We combine Artificial Immune Systems (AIS) technology with Collaborative Filtering (CF) and use it to build a movie recommendation system. We already know that Artificial Immune Systems work well as movie recommenders from previous work by Cayzer and Aickelin ([3], [4], [5]). Here our aim is to investigate the effect of different affinity measure algorithms for the AIS. Two different affinity m...

In the presented work, we discuss the Movie-Based approach to design and represent of matrix algorithms including a lot of identical operations on matrix elements. These operations usually transform an initial matrix structure into a matrix with a given structure. Therefore, it is possible to represent a matrix algorithm as a series of frames or iterations reflecting the stages of this process....

BACKGROUND The increasing popularity of commercial movies showing three dimensional (3D) images has raised concern about possible adverse side effects on viewers. METHODS AND FINDINGS A prospective carryover observational study was designed to assess the effect of exposure (3D vs. 2D movie views) on self reported symptoms of visually induced motion sickness. The standardized Simulator Sicknes...

A meaningful set of stimuli, such as a sequence of frames from a movie, triggers a set of different experiences. By contrast, a meaningless set of stimuli, such as a sequence of 'TV noise' frames, triggers always the same experience--of seeing 'TV noise'--even though the stimuli themselves are as different from each other as the movie frames. We reasoned that the differentiation of cortical res...

Movie is a kind of complex video with rich content. The analysis of movie is more complicated than other types of videos like surveillance, sport games, and documentaries. In this paper, a statistical approach using hidden Markov model to classify movie scenes is proposed. Two important kinds of movie scenes, dialogue and fighting scenes, are classified. Color and motion features are extracted ...

Given that movie attendance has been dropping significantly in recent years, movie theaters have been struggling to find additional sources of revenue. And with movie studios trying alternative release strategies—particularly by shortening the window from theatrical release to release in DVD and through video-on-demand—theater owners are now facing even more competitors for potential moviegoers...

It is often the case that a human can determine the genre of a movie by looking at its movie poster. This task is not trivial for computers. A recent advance in machine learning called deep learning allows algorithms to learn important features from large datasets. Rather than analyzing an image pixel by pixel, for example, higher level features can be used for classification. In this project I...

Listeners are often active in conversation, and the feedback they provide speakers can improve the communication. To examine how feedback influences conversation, we had 76 speaker subjects watch a movie and then summarize it to one or two listeners. Thelisteners provided varying amounts of feedback to the speaker. When two listeners were present, one could influence the speaker through feedbac...

We consider the distribution planning problem in the motion picture industry. This problem involves forecasting theater-level box office revenues for a given movie and using these forecasts to choose the best locations to screen a movie. We first develop a method that predicts theater-level box office revenues for a given movie as a function of movie attributes and theater characteristics. Thes...

IMDb is a widely known online movie platform that offers movie information, allows to rate movies and recommends interesting movies to users. The IMDb movie recommendations do not however offer any means for interactivity or user control, which inherently limits their contextual adaptability. In this work we describe our Google Chrome extension – called MovieBrain – which offers interactive mov...