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Journal articles on the topic 'Usinage intelligent'

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Author: Grafiati

Published: 20 May 2022

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1

Kishorekumar, Mr A., Mr E. Ezhilarasan, and Mr R. Parthiban. "Intelligent Drone based Personal Assistant using Artificial Intelligence AI." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1618–21. http://dx.doi.org/10.31142/ijtsrd11482.

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2

Chinagolum, Aneke Israel, Chineke Amaechi Hyacenth, and Udeh Chukwuma Callistus W. "Intelligent Routing Algorithm Using Antnet." International Journal of Trend in Scientific Research and Development Volume-3, Issue-1 (December 31, 2018): 306–14. http://dx.doi.org/10.31142/ijtsrd18990.

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3

A., Ponmalar. "Intelligent Crime Analysis System Using Pyspark." International Journal of Psychosocial Rehabilitation 24, no. 5 (March 31, 2020): 860–67. http://dx.doi.org/10.37200/ijpr/v24i5/pr201757.

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4

Liu, Hong, Haijun Wei, Lidui Wei, Jingming Li, and Zhiyuan Yang. "The Segmentation of Wear Particles Images UsingJ-Segmentation Algorithm." Advances in Tribology 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/4931502.

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This study aims to use a JSEG algorithm to segment the wear particle’s image. Wear particles provide detailed information about the wear processes taking place between mechanical components. Autosegmentation of their images is key to intelligent classification system. This study examined whether this algorithm can be used in particles’ image segmentation. Different scales have been tested. Compared with traditional thresholding along with edge detector, the JSEG algorithm showed promising result. It offers a relatively higher accuracy and can be used on color image instead of gray image with little computing complexity. A conclusion can be drawn that the JSEG method is suited for imaged wear particle segmentation and can be put into practical use in wear particle’s identification system.

5

Islam, Faraz, and Ali Faraz Syed. "PLC Based Intelligent Toll Road Traffic Control Using." International Journal of Computer Theory and Engineering 6, no. 4 (2014): 353–56. http://dx.doi.org/10.7763/ijcte.2014.v6.888.

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6

Yadav, Kiran, and Ranjit Biswas. "Finding a Shortest Path Using an Intelligent Technique." International Journal of Engineering and Technology 1, no. 2 (2009): 139–41. http://dx.doi.org/10.7763/ijet.2009.v1.25.

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7

GEORGIEV, Rumen, and Kolyo KOLEV. "COMPARATIVE ANALYSIS OF TUNING MISSILE AUTOPILOTS USING INTELLIGENT METHODS." SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE 18, no. 1 (June 24, 2016): 251–58. http://dx.doi.org/10.19062/2247-3173.2016.18.1.34.

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8

Chinagolum, Aneke Israel, Chineke Amaechi Hyacenth, and Udeh Chukwuma Callistus W. "Improving Robustness of Data Network Using Intelligent Modulation Technique." International Journal of Trend in Scientific Research and Development Volume-3, Issue-1 (December 31, 2018): 315–23. http://dx.doi.org/10.31142/ijtsrd18991.

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9

Thomas, Hephzibah, and Thyla B. "Intelligent Fall Detection Using Statistical Features and Machine Learning." International Journal of Trend in Scientific Research and Development Volume-3, Issue-1 (December 31, 2018): 609–12. http://dx.doi.org/10.31142/ijtsrd19024.

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10

COLLINĂSZY, Juraj, Marek BUNDZEL, and Iveta ZOLOTOVĂ. "IMPLEMENTATION OF INTELLIGENT SOFTWARE USING IBM WATSON AND BLUEMIX." Acta Electrotechnica et Informatica 17, no. 1 (March 1, 2017): 58–63. http://dx.doi.org/10.15546/aeei-2017-0008.

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11

Bagal, Shubhada, Tejasvini More, and Ankita Paranjape Prof Supriya Yadav. "Intelligent Agriculture Mechanism using Internet of Things and Image Processing." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (February 28, 2019): 798–800. http://dx.doi.org/10.31142/ijtsrd21485.

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12

Pimple, Omkar, Umesh Saravane, and Neha Gavankar. "Cognitive Learning Using Distributed Artificial Intelligence." International Journal of Machine Learning and Computing 5, no. 1 (February 2015): 7–11. http://dx.doi.org/10.7763/ijmlc.2015.v5.474.

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13

M, Devi, Renukasrinidhi N, and Swarnamaalika N. Mrs P. Rekha. "An intelligent device control system using augmented reality and zigbee technology." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1725–28. http://dx.doi.org/10.31142/ijtsrd11435.

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14

Oliveira, SRM. "Multi-Model for Planning High Complexity Environment using Hybrid Intelligent Architecture." International Journal of Advances in Management and Economics 01, no. 04 (July 2, 2012): 60–73. http://dx.doi.org/10.31270/ijame/01/04/2012/09.

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15

Sharma, Pallavi, and Rajesh Kochher. "Enhanced RZ-Leach using Swarm Intelligence Technique." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (February 28, 2018): 693–700. http://dx.doi.org/10.31142/ijtsrd8315.

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16

Ibargüengoytia González, Pablo Héctor, Alberto Reyes Ballesteros, Mónica Borunda Pacheco, and Uriel Alejandro García López. "Predicción de potencia eólica utilizando técnicas modernas de Inteligencia Artificial." Ingeniería Investigación y Tecnología 19, no. 4 (October 1, 2018): 1–11. http://dx.doi.org/10.22201/fi.25940732e.2018.19n4.033.

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17

Chen, Wei-Yu, Shing-Han Li, Mann-Jung Hsiao, Chung-Chiang Hu, and Kuo-Ching Tu. "Network Security Analysis by Using Business Intelligence." International Journal of Machine Learning and Computing 5, no. 6 (December 2015): 431–38. http://dx.doi.org/10.18178/ijmlc.2015.5.6.547.

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18

Paliński, Andrzej. "Prognozowanie zapotrzebowania na gaz metodami sztucznej inteligencji." Nafta-Gaz 75, no. 2 (February 2019): 111–17. http://dx.doi.org/10.18668/ng.2019.02.07.

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The paper presents contemporary trends in artificial intelligence and machine learning methods, which include, among others, artificial neural networks, decision trees, fuzzy logic systems and others. Computational intelligence methods are part of the field of research on artificial intelligence. Selected methods of computational intelligence were used to build medium-term monthly forecasts of natural gas demand for Poland. The accuracy of forecasts obtained using the artificial neural network and the decision tree with classical linear regression was compared based on historical data from a ten-year period. The explanatory variables were: gas consumption in other EU countries, average monthly temperature, industrial production, wages in the economy and the price of natural gas. Forecasting was carried out in five stages differing in the selection of the learning and testing sample, the use of data preprocessing and the elimination of some variables. For raw data and a random training set, the highest accuracy was achieved by linear regression. For the preprocessed data and the random learning set, the decision tree was the most accurate. The forecast obtained on the basis of the first eight years and tested on the last two was most accurately created by regression, but only slightly better than with the decision tree or neural network, regardless of data normalization and elimination of collinear variables. Machine learning methods showed good accuracy of monthly gas consumption forecasts, but nevertheless slightly gave way to classical linear regression, due to too narrow set of explanatory variables. Machine learning methods will be able to show higher effectiveness as the number of data increases and the set of potential explanatory variables is expanded. In the sea of data, machine learning methods are able to create prognostic models more effectively, without the analyst’s laborious involvement in data preparation and multi-stage analysis. They will also allow for the frequent updating of the form of prognostic models even after each addition of new data into the database.

19

Akintol, Sarah A. "Optimization of Drilling Cost Using Artificial Intelligence." Petroleum & Petrochemical Engineering Journal 5, no. 4 (2021): 1–8. http://dx.doi.org/10.23880/ppej-16000285.

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Drilling operations in the oil and gas industry takes most of the well cost and how fast the drilling bit penetrate and bore the formation is termed the Rate of penetration (ROP). Since most of the cost incurred during drilling is related to the drilling operations, there is need not only to drill carefully, but also to optimize the drilling process. A lot of parameters are related to the rate of penetration which are actually interdependent on each other. This makes it difficult to predict the influence of every single parameter Drilling optimization techniques have been used recently to reduce drilling operation costs. There are different approaches to optimizing the cost of drilling oil and gas wells, some of which include static and /or real time optimization of drilling parameters. A potential area for optimization of drilling cost is through bit run in the well but this is particularly difficult due to its significance in both drilling time and bit cost. In this sense, as a particular bit gets used, it gets dull as its footage increases, resulting from the reduction in the bit penetration rate. The reduction in penetration rate increases total drill time. In order to optimize bit cost, it is desirable to find a trade-off between the two by a bit change policy This study is aimed at minimizing drilling time by use of artificial intelligent for the bit program. Data obtained from a well in the Niger delta region of Nigeria was used in this study and the cost optimization modelled as a Markov decision process where the intelligent agent was to learn the optimal timings for bit change by reinforcement policy Iteration learning. This study was able to achieve its objectives as the reinforcement learning optimization process performed very well with time as the computer agent was able to figure out how to improve drilling cost over time. Better results could be obtained with a better hardware and increased training time.

20

Lee, Jae Moon, In Hwan Jung, and Kitae Hwang. "Classification of Beef by Using Artificial Intelligence." Webology 19, no. 1 (January 20, 2022): 4639–47. http://dx.doi.org/10.14704/web/v19i1/web19308.

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This paper aims to develop an application that classifies the quality of beef via Artificial Intelligence technology, which has experienced rapid technological growth in recent years. The application will allow users to obtain information including, but not limited to, cuts of beef, freshness, and marbling of the beef they are about to purchase. Deep learning image classification was used to classify the cuts of beef, and OpenCV technology was used to determine the freshness and marbling of the beef. The application was developed in a client-server system for real-time action. The mobile phone of the user (the client) will take a photo of the beef and send it to the server, and the server will analyze the received image to identify and determine the cuts of beef, freshness, and marbling of the beef. The results will then be sent back to the client from the server. Artificial Intelligence technology is used to develop applications with these functions. Image classification technology is used for the classification function of beef parts, and OpenCV's clustering technology is used to determine the freshness and marbling grade of beef. Also, Flask web server is used to apply the client-server structure. The developed system worked well for tenderloin, sirloin, and ribs. It provided high confidence over 75% for these cuts. However, it worked poor for other beef cuts. This is simply a learning problem for image classifiers.

21

Sheikh, Aabid Hussain, and Dr O. P. Malik. "Maximum Power Extraction Strategy for Wind Energy Conversion Systems using Intelligent Controllers." International Journal of Trend in Scientific Research and Development Volume-1, Issue-4 (June 30, 2017): 580–84. http://dx.doi.org/10.31142/ijtsrd2213.

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22

Z, Mouammine. "Big Data with Distributed Architecture Using Genetic Algorithm in Intelligent Transport Systems." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1405–15. http://dx.doi.org/10.5373/jardcs/v12sp7/20202243.

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23

Abolmasoumi, Amir Hossein, and Somayeh Khosravinejad. "Chaos Control in Memristor-Based Oscillators Using Intelligent Terminal Sliding Mode Controller." International Journal of Computer Theory and Engineering 8, no. 6 (December 2016): 506–11. http://dx.doi.org/10.7763/ijcte.2016.v8.1097.

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24

Vijaya Kumar, Anitha, and Akilandeswari Jeyapal. "Self-Adaptive Trust Based ABR Protocol for MANETs UsingQ-Learning." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/452362.

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Mobile ad hoc networks (MANETs) are a collection of mobile nodes with a dynamic topology. MANETs work under scalable conditions for many applications and pose different security challenges. Due to the nomadic nature of nodes, detecting misbehaviour is a complex problem. Nodes also share routing information among the neighbours in order to find the route to the destination. This requires nodes to trust each other. Thus we can state that trust is a key concept in secure routing mechanisms. A number of cryptographic protection techniques based on trust have been proposed.Q-learning is a recently used technique, to achieve adaptive trust in MANETs. In comparison to other machine learning computational intelligence techniques,Q-learning achieves optimal results. Our work focuses on computing a score usingQ-learning to weigh the trust of a particular node over associativity based routing (ABR) protocol. Thus secure and stable route is calculated as a weighted average of the trust value of the nodes in the route and associativity ticks ensure the stability of the route. Simulation results show thatQ-learning based trust ABR protocol improves packet delivery ratio by 27% and reduces the route selection time by 40% over ABR protocol without trust calculation.

25

A., Mohammed Raheel Basha. "Data Analytics Using Intelligent Optimization Technique: Profitability Analysis of Banks Using Fuzzy Logic Decision Making Technique." Journal of Advanced Research in Dynamical and Control Systems 51, SP3 (February 28, 2020): 349–73. http://dx.doi.org/10.5373/jardcs/v12sp3/20201271.

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26

Douzi, Samira, Feda A. AlShahwan, Mouad Lemoudden, and Bouabid El Ouahidi. "Hybrid Email Spam Detection Model Using Artificial Intelligence." International Journal of Machine Learning and Computing 10, no. 2 (February 2020): 316–22. http://dx.doi.org/10.18178/ijmlc.2020.10.2.937.

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27

Caridad, Daniel, Jana Hančlová, Hosn el Woujoud Bousselmi, and Lorena Caridad y López del Río. "Corporate rating forecasting using Artificial Intelligence statistical techniques." Investment Management and Financial Innovations 16, no. 2 (June 24, 2019): 295–312. http://dx.doi.org/10.21511/imfi.16(2).2019.25.

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Forecasting companies long-term financial health is provided by Credit Rating Agencies (CRA) such as S&amp;P, Moody’s, Fitch and others. Estimates of rates are based on publicly available data, and on the so-called ‘qualitative information’. Nowadays, it is possible to produce quite precise forecasts for these ratings using economic and financial information that is available in financial databases, utilizing statistical models or, alternatively, Artificial Intelligence techniques. Several approaches, both cross section and dynamic are proposed, using different methods. Artificial Neural Networks (ANN) provide better results than multivariate statistical methods and are used to estimate ratings within all the range provided by the CRAs, obtaining more desegregated results than several proposed models available for intervals of ratings. Two large samples of companies ‘public data’ obtained from Bloomberg are used to obtain forecasts of S&amp;P and Moody’s ratings directly from these data with high level of accuracy. This also permits to check the published rating’s reliability provided by different CRAs.

28

Vojtek, Jozef. "Experiment of Using Structured Techniques in Intelligence Analysis." Vojenské rozhledy 28, no. 4 (November 25, 2019): 32–43. http://dx.doi.org/10.3849/2336-2995.28.2019.04.032-043.

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29

WAKITA, Kohei, Jian HUANG, Kosuke SEKIYAMA, and Toshio FUKUDA. "Real-time Fall Detection and Prevention Control Using Intelligent Cane for Human Operator." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2010.5 (2010): 265–70. http://dx.doi.org/10.1299/jsmeicam.2010.5.265.

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30

S, Kumaravel, Premkumar M, Subash J, Rajkumar K, and Sathishkumar K. "Tracking of Soldiers Location in any Environment using Intelligent Tracking and Health Indication System by using RSSI." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 05, no. 03 (June 19, 2017): 06–10. http://dx.doi.org/10.9756/sijcsea/v5i3/05010090101.

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31

Borrero-Tigreros, Diego, and Oscar Bedoya-Leiva. "Predicción de riesgo crediticio en Colombia usando técnicas de inteligencia artificial." Revista UIS Ingenierías 19, no. 4 (May 30, 2020): 37–52. http://dx.doi.org/10.18273/revuin.v19n4-2020004.

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En este artículo se proponen modelos para la predicción de riesgo crediticio en Colombia utilizando diferentes técnicas de inteligencia artificial. Estos modelos se pueden usar como apoyo por el área de gestión de riesgo en los bancos y tienen como objetivo identificar clientes que podrían incurrir en un estado de mora generando un posible riesgo de crédito para las entidades financieras. En particular, se proponen modelos basados en tres técnicas de aprendizaje supervisado (redes neuronales, árboles de decisión y máquinas de soporte vectorial) para predecir el próximo pago de la cuota de un cliente a partir de datos básicos de la operación, del cliente y de pagos de cuotas anteriores registradas. De acuerdo con los resultados obtenidos, los árboles de decisión resultan ser más exactos que las otras técnicas utilizadas para la predicción de riesgo crediticio conun áreabajo la curva ROC de 88.29%. Los modelos propuestosalcanzan exactitudes similares y en algunos casos superan las exactitudes reportadas en algunos trabajos del estado del arte.

32

Chahhou, Mohamed, Lahcen Moumoun, Mohamed El Far, and Taoufiq Gadi. "Segmentation of 3D Meshes Usingp-Spectral Clustering." IEEE Transactions on Pattern Analysis and Machine Intelligence 36, no. 8 (August 2014): 1687–93. http://dx.doi.org/10.1109/tpami.2013.2297314.

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33

Pham, Van-Huy, Diem-Phuc Tran, and Van-Dung Hoang. "Personal Identification Based on Deep Learning Technique Using Facial Images for Intelligent Surveillance Systems." International Journal of Machine Learning and Computing 9, no. 4 (August 2019): 465–70. http://dx.doi.org/10.18178/ijmlc.2019.9.4.827.

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34

N., Yuvaraj. "Improved Intelligent Techniques of Ensemble Data Clustering Method Using Bees Swarm Optimization Ensemble Approach." International Journal of Psychosocial Rehabilitation 24, no. 5 (March 31, 2020): 1762–73. http://dx.doi.org/10.37200/ijpr/v24i5/pr201847.

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35

Lau, Henry C. W. "Computational Intelligence Approach for Process Parameter Settings Using Knowledge Representation." Lecture Notes on Software Engineering 3, no. 1 (2015): 49–52. http://dx.doi.org/10.7763/lnse.2015.v3.164.

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36

Zafrin, Mohamad Zafranudin Bin Mohamed, Subashini A/P Ganapathy, and Sajitha Smiley. "Early Childhood Education Smart Web Portal Using Intelligence Information Systems." International Journal of Trend in Scientific Research and Development Special Issue, Special Issue-ICAEIT2017 (November 30, 2018): 239–42. http://dx.doi.org/10.31142/ijtsrd19150.

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37

Mahmoud, Safaa S. "Development Intelligent Web-based Learning System Using Object-Oriented Approach for Improving Students Innovative Thinking." International Journal of Engineering and Technology 1, no. 4 (2009): 367–75. http://dx.doi.org/10.7763/ijet.2009.v1.70.

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38

Saidani, Sihem, Mohamed Radhouan Hachicha, and Moez Ghariani. "A New Phase Current Profiling with FLC f or Torque Optimization of 12/8 SRM." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 5 (October 1, 2016): 1948. http://dx.doi.org/10.11591/ijece.v6i5.11010.

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<p>The switched reluctance machine against its several merits such as simplicity, robustness, less cost manufacturing and large speed still suffers from its undesirable torque ripple and acoustic noise. Compared to different candidates of hybrid and electric vehicle engine, the frequency of use of SRM in traction drives is improved with the different optimizing torque oscillation solutions. Most of studies used the generic or specific model of switched reluctance machine in the Simulink library (6/4,8/6 and 10/8). Despite, a new non linear model simply implemented in Simulink tool usinga static finite element analysis a previous study is used in this work. Hence, a 12/8 non linear SRM drive system is simulated using MATLAB toolbox tested with an intelligent controller (FLC) in order to minimize the torque ripple of an oriented starter –alternator application of a hybrid vehicle.</p>

39

Saidani, Sihem, Mohamed Radhouan Hachicha, and Moez Ghariani. "A New Phase Current Profiling with FLC f or Torque Optimization of 12/8 SRM." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 5 (October 1, 2016): 1948. http://dx.doi.org/10.11591/ijece.v6i5.pp1948-1955.

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<p>The switched reluctance machine against its several merits such as simplicity, robustness, less cost manufacturing and large speed still suffers from its undesirable torque ripple and acoustic noise. Compared to different candidates of hybrid and electric vehicle engine, the frequency of use of SRM in traction drives is improved with the different optimizing torque oscillation solutions. Most of studies used the generic or specific model of switched reluctance machine in the Simulink library (6/4,8/6 and 10/8). Despite, a new non linear model simply implemented in Simulink tool usinga static finite element analysis a previous study is used in this work. Hence, a 12/8 non linear SRM drive system is simulated using MATLAB toolbox tested with an intelligent controller (FLC) in order to minimize the torque ripple of an oriented starter –alternator application of a hybrid vehicle.</p>

40

GOTO, Shigeaki, Takemi Asai, Yoshikazu Arai, and Wei Gao. "D33 Profile Measurement of Micro-structured Surfaces by Using SPMs(Nano/micro measurement and intelligent instruments)." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2009.5 (2009): 787–90. http://dx.doi.org/10.1299/jsmelem.2009.5.787.

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41

PENALOZA, Christian, Yasushi MAE, Tatsuo ARAI, Kenichi OHARA, and Tomohito TAKUBO. "2P1-Q04 Multi-Appearance Object Modeling using Camera Network in Household Environment(Intelligent and Robotic Room)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2011 (2011): _2P1—Q04_1—_2P1—Q04_4. http://dx.doi.org/10.1299/jsmermd.2011._2p1-q04_1.

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42

Kawasaki, M., R. Matsuzaki, and A. Todoroki. "OS16-6 Crack Visualization with Uncertainty Using Kriging Model and Time-domain Reflectometry with Microstrip Line(Mechanical properties of intelligent materials and their application,OS16 Intelligent materials and structures,APPLICATIONS)." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2015.14 (2015): 225. http://dx.doi.org/10.1299/jsmeatem.2015.14.225.

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43

ÇINAR, İ̇lkay, Murat KOKLU, and Şakir TAŞDEMİR. "Kuru Üzüm Tanelerinin Makine Görüşü ve Yapay Zeka Yöntemleri Kullanılarak Sınıflandırılması." Gazi Journal of Engineering Sciences 6, no. 3 (December 27, 2020): 200–209. http://dx.doi.org/10.30855/gmbd.2020.03.03.

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44

S, Revathi, Aniz Rizwan, and Anusha N. "OPTIMIZATION OF LOAD BALANCING IN CLOUD USING SWARM INTELLIGENCE: A SURVEY." International Journal of Current Engineering and Scientific Research 6, no. 6 (June 2019): 169–74. http://dx.doi.org/10.21276/ijcesr.2019.6.6.29.

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45

Kim, Dae Geon. "Development of High-Strength Concrete Mixed Design System Using Artificial Intelligence." Webology 19, no. 1 (January 20, 2022): 4268–85. http://dx.doi.org/10.14704/web/v19i1/web19281.

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The quality inspection of high-strength concrete construction sites consists of a compressive strength test that is considered the most important, but this can be confirmed through a compressive strength test after 28 days of high-strength concrete application. Therefore, it is of paramount importance to ship high-quality products to ready-mixed concrete factories by increasing the reliability of the mixed design that affects high-strength concrete production. In addition, there is a need to develop an efficient management system for mixed design that determines high-strength concrete quality by measuring the mixing ratio of materials in the ready-mixed concrete factory production stage. This study used matrix laboratory(MATLAB) using Deep learning, a language that performs mathematics and engineering calculations based on matrices, and presented a mixed design model by adjusting the strength through input and output variables, learning data collection, model structure determination, learning error, and repetition results. The predicted mean value of 40 MPa was measured at 40.75 MPa, showing a difference of 0.75 MPa and 40 MPa, and the error rate was confirmed to be 4.13%. And the predicted mean value of 55 MPa was measured as 55.55 MPa, showing a difference between 55 MPa and 0.55 MPa, and the error rate was confirmed to be 1.73%. Through this study, the reliability of high-strength concrete quality management is secured by applying a high-strength concrete mixed design system using artificial intelligence(AI) and adjusting it in connection with all fields of the production process.

46

Rishi, Dr O. P. "Intellectual Intelligent Tutoring System: The ITS with Emotions." International Journal of Engineering and Technology 1, no. 1 (2009): 1–6. http://dx.doi.org/10.7763/ijet.2009.v1.1.

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47

Khan, Asif, and Dr Shahab Khushnood. "Simple and Efficient Blood Glucose Measurement Technique Using Non Invasive Artificial Intelligence." Bonfring International Journal of Industrial Engineering and Management Science 7, no. 1 (March 31, 2017): 09–13. http://dx.doi.org/10.9756/bijiems.8320.

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48

Azanov, N. P., R. R. Khabirov, and U. E. Amirov. "Конкурентная разведка и принятие решений с помощью машинного обучения для обеспечения промышленной безопасности." INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES, no. 6(6) (March 14, 2022): 75–84. http://dx.doi.org/10.54309/ijict.2022.2.6.010.

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Abstract:

The purpose of this scientific article is to show what competitor data analytics can do with machine learning and neural networks. In this study, we analyzed data on potential partners of the Department of Defense Office of Hearings and Appeals (DOHA) of the USA and obtained a trained algorithm that can help in making decisions based on keywords, which can minimize reputational risks. The published dataset of the Department of Defense Office of Hearings and Appeals (DOHA) of the USA was selected for analysis of the initial data, which displayed the results of the screening of potential partners along with a text justification. This is the reason why we used Recurrent Neural Network (RNN) instead of Convolutional Neural Network (CNN). Neural networks are a very important part of machine learning. As a result, we have developed a trained machine learning model for recommending the best partners, that is, more proven partners, both professional and reputable. In addition, the developed machine learning model does not allow working with an organization of bad partners who could act in bad faith and carry reputational risks. Цель этой научной статьи показать, на что способна конкурентная разведка и анализ данных с помощью машинного обучения и нейронных сетей. В данном исследовании мы проанализировали данные о потенциальных партнерах Управления слушаний и апелляций Министерства обороны США (ДОХА) и получили обученный алгоритм, который может помочь в принятии решений на основе ключевых слов и который позволяет минимизировать репутационные риски. В качестве анализа исходных данных был выбран опубликованный набор данных Управления слушаний и апелляций Министерства обороны США (ДОХА), в котором наряду с текстовым обоснованиембыли отображены результаты скрининга потенциальных партнеров. Именно по этой причине мы использовали Рекуррентную нейронную сеть (RNN) вместо Сверточной нейронной сети (CNN). Нейронные сети -очень важная часть машинного обучения. В результате мы разработали обученную модель машинного обучения для рекомендации лучших партнеров, то есть более проверенных партнеров, как профессиональных, так и авторитетных. Кроме того, разработанная модель машинного обучения не позволяет работать организациям с неблагоприятными партнерами, которые могут действовать недобросовестно и нести репутационные риски.

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Timbal, Maricel A. "Analysis of Student-at-Risk of Dropping out (SARDO) Using Decision Tree: An Intelligent Predictive Model for Reduction." International Journal of Machine Learning and Computing 9, no. 3 (June 2019): 273–78. http://dx.doi.org/10.18178/ijmlc.2019.9.3.798.

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S., Poonguzhali. "Design of an Intelligent Foot Insole Using Dynamic Sensor Network for Prevention of Diabetic Foot Ulceration- Telemedicine Application." Journal of Advanced Research in Dynamical and Control Systems 24, no. 4 (March 31, 2020): 369–78. http://dx.doi.org/10.5373/jardcs/v12i4/20201451.

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