frontiers in artificial intelligence ranking
The ranking percentile of Frontiers in Artificial Intelligence and Applications is around 9% in the field of Artificial Intelligence. Similarly, of the 34 Frontiers journals with a 2017 CiteScore (Scopus, 2018), nine rank in the top 10% most impactful journals. Copyright © 2021 Shirai, Seneviratne, Gordon, Chen and McGuinness. If someone has a goal to reduce carbohydrates, for example, then we only should suggest substitute ingredients containing fewer carbohydrates than the original ingredient. Moreover, within each academic category our journals rank in the top Impact Factor and CiteScore percentiles. We evaluated the performance of each combination using our development dataset and selected the best combination to form our final heuristic. According to SCImago Journal Rank (SJR), this journal is ranked 0.547. UWriteMyEssay.net's services, on the other Frontiers In Artificial Intelligence hand, is a perfect match for all my written needs. Trondheim, Norway, June 26–28, 2017.. Gaillard, E., Lieber, J., and Nauer, E. (2015). Found inside – Page 157Nalisnick, E., Mitra, B., Craswell, N., Caruana, R.: Improving document ranking with dual word embeddings. In: Proceedings of the 25th International ... Frontiers in Artificial Intelligence and Applications, vol. 317, pp. 212–228. Other works typically perform user studies for evaluation (Achananuparp and Weber, 2016; Akkoyunlu et al., 2017). Figure 4: Analysis of Impact Factor rankings. When it comes to journal publications, many publications are available in the area of AI and Machine . Our contributions are as follows: Develop a novel ingredient substitutability heuristic, DIISH, which leverages explicit semantic information and word embeddings of ingredients to rank plausible substitutions (Section 2.3), Evaluate our substitution ranking heuristic using ground-truth substitutions collected from web resources and user reviews of recipes (Section 2.4), Provide a design and demonstration implementation that uses linked nutritional information and food classifications to determine “healthy” ingredient substitution options that satisfy personal dietary constraints (Section 2.2). These include views and downloads, citations, social buzz (news and social media mentions, through altmetrics.com data) and visitor demographics like location, seniority and field of interest. We use two sources of latent semantic information about ingredients as word embeddings based on ingredient names. SS conceptualized and implemented the methodology with support from OS and MG, curated evaluation data, and wrote the original manuscript draft. As another example, suggesting meat-based substitution for a vegetarian is objectively bad. Read the full analysis here. Cham, Switzerland: Springer International Publishing, 140–154. Substituting individual ingredients rather than strictly following a new meal plan allows people to eat familiar meals while maintaining their dietary goals. In constructing the DIISH, each similarity metric appears to capture slightly different aspects of good substitutions and contributes positively to improving the overall score. ★CFAIS 2020 welcomes researchers, engineers, scientists and industry professionals to an open forum where advances in the field of Artificial Intelligence and Statistics can be shared and examined. The ranking percentile of Frontiers in Artificial Intelligence and Applications is around 9% in the field of Artificial Intelligence. Our approach shows promise when compared against our collected ground-truth substitution datasets. Our group explores the two-approach relationship between pure and artificial intelligence, with an emphasis on modelling, constructing, evaluating and understanding complete programs. OS, CC, and DM conceptualized and supervised the project. It considers the number of citations received by a journal and the importance of the journals from where these citations come. UK start-up Jukedeck already uses this technology to write songs for Coca-Cola. Other existing works, such as the study by Gaillard et al. It is also the 10th most cited open-access journal in Neurosciences. Nutritional profile estimation in cooking recipes. The use of food categorization similarity is a plausible method to find simple substitutions (like different varieties of potatoes). Recall rate at k is calculated as the proportion of target ingredients for which any correct substitute option falls within the top k ranks. Thus, for example, rather than only looking at how frequently ingredients a and b were used with “yellow onions,” we can consider the frequency of use of ingredients that are linked to “onion (whole, raw),” “onion (whole),” and so on, as shown in Figure 2. For example, in a recipe consisting of (bread, peanut butter, and jelly), jelly is used in the context (bread and peanut butter). Determining how to modify a recipe can be difficult because people need to 1) identify which ingredients can act as valid replacements for the original and 2) figure out whether the substitution is “good” for their particular context, which may consider factors such as allergies, nutritional contents of individual ingredients, and other dietary restrictions. We once again use FoodOn links to better generalize the recipes by converting ingredients to their equivalent FoodOn classes. doi:10.1007/978-3-030-16667-0_11, Kalra, J., Batra, D., Diwan, N., and Bagler, G. (2020). Thanks again! Where possible, nonstandard units (e.g., “1 can of beans”) were matched against the ingredient’s common unit descriptions. Post was not sent - check your email addresses! Dev 63 (71–77), 18. doi:10.1147/JRD.2019.2893905, Keywords: ingredient substitution, semantic similarity, semantic substitution, knowledge graph, patient empowerment, health empowerment, Citation: Shirai SS, Seneviratne O, Gordon ME, Chen C-H and McGuinness DL (2021) Identifying Ingredient Substitutions Using a Knowledge Graph of Food. Frontiers journals rank among the world’s most influential in terms of citations, as reported in the 2017 Journal Citation Reports (JCR; 2018, Clarivate Analytics, formerly Thomson Reuters) and 2017 CiteScore edition (2018, Scopus, Elsevier). Tensor Completion is an important problem in big data processing. TAAABLE (Gaillard et al., 2015; Gaillard et al., 2017) performed recipe adaptations based on explicit rules and ingredient subclass information. (2019). While science fiction often portrays AI as robots with human-like characteristics, AI has been explored significantly in various biomedical fields including pharmacology. Conversely, ingredients that are dissimilar in terms of food classification may be good substitutions (e.g., potatoes and cauliflower). Recipe1m+: a dataset for learning cross-modal embeddings for cooking recipes and food images. This book presents the proceedings of COMMA 2020. Due to the Covid-19 pandemic, COMMA 2020 was held as an online event on the originally scheduled dates of 8 -11 September 2020, organised by the University of Perugia, Italy. Subclass relations of FoodOn ingredients are used to enhance our scoring metrics. Combining both explicit and implicit semantics in DIISH appears to provide some benefits of both methods, using some notion of similarity between word embeddings and applying intuitions to differentiate between “similar” and “substitutable” ingredients available through explicit semantics. But why don’t they leave? For restrictions on types of food, we use FoodOn classes to identify ingredients that violate the restriction. ⺠Journal citation analyses. Artificial Intelligence at MIT: Expanding Frontiers (Artificial Intelligence Series) [Winston, Patrick Henry, Shellard, Sarah A.] For ingredients a and b, the cosine similarity of their Word2Vec and spaCy embeddings are represented as Wa,b and Sa,b, respectively. We additionally wish to capture the notion of ingredients being used in similar recipe contexts. Top Computer Science Journals for Machine Learning, Data Mining & Artificial Intelligence . These works differ from ours in which we do not rely only on explicit substitution rules, and our focus is to provide “healthy” substitutions based on user context. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). Figure 2: As of June 2018, Frontiers articles have received more than 700,000 citations (Scopus). Relying only on explicitly annotated properties present in FoodKG would make our methods unable to leverage associations between ingredients and words relating to other aspects of cooking, such as preparation methods, utensils, or recipe names. The focus has shifted from collecting massive amounts . Found inside – Page 192For text ranking, we propose an automatic and unsupervised graph-based ranking algorithm that gives improved results when compared to other ranking algorithms. Detecting topic words is one important step in our method, ... Google ScholarBiography. Big Data is no fad. Some errors also are caused by incorrect links between FoodKG ingredients and FoodOn classes. Students will learn a blend of software and hardware skills that are applicable across multiple engineering domains. FlavorDB: a database of flavor molecules. . However, this method will struggle to discover good substitutions that are not closely related (e.g., a vegan substitution for meat will have vastly different food categorization). - Pam, 3rd Year Art Visual Studies. Willard Miller, Pathological inebriety, its causation and treatment|J W Astley. Other resources capturing recipes and associated information, such as RecipeDB (Batra et al., 2019), have also been developed. The first was a relatively small set of manually curated substitutions from “common ingredient substitution” guides published by AllRecipes5, Food Network6, Colorado State University7 (CSU), and North Dakota State University8 (NDSU). We evaluate our approach using three datasets of ingredient substitutions, curated from popular websites and user reviews. CiteScore is a similar metric covering titles in Elsevierâs Scopus database. For example, if someone wants to find gluten-free substitutions for all-purpose flour substitutions and has a goal to reduce carbohydrate intake, search results might focus solely on avoiding gluten. (ICML'13), volume 1, pages 436--444, 2013. Found inside – Page 47Heymans, S., Toma, I.: Ranking services using fuzzy hex-programs. ... Frontiers in Artificial Intelligence and Applications, ... ECAI 2008 - 18th European Conference on Artificial Intelligence, Patras, Greece, July 21-25. Frontiers in ... While examples in this study are mainly aimed at improving diets for people concerned with preventing or managing diabetes, the work can be used for a wide range of food substitution tasks. Found inside – Page 142A Rank: A multiagent based approach for ranking of cloud computing service. Scalable Computing: Practice and Experience, ... Adaptive process execution in a service cloud: Service selection and scheduling based on machine learning. For example, the “baking potato” is linked to the food “potatoes gratine” in FoodOn because it has an alternate name of “potato bake,” which was the closest available match to baking potato. “Tuuurbine: a generic CBR engine over RDFS.” in Case-based reasoning research and development, 8765. The key use case that motivates our work is to perform ingredient substitutions in recipes that will satisfy personal dietary constraints. Found inside – Page 872[2] Leila Amgoud and Jonathan Ben-Naim, 'Ranking-based semantics for argumentation frameworks', in International Conference on Scalable Uncertainty Management ... Frontiers in Artificial Intelligence and Applications, IOS Press, (2010). We also consider superclass relations to generalize the occurrence of ingredients within recipe contexts. We collected three sets of data to serve as ground-truth ingredient substitutions to compare our results against. We facilitate this using our team’s FoodKG (Haussmann et al., 2019), a knowledge graph of recipe and ingredient information. Arguably, good substitutions have the goal of replacing an ingredient to improve some criteria while maintaining the essence of the original recipe (while intentionally creating new dishes may be a valid goal, it is not in the scope of this work). Artificial Intelligence to help meet global demand for high-quality, objective peer-review in . Many of these specific types of fats are lacking exact matches to appropriate ingredients in FoodOn. As opposed to journal metrics, article- and author-level metrics indicate the quality and global impact of individual articles, give credit to individual authors and allow article- and author-level comparisons. 6
Associate Professor in US) with the Department of Data Science & AI, Faculty of Information Technology, Monash University.Prior to this, he was a Lecturer with the Centre for Artificial Intelligence (CAI), Found inside – Page 245That model is used to re-rank documents that are retrieved by the tf-idf model. The model's features include lexical words, and both tf-idf score and LDA model score of a document for a given query. The intuition is that, by including ... on Amazon.com. Frontiers in Artificial Intelligence and Applications has been ranked #207 over 227 related journals in the Artificial Intelligence research category. If we consider that many unclear measurements, such as “1 chicken breast,” may vary in weight by several ounces, this error seems within a reasonable range. Found inside – Page 342McLaren, B.M., Scheuer, O., De Laat, M., Hever, R., De Groot, R., Rose, C.P.: Using Machine Learning Techniques to Analyze and Support Mediation of Student E-Discussions. In: Frontiers in Artificial Intelligence and Applications, pp. Our work can empower people to more effectively modify their eating habits by simplifying the process of identifying plausible ingredient substitutions and comparing ingredients based on their food classification and nutritional content. This Special Issue, led by the Editor-in-Chief of AI, is open for submissions of feature papers with the aim of providing a platform for all innovative and frontier research that involves artificial intelligence (AI), including AI algorithms, AI software, AI . *Correspondence: Sola Shirai, [email protected]; Deborah McGuinness, [email protected], Front. There are normally alternatives for postgraduate analysis in our group. Front. We bring together leading scientists and practitioners with large-scale AI products deployment. The award also recognizes his contributions to the fields of mathematics, cognitive science, robotics, and philosophy. Of the 34 such Frontiers journals listed in 54 CiteScore categories, 9 rank in the top 10% most impactful journals. SCImago Journal Rank is an indicator, which measures the scientific influence of journals. 5. Artificial evolution of culinary arts,” in Computational Intelligence in Music, Sound, Art and Design, Lecture Notes in Computer Science. Hight Quality. The ISSN of Frontiers in artificial intelligence. Publishers own the rights to the articles in their journals. The first is to satisfy dietary restrictions on specific types of ingredients, such as replacing meat-based ingredients for vegetarian diets or replacing allergens such as peanuts. *FREE* shipping on qualifying offers. Managing and modifying nutritional intake from food is a meaningful way to maintain and improve personal health. View all
Artif. In more ways than one, the world is growing at an exponential rate, and so is the size of data collected across the globe. Usually, data acquired from different aspects of a multimodal phenomenon or different sensors are incomplete due to different reasons such as noise, low sampling rate or human mistake. Future work to more effectively utilize our presented approach will involve various improvements to FoodKG’s content. 2018 2019 2020 Artificial Intelligence Computer Science Applications. “Shortening” is also commonly understood to mean shortening made from vegetable oil, so it may also be appropriate to consider the top-ranked option of vegetable shortening to be correct. Figure 5: Analysis of CiteScore rankings. It can't work its magic if data is siloed between departments and in disparate IT techniques. Artificial Intelligence at MIT: Expanding Frontiers (Artificial Intelligence Series) ⺠What is journal impact? Frontiers ranks 4th most-cited with an average of 3.65 citations per article. Found inside – Page 347In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI'93), volume 1, ... Conference on Artificial Intelligence (ECAI 2006), volume 141 of Frontiers in Artificial Intelligence and Applications, ... CiteScore was launched in December 2016 and is released once a year. The data source for recipes and ingredients used in this study, FoodKG, captures links from ingredients to nutritional information and ontology of food. The scripts used to curate evaluation data as well as perform experiments are publicly available and open source10. Coefficients were selected from [0.5,1,2,4], and the powers were selected from [1/4,1/2,1,2]. The research, approach, content, Human Language Technologies The Baltic Perspective: Proceedings Of The Fifth International Conference Baltic HLT 2012 Volume 247 Frontiers In Artificial Intelligence And Applications M structure and writing style are different depending on the type of assignment. FoodEx2vec: new foods' representation for advanced food data analysis. (2014), define explicit rules for substitutions, but such rules are typically not scalable because they are only defined for a narrow set of recipes and rely on detailed annotations about recipes and ingredients. Found inside – Page 429Noro, T., Ru, F., Xiao, F., Tokuda, T.: Twitter user rank using keyword search. In: Information Modelling and Knowledge Bases XXIV. Frontiers in Artificial Intelligence and Applications, vol. 251, pp. 31–48. IOS Press (2013) 7. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. We used these descriptions to convert ingredient measurements in FoodKG into grams, using the first available common units as the default weight. To assess the correctness of our nutrition calculation method, we applied it to calculate the calorie information for a set of 8,659 recipes whose ingredients all had links to USDA foods in the FoodKG. At the top... people have to experience it. They’ve bought into myths which I will eliminate and get you on your way to growth… Myth #1: …, ...For Recruiting and Attracting The Consumer? Although we focus on dietary constraints in this study, the approach could be used in a broader range of constraint types. Frontiers In Artificial Intelligence. Frontiers in Artificial Intelligence and Applications is Subscription-based (non-OA) Journal. We also allow for a single source ingredient to have many options for substitutions (e.g., the target ingredient potato may have substitutes of cauliflower, rutabaga, and carrots). Data is becoming more meaningful and contextually relevant, breaks new ground for machine learning (ML) and artificial intelligence (AI), and even moves both of them from research labs to production. Here, the authors explain how their novel . Articles, Saints Cyril and Methodius University of Skopje, North Macedonia, Faculty of Computer Science and Engineering, Saints Cyril and Methodius University of Skopje, North Macedonia. We frame our approach as an information retrieval problem and evaluate our results using mean average precision (MAP), mean reciprocal rank (MRR), and recall rate at k (RR@k). (2018). Figure 8: Data to June 2018 (altmetic.com). Nutritional information about the ingredients, linked from the USDA, can show the patient that potatoes are the greatest contributor to carbohydrates in this recipe (both in terms of its carbohydrates per 100 g and its quantity specified in the recipe). Artificial Intelligence Research and Development, Proceedings of the 8th International Conference of the ACIA, CCIA 2005, October 26-28, 2005, Alguer, Italy F. J. Ruiz Cecilio Angulo
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