‘Super-Unsupervised’ Classification for Labelling Text: Online Political Hostility as an Illustration

Face recognition has always been a potential research area because of its demand for reliable identification of a human being especially in government and commercial sectors, such as security systems, criminal identification, border control, etc. where a large number of people interact with each other and/or with the system. The last two decades have witnessed many supervised and unsupervised learning techniques proposed by different researchers for the face recognition system. Principal component analysis (PCA), self‐organizing map (SOM), and independent component analysis (ICA) are the most widely used unsupervised learning techniques reported by research community. This article presents an analysis and comparison of these techniques. The article also includes two SOM processing methods global SOM (GSOM) and local SOM (LSOM) for performance evaluation along with PCA and ICA. We have used two different databases for our analysis. The simulation result establishes the supremacy of GSOM in general among all the unsupervised techniques. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 261–267, 2010

PurposeResearch serves to elucidate and tackle real-world issues (e.g. capitalizing opportunities and solving problems). Critical to research is the concept of validity, which gauges the extent to which research is adequate and appropriate in representing what it intends to measure and test. In this vein, this article aims to present a typology of validity to aid researchers in this endeavor.Design/methodology/approachEmploying a synthesis approach informed by the 3Es of expertise, experience, and exposure, this article maintains a sharp focus on delineating the concept of validity and presenting its typology.FindingsThis article emphasizes the importance of validity and explains how and when different types of validity can be established. First and foremost, content validity and face validity are prerequisites assessed before data collection, whereas convergent validity and discriminant validity come into play during the evaluation of the measurement model post-data collection, while nomological validity and predictive validity are crucial in the evaluation of the structural model following the evaluation of the measurement model. Additionally, content, face, convergent and discriminant validity contribute to construct validity as they pertain to concept(s), while nomological and predictive validity contribute to criterion validity as they relate to relationship(s). Last but not least, content and face validity are established by humans, thereby contributing to the assessment of substantive significance, whereas convergent, discriminant, nomological and predictive validity are established by statistics, thereby contributing to the assessment of statistical significance.Originality/valueThis article contributes to a deeper understanding of validity’s multifaceted nature in research, providing a practical guide for its application across various research stages.

Background: The widely used Rosenberg’s self-esteem scale (RSES) has not been evaluated for psychometric properties in Sweden.Aims: This study aimed at analyzing its factor structure, internal consistency, criterion, convergent and discriminant validity, sensitivity to change, and whether a four-graded Likert-type response scale increased its reliability and validity compared to a yes/no response scale.Methods: People with mental illness participating in intervention studies to (1) promote everyday life balance (N = 223) or (2) remedy self-stigma (N = 103) were included. Both samples completed the RSES and questionnaires addressing quality of life and sociodemographic data. Sample 1 also completed instruments chosen to assess convergent and discriminant validity: self-mastery (convergent validity), level of functioning and occupational engagement (discriminant validity). Confirmatory factor analysis (CFA), structural equation modeling, and conventional inferential statistics were used.Results: Based on both samples, the Swedish RSES formed one factor and exhibited high internal consistency (>0.90). The two response scales were equivalent. Criterion validity in relation to quality of life was demonstrated. RSES could distinguish between women and men (women scoring lower) and between diagnostic groups (people with depression scoring lower). Correlations >0.5 with variables chosen to reflect convergent validity and around 0.2 with variables used to address discriminant validity further highlighted the construct validity of RSES. The instrument also showed sensitivity to change.Conclusions: The Swedish RSES exhibited a one-component factor structure and showed good psychometric properties in terms of good internal consistency, criterion, convergent and discriminant validity, and sensitivity to change. The yes/no and the four-graded Likert-type response scales worked equivalently.

Objective: There is a need for more experimental research on weight stigma and physical activity, specifically among men with obesity. Yet, validated procedures are required. The goal of this study was i) to develop a 10-min video instrument for the experimental induction of physical activity-related weight stigma (including a neutral control video), and ii) to collect information with regards to its content, face, construct, and criterion validity. Methods: To ensure content and face validity, two experts gave repeated feedback on the video development. In addition, two novices reviewed the video. Subsequently, we conducted a separate experiment: 60 adult men (18-52 years, 31 with overweight, 29 with normal weight) were randomly assigned to one of the two video conditions, stratified by BMI. Results: After the treatment, the stigma group showed higher scores of negative affect and attitudes against obesity (convergent validity). As expected, there were no differences in measures of depression and general stress (discriminant validity). The BMI of the participants in the stigma group correlated positively with some indicators of negative affect (criterion validity). Conclusion: Our study provides preliminary evidence for the validity of the video tool. Therefore, it can be used in future trials to investigate the effects of weight stigma on physical activity behavior.

Customer relationship management (CRM) is the tool to enhance customer relationship in any business. Due to the exponential growth of data volume, in any field, it is significant to develop new techniques to discover the customer knowledge, automation of the system and moreover customer satisfaction to win customer lifetime value. CRM with machine learning could bring a catalytic change in business. Several supervised and unsupervised machine learning techniques are utilized to improve the customer experience and profitability of business. This paper reviews the available literature on the CRM with machine learning techniques for customer identification, customer attraction, and customer retention and customer development. This study reveals that supervised learning techniques are 48.48% utilized, unsupervised learning techniques are utilized 15.15%, and 9.09% utilized other techniques in CRM. Paradigm is also shifted toward the deep learning from machine learning as 28.28% text has been reported to deep learning. Decision tree-based algorithm and support vector machine algorithms are most utilized algorithm of supervised learning. E-commerce and telecommunication sectors are the most important areas identified with the exponential growth of the users and hence need a suitable machine learning techniques for customer satisfaction and business profitability.

Kenya is experiencing a significant increase in the prevalence of non-communicable diseases (NCDs) such as cardiovascular diseases, hypertension, Type 2 diabetes, and certain cancers (bowel, lung, prostate, and uterine). This case is not unique to Kenya but is common in many Low and Middle-Income Countries (LMICs) in Africa. Many NCDs, are linked to diets high in added sugars, sodium, saturated fat, and low in fiber. There is a notable lack of information regarding the demographic differences among supermarket customers and their purchasing habits of healthy versus unhealthy foods in some parts of Africa. This gap in knowledge hinders the ability to connect grocery purchase patterns to NCDs, including obesity. Supermarkets in LMICs offer valuable demographic insights through grocery data. This research utilizes NOVA classification tool, data mining and unsupervised machine learning techniques to analyze grocery purchase patterns in 10 supermarkets across 5 counties in Kenya between 2022 and 2023. The apriori algorithm was used to create association rules and an analysis was done on the association rules to find out the relationship between demography (location, gender, and age) with purchase patterns. Individual data was collected along with transaction data, since the supermarkets logged transactions done by loyalty card customers. The main aim is to provide guidance to policymakers in public health. We collected 3 934 122 unique transactions and each transaction was associated with a customer who was identified with a unique customer ID. Findings from this research demonstrate that 53% of food purchases from these transactions were mainly industrially processed food items and males above the age of 50 years were the main consumers of these food items. The findings lead to the conclusion that this purchase trend has a chance of rising NCDs in older people. Therefore we recommend that policymakers adopt our recommendations to safeguard public health.

Validation du questionnaire EORTC In-Patsat32 de satisfaction des patients atteints de cancer dans les établissements de soins au Maroc

Given the scarcity of instruments in Spanish to measure forgiveness, two studies were conducted in this population to obtain validity evidence of the Heartland Forgiveness Scale (HFS), an instrument that measures dispositional forgiveness of self, others, and situations. In the first study, 203 students (65% women) participated. After ensuring the linguistic adequacy and clarity of the wording of the items, a lack of congruence was found between the factors obtained in the exploratory factor analysis and the original theoretical structure of the HFS. A sample of 512 participants (63.9% women) attended the second study. This study aimed to analyze the construct validity of the HFS using confirmatory factor analysis through structural equation modelling and to explore convergent, discriminant, and criterion validity. Of the different factorial configurations tested (including the original), only a scale reduction to eight items, grouped into three factors, showed an appropriate fit. The HFS eight-item version also showed acceptable internal consistency, adequate convergent and discriminant validity, and criterion validity with respect to related variables. These findings suggest that the eight-item version of the HFS may be a valid and reliable tool for assessing forgiveness for self, others, and situations in Spanish adults.

Effective advance care planning depends on clinician-patient communication quality. However, ACP-CAT, the only validated observer-rated instrument, remains untested in real-world clinical encounters and non-Western contexts. Validating ACP-CAT in assessing clinician-patient communication quality with advanced cancer and chronic kidney disease in Hong Kong, comparing against patient and family ratings of clinician communication. Cross-sectional study of adult patients with advanced cancer or chronic kidney disease from five hospitals and one hospice service. Two independent raters evaluated audiotaped advance care planning conversations using ACP-CAT, while patients and family members assessed communication quality using the Chinese Quality of Communication Questionnaire (C-QOC). We assessed the psychometric properties, including interrater reliability, convergent and discriminant validity. One hundred and thirty-seven advance care planning conversations involving 84 cancer patients, 53 renal patients, 107 family members, and 20 clinicians. ACP-CAT demonstrated high interrater reliability (mean Gwet's AC1 = 0.81; 88.6% agreement). Convergent validity was demonstrated through significant correlations between ACP-CAT total score and patient-rated C-QOC End-of-life planning subscale (r = 0.29, p < 0.01), family-rated C-QOC summary score (r = 0.27, p < 0.05), and family-rated C-QOC End-of-life planning subscale (r = 0.34, p < 0.01). Discriminant validity was supported by absence of correlations with patients' self-rated health status and information or decision-making preferences (all p > 0.05). Clinicians frequently explored patients' fears and health goals, but rarely discussed nonmedical priorities, valued activities, or surrogate decision makers. ACP-CAT demonstrates strong concurrent validity with patient- and family-rated measures and ecological validity in real clinical settings. These findings support its reliability for assessing advance care planning communication quality across diverse cultural contexts.

Reliability and validity describe desirable psychometric characteristics of research instruments. The concept of validity is also applied to research studies and their findings. Internal validity examines whether the study design, conduct, and analysis answer the research questions without bias. External validity examines whether the study findings can be generalized to other contexts. Ecological validity examines, specifically, whether the study findings can be generalized to real-life settings; thus ecological validity is a subtype of external validity. These concepts are explained using examples so that readers may understand why the consideration of internal, external, and ecological validity is important for designing and conducting studies, and for understanding the merits of published research.

Objectives The purpose of this study is to develop a reliable and valid counseling rapport scale for clients.&#x0D; Methods This study developed a total of 95 items in an open-ended survey based on a literature review to gain preliminary items for a counseling rapport scale. The items were tested for content validity by experts and face validity by clients who finished the first sessions, which reduced the items to 50 preliminary ones. An exploratory factor analysis was conducted with 250 clients that finished the first session followed by a confirmatory factor analysis with 250 clients to test the developed scale for validation. The scale was also tested for criterion and convergent validity.&#x0D; Results First, the exploratory factor analysis results identified four factors that comprised of 21 items, including positive first impressions, favorable feelings, experiences of ease and comfort, and the counselor's honest attitude. Secondly, according to the confirmatory factor analysis results, the fitness index of the scale was good; in addition, it confirmed that the four factors from the exploratory factor analysis results were valid. Thirdly, the scale was good internal consistency, criterion validity, and convergent validity. Finally, it also had good re-test reliability.&#x0D; Conclusions Using an objective procedure this study provided a reliable and valid scale to measure counseling rapport. The scale developed in the study is significant in that it laid a foundation to diagnose and improve the level of rapport between the counselor and his or her clients at the counseling scene. In short, the study offers discussions about ways to form and promote healthy rapport in the process of developing and validating a counseling rapport scale, hopefully contributing to the better competence and higher quality of counselors.

In rarefied gas flows, discontinuity phenomena such as velocity slip and temperature jump commonly appear in the gas layer adjacent to a solid boundary. Due to the physical complexity of the interactions at the gas-solid interface, particularly in the case of systems with local nonequilibrium state, boundary models with limited number of parameters cannot completely describe the reflection of gas molecules at the boundary. In this work, the Gaussian mixture (GM) model, which is an unsupervised machine learning technique, is employed to construct a statistical gas-solid surface scattering model based on the collisional data obtained from molecular dynamics (MD) simulations. The GM model is applied to study Couette flow for different inert gases (Ar and He) confined between two parallel infinite gold walls at different temperatures. A direct comparison between the results obtained from the GM model and the Cercignani-Lampis-Lord (CLL) scattering kernel against the MD collisional data in terms of the distribution of the predicted postcollisional velocities, and accommodation coefficients has shown that the results from the GM model are an excellent match with the MD results outperforming the CLL scattering kernel. As an example, for He gas, while the predicted energy accommodation coefficient by the CLL model is more than two times higher than the MD predictions, the value computed by the GM model is in excellent agreement with the MD results. This superior performance of the GM model confirms its high potential to derive a generalized boundary condition in systems encountered with highly nonequilibrium and complex gas flow conditions.

Operational modal analysis (OMA) is required to maintain large-scale and necessary civil infrastructures. The non-contact method of OMA using digital image correlation and point tracking algorithms requires a speckle pattern placed on the structure. Alternatively, advanced computer vision methods like optical flow and phase-based video motion magnification (PBVMM) techniques are used to measure modal parameters. Despite the importance of PBVMM, the users should know the range of frequencies in which the natural structure frequency lies. A methodology based on an unsupervised machine learning technique is developed to extract the modal parameters blindly from its recorded digital video. The proposed methodology uses complex steerable pyramids and an unsupervised machine learning technique, also known as principal component analysis, and analytical mode decomposition with a random decrement technique to blindly extract the modal parameters of a structure. This study validated the proposed methodology using a multi-degree of freedom (DOF) numerical model. The results are compared with theoretical and estimated values and are in good agreement. Furthermore, it is implemented on a laboratory benchmark SDOF, MDOF, and real-time videos of the London Millennium and Tacoma Narrows bridges for blindly extracting the modal frequencies and damping ratios.

Technological developments have affected the decision-making phase in football matches. The viewing pleasure of fans and the result of the match is a matter of debate determined by the referees. Throughout time, the objectiveness of referees was questioned by the technological tools. Video Assistant Referee (VAR) is an example approach to ensure whether the referees react to similar positions of different matches in the same manner or not. One of the problems of this topic is the controversial decisions of some referees leading to unexpected results. In this research, as a different approach to the referees’ objectivity problem, referees are tried to be classified based on the statistical outcome of the matches using unsupervised machine learning techniques. Meaningful clusters should not be found to be able to state the referees are objective. This study is conducted on 10-years English Premier League between 2009–2018 data. Principal Component Analysis is going to be used for grouping the variables to perform exploratory data analysis. K-Means, hierarchical clustering, and Fuzzy C-Means are going to be used for dividing the referees into various subgroups. R programming language is used for examining data. In conclusion of the analysis, four different referee groups are defined.KeywordsFootballEnglish Premier LeagueRefereeClusteringMachine learningUnsupervised learning

ML-Diabetes is a machine learning-based predictive model for the early detection of diabetes. Diabetes is a chronic metabolic disorder that affects millions of people worldwide. Early detection of diabetes can help prevent its complications and improve patient outcomes. ML-Diabetes is designed to use demographic and clinical data to predict the likelihood of a patient developing diabetes. The model uses a combination of supervised and unsupervised machine learning techniques to analyse and classify data. ML-Diabetes uses a dataset containing demographic and clinical information of patients, including age, sex, BMI, blood pressure, and glucose levels. The dataset is preprocessed and cleaned to remove missing values and outliers. The processed data is then split into training and testing sets, and the model is trained on the training set. The model uses a combination of supervised and unsupervised machine learning techniques, including logistic regression, decision trees, and k-means clustering, to predict the likelihood of a patient developing diabetes. The model is evaluated on the testing set using various performance metrics, including accuracy, precision, recall, and F1-score. The results show that ML-Diabetes is a reliable and accurate predictive model for the early detection of diabetes. The model achieves an accuracy of 85%, precision of 90%, recall of 80%, and F1-score of 85%. The model can be used by healthcare professionals to screen patients for diabetes and provide early interventions to prevent complications.