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Sibarani, Prince, Tanika D. Sofianti y Aditya Tirta Pratama. "Improving the Overall Equipment Effectiveness (OEE) of Drum Testing Machine in Laboratory of Tire Manufacturing Using FMEA and PFMEA". Proceedings of The Conference on Management and Engineering in Industry 3, n.º 3 (4 de agosto de 2021): 56–61. http://dx.doi.org/10.33555/cmei.v3i3.84.
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Drum testing is equipment to test tire capability in the highway prototype in the tire company. Overall Equipment Effectiveness (OEE) is used to measure the productivity of the equipment. OEE has declined and has not achieved the target from Jun 2019 until June 2020. The objectives of this research are to determine the fixed parameter in the OEE calculation at the Drum Testing and to increase the OEE for achieving the company target. Process Failure Mode Effects Analysis (PFMEA) and Failure Mode Effects Analysis (FMEA) help to identify potential failure mode and its consequences, and formulate a solution to achieve the OEE target by improving the drum testing machine. Furthermore, an ideal target should be customized based on the manufacturing year and brand of the machine. This research showed PFMEA and FMEA successfully improve the OEE efficiency for five machines increases the average OEE from 53.6% to 67.2%.
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C. M. Nalayani, Thanga Akilan .V, Hariharan .S, SaranArulnathan y Venkatanathan .S. "Placement Analysis for Students using Machine Learning". September 2023 5, n.º 3 (septiembre de 2023): 223–37. http://dx.doi.org/10.36548/jitdw.2023.3.001.
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Every University/College want their students to get placed in a good company with a better package. They create the syllabus so, that students will gain the most knowledge out of the study period. But they are not sure whether the students are getting trained with proper guidance and instruction to be placed. There is a need for metric to find the progress of the student’s placement. So, the student can speed up his/her preparation to meet the demands of the minimum criteria of placement. This metric is called the placement analysis system, it takes attributes like internships completed, papers published, aptitude scores, etc to predict where the individual will get placed i.e., Dream company, Core company, Normal company or not get placed. For this the machine learning algorithms are used to predict the results using three basic algorithms Random Forest, Decision tree and K-means clustering the accuracy of the algorithms are determined to find the optimal algorithm. The past data on the placement results were fed as the training dataset and a part of it is used for testing the accuracy of the model. Then if the accuracy is good, this can be used to predict the possibility of a student getting placed. If the student is unhappy with the result, then the model can be used to find the area where the student needs to improve to get to his desired goal. If properly implemented and the students work consistently, this aids in providing solutions to meet every student's goal.
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Mustika, Nadya Intan, Bagus Nenda y Dona Ramadhan. "Machine Learning Algorithms in Fraud Detection: Case Study on Retail Consumer Financing Company". Asia Pacific Fraud Journal 6, n.º 2 (30 de diciembre de 2021): 213. http://dx.doi.org/10.21532/apfjournal.v6i2.216.
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This study aims to implement a machine learning algorithm in detecting fraud based on historical data set in a retail consumer financing company. The outcome of machine learning is used as samples for the fraud detection team. Data analysis is performed through data processing, feature selection, hold-on methods, and accuracy testing. There are five machine learning methods applied in this study: Logistic Regression, K-Nearest Neighbor (KNN), Decision Tree, Random Forest, and Support Vector Machine (SVM). Historical data are divided into two groups: training data and test data. The results show that the Random Forest algorithm has the highest accuracy with a training score of 0.994999 and a test score of 0.745437. This means that the Random Forest algorithm is the most accurate method for detecting fraud. Further research is suggested to add more predictor variables to increase the accuracy value and apply this method to different financial institutions and different industries.
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Porter, S. J., J. P. Chadwick, M. G. Owen y S. J. Page. "Evaluation of seven ultrasonic machines for estimating carcase composition in live bulls". Proceedings of the British Society of Animal Production (1972) 1988 (marzo de 1988): 46. http://dx.doi.org/10.1017/s0308229600016846.
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The application of ultrasonics to the evaluation of live cattle has been carried out in bull performance testing for several years. The machine used for this was the Scanogram. The fact that this machine is no longer being produced and the emergence of several new machines has prompted this trial to evaluate seven ultrasonic machines.A total of 49 bulls, 27 Limousin x Friesian and 22 Charolais x Friesian, were evaluated and slaughtered in four batches of approximately equal size, over four weeks. Each batch was of one breed.Age, live weight at evaluation and subjective assessments of fatness and conformation were recorded together with fat and muscle measurements by the Delphi (Delphi Instruments Ltd, New Zealand), Meritronics (Merit, Lowson and French, England), Scanogram (Ithaco Incorporated, USA), Vetscan (Company no longer in existence), Kaijo Denki (Medata Systems Ltd, England), Velocity of Sound (Prototype developed at IFR-Bristol, England) and Warren (Prototype developed by J Warren) ultrasonic machines.
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Camille Merlin S. Tan y Lawrence Y. Materum. "Cleanroom Dashboard System for Time-Reduction Checking of Disk Tester Availability". Journal of Advanced Research in Applied Sciences and Engineering Technology 43, n.º 2 (17 de abril de 2024): 237–57. http://dx.doi.org/10.37934/araset.43.2.237257.
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With the vast amount of data being processed and reviewed in production and manufacturing industries nowadays, it takes a lot of time and effort to keep track of their performance metrics. Developing an automation system to improve the overall system performance and reduce man-machine interactions is necessary. Prior to the work, a computer disk drive manufacturing facility checked its fixed-tester availability manually, which made the company unable to optimize its man and machine hours as much as possible. This work proposes a system that displays all the critical information in a dashboard system to address the problem while enabling access to data history logs and further analytic insights. Moreover, the system enables readily accessible fixed-disk tester availability. The outcomes indicate significant improvements, especially in delay reductions, necessary for optimizing man and machine interactions in a fixed-disk testing facility at a leading computer disk drive manufacturer and data storage company at Laguna Technopark.
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Sutrisno, Niantoro, Rizka Faradila, Rizka Faradila, Edison P. Sirait y Edison P. Sirait. "PENGARUH KAPASITAS MESIN DAN JUMLAH PERSEDIAAN BAHAN BAKU TERHADAP VOLUME PRODUKSI". Jurnal Akuntansi dan Bisnis 10, n.º 01 (25 de junio de 2024): 15. http://dx.doi.org/10.47686/jab.v10i01.680.
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Background of this research based on data’s observational at a company relating to capacity, amount of raw material inventory and production volume where the phenomena that occur still need to be optimized. This research uses a quantitative approach and uses primary data as a data source obtained from the results of questionnaires distributed to respondents related to activities in accordance with the specified research variables. The analytical methods used are instrument testing, classical assumption testing, multiple regression analysis, hypothesis testing and coefficient of determination (Adjusted R2). The results of this research show that:1. The machine capacity variable partially has a positive and significant effect on production volume at PT. Gema Graha Saran, Tbk Bekasi Regency.2. The raw material inventory variable partially has a positive and significant effect on production volume at PT. Gema Graha Sarana, Tbk Bekasi Regency.3. The variables of machine capacity and raw material inventory simultaneously have a positive and significant effect on production volume at PT. Gema Graha Sarana, Tbk Bekasi Regency Keywords :Raw Material Inventory, Machine Capacity, and Production Volume
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Qu, Ju Bao y Shu Juan Wang. "Intelligent Quality Control System Based on Machine Vision of Multi-Line". Applied Mechanics and Materials 513-517 (febrero de 2014): 1192–96. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1192.
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Testing for a variety of different industrial objects, this paper designed a multi on-line machine vision based on intelligent quality control system solutions. Microvision based on the company introduced the MV-8002 image acquisition card and the MV-VS1394 camera visual inspection of industrial software and hardware structure and the working principle of a high-speed online learning model of intelligent foreign recognition, it described the image acquisition card and the camera with the application and the software development. by the applications case, and the further evidence of the reliability of this system.
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Siti Nur Syamimi Mat Zain, Nor Azuana Ramli y Rose Adzreen Adnan. "CUSTOMER SENTIMENT ANALYSIS THROUGH SOCIAL MEDIA FEEDBACK: A CASE STUDY ON TELECOMMUNICATION COMPANY". International Journal of Humanities Technology and Civilization 7, n.º 2 (14 de diciembre de 2022): 54–61. http://dx.doi.org/10.15282/ijhtc.v7i2.8739.
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Customer sentiment analysis is an automated way of detecting sentiments in online interactions in order to assess customer opinions about a product, brand or service. It assists companies in gaining insights and efficiently responding to their customers. This study presents a machine learning approach to analyse how sentiment analysis detects positive and negative feedback about a telecommunication company’s products. Customer feedback data were taken from Twitter through Streaming API (Application Programming Interface), where Tweets are retrieved in real time based on search terms, time, users and likes. Responses from the twitter API are parsed into tables and stored in a CSV file. Based on the analysis, it was found that there was no negative sentiment from the customers. The data were then split into training and testing to be tested on the three different supervised learning algorithms used in this study which are Support Vector Machine, Random Forest, and Naïve Bayes. Lasty, the performance of each model was compared to select the most accurate model and from the analysis, it can be concluded that Support Vector Machine gives the best performance in terms of accuracy, Mean Squared Error, Root Mean Squared Error and Area Under the ROC curve.
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Srisattayakul, Parinya, Charnnarong Saikaew, Anurat Wisitsoraat y Naphatara Intanon. "Influence of MoN Sputtering Coating on Wear Resistance of a Fishing Net-Weaving Machine Component". Advanced Materials Research 1016 (agosto de 2014): 80–84. http://dx.doi.org/10.4028/www.scientific.net/amr.1016.80.
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Wear resistance of an upper hook, an important fishing net-weaving machine component manufactured from stainless steel, was improved by systematically investigating the influence of molybdenum nitride (MoN) sputtering coating using experimental design. Three factors of MoN coating on upper hooks including DC current, operating pressure, and Ar/N2ratio were studied and optimized for minimum wear of the machine component. After conducting wear testing on the fishing net-weaving machine in a participating company, it was found that the three coating factors influenced the wear of the machine component. In addition, the optimal operating condition for MoN sputtering coating that produced the minimum wear was obtained at DC current of 0.45 A, operating pressure of 0.01 mbar, and Ar/N2ratio of 0.5.
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Weick, S., M. Grosse y M. Steinbrueck. "The INCHAMEL facility – a new device for in-situ neutron investigations under defined temperatures with applicable mechanical load". Journal of Physics: Conference Series 2605, n.º 1 (1 de septiembre de 2023): 012035. http://dx.doi.org/10.1088/1742-6596/2605/1/012035.
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Abstract In order to in-situ quantify the hydrogen diffusion in metals or more precisely in zircaloy cladding tubes with the influence of an applied stress field, a new device was constructed in cooperation with the company ZwickRoell - the transportable INCHAMEL facility. It is a modification of ZwickRoell’s Kappa Mini 1 kN tensile testing machine. The facility is equipped with all features of a tensile testing machine, additionally a defined temperature can be applied. The machine’s design was dedicated in detail to fulfil the requirements for the usage in facilities with neutron radiation. In this manner, neutron radiography investigations can be performed in-situ with the neutron beam passing through the sample without any disturbances by installations like beam windows, thermo-couples, furnace tubes, heater wires, clamps for strain measurements, etc.
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Saikin, Saikin, Sofiansyah Fadli y Maulana Ashari. "Optimization of Support Vector Machine Method Using Feature Selection to Improve Classification Results". JISA(Jurnal Informatika dan Sains) 4, n.º 1 (20 de junio de 2021): 22–27. http://dx.doi.org/10.31326/jisa.v4i1.881.
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The performance of the organizations or companiesare based on the qualities possessed by their employee. Both of good or bad employee performance will have an impact on productivity and the impact of profits obtained by the company. Support Vector Machine (SVM) is a machine learning method based on statistical learning theory and can solve high non-linearity, regression, etc. In machine learning, the optimization model is a part for improving the accuracy of the model for data learning. Several techniques are used, one of which is feature selection, namely reducing data dimensions so that it can reduce computation in data modeling. This study aims to apply the method of machine learning to the employee data of the Bank Rakyat Indonesia (BRI) company. The method used is SVM method by increasing the accuracy of learning data by using a feature selection technique using a wrapper algorithm. From the results of the classification test, the average accuracy obtained is 72 percent with a precision value of 71 and the recall value is rounded off to 72 percent, with a combination of SVM and cross-validation. Data obtained from Kaggle data, which consists of training data and testing data. each consisting of 30 columns and 22005 rows in the training data and testing data consisting of 29 col-umns and 6000 rows. The results of this study get a classification score of 82 percent. The precision value obtained is rounded off to 82 percent, a recall of 86 percent and an f1-score of 81 percent.
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Siddiqui, Atif, Muhammad Yousuf Irfan Zia y Pablo Otero. "A Universal Machine-Learning-Based Automated Testing System for Consumer Electronic Products". Electronics 10, n.º 2 (10 de enero de 2021): 136. http://dx.doi.org/10.3390/electronics10020136.
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Consumer electronic manufacturing (CEM) companies face a constant challenge to maintain quality standards during frequent product launches. A manufacturing test verifies product functionality and identifies manufacturing defects. Failure to complete testing can even result in product recalls. In this research, a universal automated testing system has been proposed for CEM companies to streamline their test process in reduced test cost and time. A universal hardware interface is designed for connecting commercial off-the-shelf (COTS) test equipment and unit under test (UUT). A software application, based on machine learning, is developed in LabVIEW. The test site data for around 100 test sites have been collected. The application automatically selects COTS test equipment drivers and interfaces on UUT and test measurements for test sites through a universal hardware interface. Further, it collects real-time test measurement data, performs analysis, generates reports and key performance indicators (KPIs), and provides recommendations using machine learning. It also maintains a database for historical data to improve manufacturing processes. The proposed system can be deployed standalone as well as a replacement for the test department module of enterprise resource planning (ERP) systems providing direct access to test site hardware. Finally, the system is validated through an experimental setup in a CEM company.
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Siddiqui, Atif, Muhammad Yousuf Irfan Zia y Pablo Otero. "A Universal Machine-Learning-Based Automated Testing System for Consumer Electronic Products". Electronics 10, n.º 2 (10 de enero de 2021): 136. http://dx.doi.org/10.3390/electronics10020136.
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Consumer electronic manufacturing (CEM) companies face a constant challenge to maintain quality standards during frequent product launches. A manufacturing test verifies product functionality and identifies manufacturing defects. Failure to complete testing can even result in product recalls. In this research, a universal automated testing system has been proposed for CEM companies to streamline their test process in reduced test cost and time. A universal hardware interface is designed for connecting commercial off-the-shelf (COTS) test equipment and unit under test (UUT). A software application, based on machine learning, is developed in LabVIEW. The test site data for around 100 test sites have been collected. The application automatically selects COTS test equipment drivers and interfaces on UUT and test measurements for test sites through a universal hardware interface. Further, it collects real-time test measurement data, performs analysis, generates reports and key performance indicators (KPIs), and provides recommendations using machine learning. It also maintains a database for historical data to improve manufacturing processes. The proposed system can be deployed standalone as well as a replacement for the test department module of enterprise resource planning (ERP) systems providing direct access to test site hardware. Finally, the system is validated through an experimental setup in a CEM company.
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Sibarani, Ayu Anggraeni, Sugeng Waluyo, Muhammad Baharuddin Wahid Tosalili y Hilda Lutfiana. "An Approach to Combine House of Quality and Finite Element Method in Redesigning of Rotary Shaft Multi-Spindle Wheel Nutrunner Machine". Jurnal Teknik Industri 26, n.º 1 (21 de marzo de 2024): 37–48. http://dx.doi.org/10.9744/jti.26.1.37-48.
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An engineering-to-order company has developed multi-spindle wheel nutrunner machines for automotive wheel mounting. The rotating shaft component that supports that machine has experienced compressive and twisting stress during operation, resulting in damage not only to the shaft but also some parts attached to the wheel. This study uses the house of quality (HOQ) and finite element method (FEM) approaches to redesign the rotary shaft to meet quality standards for its engineers, as customers, in a systematic way by using qualitative data from interviews, documents, and questionnaires provided by five rotary shaft engineering experts. Based on the importance levels of technical specifications obtained from the HOQ results, two rotary shaft redesign models for the redesigned models 1 and 2 obtain the maximum von Mises stress from the virtual testing using FEM analysis of 277.5 MPa and 111.8 MPa, respectively, which are below the company standard maximum yield strength of 470 MPa. Hence, using the company's minimum safety factor, the redesigned model 2 is chosen for the improved version of rotary shaft design.
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Sunarti, Sunarti, Hie Ling Ting, Tiksno Widyatmoko y Herri Akhmad Bukhori. "Assessment of Students in Online Industrial Practice Activities Using Machine Learning Based on Mobile Application". Briliant: Jurnal Riset dan Konseptual 7, n.º 2 (29 de mayo de 2022): 364. http://dx.doi.org/10.28926/briliant.v7i2.926.
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All of the learning in the pandemic era uses online learning including practical in the industry that should do all of the students to apply their knowledge. The practical industry online is very difficult to assement students that the assessment is given from both company and the university. Companies have many parameters to assessment and each company has different parameters. This study uses 14 parameters that are generally used in assessment for practical students and the university side using 10 parameters. The problem is that every parameter has a different weight than it makes it confusing to give marks with manual assessment. This research uses machine learning to fix this problem based on mobile applications for the user interfaces. The result of testing this application had an average accuracy for assessment students based on parameters of companies and universities that is 83,3%.
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Novotný, Lubomír W. y Oldřich Učeň. "Methodology of Geometry Testing and Measuring of Machine Tool Components Having Big Dimensions and their Verification Using the FEM Method". Applied Mechanics and Materials 821 (enero de 2016): 378–84. http://dx.doi.org/10.4028/www.scientific.net/amm.821.378.
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The paper deals with the issue of geometric precision measuring at large parts (especially at castings having big dimensions) in their manufacturing stage. The large parts of machinetool frames are required to have their geometric tolerances in thousandths of millimetres. The production or cooperation abilities of the particular company must be adapted to these demands and it is also necessary to adapt the company metrological equipment and measuring procedures to them. The standard measuring equipment cannot be used in most cases, because the particular parts are too large. For this reason, it is necessary to search such methods and procedures which enable to perform measuring with the relevant result. It could be considered to use e. g. 3D scanners. Unfortunately, their measuring precision has not reached the required tolerances up to now. For example, the HandyPROBE 3D scanner measures with the precision of 0,022 mm [1].
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Pereira, Filipe, Luís Magalhães, Adriano A. Santos, António Ferreira da Silva, Katarzyna Antosz y José Machado. "Development of an Automated Wooden Handle Packaging System with Integrated Counting Technology". Machines 12, n.º 2 (9 de febrero de 2024): 122. http://dx.doi.org/10.3390/machines12020122.
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Manual counting and packaging processes often involve repetitive, error-prone tasks. Specifically, packaging wooden handles, utilized in gardening tools and cutlery, typically relies on labor-intensive methods with dimensions varying in diameter, length and mass. These variations complicate packaging, requiring precise counting and diverse handling solutions. This article introduces an automated counting structure tailored for a wide array of wooden handles manufactured by a company in northern Portugal. Employing standardized mechanical design methodologies, we delineate crucial stages encompassing the design, development, implementation and testing of this specialized counting equipment. The machine has been partially integrated into the management system of the company, taking into account future global integration according to the Industry 4.0 concept.
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Zhou, Bing y Jing Hui Zhao. "Design of Temperature Control System for Thermocouple Verification Based on Virtual Instrument Technology". Advanced Materials Research 706-708 (junio de 2013): 742–47. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.742.
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The thermocouple calibration temperature control system is constructed USB - 4716 200 ks/s, 16 multi-function USB module, JKH-C series thyristor phase shift trigger, PC machine as the main hardware, the U.S. National Instrument (NI) company Labview as the software development platform. According to the precious metal, base metal thermocouple wire thermoelectric EFM method to determine the temperature test point, the temperature control system using automatic control to test furnace temperature, and realize the visual operation interface. Temperature control accuracy: temperature deviation calibration point is less than ± 5 °C when testing the thermocouple, constant temperature stability: constant temperature's change is less than 0.2 °C /min in the thermocouple testing process.
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Mailasari, Mely, Monikka Nur Winnarto y Annida Purnamawati. "Penerapan Metode Waterfall dalam Pengembangan Aplikasi Schedule Maintenance Alat Produksi". Infotek : Jurnal Informatika dan Teknologi 7, n.º 1 (20 de enero de 2024): 132–40. http://dx.doi.org/10.29408/jit.v7i1.24080.
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PT. Shiroki Indonesia Cikarang is moving on the field of automotive spare parts manufacturing such as window regulator, seat recliner, sear track as well as floor lock. The activity uses a tool/machine as a support to produce spare parts that have good quality. With so many of these machines, then the company needs to do the maintenance and also the maintenance of the machine well. Scheduling of maintenance and maintenance of machinery production equipment still uses the record on board and requires information from the operator to be submitted to the technician. Therefore, it is necessary to create a web-based maintenance schedule application that can facilitate the performance of the staff in making the maintenance schedule of the production equipment more effective and efficient as well as the availability of reports from the maitenance. The research method used is the waterfall method which has five stages namely needs analysis, design, program code making, testing and support or maintenance. The schedule maintenance application was created using the PHP programming language using the CodeIgniter framework and the MySql database.
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Jendrysik, Klaudia, Monika Kiecana y Hubert Szabowicz. "Preliminary results of dry Deep Soil Mixing soil-cement composite testing". MATEC Web of Conferences 251 (2018): 01025. http://dx.doi.org/10.1051/matecconf/201825101025.
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This paper provides results of testing made for soil-cement mixtures in dry mixing technology. This technology is greatly dependent on existing soil condition; hence the results are of highly random nature. Material used in testing was distinguished with high organic content and low humidity. Tests were carried out in laboratory of Wroclaw University of Technology on 145 samples as ordered by Menard Polska Ltd. Company. Samples were prepared and stored under laboratory conditions and then, after various maturation time, were destroyed in a testing machine. The purpose was to determine the stress-strain curves used to find strength properties, strain at failure, modulus of elasticity, secondary modulus of elasticity versus cement content. Test confirmed improvement of soil strength properties after addition of cement binder. The results may be used to determine the most economic binder-to-soil ratio.
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Zhang, Chun Qing. "The Test System of Submersible Motors Performance Based on LabVIEW". Applied Mechanics and Materials 538 (abril de 2014): 335–38. http://dx.doi.org/10.4028/www.scientific.net/amm.538.335.
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This paper introduces testing system of submersible motor that used for oil field based on virtual instruments. The system can detect, analyze, process and acquire data by national instruments’ graphical programming 1anguage，LabVIEW and DAQ board. It has a perfect man-machine interface. Because of its successful application in factory, work efficiency is improved greatly. IntroductionSubmersible motor is the power component of the submersible which performance affects the normal operation of the whole unit. With the development of production, varieties and yield of the machine are increasing and routine testing work is also increasing, but the test means has lagged behind. At present, the testing technology of submersible motor has been perfect in foreign, and able to provide the accurate performance curves to the user at the same time, At home, the dynamometer test of submersible motor is limited to some basic parameters measurement, and can not map the five characteristic curves of the motor accurately. Therefore, developing a set of rodless pump dynamometer system is necessary. The system is based on the NI company Labview software development platform and hardware platform, it can test automation and draw submersible motor performance curve accurately.
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Nikam,, Snehal. "MACHINE LEARNING AND NLP BASED RESUME PARSING FRAMEWORK FOR E-RECRUITMENT". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, n.º 05 (16 de mayo de 2024): 1–5. http://dx.doi.org/10.55041/ijsrem34059.
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The growth of the Indian recruitment market has led to the emergence of specialized recruitment agencies that leverage machine learning models to streamline the recruitment process and deliver the right talent to their clients. So, the proposed model has the potential benefits of the ML model to modify the hiring process and make it more efficient and fairer. By using NLP approaches, you can extract valuable information from resumes and provide accurate ratings based on the requirements of the company. With careful testing and refinement, your web portal could be a valuable resource for both job applicants and hiring managers alike. In addition, you could consider incorporating features such as personalized feedback and suggestions for improvement in the resume. This can help applicants to understand where they may be falling short and how they can improve their chances of being hired.
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Amarnath, Raveendra N. y Gurumoorthi Gurulakshmanan. "Cloud-based machine learning algorithms for anomalies detection". Indonesian Journal of Electrical Engineering and Computer Science 35, n.º 1 (1 de julio de 2024): 156. http://dx.doi.org/10.11591/ijeecs.v35.i1.pp156-164.
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Gradient boosting machines harnesses the inherent capabilities of decision trees and meticulously corrects their errors in a sequential fashion, culminating in remarkably precise predictions. Word2Vec, a prominent word embedding technique, occupies a pivotal role in natural language processing (NLP) tasks. Its proficiency lies in capturing intricate semantic relationships among words, thereby facilitating applications such as sentiment analysis, document classification, and machine translation to discern subtle nuances present in textual data. Bayesian networks introduce probabilistic modeling capabilities, predominantly in contexts marked by uncertainty. Their versatile applications encompass risk assessment, fault diagnosis, and recommendation systems. Gated recurrent units (GRU), a variant of recurrent neural networks, emerges as a formidable asset in modeling sequential data. Both training and testing are crucial to the success of an intrusion detection system (IDS). During the training phase, several models are created, each of which can recognize typical from anomalous patterns within a given dataset. To acquire passwords and credit card details, "phishing" usually entails impersonating a trusted company. Predictions of student performance on academic tasks are improved by hyper parameter optimization of the gradient boosting regression tree using the grid search approach.
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Janásek, Adam, Robert Čep, Lenka Čepová, Jiří Kratochvíl, Vladimír Vrba y Lenka Petřkovská. "Tool Life Reliability of Indexable Cutting Inserts". Technological Engineering 9, n.º 2 (1 de diciembre de 2012): 30–34. http://dx.doi.org/10.2478/teen-2012-0008.
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Abstract This paper presents experimental testing of the cutting ability for indexable cutting inserts. The main goal will be to select a suitable test for determine the cutting abilities of cutting inserts. Nowadays all manufacturers want to achieve lower cutting forces so can permit higher speeds and feeds, without increasing the risk of chipping. For evaluating we had to design such testing procedure that it would be possible to compare and evaluate the cutting ability of the selected cutting inserts used for tests. In today’s competitive global market, quality and precision is the most important parameter. Tight tolerances and urgent deadlines are normal. In the machine tool business, companies must keep their cutting edge, or your company will lose its competitive edge.
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Carlos, José C., Pedro M. Amaral, Adriano Coelho, Andre Tavares, Jorge C. Fernandes y Luís G. Rosa. "Optimisation of Circular Sawing Conditions to Maximize Tool Productivity for each Class of Material". Key Engineering Materials 548 (abril de 2013): 106–14. http://dx.doi.org/10.4028/www.scientific.net/kem.548.106.
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The research disclosed in the present paper reports a new computer algorithm to maximize tool productivity in circular sawing processes, as a function of the stone characteristics and the quality required for the product. This algorithm is currently applied in tool testing at the company FrontWave and will be used in a new type of numeric machine to cut stone, called LeanMachine®. This optimization algorithm essentially depends on three variables: cutting depth, forward speed and rotational speed (identified as the main variables quantifying the sawing process), and how the variables are related with the forces acting on the tool. The algorithm uses the data provided by the relationships between each of the variables and the force acting on the tool (the so-called “force plots”) to determine the optimum working conditions for each tool, aiming to maximize productivity and minimize wear and energy consumption. The algorithm works with different production strategies, involving quality versus productivity, a key factor in the stone industry. A rating is subsequently attributed to each tool, allowing the establishment of tool rankings that can be used as selection criteria by machine operators or automatically in modern cutting stone machines such as LeanMachine®.
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Song, Qinghua, Tao Hu, Sattva S. Neelapu, Frederick L. Locke, Caron Jacobson, David B. Miklos, Olalekan O. Oluwole et al. "Prediction of Early Onset Cytokine Release Syndrome and Neurologic Events after Axicabtagene Ciloleucel in Large B-Cell Lymphoma Based on Machine Learning Algorithms". Blood 138, Supplement 1 (5 de noviembre de 2021): 2833. http://dx.doi.org/10.1182/blood-2021-148580.
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Abstract Background: In ZUMA-1, the pivotal study of axicabtagene ciloleucel (axi-cel) in patients with refractory large B-cell lymphoma (LBCL), Grade ≥3 cytokine release syndrome (CRS) and neurologic events (NEs) occurred in 13% and 28% of patients, respectively, and required intensive in-patient management (Neelapu, et al. NEJM. 2017;377:2531). With increased safety experience, the management of CRS and NEs has been under evaluation in several exploratory safety management cohorts of ZUMA-1. Cohort 4 evaluated levetiracetam prophylaxis and earlier corticosteroid and/or tocilizumab use on the incidence and severity of CRS and NEs (Topp, et al. Br J Haematol. 2021. In press). The impact of adding prophylactic corticosteroids to the Cohort 4 toxicity management regimen was assessed in Cohort 6 (Oluwole, et al. Br J Haematol. 2021). Notably, some treated patients have early versus late onset of CRS or NEs, warranting distinct management. To facilitate toxicity management, we developed predictive algorithms for early onset acute toxicities (within 3-4 days after axi-cel) based on machine learning from ZUMA-1 data. Methods: This post hoc analysis included patients from ZUMA-1 Phase 1 and Phase 2 Cohorts 1, 2, 4, and 6. Covariates (>1500; 227 measured before axi-cel infusion) included baseline product, patient and tumor characteristics, and proinflammatory soluble blood biomarker levels. Data from patients in Cohorts 1, 2, and 4 were randomly divided into training (70%) and testing (30%) sets. Univariate and multivariate analyses and clinical feasibility considerations were applied to select a covariate subset for further analysis. Machine learning (eg, logistic regression, random forest, XGBoost, and AdaBoost classifier) was applied to 3 categories of covariates (1, clinical; 2, mechanistic [eg, product attributes, inflammatory blood biomarkers]; 3, hybrid of 1 and 2) to build best-performing models (predictive performance evaluated by area under the curve [AUC] on test data). Optimal cutoffs for predictive scores were selected by receiver operating characteristic (ROC) or classification tree analysis. Data from patients in Cohort 6 were included to validate the best-performing model generated using training data. Results: Multivariate analysis and machine learning from data obtained from 149 evaluable patients in ZUMA-1 Cohorts 1, 2, and 4 led to several comparable predictive models for early onset CRS or NEs (best-performing models with ROC AUC >0.8 in training and >0.7 in testing). The covariates in best-performing models included product cell viability, centrally measured Day 0 (before axi-cel treatment) IL-15 and CCL2 serum levels and locally measured blood cell counts, blood chemistry analytes, tumor burden, and serum lactate dehydrogenase level. Best-performing models with <5 covariates contained only mechanistic covariates or a hybrid mix of covariates. A 3-covariate mechanistic model (product cell viability and Day 0 IL-15 and CCL2 serum levels, all positively associated with early onset toxicities) performed comparably (ROC AUC >0.7 in testing) to larger best-performing models. Classification trees with splitting based on Day 0 IL-15 and product cell viability showed a potential to categorize patients by early versus late onset of toxicities (specificity >0.85). Conclusions: Machine learning applied to covariates measured before axi-cel infusion yielded predictive models for early onset CRS or NEs that can be used for toxicity prediction, monitoring, and management. High performing hybrid or mechanistic models corroborated the importance of T-cell viability (product cell fitness) and conditioning-related elevation of factors (IL-15 and CCL2) that influence toxicities. This algorithm may be further optimized in the context of improved toxicity management and by introducing monitoring of vitals early posttreatment. Disclosures Song: Kite, a Gilead Company: Current Employment; Gilead Sciences: Current equity holder in publicly-traded company. Hu: Kite, a Gilead Company: Current Employment. Neelapu: Takeda Pharmaceuticals and related to cell therapy: Patents & Royalties; Kite, a Gilead Company, Bristol Myers Squibb, Merck, Poseida, Cellectis, Celgene, Karus Therapeutics, Unum Therapeutics (Cogent Biosciences), Allogene, Precision BioSciences, Acerta and Adicet Bio: Research Funding; Kite, a Gilead Company, Merck, Bristol Myers Squibb, Novartis, Celgene, Pfizer, Allogene, Kuur, Incyte, Precision BioSciences, Legend, Adicet Bio, Calibr, and Unum Therapeutics: Other: personal fees; Kite, a Gilead Company, Merck, Bristol Myers Squibb, Novartis, Celgene, Pfizer, Allogene Therapeutics, Cell Medica/Kuur, Incyte, Precision Biosciences, Legend Biotech, Adicet Bio, Calibr, Unum Therapeutics and Bluebird Bio: Honoraria. Locke: Gerson Lehrman Group: Consultancy; Amgen: Consultancy, Other: Scientific Advisory Role; EcoR1: Consultancy; Emerging Therapy Solutions: Consultancy; Cowen: Consultancy; Umoja: Consultancy, Other; Iovance Biotherapeutics: Consultancy, Other: Scientific Advisory Role; Kite, a Gilead Company: Consultancy, Other: Scientific Advisory Role, Research Funding; Janssen: Consultancy, Other: Scientific Advisory Role; Novartis: Consultancy, Other, Research Funding; Takeda: Consultancy, Other; GammaDelta Therapeutics: Consultancy, Other: Scientific Advisory Role; Allogene Therapeutics: Consultancy, Other: Scientific Advisory Role, Research Funding; Bluebird Bio: Consultancy, Other: Scientific Advisory Role; Wugen: Consultancy, Other; Cellular Biomedicine Group: Consultancy, Other: Scientific Advisory Role; Calibr: Consultancy, Other: Scientific Advisory Role; BMS/Celgene: Consultancy, Other: Scientific Advisory Role; Legend Biotech: Consultancy, Other; Moffitt Cancer Center: Patents & Royalties: field of cellular immunotherapy. Jacobson: Lonza: Consultancy, Honoraria, Other: Travel support; Kite, a Gilead Company: Consultancy, Honoraria, Other: Travel support; Axis: Speakers Bureau; Nkarta: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria, Other: Travel support, Research Funding; Novartis Pharmaceuticals Corporation: Consultancy, Honoraria, Other: Travel support; Clinical Care Options: Speakers Bureau; Celgene: Consultancy, Honoraria, Other: Travel support; Humanigen: Consultancy, Honoraria, Other: Travel support; Precision Biosciences: Consultancy, Honoraria, Other: Travel support. Miklos: Adaptive Biotechnologies, Novartis, Juno/Celgene-BMS, Kite, a Gilead Company, Pharmacyclics-AbbVie, Janssen, Pharmacyclics, AlloGene, Precision Bioscience, Miltenyi Biotech, Adicet, Takeda: Membership on an entity's Board of Directors or advisory committees; Kite, a Gilead Company, Amgen, Atara, Wugen, Celgene, Novartis, Juno-Celgene-Bristol Myers Squibb, Allogene, Precision Bioscience, Adicet, Pharmacyclics, Janssen, Takeda, Adaptive Biotechnologies and Miltenyi Biotechnologies: Consultancy; Pharmacyclics: Patents & Royalties; Pharmacyclics, Amgen, Kite, a Gilead Company, Novartis, Roche, Genentech, Becton Dickinson, Isoplexis, Miltenyi, Juno-Celgene-Bristol Myers Squibb, Allogene, Precision Biosciences, Adicet, Adaptive Biotechnologies: Research Funding. Oluwole: Kite, a Gilead Company: Consultancy, Research Funding; Janssen: Consultancy; Pfizer: Consultancy; Curio Science: Consultancy. Kersten: Takeda: Research Funding; Novartis: Consultancy, Honoraria, Other: Travel support; Miltenyi Biotec: Consultancy, Honoraria, Other: Travel support; BMS/Celgene: Consultancy, Honoraria; Roche: Consultancy, Honoraria, Other: Travel support, Research Funding; Celgene: Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Other: Travel support, Research Funding. Topp: Universitatklinikum Wurzburg: Current Employment; Amgen: Consultancy, Research Funding; Macrogeniecs: Research Funding; Regeneron: Consultancy, Research Funding; Gilead: Research Funding; Roche: Consultancy, Research Funding; Novartis: Consultancy; Kite, a Gilead Company: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy. Kim: Gilead Sciences: Current equity holder in publicly-traded company; Kite, a Gilead Company: Current Employment. Singh: Kite, a Gilead Company: Current Employment. Andrade: Kite, a Gilead Company: Current Employment, Other: Leadership role. Huang: Kite, a Gilead Company: Current Employment. Xue: Kite, a Gilead Company: Current Employment; Gilead Sciences: Current equity holder in publicly-traded company. Schupp: Kite, a Gilead Company: Current Employment, Honoraria, Other: Travel support; Gilead Sciences: Current equity holder in publicly-traded company. Nahas: Kite: Current Employment; Gilead: Current equity holder in publicly-traded company. Shen: Gilead Sciences: Current equity holder in publicly-traded company; Atara: Current Employment, Current equity holder in publicly-traded company, Other: Leadership role, Patents & Royalties; Kite, a Gilead Company: Current Employment, Other: Leadership role, Patents & Royalties. Bot: Gilead Sciences: Consultancy, Current equity holder in publicly-traded company, Other: Travel support; Kite, a Gilead Company: Current Employment.
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Medyanti, Wikke Alvina y Muhammad Faisal. "Early Prediction System for Employee Attrition Company “XYZ” Using Support Vector Machine Algorithm". CESS (Journal of Computer Engineering, System and Science) 8, n.º 2 (13 de julio de 2023): 429. http://dx.doi.org/10.24114/cess.v8i2.46494.
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Pergantian karyawan merupakan masalah yang signifikan bagi organisasi karena dapat berdampak negatif pada produktivitas dan kinerja. Dalam penelitian ini, dikembangkan sebuah model Support Vector Machine (SVM) untuk memprediksi pergantian karyawan berdasarkan dataset yang berisi berbagai atribut karyawan. Dataset tersebut telah melalui tahap pra-pemrosesan dengan melakukan pemetaan nilai-nilai kategorikal dan pengkodean one-hot. Fitur-fitur kemudian dibagi menjadi data latih dan data uji, serta dilakukan penskalaan menggunakan StandardScaler. Hasil penelitian menunjukkan bahwa model mencapai akurasi sebesar 88,4%. Presisi untuk karyawan yang tidak mengalami pergantian (non-attrition) tinggi, yaitu sebesar 89,3%, menunjukkan kemampuan model dalam mengidentifikasi dengan benar karyawan yang kemungkinan akan bertahan. Namun, presisi untuk karyawan yang mengalami pergantian (attrition) lebih rendah, sebesar 69,2%, mengindikasikan adanya ruang untuk perbaikan dalam mengidentifikasi karyawan yang berisiko mengalami pergantian. Recall untuk karyawan non-attrition mencapai 98,4%, menunjukkan kemampuan yang tinggi dalam mengklasifikasikan dengan benar, sedangkan recall untuk karyawan attrition sebesar 23,1%. Nilai F1-score juga mencerminkan kinerja yang lebih baik untuk karyawan non-attrition dibandingkan karyawan attrition. Secara keseluruhan, model SVM menunjukkan potensi dalam memprediksi pergantian karyawan, namun perlu dilakukan pengembangan lebih lanjut untuk meningkatkan identifikasi karyawan yang berisiko, sehingga memberikan wawasan berharga dalam pengambilan keputusan SDM dan strategi retensi.Employee attrition is a significant concern for organizations as it can have a negative impact on productivity and performance. In this study, a Support Vector Machine (SVM) model was developed to predict employee attrition based on a dataset containing various employee attributes. The dataset was preprocessed by mapping categorical values and performing one-hot encoding. The features were then split into training and testing sets, and scaled using the StandardScaler.The results showed that the model achieved an accuracy of 88.4%. The precision for non-attrition employees was high at 89.3%, indicating the model's ability to correctly identify employees who are likely to stay. However, the precision for attrition employees was lower at 69.2%, suggesting room for improvement in identifying employees at risk of attrition. The recall for non-attrition employees was 98.4%, indicating a high ability to correctly classify them, while the recall for attrition employees was 23.1%. The F1-score also reflected a better performance for non-attrition employees compared to attrition employees. Overall, the SVM model showed promise in predicting employee attrition, but further enhancements are needed to improve the identification of employees at risk, thus providing valuable insights for HR decision-making and retention strategies.
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Belousova, М. V. y V. V. Bulatov. "On the organization of the dependability service in a machine-building company". Dependability 20, n.º 1 (30 de marzo de 2020): 25–31. http://dx.doi.org/10.21683/1729-2646-2020-20-1-25-31.
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Historically, dependability services originated within design units of companies. A design engineer had his/her own ideas about the quality control of released products. As the initial application field of the dependability theory was the aerospace industry, he/she understood that the presence of errors and omissions within a product could cause catastrophic consequences [1]. Along with the dependability unit the quality and technical supervision service was developing, and that was primarily tasked with organizing and conducting acceptance testing, receiving inspection and prevention of a product’s non-compliance with technical documentation. At one point, a conflict arouse between the two branches, which lead to a general misunderstanding of responsibilities and disorganization of the product dependability control. As a result, in some companies the dependability service is integrated with the quality service, in others it is subordinated to the design bureau. Additionally, operational dependability evaluation requires an uninterruptible source of reliable information on the reliability and maintainability of the equipment. The quality of this information depends on the interaction between the dependability service and the maintenance service. The latter is to compare the repair reports that specify the recovery time and operation time of the product and promptly submit that data for dependability calculation. Thus, the following questions arise: which activities are to be performed by the dependability service, who is to be subordinated to whom, who is the owner of the processes associated with the estimation of dependability parameters? It is important to understand the purpose of establishing a dependability unit in a company, what authority its employees possess, what results the management expects to obtain. The formalization of the research findings presents a problem. As of today, there is no single approach to formalized calculations, preparation of dependability analysis reports. The research findings are to be sent to all the involved business units, therefore a convenient form of information representation must be developed. A special attention must be given to personnel training in terms of technical system dependability. Industrial products become more and more complex, new technologies are developed, and old approaches to dependability calculation and analysis do not always ensure acceptable results. That is not surprising, as the significance of the use of reliable and substantiated methods of dependability estimation is very understated. That is due to the fact, that many believe that the dependability theory is based on the research of the physical, design-specific causes of failure, physicochemical processes, etc., meaning that a dependability engineer is first and foremost a design or process engineer. However, it should not be forgotten that the general dependability theory is subdivided into the mathematical (mathematical methods of the probability theory), statistical (method of mathematical statistics) and physical (research of materials properties variations). Subsequently, a dependability service is to conduct analysis based on competent application of mathematics alongside activities associated with products design research. Proposals regarding future developments in this area, including the education system, will be welcome.Aim. To propose an approach to the organization of the dependability service in a modern machine-building company taking into account advanced methods and concepts of dependability analysis at all lifecycle stages of a product.Conclusions. The paper suggests an organizational structure of a dependability unit for a transport machine building company. The interactions between the dependability service and other business units is examined. A number of factors affecting the efficient operation of the dependability service are identified.
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Herdiansyah, Muhammad. "The Effect of Production Room Layout and Production Machine Maintenance on Production Effectiveness". Almana : Jurnal Manajemen dan Bisnis 4, n.º 2 (10 de agosto de 2020): 297–308. http://dx.doi.org/10.36555/almana.v4i2.1420.
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The intense business competition in the industrial world requires companies to always advance. Many ways did so that companies can more effective in all fields so that company productivity can increase. In relation, the Production Room Layout and Production Machine Maintenance are very influential in the creation of effectiveness in the production activities. This study aims to examine the effect of Production Room Layout and Machine Maintenance on Production Effectiveness at PT. Bintang Usaha Nasional. The population in this study is the number of employees in the production field of PT. Bintang Usaha Nasional as many as 30 people. Data collection techniques used were observation, interviews, questionnaires, and literature study. The statistical analysis method used is Path Analysis with partial hypothesis testing (t-test) and simultaneous (f test). The results of the research show that all independent variables (Production Room Layout and Machine Maintenance) have a significant effect on the effectiveness of production at PT. Bintang Usaha Nasional. The Effect of Production Room Layout and Machine Maintenance on Production Effectiveness is 85.7%, while the remaining 14.3% comes from other variables besides the production room layout and machine maintenance.
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Aljinović Barać, Željana y Mario Bilić. "The effects of company characteristics on financial reporting quality – the application of the machine learning technique". Ekonomski vjesnik 34, n.º 1 (2021): 57–72. http://dx.doi.org/10.51680/ev.34.1.5.
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Purpose: The paper aims to determine the level of financial reporting quality (FRQ) in listed companies in Croatia, as an example of a macro-based accounting system with an underdeveloped capital market, and identify company characteristics that affect it. The paper systematizes the existing key knowledge of FRQ. Furthermore, it critically analyses the principles and direction of influence of various qualitative and quantitative as well as financial and non-financial characteristics of a company. Methodology: The empirical analyses involve joint testing of the machine learning technique (MLT) and the economic hypotheses. M5 algorithm is applied to identify the factors that influence the quality of voluntary reporting as well as the direction and intensity of their influence. Results: The results show that profitability, stock market listing duration (in years), and company size positively affect the level and extent of FRQ through voluntary disclosure of information in the annual financial reports of Croatian listed companies. In addition, differences in FRQ between different areas of economic activity and depending on the type of auditor were found. Conclusion: Croatian companies should adopt good reporting practices to meet the requirements of the global market and thus contribute to the improvement of the overall transparency system. The same is expected from the relevant regulatory authorities who should encourage full disclosure. The paper provides several scientific contributions: first, the spatial dimension of the research; second, the comprehensive literature review; and third, the MLT application in the research on FRQ. An important practical implication of these findings is that they will help financial statement users in the economic decision-making process.
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NIEVES DA COSTA, JOAO PEDRO, PAULO AVILA, JOAO BASTOS y LUIS PINTO FERREIRA. "A NEW SIMPLE, FLEXIBLE AND LOW-COST MACHINE MONITORING SYSTEM". DYNA DYNA-ACELERADO (7 de junio de 2021): [ 7 pp.]. http://dx.doi.org/10.6036/10075.
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The industry 4.0 revolution provides the machines with a sensory and communicational capacity, which allows them to monitor and collect large amounts of information. This kind of data have an impact on planning, maintenance, and management of production, enabling real time reaction, efficiency increase, and the development of predictive and process improvement models. The most recent machines are prepared to communicate with the existing monitoring systems, however, many (around 60%) do not. The objective of this work is to present the proposal of a system for remote monitoring of equipment in real time that meets the requirements of low cost, simplicity, and flexibility. The system monitors the equipment in a simple and agile way, regardless of its sophistication, installation constraints and company resources. A prototype of a system was developed and tested both laboratory conditions and a productive environment. The proposed architecture of the system comprises of a sensor that transmits the machine’s signal wirelessly to a gateway which is responsible of collecting all surrounding signals and send it to the cloud. During the testing and assessment of the tools, the results validated the developed prototype. As main result, the proposed solution offers to the industrial market a new low-cost monitoring system based in mature and tested technology laid upon flexible and scalable solutions. Industry 4.0, Machine Monitoring, Beacon, Bluetooth BLE, Remote Monitoring, Low Cost, SME’s, b-Remote
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Nugraha Adz Zikri, Arif Furqon y Wiwin Suwarningsih. "Pay Later Risk Management: A Review of FMECA and Potential Customer Prediction Frameworks Through the Application of Machine Learning". International Journal of Advances in Data and Information Systems 4, n.º 2 (21 de octubre de 2023): 167–80. http://dx.doi.org/10.25008/ijadis.v4i2.1293.
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The development of technology continues to develop and gradually change the way people buy such as on online shopping sites. The increase in internet use, especially in the use of E Commerce, has given birth to great potential in the market, especially in Indonesia. These changes prompted the birth of various payment methods. One of them is Pay Later. 27% of the 3560 samples decided to use Pay Later with all the conveniences offered. However, the development of Pay Later is not synchronized with good risk management. The use of Pay Later, which is not targeted at the right consumers, causes PT. XYZ suffered losses due to 22.37% of users defaulting on Pay Later installments. The purpose of this study is to reduce Pay Later default users by answering what factors cause consumers to default. To support this study, the authors used FMECA, Cause Effect Diagrams and conducted tests using Machine Learning to improve company efficiency. Through critical matrix analysis, the author gets 3 priority failure modes, Users default, users disappear, and users experience payment delays. In solving the problems in this study, the authors provide recommendations in the form of a new framework in the form of analyzing the best Pay Later offers by analyzing consumer behavior patterns in an E Commerce by utilizing Machine Learning. However, future research will need to be conducted correlation analysis and static testing in testing attribute correlation before testing algorithms when building machine learning models. The authors also suggested comparing using other methods to improve risk management in this study.
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Zhu, Yuejia, Jiayi Zhu, Jiaoyuan Ding y Ziyan Li. "Should machine learning be applied in credit risk accessment". Applied and Computational Engineering 43, n.º 1 (26 de febrero de 2024): 83–93. http://dx.doi.org/10.54254/2755-2721/43/20230812.
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In the past, analysts evaluated whether to offer loans to particular applicants using rule-based approaches. However, due to the sudden rise in applicants and a labor shortage, financial institutions have created quantitative methods of decision-making. Credit scoring models are constructed. In this essay, random forest model, support vector machine regression model and Probit model are performed and compared according to the dataset from a major U.S. credit cards company. The result demonstrates that while machine learning techniques can improve the efficiency and accuracy of credit risk assessment, it does face some problems and limitations. Random forest model is capable of handling high-dimensional data and is not complicated to run. However, database with fewer features or samples will have lower classification accuracy. Support vector machine regression model has high accuracy and prevents overfitting to some degree. It is sensitive to the choice of kernel parameters and regularization term. By testing how important Mill Ratio is, the Probit model produces more accurate results. However, the model is more complex than the other two. In future research, we propose to enhance and extend our work by using more artificial intelligence algorithms and evaluation metrics.
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Alhamed, Mariam y M. M. Hafizur Rahman. "A Systematic Literature Review on Penetration Testing in Networks: Future Research Directions". Applied Sciences 13, n.º 12 (9 de junio de 2023): 6986. http://dx.doi.org/10.3390/app13126986.
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Given the widespread use of the internet at the individual, governmental, and nongovernmental levels, and the opportunities it offers, such as online shopping, security concerns may arise. Cyber criminals are responsible for stopping organizations’ access to internet, for stealing valuable and confidential data, and causing other damage. Therefore, the network must be protected and meet security requirements. Network penetration testing is a type of security assessment used to find risk areas and vulnerabilities that threaten the security of a network. Thus, network penetration testing is designed to provide prevention and detection controls against attacks in the network. A tester looks for security issues in the network operation, design, or implementation of the particular company or organization. Thus, it is important to identify the vulnerabilities and identify the threats that may exploit them in order to find ways to reduce their dangers.The ports at risk are named and discussed in this study. Furthermore, we discuss the most common tools used for network penetration testing. Moreover, we look at potential attacks and typical remediation strategies that can be used to protect the vulnerable ports by reviewing the related publications. In conclusion, it is recommended that researchers in this field focus on automated network penetration testing. In the future, we will use machine learning in WLAN penetration testing, which provides new insight and high efficiency in performance. Moreover, we will train machine learning models to detect a wide range of vulnerabilities in order to find solutions to mitigate the risks in a short amount of time rather that through manual WLAN penetration testing, which consumes a lot of time. This will lead to improving security and reducing loss prevention.
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Zhang, Jiaxuan, Zinan Cao, Haichen Qu y Meng Wang. "Management and prediction of employee turnover in enterprises based on big data analytics and machine learning". Applied and Computational Engineering 76, n.º 1 (16 de julio de 2024): 103–8. http://dx.doi.org/10.54254/2755-2721/76/20240573.
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In the context of aging population and social transformation, employees also put forward higher demands on corporate culture and values, which also increases the risk of employee turnover. This paper explores the correlation between various indicators and leavers through statistical analysis and visualization of various data indicators and the distribution of leavers and non-leavers on various indicators by using whether they left the company as a classification criterion. In addition, this paper also carries out Pearson correlation analysis for each indicator and draws a correlation heat map to quantitatively explore the correlation between indicators. In order to predict whether employees will leave the company, this paper uses random forest model, support vector machine model, KNN model, plain Bayesian model and logistic regression model for training and testing. The results show that the best prediction in terms of employee turnover is the Random Forest model with a prediction accuracy of 98.8%. This was followed by the Support Vector Machine model with an accuracy of 95.1%. In addition, the KNN model also achieved an accuracy of 94.8%. Ordinary Bayesian model and logistic regression model have lower accuracy rates of 80.4% and 77.2% only. This is of great significance for enterprises to realize sustainable development, and is worthy of in-depth study and practice by enterprise managers.
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Bakti, Panji Satrio y Eliyani Eliyani. "Application of J48 and Naïve Bayes Algorithms to Predict Ream Bookings at PT. Nippon Presisi Teknik". Eduvest - Journal of Universal Studies 3, n.º 6 (20 de junio de 2023): 1047–60. http://dx.doi.org/10.59188/eduvest.v3i6.834.
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In the field of goods production, demand prediction is important. By doing sales predictions, companies can make calculations and forecasts for what raw materials are mostly ordered. J48 and Naïve Bayes algorithm are two popular machine learning technique. By using these two algorithms, this study aims to develop an accurate and more reliable predictive model that help the company to make data driven decision. This study focuses on the application of quantitative methods, specifically the J48 algorithm and Naïve Bayes algorithm. This research conducted 4 times testing on each algorithm. This study produces high accuracy values with the Naïve Bayes and J48 algorithms. Both algorithm results have a fairly high accuracy value of 94% for Naïve Bayes and 98% for J48. The findings of this study implicate that by using J48 and Naïve Bayes algorithm, company can make informed decisions lead to improved operational efficiency, cost-effective, and resource utilization.
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Erico, Erico, Stephanie Wijaya, Bryan Herberth Tambela, Irpan Adiputra Pardosi y Sunaryo Winardi. "Pengembangan Sistem Monitor dan Laporan Mesin PT. Numalos Abadi Menggunakan Amazon Web Services". Jurnal SIFO Mikroskil 23, n.º 2 (27 de octubre de 2022): 111–20. http://dx.doi.org/10.55601/jsm.v23i2.886.
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PT. Numalos Abadi merupakan perusahaan distributor dan layanan purna jual mesin, salah satunya adalah decanter Flottweg Z5E. PT. Numalos Abadi memerlukan penerapan Internet of Things untuk mendapatkan informasi mesin, salah satu informasi mesin yang menjadi fokus utama adalah running hour yang merupakan penjumlahan durasi mesin berjalan. Running hour yang tidak terpantau mengakibatkan penurunan kinerja mesin yang berdampak pada hasil produksi maupun kerusakan mesin. Tidak adanya pemberitahuan dini waktu perawatan mesin mengakibatkan tidak adanya persiapan materi maupun waktu yang merugikan bagi customer. Berdasarkan masalah tersebut, dikembangkanlah sebuah solusi berupa sistem monitoring dan laporan mesin untuk mengelola waktu perawatan mesin dan mengurangi potensi kerugian bagi customer. Pengembangan sistem monitor dan laporan mesin diimplementasikan menggunakan Amazon Web Services dalam aplikasi web dan mobile. Dalam penelitian ini dikembangkan aplikasi web dan mobile menggunakan metodologi Waterfall untuk menghasilkan kualitas sistem yang baik dan dokumen pengembangan sistem yang terorganisasi. Pengujian dilakukan secara simulasi dan running hour yang telah melewati batas akan menampilkan indikasi alarm serta notifikasi. Pengujian Black Box yang dilakukan juga menunjukkan secara fungsional aplikasi dapat bekerja dengan baik dan mengeluarkan hasil yang diharapkan.PT Numalos Abadi is a distributor and after-sales service company for machinery, one of which is the Flottweg Z5E decanter. PT. Numalos Abadi required using the Internet of Things to obtain information on the machines it distributes. One of the main focuses is the running hour of a machine which is the number of hours a machine is in operational mode, if not monitored could cause a decrease in machine performance which impacts the production results and machine damage. Due to the absence of a notification Customers often requests sudden maintenance without any preparation. Based on this problem, a solution was developed in the form of a machine Monitoring and Reporting system to manage maintenance time and reduce potential losses. The development of the system is implemented using Amazon Web Services in web and mobile applications. Waterfall methodology is used in this research to produce good system quality and organized system development documents. Testing is done by simulation and running hours that have exceeded the limit will display alarm indications and notifications. Black Box testing shows that functionally the application works and produces the expected results.
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Mashuri, Chamdan, Ahmad Heru Mujianto, Hadi Sucipto y Rinaldo Yudianto Arsam. "Sistem Optimasi Penjadwalan Mesin Produksi Menggunakan Metode GUPTA Berbasis Android". JURNAL SISTEM INFORMASI BISNIS 10, n.º 1 (12 de marzo de 2020): 20–27. http://dx.doi.org/10.21456/vol10iss1pp20-27.
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Research has been carried out by developing an optimization information system for scheduling production machines by applying the Android-based GUPTA method. This android-based application is able to optimize production time, because in the android application it implements the GUPTA algorithm which uses the calculation of the comparative processing time on every machine in the company by prioritizing the smallest processing time for scheduling which aims to optimize production scheduling time, by paying attention to the value of makespan to produce product size 12 griddle, size 14 griddle, 16 size griddle, 18 size griddle and 20 size griddle so that an optimal makespan value is obtained. The GUPTA method can be used in problems with more than two machines, because this method combines the time of each process on the first and subsequent machines to find the minimum value and can only be used in pure flow shop scheduling. The advantage of this method is that it determines scheduling only on one machine group. This research resulted in an Android-based application that can schedule products to be produced by machines automatically. From the results of testing with a total of 12 pieces of production in each product with a total of 5 different sizes, the minimum value of makespan is obtained, namely 2054569 minutes with the sequence of product processing with work order 12 griddle, griddle 18, griddle 20, griddle 16, and griddle 14 The accuracy of the application test results shows 98.87% for the first time and 98.84% for the second time when compared with manual calculations.
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Wardhani, Rachmasari Pramita. "Application of Ultrasonic Methods in Super POD Thickness Measurement Examination". International Journal of Multidisciplinary Approach Research and Science 2, n.º 01 (29 de diciembre de 2023): 475–82. http://dx.doi.org/10.59653/ijmars.v2i01.554.
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NDT is a scientific field of engineering that includes testing and inspection of materials and equipment which is useful for evaluating a condition, finding deficiencies and defects, and also extending the useful life of the infrastructure used. The main difference between non-destructive testing and other forms of material evaluation is that non-destructive testing allows evaluation or inspection of parts on site without having to permanently modify or damage the part. In maintaining the credibility of the company's services, the company adopting NDT/NDE testing techniques in carrying out regular inspections or checks on the tools used, one of which is the application of the ultrasonic method on the Super POD (Programmable Optimum Density). Based on this background, the author chose the theme of the study to write a scientific paper entitled “Application of Ultrasonic Methods in Super POD Thickness Measurement Examination”. The SBF-624 Super POD (programmable optimum density, POD) is a trailer-mounted fracturing service blender that can blend and pump up to 120 barrels per minute of fracturing slurry. The Super POD computers precisely control the solid-to-liquid ratio of the prop pant at design values in either ramp or stair-step mode. The results of observations of objects that have been inspected using the ultrasonic method, namely measuring the thickness of the Super POD SBF 624, from the good results without any defects being detected, and cleaned in surface condition so it can be said that the machine has shown good effective results and can meet the standards for using tools for the company.
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Dzulfiqar, Muhammad Faiz, Aditya Rio Prabowo, Fitrian Imaduddin, Indri Yaningsih, Dominicus Danardono Dwi Pria Tjahjana, Wibawa Endra Juwana, Takahiko Miyazaki y Joung Hyung Cho. "Structural Investigation and Economical Assessment of the Designed Automatic-Brake-Pad Thickness-Checking Machine". Designs 6, n.º 4 (29 de julio de 2022): 67. http://dx.doi.org/10.3390/designs6040067.
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Thickness checking is one of the quality control procedures in the brake pad industry. This research aims to address the issue of the time effectiveness of the thickness checking by turning the technique into an automatic process, from specimen preparation to data recordings; the technical aspects, the geometric design, and the structure analysis are critical elements of the industrial machinery. However, economic analysis is considered when it becomes an investment by a company with long-term use expectations. Thus, this research provides structural analysis, time checking estimation, and simple investment feasibility studies on break-even points and a simple payback period to ensure that the new design can improve testing performance. Monte Carlo simulation offers the calculation of investment feasibility with the three possibilities of pessimistic, optimistic, and realistic results in achieving a break-even point (BEP) and a simple payback period.
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Wang, Haoqian. "Predicting Airbnb Listing Price with Different models". Highlights in Science, Engineering and Technology 47 (11 de mayo de 2023): 79–86. http://dx.doi.org/10.54097/hset.v47i.8169.
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Airbnb is a platform company that provides and directs connections between hosts and guests. People who have an open room or a vacant space can become a host on Airbnb and make it available to the world community. Airbnb offers hosts an easy way to turn otherwise wasted space into profitable space. Therefore, it is particularly necessary for hosts to forecast and analyze the price of the houses they own. Machine learning is the science of developing algorithms and statistical models. The regression model is a predictive modeling technique in machine learning. This technique is often used to discover causal relationships between variables, predictive analysis, and time series models. In this project, our goal is to predict Boston Airbnb listing prices through a variety of machine-learning methods. This paper chose four regression models, which are the random forest regression model, linear regression model, K-nearest neighbor regression model, and Gradient Boosting regression model. With one of the best regression models, this paper obtained R-squared values of 0.6593 in training and 0.7198 in testing on the Boston dataset.
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Amalia, Safira, Irene Deborah y Intan Nurma Yulita. "Comparative analysis of classification algorithm: Random Forest, SPAARC, and MLP for airlines customer satisfaction". SINERGI 26, n.º 2 (15 de junio de 2022): 213. http://dx.doi.org/10.22441/sinergi.2022.2.010.
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The airline business is one of the businesses determined by the quality of its services. Every airline creates its best service so that customers feel satisfied and loyal to using their services. Therefore, customer satisfaction is an essential metric to measure features and services provided. By having a database on customer satisfaction, the company can utilize the data for machine learning modelling. The model generated can predict customer satisfaction by looking at the existing feature criteria and becoming a decision support system for management. This article compares machine learning between Split Point and Attribute Reduced Classifier (SPAARC), Multilayer Perceptron (MLP), and Random Fores (RF) in predicting customer satisfaction. Based on the data testing, the Random Forest algorithm provides better results with the lowest training time compared to SPAARC and MLP. It has an accuracy of 95.827%, an F-score of 0.958, and a training time of 84.53 seconds.
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Nascimento, Alexandre M., Gabriel Kenji G. Shimanuki y Luiz Alberto V. Dias. "Making More with Less: Improving Software Testing Outcomes Using a Cross-Project and Cross-Language ML Classifier Based on Cost-Sensitive Training". Applied Sciences 14, n.º 11 (4 de junio de 2024): 4880. http://dx.doi.org/10.3390/app14114880.
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As digitalization expands across all sectors, the economic toll of software defects on the U.S. economy reaches up to $2.41 trillion annually. High-profile incidents like the Boeing 787-Max 8 crash have shown the devastating potential of these defects, highlighting the critical importance of software testing within quality assurance frameworks. However, due to its complexity and resource intensity, the exhaustive nature of comprehensive testing often surpasses budget constraints. This research utilizes a machine learning (ML) model to enhance software testing decisions by pinpointing areas most susceptible to defects and optimizing scarce resource allocation. Previous studies have shown promising results using cost-sensitive training to refine ML models, improving predictive accuracy by reducing false negatives through addressing class imbalances in defect prediction datasets. This approach facilitates more targeted and effective testing efforts. Nevertheless, these models’ in-company generalizability across different projects (cross-project) and programming languages (cross-language) remained untested. This study validates the approach’s applicability across diverse development environments by integrating various datasets from distinct projects into a unified dataset, using a more interpretable ML technique. The results demonstrate that ML can support software testing decisions, enabling teams to identify up to 7× more defective modules compared to benchmark with the same testing effort.
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Chlebus, Marcin, Michał Dyczko y Michał Woźniak. "Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem". Central European Economic Journal 8, n.º 55 (1 de enero de 2021): 44–62. http://dx.doi.org/10.2478/ceej-2021-0004.
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Abstract Statistical learning models have profoundly changed the rules of trading on the stock exchange. Quantitative analysts try to utilise them predict potential profits and risks in a better manner. However, the available studies are mostly focused on testing the increasingly complex machine learning models on a selected sample of stocks, indexes etc. without a thorough understanding and consideration of their economic environment. Therefore, the goal of the article is to create an effective forecasting machine learning model of daily stock returns for a preselected company characterised by a wide portfolio of strategic branches influencing its valuation. We use Nvidia Corporation stock covering the period from 07/2012 to 12/2018 and apply various econometric and machine learning models, considering a diverse group of exogenous features, to analyse the research problem. The results suggest that it is possible to develop predictive machine learning models of Nvidia stock returns (based on many independent environmental variables) which outperform both simple naïve and econometric models. Our contribution to literature is twofold. First, we provide an added value to the strand of literature on the choice of model class to the stock returns prediction problem. Second, our study contributes to the thread of selecting exogenous variables and the need for their stationarity in the case of time series models.
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Kumbhar, Chandrasekhar y Dr S. S. Sridhar. "Trend analysis of university placement by using machine learning algorithms". International Journal of Engineering & Technology 7, n.º 2.4 (10 de marzo de 2018): 178. http://dx.doi.org/10.14419/ijet.v7i2.4.13034.
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Machine learning is a method of data analysis that automates analytical model building. These models help you to make a trend analysis of university placements data, to predict a placement rate for the students of an upcoming year which will help the university to analyze the performance during placements. Many students look at universities as a means of investment which can help them make a great future by getting placed in good companies and which will relieve their stress and unease from their lives before graduating from the university. The trend will also help in giving the companies reasons as to why they should visit university again and again. Some attributes play the very important role while analyzing the student for e.g. Student’s name, Department, Company, Location and Annual package. So, classification can help you to classify those data and clustering helps to make the clusters department wise. In this paper we have used neural networks to predict the upcoming student placement and got 77% of accuracy while testing were iteration are 1000. Through extensive trend analysis of varies complex data collected from different sources, we can demonstrate that our analysis can provide a good pragmatic solution for future placement of students.
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Maslan, Andi, Kamaruddin Malik Bin Mohamad y Feresa Binti Mohd Foozy. "Feature selection for DDoS detection using classification machine learning techniques". IAES International Journal of Artificial Intelligence (IJ-AI) 9, n.º 1 (1 de marzo de 2020): 137. http://dx.doi.org/10.11591/ijai.v9.i1.pp137-145.
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Computer system security is a factor that needs to be considered in the era of industrial revolution 4.0, namely by preventing various threats to the system, as well as being able to detect and repair any damage that occurs to the computer system. DDoS attacks are a threat to the company at this time because this attack is carried out by making very large requests for a site or website server so that the system becomes stuck and cannot function at all. DDoS attacks in Indonesia and developed countries always increase every year to 6% from only 3%. To minimize the attack, we conducted a study using Machine Learning techniques. The dataset is obtained from the results of DDoS attacks that have been collected by the researchers. From the datasets there is a training and testing of data using five techniques classification: Neural Network, Naïve Bayes and Random Forest, KNN, and Support Vector Machine (SVM), datasets processed have different percentages, with the aim of facilitating in classifying. From this study it can be concluded that from the five classification techniques used, the Forest random classification technique achieved the highest level of accuracy (98.70%) with a Weighted Avg 98.4%. This means that the technique can detect DDoS attacks accurately on the application that will be developed.
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Amrin, Amrin y Omar Pahlevi. "Implementation of Logistic Regression Classification Algorithm and Support Vector Machine for Credit Eligibility Prediction". JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 5, n.º 2 (26 de enero de 2022): 433–41. http://dx.doi.org/10.31289/jite.v5i2.6220.
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Credit is a provision of money or bills that can be equated with it, the provision of loans or credit. A good credit analysis is very necessary, because it is one of the most important processes in the form of an investigation regarding the smooth or substandard credit repayments. The stages of identifying and predicting customers properly and correctly can be done before the loan process. This is done by examining the historical data of the customer's loan. At this time this activity is an effort made by the banking industry in dealing with credit risk problems. In this research, researchers will apply several data mining classification methods, including Logistic Regression algorithms and Support Vector Machines to predict creditworthiness. The dataset used 481 record motorized vehicle loan data, both problematic and non-problematic. The input variables in this study consisted of thirteen variables, including marital status, number of dependents, age, residence status, home ownership, occupation, employment status, company status, income, down payment, education, length of stay, and housing conditions. From the results of research and testing, the performance of the Logistic Regression model for predicting creditworthiness provided an accuracy rate of 94.81% with an area under the curve (AUC) value of 0.987. While the performance of the Support Vector Machine model provides an accuracy of 94.19% with an area under the curve (AUC) value of 0.978. Based on the T-Test test, the Logistic Regression method has the same performance compared to the Support Vector Machine.
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Desani, Asrian, Meidy Tangelica y Winny Irisa. "PENGARUH KOMUNIKASI DAN KOMITMEN TERHADAP KINERJA KARYAWAN PT. GARUDA MESIN AGRI". Jurnal Darma Agung 27, n.º 2 (1 de agosto de 2019): 1063. http://dx.doi.org/10.46930/ojsuda.v27i2.274.
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Garuda Mesin Agri is one of the agricultural equipment distribution companies. The fact that the level of sales of agricultural equipment from competitors who can seize market share and increased agricultural sector growth to support the performance and productivity of the company which will ultimately require agricultural equipment in case of damage, the researchers chose PT. Garuda Agri Machine as the object of research. This study aims at to testing and analyzing the effect of communication and commitment on employees’ performance. The research method used by researchers is a quantitative approach. The type of research is quantitative descriptive. Data collection methods used are interviews, distribution of questionnaires, and documentation studies. The analytical method used is multiple linear regressions, coefficient of determination, simultaneous testing (F-test), and partial testing (T-test). The population used is all employees, amounting to 75 and the total sample used is as many as 75 employees. The research findings show that: 1) communication partially had a significant positive effect on employees’ performance. 2) Commitment partially has a significant positive effect on employees’ performance at PT. Garuda Mesin Agri. 3) Simultaneous testing of independent variables communication (X1) and commitment (X2) simultaneously have a significant effect on performance (Y) at PT. Garuda Mesin Agri with a coefficient of determination of 32.1%.
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Singh, Bhupinder, Santosh Kumar Henge, Amit Sharma, C. Menaka, Pawan Kumar, Sanjeev Kumar Mandal y Baru Debtera. "ML-Based Interconnected Affecting Factors with Supporting Matrices for Assessment of Risk in Stock Market". Wireless Communications and Mobile Computing 2022 (9 de agosto de 2022): 1–15. http://dx.doi.org/10.1155/2022/2432839.
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In today’s world, people study and evaluate trading stocks to make informed decisions, based on available financial data and market information. Previous researchers relied on trend identification before making any decision to buy or sell stocks but fail to make accurate decisions due to complex systems. Some studies showed analysis to apply to stop loss on every stock transaction that got wrong levels due to limited features scaling that relied on single indicators without checking the performance metrics such as mean, standard deviation, and value at risk. Some existing models are based on theoretical implementation and they possess inaccurate success in real-time stock market transactions. Earlier risk management techniques were based on fundamental statistics of the company performance based on specific quarters that propose the future expects in the positive direction that is not every true which results in huge financial loss. Previous researchers failed to consider dynamic risk management parameters to ensure minimum loss for decision-making in fast-moving stock variations. Machine learning simply refers to learning about computers and making predictions from data. Identifying and analyzing the risk factors in the stock market are the major and crucial stage for predicting the company stock values at the national and international levels. In existing research, all risk management-related factors are analyzed based on fundamental statistics of the company performance which are measured as quarterly results, which will not give long-term true predictions and will not provide positive directions to invest in further stocks. This research majorly focused on risk management for national stock companies using the machine learning methodology and algorithms. The objective is to determine if stock market indicators are suitable decision-aid tools within the context of intraday risk management. The review of the literature revealed that while there are many studies looking to foresee changes in the stock market, there are few studies looking to improve stock market risk management methods using machine learning algorithms. The goal of this study was to fill this gap by utilizing the body of existing research on stock index forecasting combined with machine learning techniques for both short- and long-term risk managements. It has described the association between machine learning models and implicated the data with respect to discrete models based on supportive, dependable, nondependable parameters along with the name and type of the stock. This research has integrated a few crucial dependable parameters such as oil prices, on-hand projects, and future projects. It has integrated with the simple, multiple linear regression models to generate a signal for SPY growth. The proposed ML-based model has been evaluated by comparing two states of training and testing and achieved 96.3% of accuracy. The parameters used for evaluation are closing price, price differences, and daily return. The performance range of the proposed multiple regression model lies along the maximum drawn down which is 0.04411 for test cases and 1.2533 for training cases. Compare the performance of the proposed approach with that existing models with respect to the number of keys and methods associated with training and testing the data.
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Castaneda, Delio I. y Luisa F. Manrique. "Colchones Eldorado: dreaming of innovating". Emerald Emerging Markets Case Studies 2, n.º 8 (17 de octubre de 2012): 1–11. http://dx.doi.org/10.1108/20450621211320533.
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Subject area Innovation and creativity in small to medium-sized enterprises (SMEs) in Latin America. Study level/applicability The case is recommended for creativity and innovation subjects, in undergraduate and MBA levels. The case is also suggested for subjects associated with the organizational dynamics on SMEs. Case overview Colchones Eldorado is a Colombian company dedicated to the bedding industry. The company was founded in 1957 by Gumercindo Gómez Caro, a creative man who in 1959 invented a machine to make springs, which allowed the company to grow steadily for several decades. On November 18, 2004, the founder's daughter, Martha Luz Gomez, was appointed as General Manager. On April 2011 it obtained a license from Sealy, the biggest mattress making company in the USA. The license implied a challenge - testing the company's innovative capacities to adapt Sealy mattresses to satisfy consumers in the Colombian market. Expected learning outcomes Students are shown the characteristics of the creative and innovation process in a Latin American SME, and the innovation challenges which are faced. From the reading and the case discussion, the students should be able to: analyse the manifestations of the creative process in an SME; identify examples of the innovation types of an SME; and discuss the organizational conditions to answer the creativity and innovation challenges in an SME. Supplementary materials Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.