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Fingerprints are widely used as effective personal authentication systems, because they constitute unique, robust, and risk-free evidence. Fingerprinting techniques refer to biometric procedures used for identifying individuals based on their physical characteristics. A fingerprint image contains ridges and valleys forming a directionally-oriented pattern. The robustness of the fingerprint authentication technique determines the quality of the fingerprint image. This study proposed an intelligent 12-layered Convolutional Neural Network (CNN) model using Deep learning (DL) for gender determination based on fingerprints. Further, the study compared the performance of this model to existing state-of-the-art methods. The primary goal of this study was to reduce the number of comparisons within a large database obtained from automatic fingerprint recognition systems. The classification process was found to be swifter and more accurate when analysis of the DL algorithm was performed. With reference to the criteria of precision, recall, and accuracy evaluation during classification, this proposed 12-layered CNN model outperformed the Residual Neural Network with 50 Layers (ResNet-50) and Dense Convolutional Network with 201 Layers (DenseNet-201) models. The accuracies obtained were 97.0%, 95.8%, 98.0%, and 96.8% for female-left, female-right, male-left, and male-right classes respectively, while achieving an overall accuracy of 94.0%. [ABSTRACT FROM AUTHOR]

Background Studies examining the potential impact of mothers' health during pregnancy on the health of their offspring often rely on self-reported information gathered several years later. To assess the validity of this approach, we analysed data from a national case–control study of childhood cancer (diagnosed <15 years) that collected health information from both interviews and medical records. Methods Mothers' interview reports of infections and medications in pregnancy were compared with primary care records. Taking clinical diagnoses and prescriptions as the reference, sensitivity and specificity of maternal recall along with kappa coefficients of agreement were calculated. Differences in the odd ratios estimated using logistic regression for each information source were assessed using the proportional change in the odds ratio (OR). Results Mothers of 1624 cases and 2524 controls were interviewed ∼6 years (range 0–18 years) after their child's birth. Most drugs and infections were underreported; in general practitioner records, antibiotic prescriptions were nearly three times higher and infections >40% higher. Decreasing with increasing time since pregnancy, sensitivity was ⩽40% for most infections and all drugs except 'anti-epileptics and barbiturates' (sensitivity 80% among controls). ORs associated with individual drug/disease categories that were based on self-reported data varied from 26% lower to 26% higher than those based on medical records; reporting differences between mothers of cases and controls were not systematically in the same direction. Conclusions The findings highlight the scale of under-reporting and poor validity of questionnaire-based studies conducted several years after pregnancy. Future research using prospectively collected data should be encouraged to minimize measurement errors. [ABSTRACT FROM AUTHOR]

A large body of research in the study of memory has accumulated to date on the part-list cuing impairment in recall. This phenomenon refers to the lower recall of studied information in the presence of some studied words provided as retrieval cues compared to when no cues are provided. We review the current literature on the part-list cuing impairment in recall and report a meta-analysis utilizing the procedural and statistical information obtained from 109 samples (N = 5,605). In each experiment, participants studied a list of words and subsequently performed a recall task either in the presence or absence of part-list cues. The meta-analysis shows that the part-list cuing impairment is a robust, medium-sized impairment (Cohen, 1988). This recall impairment was not significantly sensitive to the number of study items provided, the relationship among study items, the number of part-list cues provided, the amount of time provided for recall, or certain other factors of interest. Our analyses also demonstrate that longer retention periods between study and retrieval mitigate the part-list cuing impairment in recall. We discuss the implications of meta-analysis results for elements of experimental design, the findings of past literature, as well as the underlying theoretical mechanisms proposed to account for this impairment in recall and the applied consequences of this recall impairment. [ABSTRACT FROM AUTHOR]

Orthopaedic devices comprise nearly 20% of devices on the market and 12% to 20% of these devices undergo a recall within 10 years. More than 95% of these devices are approved without supporting clinical data through the Food and Drug Administration's 510(k) pathway. The risk of recall of orthopaedic arthroplasty devices approved through the 510(k) pathway has not been previously studied. The FDA 510(k) database was queried for orthopaedic devices approved between January 01, 2008 and December 31, 2018 and subsequently codified to hip and knee arthroplasty devices using product codes. The database included 904 arthroplasty devices during the study period, with hip and knee making up 53.7% (485) and 46.3% (419) of devices, respectively. Information regarding numbers, dates, and reasons for recall were recorded. Cumulative incidence function was conducted to compare the risk of recall between hip and knee arthroplasty. In total, 94 (19.4%) hip and 85 (20.3%) knee devices were recalled. The hazard of recall by 10 years for hip and knee arthroplasty devices was approximately 24%, with no statistical differences between each region. The most common causes of recall were process control and device design, accounting for 29.6% and 26.3% of recalls, respectively, with no significant difference between study groups. The risk of recall for arthroplasty devices is more than that previously understood. Improved postmarket surveillance strategies along with increased physician participation in detecting and reporting device safety issues are necessary to strengthen patient safety. [ABSTRACT FROM AUTHOR]

Populism is deeply embedded in a democratic society, and a thin ideology composed of many different concepts affects people's attitudes toward political elites and their vote choices. Based on voters' populist inclination, this paper analyzes the voting in the recall of the mayor and the by-election of Kaohsiung City in 2020. In the two different voting periods, the voters' populist inclination was affected by their demographic background and political attitudes. This study uses two sets of data to analyze the voting for the recall and the byelection. One is "A Voting Study of Kaohsiung City's Third Mayoral Recall" hosted by Tsai Chia-hung and conducted by the National Chengchi University's Election Study Center, and the other is "Taiwan's Election and Democratization Study, 2020-2024(I): Kaohsiung City Mayor By-Elections: Telephone Interview hosted by Chen Luhui" conducted by the National Chengchi University's Election Study Center. We found that during the recall period, populism was affected by the age and education of the voters. During the by-election period, the populist inclination was affected by voters' party identity, Taiwanese/Chinese identity, education and occupation. In addition, the populist inclination of Kaohsiung voters did not affect their decision to vote for recall. Their satisfaction with Han Guo-yu was the main reason for voters to vote for the recall and in the by-election, voters with lower populist inclination voted for Chen Chi-mai, but it did not influence voters to vote for Li Mei-jhen. Although Han Guo-yu helped Li Mei-jhen in the campaign, it did not affect the voters' choice. [ABSTRACT FROM AUTHOR]

Retrieval-induced forgetting (RIF) is typically observed in verbal memory tasks, although a few studies have observed RIF in visual spatial tasks. This leaves an open question as to whether RIF depends on semantic identity to link across semantic properties of objects, or whether RIF depends on access to the perceptual features of objects. To explore RIF of spatial positions, we report three experiments utilizing a continuous measure of the accessibility and precision for objects that were distinguished by their shape, color, and spatial region. After a study phase, half of the objects in a single-color category were selectively practiced for their spatial position, by requiring the object to be placed in the exact spatial position seen previously. Finally, all objects were probed for their spatial position at test. No RIF occurred for objects that shared only one color feature but were located within the same spatial region (in Experiment 1) or when objects shared the same color, but were located within different spatial regions (in Experiment 3). However, RIF did occur when objects shared the same spatial region and the same color (Experiment 2). Overall, the interim recall of the spatial positions of cue-objects impairs access to the position of other cue-objects within the same color category, but only when these groups had sufficient overlapping and competing features. The finding that RIF only occurs to the accessibility of spatial positions, not the precision of visual spatial memory, was interpreted as consistent with inhibitory theories of forgetting. [ABSTRACT FROM AUTHOR]

Constructive episodic retrieval processes play an adaptive role in supporting divergent thinking (i.e., creatively combining diverse bits of information) and means-end problem solving (i.e., generating steps to solve a social problem). However, the constructive nature of episodic memory that supports these adaptive functions also leads to memory error. In three experiments we aimed to identify a direct link between divergent thinking and means-end problem solving – as assessed in the Alternative Uses Task (AUT) and Means-End Problem Solving (MEPS) task – with the generation of false memories in the Deese-Roediger-McDermott paradigm. In Experiment 1, we replicated prior findings where false memory was positively correlated with performance on the AUT, and also showed for the first time that increased performance in the MEPS task is associated with increased false recall. In Experiment 2, we demonstrated that the link between false recall and performance on the MEPS task did not extend to other forms of problem solving, as assessed with the Everyday Descriptions Task (EDT). In Experiment 3, we showed that when the EDT was preceded by the MEPS task in an attempt to influence participants to engage in a similar episodic-problem solving strategy, performance in both tasks was correlated with false memory. These findings provide evidence for a direct link between the adaptive benefits of constructive episodic processes, in the form of enhanced divergent creative thinking and problem solving, and costs, in the form of increased memory error. [ABSTRACT FROM AUTHOR]

Panoramic views offer information on heading direction and on location to visually navigating animals. This review covers the properties of panoramic views and the information they provide to navigating animals, irrespective of image representation. Heading direction can be retrieved by alignment matching between memorized and currently experienced views, and a gradient descent in image differences can lead back to the location at which a view was memorized (positional image matching). Central place foraging insects, such as ants, bees and wasps, conduct distinctly choreographed learning walks and learning flights upon first leaving their nest that are likely to be designed to systematically collect scene memories tagged with information provided by path integration on the direction of and the distance to the nest. Equally, traveling along routes, ants have been shown to engage in scanning movements, in particular when routes are unfamiliar, again suggesting a systematic process of acquiring and comparing views. The review discusses what we know and do not know about how view memories are represented in the brain of insects, how they are acquired and how they are subsequently used for traveling along routes and for pinpointing places. [ABSTRACT FROM AUTHOR]

People receiving hematopoietic stem cell transplantation (HSCT) endure long phases of therapy and immobility, which diminish their physical activity (PA) level leading to physical deconditioning. One of the reasons is a lack of clarity on the part of those who work in oncology clinical settings of their role in assessing, advising, and referring patients to exercise. Therefore, our study investigates reported physical activity counseling behavior of health care professionals (HCPs) and the patient perspective on this topic. Physicians (N = 52), nurses (N = 52) physical therapists (N = 26), and patients receiving HSCT (N = 62) participated in a nationwide cross-sectional online-survey. Patients' preferred source of information concerning PA was determined. We examined HCPs self-assessed PA counseling behavior and patients' PA recall by assessing the use of the 5As (Ask, Advice, Agree, Assist, Arrange). Analysis of survey responses was descriptive. Univariate multinomial logistic regression examined whether sociodemographic factors and patient characteristics influence the response behavior. Physicians and PA specialists were patients' preferred source of information regarding PA. A large discrepancy between HCPs' perception and the degree to which HSCT patients recall advice became apparent; profound counseling steps like making referrals were less often recalled in our patient sample. Inactive patients reported to receive less basic PA counseling by physicians. Future research should identify the requirements to increase patients' recall concerning PA counseling in the setting of HSCT. Important messages about PA need to be made more salient to those who are less active and less engaged. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to investigate the efficacy of taking Mind Lab Pro, a plant‐based nootropic on memory in a group of healthy adults. Auditory, visual, visual working memory, immediate and delayed recall (DR) were assessed. Methods: The study employed a pseudo randomised, double blinded, placebo‐controlled design. A total of 49 healthy individuals completed the study with 36 in the experimental group and 13 in the control group. Participants ranged between 20 and 68 years with a mean age of 31.4 ± 14.4 years. Pre and post taking either the Mind Lab Pro supplement or placebo for 30 days. All participants completed the Wechsler Memory Scale Fourth UK Edition (WSM‐IV UK). Results: We found that the experimental group significantly improved in all memory subtests assessed (p < 0.05) whilst the control group only significantly improved in auditory memory and immediate recall (p = 0.004 and p = 0.014 respectively). A significant difference in immediate and DR was also found between the control and experimental group (p = 0.005 and 0.034 respectively). Conclusion: The use of Mind Lab Pro for 4 weeks improves memory with the experimental group significantly improving in all sub areas of memory as assessed by the WSM‐IV UK. [ABSTRACT FROM AUTHOR]

Drug recalls may impact treatment plans or access to suitable therapies. Thus, they inadvertently affect treatment outcomes. We aimed to examine the impact of recalls on patients' safety using pantoprazole-containing products recall as a case study in terms of the occurrence of potential drug-drug interactions (pDDIs). This retrospective study used de-identified electronic health records of adult patients who had a prescription for oral proton pump inhibitors (PPIs) including pantoprazole, esomeprazole, lansoprazole, or omeprazole from April 2020 through September 2021 from a large tertiary care hospital. The study outcome definition was the prevalence of pDDIs in PPIs users before and after the recall date (March 2021). Changes in the prevalence of pDDIs were modeled using interrupted time-series. The rate ratio of pDDIs in the 12 months before and 6 months after the recall was modeled using negative binomial regression. A total of 1,826 pDDIs were identified, and the median monthly prevalence of pDDI before the recall was 102.5 which increased to 115.5 after the recall. A change in the level of pDDIs occurred immediately after the recall date, followed by a gradual decrease over time. The rate of pDDIs was 69% higher after the recall compared to the baseline (rate ratio 1.69; 95% confidence interval, 0.75–1.91). Recall of pantoprazole-containing products was associated with a higher rate of pDDIs. However, the prevalence of pDDIs gradually decreased over time. We highlight the importance of planning of recall process and coordinating all potential stakeholders to avoid potential harms. Word count: 1450. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is a logical investigation of various algorithms and factual models that PCs utilize to carry out particular operations that are not clearly programmed. This paper aims to statistically analyze different machine learning algorithms, and compare and contrast their performance for credit card fraud detection. Algorithms used are Artificial Neural Networks(ANN), Sample Vector Machine (SVM), and Kth Nearest Neighbour (KNN), Decision Tree, Logistic Regression and Random Forest. All these above mentioned algorithms are compared on basis of performance measures. It is deduced that the random forest algorithm is the best algorithm. [ABSTRACT FROM AUTHOR]

The patients are generally encouraged to attend their recall visits at regular intervals of six months, irrespective of the individual's risk of developing dental disease. The major important role of the dental recall has been seen as the prevention and early detection of dental caries and periodontal disease in particular. The recommendation of a six-month recall interval has become a mandatory practice in dental clinics. Thus, this paper reviews about the guidelines relating to the recall of the patients, various types of recall systems and how we can establish a recall visit. In addition, the paper focuses about the ideal recall visit as well. [ABSTRACT FROM AUTHOR]

The concept of agricultural sustainability, specifically sustainable beef production, is not well established, and much misinformation frames this conversation. One way agricultural communicators can educate the public on this controversial topic is through infographics. Scholars have suggested recall, or the mental process of retrieving information from the past, as a technique to understand what someone has comprehended when exposed to new information. The Limited Capacity Model of Motivated Mediated Message Processing (LC4MP) provides insight into human information processing and recall, and it guided this study's development. The purpose of this study was to determine the effect various types of data visualizations used in infographics about agricultural topics have on college students' knowledge and ability to recall attributes from the data visualizations. An experimental study was conducted where participants viewed one of three infographics with embedded data visualizations and answered questions to understand their recall of information, design elements, and information recognition. The results show pictographs were significantly more effective in participant information recognition and the free and cued recall of design; however, it was not significant for free and cued recall of information. These findings add to the agricultural communication literature as they show how the type of data visualization can impact how viewers encode, store, and retrieve information. The researchers suggest agricultural communicators implement pictographs more frequently in infographic communications strategy. Additionally, agricultural communicators must begin to train students on the use of data visualization techniques in classroom settings. [ABSTRACT FROM AUTHOR]

We report a cross-cultural study investigating musical reminiscence bumps, the phenomenon whereby adults remain emotionally invested in the music they preferentially listened to in adolescence. Using a crowdsourcing service, 4,824 participants from 102 countries were each required to recall five songs (titles and artist names), resulting in a 24,120-song study. In addition, participants provided demographic information and answered questions relating to the songs they recalled, such as age first listened to, levels of nostalgia, and associated emotions. Song titles and artist names were cleaned and genre information established through fuzzy matching recalled information to songs within an open-source music encyclopedia. These data, plus participants' demographic information, allowed reminiscence bumps differentiated by age, sex, country, and genre preference to be explored. Recency-bias effects of recalled songs were also investigated. Results demonstrated that the musical reminiscence bump phenomenon is common to all age groups and both sexes, pervasive across all countries, and is not restricted to particular genres. In sum, musical reminiscence bumps appear to be biologically and culturally ubiquitous. [ABSTRACT FROM AUTHOR]

Recently, Panama wilt disease that attacks banana leaves has caused enormous economic losses to farmers. Early detection of this disease and necessary preventive measures can avoid economic damage. This paper proposes an improved method to predict Panama wilt disease based on symptoms using an agro deep learning algorithm. The proposed deep learning model for detecting Panama wilts disease is essential because it can help accurately identify infected plants in a timely manner. It can be instrumental in large-scale agricultural operations where Panama wilts disease could spread quickly and cause significant crop loss. Additionally, deep learning models can be used to monitor the effectiveness of treatments and help farmers make informed decisions about how to manage the disease best. This method is designed to predict the severity of the disease and its consequences based on the arrangement of color and shape changes in banana leaves. The present proposed method is compared with its previous methods, and it achieved 91.56% accuracy, 91.61% precision, 88.56% recall and 81.56% F1-score.

Abstract Background This study aims to investigate growing Internet use in relation to memory and cognition. Though literature reveals human capability to utilize the Internet as a transactive memory source, the formational mechanisms of such transactive memory systems are not extensively explored. The Internet’s comparative effects on transactive memory and semantic memory are also relatively unknown. Methods This study comprises two experimental memory task survey phases utilizing null hypothesis and standard error tests to assess significance of results. Results When information is expected to be saved and accessible, recall rates are lower, regardless of explicit instructions to remember (Phase 1, N = 20). Phase 2 suggests the importance of order of attempted recall: depending on whether users first attempt to recall (1) desired information or (2) the information’s location, subsequent successful cognitive retrieval is more likely to occur for (1) only desired information or both desired information and location thereof or (2) only desired information’s location, respectively (N = 22). Conclusions This study yields several theoretical advances in memory research. The notion of information being saved online and accessible in the future negatively affects semantic memory. Phase 2 reveals an adaptive dynamic—(1) as Internet users often have a vague idea of desired information before searching for it on the Internet, first accessing semantic memory serves as an aid for subsequent transactive memory use and (2) if transactive memory access is successful, the need to retrieve desired information from semantic memory is inherently eliminated. By repeatedly defaulting to first accessing semantic memory and then transactive memory or to accessing transactive memory only, Internet users may form and reinforce transactive memory systems with the Internet, or may refrain from enhancing and decrease reliance on transactive memory systems by repeatedly defaulting to access only semantic memory; the formation and permanence of transactive memory systems are subject to users’ will. Future research spans the domains of psychology and philosophy.

Background Even with all the care taken during the production process, the pharmaceutical industries are still subject to manufacturing medicines with quality deviations, generating commercialized products without the required quality and necessitating their subsequent recall from the market. The objective of this study was to evaluate the reasons that led to the recall of medicines in Brazil in the period evaluated. Methods This is a descriptive study (using document analysis), on the recall of substandard medicines registered on the website of the National Health Surveillance Agency (ANVISA), from 2010 to 2018. The variables studied were the type of medicine (reference, generic, similar, specific, biological, herbal, simplified notification, new and radiopharmaceutical), type of pharmaceutical dosage form (solid, liquid, semi-solid and parenteral preparation), and reason for recall (Good manufacturing practices, quality and quality/good manufacturing practices). Results A total of n = 3,056 recalls of substandard medicine were recorded. Similar medicines had a higher recall index (30.1%), followed by generics (21.3%), simplified notification (20.7%) and reference (12.2%). Different dosage forms had similar recalls: solids (35.2%), liquids (31.2%) and parenteral preparations (30.0%), with the exception of semi-solids (3.4%). The reasons for the highest occurrences were related to good manufacturing practices (58.4%) and quality (40.4%). Conclusion The probable cause for this high number of recalls is the fact that, even with all the quality controls and processes in accordance with good manufacturing practices, errors can occur, both human and in automated processes, thus causing the release of batches that should not have been approved. In summary, it is necessary for manufacturers to implement a robust and well structured quality system in order to avoid such deviations, and it is up to ANVISA to apply greater oversight in the post marketing of these products.

The article is devoted to the study of the process of brand awareness formation under the influence of mass media. Knowing the parameters of this process allows us to answer the question of how many times a person must meet a brand in the media environment in order to remember it, and what extent of brand mentioning in the media must be achieved in order to get a certain level of its popularity. Two aspects of awareness are singled out: recall (knowledge without prompting) and recognition (knowledge with prompting). An empirical study was carried out, which made it possible to isolate and measure, on a sufficient level of accuracy, the influence of the Russian central press on the brand awareness of more than 20 sociological companies in Russia over a period of 15 years. It showed that the presence of a brand in the press is significant, and it affects linearly both forms of awareness – recall and recognition. A strong linear relationship allows us to calculate the effective frequency of introductions and to build a predictive model for the formation of brand awareness under the influence of the media, as well as, using plausible assumptions, to estimate the effective frequency of contacts.

Purpose: Free recall tasks target cognitive and linguistic processes such as episodic memory, lexical access, and retrieval. Consequently, the free recall paradigm is suitable for assessing memory deterioration caused by aging, which can be influenced by stimulus presentation modality. Hence, the present study aimed to determine if aging has an effect on visual and auditory recall abilities. Materials and Methods: Twenty young adults (mean age: 25.4 ± 0.99) and older adults (mean age: 63.3 ± 3.51) participated in the study. Participants performed a free recall task under two conditions – related and unrelated and two modalities – visual and auditory where they were instructed to recall as many items as possible with no specific order and time limit. Results: Free recall performance was calculated as the mean number of correctly recalled items. Although younger participants recalled a higher number of items, the performance across conditions and modality was variable. Conclusions: The findings of the present study revealed an age-related decline in the efficiency of episodic memory, which is crucial to remember recent events.

In this paper, the author analyzes the institution of recall of a mayor in the legal system of the Republika Srpska by critically examining the provisions of two legislative acts: The Election Act of Republika Srpska and the Local Self-government Act of Republika Srpska. The institution of recall has a serious democratic potential because it is an instrument enabling political control of mayors by citizens. However, the current legal solutions in Republika Srpska limit this potential because they do not encourage citizens to use this instrument of political control, which is not the case with political parties. Given the fact that the decision on initiating the recall procedure depends on a local assembly, recall has very often been used as a tool for political confrontation between a local assembly and a mayor, while the citizens have remained a second-class political subject. The Election Act of Republika Srpska does not contain adequate provisions on the protection of rights of citizens and political subjects during the recall procedure. In fact, this issue is relatively unregulated or underregulated. The Local Self-government Act of Republika Srpska protects a local assembly which triggers an unsuccessful recall procedure since there is no legal guarantee that such a local assembly would be dissolved. The author proposes different solutions which should improve the regulation of this legal institute and reduce the possibility of turning it into a tool for political manipulation.

Background. The relevance of the proposed methodology for recalling substandard products from the pharmaceutical market is determined by the requirements of GOST R ISO 9001-2015 and GOST R ISO 10393-2014. The aim of the work is to choose methods for identifying poor-quality products in the pharmaceutical market and develop a procedure for recalling them. Materials and methods. To solve this problem, the method of the matrix of consequences and probabilities was used according to the indicators "risk probability" and "risk impact level". Results. In the course of the work, a methodology was proposed for recalling poor-quality products from the pharmaceutical market, which is an integral part of the quality management system for pharmaceutical products. Conclusions. The positive results of the work open up the prospect of effective application of the methodology as a basis for organizing the recall management process in any pharmaceutical industry or for improving an existing process.

The nucleus reuniens (RE) is situated in the midline thalamus and provides a key link between the hippocampus and prefrontal cortex. This anatomical relationship positions the Re as an ideal candidate to facilitate memory consolidation. However, there is no evidence that this role extends beyond spatial memory and contextual fear memory, which are both strongly associated with hippocampal function. We, therefore, trained intact male Long–Evans rats on an odor–trace–object paired-associate task where the explicit 10-s delay between paired items renders the task sensitive to hippocampal function. Neurons in the RE showed significantly increased activation of the immediate early gene (Zif268) when rats were re-tested for previous non-spatial memory 25 days after acquisition training, compared to a group tested at 5-days post-acquisition, as well as a control group tested 25 days after acquisition but with a new pair of non-spatial stimuli, and home cage controls. The remote recall group also showed relatively augmented IEG expression in the superficial layers of the medial PFC (anterior cingulate cortex and prelimbic cortex). These findings support the conclusion that the RE is preferentially engaged during remote recall in this non-spatial task and thus has a role beyond spatial memory and contextual fear memory.

Diabetes is a chronic disease that occurs when the pancreas no longer produces insulin or when the body cannot effectively use the insulin it produces. The aim of this study is to analyze and compare the classification performance on diabetes patient dataset using four distance metric algorithms in the K-Nearest Neighbor (K-NN) method. Based on previous research, the performance values obtained were not sufficiently high, not exceeding 80%. Therefore, some actions are needed with the hope of obtaining new performance values and making comparisons with previous studies. Based on the test results using the confusion matrix, the accuracy level using Euclidean distance measurement obtained the best performance value at k=17 with 10-k fold, with an accuracy of 85.71%, precision of 86.24%, recall of 85.71%, and F-measure of 85.12%. The Manhattan distance measurement obtained the best performance value at k=25 with 10-k fold, with an accuracy of 85.53%, precision of 85.54%, recall of 85.53%, and F-measure of 85.10%. The Minkowski distance measurement obtained the best performance value at k=17 with 10-k fold, with an accuracy of 85.71%, precision of 86.24%, recall of 85.71%, and F-measure of 85.12%. On the other hand, the Hamming distance measurement obtained the best performance value at k=23 with 10-k fold, with an accuracy of 75.32%, precision of 79.27%, recall of 75.32%, and F-measure of 71.45%.

Purpose: The purpose of this paper is to derive into practical recommendations from multisensory stimulation with virtual reality (VR) and scent to help educators develop effective teaching strategies geared toward aspects of the learning experience, recall, and creativity in a stereotypical learning context. Design/methodology/approach: The paper is based on a randomized experiment in which student participants were subdivided into three treatment groups and one control group. Each group was stimulated by a different combination of visual, auditory, and olfactory stimuli (2D SMELL, VR, and VR SMELL) and the outcomes were compared against those of the control group (2D). Consistent with the Cognitive Theory of Multimedia Learning, hypotheses were constructed to study the effect of different combinations of stimuli on the learning experience and learning outcomes related to recall and creativity in a stereotypical learning context. Findings: Traditional video content alone and bundled with a coherent olfactory stimulus prompted higher self-reported ratings of perceived quality of the sensory experience. Olfactory stimulus in combination with either VR or a traditional video prompted higher self-reported ratings on perceived immersion. In a stereotypical learning context, the highest recall scores were achieved with traditional video alone. Both VR alone and bundled with an olfactory stimulus resulted in enhanced creativity. Research limitations/implications: The findings of this study should be interpreted in the context of adopting multisensory stimulations combined with VR technology as part of stereotypical learning contexts. Most professional educators do not have robust knowledge or experience in using build-on-purpose multisensory stimuli but are increasingly engaged in using multisensory tools such as VR, as part of their teaching practice. In relation to recall, the results are consistent with the hypothesis that in a stereotypical learning context, a multisensory experience involving VR and olfactory stimuli can be related to an undesired cognitive load for learners. There exists a possibility that the low-technical version of the VR goggles used, as well as the contents of the instructional video may have influenced the learning outcomes in terms of recall. Hence, future research should consider such aspects and focus on richer learning contexts. Originality/value: This work offers practical recommendations for instructional design strategies aiming to create multisensory stimulations with VR and olfactory components to foster a richer learning experience and enhanced learning outcomes, under the assumptions of a stereotypical learning context. [ABSTRACT FROM AUTHOR]

Background: The US Food and Drug Administration (FDA) regulates goods accounting for 20% of US consumers' total expenditure. The agency's potential susceptibility to corporate lobbying and political influence may adversely affect the its abilities to fulfill its duties as a vital federal agency. This study assesses whether the FDA's product recall classifications in recall scenarios are influenced by firms' lobbying activities. Methods: The universe of all FDA recalls between 2012 and 2019 is obtained from the FDA's website. Firm names are matched to federal-level lobbying data obtained from the Center for Responsive Politics – a non-profit and nonpartisan organization that tracks lobbying expenditures and campaign contributions. Analyses are conducted using ordinary-least-squares regressions, in which the dependent variable is recall classification and independent variables are three different measures of firms' lobbying activities in the one year prior to the recall. Results: Firms that engage in lobbying appear more likely to receive favourable classifications from the FDA. When examining the above results by product type, we find that classification of food recalls seems to be subject to lobbying influence, but the same does not appear to be true for drug and device recalls. Evidence is consistent with the conjecture that the distinction between medical and food firms may be a result of medical firms targeting lobbying efforts at FDA approvals, rather than recalls. Conclusions: Between 2012 and 2019, the FDA's product recall classifications seem to be significantly influenced by firms' lobbying activities. Lobbying firms appear to have received more favorable (i.e., less severe) recall classifications compared to non-lobbying firms. [ABSTRACT FROM AUTHOR]

Human health issues require estimation of heart rhythm anomalies, brain wave pattern abnormalities, blood parameter outliers, social media analysis, and more. Researchers propose many deep learning models to estimate these issues, and each uses neural optimizations to classify input parameters into disease types. But most of the existing models are either highly complex, or are not capable of capturing multiple body parameters, in order to perform comprehensive disease diagnosis. To overcome the challenges of feature representation and classification, this text proposes design of a high-density feature representation model for multidomain analysis of human health issues. The proposed model collects patient-specific datasets from multidomain sources like blood reports, electrocardiograms (ECGs), electroencephalograms (EEGs), social media posts, and physiological parameters to generate comprehensive health feature vectors. Fourier, Wavelet, Convolutional, and Gabor components transform vectors into multimodal feature sets. Representing the input vector into these components improves feature density, which helps identify multiple disease types. Bee Colony Optimization Models estimate high-variance feature sets using these features. A 1D CNN Model classifies these sets to help identify disease classes. The model performed well in accuracy, precision, recall, and AUC (area under the curve) tests for heart, brain, and psychological issues, making it suitable for clinical use. Compared to standard disease classification models, it improved accuracy, precision, recall, and fMeasure by 3.2%, 3.5%, 2.9%, and 2.5%, making it useful for real-time deployments. [ABSTRACT FROM AUTHOR]

There are multiple distinct sources through which numerous news contents that occur in digital medium tend to converge. Web contents constitute massive number of features. Complete coverage of all kinds of news is absolutely vital to retain customer confidence and to have a competitive edge over other news agencies. Aggregating such massive news content from different heterogeneous sources requires an integration of convergent computing. Classification of these online news is a challenging task in the age of Internet where news keeps flowing from several heterogeneous sources. Due to constant rise in manipulation of web contents, accurate classification of digital news is the need of the hour. Precise detection of specific news into their respective class is a major challenge in recent times. In this scenario, the need of an automated predictive-based approach can be of great use in effective organization and classification of news in a pool of web portals. This research study comprises the application of Bernoulli model to determine the effectiveness of multi-class digital news categorization that arrive in real time. The system model presented in this analysis was evaluated using python, and the result was demonstrated using six distinct classes of news with a 6000 feature size dataset from TagMyNews dataset. The classification accuracy using the Bernoulli model was computed to be 98.4%, while the evaluated precision metric was 92.7%, and recall value was 90.6%. The F-Score metric generated an optimum value of 91.4%. The execution time for Bernoulli model was only 12 s. The computed result using Bernoulli model was compared with some other related renowned existing works and the results generated by Bernoulli model gave optimum performance and the news classification efficiency is highly enhanced. [ABSTRACT FROM AUTHOR]

Recently, Panama wilt disease that attacks banana leaves has caused enormous economic losses to farmers. Early detection of this disease and necessary preventive measures can avoid economic damage. This paper proposes an improved method to predict Panama wilt disease based on symptoms using an agro deep learning algorithm. The proposed deep learning model for detecting Panama wilts disease is essential because it can help accurately identify infected plants in a timely manner. It can be instrumental in large-scale agricultural operations where Panama wilts disease could spread quickly and cause significant crop loss. Additionally, deep learning models can be used to monitor the effectiveness of treatments and help farmers make informed decisions about how to manage the disease best. This method is designed to predict the severity of the disease and its consequences based on the arrangement of color and shape changes in banana leaves. The present proposed method is compared with its previous methods, and it achieved 91.56% accuracy, 91.61% precision, 88.56% recall and 81.56% F1-score. [ABSTRACT FROM AUTHOR]

• Orthopaedic trauma patient recall of basic elements of injury and treatment course is poor, and likely impacts adherence to the postoperative care plan. • Overall, only 64% of questions regarding details of injury and treatment recommendations were answered correctly. • Only 66% of patients reported adherence to weightbearing and DVT prophylaxis recommendations. Orthopedic trauma patients may have poor recall of their injuries and treatment. This may lead to poor adherence to instructions. The purposes of this project were to quantify recall about injury and treatment information, and to assess adherence to postoperative instructions and satisfaction with care. A prospective cohort of 110 consecutive adult orthopaedic trauma patients treated for acute injury at a Level 1 trauma center were included. All had undergone surgical treatment of fractures of the pelvis or lower extremity. A brief survey to assess patient recall about injury and treatment knowledge, adherence to weightbearing and DVT recommendations and to evaluate patient satisfaction was administered during the first post-hospital clinic visit. Patients correctly answered 64% of recall-oriented questions. 82% and 83% of patients, respectively, reported adherence to their weightbearing restrictions and their DVT prophylaxis regimen, while 66% of patients reported adherence to both. Forty-two percent of non-adherent patients could not remember their weightbearing restrictions, while 78% of non-adherent patients could not remember their DVT prophylaxis regimen. Average patient satisfaction was 4.3 (range 1–5), with 15% of patients indicating neutral sentiment or dissatisfaction with their care. Orthopaedic trauma patients have poor recall, which likely reduces postoperative adherence and may impair patient satisfaction. A postoperative educational protocol focused on improving patient recall may be useful. Level of Evidence: Level 4, prognostic [ABSTRACT FROM AUTHOR]

Background: The ability to accurately recall specific reproductive health events is an integral aspect of medical decision making and evaluating a female's overall health and wellness across their lifespan. The Health and Reproductive Survey (HeRS) was developed to recall reproductive events and environmental influences on reproductive characteristics throughout the lifespan of a female. This study aimed to determine how reliably women recall certain events during menarche and early reproductive years. It was hypothesized that age at menarche, hormonal contraceptive use, and physical activity would be recalled reliably among all age ranges, while the recall reliability for cycle regularity and length would be more inconsistent with advancing age. Materials and Methods: A total of 144 participants (age: 32.73 ± 11.92), completed the HeRS on two occasions spaced 4 months apart to investigate recall reliability. Cohen's kappa coefficient was used to assess the consistency of categorical responses and 95% limits of agreement were used for continuous data. Results: Although physical activity changes had greater variability than anticipated (0.79), the recall reliability among the youngest (1) and oldest (0.89) age groups was high, and females were able to consistently recall the age of menarche (0.83), physical activity level (0.9), cessation of period during early reproductive years (0.91), and birth control use following menarche (0.85) and during the early reproductive years (0.9). Conclusions: The HeRS is a useful tool for reliably recalling reproductive history and physical activity participation across multiple age ranges and can be utilized to gather crucial information throughout the reproductive lifespan. [ABSTRACT FROM AUTHOR]

Object recognition memory (ORM) allows identification of previously encountered items and is therefore crucial for remembering episodic information. In rodents, reactivation during recall in the presence of a novel object destabilizes ORM and initiates a Zif268 and protein synthesis-dependent reconsolidation process in the hippocampus that links the memory of this object to the reactivated recognition trace. Hippocampal NMDA receptors (NMDARs) modulate Zif268 expression and protein synthesis and regulate memory stability but their possible involvement in the ORM destabilization/reconsolidation cycle has yet to be analyzed in detail. We found that, in adult male Wistar rats, intra dorsal-CA1 administration of the non-subunit selective NMDAR antagonist AP5, or of the GluN2A subunit-containing NMDAR antagonist TCN201, 5 min after an ORM reactivation session in the presence of a novel object carried out 24 h post-training impaired retention 24 h later. In contrast, pre-reactivation administration of the GluN2B subunit-containing NMDAR antagonist RO25–6981 had no effect on ORM recall or retention but impeded the amnesia caused by Zif268 silencing and protein synthesis inhibition in dorsal CA1. Our results indicate that GluN2B-containing hippocampal NMDARs are necessary for ORM destabilization whereas GluN2A-containing NMDARs are involved in ORM reconsolidation, and suggest that modulation of the relative activity of these receptor subtypes during recall regulates ORM persistence. [ABSTRACT FROM AUTHOR]

This research explores neural network models' performance and adaptability in the context of the colorectal histology dataset as it pertains to the categorization of textures. Inception, VGG19, and MobileNet, together with their federated variations, are among the models being examined. The study includes a detailed evaluation, parameter analysis, and training information. VGG19 stands out as a particularly noteworthy high performance, with remarkable accuracy, precision, and recall. Due to its lightweight design, MobileNet performs less well, but its potential is enhanced by the addition of federated learning. The accuracy and precision of federated versions of Inception, VGG19, MobileNet, and a Lightweight MobileNet model are competitive, with FL-Lightweight MobileNet achieving outstanding results. The work has important ramifications for the field of medical image analysis since it shows how federated learning may balance the need for data confidentiality and privacy with model performance. This study marks a turning point in the development of medical imaging by opening the door to in-depth investigation into the complex interactions across federated paradigms. Furthermore, these results provide a compelling story in the wider discussion of how cutting-edge technologies and the pressing needs of contemporary healthcare might work together. [ABSTRACT FROM AUTHOR]

Background: The emotion of fear bears a survival significance enabling animals to be highly vigilant in contexts of uncertainty. Expectedly, not all information available in the environment can be processed. Given that a major goal is to get rid of the source of fear in a fearful situation, the current study therefore hypothesized that information relating to the fear source would be better attended to and recalled than contextual information, which was validated by comparing participants' recall of different types of information presented in a fearful VR gaming context. Methodology: Fifty-four participants were divided into two groups and finished a specific segment of a VR game with different fear levels. Results: No significant difference in recall performance on the questions about the appearance of the fear source and the questions about the interaction with the source was observed between the fear and less-fear groups. Yet, contextual information was better recalled than information related to the fear source in both groups. Regardless of whether the contextual information was provided beforehand or not, participants still tended to focus primarily on the environment in the gameplay. Also, the immersiveness of the gameplay was positively related to participants' fear level. Discussion: The tendency of attending to contextual information was speculated to be unconditional, in line with the risk calculation observed in prey when situated in less familiar contexts. It was suggested to further validate the present primarily findings by adopting VR headset with eye-tracking function in future studies. Lastly, an understanding of fear's effect on memorization might also provide some information for developing future survival horror games. [ABSTRACT FROM AUTHOR]

The human body's temporal fluctuation is referred to as human activity. The preservation of cultural heritage, the development of video recommendation systems, the support of learners via tutoring systems will benefit from the capture and evaluation of dance-related multimedia content. Because of its detailed hand gesture, Indian classical dance (ICD) classification is still an enthralling field of study. This provides a framework for analyzing various computer vision and deep learning ideas. Automated teaching solutions across all disciplines, from traditional to online forums, become unavoidable through changes in learning habits. ICD also becomes a crucial component of a thriving culture and heritage that must be updated and preserved at all costs. The dance involves complex positions like self-hands-occlusion and full-body rotation. The main objective of this study is, in the Bharatanatyam dancing style we proposed a framework for categorizing hasta mudras. Our Convolution Neural Network - Long Short Term Memory (CNN-LSTM) deep knowledge architecture for Indian Classical Dancing (ICD) categorization now includes a new hand posture signature. By guessing where people's hands would be, we rated dance performances. This architecture assesses hand poses using information and information pruning, while a dance instructor application assesses the time and accuracy of student dances. 252 YouTube videos of the Bharatanatyam dance form has been used to make up the dataset used in our research.This study offers a methodology with three-phase deep learning techniques. Then, using the pre-trained paradigm TensorFlow EfficientNet –UNet, which aids us in determining any hand position within the frame, we extracted the appropriate joint locations of the hands from each video frame. Then, cosine similarity was used to identify or correlate the indicated action factors. Finally, using key details from the hand pose, we categorized it and trained the Convolution Neural Network - Long Short Term Memory (CNN-LSTM) network structure using the classification system's training dataset. Regarding factors like accuracy, F1-score, AUC curve, recall and precision, the proposed CNN-LSTM structure for classifying hand mudras is compared with Convolutional LSTM Long Term Recurrent Convolutional Network(LRCN), Multilayer Perceptron (MLP), LSTM and 3D Convolutional Layer (CONV3D). As a result, it was found that throughout the examination process, the proposed CNN-LSTM classification structure achieved 98.53% accuracy, 99.04% precision, 98.49% recall, 99.12% AUC score, and 98.74% F1-score. This achieves 94.03% accuracy, 93.13% precision, 94.76% recall, 96.06% AUC score, and 93.53% F1-score during the training method. [ABSTRACT FROM AUTHOR]

The recall of an episodic narrative, as a function of presentation modality (audio-visual sketch, audio-only), was examined across three cumulative experiments. Experiment 1: participants (N = 84) were asked to recall a narrative delivered via audio, or audio accompanied by a sketch. Experiment 2: participants (N = 116) were asked to recall a narrative delivered via sketch or audio-only format, both of which varied in duration/number of details to recall. Experiment 3: participants (N = 173) recalled a narrative that varied by modality (audio-only, non-concurrent sketch, hybrid sketch, concurrent sketch) and access/no access to the sketch during recall. Aggregating the data across the three experiments (mini meta-analysis) revealed that participants provided with sketch-based narratives recalled more information and had higher recall accuracy (Md = 0.80 [95% CI = 0.56, 1.04], Z = 6.51, p < 0.001) than those provided with audio-only narratives. The results also revealed that concurrent sketches led to the best recall of a narrative, and having access to the sketch during recall had minimal impact on recall. The potential implications of the results for recalling information in various contexts are discussed. [ABSTRACT FROM AUTHOR]

This paper uses Deep Learning to classify if a display panel with defects in the manufacturing line can be repaired. Both tabular data and images are fused together to make predictions, with separate feature extraction undertaken for each of the modalities. The model's predictions achieve high Average Precision as well as robust Precision values in the high Recall regions, which makes it practical for deployment. We also demonstrate superior results with multi‐modal data compared to only tabular data. [ABSTRACT FROM AUTHOR]

Over the years, there has been an increase in the number of people sent to prison for a sexual offence, yet there is a lack of research exploring the experiences of those with sexual convictions within the prison system, and the factors that help or hinder their progression towards release. This research aimed to explore the experiences of individuals with sexual convictions who have progressed to an open prison but have been recalled back to a closed prison. The research took a qualitative approach, undertaking semi-structured interviews with 10 individuals who had moved back to a closed prison. Thematic analysis was used, eliciting two main themes. First, failure was the only option, relays how participants felt they were bound to fail at open conditions, largely due to a lack of information which meant they did not know what to expect, and a lack of support upon arrival. They also felt stigmatised because of their convictions. A different world centres around participants reporting entering into an unfamiliar environment in open conditions, leaving them unsettled. It also describes the difficulties participants had adjusting to the freedom of open conditions but also the delays they experienced. Implications for practice and future research are discussed. [ABSTRACT FROM AUTHOR]

Purpose: With the advent of ChatGPT, a sophisticated generative artificial intelligence (AI) tool, maintaining academic integrity in all educational settings has recently become a challenge for educators. This paper discusses a method and necessary strategies to confront this challenge. Design/methodology/approach: In this study, a language model was defined to achieve high accuracy in distinguishing ChatGPT-generated essays from human written essays with a particular focus on "not falsely" classifying genuinely human-written essays as AI-generated (Negative). Findings: Via support vector machine (SVM) algorithm 100% accuracy was recorded for identifying human generated essays. The author discussed the key use of Recall and F2 score for measuring classification performance and the importance of eliminating False Negatives and making sure that no actual human generated essays are incorrectly classified as AI generated. The results of the proposed model's classification algorithms were compared to those of AI-generated text detection software developed by OpenAI, GPTZero and Copyleaks. Practical implications: AI-generated essays submitted by students can be detected by teachers and educational designers using the proposed language model and machine learning (ML) classifier at a high accuracy. Human (student)-generated essays can and must be correctly identified with 100% accuracy even if the overall classification accuracy performance is slightly reduced. Originality/value: This is the first and only study that used an n-gram bag-of-words (BOWs) discrepancy language model as input for a classifier to make such prediction and compared the classification results of other AI-generated text detection software in an empirical way. [ABSTRACT FROM AUTHOR]

An information retrieval system (IRS) is used to retrieve documents based on an information need. The IRS makes relevance judgements by attempting to match a query to a document. As IRS capabilities are indexing design dependent, the hybrid indexing method (IRS-H) is introduced. The objectives of this article are to examine IRS-H (as an alternative indexing method that performs exact phrase matching) and IRS-I, regarding retrieval usefulness, identification of relevant documents, and the quality of rejecting irrelevant documents by conducting three experiments and by analysing the related data. Three experiments took place where a collection of 100 research documents and 75 queries were presented to: (1) five participants answering a questionnaire, (2) IRS-I to generate data and (3) IRS-H to generate data. The data generated during the experiments were statistically analysed using the performance measurements of Precision, Recall and Specificity, and one-tailed Student's t -tests. The results reveal that IRS-H (1) increased the retrieval of relevant documents, (2) reduced incorrect identification of relevant documents and (3) increased the quality of rejecting irrelevant documents. The research found that the hybrid indexing method, using a small closed document collection of a hundred documents, produced the required outputs and that it may be used as an alternative IRS indexing method. [ABSTRACT FROM AUTHOR]

Written memoranda of conversations, or memcons, provide a near‐contemporaneous record of what was said in conversation, and offer important insights into the activities of high‐profile individuals. We assess the impact of writing a memcon on memory for conversation. Pairs of participants engaged in conversation and were asked to recall the contents of that conversation 1 week later. One participant in each pair memorialized the content of the interaction in a memcon shortly after the conversation. Participants who generated memcons recalled more details of the conversations than participants who did not, but the content of recall was equally and largely accurate for both participants. Remarkably, only 4.7% of the details of the conversation were recalled by both of the partners after a week delay. Contemporaneous note‐taking appears to enhance memory for conversation by increasing the amount of information remembered but not the accuracy of that information. These findings have implications for evaluating the testimony of participants on conversations with major political or legal ramifications. [ABSTRACT FROM AUTHOR]

An Artificial Neuron Network (ANN) is a computational model that mimics the way neurons in the human brain work. In agriculture, ANN can be used for various tasks, such as disease and insect detection, crop yield forecasting, soil fertility management, seed categorization, and quality evaluation. Marketing and development of sustainable agricultural systems rely on accurate seed classification. One major goal of this research is to create an ANN model that can reliably classify different types of dried bean seeds based on their shared characteristics. Data from the UC Irvine machine learning repository was used for this study. With the use of principal component analysis (PCA), the most informative characteristics of a dataset are isolated. Using the proposed approach, we intend to categorise seven distinct types of dry bean seeds. The suggested ANN model is then trained using the optimally tuned hyperparameters. The ANN model's effectiveness is measured by its confusion matrix, accuracy, precision, Recall, and F1-Score. After extensive testing, it was determined that the ANN with PCA model had the highest classification accuracy for dried bean seeds (94%) and the highest F1-score (95%). [ABSTRACT FROM AUTHOR]

The importance of infrared or thermal imaging can be seen in various applications based on Machine Vision. The real-time based application in surveillance systems like detection and tracking of moving objects has shown strong potential. According to available research, there exist some significant issues in colored video frames due to sudden environmental change or other external effects like varying backgrounds. In contrast, the thermal video frames are less affected by sudden light intensity and varying backgrounds. The suggested work utilizes a combined approach of Pearson's correlation and Background Subtraction (BGS) technique over thermal video frame sequences for detection of moving objects. The automatically generated threshold value boosts the efficacy of the algorithm in differentiating between background and foreground frames. The quantitative analysis shows the higher value of different performance parameters like Accuracy, F-measure, Recall, etc. Moreover, the qualitative analysis of obtained results strongly indicates that the proposed method outperforms when compared with the existing methods. [ABSTRACT FROM AUTHOR]

Citizen science data are increasingly used for biodiversity monitoring. However, concerns are often raised over the accuracy of species identifications in citizen science databases, as data are collected mostly by non‐professionals. Misidentifications can simultaneously generate two error types: false positives (erroneous reports of a species) and false negatives (lack of reports of the misidentified species). Large‐scale assessments of identification errors should provide insights into the strengths and weaknesses of citizen science data. Here we show that citizen science photographic data for birds are trustworthy overall, although problems arise in hard‐to‐identify bird groups. We reviewed over 104 000 images of 377 passerine species from the southern Neotropics (Argentina) stored in eBird – a large citizen science platform – and quantified erroneous reports to calculate precision and recall metrics as measures for data accuracy. Precision increases with fewer false positives and recall increases with fewer false negatives; hence, high values of precision and recall will mirror a higher data accuracy. We found that 97% of the photos of all species were correctly identified. Most species (77%; n = 291) showed high accuracy in their identifications (precision and recall > 95%), with 122 species showing no errors. A few hard‐to‐identify species (10%; n = 40) showed low levels of data quality (63–90% precision or recall). Similarly, few species (12%; n = 46) exhibited intermediate precision or recall scores (90–95%). Further, we uncovered the existence of a complex network of cross‐identifications composed of 272 species, with a predominance of tyrant flycatchers and ovenbirds, reflecting the strong traffic of errors that occurs within these families. To our knowledge, our study provides the first large‐scale quantification of identification errors in photos submitted by citizen science contributors. We underscore the relevance of performing such assessments to understand how identification errors are distributed across a database before analysing data, and provide tools for citizen science stakeholders to direct more specific efforts toward species that need an improvement in data quality. [ABSTRACT FROM AUTHOR]