Deep Learning - Science topic

I'm an undergraduate doing a Software Engineering degree. I'm looking for a research topic for my final year project. If anyone has any ideas or research topics or any advice on how or where to find one please post them.

Thanks in advance ✌

Is it a good idea to extract features from pre-trained, the last 1x1 convolution removed U-NET/Convolutional Autoencoder? Data will be similar and the model will be trained for image segmentation. I know everybody suggests freezing the encoder is the best option but I think there is feature extraction in the decoder part too(In both convolutional autoencoder and U-NET). They are high-level feature extractors, if my data was different, the frozen decoder part wouldn't be a good idea. But what if my data is very similar?

Recently, I am studying the use of deep learning technology to explore the extraocular diseases that can be reflected by fundus photography. I think it's difficult, because the human body is a complex system, in which there are too many confounding factors. What should I consider when designing and implementing my research？

Hi, I have been working on some Natural Language Processing research, and my dataset has several duplicate records. I wonder should I delete those duplicate records to increase the performance of the algorithms on test data?

I'm not sure whether duplication has a positive or negative impact on test or train data. I found some controversial answers online regarding this, which make me confused!

For reference, I'm using ML algorithms such as Decision Tree, KNN, Random Forest, Logistic Regression, MNB etc. On the other hand, DL algorithms such as CNN and RNN.

I am working with the UAV image data.

my study area is a dense forest.

How can I extract the dieback of the trees with deep learning techniques? do you know any package for this aim?

I want to extract the steps of the dieback trees.

What are approaches to classify patent data using deep learning? (document, text, word, labels )

How patent classification using CNN, DNN, RNN ?

Is transfer learning is effective in patent classification?

I wanted to ask this one Quora, but they no longer allow you to enter background/elaboration on a question.

I'm looking to build/purchase a workstation to use from home for research/data analysis (and probably some recreation, but anything able to handle my work needs will be able to run a game or two with ease). The most demanding applications it will be used for are image processing (specifically, 3D- and/or Z-projections of high-res, 16bit Z stacks, deconvolution, and deep learning / training DNNs and GANs). I'm looking at NVidia graphics cards, since it's so much easier to push parallel processing tasks to the GPU with something like CUDA than it is to jury-rig a workaround for an AMD card. Specifically, i'm trying to decide between a GTX and an RTX series card.

I know the primary difference is that RTX cards can do ray-tracing, while GTX cannot -- but it's not immediately apparent to me whether I should care, given that I won't be using the GPU for its ability to render realistic real-time action scenes.

Given that the GTX line is considerably more affordable, I'd like to know if the RTX equivalents will outperform the GTX counterparts in tasks like neural network training. Or even if I should bite the bullet and spring for a Quadro (though I sincerely hope that won't be the case).

I was wondering if anyone has success using AMD Radeon GPUs for deep learning because nvidia GPU is preferred in the majority of online tutorials.

I have long-term rainfall data and have calculated Mann-Kendall test statistics using the XLSTAT trial version ( addon in MS word). There is an option for asymptotic and continuity correction in XLSTAT drop-down menu.

I want to find length of construction zones appearing on drone videos. Drone is moving along the road.

Please refer me a code to find

length of the construction zones appearing on the drone videos

Calculate number and types of construction zones.

Do Serial correlation, auto-correlation & Seasonality mean the same thing? or Are they different terms? If so what are the exact differences with respect to the field of statistical Hydrology? What are the different statistical tests to determine(quantity) the serial correlation, autocorrelation & seasonality of a time series?

I am trying to make generalizations about which layers to freeze. I know that I must freeze feature extraction layers but some feature extraction layers should not be frozen (for example in transformer architecture encoder part and multi-head attention part of the decoder(which are feature extraction layers) should not be frozen). Which layers I should call “feature extraction layer” in that sense? What kind of “feature extraction” layers should I freeze?

Hello dear researchers

I used a metaheuristic algorithm for binary prediction. The input data of the model is in the form of text and has an order

Which deep learning model do you recommend? Why?

My goal is to compare the traditional machine learning model with deep learning models in solving a binary problem

CNN and LSTM performed

Thank you for your support

looking for dataset which can useful for our project.

Hi,

Thank you for help.

How to make the scheduling process in CloudSim an environment for my reinforcement learning model ?

We need large datasets to work on malware detection in android apks using deep learning

I am trying to develop an automatic segmentation system for T1 and T2 MRI (via Deep Learning) whose goal is to segment different areas of interest:

- Scalp

- Bones

- Blood vessels

- Cerebrospinal fluid

- White/gray matter

In order to be able to extract surfaces and to make calculations with.

At the beginning, I was based on an unsupervised segmentation system inspired by the W-NET model (https://arxiv.org/pdf/1711.08506.pdf).

But this system seems complicated to set up for this type of images. So I turned to other (supervised) models like U-NET or V-NET. But this kind of model requires to have the segmented mask as ground truth.

I would like to know if you have knowledge of the existence of a type of dataset where T1 and T2 brain MRI could already be segmented manually?

I found the following dataset: MRBrainS (https://github.com/looooongChen/MRBrainS-Brain-Segmentation) but it is only the brain that is segmented, not the whole head.

Thanks for your help!

I am working with nii files, that I have normalized to the T1 MNI space. All the Nifti files have been converted to 2D images, and for each NIfti file, I am getting 256 2D jpg images, some of which contain almost no information.

How do I determine which of these 2d Images should I use for training my DL model?

If I use all the images, wouldn't the images containing no or less information decrease the performance of my model?

Hi

I'm still new to deep learning.

I'm currently reading papers in my specific area of application of deep learning for text classification.

When I read those papers, I couldn't figure out how could they come out with the proposed methods.

1) So if I am just getting started, how could I find the idea of a new deep learning method?

2) And what aspect do researchers usually work on in deep learning?

how can I optimize the accuracy of the deep learning model for image segmentation by using methods optimization in python?

can anyone help me?

thank you in advance

Hello Friends,

I am applying ML algorithms (DT, RF, ANN, SVM, KNN, etc) in python to my dataset which has features and target variables as continuous data. For example, when I'm using DecisonTreeRegressor I get the r_2 square equal to 0.977. However, I'm interested to deploy the classification metrics like confusion matrix, accuracy score, etc. For this, I converted the continuous target values into categorical ones. Now when I'm applying the DecisionTreeClassifier, I get the accuracy square=1.0 which I think is overfitting. Then I applied the normality checks, and correlation techniques (spearman) but the accuracy remains the same.

My question is am I right to convert numeric data into categorical one?

Secondly, if both regressor and classifiers are used for the same dataset, will the accuracy be changed?

Need your valuable suggestions, please.

For details plz see the attached files.

Thanks for the time

Hello, I am interested converting word numerals to numbers task, e.g

- 'twenty two' -> 22

- 'hundred five fifteen eleven' -> 105 1511 etc.

And the problem I can't understand at all currently is for a number 1234567890 there are many ways we can write this number in words:

=> 12-34-56-78-90 is 'twelve thirty four fifty six seventy eight ninety'

=> 12-34-576-890 is 'twelve thirty four five hundred seventy six eight hundred ninety'

=> 123-456-78-90 is '(one)hundred twenty three four hundred fifty six seventy eight ninety'

=> 12-345-768-90 is 'twelve three hundred forty five seven hundred sixty eight ninety'

and so on (Here I'm using dash for indicating that 1234567890 is said in a few parts).

Hence, all of the above words should be converted into 1234567890.

I am reading following papers in the hopes of tackling this task:

-

-

-

-

But so far I still can't understand how would one go about solving this task.

Thank you

How will the test cases get generated with deep learning?

Cross-Validation for Deep Learning Models

The concept of Circular Economy (CE) in the Construction Industry (CI) is mainly about the R-principles: Rethink, Reduce, Reuse, Repair, and Recycle. Thus, if the design stage following an effective job site management would include consideration of the whole lifecycle of the building with further directions of the possible use of the structure elements, the waste amount could be decreased or eliminated. Analysis of the current literature has shown that CE opportunities in CI are mostly linked to materials reuse. Other top-researched areas include the development of different circularity measures, especially during the construction period.

In the last decade, AI merged as a powerful method. It solved many problems in various domains, such as object detection in visual data, automatic speech recognition, neural translation, and tumor segmentation in computer tomography scans.

Despite the broader range of works on the circular economy, AI was not widely utilized in this field. Thus, I would like to ask if you have an opinion or idea on how Artificial intelligence (AI) can be useful in developing or applying circular construction activities?

Four homogeneity tests, namely the Standard Normal Homogeneity Test(SNHT), Buishand Range(BR) and Pettitt test and Von-Neumann Ratio test (VNR) are applied for finding the break-point. Out of which SNHT, BR and Pettitt give the timestamp at which the break occurs whereas VNR measures the amount of inhomogeneity. Multiple papers have made the claim that "SNHT finds the break point at the beginning and end of the series whereas BR & Pettitt test finds the break point at the middle of the series."

Is there any mathematical proof behind that claim ? Is there any peer-reviewed work (Journal article) which has proved the claim or is there any paper which has crosschecked the claim ?

Let me say that I have a 100 years data, then start of the time series means whether it is the first 10 years or first 15 years or first 20 years? How to come to a conclusion ?

I am going to recognize both static and dynamic sign language and how to use both static and video datasets to recognize.

How can I combine three classifiers of deep learning in python language ?

For a research project we are looking for users who apply Artificial IntelligenceI/Machine Learning/Deep Learning in daily clinical practice. The participants will be asked about their experiences in a 30-minute interview.

Best,

Beat Hofer

I am trying to find datasets with cbct dental imaging for detecting periapical lesions.

Dear everyone:

I cannot find web sites or books explaining how to backpropagate attention layers in Deep Learning

Could anyone of you please teach me how to backpropagate them?

Thank you in advance and have a nice day

Most often training in computer vision task are usually in 2D or 3D. Why can't the images be presented into 1D image to access how DL models will perform?

Hello All,

I need to identify the heart region and thorax region in an automated manner in an ultrasound scan image of the heart. Could anyone tell me the step by step guide to identify the regions once an input image is given. Any assistance regarding this would a great help.

I am experimenting with Retinal Optical coherence tomography. However, the region of interest named 'irf' has a very small area and I am using Dice loss for the segmentation in Unets.

However, I am not getting satisfactory results as the input images are noisy and also the ROI is very small. Can anyone suggest to me a suitable loss for this kind of challenge?

How can cocomo model be used with deep learning?

The costs and time commitments associated with data collection and labeling might be prohibitive. A huge dataset is insufficient since the success of deep learning models is strongly dependent on the quality of training data. Cost, time, and the use of appropriate training data are all challenges. Biases, incorrect labels, and omitted values are some of the difficulties that impair the quality of deep learning training datasets.

Deep learning models require large volumes of data, and this massive data will increase the need for a system to be trained continuously.

I have come across many research articles stating the values of rmse of deep learning models such as LSTM. Some results are between 0 and 1, while some are between 0 and 50. I want to know what an ideal rmse value is for such neural network models.

I have been trying to reduce the rmse values to less than 1 from values between 3 and 7.

Note: The error values are computed after unscalling back the dataset to its original form. The errors are, however, between 0 and 1 when computed with scaled data. But, I think computing with unscaled data makes more sense.

How to request for download the dataset of DERMNET?

I want some free resources that I can learn deep learning easily. I am interested in the text mining field.

Thank You

Hi all, I am looking for GAN generated face datasets. Please share the links. Also if there is any pre-trained networks for this purpose it would be helpful.

Thank you.

Deep Learning Training

I am about to start working on 6d pose estimation for Object Grasping based point cloud. In our lab we have the following:

-AUBO i5 industrial Manipulator.

-COBOT 3D Camera that will give us a point cloud of the scene. the camera will be attach to the manipulator in the eye in hand configuration (mounted on the manipulator's gripper(end effector).

Deep learning Based method will be used for 6D pose estimation of the target object.

the 6D pose estimation will be calculated on my laptop, How can I send the final result or the pose estimation to the robot in order to control it and eventually pick and place the target object.

I need to classify patent data using deep learning. What is word embeddings in used patent classification?

How to categorized patents using transfer learning?

Which patent dataset is freely available?

Monkeypox Virus is recently spreading very fast, which is very alarming. Awareness can assist people in reducing the panic that is caused all over the world.

To do that, Is there any image dataset for monkeypox?

I'm using SPSS software to model my statistically variables. I'm used to model variables in MATLAB, R and Python, this is my first experience with SPSS software. I've create model on my observed dataset however the result of model revealing some calibration with oberserved dataset. How can I can calibrate SPSS timeseries model using programming language.

I actually want to run infinite time model on my dataset and want to set certain parameters e.g., RMSE between observed and predicted dataset fall under my descries limit I want to save that model parameters. Is there any way to do SPSS software with some programming ?

Time reply will be appreciated

Any good course on machine learning/deep learning online or on udemy, coursera?

which deep learning algorithm is best for infilling data gap in river streamflow time series??

I need to define an objective function that has several decision variables. This objective function needs to be minimised by using Adam optimiser from Tensorflow. There are a lot of examples on deep learning on internet. However, I am finding it difficult to get one that only optimises the objective function.

Among all the Neural Network structures that are introduced, RNN has received noticeable attention because of the state art included in its gradient computation with backpropagation. On the other hand, we have the invention of autograd as a gradient computation tool in many programs like Pytorch, Tensorflow,... which takes care of analytic computation of Network's gradient with chain rule and backpropagation. But even the invention of autograd doesn't impact the superiority of RNN over many other structures, because computer takes care of gradient computation with no care about the network structure which can result in a super complicated and unnecessary computations. Therefore, even applying chain rule with no inclusion of the state of art in Gradient computation can be still difficult and computer codes can crash in gradient computation. For example consider the RNN structure, if you introduce it directly to autograd with no hint of the presence of the state of art that is available for it, then autograd will have a hard time to compute its gradient, but if you hint the autograd of the specific method which exists for RNN, then the autograd will compute the gradient easily. So my question is:

I am going to introduce a network structure to autograd which includes RNN or LSTM, if I introduce the network in the standard way then it would be hard for autograd to compute its gradient, but if I tell the autograd that a specific part of my network structure is RNN, then it will compute it easily due to pre-provided tools in the autograd for famous network structures like RNN or LSTM. So How can I do that? How should I inform the autograd that my network structure has LSTM embedded inside of it?

Hello,

Is there any point clouds dataset for railway asset classification? Or are some network's weights pre-trained on such a dataset? to date, there are no datasets available in open access for this purpose.

Thank you.

We have a paper with 4 names on it in the field of deep learning in medical image analysis. Two authors had almost equal contributions. One of them is a researcher (called A) with a master's degree and the other is a last-year Ph.D. (called B) There is also another first-year Ph.D. student (called C) who had a minor contribution by participating in some meetings and finally the senior professor (called D).

Since the supervision is done by B, B's name is before the professor as supervisor, but is it okay to put an equal contribution between A and B as first and third author? Is the list of authors like follow fine?

A* C B* D

* means the equal contribution

Thanks

I have 18 rainfall time series. On calculating the variance, it was found there was an appreciable change in the value of variance from one rainfall station to other. Parametric statistical tests are sensitive to Variance, does it mean we need to apply robust statistical tests instead of the parametric test?

Hello,

In neural network pruning, we first train the network. Then, we identify redundant parts and remove them. Usually, network pruning deteriorates the model's performance, so we need to fine-tune our network. If we determine redundant parts correctly, the performance will not deteriorate.

What happens if we consider a smaller network from scratch (instead of removing redundant parts of a large network)?

I am looking for programming techniques to use in stock investment recommendation.

I am

The papers using machine-learning particularly deep-learning models in hydrological prediction (runoff, soil moisture, evapotranspiration, etc.) increase dramatically in recent years. In my viewpoint, these data-driven methods require substantial data to derive solid predictions. I am not sure what is the advantage of these models over the process-based models in predicting hydrological processes.

How effective will be endometrial cancer detection from ultrasound images using deep learning? Any kind of suggestion would be highly appreciable

At present, deep learning has been widely used in various scientific research fields. How to view the application prospect of deep learning in InSAR and the direction that it can be applied in InSAR in the future?

Three papers to start learning about science behind graph neural networks and how they work. These papers will be easy to ready if you are familiar with, not necessarily expert in, neural networks and machine learning. There are many more papers on graph neural network. Feel free to share more with your network in a message on this post. I will do the same. A Comprehensive Survey on Graph Neural Networks ( ) Kernel Graph Convolutional Neural Networks ( ) Geom-GCN: Geometric Graph Convolutional Networks ( )

Hello,

I've pruned my CNN layer-by-layer in two steps.

First, I removed a fourth of the filters of some desired layers, which led to performance degradation of around 1%. Then, I selected two layers that had the highest number of filters and caused the least performance deterioration. Next, I removed half of their filters. The second model performed even better than the original model. What is the reason?

If Model consists of CCN/LSTM layers and Deep Nueral Network(Fully connected layers) then can we called it hybrid model.

Dear Researchers.

These days machine learning application in cancer detection has been increased by developing a new method of Image processing and deep learning. In this regard, what is your idea about a new image processing method and deep learning for cancer detection?

Thank you in advance for participating in this discussion.

I was trying to get an insight into quantum computing (QC) for a research purpose. However, every source was filled with lots of technical terms with less explanation. It becomes very difficult for us those who want to learn QC with no knowledge before.

Is there any source where beginners can learn QC with zero background knowledge?

Thanks in advance.

Why rrrrrrr uuuuuuuu

I am comparing 2 deep learning models on same dataset. But I am getting different comparison on different run. For example, once I get model 1's accuracy is better than model 2, again on another run I get the opposite result. I have used same seeds before running the models.

What may be the reason? How can I get a stable comparison result?

After CLIP and DALLE, are there any latest advances when it comes to learning deep learning embeddings of image & text description together ?

In the oil and gas industry, for technical, economic, and similar reasons, well-Log running is done from special intervals. Therefore, to build comprehensive models for field development, we will need more information at different depths. Today, with advances in numerical methods, especially machine learning and deep learning methods, we can use their help to eliminate these data gaps. Of course, there are methods such as rock physics that are very practical. But according to my results, part of which is described below. It is better to combine the rock physics method with the deep learning methods, in which case the results will be amazing. I selected wells from the Poseidon Basin in Australia for testing and got good results. In this study, by combining the rock physics method and deep learning (CNN + GRU), the values ​​of density, porosity, and shear wave slowness were predicted. A comprehensive database of PEF, RHOB, LLD, GR, CGR, NPHI, DTC, DTS, and water saturation logs was prepared and used as training data for the wells. The below figure is the result of a blind well test for Torosa well in the Poseidon Basin, Australia. As you can see, the prediction results are very close to the measured values ​​of shear wave slowness in this well.

Related to early diagnosis of Alzheimer's by deep learning

I'm trying to implement a deep learning model to classify stable and converter MC

.

I was exploring differential privacy (DP) which is an excellent technique to preserve the privacy of the data. However, I am wondering what will be the performance metrics to prove this between schemes with DP and schemes without DP.

Are there any performance metrics in which a comparison can be made between scheme with DP and scheme without DP?

Thanks in advance.

How do we determine the appropriate number of hidden layers for the problem so that it positively affects the solution of the desired problem?

How do we understand the impact of adding an additional hidden layer on improving workflow? Where exactly will the improvement be?

Hello everyone,

I am looking for links of audio datasets that can be used in classification tasks in machine learning. Preferably the datasets have been exposed in scientific journals.

Thank you for your attention and valuable support.

Regards,

Cecilia-Irene Loeza-Mejía