scholarly journals Analyzing and Predicting The Success of Box Office Collection of a Movie Using Machine Learning

2021 ◽  
pp. 325-331
Author(s):  
Sanyam Jatale ◽  
Rohan Moze ◽  
Varsha Khandekar ◽  
Shubham Jain ◽  
Sanket Mokate
Keyword(s):  
Machine Learning ◽  
Social Networking ◽  
Motion Picture Industry ◽  
Learning Tools ◽  
Social Bookmarking ◽  
Business Decisions ◽  
Box Office ◽  
The World ◽  
Increased Activity ◽  
Movie Success

Our day-to-day life has always been influenced by what people think. Ideas and opinions of others have always affected our own opinions. The explosion of Web 2.0 has led to increased activity in Podcasting, Blogging, Tagging, Contributing to RSS, Social Bookmarking, and Social Networking. The motion picture industry is a multi-billion-dollar business, and there is a massive amount of data related to movies are available over the internet. The framework will foresee an estimated achievement pace of a film dependent on its productivity by dissecting verifiable information from various sources like IMDB, Rotten Tomato, Box Office Mojo and Metacritic. Utilizing distinctive AI calculations, Machine Learning Tools, and different procedures the framework will foresee a film box office benefits depending on certain highlights like caste, genre, budget, actors, and many more features. The number of movies produced in the world is growing at an exponential rate and success rate of movie is of utmost importance since billions of dollars are invested in the making of each of these movies. In such a scenario, prior knowledge about the success or failure of a particular movie and what factor affect the movie success will benefit the production houses since these predictions will give them a fair idea of how to go about with the advertising and campaigning, which itself is an expensive affair altogether. Thus, predicting the box-office will help this growing industry experts to imply some important business decisions in order to make the upcoming movie more successful.

scholarly journals Public Trolling Detection on Twitter Using Machine Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 3097-3100
Author(s):  
Miss. Pooja Dilip Dhotre
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Network ◽  
Social Networking ◽  
Social Networking Sites ◽  
Machine Learning Algorithms ◽  
The People ◽  
The World ◽  
Added Benefit ◽  
Current Popularity

Social media websites are among the internet's most far-reaching digital sites. Billions of social network users exist Users' frequent interactions with social networking sites, like Twitter, have a widespread and sometimes unfortunate effect on day-to-day life. Social networking sites make it easy for large amounts of unwanted and unrelated information to spread around the world. Twitter is a popular micro blogging service where users connect with others with similar interests. Because of the current popularity of Twitter, it is vulnerable to public shaming. Recently, Twitter has emerged as a rich source of human-generated information, with the added benefit of connecting you with customers and enabling two-way communication. It is generally accepted that when someone posts a comment in an occurrence, it is likely to humiliate the victim. The fact that shaming users' follower counts increase faster than that of the people who don't use shame is interesting. Using machine learning algorithms, users will be able to identify disrespectful words, as well as the overall negativity of those words, which is displayed in a percentage.

scholarly journals Development of On-Device Machine Learning Tools for Medication Warning Label Recognition

2021 ◽  
Author(s):  
Virendra Singh Rathore ◽  
Sabah Mohammed
Keyword(s):  
Machine Learning ◽  
English Language ◽  
Language Barrier ◽  
Learning Tools ◽  
Warning Label ◽  
Warning Labels ◽  
The World

The world that we live in today has so many languages that we speak throughout. Out of more than the six thousand languages that are spoken around the world, the most common is the English language. But there are people who do not understand it. When we speak of medications, the language barrier becomes riskier. If consumers are not able to understand the warning labels on the medicines, it might prove fatal to life. So it seems important that there be a tool or an application that would help them recognize and decode the warnings and/ or the instructions that are printed the medication pill containers. This tool will prove very useful for patients across the world and also people who consume any kind of medication without understanding the procedures/ risks that are specific to those medications.

scholarly journals Movie Success Rate Prediction Using Robust Classifier

2019 ◽  
Vol 8 (6) ◽  
pp. 3517-3522
Keyword(s):  
Machine Learning ◽  
Success Rate ◽  
Film Industry ◽  
Machine Learning Algorithms ◽  
Running Time ◽  
Box Office ◽  
Knn Classifier ◽  
Nearest Neighbours ◽  
Adaboost Classifier ◽  
Movie Success

Film industry is a multi-billion-dollar industry where each movie earns over billions of dollar. Predicting the success of the movie is a difficult task because the success rate is influenced by various factors like running time, actor, actress, genre etc. In this paper a detailed study of machine learning algorithms such as Adaboost, SVM, and K-Nearest Neighbours (KNN) were done and was implemented on IMDB dataset for predicting box office. Based on the results, Adaboost classifier gives better performance compared to SVM and KNN classifier algorithms

scholarly journals Development of On-Device Machine Learning Tools for Medication Warning Label Recognition

2021 ◽  
Author(s):  
Virendra Singh Rathore ◽  
Sabah Mohammed
Keyword(s):  
Machine Learning ◽  
English Language ◽  
Language Barrier ◽  
Learning Tools ◽  
Warning Label ◽  
Warning Labels ◽  
The World

The world that we live in today has so many languages that we speak throughout. Out of more than the six thousand languages that are spoken around the world, the most common is the English language. But there are people who do not understand it. When we speak of medications, the language barrier becomes riskier. If consumers are not able to understand the warning labels on the medicines, it might prove fatal to life. So it seems important that there be a tool or an application that would help them recognize and decode the warnings and/ or the instructions that are printed the medication pill containers. This tool will prove very useful for patients across the world and also people who consume any kind of medication without understanding the procedures/ risks that are specific to those medications.

scholarly journals YouTube Spam Comment Detection Using Support Vector Machine and K–Nearest Neighbor

2018 ◽  
Vol 12 (2) ◽  
pp. 612 ◽  
Author(s):  
Aqliima Aziz ◽  
Cik Feresa Mohd Foozy ◽  
Palaniappan Shamala ◽  
Zurinah Suradi
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Social Networking ◽  
Nearest Neighbor ◽  
Good Accuracy ◽  
Support Vector ◽  
Learning Tools ◽  
K Nearest Neighbor ◽  
Accuracy Result

<p>Social networking such as YouTube, Facebook and others are very popular nowadays. The best thing about YouTube is user can subscribe also giving opinion on the comment section. However, this attract the spammer by spamming the comments on that videos. Thus, this study develop a YouTube detection framework by using Support Vector Machine (SVM) and K-Nearest Neighbor (k-NN). There are five (5) phases involved in this research such as Data Collection, Pre-processing, Feature Selection, Classification and Detection. The experiments is done by using Weka and RapidMiner. The accuracy result of SVM and KNN by using both machine learning tools show good accuracy result. Others solution to avoid spam attack is trying not to click the link on comments to avoid any problems.</p>

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Penerapan Rinfo Sebagai Media Pendukung Untuk Proses Pembelajaran Pada Perguruan Tinggi Raharja

CCIT Journal ◽  
2014 ◽  
Vol 8 (1) ◽  
pp. 101-115
Author(s):  
Untung Rahardja ◽  
Khanna Tiara ◽  
Ray Indra Taufik Wijaya
Keyword(s):  
Learning Process ◽  
Human Life ◽  
Learning Tools ◽  
Integrated Learning ◽  
Civilizing Process ◽  
The World ◽  
The Future ◽  
Two Factors ◽  
Life Education ◽  
New Generation

Education is an important factor in human life. According to Ki Hajar Dewantara, education is a civilizing process that a business gives high values ??to the new generation in a society that is not only maintenance but also with a view to promote and develop the culture of the nobility toward human life. Education is a human investment that can be used now and in the future. One other important factor in supporting human life in addition to education, which is technology. In this globalization era, technology has touched every joint of human life. The combination of these two factors will be a new innovation in the world of education. The innovation has been implemented by Raharja College, namely the use of the method iLearning (Integrated Learning) in the learning process. Where such learning has been online based. ILearning method consists of TPI (Ten Pillars of IT iLearning). Rinfo is one of the ten pillars, where it became an official email used by the whole community’s in Raharja College to communicate with each other. Rinfo is Gmail, which is adapted from the Google platform with typical raharja.info as its domain. This Rinfo is a medium of communication, as well as a tool to support the learning process in Raharja College. Because in addition to integrated with TPi, this Rinfo was connected also support with other learning tools, such as Docs, Drive, Sites, and other supporting tools.

Platform for Analysing and Encouraging Student Activity on Contest and E-learning Systems

2018 ◽  
Vol 12 ◽  
pp. 85-98
Author(s):  
Bojan Kostadinov ◽  
Mile Jovanov ◽  
Emil STANKOV
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Educational Policy ◽  
Learning Systems ◽  
Data Sources ◽  
Or Education ◽  
Student Activity ◽  
The World ◽  
E Learning ◽  
Analyse Data

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

The role of social networking sites in the revolution of 25 January 2011 Egyptian

2019 ◽  
Vol 2 (3) ◽  
pp. 1
Author(s):  
Qassim Alwan Saeed ◽  
Khairallah Sabhan Abdullah Al-Jubouri
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Social Networking Sites ◽  
Arab Spring ◽  
The World ◽  
The Revolution

Social media sites have recently gain an essential importance in the contemporary societies، actually، these sites isn't simply a personal or social tool of communication among people، its role had been expanded to become "political"، words such as "Facebook، Twitter and YouTube" are common words in political fields of our modern days since the uprisings of Arab spring، which sometimes called (Facebook revolutions) as a result of the major impact of these sites in broadcasting process of the revolution message over the world by organize and manage the revolution progresses in spite of the governmental ascendance and official prohibition.

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