scholarly journals Predictive Analysis of Air Pollution using Collaborative Filtering Prediction Algorithm

2019 ◽  
Vol 10 (2) ◽  
pp. 66
Author(s):  
Samieksha Sharma Akanksha Gupta
Keyword(s):  
Air Pollution ◽  
Collaborative Filtering ◽  
Prediction Algorithm ◽  
Predictive Analysis

Industrialization Research and Realization of Preprocessing in Rough Set Based on Similar Prediction

2012 ◽  
Vol 251 ◽  
pp. 185-190
Author(s):  
Dun Hong Yao ◽  
Xiao Ning Peng ◽  
Jia He
Keyword(s):  
Data Processing ◽  
Collaborative Filtering ◽  
Rough Set ◽  
Data Preprocessing ◽  
Sparse Data ◽  
Data Evaluation ◽  
Prediction Algorithm ◽  
Rating Prediction ◽  
Essential Problem ◽  
Data Processing Methods

In every field which needs data processing, the sparseness of data is an essential problem that should be resolved, especially in movies, shopping sites. The users with the same commodity preferences makes the data evaluation valuable. Otherwise, without any evaluation of information, it will result in sparse distribution of the entire data obtained. This article introduces a collaborative filtering technology used in sparse data processing methods - project-based rating prediction algorithm, and extends it to the areas of rough set, the sparse information table processing, rough set data preprocessing sparse issues.

A Collaborative Filtering Recommendation Algorithm Based on Cloud Model, Item Attribute and User Data

2014 ◽  
Vol 610 ◽  
pp. 747-751
Author(s):  
Jian Sun ◽  
Xiao Ying Chen
Keyword(s):  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Cloud Model ◽  
Prediction Algorithm ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
User Data ◽  
Item Attribute ◽  
Rating Prediction ◽  
User Ratings

Aiming at the problems of extremely sparse of user-item rating data and poor recommendation quality, we put forward a collaborative filtering recommendation algorithm based on cloud model, item attribute and user data which combined with the existing literatures. A rating prediction algorithm based on cloud model and item attribute is proposed, based on idea that the similar users rating for the same item are similar and the same user ratings for the similar items are similar and stable. Through compare and analysis this paper’s and other studies experimental results, we get the conclusion that the rating prediction accuracy is improved.

A Personalized Approach to Course Recommendation in Higher Education

2021 ◽  
Vol 17 (2) ◽  
pp. 100-114
Author(s):  
Gina George ◽  
Anisha M. Lal
Keyword(s):  
Higher Education ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Pattern Mining ◽  
Education Level ◽  
Prediction Algorithm ◽  
Personal Choice ◽  
Sequence Prediction ◽  
Personalized Approach ◽  
Selection Of

The selection of elective courses, which best fits the student's personal choice, becomes a challenge, considering the variety of courses available at the higher education level. The traditional recommendation approach often uses collaborative filtering along with sequential pattern mining. Existing recommender systems also use ontology. However, these approaches have several limitations, including lack of availability of ratings at higher education level and lack of personalization based on student attributes. The proposed system intends to overcome these limitations by firstly extracting student personality and profile attributes and thereby generating a set of similar users by utilizing the versatile ontology. Secondly, it predicts courses based on a well-performing sequence prediction algorithm, the compact prediction tree (CPT). The results show that the proposed approach increases the accuracy in terms of precision to a tune of 0.97 and F1 measure to a tune of 0.58 when compared with existing systems which makes the proposed method more suitable for recommending courses.

scholarly journals MNCF: Prediction Method for Reliable Blockchain Services under a BaaS Environment

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 242
Author(s):  
Jianlong Xu ◽  
Zicong Zhuang ◽  
Zhiyu Xia ◽  
Yuhui Li
Keyword(s):  
Collaborative Filtering ◽  
Large Scale ◽  
Prediction Method ◽  
Third Party ◽  
Superior Performance ◽  
Prediction Algorithm ◽  
Service Reliability ◽  
Vast Number ◽  
Personalized Services ◽  
Service Oriented

Blockchain is an innovative distributed ledger technology that is widely used to build next-generation applications without the support of a trusted third party. With the ceaseless evolution of the service-oriented computing (SOC) paradigm, Blockchain-as-a-Service (BaaS) has emerged, which facilitates development of blockchain-based applications. To develop a high-quality blockchain-based system, users must select highly reliable blockchain services (peers) that offer excellent quality-of-service (QoS). Since the vast number of blockchain services leading to sparse QoS data, selecting the optimal personalized services is challenging. Hence, we improve neural collaborative filtering and propose a QoS-based blockchain service reliability prediction algorithm under BaaS, named modified neural collaborative filtering (MNCF). In this model, we combine a neural network with matrix factorization to perform collaborative filtering for the latent feature vectors of users. Furthermore, multi-task learning for sharing different parameters is introduced to improve the performance of the model. Experiments based on a large-scale real-world dataset validate its superior performance compared to baselines.

scholarly journals An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1956 ◽  
Author(s):  
Sami Kabir ◽  
Raihan Ul Islam ◽  
Mohammad Shahadat Hossain ◽  
Karl Andersson
Keyword(s):  
Air Pollution ◽  
Deep Learning ◽  
Prediction Accuracy ◽  
Integrated Approach ◽  
Sensor Data ◽  
Weather Data ◽  
Bayesian Probability ◽  
Prediction Algorithm ◽  
Rule Base ◽  
Belief Rule Base

Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT). Reasoning is applied on such sensor data in order to compute prediction. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian probability theory. Contrarily, Deep Learning is a data-driven technique, which constitutes a part of Artificial Intelligence (AI). By applying analytics on huge amount of data, Deep Learning learns the hidden representation of data. Thus, Deep Learning can infer prediction by reasoning over available data, such as historical data and sensor data streams. Combined application of BRBES and Deep Learning can compute prediction with improved accuracy by addressing sensor data uncertainties while utilizing its discovered data pattern. Hence, this paper proposes a novel predictive model that is based on the integrated approach of BRBES and Deep Learning. The uniqueness of this model lies in the development of a mathematical model to combine Deep Learning with BRBES and capture the nonlinear dependencies among the relevant variables. We optimized BRBES further by applying parameter and structure optimization on it. Air pollution prediction has been taken as use case of our proposed combined approach. This model has been evaluated against two different datasets. One dataset contains synthetic images with a corresponding label of PM2.5 concentrations. The other one contains real images, PM2.5 concentrations, and numerical weather data of Shanghai, China. We also distinguished a hazy image between polluted air and fog through our proposed model. Our approach has outperformed only BRBES and only Deep Learning in terms of prediction accuracy.

scholarly journals Atmospheric PM2.5Concentration Prediction Based on Time Series and Interactive Multiple Model Approach

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Jihan Li ◽  
Xiaoli Li ◽  
Kang Wang
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
State Equation ◽  
Ar Model ◽  
Prediction Algorithm ◽  
Time Series Models ◽  
Multiple Model ◽  
Concentration Data ◽  
Interactive Multiple Model

Urbanization, industrialization, and regional economic integration have developed rapidly in China in recent years. Air pollution has attracted more and more attention. However, PM2.5is the main particulate matter in air pollution. Therefore, how to predict PM2.5accurately and effectively has become a concern of experts and scholars. For the problem, atmosphere PM2.5concentration prediction algorithm is proposed based on time series and interactive multiple model in this paper. PM2.5concentration is collected by using the monitor at different air quality levels. The time series models are established by historical PM2.5concentration data, which were given by the autoregressive model (AR). In the paper, three PM2.5time series models are established for three different air quality levels. Then, the three models are converted to state equation, respectively, by autoregressive integrated with Kalman filter (AR-Kalman) approaches. Besides, the proposed interactive multiple model (IMM) algorithm is, respectively, compared with autoregressive (AR) model algorithm and AR-Kalman prediction algorithm. It is turned out the proposed IMM algorithm is more accurate than the other two approaches for PM2.5prediction, and it is effective.

scholarly journals An Item–Item Collaborative Filtering Recommender System Using Trust and Genre to Address the Cold-Start Problem

2019 ◽  
Vol 3 (3) ◽  
pp. 39 ◽  
Author(s):  
Mahamudul Hasan ◽  
Falguni Roy
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measure ◽  
Recommender System ◽  
Similarity Measures ◽  
Cold Start ◽  
Absolute Error ◽  
Prediction Algorithm ◽  
Rating Data ◽  
Asymmetric Correlation ◽  
Cold Start Problem

Item-based collaborative filtering is one of the most popular techniques in the recommender system to retrieve useful items for the users by finding the correlation among the items. Traditional item-based collaborative filtering works well when there exists sufficient rating data but cannot calculate similarity for new items, known as a cold-start problem. Usually, for the lack of rating data, the identification of the similarity among the cold-start items is difficult. As a result, existing techniques fail to predict accurate recommendations for cold-start items which also affects the recommender system’s performance. In this paper, two item-based similarity measures have been designed to overcome this problem by incorporating items’ genre data. An item might be uniform to other items as they might belong to more than one common genre. Thus, one of the similarity measures is defined by determining the degree of direct asymmetric correlation between items by considering their association of common genres. However, the similarity is determined between a couple of items where one of the items could be cold-start and another could be any highly rated item. Thus, the proposed similarity measure is accounted for as asymmetric by taking consideration of the item’s rating data. Another similarity measure is defined as the relative interconnection between items based on transitive inference. In addition, an enhanced prediction algorithm has been proposed so that it can calculate a better prediction for the recommendation. The proposed approach has experimented with two popular datasets that is Movielens and MovieTweets. In addition, it is found that the proposed technique performs better in comparison with the traditional techniques in a collaborative filtering recommender system. The proposed approach improved prediction accuracy for Movielens and MovieTweets approximately in terms of 3.42% & 8.58% mean absolute error, 7.25% & 3.29% precision, 7.20% & 7.55% recall, 8.76% & 5.15% f-measure and 49.3% and 16.49% mean reciprocal rank, respectively.

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