scholarly journals Ekstraksi Citra Digital Tulisan Mandarin Ke Dalam Terjemahan Tulisan Latin Bahasa Indonesia Dengan Stroke Extraction Dan Template Matching

2018 ◽  
Vol 4 (3) ◽  
pp. 138-148
Author(s):  
Robert Robert ◽  
Ananda Ananda ◽  
Ibnu Surya
Keyword(s):  
Receiver Operating Characteristic ◽  
Template Matching ◽  
Operating Characteristic ◽  
Stroke Extraction ◽  
Bahasa Indonesia ◽  
Receiver Operating

Bahasa merupakan sistem lambang bunyi arbitrer, yang digunakan oleh masyarakat untuk berkomunikasi. Bahasa Mandarin menduduki peringkat pertama bahasa yang paling banyak digunakan menurut survei dari Nationalencyklopedin, dan bahasa Mandarin menggunakan huruf yang disebut dengan Hanzi. Manusia memiliki berbagai cara dalam berkomunikasi, salah satunya dengan tulisan. Perangkat lunak dapat mengenali tulisan melalui gambar dengan menerapkan Pengolahan Citra Digital. Penelitian ini bertujuan untuk membangun aplikasi yang dapat mendeteksi Hanzi Mandarin dengan menerapkan Pengolahan Citra Digital. Pengolahan Citra Digital merujuk pada pemrosesan gambar dua dimensi dengan menggunakan komputer. Metode Pengolahan Citra dengan Stroke Extraction dan Template Matching diimplementasikan sebagai solusi masalah. Stroke Extraction digunakan untuk melakukan ekstraksi goresan dasar (pi hua) pada citra Hanzi Mandarin dan kemudian dikenali dengan menggunakan Template Matching. Aplikasi diuji dengan Receiver Operating Characteristic sehingga didapat digambarkan sebuah kurva yang dapat membantu proses analisis. Berdasarkan kurva ROC yang tergambarkan metode Stroke Extraction masih perlu pengembangan karena rendahnya tingkat keberhasilan dalam menghitung goresan. Aplikasi yang dibangun berhasil melakukan ekstraksi Hanzi Mandarin tulisan tangan maupun cetak, dan diterjemahkan ke bahasa Indonesia dengan tingkat akurasi 76,47%. Disimpulkan bahwa metode Stroke Extraction dan Template Matching cukup efisien dalam memproses citra digital Hanzi Mandarin untuk diterjemahkan ke tulisan bahasa Indonesia.

scholarly journals Ekstraksi Citra Digital Tulisan Mandarin Ke Dalam Terjemahan Tulisan Latin Bahasa Indonesia Dengan Stroke Extraction Dan Template Matching

2018 ◽  
Vol 4 (3) ◽  
pp. 138-148
Author(s):  
Robert Robert ◽  
Ananda Ananda ◽  
Ibnu Surya
Keyword(s):  
Image Processing ◽  
Receiver Operating Characteristic ◽  
Digital Image Processing ◽  
Digital Image ◽  
Template Matching ◽  
Operating Characteristic ◽  
Stroke Extraction ◽  
Bahasa Indonesia ◽  
Receiver Operating

Bahasa adalah sistem simbolik dari suara acak, yang digunakan oleh orang untuk berkomunikasi. Mandarin berada di peringkat pertama bahasa yang paling banyak digunakan menurut survei Nationalencyklopedin, dan Mandarin menggunakan huruf yang disebut Hanzi. Manusia memiliki berbagai cara berkomunikasi, salah satunya dengan menulis. Perangkat lunak ini dapat mengenali tulisan melalui gambar dengan menerapkan Pemrosesan Gambar Digital. Penelitian ini bertujuan untuk membangun aplikasi yang dapat mendeteksi Hanzi Mandarin dengan menerapkan Digital Image Processing. Digital Image Processing mengacu pada pemrosesan gambar dua dimensi menggunakan komputer. Metode pemrosesan gambar dengan Stroke Extraction dan Template Mathcing diterapkan solusi untuk masalah ini. Stroke Extraction digunakan untuk melakukan ekstraksi stroke dasar (pi hua) pada gambar Mandarin Hanzi dan kemudian mengenalinya menggunakan Template Mathcing. Aplikasi diuji dengan Receiver Operating Characteristic sehingga kurva yang dapat membantu proses analisis dapat ditarik. Berdasarkan hasil kurva ROC yang dijelaskan, metode Stroke Extraction masih membutuhkan pengembangan karena tingkat keberhasilan yang rendah dalam menghitung sejumlah stroke. Aplikasi yang dibangun berhasil mengekstraksi Mandarin Hanzi baik tulisan tangan dan dicetak dan diterjemahkan ke dalam bahasa Indonesia dengan akurasi 76,47%. Dapat disimpulkan bahwa metode Stroke Extraction dan Template Matching cukup efisien dalam mengolah gambar digital Mandarin Hanzi untuk diterjemahkan ke dalam bahasa Indonesia.

Übereinstimmung von klinischer Diagnose, strukturierten Interviews und Selbstbeurteilungsfragebögen bei Depression im Kindes- und Jugendalter

2012 ◽  
Vol 40 (6) ◽  
pp. 405-414 ◽  
Author(s):  
Kathrin Dolle ◽  
Gerd Schulte-Körne ◽  
Nikolaus von Hofacker ◽  
Yonca Izat ◽  
Antje-Kathrin Allgaier
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Kinder Und Jugendliche ◽  
Prädiktive Werte ◽  
Receiver Operating

Fragestellung: Die vorliegende Studie untersucht die Übereinstimmung von strukturierten Kind- und Elterninterviews sowie dem klinischen Urteil bei der Diagnostik depressiver Episoden im Kindes- und Jugendalter. Zudem prüft sie, ob sich die Treffsicherheit und die optimalen Cut-off-Werte von Selbstbeurteilungsfragebögen in Referenz zu diesen verschiedenen Beurteilerperspektiven unterscheiden. Methodik: Mit 81 Kindern (9–12 Jahre) und 88 Jugendlichen (13–16 Jahre), die sich in kinder- und jugendpsychiatrischen Kliniken oder Praxen vorstellten, und ihren Eltern wurden strukturierte Kinder-DIPS-Interviews durchgeführt. Die Kinder füllten das Depressions-Inventar für Kinder und Jugendliche (DIKJ) aus, die Jugendlichen die Allgemeine Depressions-Skala in der Kurzform (ADS-K). Übereinstimmungen wurden mittels Kappa-Koeffizienten ermittelt. Optimale Cut-off-Werte, Sensitivität, Spezifität sowie positive und negative prädiktive Werte wurden anhand von Receiver operating characteristic (ROC) Kurven bestimmt. Ergebnisse: Die Interviews stimmten untereinander sowie mit dem klinischen Urteil niedrig bis mäßig überein. Depressive Episoden wurden häufiger nach klinischem Urteil als in den Interviews festgestellt. Cut-off-Werte und Validitätsmaße der Selbstbeurteilungsfragebögen variierten je nach Referenzstandard mit den schlechtesten Ergebnissen für das klinische Urteil. Schlussfolgerungen: Klinische Beurteiler könnten durch den Einsatz von strukturierten Interviews profitieren. Strategien für den Umgang mit diskrepanten Kind- und Elternangaben sollten empirisch geprüft und detailliert beschrieben werden.

Use of Receiver Operating Characteristic (ROC) Curves to Evaluate Computer Confidence Threshold and Clinical Performance in the Diagnosis of Appendicitis

1978 ◽  
Vol 17 (03) ◽  
pp. 157-161 ◽  
Author(s):  
F. T. De Dombal ◽  
Jane C. Horrocks
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Clinical Performance ◽  
Roc Curves ◽  
Final Diagnosis ◽  
Confidence Threshold ◽  
Computer Aided ◽  
Computer Confidence ◽  
Overall Performance ◽  
Receiver Operating

This paper uses simple receiver operating characteristic (ROC) curves (i) to study the effect of varying computer confidence of threshold levels and (ii) to evaluate clinical performance in the diagnosis of acute appendicitis. Over 1300 patients presenting to five centres with abdominal pain of short duration were studied in varying detail. Clinical and computer-aided diagnostic predictions were compared with the »final« diagnosis. From these studies it is concluded the simplistic setting of a 50/50 confidence threshold for the computer program is as »good« as any other. The proximity of a computer-aided system changed clinical behaviour patterns; a higher overall performance level was achieved and clinicians performance levels became associated with the »mildly conservative« end of the computers ROC curve. Prior forecasts of over-confidence or ultra-caution amongst clinicians using the computer-aided system have not been fulfilled.

A validated novel preoperative index to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology: A cohort study

2019 ◽  
Vol 30 (7-8) ◽  
pp. 221-228
Author(s):  
Shahab Hajibandeh ◽  
Shahin Hajibandeh ◽  
Nicholas Hobbs ◽  
Jigar Shah ◽  
Matthew Harris ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Emergency Laparotomy ◽  
Contamination Index ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Aims To investigate whether an intraperitoneal contamination index (ICI) derived from combined preoperative levels of C-reactive protein, lactate, neutrophils, lymphocytes and albumin could predict the extent of intraperitoneal contamination in patients with acute abdominal pathology. Methods Patients aged over 18 who underwent emergency laparotomy for acute abdominal pathology between January 2014 and October 2018 were randomly divided into primary and validation cohorts. The proposed intraperitoneal contamination index was calculated for each patient in each cohort. Receiver operating characteristic curve analysis was performed to determine discrimination of the index and cut-off values of preoperative intraperitoneal contamination index that could predict the extent of intraperitoneal contamination. Results Overall, 468 patients were included in this study; 234 in the primary cohort and 234 in the validation cohort. The analyses identified intraperitoneal contamination index of 24.77 and 24.32 as cut-off values for purulent contamination in the primary cohort (area under the curve (AUC): 0.73, P < 0.0001; sensitivity: 84%, specificity: 60%) and validation cohort (AUC: 0.83, P < 0.0001; sensitivity: 91%, specificity: 69%), respectively. Receiver operating characteristic curve analysis also identified intraperitoneal contamination index of 33.70 and 33.41 as cut-off values for feculent contamination in the primary cohort (AUC: 0.78, P < 0.0001; sensitivity: 87%, specificity: 64%) and validation cohort (AUC: 0.79, P < 0.0001; sensitivity: 86%, specificity: 73%), respectively. Conclusions As a predictive measure which is derived purely from biomarkers, intraperitoneal contamination index may be accurate enough to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology and to facilitate decision-making together with clinical and radiological findings.

scholarly journals MicroRNAs-1299, -126-3p and -30e-3p as Potential Diagnostic Biomarkers for Prediabetes

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 949
Author(s):  
Cecil J. Weale ◽  
Don M. Matshazi ◽  
Saarah F. G. Davids ◽  
Shanel Raghubeer ◽  
Rajiv T. Erasmus ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Roc Curves ◽  
Tolerance Test ◽  
Cross Sectional Study ◽  
Oral Glucose ◽  
Cross Sectional ◽  
Receiver Operating

This cross-sectional study investigated the association of miR-1299, -126-3p and -30e-3p with and their diagnostic capability for dysglycaemia in 1273 (men, n = 345) South Africans, aged >20 years. Glycaemic status was assessed by oral glucose tolerance test (OGTT). Whole blood microRNA (miRNA) expressions were assessed using TaqMan-based reverse transcription quantitative-PCR (RT-qPCR). Receiver operating characteristic (ROC) curves assessed the ability of each miRNA to discriminate dysglycaemia, while multivariable logistic regression analyses linked expression with dysglycaemia. In all, 207 (16.2%) and 94 (7.4%) participants had prediabetes and type 2 diabetes mellitus (T2DM), respectively. All three miRNAs were significantly highly expressed in individuals with prediabetes compared to normotolerant patients, p < 0.001. miR-30e-3p and miR-126-3p were also significantly more expressed in T2DM versus normotolerant patients, p < 0.001. In multivariable logistic regressions, the three miRNAs were consistently and continuously associated with prediabetes, while only miR-126-3p was associated with T2DM. The ROC analysis indicated all three miRNAs had a significant overall predictive ability to diagnose prediabetes, diabetes and the combination of both (dysglycaemia), with the area under the receiver operating characteristic curve (AUC) being significantly higher for miR-126-3p in prediabetes. For prediabetes diagnosis, miR-126-3p (AUC = 0.760) outperformed HbA1c (AUC = 0.695), p = 0.042. These results suggest that miR-1299, -126-3p and -30e-3p are associated with prediabetes, and measuring miR-126-3p could potentially contribute to diabetes risk screening strategies.

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