Expert answer:Return – Course Project

Expert answer:Pick whatever project from this list that is easy ans simple. You can think of (4-C, 4-E, 4-F) they look similar to each other to what you want., but not (4-D) which is my friend’s project. My friend have used API algorithm! Don’t use this one please. Any other algorithm or title.Requirements:1) Research paper IEEE about this project as a word document. (There is no limit, but you can consider of minimum of 6 pages including references).2) An explanation of function codes as a separate word document. (There is no limit, and it depends on project functions have used, but you can consider of minimum of 2 pages).3) Attach all references that used (articles and websites links … etc.) as a zip file. References that are 20 to 40. I need at least 20 references.4) A presentation about the project.The project codes must run on my machine, and all requirements from 1 up to 4 need to be submitted including the project codes.Here is the requirements and time for the project submission and presentation**************************************************************************Please compress the project folder and submit to the email address by Dec 7 2017.The project folder should include the following two kinds of documents:1. Research reportThe research report should include the problem, the proposed algorithm to solve the problem, comp rations between the proposed algorithm with other previous algorithms that deal with the same problem, features of the proposed algorithm, simulations results. The format of the research report should follow the IEEE conference paper format http://www.ieee.org/conferences_events/conferences/publishing/templates.html.The research report should include abstract, key words, introduction, previous theories/methods, proposed algorithm, simulations, conclusions and/or future works, and reference. The length of the report should be at least 6 pages without counting references. Please use the red words to show your innovative part of this project.Students should write your report in his/her own words, not directly copy the original sentences from others? works. In case if a student has to directly copy the original sentences from others? works (exp. definition of a concept), please put these sentences in double quotation and indicate where get these sentences.2. Simulation codes and the file to explain how to run the codesEvery student must code by himself/herself. In case if student has to reference library functions, please use red color to highlight these codes and indicate where get these codes. The major part of the codes must be coded by the student, not referenced from library functions.Each student will give a presentations (20mins) for this coding project. The presentation should include, your topic, algorithm, your innovative part, simulation results, comparison between your algorithm and others, demonstration of running your codes and conclusion. The Presentation time will be the week Dec. 4 2017 to Dec. 8 2017 ( Please send me an email to make an appointment for presentation, if you would like to make presentations earlier please let me know your prefer time)Please, this project must have free plagiarism that’s mean NO PLAGIARISM at all because my advisor will check. If you have any question, please ask me. Good Luck 🙂
20171112165858project_lists.pdf

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Research Project List
1. Deep Networks for Visual Recognition (http://cs231n.stanford.edu/ )
a. Image Classification: Implement image classification system using Neural Network or
RNNs.
b. Image Captioning: Implement image caption system using RNNs or LSTMs. Use
TensorFlow or Caffe to implement Deep Learning module.
2. Computer Vision (http://vision.stanford.edu/teaching/cs231b_spring1415/)
a. Graph Cut: Implement an Interactive Image Segmentation tool (SIGGRAPH2004).
b. Target Tracker: Implement the learning and detection components of an online target
tracker. Implement single target or multiple targets tracking algorithm on movies.
c. Object Detector: Implement the R-CNN based object detection algorithm (CVPR2014).
3. Deep Learning for Natural Language Processing (http://cs224d.stanford.edu/)
a. Named Entity Recognition: Use TensorFlow to build a deep network to predict whether
a given word represents one of four categories (Person, Organization, Location, and
Miscellaneous).
b. Recurrent neural network (RNN): Use a TensorFlow to build a language modeling to
predict a vocabulary with the given series of words.
4. Data Mining / Machine Learning
a. Phishing / Spam classification: build a model to classify phishing website or spam email
with various data mining algorithms.
b. Anomaly detection (Intrusion detection): build a model to detect anomaly transactions
or server log.
c. Recommendation System: build a collaborative filtering recommender system to
recommend movie or restaurant.
d. Sentiment Analysis with Text Data: build a system to analyze the customer sentiment
based on the Twitter or Blog text data.
e. Social Network Analysis: build a system to analyze the characteristics of social network
graphs.
f. Social Media Analysis: build a system to analyze the social media data. Especially,
sentiment analysis on a product or service.
g. Price prediction: build a model to predict a time series price. Implement ARIMA or other
multiple regression types algorithms.
h. Vandalism detection: Implement a vandalism detection algorithm on Wikipedia page.
5. Deep Learning Computer Vision (Advanced research topics)
a. Classifying and Segmenting Microscopy Images using Convolutional multiple instance
learning.
b. Fast Scene Understanding with Generative Models.
c. Instance Segmentation (ICCV 2015, CVPR 2016).
d. Neural Algorithm of Artistic Style (CVPR2015).
e. Pose Aligned Network for Deep Attribute Modeling (CVPR 2014).
f. Audio Generation and Extraction.
g. Recurrent Model of Visual Attention (CVPR 2014)
h. Neural Mapping of Navigational Instructions to Action Sequence.
i. Recursive Deep Models for Semantic Compositionality (EMNLP2013).
…
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