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Littérature scientifique sur le sujet « HYBRID MOVIE »

Auteur : Grafiati

Publié le 26 octobre 2023

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Articles de revues sur le sujet "HYBRID MOVIE"

Nosshi, Anthony, Aziza Asem et Mohamed Badr Senousy. « Hybrid Recommender System via Personalized Users’ Context ». Cybernetics and Information Technologies 19, n^o 1 (1 mars 2019) : 101–15. http://dx.doi.org/10.2478/cait-2019-0006.

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Abstract In movie domain, finding the appropriate movie to watch is a challenging task. This paper proposes a recommender system that suggests movies in cinema that fit the user’s available time, location, mood and emotions. Conducted experiments for evaluation showed that the proposed method outperforms the other baselines.

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Wang, Yibo, Mingming Wang et 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.

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Movie recommendation in mobile environment is critically important for mobile users. It carries out comprehensive aggregation of user’s preferences, reviews, and emotions to help them find suitable movies conveniently. However, it requires both accuracy and timeliness. In this paper, a movie recommendation framework based on a hybrid recommendation model and sentiment analysis on Spark platform is proposed to improve the accuracy and timeliness of mobile movie recommender system. In the proposed approach, we first use a hybrid recommendation method to generate a preliminary recommendation list. Then sentiment analysis is employed to optimize the list. Finally, the hybrid recommender system with sentiment analysis is implemented on Spark platform. The hybrid recommendation model with sentiment analysis outperforms the traditional models in terms of various evaluation criteria. Our proposed method makes it convenient and fast for users to obtain useful movie suggestions.

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Nosshi, Anthony, Aziza Saad Asem et Mohammed Badr Senousy. « Hybrid Recommender System Using Emotional Fingerprints Model ». International Journal of Information Retrieval Research 9, n^o 3 (juillet 2019) : 48–70. http://dx.doi.org/10.4018/ijirr.2019070104.

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With today's information overload, recommender systems are important to help users in finding needed information. In the movies domain, finding a good movie to watch is not an easy task. Emotions play an important role in deciding which movie to watch. People usually express their emotions in reviews or comments about the movies. In this article, an emotional fingerprint-based model (EFBM) for movies recommendation is proposed. The model is based on grouping movies by emotional patterns of some key factors changing in time and forming fingerprints or emotional tracks, which are the heart of the proposed recommender. Then, it is incorporated into collaborative filtering to detect the interest connected with topics. Experimental simulation is conducted to understand the behavior of the proposed approach. Results are represented to evaluate the proposed recommender.

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Tripathi, Jyoti, Sunita Tiwari, Anu Saini et 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, n^o 3 (1 mars 2023) : 1750. http://dx.doi.org/10.11591/ijeecs.v29.i3.pp1750-1757.

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<span lang="EN-US">Nowadays, predicting the success of a new movie is a crucial task. In this work, the hybrid approach considers the movie features as well as sentiment expressed in the movie review to predict the success rate of a movie. Multiple movie features such as title, director, star cast, and writer. Are considered for prediction. The related raw data is collected from the internet movie database (IMDb) website and after pre-processing, the collected data is used to generate the supervised machine learning model. Different supervised learning models are compared and the one with the best results is used further. The mean squared error, root mean squared error and r2 score of the models generated are comparable with existing models. Further, sentiment analysis of the movie-related tweets is performed. The accuracy of best sentiment analysis model is 88.47%. Finally, the two models are combined to give the success prediction rating of new movies and the results of the hybrid model are encouraging. The proposed model may be used to find the top-rated movies of a particular calendar year.</span>

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Bohra, Sneha, Amit Gaikwad et Ghanapriya Singh. « Hybrid Machine Learning Based Recommendation Algorithm for Multiple Movie Dataset ». Indian Journal Of Science And Technology 16, n^o 37 (9 octobre 2023) : 3121–28. http://dx.doi.org/10.17485/ijst/v16i37.2065.

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Mohile, Sara, Hemant Ramteke, Pragati Shelgaonkar, Hritika Phule et M. M. Phadtare. « A Movie Recommender System Using Hybrid Approach : A Review ». International Journal for Research in Applied Science and Engineering Technology 10, n^o 3 (31 mars 2022) : 1834–37. http://dx.doi.org/10.22214/ijraset.2022.41014.

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Abstract: The topic of this paper is movie suggestions. Because of its ability to provide improved entertainment, a movie recommendation is vital in our social lives. Users can be recommended a set of movies depending on their interests or admiration for the films by such a system. A recommendation system is used to make suggestions for things to buy or see. They employ a big collection of information to steer consumers to the things that will best match their needs. A recommender system, also known as a recommendation system, is a type of material filtering system that attempts to forecast a user's "rating" or "preference" for an item. They're mostly employed for commercial purposes. MOVREC also assists users in efficiently and effectively locating movies of their choice based on the movie experiences of other users, without wasting time in pointless searching. Keywords: Filtering, Recommendation System, Recommender.

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Lekakos, George, et Petros Caravelas. « A hybrid approach for movie recommendation ». Multimedia Tools and Applications 36, n^o 1-2 (21 décembre 2006) : 55–70. http://dx.doi.org/10.1007/s11042-006-0082-7.

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Jadhav, Prof Rupali. « Implementing a Movie Recommendation System in Machine Learning Using Hybrid Approach ». International Journal for Research in Applied Science and Engineering Technology 11, n^o 5 (31 mai 2023) : 6601–3. http://dx.doi.org/10.22214/ijraset.2023.53204.

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Abstract: In this paper, we have proposed a movie recommendation system using hybrid recommendation system. Today, there are a lot of recommendation systems available which are practically implemented in various websites and mobile apps. Variety exists in types of recommendation systems, user interfaces but most importantly, the accuracy of the recommendation systems. Determining a user’s possible future preference of movie or TV shows to watch is a complex task which requires a lot of relevant user data such as watch history of user, genres liked by the user, favorite actor or director, etc. Hence, the aim of this proposed system is to refine the search engine and make it more enhanced and accurate in terms of prediction. The system recommends the movies graphically based on both, user preference and similarity of individual user with other users. It also shows top rated movies worldwide and updates the recommendation after every choice of movie or show by the user

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Ez-zahout, Abderrahmane, Hicham Gueddah, Abir Nasry, Rabie Madani et 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, n^o 1 (1 avril 2022) : 434. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp434-441.

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On the subject of broadcasting the information, finding someone’s favorite book or movie in a sea of data containing books and movies has become a crucial issue. In an era when there are so many genres and types of movies and books, the customer may find it difficult to choose which to discover in the first place. Thus, personalized recommendation systems play an important role because of the value that is attributed to movies and books nowadays, and considering that there are so many to choose from that the user may not be able to have a specific target. In this context, our proposed work, design and implement a prototype of movie recommendation system while taking into consideration the real requirement for the search of movies and books. The research of movie recommendation system by using the k-nearest neighbors approach and collaborative filtering algorithm are adopted to extract the criteria for a good use case on recommender systems. At last, the results are as what was expected as they showed that the system has a good recommendation effect.

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Huang, Yi-Ting, et Ping-Feng Pai. « Using the Least Squares Support Vector Regression to Forecast Movie Sales with Data from Twitter and Movie Databases ». Symmetry 12, n^o 4 (15 avril 2020) : 625. http://dx.doi.org/10.3390/sym12040625.

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Due to the rapid prominence and popularity of social media, social broadcasting networks with voluntary information sharing have become one of the most powerful ways to spread word-of-mouth opinions, and thus, have influence on consumers’ preferences toward products. Therefore, sentiment analysis data from social media have become more important in forecasting product sales. For the movie industry, the opinions expressed on social media have increasing impacts on movie sales. In addition, some databases, such as the Box Office Mojo and Internet Movie Database (IMDb), contain structured data for predicting movie sales. Thus, three categories of data—data of movie databases, data of tweets, and hybrid data including movies databases and tweets—are employed symmetrically in this study. The aim of this study is to employ the least squares support vector regression (LSSVR) to forecast movie sales worldwide according to these three forms of data. In addition, three other forecasting techniques—namely, the back propagation neural network (BPNN), the generalized regression neural network (GRNN), and the multivariate linear regression (MLR) model—were used to forecast movie sales with the three types of data. The empirical results show that the LSSVR model with hybrid data can obtain more accurate results than the other forecasting models with all data types. Thus, forecasting movie sales using the LSSSVR model with data containing movie databases and tweets is a feasible and prospective method to forecast movie sales.

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Thèses sur le sujet "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.

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Recommender systems are information retrieval tools helping users in their information seeking tasks and guiding them in a large space of possible options. Many hybrid recommender systems are proposed so far to overcome shortcomings born of pure content-based (PCB) and pure collaborative filtering (PCF) systems. Most studies on recommender systems aim to improve the accuracy and efficiency of predictions. In this thesis, we propose an online hybrid recommender strategy (CBCFdfc) based on content boosted collaborative filtering algorithm which aims to improve the prediction accuracy and efficiency. CBCFdfc combines content-based and collaborative characteristics to solve problems like sparsity, new item and over-specialization. CBCFdfc uses fuzzy clustering to keep a certain level of prediction accuracy while decreasing online prediction time. We compare CBCFdfc with PCB and PCF according to prediction accuracy metrics, and with CBCFonl (online CBCF without clustering) according to online recommendation time. Test results showed that CBCFdfc performs better than other approaches in most cases. We, also, evaluate the effect of user-specified parameters to the prediction accuracy and efficiency. According to test results, we determine optimal values for these parameters. In addition to experiments made on simulated data, we also perform a user study and evaluate opinions of users about recommended movies. The results that are obtained in user evaluation are satisfactory. As a result, the proposed system can be regarded as an accurate and efficient hybrid online movie recommender.

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Sommar, Fredrik, et 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.

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Sentiment classification is the process of categorizing data into categories based on its polarity with a wide array of applications across several industries. This report examines a combination of two prominent approaches to sentiment classification using a lexicon of weighted words and machine learning respectively. These approaches are compared with the combined hybrid approach in order to give an account of their relative strengths and weaknesses. When run on a set of IMDb movie reviews the results indicate that the hybrid model performs better than the lexicon-based approach, in turn being outperformed by the learning-based approach. However, the gain in convenience brought on by eliminating the need for training data makes the hybrid model an appealing alternative to the other approaches with a slight trade-off in performance.
Att 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.

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Lokesh, Ashwini. « A Comparative Study of Recommendation Systems ». TopSCHOLAR®, 2019. https://digitalcommons.wku.edu/theses/3166.

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Recommendation Systems or Recommender Systems have become widely popular due to surge of information at present time and consumer centric environment. Researchers have looked into a wide range of recommendation systems leveraging a wide range of algorithms. This study investigates three popular recommendation systems in existence, Collaborative Filtering, Content-Based Filtering, and Hybrid recommendation system. The famous MovieLens dataset was utilized for the purpose of this study. The evaluation looked into both quantitative and qualitative aspects of the recommendation systems. We found that from both the perspectives, the hybrid recommendation system performs comparatively better than standalone Collaborative Filtering or Content-Based Filtering recommendation system

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Marsh, 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.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.
Includes 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.

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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.

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Ce travail présente une approche d’intégration de semiconducteurs III-V sur silicium. L’objectif est de réaliser des sources laser multi-longueur d’onde émettant en bande C pour les télécommunications optiques à partir d’une croissance épaisse de matériaux III-V sur fine couche d’InP collée sur silicium oxydé (InP-SiO2/Si = InPoSi).Afin d’étudier la compatibilité du procédé de collage avec l’élévation en température nécessaire à l’étape de recroissance, de l’ordre de 600°C par MOVPE, une étude de la stabilité en température des substrats InPoSi a été menée. Cette dernière a mis en évidence le délaminage de l'InPoSi avec apparition de "bulles" liée au décollement de la couche d’InP provoqué par la désorption d’hydrogène à 400°C. Une étude de la diffusion latérale de l’hydrogène le long de l’interface de collage a permis de mesurer une longueur de diffusion de l’ordre de 100 µm dans nos conditions expérimentales. Le développement de tranchées de dégazage espacées de 200 µm a ainsi permis d’effectuer la recroissance de matériaux III-V de haute qualité sur InPoSi sans apparition de défectivité de type "bulles" entre ces tranchées.Par la suite, l'amélioration constante des étapes de préparation des surfaces à coller a permis d'obtenir une qualité de matériau InPoSi optimale pour la recroissance à haute température sans faire appel à des procédés de dégazage. L’étude d’une structure active composée de multipuits quantiques (MQWs) à base de matériaux AlGaInAs a été menée par caractérisation in-situ pendant la croissance sur InPoSi. Par la mesure en temps réel de la courbure du substrat InPoSi à température d’épitaxie, une contrainte thermique de 390 ppm a été quantifiée. Cette dernière est créée par la différence de coefficients d’expansion thermique entre InP et Si. Malgré cette contrainte thermique, la recroissance d’une structure diode laser de 3 µm d’épaisseur de grande qualité cristalline a été démontrée sur InPoSi. Des lasers à contact large basée sur cette structure ont été fabriqués et les performances ont été comparées à celles obtenues pour le même composant fabriqué sur substrat InP pour référence. Des courants de seuil de 0,4 kA/cm² à 20°C en régime pulsé ont été obtenus sur InPoSi. La comparaison des lasers sur InPoSi et InP a montré des courants de seuil, des rendements et une température caractéristique similaires. Ce résultat démontre que la structure épaisse épitaxiée sur InPoSi ne subit pas de dégradation matériau.Enfin, un nouveau procédé de croissance sélective (SAG : Selective Area Growth) a été développé spécifiquement sur InPoSi. Pour cela, la silice de l'InPoSi est déterrée localement par gravure de la couche d'InP afin d'offrir des surfaces diélectriques de tailles variables pour le procédé SAG. La variation des épaisseurs des puits quantiques obtenus par épitaxie sélective en fonction de la surface des masques permet d'atteindre une très large extension en longueur d’onde de photoluminescence, de 1490 à 1650 nm. En utilisant la SAG, des lasers Fabry-Pérot ont été fabriqués en structure shallow-ridge et des émissions laser couvrant 155 nm d’extension spectrale ont été obtenues. Pour une barrette de 500 µm de long, des courants de seuil en dessous de 30 mA à 20°C ont été obtenus en régime continu pour les lasers en bande C. A 70°C, les courants de seuil demeurent en dessous de 60 mA, ce qui traduit une très bonne tenue en température des lasers. L'ensemble de ces résultats valide la méthode d'intégration de III-V sur silicium
This 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

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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.

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Uno de los retos a los que se enfrenta la industria de las comunicaciones móviles e inalámbricas es proporcionar servicios multimedia masivos a bajo coste, haciéndolos asequibles para los usuarios y rentables a los operadores. El servicio más representativo es el de TV móvil, el cual se espera que sea una aplicación clave en las futuras redes móviles. Actualmente las redes celulares no pueden soportar un consumo a gran escala de este tipo de servicios, y las nuevas redes de radiodifusión móvil son muy costosas de desplegar debido a la gran inversión en infraestructura de red necesaria para proporcionar niveles aceptables de cobertura. Esta tesis doctoral aborda el problema de la provisión eficiente de servicios multimedia masivos a dispositivos móviles y portables utilizando la infraestructura de radiodifusión y celular existente. La tesis contempla las tecnologías comerciales de última generación para la radiodifusión móvil (DVB-H) y para las redes celulares (redes 3G+ con HSDPA y MBMS), aunque se centra principalmente en DVB-H. El principal paradigma propuesto para proporcionar servicios multimedia masivos a bajo coste es evitar el despliegue de una red DVB-H con alta capacidad y cobertura desde el inicio. En su lugar se propone realizar un despliegue progresivo de la infraestructura DVB-H siguiendo la demanda de los usuarios. Bajo este contexto, la red celular es fundamental para evitar sobre-dimensionar la red DVB-H en capacidad y también en áreas con una baja densidad de usuarios hasta que el despliegue de un transmisor o un repetidor DVB-H sea necesario. Como principal solución tecnológica la tesis propone realizar una codificación multi-burst en DVB-H utilizando códigos Raptor. El objetivo es explotar la diversidad temporal del canal móvil para aumentar la robustez de la señal y, por tanto, el nivel de cobertura, a costa de incrementar la latencia de la red.
Gómez 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
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SINGH, YOGENDRA. « A PERSONALIZED HYBRID MOVIE RECOMMENDATION SYSTEM FOR USERS ». Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15145.

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We describe a rating logical thinking approach to incorporating matter user reviews into Collaborative Filtering (CF) algorithms. The main motive of our approach is to use user preferences which is expressed in movie reviews and then convert such user’s preferences into some rating that may be understood by existing CF algorithms. The linguistics score of subjective sentence is fetched from SentiWordNet Library to calculate their sentiments as +ve, -ve or neutral based on the textual review. We’ve used SentiWordNet library as a dataset with two completely different approaches of alternatives comprising of adverbs and verbs, adjectives and n-gram feature extraction. We have a tendency to conjointly used our SentiWordNet library to figure the document level sentiment for every movie reviewed and compared its label with results obtained victimization Alchemy API. We conjointly developed and evaluated a model of the planned framework. Preliminary results valid the effectiveness of varied tasks within the planned framework, and recommend that the framework doesn't admit an oversized coaching corpus to operate. Additional development of our rating logical thinking framework is in progress. A comprehensive analysis of the framework are administered and reported during a follow-up article.

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Lin, Chung-Yu, et 林重佑. « A Study on LVQ Based Switching Hybrid Movie Recommendation ». Thesis, 2012. http://ndltd.ncl.edu.tw/handle/11873132375795364614.

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碩士
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%.

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Lee, Chia-Hsing, et 李佳馨. « Integration of Content-based approach and Hybrid Collaborative Filtering for Movie Recommendation ». Thesis, 2013. http://ndltd.ncl.edu.tw/handle/64f874.

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碩士
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.

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RATHI, ISHAN. « A COLLABORATIVE FILTERING-BASED RECOMMENDER SYSTEM ALLEVIATING COLD START PROBLEM ». Thesis, 2019. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16694.

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Consumers currently have a surplus of items available to purchase via online stores. Surplus of goods enables users to have huge variety but it often leads to inconvenience for users. Consumers have to spend a lot of time going through items to find goods of their preference. To automate the process of sharing relevant suggestions, recommender systems are used. Recommender systems are making their presence felt in a number of domains, be it for ecommerce or education, social networking etc. With huge growth in number of consumers and items in recent years, recommender systems face some key challenges. These are: producing high quality recommendations and performing many recommendations per second for millions of consumers and items. New recommender system technologies are needed to scale themselves for new items as well as in new user in the system in order to get high quality recommendations. In this thesis, we focus on collaborative approach-based recommender systems to solve the issue of cold start problem. We have compared multiple algorithms which aim to solve cold start problem and proposed a new hybrid algorithm. This new algorithm is implemented on Movie-Lens 1Million Dataset.

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Livres sur le sujet "HYBRID MOVIE"

Haubitz + Zoche : Hybrid Modernism : Movie Theaters in South India. Dreen, Markus, Anne König u. Jan Wenzel. Spectormag GbR, 2016.

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Solomonova, Elizaveta. Sleep Paralysis. Sous la direction de Kalina Christoff et Kieran C. R. Fox. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190464745.013.20.

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Sleep paralysis is an experience of being temporarily unable to move or talk during the transitional periods between sleep and wakefulness: at sleep onset or upon awakening. The feeling of paralysis may be accompanied by a variety of vivid and intense sensory experiences, including mentation in visual, auditory, and tactile modalities, as well as a distinct feeling of presence. This chapter discusses a variety of sleep paralysis experiences from the perspective of enactive cognition and cultural neurophenomenology. Current knowledge of neurophysiology and associated conditions is presented, and some techniques for coping with sleep paralysis are proposed. As an experience characterized by a hybrid state of dreaming and waking, sleep paralysis offers a unique window into phenomenology of spontaneous thought in sleep.

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Petmesidou, Maria. Welfare Reform in Greece. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198790266.003.0008.

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Greece developed a pension-heavy, clientelist, hybrid Mediterranean welfare state with many gaps in coverage. The global financial crisis of 2008 triggered a severe sovereign debt crisis, compelling the country to accept three bailout packages with stringent conditions as to spending cuts, privatization, and openness to international competition. Severe austerity has caused a protracted recession: the economy lost more than a quarter of its GDP between 2008 and 2015. The Mediterranean refugee crisis impacted severely on the country. New parties of the extreme left (SYRIZA) and extreme right (Golden Dawn) have gained support. SYRIZA was elected on an anti-austerity platform but failed to deliver and a fourth rescue package is under negotiation. The more likely future direction consists in an ever-tighter austerity programme with the immizeration of large sections of the population. A move towards neo-Keynesian intervention and social investment seems unlikely, given the level of debt and the bailout conditions.

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Mattox, Gale A. The Transatlantic Security Landscape in Europe. Sous la direction de Derek S. Reveron, Nikolas K. Gvosdev et John A. Cloud. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190680015.013.26.

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The geopolitical and strategic landscape in Europe has transformed fundamentally under the Russian challenge to the Transatlantic Alliance. The alliance response to the annexation of Crimea and Russian hybrid warfare in Ukraine strengthened and demonstrated resolve on the part of the North Atlantic Treaty Organization (NATO) in the Baltic states and Poland with an Enhanced Forward Presence of rotational troops. Since the fall of the Berlin Wall and disintegration of the Soviet Union, NATO has accepted new members that pursued democracy, free markets, rule of law, and human rights as well as a stable European and international order. The future of Transatlantic relations will be impacted by European defense spending, the implications of U.K. withdrawal from the European Union, Russian foreign policy, and the ability of the Atlantic Alliance to move from assurance to a strong deterrence and defense posture in the East and at the same time confront the challenges from the south. The chapter addresses the major challenges to transatlantic security, focuses on the UK, France, and Germany and lays out future challenges.

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Özkazanç-Pan, Banu. Transnational Migration and the New Subjects of Work. Policy Press, 2019. http://dx.doi.org/10.1332/policypress/9781529204544.001.0001.

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This book brings about insights and key concepts from the field of transnational migration studies to bear upon the field of organization studies. It expands upon multiscalar global perspective, moving beyond methodological nationalism, and historical global conjuncturesas relevant transnational concepts for studying people and difference in novel ways including agentic, reflexive mobile subjectivities as the new subjects of diversity research that emerge in a ‘post-identitarian’ world. Specifically, the book offers transmigrant, hybrid, and cosmopolitan subjectivities as new the subjects of diversity research. Beyond new subjectivities, mobility ontology requires rethinking the epistemology of multiculturalism, examining inequalities, and redirecting the methodologies adopted to attend to difference. In expanding on these, the book offers new frameworks for the study of people on-the-move and organizations through a mobility ontology that foregrounds movement as the natural order of the social world. It also calls into question the ways existing research paradigms and approaches have potentially replicated the creation of boundaries and borders through implicit assumptions about difference, race/ethnicity and belonging. By shifting the ontological premise upon which the field of organization studies rests, this book provides novel ways of theorizing difference, people and work beyond static epistemologies guiding much of the field.

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Sarkar, B. K., et Reena Singh. Hydrogen Fuel Cell Vehicles Current Status. Namya Press, 2022. http://dx.doi.org/10.56962/9789355451118.

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Abstract: The hazardous effects of pollutants from conventional fuel vehicles have caused the scientific world to move towards environmentally friendly energy sources. Though we have various renewable energy sources, the perfect one to use as an energy source for vehicles is hydrogen. Like electricity, hydrogen is an energy carrier that has the ability to deliver incredible amounts of energy. On-board hydrogen storage in vehicles is an important factor that should be considered when designing fuel cell vehicles. In this study, a recent development in hydrogen fuel cell engines is reviewed to scrutinize the feasibility of using hydrogen as a major fuel in transportation systems. A fuel cell is an electrochemical device that can produce electricity by allowing chemical gases and oxidants as reactants. With anodes and electrolytes, the fuel cell splits the cation and the anion in the reactant to produce electricity. Fuel cells use reactants, which are not harmful to the environment and produce water as a product of the chemical reaction. As hydrogen is one of the most efficient energy carriers, the fuel cell can produce direct current (DC) power to run the electric car. By integrating a hydrogen fuel cell with batteries and the control system with strategies, one can produce a sustainable hybrid car.

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Oswald, Laura R. Doing Semiotics. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198822028.001.0001.

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Structural semiotics is a hybrid of communication science and anthropology that accounts for the deep cultural codes that structure communication and sociality, endow things with value, move us through constructed space, and moderate our encounters with change. Doing Semiotics: A Research Guide for Marketers at the Edge of Culture, shows readers how to leverage these codes to solve business problems, foster innovation, and create meaningful experiences for consumers. In addition to the basic principles and methods of applied semiotics, the book introduces the reader to branding basics, strategic decision-making, and cross-cultural marketing management. The guide can be used to supplement my previous books, Marketing Semiotics (2012) and Creating Value (2015), with practical exercises, examples, extended team projects and evaluation criteria. The work guides students through the application of learnings to all phases of semiotics-based projects for communications, brand equity management, design strategy, new product development, and public policy management. In addition to grids and tables for sorting data and mapping cultural dimensions of a market, the book includes useful interview protocols for use in focus groups, in-depth interviews, and ethnographic studies. Each chapter also includes expert case studies and essays from the perspectives of Marcel Danesi, Rachel Lawes, Christian Pinson, Laura Santamaria, and Laura Oswald.

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Boydstun, Amber E., et 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.

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Media coverage does not ebb and flow. Rather, media coverage rapidly moves from crisis to stasis and back again. The result of these attention dynamics is news reporting that is disproportional to the breadth and pace of policy problems in the world, where some balloon in the news beyond expectations and others fade quickly (or never make the news at all). These patterns of news coverage result from the powerful role that momentum plays in the news-generation process. Forces of positive feedback drive news outlets to chase each new hot story quickly, while negative feedback forces drive news outlets to stay locked onto a hot story at hand. Together, these forces drive news coverage to lurch and fixate, lurch and fixate, again and again. Thus, although previous research has conceived of the news-generation process functioning either as a “patrol” system (where news outlets act as sentinels, tracking each policy problem as it unfolds in the world) or as an “alarm” system (where news outlets move in quick bursts from one policy problem to the next, with little to no in-depth coverage), both these previous models tell only half the story. Rather, the news-generation process is best understood through the alarm/patrol hybrid model, where news outlets often lurch from one hot item to the next but sometimes become entrenched in an unfolding storyline. The alarm/patrol hybrid model helps explain the particular phenomenon of “media storms” that can occur, where a sudden surge in media attention can vault a previously ignored issue into the center of public and political attention; think of the Catholic priest abuse scandal, or the scene in Ferguson, Missouri, after Michael Brown’s death. The lurching/fixating dynamics of media attention have far-ranging implications for citizen information and political response, contributing to a wider system of disproportionate information processing where some topics are attended to and others are largely ignored. In particular, because policymakers take so many of their cues from the news, it is likely the case that the lurching/fixating patterns of our media system exacerbate the punctuated patterns of government in turn.

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Jenkins, Ryan, David Cerny et Tomas Hribek, dir. Autonomous Vehicle Ethics. Oxford University PressNew York, 2022. http://dx.doi.org/10.1093/oso/9780197639191.001.0001.

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Abstract “A runaway trolley is speeding down a track . . .” So begins what is perhaps the most fecund thought experiment of the past several decades since its invention by Philippa Foot. Since then, moral philosophers have applied the “trolley problem” as a thought experiment to study many different ethical conflicts—and chief among them is the programming of autonomous vehicles (AVs). Nowadays, however, very few philosophers accept that the trolley problem is a perfect analogy for driverless cars or that the situations AVs face will resemble the forced choice of the unlucky bystander in the original thought experiment. This book represents a substantial and purposeful effort to move the academic discussion beyond the trolley problem to the broader ethical, legal, and social implications that AVs present. There are still urgent questions waiting to be addressed, for example: how AVs might interact with human drivers in mixed or “hybrid” traffic environments; how AVs might reshape our urban landscapes; what unique security or privacy concerns are raised by AVs as connected devices in the “Internet of Things”; how the benefits and burdens of this new technology, including mobility, traffic congestion, and pollution, will be distributed throughout society; and more. This book is an attempt to map the landscape of these next-generation questions and to suggest preliminary answers, with input from the disciplines of philosophy, sociology, economics, urban planning and transportation engineering, business ethics, and more, and represents a worldwide variety of perspectives.

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Andrew, Nell. Moving Modernism. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190057275.001.0001.

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This book reenacts the simultaneous eruption of three spectacular revolutions—the development of pictorial abstraction, the first modern dance, and the birth of cinema—which together changed the artistic landscape of early twentieth-century Europe and the future of modern art. Rather than seeking dancing pictures or pictures of dancing, however, this study follows the chronology of the historical avant-garde to show how dance and pictures were engaged in a kindred exploration of the limits of art and perception that required the process of abstraction. Recovering the performances, methods, and circles of aesthetic influence of avant-garde dance pioneers and experimental filmmakers from the turn of the century to the interwar period, this book challenges modernism’s medium-specific frameworks by demonstrating the significant role played by the arts of motion in the historical avant-garde’s development of abstraction: from the turn-of-the-century dancer Loïe Fuller, who awakened in symbolist artists the possibility of prolonged vision; to cubo-futurist and neosymbolist artists who reached pure abstraction in tandem with the radical dance theory of Valentine de Saint-Point; to Sophie Taeuber’s hybrid Dadaism between art and dance; to Akarova, a prolific choreographer whose dancing Belgian constructivist pioneers called “music architecture”; and finally to the dancing images of early cinematic abstraction from the Lumière brothers to Germaine Dulac. Each chapter reveals the emergence of abstractionas an apparatus of creation, perception, and reception deployed across artistic media toward shared modernist goals. The author argues that abstraction can be worked like a muscle, a medium through which habits of reception and perception are broken and art’s viewers are engaged by the kinesthetic sensation to move and be moved.

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Chapitres de livres sur le sujet "HYBRID MOVIE"

Bharatiya, Nidhi, Shatakshi Bhardwaj, Kartik Sharma, Pranjal Kumar et Jeny Jijo. « Movie Recommendation System Using Hybrid Approach ». Dans 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.

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Parikh, Dhairya, Dilpreet Kaur, Kajal Parikh, Prakhar Yadav et Hemant Rathore. « Movie Recommendation System Addressing Changes in User Preferences with Time ». Dans Hybrid Intelligent Systems, 473–83. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73050-5_48.

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Arfaoui, Nouha. « Movie Sentiment Analysis Based on Machine Learning Algorithms : Comparative Study ». Dans Hybrid Intelligent Systems, 401–11. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27409-1_36.

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Liu, Xiangyong, Guojun Wang, Wenjun Jiang et Yinong Long. « DHMRF : A Dynamic Hybrid Movie Recommender Framework ». Dans Lecture Notes in Computer Science, 491–503. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49178-3_37.

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Jain, Kartik Narendra, Vikrant Kumar, Praveen Kumar et Tanupriya Choudhury. « Movie Recommendation System : Hybrid Information Filtering System ». Dans Intelligent Computing and Information and Communication, 677–86. Singapore : Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7245-1_66.

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Parida, Prajna Paramita, Mahendra Kumar Gourisaria, Manjusha Pandey et Siddharth Swarup Rautaray. « Hybrid Movie Recommender System - A Proposed Model ». Dans Communications in Computer and Information Science, 475–85. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1480-4_43.

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Lavanya, R., V. S. Bharat Raam et Nikil Pillaithambi. « Enhanced Movie Recommender System Using Hybrid Approach ». Dans 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.

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Kaveri, V. Vijeya, P. Hari Prasath, M. M. Kamalika, A. Devadharsika et S. Arthik Sankar. « Machine Learning-Based Hybrid Movie Recommendation System ». Dans 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.

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Karak, Gahina, Shubham Mishra, Arkadyuti Bandyopadhyay, Pavirala Ranga Sai Rohith et Hemant Rathore. « Sentiment Analysis of IMDb Movie Reviews : A Comparative Analysis of Feature Selection and Feature Extraction Techniques ». Dans Hybrid Intelligent Systems, 283–94. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96305-7_27.

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Actes de conférences sur le sujet "HYBRID MOVIE"

Subramaniam, Rajan, Roger Lee et Tokuro Matsuo. « Movie Master : Hybrid Movie Recommendation ». Dans 2017 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2017. http://dx.doi.org/10.1109/csci.2017.56.

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Pathak, Dharmendra, S. Matharia et C. N. S. Murthy. « ORBIT : Hybrid movie recommendation engine ». Dans 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.

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Hasan, Md Mehedi, Sadia Tamim Dip, Tasmiah Rahman, Mst Sonia Akter et Imrus Salehin. « Multilabel Movie Genre Classification from Movie Subtitle : Parameter Optimized Hybrid Classifier ». Dans 2021 4th International Symposium on Advanced Electrical and Communication Technologies (ISAECT). IEEE, 2021. http://dx.doi.org/10.1109/isaect53699.2021.9668427.

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Salmani, Sakina, et Sarvesh Kulkarni. « Hybrid Movie Recommendation System Using Machine Learning ». Dans 2021 International Conference on Communication information and Computing Technology (ICCICT). IEEE, 2021. http://dx.doi.org/10.1109/iccict50803.2021.9510058.

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Almuhaimeed, Abdullah, et Maria Fasli. « A hybrid semantic method for enhancing movie recommendations ». Dans 2017 International Conference on the Frontiers and Advances in Data Science (FADS). IEEE, 2017. http://dx.doi.org/10.1109/fads.2017.8253188.

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Wei, Shouxian, Litao Xiao, Xiaolin Zheng et Deren Chen. « A Hybrid Movie Recommendation Approach via Social Tags ». Dans 2014 IEEE 11th International Conference on e-Business Engineering (ICEBE). IEEE, 2014. http://dx.doi.org/10.1109/icebe.2014.55.

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Xiong, Wei, et Chengwan He. « Personalized Movie Hybrid Recommendation Model Based on GRU ». Dans 2021 4th International Conference on Robotics, Control and Automation Engineering (RCAE). IEEE, 2021. http://dx.doi.org/10.1109/rcae53607.2021.9638949.

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Krishnathasan, Mathangi. « Movie Recommendation System Using Concurrent Hybrid Variational Autoencoders ». Dans 2021 21st International Conference on Advances in ICT for Emerging Regions (ICter). IEEE, 2021. http://dx.doi.org/10.1109/icter53630.2021.9774813.

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Christakou, C., et A. Stafylopatis. « A hybrid movie recommender system based on neural networks ». Dans 5th International Conference on Intelligent Systems Design and Applications (ISDA'05). IEEE, 2005. http://dx.doi.org/10.1109/isda.2005.9.

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Al-Shamri, Mohammad Yahya H., et Kamal K. Bharadwaj. « A Compact User Model for Hybrid Movie Recommender System ». Dans International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007). IEEE, 2007. http://dx.doi.org/10.1109/iccima.2007.15.

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Rapports d'organisations sur le sujet "HYBRID MOVIE"

Caparini, Marina. Conflict, Governance and Organized Crime : Complex Challenges for UN Stabilization Operations. Stockholm International Peace Research Institute, décembre 2022. http://dx.doi.org/10.55163/nowm6453.

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This SIPRI Report examines how organized crime is intertwined with armed conflict and hybrid governance systems in three states that currently host United Nations stabilization missions. It surveys the conflict/crime/governance nexus in the Central African Republic (CAR), the Democratic Republic of the Congo (DRC) and Mali, and how UN stabilization missions, in particular the UN Police, have engaged with the challenge of organized crime. The report argues that improving how UN stabilization interventions engage with organized crime will require a frank assessment of the significance of organized crime in systems of governance and patronage, of its role as a driver and enabler of armed conflict by non-state armed groups, and of the involvement of state-embedded actors in illicit markets. The complex links between conflict and governance actors and organized crime in the settings examined raise fundamental questions about the assumptions underlying peace operations. The report concludes with a set of recommendations on how to move to more realistic analyses and bases for peace operations.

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Mosalam, Khalid, Amarnath Kasalanati et Selim Gunay. PEER Annual Report 2017 - 2018. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, juin 2018. http://dx.doi.org/10.55461/fars6451.

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The Pacific Earthquake Engineering Research Center (PEER) is a multi-institutional research and education center with headquarters at the University of California, Berkeley. PEER’s mission is to (1) develop, validate, and disseminate performance-based engineering (PBE) technologies for buildings and infrastructure networks subjected to earthquakes and other natural hazards, with the goal of achieving community resilience; and (2) equip the earthquake engineering and other extreme-event communities with the 21st -century tools that define the current digital revolution. This reports presents the activities of the Center over the period of July 1, 2017 to June 30, 2018. PEER staff, in particular Grace Kang, Erika Donald, Claire Johnson, Christina Bodnar-Anderson, and Zulema Lara, helped in preparation of this report. Key activities of the past academic year include the following: -Continuation of major projects such as Tall Building Initiative (TBI) and Next Generation Attenuation (NGA) projects, and start of work on the major project funded by the California Earthquake Authority (CEA). The TBI was completed in 2017, and NGA projects are nearing completion soon. -Addition of University of Nevada, Reno (UNR) as a core institution. -Re-establishment of the PEER Research Committee. -Issuing a Request for Proposal (RFP) from TSRP funds and funding 17 projects as a result of this RFP. Together with the ongoing projects, the total number of projects funded in 2017 is 24. -Organization of several workshops focused on Liquefaction, Structural Health Monitoring (SHM), High-Performance Computing (HPC), Bridge Component Fragility Development, Physics-Based Ground Motions, Hybrid Simulation, and Research Needs for Resilient Buildings. -Rollout of TBI seminars and HayWired activities as part of outreach. -Conducting a blind prediction contest with robust participation and instructive findings on current modeling approaches. -Organization of the PEER Annual Meeting with participation of 240 attendees -Continuing participation in board of directors of international organizations such as Global Alliance of Disaster Research Institutes (GADRI) and International Laboratory of Earthquake Engineering (ILEE). Going forward, PEER aims to hold more focused workshops, form new committees, and draw on existing resources and experience on PBE to systematically move towards Resilient Design for Extreme Events (RDEE).

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