for preparation. The set of local voltage profile modes and their corresponding optimal control solutions are stored as knowledge for each emergency. Second, fast online control is realized and the control solution is configured according to a similarity ranking among local voltage profile modes. Furthermore, a learning process is applied to accumulate knowledge in case of unprepared situations. Thus, improved control performances in the future are ensured. This article depends on a new concept of local voltage profile mode with which knowledge preparation, emergency identification, configuration of control, and knowledge accumulation work together to perform an effective response. Simulations of the IEEE 39-bus system verify the outcomes of this voltage control scheme.

DCR‐Net: Dilated convolutional residual network for fashion image retrieval

AbstractFashion image retrieval is an important branch of image retrieval technology. With the rapid development of online shopping, fashion image retrieval technology has made a breakthrough from text‐based to content‐based. But there is still not a proper deep learning method used for fashion image retrieval. This article proposes a fashion image retrieval framework based on dilated convolutional residual network which consists of two major parts, image feature extraction and feature distance measurement. For image feature extraction, we first extract the shallow features of the input image by a multi‐scale convolutional network, and then develop a novel dilated convolutional residual network to obtain the deep features of the image. Finally, the extracted features are transformed into high‐dimensional features vector by a binary retrieval vector module. For feature distance measurement, we first use PCA to reduce the dimension of the extracted high‐dimensional vectors. Then we propose a mixed distance measurement algorithm combined with cosine distance and Mahalanobis distance to calculate the spatial distance of the feature vectors for similarity ranking, which solves the problems of poor robustness in complex background fashion image retrieval and the inefficiency calculation of Mahalanobis distance. The experimental results show the superiority of our fashion image retrieval framework over existing state‐of‐the‐art methods.

Use of Real-Life Safety Data From International Pharmacovigilance Databases to Assess the Importance of Symptoms Associated With Gadolinium Exposure.

ObjectiveRecent scientific publications have reported cases of patients who complained from a variety of symptoms after they received a gadolinium-based contrast agent (GBCA). The aim of this study was to appreciate the importance of these clinical manifestations in the overall population by assessing the weight of “symptoms associated with gadolinium exposure” (SAGE) among the bulk of safety experiences reported to major health authorities.Materials and MethodsSymptoms associated with gadolinium exposure were identified from a review of the scientific literature, and the corresponding preferred terms were searched in each system organ class (SOC) category recorded in the European and North American pharmacovigilance databases EudraVigilance (EV) and FDA Adverse Event Reporting System (FAERS), respectively. The numbers of SAGE per preferred term, and cumulatively per SOC, were recorded and their weights in the overall spectrum of adverse events (AEs) were determined for each GBCA.ResultsThe analysis of the selected AEs revealed a significantly higher SAGE weight for gadobenate dimeglumine (EV: 25.83%, FAERS: 32.24%) than for gadoteridol (EV: 15.51%; FAERS: 21.13%) and significantly lower SAGE weights for gadobutrol (EV: 7.75%; FAERS: 13.31%) and gadoterate meglumine (EV: 8.66%; FAERS: 12.99%). A similar ranking was found for most of the SOCs except for “nervous system disorders,” probably owing to a limitation in the methods of data selection. Furthermore, this analysis showed a greater percentage of reports mentioning a decrease in the quality of life of the patients when they were exposed to gadobenate dimeglumine or gadoteridol than to gadobutrol or gadoterate meglumine.ConclusionThis study showed that SAGE represent a significant percentage of the bulk of AEs reported to the health authorities for each GBCA. It provided real-life arguments suggesting that SAGE may be more prevalent with linear than macrocyclic GBCAs and that gadoteridol may present a higher SAGE risk than the other macrocyclic contrast agents.

Susceptibility of Cider Apple Accessions to European Canker-Comparison between Evaluations in Field Planted Trees and Rapid Screening Tests.

European canker, caused by Neonectria ditissima Bres., is an economically damaging fungal disease of apple. Breeding new cultivars with a high level of resistance to European canker is the main aim of apple breeding programs. Observations of symptoms in naturally infected trees were carried out in 400 apple accessions in Asturias (north-western Spain). Young and mature field planted trees were assessed under conditions highly conducive for N. ditissima development. The results demonstrated that juvenile trees (4-year-old) barely showed noticeable symptoms whereas a wide variability in the levels of resistance among accession was observed in mature trees (14-year-old). Around 28% of the locally maintained collection resulted to be highly resistant to this disease in the region. Field observations on mature trees were also compared to four rapid screening tests based on artificially induced lesions. Spearman correlation analysis using two resistance parameters revealed that none of the methods resulted in similar rankings of cultivar susceptibility as some accessions that were ranked as resistant for a given test turned out to be susceptible in the field. This study might suggest that whilst conventional resistance phenotyping techniques are time-consuming, the outcomes of this approach still seem the preferred option to assess the response to N. ditissima of apple accessions.

Consideration of a Tack Coat Bond Strength Test to Minimize the Potential for Slippage Failures on Flexible Airfield Pavements

Slippage failures on flexible pavements are a potential problem for airports given the large wheel loads and high tire pressures of modern aircraft. While not common, when slippage failures do occur the impact can be significant on flexible pavement performance. This paper has three components: (1) case studies of slippage failures that have occurred on airfield pavements; (2) a literature review of relevant interlayer bond strength test procedures; and (3) the results of an analytical, field, and laboratory research investigation. Component (3) was conducted to assess the suitability of interface bond strength tests to evaluate the sufficiency of interlayer bond strength between lifts of asphalt pavement, either new construction or an overlay of existing pavement. The analytical study found that for a fully bonded pavement (no-slip condition), subgrade strength was the primary factor affecting horizontal stress at the interface followed by wheel load. Field cores were obtained from recently constructed asphalt pavements and brought to the laboratory for monotonic direct shear testing to determine bond strength as well as monotonic indirect tensile testing to determine mixture strength. Testing was conducted at 77°F (25°C) and 136°F (58°C). Results showed better repeatability at room temperature with similar relative performance ranking to the critical case high temperature test. Bond strength was influenced by in-place mixture density. Overall, the bond strength test method was practicable for use in field laboratories for acceptance testing.

Inter-Provincial Differences in Potential Obstacles to Land Transfer Income to Support Rural Revitalization in China

This study analyzes the triple path obstacles of land transfer income in supporting rural revitalization under the premise of existing policy support. It is innovative to try to de-qualitatively analyze the obstacle factors and quantitatively measure the degree of obstacles. We examined the potential obstacles to land transfer income supporting rural revitalization in China, measured the potential comprehensive obstacle degree of 31 provincial-level administrative divisions’ land transfer income supporting rural revitalization, and constructed a way to resolve the differences. We employed inter alia the entropy method, ideal solution similarity ranking method, and cluster analyses. Potential obstacles were divided into nine types. The resolution path was constructed by combining the original and weighted values of various obstacle factors in the top three obstacle factors. It is of great significance to ensure the effective implementation of China’s “opinions on adjusting and improving the scope of land transfer income to give priority to supporting rural revitalization” (2020). This study addresses the theoretical gap in the analysis of possible paths needed to support rural revitalization in China.

Simultaneous selection for yield and stability of Chickpea (Cicer arietinum) germplasm through AMMI based parameters

Identifying a stable variety is essential before recommending it for cultivation at a particular location. In the present study, 23 chickpea (Cicer arietinum L.) genotypes were evaluated for pod yield per plot in randomized complete block design at Hebbal, Gouribidanur, and Nandyal during 2015–17. Among the three environments, Nandyal had the highest environmental index with a high mean yield. Pooled analysis of variance indicated significant differences due to environment, genotypes, and their interaction, necessitating the selection of genotypes with general and specific adaptation. AMMI (Additive main effects and multiplicative interaction) analysis extracted two significant principal components which explained all the variation. Univariate parametric measures like Francis’ coefficient of variation, Wricke’s ecovalence, Lin and Binns’ superiority index, Eberhart and Russel’s regression coefficient (bi), and deviation from linearity (S2di) were estimated along with AMMI based parameters, which include either one or both significant principal components. Most of these measures had similar rankings for genotypes. Rank sums were calculated for genotypes over these stability parameters, where DCP-92-3, BG-212, and KAK-2 had the best ranks. Simultaneous selection indices were constructed assigning equal weightage to yield and stability for each of the AMMI-based parameters, and the genotypes DCP-92-3, BG-212, and KAK-2 had the highest index values. All the analyses were performed in R studio. Based on the above measures, it can be concluded that these three genotypes are stable and can be cultivated to obtain stable yields across environments.

Adolescent anxiety and depression: burden of disease study in 53,894 secondary school pupils in the Netherlands

BackgroundPrevalence rates of anxiety and depression in adolescence are rising markedly in early adolescence. It is important to quantify the non-fatal disease burden of anxiety and depression, such that early interventions can be well targeted, and resources can be allocated in a just and optimal way. This study aimed to estimate the non-fatal disease burden of anxiety and depression with and without suicidal ideation in girls and boys aged 13, 14, and 15 years.MethodsParticipants were 53,894 secondary school pupils who completed health questionnaires between September 2018 and July 2019. A design-based approach was used for complex survey data with post-stratification weights and taking clustering at school-level into account. At individual level, disability weights (DWs) were calculated for each disorder. At population level, DWs were multiplied by the point-prevalence per one thousand population of the respective disorders to compute years lived with disability (YLD). DWs and YLD of anxiety and depression were calculated with and without adjustment for comorbid eating disorders, substance use disorders and somatic illnesses.ResultsThe unadjusted DW of depression with suicidal ideation (0.30) was greater than without suicidal ideation (0.26), and both were greater than the DW of anxiety (0.24). A similar ranking was obtained after adjusting for comorbidities. At population level, where the prevalence of the disorders come into play, the YLD disease burden was greatest for anxiety, followed by depression with suicidal ideation and depression without suicidal ideation with 17.40, 9.85, and 5.28 YLD per one thousand population, unadjusted for comorbidities. This pattern was the same after adjustment, but then the total YLD of depression with and without suicidal ideation was similar to the YLD of anxiety (12.47 and 12.46, respectively). Girls showed a significantly greater YLD burden of anxiety and depression than boys, but no differences were found between different age groups.ConclusionsFrom an individual clinical perspective, depression, especially when accompanied by suicidal ideation, was identified as a major health concern, especially in girls. From a public health perspective, both anxiety and depression, especially when accompanied by suicidal ideation, were identified as major drivers of disease burden, again most notably in girls.

Toxicity of curcumin nanoparticles towards alveolar macrophage: Effects of surface charges

Curcumin has been used for chronic lung diseases management due to its diversified molecular actions. However, the potential cytotoxicity which occurs in cells following the exposure to high concentrations of curcumin has been overlooked. This study evaluated the toxic events of curcumin nanoparticles (Cur-NPs) with alterable surface polarity in alveolar macrophages (NR8383). We aimed to establish the correlation between the toxicity of Cur-NPs with different surface charges and the internalization mechanisms of the NPs. Toxicity data showed that positively charged Cur-NPs (IC50: 9.77 ± 0.5 μg/mL) was the most potent against NR8383, followed by negatively charged Cur-NPs (IC50:13.33 ± 0.9 μg/mL) and neutral Cur-NPs (IC50:18.68 ± 1.2 μg/mL). Results from mitochondrial membrane potential, ATP content and intracellular ROS in NR8383 showed similar ranking to the toxicity assay. The predominant uptake pathway for positively and negatively charged Cur-NPs was via clathrin-mediated endocytosis, while neutral Cur-NPs was internalized via phagocytosis, micropinocytosis and clathrin-mediated endocytosis. Positively charged Cur-NPs mediates the cytotoxicity of NR8383 via lysosomal and mitochondrial-associated destabilization upon entry. In conclusion, the cytotoxicity of Cur-NPs on NR8383 is surface-charge dependent, which in turn is associated to the uptake pathway and localization of Cur-NPs in cells.

A Set of Comprehensive Evaluation System for Different Data Augmentation Methods

Data augmentation is an effective method to prevent model overfitting in deep learning, especially in medical image classification where data samples are small and difficult to obtain. In recent years, different data augmentation methods, such as those based on single data transformation, multiple data mixing, and learning data distribution, have been proposed one after another, but there has never been a systematic system to evaluate various data augmentation methods. An impartial and comprehensive data augmentation evaluation system not only can assess the benefits and drawbacks of existing augmentation approaches in a specific medical image classification but also can provide an effective research direction for the subsequent proposal of new medical image data augmentation methods, thereby advancing the development of auxiliary diagnosis technology based on medical images. Therefore, this paper proposes an objective and universal evaluation system for different data augmentation methods. In this method, different augmented methods are evaluated objectively and comprehensively in terms of classification accuracy and data diversity by using existing large public data sets. The method is universal and easy to operate. To imitate the prevalent small-sized data sets in deep learning, an equal-interval sampling technique based on similarity ranking is presented to select samples from large public data sets and construct a subset that can fully reflect the original set. The augmented data sets are then created using various data augmentation approaches based on the small-sized data sets. Finally, different data augmentation strategies are objectively and fully evaluated based on the comprehensive scores of classification accuracy and data diversity following data augmentation. The validity and feasibility of the suggested sampling method and assessment system in this study are demonstrated by experimental findings on numerous data sets.