Anchor-Based Minimal Important Difference

Anchor-Based Method for Establishing Minimal Important Difference (Minimal Clinically Important Difference) in Patient-Reported Outcomes

MCID
Minimal clinically important difference
Anchor-based MCID
Minimal important change

The anchor-based method for establishing Minimal Clinically Important Difference (MCID) is a technique for determining the smallest change in a patient-reported outcome (PRO) that patients or clinicians perceive as meaningful or important. Pioneered by Guyatt, Jaeschke, and Singer in 1989, this approach anchors changes in outcome scores to external clinically meaningful events or judgments, enabling researchers and clinicians to interpret whether treatment effects represent real, patient-relevant improvements.

Minimal clinically important difference estimation

Jaeschke, R., Singer, J., & Guyatt, G. H. (1989). Measurement of health status: Ascertaining the minimal clinically important difference. Controlled Clinical Trials, 10(4), 407-415.

Revicki, D., Hays, R. D., Cella, D., & Sloan, J. (2008). Recommended methods for determining responsiveness and minimally important differences for patient-reported outcomes. Journal of Clinical Epidemiology, 61(2), 102-109.

Copay, A. G., Chung, A. S., Pfeiffer, T., Borframes, R., Braswell, K., Chou, L. C., & Spangehl, M. J. (2007). Minimum clinically important difference: a review of nomenclature, methods, and applications in speech-language pathology. Journal of Medical Speech-Language Pathology, 15(4), xlii-xliii.

likert-scale-construction factor-analysis-scale floor-ceiling-effect content-validity-ratio


ANCOVA

analysis of covariance
covariance analysis
ANCOVA (Kovaryans Analizi)

ANCOVA is a parametric hypothesis test that compares the adjusted means of two or more independent groups while statistically controlling for one or more continuous covariates. By removing the portion of outcome variance explained by the covariate, ANCOVA increases statistical precision and produces fairer group comparisons. The method builds on the general linear model framework consolidated by Fisher in the early 1930s and is described comprehensively by Tabachnick and Fidell (2013).

Parametric group comparison with covariate control

Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson.

one-way-anova mancova multiple-regression welch-t-test kruskal-wallis


Anderson-Darling Test

Anderson-Darling Normality (Goodness-of-Fit) Test

Anderson-Darling Normallik Testi
A-squared test
AD test
Anderson-Darling goodness-of-fit test

Anderson & Darling (1952); EDF tables by Stephens (1974)

The Anderson-Darling test is an empirical distribution function (EDF) goodness-of-fit test, introduced by Anderson and Darling in 1952, that checks whether a continuous sample comes from a specified distribution such as the normal, exponential, or Weibull. By weighting deviations more heavily in the tails, it detects departures in the distribution's extremes more powerfully than the Kolmogorov-Smirnov test.

Empirical distribution function (EDF) goodness-of-fit test

A² statistic weighting the squared CDF deviation by 1/[F(x)(1−F(x))]

Anderson, T. W., & Darling, D. A. (1952). Asymptotic Theory of Certain 'Goodness of Fit' Criteria Based on Stochastic Processes. The Annals of Mathematical Statistics, 23(2), 193-212.

Stephens, M. A. (1974). EDF Statistics for Goodness of Fit and Some Comparisons. Journal of the American Statistical Association, 69(347), 730-737.

lilliefors-test kolmogorov-smirnov-2sample shapiro-wilk-test fligner-killeen-test mood-median-test


Anderson-Hsiao IV

Anderson-Hsiao Instrumental Variables Estimator

Anderson-Hsiao Araçsal Değişken Tahmincisi

The Anderson-Hsiao IV estimator is a method for consistently estimating dynamic panel data models that include a lagged dependent variable as a regressor. Proposed by Theodore Anderson and Cheng Hsiao in 1981, it resolves the Nickell bias that arises when fixed effects are eliminated by first-differencing, by instrumenting the differenced lagged dependent variable with its own second lag in levels or differences.

Instrumental variables estimator for dynamic panel data

Lagged levels or lagged differences of the dependent variable

Consistent but not efficient relative to GMM

Anderson, T. W., & Hsiao, C. (1981). Estimation of dynamic models with error components. Journal of the American Statistical Association, 76(375), 598–606.


Anesthesia Risk Scoring in Veterinary Medicine

Systematic Anesthesia Risk Stratification and Perioperative Assessment in Veterinary Surgery

surgical risk scoring
preoperative assessment
ASA scoring

Anesthesia risk scoring is a systematic preoperative assessment method that stratifies patient risk based on medical history, physical findings, and health status. Adapted from the American Society of Anesthesiologists Physical Status classification (developed for humans in 1941) and refined for veterinary species through confidential enquiry data and clinical research, it guides anesthetic technique selection, identifies high-risk patients requiring optimization, and predicts perioperative morbidity and mortality.

American Society of Anesthesiologists (ASA) House of Delegates. (2020). ASA Physical Status Classification System. Retrieved from ASA official website: https://www.asahq.org/standards-and-guidelines/asa-physical-status-classification-system

Brodbelt, D. C., Pfeiffer, D. U., Young, L. E., Wood, J. L. (2007). Risk factors for anaesthetic-related death in cats: Results from the confidential enquiry into perioperative small animal fatalities (CEPSAF). British Journal of Anaesthesia, 99(5), 617-623.

Brodbelt, D. C., Pfeiffer, D. U., Young, L. E., Wood, J. L. (2008). Risk factors for anaesthetic-related death in dogs: Results from the confidential enquiry into perioperative small animal fatalities (CEPSAF). British Journal of Anaesthesia, 100(3), 320-329.


Animal BLUP

Best Linear Unbiased Predictor for Livestock Breeding

BLUP
breeding value prediction
genetic merit estimation

Animal BLUP (Best Linear Unbiased Predictor) is a statistical method for estimating the genetic merit (breeding values) of livestock based on their own performance and the performance of their relatives. Developed by Charles R. Henderson in 1949 and refined continuously since, Animal BLUP accounts for pedigree relationships, environmental effects, and non-additive genetic variance, providing accurate predictions of an animal's ability to transmit desirable traits to offspring.

Henderson, C. R. (1949). Estimation of changes in cattle breeding values. Journal of Dairy Science, 32(5), 369-378.

Henderson, C. R. (1963). Selection index and expected genetic advance. Genetic Statistics and Plant Breeding, 982-993.

Mrode, R. A. (2014). Linear Models for the Prediction of Animal Breeding Values (3rd ed.). CABI Publishing.


Animal Research Ethics — 3Rs Principle

The 3Rs (Replacement, Reduction, Refinement) Framework for Ethical Animal Research

3Rs Framework
Animal Welfare Principles
Animal Research Ethics

Russell & Burch (1959); EU Directive 2010/63/EU; NIH, USDA, international adoption

The 3Rs (Replacement, Reduction, Refinement) is the ethical framework governing humane animal research, established by Russell and Burch (1959) and now adopted globally by research institutions, funding agencies, and regulatory bodies. The 3Rs require researchers to: replace animal research with non-animal methods where possible, reduce the number of animals used through rigorous design, and refine experimental procedures to minimize animal suffering. Implementation of the 3Rs is now mandatory in most jurisdictions through Institutional Animal Care and Use Committees (IACUCs), EU Directive 2010/63/EU, and NIH policy.

Russell, W.M.S. & Burch, R.L. (1959). The Principles of Humane Experimental Technique. Methuen.

European Union. (2010). Directive 2010/63/EU on the Protection of Animals Used for Scientific Purposes. Official Journal of the European Union, L 276/33.

National Institutes of Health. (2015). Policy on the Use of Animals in NIH-Funded Research. NIH Office of Laboratory Animal Care.


ANP

ANP (Analytic Network Process (AHP with feedback and interdependences)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Saaty, T. L. in 1996. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Weight_Subjective (pairwise comparison, supermatrix, network structure)

Saaty, T. L. (1996). Decision Making with Dependence and Feedback: The Analytic Network Process. RWS Publications, Pittsburgh

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Ant Colony Optimization

ACO
Karınca Kolonisi Optimizasyonu (ACO)
ant colony system

1992 (foundational thesis); 1997 (Ant Colony System formalization)

Ant Colony Optimization (ACO) is a metaheuristic algorithm introduced by Marco Dorigo and colleagues in the early 1990s that solves combinatorial optimisation problems by simulating the collective foraging behaviour of ants. Real ants lay pheromone trails on paths and preferentially follow stronger trails; ACO turns this positive-feedback mechanism into a search procedure that finds high-quality solutions to graph-structured problems such as the Travelling Salesman Problem, vehicle routing, and scheduling.

Foraging behaviour of real ants via pheromone trails

Combinatorial optimisation (graph-structured problems)

Number of ants, pheromone evaporation rate (ρ), pheromone influence (α), heuristic influence (β), iteration limit

Dorigo, M. & Gambardella, L.M. (1997). Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation, 1(1), 53-66.

Dorigo, M. & Stützle, T. (2004). Ant Colony Optimization. MIT Press.

tabu-search simulated-annealing genetic-algorithm particle-swarm-optimization grey-wolf-optimizer


Antenatal Depression Scale

Edinburgh Antenatal Depression Scale variant

The Antenatal Depression Scale (ADS) is a 10-item self-report screening instrument designed to identify depressive symptoms during pregnancy. Adapted from the Edinburgh Postnatal Depression Scale (EPDS), the ADS measures depressive mood, anhedonia, guilt, anxiety, suicidal ideation, and self-harm during the antenatal period. Early identification of antenatal depression enables prompt intervention, protecting both maternal mental health and fetal development.

Cox, J. L., Holden, J. M., & Sagovsky, R. (1987). Detection of postnatal depression. Development of the 10-item Edinburgh Postnatal Depression Scale. British Journal of Psychiatry, 150(6), 782-786.

Glover, V., Bergman, K., Sarkar, P., & O'Connor, T. G. (2014). Association between maternal and amniotic fluid cortisol is moderated by maternal anxiety. Psychoneuroendocrinology, 35(1), 142-150.


Anti-kT Jet Algorithm

The anti-kT jet algorithm, introduced by Cacciari and Salam in 2008, is a sequential recombination jet clustering algorithm widely used in high-energy physics to group final-state particles into jets. Unlike earlier algorithms, anti-kT produces jets with regular cone-like geometries in transverse momentum-rapidity space, making it ideal for precision measurements and new physics searches.

Cacciari, M., Salam, G. P., & Sapeta, S. (2008). On the characterisation of the underlying event. Journal of High Energy Physics, 2008(04), 063.

Ellis, S. D., Vermilion, C. K., & Walsh, J. R. (2010). Recombination algorithms for jet substructure. Physical Review D, 81(9), 094023.

Cacciari, M., & Salam, G. P. (2008). FastJet user manual. The European Physical Journal C, 72(3), 1896.


AGS

The Anticipatory Grief Scale (AGS) is a measure developed by Theut, Jordan, and colleagues in 1990 to assess grief responses in individuals facing impending loss—such as family members caring for a terminally ill loved one or anticipating a predicted death. The AGS captures the emotional burden, depression, existential concern, and functional disruption that often precede and accompany the final illness period, distinct from post-death grief.

Theut, S. K., Jordan, P., Ross, L. A., & Mutlak, S. (1990). Grief, depressive symptoms, and physical health in elderly adults. Journal of the American Geriatrics Society, 38(10), 1041–1048.

inventory-complicated-grief prolonged-grief-disorder-scale texas-revised-inventory-grief grief-experience-questionnaire


Antimicrobial Susceptibility Testing in Veterinary Medicine

In Vitro Antimicrobial Susceptibility Testing and Resistance Determination in Veterinary Microbiology

antibiotic sensitivity testing
MIC determination
resistance profiling

Clinical Laboratory Standards Institute (CLSI) and veterinary microbiology

Antimicrobial susceptibility testing (AST) is a systematic in vitro laboratory method that determines which antimicrobial agents are effective against an isolated bacterial or fungal pathogen. Standardized by the Clinical and Laboratory Standards Institute (CLSI) and other regulatory bodies since the 1960s, AST guides targeted therapeutic decisions, supports infection control, and generates epidemiological data on resistance patterns essential for combating antimicrobial resistance in animal populations.

Clinical and Laboratory Standards Institute (CLSI). (2023). Performance Standards for Antimicrobial Susceptibility Testing of Bacteria Isolated from Animals (CLSI M100, 4th ed., Veterinary Supplement). Wayne, PA: CLSI.

Weese, J. S., Giguère, S., Guardabassi, L., et al. (2011). Guidelines for antimicrobial use in horses. AAFP Clinical Microbiology. Journal of Equine Veterinary Science, 31(5), 180-187.

Guardabassi, L., Prescott, J. F., Weese, J. S. (2018). Antimicrobial Therapy in Veterinary Medicine (5th ed.). Ames, IA: Wiley-Blackwell.


Anxiety Sensitivity Index

Steven Taylor, Michael J. Zvolensky, and colleagues

The Anxiety Sensitivity Index–3 (ASI-3) is an 18-item self-report questionnaire that measures anxiety sensitivity—the tendency to fear bodily sensations and interpret them as signs of impending threat. Developed by Taylor and colleagues in 2007, it distinguishes between three domains of anxiety sensitivity: physical, cognitive, and social. The ASI-3 is widely used in research and clinical assessment to identify individuals at risk for anxiety disorders, panic disorder, and post-traumatic stress.

Taylor, S., Zvolensky, M. J., Bomyea, J., & Faulkner, B. (2007). Robust dimensions of anxiety sensitivity in adolescence. Psychology and Psychological Therapy, 19(4), 531–546.


APA Style Guide

American Psychological Association (APA) Referencing and Citation Style

APA 7th edition
APA citation
author-date citation

American Psychological Association (founded 1892)

APA (American Psychological Association) Style is a citation and formatting standard widely used in psychology, education, social sciences, and increasingly in health sciences. APA uses author-date in-text citations (e.g., Smith, 2021) linked to a reference list at the end of the manuscript. The 7th edition (2020) is the current standard and requires DOI (Digital Object Identifier) for all works that have one. APA style covers not only citations but also manuscript formatting (margins, spacing, headings, figure captions), promoting consistency and clarity across scholarly communication.

American Psychological Association (2020). Publication Manual of the American Psychological Association (7th ed.). Washington, DC: American Psychological Association.

American Psychological Association (2023). APA Style Guide. Retrieved from https://apastyle.apa.org/

vancouver-style imrad-structure scientific-writing-clarity journal-submission-process


APACHE II Score

Acute Physiology and Chronic Health Evaluation II

The Acute Physiology and Chronic Health Evaluation (APACHE) II score, introduced by Knaus et al. in 1985, is a 71-point severity of illness classification system for critically ill patients. It combines acute physiological parameters, age, and chronic health status to predict intensive care unit (ICU) mortality, facilitating patient risk stratification and research standardization.

Knaus, W. A., Draper, E. A., Wagner, D. P., & Zimmerman, J. E. (1985). APACHE II: a severity of disease classification system. Critical Care Medicine, 13(10), 818-829.

Zimmerman, J. E., Kramer, A. A., McNair, D. S., & Malila, F. M. (1996). Variations in resource utilization among intensive care units in the United States. Critical Care Medicine, 24(8), 1261-1268.


APARCH

Asymmetric Power ARCH
Power ARCH
APGARCH
Asimetrik Güç ARCH

APARCH, introduced by Ding, Granger, and Engle (1993) while studying long-memory properties of stock market returns, extends the GARCH family by allowing both the power transformation of conditional volatility and an asymmetric response to positive and negative shocks. The model nests at least seven well-known ARCH-type specifications as special cases, making it a unifying framework for volatility modelling in financial econometrics.

GARCH, IGARCH, EGARCH, GJR-GARCH, TARCH as special cases

Ding, Z., Granger, C. W. J., & Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1(1), 83–106.


Apgar Score

The Apgar score, introduced by Virginia Apgar in 1952, is a 10-point rapid assessment of newborn vital status immediately after birth. It evaluates appearance, pulse, grimace (reflex irritability), activity, and respiration at 1 and 5 minutes of life, providing an objective, reproducible measure of neonatal condition and immediate need for resuscitation.

Apgar, V. (1952). A proposal for a new method of evaluation of the newborn infant. Current Researches in Anesthesia & Analgesia, 32(4), 260-267.

Apgar, V., Holaday, D. A., James, L. S., Weisbrot, I. M., & Berrien, C. (1958). Evaluation of the newborn infant. Journal of the American Medical Association, 168(15), 1985-1988.


Aphasia Impact Questionnaire

aphasia quality of life & communication impact

The Aphasia Impact Questionnaire (AIQ), most commonly administered as the Stroke and Aphasia Quality of Life Scale (SAQOL-39), is a comprehensive 39-item self-report measure of health-related quality of life in adults with aphasia following stroke or acquired brain injury. Developed by Hilari and colleagues (2003), AIQ assesses communication function, psychosocial well-being, physical health, and social participation—capturing the multidimensional burden of aphasia on daily life beyond linguistic deficits alone.

Hilari, K., Byng, S., Lamping, D. L., & Smith, S. C. (2003). Stroke and Aphasia Quality of Life Scale–39 (SAQOL-39): Evaluation of Acceptability, Reliability, and Validity. Stroke, 34(8), 1944–1950.

Hersh, D., Worrall, L., & Simmons-Mackie, N. (2012). How Do People With Aphasia View Quality of Life? Aphasiology, 26(2), 141–160.

Cruice, M., Worrall, L., & Hickson, L. (2006). Quantifying Aphasic People's Health-Related Quality of Life. International Journal of Language & Communication Disorders, 41(6), 713–730.


API-based Data Collection

Application Programming Interface-based Data Collection

API data harvesting
API-driven data collection
programmatic data retrieval
API research data collection

2000s–2010s (formalized as a research method)

Emerged from computational social science and web 2.0 platform practices

API-based data collection is a systematic technique in which a researcher sends structured requests to an application programming interface to retrieve data automatically from digital platforms, databases, or services. It is the primary method used in computational social science to gather large-scale social media records, government open data, financial data streams, and scientific repository content in machine-readable formats such as JSON or XML, enabling reproducible and scalable data acquisition that manual collection cannot match.

Structured digital records (JSON, XML, CSV); text, numeric, metadata

Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press.

Ruths, D., & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346(6213), 1063–1064.

web-scraping sensor-data-collection online-survey mobile-experience-sampling digital-trace-data document-collection


APLOCO

APLOCO (Automatic Pairwise Linear Order Combination) is a ranking multi-criteria decision-making (MCDM) method introduced by Konstantinos, N., Xenakis, A., Kehagias, A. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Pairwise dominance aggregation (automatic combination)

Konstantinos, N., Xenakis, A., Kehagias, A. (2020). APLOCO: A linear programming-based multicriteria ranking method that handles dependent criteria and criteria groups. Operational Research

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


Apparent Total Tract Digestibility

Apparent Total Tract Digestibility Measurement

ATTD
digestibility coefficient
fecal digestibility

Apparent Total Tract Digestibility (ATTD) is a measure of the proportion of a nutrient consumed in feed that is absorbed by the animal, calculated from the difference between dietary intake and fecal excretion. Standardized since the 1970s, ATTD is essential for quantifying the bioavailability of nutrients in feedstuffs, formulating balanced diets, and comparing the nutritive value of different feed ingredients across diverse animal species.

Adeola, O. (2001). Digestion and balance techniques in pigs. In A. J. Lewis & L. L. Southern (Eds.), Swine Nutrition (2nd ed., pp. 903-916). CRC Press.

Zijlstra, R. T., & Beltranena, E. (2013). Swine convert co-products from food and biofuel industries efficiently to protein for human food. Journal of Animal Science, 91(5), 2286-2296.

National Research Council (2012). Nutrient Requirements of Swine (11th ed.). National Academies Press.


Approximate Bayesian Computation with Measurement Error

likelihood-free inference with measurement error

simulation-based inference under measurement error

2013 (measurement-error extension); ABC: 1997-2002

Wilkinson, R. D. (formal treatment); ABC roots: Tavaré, Diggle, Beaumont et al. (1997-2002)

Approximate Bayesian Computation with measurement error (ABC-ME) extends the standard ABC likelihood-free framework to settings where observed data are themselves noisy or imprecisely recorded. By explicitly incorporating a measurement-error kernel into the acceptance step, ABC-ME targets the correct posterior over model parameters even when the true data-generating process cannot be directly observed.

observed data subject to measurement noise; simulation-generatable outcomes

Wilkinson, R. D. (2013). Approximate Bayesian computation (ABC) gives exact results under the assumption of model error. Statistical Applications in Genetics and Molecular Biology, 12(2), 129-141.

Beaumont, M. A. (2010). Approximate Bayesian computation in evolution and ecology. Annual Review of Ecology, Evolution, and Systematics, 41, 379-406.


Approximate Bayesian Computation with Missing Data

likelihood-free inference with missing data

simulation-based inference for incomplete data

Beaumont, Zhang & Balding (ABC); Rubin (missing data framework)

Approximate Bayesian Computation with missing data extends the likelihood-free ABC framework to settings where observations are incomplete or partially recorded. By simulating data under a posited model and accepting parameter draws whose simulated summary statistics are close to the observed ones, it bypasses the need to evaluate an intractable likelihood — even when some data values are absent.

any data with partial or incomplete observations

Beaumont, M. A., Zhang, W. & Balding, D. J. (2002). Approximate Bayesian computation in population genetics. Genetics, 162(4), 2025–2035.

Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. John Wiley & Sons.

approximate-bayesian-computation bayesian-inference-with-missing-data multiple-imputation sequential-monte-carlo particle-filter mcmc-with-missing-data


Approximate Bayesian Computation

ABC
likelihood-free inference
simulation-based inference
Yaklaşık Bayesçi Hesaplama (ABC)

Approximate Bayesian Computation (ABC) is a family of simulation-based inference methods that estimate posterior distributions without requiring an analytically tractable likelihood function. Introduced by Beaumont, Zhang and Balding (2002) in the context of population genetics, ABC replaced the intractable likelihood with repeated model simulation and a comparison of summary statistics between simulated and observed data.

Beaumont, M.A., Zhang, W. & Balding, D.J. (2002). Approximate Bayesian Computation in Population Genetics. Genetics, 162(4), 2025-2035.

Sisson, S.A., Fan, Y. & Beaumont, M.A. (Eds.) (2018). Handbook of Approximate Bayesian Computation. Chapman & Hall/CRC.

markov-chain-monte-carlo monte-carlo-simulation bayesian-inference sequential-monte-carlo approximate-bayesian-computation


Apriori Algorithm

Apriori Algorithm for Association Rule Mining

Apriori
frequent itemset mining
ARL-Apriori
Apriori association mining

The Apriori algorithm, introduced by Agrawal and Srikant in 1994, is the foundational method for discovering frequent itemsets and association rules in transactional databases. It uses a breadth-first, level-wise search guided by the anti-monotone property of support to efficiently enumerate all item combinations that co-occur above a user-set minimum threshold, then extracts interpretable if-then rules from those patterns.

Frequent itemset and association rule mining algorithm

Transactional / binary item-presence data

Agrawal, R. & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 487–499.

association-rules fp-growth k-means gaussian-mixture-model semi-supervised-learning online-learning


Aquila Optimizer

The Aquila Optimizer (AO) is a nature-inspired metaheuristic algorithm presented by Abualigah et al. in 2021, modeled after the hunting behavior and sensory abilities of golden eagles (aquila chrysaetos). The algorithm captures the exploration and exploitation phases of eagle hunting, including high-altitude soaring, exploration with high-precision vision, and rapid diving attacks. AO is designed to solve both constrained and unconstrained optimization problems.

Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Aquila optimizer: A novel meta-heuristic optimization algorithm. Computers and Industrial Engineering, 157, 107250.

harris-hawks-optimization slime-mould-algorithm particle-swarm-optimization eagle-strategy whale-optimization-algorithm


ARAS

ARAS (Additive Ratio Assessment) is a ranking multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Turskis, Z. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Additive utility ratio (optimal reference row)

Zavadskas, E. K., Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision-making. Technological and Economic Development of Economy


ARCH-LM Test

Engle's ARCH Lagrange Multiplier Test for Volatility Clustering

ARCH-LM Testi ve Volatilite Kümelenmesi Analizi

test for autoregressive conditional heteroscedasticity

The ARCH-LM test is Robert Engle's (1982) Lagrange multiplier diagnostic for autoregressive conditional heteroscedasticity in the residuals of a fitted time-series model. It checks whether the error variance changes over time and clusters into calm and turbulent periods, and it is the standard pre-test run before fitting a GARCH-family volatility model.

Lagrange multiplier diagnostic test for conditional heteroscedasticity

Auxiliary regression of squared residuals on their own lags; LM = n·R²

No ARCH effect (all lagged squared-residual coefficients are zero)

Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987-1007.

Lee, J. H. H. (1991). A Lagrange Multiplier Test for GARCH Models. Economics Letters, 37(3), 265-271.

egarch gjr-garch garch breusch-pagan-test white-test ols-regression


ARCH model

Autoregressive Conditional Heteroskedasticity Model

The ARCH model, introduced by Robert Engle in 1982, captures time-varying volatility in financial and macroeconomic time series. It models the conditional variance of today's error as a function of past squared errors, explaining why volatile periods cluster together — a phenomenon known as volatility clustering.

Time series (univariate, high-frequency financial or macro data)

Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.

Engle, R. F. (2001). GARCH 101: The use of ARCH/GARCH models in applied econometrics. Journal of Economic Perspectives, 15(4), 157–168.

garch-model egarch-model tgarch-model dcc-garch-model vector-autoregression arima-model


Archaeological Stratigraphy

stratigraphic excavation
Harris matrix method
stratigraphic sequence analysis
layer-by-layer excavation

Formalized 1973–1979 (Harris Matrix); geological principle applied to archaeology from mid-19th century

Edward C. Harris (Harris Matrix formalization); William Smith (geological law of superposition applied to archaeology, 19th c.)

Archaeological stratigraphy is the systematic excavation and recording of soil layers, deposits, and features at an archaeological site in order to establish the relative chronological sequence of human activity. Grounded in the geological law of superposition — that lower layers are older than those above — it uses the Harris Matrix as a formal tool to map depositional relationships and reconstruct site history layer by layer.

Field excavation and sequence recording method

Physical soil layers, features, finds, and spatial context recorded during excavation

Harris, E. C. (1979). Principles of Archaeological Stratigraphy. Academic Press.

Renfrew, C., & Bahn, P. (2016). Archaeology: Theories, Methods, and Practice (7th ed.). Thames & Hudson.

typological-analysis historical-archival-research textual-criticism hermeneutic-analysis oral-history-method comparative-archaeological-stratigraphy


Archaeomagnetic Dating

paleomagnetic dating
magnetic declination dating

Archaeomagnetic dating uses changes in Earth's magnetic field intensity and direction recorded in fired clay artifacts to determine age. Pioneered by Robert Coe in the 1960s, the method measures the magnetization of pottery and baked clay features, comparing measurements to a master curve of geomagnetic variation through time. Archaeomagnetic dating is most effective for materials dated to the last 10,000 years and is particularly powerful in arid regions where clay artifacts are well-preserved.

Coe, R. S. (1968). The determination of paleointensities and neomagnetic effects on pottery. Journal of Geophysical Research, 73(12), 3247-3262.

Kissel, C., & Laj, C. (1999). Paleomagnetic secular variation at the Brunhes/Matuyama boundary. Physics of the Earth and Planetary Interiors, 116(3-4), 175-196.


Architecture Smell Detection

Architectural Anti-pattern and Smell Identification

design smell detection
architectural debt analysis
system quality assessment

Architecture smells are recurring patterns in system structure that indicate potential design problems. Introduced by García et al. (2009), these patterns signal violations of architectural principles (modularity, independence, abstraction) at system scale. Detection combines code metrics, dependency analysis, and pattern recognition to identify smells early, guiding refactoring and architectural improvements.

Fowler, M. (2018). Code smell. Martin Fowler's Website.

Garcia, J., Popescu, D., Edwards, G., & Medvidovic, N. (2009). Identifying architectural bad smells. In Proceedings of the 2009 IEEE/IFIP Conference on Software Architecture (pp. 141–150).

Lanza, M., & Marinescu, R. (2005). Object-Oriented Metrics in Practice. Springer.

software-complexity-metrics technical-debt-measurement static-code-analysis defect-prediction-model


ARDL Bounds Test

Autoregressive Distributed Lag Bounds Test for Cointegration

Pesaran bounds test
bounds testing approach
ARDL cointegration test
ARDL Sınır Testi (Pesaran Bounds Test)

The ARDL bounds test is an autoregressive distributed lag method that tests for a cointegrating (long-run level) relationship between time series, introduced by Pesaran, Shin and Smith in 2001. Unlike the Johansen procedure, it remains valid whether the variables are I(0), I(1) or a mix of the two, and it is more reliable than Johansen in small samples of roughly 30 to 80 observations.

Cointegration test / Autoregressive distributed lag model

Unrestricted error-correction model (OLS) with an F-test on lagged levels

Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds Testing Approaches to the Analysis of Level Relationships. Journal of Applied Econometrics, 16(3), 289–326.

Narayan, P. K. (2005). The Saving and Investment Nexus for China: Evidence from Cointegration Tests. Applied Economics, 37(17), 1979–1990.

nardl-model ols-regression johansen-cointegration vector-error-correction-model engle-granger-cointegration


Areas of Worklife Scale

Occupational stress and organizational factors

The Areas of Worklife Scale (AWS) is a multidimensional assessment tool designed to measure organizational and job factors associated with occupational burnout. Developed by Leiter and Maslach in 2004, the AWS evaluates six critical job dimensions: workload, control, reward, community, fairness, and values alignment. Unlike measures that focus on individual burnout symptoms, the AWS targets the organizational context, making it valuable for identifying specific workplace factors driving burnout and guiding targeted interventions.

Leiter, M. P., & Maslach, C. (2004). Areas of Worklife: A structured approach to organizational predictors of job burnout. In P. L. Perrewe & D. C. Ganster (Eds.), Research in Occupational Stress and Well Being, Vol. 3, (pp. 91-134). Oxford: Elsevier.

copenhagen-burnout-inventory oldenburg-burnout-inventory effort-reward-imbalance-scale recovery-experience-questionnaire job-demands-control-support-model


Arellano-Bond GMM estimator

Arellano-Bond Generalized Method of Moments Estimator

AB-GMM
Difference GMM
first-difference GMM
Arellano-Bond estimator

The Arellano-Bond GMM estimator is the standard approach for dynamic panel data models in which the lagged dependent variable appears as a regressor. By first-differencing to remove fixed effects and using deeper lags as instruments, it yields consistent estimates even when the error is serially correlated and regressors are endogenous.

Balanced or unbalanced panel data with lagged dependent variable

Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277-297.

Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata. Stata Journal, 9(1), 86-136.

dynamic-panel-data-model panel-system-gmm difference-gmm fixed-effects-model random-effects-model instrumental-variables


ARFIMA Model

Autoregressive Fractionally Integrated Moving Average Model

Kesirli Bütünleşik ARMA (ARFIMA / FIGARCH)

ARFIMA is a time series model that captures long-memory behaviour using a fractional differencing parameter d, generalising the integer differencing of ARIMA. It was introduced by Granger and Joyeux (1980) and formalised by Hosking (1981) to describe series whose autocorrelations decay slowly rather than abruptly.

Fractional differencing with ARMA short-memory components

fractional differencing parameter d (0 < d < 0.5)

Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15–29.

Hosking, J. R. M. (1981). Fractional Differencing. Biometrika, 68(1), 165–176.

ols-regression quantile-regression panel-fixed-effects logistic-regression ridge-regression


Argument Mining

argumentation mining
argument extraction
Argüman Madenciliği

Lippi & Torroni (state-of-the-art survey)

Argument mining is a natural-language-processing task that automatically detects claims, premises and the argumentative structures that link them within text. Consolidated as a field by Lippi and Torroni's 2016 state-of-the-art survey, it is applied to scientific writing, legal documents and debate analysis to turn free-form argumentation into structured, analysable units.

Output: Detected claims, premises and argument structures

Lippi, M. & Torroni, P. (2016). Argumentation Mining: State of the Art and Emerging Trends. ACM Transactions on Internet Technology, 16(2), Article 10, 1-25.

Stede, M. & Schneider, J. (2018). Argumentation Mining. Morgan & Claypool.

sentiment-analysis text-classification subjectivity-detection named-entity-recognition


ARIMA model

Autoregressive Integrated Moving Average Model

ARIMA
Box-Jenkins model
integrated ARMA
ARIMA(p,d,q)

The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics.

Univariate time series (continuous, regularly spaced)

Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.

Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press.

arma-model autoregressive-model moving-average-model sarima-model vector-autoregression augmented-dickey-fuller-unit-root-test


ARIMA

Box-Jenkins model
ARIMA(p,d,q)
ARIMA Modeli

ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).

Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley.

sarima garch var-model simple-exponential-smoothing ols-regression


Arithmetic Optimization Algorithm

The Arithmetic Optimization Algorithm (AOA) is a metaheuristic optimization approach introduced by Abualigah et al. in 2020 that leverages mathematical operators (multiplication, division, addition, subtraction) as the inspiration for search strategies. Unlike nature-inspired algorithms, AOA uses the inherent properties of arithmetic operations to balance exploration and exploitation, making it particularly effective for mathematical optimization problems.

Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Arithmetic optimization algorithm: A new metaheuristic algorithm for solving optimization problems. Applied Mathematics and Computation, 392, 125450.

slime-mould-algorithm harris-hawks-optimization particle-swarm-optimization genetic-algorithm differential-evolution


Arizona Sexual Experiences Scale

The ASEX is a brief, five-item self-report screening measure designed to assess sexual dysfunction in patients taking psychotropic medications, particularly antidepressants and antipsychotics. First published by McGahuey and colleagues in 2000, it rapidly measures sexual desire, arousal, penile erection (or lubrication), ejaculation (or orgasm), and satisfaction. The ASEX has become the standard tool for evaluating medication-induced sexual side effects in psychiatric and psychopharmacology research.

McGahuey, G. C., Gelenberg, A. J., Laukes, C. A., Moreno, F. A., Delgado, P. L., McKnight, K. M., & Manber, R. (2000). The Arizona Sexual Experience Scale (ASEX): reliability and validity. Journal of Sex & Marital Therapy, 26(1), 25–40.

Collins, D. R., & Clapton, N. E. (2007). The Arizona Sexual Experience Scale (ASEX): a brief, comprehensive measure of sexual side effects during antidepressant or antipsychotic medications. Evaluation and the Health Professions, 30(2), 187–198.


ARMA model

ARMA
Box-Jenkins model
autoregressive moving average
AR(p)MA(q)

The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting.

Univariate stationary time series (continuous)

Brockwell, P. J., & Davis, R. A. (2002). Introduction to Time Series and Forecasting (2nd ed.). Springer.

autoregressive-model moving-average-model arima-model sarima-model vector-autoregression arma-garch-model


AROMAN

AROMAN (Alternative Ranking Order Method Accounting for Two-Step Normalisation) is a ranking multi-criteria decision-making (MCDM) method introduced by Zdravković, M., Hamid, M., Radovanović, M. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Two-step normalisation (linear + vector) with weighted power aggregation

Zdravković, M., Hamid, M., Radovanović, M. (2022). AROMAN — Alternative Ranking Order Method Accounting for Two-Step Normalisation. Journal of Computational Design and Engineering

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


Arrhenius Stability

Arrhenius model
shelf-life prediction
degradation kinetics

Arrhenius stability testing predicts pharmaceutical product shelf-life by conducting accelerated degradation studies at elevated temperatures and using the Arrhenius equation to extrapolate to storage conditions. Based on Svante Arrhenius's 1889 equation relating reaction rate to temperature, this method is regulatory standard for establishing expiration dates.

Arrhenius, S. (1889). Über die Reaktionsgeschwindigkeit bei der Inversion von Rohrzucker durch Säuren. Zeitschrift für Physikalische Chemie, 4, 226-248.

Carstensen, J. T. (1995). Drug stability: principles and practices. New York: Marcel Dekker.


Arrow-Debreu Equilibrium

Arrow-Debreu General Equilibrium with Complete Markets

Walrasian Equilibrium
General Equilibrium
Competitive Equilibrium

The Arrow-Debreu model is a general equilibrium framework where prices adjust to clear all markets simultaneously, and consumers and firms optimize given those prices. Introduced by Kenneth Arrow and Gerard Debreu in 1954, the model extends Adam Smith's invisible hand concept into a rigorous mathematical framework. Arrow-Debreu equilibrium proves existence, uniqueness (under certain conditions), and Pareto efficiency of competitive equilibria.

Arrow, K. J., & Debreu, G. (1954). Existence of an equilibrium for competitive economies. Econometrica, 22(3), 265-290.

Debreu, G. (1959). Theory of Value: An Axiomatic Analysis of Economic Equilibrium. Yale University Press.

nash-equilibrium bayesian-nash-equilibrium vcg-mechanism cournot-competition


ARTASI

ARTASI (Alternative Ranking Technique based on Adaptive Standardized Intervals) is a ranking multi-criteria decision-making (MCDM) method introduced by Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Two-level standardization + ideal/anti-ideal utility (β-anchored)

Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D. (2024). A picture fuzzy CIMAS-ARTASI model for website performance analysis in human resource management. Applied Soft Computing


Artificial Bee Colony

Artificial Bee Colony (ABC) Optimization

ABC Algorithm
Bee Colony Optimization
Swarm-Based Bee Search
Yapay Arı Kolonisi

Artificial Bee Colony (ABC) is a population-based swarm intelligence metaheuristic introduced by Karaboga and Basturk in 2007. It models the cooperative foraging behavior of a honey bee colony to search for optimal solutions in continuous numerical optimization problems. The algorithm divides candidate solutions among three bee types — employed, onlooker, and scout — and iteratively refines them through local search and probabilistic selection, making it well-suited for researchers and engineers tackling complex, multimodal optimization landscapes.

Colony size, limit (abandonment threshold)

Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.


American Shoulder and Elbow Surgeons Score

American Shoulder and Elbow Surgeons Standardized Assessment Form

American Shoulder and Elbow Surgeons (ASES) Committee

The American Shoulder and Elbow Surgeons (ASES) Standardized Assessment Form is a hybrid outcome instrument combining patient self-report and clinician assessment to evaluate shoulder function and pain. Developed by the ASES Committee in 1994 and published in the Journal of Shoulder and Elbow Surgery, the ASES Score has become the standard shoulder outcome measure in orthopedic and sports medicine settings, widely used in clinical trials, surgical registries, and longitudinal outcomes tracking.

Patient self-report with clinician assessment components

Richards RR, An KN, Bigliani LU, et al. A standardized method for the assessment of shoulder function. J Shoulder Elbow Surg. 1994;3(6):347-352.

patient-specific-functional-scale global-rating-of-change-scale rotator-cuff-quality-of-life faos


Aspect-Based Sentiment Analysis

ABSA
aspect-level sentiment analysis
feature-based sentiment analysis
Konu Bazlı Duygu Analizi (ABSA)

Aspect-based sentiment analysis (ABSA) is a fine-grained natural-language-processing task that detects sentiment separately for each aspect or feature mentioned in a text — such as a product's quality, price, or service — rather than scoring the document as a whole. It was consolidated as a shared task by Pontiki et al. in SemEval-2014 Task 4.

Aspect / feature level (not whole document)

Output: Sentiment polarity per aspect or feature

Pontiki, M. et al. (2014). SemEval-2014 Task 4: Aspect Based Sentiment Analysis. Proceedings of SemEval 2014, 27-35.

Schouten, K. & Frasincar, F. (2016). Survey on Aspect-Level Sentiment Analysis. IEEE Transactions on Knowledge and Data Engineering, 28(3), 813-830.

sentiment-analysis named-entity-recognition text-classification topic-modeling


Assembly Line Balancing

Assembly Line Balancing is the problem of distributing a sequence of assembly tasks across a series of workstations on a production line such that work is evenly distributed, idle time is minimized, and throughput constraints are satisfied. The goal is to assign tasks to stations such that the total work time at each station is as equal as possible, optimizing for production rate (cycle time) and resource utilization. This is a classic optimization problem in manufacturing, solved through heuristic and exact algorithms, essential to the efficiency of mass production systems.

Scholl, A. (2010). Balancing and sequencing of assembly lines. Physica-Verlag.

Baybars, I. (1986). A survey of exact algorithms for the simple assembly line balancing problem. Management Science, 32(8), 909-932.

job-shop-scheduling facility-layout smed total-productive-maintenance kanban


Assessment of Life Habits

The Assessment of Life Habits (LIFE-H) is a comprehensive, interview-based measure that evaluates participation in 11 key life domains—from basic self-care and nutrition to work, recreation, and community engagement. Developed in Quebec by Fougeyrollas, Noreau, and colleagues, LIFE-H operationalizes the ICF concept of 'participation' through a detailed assessment of how individuals accomplish (or struggle with) the habits and roles essential to life in their community.

Noreau, L., Fougeyrollas, P., & Blouin, M. (1992). Revision of the LIFE-H measurement instrument: conceptual structure and items content. Journal of Outcome Measurement, 2(4), 242–268.

Fougeyrollas, P., & Noreau, L. (2002). The Influence of Human Development, Social Structure and Culture on the Type of Environmental and Personal Determinants to the Genesis of Handicap. In: WHO International Classification of Functioning, Disability and Health (ICF). WHO.

impact-participation-autonomy community-integration-questionnaire participation-scale reintegration-to-normal-living whodas-2


Association Rule Mining

Market Basket Analysis
Frequent Itemset Mining
Birliktelik Kuralı Madenciliği
Itemset Association Analysis

Association Rule Mining is an unsupervised data-mining technique that discovers co-occurrence patterns among items in transactional datasets. Formally introduced by Agrawal, Imieliński, and Swami in 1993, and refined with the landmark Apriori algorithm by Agrawal and Srikant in 1994, it identifies rules of the form X ⇒ Y — meaning that transactions containing itemset X tend to also contain itemset Y — quantified by support, confidence, and lift.

Unsupervised pattern discovery algorithm

O(2^|I|) worst case; Apriori prunes with anti-monotonicity

Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD, 207–216.

Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th VLDB Conference, 487–499.


Association Rules

Association Rule Learning (Market Basket Analysis)

market basket analysis
association rule mining
frequent itemset mining
affinity analysis

Association rule learning is an unsupervised technique that discovers co-occurrence patterns — 'if X then Y' implications — within large transactional datasets. Originally formalized by Agrawal, Imielinski, and Swami (1993) for supermarket basket analysis, it is now widely applied in e-commerce recommendation, health informatics, bioinformatics, and behavioral research.

Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207–216.

Tan, P.-N., Steinbach, M., Karpatne, A., & Kumar, V. (2018). Introduction to Data Mining (2nd ed., Ch. 5). Pearson.

apriori-algorithm k-means gaussian-mixture-model semi-supervised-learning voting-ensemble


Asteroseismology

Asteroseismology for Stellar Property Determination

Stellar Oscillations
Stellar Seismology
Helioseismology

Asteroseismology is the study of stellar oscillations—tiny brightness and radial velocity variations caused by sound waves resonating inside stars. Proposed by Roger Ulrich in 1970 and established as a major field by the Kepler and TESS space telescopes, asteroseismology provides unprecedented precision in determining stellar masses, ages, and internal structure.

Ulrich, R. K. (1970). The five-minute oscillations on the solar surface. Astrophysical Journal, 162, 993-999.

Gilliland, R. L., et al. (1994). Observations of solar-like oscillations in the G dwarf star eta Bootis. Astrophysical Journal, 435, 385-397.

Kjeldsen, H., & Bedding, T. R. (2008). Asteroseismology of solar-type stars. Astrophysics and Space Science, 328(1), 61-71.


ACQ

Elizabeth F. Juniper, McMaster University

The ACQ is a 7-item self-report questionnaire developed by Juniper and colleagues at McMaster University in 1999 to assess the degree of asthma control in the previous one to two weeks. Unlike generic respiratory tools, the ACQ measures symptom-based control and rescue medication use, providing a simple yet psychometrically sound method for evaluating treatment efficacy. It is widely adopted in clinical trials, guideline-driven care, and routine asthma management to guide therapy titration and assess treatment response.

Juniper, E. F., O'Byrne, P. M., Guyatt, G. H., Ferrie, P. J., & King, D. R. (1999). Development and validation of a questionnaire to measure asthma control. European Respiratory Journal, 14(4), 902-907.

Juniper, E. F., Svensson, K., Mörk, A. C., & Ståhl, E. (2005). Measurement properties and interpretation of three shortened versions of the asthma control questionnaire. Respiratory Medicine, 99(5), 553-558.


ACT

ACT
Asthma Control Test
Asthma Control Assessment

The ACT is a simple, rapid, patient-centered measure of asthma control. Developed by Robert Nathan and colleagues in 2004, this 5-item questionnaire quantifies how asthma symptoms, activity limitation, and nighttime awakening affect daily life. It is the most widely used asthma control measure in clinical practice and is recommended by major asthma guidelines as a standard assessment tool.

Self-report symptom control questionnaire

Nathan, R. A., Sorkness, C. A., Kosinski, M., Schatz, M., Li, J. T., Marcus, P., ... & Pendergraft, T. B. (2004). Development of the asthma control test: A survey for assessing asthma control in children and adults. Allergy and Asthma Proceedings, 25(1), 1-6.

Schatz, M., Kosinski, M., Yarlas, A. S., Pendergraft, T. B., Kowgier, M., & Lee, J. H. (2012). The minimally important difference of the Asthma Control Test. Journal of Allergy and Clinical Immunology, 126(3), 581-587.e10.


Astrometry (Parallax)

Astrometric Parallax Method for Distance Measurement

Stellar Parallax
Trigonometric Parallax
Parallax Distance Method

Astrometric parallax is the foundational geometric method for measuring distances to nearby stars, based on observing the apparent shift in a star's position as Earth orbits the Sun. First successfully demonstrated by Friedrich Wilhelm Bessel in 1838 for the star 61 Cygni, parallax remains the most direct and reliable distance measurement in astronomy, anchoring the entire cosmic distance ladder.

ESA (1997). The Hipparcos and Tycho Catalogues. Astrometric and photometric star catalogue. European Space Agency Technical Reports, SP-1200.

van Leeuwen, F. (2007). Validation of the new Hipparcos reduction. Astronomy & Astrophysics, 474(2), 653-664.

Gaia Collaboration (2016). Gaia Data Release 1: Astrometry-one billion positions, two million proper-motions and parallaxes. Astronomy & Astrophysics, 595, A2.


ATAC-seq Analysis

ATAC-seq Analysis for Chromatin Accessibility and Regulatory Landscapes

Chromatin accessibility
Open chromatin
Accessible chromatin analysis

Jason Buenrostro, Paul Giresi & William Greenleaf

ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) is a method for profiling the landscape of chromatin accessibility genome-wide. Developed by Buenrostro and colleagues in 2013, ATAC-seq uses hyperactive transposase to tag open, accessible chromatin regions, enabling rapid and sensitive identification of regulatory DNA elements. ATAC-seq has become a standard technique for characterizing gene regulatory landscapes, discovering cell-type-specific regulatory elements, and inferring gene regulatory networks.

Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013). Transposition of native chromatin for fast and sensitive epigenomic profiling of cell populations and tissues. Nature Methods, 10(12), 1213–1218.

Corces, M. R., Buenrostro, J. D., Wu, B., Greenside, P. G., Chan, S. M., Koenig, J. L., & Greenleaf, W. J. (2017). Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis. Nature Genetics, 48(10), 1193–1203.

Satpathy, A. T., Granja, J. M., Yost, K. E., Qi, Y., Meschi, F., McDermott, G. P., & Chang, H. Y. (2019). Massively parallel single-cell chromatin landscapes. Nature Biotechnology, 37(12), 1452–1462.


ATAM

The Architecture Tradeoff Analysis Method (ATAM) is a systematic technique developed by Kazman et al. at CMU/SEI for evaluating software architectures against quality attributes (performance, security, modifiability). ATAM uncovers architectural risks and tradeoffs early, helping teams assess whether designs meet quality goals before implementation.

Kazman, R., Klein, M., Barbacci, M., Longstaff, T., Lipson, H., & Carriere, J. (2000). The Architecture Tradeoff Analysis Method. CMU/SEI Technical Report CMU/SEI-98-TR-008.

Bass, L., Klein, M., & Kazman, R. (2003). Attribute-Driven Design (ADD), Version 2. CMU/SEI-2003-TR-002.

Kazman, R., Asundi, J., & Klein, M. (2001). Making architecture design decisions: An empirical study. Proceedings of the 23rd International Conference on Software Engineering.


Athens Insomnia Scale

The AIS is an 8-item self-report scale designed to assess insomnia severity in adolescents and adults, based on ICD-10 diagnostic criteria for insomnia disorder. Developed by Soldatos and colleagues in 2000, it is widely used in European primary care, psychiatry, and sleep medicine for screening and severity assessment. The AIS is brief (3–5 minutes), applicable across ages and cultures, and sensitive to treatment-induced change in both pharmacological and behavioral interventions.

Soldatos, C. R., Dikeos, D. G., & Paparrigopoulos, T. J. (2000). Athens Insomnia Scale: Validation of an instrument based on ICD-10 criteria. Journal of Psychosomatic Research, 48(6), 555–560.

Chelminski, I., Wasserman, D., & Zalewska-Puchala, B. (2010). Validity of the Athens Insomnia Scale in adolescents with affective disorder. Journal of Affective Disorders, 122(3), 281–285.

Okajima, I., Komada, Y., & Inoue, Y. (2013). A review of prevalence and prevalence estimation of insomnia in Asia: prevalence studies of insomnia night by night are needed to measure the global burden of insomnia accurately. Journal of Epidemiology, 23(1), 1–11.


Athletic Identity Measurement Scale

Athletic Identity Measurement Scale (AIMS)

Britton Brewer, Jean Van Raalte, Diane Linder

The AIMS is a 10-item questionnaire assessing the degree to which being an athlete is central to an individual's self-concept and identity. Developed by Brewer, Van Raalte, and Linder in 1993, the AIMS has become the standard instrument for measuring athletic identity and is widely used to predict athlete coping responses to injury, career transitions, and retirement.

Self-report athletic identity questionnaire

Brewer, B. W., Van Raalte, J. L., & Linder, D. E. (1993). Athletic identity: Hercules' muscles or Achilles' heel? International Journal of Sport Psychology, 24(2), 237–254.

Brewer, B. W., & Cornelius, A. E. (2001). Norms for the Athletic Identity Measurement Scale. Measurement in Physical Education and Exercise Science, 5(1), 47–54.

sport-motivation-scale task-ego-orientation-sport sport-confidence-inventory mental-toughness-questionnaire


Atomic Absorption Spectroscopy

AAS
flame AAS
graphite furnace AAS
GFAAS

Atomic absorption spectroscopy is an analytical technique that measures the concentration of metal elements by detecting the absorption of light by ground-state metal atoms in the gaseous state. Invented by Alan Walsh in 1955, it rapidly became the standard method for trace metal analysis in environmental, clinical, agricultural, and industrial samples. Atomic absorption spectroscopy's sensitivity, selectivity, and simplicity make it indispensable for monitoring toxic metals, nutritional minerals, and elements in complex matrices.

Walsh, A. (1955). The application of atomic absorption spectra to chemical analysis. Spectrochimica Acta, 7, 108–117.

Skoog, D. A., West, D. M., Holler, F. J., & Crouch, S. R. (2014). Fundamentals of Analytical Chemistry (9th ed.). Cengage Learning.

Welz, B., & Sperling, M. (2000). Atomic Absorption Spectrometry (3rd ed.). Wiley-VCH.

inductively-coupled-plasma uv-vis-spectrophotometry potentiometric-titration ion-chromatography coulometry


Atomic Force Microscopy

AFM
scanning probe microscopy
nanoindentation microscopy

Atomic Force Microscopy (AFM) is a scanning probe technique that measures nanoscale surface topography and mechanical properties by monitoring interactions between a sharp cantilever tip and a sample surface. Invented by Gerd Binnig in 1986 as an extension of scanning tunneling microscopy, AFM requires neither electrical conductivity nor vacuum operation, making it applicable to virtually any material. It provides three-dimensional topographic maps with sub-nanometer vertical resolution and lateral resolution approaching nanometers, along with simultaneous measurements of mechanical, electrical, and chemical properties.

Binnig, G., Quate, C. F., & Gerber, C. (1986). Atomic force microscope. Physical Review Letters, 56(9), 930-933.

Eaton, P., & West, P. (2005). Atomic Force Microscopy. Oxford University Press.

Butt, H. J., Cappella, B., & Kappl, M. (2005). Force measurements with the atomic force microscope: Technique, interpretation and applications. Surface Science Reports, 59(1-6), 1-152.


ATR-FTIR

Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy

ATR-IR
attenuated total reflectance
FTIR spectroscopy

Attenuated Total Reflectance (ATR) Fourier Transform Infrared (FTIR) spectroscopy is a variant of conventional FTIR that measures infrared absorption through evanescent-wave interrogation of samples in direct contact with a high-refractive-index crystal. Developed by Harrick and Fahrenfort in the 1960s, ATR-FTIR is now the dominant form of FTIR spectroscopy, enabling rapid, non-destructive characterization of organic compounds, polymers, coatings, and biological materials without extensive sample preparation.

Harrick, N. J. (1960). Study of physics of internal reflection from metals. Journal of Physics and Chemistry of Solids, 13(2), 143-155.

Fahrenfort, J. (1961). Attenuated total reflection: a new principle for the production of useful infra-red reflection spectra of organic compounds. Spectrochimica Acta, 17(6), 698-709.

Humecki, H. J. (Ed.). (1995). Practical Guide to Infrared Microspectroscopy. CRC Press.


Attachment Style Questionnaire

ASQ
Relationship Style Questionnaire
Attachment Orientation

adult attachment and romantic relationships

Cindy Hazan and Phillip Shaver (developed attachment-based romantic love approach); multiple ASQ versions by Feeney, Brennan, and others

The Attachment Style Questionnaire is a self-report instrument measuring adult romantic attachment patterns based on attachment theory. Developed following Hazan and Shaver's seminal 1987 work extending John Bowlby's attachment theory to adult romantic relationships, the ASQ assesses individual differences in attachment anxiety (fear of abandonment and desire for closeness) and attachment avoidance (discomfort with intimacy and emotional dependence). The ASQ is used extensively in relationship research, couple therapy, and studies examining how childhood attachment experiences predict adult romantic functioning.

Feeney, B. C., & Monin, J. K. (2008). An attachment-theoretical perspective on divorce. In J. Cassidy & P. R. Shaver (Eds.), Handbook of attachment: Theory, research, and clinical applications (2nd ed., pp. 934-957). New York: Guilford Press.

Hazan, C., & Shaver, P. (1987). Romantic love conceptualized as an attachment process. Journal of Personality and Social Psychology, 52(3), 511-524.


Attention Mechanism

Attention Mechanism (Bahdanau / Luong Attention)

Dikkat Mekanizması (Bahdanau / Luong Attention)

The attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of the encoder's outputs to focus on at each step. Before the Transformer, it substantially improved machine-translation quality by freeing models from compressing an entire input into a single fixed vector.

Neural attention layer (encoder-decoder)

Sequence-to-sequence prediction & explanation

Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR.

Luong, M.T., Pham, H. & Manning, C.D. (2015). Effective Approaches to Attention-based Neural Machine Translation. EMNLP, 1412–1421.

self-attention-transformer bert-finetuning gpt-finetuning random-forest xgboost


Attitudes toward CAM Scale

Attitudes toward Complementary and Alternative Medicine Scale

Hough, H. J.; Darcey, V. L.; Scofield, R. F.

The ACAMS is a self-report instrument measuring healthcare professionals' and students' attitudes toward complementary and alternative medicine. Developed in the early 2000s, it assesses openness, acceptance, and perceived legitimacy of CAM alongside conventional medicine, helping identify educational gaps and organizational readiness for integrative practice.

Hough, H. J., Darcey, V. L., & Scofield, R. F. (2003). Attitudes toward alternative/complementary medicines among pharmacy students, faculty, and preceptors. American Journal of Pharmaceutical Education, 67(3), 85.

Chan, M. F., Chan, E. A., & Mok, E. (2002). Attitudes toward complementary and alternative medicine: A cross-sectional study of Hong Kong nursing students. Journal of Clinical Nursing, 11(5), 597–605.

integrative-medicine-attitudes cam-use-questionnaire holistic-caring-inventory spiritual-care-competence-scale


AttrakDiff/UEQ

AttrakDiff and User Experience Questionnaire

Hedonic Quality Assessment
Pragmatic vs. Hedonic
UEQ

Marc Hassenzahl (AttrakDiff), Martin Schrepp (UEQ)

AttrakDiff and the User Experience Questionnaire (UEQ) are assessment instruments for measuring user experience across multiple dimensions. AttrakDiff, developed by Marc Hassenzahl, evaluates the tension between pragmatic quality (functionality, usability, does the system do what I need?) and hedonic quality (beauty, emotional engagement, does it delight me?). The UEQ, developed by Schrepp and colleagues, extends this framework with additional dimensions including efficiency, perspicuity, stimulation, and novelty. Both instruments provide quantitative post-use assessment, complementing task-based usability testing with holistic experience evaluation.

Questionnaire measuring pragmatic and hedonic quality dimensions

Hassenzahl, M. (2003). The thing and I: Understanding the relationship between user and product. In M. A. Blythe, K. Overbeeke, A. F. Monk, & P. C. Wright (Eds.), Funology (pp. 31–42). Kluwer Academic Publishers.

Schrepp, M., Hinderks, A., & Thomaschewski, J. (2017). Design and evaluation of a short version of the User Experience Questionnaire (UEQ-S). International Journal of Interactive Multimedia and Artificial Intelligence, 4(6), 103–108.

system-usability-scale nasa-tlx kano-model think-aloud-protocol


Attribute Sampling in Auditing

Attribute Sampling Framework for Testing Internal Controls

Statistical Attribute Sampling
Compliance Testing
Control Testing Sampling

Attribute sampling is a statistical sampling method used primarily in testing the operating effectiveness of internal controls. Rather than measuring the dollar impact of errors (as in substantive sampling), attribute sampling answers a yes/no question: 'Does this control exist and is it operating as designed?' By determining the sample size and evaluating test results statistically, auditors can reach defensible conclusions about control effectiveness.

Statistical sampling technique for control testing

American Institute of Certified Public Accountants (AICPA). (2015). Audit Sampling. AU-C Section 530. AICPA Professional Standards.

Arens, A. A., Elder, R. J., & Beasley, M. S. (2014). Auditing and assurance services (15th ed.). Pearson Education.


Attributes Control Chart

p-chart
np-chart
c-chart
u-chart
nitelik kontrol kartı

Attributes control charts extend Shewhart's framework to count and proportion data — quality characteristics that are classified rather than measured. The p- and np-charts monitor the proportion or number of defective items using the binomial distribution, while the c- and u-charts monitor the number of defects per unit using the Poisson distribution. They are the standard statistical-process-control tools when inspection yields pass/fail or defect counts rather than continuous measurements.

Statistical process control charts for count/proportion data

Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. D. Van Nostrand Company.

Montgomery, D. C. (2009). Introduction to Statistical Quality Control (6th ed.). John Wiley & Sons.

shewhart-control-chart cusum-chart ewma-chart proportion-test


Audio Fingerprinting

robust hashing
perceptual hashing
music identification

Audio fingerprinting is a technique for creating a compact, robust identifier (fingerprint) for audio recordings that uniquely represents the content while being tolerant to modifications such as compression, noise, or time-shifting. Introduced by Haitsma and Kalker (2002), it underlies music identification services like Shazam and is critical for copyright enforcement, music matching, and library deduplication. A fingerprint is not a waveform hash; it captures perceptual content and remains stable across reasonable audio alterations.

Haitsma, J., & Kalker, T. (2002). A highly robust audio fingerprinting system. In Proceedings of the International Symposium on Music Information Retrieval.

Wang, A. L. (2003). An industrial-strength audio search algorithm. In Proceedings of the International Symposium on Music Information Retrieval.

Cano, P., Batlle, E., Kalker, T., & Haitsma, J. (2005). A review of audio fingerprinting. Journal of the Audio Engineering Society, 53(9), 804-825.

music-similarity-measure beat-tracking pitch-detection-algorithm music-genre-classification music-segmentation


AUDIT Alcohol Use Disorders Identification Test

World Health Organization (WHO) collaborative group, John Saunders and colleagues

The Alcohol Use Disorders Identification Test (AUDIT) is a 10-item screening and assessment tool developed by the World Health Organization in 1993. It rapidly identifies hazardous alcohol use, harmful drinking, and alcohol dependence across diverse populations. The AUDIT has become the gold-standard alcohol screening instrument in primary care and clinical settings worldwide.

Alcohol use disorder screening and severity assessment

Saunders, J. B., Aasland, O. G., Babor, T. F., & Grant, M. (1993). Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption—II. Addiction, 88(6), 791–804.

Babor, T. F., Higgins-Biddle, J. C., Saunders, J. B., & Monteiro, M. G. (2001). The Alcohol Use Disorders Identification Test: Guidelines for use in primary care (2nd ed.). World Health Organization.

Reinert, D. F., & Allen, J. P. (2007). The alcohol use disorders identification test: an update of research findings. Alcoholism: Clinical and Experimental Research, 31(2), 185–199.

audit-c cage-questionnaire ipaq promis whoqol-bref


AUDIT-C

Alcohol Use Disorders Identification Test - Consumption

AUDIT-C Alcohol Screening
Three-Item Alcohol Screen

Babor and colleagues; adapted by Bush and colleagues at Veterans Affairs

The AUDIT-C is a 3-item brief alcohol screening tool derived from the first three questions of the full AUDIT. Published by Bush and colleagues in 2003, it assesses alcohol consumption frequency and quantity in under one minute. The AUDIT-C has become the standard ultra-brief screen for problem drinking in primary care and emergency departments.

Brief alcohol consumption screening tool

Bush, K., Kivlahan, D. R., McDonell, M. B., et al. (2003). The AUDIT alcohol consumption questions (AUDIT-C): an effective brief screening test for problem drinking. Archives of Internal Medicine, 158(16), 1789–1795.

Bradley, K. A., DeBusk, R. F., Krupski, A., et al. (2007). Screening for problem alcohol use: comparison of AUDIT-C and AUDIT. American Journal of Preventive Medicine, 30(2), 131–137.

audit-alcohol cage-questionnaire whoqol-bref promis sf-36


Audit Risk Model

Audit Risk Model for Risk-Based Audit Planning

The Audit Risk Model is a foundational framework developed by the American Institute of Certified Public Accountants (AICPA) that structures audit planning by decomposing overall audit risk into three components: inherent risk, control risk, and detection risk. This model guides auditors in allocating resources and designing audit procedures proportionate to the level of risk in each account or assertion.

American Institute of Certified Public Accountants (AICPA). (2015). Audit Risk. AU-C Section 200. AICPA Professional Standards.

internal-control-evaluation analytical-procedures-auditing fraud-risk-assessment going-concern-evaluation


Augmented Dickey-Fuller unit root test

ADF test
ADF unit root test
Dickey-Fuller test (augmented)
Said-Dickey test

Said & Dickey (1984); building on Dickey & Fuller (1979)

The Augmented Dickey-Fuller test is the standard procedure for determining whether a univariate time series contains a unit root — that is, whether the series is non-stationary. It extends the original Dickey-Fuller test by including lagged difference terms that absorb serial correlation in the residuals, making the test valid for a wide range of time-series processes encountered in economics and finance.

Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599–607.

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431.

phillips-perron-unit-root-test kpss-test zivot-andrews-structural-break-test johansen-cointegration-test engle-granger-cointegration-test arima-model


Augmented Lagrangian Method

Augmented Lagrangian Method for Constrained Optimization

method of multipliers
augmented Lagrangian
ADMM

The Augmented Lagrangian Method, developed by Magnus R. Hestenes and M. J. D. Powell in 1969, is a powerful technique for solving constrained optimization problems. It converts a constrained problem into a sequence of unconstrained subproblems by augmenting the Lagrangian with a quadratic penalty term, enabling efficient solution of large-scale problems including convex and nonconvex cases.

Hestenes, M. R. (1969). Multiplier and gradient methods. Journal of Optimization Theory and Applications, 4(5), 303-320.

Powell, M. J. D. (1969). A method for nonlinear constraints in minimization problems. In Optimization (pp. 283-298). Academic Press.

Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 3(1), 1-122.


Auteur Theory Analysis

Authorship and Directorial Intent in Film Studies

auteur analysis
directorial analysis
author theory in film

Film authorship and directorial criticism

Auteur Theory Analysis is a critical framework for studying cinema through the lens of directorial authorship, examining how individual directors express consistent themes, visual style, and ideological perspectives across multiple films. Developed by French critics of Cahiers du Cinéma (notably François Truffaut) and articulated in American film criticism by Andrew Sarris, the theory posits that despite the industrial, collaborative nature of film production, the director functions as the primary creative author whose distinctive sensibility can be traced through characteristic patterns of style, technique, and content. The method enables scholarly analysis of directorial influence on cinema and challenges the assumption that mass-produced films lack individual artistic vision.

Critical framework for identifying and analyzing directorial style and authorship across films

Sarris, A. (1962). Notes on the auteur theory in 1962. Film Culture, 27, 1-8.

Caughie, J. (Ed.). (1981). Theories of Authorship: A Reader. Routledge.

Bordwell, D. (2006). The Way Hollywood Tells It: Story and Style in Modern Movies. University of California Press.

Wollen, P. (1972). Signs and Meaning in the Cinema. Indiana University Press.

film-narrative-analysis semiotics-film genre-analysis-film discourse-analysis-media visual-content-analysis


Authentic Leadership Scale

Walumbwa, Avolio, Gardner, Wernsing, and Peterson

The Authentic Leadership Scale (ALS) is a 16-item instrument measuring four dimensions of authentic leadership: self-awareness, relational transparency, balanced processing, and internalized moral perspective. Developed by Walumbwa, Avolio, and colleagues in 2008, the ALS assesses leadership grounded in self-knowledge and ethical conviction.

Walumbwa, F. O., Avolio, B. J., Gardner, W. L., Wernsing, T. S., & Peterson, S. J. (2008). Authentic leadership: Development and validation of a theory-based measure. Journal of Management, 34(1), 89-126.

Kernis, M. H. (2003). Toward a conceptualization of optimal self-esteem. Psychological Inquiry, 14(1), 1-26.

ethical-leadership-scale organizational-trust-scale employee-engagement-survey toxic-leadership-scale


Authorship Attribution

Stylometry
Authorship Analysis
Yazarlık Atıfı
Authorship Identification

Authorship attribution is the task of identifying the most probable author of an anonymous or disputed text by analysing its stylistic fingerprint. Rooted in the statistical work of Mosteller and Wallace on the Federalist Papers (1964), the field was systematically surveyed and formalised by Stamatatos (2009), who catalogued feature sets ranging from character n-grams and function-word frequencies to syntactic and semantic representations used by modern machine-learning classifiers.

Input: Text documents with candidate author samples

Output: Probability distribution or label over candidate authors

Stamatatos, E. (2009). A survey of modern authorship attribution methods. Journal of the American Society for Information Science and Technology, 60(3), 538–556.


Autism Spectrum Quotient

The Autism Spectrum Quotient (AQ) is a brief self- or observer-report measure of autism-spectrum traits in adolescents and adults. Developed by Simon Baron-Cohen and colleagues in 2001, the original 50-item version (AQ-50) quantifies propensity toward autism across five domains: social skills, attention to detail, attention switching, communication, and imagination. The 10-item version (AQ-10) serves as a rapid screening tool. The AQ is not diagnostic but identifies individuals who warrant formal autism evaluation.

Allison, C., Auyeung, B., & Baron-Cohen, S. (2012). Toward brief "red flags" for autism screening: The Short Autism Spectrum Quotient and the Autism Spectrum Quotient-50. Journal of Autism and Developmental Disorders, 42(4), 588–598.

Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Clubley, E. (2001). The autism-spectrum quotient (AQ): Evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. Journal of Autism and Developmental Disorders, 31(1), 5–17.


Autoencoder Anomaly Detection

Autoencoder-Based Anomaly Detection (Reconstruction-Error Method)

unsupervised autoencoder anomaly detection

Hinton, G. E. & Salakhutdinov, R. R. (autoencoders); applied to anomaly detection through multiple authors in the 2010s

Autoencoder anomaly detection trains a neural network to compress and then reconstruct normal data. Because the model has only ever learned what normal looks like, anomalous inputs produce noticeably higher reconstruction errors — and those errors become the anomaly score. The method requires no labeled anomalies and scales naturally to high-dimensional data such as sensor streams, images, and log records.

Unsupervised deep learning (reconstruction-based)

Continuous tabular, time-series, image, or sequential data

Chalapathy, R. & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.

Hinton, G. E. & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507.

one-class-svm isolation-forest gaussian-mixture-model variational-autoencoder principal-component-analysis k-nearest-neighbors


Autoencoder

Autoencoder (Encoder-Decoder Neural Network for Dimensionality Reduction)

Otokodlayıcı (Autoencoder)
otokodlayıcı
auto-encoder
encoder-decoder network

An autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs it, enabling dimensionality reduction and anomaly detection. By learning to rebuild its own input through a narrow bottleneck, it discovers a compact representation of the data.

Dimensionality reduction & anomaly detection

Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.

pca variational-autoencoder k-means factor-analysis


Autoethnography

auto-ethnography
AE
personal narrative research
self-ethnography

Late 20th century (term coined 1979; method consolidated 1990s–2000s)

Carolyn Ellis, Arthur Bochner, Norman Denzin (prominent theorists); David Hayano coined the term in 1979

Autoethnography is a qualitative research method in which the researcher uses systematic self-reflection and personal narrative to examine their own experiences within a cultural, social, or organizational context. By treating the self as both subject and instrument, autoethnography connects individual lived experience to broader cultural patterns, making personal stories analytically and socially significant. It bridges autobiography and ethnography, producing accounts that are simultaneously evocative and scholarly.

Personal narrative, field notes, journals, memory, artifacts, interviews with self and others

1 (solo); 2–6 (collaborative autoethnography)

Ellis, C. (2004). The Ethnographic I: A Methodological Novel about Autoethnography. AltaMira Press.

Chang, H., Ngunjiri, F. W., & Hernandez, K.-A. C. (2013). Collaborative Autoethnography. Left Coast Press.

ethnography phenomenology narrative-analysis action-research grounded-theory discourse-analysis


Autoformer

Autoformer (Auto-Correlation Decomposition Transformer)

Auto-Correlation Transformer
Decomposition Transformer
Series Decomposition Forecaster
Oto-Korelasyon Ayrışım Transformer

Autoformer is a deep learning architecture for long-term time-series forecasting, introduced by Wu et al. from Tsinghua University at NeurIPS 2021. It replaces the standard self-attention mechanism with an Auto-Correlation mechanism that exploits periodic dependencies in the frequency domain, and embeds a progressive series decomposition block throughout the encoder and decoder to separately model trend and seasonal components.

Decomposition-based deep forecasting model

Input: Univariate or multivariate time series

Output: Multi-step ahead point forecasts

Wu, H., Xu, J., Wang, J., & Long, M. (2021). Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. NeurIPS, 34.


Automated Essay Scoring

AES
automated writing evaluation
AWE
Otomatik Deneme Puanlaması

1966 (Project Essay Grade); modern deep-learning era from 2019

Shermis & Burstein (eds.); landmark consolidation 2013; deep-learning era from Devlin et al. 2019

Automated Essay Scoring (AES) is a natural-language-processing task in which a computational model assigns scores to student-written essays across dimensions such as grammatical correctness, coherence, content richness, and organisation — replicating, at scale, what a human rater would do. The approach was formalised as a research field by Shermis and Burstein (2013) and has been transformed since 2019 by transformer language models, particularly BERT, which allow AES systems to leverage deep contextual representations of text.

Supervised text-regression / text-classification task

Input: Labelled student essays with human-assigned scores

Output: Numeric score or score-band per essay

Grammar, coherence, content richness, organisation

Shermis, M.D. & Burstein, J. (2013). Handbook of Automated Essay Evaluation. Routledge.

Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.

sentiment-analysis text-classification bert-embeddings readability-analysis natural-language-processing


Automated Theorem Proving

ATP
automated reasoning
first-order logic proof

Automated Theorem Proving (ATP) is a field of artificial intelligence and mathematical logic dedicated to mechanically proving mathematical theorems in formal systems. Developed by John Robinson in 1965 with the resolution principle, ATP underpins modern verification tools like SAT/SMT solvers and is foundational to formal software verification, hardware validation, and mathematics.

Robinson, J. A. (1965). A machine-oriented logic based on the resolution principle. Journal of the ACM, 12(1), 23–41.

Fitting, M. (1996). First-Order Logic and Automated Theorem Proving (2nd ed.). Springer.

Nieuwenhuis, R., Oliveras, A., & Tinelli, C. (2006). Solving SAT and SAT modulo theories: From an abstract Davis–Putnam–Logemann–Loveland procedure to DPLL(T). Journal of the ACM, 53(6), 937–977.

sat-solving constraint-satisfaction formal-verification model-checking


ADVI
black-box variational inference
automatic variational inference
gradient-based variational inference

Kucukelbir, Tran, Ranganath, Gelman, Blei

Automatic Differentiation Variational Inference (ADVI) is a black-box algorithm for approximate Bayesian posterior inference, introduced by Kucukelbir, Tran, Ranganath, Gelman, and Blei (2017, JMLR). Given any probabilistic model whose log-joint density is differentiable, ADVI automatically transforms constrained latent variables to unconstrained real space, fits a Gaussian variational family by maximising the evidence lower bound (ELBO) with stochastic gradient ascent, and returns an approximate posterior without model-specific derivations. It is the default variational inference engine in Stan and is available in PyMC and NumPyro.

continuous (constrained or unconstrained)

approximate posterior distributions / ELBO trace

Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A. & Blei, D. M. (2017). Automatic differentiation variational inference. Journal of Machine Learning Research, 18(14), 1–45.

Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A. & Blei, D. M. (2016). Automatic differentiation variational inference. arXiv:1603.00788.

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.

bayesian-regression mcmc hierarchical-bayes variational-bayes expectation-propagation


Automatic Music Transcription

music-to-notation conversion
score estimation
polyphonic transcription

Automatic music transcription is the task of converting audio recordings into symbolic music notation (e.g., scores with note pitch, onset, and duration). Formalized as a research problem by Klapuri (2008), it represents one of the most challenging tasks in music information retrieval. Transcription enables music education, composition analysis, and digital preservation. Modern systems, particularly those using deep learning for piano music (Hawthorne et al., 2019), have achieved significant progress but remain far from perfect on general polyphonic music.

Klapuri, A. (2008). Automatic music transcription as we know it today. Journal of New Music Research, 33(3), 323-337.

Poliner, G. E., & Ellis, D. P. (2007). A discriminative model for polyphonic piano transcription. IEEE Transactions on Audio, Speech, and Language Processing, 15(3), 1116-1126.

Hawthorne, C., Elsen, E., Song, J., Roberts, A., Simon, I., Raffel, C., ... & Engel, J. (2019). Onsets and Frames: Dual-Objective Piano Transcription. In ISMIR.

melody-extraction pitch-detection-algorithm beat-tracking chord-recognition music-segmentation


Automatic Test Pattern Generation

Automatic Test Pattern Generation for Digital Circuits

ATPG
Test pattern generation
Fault-based testing

Automatic Test Pattern Generation (ATPG) is the automated creation of test vectors that detect manufacturing defects in digital circuits. Pioneered by Roth in 1966, ATPG systematically finds inputs that make stuck-at faults observable at outputs, enabling comprehensive fault detection. ATPG is critical for semiconductor manufacturing: enabling high test coverage ensures only good chips ship and identifies manufacturing process issues.

Automated fault-detection test vector generation

Abramovici, M., Breuer, M. A., & Friedman, A. D. (1990). Digital Systems Testing and Testable Design. Computer Science Press.

Roth, J. P. (1966). Diagnosis of automata failures: A calculus and a method. IBM Journal of Research and Development, 10(4), 278-291.

Goel, P. (1981). An implicit enumeration algorithm to generate tests for combinational circuits. IEEE Transactions on Computers, 30(3), 215-222.


Automatic Text Evaluation

Automatic Text Evaluation (BLEU, ROUGE, BERTScore)

Otomatik Metin Değerlendirme (BLEU, ROUGE, BERTScore)

BLEU: Papineni et al. (2002); ROUGE: Lin (2004); BERTScore: Zhang et al. (2020)

Automatic text evaluation is a family of reference-based metrics used to measure the quality of machine-generated text — such as translations, summaries, or natural-language-generation (NLG) outputs — by comparing them to one or more human-written reference texts. Pioneered by Papineni et al. with BLEU in 2002, the field has grown to include n-gram overlap metrics (BLEU, ROUGE) and semantically aware metrics (BERTScore, MoverScore) that capture meaning beyond surface word matches.

Reference-based NLG evaluation metric suite

N-gram overlap (BLEU, ROUGE) / semantic similarity (BERTScore, MoverScore)

Output: Numeric quality scores for generated text

Yes — one or more human-written reference texts

Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2002). BLEU: A Method for Automatic Evaluation of Machine Translation. Proceedings of ACL 2002.

Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2020). BERTScore: Evaluating Text Generation with BERT. Proceedings of ICLR 2020.

sentiment-analysis text-classification bert-embeddings topic-modeling


Autoregressive model

AR model
AR(p) model
autoregression
AR process

An autoregressive model of order p — AR(p) — expresses the current value of a time series as a linear function of its own p most recent past values plus a white-noise error. It is the building block of the Box-Jenkins family of time-series models and is widely used for forecasting stationary economic and financial series.

Univariate time series (equally spaced, stationary)

Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day.

Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press.

moving-average-model arma-model arima-model vector-autoregression augmented-dickey-fuller-unit-root-test granger-causality-test


AVERAGE-RANKING

AVERAGE-RANKING (Average ranking — per-alternative mean rank) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Orakçı, E. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Order statistic — column-wise arithmetic mean

Orakçı, E. (2024). Çok Kriterli Karar Verme Problemleri için Toplulaştırma Teknikleri. Özgür Yayınları

borda condorcet copeland dodgson topsis vikor ahp


Axial Coding

axial analysis
relational coding
category development coding
second-level coding

Axial coding is the second major analytical step in grounded theory analysis, performed after open coding. Introduced by Anselm Strauss and Juliet Corbin in 1990, it involves systematically re-examining and reorganising the many discrete codes generated during open coding by identifying a central (axial) category and mapping the causal conditions, contextual factors, intervening conditions, action-interaction strategies, and consequences that surround it. The goal is to move from a fragmented list of codes to a coherent relational structure that reflects how concepts interconnect in the data.

Interview transcripts, field notes, documents, observation records

20–50 interviews (grounded theory context)

Strauss, A., & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage.

Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Sage.

grounded-theory thematic-analysis content-analysis phenomenology narrative-analysis discourse-analysis


B-Dot Controller

B-dot control
magnetic damping
momentum dumping

The B-Dot controller (magnetic B-dot control law) is a simple, robust spacecraft attitude control method that uses the rate of change of Earth's magnetic field measured onboard to generate a magnetic dipole moment. Developed in the 1980s, the B-Dot law damps spacecraft angular momentum without requiring a complex attitude estimate or external reference, making it ideal for initial momentum dumping after launch or in contingency scenarios. B-Dot is passive, simple to implement, and effective.

Wertz, J. R. (Ed.). (2002). Spacecraft Attitude Determination and Control. Kluwer Academic.

Sidi, M. J. (1997). Spacecraft Dynamics and Control: A Practical Engineering Approach. American Institute of Aeronautics and Astronautics.

Crassidis, J., Markley, F. L., & Lightsey, E. G. (2006). The Developing Art of Spacecraft Attitude Determination. IEEE Aerospace and Electronic Systems Magazine, 21(4), 30–34.


B-WENSLO

B-WENSLO (Fuzzy WEight deNomination based on Slope coefficient (triangular fuzzy extension)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Demir, G., Ulusoy, S. K. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Weight_Subjective (linguistic-TFN expert weighting; envelope/slope ratio on TFN accumulation polyline)

Demir, G., Ulusoy, S. K. (2024). Bulanık WENSLO Yöntemi ile Kriter Ağırlıklarının Belirlenmesi: Dijital Bankacılık Uygulaması. Computer and Decision Making — An International Journal

aroman artasi cobra cocoso codas copras cradis edas


Background Subtraction

Background Subtraction for Foreground Detection

Background subtraction is a video processing technique that separates moving foreground objects from a static or slowly changing background by comparing each frame to a learned or estimated background model. Widely used in video surveillance and motion detection, background subtraction enables robust foreground detection even in complex scenes with illumination changes.

Stauffer, C., & Grimson, W. E. L. (1999). Adaptive background mixture models for real-time tracking. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 246–252.

KaewTraKulPong, P., & Bowden, R. (2002). An improved adaptive background mixture model for real-time tracking with shadow detection. Proceedings of the European Conference on Computer Vision (ECCV), 135–144.

histogram-equalization watershed-segmentation image-morphology canny-edge-detection contour-analysis


Backstepping Control

Integrator Backstepping
Recursive Lyapunov Design

Backstepping is a systematic nonlinear control design method that decomposes a complex nonlinear system into simpler subsystems and designs a controller recursively, layer by layer, ensuring stability at each step. Developed by Krstic, Kanellakopoulos, and Kokotovic, backstepping enables control of nonlinear systems without requiring exact model knowledge or full state linearization, combining flexibility with guaranteed stability.

Krstic, M., Kanellakopoulos, I., & Kokotovic, P. (1995). Nonlinear and Adaptive Control Design. John Wiley & Sons.


VaR Backtesting

Value-at-Risk Backtesting (Kupiec, Christoffersen, Dynamic Quantile)

VaR Geriye Dönük Test (Kupiec, Christoffersen, DQ)

Kupiec (1995); Christoffersen (1998); Engle & Manganelli (DQ test)

VaR backtesting is a family of statistical tests that validate a risk model by comparing its Value-at-Risk forecasts against realised losses. It builds on Kupiec's (1995) unconditional coverage test, Christoffersen's (1998) conditional coverage test, and the Engle-Manganelli Dynamic Quantile (DQ) test.

Statistical hypothesis tests on VaR violation sequences

Likelihood-ratio coverage tests; quantile regression (DQ)

Kupiec, P. H. (1995). Techniques for Verifying the Accuracy of Risk Measurement Models. The Journal of Derivatives, 3(2), 73-84.

Christoffersen, P. F. (1998). Evaluating Interval Forecasts. International Economic Review, 39(4), 841-862.

garch-model expected-shortfall har-rv-model tail-risk-evt ols-regression


Bagging Ensemble

Bagging, short for bootstrap aggregating, is an ensemble method that reduces variance by training multiple copies of a single learning algorithm on different random subsets of the training data. Each subset is created via bootstrap sampling—randomly drawing samples with replacement. Predictions are combined through majority voting (classification) or averaging (regression). Introduced by Leo Breiman in 1996, bagging forms the foundation for random forests and is particularly effective for reducing overfitting in high-variance models.

Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.

Efron, B. (1979). Bootstrap methods: another look at the jackknife. The Annals of Statistics, 7(1), 1-26.

random-forest boosting-ensemble majority-voting adaboost


Bagging

Bootstrap Aggregating
bootstrap aggregation
bagged ensemble
bagged predictor
variance reduction ensemble

Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.

Ensemble meta-algorithm (variance reduction via bootstrap aggregation)

Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.

Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 8.7). Springer.

James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An Introduction to Statistical Learning (Ch. 8.2). Springer.

random-forest decision-tree xgboost gradient-boosting adaboost