Lens Dataset Research Articles

Design and Development of an Efficient Demographic-based Movie Recommender System using Hybrid Machine Learning Techniques

Movie Recommender systems are frequently used in academics and industry to give users with relevant, engaging material based on their rating history. However, most traditional methods suffer from the cold-start problem, which is the initial lack of item ratings and data sparsity. The Hybrid Machine Learning (ML) technique is proposed for a movie recommendation system. Demographic data is collected from the Movie Lens dataset, and attributes are evaluated using the Attribute Analysis module. The Aquila Optimization Algorithm is used to select the best attributes, while Random Forest classifier is used for classification. Data is clustered using Fuzzy Probabilistic Cmeans Clustering Algorithm (FPCCA), and the Correspondence Index Assessment Phase (CIAP) uses Bhattacharyya Coefficient in Collaborative Filtering (BCCF) for similarity index calculation. The Outcomes gives the proposed method obtained low error, such as MAE has 0.44, RMSE has 0.63 compared with the baseline methods.

A Comparison of Madaline and Perceptron Algorithms on Classification with Quantum Computing Approach

The fundamental problem in this research is to explore a more profound understanding regarding both performance and efficiency in quantity computing. Successful implementation of algorithms in computational computing environments can unlock the potential for significant improvements in information processing and neural network modelling. This research focuses on developing the Madaline and Perceptron algorithm using a quantum approach. This study compares the two algorithms regarding the accuracy and epoch of the test results. The dataset used in this study is using a lens dataset. There are four attributes: 1) patient age: young, pre-presbyopia, presbyopia 2) eyeglass prescription: myopia, hypermetropia 3) astigmatic: no, yes. 4) tear production rate: reduced, normal. There are three classes: 1) patients must have hard contact lenses installed, 2) patients must have soft contact lenses installed, and 3) patients cannot have contact lenses installed. The number of data is 24 data. The result of this research is the development of the Madaline and Perceptron algorithms with a quantum computing approach. Comparing these algorithms shows the best accuracy is the Perceptron algorithm, namely 100%. In comparison, Madaline is 62.5%, and the smallest epoch is the Madaline algorithm, namely 4 epochs, while the smallest Perceptron epoch is 317. This research significantly contributes to developing computing and neural networks, with potential applications extending from data processing to more accurate modelling in artificial intelligence, data analysis and understanding complex patterns.

Movie Recommendation System Using Optimized RNN Approach.

This paper proposes a movie recommendation system that utilizes an optimized Recurrent Neural Network (RNN) approach. The proposed system is designed to provide users with personalized movie recommendations based on their previous movie preferences and the sentiments. The system works by taking user input, analyzing their movie preferences using content-based filtering techniques, and generating a list of recommended movies. The RNN architecture used in this system is optimized using a combination of techniques such as dropout regularization, early stopping, and parameter tuning. The proposed optimization techniques aim to reduce overfitting, improve convergence speed, and increase the model's overall accuracy. To evaluate the effectiveness of the proposed approach, we conducted experiments on the Movie Lens dataset. The results indicate that the optimized RNN-based movie recommendation system outperforms other existing recommendation systems such as collaborative filtering, content-based filtering, and standard RNN models. Furthermore, the proposed system achieved a significant improvement in accuracy and provided highly personalized recommendations to users. Overall, the proposed movie recommendation system using optimized RNN approach is a promising solution for providing personalized movie recommendations to users. It can be implemented in various platforms such as movie streaming websites, social media, and other movie-related platforms to improve the user experience and increase engagement

Hybrid CNN-based Recommendation System

Recommendation systems are now being used to address the problem of excess information in several sectors such as entertainment, social networking, and e-commerce. Although conventional methods to recommendation systems have achieved significant success in providing item suggestions, they still face many challenges, including the cold start problem and data sparsity. Numerous recommendation models have been created in order to address these difficulties. Nevertheless, including user or item-specific information has the potential to enhance the performance of recommendations. The ConvFM model is a novel convolutional neural network architecture that combines the capabilities of deep learning for feature extraction with the effectiveness of factorization machines for recommendation tasks. The present work introduces a novel hybrid deep factorization machine (FM) model, referred to as ConvFM. The ConvFM model use a combination of feature extraction and convolutional neural networks (CNNs) to extract features from both individuals and things, namely movies. Following this, the proposed model employs a methodology known as factorization machines, which use the FM algorithm. The focus of the CNN is on the extraction of features, which has resulted in a notable improvement in performance. In order to enhance the accuracy of predictions and address the challenges posed by sparsity, the proposed model incorporates both the extracted attributes and explicit interactions between items and users. This paper presents the experimental procedures and outcomes conducted on the Movie Lens dataset. In this discussion, we engage in an analysis of our research outcomes followed by provide recommendations for further action.

Exploration of Digital Communication Mechanism of Film and Television Media Industry in the Background of Artificial Intelligence

Abstract This paper analyzes the fission information dissemination mode from the digital information media mode of the film and television media industry. Using the correlation algorithm to analyze the influence of TV drama ratings and broadcasting accounted for and selecting cluster analysis to explore the relationship between TV drama broadcasting and TV type and rating. Take the ratings as the dependent variable, set the independent variables, establish the multivariate statistical model, and use SPSS software to calculate factor analysis of TV drama ratings. By combining user opinions, optimize the heterogeneous graph neural network film and television communication model based on attribute information. Test the MAE value and effect of the propagation algorithm proposed in this paper using the real Movies Lens dataset. When N=5, the recall, precision and F 1 of this paper’s algorithm are 0.295, 0.751, and 0.425, respectively. The difference of the three metrics with the resource diffusion algorithm based on the three-step graph is 0.25, 0.634, and 0.36. When N=50, the difference of the three metrics between this paper’s algorithm and the social diffusion algorithm based on labels is 0.197, 0.071, and 0.101.

Improvement and Research of Collaborative Filtering Algorithm Based on Penalty Factor

The rapid development of the Internet has pushed society into the era of information explosion, and people are faced with more and more information screening and choices. A recommendation system is an effective way to process massive amounts of information, and it is also a tool that can make recommendations based on user behavior. Traditional collaborative filtering algorithms generally use the cosine similarity formula to calculate the similarity between users or items to make recommendations. Due to the popularity of the Internet, more and more popular items have appeared. The appearance of popular items not only affects the recommendation results, but also fails to reflect the real needs of users. This paper proposes an improved formula for cosine similarity with a penalty factor, which can restrain the influence of popular items on the recommendation result. Finally, the Movie Lens data set is used to verify that the recommended performance indicators have been improved to a certain extent.

The Refractive Error and Vision Impairment Estimation with Spectacle Data Study.

PurposeTo investigate whether spectacle lens sales data can be used to estimate the population distribution of refractive error among patients with ametropia and hence to estimate the current and future risk of vision impairment.DesignCross-sectional study.ParticipantsA total of 141 547 436 spectacle lens sales records from an international European lens manufacturer between 1998 and 2016.MethodsAnonymized patient spectacle lens sales data, including refractive error information, was provided by a major European spectacle lens manufacturer. Data from the Gutenberg Health Survey was digitized to allow comparison of a representative, population-based sample with the spectacle lens sales data. A bootstrap analysis was completed to assess the comparability of both datasets. The expected level of vision impairment resulting from myopia at 75 years of age was calculated for both datasets using a previously published risk estimation equation combined with a saturation function.Main Outcome MeasuresComparability of spectacle lens sales data on refractive error with typical population surveys of refractive error and its potential usefulness to predict vision impairment resulting from refractive error.ResultsEquivalent estimates of the population distribution of spherical equivalent refraction can be provided from spectacle lens data within limits. For myopia, the population distribution was equivalent to the Gutenberg Health Survey (≤ 5% deviation) for levels of –2.0 diopters (D) or less, whereas for hyperopia, the distribution was equivalent (≤ 5% deviation) for levels of +3.0 D or more. The estimated rates of vision impairment resulting from myopia were not statistically significantly different (chi-square, 182; degrees of freedom, 169; P = 0.234) between the spectacle lens dataset and Gutenberg Health Survey dataset.ConclusionsThe distribution of refractive error and hence the risk of vision impairment resulting from refractive error within a population can be determined using spectacle lens sales data. Pooling this type of data from multiple industry sources could provide a cost-effective, timely, and globally representative mechanism for monitoring the evolving epidemiologic features of refractive error and associated vision impairment.

Comparison Study on Improved Movie Recommender Systems

Recommendation system is a crucial part of offering items especially in services that offer streaming. For streaming movie services on OTT, RS are a helping hand for users in finding new movies for leisure. In this paper, we propose a machine learning an approach based on auto encoders to produce a CF system which outputs movie rating for a user based on a huge DB of ratings from other users. Utilising Movie Lens dataset, we explore the use of deep learning neural network based Stacked Auto encoders to predict user s ratings on new movies, thereby enabling movie recommendations. We consequently implement Singular Value Decomposition (SVD) to recommend movies to users. The experimental result showcase that our R S out performs a user-based neighbourhood baseline in terms of MSE on predicted ratings and in a survey in which user judge between recommendation s from both systems.

PyAutoLens: Open-Source Strong Gravitational Lensing

Strong gravitational lensing, which can make a background source galaxy appears multiple times due to its light rays being deflected by the mass of one or more foreground lens galaxies, provides astronomers with a powerful tool to study dark matter, cosmology and the most distant Universe. PyAutoLens is an open-source Python 3.6+ package for strong gravitational lensing, with core features including fully automated strong lens modeling of galaxies and galaxy clusters, support for direct imaging and interferometer datasets and comprehensive tools for simulating samples of strong lenses. The API allows users to perform ray-tracing by using analytic light and mass profiles to build strong lens systems. Accompanying PyAutoLens is the autolens workspace (see https://github.com/Jammy2211/autolens_workspace), which includes example scripts, lens datasets and the HowToLens lectures in Jupyter notebook format which introduce non experts to strong lensing using PyAutoLens. Readers can try PyAutoLens right now by going to the introduction Jupyter notebook on Binder (see https://mybinder.org/v2/gh/Jammy2211/autolens_workspace/master) or checkout the readthedocs (see https://pyautolens.readthedocs.io/en/latest/) for a complete overview of PyAutoLens's features.

Model of socio-economic factors in the spread of SARS-CoV-2 across Russian regions

The worldwide spread of a new infection SARS-CoV-2 makes relevant the analysis of the different factors that lead to the vulnerability of modern civilization to previously unknown diseases. In this regard, the development of mathematical models describing the evolution of epidemics like COVID-19 and the identification of socio-economic factors affecting the epidemiological situation in regions is an important research task. The paper proposes a probabilistic mathematical model for the spread of the COVID-19 epidemic, which allows to analyze the evolution of the main characteristics of the disease and to assess main factors influencing them. The study is based on the official statistical data on the spread of the COVID 19 presented on coronavirus sites in the Russian Federation and other countries, the Yandex Data Lens dataset service, as well as the data from the Federal State Statistics Service. In the research some data mining methods were used for evaluation the model’s parameters. The model equations allow to predict the evolution of the disease and estimate the confidence interval of such prognosis. We estimated the ratio of detected and hidden cases of the disease, the distribution of the disease’s duration probability and its average value for different regions. It has been mathematically proven that the vaccination is the necessary and sufficient condition of achievement a stationary stable state - the cessation of a pandemic. The regions of Russian Federation were clustered by the course of the disease COVID-19 on the base of k-means method. The analysis of the most important socio-economic factors affecting the epidemiological situation was provided separately for each cluster.

Comparative Study of Machine Learning Algorithms for Recommendation System

The role of recommender system is very vital in recent times for a lot of individuals. It helps in taking decisions without exploring physically. Broadly there are two types of recommender system: Content based and Collaborative Filtering. The first one focus on user’s history and takes decisions. But there could be times when decisions based on only user history is not sufficient. For this, there is a need to analyze many parameters influencing the decision such as previous history, Age, gender, location etc. In the second approach it finds similar group of users based on several parameters and then takes decisions. Over the last few decades machine learning algorithms have proved their worth in this area because of their ability to learn from the given data and identify various hidden patterns. With this learning, these algorithms are able to generalize very well for unknown data. In this research work, a survey on three different machine learning based collaborative filtering methods are presented using Movie Lens dataset. The comparison of all three methods based on RMSE and MAE error is also discussed.

Fundamental Limits on the Regret of Online Network-Caching

Optimal caching of files in a content distribution network (CDN) is a problem of fundamental and growing commercial interest. Although many different caching algorithms are in use today, the fundamental performance limits of the network caching algorithms from an online learning point-of-view remain poorly understood to date. In this paper, we resolve this question in the following two settings: (1) a single user connected to a single cache, and (2) a set of users and a set of caches interconnected through a bipartite network. Recently, an online gradient-based coded caching policy was shown to enjoy sub-linear regret. However, due to the lack of known regret lower bounds, the question of the optimality of the proposed policy was left open. In this paper, we settle this question by deriving tight non-asymptotic regret lower bounds in the above settings. In addition to that, we propose a new Follow-the- Perturbed-Leader-based uncoded caching policy with near-optimal regret. Technically, the lower-bounds are obtained by relating the online caching problem to the classic probabilistic paradigm of balls-into-bins. Our proofs make extensive use of a new result on the expected load in the most populated half of the bins, which might also be of independent interest.We evaluate the performance of the caching policies by experimenting with the popular Movie- Lens dataset and conclude the paper with design recommendations and a list of open problems.

Social Balance Theory Based Hybrid Movie Recommendation System

Suggesting people exactly according to their likeness is most challenging in today’s generation. Present websites fail to provide the recommendation that is appropriate for people. There are several reasons such as there is either inadequate information about people or absence of feedback from the movies that they have watched. In this Situation considering those few/Sparse scores that are given that are collected from the people a socially balanced concept came into picture. Socially balanced theory Concept (hybrid) uses a integrated recommendation by combining both substance—oriented and community organized approach i.e., recommends based on both on viewers as well as movie. Socially balance theory helps to get better suggestion even when there is less information or inappropriate content by finding the opponent for the end users later discover the end users companion i.e., “opponents opponent is a companion” rule in social balance theory. So that suggestions can be based on both customer as well as goods based. For this, initially grouping the community is required to find the similarity between them. Finally the workability of integrated—recommendation is evaluated by considering film lens dataset – 10 M.

Hybrid Approach to Speed-Up the Privacy Preserving Kernel K-means Clustering and its Application in Social Distributed Environment

In this most revolutionized world, the social network plays a vital role in each and everyone’s life. Social networking is a pervasive communication platform where the users can search whole over the world via the Internet. Users have similar interest to connect and interact with one another and to share their private and personal interest. In this paper, we examine privacy concern for the social networking users by distributed clustering method. In the proposed scheme, to speed-up, the Kernel k-means algorithm, a prototype based hybrid kernel k-means algorithm is involved in distributing the users into the cluster. Since we are using a large data set, we use a hybrid approach to speed-up the kernel k-means clustering (HSKK). The clustering process used here is to partition a similar set of objects in a dataset. Additionally, in the clustering process, a cryptographic protocol such as homomorphic encryption is involved in every dataset to achieve the goal to protect the private data. To prove the efficiency of the proposed approach, the experiment is done on Movie lens dataset. The experimental study of HSKK shows that the proposed method can significantly reduce the computation time and the private data of users is hidden from the service provider.

Hybrid Model for Movie Recommendation System Using Fireflies and Fuzzy C-Means

In the era of Big Data, extremely complicated data is delivered from the system, of which it is impossible to collect the correct information with an online platform. In this research work, it provides a hybrid model for a movie-based recommender system; based on meta-heuristic firefly algorithm and fuzzy c-means (FCM) clustering technique to evaluate rating of a movie for a specific user based on the similarity of users and historical data. The firefly algorithm was employed in the movie lens dataset to get the initial cluster and also to initialize the position of clusters. FCM is used to classify the similarity of the user ratings. The proposed collaborative recommender system performed well regarding accuracy and precision. Various metrics are used in a movie lens dataset like mean absolute error (MAE), precision, and recall. The experimental result delivered by the system provides more efficient performance compared to the existing system in term of mean absolute error (MAE).

A Study on the Accuracy of Prediction in Recommendation System Based on Similarity Measures

Recommender Systems are tools to understand the huge amount of data available in the internet world. Collaborative filtering (CF) is one of the most knowledge discovery methods used positively in recommendation system. Memory collaborative filtering emphasizes on using facts about present users to predict new things for the target user. Similarity measures are the core operations in collaborative filtering and the prediction accuracy is mostly dependent on similarity calculations. In this study, a combination of weighted parameters and traditional similarity measures are conducted to calculate relationship among users over Movie Lens data set rating matrix. The advantages and disadvantages of each measure are spotted. From the study, a new measure is proposed from the combination of measures to cope with the global meaning of data set ratings. After conducting the experimental results, it is shown that the proposed measure achieves major objectives that maximize the accuracy Predictions.

A Study on the Accuracy of Prediction in Recommendation System Based on Similarity Measures

Recommender Systems are tools to understand the huge amount of data available in the internet world. Collaborative filtering (CF) is one of the most knowledge discovery methods used positively in recommendation system. Memory collaborative filtering emphasizes on using facts about present users to predict new things for the target user. Similarity measures are the core operations in collaborative filtering and the prediction accuracy is mostly dependent on similarity calculations. In this study, a combination of weighted parameters and traditional similarity measures are conducted to calculate relationship among users over Movie Lens data set rating matrix. The advantages and disadvantages of each measure are spotted. From the study, a new measure is proposed from the combination of measures to cope with the global meaning of data set ratings. After conducting the experimental results, it is shown that the proposed measure achieves major objectives that maximize the accuracy Predictions.

Top-k Recommended Items: Applying Clustering Technique for Recommendation

This research proposes the Top-k Items Recommendation System which uses clustering techniques based on memory-based collaborative filtering technique. Currently, data sparsity and quantity of system are problems in memory-based collaborative filtering technique. We offer recommend or show some items set for user’s preference. In this research, we propose methods for recommended items set to user preference on data sparsity, movie lens datasets (1M) consisting of 671 users and 163,949 product items were used by determining the preference level between 1 and 5 and user satisfaction levels of all 98,903 items being build and test the models. Methods was divided into three parts included 1) Simple Agent Module 2) Neighbor Filtering and 3) Prediction and Recommend. Simple clustering was used to create a system to provide suggestions for sparsity data. Datasets obtained from clustering represented the sample agent of dataset to being create the recommendation system. Datasets were divided into two categories, 1) Traditional Data (TD) and 2) Statistic Data (SD), and each dataset clustered by k-means clustering. The experimental results demonstrated that the number of item types in the system were recommended in the TD and Euclidean (DIS). DIS was used to find the nearest value in TD for the item list recommendation to active users in the system with the a lot of number choice of recommendation system.

Movie Recommended System by Using Collaborative Filtering

Recommendation system helps people in decision making an item/person. Recommender systems are now pervasive and seek to make profit out of customers or successfully meet their needs. Companies like Amazon use their huge amounts of data to give recommendations for users. Based on similarities among items, systems can give predictions for a new item’s rating. Recommender systems use the user, item, and ratings information to predict how other users will like a particular item. In this project, we attempt to under- stand the different kinds of recommendation systems and compare their performance on the Movie Lens dataset. Due to large size of data, recommendation system suffers from scalability problem. Hadoop is one of the solutions for this problem.

A Non‐Gaussian Spatio‐Temporal Model for Daily Wind Speeds Based on a Multi‐Variate Skew‐t Distribution

Facing increasing domestic energy consumption from population growth and industrialization, Saudi Arabia is aiming to reduce its reliance on fossil fuels and to broaden its energy mix by expanding investment in renewable energy sources, including wind energy. A preliminary task in the development of wind energy infrastructure is the assessment of wind energy potential, a key aspect of which is the characterization of its spatio‐temporal behavior. In this study we examine the impact of internal climate variability on seasonal wind power density fluctuations over Saudi Arabia using 30 simulations from the Large Ensemble Project (LENS) developed at the National Center for Atmospheric Research. Furthermore, a spatio‐temporal model for daily wind speed is proposed with neighbor‐based cross‐temporal dependence, and a multi‐variate skew‐t distribution to capture the spatial patterns of higher‐order moments. The model can be used to generate synthetic time series over the entire spatial domain that adequately reproduce the internal variability of the LENS dataset.