System Design of an Intelligent Nutrition Consultation and Recommendation Model

Lin, En-Yu; Yang, Don‐Lin; Hung, Ming-Chuan

doi:10.1109/uic-atc.2012.80

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…Pemberian prediksi untuk preferensi dari pengguna saat ini sudah banyak digunakan di berbagai sektor baik itu sektor pariwisata, penyedia akomodasi, perusahaan ritel, industri perfilman hingga perusahaan besar seperti Google, Ebay yang menyediakan layanan kepada pengguna (Ricci, dkk., 2011). Prediksi juga diterapkan dalam mengurangi resiko penyakit (Lin dan Yang, 2012), risiko kerusakan lingkungan (Kretser, 2015) dan mengukur tingkat produktivitas ataupun keuntungan perusahaan (Wang dan Chuang, 2015). Pemberian prediksi kinerja petugas pada (Al-radaideh dkk, 2012) yang dilakukan di suatu perusahaan ternyata memberikan keuntungan dalam hal efektifitas biaya dan waktu.…”

Section: Gambar 1 Lama Keterlambatan Rilis Data Kegiatan Pendataan Punclassified

Kombinasi Metode Ensemble, CFS Dan Pohon Keputusan Untuk Prediksi Kinerja Petugas Studi Kasus: Survey Podes Badan Pusat Statistik

Hardiyanto

2020

JTI

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Kegiatan rilis data pada pendataan Potensi Desa (Podes) Badan Pusat Statistik pada kurun waktu sembilan tahun terakhir, selalu mengalami keterlambatan. Untuk meminimalisir agar keterlambatan tidak terjadi secara berulang, penelitian bertujuan memprediksi kinerja petugas berdasarkan faktor internal dan eksternal sebagai informasi kegiatan di Badan Pusat Statistik (BPS). Pemilihan atribut pada penelitian ini menggunaan nilai gain informasi dan Correlation feature selection (CFS), selanjutnya dilakukan pemodelan dengan algoritma Pohon Keputusan. Hasil penelitian ini menunjukkan akurasi prediksi pada petugas organik BPS meningkat sebesar 10.57 % dari 63,19 % menjadi 69,87 % dengan faktor penentu keterlambatan adalah beban kerja, track record dan kemudahan lokasi, sedangkan pada petugas mitra dengan menggunakan metode CFS akurasi prediksi meningkat sebesar 24,11 % dari 65,78 % menjadi 81,643 % dengan faktor penentu keterlambatan adalah nilai pendalaman,kemampuan bekerja dalam tim, dan profesionalitas.

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Section: Gambar 1 Lama Keterlambatan Rilis Data Kegiatan Pendataan Punclassified

Kombinasi Metode Ensemble, CFS Dan Pohon Keputusan Untuk Prediksi Kinerja Petugas Studi Kasus: Survey Podes Badan Pusat Statistik

Hardiyanto

2020

JTI

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“…The study didn't focus on user choices, their cultures and health issues. An application named SHADE is developed [13] which produced diet with food lists with portion size as per users' preferences. It also suggested individual's which exercises had to performed with duration and intensity.…”

Section: Related Approachesmentioning

confidence: 99%

Design of Personalized Diet and Physical Activities Recommendation Framework for Hypothyroid Patients

Vairale*¹,

Shukla²

2019

IJRTE

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These days, hypothyroid disease is quickly growing among individuals. In India, one out of eight women experiences hypothyroid disorder because of iodine deficiency. It is necessary to maintain the thyroid hormone levels because it may lead to thyroid cancer. There is a need to consume an adequate amount of iodine intake and other nutrients required to balance thyroid hormones levels. So, patients should follow a customized daily diet and exercise plan to meet their nutritional needs. These recommendations help hypothyroid patients to enhance their metabolism and to adjust thyroid hormones levels. Most of the existing online systems usually provide diet recommendations in general forms. Such recommendations are insufficient for any patient suffering from a specific disease. This paper provides a personalized recommendation framework to provide appropriate diet plans and physical activities to patients. These recommendations are based on their clinical data and personal choices. Validation of recommendations can be made by combining both domains like human expertise and computer technologies.

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“…The output data can be used to set up a weekly dietary plan as has been proposed Lin et al [38]. This results in a more concrete food plan, which instead of just recommending "1/8 fruits" could say "1 apple" (or, in line with the stated preferences of the individual patient, "1 bunch of grapes").…”

Section: Foods: a Food-oriented Ontology-driven System-diabetes Editionmentioning

confidence: 99%

Towards an “Internet of Food”: Food Ontologies for the Internet of Things

et al. 2015

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Automated food and drink recognition methods connect to cloud-based lookup databases (e.g., food item barcodes, previously identified food images, or previously classified NIR (Near Infrared) spectra of food and drink items databases) to match and identify a scanned food or drink item, and report the results back to the user. However, these methods remain of limited value if we cannot further reason with the identified food and drink items, ingredients and quantities/portion sizes in a proposed meal in various contexts; i.e., understand from a semantic perspective their types, properties, and interrelationships in the context of a given user's health condition and preferences. In this paper, we review a number of "food ontologies", such as the Food Products Ontology/FOODpedia (by Kolchin and Zamula), Open Food Facts (by Gigandet et al.), FoodWiki (Ontology-driven Mobile Safe Food Consumption System by Celik), FOODS-Diabetes Edition (A Food-Oriented Ontology-Driven System by Snae Namahoot and Bruckner), and AGROVOC multilingual agricultural thesaurus (by the UN Food and Agriculture Organization-FAO). These food ontologies, with appropriate modifications (or as a basis, to be added to and further OPEN ACCESSFuture Internet 2015, 7 373 expanded) and together with other relevant non-food ontologies (e.g., about diet-sensitive disease conditions), can supplement the aforementioned lookup databases to enable progression from the mere automated identification of food and drinks in our meals to a more useful application whereby we can automatically reason with the identified food and drink items and their details (quantities and ingredients/bromatological composition) in order to better assist users in making the correct, healthy food and drink choices for their particular health condition, age, body weight/BMI (Body Mass Index), lifestyle and preferences, etc.

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System Design of an Intelligent Nutrition Consultation and Recommendation Model

Cited by 8 publications

References 4 publications

Kombinasi Metode Ensemble, CFS Dan Pohon Keputusan Untuk Prediksi Kinerja Petugas Studi Kasus: Survey Podes Badan Pusat Statistik

Kombinasi Metode Ensemble, CFS Dan Pohon Keputusan Untuk Prediksi Kinerja Petugas Studi Kasus: Survey Podes Badan Pusat Statistik

Design of Personalized Diet and Physical Activities Recommendation Framework for Hypothyroid Patients

Towards an “Internet of Food”: Food Ontologies for the Internet of Things

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