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computer algorithms of lower-order confounding in regular designs

COMPUTATIONAL STATISTICS 2024年第2期39卷 653-676页

作者： Li, Zhi Li, Zhiming Xinjiang Univ Coll Math & Syst Sci Urumqi 830046 Peoples R China

In the design of experiments, an optimal design should minimize the confounding between factorial effects, especially main effects and two-factor interaction effects. The general minimum lower-order confounding (GMC) criterion can be used to choose optimal regular designs based on the aliased component-number pattern. This paper aims to study the confounding properties of lower-order effects and provide several computer algorithms to calculate the lower-order confounding in regular designs. We provide a search algorithm to obtain GMC designs. Through python software, we conduct these algorithms. Some examples are analyzed to illustrate the effectiveness of the proposed algorithms.

关键词： Regular design Aliased component-number pattern General minimum lower-order confounding criterion computer algorithm

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Performance of artificial intelligence on cervical vertebral maturation assessment: a systematic review and meta-analysis

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BMC ORAL HEALTH 2025年第1期25卷 1-21页

作者： Sadeghi, Termeh Sarrafan Ourang, Seyed AmirHossein Sohrabniya, Fatemeh Sadr, Soroush Shobeiri, Parnian Motamedian, Saeed Reza Shahid Beheshti Univ Med Sci Res Inst Dent Sci Dentofacial Deform Res Ctr Tehran Iran Hamadan Univ Med Sci Sch Dent Dept Endodont Hamadan Iran Mem Sloan Kettering Canc Ctr Dept Radiol New York NY 10065 USA Shahid Beheshti Univ Med Sci Sch Dent Dept Orthodont Daneshjoo BlvdShahid Chamran Highway Tehran *** Iran

BackgroundArtificial intelligence (AI) methods, including machine learning and deep learning, are increasingly applied in orthodontics for tasks like assessing skeletal maturity. Accurate timing of treatment is crucial, but traditional methods such as cervical vertebral maturation (CVM) staging have limitations due to observer variability and complexity. AI has the potential to automate CVM assessment, enhancing reliability and user-friendliness. This systematic review and meta-analysis aimed to evaluate the overall performance of artificial intelligence (AI) models in assessing cervical vertebrae maturation (CVM) in radiographs, when compared to *** databases of Medline (via PubMed), Google Scholar, Scopus, Embase, IEEE ArXiv and MedRxiv were searched for publications after 2010, without any limitation on language. In the present review, we included studies that reported AI models' performance on CVM assessment. Quality assessment was done using Quality assessment and diagnostic accuracy Tool-2 (QUADAS-2). Quantitative analysis was conducted using hierarchical logistic regression for meta-analysis on diagnostic accuracy. Subgroup analysis was conducted on different AI subsets (Deep learning, and Machine learning).ResultsA total of 1606 studies were screened of which 25 studies were included. The performance of the models was acceptable. However, it varied based on the methods employed. Eight studies had a low risk of bias in all domains. Twelve studies were included in the meta-analysis and their pooled values for sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio (DOR) were calculated for each cervical stage (CS). The most accurate CVM evaluation was observed for CS1, boasting a sensitivity of 0.87, a specificity of 0.97, and a DOR of 213. Conversely, CS3 exhibited the lowest performance with a sensitivity of 0.64, and a specificity of 0.96, yet maintaining a DOR of *** has demonstrated

关键词： Artificial intelligence Growth and development Cervical vertebrae Orthodontics computer algorithm

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computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017

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JOURNAL OF THE AMERICAN ACADEMY OF DERMATOLOGY 2020年第3期82卷 622-627页

作者： Marchetti, Michael A. Liopyris, Konstantinos Dusza, Stephen W. Codella, Noel C. F. Gutman, David A. Helba, Brian Kalloo, Aadi Halpern, Allan C. Soyer, H. Peter Curiel-Lewandrowski, Clara Kittler, Harald Caffery, Liam Malvehy, Josep Wellenhof, Rainer Hofmann Mem Sloan Kettering Canc Ctr Dept Med Dermatol Serv 16 East 60th St New York NY 10022 USA Thomas J Watson Res Ctr IBM Res Div Yorktown Hts NY USA Emory Univ Sch Med Dept Neurol Atlanta GA 30322 USA Emory Univ Sch Med Dept Psychiat Atlanta GA 30322 USA Emory Univ Sch Med Dept Biomed Informat Atlanta GA USA Kitware Inc Clifton Pk NY USA Univ Queensland Dermatol Res Ctr Brisbane Qld Australia Univ Arizona Canc Ctr Tucson AZ USA Med Univ Vienna Dept Dermatol Vienna Austria Univ Queensland Brisbane Qld Australia Hosp Clin Barcelona Barcelona Spain Med Univ Graz Graz Austria

Background: computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain. Objective: To determine if computer algorithms from an international melanoma detection challenge can improve dermatologists' accuracy in diagnosing melanoma. Methods: In this cross-sectional study, we used 150 dermoscopy images (50 melanomas, 50 nevi, 50 seborrheic keratoses) from the test dataset of a melanoma detection challenge, along with algorithm results from 23 teams. Eight dermatologists and 9 dermatology residents classified dermoscopic lesion images in an online reader study and provided their confidence level. Results: The top-ranked computer algorithm had an area under the receiver operating characteristic curve of 0.87, which was higher than that of the dermatologists (0.74) and residents (0.66) (P < .001 for all comparisons). At the dermatologists' overall sensitivity in classification of 76.0%, the algorithm had a superior specificity (85.0% vs. 72.6%, P = .001). Imputation of computer algorithm classifications into dermatologist evaluations with low confidence ratings (26.6% of evaluations) increased dermatologist sensitivity from 76.0% to 80.8% and specificity from 72.6% to 72.8%. Limitations: Artificial study setting lacking the full spectrum of skin lesions as well as clinical metadata. Conclusion: Accumulating evidence suggests that deep neural networks can classify skin images of melanoma and its benign mimickers with high accuracy and potentially improve human performance.

关键词： automated melanoma diagnosis computer algorithm computer vision deep learning dermatologist International Skin Imaging Collaboration International Symposium on Biomedical Imaging machine learning melanoma reader study skin cancer

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computer simulation of ion chromatography separation: an algorithm enabling continuous monitoring of anion distribution on an ion-exchange chromatography column

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JOURNAL OF CHROMATOGRAPHY A 2001年第1-2期922卷 1-11页

作者： Novic, M Zupan, J Novic, M Natl Inst Chem SI-1001 Ljubljana Slovenia

A computer algorithm for the calculation of ion chromatography separation is presented. It is based on the calculation of equilibrium concentrations of present analyte in discrete column segments. The continuous column is treated as a number of discrete cells or segments where the equilibration process between the stationary phase and the eluent is simulated. The ion-exchange equilibration process is supposed to be instantaneous and quantitative. The continuous flow of the eluent is rendered by discrete transfers. The size of each transfer of the eluent corresponds to a portion of the volume contained in one column segment. The equilibrium calculations in all column segments are repeated for each transfer of the eluent, through all the stages of the chromatographic process. The distribution of the analytes between the stationary phase and the eluent can be monitored at any step and in any column segment which means that the described algorithm provides the spatial and time concentration profiles. The simulated chromatogram is acquired as a time-concentration profile in the last column segment. The obtained chromatograms are in good agreement with the experimental ones. The distribution of ions between the stationary phase and the eluent in the early stages of the ion chromatographic process can thus be studied with confidence. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： computer algorithm on-column distribution anions

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computer algorithms in Assessment of Obstructive Sleep Apnoea Syndrome and Its Application in Estimating Prevalence of Sleep Related Disorders in Population

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INDIAN JOURNAL OF OTOLARYNGOLOGY AND HEAD & NECK SURGERY 2019年第3期71卷 352-359页

作者： Katyayan, Angira Yadav, Vishal Mishra, Pranav Mishra, Anupam Saxena, Mallika Kant, Surya Garg, Rajiv Srivastava, Anand Verma, Veerendra SRM Univ Comp Engn Undergrad Chennai Tamil Nadu India KG Med Univ Dept Otorhinolaryngol & Head & Neck Surg Lucknow Uttar Pradesh India Lynbrooke Highsch Sch San Jose CA USA San Francisco High Sch San Francisco CA USA KG Med Univ Pulm Med Lucknow Uttar Pradesh India

The incorporation of telemedicine and artificial intelligence for early screening and assessment of severity of life-style disorders has a great potential for better assessment in a busy outpatient clinic and thereby curtail down the related morbidities. A computer based algorithm based upon standardized questionnaire (from established assessment tools) is designed to assess the risk of obstructive sleep apnoea syndrome (OSAS). In addition the incorporation of basic screening questions of anamnesis help in suggesting a probable diagnosis of sleep related disorder as well. The overall data at our center has been analyzed to establish the existing pattern of sleep related disorders. Of 850 healthy subjects screened, prevalence of snoring was 20.47% while OSAS was seen in 4.20% (N = 25) in males and 2.64% (N = 8) in females. The parasomnia was most prevalent (14.71%), followed by insomnia (10.24%), periodic leg movement (6.59%), bruxism (1.65%) and narcolepsy (0.59%). Hypertension, laryngopharyngeal reflux and obesity were the common co-morbidities in OSAS while family history of hypertension and diabetes were common in snorers. A significant association with OSA was seen with diabetes mellitus, neck circumference and nasal obstruction, while, obesity and apnoeic episodes were more significantly associated with OSA than snorers. Increased waist to hip ratio was appreciated in both the OSAS and snorers. The algorithm based online assessment is likely to diagnose the occult clinical cases as well as assess the risk of OSAS. In routine outpatient clinic, a clinician may better assess the patient morbidity with a comprehensive availability of symptoms and moreover enhance the post-treatment compliance. In addition a smartphone based computerized assessment for general population may be designed for other lifestyle disorders as well.

关键词： computer algorithm Artificial intelligence Obstructive sleep apnoea syndrome Periodic leg movement Narcolepsy Parasomnia Insomnia

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Combining computer algorithms with experimental approaches permits the rapid and accurate identification of T cell epitopes from defined antigens

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JOURNAL OF IMMUNOLOGICAL METHODS 2001年第1-2期257卷 1-16页

作者： Schirle, M Weinschenk, T Stevanovic, S Univ Tubingen Inst Cell Biol Dept Immunol D-72076 Tubingen Germany

The identification of T cell epitopes from immunologically relevant antigens remains a critical step in the development of vaccines and methods for monitoring of T cell responses. This review presents an overview of strategies that employ computer algorithms for the selection of candidate peptides front defined proteins and subsequent verification of their in vivo relevance by experimental approaches. Several computer algorithms are currently being used for epitope prediction of various major histocompatibility complex WHO class I and II molecules, based either on the analysis of natural MHC ligands or on the binding properties of synthetic peptides. Moreover, the analysis of proteasomal digests of peptides and whole proteins has led to the development of algorithms for the prediction of proteasomal cleavages, In order to verify the generation of the predicted peptides during antigen processing in vivo as well as their immunogenic potential, several experimental approaches have been pursued in the recent past, Mass spectrometry-based bioanalytical approaches have been used specifically to detect predicted peptides among isolated natural ligands. Other strategies employ various methods for the stimulation of primary T cell responses against the predicted peptides and subsequent testing of the recognition pattern towards target cells that express the antigen. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： T cell epitope prediction HLA peptide computer algorithm

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Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images

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JOURNAL OF THE AMERICAN ACADEMY OF DERMATOLOGY 2018年第2期78卷 270-+页

作者： Marchetti, Michael A. Codella, Noel C. F. Dusza, Stephen W. Gutman, David A. Helba, Brian Kalloo, Aadi Mishra, Nabin Carrera, Cristina Emre Celebi, M. DeFazio, Jennifer L. Jaimes, Natalia Marghoob, Ashfaq A. Quigley, Elizabeth Scope, Alon Yelamos, Oriol Halpern, Allan C. Mem Sloan Kettering Canc Ctr Dept Med Dermatol Serv 16 E 60th St New York NY 10022 USA IBM Res Div Thomas J Watson Res Ctr Yorktown Hts NY USA Emory Univ Sch Med Dept Neurol Atlanta GA 30322 USA Emory Univ Sch Med Dept Psychiat Atlanta GA 30322 USA Emory Univ Sch Med Dept Biomed Informat Atlanta GA USA Kitware Inc Clifton Pk NY USA Stoecker & Associates Rolla MO USA Univ Barcelona Inst Invest Biomed August Pi & Sunyer Inst Salud Carlos III Melanoma UnitDept DermatolHosp ClinCIBER Enfer Barcelona Spain Univ Cent Arkansas Dept Comp Sci Conway AR USA Aurora Ctr Especializado Canc Piel Dermatol Serv Medellin Colombia Univ Miami Miller Sch Med Dept Dermatol & Cutaneous Surg Miami FL 33136 USA Tel Aviv Univ Sackler Sch Med Sheba Med Ctr Dept Dermatol Tel Aviv Israel

Background: computer vision may aid in melanoma detection. Objective: We sought to compare melanoma diagnostic accuracy of computer algorithms to dermatologists using dermoscopic images. Methods: We conducted a cross-sectional study using 100 randomly selected dermoscopic images (50 melanomas, 44 nevi, and 6 lentigines) from an international computer vision melanoma challenge dataset (n = 379), along with individual algorithm results from 25 teams. We used 5 methods (nonlearned and machine learning) to combine individual automated predictions into "fusion" algorithms. In a companion study, 8 dermatologists classified the lesions in the 100 images as either benign or malignant. Results: The average sensitivity and specificity of dermatologists in classification was 82% and 59%. At 82% sensitivity, dermatologist specificity was similar to the top challenge algorithm (59% vs. 62%, P =.68) but lower than the best-performing fusion algorithm (59% vs. 76%, P = .02). Receiver operating characteristic area of the top fusion algorithm was greater than the mean receiver operating characteristic area of dermatologists (0.86 vs. 0.71, P = .001). Limitations: The dataset lacked the full spectrum of skin lesions encountered in clinical practice, particularly banal lesions. Readers and algorithms were not provided clinical data (eg, age or lesion history/symptoms). Results obtained using our study design cannot be extrapolated to clinical practice. Conclusion: Deep learning computer vision systems classified melanoma dermoscopy images with accuracy that exceeded some but not all dermatologists.

关键词： computer algorithm computer vision dermatologist International Skin Imaging Collaboration International Symposium on Biomedical Imaging machine learning melanoma reader study skin cancer

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An Improved algorithm Design and Implementation Based on Unsupervised Learning in computer Games 34

An Improved Algorithm Design and Implementation Based on Uns...

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34th Chinese Control and Decision Conference (CCDC)

作者： Wu, Gui Jianghan Univ Educ Adm Off Wuhan 430056 Peoples R China

ISBN: (纸本)9781665478960

At present, the computer games with the intelligent algorithm optimization are always a popular research direction of the artificial intelligence. In this research field, the key concerned technologies and the important resolve are how to continue to gain the final winning rate constantly by independent identification realization, movement optimization and real-time update games based on the unsupervised learning algorithm in the computer chess games. According to the typical artificial intelligence application scene of computer games, an improved algorithm based on the unsupervised learning is designed and proposed in the paper. This algorithm uses alfa-beta search tree to select the best matching pattern and applies the concept of feature learning to make the image recognition innovatively. With the characteristics of the Checkers and the common search methods, it proposes a feature and pattern learning method based on the K-means clustering algorithm. By the experimental data and results, the improved algorithm proposed in the paper can gain and keep a high winning rate in the real games.

关键词： Unsupervised Learning computer algorithm computer Games Machine Learning

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The Application of computer algorithms in Marketing Data Analysis and Prediction 2

The Application of Computer Algorithms in Marketing Data Ana...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Jiao, Pengfei Guo, Chunxiang Shaanxi Fashion Engineering University School of Economics and Management Shaanxi Xi'an China

ISBN: (纸本)9798350317558

Many enterprises face data quality issues such as missing data, data errors, data inconsistencies, etc. Using data mining algorithms to analyze and predict market data has become an important way for enterprises to improve their competitiveness and market efficiency. This article adopts data mining algorithms, uses large-scale market data for analysis and modeling, and constructs a data set containing multiple characteristic variables by collecting and sorting historical market data. This article chooses data mining algorithms to train and model the data set, and optimizes the performance and accuracy of the model by repeatedly adjusting model parameters and evaluation indicators. Research results show that the algorithm's accuracy test data is between 85% and 94%. By using algorithms for data analysis, companies can more accurately predict market trends, customer behavior and sales performance. This helps companies develop more precise marketing strategies and resource allocation plans, and improve marketing effectiveness and return rates. © 2024 IEEE.

关键词： computer algorithm Data Analysis and Prediction Data Mining Marketing Strategy

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Classification of positive blood cultures: computer algorithms versus physicians' assessment - development of tools for surveillance of bloodstream infection prognosis using population-based laboratory databases

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BMC MEDICAL RESEARCH METHODOLOGY 2012年第1期12卷 139-139页

作者： Gradel, Kim O. Knudsen, Jenny Dahl Arpi, Magnus Ostergaard, Christian Schonheyder, Henrik C. Sogaard, Mette Univ So Denmark Inst Clin Res Res Unit Clin Epidemiol Odense Denmark Odense Univ Hosp Ctr Natl Clin Databases DK-5000 Odense Denmark Univ Copenhagen Hvidovre Hosp Copenhagen Univ Hosp Dept Clin Microbiol DK-2650 Hvidovre Denmark Herlev Hosp Copenhagen Univ Hosp Dept Clin Microbiol DK-2730 Herlev Denmark Aarhus Univ Hosp Aalborg Hosp Dept Clin Microbiol Aalborg Denmark

Background: Information from blood cultures is utilized for infection control, public health surveillance, and clinical outcome research. This information can be enriched by physicians' assessments of positive blood cultures, which are, however, often available from selected patient groups or pathogens only. The aim of this work was to determine whether patients with positive blood cultures can be classified effectively for outcome research in epidemiological studies by the use of administrative data and computer algorithms, taking physicians' assessments as reference. Methods: Physicians' assessments of positive blood cultures were routinely recorded at two Danish hospitals from 2006 through 2008. The physicians' assessments classified positive blood cultures as: a) contamination or bloodstream infection;b) bloodstream infection as mono- or polymicrobial;c) bloodstream infection as community-or hospital-onset;d) community-onset bloodstream infection as healthcare-associated or not. We applied the computer algorithms to data from laboratory databases and the Danish National Patient Registry to classify the same groups and compared these with the physicians' assessments as reference episodes. For each classification, we tabulated episodes derived by the physicians' assessment and the computer algorithm and compared 30-day mortality between concordant and discrepant groups with adjustment for age, gender, and comorbidity. Results: Physicians derived 9,482 reference episodes from 21,705 positive blood cultures. The agreement between computer algorithms and physicians' assessments was high for contamination vs. bloodstream infection (8,966/9,482 reference episodes [96.6%], Kappa = 0.83) and mono- vs. polymicrobial bloodstream infection (6,932/7,288 reference episodes [95.2%], Kappa = 0.76), but lower for community-vs. hospital-onset bloodstream infection (6,056/7,288 reference episodes [83.1%], Kappa = 0.57) and healthcare-association (3,032/4,740 reference episodes [64

关键词： Blood Culture computer algorithm Bloodstream Infection Positive Blood Culture Blood Culture Bottle

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