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chute type classifier

create a classifier | microsoft docs

create a classifier | microsoft docs

A classifier is a type of model that you can use to automate identification and classification of a document type. For example, you may want to identify all Contract Renewal documents that are added to your document library, such as is shown in the following illustration.

While your model uses a classifier to identify and classify document types, you can also choose to pull specific pieces of information from each file identified by the model. Do this by creating an extractor to add to your model. See Create an extractor.

When you create a model, you are also creating a new site content type. A content type represents a category of documents that have common characteristics and share a collection of columns or metadata properties for that particular content. SharePoint content types are managed through the Content types gallery. For this example, when you create the model, you are creating a new Contract Renewal content type.

Select Advanced settings if you want to map this model to an existing enterprise content type in the SharePoint Content types gallery to use its schema. Enterprise content types are stored in the Content Type Hub in the SharePoint admin center and are syndicated to all sites in the tenant. Note that while you can use an existing content type to leverage its schema to help with identification and classification, you still need to train your model to extract information from files it identifies.

You should use the same files for both classifier and extractor training. You always have the option to add more later, but typically you add a full set of example files. Label some to train your model, and test the remaining unlabeled ones to evaluate model fitness.

On the Select example files for your model page, select your example files from the Training files library in the content center. If you had not already uploaded them there, choose to upload them now by clicking Upload to copy them to the Training files library.

From the model home page, on the Classify files and run training tile, click Train classifier. This displays the label page that shows a listing of your example files, with the first file visible in the viewer.

In the viewer on the top of the first example file, you should see text asking if the file is an example of the model you just created. If it is a positive example, select Yes. If it is a negative example, select No.

The next step is for you to create an explanation on the Train page. An explanation helps the model understand how to recognize the document. For example, the Contract Renewal documents always contain a Request for additional disclosure text string.

On the Create an explanation page: a. Type the Name (for example, "Disclosure Block"). b. Select the Type. For the sample, select Phrase list, since you add a text string. c. In the Type here box, type the string. For the sample, add "Request for additional disclosure". You can select Case sensitive if the string needs to be case sensitive. d. Click Save.

The Content Center now checks to see if the explanation you created is complete enough to identify the remaining labeled example files correctly, as positive and negative examples. In the Trained Files section, check the Evaluation column after the training has completed to see the results. The files show a value of Match, if the explanations you created was enough to match what you labeled as positive or negative.

If you receive a Mismatch on the labeled files, you may need to create an additional explanation to provide the model more information to identify the document type. If this happens, click on the file to get more information about why the mismatch occurred.

If you received a match on your labeled sample files, you can now test your model on your remaining unlabeled example files that the model has not seen before. This is optional, but a useful step to evaluate the fitness or readiness of the model before using it, by testing it on files the model hasnt seen before.

In the Test files list, your example files display and shows if the model predicted them to be positive or negative. Use this information to help determine the effectiveness of your classifier in identifying your documents.

types of classifiers in mineral processing

types of classifiers in mineral processing

In mineral processing, the Akins AKA spiral or screw Classifier has been successfully used for so many years that most mill operators are familiar with its principle and operation. This classifier embodies the simplest design, smallest number of wearing parts, and an absence of surge in the overflow. It separates coarse and fine solids, carried in liquids, with a high degree of accuracy and with lowest possible power and maintenance costs. Additional information on Akins Classifiers will be sent upon request.

If the pulp (solids and liquid) contains more than 5% of solids larger than 48 mesh, the type 40 Allen Sand Cone is almost always used, where as if the solids contain less than 5% coarser than 48 mesh it is generally advisable to install the type 50 Slime Cone.

The feed enters the cone through an inner truncated cone, settled solids gradually building up in the cone to a point restricting the out-flow of the inner truncated cone. This causes the water level in the truncated cone to rise lifting a float which operates a series of levers to operate a ball valve to open a spigot in the bottom of the cone which discharges the settled material. This arrangement maintains an even depth of sand regardless of increase or decrease in the feed rate. If a foreign object should obstruct the opening retarding the flow the valve opens wider, thus allowing the passage of the obstruction.

The operation of the Allen Slime Cone follows the same general principle as described above except the float is located near the bottom of the cone and is operated by the buoyancy of the settled slime. The float operates a ball valve and spigot same as for the sand cone.

CONE Classifiers are built in two types, one operating on the density of the pulp in the cone, and the other on the hydraulic or mechanical movement of the pulp. The Dewatering Cone is of the former type. The weight of the pulp in the cone actuates a lever with an adjusting weight, automatically controlling the discharge valve. This method gives a constant density discharge. The unit can be used as either a dewaterer or classifier, requires no power, and is entirely automatic.

The body of the separator can be conical or pyramidal in shape, to suit best the physical requirements of the location. The classifier can be used to advantage in dewatering and controlling feed to a regrind ball or rod mill, or material from tables, jigs, or flotation.

CLASSIFIERS having a helical (often improperly called spiral) flight for removing settled coarse material have long been in successful use and most mill operators are familiar with their operation. The Cross-Flow Classifier is of this general type but has many improvements which result in improved metallurgical efficiency, longer life and less repair. Among these improvements may be mentioned:

An auxiliary weir is provided at the end of the classifier. Both the main and auxiliary weirs are provided with weir blocks by which the depth of the pulp and pool area may be adjusted, thus controlling the size of separation.

The Cross-Flow Classifier is ruggedly constructed and the tank is thoroughly reinforced. Bearings are Jarge and all gears are enclosed. The 6, 9, and 12 sizes have replaceable hard cast iron flight sections on a square shaft. The 18, 24, and 30 have replaceable hard cast iron flight sections. The 36, 42, 48, 54, and 60 sizes have replaceable steel flights and replaceable hard iron wearing shoes. Sizes 30 and larger are provided with lifting device by which the lower end of the flight may be raised and the conveyor operated while it is slowly lowered, thus gradually removing the bed of solids which settles during a shutdown.

Hydraulic Classifier is designed for use in gravity concentration mills for preparing a classified feed for table concentration. The classifier compartments are provided with glass sides so that the conditions existing in each chamber can readily be observed. Only enough water is necessary to keep the solids in full teeter. As the sands accumulate in the classifier pockets the effective density of sand-water mixture increases and thumbscrews onthe valve rod assemblies may be adjusted to discharge sand fromeach compartment as required. The finer the sand the less water is required. When the pressure regulating valves and the product valves are set, no further adjustment is necessary.

The Hydraulic Classifier is made in 2, 4, 6, and 8 compartments with two sizes of compartments, namely 4x 4 and 8x 8. The standard units are of steel construction. Capacity depends on specific gravity of the material and range of size taken from each compartment. The 4x 4 has a capacity from 5 to 10 tons per compartment in 24 hours. The 8x 8 has a capacity from 20 to 50 tons per compartment in 24 hours. Additional data gladly furnished upon request.

TheHydroclassifieris designed to make a separation according to size in the range from 100 mesh to that of colloidal particles. It is particularly suited to the removal of slimes prior to further treatment by flotation, cyanidation, or chemical processes. The separation in this fine size range requires a large quiet, pool area, such as isprovided by the Hydroclassifier, combined withaccurate control over the removal and washing of thesettled material for maximum classification efficiency.

In the Hydroclassifier the feed enters a feed well in the center of a large settling tank and flows without agitation into the pool. The slimes and fine material move radially to the overflow weir at the rim of the tank and overflow into the overflow launder. As the slimes move to the weir, the coarse particles settle on the sloping tank bottom, where slowly moving spiral rakes continuously move these settled solids to a central discharge cone. In the discharge cone water is added below a perforated plate while the settled solids are rabbled above the plate, thoroughly washing out slimes and fines from the coarse material, as it works through the openings in the plate. The coarse material is usually removed in the form of a sludge by means of a Adjustable Stroke Diaphragm Pump.

The tank is steel, thoroughly reinforced, and supported on steel columns, thoroughly braced. The mechanism driving the rakes is an alloy steel gear and bronze worm, supported on anti-friction bearings enclosed and running in oil. The vertical shaft and rakes may be raised or lowered while the mechanism is in operation. Extra heavy construction is provided throughout, resulting in long life and a minimum of repair. Additional data gladly furnished upon request.

In addition to these standard machines we can furnish larger sizes where mechanism is placed in customers concrete tanks. Mechanism sizes 22 to 40 can be furnished for concrete tanks. These sizes can be furnished in Bolted Steel Tanks. Capacity range depending on mesh separation, specific gravity and per cent solids in products. Horsepower depends on the amount of solids settling and specific gravity.

The Rake Classifier is designed for either open or closed circuit operation. It is made in two types, type C for light duty and type D for heavy duty. The mechanism and tank of both units are of sturdiest construction to meet the need for 24 hour a day service. Both type C and type D Rake Classifiers have a tank made of heavy steel plate with the seams double electric welded both inside and out. Tanks are arranged so that feed may enter from either side. Rakes are made either of heavy steel angles welded to channel supports which are carried by the actuating mechanism or channel irons split lengthwise and welded to supports, whichever type of duty the classifier will be expected to perform. Raking movement is accomplished by a cam and roller drive. Cams are mounted on an oversize shaft and driven by a semi-steel cast tooth gear and pinion on the light duty unit or heavy molychrome steel cams operated by machine cut gears are used on the heavy duty unit. Mechanism supports are of the strongest possible construction and have a large safety factor to withstand severe load conditions. Also, the babbitted pillow block drive bearings on the light duty unit and the bronze bushed drive bearings on the heavy duty unit are of ample size to handle most any overload condition.

A rake lifting device, utilizing worm gears, a hand crank, and steel cables, is provided on both units and this rake lifting device is so designed that one man can quickly and easily raise or lower the rake. Classifiers can be furnished either belt or motor driven. On the belt driven type a right angle drive can be supplied if desired. The standard motor drive is V to flat with 3-phase, 60 and 50 cycle, 220, 440 or 550 volt motor.

The standard length of C type simplex classifier is 148, with widths of 16, 20, 23, 30. Type C duplex standard lengths are 120, 148, 164 and 180, with widths of 40, 46, 50 and 60. Model D simplex has tank lengths of 184, 20,0, 218, 234, 250, or 268, with width of 30 and 40. Heavy duty model D duplex comes with tank lengths the same as model D simplex, and widths of 50, 60, 70 and 80.

The Rotary High Weir Classifier is designed to give best classification of fine and coarse material. The horsepower is low and maintenance is reduced to a minimum. The classifier is made with either a high or low weir, the highweir with its larger pool areabeing used where a fine separation is necessary. Rotary High Weir Classifier is built with replaceable wearing flights, steel tank mounted on base, and belt or gear-motor drive. Standard drive is a single bevel gear, although spur gear and double reduction drive units can be furnished.

The two conveyor shaft bearings are mounted outside ofthe tank away from pulp and splash. The conveyor shaftis effectively sealed at the slime overflow end of the tankby means of a specially designed stuffing box which contains a distributing ring in addition to the conventional packing rings. Admission of water to the interior of the stuffing box prevents any dirt from entering. This classifier can also be made of corrosion-resisting material so as to handle successfully products such as sulphuric acid.

spiral classifier for mineral processing

spiral classifier for mineral processing

In Mineral Processing, the SPIRAL Classifier on the other hand is rotated through the ore. It doesnt lift out of the slurry but is revolved through it. The direction of rotation causes the slurry to be pulled up the inclined bed of the classifier in much the same manner as the rakes do. As it is revolved in the slurry the spiral is constantly moving the coarse backwards the fine material will flow over the top and be travelling fast enough to be able to work its way downwards to escape. The Variables of these two types of classifiers are The ANGLE of the inclined bed, this is normally a fixed angle the operator will not be able to adjust it.

The SPEED of the rakes or spirals, the DENSITY of the slurry, the TONNAGE throughput and finally the SETTLING RATE of the ore itself.To be effective all of these variables must be balanced. If the incline is too steep the flow of slurry will be too fast for the rakes or spirals to separate the ore. If the angle is too flat the settling rate will be too high and the classifier will over load. The discharge rate will be lower than the feed rate, in this case. The load on the rakes will continue to build until the weight is greater than the rake or spiral mechanism is able to move. This will cause the classifier to stop and is known as being SANDED UP. If the speed of the rakes or spirals are too fast, too much will be pulled, out the top. This will increase the feed to the mill and result in an overload in either the mill or classifier as the circuit tries to process the increased CIRCULATING LOAD.

The DENSITY of the slurry is very important, too high the settling will be hampered by too many solids. Each particle will support each other preventing the heavier material from quickly reaching the bottom of the slurry. This will not allow a separation to take place quickly. The speed at which the slurry will be travelling will be slow and that will hamper effective classification. Another variable is the TONNAGE. All equipment has a limit on the throughput that anyone is able to process, classifiers are no different. This and the other factors will have to be adjusted to compensate for the last variable, the ore itself. Every ore type has a different rate of settling. To be effective each of the previous variables will have to be adjusted to conform to each ones settling characteristics.

The design of these classifiers (rake, spiral, screw) have inherent problems, First, they are very susceptible to wear, caused by the scrubbing action of the ore, that plus all of the mechanical moving parts create many worn areas to contend with. The other problem that these classifiers have is that they are easily overloaded. An overloaded classifier can quickly deteriorate into a sanded-up classifier. Once that happens the results are lost operating time, spillage and a period of poor Mineral Processing and Separation performance.

Another mechanical classifier is the spiral classifier. The spiral classifier such as the Akins classifier consists of a semi-cylindrical trough (a trough that is semicircular in cross-section) inclined to the horizontal. The trough is provided with a slow-rotating spiral conveyor and a liquid overflow at the lower end. The spiral conveyor moves the solids which settle to the bottom upward toward the top of the trough.

The slurry is fed continuously near the middle of the trough. The slurry feed rate is so adjusted that fines do not have time to settle and are carried out with the overflow .liquid. Heavy particles have time to settle, they settle to the bottom of the trough and the spiral conveyor moves the settled solids upward along the floor of the trough toward the top of the trough/the sand product discharge chute.

screw grit classifier

screw grit classifier

Screw Grit Classifiers (SGC) from Napier Reid offer a solution to removing, washing, and dewatering grit and sand, in addition to protecting downstream mechanical equipment and improving the performance and reliability of drainage systems.

As the grit and other heavy materials are transported up the inclined plane, the specially designed conveyor tumbles and washes the grit in order to remove the organics from it. The washed organics are returned to the effluent connection by an organic return connection.

Once the grit and heavier material is elevated above the classifiers water level, the solids are further dewatered by transferring them to a convenient discharge height. The grit falls through the grit discharge chute into a grit collection box or into a screw conveyor for disposal.

Company Headquarters: 10 Alden Road Markham, Ontario Canada L3R 2S1 Phone: +1-905-475-1545 Email: [email protected]

github - sxslabjhu/celltypeclassification: deep learning based cell type classification with low-res brightfield single cell images

github - sxslabjhu/celltypeclassification: deep learning based cell type classification with low-res brightfield single cell images

Our group utilize Deep Learning algorithms (Convolutional Neural Networks) to classify different cell types or cells from different stages of cell differentitation, simply based on small-sized brightfield single cell images acquired from everyday use benchtop microscope. Please refer to bioRxiv preprint for detailed introduction of the project.

This repository contains codes for several parts of the proposed cell type / cell morphology classification pipeline, including data preparation and neural network model training and application. All files are able to be compiled under MATLAB 2017b and later versions (MathWorks Inc.):

Please run FieldImageGrayScaling.m first to scale all brightfield images in the specified folder to grayscale images with one single color channel. Then you can run CellCropping.m which provides you with a simple UI interface in MATLAB figure window for manually cropping single cells out of multi-cell frames.

Please load the reference cell image refIm.jpg first (here we provided one reference cell image, but it can be any cell of your own choice). Next, run QuanNorm.m in the specified folder to quantile-normalize each image file in the folder and modify the image file to the normalized version.

As an example of the usage of the augmentation functions and algorithms, please load exDbl.mat file first and then compile ExampleRun.m in the same folder. This will give you a feeling of how the rotation and translation, as well as random blobbing work in our augmentation package. Please also feel free to make use of any part of the codes at your own discretion.

Please place Training.m in the same folder as the two folders of different cell types to classify. Here we provided examples of two cell types in our work: HEK-293A (human embryonic kidney 293 cells) and HT1080 (fibrosarcoma cancer cell line), each cell type forms a folder with hundreds of single cell images of the cell type prepared with the above described experimental and computational procedure. Then you can run Training.m which starts the training process of the network with the training option and network structure designed in the code.

After the training you should get a file net.mat, with which you can then test new single cell images of one of the two cell types to classify which cell type does the new cell belong to. Simply apply the command of classify(net,imdsTest) where net is our trained neural network model and imdsTest is the imageDatastore for the test files of your choice.

learn about trainable classifiers - microsoft 365 compliance | microsoft docs

learn about trainable classifiers - microsoft 365 compliance | microsoft docs

This method requires human judgment and action. An admin may either use the pre-existing labels and sensitive information types or create their own and then publish them. Users and admins apply them to content as they encounter it. You can then protect the content and manage its disposition.

This classification method is particularly well suited to content that isn't easily identified by either the manual or automated pattern matching methods. This method of classification is more about training a classifier to identify an item based on what the item is, not by elements that are in the item (pattern matching). A classifier learns how to identify a type of content by looking at hundreds of examples of the content you're interested in classifying. You start by feeding it examples that are definitely in the category. Once it processes those, you test it by giving it a mix of both matching and non-matching examples. The classifier then makes predictions as to whether any given item falls into the category you're building. You then confirm its results, sorting out the true positives, true negatives, false positives, and false negatives to help increase the accuracy of its predictions.

When you publish the classifier, it sorts through items in locations like SharePoint Online, Exchange, and OneDrive, and classifies the content. After you publish the classifier, you can continue to train it using a feedback process that is similar to the initial training process.

Both built-in classifiers and trainable classifiers are available as a condition for Office autolabeling with sensitivity labels, auto-apply retention label policy based on a condition and in communication compliance.

We are deprecating the Offensive Language pre-trained classifier because it has been producing a high number of false positives. Don't use it and if you are currently using it, you should move your business processes off of it. We recommend using the Threat, Profanity, and Harassment pre-trained classifiers instead.

Please note that the offensive language, harassment, profanity, and threat classifiers only work with searchable text are not exhaustive or complete. Further, language and cultural standards continually change, and in light of these realities, Microsoft reserves the right to update these classifiers in its discretion. While the classifiers may assist your organization in monitoring offensive and other language used, the classifiers do not address consequences of such language and are not intended to provide your organization's sole means of monitoring or responding to the use of such language. Your organization, and not Microsoft or its subsidiaries, remains responsible for all decisions related to monitoring, enforcement, blocking, removal and retention of any content identified by a pre-trained classifier.

When the pre-trained classifiers don't meet your needs, you can create and train your own classifiers. There's significantly more work involved with creating your own, but they'll be much better tailored to your organizations needs.

Creating and publishing a classifier for use in compliance solutions, such as retention policies and communication supervision, follows this flow. For more detail on creating a custom trainable classifier see, Creating a custom classifier.

You can help improve the accuracy of all custom classifiers and some pre-trained classifiers by providing them with feedback on the accuracy of the classification that they perform. This is called retraining and follow this workflow.

get started with trainable classifiers - microsoft 365 compliance | microsoft docs

get started with trainable classifiers - microsoft 365 compliance | microsoft docs

A Microsoft 365 trainable classifier is a tool you can train to recognize various types of content by giving it samples to look at. Once trained, you can use it to identify item for application of Office sensitivity labels, Communications compliance policies, and retention label policies.

Creating a custom trainable classifier first involves giving it samples that are human picked and positively match the category. Then, after it has processed those, you test the classifiers ability to predict by giving it a mix of positive and negative samples. This article shows you how to create and train a custom classifier and how to improve the performance of custom trainable classifiers and pre-trained classifiers over their lifetime through retraining.

Opt-in is required the first time for trainable classifiers. It takes twelve days for Microsoft 365 to complete a baseline evaluation of your organizations content. Contact your global administrator to kick off the opt-in process.

When you want a trainable classifier to independently and accurately identify an item as being in particular category of content, you first have to present it with many samples of the type of content that are in the category. This feeding of samples to the trainable classifier is known as seeding. Seed content is selected by a human and is judged to represent the category of content.

You need to have at least 50 positive samples and as many as 500. The trainable classifier will process up to the 500 most recent created samples (by file created date/time stamp). The more samples you provide, the more accurate the predictions the classifier will make.

Once the trainable classifier has processed enough positive samples to build a prediction model, you need to test the predictions it makes to see if the classifier can correctly distinguish between items that match the category and items that don't. You do this by selecting another, hopefully larger, set of human picked content that consists of samples that should fall into the category and samples that won't. You should test with different data than the initial seed data you first provided. Once it processes those, you manually go through the results and verify whether each prediction is correct, incorrect, or you aren't sure. The trainable classifier uses this feedback to improve its prediction model.

Collect between 50-500 seed content items. These must be only samples that strongly represent the type of content you want the trainable classifier to positively identify as being in the classification category. See, Default crawled file name extensions and parsed file types in SharePoint Server for the supported file types.

Make sure the items in your seed set are strong examples of the category. The trainable classifier initially builds its model based on what you seed it with. The classifier assumes all seed samples are strong positives and has no way of knowing if a sample is a weak or negative match to the category.

Within 24 hours the trainable classifier will process the seed data and build a prediction model. The classifier status is In progress while it processes the seed data. When the classifier is finished processing the seed data, the status changes to Need test items.

Collect at least 200 test content items (10,000 max) for best results. These should be a mix of items that are strong positives, strong negatives and some that are a little less obvious in their nature. See, Default crawled file name extensions and parsed file types in SharePoint Server for the supported file types.

When the trainable classifier is done processing your test files, the status on the details page will change to Ready to review. If you need to increase the test sample size, choose Add items to test and allow the trainable classifier to process the additional items.

Microsoft 365 will present 30 items at a time. Review them and in the We predict this item is "Relevant". Do you agree? box choose either Yes or No or Not sure, skip to next item. Model accuracy is automatically updated after every 30 items.

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