Classifier - Science topic

It is possible to distinguish different types of water bodies using hyperspectral data, including coastal waters, inland waters, ponds, and inland aquaculture. Hyperspectral imaging measures the reflectance of water bodies across a wide range of wavelengths, which can be used to identify different water properties and characteristics.

Different water bodies have unique spectral signatures, which can be analyzed to identify their specific land use classifications. For example, coastal waters typically have high reflectance in the blue and green parts of the spectrum due to the presence of phytoplankton and other water constituents. Inland waters may have different spectral characteristics depending on their location, depth, and surrounding vegetation.

Ponds and inland aquaculture can also be distinguished using hyperspectral data by analyzing the reflectance patterns of water and surrounding vegetation. For example, ponds may have different spectral signatures depending on their size, depth, and the type of vegetation present around them. Inland aquaculture, on the other hand, may have unique spectral signatures based on the type of fish species and feed used, as well as water quality and management practices.

However, accurate classification of water bodies using hyperspectral data requires careful data processing and analysis, as well as accurate ground truthing data for calibration and validation. Additionally, it is important to consider the specific environmental and management factors that may affect the spectral characteristics of different water bodies, in order to accurately classify them.

Please, could you provide more details? What type of images do you have?

some common feature extraction techniques:

Hope this will help to start your productive work with collaboration.

As far as I know it depends on how you decide to have the company take the sample. In any case it will be a non probability sample so standard statistical methods probably don't apply. That's the real problem. Best wishes David Booth

There is no universally agreed-upon classification scheme for Shannon's Diversity Index that categorizes it as "high", "medium", "low", or "very low". The interpretation of Shannon's Diversity Index can vary depending on the context in which it is used and the specific values obtained.

Shannon's Diversity Index ranges from 0 to a maximum value, which depends on the number of species or categories being analyzed and their relative abundances. Higher values of Shannon's Diversity Index generally indicate greater diversity or evenness of the distribution of species or categories, while lower values indicate lower diversity or greater dominance of certain species or categories.

You might be interested in this recent publication on gaps regarding Sleep Apnea...see https://click.mail.medscape.com/?qs=fa3bda5c01341501ef46724b48d0134148fdb4a41f8eacd5ace621ae9dbd81477bca1a969bf104ff106c7d749ebf445efe1b1d4c28565e6f9c26b17f571a2dd3

The perpetual coexistence of developing countries (with high income inequality and poor standard of living) and developed countries (with low income inequality and high standard of living) can be explained under the following headings.

*1.Technological Advancement and Colonization.*

As a result of development of superior military and industrial technology, the Western Capitalist Powers were able to colonize less technologically advanced segments of the world. Thereby giving developed countries an edge in international trade

*2. Old International Division of Labour.*

Given colonization and technology advancement, trade between developed economies (Colonial masters) and developing countries (colonies) were based on old international division of labour in which developing countries specialize in exportation of primary products (and importation of manufactured goods) while developed countries specialize in exportation of manufactured goods (and importation of raw materials)

Developing economies dependence on primary exports implies little or no value addition which in turn inhibits job creation; development of infrastructure etc. These negative effects of dependence on primary export further worsen global inequality.

*3. Post Colonization and New International Division of Labour.*

After attainment of political independence from the colonial masters, one would expect the indigenous leaders of the erstwhile colonies to re-write their history and progress from dependence on primary export to manufactured export (new international division of labour).

However, the few elites in developing countries (for personal interest) chose to maintain the status quo at the expense of the masses. Consequently most developing countries only got political independence and not economic independence.

*Conclusion.*

Global inequality will persist until developing countries metamorphose from primary-export dependent economies to manufactured export dependent economies.

To resolve this issue, you should first check if your clarifier data contains the same number of classifiers as the current dataset in MorpheoJ. If the number of classifiers is not consistent, you may need to modify your clarifier data to align with the current dataset.

It may also be helpful to double-check the format of your clarifier data to ensure that it is in a compatible format for importing into MorpheoJ. You can check the MorpheoJ manual or website for information on the supported file formats for importing data.

If you've already tried these steps and are still encountering the same issue, you may want to consider reaching out to the MorpheoJ support team for further assistance. They may be able to provide more specific guidance on how to resolve the issue you're encountering.

Follow these steps:

1. First, you'll need to gather a collection of dirt photos to categorize. This dataset should be divided into two parts: training and testing.

2. The photos must then be pre-processed, such as resizing and normalizing the pixel values.

3. After that, import the VGG19 model and delete the final completely linked layer.

4. Then, you will need to add new layers to the model and train it using the training dataset of soil images.

5. Once the model is trained, you can test it on the testing dataset of soil images and evaluate the performance using metrics such as accuracy, precision, recall, and F1-score.

Thankyou :)

Renal cell carcinomas (RCCs) are a type of cancer that affects the cells of the kidney. These tumors are now commonly classified based on their genetic characteristics, and various immunohistochemical stains and other genetic testing methods are used to identify the specific subtype of RCC.

In terms of the minimum panel of immunostains that should be available for classifying RCCs, it is important to have a range of markers that can help to identify the specific subtype of RCC and guide treatment decisions. Some commonly used immunostains for RCC include:

In addition to these markers, it may also be helpful to have access to other conventional markers, such as CD10, vimentin, and PAX2, which are commonly used to differentiate RCCs from other types of renal tumors.

In terms of FISH probes, some commonly used probes for RCC include those that target specific genetic abnormalities,

For dichotomous dependent variables, you could use logistic regression.

However, I would discourage you from dichotomizing your independent variables (leave them in their original metric). Dichotomizing leads to a loss of information and can reduce statistical power. There is almost never a need to dichotomize variables from a statistical perspective because there are many statistical procedures available that can handle ordinal-polytomous and/or continuous (metrical, interval-scale) variables. When you type "dichotomizing continuous variables" into Google Scholar, you will find a large number of relevant references that explain why this is a bad idea.

The Central Water Commission of India provides daily discharge data of Indian rivers on its website. The data can be accessed at http://www.cwc.gov.in/main/hydrometric_observations.html.

Booth are commenly used, however., for the case of data mining people use tabular data, whilst for computer vision applications, images data is efficient and usual.

I found the Semi-Automatic Classification Plugin in QGIS very useful and easy to use. It is open-source, easily accessible, and can be learned through youtube tutorials.

Here's one from the creator:

you can find the SVM classifier as a ready made algorithm in ENVI

or you can use QGIS with open CV library which has a ready made codes for some machine learning algorithms

Hi,

I suggest you see the links on the topic.

Best regards

Dear Laxmisagar Hs ,

The same argument applies to Machine Learning Algorithms. Selecting the best ML algorithm in terms of computational complexity for a given problem is an important part of an ML practitioner. This article will be very specific in context to SVM.

Regards,

Shafagat

Misspelled — reading comments should be roading— my computer thinks it knows what I mean — not always so.

Hi Tim Albiges

Wow. This is very helpful. Thank you so much.

Time-dependent ROC curve analysis in medical research: current methods and applications

Adina Najwa Kamarudin* , Trevor Cox and Ruwanthi Kolamunnage-Dona

You can request here to get the dataset:

Hello Liam,

The extraction of components is such that the first PC will always be that linear combination of variables which accounts for the largest possible portion of total variance in the data set. As subsequent components must be linearly independent of previously extracted components, PC2 will always account for a lower proportion of total variance than the first extracted component, and so on. The k-th PC extracted (for a set of k variables) will account for the least amount of total variation.

Sometimes, you will encounter studies in which the researcher has chosen to rotate the chosen solution in multi-dimensional space (when the chosen solution has more than one component). This can and will redistribute the amount of variance associated with a given component, when compared to the amount associated with that component upon extraction. The total variance accounted for by a set (or subset) of components does not change due to rotation, just the allocation by individual components.

Rotated or not, whether the resultant components make conceptual sense is in no way guaranteed for your specific sift of variables (and their specific method of quantification), given your specific sample. So, labels such as "dominant" may or may not be meaningful; as well, they can change if one rotates a given structure.

Good luck with your work.

Hasan Habib This might help https://open-research-europe.ec.europa.eu/faqs

This is an old question indeed. The one-vs-one approach was first proposed in 1990, as an application of the divide-and-conquer strategy, which has been very successful in many areas of science: turn a complex problem into a set of simple

ones:

This article, which received 1200+ citations (Google Scholar count), describes the principle of one-vs-one classification, and a small academic example.

The first real-life application of one-vs-one classification was published in 1992:

The main reasons for the success of one-vs-one as opposed to one-vs-all are the following.

1) Assume that you want to solve an N class classification problem. In a one-vs-all-approach, you have to discriminate 1 class from the N-1 others. If all classes have the same number of examples n, this is a classification problem with imbalanced classes, as class "1" has n examples, while class "N-1 others" has n(N-1) examples. Classification with imbalanced classes is a notoriously difficult problem; thus, the one-vs-all approach requires the design of N complex classifiers, with imbalanced classes. By contrast, the one-vs-one approach requires the design of N(N-1)/2 simple classifiers, with balanced classes.

2) A very important, seldom mentioned, advantage of the one-vs-one approach in real-life problems is the following: very often, the variables that are relevant for discriminating class A from class B are not the same as the variables that are relevant for discriminating class A from class C. Hence, for each one-vs-one classifier, one can perform variable selection in order to design a classifier that has only the relevant variables, whose number is very frequently much smaller than the total number of variables necessary for one-vs-all classification.

3) Once the one-vs-one classifiers are designed and tested, the global classification result can be derived, in closed form, by an exact relation:

GOOD LUCK

It sounds like your study is essentially descriptive,. So, I would report the four quartiles for your measure and let the readers draw their own conclusions, because there is no hard and fast rule for what is a "satisfactory" level of knowledge.

Heba Mohammed , if your response to David Morse 's answer was meaning how to do IRT within SPSS, this is addressed in https://www.ibm.com/support/pages/item-response-theoryrasch-models-spss-statistics though it will likely be easier in other software. The rest of his recommendations should be straightforward for anyone having taken courses with SPSS.

There seems to be a widely shared misconception here: 73 samples is a "small" number. The number of samples alone is irrelevant. If you are performing classification in two dimensions (i.e. your classifiers have two variables), 73 samples may be quite enough; if your classifiers have 100 variables, 73 samples is a hopelessly small number. That crucial piece of information is not provided in your question, so that no scientifically valid answer can be provided. The only meaningful figure is the ratio of the number of samples to the complexity of the model, which must be as large as possible. Roughly, the complexity is the number of parameters of the model. In most models the number of parameters can be computed easily; in SVMs it can be estimated after training by the number of support vectors, which must be much smaller than the number of samples. Obviously the complexity of the model increases with the number of variables; hence the first step to take is not to try to increase the number of samples, which may be a lengthy and costly process, if at all possible. The first step is to make sure that all your variables are really relevant and discard irrelevant ones, i.e. perform variable selection with a serious, statistically meaningful variable selection method.

Another crucial missing data: do you get 100 % accuracy on the training set or on the test set? 100% accuracy can be very easily obtained on the training set, by using a model with a large number of parameters ("large" meaning as large as, or larger than, the number of samples of the training set). Getting 100% accuracy on a test set, i.e. a set of data that were never used at any step of the model design, is very unusual when dealing with real experimental data. If that happens, all the steps of the design method must be checked very carefully with a critical eye.

Another critical missing information: are your classes balanced, i.e.do they have roughly the same number of samples? If they are seriously imbalanced, you can very easily obtain near-100% accuracy on the training set and a disaster on the test set.

My advice is the following: turn off your computer, go to the library of your university and read a couple of good textbooks on machine learning and classification; my first choice would be: C. Bishop - Pattern Recognition And Machine Learning - Springer 2006. It is rightfully said that one week in the library may save you months in front of your computer, trying this and that, following advice from blogs written by self-proclaimed experts, without really understanding what's going on.

There are many journals specialized in strategy management, such as, Journal of Strategic Studies and Strategy and Leadership. These journals are listed in Scopus.

Hello,

Landscape metrics calculated through Fragstats may be of help. There are some indices regarding the spatial pattern analysis.

It should not contain toxic materials or high percent of saturated fatty acids. Regards.

Some models such as Artificial Neural networks and their derivatives.

Yes, there is. You can assess now all types of negotiations in all scenarios. I addressed this issue on THE FOUR-TYPE NEGOTIATION MATRIX: A MODEL FOR ASSESSING NEGOTIATION PROCESSES. Check it out:

It's correct. Ref: FEMA 450 commentary and tall building (taranath)

If you have only looked at correct RTs then there can be some issues with just using the means as the cells sizes will be unbalanced. One option to consider is model the individual trials in a multilevel model or models such as the diffusion model (which can cope with modeling accuracy and RT simultaneously).

Hey!

What do you mean by "empty phylum/order/genus/species"? not-assigned taxa? I'll assume so.

If so, it's expected that some OTUs can't be identified precisely because most microbes are not yet known/described/sequenced (so, theres no rRNAs sequences available on databases from it).

Also, some taxa can't be identified to higher taxonomic levels due to lack of resolution (e.g. some genus have species with very similar SSU sequences that prevents the species diferentiation using such approach).

Lastly, as far as I know, GreenGenes is outdated.

you can refer to:

You could use RDP database as an alternative (assuming you're using 16S sequences - since you've used GreenGenes until now).

But then two people who are only a point apart on your full scale could end up in different categories, or two people who are apart would be lumped into the same score. That is what I mean by "lose information."

For those following this question - the solution was:

1 joining time series data

2. concatenating the stationary data.

// JOIN - Combine/join image collections for training

var joinA = GW_anomaly2.select('GW_anomaly');

var joinB = CHIRPS_resampleD.select('precipitation');

var joinC = MODIS_resampleD.select('ET','PET');

var filter2 = ee.Filter.equals({

leftField: 'system:time_start',

rightField: 'system:time_start'

});

var simpleJoinB = ee.Join.inner();

var innerJoinB = ee.ImageCollection(simpleJoinB.apply(joinA, joinB, filter2));

var joinedB = innerJoinB.map(function(feature) {

return ee.Image.cat(feature.get('primary'),feature.get('secondary'));

});

var simpleJoinC = ee.Join.inner();

var innerJoinC = ee.ImageCollection(simpleJoinC.apply(joinedB, joinC, filter2));

var joinedC = innerJoinC.map(function(feature) {

return ee.Image.cat(feature.get('primary'),feature.get('secondary'));

});

// Concatenate (combine into single collection with multiple bands) the given long term mean (single band)

function add_topo(image){

var concatenate2 = ee.Image.cat([DEM_resampleD]);

return image.addBands(concatenate2);

}

//Map function over entire time-series collection

var combined_input = joinedC.map(add_topo);

Angel Martin Bastida Take a look https://www.youtube.com/watch?v=oGDvD3wwXsU

Interesting question.... KINDLY CHECK THE FORMULAR FOR EITHER CU OR CC....

IF YOUR ANSWERS ARE TRULY CORRECT I WILL URGE YOU TO STICK TO ONE... CU.... AND YOU WILL BE OKAY.

After you choose a particular type of model to train, for example a decision tree or a support vector machine (SVM), you can tune your model by selecting different advanced options. For example, you can change the maximum number of splits for a decision tree or the box constraint of an SVM. Some of these options are internal parameters of the model, or hyperparameters, that can strongly affect its performance. Instead of manually selecting these options, you can use hyperparameter optimization within the Classification Learner app to automate the selection of hyperparameter values. For a given model type, the app tries different combinations of hyperparameter values by using an optimization scheme that seeks to minimize the model classification error, and returns a model with the optimized hyperparameters. You can use the resulting model as you would any other trained model.

In my opinion, if you will use pre-trained CNN based on ImageNet then better results can give Inception V3 or Xception.

I agree with Akhil Kumar that it will depend on a dataset.

Dear Dalia Samir Mahdy ,

Automated cell classification is an important yet a challenging computer vision task with significant benefits to biomedicine. In recent years, there have been several studies attempted to build an artificial intelligence-based cell classifier using label-free cellular images obtained from an optical microscope.

Regards,

Shafagat

Yes it is possible, but I think it is very limited

Greater human mobility, largely driven by air travel, is leading to an increase in the frequency and reach of infectious disease epidemics. Air travel can rapidly connect any two points on the planet, and this has the potential to cause swift and broad dissemination of emerging and re-emerging infectious diseases that may pose a threat to global health security.

Population migration, spread of COVID-19, and epidemic prevention and control: empirical evidence from China | BMC Public Health | Full Text (biomedcentral.com)

Human Mobility and the Global Spread of Infectious Diseases: A Focus on Air Travel (nih.gov)

Dear Adam Weidling ,

Anomaly detection is one of the most common use cases of machine learning. Finding and identifying outliers helps to prevent fraud, adversary attacks, and network intrusions that can compromise your company’s future.

Regards,

Shafagat

Dear Tiziana Ziltener:

You can benefit from these valuable Qs/As about your topic:

@https://www.researchgate.net/topic/DEA

I hope it will be helpful..

Best wishes...

Hello dear Saad Benslimane,

I would suggest you the attached paper : https://www.sciencedirect.com/science/article/abs/pii/S1364032117308523

I hope this helps you!

Interesting question. Yes - I suspect that the network will quickly overfit because it will essentially start training near a local minima. However, if you start with a large learning rate, it is possible that a jump away from the local minima happens so it might not overfit. My advice would be to give it a try, record your results and let us know.

can anyone tell how am i ensemble deep learning models those having different input shape array?

as Dnn using 2D input array shape while CNN and RNN using 3D shape input arrays.

all of the models are already fitted

Chi square test for association of categorical variables.

Hello Howaida Mansour

I think these are sully generated by that journal or publisher so they calculate the number of published manuscript divided by the number of submissions. This may be an indicator of the quality of the journal or how hard it is to get “in” - they seem to say look how few we accept of the ones that we receive.

To me, that also means if the % is low, please do not be disappointed if your paper is not accepted.

Something you could do is to identify linguistic rules that suggest a certain order. For example, before a personal pronoun can be used, a distinct name must be introduced, and the genus must agree with it.

He gave her an envelope.

She went to Peter.

Mary entered the room.

A knowledge base must provide information about actions that are done by objects of certain type, such that giving is an action performed by humans (and not by rooms), and information about gender of names.

Regards,

Joachim

I think I would try with the Kendall-tau test.

Total MDSCs in mouse spleens are CD11b+ and Gr1+. Further, they can be classified as:

1. CD11b+Ly6CloLy6G+ : polymorphonuclear-MDSC

2. CD11b+Ly6ChiLy6G− : monocytic-MDSC.

For more information in detail, lease see:

Good Luck.

Dear Aditya Susarla:

You can benefit from this valuable article about your topic:

"Role of soft computing techniques for classification of power quality parameters and state-of-the-art mitigation techniques in Smart-Micro-Grid: A extensive review by ELKRC".

ABSTRACT:

The power system at a worldwide level is growing with the help of revolutionary transformation because of the integration of several distributed components such as advanced metering infrastructure, communication infrastructure, and electric vehicle distributed energy sources. The distributed components help to improve the reliability and increase the efficiency of the energy management system and ensure the security of the future power system. The study also discusses how artificial intelligence techniques are applied to provide effective support in the integration of renewable energy sources, integration of energy storage systems, management of the grid and home energy, and security and demand-responsive. It was found that the involvement of the smart grid uses different battery technologies to increase the efficacy of the grid. It includes using lead-acid (L/A) batteries, lithium-ion (Li-ion) batteries, sodium-sulfur (NAS) batteries, and vanadium redox flow batteries (VRB). It was also found that challenges faced in smart grid power quality that create an issue in fault detection and conduction of safety analysis.

@https://www.elkrc.com/news-detail/role-of-soft-computing-techniques-for-classification-of-power-quality-parameters-and-state-of-the-art-mitigation-techniques-in-smart-micro-grid-a-extensive-review-by-elkrc-12.

The current research analyzes how artificial intelligence and market liberalization can potentially help to increase the overall social welfare of the grid. The facts related to the comprehensive review of the state-of-the-art artificial intelligence techniques to support various applications in a distributed smart grid and are also discussed in the research.

I hope it will be helpful...

Best wishes....

First, please study the basic Concept such as:

Then, focus on CNN's applications

A Convolutional neural network (CNN) is a neural network that has one or more convolutional layers and is used mainly for image processing, classification, segmentation, and also for other autocorrelated data

GOOD LUCK

Thanks all for your answers. After some research, in my specific case, I think the better approach would be to use two-poportions Z-test (at this link a nice explanation and working examples in R: http://www.sthda.com/english/wiki/two-proportions-z-test-in-r). This is because running a leave-one-out test, as proposed by Ankit Agrawal, I have each outcome of the prediction as 1 (success) or 0 (failure), and t-test is not suited to work on binary values.

Each classifier has their strength and limitation. You can read about SVM and kNN in this article for an overview:

https://link.springer.com/chapter/10.1007/978-981-15-2317-5_47

Kindly see also the following useful RG:

Bayesian analysis are based on Maximum Likelihood equation but add a multiplier in the numerator describing the prior probability that makes the ML probaility conditioned on the prior probability. This is the subject of and entire course on statistical inference so is difficult to describe unless you have a background in probabilities and Bayes theorem.

I believe the attached article can give more light on Salimi Publication.

Thank you. Can you please clarify what does 'all k classes' mean in your first sentence. Do I have to train my Anomalous data also in autoencoder? Also please let me know if there is any resource I can follow to figure out this process. Thank you.

Pleasure Guelib Bouchra

The typical ROC curve has the following axes: vertical true positive rate (sensitivity); horizontal false positive rate (1-specificity). That is why the presented curves (red and yellow go down at their ends (here the axes are different than in typical ROC).

Let's define the meaning of axes:

Precision = True positive/(true positive + false positive)

Recall = True positive/(true positive + false negative)

Now, reading the meaning of the presented curves is possible.

Only for the Gaussian model Precision=1 is achievable (it means false positive=0)

For Precision=1, the highest Recall is 0.48 (it means the lowest number of false positive). I've marked this with violet dot on the attached figure.

The remaining models (poly and linear) are worse in this meaning.

If the highest recall is the most desirable, it is achieved by all three models (poly, linear and gaussian) but.... for recall=1 (false negative = 0) the gaussian model gives the highest precision (0.85). I've marked it with gray dot.

In fact, each presented curve is a set of models - based on gaussian are the best.

There are two of them (violet and gray) that gives the best results:

the best precision (1) and the highest possible recall then = 0.48

or

the best recall (1) and the highest possible precision then = 0.85

Best regards

Hubert

Hi Dr Zuñiga

I hope that everything is alright at your end.

The answer of your question is based on interactions between you and the suggested answers. Questions like: level (city, hinterlands, rural areas, district, etc....). Exact information (very accurate, accurate, sensible, etc.). Available/used data (km, 100m, 30m, 15m, 43cm, etc...). Classification (Super/unsuper-vised, ----------many), (pixel-based, object-based, ------ etc...). Classifier (parametric, non-parametric, --------------- etc...), and other factors.

Keep in mind ________ It is an approach issue_________.

From your aforementioned answer, I may suggest, Object-based supervised classification using Support vector machines-SVM to analyse medium-high resolution remote sensing data.

Meanwhile, after you give us other hints of your investigation, the community will provide you other suggestions.

Enjoy your studies.

Emad Hawash

Natural Resources Dept.,

Faculty of African Postgraduate Studies-FAPS,

Cairo University.

Optimizing the DNA fragment assembly using metaheuristics is one of the intriguing things in this field.

Dear Abdur Rasheed

As others mentioned, it is possible. It is better to check your input and target data.

Kindly check these links: