{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":11,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":11,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"ff92c11c533a","filters":{"venue":"International Journal of Artificial Intelligence & Applications"}},"results":[{"id":"W2012684637","doi":"10.5121/ijaia.2010.1301","title":"An Efficient Automatic Mass Classification Method In Digitized Mammograms Using Artificial Neural Network","year":2010,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence & Applications","topic":"AI in cancer detection","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial intelligence; Artificial neural network; Kurtosis; Pattern recognition (psychology); Computer science; Standard deviation; Skewness; Entropy (arrow of time); Computer-aided diagnosis; Sensitivity (control systems); Mathematics; Statistics; Engineering","authors":[{"name":"Mohammed Jahirul Islam","is_ca":false},{"name":"Majid Ahmadi","is_ca":true},{"name":"M.A. Sid-Ahmed","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05774316565455551,"gpt":0.3816841791298209,"spread":0.3239410134752654,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001515631,0.000213426,0.0002749244,0.0005254552,0.0001844311,0.0006022454,0.002084543,0.0001496419,0.00005470388],"category_scores_gemma":[0.0001197514,0.0002176917,0.0001683725,0.001057948,0.000136926,0.0008253383,0.00009506691,0.0006941731,0.00003249109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002941476,"about_ca_system_score_gemma":0.0002650022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006546221,"about_ca_topic_score_gemma":0.0001592711,"domain_scores_codex":[0.9967099,0.0001743411,0.00145714,0.0004406752,0.0008626705,0.0003553109],"domain_scores_gemma":[0.9970766,0.0002972754,0.0009414146,0.0005706234,0.000928807,0.000185325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003550881,0.000286063,0.0001757486,0.000002882649,0.00002494356,0.000008327786,0.0002196055,0.2443999,0.07164633,0.1661522,0.000005781194,0.5170427],"study_design_scores_gemma":[0.00004813148,0.00006798843,0.0005206152,0.00002271756,0.00001456639,0.0001225302,0.0001639367,0.8606563,0.012101,0.1256663,0.0004347859,0.0001811694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1005582,0.00002173962,0.8955182,0.0009444149,0.002344331,0.0004210132,0.000005064957,0.00009650106,0.00009052785],"genre_scores_gemma":[0.6838106,0.000005592432,0.3149546,0.00008580094,0.001045659,0.0000758701,0.000004349367,0.00001548757,0.000002013199],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6162563,"threshold_uncertainty_score":0.8877209,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4297794705","doi":"10.5121/ijaia.2022.13404","title":"Deep Learning-based ECG Classification on Raspberry PI using a Tensorflow Lite Model based on PTB-XL Dataset","year":2022,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence & Applications","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"","keywords":"Raspberry pi; Artificial intelligence; Computer science; Machine learning; Deep learning; Coronavirus disease 2019 (COVID-19); Internet of Things; Embedded system; Medicine","authors":[{"name":"K. Venkatesh Sharma","is_ca":true},{"name":"Rasit Eskicioglu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09041596806005482,"gpt":0.3818925607178578,"spread":0.291476592657803,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006386936,0.000187402,0.0002693307,0.0007703144,0.0003841088,0.00009127164,0.0005186333,0.00006340924,0.0002838473],"category_scores_gemma":[0.0002388314,0.0001833322,0.0002625935,0.0005018244,0.000087098,0.0001057028,0.00004597944,0.0007919268,0.0000606849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005446398,"about_ca_system_score_gemma":0.0003193516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003511759,"about_ca_topic_score_gemma":0.00000511339,"domain_scores_codex":[0.9973122,0.0001174015,0.0008390659,0.0003323987,0.001196673,0.0002022997],"domain_scores_gemma":[0.9977881,0.0002712265,0.000656923,0.0003741758,0.000742322,0.0001672652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005383889,0.001018421,0.0007026568,0.000006665939,0.00009819259,0.0000203692,0.00007714637,0.9282461,0.008719274,0.0007711031,0.0002035131,0.05959814],"study_design_scores_gemma":[0.0001557238,0.0003497501,0.0001942214,0.0000720836,0.000156234,0.00003164797,0.0003518938,0.9850745,0.004468248,0.000952659,0.008041628,0.0001514483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02537502,0.00004449377,0.9690148,0.004477161,0.0002986014,0.0003200142,0.0002392196,0.00003983665,0.0001908313],"genre_scores_gemma":[0.9848167,0.00001678408,0.01250221,0.001031243,0.000738437,0.000126308,0.0006544011,0.00003150275,0.0000824448],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9594417,"threshold_uncertainty_score":0.7476069,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3048896729","doi":"10.5121/ijaia.2020.11405","title":"Log Message Anomaly Detection with Oversampling","year":2020,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence & Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"","keywords":"Oversampling; Anomaly detection; Anomaly (physics); Computer science; Artificial intelligence; Physics; Computer network; Bandwidth (computing); Condensed matter physics","authors":[{"name":"Amir Farzad","is_ca":true},{"name":"T. Aaron Gulliver","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0389444143972389,"gpt":0.302322601934411,"spread":0.2633781875371721,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001929326,0.0001346813,0.0001569505,0.0001826931,0.0001498144,0.0002487827,0.001366777,0.000060463,0.00004997817],"category_scores_gemma":[0.00004230425,0.0001205631,0.000121249,0.000625131,0.0000830388,0.0006211761,0.0001141927,0.0002687136,0.00007568383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008519561,"about_ca_system_score_gemma":0.0001020603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002265443,"about_ca_topic_score_gemma":0.00001622704,"domain_scores_codex":[0.9984556,0.0000254033,0.0006173432,0.0002734812,0.0004776717,0.0001505146],"domain_scores_gemma":[0.9981331,0.00008817855,0.0005247869,0.0002421928,0.0008469279,0.0001648047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001066523,0.0002359289,0.0002150737,0.000006606256,0.0001344088,0.00001782187,0.0006009302,0.004338018,0.05648951,0.2582744,0.0001035654,0.679477],"study_design_scores_gemma":[0.000187868,0.0009891063,0.0005071473,0.00005815245,0.00007363359,0.0005159279,0.00084451,0.09442493,0.6973218,0.06725648,0.1371437,0.0006767411],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003275113,0.00004351657,0.9916487,0.003971742,0.0001218056,0.0002696253,0.000005886332,0.0001249247,0.0005386366],"genre_scores_gemma":[0.9098792,0.00004716411,0.08888455,0.0006010348,0.0004666365,0.00009541931,0.00000188049,0.00001140442,0.00001272196],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9066041,"threshold_uncertainty_score":0.4916419,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3155456212","doi":"10.5121/ijaia.2021.12201","title":"A Modified CNN-Based Face Recognition System","year":2021,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence & Applications","topic":"Face recognition and analysis","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"","keywords":"Softmax function; Preprocessor; Artificial intelligence; Computer science; Pattern recognition (psychology); Facial recognition system; Face (sociological concept); Data pre-processing; Classifier (UML); Deep learning","authors":[{"name":"Jayanthi Raghavan","is_ca":true},{"name":"Majid Ahmadi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06193818704515913,"gpt":0.3173775742855616,"spread":0.2554393872404025,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003767115,0.0001256014,0.0001954909,0.0003381093,0.000117039,0.0003610265,0.0009997312,0.00006783369,0.000128205],"category_scores_gemma":[0.0001201802,0.000125639,0.0002681895,0.0006854377,0.00005223604,0.0004202902,0.000074937,0.0002065716,0.0003885064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001658421,"about_ca_system_score_gemma":0.0003275195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001128303,"about_ca_topic_score_gemma":0.0000139154,"domain_scores_codex":[0.9980217,0.00009133983,0.0008142817,0.0002624677,0.0006511914,0.0001590094],"domain_scores_gemma":[0.9964161,0.0001776965,0.0004874007,0.0002810891,0.002501109,0.0001366129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002418768,0.0004523297,0.00002514649,0.00001395412,0.0001677209,0.00007389913,0.0001942792,0.01757072,0.007973216,0.1051232,0.0001307039,0.8682507],"study_design_scores_gemma":[0.0002146156,0.00009560659,0.00006489637,0.000276499,0.0001151504,0.0004616163,0.002300535,0.5544716,0.3642763,0.0655382,0.01167685,0.000508095],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002982785,0.0001057887,0.9913372,0.003505156,0.0005080136,0.0001243995,0.00002225964,0.00006427008,0.001350147],"genre_scores_gemma":[0.9464957,0.0000535894,0.05253454,0.0004159914,0.0003345977,0.00006389141,0.00003066525,0.00000898599,0.00006205264],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9435129,"threshold_uncertainty_score":0.5123408,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385841855","doi":"10.5121/ijaia.2023.14404","title":"Segmentation of the Gastrointestinal Tract MRI Using Deep Learning","year":2023,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence & Applications","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Laurentian University","funders":"","keywords":"Computer science; Contouring; Segmentation; Artificial intelligence; Deep learning; Hausdorff distance; Sørensen–Dice coefficient; Gastrointestinal tract; Dice; Magnetic resonance imaging; Rendering (computer graphics); Market segmentation; Computer vision; Image segmentation; Radiology; Medicine; Computer graphics (images); Internal medicine; Mathematics","authors":[{"name":"Jitendra Nath Roy","is_ca":true},{"name":"Amr Abdel-Dayem","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03822823701453593,"gpt":0.3741652881450615,"spread":0.3359370511305256,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005513319,0.00008408828,0.000157651,0.0002440435,0.000104459,0.00003578103,0.0003336159,0.00002732756,0.00008718482],"category_scores_gemma":[0.0004805426,0.0000638214,0.0001758888,0.0004374587,0.0001507942,0.0001018497,0.00005232568,0.0004459519,0.00002197526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009544659,"about_ca_system_score_gemma":0.0001303049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002185828,"about_ca_topic_score_gemma":0.00000246028,"domain_scores_codex":[0.9984524,0.00004928574,0.0006682244,0.0001109207,0.0005885552,0.0001306031],"domain_scores_gemma":[0.998423,0.0001772359,0.0005627321,0.0001133514,0.0006444813,0.000079231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001678369,0.000469693,0.07546633,0.00003799366,0.0001188074,0.00005051479,0.001018094,0.1694227,0.3165556,0.008588254,0.000118348,0.4279859],"study_design_scores_gemma":[0.0004029492,0.0004409945,0.04780044,0.0009427025,0.0003435122,0.004634104,0.005444669,0.8367516,0.07694802,0.01810084,0.007896829,0.0002932764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3136248,0.00004217415,0.6824934,0.003049171,0.0003267259,0.0002272649,0.000002720912,0.00002192423,0.0002117867],"genre_scores_gemma":[0.9788356,0.00006933219,0.02037649,0.0001138778,0.0005104704,0.00001158061,0.000008477652,0.00001461125,0.0000595139],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.667329,"threshold_uncertainty_score":0.2602561,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4297794681","doi":"10.5121/ijaia.2022.13406","title":"New Local Binary Pattern Feature Extractor with Adaptive Threshold for Face Recognition Applications","year":2022,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence & Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"","keywords":"Local binary patterns; Pattern recognition (psychology); Thresholding; Artificial intelligence; Computer science; Pixel; Feature extraction; Facial recognition system; Binary number; Support vector machine; Histogram; Computer vision; Mathematics; Image (mathematics)","authors":[{"name":"Soroosh Parsai","is_ca":true},{"name":"Majid Ahmadi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04913380572319091,"gpt":0.3072572551650916,"spread":0.2581234494419007,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003561388,0.000170762,0.0001800723,0.0003148657,0.0003489028,0.0001819159,0.001454284,0.00006281622,0.0001690281],"category_scores_gemma":[0.00001363829,0.0001573953,0.000159381,0.0004561023,0.00006518594,0.0006459905,0.0001795591,0.0004208014,0.00006299792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002149731,"about_ca_system_score_gemma":0.0003195751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002321246,"about_ca_topic_score_gemma":0.00001738685,"domain_scores_codex":[0.9981076,0.00005241162,0.0005420928,0.0003528898,0.0007355601,0.0002094635],"domain_scores_gemma":[0.9978245,0.0002432111,0.0005624971,0.0002807034,0.0009268729,0.0001621376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002528216,0.0003808601,0.00002322699,0.000005637064,0.00009155645,0.000008424108,0.000343453,0.01027294,0.004354044,0.01629715,0.002539224,0.9654307],"study_design_scores_gemma":[0.0009854011,0.002827079,0.000330826,0.0002784621,0.0002009797,0.00111906,0.009434505,0.1416878,0.1065559,0.4100789,0.3250367,0.001464449],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008334154,0.0001378825,0.9935762,0.003736711,0.0003580118,0.0009677761,0.000143375,0.00005263834,0.0001939856],"genre_scores_gemma":[0.8628257,0.00006024706,0.1333321,0.0006281888,0.0007595742,0.002063363,0.0001468156,0.00002849414,0.0001555527],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9639662,"threshold_uncertainty_score":0.6418394,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4399714634","doi":"10.5121/ijaia.2024.15305","title":"Classifying Emergency Patients into Fast-Track and Complex Cases using Machine Learning","year":2024,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence & Applications","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"","keywords":"Triage; Fast track; Specialty; Medical emergency; Emergency department; Medicine; Track (disk drive); Artificial intelligence; Computer science; Machine learning; Emergency medicine; Family medicine; Nursing; Surgery","authors":[{"name":"Ala' Karajeh","is_ca":true},{"name":"Rasit Eskicioglu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08642299157486028,"gpt":0.4007727330459158,"spread":0.3143497414710555,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000187775,0.0001389389,0.0001985122,0.0002865426,0.0001838541,0.00006106719,0.0001624695,0.00004772349,0.0003138238],"category_scores_gemma":[0.0002447159,0.000120699,0.000142508,0.0002602335,0.0001089905,0.0002197712,0.00007574544,0.0003334538,0.00002647147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001126635,"about_ca_system_score_gemma":0.00007080066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009266588,"about_ca_topic_score_gemma":0.00005577471,"domain_scores_codex":[0.9984904,0.0000268162,0.0007445699,0.0001973632,0.0004051589,0.0001356846],"domain_scores_gemma":[0.9985225,0.0001302959,0.0002009513,0.000081185,0.0009626722,0.0001024291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002671522,0.0006864386,0.05252969,0.0002094974,0.001136835,0.0001859159,0.003971674,0.0008909917,0.05078324,0.02360484,0.001419717,0.864314],"study_design_scores_gemma":[0.0005424347,0.002052672,0.01579878,0.002044732,0.001713228,0.002890005,0.01389943,0.1744033,0.03060152,0.05866021,0.6958516,0.001542067],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6899617,0.01102542,0.2888052,0.00494796,0.003265941,0.0007077479,0.00006823034,0.0001140403,0.001103724],"genre_scores_gemma":[0.9914535,0.002717457,0.004809351,0.00009517065,0.0007746411,0.00001785777,0.00003301368,0.00001949031,0.00007951006],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8627719,"threshold_uncertainty_score":0.4921964,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4293070708","doi":"10.5121/ijaia.2022.13305","title":"Data Standardization using Deep Learning for Healthcare Insurance Claims","year":2022,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence & Applications","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Laurentian University","funders":"","keywords":"Standardization; Computer science; Metadata; Receipt; Deep learning; Task (project management); Data mining; Artificial intelligence; Data science; Machine learning; Information retrieval; World Wide Web; Engineering","authors":[{"name":"Kaelan Renault","is_ca":true},{"name":"Amr Abdel-Dayem","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.4530248982000601,"gpt":0.497278961173409,"spread":0.04425406297334894,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002764675,0.0001217848,0.0002313752,0.0004665683,0.0008377697,0.0003153197,0.004546294,0.00005734786,0.0002113816],"category_scores_gemma":[0.00119629,0.0001144947,0.0001257393,0.001016589,0.0001380248,0.0005840638,0.0008372273,0.0004472707,0.00001996734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002437427,"about_ca_system_score_gemma":0.0002495768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002703415,"about_ca_topic_score_gemma":0.00006288179,"domain_scores_codex":[0.9961835,0.0001203552,0.001442014,0.000440781,0.001610522,0.0002028829],"domain_scores_gemma":[0.9948333,0.0007378826,0.001330509,0.0007901334,0.002223102,0.00008509542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009654676,0.0001652191,0.0008263672,0.000002259433,0.00004445982,0.000001736578,0.0001579331,0.09308926,0.00188228,0.1002786,0.0007266341,0.8027287],"study_design_scores_gemma":[0.00006890553,0.0001237726,0.0001881379,0.000009327702,0.00001967922,0.00008848519,0.005292367,0.117706,0.001282179,0.3047899,0.5702663,0.0001650018],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007179593,0.0003193905,0.9832578,0.006586808,0.000571328,0.0005338883,0.001472565,0.00003608411,0.00004254953],"genre_scores_gemma":[0.9614671,0.0001328208,0.03721066,0.0002156213,0.0004544253,0.0002239497,0.0002464233,0.00001701055,0.00003201262],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9542875,"threshold_uncertainty_score":0.8448222,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4403440036","doi":"10.5121/ijaia.2024.15504","title":"Transformer-Based Regression Models for Assessing Reading Passage Complexity: A Deep Learning Approach in Natural Language Processing","year":2024,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence & Applications","topic":"Text Readability and Simplification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Laurentian University","funders":"","keywords":"Computer science; Transformer; Artificial intelligence; Natural language processing; Deep learning; Regression; Machine learning; Statistics; Electrical engineering; Mathematics","authors":[{"name":"Harmanpreet Sidhu","is_ca":true},{"name":"Amr Abdel-Dayem","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07330698097469918,"gpt":0.3753080358454225,"spread":0.3020010548707234,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009743232,0.0001497259,0.000196594,0.0005123738,0.0001710002,0.0008454521,0.0009430897,0.00007822826,0.000005049443],"category_scores_gemma":[0.0000774568,0.0001307864,0.0001631721,0.0005840398,0.0000919369,0.001597522,0.00003148744,0.0004671058,0.000004078368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002534438,"about_ca_system_score_gemma":0.000228648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001831921,"about_ca_topic_score_gemma":0.00002107361,"domain_scores_codex":[0.9980775,0.00007437984,0.0008009907,0.000342355,0.0004905021,0.0002142909],"domain_scores_gemma":[0.998689,0.0003252007,0.0002726462,0.0001690635,0.0004757313,0.00006834185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002551037,0.0001518479,0.00002007839,0.00005074373,0.00001532387,0.00000527729,0.002441347,0.03758528,0.006089941,0.1206748,0.000003587867,0.8329363],"study_design_scores_gemma":[0.00004393717,0.00002634357,0.00004437235,0.0002183732,0.00001064343,0.00003865933,0.001105184,0.9240958,0.005317767,0.06829938,0.0006703184,0.0001292312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003759749,0.0009898128,0.9925464,0.001639859,0.0002545381,0.0003995018,0.000004405224,0.00008508869,0.0003206666],"genre_scores_gemma":[0.8607295,0.00001892605,0.1388113,0.00005277064,0.0002049633,0.0001327866,0.00002464545,0.00001268721,0.00001245308],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8865105,"threshold_uncertainty_score":0.8152714,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4394825026","doi":"10.5121/ijaia.2024.15205","title":"Immunizing Image Classifiers Against Localized Adversary Attack","year":2024,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence & Applications","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Adversarial system; Convolutional neural network; Computer science; Adversary; Deep learning; Artificial intelligence; Vulnerability (computing); Machine learning; Convolution (computer science); Image (mathematics); Scale (ratio); Deep neural networks; Artificial neural network; Pattern recognition (psychology); Computer security; Geography","authors":[{"name":"Henok Ghebrechristos","is_ca":false},{"name":"Gita Alaghband","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03972670245325821,"gpt":0.356184689767243,"spread":0.3164579873139848,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000781182,0.0001927643,0.0002178147,0.0005461723,0.0001711205,0.0007321886,0.00258928,0.00008998081,0.0000934263],"category_scores_gemma":[0.0002086207,0.0001845726,0.0002674046,0.0007118505,0.0001882595,0.001442507,0.0003426424,0.0006992336,0.0003353827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002828568,"about_ca_system_score_gemma":0.0003195111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001387546,"about_ca_topic_score_gemma":0.000004251425,"domain_scores_codex":[0.9975699,0.00009935822,0.0009680903,0.0003419601,0.000766673,0.0002539862],"domain_scores_gemma":[0.9979314,0.0004172833,0.000366437,0.0003854963,0.0007610265,0.0001383419],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003213672,0.0001123569,0.00001961077,0.00001330999,0.0002028911,0.0001664025,0.0009001897,0.03918642,0.01185577,0.2849899,0.0006917524,0.6618292],"study_design_scores_gemma":[0.00008968395,0.00007782637,0.00002920269,0.0002587722,0.00004538407,0.0002981072,0.001007623,0.7340447,0.01157499,0.0595521,0.1926098,0.0004118057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001301452,0.0004896724,0.9886119,0.004518027,0.002166923,0.0001985168,0.000006344869,0.0001503802,0.00255679],"genre_scores_gemma":[0.7847777,0.0003129921,0.2129179,0.0003660685,0.001385983,0.00005183748,0.00001217995,0.00003457082,0.0001407326],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7834763,"threshold_uncertainty_score":0.7526651,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4387654814","doi":"10.5121/ijaia.2023.14501","title":"Performance Evaluation of Block-Sized Algorithms for Majority Vote in Facial Recognition","year":2023,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence & Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Computer science; Feature extraction; Support vector machine; Facial recognition system; Preprocessor; Histogram equalization; Principal component analysis; Histogram; Linear discriminant analysis; Block (permutation group theory); Local binary patterns; Mathematics; Image (mathematics)","authors":[{"name":"Andrea Ruiz-Hernandez","is_ca":true},{"name":"Jennifer Lee","is_ca":true},{"name":"Nawal Rehman","is_ca":true},{"name":"Jayanthi Raghavan","is_ca":true},{"name":"Majid Ahmadi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1268462707714424,"gpt":0.3837447680990099,"spread":0.2568984973275675,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00191729,0.0001018307,0.0001731523,0.0006061303,0.00007566933,0.00007034983,0.0007637671,0.0000771506,0.00002102386],"category_scores_gemma":[0.0002659622,0.0001014974,0.0001264242,0.0006878813,0.00005255466,0.0006060411,0.0000726064,0.000148399,0.00005378751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001114481,"about_ca_system_score_gemma":0.0002287258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002276862,"about_ca_topic_score_gemma":0.00003531851,"domain_scores_codex":[0.9977931,0.00007363121,0.0009003321,0.0002069581,0.0008627765,0.000163156],"domain_scores_gemma":[0.9966399,0.0002203275,0.0005318219,0.0001632755,0.002388888,0.00005578053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006507952,0.000175592,0.0001506231,0.000007321512,0.00002759701,9.500276e-7,0.0003675864,0.005593756,0.01112201,0.001229352,0.0001148201,0.9811453],"study_design_scores_gemma":[0.0004203167,0.0001784697,0.002628815,0.0001767579,0.00004282984,0.00002527662,0.0003270057,0.7152501,0.161537,0.1169189,0.002271069,0.0002234173],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.40684,0.00003747776,0.5892437,0.001581702,0.001086542,0.0008927653,0.00007307292,0.00004375177,0.0002010259],"genre_scores_gemma":[0.9777899,0.0001443274,0.02126542,0.00004576416,0.0003362852,0.00034406,0.00005491474,0.00000774601,0.00001154441],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9809219,"threshold_uncertainty_score":0.4138945,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}