I need to do implementation to biology model ( it is applied the functions of organelle in living cell ) using biopython or any suitable programming language.
I am beginner in this field.
What is suitable programming language for this problem?
Can any one help me ?
How I can start?
KNIME with its cheminformatics extensions is often used to model chemical transformations.
Related
Part of my thesis work is to evaluate number of language detection methods that are already available and then finally implement one them.
For this I have chosen the following methods,
N-Gram-Based Text Categorization by Cavnar and Trenkle
Statistical Identification of Language by Ted Dunning
Using compression-based language models for text categorization by Teahan and Harper
Character Set Detection
A composite approach to language/encoding detection
I have to first evaluate the methods and preferably present a table with accuracy for each of these methods. My question is that in order to find the accuracy of each of these methods, do I need to go ahead a build the language models using training data, then test them and record the accuracy or is there any other approach that I can follow here. Though most of the researches already include these accuracy tables, I am not sure if it's accepted in my education to simply grab it and present in the report.
Appreciate any thoughts on this.
I would also suggest asking your thesis advisor. Implementing all of them will be a lot of work, and it is very difficult to really compare them without being able to test them. If I remember correctly the last three have not been well evaluated in the literature, so it would be difficult to compare their results. I have implemented (and evaluated) only the first one of those myself. One big question is also how big a part of your thesis this LI evaluation and implementation is?
I am trying to develop a Sinhala (My native language) to English translator.
Still I am thinking for an approach.
If I however parse a sentence of my language, then can use that for generating english sentence with the help of stanford parser. Or is there any other method you can recommend.
And I am thinking of a bottom up parser for my language, but still have no idea how to implement. Any suggestions for steps I can follow.
Thanks
Mathee
If you have enough amounts of bilingual corpora then the best thing you can do is to train a model using an existing statistical machine translation system like Moses. Modern phrase-based SMT systems resemble a reverse parsing mechanism in the sense that they find the most probable combination of target-language phrases for translating a specific source-language sentence. More information in this Wikipedia article.
There are several (many?) programming/design systems where the user constructs a (node-edge) graph to represent the algorithm, and can then run the resulting algorithm to obtain results.
The two examples that I know off the top of my head are
Simulink
Pure Data
but I want to look into the general features of this approach for designing a user interface for setting up numerical processing problems, so I need to know some general terms for concisely describing this interface design.
I'm sort of looking for:
I type in "What programming systems (environments) use an XXX interface" into Google,
and amongst the answers are Simulink and Pure Data.
I find the Wikipedia page on XXX user interface and it includes in its list of systems, Simulink and Pure Data.
Someone wrote an academic paper "AmazingSoftware: an XXX system for modelling ecosystems", where they constructed a system, with this type of node/edge interface, that allows for modelling population dynamics in some way (I'm not particularly interested in ecology, rather I'd want to find this to understand what they did with respect to the interface itself).
Pure Data is generally described as "real-time graphical dataflow programming", so there are three key words there:
real-time: its a real-time system, so there is a built in sense of time and concurrency, and "guarantees" a response within strict time constraints
graphical: the programming is performed and represented graphically rather than text or punch cards or whatever (this could also be labeled visual)
dataflow: the programming logic is based on the flow of the data, versus object-oriented or procedural
My guess is that you are most interested in the graphical/visual part of that.
I'm about to take a course in AI and I want to practice before. I'm using a book to learn the theory, but resources and concrete examples in any language to help with the practice would be amazing. Can anyone recommend me good sites or books with plenty of examples and tutorials ?
Thanks !
Edit: My course will deal with Perceptrons, Neural networks and Bayesian AI.
Really depends on what area you want to specialize on. There is the startup - resource : is
here. I learned about neural nets from their example. Can you elaborate what kind of AI it should be?
Ah and i forgot: this link is a very nice forum where you can look at problems other people have and learn from that.
Cheers.
My advice would be to learn it by trying to implement the various types of learners yourself. See if you can find yourself a dataset related to some interest you have (sports, games, health, etc.) and then try and create a learner to do some kind of classification (predicting a winner in a sports game, learning how to classify backgammon positions, detecting cancer based on patient data, etc.) using different methods. Start with Decision Trees if that's part of your future course work since they're relatively simple, then move on to neural networks.
Here is a set of sources, each one of which i recommend highly--for the quality of the explanation, for the quality of the code, and for the 'completeness' of the algorithm demo.
Least-Squares Regression
(Python)
k-means clustering (Python)
Multi-Layer Perceptron (Python)
Hopfield Network (Python)
Decision Tree (ID3 & C4.5)
In addition, the excellent textbook Elements of Statistical Learning by Hastie, et al. is actually free to download. The authors have an R package that accompanies this textbook which contains all of the code. This book includes detailed discussion of most (if not all) of the major classes of ML algorithms, with specific examples that refer to working code and 'real-world' data.
Personally I would recommend this M.Tim.Jones book on AI.
Has many many topics on AI and almost every type of AI discussion is followed by C example code. Very pragmatic book on AI indeed !!
Russel & Norvig have a good survey of the broad field.
i am a final year student of computer engg.i am doing a project on sketch generation from an image.The generated sketch will then be given the feature of lip synch.
I wanted to know what type of programming languages would be suitable for this kind of application?
Thanking you in advance
Since nobody has the answer for this,After a few months of search, i found out myself.The language which can be used are either pure Matlab for image processing and lip sync(however it is difficult in Matlab due to absence of multi threading,but still can be done using a TimerFcn).Java can be used as language here,but the image processing and sound processing is a bit complicated in Java.Flash is a language which can be used the best for this type of processing though i doubt if it satisfies the criterion of a 'programming language'.other readily available tools are present which may be used of the implementation of the project.