Book Recommender System using Fuzzy Linguistic Quantifier and Opinion Mining

Abstract. Pneumonia is an infection of the lungs that is caused by bacteria, viruses, fungi, or parasites. It is characterized primarily by inflammation of the alveoli in the lungs or by alveoli that are filled with fluid (alveoli are microscopic sacs in the lungs that absorb oxygen). The shortage of medical personnel at health community centers to serve a population often results in care delays for pneumonia patients. The purpose of this research is to make a modelling of group decision support system in diagnosing pneumonia in adult patients. The system is designed as a tool for medical personnel in diagnosing pneumonia patients.Group Decision Support System (GDSS) is developed by using Fuzzy Simple Additive weighting methods. The preference scoring of three experts i.e. a pulmonary specialist, an internist and a pharmacist is carried out by applying triangular fuzzy numbers. In the aggregation stage, preferences makes use of Fuzzy Linguistic quantifier, stage rangking employs Simple Additive Weighting while Forward Chaining is employed in the inference process. The system is tested by inputting the symptoms of pneumonia without the involvement of an expert. The results shows that the system is capable in diagnosing pneumonia.Keywords: GDSS, Fuzzy, Simple Additive Weighting, PneumoniaÂ Abstrak. Pneumonia adalah infeksi paru-paru yang disebabkan oleh bakteri, virus, jamur, atau parasit. Hal ini ditandai terutama oleh peradangan alveoli di paru-paru atau alveoli yang berisi cairan (alveoli adalah kantung mikroskopis di paru-paru yang menyerap oksigen), terbatasnya tenaga medis di puskesmas disbanding dengan jumlah penduduk berakibat sering terlambatnya pelayanan terhadap pasien pneumonia. Tujuan penelitian ini adalah membuat pemodelan sistem pendukung keputusan kelompok untuk mendiagnosis pasien pneumonia pada orang dewasa. Sistem ini dirancang sebagai alat bantu tenaga medis dalam mendiagnosis pasien pneumonia. Sistem Pendukung Keputusan Kelompok (SPKK) dikembangkan menggunakan metode Fuzzy Simple Additive Weighting. Pemberian nilai preferensi tiga orang pakar antara lain ahli paru-paru, ahli internis dan ahli farmasi menggunakan bilangan fuzzy segitiga. Pada tahap agregasi preferensi digunakan Fuzzy Linguistic Quantifier, tahap perangkingan menggunakan Simple Additive Weighting dan proses inferensi menggunakan Forward Chaining. Sistem diuji dengan cara memasukkan gejala-gejala pneumonia tanpa melibatkan seorang pakar. Hasil penelitian menunjukkan bahwa sistem dapat mendiagnosis penyakit pneumonia.Kata Kunci: SPKK, Fuzzy, Simple Additive Weighting, Pneumonia

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A risk-aware fuzzy linguistic knowledge-based recommender system for hedge funds

Procedia Computer Science ◽

10.1016/j.procs.2019.12.068 ◽

2019 ◽

Vol 162 ◽

pp. 916-923

Author(s):

Álvaro Tejeda-Lorente ◽

Juan Bernabé-Moreno ◽

Julio Herce-Zelaya ◽

Carlos Porcel ◽

Enrique Herrera-Viedma

Keyword(s):

Recommender System ◽

Hedge Funds ◽

Linguistic Knowledge ◽

Knowledge Based ◽

Fuzzy Linguistic

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An Innovative Recommender System for E-Commerce Websites using Natural Language Processing

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f7168.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 4085-4089

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Sentiment Analysis ◽

Recommender Systems ◽

Language Processing ◽

Recommender System ◽

Opinion Mining ◽

The Internet

A Recommender System has become the go-to application for the internet generation these days. Mono-variate, bi-variate and multi-variate Recommender Systems are available to consumers of various products and services for the last 10 years or so only. In this paper, opinion mining dependent sentiment analysis using NLP tools will be used to recommend products to their purchasers on e-commerce websites. The application can be developed on the Python platform can be commercially used and will be precisely used to people who have to spend money without traditionally touching or feeling the item

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