scholarly journals Network Course Recommendation System Based on Double-Layer Attention Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qianyao Zhu
Keyword(s):  
Double Layer ◽  
Recommendation System ◽  
Attention Mechanism ◽  
Inverse Document Frequency ◽  
Network Teaching ◽  
Teaching Platform ◽  
Document Frequency ◽  
Different Types ◽  
Recommendation Accuracy ◽  
Selection Of

In view of the lack of accurate recommendation and selection of courses on the network teaching platform in the new form of higher education, a network course recommendation system based on the double-layer attention mechanism is proposed. First of all, the collected data are preprocessed, while the data of students and course information are normalized and classified. Then, the dual attention mechanism is introduced into the parallel neural network recommendation model so as to improve the model’s ability to mine important features. TF-IDF (term frequency-inverse document frequency) based on the student score and course category is improved. The recommendation results are classified according to the weight of course categories, so as to construct different types of course groups and complete the recommendation. The experimental results show that the proposed algorithm can effectively improve the model recommendation accuracy compared with other algorithms.

scholarly journals Pemodelan Topik dengan LDA untuk Temu Kembali Informasi dalam Rekomendasi Tugas Akhir

2021 ◽  
Vol 5 (3) ◽  
pp. 421-428
Author(s):  
Diana Purwitasari ◽  
Aida Muflichah ◽  
Novrindah Alvi Hasanah ◽  
Agus Zainal Arifin
Keyword(s):  
Learning Outcomes ◽  
Recommendation System ◽  
Latent Dirichlet Allocation ◽  
Cluster Number ◽  
Inverse Document Frequency ◽  
Final Project ◽  
Document Frequency ◽  
Student Graduation ◽  
Undergraduate Thesis ◽  
Optimal Cluster

Undergraduate thesis as the final project, or in Indonesian called as Tugas Akhir, for each undergraduate student is a pre-requisite before student graduation and the successfulness in finishing the project becomes as one of learning outcomes among others. Determining the topic of the final project according to the ability of students is an important thing. One strategy to decide the topic is reading some literatures but it takes up more time. There is a need for a recommendation system to help students in determining the topic according to their abilities or subject understanding which is based on their academic transcripts. This study focused on a system for final project topic recommendations based on evaluating competencies in previous academic transcripts of graduated students. Collected data of previous final projects, namely titles and abstracts weighted by term occurences of TF-IDF (term frequency–inverse document frequency) and grouped by using K-Means Clustering. From each cluster result, we prepared candidates for recommended topics using Latent Dirichlet Allocation (LDA) with Gibbs Sampling that focusing on the word distribution of each topic in the cluster. Some evaluations were performed to evaluate the optimal cluster number, topic number and then made more thorough exploration on the recommendation results. Our experiments showed that the proposed system could recommend final project topic ideas based on student competence represented in their academic transcripts.

An innovative citation recommendation model for draft papers with varying degrees of information completeness

2019 ◽  
Vol 53 (4) ◽  
pp. 562-576 ◽  
Author(s):  
Yen-Liang Chen ◽  
Cheng-Hsiung Weng ◽  
Cheng-Kui Huang ◽  
Duo-Jia Shih
Keyword(s):  
Structural Factors ◽  
Improve Performance ◽  
Research Papers ◽  
Inverse Document Frequency ◽  
Content Type ◽  
Document Frequency ◽  
Recommendation Algorithms ◽  
Recommendation Accuracy ◽  
Reference Citation ◽  
Draft Paper

Purpose As researchers are writing a draft paper with incomplete structure or text, one of burdensome tasks is to deliberate about which references should be cited for one sentence or paragraph of this draft. In view of the rapid increase in the number of research papers, researchers desire to figure out a better way to do citation recommendations in developing their draft papers. The purpose of this paper is to propose citation recommendation algorithms that enable the acquisition of relevant citations for research papers that are still at the drafting stage. This study attempts to help researchers to select appropriate references among the vast amount of available papers and make draft papers complete in reference citation. Design/methodology/approach This study adopts a model for recommending citations for incomplete drafts. Four algorithms are proposed in this study. The first and second algorithms are unsupervised models, applying term frequency-inverse document frequency and WordNet technologies, respectively. The third and fourth algorithms are based on the second algorithm to integrate different weight adjustment strategies to improve performance. Findings The proposed recommendation method adopts three techniques, including using WordNet to transform vector and setting adjustment weights according to structural factors and the information completeness degree, to generate citation recommendation for incomplete drafts. The experiments show that all these three techniques can significantly improve the recommendation accuracy. Originality/value None of the methods employed in previous studies can recommend articles as references for incomplete drafts. This paper addresses the situation that a draft paper can be incomplete either in structure or text or both. Recommended references, however, can be still generated and inserted into any desired sentence of the draft paper.

Sistem Perekomendasi Dosen Pembimbing berdasarkan Relevansi Topik Tugas Akhir menggunakan Metode Okapi BM25

Repositor ◽  
2020 ◽  
Vol 2 (9) ◽  
Author(s):  
Meilina Agustina ◽  
Yufiz Azhar ◽  
Nur Hayatin
Keyword(s):  
Recommendation System ◽  
School Teacher ◽  
Primary School Teacher ◽  
Education Department ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Document Frequency ◽  
Teacher Education Department ◽  
Primary School Teacher Education ◽  
The University

AbstrakSistem rekomendasi adalah sebuah perangkat lunak untuk memberikan rekomendasi kepada pengguna mengenai produk yang dapat digunakannya. Masalah administrasi di kantor jurusan Pendidikan Guru Sekolah Dasar Universitas Muhammadiyah Malang merupakan salah satu permasalahan yang selalu dihadapi oleh para staf TU dan part timer. Penggunaan sistem manual yang masih berjalan saat ini dinilai kurang efektif terhadap waktu, tempat, dan tenaga sehingga diperlukan adanya bantuan berupa sistem informasi. Pada perancangan sistem informasi ini akan menggunakan metode Okapi BM25 dimana metode ini merupakan fungsi peringkat yang digunakan oleh mesin pencari (search engine) untuk peringkat dokumen pencocokan sesuai relevansinya dengan permintaan pencarian yaitu berupa topik tugas akhir. BM25 memiliki fungsi yang sesuai dengan 3 prinsip pembobotan yang baik, yaitu memiliki inverse document frequency (idf), term frequency (tf), dan memiliki fungsi normalisasi dari panjang dokumen (document length normalization).Abstract The recommendation system is a software to provide recommendations to users about the products they can use. The administrative problem in the office of the Primary School Teacher Education department at the University of Muhammadiyah Malang is one of the problems faced by the Administration staff and part timers. The use of manual systems that are still running at this time is considered to be less effective against time, place, and energy, so that assistance in the form of information systems is needed. In designing this information system will use the Okapi BM25 method where this method is a ranking function used by search engines for matching document rankings according to their relevance to search queries, namely in the form of final assignment topics. BM25 has functions that are in accordance with the 3 principles of good weighting, which has an inverse document frequency (idf), term frequency (tf), and has a document length normalization function.

scholarly journals Design and Implementation of a Big Data Evaluator Recommendation System Using Deep Learning Methodology

2020 ◽  
Vol 10 (22) ◽  
pp. 8000
Author(s):  
Sukil Cha ◽  
Mun Y. Yi ◽  
Sekyoung Youm
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Full Text ◽  
Recommendation System ◽  
Selection Process ◽  
Korean Literature ◽  
Inverse Document Frequency ◽  
Design And Implementation ◽  
Research Fields ◽  
Document Frequency

As the number of researchers in South Korea has grown, there is increasing dissatisfaction with the selection process for national research and development (R&D) projects among unsuccessful applicants. In this study, we designed a system that can recommend the best possible R&D evaluators using big data that are collected from related systems, refined, and analyzed. Our big data recommendation system compares keywords extracted from applications and from the full-text of the achievements of the evaluator candidates. Weights for different keywords are scored using the term frequency–inverse document frequency algorithm. Comparing the keywords extracted from the achievement of the evaluator candidates’, a project comparison module searches, scores, and ranks these achievements similarly to the project applications. The similarity scoring module calculates the overall similarity scores for different candidates based on the project comparison module scores. To assess the performance of the evaluator candidate recommendation system, 61 applications in three Review Board (RB) research fields (system fusion, organic biochemistry, and Korean literature) were recommended as the evaluator candidates by the recommendation system in the same manner as the RB’s recommendation. Our tests reveal that the evaluator candidates recommended by the Korean Review Board and those recommended by our system for 61 applications in different areas, were the same. However, our system performed the recommendation in less time with no bias and fewer personnel. The system requiresrevisions to reflect qualitative indicators, such as journal reputation, before it can entirely replace the current evaluator recommendation process.

scholarly journals Data Leak Identification in Social Networks using K Means Clustering & Tabu K Means Clustering

2019 ◽  
Vol 9 (2) ◽  
pp. 2777-2783
Keyword(s):  
Social Networks ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Network Access ◽  
Inverse Document Frequency ◽  
Document Frequency ◽  
Lower False Positive Rate ◽  
Different Types ◽  
Positive Rate ◽  
The Right

The prevention of leakage of data has been defined as a process or solution which identifies data that is confidential, tracks the data in a way in which it moves in and out of its enterprise to prevent any unauthorized data disclosure in an intentional or an unintentional manner. As data that is confidential is able to reside on various computing devices and move through several network access points or different types of social networks such as emails. Leakage of emails has been defined as if the email either deliberately or accidentally goes to an addressee to whom it should not be addressed. Data Leak Prevention (DLP) is the technique or product that tries mitigating threats to data leaks. In this work, the technique of clustering will be combined with the frequency of the term or the inverse document frequency in order to identify the right centroids for analysing the various emails that are communicated among members of an organization. Every member will fit in to various topic clusters and one such topic cluster can also comprise of several members in the organization who have not communicated with each other earlier. At the time when a new email is composed, every addressee will be categorized to be a potential leak recipient or one that is legal. Such classification was based on the emails sent among the sender and the receiver and also on their topic clusters. The work had investigated the technique of K-Means clustering and also proposed a Tabu - K-Means (TABU-KM) technique of clustering to identify points of optimal clustering. The proposed TABU-KM optimizes the K-Means clustering. Experimental results demonstrated that the proposed method achieves higher True Positive Rate (TPR) for known and unknown recipient and lower False Positive Rate (FPR) for known and unknown recipient

scholarly journals Hybrid News Recommendation System using TF-IDF and Similarity Weight Index

2020 ◽  
Vol 10 (3) ◽  
pp. 5-9
Keyword(s):  
Recommendation System ◽  
Comparison Study ◽  
Important Place ◽  
Inverse Document Frequency ◽  
Fast Running ◽  
Java Technology ◽  
Document Frequency ◽  
Current Affairs ◽  
Digital News ◽  
News Recommendation

As the usage of internet is increasing, we are getting more dependent on it in our daily life. The Internet plays an essential role to simplify our tight schedules. In such tough lives, it is very important to stay aware of current affairs. Now for different people coming from different backgrounds and professions, the preferences are different too. Here come Data mining techniques in the picture, which gives us “Recommender system” as the output, capable of delivering more relevant and worthy outcomes. Newspapers are the basic obligation asked by almost every person to stay updated and aware of the world. But as we observe that nowadays, various solutions are been developed to convert paper news system to digital news and raise the bar of the quick news. And that’s how News Recommender systems are have made an important place in our fast running lives.This research paper has investigated the News Recommendation solution right from its core, including the importance, performance, and improvement suggestions. This paper talks about enhancing the performance of states solution by using modified Term Frequency-Inverse Document Frequency (TF-IDF) algorithms. Proposed solution advocates the usage of JAVA technology which reflects fruitful results in the final graphs of accuracy, precision, and F-score. Here, BBC dataset has been used for comparison study purposes.

scholarly journals Recommendation System Using Weighted TF-IDF and Naive Bayes Classifiers on RSS Contents

2010 ◽  
Vol 14 (6) ◽  
pp. 631-637
Author(s):  
Incheon Paik ◽  
◽  
Hiroshi Mizugai ◽  
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Document Frequency ◽  
Rss Feeds ◽  
Enormous Quantity

A recent increase in RDF Site Summary (RSS) feeds, used for news updates and blogs, has been caused by the widespread use of blogs. This means that much effort is now needed to search the contents of RSS feeds because of this enormous quantity of material. To solve this problem, recommendation systems enable users to obtain relevant RSS contents easily and quickly. In previous research, an RSS recommendation system was proposed that used the similarity between the Term Frequency (TF) of the RSS contents and the TF derived from the contents of the user’s browsing history for RSS feeds. In this paper, we use Term Frequency-Inverse Document Frequency (TF-IDF) calculations to propose a Weighted TF-IDF method, which focuses on the terms folded by the title tags in RSS contents as characteristic terms. In addition, we propose a new recommendation method, which uses a Naive Bayes classifier in a Machine Learning-based approach. Via experiments, we compare the proposed methods and the existing method in a prototype recommendation system, and we show that the proposed methods outperform the existing method with respect to several evaluation measurements.

scholarly journals Multi-Criteria Selection of Additives in Porous Asphalt Mixtures Using Mechanical, Hydraulic, Economic, and Environmental Indicators

2021 ◽  
Vol 13 (4) ◽  
pp. 2146
Author(s):  
Anik Gupta ◽  
Carlos J. Slebi-Acevedo ◽  
Esther Lizasoain-Arteaga ◽  
Jorge Rodriguez-Hernandez ◽  
Daniel Castro-Fresno
Keyword(s):  
Cellulose Fiber ◽  
Environmental Indicators ◽  
Asphalt Mixtures ◽  
Environmental Benefits ◽  
Modified Bitumen ◽  
Porous Asphalt ◽  
Different Types ◽  
Product Assessment ◽  
Polymer Modified Bitumen ◽  
Selection Of

Porous asphalt (PA) mixtures are more environmentally friendly but have lower durability than dense-graded mixtures. Additives can be incorporated into PA mixtures to enhance their mechanical strength; however, they may compromise the hydraulic characteristics, increase the total cost of pavement, and negatively affect the environment. In this paper, PA mixtures were produced with 5 different types of additives including 4 fibers and 1 filler. Their performances were compared with the reference mixtures containing virgin bitumen and polymer-modified bitumen. The performance of all mixes was assessed using: mechanical, hydraulic, economic, and environmental indicators. Then, the Delphi method was applied to compute the relative weights for the parameters in multi-criteria decision-making methods. Evaluation based on distance from average solution (EDAS), technique for order of the preference by similarity to ideal solution (TOPSIS), and weighted aggregated sum product assessment (WASPAS) were employed to rank the additives. According to the results obtained, aramid pulp displayed comparable and, for some parameters such as abrasion resistance, even better performance than polymer-modified bitumen, whereas cellulose fiber demonstrated the best performance regarding sustainability, due to economic and environmental benefits.

scholarly journals Methods for Preventing Visual Attacks in Convolutional Neural Networks Based on Data Discard and Dimensionality Reduction

2021 ◽  
Vol 11 (11) ◽  
pp. 5235
Author(s):  
Nikita Andriyanov
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Inference ◽  
Recognition Accuracy ◽  
Network Architectures ◽  
Reduced Dimensions ◽  
Different Types ◽  
Rectangular Area ◽  
Noise Impulse ◽  
Selection Of

The article is devoted to the study of convolutional neural network inference in the task of image processing under the influence of visual attacks. Attacks of four different types were considered: simple, involving the addition of white Gaussian noise, impulse action on one pixel of an image, and attacks that change brightness values within a rectangular area. MNIST and Kaggle dogs vs. cats datasets were chosen. Recognition characteristics were obtained for the accuracy, depending on the number of images subjected to attacks and the types of attacks used in the training. The study was based on well-known convolutional neural network architectures used in pattern recognition tasks, such as VGG-16 and Inception_v3. The dependencies of the recognition accuracy on the parameters of visual attacks were obtained. Original methods were proposed to prevent visual attacks. Such methods are based on the selection of “incomprehensible” classes for the recognizer, and their subsequent correction based on neural network inference with reduced image sizes. As a result of applying these methods, gains in the accuracy metric by a factor of 1.3 were obtained after iteration by discarding incomprehensible images, and reducing the amount of uncertainty by 4–5% after iteration by applying the integration of the results of image analyses in reduced dimensions.

An algebraic approach to N-soft sets with application in decision-making using TOPSIS

2021 ◽  
pp. 1-21
Author(s):  
Muhammad Shabir ◽  
Rimsha Mushtaq ◽  
Munazza Naz
Keyword(s):  
Decision Making ◽  
Mathematical Models ◽  
Real World ◽  
Algebraic Approach ◽  
Multi Criteria Decision Making ◽  
Commutative Monoids ◽  
Soft Sets ◽  
Algebraic Properties ◽  
Different Types ◽  
Selection Of

In this paper, we focus on two main objectives. Firstly, we define some binary and unary operations on N-soft sets and study their algebraic properties. In unary operations, three different types of complements are studied. We prove De Morgan’s laws concerning top complements and for bottom complements for N-soft sets where N is fixed and provide a counterexample to show that De Morgan’s laws do not hold if we take different N. Then, we study different collections of N-soft sets which become idempotent commutative monoids and consequently show, that, these monoids give rise to hemirings of N-soft sets. Some of these hemirings are turned out as lattices. Finally, we show that the collection of all N-soft sets with full parameter set E and collection of all N-soft sets with parameter subset A are Stone Algebras. The second objective is to integrate the well-known technique of TOPSIS and N-soft set-based mathematical models from the real world. We discuss a hybrid model of multi-criteria decision-making combining the TOPSIS and N-soft sets and present an algorithm with implementation on the selection of the best model of laptop.

Sign in / Sign up

Export Citation Format

Share Document