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arXiv 2021年

作者： Muñoz-Gil, Gorka Volpe, Giovanni Garcia-March, Miguel Angel Aghion, Erez Argun, Aykut Hong, Chang Beom Bland, Tom Bo, Stefano Conejero, J. Alberto Firbas, Nicolás Orts, Òscar Garibo Gentili, Alessia Huang, Zihan Jeon, Jae-Hyung Kabbech, Hélène Kim, Yeongjin Kowalek, Patrycja Krapf, Diego Loch-Olszewska, Hanna Lomholt, Michael A. Masson, Jean-Baptiste Meyer, Philipp G. Park, Seongyu Requena, Borja Smal, Ihor Song, Taegeun Szwabinski, Janusz Thapa, Samudrajit Verdier, Hippolyte Volpe, Giorgio Widera, Arthur Lewenstein, MacIej Metzler, Ralf Anzo, Carlo 08860 Spain Department of Physics University of Gothenburg Origovägen 6B GothenburgSE-41296 Sweden Instituto Universitario de Matemática Pura y Aplicada Universitat Politècnica de València Spain Max Planck Institute for the Physics of Complex Systems Nöthnitzer Straße 38 DresdenDE-01187 Germany Department of Physics Pohang University of Science and Technology Pohang37673 Korea Republic of Francis Crick Institute 1 Midland Road LondonNW1 1AT United Kingdom Department of Chemistry University College London 20 Gordon Street LondonWC1H 0AJ United Kingdom School of Physics and Electronics Hunan University Changsha410082 China Department of Cell Biology Erasmus Mc Rotterdam Netherlands Faculty of Pure and Applied Mathematics Hugo Steinhaus Center Wroclaw University of Science and Technology Wroclaw Poland Department of Electrical and Computer Engineering Colorado State University Fort CollinsCO80523 United States PhyLife Department of Physics Chemistry and Pharmacy University of Southern Denmark Odense MDK-5230 Denmark Institut Pasteur Decision and Bayesian Computation Lab Paris France Center for Ai and Natural Sciences Korea Institute for Advanced Study Seoul Korea Republic of Institute for Physics and Astronomy University of Potsdam Karl-Liebknecht-Str 24/25 Potsdam-GolmD-14476 Germany Sackler Center for Computational Molecular and Materials Science Tel Aviv University Tel Aviv69978 Israel School of Mechanical Engineering Tel Aviv University Tel Aviv69978 Israel Institut Pasteur Decision and Bayesian Computation Lab Paris France Department of Physics Research Center Optimas Technische Universität Kaiserslautern Kaiserslautern67663 Germany Icrea Pg. Lluís Companys 23 Barcelona08010 Spain C. de la Laura 13 Vic08500 Spain

Deviations from Brownian motion leading to anomalous diffusion are ubiquitously found in transport dynamics, playing a crucial role in phenomena from quantum physics to life sciences. The detection and characterization of anomalous diffusion from the measurement of an individual trajectory are challenging tasks, which traditionally rely on calculating the mean squared displacement of the trajectory. However, this approach breaks down for cases of important practical interest, e.g., short or noisy trajectories, ensembles of heterogeneous trajectories, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams independently applied their own algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, providing practical advice for users and a benchmark for developers. Copyright © 2021, The Authors. All rights reserved.

关键词： Trajectories

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Pseudorapidity densities of charged particles with transverse momentum thresholds in pp collisions at s=5.02 and 13 TeV

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Physical Review D 2023年第7期108卷 072008-072008页

作者： S. Acharya D. Adamová A. Adler G. Aglieri Rinella M. Agnello N. Agrawal Z. Ahammed S. Ahmad S. U. Ahn I. Ahuja A. Akindinov M. Al-Turany D. Aleksandrov B. Alessandro H. M. Alfanda R. Alfaro Molina B. Ali A. Alici N. Alizadehvandchali A. Alkin J. Alme G. Alocco T. Alt I. Altsybeev M. N. Anaam C. Andrei A. Andronic V. Anguelov F. Antinori P. Antonioli N. Apadula L. Aphecetche H. Appelshäuser C. Arata S. Arcelli M. Aresti R. Arnaldi J. G. M. C. A. Arneiro I. C. Arsene M. Arslandok A. Augustinus R. Averbeck M. D. Azmi A. Badalà J. Bae Y. W. Baek X. Bai R. Bailhache Y. Bailung A. Balbino A. Baldisseri B. Balis D. Banerjee Z. Banoo R. Barbera F. Barile L. Barioglio M. Barlou G. G. Barnaföldi L. S. Barnby V. Barret L. Barreto C. Bartels K. Barth E. Bartsch N. Bastid S. Basu G. Batigne D. Battistini B. Batyunya D. Bauri J. L. Bazo Alba I. G. Bearden C. Beattie P. Becht D. Behera I. Belikov A. D. C. Bell Hechavarria F. Bellini R. Bellwied S. Belokurova V. Belyaev G. Bencedi S. Beole A. Bercuci Y. Berdnikov A. Berdnikova L. Bergmann M. G. Besoiu L. Betev P. P. Bhaduri A. Bhasin M. A. Bhat B. Bhattacharjee L. Bianchi N. Bianchi J. Bielčík J. Bielčíková J. Biernat A. P. Bigot A. Bilandzic G. Biro S. Biswas N. Bize J. T. Blair D. Blau M. B. Blidaru N. Bluhme C. Blume G. Boca F. Bock T. Bodova A. Bogdanov S. Boi J. Bok L. Boldizsár A. Bolozdynya M. Bombara P. M. Bond G. Bonomi H. Borel A. Borissov A. G. Borquez Carcamo H. Bossi E. Botta Y. E. M. Bouziani L. Bratrud P. Braun-Munzinger M. Bregant M. Broz G. E. Bruno M. D. Buckland D. Budnikov H. Buesching S. Bufalino O. Bugnon P. Buhler Z. Buthelezi S. A. Bysiak M. Cai H. Caines A. Caliva E. Calvo Villar J. M. M. Camacho P. Camerini F. D. M. Canedo M. Carabas A. A. Carballo F. Carnesecchi R. Caron L. A. D. Carvalho J. Castillo Castellanos F. Catalano C. Ceballos Sanchez I. Chakaberia P. Chakraborty S. Chandra S. Chapeland M. Chartier S. Chattopadhyay T. G. Chavez T. Cheng C. Cheshkov B. Cheynis V. Chibante Barroso D. D. Chinellato E. S. Chizzali J. Cho S. Cho P. Chochula P. Chri Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France Nuclear Physics Institute of the Czech Academy of Sciences Husinec-Řež Czech Republic Johann-Wolfgang-Goethe Universität Frankfurt Institut für Informatik Fachbereich Informatik und Mathematik Frankfurt Germany European Organization for Nuclear Research (CERN) Geneva Switzerland Dipartimento DISAT del Politecnico and Sezione INFN Turin Italy INFN Sezione di Bologna Bologna Italy Variable Energy Cyclotron Centre Homi Bhabha National Institute Kolkata India Department of Physics Aligarh Muslim University Aligarh India Korea Institute of Science and Technology Information Daejeon Republic of Korea Faculty of Science P.J. Šafárik University Košice Slovak Republic Affiliated with an institute covered by a cooperation agreement with CERN Research Division and ExtreMe Matter Institute EMMI GSI Helmholtzzentrum für Schwerionenforschung GmbH Darmstadt Germany INFN Sezione di Torino Turin Italy Central China Normal University Wuhan China Instituto de Física Universidad Nacional Autónoma de México Mexico City Mexico Dipartimento di Fisica e Astronomia dell’Università Bologna Italy Sezione INFN Bologna Italy University of Houston Houston Texas USA Department of Physics and Technology University of Bergen Bergen Norway INFN Sezione di Cagliari Cagliari Italy Institut für Kernphysik Johann Wolfgang Goethe-Universität Frankfurt Frankfurt Germany Horia Hulubei National Institute of Physics and Nuclear Engineering Bucharest Romania Westfälische Wilhelms-Universität Münster Institut für Kernphysik Münster Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany INFN Sezione di Padova Padova Italy Lawrence Berkeley National Laboratory Berkeley California USA SUBATECH IMT Atlantique Nantes Université CNRS-IN2P3 Nantes France Laboratoire de Physique Subatomique et de Cosmologie Université Grenoble-Alpes CNRS-IN2P3 Grenoble France Universidade de São Paulo (USP

The pseudorapidity density of charged particles with minimum transverse momentum (pT) thresholds of 0.15, 0.5, 1, and 2 GeV/c is measured in pp collisions at the center of mass energies of s=5.02 and 13 TeV with the ALICE detector. The study is carried out for inelastic collisions with at least one primary charged particle having a pseudorapidity (η) within ±0.8 and pT larger than the corresponding threshold. In addition, measurements without pT-thresholds are performed for inelastic and nonsingle-diffractive events as well as for inelastic events with at least one charged particle having |η|<1 in pp collisions at s=5.02 TeV for the first time at the LHC. These measurements are compared to the pythia 6, pythia 8, and epos-lhc models. In general, the models describe the η dependence of particle production well. However, discrepancies are observed for the highest transverse momentum threshold (pT>2 GeV/c), highlighting the importance of such measurements for tuning event generators. The new measurements agree within uncertainties with results from the ATLAS and CMS experiments obtained at s=13 TeV.

关键词： Hadron-hadron interactions Particle production QCD phenomenology

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Abstracts

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JOURNAL OF SPINAL CORD MEDICINE 2021年第SUPPL 1期44卷 S278-S324页

作者： [Anonymous] Department of Medicine Division of Physical Medicine and Rehabilitation University of Toronto Toronto ON CA KITE - Toronto Rehabilitation Institute University Health Network Toronto ON CA Institute for Health Policy Management and Evaluation University of Toronto Toronto ON CA Rehabilitation Sciences Institute University of Toronto Toronto ON CA Institute of Medical Sciences University of Toronto Toronto ON CA. Rehabilitation Sciences Institute University of Toronto ON CA KITE Research Institute-Toronto Rehab-University Health Network Toronto ON CA Physical Therapy Department University of Toronto ON CA Praxis Spinal Cord Institute Vancouver BC Université de Montréal (École de réadaptation) Montreal QC CA Institut de réadaptation Gingras-Linsay-de-Montréal Montreal QC CA Division of Physical Medicine & Rehabilitation Department of Medicine Faculty of Medicine & Dentistry University of Alberta Edmonton AB CA University of Calgary Calgary AB CA Alberta Health Services Centre for Family Medicine Kitchener ON CA McMaster University Department of Family Medicine Hamilton ON CA Dept. of Physical Therapy University of Toronto Toronto ON CA KITE-University Health Network Toronto ON CA Praxis Spinal Cord Institute Vancouver BC CA Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS) Quebec City QC CA Dept. of Rehabilitation Laval University Quebec City QC CA Institute of Health Policy Management and Evaluation University of Toronto Toronto ON CA School of Rehabilitation Science University of Saskatchewan Saskatoon SK CA Ontario Neurotrauma Foundation Toronto ON CA Canadian Spinal Research Organization Toronto ON CA CIUSSS du Centre-Sud-de-l ‘Île-de-Montréal (Installation Gingras-Lindsay) Montréal QC CA École de réadaptation Université de Montréal Montréal QC CA First Steps Wellness Centre Regina SK CA Stan Cassidy Centre for Rehabilitation Horizon Health Network Fredericton NB CA Departmen

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Preparation of Nanocellulose-Alginate Nanocomposites for Chlorhexidine Digluconate Drug Carrier

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IOP Conference Series: Materials science and engineering 2019年第1期547卷

作者： Angela Evelyna Tiara Khumaira Astifanni Imelda Ruth Lia Asri Bambang Sunendar Purwasasmita Faculty of Dentistry Maranatha Christian University Indonesia Materials Science and Engineering Bandung Institute of Technology Indonesia Advanced Materials Processing Group Engineering Physics Study Program Bandung Institute of Technology Indonesia Research Center for Nanosciences and Nanotechnology Bandung Institute of Technology Indonesia.

Root canal treatment failures generally caused by microorganisms that have ability to invade dentinal tubules. Chlorhexidine is widely known to effectively eradicate broad spectrum bacteria that infected dentinal tubules. The delivery of chlorhexidine to the infected site of root canal still becomes an issue. In order to increase the stability and to regulate the release of chlorhexidine, encapsulation of chlorhexidine digluconate was conducted by employing nanocellulose and alginate nanocomposites. Nanocellulose was prepared from palm kernel cake using acid hydrolysis method. Transmission electron microscopy images showed the formation of whisker and fiber nanocellulose with the average diameter of 20 nm. Resulting nanocellulose was combined with alginate in the presence of Ca2+ crosslinker. Scanning electron microscopy images displayed sphere and oval morphologies of microcapsules with diameter of 500 nm. The microcapsules were further loaded with chlorhexidine digluconate 2% (w/v). In vitro drug release properties were evaluated in PBS at pH 7.4 (normal tooth environment) and at pH 5.5 (infected tooth environment). The UV-VIS results showed that chlorhexidine digluconate has higher release rate at pH 5.5 than at pH 7.4, suggesting that the microcapsule is a good candidate for the delivery of chlorhexidine digluconate to the infected root canal tooth environment.

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The complete mitogenome of Bagrid catfishChrysichthys nigrodigitatus(Siluriformes: Claroteidae)

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Mitochondrial DNA Part B 2018年第2期3卷 1239-1240页

作者： Nack-Keun Kim Fantong Zealous Gietbong Sapto Andriyono Ah Ran Kim Hyun-Woo Kim Interdisciplinary Program of Biomedical Mechanical and Electrical Engineering Pukyong National University Busan Republic of Korea Department of Marine Biology Pukyong National University Busan Republic of KoreaKOICA-PKNU International Graduate Program of Fisheries Science Graduate School of Global Fisheries Pukyong National University Busan Republic of Korea Interdisciplinary Program of Biomedical Mechanical and Electrical Engineering Pukyong National University Busan Republic of KoreaDepartment of Marine Fisheries and Marine Faculty Universitas Airlangga C Campus Jl. Mulyorejo Surabaya East Java Indonesia Department of Marine Biology Pukyong National University Busan Republic of Korea Interdisciplinary Program of Biomedical Mechanical and Electrical Engineering Pukyong National University Busan Republic of KoreaDepartment of Marine Biology Pukyong National University Busan Republic of Korea

We here report the complete mitochondrial genome ofChrysichthys nigrodigitatus, which is 16,514 bp in length. Mitogenome ofC. nigrodigitatusshowed the conserved 13 protein-coding genes, 22 tRNA genes, two rRNA genes, and two noncoding regions including the light-strand replication origin (OL) and a putative control region (CR). All tRNA genes were predicted to fold into the typical cloverleaf secondary structures with the typical base-pairing except for tRNA-Ser(AGC). Phylogenetic analysis with currently known complete mitogenome sequences in Siluriformes showed thatC. nigrodigitatusis most closely related toAuchenoglanis occidentalisforming a family Claroteidae cluster.

关键词： Chrysichthys nigrodigitatus mitochondrial genome catfish Cameroon Africa

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Retracted: LUC Algorithm in Visual Cryptography

Retracted: LUC Algorithm in Visual Cryptography

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2018 Workshop on Multidisciplinary and Its Applications: Applied Mathematics, Computer science, Information Systems, and Information Technology, WMA-Mathcomtech 2018

作者： Achmad Daengs, G.S. Ratnadewi Alpha, Christian Dio Napitupulu, Darmawan Nurdiyanto, Heri Kurniasih, Nuning Hasibuan, Abdurrozaq Daengs, Achmad Manurung, Rosida Tiurma Mulyaningsih, Indrya Rahim, Robbi Sugianto, Castaka Agus Abdullah, Dahlan Department of Electrical Engineering Universitas Kristen Maranatha Bandung Indonesia Research Center for Quality System and Testing Technology Indonesian Institute of Sciences Banten Indonesia STMIK Dharma Wacana Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Universitas Islam Sumatera Utara Indonesia University of 45 Surabaya Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Bandung Indonesia Department of Education Institut Agama Islam Negeri Syekh Nurjati Cirebon Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Kubang Gajah Malaysia Politeknik TEDC Bandung Indonesia Department of Informatics Universitas Malikussaleh Aceh Utara Indonesia

Nowadays, messaging technology in digital data form more often used and not less messages that confidentially wanted. Then it should be modified so that can be understood only by the sender and the intended recipients. All of this system can be realization by using cryptography. LUC algorithms are introduced by Smith and Lennon in 1993. LUC algorithm is an algorithm that based on the use of Lucas sequence (specific arithmetic operations derived from Lucas row) that rarely used to enhance the security of the messages. In this research, LUC algorithm is combined with visual cryptography to process the encryption and the description of a colored image. Four different images were used in the trial here. The performance of the system is assessed using Structural similarity (SSIM) which has an assessment similar to human eye. If the image quality is the same as the original image, then the SSIM value is one. Whereas if the quality of decryption image is very different from the original image then the value of SSIM is zero. © Published under licence by IOP Publishing Ltd.

关键词： Information systems

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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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Making up for the deficit in a marathon run 17

Making up for the deficit in a marathon run

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1st International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence, ISMSI 2017

作者： Fister, Iztok Fister, Dušan Deb, Suash Mlakar, Uroš Brest, Janez University of Maribor Faculty of Electrical Engineering and Computer Science Smetanova 17 Maribor2000 Slovenia University of Maribor Faculty of Mechanical Engineering Smetanova 17 Maribor2000 Slovenia IT and Educational RanchiJharkhand India Decision Sciences and Modelling Program Victoria University Melbourne Australia

ISBN: (纸本)9781450347983

To predict the final result of an athlete in a marathon run thoroughly is the eternal desire of each trainer. Usually, the achieved result is weaker than the predicted one due to the objective (e.g., environmental conditions) as well as subjective factors (e.g., athlete's malaise). Therefore, making up for the deficit between predicted and achieved results is the main ingredient of the analysis performed by trainers after the competition. In the analysis, they search for parts of a marathon course where the athlete lost time. This paper proposes an automatic making up for the deficit by using a Differential Evolution algorithm. In this case study, the results that were obtained by a wearable sports-watch by an athlete in a real marathon are analyzed. The first experiments with Differential Evolution show the possibility of using this method in the future. © 2017 ACM.

关键词： Optimization

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Data Mining: The Classification Method to Predict the Types of Motorcycle Spare Parts to be Restocked

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IOP Conference Series: Materials science and engineering 2019年第2期662卷

作者： S L B Ginting Y R Ginting Sutono A Rakhman Computer System Study Program Faculty of Engineering and Computer Science Universitas Komputer Indonesia Jl. Dipati Ukur 112-116 Bandung 40132 Indonesia Department of Mechanical Engineering Universitas Riau Kampus Bina Widya KM 12 5 Simpang Baru Pekanbaru 28293 Indonesia

The research intends to create an application which is able to analyse sales data in a motorcycle company to predict the types of spare parts which should be stocked. This prediction is crucial since problems are often encountered while restocking. For instance, when there have been some imprecisions occurring in deciding regarding the types of spare parts to restock, the spare parts accumulate. It can cause inefficiency in terms of storage, the products quality deteriorates due to having been stored for too long, and sometimes the best-selling products are not available in the warehouse. This application is developed with Naïve Bayes Classifier (NBC) method which has a high accuracy in predicting future occurrences. This method works by calculating the probability value in each attribute class and determining the optimal probability value. From the test results, 4500 training data with 200 sample test data has 90% similarity with the results of the restock decision without application. For 500 test data, the similarity was 96%. It is proven that this method has a high accuracy so that it can help the decision makers solved the company problem in predicting the types of motorcycle parts to be restocked.

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Increasing accuracy of classification physical activity based on smartphone using ensemble logistic regression with boosting method

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Journal of Physics: Conference Series 2019年第4期1341卷

作者： A Lawi F Aziz S L Wungo Department of Computer Science Universitas Hasanuddin Makassar Indonesia. 90245 Faculty of Mathematics and Natural Sciences Universitas Pancasakti Makassar Indonesia. 90132 Program Study Informatics Engineering STMIK Kreatindo Monokwari Indonesia. 98300

At present, the smartphone is equipped with several sensors such as Accelerometer, Gravity sensor, and Gyroscope which can be used to recognize human physical activities such as walking upstair and walking downstairs etc. Machine learning is needed to group data and get information. Statistical methods have poor performance in classifying because procedures must be met. To overcome this, an ensemble technique was used. This study proposes the application of the gradientboost ensemble method to classify walking upstair and walking downstairs. The *** system is designed for data retrieval using a smartphone. Then, the dataset will be partitioned into 70% training data and 30% test data. The results show that the performance of the ensemble boosting method produces 81.82% accuracy, 86.11% sensitivity and 77.50% specificity.

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