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Market Basket Analysis Using Apriori and FP-Growth for Analysis Consumer Expenditure Patterns at Berkah Mart in Pekanbaru Riau

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： Mustakim Della Maulina Herianda Ahmad Ilham Achmad Daeng GS Folkes E. Laumal Nuning Kurniasih Akbar Iskandar Gloria Manulangga Ida Bagus Ary Indra Iswara Robbi Rahim Puzzle Research Data Technology UIN Sultan Syarif Kasim Riau Indonesia Department of Information System UIN Sultan Syarif Kasim Riau indonesia Informatics Department Universitas Muhammadiyah Semarang Semarang Indonesia Department of Management University of 45 Surabaya Surabaya Indonesia Department of Electrical Engineering Politeknik Negeri Kupang Kupang Indonesia Faculty of Communication Sciences Library and Information Science Program UniversitasPadjadjaran Bandung Indonesia Informatics Engineering STMIK AKBA Makassar Indonesia Informatics Department STMIK STIKOM Indonesia Denpasar Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Kubang Gajah Malaysia

Berkah Mart is one of the new minimarkets in Pekanbaru city which began to develop as a retail business to meet the needs of the community. In his operation, Berkah Mart still uses the traditional store management perception that the placement of product layout on sale shelves has not been through sales analysis. Existing products have not been reviewed from the consumer's point of view, whereas the marketing economics concept provides a variety of product and consumer management methods for business management in Berkah Mart, including market bucket analysis techniques and various itemset search algorithms. In this research, Market Basket Basket Analysis with FP-Growth algorithm is proposed to determine the layout and planning of goods availability. The application of FP-Growth algorithm proved to be useful in generating many and informative association rules to find out the consumer spending pattern at Berkah Mart in Pekanbaru. Moreover, from the rules of the customer association can be segmented separately to meet the specific needs of customers with cost-effective by using some special promotions for the general group. The experimental results show that FP-Growth algorithm can analyze quickly and efficiently informing consumer shopping pattern at Berkah Mart and can increase Berkah Mart revenue.

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Correction: After the pandemic: the global seafood trade market forecasts in 2030

Humanities and Social Sciences Communications

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Humanities and Social sciences Communications 2023年第1期10卷 1页

作者： Chunzhu Wei Wei Chen Desheng Xue Yong Ge Mo Zhang Die Zhang Daoping Wang Qiuming Cheng Changxiu Cheng Wenguang Zhang School of Geography and Planning Sun Yat-sen University 510006 Guangzhou China Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) 519080 Zhuhai China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences 100101 Beijing China College of Resources and Environment University of Chinese Academy of Sciences 100049 Beijing China Department of Computer Science and Technology University of Cambridge Cambridge CB3 0FD UK Department of Geography King’s College London London WC2R 2LS UK School of Earth Science and Engineering Sun Yat-Sen University 519000 Zhuhai China Key Laboratory of Environmental Change and Natural Disaster Beijing Normal University 100875 Beijing China Faculty of Geographical Science Beijing Normal University 100875 Beijing China School of Government Beijing Normal University 100875 Beijing China

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The link between brain functional network connectivity and genetic risk of Alzheimer's disease

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Alzheimer's & Dementia 2022年第S4期17卷

作者： Mohammad S.E. Sendi Elaheh Zendehrouh Robyn L. Miller Elizabeth C. Mormino David H. Salat Vince D. Calhoun School of Electrical and Computer Engineering (ECE) at the Georgia Institute of Technology Atlanta GA USA Tri-Institutional Georgia State University/Georgia Institute of Technology/Emory University Center for Translational Research in Neuroimaging and Data Science Atlanta GA USA Wallace H. Coulter Department of Biomedical Engineering at Emory University and Georgia Tech Atlanta GA USA Georgia State University Atlanta GA USA Stanford University School of Medicine Palo Alto CA USA Stanford University Stanford CA USA Neuroimaging Research for Veterans Center VA Boston Healthcare System Boston MA USA Massachusetts General Hospital Charlestown MA USA Harvard Medical School Boston MA USA

Background Individuals carrying the ɛ4 allele have the highest risk of Alzheimer’s disease (AD) compared with those carrying ɛ3 and ɛ2 allele, whereas ɛ2 allele has the lowest risk. Although previous studies explored the link between the genetic risk of AD and static functional network connectivity (sFNC), limited studies have evaluated the association between dynamic FNC (dFNC) and AD risk. Here, we explore how the dFNC differs between individuals with genetic risk for AD. Method We used rs-fMRI data of 991 healthy brains (404 females) and their demographic information from the Open Access Series of Imaging Studies-3 cohort. The participants' age at scanning time was ranging from 42.46 to 95.39, with a mean of 69.81. We put the data into three groups including group1 (N=135, 63 females) including subjects with ɛ2 allele (i.e., ɛ2/ ɛ2, ɛ2/ ɛ3, and ɛ3/ɛ2), group2 (N=558, 219 females) including subjects with only ɛ3 allele (i.e., ɛ3/ ɛ3), and group3 (N=298, 122 females) including subjects with ɛ4 allele (i.e., ɛ3/ ɛ4, ɛ4/ ɛ3, and ɛ4/ɛ4). Age and gender were not significantly different across groups. Group-ICA was used to extract 53 components. The sliding window and Pearson correlation were used to measure the dFNC among components K-means algorithm was applied to partition dFNC windows into three distinct states. We calculated each subject's occupancy rate (OCR) in each state. A two-sample t-test was used to compare the OCR of groups in each state (Fig. 1). Result Subject with a lower AD risk spend more time in state1 with more positive connectivity within cognitive control network (CCN) and between CCN and sensory network (Fig. 2A and Fig. 2B: p corrected <0.05). Interestingly, in this state, the difference of OCR among subjects with different AD risk was more significant in females (Fig. 2C), while males did not show any significant difference in their OCR across three groups (Fig. 2D). Females with higher AD risk had more OCR in state 3 with relatively lower withi

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Front Cover: AuNP-Modulated qPCR: An Optimized system for Detecting MIR Biophotons Released in DNA Replication (Chem. Eur. J. 8/2023)

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Chemistry – A European Journal 2023年第8期29卷

作者： Yu Yang Prof. Dr. Daoling Peng Prof. Dr. Zhenglong Gu Prof. Dr. Lei Jiang Prof. Dr. Bo Song State Key Laboratory of Genetic Engineering Collaborative Innovation Center of Genetics and Development School of Life Sciences Fudan University 200438 Shanghai P. R. China Key Laboratory of Theoretical Chemistry of Environment Ministry of Education School of Environment South China Normal University 510006 Guangzhou P. R. China Key Laboratory of Optical Technology and Instrument for Medicine Ministry of Education Shanghai Key Lab of Modern Optical System School of Optical-Electrical Computer Engineering University of Shanghai for Science and Technology 200093 Shanghai P. R. China Key Laboratory of Bio-inspired Materials and Interfacial Science Technical Institute of Physics and Chemistry Chinese Academy of Sciences 100190 Beijing P. R. China School of Future Technology University of Chinese Academy of Sciences 100049 Beijing P. R. China

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HARNESSING DIGITAL PATHOLOGY AND CAUSAL LEARNING TO IMPROVE EOSINOPHILIC ESOPHAGITIS DIETARY TREATMENT ASSIGNMENT

arXiv

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arXiv 2023年

作者： Aknin, Eliel Larey, Ariel Caldwell, Julie M. Collins, Margaret H. Rothenberg, Marc E. Savir, Yonatan Abonia, Juan P. Aceves, Seema S. Arva, Nicoleta C. Chehade, Mirna Dellon, Evan S. Gonsalves, Nirmala Gupta, Sandeep K. Leung, John Peterson, Kathryn A. Shoda, Tetsuo Spergel, Jonathan M. Department of Physiology Biophysics and System Biology Faculty of Medicine Technion Israel Institute of Technology Haifa Israel Faculty of Industrial Engineering Technion Israel Institute of Technology Haifa Israel Faculty of Computer Science Technion Israel Institute of Technology Haifa Israel Division of Allergy and Immunology Cincinnati Children's Hospital Medical Center CincinnatiOH United States Dept. of Pediatrics University of Cincinnati College of Medicine CincinnatiOH United States Division of Pathology and Laboratory Medicine Cincinnati Children's Hospital Medical Center CincinnatiOH United States Division of Allergy and Immunology University of California at San Diego Rady Children's Hospital San DiegoCA United States Dept. of Pathology and Laboratory Ann and Robert H Lurie Children's Hospital Northwestern University Feinberg School of Medicine ChicagoIL United States Mount Sinai Center for Eosinophilic Disorders Icahn School of Medicine at Mount Sinai New YorkNY United States Division of Gastroenterology and Hepatology University of North Carolina School of Medicine Chapel HillNC United States Division of Gastroenterology and Hepatology Northwestern University Feinberg School of Medicine ChicagoIL United States Division of Pediatric Gastroenterology Hepatology and Nutrition Children's of Alabama University of Alabama at Birmingham BirminghamAL United States Boston Specialists BostonMA United States Division of Gastroenterology University of Utah Salt Lake CityUT United States Division of Allergy and Immunology Children's Hospital of Philadelphia Dept. of Pediatrics Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States

Eosinophilic esophagitis (EoE) is a chronic, food antigen-driven, allergic inflammatory condition of the esophagus associated with elevated esophageal eosinophils. EoE is a top cause of chronic dysphagia after GERD. Diagnosis of EoE relies on counting eosinophils in histological slides, a manual and time-consuming task that limits the ability to extract complex patient-dependent features. The treatment of EoE includes medication and food elimination. A personalized food elimination plan is crucial for engagement and efficiency, but previous attempts failed to produce significant results. In this work, on the one hand, we utilize AI for inferring histological features from the entire biopsy slide, features that cannot be extracted manually. On the other hand, we develop causal learning models that can process this wealth of data. We applied our approach to the 'Six-Food vs. One-Food Eosinophilic Esophagitis Diet Study', where 112 symptomatic adults aged 18-60 years with active EoE were assigned to either a six-food elimination diet (6FED) or a one-food elimination diet (1FED) for six weeks. Our results show that the average treatment effect (ATE) of the 6FED treatment compared with the 1FED treatment is not significant, that is, neither diet was superior to the other. We examined several causal models and show that the best treatment strategy was obtained using T-learner with two XGBoost modules. While 1FED only and 6FED only provide improvement for 35%-38% of the patients, which is not significantly different from a random treatment assignment, our causal model yields a significantly better improvement rate of 58.4%. This study illustrates the significance of AI in enhancing treatment planning by analyzing molecular features' distribution in histological slides through causal learning. Our approach can be harnessed for other conditions that rely on histology for diagnosis and treatment. Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

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Resting-state dynamic functional network connectivity predicts cognition in 37,784 participants of UK Biobank

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Alzheimer's & Dementia 2023年第S3期19卷

作者： Mohammad S.E. Sendi Elaheh Zendehrouh Robyn L Miller David H Salat Vince D Calhoun School of Electrical and Computer Engineering (ECE) at the Georgia Institute of Technology Atlanta GA USA Tri-Institutional Georgia State University/Georgia Institute of Technology/Emory University Center for Translational Research in Neuroimaging and Data Science Atlanta GA USA Wallace H. Coulter Department of Biomedical Engineering at Emory University and Georgia Tech Atlanta GA USA Georgia State University Atlanta GA USA Neuroimaging Research for Veterans Center VA Boston Healthcare System Jamaica Plain MA USA Massachusetts General Hospital Charlestown MA USA Harvard Medical School Boston MA USA Athinoula A. Martinos Center for Biomedical Imaging Charlestown MA USA

Background Age-related cognitive decline after 65 is a well-known phenomenon, but little is known about how brain functional changes are related to cognitive decline. To this end, previous studies explored the link between functional network connectivity (FNC) estimated from resting-state functional MRI (rs-fMRI) and cognitive scores in healthy adults. FNC is often assumed to be static over time. However, this assumption runs contrary to the dynamic nature of brain FNC, and dynamic FNC (dFNC) has been recently introduced to overcome this limitation. The current study investigated the relationship between dFNC estimated from 36,263 individuals from UK Biobank and cognition. Method We used the resting-state fMRI (duration: 5min) data of 37,784 (20,157 females) adults’ brains, demographic information (age:64.06± 7.51), and cognitive scores from the UK Biobank in which the cognitive scores include fluid intelligence (FI), reaction time (RT), and pairs matching (Pairs). We adapted group independent component analysis to extract 53 data-driven components for the whole brain using a fully automated approach (Fig.1: Step1). Next, we used the sliding window and Pearson correlation to estimate the dFNC among 53 components (Fig.1: Step2). We used k-means clustering to put dFNCs of all individuals into three separated states and calculated individual state vectors (Fig.1: Step3). Then, we estimated 32 dFNC features based on three estimated states and state vectors (Fig.1: Step4). Finally, we trained a two-fold cross-validation support vector regression to predict the cognitive scores (Fig.1: Step5). Result The estimated dFNC feature predicted FI score with high accuracy (the correlation between measure and predicted score is R = 0.043, p = 6.6e −17 , Fig.2A), mean time to correctly identify match in RT task (R = 0.065, p = 1.9 −34 , Fig.2B), and time to complete Pairs task (R = 0.049, p = 1.3e −20 , Fig.2C). Conclusion Here we explored the link between dFNC features and cogniti

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Learning from learning machines: A new generation of AI technology to meet the needs of science

arXiv

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arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems

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computer AIDS FOR SHIP DESIGN, INTEGRATION AND CONTROL

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NAVAL ENGINEERS JOURNAL 1980年第2期92卷 73-87页

作者： CARLSON, CM JOHNSON, RA HELMING, FW Mr. Craig M. Carlson received his B.S. degree in Naval Architecture and Marine Engineering from the University of Michigan in 1970 and began his career with the Department of the Navy at the Naval Ship Engineering Center (NAVSEC). In 1972. he returned to the University of Michigan under the NAVSEC Long Term Training Program and received his M.S. degree in Naval Architecture and Marine Engineering. After returning to the Ship Arrangements Branch at NAVSEC. he was assigned as Task Leader for General Arrangements for the PGG PCG PHM. and MCM ship designs and was awarded Outstanding Performance Awards in 1974 and 1975. In addition he was Manager of the Arrangement Subsystem of the Navy's Computer-Aided Ship Design and Construction Program (CASDAC). In October 1979. he became Manager of the CASDAC Hull Design System. Currently. he also is enrolled in the M.S. of Computer Science Program at Johns Hopkins University. Mr. Carlson previously has presented technical papers at ASNE Day 1974 and 1978 as well as at the 1979 DOD Manufacturing Technology Advisory Group Conference. Besides ASNE. which he joined in 1972. he is a member of SNAME. ASE. and the U.S. Naval Institute. Mr. Robert A. Johnson is a Naval Architect in Surface Combatants Design (SEA 03D3). Ship Design Integration Directorate Naval Sea Systems Command. He received an Associate in Engineering degree in Drafting and Design Technology in 1959. his B.S. degree in Aerospace Engineering in 1965. and his M.S. degree in Engineering Mechanics in 1970. all from the Pennsylvania State University. In 1973. he was selected for the NA VSEC Hull Division s Long Term Training Program at the University of Michigan subsequently receiving his M.S.E. degree in Naval Architecture in 1974. Mr. Johnson began his career with the Ordnance Research Laboratory at Pennsylvania State University in 1959 where he worked. on the design of hydroelastic submarine models and conducted research in the area of flow induced structural vibrations. In 1967 he joined HRB-Singer at State Colle

This paper presents an integrated approach to computer-Aided Ship Design for U.S. Navy preliminary and contract design. An integrated Hull Design system (HDS), currently under development by the Hull Group of the Naval Sea systems Command (NAVSEA 32). is the vehicle for the discussion. This paper is directed toward practicing ship design professionals and the managers of the ship design process. Primary emphasis of this paper, and of the development effort currently under way, is on aiding ship design professionals in their work. Focus is on integration and management control of the extremely complex set of processes which make up naval ship design. The terminology of the Ship Designer and Design Manager is used. The reader needs no familiarity with the technologies of computer science.

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Democrats are better than Republicans at discerning true and false news but do not have better metacognitive awareness

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Communications psychology 2023年第1期1卷 46页

作者： Mitch Dobbs Joseph DeGutis Jorge Morales Kenneth Joseph Briony Swire-Thompson Network Science Institute and Department of Psychology Northeastern University Boston MA USA. Boston Attention and Learning Laboratory VA Boston Healthcare System Boston MA USA. Department of Psychiatry Harvard Medical School Boston MA USA. Department of Psychology Northeastern University Boston MA USA. Department of Philosophy Northeastern University Boston MA USA. Department of Computer Science and Engineering University at Buffalo Buffalo NY USA. Network Science Institute and Department of Psychology Northeastern University Boston MA USA. b.swire-thompson@northeastern.edu. Department of Political Science Northeastern University Boston MA USA. b.swire-thompson@northeastern.edu.

Insight into one's own cognitive abilities is one important aspect of metacognition. Whether this insight varies between groups when discerning true and false information has yet to be examined. We investigated whether demographics like political partisanship and age were associated with discernment ability, metacognitive efficiency, and response bias for true and false news. Participants rated the veracity of true and false news headlines and provided confidence ratings for each judgment. We found that Democrats and older adults were better at discerning true and false news than Republicans and younger adults. However, all demographic groups maintained good insight into their discernment ability. Although Republicans were less accurate than Democrats, they slightly outperformed Democrats in metacognitive efficiency when a politically equated item set was used. These results suggest that even when individuals mistake misinformation to be true, they are aware that they might be wrong.

关键词： Human behaviour Human behaviour

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Control and Notification Automatic Water Pump with Arduino and SMS Gateway

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IOP Conference Series: Materials science and engineering 2018年第1期407卷

作者： R Ratnadewi H Nurdiyanto A Najmurrokhman C Prabowo R Idmayanti H Eteruddin C A Sugianto N Kurniasih H K Siburian D Nababan R Rahim Electrical Engineering Universitas Kristen Maranatha Bandung Indonesia Department of Informatics STMIK Dharma Wacana Metro Lampung Indonesia Department of Electrical Engineering Universitas Jenderal Achmad Yani Cimahi Indonesia Department of Information Technology Politeknik Negeri Padang Padang Indonesia Department of Electrical Universitas Lancang Kuning Pekanbaru Indonesia Departement of Informatics Politeknik TEDC Bandung Bandung Indonesia Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Informatics Sekolah Tinggi Manajemen Informatika dan Komputer Medan Indonesia Department of Information System Universitas Pelita Harapan Medan Medan Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Kubang Gajah Malaysia

This paper aims to facilitate the owner to control water pump machine from the vacuum, and the condition of the water is it full or not in house or apartment. The current control system still has many limitations in controlling the water pump machine at home or apartment. The water machine control system was designed with device that has a smart surveillance system that can be accessed with Android smartphone and all information from or to machine control system will command by using SMS, this device create a development of Arduino UNO microcontroller technology that allows the reading of the GSM Module and connected with SMS Gateway. Experiment results facilitate real-time water control and much easy for the owner to control water tank.

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