Journal articles: 'Jaccard coefficient'

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Chahal, Manoj. "Information Retrieval using Jaccard Similarity Coefficient." International Journal of Computer Trends and Technology 36, no. 3 (June 25, 2016): 140–43. http://dx.doi.org/10.14445/22312803/ijctt-v36p124.

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Rahman, Zahid, Altaf Hussain, Hussain Shah, and Muhammad Arshad. "Urdu News Clustering Using K-Mean Algorithm On The Basis Of Jaccard Coefficient And Dice Coefficient Similarity." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 10, no. 4 (February 8, 2022): 381–99. http://dx.doi.org/10.14201/adcaij2021104381399.

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Clustering is the unsupervised machine learning process that group data objects into clusters such that objects within the same cluster are highly similar to one another. Every day the quantity of Urdu text is increasing at a high speed on the internet. Grouping Urdu news manually is almost impossible, and there is an utmost need to device a mechanism which cluster Urdu news documents based on their similarity. Clustering Urdu news documents with accuracy is a research issue and it can be solved by using similarity techniques i.e., Jaccard and Dice coefficient, and clustering k-mean algorithm. In this research, the Jaccard and Dice coefficient has been used to find the similarity score of Urdu News documents in python programming language. For the purpose of clustering, the similarity results have been loaded to Waikato Environment for Knowledge Analysis (WEKA), by using k-mean algorithm the Urdu news documents have been clustered into five clusters. The obtained cluster’s results were evaluated in terms of Accuracy and Mean Square Error (MSE). The Accuracy and MSE of Jaccard was 85% and 44.4%, while the Accuracy and MSE of Dice coefficient was 87% and 35.76%. The experimental result shows that Dice coefficient is better as compared to Jaccard similarity on the basis of Accuracy and MSE.

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Zhang, YICHENG. "Design of Branch Predictor Based on Jaccard Coefficient." Journal of Physics: Conference Series 1487 (March 2020): 012003. http://dx.doi.org/10.1088/1742-6596/1487/1/012003.

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Lee, Soojung. "Improving Performance of Jaccard Coefficient for Collaborative Filtering." Journal of the Korea Society of Computer and Information 21, no. 11 (November 30, 2016): 121–26. http://dx.doi.org/10.9708/jksci.2016.21.11.121.

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Taylor, Paul J., Ian J. Donald, Karen Jacques, and Stacey M. Conchie. "Jaccard's heel: Radex models of criminal behaviour are rarely falsifiable when derived using Jaccard coefficient." Legal and Criminological Psychology 17, no. 1 (March 18, 2011): 41–58. http://dx.doi.org/10.1348/135532510x518371.

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Huynh, Hao Tuan, Nghia Duong-Trung, Dinh Quoc Truong, and Hiep Xuan Huynh. "Vietnamese Text Classification with TextRank and Jaccard Similarity Coefficient." Advances in Science, Technology and Engineering Systems Journal 5, no. 6 (2020): 363–69. http://dx.doi.org/10.25046/aj050644.

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Nugraheni, Murien. "Detection Coronavirus using Cased-Based Reasoning with Extended Jaccard Coefficient." IJISTECH (International Journal of Information System & Technology) 5, no. 1 (June 30, 2021): 31. http://dx.doi.org/10.30645/ijistech.v5i1.112.

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Coronavirus Disease 2019 or known as COVID-19 is a new disease that can cause respiratory problems and pneumonia. This disease is caused by infection with Severe Acute Respiratory Syndrome Me Coronavirus 2 (SARS-CoV-2). Some of the clinical symptoms that appear vary, ranging from symptoms such as influenza, cough, cold, throat pain, muscle aches, headaches to those with serious complications such as pneumonia or sepsis. This research to build case-based reasoning for early detection of COVID-19 by looking at the characteristics of clinical symptoms seen in a person using the Extended Jaccard Coefficient method. The results show case-based reasoning for early detection of COVID-19 using the Extended Jaccard Coefficient method can model the level of similarity of a new case to an old case.

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Samanthula, Bharath K., and Wei Jiang. "Secure Multiset Intersection Cardinality and its Application to Jaccard Coefficient." IEEE Transactions on Dependable and Secure Computing 13, no. 5 (September 1, 2016): 591–604. http://dx.doi.org/10.1109/tdsc.2015.2415482.

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Chaitra, Nada Filsa, Muhammad Ainul Yaqin, Rodhiyatus Saadah, and Riska Dwi Anggraeni. "Analisis dan Perancangan Software Pengukur Kemiripan Desain Database Relasional." ILKOMNIKA: Journal of Computer Science and Applied Informatics 2, no. 3 (December 30, 2020): 229–39. http://dx.doi.org/10.28926/ilkomnika.v2i3.60.

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Pengukuran kemiripan entity relational diagram (ERD) dilakukan untuk mendapatkan nilai kemiripan secara semantik dan struktural pada dua ERD yang dibandingkan. Kemiripan struktural didapatkan dengan mendapatkan elemen-elemen yang ada pada ERD. kemiripan semantik dilakukan dengan membandingkan setiap kata pada data definition language (DDL) dari ERD. Untuk mengetahui nilai kesamaan tersebut pengecekan dilakukan dengan secara manual. Hal seperti ini membutuhkan waktu yang cukup lama. Selain itu, terjadinya human error juga sangat mungkin terjadi. Dalam penelitian ini penulis akan menganalisa dan merancang sebuah sistem yang dapat mengukur kemiripan ERD. Ada banyak metode yang digunakan untuk menganalisa kemiripan desain basis data relasional. Untuk kemiripan struktural menggunakan metode jaccard similarity, cosine coefficient, dice’s coefficient dan overlap coefficient. dan untuk kemiripan semantic digunakan levensthein. Hasil kemiripan ERD berupa nilai dari hasil perhitungan menggunakan metode jaccard similarity, cosine coefficient, dice’s coefficient, serta overlap coefficient untuk kemiripan struktural dan metode levensthein untuk kemiripan semantik

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Abdul Malek, Aminah, Nurhanani Abdul Rahim, Nor Farah Nabilah Mushtafa, Nadhirah Afiqah Zailan, and Norlyda Mohamed. "Multiregion segmentation of microcalcificationin mammogram images by using Parametric Kernel Graph Cut algorithm." International Journal of Software Engineering and Computer Systems 7, no. 1 (February 28, 2021): 1–11. http://dx.doi.org/10.15282/ijsecs.7.1.2021.1.0077.

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Early detection of breast cancer can be detected through screening mammography. However, the potential abnormality such as microcalcification can hardly be differentiated by the radiologists due to the tiny size, which sometimes be hidden behind the density of breast tissue. Therefore, image segmentation technique is required. This paper proposes the potential use of Parametric Kernel Graph Cut Algorithm in segmenting microcalcification. The performances of this method were measured based on accuracy, sensitivity, Dice and Jaccard coefficient. All the experimental results generated satisfying results, whereby all images produced the average of 91.67% for Dice coefficient and 84.72% for Jaccard coefficient. Meanwhile, both accuracy and sensitivity results acquired 97.84% and 96%, respectively. Therefore, Parametric Kernel Graph Cut algorithm had proved its ability to segment the microcalcification robustly and efficiently.

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Daniella, Cherry, Intan Yuniar Purbasari, and Basuki Rahmat. "Implementasi Case Based Reasoning Pada Sistem Diagnosis Penyakit Kulit Anjing." Jurnal Informatika dan Sistem Informasi 2, no. 2 (July 21, 2021): 224–33. http://dx.doi.org/10.33005/jifosi.v2i2.320.

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Anjing merupakan salah satu hewan yang sering dijadikan hewan peliharaan bagi manusia. Seringkali pemilik tidak mengetahui penyakit kulit yang diderita oleh anjing peliharaan yang dapat menyebabkan menjadi parah ataupun kesalahan fatal bahkan kematian pada anjing peliharaan. Adanya sistem ini diharapkan dapat membantu pemilik dan dokter hewan untuk mengetahui mengenai diagnosa awal terhadap penyakit kulit yang diderita oleh anjing peliharaanya sehingga mendapatkan penanganan yang tepat setelahnya. Penelitian ini akan menerapkan Case Based Reasoning pada sistem diagnosis penyakit kulit anjing dengan menerapkan metode Jaccard Coefficient (JC) dan Simple Matching Coefficient (SMC). Kedua metode digunakan untuk membandingkan diantara kedua metode ini, hasil akurasi mana yang mendekati sempurna akan digunakan untuk mendapat hasil terbaik dalam pengambilan keputusan. Data yang digunakan berupa gejala-gejala dari penyakit yang ada. Hasil diagnosa didapatkan dari gejala yang telah dimasukkan oleh pengguna yang akan mencari kemiripannya dengan kasus lama dengan menghitung nilai similaritas menggunakan metode Jaccard Coefficient dan Simple Matching Coefficient. Nilai similaritas yang paling mendekati 1 akan dijadikan sebagai solusi dari kasus baru. Apabila nilai similaritas < treshold, maka kasus baru akan dievaluasi kembali untuk mendapatkan solusi. Setelah proses tersebut, kasus baru akan dimasukkan kedalam basis pengetahuan untuk digunakan lagi sebagai pembanding dalam proses diagnosa selanjutnya. Hasil pengujian sistem mendapatkan nilai akurasi sebesar 93,75%, nilai precision sebesar 93,75%, dan nilai recall sebesar 100%. Sementara itu untuk perbandingan metode, dibuktikan bahwa metode Simple Matching Coefficient lebih baik dengan perbandingan akurasi yang mana metode Simple Matching Coefficient mendapat nilai akurasi 93,75%, sementara metode Jaccard Coefficient mendapat nilai akurasi sebesar 87,5%.

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Lee, Joon Kyu, Chang Liu, Mohamed A. Elshaikh, and Ning Wen. "Multiparametric MRI-based intraprostatic tumor volume delineation in localized prostate cancer." Journal of Clinical Oncology 36, no. 6_suppl (February 20, 2018): 22. http://dx.doi.org/10.1200/jco.2018.36.6_suppl.22.

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22 Background: Multiparametric MR imaging (mpMRI) has shown promising results in the diagnosis and localization of prostate cancer. Furthermore, mpMRI may play an important role in identifying a suitable target volume for intraprostatic radiotherapy boost. We sought to investigate the level of correlation between dominant tumor foci contoured on various mpMRI sequences. Methods: mpMRI data from 18 patients with MR-guided biopsy-proven prostate cancer were obtained from the SPIE-AAPM-NCI Prostate MR Classification Challenge. Each case consisted of T2-weighted, apparent diffusion coefficient (ADC), and ktrans images computed from dynamic contrast-enhanced sequences. All image sets were rigidly co-registered, and the dominant tumor foci were identified and contoured for each MRI sequence. Hausdorff distance (HD), mean distance to agreement (MDA), and Dice and Jaccard coefficients were calculated between the contours for each pair of MRI sequences (i.e., T2 vs. ADC, T2 vs. ktrans, and ADC vs. ktrans). The Pearson correlation coefficient (PCC) was also obtained for Dice and Jaccard between these image pairs. Results: The dominant tumor foci were located in the peripheral zone, transition zone, and anterior fibromuscular stroma in 5 (28%), 7 (39%), and 6 (33%) patients, respectively. Mean tumor volumes in the T2-weighted, ADC, and ktrans sequences were 2.71 +/- 2.74 mL, 2.71 +/- 2.67 mL, and 2.21 +/- 1.86 mL, respectively. Mean HD and MDA were lowest (4.34 +/- 1.52 mm and 1.00 +/- 0.52 mm) and Dice and Jaccard coefficients highest (0.74 +/- 0.12 and 0.60 +/- 0.15) for T2 vs. ADC. The PCC for Dice was 0.15 between T2 vs. ADC and T2 vs. ktrans, 0.37 between T2 vs. ADC and ADC vs. ktrans, and 0.62 between T2 vs. ktrans and ADC vs. ktrans, and similar values were obtained for Jaccard (0.12, 0.32, and 0.67, respectively). Four patients were excluded in the PCC calculation as no vascular permeability was visible in the ktrans maps. Conclusions: This analysis suggests that T2-weighted and ADC sequences have high correlation in identifying a suitable intraprostatic radiotherapy boost volume for localized prostate cancer. Furthermore, ktrans maps may provide additional information for tumor volume delineation.

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Anantharajan, Shenbagarajan, Shenbagalakshmi Gunasekaran, and havasi Subramanian. "Brain Tumor Segmentation based on Red-Bellied Woodpecker Mating Optimization Algorithm." NeuroQuantology 20, no. 5 (May 18, 2022): 785–90. http://dx.doi.org/10.14704/nq.2022.20.5.nq22235.

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Earlier, many researchers proposed various segmentation algorithms to segment tumor from MRI Brain image. The method of a nature-inspired meta heuristic-based woodpecker characteristics approach is used to segment the tumored area of this proposed study. In this automated MRI brain tumor segmentation, the MRI brain image gets enhanced for improving the performance of the segmentation accompanied by the skull elimination phase to eliminate the morphological operations of all non-brain tissues. In the end, the RBWMOA (Red-Bellied Woodpecker Mating Optimization Algorithm) is suggested for the segmentation of tumor. An assessment of the experimental outcomes of the methodology suggested was focused on the coefficient of dice similarity, Hausdorff distance, Jaccard coefficient, Precision, Recall, Accuracy and F-measure. The experimental result of RBWMOA obtain better performance and shows0.845 Dice Similarity Coefficient, 7.231 Hausdorff distance in mm, 0.6981 Jaccard Coefficient, 95.67 % Precision, 94.72 % Recall, 98.29 % Accuracy and 95.19 % F-measure.

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Fatmayati, Fryda, Kusrini, and Emha Taufiq Lutfi. "Implementasi Case Base Reasoning Untuk Mendiagnosa Penyakit Gigi dan Mulut." Techno.Com 16, no. 1 (February 6, 2017): 70–79. http://dx.doi.org/10.33633/tc.v16i1.1331.

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Penyakit gigi dan mulut dapat dialami oleh semua orang mulai dari anak-anak hingga dewasa.Namun karena biaya berobat ke dokter gigi yang mahal maka masyarakat enggan memeriksanakan keluhannya terutama pada masyarakat kalangan menengah ke bawah. Padahal jika penyakit gigi dan mulut tidak segera dirawat akan bertambah parah. Case-Based Reasoning meniru kemampuan manusia, yaitu menyelesaikan masalah baru menggunakan jawaban atau pengalaman dari masalah lama.Penyajian pengetahuan (knowledge representation) dibuat dalam bentuk kasus-kasus (case).Setiap kasus berisi masalah dan jawaban, sehingga kasus lebih mirip dengan suatu pola tertentu.Cara kerja Case-Based Reasoning adalah dengan membandingkan kasus baru dengan kasus lama. Jika tidak ada yang cocok maka Case-Based Reasoning akan melakukan adaptasi, dengan cara memasukkan kasus baru tersebut ke dalam database penyimpanan kasus (case base), sehingga secara tidak langsung pengetahuan CBR akan bertambah. Tujuan dari penelitian ini, yaitu mengetahui kemiripan kasus baru dan kasus lama dengan penerapan Case-Based Reasoning (CBR) dan membandingkan dua metode yang digunakan, yaitu Extended Jaccard Coefficient (Tanimoto Coefficient) dan Euclidean Distance similarity dengan memilih hasil akurasi terbaik dari kedua metode tersebut. Hasil pengujian terhadap data uji penyakit gigi dan mulut menunjukkan sistem memiliki unjuk kerja dengan tingkat akurasi menggunakan metode Extended Jaccard Coefficient sebesar 95.24% dan Euclidean Distance Similarity sebesar 100%. Kata kunci—Case Base Reasoning, Extended Jaccard Coefficient, Euclidean Distance Similarity, penyakit gigi dan mulut

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Hoggarth, Michael, and Michael Grumney. "The Distribution and Abundance of mussels (Bivalvia: Unionidae) in Lower Big Walnut Creek from Hoover Dam to its Mouth, in Franklin and Pickaway Counties, Ohio." Ohio Journal of Science 116, no. 2 (August 31, 2016): 48. http://dx.doi.org/10.18061/ojs.v116i2.5151.

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Big Walnut Creek in central Ohio once supported a mussel fauna of 40 species, but no systematic study of the mussels of the creek has been done. The objective of the current study was to determine the distribution and abundance of mussels in Big Walnut Creek downstream of Hoover Dam (RM 36.7) to its mouth with the Scioto River. The extant (living and freshly dead shells) and total (extant plus weathered and subfossil shells) mussel communities were determined at 21 sites. Two techniques were used to determine the mussel community at each site: timed searches and transect/quadrat sampling. Shannon-Weiner (H’) values, Jaccard Coefficient of similarity values, and percent extant species were calculated for the mussel communities at each location. Student T-tests were used to determine where either a significant change in community structure occurred based on the metrics listed above. The mussel communities from Hoover Dam to Whitehall (RM 22.0) had maintained their diversity. The historic and extant communities in this reach were essentially the same (Jaccard Coefficient = 83% and percent extant species = 78%) with H’ values for this reach not significantly different when comparing the total and extant communities (t = 1.08, p > 0.05). The communities from RM 22.0 to RM 15.0 (just downstream of Three Rivers MetroPark) had fewer extant species (Jaccard Coefficient and percent extant values of 62% and 36%, respectively), and significantly diminished species diversity (t = 2.35, p < 0.05). Diminished species diversity continued to be expressed downstream (t = 2.48, p < 0.05), with some recovery (Jaccard Coefficient = 67% and percent extant = 42%) as we approached the mouth of the creek. This improvement may be a result of movement of mussels (as larvae attached to fish hosts) from the Scioto River.

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Normohamadi, Sobhan, Mahmood Solouki, and Forouzan Heidari. "Diversity in Cucumber Genotypes Based on Morphological Traits and SSR Molecular Markers." Biosciences, Biotechnology Research Asia 14, no. 2 (June 25, 2017): 775–82. http://dx.doi.org/10.13005/bbra/2507.

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ABSTRACT: Biodiversity is one of the most important factors in the survival and improvement of any species. Therefore, germplasm collection is the first step for plant improvement. To investigate their genetic and morphological relationships, 10 morphological traits of 20 genotypes of local cucumbers were evaluated using 9 SSR primers. A high genetic variability was observed for the number of flowers per plant. The values of the Jaccard similarity coefficient ranged between 0.51 and 0.92, indicating a high diversity of the genotypes. To evaluate the genetic similarity among genotypes, a cluster analysis using the UPGMA method was performed based on the Jaccard similarity coefficient. The average genetic distance between genotypes (using the Jaccard similarity coefficient) was 0.74 and the mean polymorphic information content (PIC) was 0.69. The primer SSR13251 had the highest PIC (0.8). The clustering pattern of the SSR markers did not coincide with the groupings based on quantitative traits. A dendrogram of the cluster analysis of molecular data showed a high diversity among the studied genotypes. The highest genetic similarity was between genotypes 2 and 3 (0.94), and the lowest genetic similarity was between genotypes 6 and 12 (0.51). The results suggest that SSR markers are a suitable tool to study the genetic diversity and relationships among different genotypes in cucumber.

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Ghasemi, Ali, Ahmad Golparvar, and Mehdi Isfahani. "Analysis of genetic diversity of sugar beet genotypes using random amplified polymorphic DNA marker." Genetika 46, no. 3 (2014): 975–84. http://dx.doi.org/10.2298/gensr1403975g.

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Plant breeding programs are formulated based on the diversity and selection of superior quantitative and qualitative traits. Hence, assessment of genetic diversity is the first step of every plant breeding program. In this regard, use of new methods for studying genetic diversity seems necessary. In the present study, the genetic diversity of thirty sugar beet genotypes was determined using Random Amplified Polymorphic DNA (RAPD) marker. Following the DNA extraction and optimization of experiment conditions, of the 40 primers under study, 10 primers that induced polymorphism and produced good and clear bands in the genotypes of sugar beet were randomly selected. Statistical calculations were carried out based on the Jaccard similarity coefficient and UPGMA-based grouping in the NTSYS software (version 2.02). The amplitude of the multiplied bands varied between 100 and 3000 of alkaline pair. The polymorphism of all primers was 82.33% within the similarity limit. The Cophenetic coefficient for the similarity matrix and the resulting curve was obtained to be r=0.75. Genotypes 4 and 18 with a similarity coefficient of 0.91% demonstrated the highest similarity while genotypes 21 and 15 with a similarity coefficient of 0.63% showed the lowest similarity. Of the primers in use, the OPB-18 primer produced 12 bands (the highest number of bands) and the OPA-09 primer produced 5 bands (the lowest number of bands). Cluster analysis also confirmed the results obtained from the profiles produced in the genetic differentiation of cultivars under study as well as the correlations resulting from the Jaccard similarity coefficient. Finally, genotypes were categorized into 13 groups based on the results and resulting dendrogram. Results of the cluster analysis performed using the Jaccard similarity coefficient revealed the genetic diversity among genotypes that emphasize on efficiency of selection in sugar beet genotypes.

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Li, Xin Ye. "XML Document Clustering Based on Spectral Analysis Method." Advanced Materials Research 219-220 (March 2011): 304–7. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.304.

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While K-Means algorithm usually gets local optimal solution, spectral clustering method can obtain satisfying clustering results through embedding the data points into a new space in which clusters are tighter. Since traditional spectral clustering method uses Gauss Kernel Function to compute the similarity between two points, the selection of scale parameter σ is related with domain knowledge usually. This paper uses spectral method to cluster XML documents. To consider both element and structure of XML documents, this paper proposes to use path feature to represent XML document; to avoild the selection of scale parameter σ, it also proposes to use Jaccard coefficient to compute the similarity between two XML documents. Experiment shows that using Jaccard coefficient to compute the similarity is effective, the clustering result is correct.

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Kanno, Junji, Takuhei Shoji, Hirokazu Ishii, Hisashi Ibuki, Yuji Yoshikawa, Takanori Sasaki, and Kei Shinoda. "Deep Learning with a Dataset Created Using Kanno Saitama Macro, a Self-Made Automatic Foveal Avascular Zone Extraction Program." Journal of Clinical Medicine 12, no. 1 (December 26, 2022): 183. http://dx.doi.org/10.3390/jcm12010183.

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The extraction of the foveal avascular zone (FAZ) from optical coherence tomography angiography (OCTA) images has been used in many studies in recent years due to its association with various ophthalmic diseases. In this study, we investigated the utility of a dataset for deep learning created using Kanno Saitama Macro (KSM), a program that automatically extracts the FAZ using swept-source OCTA. The test data included 40 eyes of 20 healthy volunteers. For training and validation, we used 257 eyes from 257 patients. The FAZ of the retinal surface image was extracted using KSM, and a dataset for FAZ extraction was created. Based on that dataset, we conducted a training test using a typical U-Net. Two examiners manually extracted the FAZ of the test data, and the results were used as gold standards to compare the Jaccard coefficients between examiners, and between each examiner and the U-Net. The Jaccard coefficient was 0.931 between examiner 1 and examiner 2, 0.951 between examiner 1 and the U-Net, and 0.933 between examiner 2 and the U-Net. The Jaccard coefficients were significantly better between examiner 1 and the U-Net than between examiner 1 and examiner 2 (p < 0.001). These data indicated that the dataset generated by KSM was as good as, if not better than, the agreement between examiners using the manual method. KSM may contribute to reducing the burden of annotation in deep learning.

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Yin, Lifeng, Menglin Li, Huayue Chen, and Wu Deng. "An Improved Hierarchical Clustering Algorithm Based on the Idea of Population Reproduction and Fusion." Electronics 11, no. 17 (August 30, 2022): 2735. http://dx.doi.org/10.3390/electronics11172735.

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Aiming to resolve the problems of the traditional hierarchical clustering algorithm that cannot find clusters with uneven density, requires a large amount of calculation, and has low efficiency, this paper proposes an improved hierarchical clustering algorithm (referred to as PRI-MFC) based on the idea of population reproduction and fusion. It is divided into two stages: fuzzy pre-clustering and Jaccard fusion clustering. In the fuzzy pre-clustering stage, it determines the center point, uses the product of the neighborhood radius eps and the dispersion degree fog as the benchmark to divide the data, uses the Euclidean distance to determine the similarity of the two data points, and uses the membership grade to record the information of the common points in each cluster. In the Jaccard fusion clustering stage, the clusters with common points are the clusters to be fused, and the clusters whose Jaccard similarity coefficient between the clusters to be fused is greater than the fusion parameter jac are fused. The common points of the clusters whose Jaccard similarity coefficient between clusters is less than the fusion parameter jac are divided into the cluster with the largest membership grade. A variety of experiments are designed from multiple perspectives on artificial datasets and real datasets to demonstrate the superiority of the PRI-MFC algorithm in terms of clustering effect, clustering quality, and time consumption. Experiments are carried out on Chinese household financial survey data, and the clustering results that conform to the actual situation of Chinese households are obtained, which shows the practicability of this algorithm.

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N, Swathi, and T. Christy Bobby. "BREAST CANCER SEGMENTATION OF MAMMOGRAPHICS IMAGES USING GENERATIVE." Biomedical Sciences Instrumentation 57, no. 2 (April 1, 2021): 247–55. http://dx.doi.org/10.34107/yhpn9422.04247.

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Segmentation of breast cancer tumor plays an important role in identifying the location of the tumor, to know the shape of tumor and hence the stage of breast cancer. This paper deals with the segmentation of tumor from whole mammographic mass images using Generative Adversarial Network (GAN). A mini dataset was considered with mammograms and their corresponding ground truth images. Pre-processing like image format conversion, enhancement, pectoral muscle removal and resizing was performed on raw mammogram images. GANs have two neural nets called generative and discriminative networks that compete against each other to obtain the segmentation output. PIX2PIX is a conditional GAN variant which has U-Net as the Generator network and a simple deep neural net as the discriminator. The input to the network was pair of pre-processed mass image and the associated ground truth. A binary image with highlighted tumor was obtained as output. The performance of GAN was evaluated by plotting Generator and discriminator loss. The segmented output was compared with corresponding ground truth. Metrics like Jaccard index, Jaccard distance and Dice-coefficient were calculated. A Dice-coefficient and Jaccard index of 90% and 88.38% was achieved. In future, higher accuracy could be achieved by involving larger dataset to make the system robust.

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Song, Kwangho, Jihong Min, Gayoung Lee, Sang Chul Shin, and Yoo-Sung Kim. "An Improvement of Plagiarized Area Detection System Using Jaccard Correlation Coefficient Distance Algorithm." Computer Science and Information Technology 3, no. 3 (May 2015): 76–80. http://dx.doi.org/10.13189/csit.2015.030304.

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Ayub, Mubbashir, Mustansar Ali Ghazanfar, Tasawer Khan, and Asjad Saleem. "An Effective Model for Jaccard Coefficient to Increase the Performance of Collaborative Filtering." Arabian Journal for Science and Engineering 45, no. 12 (May 12, 2020): 9997–10017. http://dx.doi.org/10.1007/s13369-020-04568-6.

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Guelzim, Ibrahim, Amina Amkoui, and Hammadi Nait-Charif. "Part-Based Lumbar Vertebrae Tracking in Videofluoroscopy Using Particle Filter." International Journal of Computer Vision and Image Processing 10, no. 2 (April 2020): 29–44. http://dx.doi.org/10.4018/ijcvip.2020040103.

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Vertebrae tracking in videofluoroscopy is a challenging problem because of the low quality ‎of ‎image ‎sequences, like poor image contrast, ambiguous geometry details, and vertebrae rotation. The aim of this article is to tackle this problem by ‎proposing a ‎method for rigid object tracking based on the ‎fragmentation of the tracked object. The proposed method ‎is based on the particle filter using the calculation of the similarity between the ‎respective‏ ‏fragments of ‎objects instead of the whole objects. The similarity measures used are the Jaccard index, the ‎correlation ‎coefficient, and the Bhattacharyya coefficient. The tracking starts with a semi-automatic initialization. ‎The results show that the fragments-based object tracking method outperforms the classical ‎method ‎‎(without fragmentation) for each of the used similarity measures. The results show that the ‎tracking based on the Jaccard index is more stable and outperforms methods based on ‎other similarity ‎measures.‎

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Tomaz, Francisco Linco de Souza, Ana Paula Moura da Silva, Linda Brenna Ribeiro Araújo, Jonas Cunha Neto, and Cândida Hermínia Campos de Magalhães Bertini. "COEFICIENTES DE SIMILARIDADE PARA AVALIAÇÃO DA DIVERSIDADE GENÉTICA EM PINHÃO-MANSO POR MARCADORES ISSR." Nativa 8, no. 4 (July 31, 2020): 456–63. http://dx.doi.org/10.31413/nativa.v8i4.9685.

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Objetivou-se com este trabalho, avaliar a eficiência da utilização de diferentes coeficientes de similaridade na estimação da diversidade genética de Jatropha curcas L. utilizando marcadores moleculares ISSR. O DNA genômico foi extraído a partir de folhas jovens de 43 acessos de pinhão-manso. Matrizes de dissimilaridade genética foram obtidas a partir dos coeficientes Baroni, Coincidência Simples, Hamann, Índice II, Índice III, Jaccard, Nei e Li, Ochiai I, Ochiai II, Rogers e Tanimoto e Sokal e Sneath. Os dendrogramas foram construídos utilizando o método UPGMA e comparados mediante os parâmetros de coeficiente de correlação cofenético, estresse e distorção. Foram estimadas as correlações entre os pares de matrizes pelo teste de Mantel. Houve concordância entre as matrizes originais e as matrizes resultantes do processo de agrupamento para todos os coeficientes estudados. Os índices de Jaccard e Nei e Li não diferiram quanto ao ordenamento dos acessos avaliados e permitiram maior discriminação destes, sendo os mais adequados para avaliar a diversidade genética em pinhão-manso baseada em marcadores moleculares ISSR.Palavras-chave: dissimilaridade genética; análise de agrupamento; Jatropha curcas L. SIMILARITY COEFFICIENTS FOR EVALUATION OF GENETIC DIVERSITY IN JATROPHA BY ISSR MARKERS ABSTRACT:The aim of this work was to evaluate the efficiency of using different similarity coefficients in the estimation of Jatropha curcas L. genetic diversity using ISSR molecular markers. Genomic DNA was extracted from young leaves of the 43 jatropha accessions. Genetic dissimilarity matrices were obtained from the Baroni, Simple Matching, Hamann, Index II, Index III, Jaccard, Nei and Li, Ochiai I, Ochiai II, Rogers and Tanimoto and Sokal and Sneath coefficients. The dendrograms were constructed using the UPGMA method and compared using the co-phenetic correlation coefficient, stress and distortion parameters. Correlations between pairs of matrices were estimated by the Mantel test. There was agreement between the original matrices and the matrices resulting from the grouping process for all the studied coefficients. The Jaccard and Nei and Li indices did not differ in terms of the order of the evaluated accessions and allowed for greater discrimination of these, being the most suitable for assessing genetic diversity in physic nut based on ISSR molecular markers.Keywords: genetic dissimilarity; cluster analysis; Jatropha curcas L.

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Deng, Zhanao, Fahrettin Goktepe, Brent K. Harbaugh, and Jinguo Hu. "Assessment of Genetic Diversity and Relationships Among Caladium Cultivars and Species Using Molecular Markers." Journal of the American Society for Horticultural Science 132, no. 2 (March 2007): 219–29. http://dx.doi.org/10.21273/jashs.132.2.219.

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Caladium (Caladium ×hortulanum Birdsey) is an important aroid widely used in the ornamental plant industry. Concerns have been raised about possible loss of genetic diversity due to a drastic decline in the number of cultivars in the last century. This study assessed genetic diversity and relationships among caladium cultivars and species accessions. Forty-five major cultivars and 14 species accessions were analyzed based on 297 DNA fragments produced by the target-region amplification polymorphism marker system. A low level of diversity (44.4% polymorphism) was exhibited in cultivars, while a high level of diversity (96.8% polymorphism) was present among seven accessions of Caladium bicolor (Aiton) Vent., Caladium marmoratum Mathieu, Caladium picturatum C. Koch, and Caladium schomburgkii Schott. A small percentage (7.6%) of DNA fragments was present in cultivars but absent in the seven species accessions, while a high percentage (32.2%) of DNA fragments was present in the seven species accessions but absent in cultivars. Cultivars shared a higher level of similarity at the molecular level with an average Jaccard coefficient at 0.802, formed a large group in cluster analysis, and concentrated in the scatter plot from a principal-coordinate analysis. Two accessions of C. bicolor and C. schomburgkii were very similar to cultivars with Jaccard similarity coefficients from 0.531 to 0.771, while the rest of the species accessions had small similarity coefficients with cultivars (0.060 to 0.386). Caladium steudnirifolium Engler and Caladium lindenii (André) Madison were very dissimilar to C. bicolor, C. marmoratum, C. picturatum, and C. schomburgkii, with Jaccard similarity coefficients from 0.149 to 0.237 (C. steudnirifolium) and from 0.060 to 0.118 (C. lindenii). There is a limited amount of molecular diversity in caladium cultivars, but the great repertoire of unique genes in species accessions could be used to enhance the diversity in future cultivars and reduce potential genetic vulnerability.

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Jiao, Han, Xinhua Jiang, Zhiyong Pang, Xiaofeng Lin, Yihua Huang, and Li Li. "Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI." Computational and Mathematical Methods in Medicine 2020 (May 5, 2020): 1–12. http://dx.doi.org/10.1155/2020/2413706.

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Breast segmentation and mass detection in medical images are important for diagnosis and treatment follow-up. Automation of these challenging tasks can assist radiologists by reducing the high manual workload of breast cancer analysis. In this paper, deep convolutional neural networks (DCNN) were employed for breast segmentation and mass detection in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). First, the region of the breasts was segmented from the remaining body parts by building a fully convolutional neural network based on U-Net++. Using the method of deep learning to extract the target area can help to reduce the interference external to the breast. Second, a faster region with convolutional neural network (Faster RCNN) was used for mass detection on segmented breast images. The dataset of DCE-MRI used in this study was obtained from 75 patients, and a 5-fold cross validation method was adopted. The statistical analysis of breast region segmentation was carried out by computing the Dice similarity coefficient (DSC), Jaccard coefficient, and segmentation sensitivity. For validation of breast mass detection, the sensitivity with the number of false positives per case was computed and analyzed. The Dice and Jaccard coefficients and the segmentation sensitivity value for breast region segmentation were 0.951, 0.908, and 0.948, respectively, which were better than those of the original U-Net algorithm, and the average sensitivity for mass detection achieved 0.874 with 3.4 false positives per case.

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Rathna Priya, T. S., and Annamalai Manickavasagan. "Evaluation of segmentation methods for RGB colour image-based detection of Fusarium infection in corn grains using support vector machine (SVM) and pre-trained convolution neural network (CNN)." Canadian Biosystems Engineering 64, no. 1 (December 31, 2022): 7.09–7.20. http://dx.doi.org/10.7451/cbe.2022.64.7.9.

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This study evaluated six segmentation methods (clustering, flood-fill, graph-cut, colour-thresholding, watershed, and Otsu’s-thresholding) for segmentation accuracy and classification accuracy in discriminating Fusarium infected corn grains using RGB colour images. The segmentation accuracy was calculated using Jaccard similarity index and Dice coefficient in comparison with the gold standard (manual segmentation method). Flood-fill and graph-cut methods showed the highest segmentation accuracy of 77% and 87% for Jaccard and Dice evaluation metrics, respectively. Pre-trained convolution neural network (CNN) and support vector machine (SVM) were used to evaluate the effect of segmentation methods on classification accuracy using segmented images and extracted features from the segmented images, respectively. The SVM based two-class model to discriminate healthy and Fusarium infected corn grains yielded the classification accuracy of 84%, 79%, 78%, 74%, 69% and 65% for graph-cut, watershed, clustering, flood-fill, colour-thresholding, and Otsu’s-thresholding, respectively. In pretrained CNN model, the classification accuracies were 93%, 88%, 87%, 84%, 61% and 59% for flood-fill, graph-cut, colour-thresholding, clustering, watershed, and Otsu’s-thresholding, respectively. Jaccard and Dice evaluation metrics showed the highest correlation with the pretrained CNN classification accuracies with R2 values of 0.9693 and 0.9727, respectively. The correlation with SVM classification accuracies were R2–0.505 for Jaccard and R2–0.5151 for Dice evaluation metrics.

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Chen, Xiaoqun, Rong Lu, and Feng Zhao. "Convolutional Neural Network-Processed MRI Images in the Diagnosis of Plastic Bronchitis in Children." Contrast Media & Molecular Imaging 2021 (September 13, 2021): 1–8. http://dx.doi.org/10.1155/2021/2748830.

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Objective. The study focused on the features of the convolutional neural networks- (CNN-) processed magnetic resonance imaging (MRI) images for plastic bronchitis (PB) in children. Methods. 30 PB children were selected as subjects, including 19 boys and 11 girls. They all received the MRI examination for the chest. Then, a CNN-based algorithm was constructed and compared with Active Appearance Model (AAM) algorithm for segmentation effects of MRI images in 30 PB children, factoring into occurring simultaneously than (OST), Dice, and Jaccard coefficient. Results. The maximum Dice coefficient of CNN algorithm reached 0.946, while that of active AAM was 0.843, and the Jaccard coefficient of CNN algorithm was also higher (0.894 vs. 0.758, P < 0.05 ). The MRI images showed pulmonary inflammation in all subjects. Of 30 patients, 14 (46.66%) had complicated pulmonary atelectasis, 9 (30%) had the complicated pleural effusion, 3 (10%) had pneumothorax, 2 (6.67%) had complicated mediastinal emphysema, and 2 (6.67%) had complicated pneumopericardium. Also, of 30 patients, 19 (63.33%) had lung consolidation and atelectasis in a single lung lobe and 11 (36.67%) in both two lung lobes. Conclusion. The algorithm based on CNN can significantly improve the segmentation accuracy of MRI images for plastic bronchitis in children. The pleural effusion was a dangerous factor for the occurrence and development of PB.

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Febriansyah, Luke Michael, and Shinta Estri Wahyuningrum. "ANALYSIS WINNOWING ALGORITHM FOR TEXT PLAGIARISM DETECTION USING THREE METHOD SIMILARITY." Proxies : Jurnal Informatika 2, no. 2 (March 10, 2021): 42. http://dx.doi.org/10.24167/proxies.v2i2.3208.

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Cases of plagiarism in recent years has been an issues. Based on that issues, this research will create a system to detect similarity in a text. There is an aspect as reference of the research that is analyze the plagiarism algorithm. This research will analyze the accuracy one of plagiarism check algorithm, winnowing algorithm. Winnowing algorithm is a plagiarism detection algorithm based on document fingerprinting. To calculate percentage similarity of document fingerprinting in text, there are 3 methods to measure similarity that will be used in this research, which is jaccard similarity coefficient, sorensen dice similarity coefficient, and berg similarity coefficient.

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Pana, Lenuta, Simona Moldovanu, Nilanjan Dey, Amira S. Ashour, and Luminita Moraru. "Brain Tissue Evaluation Based on Skeleton Shape and Similarity Analysis between Hemispheres." Computation 8, no. 2 (April 15, 2020): 31. http://dx.doi.org/10.3390/computation8020031.

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Background: The purpose of this article is to provide a new evaluation tool based on skeleton maps to assess the tumoral and non-tumoral regions of the 2D MRI in PD-weighted (proton density) and T2w (T2-weighted type) brain images. Methods: The proposed method investigated inter-hemisphere brain tissue similarity using a mask in the right hemisphere and its mirror reflection in the left one. At the hemisphere level and for each ROI (region of interest), a morphological skeleton algorithm was used to efficiently investigate the similarity between hemispheres. Two datasets with 88 T2w and PD images belonging to healthy patients and patients diagnosed with glioma were investigated: D1 contains the original raw images affected by Rician noise and D2 consists of the same images pre-processed for noise removal. Results: The investigation was based on structural similarity assessment by using the Structural Similarity Index (SSIM) and a modified Jaccard metrics. A novel S-Jaccard (Skeleton Jaccard) metric was proposed. Cluster accuracy was estimated based on the Silhouette method (SV). The Silhouette coefficient (SC) indicates the quality of the clustering process for the SSIM and S-Jaccard. To assess the overall classification accuracy an ROC curve implementation was carried out. Conclusions: Consistent results were obtained for healthy patients and for PD images of glioma. We demonstrated that the S-Jaccard metric based on skeletal similarity is an efficient tool for an inter-hemisphere brain similarity evaluation. The accuracy of the proposed skeletonization method was smaller for the original images affected by Rician noise (AUC = 0.883 (T2w) and 0.904 (PD)) but increased for denoised images (AUC = 0.951 (T2w) and 0.969 (PD)).

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Wahba, Lydia E., Nor Hazlina, A. Fadelah, and Wickneswari Ratnam. "Genetic Relatedness among Dendrobium (Orchidaceae) Species and Hybrids Using Morphological and AFLP Markers." HortScience 49, no. 5 (May 2014): 524–30. http://dx.doi.org/10.21273/hortsci.49.5.524.

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Dendrobium is one of the largest genera in the Orchidaceae family. Information on the genetic diversity and relationships among species and hybrids is important for breeding purposes and species conservation. The objectives of this study were to assess genetic relatedness and to determine whether morphological, molecular, or combined analysis can discriminate among Dendrobium species, commercial hybrids, and interspecific hybrids. A total of 81 Dendrobium accessions were characterized with 12 amplified fragment length polymorphism (AFLP) primer pairs and 21 morphological characters. Mean genetic relatedness for morphological characters, AFLP analysis, and combined analysis were 0.61, 0.37, and 0.43, respectively. Dendrograms were generated using an unweighted pair group method with arithmetic averages (UPGMA); the analysis was performed on a Jaccard similarity coefficient matrix. The data from morphological characters revealed that the Jaccard similarity coefficient ranged from 0.20 to 1.0, where the tested 81 Dendrobium accessions could be grouped into four clusters. For the AFLP analysis, the number of polymorphic fragments for each primer varied from 80 to 284 with 78% average percentage of polymorphic loci and the similarity coefficient ranging from 0.125 to 1.0 with Dendrobium accessions grouped into three clusters. The similarity coefficients estimated through a combined analysis of morphological and AFLP data ranged from 0.21 to 1.0 and the Dendrobium accessions appeared clustered into two groups. The results revealed some similarities among the three data sets. The combined data set was the most useful in discriminating Dendrobium accessions based on species sections and relationship among species and their hybrids. The correlation between the AFLP data and the combined data was highly significant (r = 0.98, P > 0.001), indicating the usefulness of AFLP data for species discrimination and hybrid identity in the absence of floral morphological characters.

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Sakira Kamaruddin, Siti, Yuhanis Yusof, Nur Azzah Abu Bakar, Mohamed Ahmed Tayie, and Ghaith Abdulsattar A.Jabbar Alkubaisi. "Graph-based Representation for Sentence Similarity Measure : A Comparative Analysis." International Journal of Engineering & Technology 7, no. 2.14 (April 6, 2018): 32. http://dx.doi.org/10.14419/ijet.v7i2.14.11149.

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Textual data are a rich source of knowledge; hence, sentence comparison has become one of the important tasks in text mining related works. Most previous work in text comparison are performed at document level, research suggest that comparing sentence level text is a non-trivial problem. One of the reason is two sentences can convey the same meaning with totally dissimilar words. This paper presents the results of a comparative analysis on three representation schemes i.e. term frequency inverse document frequency, Latent Semantic Analysis and Graph based representation using three similarity measures i.e. Cosine, Dice coefficient and Jaccard similarity to compare the similarity of sentences. Results reveal that the graph based representation and the Jaccard similarity measure outperforms the others in terms of precision, recall and F-measures.

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Lee, Deun, and Sunoh Choi. "Similar Word Replacement Method for Improving News Commenter Analysis." Applied Sciences 12, no. 13 (July 5, 2022): 6803. http://dx.doi.org/10.3390/app12136803.

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In Korea, it is common to read and comment on news stories on portal sites. To influence public opinion, some people write comments repeatedly, some of which are similar to those posted by others. This has become a serious social issue. In our previous research, we collected approximately 2.68 million news comments posted in April 2017. We classified the political stance of each author using a deep learning model (seq2seq), and evaluated how many similar comments each user wrote, as well as how similar each comment was to those posted by other people, using the Jaccard similarity coefficient. However, as our previous model used Jaccard’s similarity only, the meaning of the comments was not considered. To solve this problem, we propose similar word replacement (SWR) using word2vec and a method to analyze the similarity between user comments and classify the political stance of each user. In this study, we showed that when our model used SWR rather than Jaccard’s similarity, its ability to detect similarity between comments increased 3.2 times, and the accuracy of political stance classification improved by 6%.

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Kroth, Mariela Aparecida, Micheline Sandra Ramella, Caroline Tagliari, Alicia de Francisco, and Ana Carolina Maisonnave Arisi. "Genetic similarity of Brazilian hull-less and malting barley varieties evaluated by RAPD markers." Scientia Agricola 62, no. 1 (January 2005): 36–39. http://dx.doi.org/10.1590/s0103-90162005000100007.

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Barley (Hordeum vulgare L.) is widely used for brewing and animal feed. Recently, it has become desirable for human consumption due to its high nutritional significance, specially hull-less or naked barley. There are differences in nutritional and malting characteristics among barley varieties. RAPD procedure is able to separate barley varieties at various similarity levels. The aim of this work was the RAPD analysis of six Brazilian hull-less varieties and seven malting varieties. PCR reactions were performed with eleven random primers. A total of 34 RAPD fragments was obtained with five primers. A dendrogram was constructed based on the Jaccard similarity coefficient. Barley varieties displayed an average similarity coefficient of 0.53. Intravarietal monomorphic fragments allowed differentiation among varieties. The averages of intravarietal similarity coefficients ranged from 0.53 to 0.85. RAPD markers, detected in this work, were suitable for differentiation among Brazilian barley varieties.

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Taravat, Alireza, Matthias P. Wagner, Rogerio Bonifacio, and David Petit. "Advanced Fully Convolutional Networks for Agricultural Field Boundary Detection." Remote Sensing 13, no. 4 (February 16, 2021): 722. http://dx.doi.org/10.3390/rs13040722.

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Accurate spatial information of agricultural fields is important for providing actionable information to farmers, managers, and policymakers. On the other hand, the automated detection of field boundaries is a challenging task due to their small size, irregular shape and the use of mixed-cropping systems making field boundaries vaguely defined. In this paper, we propose a strategy for field boundary detection based on the fully convolutional network architecture called ResU-Net. The benefits of this model are two-fold: first, residual units ease training of deep networks. Second, rich skip connections within the network could facilitate information propagation, allowing us to design networks with fewer parameters but better performance in comparison with the traditional U-Net model. An extensive experimental analysis is performed over the whole of Denmark using Sentinel-2 images and comparing several U-Net and ResU-Net field boundary detection algorithms. The presented results show that the ResU-Net model has a better performance with an average F1 score of 0.90 and average Jaccard coefficient of 0.80 in comparison to the U-Net model with an average F1 score of 0.88 and an average Jaccard coefficient of 0.77.

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Calvo-Rodriguez, Sofía, Julio Calvo-Alvarado, Mario M. Do Espírito-Santo, and Yule R. F. Nunes. "Changes in forest structure and composition in a successional tropical dry forest." Revista Forestal Mesoamericana Kurú 14, no. 35 (June 26, 2017): 12. http://dx.doi.org/10.18845/rfmk.v14i35.3149.

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We describe changes in forest structure and floristic composition of three successional stages for Mata Seca State Park, in Minas Gerais, Brazil, through the measurement of all trees greater than 5 cm of diameter at breast height (DBH) of 18 permanent plots (6 per stage) for early, intermediate, and late successional stages of a tropical dry forest during a 5-year period. Using this information, we calculated the Importance Value Index (IVI), Holdridge Complexity Index, Jaccard Similarity Coefficient, and Shannon Diversity Index for each stage of succession. The floristic composition and structure of the successional stages expressed by the Holdridge Complexity Index, showed that complexity increases gradually as we advance through the successional stages, while the Shannon Diversity Index indicated that species diversity was higher in the intermediate stage of succession. The Jaccard Similarity Coefficients showed that the intermediate and late successional stages had high similarity, whereas the early successional stage had low similarity with these two successional stages. Mortality rates were higher in the early stage, especially in stems with smaller diameters (5-10cm). This information contributes to the dissemination of important knowledge for the conservation of the tropical dry forests of Brazil, which are the most threatened ecosystems in this country and, at the same time, the least studied.

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Elkabani, Islam, and Roa A. Aboo Khachfeh. "Homophily-Based Link Prediction in The Facebook Online Social Network: A Rough Sets Approach." Journal of Intelligent Systems 24, no. 4 (December 1, 2015): 491–503. http://dx.doi.org/10.1515/jisys-2014-0031.

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AbstractOnline social networks are highly dynamic and sparse. One of the main problems in analyzing these networks is the problem of predicting the existence of links between users on these networks: the link prediction problem. Many studies have been conducted to predict links using a variety of techniques like the decision tree and the logistic regression approaches. In this work, we will illustrate the use of the rough set theory in predicting links over the Facebook social network based on homophilic features. Other supervised learning algorithms are also employed in our experiments and compared with the rough set classifier, such as naive Bayes, J48 decision tree, support vector machine, logistic regression, and multilayer perceptron neural network. Moreover, we studied the influence of the “common groups” and “common page likes” homophilic features on predicting friendship between users of Facebook, and also studied the effect of using the Jaccard coefficient in measuring the similarity between users’ homophilic attributes compared with using the overlap coefficient. We conducted our experiments on two different datasets obtained from the Facebook online social network, where users in each dataset live within the same geographical region. The results showed that the rough set classifier significantly outperformed the other classifiers in all experiments. The results also demonstrated that the common groups and the common page likes features have a significant influence on predicting the friendship between users of Facebook. Finally, the results revealed that using the overlap coefficient homophilic features provided better results than that of the Jaccard coefficient features.

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Díaz, Macarena, Jorge Novo, Manuel G. Penedo, and Marcos Ortega. "Automatic Segmentation and Measurement of Vascular Biomarkers in OCT-A Images." Proceedings 2, no. 18 (September 17, 2018): 1169. http://dx.doi.org/10.3390/proceedings2181169.

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We propose an automatic methodology that identifies the vascularity zones in OCT-A images and their measurement for its use in clinical analysis and diagnostic processes. The segmentation and measurement contributes objectivity and repeatability in the results, desirable characteristics in any diagnosis and monitoring process. In the validation of the method, the correlation coefficient of Pearson and Jaccard index were used, obtaining satisfactory results.

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Rozinek, Ondřej, and Jan Mareš. "The Duality of Similarity and Metric Spaces." Applied Sciences 11, no. 4 (February 22, 2021): 1910. http://dx.doi.org/10.3390/app11041910.

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We introduce a new mathematical basis for similarity space. For the first time, we describe the relationship between distance and similarity from set theory. Then, we derive generally valid relations for the conversion between similarity and a metric and vice versa. We present a general solution for the normalization of a given similarity space or metric space. The derived solutions lead to many already used similarity and distance functions, and combine them into a unified theory. The Jaccard coefficient, Tanimoto coefficient, Steinhaus distance, Ruzicka similarity, Gaussian similarity, edit distance and edit similarity satisfy this relationship, which verifies our fundamental theory.

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Vaidya, Praveenkumar, and Harinarayana N. S. "Social Semantics and Similarities from User-generated Keywords to Information Retrieval: A Case Study of Social Tags in Marine Science." DESIDOC Journal of Library & Information Technology 38, no. 1 (January 2, 2018): 11. http://dx.doi.org/10.14429/djlit.38.1.10969.

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Of late, social tagging has become popular trend in information organisation. In context of digital resources the tags assigned by users also play vital role in information retrieval. For information discovery the ‘terms’ used to retrieve the results also depend upon the ‘relevancy’ or ‘weightage’ of the keywords. This study investigates ‘relevancy ranking’ of terms used in the full text of the resource. The common words present in both full text of the article and social tags were considered for the study by employing TF-IDF statistical technique and Jaccard similarity test. The results show that it is possible to assign ‘weight’ to keywords for better results and also determine the significant tags assigned by the user. The Jaccard similarity coefficient test adopted to understand the word similarity between full text words of an article and marine social tags. This work reveals the social tags can enrich metadata for information retrieval.

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VilasBoas-Ribeiro, Iva, Sergio Curto, Gerard C. van Rhoon, Martine Franckena, and Margarethus M. Paulides. "MR Thermometry Accuracy and Prospective Imaging-Based Patient Selection in MR-Guided Hyperthermia Treatment for Locally Advanced Cervical Cancer." Cancers 13, no. 14 (July 13, 2021): 3503. http://dx.doi.org/10.3390/cancers13143503.

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The efficacy of a hyperthermia treatment depends on the delivery of well-controlled heating; hence, accurate temperature monitoring is essential for ensuring effective treatment. For deep pelvic hyperthermia, there are no comprehensive and systematic reports on MR thermometry. Moreover, data inclusion generally lacks objective selection criteria leading to a high probability of bias when comparing results. Herein, we studied whether imaging-based data inclusion predicts accuracy and could serve as a tool for prospective patient selection. The accuracy of the MR thermometry in patients with locally advanced cervical cancer was benchmarked against intraluminal temperature. We found that gastrointestinal air motion at the start of the treatment, quantified by the Jaccard similarity coefficient, was a good predictor for MR thermometry accuracy. The results for the group that was selected for low gastrointestinal air motion improved compared to the results for all patients by 50% (accuracy), 26% (precision), and 80% (bias). We found an average MR thermometry accuracy of 2.0 °C when all patients were considered and 1.0 °C for the selected group. These results serve as the basis for comprehensive benchmarking of novel technologies. The Jaccard similarity coefficient also has good potential to prospectively determine in which patients the MR thermometry will be valuable.

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Fan, Xiaojie, Xiaoyu Zhang, Zibo Zhang, and Yifang Jiang. "Deep Learning on MRI Images for Diagnosis of Lung Cancer Spinal Bone Metastasis." Contrast Media & Molecular Imaging 2021 (July 14, 2021): 1–9. http://dx.doi.org/10.1155/2021/5294379.

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This paper aimed to explore the adoption of deep learning algorithms in lung cancer spinal bone metastasis diagnosis. Comprehensive analysis was carried out with the aid of AdaBoost algorithm and Chan-Vese (CV) algorithm. 87 patients with lung cancer spinal bone metastasis were taken as research subjects, and comprehensive evaluation was made in terms of preliminary classification of images, segmentation results, Dice index, and Jaccard coefficient. After the case of misjudgment on whether there was hot spot was excluded, the initial classification accuracy of the AdaBoost algorithm can reach 96.55%. True positive rate (TPR) was 2.3%, and false negative rate (FNR) was 1.15%. 45 MRI images with hot spots were utilized as test set to detect the segmentation accuracy of CV, maximum between-cluster variance method (OTSU), and region growing algorithm. The results showed that the Dice index and Jaccard coefficient of the CV algorithm were 0.8591 and 0.8002, respectively, which were considerably superior to OTSU (0.6125 and 0.5541) and region growing algorithm (0.7293 and 0.6598). In summary, the AdaBoost algorithm was adopted for image preliminary classification, and CV algorithm for image segmentation was ideal for the diagnosis of lung cancer spinal bone metastasis and it was worthy of clinical promotion.

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Puspaningrum, Eva Y., Budi Nugroho, Ariyono Setiawan, and Nuraini Hariyanti. "Detection of Text Similarity for Indication Plagiarism Using Winnowing Algorithm Based K-gram and Jaccard Coefficient." Journal of Physics: Conference Series 1569 (July 2020): 022044. http://dx.doi.org/10.1088/1742-6596/1569/2/022044.

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Markov, Zlata, Snezana Popov, Sonja Mudri-Stojnic, Snezana Radenkovic, and Ante Vujic. "Hoverfly diversity assesment in grassland and forest habitats in Autonomous Province of Vojvodina based on a recent monitoring study." Zbornik Matice srpske za prirodne nauke, no. 135 (2018): 93–102. http://dx.doi.org/10.2298/zmspn1835093m.

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Pollination is a required process for survival of numerous plant species and crops. Hoverflies (Diptera: Syrphidae) play a significant role in this phenomenon. Due to raising environmental pressures, pollinator diversity and pollination services are at risk. Faunistic studies and biodiversity research are the essential elements and steps in the process of species preservation. This study aimed to analyze diversity of hoverflies in two CORINE land cover types (Broad-leaved forest and Natural grasslands), based on a recent one-year study. To achieve this goal, Shannon?s diversity index (H), Shannon?s equitability (EH), and Jaccard similarity coefficient (Jt) were calculated. Values of indices and coefficients indicate which parts of Vojvodina and what land cover types can be considered as hoverfly reservoirs.

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Favoretto, Patrícia, Elizabeth Ann Veasey, and Paulo César Tavares de Melo. "Molecular characterization of potato cultivars using SSR markers." Horticultura Brasileira 29, no. 4 (December 2011): 542–47. http://dx.doi.org/10.1590/s0102-05362011000400017.

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The potato crop has a very narrow genetic base, so the use of molecular markers is a very important tool in the characterization of germplasm banks and evaluation of genetic divergence. The objective of this study was to identify, using microsatellite or simple sequence repeat (SSR) markers, 38 accessions of potato from two collections of commercial cultivars. For the molecular characterization 10 loci were used, generating a total of 46 alleles, which were analyzed as binary data. A cluster analysis was performed with the Jaccard´s similarity coefficient and the UPGMA method, using the software NTSYSpc. On average, the number of alleles per locus was 4.6, ranging from two alleles for primers STM1049, STM 1053 and STM 1104 to 12 alleles per locus for primer STM0019a. Of the 46 alleles, only five were monomorphic, therefore presenting 89.1% polymorphism. The polymorphism information content (PIC) varied from 0.13 to 0.86, with an average of 0.54. The Jaccard´s coefficient varied from 0.41 to 0.93, showing high genetic variability among accessions. Two possible duplicates [Atlantic (Canada) and Atlantic (Chile), and Colorado and Ágata (EPAMIG) (duplicates with these SSRs, which did not separate them)] were identified. High similarity was also shown by cultivars Chipie and Melodie (EPAMIG), Voyager and Gourmandine (EPAMIG), Eole and Caesar (EPAMIG), and Cupido and Santé (Pirassu). The most genetically divergent accessions (Lady Rosetta and HPC-7B) were also identified.

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Mossi, AJ, RL Cansian, O. Leontiev-Orlov, JL Cechet, AZ Carvalho, G. Toniazzo, and S. Echeverrigaray. "Genetic diversity and conservation of native populations of Maytenus Ilicifolia Mart. ex Reiss." Brazilian Journal of Biology 69, no. 2 (May 2009): 447–53. http://dx.doi.org/10.1590/s1519-69842009000200030.

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The aim of this work was to analyze genetic variability in 18 populations of Maytenus ilicifolia, and representatives of Maytenus aquifolia and Maytenus evonymoidis, collected in the states of Mato Grosso do Sul, Paraná, Santa Catarina and Rio Grande do Sul, using RAPD molecular markers. Considering total samples of the three species, 263 amplified fragments were identified, of which 72.2% showed to be polymorphous. The index of similarity (Jaccard coefficient) was on average 0.64 between M. ilicifolia and M. aquifolia; 0.47 between M. ilicifolia and M. evonymoidis; and 0.44 between M. aquifolia and M. evonymoidis. The analysis of groupings by the UPGMA algorithm allowed to clearly separate the three analyzed species. In determining the variability in M. ilicifolia, 222 bands were identified, on average 11.1 bands per primer, being 43.2% polymorphous. The index of similarity (Jaccard coefficient) in the bulks of each population in M. ilicifolia was, on average, 0.92 and the index of similarities among the populations was 0.83. The analysis of groupings with the UPGMA algorithm and the analysis of the main coordination (PCO), allowed the separation of the analyzed populations into three groups, the populations from the south of Rio Grande do Sul and the population from Mato Grosso do Sul standing out. A relation between the groupings found and the edaphoclimatic conditions of the collecting places was observed.

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Soyusiawaty, Dewi, and Indriyani Putri Utami. "Implementation of Jaccard Coefficient Method for Searching Report Findings of Internal Quality Audit in Ahmad Dahlan University." International Journal of Computer Applications 175, no. 38 (December 17, 2020): 28–35. http://dx.doi.org/10.5120/ijca2020920954.

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Devanand, Pachanoor S., C. Thomas Chao*, Jianjn Chen, and Richard J. Henny. "AFLP Analysis of Genetic Relationships among Container-grown Anthurium Cultivars." HortScience 39, no. 4 (July 2004): 861C—861. http://dx.doi.org/10.21273/hortsci.39.4.861c.

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Anthurium is the largest genus in the family Araceae, consisting of about 1000 species. Anthuriums are valued for their colorful spathes and traditionally used as cut flowers. With the introduction of compact cultivars through breeding, a series of container-grown cultivars have been released and widely produced as flowering foliage plants. However, limited information is available about genetic relatedness among these container-grown cultivars. This study analyzed genetic relationships of 58 cultivars using amplified fragment length polymorphism (AFLP) markers with near infrared fluorescence labeled primers. Forty-eight EcoR I + 2/Mse I + 3 primer set combinations were screened from which six primer sets were selected and used in this investigation. Each selected primer set generated 94 to 115 scorable fragments. A total of 647 AFLP fragments were detected of which 401 were polymorphic (67%). All cultivars were clearly differentiated by their AFLP finger-prints. A dendrogram was constructed using the unweighted pair-group method of arithmetic averages (UPGMA) technique and a principal coordinated analysis (PCA) was used to analyze the relationships. The 58 cultivars were divided into three clusters; clusters I, II, and III had 40, 10, and 8 cultivars, respectively. Most commonly grown cultivars were positioned in cluster I, where had Jaccard similarity coefficients among them ranged from 0.7 to 0.98. Eighteen of the 40 shared Jaccard similarity coefficient of 0.8 or higher, indicating that genetic diversity for cultivated container-grown Anthurium is needed.

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Taleb, Aya, Rizik M. H. Al-Sayyed, and Hamed S. Al-Bdour. "JKRW Link Prediction – A New Ensemble Technique Based on Merging Other Known Techniques in The Social Network Analysis." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 12 (June 18, 2021): 125. http://dx.doi.org/10.3991/ijim.v15i12.22831.

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In this research, a new technique to improve the accuracy of the link prediction for most of the networks is proposed; it is based on the prediction ensemble approach using the voting merging technique. The new proposed ensemble called Jaccard, Katz, and Random models Wrapper (JKRW), it scales up the prediction accuracy and provides better predictions for different sizes of populations including small, medium, and large data. The proposed model has been tested and evaluated based on the area under curve (AUC) and accuracy (ACC) measures. These measures applied to the other models used in this study that has been built based on the Jaccard Coefficient, Katz, Adamic/Adar, and Preferential attachment. Results from applying the evaluation matrices verify the improvement of JKRW effectiveness and stability in comparison to the other tested models. The results from applying the Wilcoxon signed-rank method (one of the non-parametric paired tests) indicate that JKRW has significant differences compared to the other models in the different populations at <strong>0.95</strong> confident interval.

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Journal articles on the topic 'Jaccard coefficient'

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