Program Plagiarism Detection using Data Dependency Matrix Method

2010 ◽  
Author(s):  
Seema Kolkur ◽  
Madhavi M. Naik (Samant)
Keyword(s):  
Matrix Method ◽  
Data Dependency ◽  
Plagiarism Detection ◽  
Dependency Matrix ◽  
Using Data

scholarly journals Plagiarism Detection Process using Data Mining Techniques

2017 ◽  
Vol 5 (4) ◽  
pp. 68
Author(s):  
Mahwish Abid ◽  
Muhammad Usman ◽  
Muhammad Waleed Ashraf
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Computer Systems ◽  
Plagiarism Detection ◽  
Data Mining Techniques ◽  
Detection Process ◽  
Using Data ◽  
Day By Day

<strong>As the technology is growing very fast and usage of computer systems is increased  as compared to the old times, plagiarism is the phenomenon which is increasing day by day. Wrongful appropriation of someone else’s work is known as plagiarism. Manually detection of plagiarism is difficult so this process should be automated. There are various tools which can be used for plagiarism detection. Some works on intrinsic plagiarism while other work on extrinsic plagiarism. Data mining the field which can help in detecting the plagiarism as well as can help to improve the efficiency of the process. Different data mining techniques can be used to detect plagiarism. Text mining, clustering, bi-gram, tri-grams, n-grams are the techniques which can help in this process</strong>

scholarly journals PENENTUAN RUTE DISTRIBUSI KERAMIK DENGAN METODE SAVING MATRIX (STUDI KASUS: PT. LUCRO INDO JAYA)

SISTEMASI ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 398
Author(s):  
Ismi Ana Sulasiyah ◽  
Dani Abdul Malik ◽  
Qurrotul Aini
Keyword(s):  
Matrix Method ◽  
Nearest Neighbor ◽  
Operating Costs ◽  
Long Distance ◽  
Shipping Cost ◽  
Distance Data ◽  
Distance Minimization ◽  
Using Data ◽  
Retail Demand ◽  
Distribution Distance

ABSTRACTDistribution is part of logistics, with a fundamental function for the company. Distribution made from a source to various destinations makes this activity quite complex because the distribution channels that are adrift with a long-distance make the number of costs incurred for shipping. This study aims to minimize the delivery distance traveled in the distribution of ceramics to more than one retail by using the saving matrix method. The saving matrix method can schedule vehicles to distribute products from the central warehouse to several shipping routes. This research was conducted using data in the form of ceramic supply at the company, retail demand data, distance data, and shipping cost of ceramics in January-December 2018 with a total of 10 (ten) customer destinations located in Tangerang-Jakarta. The saving matrix method is optimized by using the method of Farthest Insert, Nearest insert and Nearest Neighbor in determining vehicle scheduling by sorting shipments that are considered more optimal. The results of this study are obtained savings of 14.5% with a total distance that was previously 302.9 km to 258.9 km and savings in shipping costs by 15% which previously was Rp 1,756,150,000 to Rp 1,501,047,326. The use of the saving matrix method is able to improve the order of shipping routes of the company's distribution so that it reduces shipping mileage and reduces the company's operating costs in terms of product distribution.Keywords: distribution, distance minimization, routing, saving matrixABSTRAKDistribusi merupakan bagian dari logistik, dengan fungsi fundamental bagi perusahaan. Pendistribusian yang dilakukan dari suatu sumber keberbagai tempat tujuan menjadikan kegiatan ini cukup komplek, dikarenakan jalur distribusi yang terpaut dengan jarak yang panjang menjadikan besarnya biaya yang dikeluarkan untuk pengiriman. Penelitian ini bertujuan meminimumkan jarak pengiriman yang ditempuh dalam distribusi keramik ke lebih dari satu retail dengan menggunakan metode saving matrix. Metode saving matrix dapat menjadwalkan kendaraan untuk melakukan pendistribusikan produk dari gudang central ke beberapa rute pengiriman. Penelitian ini dilakukan dengan menggunakan data berupa persediaan (supply) keramik pada perusahaan, data permintaan (demand) kebutuhan retail, data jarak dan biaya pengiriman keramik pada bulan Januari-Desember 2018 dengan total 10 (sepuluh) tujuan pelanggan yang berlokasi di Tangerang-Jakarta. Metode saving matrix dioptimalkan dengan penggunaan metode Farthest Insert, Nearest insert dan Nearest Neighbor dalam menentukan penjadwalan kendaraan dengan mengurutkan pengiriman yang dinilai lebih optimal. Hasil dari penelitian ini adalah diperolehnya penghematan sebesar 14,5% dengan total jarak yang sebelumnya 302,9 km menjadi 258,9 km dan penghematan biaya pengiriman sebesar 15% yang sebelumnya sebesar Rp 1.756.150.000 menjadi Rp 1.501.047.326. Penggunaan metode saving matrix mampu memperbaiki urutan pengiriman rute distribusi perusahaan sehingga mengurangi jarak tempuh pengiriman dan mengurangi biaya operasional perusahaan dalam hal distribusi produk.Kata Kunci: distribusi, minimasi jarak, routing, saving matrix

scholarly journals Improved Text Mining Algorithm for Fault Detection using Combined D-Matrix

2019 ◽  
Vol 8 (4) ◽  
pp. 1376-1379
Keyword(s):  
Signs And Symptoms ◽  
Fault Analysis ◽  
Data Dependency ◽  
Dependency Matrix ◽  
Unstructured Text ◽  
Content Mining ◽  
Fault Dependency ◽  
Preprocessing Technique ◽  
Textual Content ◽  
Combined Data

Systematic diagnostic version of Fault dependency (D-matrix) mostly use for setup the fault method records and its contributing courting on the classified system-degree. It includes dependencies and association between recognizable failure approaches and signs and symptoms related to a machine. Proposed system in this paper describes an relations of domain primarily based textual content repository for construction and renovate combined data dependency matrix through mining lacks of the tuple exact unstructured text ,cumulative during the analysis incidents. Here paradigm is combined D matrix and then fault analysis through textual content mining using advance data preprocessing technique approach to pick out dependencies. Using real-existence statistics accumulated and validated in proposed method

scholarly journals Using data envelopment analysis to perform benchmarking in intensive care units

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260025
Author(s):  
Bianca B. P. Antunes ◽  
Leonardo S. L. Bastos ◽  
Silvio Hamacher ◽  
Fernando A. Bozza
Keyword(s):  
Intensive Care ◽  
Data Envelopment Analysis ◽  
Intensive Care Units ◽  
Matrix Method ◽  
Performance Metrics ◽  
Nursing Workload ◽  
Data Envelopment Analysis Model ◽  
Standardized Mortality Ratio ◽  
Data Envelopment ◽  
Using Data

Background Studies using Data Envelopment Analysis to benchmark Intensive Care Units (ICUs) are scarce. Previous studies have focused on comparing efficiency using only performance metrics, without accounting for resources. Hence, we aimed to perform a benchmarking analysis of ICUs using data envelopment analysis. Methods We performed a retrospective analysis on observational data of patients admitted to ICUs in Brazil (ORCHESTRA Study). The outputs in our data envelopment analysis model were the performance metrics: Standardized Mortality Ratio (SMR) and Standardized Resource Use (SRU); whereas the inputs consisted of three groups of variables that represented staffing patterns, structure, and strain, thus resulting in three models. We compared efficient and non-efficient units for each model. In addition, we compared our results to the efficiency matrix method and presented targets to each non-efficient unit. Results We performed benchmarking in 93 ICUs and 129,680 patients. The median age was 64 years old, and mortality was 12%. Median SMR was 1.00 [interquartile range (IQR): 0.79–1.21] and SRU was 1.15 [IQR: 0.95–1.56]. Efficient units presented lower median physicians per bed ratio (1.44 [IQR: 1.18–1.88] vs. 1.7 [IQR: 1.36–2.00]) and nursing workload (168 hours [IQR: 168–291] vs 396 hours [IQR: 336–672]) but higher nurses per bed ratio (2.02 [1.16–2.48] vs. 1.71 [1.43–2.36]) compared to non-efficient units. Units from for-profit hospitals and specialized ICUs presented the best efficiency scores. Our results were mostly in line with the efficiency matrix method: the efficiency units in our models were mostly in the “most efficient” quadrant. Conclusion Data envelopment analysis provides managers the information needed to identify not only the outcomes to be achieved but what are the levels of resources needed to provide efficient care. Different perspectives can be achieved depending on the chosen variables. Its use jointly with the efficiency matrix can provide deeper understanding of ICU performance and efficiency.

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