Film Industry is not only a industry or a centre of entertainment, rather it is now a centre of global business. All over the world is now excited about a movie's box office success, popularity etc. A huge data is available online about these movies success or popularity. We have used hollywood movie list from Wikipedia and their rating from IMDb movie rating website to create our data set. Then machine learning classification algorithms are applied of the data set. Lastly an efficient model is developed to predict a movie's IMDb rating. The model gives good classification measures with the data set.

Predicting IMDb Rating of Movies by Machine Learning Techniques

The main purpose of this paper is to develop a model for predicting the success of movie (Bollywood movie) being a Hit or Flop, long before a movie is actually released. Using Logistics Regression algorithm, a supervised machine leaning algorithm, a model has been developed for predicting Hit Bollywood movies. The model developed has been able to predict the Hit movies with an accuracy up to 80%. The proposed model is unique in the sense that it uses MusicRating of a movie as a predictor which is a unique feature of Bollywood movies. The study holds lot of relevance to practitioners as well as to academicians as the former may benefit by using this model to predict the success of a Bollywood movie before its release and latter may gain new insights into this body of knowledge.

Predicting Bollywood Movies Success Using Machine Learning Technique

Parking occupancy detection systems help to identify the available parking spaces and direct vehicles efficiently to unoccupied lots by reducing time and energy. This paper presents an approach for the design and development of an end-to-end automated vehicle parking occupancy detection system. The novelty of this study lies in the methodology followed for the object detection process using RetinaNet one stage detector and region-based convolutional neural network deep learning technique. The proposed software architecture consists of low coupled components that support scalability and reliability. The developed web-based and mobile-based client applications assist to find parking spaces easily and efficiently. The existing solutions utilize dedicated sensors and depend on manual segmentation of surveillance footage to detect the state of parking spaces. The proposed approach eliminates existing limitations while maintaining reasonable accuracy.

Automated Vehicle Parking Occupancy Detection in Real-Time

Professional reviews as service: A mix method approach to assess the value of recommender systems in the entertainment industry

This research developed a smart parking system through video data analysis using deep learning techniques that automatically determine the availability of vacant parking spaces. This system has two main stages. The first is the stage of marking the parking position on the image of a parking lot captured by the camera. This research proposes a Preprocessed Region-based Convolutional Neural Network (Mask R-CNN) to mark the parking position on the input image of a full parking lot. The preprocess that combining contrast enhancement using the Exposure Fusion framework, aims to overcome the problem of lighting variations in images taken in an open area. In the second stage, each parking position is examined whether the position is vacant or not using mAlexNet. A series of trials on images with varying light conditions indicate that the Preprocessed Mask R-CNN can improve marking the parking positions with an accuracy of Intersection over Union (IoU) reach 85.80%. The result of marking the parking position is then used in the trial of the availability of parking space on video data using mAlexNet, and achieving an accuracy of 73.73%.

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Preprocessed Mask RCNN for Parking Space Detection in Smart Parking Systems

In this project, we developed a mathematical model to predict the success and failure of the upcoming movies based on several attributes. Some of the criteria in calculating movie success included budget, actors, director, producer, set locations, story writer, movie release day, competing movie releases at the same time, music, release location and target audience. The success prediction of a movie plays a vital role in movie industry because it involves huge investments. However, success cannot be predicted based on a particular attribute. So, we have built a model based on interesting relation between attributes. The movie industry can use this model to modify the movie criteria for obtaining likelihood of blockbusters. Also, this model can be used by movie watchers in determining a blockbuster before purchasing a ticket. Each of the criteria involved was given a weight and then the prediction was made based on these. For example, if a movies budget was below 5 million, the budget was given a lower weight. Depending on the number of actors, directors and producers past successful movies, each of these categories was given a weight. If the movie was to be released on a weekend, it was given higher weight because the chances of success were greater. If with the release of a movie, there was another high success movie released, a lower weight was given to the release time indicating that the chances of movie success were low due to the competition. The criteria were not limited just to the ones mentioned. There were be additional factors discussed in this work. We have conducted our work with simulation data.

Movie success prediction using data mining

During peak hours, finding a parking spot is challenging particularly in urban areas. This remains a big issue as such areas are densely populated and there are limited parking spaces available. People rush to work and meetings during busy hours but they have to drive to multiple parking lots hoping that they will get lucky in occupying a spot successfully. Driving around still does not guarantee them a parking spot and they waste their time in blindly looking for one. There is a need to find solution to this problem. In an effort to provide solution to this problem, this study has been conducted. The purpose of this study is to introduce a car parking locating system using MRCNN (Mask Region-based Convolutional Neural Networks) to find available parking spaces effectively. The system provides a way to find the parking through MRCNN without the need to look for the parking manually. This addresses current parking issues where drivers need to spend significant time looking for the parking without any guarantee for success. MRCNN signals the free parking spots which can be occupied by the drivers.

Parking Lot Monitoring using MRCNN

The growth of IoT apps poses increasing concerns about sensitive data leaks. While privacy policies are required to describe how IoT apps use private user data (i.e., data practice), problems such as missing, inaccurate, and inconsistent policies have been repeatedly reported. Therefore, it is important to assess the actual data practice in IoT apps and identify the potential gaps between the actual and declared data usage. In this work, we conducted a measurement study using our framework called IoTPrivComp, which applies an automated analysis of IoT apps’ code and privacy policies to identify compliance gaps. We collected 1,489 IoT apps with English privacy policies from the Play Store. IoTPrivComp found 532 apps with sensitive external data flows, among which 408 (76.7%) apps had undisclosed data leaks. Moreover, 63.4% of the data flows that involved health and wellness data was inconsistent with the practices disclosed in the apps’ privacy policies. 

Javaria Ahmad

Papers

Movie success prediction using data mining

Parking Lot Monitoring using MRCNN

IoTPrivComp: A Measurement Study of Privacy Compliance in IoT Apps