PNTRS

2022 ◽  
Vol 24 (3) ◽  
pp. 1-19
Author(s):  
Sunita Tiwari ◽  
Sushil Kumar ◽  
Vikas Jethwani ◽  
Deepak Kumar ◽  
Vyoma Dadhich
Keyword(s):  
Fuzzy Inference System ◽  
Recommendation System ◽  
Naive Bayes ◽  
Fuzzy Inference ◽  
Recommendation Systems ◽  
User Feedback ◽  
Satisfaction Level ◽  
Inference System ◽  
News Recommendation ◽  
Feedback Rating

A news recommendation system not only must recommend the latest, trending and personalized news to the users but also give opportunity to know about the people’s opinion on trending news. Most of the existing news recommendation systems focus on recommending news articles based on user-specific tweets. In contrast to these recommendation systems, the proposed Personalized News and Tweet Recommendation System (PNTRS) recommends tweets based on the recommended article. It firstly generates news recommendation based on user’s interest and twitter profile using the Multinomial Naïve Bayes (MNB) classifier. Further, the system uses these recommended articles to recommend various trending tweets using fuzzy inference system. Additionally, feedback-based learning is applied to improve the efficiency of the proposed recommendation system. The user feedback rating is taken to evaluate the satisfaction level and it is 7.9 on the scale of 10.

Movie Recommendation System Based on Fuzzy Inference System and Adaptive Neuro Fuzzy Inference System

Fuzzy Systems ◽  
2017 ◽  
pp. 573-608
Author(s):  
Mahfuzur Rahman Siddiquee ◽  
Naimul Haider ◽  
Rashedur M. Rahman
Keyword(s):  
Fuzzy Inference System ◽  
Recommendation System ◽  
Fuzzy Inference ◽  
Pearson Correlation ◽  
Similarity Measures ◽  
Manhattan Distance ◽  
Comparative Performance ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Similarity Calculation

One of most prominent features that social networks or e-commerce sites now provide is recommendation of items. However, the recommendation task is challenging as high degree of accuracy is required. This paper analyzes the improvement in recommendation of movies using Fuzzy Inference System (FIS) and Adaptive Neuro Fuzzy Inference System (ANFIS). Two similarity measures have been used: one by taking account similar users' choice and the other by matching genres of similar movies rated by the user. For similarity calculation, four different techniques, namely Euclidean Distance, Manhattan Distance, Pearson Coefficient and Cosine Similarity are used. FIS and ANFIS system are used in decision making. The experiments have been carried out on Movie Lens dataset and a comparative performance analysis has been reported. Experimental results demonstrate that ANFIS outperforms FIS in most of the cases when Pearson Correlation metric is used for similarity calculation.

Tourist spot recommendation system using fuzzy inference system

2017 ◽  
Author(s):  
Haymontee Khan ◽  
Noel Mannan ◽  
Shahnoor Chowdhury Eshan ◽  
Md. Mustafizur Rahman ◽  
K. M. Mehedi Hasan Sonet ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Recommendation System ◽  
Fuzzy Inference ◽  
Inference System

scholarly journals Sistem Rekomendasi Profesi Berdasarkan Dimensi Big Five Personality Menggunakan Fuzzy Inference System Tsukamoto

2019 ◽  
Vol 6 (5) ◽  
pp. 457
Author(s):  
Farhanna Mar'i ◽  
Wayan Firdaus Mahmudy ◽  
Cleoputri Yusainy
Keyword(s):  
Personality Traits ◽  
Big Five ◽  
Fuzzy Inference System ◽  
Recommendation System ◽  
Fuzzy Inference ◽  
Big Five Personality Traits ◽  
Big Five Personality ◽  
Accuracy Score ◽  
Personality Dimension ◽  
Inference System

<p>Sistem rekomendasi dapat dimanfaatkan sebagai alat bantu untuk pengambilan keputusan. Pada sebuah perusahaan, sistem rekomendasi profesi bisa digunakan untuk menempatkan seorang karyawan pada posisi yang tepat. Pada penelitian ini diusulkan sistem rekomendasi profesi berdasarkan <em>Big Five Personality traits</em> yang meliputi <em>Extraversion, Aggreableness, Conscentiousness, Neuroticm, </em>dan <em>Opennes</em>. <em>Input</em> yang digunakan ialah parameter dimensi <em>Big Five Personality</em> yang dirumuskan oleh John. Metode yang digunakan adalah <em>Fuzzy</em> <em>Inference System</em> (FIS) <em>Tsukamoto</em>. Keakuratan sistem dihitung dengan membandingkan <em>output</em> sistem dengan dengan acuan <em>Top Ranked Personality - Based Work Styles for 22 Job Families</em> yang menghasilkan nilai akurasi sebesar 63%.</p><p><em><strong>Abstract</strong></em></p><p><em>Recommendation systems can be used as a tool for decision making. In a company, a professional recommendation system can be used to place an employee in the right position. In this study proposed system of professional recommendation based on Big Five Personality traits which includes Extraversion, Aggreableness, Conscentiousness, Neuroticm, and Opennes. The input used is the Big Five Personality dimension parameter formulated by John. The method used is Fuzzy Inference System (FIS) Tsukamoto. The accuracy of the system is calculated by comparing the output of the system with the reference Top Personality - Based Work Styles for 22 Job Families that produce an accuracy score of 63%.</em><em></em></p><p><em><strong><br /></strong></em></p>

Movie Recommendation System Based on Fuzzy Inference System and Adaptive Neuro Fuzzy Inference System

2015 ◽  
Vol 4 (4) ◽  
pp. 31-69 ◽  
Author(s):  
Mahfuzur Rahman Siddiquee ◽  
Naimul Haider ◽  
Rashedur M. Rahman
Keyword(s):  
Fuzzy Inference System ◽  
Recommendation System ◽  
Fuzzy Inference ◽  
Pearson Correlation ◽  
Similarity Measures ◽  
Manhattan Distance ◽  
Comparative Performance ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Similarity Calculation

One of most prominent features that social networks or e-commerce sites now provide is recommendation of items. However, the recommendation task is challenging as high degree of accuracy is required. This paper analyzes the improvement in recommendation of movies using Fuzzy Inference System (FIS) and Adaptive Neuro Fuzzy Inference System (ANFIS). Two similarity measures have been used: one by taking account similar users' choice and the other by matching genres of similar movies rated by the user. For similarity calculation, four different techniques, namely Euclidean Distance, Manhattan Distance, Pearson Coefficient and Cosine Similarity are used. FIS and ANFIS system are used in decision making. The experiments have been carried out on Movie Lens dataset and a comparative performance analysis has been reported. Experimental results demonstrate that ANFIS outperforms FIS in most of the cases when Pearson Correlation metric is used for similarity calculation.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

ZERO-DAY POLYMORPHIC CYBERATTACKS DETECTION USING FUZZY INFERENCE SYSTEM

2020 ◽  
pp. 8-13
Author(s):  
V. V. Fesokha ◽  
I. Y. Subach ◽  
V. O. Kubrak ◽  
A. V. Mykytiuk ◽  
S. O. Korotaiev
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System
Sign in / Sign up

Export Citation Format

Share Document