Id3 classifier työt

Using the ESP32C3 DevkitC-02 board, we want to extend the IDF ble_mesh_node examples, creating a mesh network of 4 nodes: 3 servers and 1 client. In the server, add an int32_t counter that increments its value by +1, every second that the green LED is on. From the client, have the service set any green led in the other nodes in status on or off. From the client, have the service f...client exposes the services like: a) Green LED (on/off): switch on and off the green LEDs in any server node in the ble-mesh network. b) Counter service node "ID1" (read/write) Read and write the counter in the service node "ID1". Where "ID1" is an identification of one specific service node. It could be its remote address. b) Counter service node "ID2" (read/write) ...

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...with overcurrent. An additional objective: - i. To involve international standard power system with microgrid and conventional distance protective relay. ii. To investigate the performance of the modelled distance relay in the presence of various multitude faults in grid-connected and islanded. iii. To improve distance protection relay reliability with communication assisted or Machine Learning Classifier Algorithm. PROBLEM STATE-MENT: In both modes which is islanded and grid connected of Microgrids (MGs) have different short circuit levels. The fault level in islanded MGs is lower than grid connected MGs due to fault contribution of distributed generators is lesser than contribution of microgrids. PV inverters as one of main part in PV system also has fault contribution and it...

i want a LR about facial expression classifiers, and i want to conclude the SVM and CNN has a high and good accurecy . the LR from two pages 1.5 space and has the table summrize the all LR at the end the file as example

...with overcurrent. An additional objective: - i. To involve international standard power system with microgrid and conventional distance protective relay. ii. To investigate the performance of the modelled distance relay in the presence of various multitude faults in grid-connected and islanded. iii. To improve distance protection relay reliability with communication assisted or Machine Learning Classifier Algorithm. PROBLEM STATE-MENT: In both modes which is islanded and grid connected of Microgrids (MGs) have different short circuit levels. The fault level in islanded MGs is lower than grid connected MGs due to fault contribution of distributed generators is lesser than contribution of microgrids. PV inverters as one of main part in PV system also has fault contribution and it...

Type2 fuzzy logic as a classifier for power transformer protection

Need to use YOLO codes to identify players and squash ball. and use YOLO codes to identify the relative object position within the squash court and build birdeye view. Then crop the video clips into useful footage according to the conditions (i.e., stroke, let, and no let) specified at the excel spreadsheet at dropbox. At last, build classifier to differentiate the 3 conditions.

To perform an svd analysis on two data sets and and design a gender classifier

Image processing classifier API is needed using cnn or better modeling technique python code program will be used as rest api through docker container connected to database msql, ms sql, or pgadmin though env file credentials to docker container the model should be reusable after learning, by trigger on rest api or by periodic classify of new db records several models to be created over distinct sets based on database connected to optimized for very large number of colored images with inconsistent sizes.

You will be doing the social learning binary classifier problem. The problem have 3 parts a, b and c and it needs the solution to be provided within 6 hours from the time of awarding the work

Need someone having good understanding with binary classifier

Need to do calculations from scratch with the algorithms equation,, and coding parts for Linear Classifier and Neural Network.

Do SVD analysis for 2 datasets in MATLAB and compare the differences between cropped and uncropped images, build a gender classifier capable of recognizing men from women in training set.

Train the classifier on expressions from user A. Use an appropriate classification method among those you know (e.g. hard SVM, soft SVM, K-NN, CNN, etc.) to train the classifier on expressions from user A, broken down into: a...

Train the classifier on expressions from user A. Use an appropriate classification method among those you know (e.g. hard SVM, soft SVM, K-NN, CNN, etc.) to train the classifier on expressions from user A, broken down into: a. Basic implementation, using an off-the-shelf classifier (e.g. a standard R command), on a single facial expression. b. Repeat the training for a different facial expression. c. Extra marks for coding your own implementation of the chosen classifier. 2. Evaluate accuracy. Employ this trained classifier on the expressions performed by user B, and determine the accuracy, broken down into: a. Test on a single facial expression, using an off-the-shelf classifier, and comment on the results. b. Test on a different facial expression...

Several coding (Python) and theory tasks for Naive Bayes, Decision Tree, Linear Classifier, Neural Network.

...analysis project (I will provide most of the necessary code to implement the project in R---see the attachment above), and for this project, you will need to turn in a ~5 page written report on your data analyses. Your document should be professionally presented (imagine giving it to your future boss as a report), and pertinent statistical decisions (e.g., parameter selection in classifier models, statements about classifier efficacy, etc.) should be justified in the text. The document should include paragraphs describing the applied methods (including why you do things like CV, dimension reduction, introduce non-linear kernels, etc.). All methods should be explained in text paragraphs. 2. All decisions/analyses should be explained in text paragraphs. 3. Figures and ta...

Several coding (Python) and theory tasks for Naive Bayes, Decision Tree, Linear Classifier, Neural Network.

In this Project, there is a notebook which operate in google colab lab because it use gpu/cpu. So, It is Natural Language Programming Project in which python and other data science skills are use. I have to prepare the data and tokenize it into subwords, and finally use it as input to some pretrained models, for ex...in google colab lab because it use gpu/cpu. So, It is Natural Language Programming Project in which python and other data science skills are use. I have to prepare the data and tokenize it into subwords, and finally use it as input to some pretrained models, for example BERT. I have to: implement BPE algorithm use ELMO and compare it with word2vec embeddings explore the usage of BERT train a classifier using BERT embeddings to solve COLA classification task use prepared...

The aim is to build an AI classifier which will accurately predict whether a given URL link is malicious or not based on the datasets that the classifier has used for training and testing the data. I will provide the webpage classification train and test data so that the classifier can be built. The aim is get any URL and the relevant parameters (extracted using an API) and pass it to this AI classifier hosted on the cloud - which will determine whether it is malicious or not based on the same parameters used within the provided datasets.

...analysis project (I will provide most of the necessary code to implement the project in R---see the attachment above), and for this project, you will need to turn in a ~5 page written report on your data analyses. Your document should be professionally presented (imagine giving it to your future boss as a report), and pertinent statistical decisions (e.g., parameter selection in classifier models, statements about classifier efficacy, etc.) should be justified in the text. The document should include 1. Text paragraphs describing the applied methods (including why you do things like CV, dimension reduction, introduce non-linear kernels, etc.). All methods should be explained in text paragraphs. 2. All decisions/analyses should be explained in text paragraphs. 3. Figures a...

Request details You will need to build a program that can look at a video of a person/s and be able to label them with a behavior/emotion based on their body language. The feature of motion must be represented to describe the movements in temporal and spatial sequences such as MHI or MEI. produce an SVM classifier and one other and train it to label these features and whenever a new feature is seen the prediction will be able to estimate which label the feature is. An evaluation method ie K-fold cross-validation needs to be used to test the performance of the system. The basic functionalities would be a feature detection and extraction SIFT feature Feature representation MHI MEI Data training and recognition using Support Vector Machine SVM and Experimental results analysis If poss...

...lectures and/or workshops should be included as baselines. In addition, at least one of your proposed classifiers should attempt to go beyond the module in terms of architectural, approach, and/or algorithmic details. You will then investigate their performance, compare and critique them to justify your recommended classifier(s). This should include metrics such as TP/FP rates, Precision-Recall, F-measure, and any other relevant metrics. In this assignment you are free to train any classifier, to do any pre-processing of the data, and to implement your own algorithm(s) instead of only using libraries. While you are encouraged to make your own implementations, you can use libraries (such as Tensorflow or Pytorch) to train your your deep neural networks. But you should clear...

I have a tag editor already that enables me to change the id3 tags for any mp3 files (such as changing metadata, including title, artist, album, and cover image of any mp3 file). It hosted on my shared hosting.. So i want to be able to add an audio watermark at time intervals to any mp3 i uploaded. For example a voice signature like this () will be added (mixed together) to any audio i uploaded at a desired time interval ( maybe at 10 sec or 2.53 mins) and the output will be stored on my server. So when people here the audio any where. They would know it was downloaded fron my website.

...RAW speech or features such as MFCCs or LPCs may be used as input space. 2.5 Generative Models In this module, we build generative models on Tinyimage net (). Use Frechet Inception Distance (FID) between 1000 generated and real data as the metric for evaluation. 3 Models for Assignment 3.1 Bayes’s Classifier with several Class Conditional Densities such as Gaussian, GMMs (Have to code up EM) 3.2 Bayes’s Classifier with different density estimates (ML, MAP and Parzen Window and nearest neighbor estimates) 5 Also using any one data set do the following:- 5.1 Train a DCGAN for one of the datasets given. Compute FID and plot the generated images in a 10×10 grids.  5.2 Train a VAE on the same dataset and repeat the above experiment.  5.3 Plo...

...RAW speech or features such as MFCCs or LPCs may be used as input space. 2.5 Generative Models In this module, we build generative models on Tinyimage net (). Use Frechet Inception Distance (FID) between 1000 generated and real data as the metric for evaluation. 3 Models for Assignment 3.1 Bayes’s Classifier with several Class Conditional Densities such as Gaussian, GMMs (Have to code up EM) 3.2 Bayes’s Classifier with different density estimates (ML, MAP and Parzen Window and nearest neighbor estimates) 5 Also using any one data set do the following:- 5.1 Train a DCGAN for one of the datasets given. Compute FID and plot the generated images in a 10×10 grids.  5.2 Train a VAE on the same dataset and repeat the above experiment.  5.3 Plo...

A project relates to data cleaning/preparation and then building a ML model based on Neural Network classifier

Two hidden layers here means (input - hidden1 - hidden2 - output). You must not use flax, optax, or any other library for this task. Use MNIST dataset with 80:20 train:test split. Manually optimize the number of neurons in hidden layers. Use gradient descent from scratch to optimize your network. You should use the Pytree concept of JAX to do this elegantly. Plot loss v/s iterations curve with matplotlib. Evaluate the model on test data with various classification metrics and briefly discuss their implications.

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As the social networking sites get more popular, spammers target these sites to spread spam posts. Twitter is one of the most popular online social networking sites where users communicate and interact on various topics. Most of the current spam filtering methods in Twitter focus on detectin...based on convolutional neural networks (CNNs). Five CNNs and one feature-based model are used in the ensemble. Each CNN uses different word embeddings (Glove, Word2vec) to train the model. The feature-based model uses contentbased, user-based, and n-gram features. Our approach combines both deep learning and traditional feature-based models using a multilayer neural network which acts as a meta-classifier. We evaluate our method on two data sets, one data set is balanced, and another one is i...

This is a mobile app tracking data from users regarding mens mental wellbeing such as sleep hours etc. The goal is to add a decision tree classifier to predict how the user's actions are affecting their mood. Need a developer who has React experience. Currently have created a decision tree classifier but am struggling to display it in a user friendly way due to a lack of experience.

'Suppose that we have data points (xi,yi) for i ∈ [N], where xi ∈ Rn are feature vectors and yi ∈ {±1} are binary outputs. The linear classification problem aims to find a linear function f(x) = a⊤x + b such that the sign of f(xi) agrees with yi for as many data points i ∈ [N] as possible. You have seen in QBUS1040 the least squares classification technique, which is to get ...classification problem aims to find a linear function f(x) = a⊤x + b such that the sign of f(xi) agrees with yi for as many data points i ∈ [N] as possible. You have seen in QBUS1040 the least squares classification technique, which is to get a and b by minimizing �i∈[N](a⊤i x + b − yi)2. In this question, we will explore two more popular techniques: the support vector ...

I need a Few Shot learning-based binary classifier for my images with an algorithmic novelty so that I can call it a novel classification system. The novelty can be inspired either from other algorithms or the kind of image inputs and is open to discussion. It doesn't necessarily needs to be anything major, but necessarily novel. Implementation in PyTorch is preferred

I have a data set that has to be run through a few classifier models and their statistics have to be put into a Roc curve.

Looking for a robust python project and it's deployment instructions. The software should be able to: - receive a .mp4 video - identify the existence of a smartphone or multiple smartphones on the video - log on a file the name of the video, the second / frame in which the smartphone(s) were detected Plus - Identify if all smartphone edges are fully visible Big Plus - Identify cracks on the smartphone screen Timeline: one week Your financial proposal is final. Please make questions before submitting the proposal.

Topic: Instance segment use method: Mask RCNN Dataset: CURE dataset (I have divided 51/195 pill layers into 09 pill shape layers, pixel annotated more than 1,000 photos) Requirements: - Annotate about 500 images missed, then config Mask RCNN model to identify of the shape of the pill - IoU score >= 94% - Compare the results with some traditional method, as: Find contour + SVM, watershed + classifier - Dead line: 22/3/2022

1. Describe and summarize your dataset by running some codes. 2. Split the dataset using hold out approach on 75:25 split. 3. Run neural networks classifier with any parameters setting on your own choice on the given dataset. 4. Print the confusion matrix and classification report. 5. Repeat step 2-4 by using K-fold Cross Validation, where K=10. 6. Compare the performance of neural network classifier on hold out and cross validation. Which method gives the best performance? 7. Explain the confusion matrix for the best method. 8. Comment on whether the neural networks classifier is sufficient to predict the selected problem for your selected dataset based on the confusion matrix and classification report. 9. Fine-tune any 2 parameters using GridSearchCV to find the opti...

Need to use YOLO codes to identify players and squash ball. and use YOLO codes to identify the relative object position within the squash court and build birdeye view. Then crop the video clips into useful footage according to the conditions (i.e., stroke, let, and no let) specified at the excel spreadsheet at dropbox. At last, build classifier to differentiate the 3 conditions.

Problem Overview Sarcasm is the caustic use of words, often in a humorous way, to mock someone or something. Sarcasm is a nice trait to have. However, there is a thin line between sarcasm and foul language. You have to define that thin line by building a classic NLP classification model using the provided dataset. Metric Evaluate your models on F1_score: which combines the precision and recall of a classifier into a single metric by taking their harmonic mean. Dataset Details A university has chat groups on different topic. Students & their parents both have access to these chat groups. The dataset contains chat extract from the chat groups along with topic name and few other parameters, out of which three parameters description are classified (not disclosed). This dataset conta...

Using Friedman and Nemenyi post- hoc test, I want to assess the performance results of these...performance results of these algorithms statistically. It will be help me know whether the classifiers are significantly different from each other or not. The null hypothesis (H0) considered in this case is that there is no performance difference among classifiers. While alternative hypothesis (HA) is that there is at least one classifier that performs significantly different than at least one other classifier for each performance metrics. Secondly, I need to find which classifier pairs performs significantly different. Obtain the p value of all the pairwise comparisons using Nemenyi post-hoc test. Let’s assume the classifiers being tested are ‘LR’ and &lsq...

First of all there are two problem set One is window 7 One is window 10 In each of the dataset file there should be som... The task is to designed the automative chat bot web interface with natural language procesing and machine learning that give accurate result... while typing the question The nlp algorithm should neglect all the words that dont have any meaning. And the machine learning classified the problem into sub catgeories like window 7 have various problem like interent, harddrive, pc performance so classifier separate all these problem. Lets suppose in dataset we have problem That Internet explorer is not working What if user type internet explorer not functioning These both sentence hava same meaning but written in different context. So the chat bot will return sa...

Dataset: 4.6k records, 1.8k spam Columns, e.g. word_freq_make, word_freq_address, capital_run_length_average, etc. Tasks: 1. build a fully connected classifier network that will classify the mails. 2. check which parameters affect decision the most (biggest gradient wrt to answer) 3. build a classifier that doesn't use 1, 2, 10 most significant parameters. Does it still perform better then random? I would need a full python notebook with the 3 tasks above done for the attached dataset. The dataset is from the following link: It needs to be done usin tensorflow and the output should be a python notebook. I have attached the start of the workbook you can continue Dataset description: Our collection of spam e-mails came from our

...preprocessing includes the following > standardisation to a standard size (120*120) > Images are rotated by 90 degree in all directions and flipped to enhance dataset > noise reduction (like hair and skin pigments) > image enhancement(brings affected parts into focus by enhancing the area) > segmentation into various segments to differentiate from normal skin > feature extraction - Softmax classifier is used for skin disease classification - Convolutional Neural Network is used with initial batch size as 20 and epoch size as 25 - model is saved - backend using jupyter lab and basic front end using .net framework to take webcam input and display prediction. - primary focus on acne and Shingles while classifying others as well - project is to be replicated ...

Using CNN or LSTM, develop a neural network application using python to detect vulnerabilities. Diverse machine learning techniques such as Deep Neural Networks have been successfully applied to malware static analysis. By directly using the source code as input, or the opcodes derived from it, a classifier is able to distinguish between benign or malicious software. A similar approach can be applied to the source code or compiled bytecode to detect potential vulnerabilities of the software ().The goal of this project is to develop a software analysis framework based on neural networks, which will effectively detect one or several potential vulnerabilities in Java programs. First, the source code will be translated to bytecode ()

one vs rest classification of a dataset using perceptron binary classifier

I need to build a speech emotion detection classifier mobile application. It will be a very basic mobile application where users can speak and record, and based on the speech we will identify the tone/emotions of the user. The application should also have a sign-in/sign-out option. Speech Emotion Recognition, abbreviated as SER, is the act of attempting to recognize human emotion and affective states from speech. This is capitalizing on the fact that voice often reflects underlying emotion through tone and pitch. Emotions: 1:'neutral', 2:'calm', 3:'happy', 4:'sad', 5:'angry', 6:'fear', 7:'disgust', 8:'surprise' etc.

Manually implement a custom bagging classifier using soft voting which works with sci-kit API

- You will be given a target dataset & working code for a baseline audio classifier. - You need to implement augmentation methods to improve the performance of the classifier. - Subsequently you will need to re-train the baseline model with these augmentations and keep detailed logs of performance. - Requires a GPU or we will provide access to a cloud instance (self-owned is preferred)

I need a python code that can perform the following tasks 1. Image Scraping the web and automatically creating a database of faces of notable people 2. Run a facial recognition algorithm and crop the faces from the images 3. Run a frontal face classifier algorithm that can score faces on how frontal they are a. Select only pictures that are “frontal” (i.e. above a certain threshold of the frontal score) 4. Run a facial expression recognition of the pictures that can score how neutral the faces are a. Select only pictures that are “neutral” (i.e. above a certain threshold of the frontal score) 5. Run a facial landmarks detection algorithm to detect the points we need. 6. Calculate a number of facial metrics from the landmarks calculated above. At the end of th...

I want suggestions of a one class classifier model that will effectively work for real fingerprint classification. After that I will want it to be implemented. details will be given later.