Automatically Populating the Biomimicry Taxonomy for Scalable Systematic Biologically-Inspired Design

2012 ◽  
Author(s):  
Dennis Vandevenne ◽  
Paul-Armand Verhaegen ◽  
Simon Dewulf ◽  
Joost R. Duflou
Keyword(s):  
Hierarchical Classification ◽  
Biological Database ◽  
Support Vector ◽  
Biologically Inspired ◽  
Cross Domain ◽  
Large Numbers ◽  
Vector Machines ◽  
Database Size ◽  
Design By Analogy ◽  
Biologically Inspired Design

Although Biologically-Inspired Design (BID) is gaining popularity, state-of-the-art approaches for systematic BID are still limited by the required interactive work which is proportional to the applied biological database size. This interactive work, depending on the adopted methodology, might encompass model instantiation for each strategy in the biological database, classification into a predefined scheme or extensive result filtering. This contribution presents a first scalable approach to systematic BID with the potential to leverage large numbers of biological strategies. First, a focused webcrawler, based on a combination of Support Vector Machines (SVM), continuously searches for biological strategies on the Internet. The solution to this needle-in-a-haystack task is shown to produce biological strategies interesting for cross-domain Design-by-Analogy (DbA). These resources are then automatically positioned into Ask Nature’s well-known Biomimicry Taxonomy; a 3-level hierarchical classification scheme that enables designers to identify biological strategies relevant to their specific design problem. This paper details the architecture of the proposed system, and presents results indicating the feasibility of the applied approach.

scholarly journals SEABIRD: Scalable search for systematic biologically inspired design

2015 ◽  
Vol 30 (1) ◽  
pp. 78-95 ◽  
Author(s):  
Dennis Vandevenne ◽  
Paul-Armand Verhaegen ◽  
Simon Dewulf ◽  
Joost R. Duflou
Keyword(s):  
Design Process ◽  
State Of The Art ◽  
Knowledge Bases ◽  
Biological Database ◽  
Biologically Inspired ◽  
Growing Body ◽  
Design By Analogy ◽  
Biologically Inspired Design ◽  
Scalable Search ◽  
Design Inspiration

AbstractAs more and more people are increasingly turning to nature for design inspiration, tools and methodologies are developed to support the systematic bioideation process. State-of-the-art approaches struggle with expanding their knowledge bases because of interactive work required by humans per biological strategy. As an answer to this persistent challenge, a scalable search for systematic biologically inspired design (SEABIRD) system is proposed. This system leverages experience from the product aspects in design by analogy tool that identifies candidate products for between-domain design by analogy. SEABIRD is based on two conceptual representations, product and organism aspects, extracted from, respectively, a patent and a biological database, that enable leveraging the ever growing body of natural-language biological texts in the systematic bioinspired design process by eliminating interactive work by humans during corpus expansion. SEABIRD's search is illustrated and validated with three well-known biologically inspired design cases.

A content account of creative analogies in biologically inspired design

2010 ◽  
Vol 24 (4) ◽  
pp. 467-481 ◽  
Author(s):  
Swaroop S. Vattam ◽  
Michael E. Helms ◽  
Ashok K. Goel
Keyword(s):  
Sustainable Development ◽  
Engineering Design ◽  
Ad Hoc ◽  
Extended Period ◽  
Biological Knowledge ◽  
Biologically Inspired ◽  
Cross Domain ◽  
Engineering Problems ◽  
Biologically Inspired Design ◽  
Content Theory

AbstractThe growing movement of biologically inspired design is driven in part by the need for sustainable development and in part by the recognition that nature could be a source of innovation. Biologically inspired design by definition entails cross-domain analogies from biological systems to problems in engineering and other design domains. However, the practice of biologically inspired design at present typically isad hoc, with little systemization of either biological knowledge for the purposes of engineering design or the processes of transferring knowledge of biological designs to engineering problems. In this paper we present an intricate episode of biologically inspired engineering design that unfolded over an extended period of time. We then analyze our observations in terms ofwhy,what,how, andwhenquestions of analogy. This analysis contributes toward a content theory of creative analogies in the context of biologically inspired design.

scholarly journals Hierarchy-Based File Fragment Classification

2020 ◽  
Vol 2 (3) ◽  
pp. 216-232
Author(s):  
Manish Bhatt ◽  
Avdesh Mishra ◽  
Md Wasi Ul Kabir ◽  
S. E. Blake-Gatto ◽  
Rishav Rajendra ◽  
...  
Keyword(s):  
Cross Validation ◽  
Hierarchical Classification ◽  
Future Research ◽  
Support Vector ◽  
Challenging Problem ◽  
Fine Grain ◽  
Average Accuracy ◽  
Vector Machines ◽  
Essential Problem ◽  
Fold Cross Validation

File fragment classification is an essential problem in digital forensics. Although several attempts had been made to solve this challenging problem, a general solution has not been found. In this work, we propose a hierarchical machine-learning-based approach with optimized support vector machines (SVM) as the base classifiers for file fragment classification. This approach consists of more general classifiers at the top level and more specialized fine-grain classifiers at the lower levels of the hierarchy. We also propose a primitive taxonomy for file types that can be used to perform hierarchical classification. We evaluate our model with a dataset of 14 file types, with 1000 fragments measuring 512 bytes from each file type derived from a subset of the publicly available Digital Corpora, the govdocs1 corpus. Our experiment shows comparable results to the present literature, with an average accuracy of 67.78% and an F1-measure of 65% using 10-fold cross-validation. We then improve on the hierarchy and find better results, with an increase in the F1-measure of 1%. Finally, we make our assessment and observations, then conclude the paper by discussing the scope of future research.

Quality Assessment in Vocational Education Based on a Hierarchical Classification Technique

2011 ◽  
Vol 403-408 ◽  
pp. 3724-3728
Author(s):  
Chantima Ekwong ◽  
Sageemas Na Wichain ◽  
Choochart Haruechaiyasak
Keyword(s):  
Quality Assessment ◽  
Vocational Education ◽  
Hierarchical Classification ◽  
Unstructured Data ◽  
Classification Model ◽  
Support Vector ◽  
Education Standards ◽  
National Education ◽  
Vector Machines

According to the laws of education in Thailand, the Office for National Education Standards and Quality Assessment is responsible for assessing the external educational institutes in order to develop the quality and educational standards. The external quality assessment reports are represented in both structured and unstructured data. In this paper, we focus on the analysis of unstructured data, i.e., to automatically classify strength and weakness points. We propose and evaluate two different classification models: Flat Classification and Hierarchical Classification. Three algorithms, Naive Bayes, Support Vector Machines (SVM) and Decision Tree, were used in the experiments. The results showed that classification viathe Hierarchical Classification model by using the SVM yielded the best performance. The classification of strength and weakness points yielded the F-measure equal to 0.843 and 0.893, respectively. The proposed approach can be applied as a decision support function for quality assessment in vocational education.

scholarly journals A scalable approach for ideation in biologically inspired design

2014 ◽  
Vol 29 (1) ◽  
pp. 19-31 ◽  
Author(s):  
Dennis Vandevenne ◽  
Paul-Armand Verhaegen ◽  
Simon Dewulf ◽  
Joost R. Duflou
Keyword(s):  
Natural Language ◽  
Information Structure ◽  
Biological Information ◽  
Reference Database ◽  
Automated Classification ◽  
Biologically Inspired ◽  
Large Numbers ◽  
Growing Body ◽  
Biologically Inspired Design

AbstractThis paper presents a bioinspiration approach that is able to scalably leverage the ever-growing body of biological information in natural-language format. The ideation tool AskNature, developed by the Biomimicry 3.8 Institute, is expanded with an algorithm for automated classification of biological strategies into the Biomimicry Taxonomy, a three-level, hierarchical information structure that organizes AskNature's database. In this way, the manual work entailed by the classification of biological strategies can be alleviated. Thus, the bottleneck is removed that currently prevents the integration of large numbers of biological strategies. To demonstrate the feasibility of building a scalable bioideation system, this paper presents tests that classify biological strategies from AskNature's reference database for those Biomimicry Taxonomy classes that currently hold sufficient reference documents.

scholarly journals Email Spam Detection using SVM

2021 ◽  
Vol 9 (VI) ◽  
pp. 669-672
Author(s):  
Azhar Baig
Keyword(s):  
Support Vector Machines ◽  
Personal Information ◽  
Support Vector ◽  
Spam Detection ◽  
Large Numbers ◽  
Vector Machines ◽  
E Mail ◽  
Email Spam

E-mail contributes to internet messaging as a necessary component. Spam mails are unwanted messages that appear in large numbers and are exploited by spammers to divulge personal information of the user. These e-mails are frequently company/control announcements or malware that the user receives suddenly. Email spamming is one of the Internet's unsolved challenges, causing inconvenience to users and loss to businesses. Filtering is one of the foremost widely used and important methods for preventing spam emails. Email filters are commonly wont to organize incoming emails, protect computers from viruses, and eliminate spam. We present this method to classifying spam emails using support vector machines during this study, the SVM outperformed other classifiers.

scholarly journals Detecting Deceptive Opinions: Intra and Cross-Domain Classification Using an Efficient Representation

2017 ◽  
Vol 25 (Suppl. 2) ◽  
pp. 151-174 ◽  
Author(s):  
Leticia C. Cagnina ◽  
Paolo Rosso
Keyword(s):  
Support Vector Machines ◽  
State Of The Art ◽  
Support Vector ◽  
Purchase Decisions ◽  
Business Decisions ◽  
Cross Domain ◽  
Vector Machines ◽  
Low Dimensionality ◽  
Efficient Representation ◽  
Fake Reviews

Online opinions play an important role for customers and companies because of the increasing use they do to make purchase and business decisions. A consequence of that is the growing tendency to post fake reviews in order to change purchase decisions and opinions about products and services. Therefore, it is really important to filter out deceptive comments from the retrieved opinions. In this paper we propose the character n-grams in tokens, an efficient and effective variant of the traditional character n-grams model, which we use to obtain a low dimensionality representation of opinions. A Support Vector Machines classifier was used to evaluate our proposal on available corpora with reviews of hotels, doctors and restaurants. In order to study the performance of our model, we make experiments with intra and cross-domain cases. The aim of the latter experiment is to evaluate our approach in a realistic cross-domain scenario where deceptive opinions are available in a domain but not in another one. After comparing our method with state-of-the-art ones we may conclude that using character n-grams in tokens allows to obtain competitive results with a low dimensionality representation.

Change Detection in SAR Images Based on Semi-Supervised Learning

2014 ◽  
Vol 596 ◽  
pp. 484-489
Author(s):  
Song Tian ◽  
Jian She Song ◽  
Shu Bing Tian ◽  
Wei Gong
Keyword(s):  
Support Vector Machine ◽  
Change Detection ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Detection Accuracy ◽  
Sar Image ◽  
Training Set ◽  
Sar Images ◽  
Large Numbers ◽  
Vector Machines

Support Vector Machine (SVM) is a supervised approach, which needs large numbers of labeled samples. However, it is difficult to obtain such samples for change detection based on SAR images and the available labeled samples are very limited. this paper proposes a semi-supervised support vector machine (S3VM) unsupervised SAR image change detection. Using of K-means clustering method obtain threshold of image; introduce offsets which are automatically selected to achieve a pseudo-training set and unlabeled set; Finally, based on the statistical characteristics of semi-supervised support vector machines for image change and non-change class. The experimental results showed that: In the case without using noise reduction and the reduction in the number of samples, the proposed algorithm can maintain better classification, generalization and more stable detection accuracy.

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