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Статті в журналах з теми "HYBRID MOVIE"
Nosshi, Anthony, Aziza Asem, and Mohamed Badr Senousy. "Hybrid Recommender System via Personalized Users’ Context." Cybernetics and Information Technologies 19, no. 1 (March 1, 2019): 101–15. http://dx.doi.org/10.2478/cait-2019-0006.
Повний текст джерелаWang, Yibo, Mingming Wang, and Wei Xu. "A Sentiment-Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework." Wireless Communications and Mobile Computing 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/8263704.
Повний текст джерелаNosshi, Anthony, Aziza Saad Asem, and Mohammed Badr Senousy. "Hybrid Recommender System Using Emotional Fingerprints Model." International Journal of Information Retrieval Research 9, no. 3 (July 2019): 48–70. http://dx.doi.org/10.4018/ijirr.2019070104.
Повний текст джерелаTripathi, Jyoti, Sunita Tiwari, Anu Saini, and Sunita Kumari. "Prediction of movie success based on machine learning and twitter sentiment analysis using internet movie database data." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 3 (March 1, 2023): 1750. http://dx.doi.org/10.11591/ijeecs.v29.i3.pp1750-1757.
Повний текст джерелаBohra, Sneha, Amit Gaikwad, and Ghanapriya Singh. "Hybrid Machine Learning Based Recommendation Algorithm for Multiple Movie Dataset." Indian Journal Of Science And Technology 16, no. 37 (October 9, 2023): 3121–28. http://dx.doi.org/10.17485/ijst/v16i37.2065.
Повний текст джерелаMohile, Sara, Hemant Ramteke, Pragati Shelgaonkar, Hritika Phule, and M. M. Phadtare. "A Movie Recommender System Using Hybrid Approach: A Review." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 1834–37. http://dx.doi.org/10.22214/ijraset.2022.41014.
Повний текст джерелаLekakos, George, and Petros Caravelas. "A hybrid approach for movie recommendation." Multimedia Tools and Applications 36, no. 1-2 (December 21, 2006): 55–70. http://dx.doi.org/10.1007/s11042-006-0082-7.
Повний текст джерелаJadhav, Prof Rupali. "Implementing a Movie Recommendation System in Machine Learning Using Hybrid Approach." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 6601–3. http://dx.doi.org/10.22214/ijraset.2023.53204.
Повний текст джерелаEz-zahout, Abderrahmane, Hicham Gueddah, Abir Nasry, Rabie Madani, and Fouzia Omary. "A hybrid big data movies recommendation model based k-nearest neighbors and matrix factorization." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (April 1, 2022): 434. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp434-441.
Повний текст джерелаHuang, Yi-Ting, and Ping-Feng Pai. "Using the Least Squares Support Vector Regression to Forecast Movie Sales with Data from Twitter and Movie Databases." Symmetry 12, no. 4 (April 15, 2020): 625. http://dx.doi.org/10.3390/sym12040625.
Повний текст джерелаДисертації з теми "HYBRID MOVIE"
Gurcan, Fatih. "A Hybrid Movie Recommender Using Dynamic Fuzzy Clustering." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/2/12611667/index.pdf.
Повний текст джерелаSommar, Fredrik, and Milosz Wielondek. "Combining Lexicon- and Learning-based Approaches for Improved Performance and Convenience in Sentiment Classification." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166430.
Повний текст джерелаAtt klassificera text i kategorier baserat på känslan de uttrycker är ett aktuellt område idag och kan tillämpas inom många industrier. Rapporten undersöker en kombination av de två framstående tillvägagångssätten till denna typ av klassificering baserade på ett lexikon med definerade ordvikter respektive maskininlärning. Denna hybridlösning jämförs mot de två andra tillvägagångssätten för att framlägga deras relativa styrkor och svagheter. På ett dataset med filmrecensioner från IMDb får maskininlärningsklassificeraren bäst resultat, följt av hybridlösningen och sist den lexikonbaserade lösningen. Trots det kan hybridlösningen vara att föredra i situationer där det är ogenomförbart eller oskäligt att förbereda träningsdata för maskininlärningsklassificeraren, dock med ett visst avkall på prestanda.
Lokesh, Ashwini. "A Comparative Study of Recommendation Systems." TopSCHOLAR®, 2019. https://digitalcommons.wku.edu/theses/3166.
Повний текст джерелаMarsh, Eric Allen. "Inertially stabilized platforms for SATCOM on-the-move applications : a hybrid open/closed-loop antenna pointing strategy." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45259.
Повний текст джерелаIncludes bibliographical references (p. 213-216).
The increasing need for timely information in any environment has led to the development of mobile SATCOM terminals. SATCOM terminals seeking to achieve high data-rate communications require inertial antenna pointing to within fractions of a degree. The base motion of the antenna platform complicates the pointing problem and must be accounted for in mobile SATCOM applications. Antenna Positioner Systems (APSs) provide Inertially Stabilized Platforms (ISPs) for accurate antenna pointing and may operate in either an open or closed-loop fashion. Closed-loop antenna pointing strategies provide greater inertial pointing accuracies but typically come at the expense of more complex and costly systems. This thesis defines a nominal two-axis APS used on an EHF SATCOM terminal on a 707 aircraft. The nominal APS seeks to accomplish mobile SATCOM using the simplest possible system; therefore, the system incorporates no hardware specific to closed-loop pointing. This thesis demonstrates that the nominal APS may achieve accurate antenna pointing for an airborne SATCOM application using a hybrid open/closed-loop pointing strategy. The nominal APS implements the hybrid pointing strategy by employing an open-loop pedestal feedback controller in conjunction with a step-tracking procedure. The open-loop feedback controller is developed using optimal control techniques, and the pointing performance of the controller with the nominal APS is determined through simulation. This thesis develops closed-loop step-tracking algorithms to compensate for open-loop pointing errors.
(cont.) The pointing performance of several step-tracking algorithms is examined in both spatial pull-in and tracking simulations in order to determine the feasibility of employing hybrid pointing strategies on mobile SATCOM terminals. Keywords: Mobile SATCOM, Antenna Pointing, Inertially Stabilized Platform, Two-axis Positioner, Linear Quadratic Gaussian Control, Nonlinear Optimization.
by Eric Allen Marsh.
S.M.
Besancon, Claire. "Intégration hybride de sources laser III-V sur Si par collage direct et recroissance pour les télécommunications à haut débit." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALT044.
Повний текст джерелаThis thesis focuses on the integration of III-V semiconductors on silicon. The objective is to process multi-wavelength laser sources emitting in the C-band for optical telecommunications. The process is based on the regrowth of a thick III-V structure on a thin InP layer bonded onto an oxidized silicon wafer (InP-SiO2/Si = InPoSi).In order to study the bonding process compatibility with high temperature annealing, around 600°C, required for MOVPE growth, a study of the thermal stability behavior of InPoSi substrate was carried out. The latter showed InPoSi delamination with "bubble" appearance due to the debonding of the InP layer caused by hydrogen desorption at 400°C. A study of the hydrogen lateral diffusion along the bonding interface enabled the assessment of a diffusion length of 100 µm. The development of outgassing trenches spaced 200 µm apart has permitted to obtain III-V material of high-quality regrown onto InPoSi without emergence of any void defect between the trenches.Then, the constant improvement of the preparation steps of the surfaces to be bonded enabled to obtain optimal material quality of InPoSi for regrowth at high temperature without the use of any outgassing method. The study of an active structure composed of AlGaInAs-based multi-quantum wells (MQWs) was carried out during growth on InPoSi. A thermal strain of 390 ppm was assessed at growth temperature thanks to real-time curvature measurement. The latter is due to the difference of thermal coefficients between InP and Si. Despite this thermal strain, the regrowth of a 3 µm-thick laser structure of high crystal quality was successfully obtained on InPoSi. Based on this structure, broad-area lasers were processed and their performance was compared to the ones obtained with the same component made on InP substrate as a reference. Threshold current densities as low as 0.4 kA/cm² at 20°C in pulse regime were obtained on InPoSi. The laser comparison on InPoSi and InP showed that threshold currents, laser efficiency and characteristic temperatures were similar. This result demonstrates that the thick structure grown on InPoSi does not suffer from material degradation.Finally, a new selective area growth (SAG) process was specifically developed on InPoSi. To do so, the silica from InPoSi was locally digged out by the etching of the InP layer in order to open the variable-sized dielectric surfaces for the SAG process. The thickness variation of the quantum wells obtained by SAG with the masks’ dimensions has enabled to obtain a very large photoluminescence wavelength extension, from 1490 to 1650 nm. Shallow-ridge Fabry-Pérot laser arrays were processed using SAG, and laser emissions covering a 155 nm-wide spectral range were successfully obtained. Threshold currents below 30 mA were obtained at 20°C under continuous-wave operation for 500 µm-long bars. At 70°C, threshold currents remain below 60 mA, which shows the high thermal stability of the lasers. Altogether, these results validate the III-V integration process on silicon
Gómez, Barquero David. "COST EFFICIENT PROVISIONING OF MASS MOBILE MULTIMEDIA SERVICES IN HYBRID CELLULAR AND BROADCASTING SYSTEMS." Doctoral thesis, Universitat Politècnica de València, 2010. http://hdl.handle.net/10251/6881.
Повний текст джерелаGómez Barquero, D. (2009). COST EFFICIENT PROVISIONING OF MASS MOBILE MULTIMEDIA SERVICES IN HYBRID CELLULAR AND BROADCASTING SYSTEMS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/6881
Palancia
SINGH, YOGENDRA. "A PERSONALIZED HYBRID MOVIE RECOMMENDATION SYSTEM FOR USERS." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15145.
Повний текст джерелаLin, Chung-Yu, and 林重佑. "A Study on LVQ Based Switching Hybrid Movie Recommendation." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/11873132375795364614.
Повний текст джерела國立雲林科技大學
資訊管理系碩士班
100
The great development of Internet technology brings more and more people to use computers to extract abundant content from this platform by their high speed computing ability. To keep the most valued consumers, most corporations have launched to the electronic environment in order to provide personalized services to their consumers. Content-based filtering and collaborative filtering are widely used techniques in recommendation system. The former method analyzes used records from users to make recommendation. The latter one takes the advantage of user preferences to recommend suitable products. Although they can offer proper recommendations, some shortcomings are existed individually. Thus, the hybrid recommendation technique combines the above advantages to recommend content corresponded with users’ requirements. Recently, hybrid recommendation technique is affected by neural network’s learning ability. A lot of supervised neural networks are combined with hybrid recommendation. Previous studies adopted three layers or multiple layers to construct recommendation. Their drawbacks are slow convergence and hard to design. In this paper, we present a novel switching hybrid recommendation framework based on Learning Vector Quantization (LVQ) and collaborative filtering to provide personalized recommendation. Our approach applies the two-layer architecture in LVQ and collaborative filtering to build switching hybrid recommendation. MovieLens data set is used to test our framework. Results show that switching hybrid strategy provides promising personalized recommendation. Our experiment gains 79% of precision, and the recall rate also reaches 82%.
Lee, Chia-Hsing, and 李佳馨. "Integration of Content-based approach and Hybrid Collaborative Filtering for Movie Recommendation." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/64f874.
Повний текст джерела國立臺北科技大學
資訊與運籌管理研究所
101
As the scale of e-commerce continues to expand, personalized recommendation systems have been developed for general users in the hope of saving their search cost and time. In the core methods of personalized recommendation systems, collaborative filtering, one of the most widely-used recommended methods, still leaves two major problems. One is sparsity problem, the difficulty of finding similar users results in poor accuracy. The other is cold start, new users and new items make it hardly possible to estimate the preferences because of the lack of past ratings. This work simulates a real environment for movie recommendation. In the case of considering the factors of the new users and new movies in the sparse rating matrix, we conduct a content-based approach based on movie genre to predict user ratings on new movies. Furthermore, we integrate the modification of similar measures in memory-based collaborative filtering with matrix factorization(model-based collaborative filtering). In experiments, we observe our methodology brought out a lower MAE in overall rating prediction. Finally, our approach has been shown to have better recommendation quality than basic collaborative filtering in different sparsity level dataset.
RATHI, ISHAN. "A COLLABORATIVE FILTERING-BASED RECOMMENDER SYSTEM ALLEVIATING COLD START PROBLEM." Thesis, 2019. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16694.
Повний текст джерелаКниги з теми "HYBRID MOVIE"
Haubitz + Zoche : Hybrid Modernism: Movie Theaters in South India. Dreen, Markus, Anne König u. Jan Wenzel. Spectormag GbR, 2016.
Знайти повний текст джерелаSolomonova, Elizaveta. Sleep Paralysis. Edited by Kalina Christoff and Kieran C. R. Fox. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190464745.013.20.
Повний текст джерелаPetmesidou, Maria. Welfare Reform in Greece. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198790266.003.0008.
Повний текст джерелаMattox, Gale A. The Transatlantic Security Landscape in Europe. Edited by Derek S. Reveron, Nikolas K. Gvosdev, and John A. Cloud. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190680015.013.26.
Повний текст джерелаÖzkazanç-Pan, Banu. Transnational Migration and the New Subjects of Work. Policy Press, 2019. http://dx.doi.org/10.1332/policypress/9781529204544.001.0001.
Повний текст джерелаSarkar, B. K., and Reena Singh. Hydrogen Fuel Cell Vehicles Current Status. Namya Press, 2022. http://dx.doi.org/10.56962/9789355451118.
Повний текст джерелаOswald, Laura R. Doing Semiotics. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198822028.001.0001.
Повний текст джерелаBoydstun, Amber E., and Annelise Russell. From Crisis to Stasis: Media Dynamics and Issue Attention in the News. Oxford University Press, 2016. http://dx.doi.org/10.1093/acrefore/9780190228637.013.56.
Повний текст джерелаJenkins, Ryan, David Cerny, and Tomas Hribek, eds. Autonomous Vehicle Ethics. Oxford University PressNew York, 2022. http://dx.doi.org/10.1093/oso/9780197639191.001.0001.
Повний текст джерелаAndrew, Nell. Moving Modernism. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190057275.001.0001.
Повний текст джерелаЧастини книг з теми "HYBRID MOVIE"
Bharatiya, Nidhi, Shatakshi Bhardwaj, Kartik Sharma, Pranjal Kumar, and Jeny Jijo. "Movie Recommendation System Using Hybrid Approach." In Lecture Notes in Networks and Systems, 415–29. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5166-6_28.
Повний текст джерелаParikh, Dhairya, Dilpreet Kaur, Kajal Parikh, Prakhar Yadav, and Hemant Rathore. "Movie Recommendation System Addressing Changes in User Preferences with Time." In Hybrid Intelligent Systems, 473–83. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73050-5_48.
Повний текст джерелаArfaoui, Nouha. "Movie Sentiment Analysis Based on Machine Learning Algorithms: Comparative Study." In Hybrid Intelligent Systems, 401–11. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27409-1_36.
Повний текст джерелаLiu, Xiangyong, Guojun Wang, Wenjun Jiang, and Yinong Long. "DHMRF: A Dynamic Hybrid Movie Recommender Framework." In Lecture Notes in Computer Science, 491–503. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49178-3_37.
Повний текст джерелаJain, Kartik Narendra, Vikrant Kumar, Praveen Kumar, and Tanupriya Choudhury. "Movie Recommendation System: Hybrid Information Filtering System." In Intelligent Computing and Information and Communication, 677–86. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7245-1_66.
Повний текст джерелаParida, Prajna Paramita, Mahendra Kumar Gourisaria, Manjusha Pandey, and Siddharth Swarup Rautaray. "Hybrid Movie Recommender System - A Proposed Model." In Communications in Computer and Information Science, 475–85. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1480-4_43.
Повний текст джерелаLavanya, R., V. S. Bharat Raam, and Nikil Pillaithambi. "Enhanced Movie Recommender System Using Hybrid Approach." In Proceedings of International Conference on Deep Learning, Computing and Intelligence, 539–50. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5652-1_48.
Повний текст джерелаLavanya, R., V. S. Bharat Raam, and Nikil Pillaithambi. "Enhanced Movie Recommender System Using Hybrid Approach." In Proceedings of International Conference on Deep Learning, Computing and Intelligence, 539–50. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5652-1_48.
Повний текст джерелаKaveri, V. Vijeya, P. Hari Prasath, M. M. Kamalika, A. Devadharsika, and S. Arthik Sankar. "Machine Learning-Based Hybrid Movie Recommendation System." In Advances in Intelligent Systems and Computing, 157–68. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3608-3_11.
Повний текст джерелаKarak, Gahina, Shubham Mishra, Arkadyuti Bandyopadhyay, Pavirala Ranga Sai Rohith, and Hemant Rathore. "Sentiment Analysis of IMDb Movie Reviews: A Comparative Analysis of Feature Selection and Feature Extraction Techniques." In Hybrid Intelligent Systems, 283–94. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96305-7_27.
Повний текст джерелаТези доповідей конференцій з теми "HYBRID MOVIE"
Subramaniam, Rajan, Roger Lee, and Tokuro Matsuo. "Movie Master: Hybrid Movie Recommendation." In 2017 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2017. http://dx.doi.org/10.1109/csci.2017.56.
Повний текст джерелаPathak, Dharmendra, S. Matharia, and C. N. S. Murthy. "ORBIT: Hybrid movie recommendation engine." In 2013 International Conference on Emerging Trends in Computing, Communication and Nanotechnology (ICE-CCN). IEEE, 2013. http://dx.doi.org/10.1109/ice-ccn.2013.6528589.
Повний текст джерелаHasan, Md Mehedi, Sadia Tamim Dip, Tasmiah Rahman, Mst Sonia Akter, and Imrus Salehin. "Multilabel Movie Genre Classification from Movie Subtitle: Parameter Optimized Hybrid Classifier." In 2021 4th International Symposium on Advanced Electrical and Communication Technologies (ISAECT). IEEE, 2021. http://dx.doi.org/10.1109/isaect53699.2021.9668427.
Повний текст джерелаSalmani, Sakina, and Sarvesh Kulkarni. "Hybrid Movie Recommendation System Using Machine Learning." In 2021 International Conference on Communication information and Computing Technology (ICCICT). IEEE, 2021. http://dx.doi.org/10.1109/iccict50803.2021.9510058.
Повний текст джерелаAlmuhaimeed, Abdullah, and Maria Fasli. "A hybrid semantic method for enhancing movie recommendations." In 2017 International Conference on the Frontiers and Advances in Data Science (FADS). IEEE, 2017. http://dx.doi.org/10.1109/fads.2017.8253188.
Повний текст джерелаWei, Shouxian, Litao Xiao, Xiaolin Zheng, and Deren Chen. "A Hybrid Movie Recommendation Approach via Social Tags." In 2014 IEEE 11th International Conference on e-Business Engineering (ICEBE). IEEE, 2014. http://dx.doi.org/10.1109/icebe.2014.55.
Повний текст джерелаXiong, Wei, and Chengwan He. "Personalized Movie Hybrid Recommendation Model Based on GRU." In 2021 4th International Conference on Robotics, Control and Automation Engineering (RCAE). IEEE, 2021. http://dx.doi.org/10.1109/rcae53607.2021.9638949.
Повний текст джерелаKrishnathasan, Mathangi. "Movie Recommendation System Using Concurrent Hybrid Variational Autoencoders." In 2021 21st International Conference on Advances in ICT for Emerging Regions (ICter). IEEE, 2021. http://dx.doi.org/10.1109/icter53630.2021.9774813.
Повний текст джерелаChristakou, C., and A. Stafylopatis. "A hybrid movie recommender system based on neural networks." In 5th International Conference on Intelligent Systems Design and Applications (ISDA'05). IEEE, 2005. http://dx.doi.org/10.1109/isda.2005.9.
Повний текст джерелаAl-Shamri, Mohammad Yahya H., and Kamal K. Bharadwaj. "A Compact User Model for Hybrid Movie Recommender System." In International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007). IEEE, 2007. http://dx.doi.org/10.1109/iccima.2007.15.
Повний текст джерелаЗвіти організацій з теми "HYBRID MOVIE"
Caparini, Marina. Conflict, Governance and Organized Crime: Complex Challenges for UN Stabilization Operations. Stockholm International Peace Research Institute, December 2022. http://dx.doi.org/10.55163/nowm6453.
Повний текст джерелаMosalam, Khalid, Amarnath Kasalanati, and Selim Gunay. PEER Annual Report 2017 - 2018. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, June 2018. http://dx.doi.org/10.55461/fars6451.
Повний текст джерела