Detection of shilling attack in recommender system for YouTube video statistics using machine learning techniques

Saini

Wireless Communications and Mobile Computing

et al. 2022

Clustering, also known as unsupervised learning, is one of the most significant topics of machine learning because it divides data into groups based on similarity with the aim of extracting or summarizing new information. It is one of the most often used machine learning techniques. The most significant problem encountered in this subject is the sheer volume of electronic text documents accessible, which is increasing at an exponential rate, necessitating the development of efficient ways for dealing with these papers. Furthermore, it is not practicable to consolidate all of the papers from numerous locations into a single area for processing. In this study, the primary goal is to enhance the performance of the distributed document clustering approach for clustering big, high-dimensional distributed document datasets. For distributed storage and analysis, one of the most prominent open-source implementations of the big data analytic-based MapReduce model, such as the Hadoop framework, is used in conjunction with a distributed file system and is known as the Hadoop Distributed File System, to achieve the desired results. This necessitates an improvement in the approach of the clustering operation with Elephant Herding Optimization, which will be accomplished by applying a hybridized clustering procedure. In conjunction with the MapReduce framework, this hybridized strategy is able to solve the obstacles associated with the K -means clustering method, including the initial centroids difficulty and the dimensionality problem. In this paper, we analyze the performance of the whole distributed document clustering technique for big document datasets by using a distributed document clustering framework such as Hadoop and the associated MapReduce methodology. In the end, this decides how quickly computations may be completed.

Section: Multivariate Empirical Mode Decomposition-based Gradient Sup...mentioning

confidence: 99%

Section: Gradient Support Vector Entropy Boosting Classifiermentioning

confidence: 99%

[Retracted] Big Data Analytics Model for Distributed Document Using Hybrid Optimization with K‐Means Clustering

Saini

Wireless Communications and Mobile Computing

et al. 2022

“…The first one is to explore the clear image without reference for training the dehazing network [45,46,51,52]. The second is to explore video dehazing [47][48][49][50].…”

Section: Discussionmentioning

confidence: 99%

GGADN: Guided generative adversarial dehazing network

2021

Image dehazing has always been a challenging topic in image processing. The development of deep learning methods, especially the Generative Adversarial Networks(GAN), provides a new way for image dehazing. In recent years, many deep learning methods based on GAN have been applied to image dehazing. However, GAN has two problems in image dehazing. Firstly, For haze image, haze not only reduces the quality of the image, but also blurs the details of the image. For Gan network, it is difficult for the generator to restore the details of the whole image while removing the haze. Secondly, GAN model is defined as a minimax problem, which weakens the loss function. It is difficult to distinguish whether GAN is making progress in the training process. Therefore, we propose a Guided Generative Adversarial Dehazing Network(GGADN). Different from other generation adversarial networks, GGADN adds a guided module on the generator. The guided module verifies the network of each layer of the generator. At the same time, the details of the map generated by each layer are strengthened. Network training is based on the pre-trained VGG feature model and L1-regularized gradient prior which is developed by new loss function parameters. From the dehazing results of synthetic images and real images, proposed method is better than the state-of-the-art dehazing methods.

“…Netflix Dataset [ 51 ]: It contains 100480507 ratings of 17770 movies by 480189 users. The minimum user rating is 1 point, the maximum is 5 points, and the increment is 1 point, provided by the Netflix Prize recommendation algorithm competition.…”

Section: Experiments Setupmentioning

confidence: 99%

FPLV: Enhancing recommender systems with fuzzy preference, vector similarity, and user community for rating prediction

Su,

Yang,

2023

PLoS ONE

Rating prediction is crucial in recommender systems as it enables personalized recommendations based on different models and techniques, making it of significant theoretical importance and practical value. However, presenting these recommendations in the form of lists raises the challenge of improving the list’s quality, making it a prominent research topic. This study focuses on enhancing the ranking quality of recommended items in user lists while ensuring interpretability. It introduces fuzzy membership functions to measure user attributes on a multi-dimensional item label vector and calculates user similarity based on these features for prediction and recommendation. Additionally, the user similarity network is modeled to extract community information, leading to the design of a set of corresponding recommendation algorithms. Experimental results on two commonly used datasets demonstrate the effectiveness of the proposed algorithm in enhancing list ranking quality, reducing prediction errors, and maintaining recommendation diversity and accurate user preference classification. This research highlights the potential of integrating heuristic methods with complex network theory and fuzzy techniques to enhance recommendation system performance with interpretability in mind.