Open-source Machine Learning Library Research Articles

Enhancing Health and Wellness: Deep Learning Powered Exercise Recognition and Tracking

Abstract: This project aims to develop a React-based web application that enables users to track, analyze, manage, and schedule their workout routines. Additionally, the application incorporates a machine learning model that utilizes the user’s web camera to track their movements and count reps, sets, and calories burned. This innovative feature provides users with a more accurate representation of their workout progress and empowers them to make data-driven decisions regarding their fitness goals. To achieve this, we utilized various technologies such as React for the frontend, Node.js for the backend, and MongoDB for the database. We also integrated TensorFlow.js, an open-source machine learning library, to build the ML model that powers the application’s tracking and analysis capabilities. Furthermore, we designed the user interface using Bootstrap with Vanilla CSS to ensure a responsive and user-friendly experience. Through this project, our primary objective was to address the limitations of existing workout tracking applications that heavily rely on subjective and often inaccurate user input. Our solution offers a more objective and efficient approach to tracking fitness progress. We firmly believe that this technology has the potential to revolutionize the fitness industry. Overall, our project showcases the power of machine learning and its ability to enhance people’s lives in meaningful ways.

Protecting Children from Harmful Audio Content: Automated Profanity Detection From English Audio in Songs and Social-Media

A novel approach for the automated detection of profanity in English audio songs using machine learning techniques. One of the primary drawbacks of existing systems is only confined to textual data. The proposed method utilizes a combination of feature extraction techniques and machine learning algorithms to identify profanity in audio songs. Specifically, the approach employs the popular feature extraction techniques of Term frequency–inverse document frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT) and Doc2vec to extract relevant features from the audio songs. TF-IDF is used to capture the frequency and importance of each word in the song, while BERT is utilized to extract contextualized representations of words that can capture more nuanced meanings. To capture the semantic meaning of words in audio songs, also explored the use of the Doc2Vec model, which is a neural network-based approach that can extract relevant features from the audio songs. The study utilizes Open Whisper, an open-source machine learning library, to develop and implement the approach. A dataset of English audio songs was used to evaluate the performance of the proposed method. The results showed that both the TF-IDF and BERT models outperformed the Doc2Vec model in terms of accuracy in identifying profanity in English audio songs. The proposed approach has potential applications in identifying profanity in various forms of audio content, including songs, audio clips, social media, reels, and shorts.

TorchDIVA: An extensible computational model of speech production built on an open-source machine learning library.

The DIVA model is a computational model of speech motor control that combines a simulation of the brain regions responsible for speech production with a model of the human vocal tract. The model is currently implemented in Matlab Simulink; however, this is less than ideal as most of the development in speech technology research is done in Python. This means there is a wealth of machine learning tools which are freely available in the Python ecosystem that cannot be easily integrated with DIVA. We present TorchDIVA, a full rebuild of DIVA in Python using PyTorch tensors. DIVA source code was directly translated from Matlab to Python, and built-in Simulink signal blocks were implemented from scratch. After implementation, the accuracy of each module was evaluated via systematic block-by-block validation. The TorchDIVA model is shown to produce outputs that closely match those of the original DIVA model, with a negligible difference between the two. We additionally present an example of the extensibility of TorchDIVA as a research platform. Speech quality enhancement in TorchDIVA is achieved through an integration with an existing PyTorch generative vocoder called DiffWave. A modified DiffWave mel-spectrum upsampler was trained on human speech waveforms and conditioned on the TorchDIVA speech production. The results indicate improved speech quality metrics in the DiffWave-enhanced output as compared to the baseline. This enhancement would have been difficult or impossible to accomplish in the original Matlab implementation. This proof-of-concept demonstrates the value TorchDIVA can bring to the research community. Researchers can download the new implementation at: https://github.com/skinahan/DIVA_PyTorch.

Impact of data quality on supervised machine learning: Case study on drilling vibrations

Training complex machine learning and deep learning models has become straightforward with the advent of highly efficient, open-source machine learning libraries. Supervised classification techniques such as logistic regression, random forests, and neural networks have also gained popularity in the drilling industry on the back of promising results. As a result, these techniques have been increasingly researched, especially in the domain of drilling vibrations. However, much of this research interest has been limited to finding the best classification model for estimating severity of downhole vibrations. While the choice of classification model is important, we argue that the successful implementation and adoption of machine learning technologies is equally dependent on correctly studying, cleaning, pre-processing the vibration drilling data before applying machine learning techniques. We show that, in certain cases, correctly pre-processing the data guarantees competitive classification performance regardless of the choice of classification model. Specifically, we empirically investigate how factors such as data sampling frequency, data labeling technique, feature extraction technique, and class imbalance impact the performance of different popular classifiers, when dealing with drilling data. We make recommendations specific to vibration classification and highlight pitfalls of certain techniques in that context. Finally, we also develop a step-by-step workflow which enables users to select the correct parameters and techniques at every step, from data collection to model training.

DeePKS-kit: A package for developing machine learning-based chemically accurate energy and density functional models

We introduce DeePKS-kit, an open-source software package for developing machine learning based energy and density functional models. DeePKS-kit is interfaced with PyTorch, an open-source machine learning library, and PySCF, an ab initio computational chemistry program that provides simple and customized tools for developing quantum chemistry codes. It supports the DeePHF and DeePKS methods. In addition to explaining the details in the methodology and the software, we also provide an example of developing a chemically accurate model for water clusters. Program summaryProgram Title: DeePKS-kitCPC Library link to program files:https://doi.org/10.17632/x54bnz5vxk.1Developer's repository link:https://github.com/deepmodeling/deepks-kitLicensing provisions: LGPLProgramming language: PythonNature of problem: Modeling the energy and density functional in electronic structure problems with high accuracy by neural network models. Solving electronic ground state energy and charge density using the learned model.Solution method: DeePHS and DeePKS methods are implemented, interfaced with PyTorch and PySCF for neural network training and self-consistent field calculations. An iterative learning procedure is included to train the model self-consistently.

Towards scalable online machine learning collaborations with OpenML

Is massively collaborative machine learning possible? Can we share and organize our collective knowledge of machine learning to solve ever more challenging problems? In a way, yes: as a community, we are already very successful at developing high-quality open-source machine learning libraries, thanks to frictionless collaboration platforms for software development. However, code is only one aspect. The answer is much less clear when we also consider the data that goes into these algorithms and the exact models that are produced. A tremendous amount of work and experience goes into the collection, cleaning, and preprocessing of data and the design, evaluation, and finetuning of models, yet very little of this is shared and organized in a way so that others can easily build on it. Suppose one had a global platform for sharing machine learning datasets, models, and reproducible experiments in a frictionless way so that anybody could chip in at any time to share a good model, add or improve data, or suggest an idea. OpenML is an open-source initiative to create such a platform. It allows anyone to share datasets, machine learning pipelines, and full experiments, organizes all of it online with rich metadata, and enables anyone to reuse and build on them in novel and unexpected ways. All data is open and accessible through APIs, and it is readily integrated into popular machine learning tools to allow easy sharing of models and experiments. This openness also allows a budding ecosystem of automated processes to scale up machine learning further, such as discovering similar datasets, creating systematic benchmarks, or learning from all collected results how to build the best machine learning models and even automatically doing so for any new dataset. We welcome all of you to become a part of it.

Potential for Big Data Analysis Using AI in the Field of Clinical Pharmacy

Industrial reforms utilizing artificial intelligence (AI) have advanced remarkably in recent years. The application of AI to big data analysis in the medical information field has also been advancing and is expected to be used to find drug adverse effects that cannot be predicted by conventional methods. We have developed an adverse drug reactions analysis system that uses machine learning and data from the Japanese Adverse Drug Event Report (JADER) database. The system was developed using the C# programming language and incorporates the open source machine learning library Accord.Net. Potential analytical capabilities of the system include discovering unknown drug adverse effects and evaluating drug-induced adverse events in pharmaceutical management. However, to apply the system to pharmaceutical management, it is important to examine the characteristics and suitability of the level of AI used in the system and to select statistical methods or machine learning when appropriate. If these points are addressed, there is potential for pharmaceutical management to be individualized and optimized in the clinical setting by using the developed system to analyze big data. The system also has the potential to allow individual healthcare facilities such as hospitals and pharmacies to contribute to drug repositioning, including the discovery of new efficacies, interactions, and drug adverse events.

Convolutional neural networks performance evaluation applied to automated pavement crack detection

Neste artigo é avaliado o potencial de Redes Neurais Convolucionais (RNC) como ferramenta automatizada para detecção de trincas em superfícies de pavimentos. Foram utilizadas fotografias da superfície de diferentes segmentos de um pavimento do tipo Cheapseal, obtidas a partir de câmeras fotográficas montadas em veículos. As imagens foram avaliadas a partir da proposta do uso de duas arquiteturas de redes neurais convolutionais e implementadas com o auxílio da biblioteca de aprendizado de máquina PyTorch, o qual possui código aberto e disponível na forma de script em linguagem Python. As imagens foram processadas com o uso de três técnicas diferentes, com o intuito de avaliar a influência da complexidade dos algoritmos propostos. Para análise da performance da rede neural, foram utilizadas como métricas de avaliação a acurácia, a precisão, o recall e o F1 score. Os resultados apontaram que a arquitetura da rede neural escolhida apresentou desempenho satisfatório na detecção de trincas, bem como indicam que a complexidade da rede é um dos fatores a ser considerado durante o processo de classificação das imagens.

CNN Modelling Untuk Deteksi Wajah Berbasis Gender Menggunakan Python

Pendeteksian wajah (Face Detection) merupakan Pemanfaatan data Biologis (Biometrics) dengan mengidentifikasi ciri-ciri fisik yang ada pada manusia. Digitalisasi pengenalan gender sebagai sebuah teknologi untuk mengenali gender manusia dengan membedakan wajah wanita dan wajah pria berdasarkan fitur Ekstraksi. Keberadaan sistem ini secara implementatif dapat diaplikasikan untuk sistem pengawasan otomatis dan sistem monitoring atau segmentasi pasar berdasarkan trend demografis serta juga dapat diaplikasikan untuk pembatasan akses suatu ruangan. Penelitian ini menggunakan Convolutional Neural Network (CNN). CNN merupakan salah satu jenis neural network dimana metode ini dapat digunakan pada data image. CNN memiliki kemampuan untuk mengenali objek pada suatu image. Secara keseluruhan dataset yang digunakan memiliki 40 anotasi atribut untuk mendeskripsikan image wanita dan pria. Sistem deteksi wajah ini menggunakan python dan Keras sebagai library Machine Learning open source untuk nerve network,dikembangkan untuk membuat penerapan model pembelajaran yang mendalam. Dengan adanya system ini memberikan analisa keakuratan dalam pendeteksian gender sehingga dapat dikembangkan untuk penelitian yang lebih implementatif. Jumlah gambar harus seimbang untuk mendapatkan kinerja yang baik untuk permodelan, masing-masing model akan memiliki folder pelatihan, validasi dan data uji. Jumlah gambar yang tidak seimbang dapat berpengaruh pada kinerja model CNN. Model yang dibangun menggunakan transfer learning dari InceptionV3 dimana permodelan dapat mengenali gender dengan akurasi sebesar 92,6%

GPU-Accelerated Vision for Robots: Improving System Throughput Using OpenCV and CUDA

OpenCV is an open source computer vision and machine learning library for C/C++/Python available for Windows, Linux, macOS, and Android platforms. It contains low-level image processing functions as well as high-level algorithms such as object identification, face recognition, and action classification in videos. OpenCV has become very popular, with more than 47,000 people in its user community and 18 million downloads (see https://opencv.org/about/). Under a Berkeley Software Distribution (BSD) license, it can be used for both academic and commercial applications.

Data Pre Processing for Machine Learning Models using Python Libraries

Data pre-processing is the process of transforming the raw data into useful dataset. Data pre-processing is one of the most important phase of any machine learning model because the quality and efficiency of any machine learning model directly depends upon the data-set, if we skip this step and design a model with data sets containing missing values then the model we have designed will not be that efficient and will be inconsistent model. This paper describes the methodology for pre-processing the data in seven sequence of steps using python powerful libraries which are open source machine learning libraries that support both supervised and unsupervised learning like pandas is a high level data manipulation tool, scikit learn which provides various tools for model fitting, data pre-processing, model selection and many other utilities. These steps include dealing with missing value, categorical values, importing data sets etc. This analysis helps in cleaning and transforming the datasets which future applied to any learning model and produce a efficient machine learning model.

Deciphering tip-enhanced Raman imaging of carbon nanotubes with deep learning neural networks.

Recent release of open-source machine learning libraries presents opportunities to unify machine learning with nanoscale research, thus improving effectiveness of research methods and characterization protocols. This paper outlines and demonstrates the effectiveness of such a synergy with artificial neural networks to provide for an accelerated and enhanced characterization of individual carbon nanotubes deposited over a surface. Our algorithms provide a rapid diagnosis and analysis of tip-enhanced Raman spectroscopy mappings and the results show an improved spectral assignment of spectral features and spatial contrast of the collected images. Using several examples, we demonstrate the robustness and versatility of our deep learning neural network models. We highlight the use of machine learning and data science in tandem with tip-enhanced Raman spectroscopy technique enables a fast and accurate understanding of experimental data, thus leading to a powerful and comprehensive imaging analysis applied to spectroscopic measurements.

Implementation of Tensor Flow for Real-time Object Detection

Tensor Flow is an open-source Machine Learning library for research and creation. Tensor Flow offers APIs for beginners and specialists to create for work desktop, mobile, web, and cloud. The best utilizations of Google's Tensor flow are the best applications for deep learning . Deep Learning is extraordinary at example acknowledgment/machine recognition, and it's being connected to pictures, video, sound, voice, content and time arrangement information. It groups and bunch information like that with now and again superhuman precision. This can be actualized for the acknowledgment of the diverse items, for example, Ball, Cat, Bottle, Car and so forth. It can utilize Android as its stage with to utilize the cell phone's camera to prepare the informational indexes and perceive diverse items in ongoing process.

Multi-pattern algorithm for first-break picking employing open-source machine learning libraries

Accurate first-break (FB) onsets are crucial in processing seismic data, e.g. for deriving statics solution or building velocity models via tomography. Ever-increasing volumes of seismic data require automatic algorithms for FB picking. Most of the existing techniques are based either on simple triggering algorithms that rely on basic properties of seismic traces or on neural network architectures that are too complex to be easily trained and re-used in another survey. Here we propose a solution that addresses the issue of multi-level analysis using a time-sequence pattern-recognition process implemented in the newest open-source machine learning library like Keras or Scikit-Learn. We use well-established methods such as STA/LTA, entropy, fractal dimension or higher-order statistics to provide patterns required for generative model training with artificial neural networks (ANN), Support Vector Regression (SVR) and an implementation of gradient boosted decision trees – XGBoost (Extreme Gradient Boosting). FB picking is cast as the binary classification that requires a model to differentiate FB sample from all other samples in a seismic trace. Our workflow (provided freely as a Jupyter Notebook) is robust, easily adaptable and flexible in a way of adding new pattern generators that might contribute to even better performance, while already-trained models can be saved and re-used for another dataset collected with similar acquisition parameters (e.g., in multi-line surveys). Application to real seismic data showed that the models trained on 1000 and 340,100 manually-picked FB onsets are able to predict FB on the rest of 470,000 of traces with the success rate of nearly 90% and 95%, respectively.

Enterprise search: A state of the art

Enterprise search is changing. The explosion of information within organizations, technological advances and availability of free OpenSource machine learning libraries offer many possibilities. Eighteen informants from practice, academia, search technology vendors and large organizations (Oil and Gas, Governments, Pharmaceuticals, Aerospace and Retail) were interviewed to assess challenges and future directions. The findings confirmed the existence of the ‘Google Habitus’, technology propaganda and a need to transcend disciplines for a Systems thinking approach toward enterprise search. This encompasses information management, user search literacy, governance, learning feedback loops as well as technology. A novel four-level model for enterprise search use cases is presented, covering search as a utility, search as an answer machine, search task apps and a discovery engine. This could be used to reframe enterprise search perceptions, expanding possibilities and improving business outcomes.

Gamma/Hadron Separation in Imaging Air Cherenkov Telescopes Using Deep Learning Libraries TensorFlow and PyTorch

In this work we compare two open source machine learning libraries, PyTorch and TensorFlow, as software platforms for rejecting hadron background events detected by imaging air Cherenkov telescopes (IACTs). Monte Carlo simulation for the TAIGA-IACT telescope is used to estimate background rejection quality. A wide variety of neural network algorithms provided by both libraries can easily be tested on various types of data, which is useful for various imaging air Cherenkov experiments. The work is a component of the Astroparticle.online project, which collaborates with the TAIGA and KASCADE experiments and welcomes any astroparticle experiment to join.

Development of Predictive Emissions Monitoring System Using Open Source Machine Learning Library – Keras: A Case Study on a Cogeneration Unit

The study provides an overview of Predictive Emissions Monitoring System's (PEMS) research, application, installation, and regulatory framework as well as develops predictive models for NOx emissions from a natural gas fired cogeneration unit using an open source machine learning library, Keras, and open source programming languages, Python and R. Nine neural network based predictive models were trained with 12 086 examples and tested with 3020 examples. The neural network-based models use eight process parameters as inputs to predict NOx emissions. All models meet the regulatory requirements for precision. The best model (32-64-64-64) has four hidden layers and uses the Nadam method for optimization. The best model has a mean absolute error of 0.5982, r-value of 0.9451, and a difference of 0.14% between the measured and predicted emission values using the test dataset. The study demonstrated the feasibility of using open source machine learning library in PEMS development. It also provides guidance to facility operators to develop their own PEMS models for monitoring emissions.

Solving Fourier ptychographic imaging problems via neural network modeling and TensorFlow.

Fourier ptychography is a recently developed imaging approach for large field-of-view and high-resolution microscopy. Here we model the Fourier ptychographic forward imaging process using a convolutional neural network (CNN) and recover the complex object information in a network training process. In this approach, the input of the network is the point spread function in the spatial domain or the coherent transfer function in the Fourier domain. The object is treated as 2D learnable weights of a convolutional or a multiplication layer. The output of the network is modeled as the loss function we aim to minimize. The batch size of the network corresponds to the number of captured low-resolution images in one forward/backward pass. We use a popular open-source machine learning library, TensorFlow, for setting up the network and conducting the optimization process. We analyze the performance of different learning rates, different solvers, and different batch sizes. It is shown that a large batch size with the Adam optimizer achieves the best performance in general. To accelerate the phase retrieval process, we also discuss a strategy to implement Fourier-magnitude projection using a multiplication neural network model. Since convolution and multiplication are the two most-common operations in imaging modeling, the reported approach may provide a new perspective to examine many coherent and incoherent systems. As a demonstration, we discuss the extensions of the reported networks for modeling single-pixel imaging and structured illumination microscopy (SIM). 4-frame resolution doubling is demonstrated using a neural network for SIM. The link between imaging systems and neural network modeling may enable the use of machine-learning hardware such as neural engine and tensor processing unit for accelerating the image reconstruction process. We have made our implementation code open-source for researchers.

Machine learning “red dot”: open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification

To develop a machine learning-based model for the binary classification of chest radiography abnormalities, to serve as a retrospective tool in guiding clinician reporting prioritisation. The open-source machine learning library, Tensorflow, was used to retrain a final layer of the deep convolutional neural network, Inception, to perform binary normality classification on two, anonymised, public image datasets. Re-training was performed on 47,644 images using commodity hardware, with validation testing on 5,505 previously unseen radiographs. Confusion matrix analysis was performed to derive diagnostic utility metrics. A final model accuracy of 94.6% (95% confidence interval [CI]: 94.3-94.7%) based on an unseen testing subset (n=5,505) was obtained, yielding a sensitivity of 94.6% (95% CI: 94.4-94.7%) and a specificity of 93.4% (95% CI: 87.2-96.9%) with a positive predictive value (PPV) of 99.8% (95% CI: 99.7-99.9%) and area under the curve (AUC) of 0.98 (95% CI: 0.97-0.99). This study demonstrates the application of a machine learning-based approach to classify chest radiographs as normal or abnormal. Its application to real-world datasets may be warranted in optimising clinician workload.

REAL TIME DETECTION AND RECOGNITION OF HAND HELD OBJECTS TO ASSIST BLIND PEOPLE

In this paper, we have implemented our idea to help blind individuals with their daily struggle. We have used Google’s TensorFlow library in our project. TensorFlow is an open source, fast and scalable open source machine learning library. TesnsorFlow is used by many internal Google products including: Gmail, translate, YouTube, and Play.