Expressing reviews in the form of sentiments or ratings for item used or movie seen is the part of human habit. These reviews are easily available on different social websites. Based on interest pattern of a user, it is important to recommend him the items. Recommendation system is playing a vital role in everyone’s life as demand of recommendation for user’s interest increasing day by day. Movie recommendation system based on available ratings for a movie has become interesting part for new users. Till today, a lot many recommendation systems are designed using several machine learning algorithms. Still, sparsity problems, cold start problem, scalability, grey sheep problem are the hurdles for the recommendation systems that must be resolved using hybrid algorithms. We proposed in this paper, a movie rating system using a k-nearest neighbor (KNN-based) collaborative filtering (CF) approach. We compared user’s ratings for different movies to get top K users. Then we have used this top K set to find missing ratings by user for a movie using CF. Our proposed system when evaluated for various criteria shows promising results for movie recommendations compared with existing systems.
A REVIEW PAPER ON BFO AND PSO BASED MOVIE RECOMMENDATION SYSTEM | J4RV4I1015Journal For Research
Recommendation system plays important role in Internet world and used in many applications. It has created the collection of many application, created global village and growth for numerous information. This paper represents the overview of Approaches and techniques generated in recommendation system. Recommendation system is categorized in three classes: Collaborative Filtering, Content based and hybrid based Approach. This paper classifies collaborative filtering in two types: Memory based and Model based Recommendation .The paper elaborates these approaches and their techniques with their limitations. The result of our system provides much better recommendations to users because it enables the users to understand the relation between their emotional states and the recommended movies.
A recommender system-using novel deep network collaborative filteringIAESIJAI
The recommendation model aims to predict the user’s preferred items among million through analyzing the user-item relations; furthermore, Collaborative Filtering has been utilized as one of the successful recommendation approaches in last few years; however, it has the issue of sparsity. This research work develops a deep network collaborative filtering (DeepNCF), which incorporates graph neural network (GNN), and novel network collaborative filtering (NCF) for performance enhancement. At first user-item dual network is constructed, thereafter-custom weighted dual mode modularity is developed for edge clustering. Furthermore, GNN is utilized for capturing the complex relation between user and item. DeepNCF is evaluated considering the two distinctive. The experimental analysis is carried out on two datasets for Amazon and movielens dataset for recall@20 and recall@50 and the normalized discounted cumulative gain (NDCG) metric is evaluated for Amazon Dataset for NDCG@20 and NDCG@50. The proposed method outperforms the most relevant research and is accurate enough to give personalized recommendations and diversity.
Recommendation System using Machine Learning TechniquesIRJET Journal
This document discusses implementing a movie recommendation system using machine learning techniques. It first reviews different recommendation approaches like content filtering, collaborative filtering, and their advantages/disadvantages. It then proposes building a movie recommendation system that uses machine learning models trained on movie metadata and user ratings data. Specifically, it will use content and collaborative filtering approaches along with cosine similarity and build recommendation models to suggest movies to users based on their preferences. The system will be tested and the best performing model will be used to create recommendations in a user interface.
A Recommendation Engine For Predicting Movie Ratings Using A Big Data ApproachFelicia Clark
The document describes a study that developed a movie recommendation system using collaborative filtering and the alternating least squares (ALS) model on Apache Spark. Key points:
- The system predicts the top movie ratings for users by analyzing a user's past movie searches and ratings to recommend similar movies.
- It uses a model-based approach with matrix factorization to learn latent features and solve problems like cold starts and sparsity.
- The study tests different ALS parameters and achieves a root mean squared error of 0.9761, predicting the top 1000 movie ratings with 97% accuracy in 517 seconds.
Mixed Recommendation Algorithm Based on Content, Demographic and Collaborativ...IRJET Journal
The document describes a proposed hybrid recommendation algorithm that incorporates content filtering, collaborative filtering, and demographic filtering. It begins with an overview of recommendation systems and different filtering techniques. Then, it discusses related work incorporating various filtering approaches. The methodology section outlines the original algorithm, which develops user profiles based on browsing history and ratings. It provides recommendations by calculating similarities between user and item profiles. The proposed methodology enhances this by incorporating demographic attributes into user profiles and using fuzzy logic to validate recommendations. It claims this integrated approach can provide more accurate and personalized recommendations.
IRJET- Searching an Optimal Algorithm for Movie Recommendation SystemIRJET Journal
This document discusses and compares different algorithms for movie recommendation systems, including collaborative filtering, content-based filtering, demographic filtering, clustering using k-means, and knowledge-based recommendation. It provides details on deep neural network models, word2vec algorithms, and classification of recommendation systems based on datasets and algorithms used. The objective is to design and implement an optimal movie recommendation system by analyzing different machine learning approaches.
An Adaptive Framework for Enhancing Recommendation Using Hybrid Techniqueijcsit
Recommender systems provide useful recommendations to a collection of users for items or products that
might be of concern or interest to them. Several techniques have been proposed for recommendation such
as collaborative filtering, content-based, knowledge-based, and demographic filtering. Each of these
techniques suffers from scalability, data sparsity, and cold-start problems when applied individually
resulting in poor recommendations. This paper proposes an adaptive hybrid recommender system that
combines multiple techniques together to achieve some synergy between them. Collaborative filtering and
demographic techniques are combined in a weighted linear formula. Different experiments applied using
movieLen dataset confirm that the proposed adaptable hybrid framework outperforms the weaknesses
resulted when using traditional recommendation techniques.
A REVIEW PAPER ON BFO AND PSO BASED MOVIE RECOMMENDATION SYSTEM | J4RV4I1015Journal For Research
Recommendation system plays important role in Internet world and used in many applications. It has created the collection of many application, created global village and growth for numerous information. This paper represents the overview of Approaches and techniques generated in recommendation system. Recommendation system is categorized in three classes: Collaborative Filtering, Content based and hybrid based Approach. This paper classifies collaborative filtering in two types: Memory based and Model based Recommendation .The paper elaborates these approaches and their techniques with their limitations. The result of our system provides much better recommendations to users because it enables the users to understand the relation between their emotional states and the recommended movies.
A recommender system-using novel deep network collaborative filteringIAESIJAI
The recommendation model aims to predict the user’s preferred items among million through analyzing the user-item relations; furthermore, Collaborative Filtering has been utilized as one of the successful recommendation approaches in last few years; however, it has the issue of sparsity. This research work develops a deep network collaborative filtering (DeepNCF), which incorporates graph neural network (GNN), and novel network collaborative filtering (NCF) for performance enhancement. At first user-item dual network is constructed, thereafter-custom weighted dual mode modularity is developed for edge clustering. Furthermore, GNN is utilized for capturing the complex relation between user and item. DeepNCF is evaluated considering the two distinctive. The experimental analysis is carried out on two datasets for Amazon and movielens dataset for recall@20 and recall@50 and the normalized discounted cumulative gain (NDCG) metric is evaluated for Amazon Dataset for NDCG@20 and NDCG@50. The proposed method outperforms the most relevant research and is accurate enough to give personalized recommendations and diversity.
Recommendation System using Machine Learning TechniquesIRJET Journal
This document discusses implementing a movie recommendation system using machine learning techniques. It first reviews different recommendation approaches like content filtering, collaborative filtering, and their advantages/disadvantages. It then proposes building a movie recommendation system that uses machine learning models trained on movie metadata and user ratings data. Specifically, it will use content and collaborative filtering approaches along with cosine similarity and build recommendation models to suggest movies to users based on their preferences. The system will be tested and the best performing model will be used to create recommendations in a user interface.
A Recommendation Engine For Predicting Movie Ratings Using A Big Data ApproachFelicia Clark
The document describes a study that developed a movie recommendation system using collaborative filtering and the alternating least squares (ALS) model on Apache Spark. Key points:
- The system predicts the top movie ratings for users by analyzing a user's past movie searches and ratings to recommend similar movies.
- It uses a model-based approach with matrix factorization to learn latent features and solve problems like cold starts and sparsity.
- The study tests different ALS parameters and achieves a root mean squared error of 0.9761, predicting the top 1000 movie ratings with 97% accuracy in 517 seconds.
Mixed Recommendation Algorithm Based on Content, Demographic and Collaborativ...IRJET Journal
The document describes a proposed hybrid recommendation algorithm that incorporates content filtering, collaborative filtering, and demographic filtering. It begins with an overview of recommendation systems and different filtering techniques. Then, it discusses related work incorporating various filtering approaches. The methodology section outlines the original algorithm, which develops user profiles based on browsing history and ratings. It provides recommendations by calculating similarities between user and item profiles. The proposed methodology enhances this by incorporating demographic attributes into user profiles and using fuzzy logic to validate recommendations. It claims this integrated approach can provide more accurate and personalized recommendations.
IRJET- Searching an Optimal Algorithm for Movie Recommendation SystemIRJET Journal
This document discusses and compares different algorithms for movie recommendation systems, including collaborative filtering, content-based filtering, demographic filtering, clustering using k-means, and knowledge-based recommendation. It provides details on deep neural network models, word2vec algorithms, and classification of recommendation systems based on datasets and algorithms used. The objective is to design and implement an optimal movie recommendation system by analyzing different machine learning approaches.
An Adaptive Framework for Enhancing Recommendation Using Hybrid Techniqueijcsit
Recommender systems provide useful recommendations to a collection of users for items or products that
might be of concern or interest to them. Several techniques have been proposed for recommendation such
as collaborative filtering, content-based, knowledge-based, and demographic filtering. Each of these
techniques suffers from scalability, data sparsity, and cold-start problems when applied individually
resulting in poor recommendations. This paper proposes an adaptive hybrid recommender system that
combines multiple techniques together to achieve some synergy between them. Collaborative filtering and
demographic techniques are combined in a weighted linear formula. Different experiments applied using
movieLen dataset confirm that the proposed adaptable hybrid framework outperforms the weaknesses
resulted when using traditional recommendation techniques.
ENTERTAINMENT CONTENT RECOMMENDATION SYSTEM USING MACHINE LEARNINGIRJET Journal
This document describes a content-based movie recommendation system using machine learning techniques. It discusses how content-based filtering utilizes metadata like plot, cast, and genre to recommend similar movies. Term frequency-inverse document frequency and cosine similarity are used to measure similarity between movies. Sentiment analysis with naive Bayes classification determines if reviews are positive or negative. The system was tested on IMDb data and achieved 98.77% accuracy for sentiment analysis. Users can search movies and receive recommendations, view movie details, and rate results to improve recommendations. Future work includes incorporating location data and ratings from other sites into a hybrid recommendation model.
This document discusses analyzing a movie recommendation system using the MovieLens dataset. It compares user-based and item-based collaborative filtering approaches. For user-based filtering, it calculates user similarity using cosine similarity and predicts ratings. For item-based filtering, it also uses cosine similarity to find similar items and predicts ratings. It evaluates the performance of both approaches using root mean square deviation and finds that item-based collaborative filtering has lower error compared to user-based filtering.
Internet becomes the most popular surfing environment which increases the
service oriented data size. As the data size grows, finding and retrieving the most
similar data from the large volume of data would become more difficult task. This
problem is focused in the various research methods, which attempts to cluster the
large volume of data. In the existing research method Clustering-based Collaborative
Filtering approach (ClubCF) is introduced whose main goal is to cluster the similar
kind of data together, so that retrieval time cost can be reduced considerably.
However, existing research methods cannot find the similar reviews accurately which
needs to be focused more for efficient and accurate recommendation system. This is
ensured in the proposed research method by introducing the novel research technique
namely Modified Collaborative Filtering and Clustering with Regression (MoCFCR).
In this research method, initially k means algorithm is used to cluster the similar
movie reviewer together, so that recommendation process can be done in the easier
way. In order to handle the large volume of data this research work adapts the map
reduce framework which will divide the entire data into subsets which will assigned
on separate nodes with individual key values. After clustering, the clustered outcome
is merged together using inverted index procedure in which similarity between movies
would be calculated. Here collaborative filtering is applied to remove the movies that
are not relevant to input. Finally recommendations of movies are made in the accurate
way by using the logistic regression method. The overall evaluation of the proposed
research method is done in Hadoop from which it can be proved that the proposed
research technique can lead to provide better outcome than the existing research
techniques
Hybrid Personalized Recommender System Using Modified Fuzzy C-Means Clusterin...Waqas Tariq
Recommender Systems apply machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given resource. This paper proposes a novel Modified Fuzzy C-means (MFCM) clustering algorithm which is used for Hybrid Personalized Recommender System (MFCMHPRS). The proposed system works in two phases. In the first phase, opinions from the users are collected in the form of user-item rating matrix. They are clustered offline using MFCM into predetermined number clusters and stored in a database for future recommendation. In the second phase, the recommendations are generated online for active users using similarity measures by choosing the clusters with good quality rating. We propose coefficient parameter for similarity computation when weighting of the users’ similarity. This helps to get further effectiveness and quality of recommendations for the active users. The experimental results using Iris dataset show that the proposed MFCM performs better than Fuzzy C-means (FCM) algorithm. The performance of MFCMHPRS is evaluated using Jester database available on website of California University, Berkeley and compared with fuzzy recommender system (FRS). The results obtained empirically demonstrate that the proposed MFCMHPRS performs superiorly.
Tourism Based Hybrid Recommendation SystemIRJET Journal
This paper proposes a hybrid tourism recommendation system that combines collaborative filtering, content-based filtering, and aspect-based sentiment analysis to improve accuracy and address cold start problems. The system analyzes user ratings and reviews to predict ratings for other tourism packages. It stores ratings, reviews, and sentiment information in a database to enhance recommendations. Results showed the hybrid approach increased efficiency over conventional methods. Future work could include testing on additional datasets and expanding the system.
A Review Study OF Movie Recommendation Using Machine LearningIRJET Journal
This document reviews machine learning techniques for movie recommendation systems. It discusses content-based filtering, collaborative filtering, and hybrid filtering as the most common approaches. Specific machine learning algorithms described for movie recommendations include k-means clustering to group similar users, k-nearest neighbors to find the closest matches to a user's preferences, and support vector machines to classify movies a user may like. The document also reviews challenges like cold start problems and potential solutions explored in previous literature.
Recommendation System Using Social Networking ijcseit
With the proliferation of electronic commerce and knowledge economy environment both organizations and
individuals generate and consume a large amount of online information. With the huge availability of
product information on website, many times it becomes difficult for a consumer to locate item he wants to
buy. Recommendation Systems [RS] provide a solution to this. Many websites such as YouTube, e-Bay,
Amazon have come up with their own versions of Recommendation Systems. However Issues like lack of
data, changing data, changing user preferences and unpredictable items are faced by these
recommendation systems. In this paper we propose a model of Recommendation systems in e-commerce
domain which will address issues of cold start problem and change in user preference problem. Our work
proposes a novel recommendation system which incorporates user profile parameters obtained from Social
Networking website. Our proposed model SNetRS is a collaborative filtering based algorithm, which
focuses on user preferences obtained from FaceBook. We have taken domain of books to illustrate our
model.
On the benefit of logic-based machine learning to learn pairwise comparisonsjournalBEEI
This document describes a study that used a logic-based machine learning approach called APARELL to learn user preferences from pairwise comparisons in a recommender system. APARELL learns description logic rules from examples of users' pairwise item preferences and background knowledge about item properties. It was implemented in a used car recommender system. A user study evaluated the pairwise preference elicitation method compared to a standard list interface. Results showed the pairwise interface was significantly better in recommending items that matched users' preferences.
IRJET- Review on Different Recommendation Techniques for GRS in Online Social...IRJET Journal
This document reviews different recommendation techniques for group recommender systems (GRS) in online social networks. It discusses traditional recommender approaches like content-based filtering and collaborative filtering. It also reviews related work applying opinion dynamics models and weight matrices to GRS. The document concludes that using a smart weights matrix to consider relationships between group members' preferences in a recommendation process improves aggregation and ensures consensus, providing the best way to recommend items to a complete group.
MTVRep: A movie and TV show reputation system based on fine-grained sentiment ...IJECEIAES
Customer reviews are a valuable source of information from which we can extract very useful data about different online shopping experiences. For trendy items (products, movies, TV shows, hotels, services . . . ), the number of available users and customers’ opinions could easily surpass thousands. Therefore, online reputation systems could aid potential customers in making the right decision (buying, renting, booking . . . ) by automatically mining textual reviews and their ratings. This paper presents MTVRep, a movie and TV show reputation system that incorporates fine-grained opinion mining and semantic analysis to generate and visualize reputation toward movies and TV shows. Differently from previous studies on reputation generation that treat the task of sentiment analysis as a binary classification problem (positive, negative), the proposed system identifies the sentiment strength during the phase of sentiment classification by using fine-grained sentiment analysis to separate movie and TV show reviews into five discrete classes: strongly negative, weakly negative, neutral, weakly positive and strongly positive. Besides, it employs embeddings from language models (ELMo) representations to extract semantic relations between reviews. The contribution of this paper is threefold. First, movie and TV show reviews are separated into five groups based on their sentiment orientation. Second, a custom score is computed for each opinion group. Finally, a numerical reputation value is produced toward the target movie or TV show. The efficacy of the proposed system is illustrated by conducting several experiments on a real-world movie and TV show dataset.
IRJET- Analysis of Rating Difference and User InterestIRJET Journal
This document summarizes a research paper that proposes a collaborative filtering recommendation algorithm that incorporates rating differences and user interests. It first adds a rating difference factor to the traditional collaborative filtering algorithm. It then calculates user interests based on item attributes and the similarity between user interests. Recommendations are made by weighting user rating differences and interest similarities. The proposed algorithm is shown to reduce error rates and improve accuracy compared to traditional collaborative filtering.
A Brief Survey on Recommendation System for a Gradient Classifier based Inade...Christo Ananth
Recommender systems are a common and successful feature of modern internet services. (RS). A service that connects users to tasks is known as a recommendation system. Making it simpler for customers and project providers to identify and receive projects and other solutions achieves this. A recommendation system is a strong device that may be advantageous to a business or organisation. This study explores whether recommender systems may be utilised to solve cold-start and data-sparsely issues with recommender systems, as well as delays and business productivity. Recommender systems make it easier and more convenient for people to get information. Over the years, several different methods have been created. We employ a potent predictive regression method known as the slope classifier algorithm, which minimises a loss function by repeatedly choosing a function that points in the direction of the weak hypothesis or the negative gradient. A group that is experiencing trouble handling cold beginnings and data sparsity will send enormous datasets to the suggested systems team. The users have to finish their job by the deadline in order to overcome these challenges.
Provide individualized suggestions
of data or products related to users’ needs
by Recommender systems (RSs). Even
if RSs have created substantial progresses
in theory and formula development and
have achieved many business successes, a
way to operate the wide accessible info in
online social Networks (OSNs) has been
mainly overlooked. Noticing such a gap in
the existing research in RSs and taking
into account a user’s choice being greatly
influenced by his/her trustworthy friends
and their opinions; this paper proposes a,
Fact Finder technique that improves the
prevailing recommendation approaches by
exploring a new source of data from
friends’ short posts in microbloggings as
micro-reviews.Degree of friends’
sentiment and level being sure to a user’s
choice are known by victimisation
machine learning strategies as well as
Naive Bayes, Logistic Regression and
Decision Trees. As the verification of the
proposed Fact finder, experiments
victimisation real social data from Twitter
microblogger area unit given and results
show the effectiveness and promising of
the planned approach.
Context Based Classification of Reviews Using Association Rule Mining, Fuzzy ...journalBEEI
The Internet has facilitated the growth of recommendation system owing to the ease of sharing customer experiences online. It is a challenging task to summarize and streamline the online textual reviews. In this paper, we propose a new framework called Fuzzy based contextual recommendation system. For classification of customer reviews we extract the information from the reviews based on the context given by users. We use text mining techniques to tag the review and extract context. Then we find out the relationship between the contexts from the ontological database. We incorporate fuzzy based semantic analyzer to find the relationship between the review and the context when they are not found therein. The sentence based classification predicts the relevant reviews, whereas the fuzzy based context method predicts the relevant instances among the relevant reviews. Textual analysis is carried out with the combination of association rules and ontology mining. The relationship between review and their context is compared using the semantic analyzer which is based on the fuzzy rules.
Unification Algorithm in Hefty Iterative Multi-tier Classifiers for Gigantic ...Editor IJAIEM
Dr.G.Anandharaj1, Dr.P.Srimanchari2
1Associate Professor and Head, Department of Computer Science
Adhiparasakthi College of Arts and Science (Autonomous), Kalavai, Vellore (Dt) -632506
2 Assistant Professor and Head, Department of Computer Applications
Erode Arts and Science College (Autonomous), Erode (Dt) - 638001
ABSTRACT
In unpredictable increase in mobile apps, more and more threats migrate from outmoded PC client to mobile device. Compared
with traditional windows Intel alliance in PC, Android alliance dominates in Mobile Internet, the apps replace the PC client
software as the foremost target of hateful usage. In this paper, to improve the confidence status of recent mobile apps, we
propose a methodology to estimate mobile apps based on cloud computing platform and data mining. Compared with
traditional method, such as permission pattern based method, combines the dynamic and static analysis methods to
comprehensively evaluate an Android applications The Internet of Things (IoT) indicates a worldwide network of
interconnected items uniquely addressable, via standard communication protocols. Accordingly, preparing us for the
forthcoming invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve
progression efficiency and provide advanced intelligence. In this paper, we propose an efficient multidimensional fusion
algorithm for IoT data based on partitioning. Finally, the attribute reduction and rule extraction methods are used to obtain the
synthesis results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is
illustrated. This paper introduces and investigates large iterative multitier ensemble (LIME) classifiers specifically tailored for
big data. These classifiers are very hefty, but are quite easy to generate and use. They can be so large that it makes sense to use
them only for big data. Our experiments compare LIME classifiers with various vile classifiers and standard ordinary ensemble
Meta classifiers. The results obtained demonstrate that LIME classifiers can significantly increase the accuracy of
classifications. LIME classifiers made better than the base classifiers and standard ensemble Meta classifiers.
Keywords: LIME classifiers, ensemble Meta classifiers, Internet of Things, Big data
A Novel Latent Factor Model For Recommender SystemAndrew Parish
The document proposes a novel latent factor model for recommender systems that is less complex than Funk SVD while maintaining comparable accuracy. It introduces the proposed model, which transforms users into a one-dimensional latent space and items into a multidimensional latent space, with ratings predicted as the product of the user's latent factor and the sum of the item's latent factors. An evaluation on movie and product rating datasets found the proposed model has comparable accuracy to Funk SVD but lower complexity due to requiring fewer latent features.
An Unsupervised Approach For Reputation GenerationKayla Jones
This document describes a proposed unsupervised approach for generating reputation values based on opinion clustering and semantic analysis. The approach involves collecting opinion data, preprocessing it, applying latent semantic analysis and k-means clustering to group opinions into clusters, and then aggregating statistics from the clusters to generate a single reputation value for an entity. The approach is evaluated on a dataset of movie reviews from IMDB by comparing the generated reputation values to IMDB's weighted average user ratings. The results show the approach can accurately generate reputation values when choosing an optimal number of clusters.
The document discusses a content-based recommendation system with sentiment analysis. It provides an overview of recommendation systems and their importance. The objectives are to provide personalized recommendations to users based on their preferences using information filtering techniques. Existing systems faced issues like scalability, sparsity, and cold starts. The proposed system is a hybrid approach that combines item-based collaborative filtering with user clustering to make predictions. It will be scalable while addressing cold starts. Tools like Flask, JavaScript, Python are used. Cosine similarity and sentiment analysis techniques are also discussed. The conclusion is that the proposed system can recommend less popular items and future work could include other factors in recommendations.
Develop a robust and effective book recommendation system that provides personalized suggestions to users, enhancing their reading experience and promoting diverse literary exploration.
Costomization of recommendation system using collaborative filtering algorith...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
More Related Content
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ENTERTAINMENT CONTENT RECOMMENDATION SYSTEM USING MACHINE LEARNINGIRJET Journal
This document describes a content-based movie recommendation system using machine learning techniques. It discusses how content-based filtering utilizes metadata like plot, cast, and genre to recommend similar movies. Term frequency-inverse document frequency and cosine similarity are used to measure similarity between movies. Sentiment analysis with naive Bayes classification determines if reviews are positive or negative. The system was tested on IMDb data and achieved 98.77% accuracy for sentiment analysis. Users can search movies and receive recommendations, view movie details, and rate results to improve recommendations. Future work includes incorporating location data and ratings from other sites into a hybrid recommendation model.
This document discusses analyzing a movie recommendation system using the MovieLens dataset. It compares user-based and item-based collaborative filtering approaches. For user-based filtering, it calculates user similarity using cosine similarity and predicts ratings. For item-based filtering, it also uses cosine similarity to find similar items and predicts ratings. It evaluates the performance of both approaches using root mean square deviation and finds that item-based collaborative filtering has lower error compared to user-based filtering.
Internet becomes the most popular surfing environment which increases the
service oriented data size. As the data size grows, finding and retrieving the most
similar data from the large volume of data would become more difficult task. This
problem is focused in the various research methods, which attempts to cluster the
large volume of data. In the existing research method Clustering-based Collaborative
Filtering approach (ClubCF) is introduced whose main goal is to cluster the similar
kind of data together, so that retrieval time cost can be reduced considerably.
However, existing research methods cannot find the similar reviews accurately which
needs to be focused more for efficient and accurate recommendation system. This is
ensured in the proposed research method by introducing the novel research technique
namely Modified Collaborative Filtering and Clustering with Regression (MoCFCR).
In this research method, initially k means algorithm is used to cluster the similar
movie reviewer together, so that recommendation process can be done in the easier
way. In order to handle the large volume of data this research work adapts the map
reduce framework which will divide the entire data into subsets which will assigned
on separate nodes with individual key values. After clustering, the clustered outcome
is merged together using inverted index procedure in which similarity between movies
would be calculated. Here collaborative filtering is applied to remove the movies that
are not relevant to input. Finally recommendations of movies are made in the accurate
way by using the logistic regression method. The overall evaluation of the proposed
research method is done in Hadoop from which it can be proved that the proposed
research technique can lead to provide better outcome than the existing research
techniques
Hybrid Personalized Recommender System Using Modified Fuzzy C-Means Clusterin...Waqas Tariq
Recommender Systems apply machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given resource. This paper proposes a novel Modified Fuzzy C-means (MFCM) clustering algorithm which is used for Hybrid Personalized Recommender System (MFCMHPRS). The proposed system works in two phases. In the first phase, opinions from the users are collected in the form of user-item rating matrix. They are clustered offline using MFCM into predetermined number clusters and stored in a database for future recommendation. In the second phase, the recommendations are generated online for active users using similarity measures by choosing the clusters with good quality rating. We propose coefficient parameter for similarity computation when weighting of the users’ similarity. This helps to get further effectiveness and quality of recommendations for the active users. The experimental results using Iris dataset show that the proposed MFCM performs better than Fuzzy C-means (FCM) algorithm. The performance of MFCMHPRS is evaluated using Jester database available on website of California University, Berkeley and compared with fuzzy recommender system (FRS). The results obtained empirically demonstrate that the proposed MFCMHPRS performs superiorly.
Tourism Based Hybrid Recommendation SystemIRJET Journal
This paper proposes a hybrid tourism recommendation system that combines collaborative filtering, content-based filtering, and aspect-based sentiment analysis to improve accuracy and address cold start problems. The system analyzes user ratings and reviews to predict ratings for other tourism packages. It stores ratings, reviews, and sentiment information in a database to enhance recommendations. Results showed the hybrid approach increased efficiency over conventional methods. Future work could include testing on additional datasets and expanding the system.
A Review Study OF Movie Recommendation Using Machine LearningIRJET Journal
This document reviews machine learning techniques for movie recommendation systems. It discusses content-based filtering, collaborative filtering, and hybrid filtering as the most common approaches. Specific machine learning algorithms described for movie recommendations include k-means clustering to group similar users, k-nearest neighbors to find the closest matches to a user's preferences, and support vector machines to classify movies a user may like. The document also reviews challenges like cold start problems and potential solutions explored in previous literature.
Recommendation System Using Social Networking ijcseit
With the proliferation of electronic commerce and knowledge economy environment both organizations and
individuals generate and consume a large amount of online information. With the huge availability of
product information on website, many times it becomes difficult for a consumer to locate item he wants to
buy. Recommendation Systems [RS] provide a solution to this. Many websites such as YouTube, e-Bay,
Amazon have come up with their own versions of Recommendation Systems. However Issues like lack of
data, changing data, changing user preferences and unpredictable items are faced by these
recommendation systems. In this paper we propose a model of Recommendation systems in e-commerce
domain which will address issues of cold start problem and change in user preference problem. Our work
proposes a novel recommendation system which incorporates user profile parameters obtained from Social
Networking website. Our proposed model SNetRS is a collaborative filtering based algorithm, which
focuses on user preferences obtained from FaceBook. We have taken domain of books to illustrate our
model.
On the benefit of logic-based machine learning to learn pairwise comparisonsjournalBEEI
This document describes a study that used a logic-based machine learning approach called APARELL to learn user preferences from pairwise comparisons in a recommender system. APARELL learns description logic rules from examples of users' pairwise item preferences and background knowledge about item properties. It was implemented in a used car recommender system. A user study evaluated the pairwise preference elicitation method compared to a standard list interface. Results showed the pairwise interface was significantly better in recommending items that matched users' preferences.
IRJET- Review on Different Recommendation Techniques for GRS in Online Social...IRJET Journal
This document reviews different recommendation techniques for group recommender systems (GRS) in online social networks. It discusses traditional recommender approaches like content-based filtering and collaborative filtering. It also reviews related work applying opinion dynamics models and weight matrices to GRS. The document concludes that using a smart weights matrix to consider relationships between group members' preferences in a recommendation process improves aggregation and ensures consensus, providing the best way to recommend items to a complete group.
MTVRep: A movie and TV show reputation system based on fine-grained sentiment ...IJECEIAES
Customer reviews are a valuable source of information from which we can extract very useful data about different online shopping experiences. For trendy items (products, movies, TV shows, hotels, services . . . ), the number of available users and customers’ opinions could easily surpass thousands. Therefore, online reputation systems could aid potential customers in making the right decision (buying, renting, booking . . . ) by automatically mining textual reviews and their ratings. This paper presents MTVRep, a movie and TV show reputation system that incorporates fine-grained opinion mining and semantic analysis to generate and visualize reputation toward movies and TV shows. Differently from previous studies on reputation generation that treat the task of sentiment analysis as a binary classification problem (positive, negative), the proposed system identifies the sentiment strength during the phase of sentiment classification by using fine-grained sentiment analysis to separate movie and TV show reviews into five discrete classes: strongly negative, weakly negative, neutral, weakly positive and strongly positive. Besides, it employs embeddings from language models (ELMo) representations to extract semantic relations between reviews. The contribution of this paper is threefold. First, movie and TV show reviews are separated into five groups based on their sentiment orientation. Second, a custom score is computed for each opinion group. Finally, a numerical reputation value is produced toward the target movie or TV show. The efficacy of the proposed system is illustrated by conducting several experiments on a real-world movie and TV show dataset.
IRJET- Analysis of Rating Difference and User InterestIRJET Journal
This document summarizes a research paper that proposes a collaborative filtering recommendation algorithm that incorporates rating differences and user interests. It first adds a rating difference factor to the traditional collaborative filtering algorithm. It then calculates user interests based on item attributes and the similarity between user interests. Recommendations are made by weighting user rating differences and interest similarities. The proposed algorithm is shown to reduce error rates and improve accuracy compared to traditional collaborative filtering.
A Brief Survey on Recommendation System for a Gradient Classifier based Inade...Christo Ananth
Recommender systems are a common and successful feature of modern internet services. (RS). A service that connects users to tasks is known as a recommendation system. Making it simpler for customers and project providers to identify and receive projects and other solutions achieves this. A recommendation system is a strong device that may be advantageous to a business or organisation. This study explores whether recommender systems may be utilised to solve cold-start and data-sparsely issues with recommender systems, as well as delays and business productivity. Recommender systems make it easier and more convenient for people to get information. Over the years, several different methods have been created. We employ a potent predictive regression method known as the slope classifier algorithm, which minimises a loss function by repeatedly choosing a function that points in the direction of the weak hypothesis or the negative gradient. A group that is experiencing trouble handling cold beginnings and data sparsity will send enormous datasets to the suggested systems team. The users have to finish their job by the deadline in order to overcome these challenges.
Provide individualized suggestions
of data or products related to users’ needs
by Recommender systems (RSs). Even
if RSs have created substantial progresses
in theory and formula development and
have achieved many business successes, a
way to operate the wide accessible info in
online social Networks (OSNs) has been
mainly overlooked. Noticing such a gap in
the existing research in RSs and taking
into account a user’s choice being greatly
influenced by his/her trustworthy friends
and their opinions; this paper proposes a,
Fact Finder technique that improves the
prevailing recommendation approaches by
exploring a new source of data from
friends’ short posts in microbloggings as
micro-reviews.Degree of friends’
sentiment and level being sure to a user’s
choice are known by victimisation
machine learning strategies as well as
Naive Bayes, Logistic Regression and
Decision Trees. As the verification of the
proposed Fact finder, experiments
victimisation real social data from Twitter
microblogger area unit given and results
show the effectiveness and promising of
the planned approach.
Context Based Classification of Reviews Using Association Rule Mining, Fuzzy ...journalBEEI
The Internet has facilitated the growth of recommendation system owing to the ease of sharing customer experiences online. It is a challenging task to summarize and streamline the online textual reviews. In this paper, we propose a new framework called Fuzzy based contextual recommendation system. For classification of customer reviews we extract the information from the reviews based on the context given by users. We use text mining techniques to tag the review and extract context. Then we find out the relationship between the contexts from the ontological database. We incorporate fuzzy based semantic analyzer to find the relationship between the review and the context when they are not found therein. The sentence based classification predicts the relevant reviews, whereas the fuzzy based context method predicts the relevant instances among the relevant reviews. Textual analysis is carried out with the combination of association rules and ontology mining. The relationship between review and their context is compared using the semantic analyzer which is based on the fuzzy rules.
Unification Algorithm in Hefty Iterative Multi-tier Classifiers for Gigantic ...Editor IJAIEM
Dr.G.Anandharaj1, Dr.P.Srimanchari2
1Associate Professor and Head, Department of Computer Science
Adhiparasakthi College of Arts and Science (Autonomous), Kalavai, Vellore (Dt) -632506
2 Assistant Professor and Head, Department of Computer Applications
Erode Arts and Science College (Autonomous), Erode (Dt) - 638001
ABSTRACT
In unpredictable increase in mobile apps, more and more threats migrate from outmoded PC client to mobile device. Compared
with traditional windows Intel alliance in PC, Android alliance dominates in Mobile Internet, the apps replace the PC client
software as the foremost target of hateful usage. In this paper, to improve the confidence status of recent mobile apps, we
propose a methodology to estimate mobile apps based on cloud computing platform and data mining. Compared with
traditional method, such as permission pattern based method, combines the dynamic and static analysis methods to
comprehensively evaluate an Android applications The Internet of Things (IoT) indicates a worldwide network of
interconnected items uniquely addressable, via standard communication protocols. Accordingly, preparing us for the
forthcoming invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve
progression efficiency and provide advanced intelligence. In this paper, we propose an efficient multidimensional fusion
algorithm for IoT data based on partitioning. Finally, the attribute reduction and rule extraction methods are used to obtain the
synthesis results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is
illustrated. This paper introduces and investigates large iterative multitier ensemble (LIME) classifiers specifically tailored for
big data. These classifiers are very hefty, but are quite easy to generate and use. They can be so large that it makes sense to use
them only for big data. Our experiments compare LIME classifiers with various vile classifiers and standard ordinary ensemble
Meta classifiers. The results obtained demonstrate that LIME classifiers can significantly increase the accuracy of
classifications. LIME classifiers made better than the base classifiers and standard ensemble Meta classifiers.
Keywords: LIME classifiers, ensemble Meta classifiers, Internet of Things, Big data
A Novel Latent Factor Model For Recommender SystemAndrew Parish
The document proposes a novel latent factor model for recommender systems that is less complex than Funk SVD while maintaining comparable accuracy. It introduces the proposed model, which transforms users into a one-dimensional latent space and items into a multidimensional latent space, with ratings predicted as the product of the user's latent factor and the sum of the item's latent factors. An evaluation on movie and product rating datasets found the proposed model has comparable accuracy to Funk SVD but lower complexity due to requiring fewer latent features.
An Unsupervised Approach For Reputation GenerationKayla Jones
This document describes a proposed unsupervised approach for generating reputation values based on opinion clustering and semantic analysis. The approach involves collecting opinion data, preprocessing it, applying latent semantic analysis and k-means clustering to group opinions into clusters, and then aggregating statistics from the clusters to generate a single reputation value for an entity. The approach is evaluated on a dataset of movie reviews from IMDB by comparing the generated reputation values to IMDB's weighted average user ratings. The results show the approach can accurately generate reputation values when choosing an optimal number of clusters.
The document discusses a content-based recommendation system with sentiment analysis. It provides an overview of recommendation systems and their importance. The objectives are to provide personalized recommendations to users based on their preferences using information filtering techniques. Existing systems faced issues like scalability, sparsity, and cold starts. The proposed system is a hybrid approach that combines item-based collaborative filtering with user clustering to make predictions. It will be scalable while addressing cold starts. Tools like Flask, JavaScript, Python are used. Cosine similarity and sentiment analysis techniques are also discussed. The conclusion is that the proposed system can recommend less popular items and future work could include other factors in recommendations.
Develop a robust and effective book recommendation system that provides personalized suggestions to users, enhancing their reading experience and promoting diverse literary exploration.
Costomization of recommendation system using collaborative filtering algorith...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
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Forecasting movie rating using k-nearest neighbor based collaborative filtering
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 6, December 2022, pp. 6506~6512
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i6.pp6506-6512 6506
Journal homepage: http://ijece.iaescore.com
Forecasting movie rating using k-nearest neighbor based
collaborative filtering
Prakash Pandharinath Rokade, PVRD Prasad Rao, Aruna Kumari Devarakonda
1
Department of Computer Science and Engineering, KLEF Deemed University, Vaddeswaram, India
Article Info ABSTRACT
Article history:
Received May 13, 2021
Revised Jun 6, 2022
Accepted Jul 1, 2022
Expressing reviews in the form of sentiments or ratings for item used or
movie seen is the part of human habit. These reviews are easily available on
different social websites. Based on interest pattern of a user, it is important
to recommend him the items. Recommendation system is playing a vital role
in everyone’s life as demand of recommendation for user’s interest
increasing day by day. Movie recommendation system based on available
ratings for a movie has become interesting part for new users. Till today, a
lot many recommendation systems are designed using several machine
learning algorithms. Still, sparsity problems, cold start problem, scalability,
grey sheep problem are the hurdles for the recommendation systems that
must be resolved using hybrid algorithms. We proposed in this paper, a
movie rating system using a k-nearest neighbor (KNN-based) collaborative
filtering (CF) approach. We compared user’s ratings for different movies to
get top K users. Then we have used this top K set to find missing ratings by
user for a movie using CF. Our proposed system when evaluated for various
criteria shows promising results for movie recommendations compared with
existing systems.
Keywords:
Collaborative filtering
K-nearest neighbor
Machine learning
Recommended system
This is an open access article under the CC BY-SA license.
Corresponding Author:
Prakash Pandharinath Rokade
Department of Computer Science and Engineering, KLEF Deemed University
Vaddeswaram, India
Email: prakashrokade2005@gmail.com
1. INTRODUCTION
Now a days internet has become an integral part of life. People are strongly connected to social
media for sharing their emotions and reviews on different websites. These emotions are in the form of
sentiments or ratings for a product or service. Business intelligence for provider or demander is guided by
recommendation system. Recommendation system suggests items or services to the user of his interest like
books, movies, shares. A large dataset with ratings by different users is available online. The movie rating
database with users and different parameters for movies is available on several popular websites like Kaggle.
Decisions made by support of multiple stronger historical impressions to resolve an issue are always superior
to the decisions made with single impression by any user. Rather than collecting all of the reviews or ratings,
only the users having stronger relevance of ratings between them are collected. By using k-nearest neighbor
(KNN) recommendation system finds a small set of data which is the most similar.
Collaborative filtering (CF) takes input from KNN clustering algorithm. KNN gives top K users
with ratings among available huge dataset. Movies genres may be of entertainment, educational, horror, and
comedy. Movie recommendation system will help user to rate unrated or new movie for which old users have
given ratings already. The increasing amount of data or internet raised challenges for the users to manage the
information for getting suggestion to them to predict the rating for their interest.
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2. LITERATURE REVIEW
Godbole et al. [1] proposed a mechanism for assigning positive or negative scores to each entity in a
text collection. Annett and Kondrak [2] discuss an innovative application of support vector machines (SVM).
They used movie data to compare their method to lexical-based [2]. The results of different classifiers
suggest that using numerous classifiers in a hybrid approach increases sentiment classification quality [3].
For sentiment analysis (SA) of reviews for travel sentiments, three alternative machine learning methods are
compared [4]. SentiWordNet is proposed as a source for building data set, and advancement is proposed by
calculating positive and negative scores of phrases to identify sentiment orientation [5].
Shambour and Lu [6] addressed how e-governments assist in the search for a reliable corporate
partner to conduct business. Moshfeghi et al. [7] discuss a novel strategy for CF that uses a combined
approach of semantic, emotional, and rating information. The product features for which an opinion is
offered are mined in an opinion summary, which varies from text summarization [8]. By clients Erik Cambria
outlined how SA may be used in recommendation systems in great detail. The comment is replaced by a
rating, followed by a discussion of rating approach [9]. The influence of domain information is considered
when choosing feature vectors. Different classifiers are used to determine their impact on a specific domain
and feature vector [10]. Pappas and Popescu-Belis [11] proposed a sentiment-aware closest neighbor
algorithm for multimedia recommendations based on user comments. A high-prediction-accuracy explicit
factor model for explainable suggestions is proposed [12]. Tang et al. [13] discusses how feature-based
twitter sentiment is superior than standard neural networks. A novel memory-based CF technique is
suggested, which models a CF-based recommender system using user reviews and numeric ratings [14].
Yang et al. [15] proposed a clique-based data smoothing approach to solve the data sparsity problem
in CF using traditional user based nearest neighbor algorithm. Trends and patterns in client priorities have
been studied by Subramaniyaswamy et al. [16] can be used with filtering and clustering techniques to
identify items of interest. Nilashi et al. [17] presents a dimensionality reduction strategy to overcome the
sparsity and scalability problems in the recommended system. The merits and limitations of basic sentiment
analysis concepts are thoroughly explained and compared [18]. On several datasets, Özdemir et al. [19]
demonstrated how to evaluate the efficacy of various data classifier algorithms. Mohemad et al. [20]
proposed the construction of a new ontology model in the education sector in order to give early detection of
children with learning problems.
Babeetha et al. [21] proposed a prediction strategy for smoothing sparse original rating matrices and
clustering, as well as a discussion of the proposed methods’ accuracy and processing time. Rahim et al. [22]
looked into the importance of a number of important variables for innovative digital marketing. Heart disease
and breast cancer can be predicted. Saranya and Pravin [23] worked on developing successful prediction
models and methodologies. Behera et al. [24] described how collaborative filtering and content-based
recommendation are well-known strategies for selecting movies from a huge collection and determining their
attribute-based similarity. According to Ez-Zahout et al. [25] matrix factorization is the superior technique,
and it delivers a satisfactory result with a high precision score for the MovieLens dataset. Mawane et al. [26]
suggested a platform whose purpose is to try to discover the suitable parameters of the Kohonen maps that can
help satisfy the relevance of recommended items by dividing learners into homogeneous groups before
generating recommendations. El Fazziki et al. [27] examined user-based CF on two datasets, film trust and
MovieLens, and found that it works well and enhances prediction accuracy. Rawat et al. [28] proposed a
method for predicting virtual machine failure based on a time series stochastic model that accurately predicts
failure. Various evaluation techniques like precision, recall, F-measure, accuracy for machine learning based
algorithm are discussed by Yadla and Rao [29].
3. RESEARCH CHALLENGES AND OBJECTIVES
Today, several recommender systems have been evolved for specific domains but, those are not
particular enough to fulfill the information desires of users. So, it is important to build better recommender
system. In designing such system, we face numerous issues.
3.1. Sparsity problems
A recommendation quality depends on data sparsity. Data sparsity occurs when dimensionality of
the data set increases and some users do not rate few items. Good research is carried out to minimize this
problem. Dimensionality reduction can solve this issue up to a certain extent.
3.2. Cold start problem
The cold start problem occurs as new users or items enter the system. This issue is classified as a
new user issue, a new item issue, or a new system issue. This issue degrades the performance of collaborative
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filtering for recommendation. We may look into appropriate user group rather than entire data set-to resolve
this issue.
3.3. Scalability
Small dataset can be evaluated to recommend users for their choices. As dataset increases, accuracy
in result reduces. We can use advanced large scale assessment methods to improve scalability.
3.4. Grey sheep problem
Sometimes user’s behavior is suspicious as the user may rate related items inconsistently. This will
degrade the performance of recommendation system. One way omits these users from training dataset and
choose only top K users we have rated all items with consistency to related items. Objectives of work: i) to
analyze the given ratings by different users for movies; ii) to minimize sparsity, cold start issue; and iii) to
compare proposed approach with existing approaches.
4. PROPOSED METHOD ARCHITECTURE
As shown in Figure 1, the input dataset is of Movielens from Kaggle database. KNN algorithm is
applied on this dataset to find Top K similar users who have rated all movies. Collaborative filtering
algorithm is applied on this Top K dataset for recommending rating for unrated movie.
Figure 1. The proposed system’s architecture
4.1. Input dataset
The input dataset contains following features: i) user Id; ii) movie Id; and iii) rating. In the Table 1,
the record set contains ratings given by various users to various movies. Six users have rated 4 movies. The
ratings given by users vary from 1 to 5. The record set additionally contains few rows where rating data is
missing, which will be computed using CF. To find missing rating, we first find top K users having higher
similarity between them.
Table 1. Ratings for movies by users
User Vs Movie 1 2 3 4
A 3 4.5 2 4
B 4 1 2 5
C 3 4.5 2 4
D 5 4.5 3 1
E 3 1 5 5
X 3 2 3
4.2. Finding top K users
Here our aim is to find top K similar users whose ratings for all movies are closer than remaining
users. To attain this, we are going to use KNN clustering algorithm. K in KNN stands for the number of
neighbors. In training period KNN does not learn anything. It saves the training data set and uses it to make
real-time predictions. Due to this KNN algorithm is much faster than support vector machine, regression
which require training before prediction. To implement KNN we require value of K and distance function.
We are going to use Euclidean distance function to find top K users amongst all used set. KNN algorithm:
i) select the K users who have given ratings to all movies, ii) find the Euclidean distance of each user with
remaining all users, iii) select top K users with maximum weight to formulate a set of top K users, and iv) our
model is ready.
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4.3. Formula for Euclidean distance
To calculate the distance between user A and B, we will put values of their ratings in (1). Ratings of
user A will occupy positions of variable x whereas ratings of user B will occupy positions of variable y. For
calculating the Euclidian distances, we will consider all the users who have rated all movies. As shown in
Table 2, first we will find the average rating by every user.
Table 2. Average ratings by users
User Vs Movie 1 2 3 4 Average Rating
A 3 4.5 2 4 3.16
B 4 1 2 5 2.33
C 3 4.5 2 4 3.16
E 3 1 5 5 3
𝐷(𝑥, 𝑦) = √(𝑥1 − 𝑦1)2 + (𝑥2 − 𝑦2)2 + … . (𝑥𝑛 − 𝑦𝑛)2 (1)
By putting the values from Table 2 in the formula we will get the Euclidean distance between A and B.
𝐷(𝐴, 𝐵) = √(3 − 4)2 + (4.5 − 1)2 + (3 − 2)2 + (4 − 5)2
𝐷(𝐴, 𝐵) = 3.9
Similarly, by calculating Euclidean distances for user pairs (A,C), (A,D), (A,E), (B,C), (B,D), (B,E),
(C,D), (C,E), (D,E) we will get following results shown in Table 3. As shown in Table 4, if we consider K=1
we will get smallest 1 distance that is 0 for (A,C), for K=2 we will get smallest 2 distances 0 and 3.16 for
(A,C) and (B,E) respectively. Similarly for K=3 we will get smallest 3 distances 0 for (A,C), 3.16 for (B,E),
3.74 for (A,D) and (C,D).
Table 3. Euclidean distance between users
User pair Distance between users
A,B 3.9
A,C 0
A,D 3.74
A,E 4.71
B,C 3.77
B,D 5.5
B,E 3.16
C,D 3.74
C,E 4.71
D,E 6.02
Table 4. User pair set for different K values
K value Distance User pairs
K=1 0 (A,C)
K=2 0,3.16 (A,C),(B,E)
K=3 0,3.16,3.74 (A,C),(B,E),(A,D),(C,D)
If we consider value of K very small, we will face the problem of under fitting in which noisy data
or outlier has huge impact on our classifier. In opposite case, if we consider value of K very large, we will
face the problem of over fitting in which classifier has tendency to predict majority classes regardless of
which neighbors are nearest. To avoid under fitting and over fitting we will consider value of K=2. Then we
will get following in Table 5. In Table 5 only the users by considering value of K=2 is shown with their
ratings.
Table 5. User with ratings for K=2
K value Distance User Pairs
K=1 0 (A,C)
K=2 0,3.16 (A,C), (B,E)
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4.4. Collaborative filtering
The aim of CF is to predict ratings for unrated items by considering the top K user set with their
ratings. We will not consider the column with missing ratings. As shown in Table 5, we will ignore the
column for movie 4 as user X has not rated it. Now we will find average of all ratings provided by all users
for movie 1, 2, 3. The average ratings are shown in Table 6. Now we will find the similarity between user X
with all remaining users. We will attain this by cosine similarity formula.
Table 6. Average ratings by users without considering missing value column
User Vs Movie 1 2 3 4 Average Rating
A 3 4.5 2 4 3.16
B 4 1 2 5 2.33
C 3 4.5 2 4 3.16
E 3 1 5 5 3
X 3 2 3 2.66
4.5. Comparing the similarity between users
Now, we will compare ratings of user A, B, C, E with the ratings of user X to get how close these
users are with user X. To achieve this, we preferred to use cosine similarity formula. Cosine similarity is a
metric, helpful in determining, how similar the data objects are irrespective of their size. Cosine similarity
formula:
sim(𝑎, 𝑏) = 𝑎 .
→
𝑏
→
|𝑎|.
→
|𝑏|
→
(2)
sim(𝐶𝑖, 𝐶𝑗) =
∑[(𝑟𝑖𝑝−𝑟𝑖 𝑎𝑣𝑔 )−(𝑟𝑗𝑝−𝑟𝑗 𝑎𝑣𝑔 )]
√∑(𝑟𝑖𝑝−𝑟𝑖 𝑎𝑣𝑔 )
2
√∑(𝑟𝑗𝑝−𝑟𝑗 𝑎𝑣𝑔 )2
(3)
where, rip is current/particular rating of customer i, rjp is current/particular rating of customer j, riavg is average
rating of customer I and rjavg is average rating of customer j.
The similarity varies in between -1 to 1 only. To calculate similarity between user A and X we will
put values in (3).
sim(𝐴, 𝑋) =
(3−3.16)(3−2.66)+(4.5−3.16)(2−2.66)
+(2−3.16)(3−2.66)
(√(3−3.16)2+(4.5−3.16)2+(2−3.16)2 )
∗
(√(3−2.66)2+(2−2.66)2+(3−2.66)2 )
=
(−0.16)(0.34)+(1.34)(−0.66)+(−1.66)(1.66)
√(−0.16)2+(1.34)2+(−1.16)2 √(0.34)2+(−0.66)2+(1.66)2
=-0.89
Similarly, by calculating cosine similarity for (B,X), (C,X) and (E,X) we get results as shown in Table 6.
From Table 7, we will get lowest similarity weight -0.89 for user pair (A,X) whereas highest
similarity weight 0.31 for user pair (E.X). Hence as similarity between ratings of pair (E,X) is highest; we
can assign user E’s rating for movie 4 to missing rating for movie 4 by user X. So, the missing rating for user
X for movie 4 will be 5.
Table 7. Cosine similarity between user pairs
Sim (Ci,Cj ) Similarity weight Remark
(A,X) -0.89 Lowest similarity
(B,X) 0.22 Moderate similarity
(C,X) -0.28 Moderate similarity
(E,X) 0.31 Highest similarity
5. CONCLUSION
In this paper, we proposed a recommended system based on collaborative filtering with k-nearest
neighbors (KNN). We got unknown rating by comparing the ratings for top k-movies. Selection of proper
value of K improved our result of prediction. We have resolved both the issues of underfitting and overfitting
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Forecasting movie rating using k-nearest neighbor based collaborative … (Prakash Pandharinath Rokade)
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by selecting proper K value. In future, we should use deep learning techniques for movie rating prediction or
movie recommendations. Also, we may prefer some standard database directly from the organizations
working in the same domain to get more accurate results.
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BIOGRAPHIES OF AUTHORS
Prakash Pandharinath Rokade graduated from Pune University in Maharashtra,
India, with a bachelor’s degree in computer engineering in 2005. In 2011, he obtained his
M.Tech. in Computer Engineering at Bharti Vidyapeerth Pune, Maharashtra, India. He is
currently pursuing his Ph.D. in Computer Science and Engineering at Koneru Lakshmaiah
Education Foundation, previously K L University in Vaddeswaram, Andhra Pradesh, India.
Sentiment analysis, opinion mining, and machine learning are some of his research interests.
He can be contacted at email: prakashrokade2005@gmail.com.
PVRD Prasad Rao has 23 years of experience in industry and academia. In the
field of Data Science, he obtained his M.Tech in CSE from Andhra University in 1998 and his
Ph.D. in Computer Science from Acharya Nagarjuna University in 2014. He is currently
employed as a Professor at KL University’s Department of Computer Science and
Engineering. Data science, bioinformatics, IoT, and cyber security are among his research
interests. He has around 70 research publications published in journals and conference
proceedings from SCI, WoS, and Scopus. He is also an Associate Dean (P&P), a Reviewer,
and an Editorial member for a number of journals. He can be contacted at email:
pvrdprasad@kluniversity.in.
Aruna Kumari Devarakonda earned her Ph.D. in Computer Science and
Engineering from K L University in Vaddeswaram, Andhra Pradesh, India. Professor at
Koneru Lakshmaiah Education Foundation, formerly K L University, is her current position.
Her teaching and research interests include data mining and machine learning, and she has
over 50 publications published in national and international journals. The DST Young Scientist
Award has been given to her (Government of India). She can be contacted at email:
arunakumari@vjit.ac.in.