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mahiworld-blog1 · 5 years ago

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Important libraries for data science and Machine learning.

Python has more than 137,000 libraries which is help in various ways.In the data age where data is looks like the oil or electricity .In coming days companies are requires more skilled full data scientist , Machine Learning engineer, deep learning engineer, to avail insights by processing massive data sets.

Python libraries for different data science task:

Python Libraries for Data Collection

Beautiful Soup

Scrapy

Selenium

Python Libraries for Data Cleaning and Manipulation

Pandas

PyOD

NumPy

Spacy

Python Libraries for Data Visualization

Matplotlib

Seaborn

Bokeh

Python Libraries for Modeling

Scikit-learn

TensorFlow

PyTorch

Python Libraries for Model Interpretability

Lime

H2O

Python Libraries for Audio Processing

Librosa

Madmom

pyAudioAnalysis

Python Libraries for Image Processing

OpenCV-Python

Scikit-image

Pillow

Python Libraries for Database

Psycopg

SQLAlchemy

Python Libraries for Deployment

Flask

Django

Best Framework for Machine Learning:

1. Tensorflow :

If you are working or interested about Machine Learning, then you might have heard about this famous Open Source library known as Tensorflow. It was developed at Google by Brain Team. Almost all Google’s Applications use Tensorflow for Machine Learning. If you are using Google photos or Google voice search then indirectly you are using the models built using Tensorflow.

Tensorflow is just a computational framework for expressing algorithms involving large number of Tensor operations, since Neural networks can be expressed as computational graphs they can be implemented using Tensorflow as a series of operations on Tensors. Tensors are N-dimensional matrices which represents our Data.

2. Keras :

Keras is one of the coolest Machine learning library. If you are a beginner in Machine Learning then I suggest you to use Keras. It provides a easier way to express Neural networks. It also provides some of the utilities for processing datasets, compiling models, evaluating results, visualization of graphs and many more.

Keras internally uses either Tensorflow or Theano as backend. Some other pouplar neural network frameworks like CNTK can also be used. If you are using Tensorflow as backend then you can refer to the Tensorflow architecture diagram shown in Tensorflow section of this article. Keras is slow when compared to other libraries because it constructs a computational graph using the backend infrastructure and then uses it to perform operations. Keras models are portable (HDF5 models) and Keras provides many preprocessed datasets and pretrained models like Inception, SqueezeNet, Mnist, VGG, ResNet etc

3.Theano :

Theano is a computational framework for computing multidimensional arrays. Theano is similar to Tensorflow , but Theano is not as efficient as Tensorflow because of it’s inability to suit into production environments. Theano can be used on a prallel or distributed environments just like Tensorflow.

4.APACHE SPARK:

Spark is an open source cluster-computing framework originally developed at Berkeley’s lab and was initially released on 26th of May 2014, It is majorly written in Scala, Java, Python and R. though produced in Berkery’s lab at University of California it was later donated to Apache Software Foundation.

Spark core is basically the foundation for this project, This is complicated too, but instead of worrying about Numpy arrays it lets you work with its own Spark RDD data structures, which anyone in knowledge with big data would understand its uses. As a user, we could also work with Spark SQL data frames. With all these features it creates dense and sparks feature label vectors for you thus carrying away much complexity to feed to ML algorithms.

5. CAFFE:

Caffe is an open source framework under a BSD license. CAFFE(Convolutional Architecture for Fast Feature Embedding) is a deep learning tool which was developed by UC Berkeley, this framework is mainly written in CPP. It supports many different types of architectures for deep learning focusing mainly on image classification and segmentation. It supports almost all major schemes and is fully connected neural network designs, it offers GPU as well as CPU based acceleration as well like TensorFlow.

CAFFE is mainly used in the academic research projects and to design startups Prototypes. Even Yahoo has integrated caffe with Apache Spark to create CaffeOnSpark, another great deep learning framework.

6.PyTorch.

Torch is also a machine learning open source library, a proper scientific computing framework. Its makers brag it as easiest ML framework, though its complexity is relatively simple which comes from its scripting language interface from Lua programming language interface. There are just numbers(no int, short or double) in it which are not categorized further like in any other language. So its ease many operations and functions. Torch is used by Facebook AI Research Group, IBM, Yandex and the Idiap Research Institute, it has recently extended its use for Android and iOS.

7.Scikit-learn

Scikit-Learn is a very powerful free to use Python library for ML that is widely used in Building models. It is founded and built on foundations of many other libraries namely SciPy, Numpy and matplotlib, it is also one of the most efficient tool for statistical modeling techniques namely classification, regression, clustering.

Scikit-Learn comes with features like supervised & unsupervised learning algorithms and even cross-validation. Scikit-learn is largely written in Python, with some core algorithms written in Cython to achieve performance. Support vector machines are implemented by a Cython wrapper around LIBSVM.

Below is a list of frameworks for machine learning engineers:

Apache Singa is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users.

Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology. Amazon Machine Learning provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. It connects to data stored in Amazon S3, Redshift, or RDS, and can run binary classification, multiclass categorization, or regression on said data to create a model.

Azure ML Studio allows Microsoft Azure users to create and train models, then turn them into APIs that can be consumed by other services. Users get up to 10GB of storage per account for model data, although you can also connect your own Azure storage to the service for larger models. A wide range of algorithms are available, courtesy of both Microsoft and third parties. You don’t even need an account to try out the service; you can log in anonymously and use Azure ML Studio for up to eight hours.

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Models and optimization are defined by configuration without hard-coding & user can switch between CPU and GPU. Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU.

H2O makes it possible for anyone to easily apply math and predictive analytics to solve today’s most challenging business problems. It intelligently combines unique features not currently found in other machine learning platforms including: Best of Breed Open Source Technology, Easy-to-use WebUI and Familiar Interfaces, Data Agnostic Support for all Common Database and File Types. With H2O, you can work with your existing languages and tools. Further, you can extend the platform seamlessly into your Hadoop environments.

Massive Online Analysis (MOA) is the most popular open source framework for data stream mining, with a very active growing community. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. Related to the WEKA project, MOA is also written in Java, while scaling to more demanding problems.

MLlib (Spark) is Apache Spark’s machine learning library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.

mlpack, a C++-based machine learning library originally rolled out in 2011 and designed for “scalability, speed, and ease-of-use,” according to the library’s creators. Implementing mlpack can be done through a cache of command-line executables for quick-and-dirty, “black box” operations, or with a C++ API for more sophisticated work. Mlpack provides these algorithms as simple command-line programs and C++ classes which can then be integrated into larger-scale machine learning solutions.

Pattern is a web mining module for the Python programming language. It has tools for data mining (Google, Twitter and Wikipedia API, a web crawler, a HTML DOM parser), natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM), network analysis and visualization.

Scikit-Learn leverages Python’s breadth by building on top of several existing Python packages — NumPy, SciPy, and matplotlib — for math and science work. The resulting libraries can be used either for interactive “workbench” applications or be embedded into other software and reused. The kit is available under a BSD license, so it’s fully open and reusable. Scikit-learn includes tools for many of the standard machine-learning tasks (such as clustering, classification, regression, etc.). And since scikit-learn is developed by a large community of developers and machine-learning experts, promising new techniques tend to be included in fairly short order.

Shogun is among the oldest, most venerable of machine learning libraries, Shogun was created in 1999 and written in C++, but isn’t limited to working in C++. Thanks to the SWIG library, Shogun can be used transparently in such languages and environments: as Java, Python, C#, Ruby, R, Lua, Octave, and Matlab. Shogun is designed for unified large-scale learning for a broad range of feature types and learning settings, like classification, regression, or explorative data analysis.

TensorFlow is an open source software library for numerical computation using data flow graphs. TensorFlow implements what are called data flow graphs, where batches of data (“tensors”) can be processed by a series of algorithms described by a graph. The movements of the data through the system are called “flows” — hence, the name. Graphs can be assembled with C++ or Python and can be processed on CPUs or GPUs.

Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray). Using Theano it is possible to attain speeds rivaling hand-crafted C implementations for problems involving large amounts of data. It was written at the LISA lab to support rapid development of efficient machine learning algorithms. Theano is named after the Greek mathematician, who may have been Pythagoras’ wife. Theano is released under a BSD license.

Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community.

Veles is a distributed platform for deep-learning applications, and it’s written in C++, although it uses Python to perform automation and coordination between nodes. Datasets can be analyzed and automatically normalized before being fed to the cluster, and a REST API allows the trained model to be used in production immediately. It focuses on performance and flexibility. It has little hard-coded entities and enables training of all the widely recognized topologies, such as fully connected nets, convolutional nets, recurent nets etc.

#datascience machineLearning.

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digitaltechnologyhub-blog · 6 years ago

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An Introduction to Deep Learning

Deep Learning is at the cutting edge of what machines can do, and developers and magnate definitely need to comprehend what it is and how it works. This special kind of algorithm has far gone beyond any previous benchmarks for category of images, text, and voice. Are you interest in learning "Deep Learning" you can follow best Coursera deep learning courses for more information It also powers some of the most intriguing applications worldwide, like autonomous lorries and real-time translation. There was definitely a bunch of excitement around Google's Deep Learning based AlphaGo beating the very best Go gamer in the world, however business applications for this innovation are more immediate and potentially more impactful. This post will break down where Deep Learning fits into the environment, how it works, and why it matters.

What is Deep Learning?

To understand what deep learning is, we first need to comprehend the relationship deep learning has with machine learning, neural networks, and artificial intelligence. The very best way to consider this relationship is to imagine them as concentric circles: Deep learning is a specific subset of Machine Learning, which is a particular subset of Artificial Intelligence. For private meanings: Artificial Intelligence is the broad required of developing machines that can think intelligently Machine Learning is one method of doing that, by utilizing algorithms to glean insights from data (see our mild introduction here). Deep Learning is one way of doing that, using a specific algorithm called a Neural Network. Do not get lost in the taxonomy-- Deep Learning is simply a type of algorithm that appears to work truly well for forecasting things. Deep Learning and Neural Nets, for most purposes, are efficiently synonymous. If individuals try to puzzle you and argue about technical meanings, do not stress over it: like Neural Nets, labels can have numerous layers of significance. Neural networks are influenced by the structure of the cerebral cortex. At the fundamental level is the perceptron, the mathematical representation of a biological nerve cell. Like in the cortex, there can be numerous layers of interconnected perceptrons. Input values, or to put it simply our underlying data, get travelled through this "network" of covert layers till they eventually assemble to the output layer. The output layer is our prediction: it may be one node if the design simply outputs a number, or a couple of nodes if it's a multiclass classification issue. The covert layers of a Neural Net perform modifications on the data to ultimately feel out what its relationship with the target variable is. Each node has a weight, and it multiplies its input worth by that weight. Do that over a couple of different layers, and the Web is able to essentially control the data into something significant. To figure out what these small weights need to be, we usually use an algorithm called Backpropagation. The great reveal about Neural Nets (and a lot of Machine Learning algorithms, really) is that they aren't all that wise-- they're basically simply feeling around, through trial and error, to attempt and discover the relationships in your data. In his popular Coursera course on Machine Learning, Teacher Andrew Ng uses the analogy of a lazy hiker to describe how most algorithms end up working: "we position a fictional hiker at various points with simply one instruction: Walk only downhill up until you can't walk down anymore.". The hiker doesn't actually understand where she's going-- she simply feels around to discover a course that might take her down the mountain. Our algorithm is the very same-- it's probing to figure out how to make the most precise predictions. The last worths that each our our nodes in a Neural Web takes on is a reflection of that procedure. In the 1980s, a lot of neural networks were a single layer due to the cost of calculation and accessibility of data. Nowadays we can afford to have more covert layers in our Neural Nets, hence the name "Deep" Learning. The various types of Neural Networks available for usage have actually also proliferated. Designs like Convolutional Neural Webs, Reoccurring Neural Internet, and Long Short-Term Memory are discovering compelling usage cases across the board.

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leedsomics · 2 years ago

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MOT: a Multi-Omics Transformer for multiclass classification tumour types predictions

Motivation Breakthroughs in high-throughput technologies and machine learning methods have enabled the shift towards multi-omics modelling as the preferred means to understand the mechanisms underlying biological processes. Machine learning enables and improves complex disease prognosis in clinical settings. However, most multi-omic studies primarily use transcriptomics and epigenomics due to their over-representation in databases and their early technical maturity compared to others omics. For complex phenotypes and mechanisms, not leveraging all the omics despite their varying degree of availability can lead to a failure to understand the underlying biological mechanisms and leads to less robust classifications and predictions. Results We proposed MOT (Multi-Omic Transformer), a deep learning based model using the transformer architecture, that discriminates complex phenotypes (herein cancer types) based on five omics data types: transcriptomics (mRNA and miRNA), epigenomics (DNA methylation), copy number variations (CNVs), and proteomics. This model achieves an F1-score of $98.37%$ among 33 tumour types on a test set without missing omics views and an F1-score of $96.74%$ on a test set with missing omics views. It also identifies the required omic type for the best prediction for each phenotype and therefore could guide clinical decision-making when acquiring data to confirm a diagnostic. The newly introduced model can integrate and analyze five or more omics data types even with missing omics views and can also identify the essential omics data for the tumour multiclass classification tasks. It confirms the importance of each omic view. Combined, omics views allow a better differentiation rate between most cancer diseases. Our study emphasized the importance of multi-omic data to obtain a better multiclass cancer classification. Availability and implementation: MOT source code is available at url{https://github.com/dizam92/multiomic_predictions}. http://dlvr.it/ScthNX

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dblacklabel · 2 years ago

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Why Do We Need NLP?

Why Do We Need NLP? Natural language processing (NLP) is the process of analyzing words and phrases to determine their meaning. However, this process is far from perfect. Some of the challenges include semantic analysis, which is not easy for programs to grasp. The abstract nature of language can also be difficult for programs to process. Furthermore, a sentence can have multiple meanings depending on the speaker's inflection or stress. Another challenge is that NLP algorithms might not pick up subtle changes in voice tone. NLTK The NLTK is a framework that reduces the amount of infrastructure required for advanced projects. NLTK provides predefined interfaces and data structures, which help users create new modules with minimal effort. This way, they can concentrate on the more difficult problems and not on the underlying infrastructure. NLTK is open-source, which means that anyone can contribute to it. To get started with NLTK, you need Python installed. Then, you should install the python compiler and All NLP packages. When this is done, you should open a dialogue box and select "Tokenize text." Tokenization is the process of breaking text into words, sentences, and characters. Two types of tokenizing are used in NLP: nominalization and lexical tokenization. SpaCy SpaCy is a Python package that tokenizes text, processes it into a Doc object, and then returns a result. Its processing pipeline is composed of several components: a lemmatizer, tagger, parser, and entity recognizer. Each component returns a processed Doc. You can learn more about each of these components in the usage guide. SpaCy allows you to create a processing pipeline that includes machine learning components. The first component is a tokenizer, which acts on text to generate a result. From there, you can add a parser or a statistical model. You can also use custom components. Another component is POS tagging. This algorithm tags words with the appropriate part of speech, and changes with context. In this way, spaCy can predict which words are more likely to appear in a given text. Naive Bayes Algorithm The Naive Bayes algorithm is a fast machine learning algorithm that can classify data into binary and multi-class categories. This algorithm is useful in many practical applications. There are several ways to apply Naive Bayes, including regularization and small-sample correction. One of the most popular Naive Bayes variants is the Multinomial Naive Bayes, which is typically used with multivariate, Bernoulli distributions. This version of the Naive Bayes algorithm is fast and extensible, and can classify binary and multiclass data. This algorithm is also computationally cheap. In contrast, it would take a lot of time to build a classifier from scratch. Naive Bayes classifiers take the average of a number of features and assign them to different classes. This makes them ideal for text classification. Masked Language Model A Masked Language Model (MLM) is a machine learning technique that predicts the masked token in a given sentence based on other words in the sentence. Its bidirectional nature allows it to learn from words on both sides of a masked word. The model is often trained with a specific learning objective. It can be applied in many NLP tasks. In particular, it can be applied to speech recognition, question answering, and search. It can be trained using a fraction of the input text and combines that information to make a more accurate representation. This technology is highly computationally efficient and is expected to improve performance on NLP tasks. A Masked Language Model works by taking an entire sentence as input and masking about fifteen percent of words. The model can then predict the words that are left unmasked. It can also learn to represent sentences in a bidirectional manner. It can even learn to predict words from two masked sentences by concatenating them. Conversational AI Conversational AI is an emerging field in computer science. It is a branch of artificial intelligence that uses natural language processing (NLP) to recognize and understand conversations. Until now, conversational AI was limited to speech recognition in the internet. However, with advances in AI and machine learning, conversational AI can now be used in a number of real-world applications. The use of conversational AI in customer service is becoming more widespread. This technology can power intelligent virtual agents that can offer assistance and resolve customer issues. It is already entering the mainstream, and 79% of contact center leaders plan to invest in greater AI capabilities in the next two years. Read the full article

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globalteachonline · 2 years ago

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What you'll learn Create a Machine Learning app with C#Use TensorFlow or ONNX model with dotnet appUsing Machine Learning model in ASP dotnetUse AutoML to generate ML dotnet modelNote: This course is designed with ML.Net 1.5.0-preview2Machine Learning is learning from experience and making predictions based on its experience.In Machine Learning, we need to create a pipeline, and pass training data based on that Machine will learn how to react on data.ML.NET gives you the ability to add machine learning to .NET applications.We are going to use C# throughout this series, but F# also supported by ML.Net.ML.Net officially publicly announced in Build 2019.It is a free, open-source, and cross-platform.It is available on both the dotnet core as well as the dotnet framework.The course outline includes:Introduction to Machine Learning. And understood how it’s different from Deep Learning and Artificial Intelligence.Learn what is ML.Net and understood the structure of ML.Net SDK.Create a first model for Regression. And perform a prediction on it.Evaluate model and cross-validate with data.Load data from various sources like file, database, and binary.Filter out data from the data view.Export created the model and load saved model for performing further operations.Learn about binary classification and use it for creating a model with different trainers.Perform sentimental analysis on text data to determine user’s intention is positive or negative.Use the Multiclass classification for prediction.Use the TensorFlow model for computer vision to determine which object represent by images.Then we will see examples of using other trainers like Anomaly Detection, Ranking, Forecasting, Clustering, and Recommendation.Perform Transformation on data related to Text, Conversion, Categorical, TimeSeries, etc.Then see how we can perform AutoML using ModelBuilder UI and CLI.Learn what is ONNX, and how we can create and use ONNX models.Then see how we can use models to perform predictions from ASP.Net Core.Who this course is for:This is for newbies who want to learn Machine LearningDeveloper who knows C# and want to use those skills for Machine Learning tooA person who wants to create a Machine Learning model with C#Developer who want to create Machine Learning

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myprogrammingsolver · 3 years ago

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Machine Learning Homework 3 Solution

1 SVM vs. Neural Networks In this section, I did experiments on the SVM and MLP using following two datasets: Table 1: Datasets Dataset Classes Size Features breast-cancer 2 683 10 dna 3 3186 180 I tried both binary and multiclass classification tasks on the two classifiers. The size and features of the two datasets is different. Thus, we can compare the performance of SVM and MLP in…

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learnbaydatascience · 3 years ago

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Top 10 Deep Learning projects for beginners

MNIST

MNIST stands for ‘Modified National Institute of Standards and Technology database’. It is a database consisting of handwritten digits. The objective is to identify the correct number. This project is straightforward; it should familiarize you with your deep learning framework and teach you how to build and train your first Artificial Neural Network. It also shows you how to solve multiclass classification problems rather than just binary ones.

TensorFlow and PyTorch both can load MNIST.

CIFAR-10

CIFAR-10 stands for ‘Canadian Institute For Advanced Research’. This dataset comprises 60000 colour images of size 32x32 in ten classes, each with 6000 photos. This project is similar to the previous, although it is a little more complex. It includes colour photos of aeroplanes, birds, dogs, and other objects from ten different classes. It’s a little more challenging to come up with a good classification model here. Instead of utilising a simple neural network, you should use a Convolutional Neural Network and discover how it works.

TensorFlow and PyTorch both can load CIFAR-10.

Image Recognition

The task of finding objects of interest within an image and determining which category they belong to is known as image recognition. We naturally recognize items as separate instances and associate them with specific definitions when we view them visually. Visual recognition, on the other hand, is a challenging assignment for machines to do.

In the realm of computer vision, image recognition using artificial intelligence has been a long-standing research challenge. While numerous methods have evolved, image recognition’s unifying purpose is to classify observed objects into multiple categories. Hence, they are also called object recognition.

Diagnosis of a disease

For decades, the disease diagnosis process has remained the same: a clinician would examine symptoms, conduct lab testing, and consult medical diagnostic standards. Recent breakthroughs in AI/machine learning/deep learning, on the other hand, have enabled computers to diagnose and identify diseases with human precision. The Deep Learning technology is successfully used in the diagnosis of the following:

Cancer prognosis and detection.

Organ Failure.

Autistic disorder.

Diabetic retinopathy disease diagnosis.

Psoriasis disease diagnosis.

Alzheimer’s disease.

Parkinson’s disease.

Recognition/ Classification of Tweets

This project falls under the category of Natural Language Processing. Another subset of NLP is Sentiment Analysis, where we find the sentiment of the given text. Text classification is a machine learning technique for categorizing open-ended text into a collection of predetermined categories. Text classifiers can organize, arrange, and organize almost any type of text, including documents, medical research, and files, as well as text found on the internet.

Unstructured data accounts for over 80% of all data, with text being one of the most common categories. Because analyzing, comprehending, organizing, and sifting through text data is difficult and time-consuming due to its messy nature, most businesses do not exploit it to its full potential.

Text classifiers enable businesses to rapidly and cost-effectively arrange various forms of relevant text, including email messages, legal documentation, media platforms, chatbots, surveys, and more. This has allowed companies to save time studying text data, automate business processes, and make data-driven business choices.

Recommendation System

Major music and video streaming services such as Spotify and Netflix use Deep learning models to curate a playlist/ watchlist for you. Using data obtained from their interactions, such as impressions, clicks, likes, and purchases, recommender systems are trained to comprehend individuals’ preferences, previous decisions, and characteristics.

Recommender systems aid in the reduction of information overload by assisting consumers in locating relevant items from a large number of options by delivering customized content. Since their ability to predict customer interests and desires on a highly customized level, recommender systems are a favorite of content and product suppliers because they push consumers to just about any product or service that interests them, from books to movies.

Time Series Forecasting

Time series forecasting is a critical application of machine learning. While the time component provides more information, time-series issues are more difficult to anticipate than many other tasks. As the name implies, time-series data differ from different data types in that the temporal aspect is significant. On the plus side, this provides us with more information that we can use when creating our machine learning model. The input features and the changes in input/output over time include helpful information.

Chatbot

Through human-to-human dialogue, a deep learning chatbot learns everything from data. The more data you feed it, the more effective it will become at learning. Chatbots will enhance their accuracy if they are trained extensively. Retrieval-based and generative deep learning chatbots are the two primary forms. Retrieval-based chatbots have a ‘repository’ of responses to questions, whereas generative chatbots don’t.

Existing interactions between customers and support workers can train deep learning chatbots, which should be as thorough and varied as feasible. Data reshaping (generating message-response combinations that the machine will recognise) and pre-processing are also part of the training process (adding grammar so that the chatbot can understand errors correctly).

Object Detection

A subset of computer vision, object detection is an automated method for detecting necessary details in an image with respect to the background. Placing a tight bounding box around these things and linking the relevant object category with each bounding box is the key to solving the object detection challenge. Deep learning, like other computer vision tasks, is the most advanced way of detecting objects.

The number of things in the foreground can change across photos, which complicates object recognition. Consider restricting the object detection problem by assuming that each image has only one thing to understand better how it works. When there is only one object per image, determine a bounding box and categorize the object. Because the bounding box comprises four values, knowing its position makes it a regression issue. The object is then classified, which is a classification problem.

The convolutional neural network (CNN) solves the regression and classification difficulties for our constrained object detection task. Unlike other traditional computer vision tasks like image recognition, key-point detection, and semantic segmentation, our constrained object identification issue has a set number of targets. Modelling the targets as a fixed number of classification or regression tasks can be used to fit them.

Style Transfer

Neural style transfer is a technique for blending two images—a content image and a style reference image (such as a famous painter’s work)—so that the output image appears like the content image but is ‘painted’ in the manner of the style reference image.

This is accomplished by adjusting the output image’s content statistics to match the content image’s content statistics and the style reference image’s style statistics. A convolutional network extracts these data from the pictures.

In this blog, you will get to know about “Top 10 Deep Learning Projects for Beginners” For more such information, visit Learnbay.co.

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thedatasciencehyderabad · 2 years ago

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The Method To Take Care Of Imbalanced Classification And Imbalanced Regression Data?

It will also provide a step-by-step approach to carry out multiclass classification using machine studying algorithms. Cost-Sensitive Learning takes the misclassification of prices into consideration by minimizing the total price. The aim of this system is mainly to pursue an excessive accuracy of classifying examples into a set of identified classes. It is enjoyed as one of the important roles in the machine studying algorithms including real-world knowledge mining purposes. Oversampling is implemented when the amount of data is inadequate.

It would have a very excessive accuracy of 99.8% as a result of all the testing samples belonging to “0”, but in actuality, it would present no meaningful data for us. The disadvantage of SMOTE and Tomek links are eliminated by the hybrid sampling method. This method is used for better-defined class clusters among majority and minority classes. Under-sampling, by eradicating some of the majority class so it has less effect on the machine learning algorithm.

The variety of corrections can additionally be less, so it is a highly efficient technique. The architecture utilized in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the identical time, the testing structure is 3D-VAE, which has an encoder and a decoder.

Taking one other case as the place we want to predict whether or not a person will have heart illness or not. So for this task, the mannequin mustn't predict a person with coronary heart illness won't have it so recall ought to be excessive. This technique avoids the pre-selection of parameters and auto-adjusts the decision hyperplane. AIM discovers new ideas and breakthroughs that create new relationships, new industries, and new methods of considering. AIM is the crucial source of information and ideas that make sense of a reality that is at all times changing. Our mission is to bring about better-informed and extra-conscious selections about expertise via authoritative, influential, and reliable journalism.

So we should always create a mannequin with excessive precision as if we predict a non-buyer is going to purchase so marketing cash might be spent on it. Cross-validation must be utilized properly when making use of over-sampling to handle imbalance problems. This represents a harmonic mean between recall and precision. In follow, a high F-measure value ensures that both recall and precision is moderately excessive. Accordingly, synthetic examples may be generated by repeating the above steps.

So in such a case, we ought to always know which metrics may help me to get a generalized mannequin. Run-time could be improved by reducing the quantity of the training dataset. Shiva Prasad Koyyada is a data scientist training technical and nontechnical people in data science, consulting with varied purchasers throughout domains since 2016. He has worked as a school in various reputed engineering institutions for 6 years. Shiva is enthusiastic about connecting with students and hence his love for educating continues. He is known for his endurance and the instant rapport he builds with people.

But at a later stage, it was found the shopper defaulted on credit. So right here our goal must be to create a mannequin that shouldn't predict a defaulter customer as a non-defaulter and non-defaulter as a defaulter, so mainly each Precision and Recall should be high here. Please notice that when we run the models multiple instances there might be a slight change within the results as sample modifications every time. Sampling Majority samples into different subsets with (70-75)-(25-30) ratio.

To examine options, we are going to use different metrics instead of the common accuracy of counting the variety of errors. Given a dataset of transaction data, we would like to find out which are fraudulent and which are genuine. Now, it's highly cost to the e-commerce firm if a fraudulent transaction goes through as this impacts our customer's trust in us, and costs us money. So we need to catch as many fraudulent transactions as possible. It is the problem in machine studying where the total number of a category of knowledge is far less than the whole number of another class of data. Instead of relying on random samples to cowl the number of the training samples, cluster the ample class in r teams, with r being the variety of circumstances in r.

On the left facet is the end result of simply making use of a common machine-studying algorithm without utilizing undersampling. Oversampling & under-sampling are the methods to vary the ratio of the classes in an imbalanced modeling dataset. So, let’s say you have a thousand records out of which 900 are most cancers and 100 are non-cancer. This is an example of an imbalanced dataset as the end result of your majority class is about 9 occasions greater than the bulk class. Class imbalance is the largest challenge within the classification task of machine learning algorithms. This happens when one label of goal variable information factors is less as compared to one other label.

Combining these methods with your long-term marketing strategy will deliver outcomes. However, there will be challenges with the method in which, where you should adapt as per the necessities to benefit from it. At the same time, introducing new technologies like AI and ML can also solve such points easily. To learn more about the use of AI and ML and how they are reworking businesses, keep referring to the weblog section of E2E Networks.

Interested readers could look into newer literature regarding RUSBoost, SMOTEBagging, and Underbagging, which are all regarded as extra promising approaches since SMOTE. It can be utilized for re-modeling ruins at ancient architectural sites. The rubble or the particle stubs of constructions can be used to recreate the complete constructing construction and get a thought of the means it looked prior to now.

Zhu, B., Baesens, B., Backiel, A., & Vanden Broucke, S. K. Benchmarking sampling methods for imbalance studying in churn prediction. Journal of the Operational Research Society, sixty-nine, 49-65.

Note that in a few fashions, the class_weight parameter was additionally used. We can now visualize relying on the plot to have an equal number of samples for each class in the goal. We select a dataset as explained earlier in level number 1.

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Imbalance information distribution is an important part of machine learning workflow. An imbalanced dataset means cases of one of many two lessons are greater than the opposite, in one other way, the variety of observations is not identical for all of the lessons in a classification dataset. This problem is confronted not solely within the binary class information but also in the multi-class information. This listing is not complete and should only be used as a beginning point, but it’s a fantastic place to get begun if you are having bother with imbalanced knowledge. There isn’t one greatest method that applies to all problems, so strive for completely different techniques and models to see what works greatest for you. Try to be creative when making use of different approaches, and don’t forget that in many industries (e.g., fraud detection, real-time bidding), trade guidelines can change as time goes on.

In this process, we increase the size of the uncommon samples to balance the dataset. The samples are generated utilizing techniques like SMOTE, bootstrapping, and repetitions. The most typical approach used while oversampling is ‘Random Over Sampling’, whereby random copies are added to the minority class to stabilize with the majority class. The drawback with the normal methods of testing imbalanced data is that the data is all time resampled. But you don’t need to resample if you’re utilizing a mannequin specifically educated to work with imbalanced information, like XGBoost. Of course, you’ll nonetheless have to resample the data, however, not all fashions require this.

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superspectsuniverse · 3 years ago

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How Naïve Bayes Algorithm works?

Naïve Bayes is a simple supervised machine learning algorithm that is used primarily for classification problems. It is one of the algorithms that are mainly used for text classification that has a high – dimensional training datasets. It is also the most effective classification algorithm which can be used in building fast machine learning models that makes quick predictions.

It works on the principle of prediction. Popular examples where this algorithm used are Spam filtration, classification of articles and Sentimental Analysis.

Naïve Bayes Algorithm consists of two words: Naïve and Bayes, Naïve because it assumes occurrence of a certain feature is independent of occurrence of other features and Bayes because it depends on Bayes’ Theorem.

This algorithm is able to effectively solve many complex problems such as text classifiers than the much hyped neural networks. The model works well with insufficient and mislabelled data. Probability is a field of math that helps us to reason about uncertainty and calculates the likelihood of some events. When we work with a predictive machine learning model, such as Naïve Bayes Algorithm, we have to predict uncertain future.

Classification is the most used form of prediction. For binary classification, prediction results in classification to 0 or 1, such as spam or not spam. In case of multiclass classification, it aims to predict the class of record to wide variety of classes rather than 0 or 1.The Naïve Bayes machine learning algorithm is one of the methods to deal with uncertainty with the help of probabilistic methods.

When dealing with classification problem in supervised learning, we use labelled data where target class of the records is known. We use these data to train the model using these data and apply this particular trained model to new data where classification has to be done.

Advantages of Naïve Bayes Classifier are that it is fast and easy and can be used for binary as well as multiclass classification. It is the most foremost choice for text classification problems. One of the disadvantages is that it assumes that all features are independent or unrelated, so it cannot learn the relationship between the features.

The most important applications of the algorithm are Credit scoring, Medical data classification, real time predictions and Text classification such as Spam filtering and Sentimental Analysis.

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juniperpublishers-agriculture · 3 years ago

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Classify Rice Disease Using Self-Optimizing Models and Edge Computing with Agricultural Implications- Juniper Publishers

Rice continues to be a primary food for the world’s population. Over its complex history, dating as far back as 8,000 B.C., there have been agricultural challenges, such as a variety of diseases. A consequence of disease in rice plants may lead to no harvest of grain; therefore, detecting disease early and providing expert remedies in a low-cost solution is highly desirable. In this article, we study a pragmatic approach for rice growers to leverage artificial intelligence solutions that reduce cost, increase speed, improve ease of use, and increase model performance over other solutions, thereby directly impacting field operations. Our method significantly improves upon prior methods by combining automated feature extraction for image data, exploring thousands of traditional machine learning configurations, defining a search space for hyper-parameters, deploying a model using edge computing field usability, and suggesting remedies for rice growers. These results prove the validity of the proposed approach for rice disease detection and treatments.

Keywords:Agriculture Technology; Machine Learning Applications; Rice Production, Edge Computing; Precision Farming; Agriculture Education; Pre-Trained Models For Image Classification; Deep Learning Applications; Farming Knowledge; Rice Disease Management

Introduction

Rice supports more than half the world’s population as a primary food source [1]. The quality and quantity of rice production are significantly affected by rice disease. In general, identification of rice disease is made by visual observation of experienced producers in the field. This method requires constant surveillance from manual labor, which could be prohibitively expensive for large farms. However, with the advances in image processing and pattern recognition, a cost-effective method for disease identification is demonstrated. Advances in research continue on image processing and pattern recognition as a result of innovations with digital cameras and the increase in computational capacity. These tools have been effectively applied in many areas [2-5]. Prajapati et al., [6] developed a rice plant disease classification system after detailed experimental analysis of various techniques. Four techniques of background removal and three techniques of segmentation were empirically evaluated. It was proposed for accurate feature extraction, a centroid feedingbased K-means clustering for segmentation of disease from a leaf image was necessary. The output from K-means clustering was enhanced by removing green pixels in the disease portion. Additional feature extraction was done under three categories: (1) color, (2) shape, and (3) texture. Ultimately, Support Vector Machines was chosen to perform a multiclass classifier (Figure 1).

Generally, rice growers identify plant disease through leaves as the first source. This can be detected automatically using computer vision techniques. Until now, there have been several researchers who have conducted experiments with very little utility for rice farms. Considerations for farmers are cost, speed, ease of use, model performance, and direct impact on the field. There has been little attention to structuring a useful machine learning approach that is end-to-end in agriculture. Previous investigations have successfully demonstrated the potential of deep learning algorithms in plant disease detection; yet, the cost associated with such architecture makes this unattainable for many rice growers. The length of training time required for such deep learning models has historically been lengthy, and specialty hardware is needed. Additionally, the expertise necessary to maintain and optimize deep learning network hyper-parameters, such as (a) a comparison of activation functions like ReLU, Sigm, and Tanh, (b) learning rate, (c) quantity of neurons per layer, (d) quantity of hidden layers, and (e) dropout regularization remains unrealistic. Much of the research to date has been concerned with many pre-processing steps and augmentation techniques for images to maximize model performance: (a) resize images, (b) denoise, (c) segmentation, and (d) morphology. In almost all the research, model performance has suffered from over-fitting, evidenced by high accuracy scores for training sets but significantly lower accuracy for validation sets.

Given that growers value more what is likely to happen in day-to-day utilization, the emphasis on a practical solution suggests validation scores matter more than training scores. It will measure how well a solution performs. Lastly, there is little to no connection between identifying plant disease and what action rice farms should do next to experience the benefit of an algorithm detecting a plant disease early. In this work, we studied the benefits of crafting an end-to-end solution for rice farmers using an automated machine learning platform with the aim of building a production-grade solution for agriculture that provides real-time decision support for rice farms. We combine several methods, namely, employing automated feature extraction for image data, exploring thousands of possible traditional machine learning configurations, defining a search space for hyper-parameters, deploying a model built for edge computing for field usability, and suggesting remedies for rice growers. This journal article comprises the following sections: methods and materials, results, discussion, and conclusion.

Methods and Materials

Data Acquisition

The dataset contains 120 jpeg images of disease-infected rice leaves. There are 3 classes of images based on the type of disease, each containing 40 images, and captured with a NIKON D90 digital SLR camera with 12.3 megapixels. This dataset was curated by the research team at the Department of Information Technology, Dharmsinh Desai University, and is made publicly available. The authors gathered these leaves from a rice field in a village called Shertha in Gujarat, India, and in consultation with farmers, grouped the leaves into the aforementioned-diseases categories (Figure 2).

LogicPlum

As part of our research and analysis, we opted to use an A.I. innovation platform named LogicPlum. LogicPlum includes a library of proprietary and open-source model types, including linear, non-linear, and deep learning approaches [7]. While manual interventions are possible during model development, in this study, the autonomous model builder was used; specifically, the platform was provided only with the original data images, and it decided appropriate configurations for machine learning models automatically. Additionally, we chose two different autonomous run types within the LogicPlum platform. The first was Rapid mode, which is designed for model development under 5 minutes. We also used Intensive mode, which is intended for model development that allows for an undefined time but stops after several rounds of non-improvement with a given model evaluation metric. The software considers several families of algorithms and ranks them according to model performance based on the machine learning task. Lastly, a combination of base models is automatically evaluated, and a subsequent composite model is tested for increased lift before a final solution.

Deep Learning for Computer Vision

Within research, education, and industry applications, the most essential step in a computer vision process is to extract features from the images in a dataset. In this context, a feature is a tangible piece of information about a given image, such as color, line, edges, etc. A model needs to observe in order to learn the characteristics of a given image and thereby classify it correctly. Traditional machine learning approaches allow for several different feature extraction methods, which require manual feature selection and engineering. This process relies heavily on domain knowledge, both in computer vision and rice plant disease, to create model inputs that make machine learning algorithms work better. To increase speed to market for the solution and eliminate the need for expertise in machine learning and plant pathology, we explored automatically extracting features using deep learning. The network automatically extracts features and learns their importance based on the output by applying weights to its connections. In practice, an individual feeds the raw image to the network and, as it passes through the network layers, the network identifies patterns within the image to create features.

We use the SqueezeNet network to extract features from the images. SqueezeNet is a lightweight architecture that is extremely useful in low bandwidth scenarios like mobile platforms and has ImageNet accuracy similar to AlexNet, the convolution neural network that began the deep learning revolution in 2012 (Figure 3). The first layer demonstrates that the first layer is a squeeze layer comprised of a 1×1 convolution that reduces the amount of channels, for example, from 64 to 16 in each image. The squeeze layer aims to compress the data so that the 3×3 convolution does not need to learn so many parameters. This is followed by an expand block with two parallel convolution layers: one with a 1×1 kernel, the other with a 3×3 kernel. These convolution layers also increase the quantity of channels again, from 16 back to 64. Their outputs are joined together so the output of this fire module has 128 channels overall. SqueezeNet has 8 of these Fire modules in succession, sometimes with max-pooling layers between them. There are zero fully-connected layers. At the end of the process is a convolution layer that performs the classification, followed by the global average [8] (Figure 4).

Determined Architecture with Rapid Mode

Modeling with ExtraTrees

ExtraTrees Classifier was selected as the top performer. This classifier fits many randomized decision trees on numerous sub-samples of the dataset and uses averaging to enhance the predictive accuracy and control over-fitting. ExtraTrees is considered a perturb-and-combine technique specifically designed for trees. Effectively this means that a diverse set of classifiers is designed by introducing randomness in the classifier construction. The prediction of the collection of weak learners is given as the averaged prediction of the individual classifiers (Figure 5).

Determined Architecture with Autonomous Mode

For a composite model to outperform base models, some samples must be better predicted by one model, and other samples by another model. Stacking is an ensemble learning technique to bring together multiple classification models via a meta-classifier [9]. LogicPlum extends the standard stacking algorithm using cross-validation to arrange the input data for the level-2 classifier.

In the usual stacking procedure, the first-level classifiers fit the same training set used to arrange the inputs for the level-2 classifier, which may lead to overfitting. However, the LogicPlum approach uses the concept of cross-validation: the dataset is split into k-folds, and in k successive sequences, k-1 folds are used to fit the first level classifier. The first-level classifiers are then utilized on the remaining 1 subset that was not used for model fitting in each iteration in each round. The resulting predictions are then stacked and provided – as input data – to the second-level classifier. After the training of the StackedCVClassifier, the first-level classifiers are fit to the entire dataset, as illustrated in the figure below. More formally, the Stacking Cross-Validation algorithm can be summarized as follows: (Table 1)

Modeling with Stochastic Gradient Descent

This estimator applies regularized linear models with stochastic gradient descent learning; the loss’s gradient is estimated each sample at a time. The model is revised along the way with a decreasing learning rate [10]. This implementation makes use of data represented as dense or sparse arrays of floating-point values for the features (Figure 6). The model it fits can be monitored with the loss parameter; by default, it fits a linear support vector machine. The regularizer is a consequence added to the loss function that shrinks model parameters in the direction of the zero vector using the squared Euclidean norm (L2), the absolute norm (L1), or a mixture of both (Elastic Net). Many hyperparameters were considered in optimizing for the Stochastic Gradient Descent Classifier. The constant that multiplies the regularization term, alpha, is set to 0.0001. In general, the higher the value, the stronger the regularization. We did not compute the average Stochastic Gradient Descent weights across all updates and therefore did not store the results as coefficients. We did not set class weights, and thus, all classes are assigned to a weight of one. Early stopping was not engaged, forcing us to not terminate training when validation scores did not improve. The initial learning rate set was 1.0. We did not assume the data was already centered, and chose to estimate the intercept. We used a squared hinge loss function that is equivalent to Support Vector Classification, but is quadratically penalized. For the exponent for inverse scaling learning rate, we used a power_t =0.1. We set the maximum number of passes over the training data to be 1,000. The L1 ratio is defined with a range of 0 to 1, and we set it to 1.0. We used Elastic Net as the penalty term, which brought sparsity to the model. The learning rate schedule used was inverse scaling,

Where eta0 and t power _t are hyperparameters chosen by LogicPlum.

Modeling with Gaussian Naïve Bayes

We implemented the Gaussian Naïve Bayes algorithm for classification. The likelihood of the features is believed to be Gaussian:

Where the parameters y σ and y μ are estimated using maximum likelihood.

The classes’ prior probabilities were not specified as part of our experiment and therefore were not adjusted according to the data. It was determined that variable smoothing should be 1e-9, which was the most considerable variance of all features added to variances for calculation stability.

Connected to farmers for usability

Results

Evaluation Metrics

We use a ground-truth-based approach to compare the results of various machine learning models. Ground truth is a term used in multiple fields to refer to the information provided by direct observation instead of the information provided by inference. We understood the machine learning task to be a multiclass classification problem that could be realized in a binary classification model framework.

The model results were compared concerning the ground truth as follows:

Given the definitions of terms within table 2, we can generate standard evaluation metrics for machine learning classification models:

Accuracy is defined as the number of items correctly identified as either true positive or true negative out of the total number of items. Mathematically described as,

Recall is defined as the number of items correctly identified as positive out of the total actual positives. Mathematically described as,

Precision is defined as the number of items correctly identified as positive out of the total items identified as positive. Mathematically described as,

F1 score is defined as the harmonic average of precision and recall, measures the effectiveness of identification when just as much significance is given to recall as to precision. Mathematically, described as,

Macro Average computes the metric independently for each class then averages the results. Mathematically, described as,

Weighted average weights are calculated by the frequency of a class. Mathematically, described as,

LogicPlum randomly selected 30 images from each class and formed a training dataset of 90 images. The remaining 30 images were portioned as a test set. The data in both train and test consisted of 513 features (Table 3).

Model Performance

Statistics

Our primary evaluation metric for model performance was accuracy. We observed accuracy of 0.90 across all rice plant disease classifications on the Rapid model’s validation dataset. Recall, as it relates to Leaf Smut, is the lowest secondary evaluation metric for model performance. This measure aims to answer the question, “What proportion of actual positives was identified correctly?” In the context of the Intensive model, which was completed in 60 minutes, we observed accuracy of 92.5% across all rice disease classes. However, the lowest secondary measure is recall as it relates to Leaf Smut (Table 4).

To completely evaluate the effectiveness of a model, we must examine both precision and recall. Precision and recall are often in tension. That is, improving precision typically reduces recall and vice versa. Thus, many machine learning practitioners rely on the F1 score, which combines the effects of both precision and recall. An F1 score is considered perfect if it reaches 1.0. When comparing the F1 score from both the Rapid and Intensive mode, we can observe that the Intensive mode does significantly better at classifying Leaf Smut than the Rapid mode, with a 15.68% increase. It is worth noting that while the Intensive mode is superior in almost every respect, it does show a percentage decrease of 3.21% when considering the F1 score for Bacterial Leaf Blight.

Confusion Matrix

Below are the confusion matrices for both models.

Table 5 illustrates where the Rapid mode made incorrect predictions: Leaf Smut for the True Class should be 11, and instead we have 7. We incorrectly classified 4 Leaf Smut cases as Bacterial Leaf Blight in two instances and Brown Spot in the remaining instances (Table 6). In the case of the Intensive mode, there was misclassification that occurs in two classes, Brown Spot and Leaf Smut. However, the total misclassification rate for Intensive was lower by 25% over Rapid mode. Additionally, Bacterial Leaf Blight offered new improvement, and Brown Spot created some minor confusion for the Intensive mode.

Comparative Accuracy of Models

Our experiment was conducted in LogicPlum cloud and only leveraged a CPU configuration. As seen in Table 4, we achieve test accuracy of 90% with the Rapid results model, whereas with the Intensive results model, accuracy goes up to 92.5%. Barring one image, all the test images belonging to Bacteria Leaf Light and Brown Spot are correctly classified.

Discussion

Summary of conclusions

This paper proposed two new approaches for detecting disease in rice plants, Rapid mode and Intensive mode, using a meager number of images for training a classifier. We provide an indepth analysis of our methods, which outperform the original paper results on the same dataset with significantly fewer machine learning techniques. Future work involves exploring the edge computing capabilities of these methods.

Relation to other results

We achieved 90.0% on the test dataset with Rapid mode, which builds the A.I. solution from data upload to prediction within 2 minutes. Additionally, we achieved 92.5% accuracy on the test dataset, which has a training time that completes within 60 minutes. Both approaches increase detection accuracy for rice plant disease over the prior research, which achieved 73.33% accuracy on the dataset [6]. As it relates to model performance, the Rapid mode exhibits a 22.73% increase over the prior research, while the Intensive mode demonstrates a 26.14% percent increase. Furthermore, we reduced the number of technical steps taken by practitioners in the preceding study, from 11 steps to 5 steps in the case of Rapid mode, and 6 steps in the Intensive mode—a 54.54% and 45.45% decrease, respectively, over the prior research (Figure 7).

Prior methods

This paper evaluated four techniques of background removal by applying masks generated based on the following: (1) original image, (2) hue component values of the image in HSV color space, (3) value components of the image in HSV color space, and finally (4) saturation component values of the image in HSV color space. Three techniques of segmentation were utilized: (1) LAB color space based K-means clustering, (2) Otsu’s segmentation technique, and (3) HSV color space based K-means clustering. Using various features under three categories: color, texture, and shape, the authors extracted 88 features from the disease portion of a leaf image. Finally, the paper used Support Vector Machines with Gaussian kernel for multiclass classification of the leaf images.

Implications

Edge computing for smartphone users

Edge computing has the capability to address the concerns of bringing machine learning approaches to the farming fields. Specifically, edge computing deals with response time requirements, battery life consumption, bandwidth cost savings, and data safety and privacy. Edge computing is at the center of several IoT agricultural applications, such as pest identification, safety traceability of farm products, unmanned agrarian machinery, agrarian technology promotion, and in this case, classifying diseases from the images of rice leaves purely because of its speed and efficiency compared to the cloud infrastructure. It offers a potentially tractable model for mainstreaming smart agriculture [11]. Agriculture IoT systems can make informed decisions in the field when using edge computing [12].

We propose an approach that allows for access to our A.I. solution without an internet connection in the field. Figure 8 (A) illustrates the process of a farmer in a field who needs access to rice plant disease classification via her smartphone and does not have access to a network connection. The farmer can make use of the classification algorithm as it is embedded on the phone. (B) demonstrates that the trained model is converted to a LogicPlum Lite file type, which is how the model becomes executable on a mobile phone device. Figure 8.C exemplifies the concept of returning to a location that supplies network connection, and a transfer occurs. If an update exists, then an update is made available.

Borrowing knowledge from plant experts

Making expert plant knowledge readily available to farmers in the field promises a meaningful impact. Edge computing allows farmers with a mobile app to capture the image of infected rice leaf and classify the disease, thereby greatly reducing the need for consultation with plant pathologists, which can be a time-consuming process. Furthermore, once a condition is detected, appropriate expert measures can be applied with various management strategies, namely preventive methods, cultural methods, and chemical methods (Figure 9). The Next Action Model is built on a concept of just-in-time learning, which meets farmers where they are instead of requiring structured education to form concept knowledge. The advent of our machine learning approach, coupled with edge computing and remedies for specific management strategies of rice plant disease, shifts farming further into the 21st century. Many education areas have evolved into a self-paced process of finding information or learning skills exactly when and where they are needed, and farming is no different. Our approach offers flexible delivery of learning to farmers with an “anytime, anyplace” framework. This approach allows farmers to independently access information from plant pathology databases in the context of what they observe within their environment. This approach is linked to the idea of lifelong learning, learner-driven learning, and project- based learning. We have organized expert remedies for each of the three rice disease classes we analyzed: Rice Leaf Blight, Brow Spot, and Leaf Smut. According to Tamil Naud Agricultural University, each of these rice diseases has three management strategies categorized as preventive, cultural, and chemicall (Tables 8-14).

Data science knowledge

Our approach leverages an automated machine learning process that allows for rapid experimentation on real-world problems. This approach covers the entire process from beginning to end, more specifically, from uploading the data to the deployment of a machine learning classifier, with little to no human interaction. This approach has data science expertise built into the process, offering guardrails for lay users of machine learning. In this approach, the emphasis is placed on the creative use of the technology rather than the details of a given algorithm.

Effects of age, educational level, and adoption of farming practices

Children who were raised on family farms are familiar with the farming practices that have proven successful for their parents. So, even when younger family members don’t make identical decisions to those of their parents, their decisions will continue to be informed by years spent under their parents’ guidance [13]. This is known as multi-generational farming, which often doesn’t involve technology in agriculture.

According to Moore’s law, computer processing speed doubles every 18 or so months, and a generation is generally understood to be between 20 and 30 years. This means that processing speeds may double 20 times during a given farming generation, allowing for more insight and actionable machine learning models (Koleva, 2021). Although former generations may not have been raised with digital technology, such significant enhancements in machine learning model performance, along with edge computing, should encourage adoption within agriculture, requiring new behaviors and ways of thinking. We believe that just like rakes, hoes, and shovels are essential for today’s farmers, machine learning will be added to the basic set of farming tools in the 21st century.

Digital farming techniques

Our approach is additive in the context of modern agricultural methods. Successfully delivering productive and sustainable agricultural systems worldwide will help form the foundations for overcoming food insecurity and hunger. Economic viability makes edge commuting one of the emerging technologies staged to transform the agricultural industry. With sensors, actuators, and real-time data-driven models, digitization can help us overcome some of the biggest challenges of our time [14]. Autonomous tractors and robotic machinery, often known as Agribots, can run on autopilot, communicating with nearby sensors to acquire the necessary data about the surrounding environment. The introduction of drones has shown great promise with agricultural implications. These unmanned aerial vehicles can help in various ways, such as monitoring livestock and crop growth, and increasing output with real-time insights. Additionally, the introduction of the 5G mobile network, which is designed to connect virtually everyone and everything together, including machines, objects, and devices, will further drive the adoption of digital farming techniques.

Precision farming

Technology has become an imperative consideration for every stakeholder involved in agriculture, starting from farmer to agronomist. Precision farming makes farming more accurate and controlled when it comes to growing crops and raising livestock. It can decide on and carry out the best technical intervention in the right place at the best possible moment. It makes it simpler to plan ahead of time and to act precisely in terms of space. A vital component of the precision farming management approach is the use of technology with its wide array of instruments, such as robotics, drones, variable rate technology, sensors, GPS-based soil sampling, telematics, and software. A balance must be found between precision farming, capable of determining the correct, limited scale of mediation at the right time and in the right place, and a preventive, systemic approach empowering a cultivated ecosystem to produce without the need for curative treatments. Digital technology will make it possible for targeted interventions, through data processing, forecasting and anticipating, simulating, and safeguarding [15].

Conclusion

The best prediction statistics were achieved with a Gaussian Naïve Bayes stacked classifier that used Stochastic Gradient Descent Classifiers predictions as model inputs. The automated model construction approach resulted in a validation set of 92.5% accuracy. Therefore, it can be recommended for use, with little to no involvement from a machine learning expert or trained plant pathologist. Our approach ranged from as much as 60 minutes in total time to 2 minutes. Since our method was automated compared to a manually crafted process, it is faster loading the data, model construction, optimization, and deployment. This method is inexpensive compared to other methods, not only in time but in economic terms, as our method only uses CPU rather than GPU architecture. Our approach cut the number of steps in half compared to prior methods and is also self-optimizing, permitting users of this approach to be hands-free. Additionally, our process does not end with the identification of rice plant disease. Instead, we combined management strategies for specific rice diseases from known plant experts using edge computing. This was chosen to increase accessibility to the machine learning approach, and allows for our system to meet more farmers where they are and when they need it [16-20].

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