Software and algorithm implementation with python, java, golang, and scala using functional and objected oriented programming
Here, we strive to make sure every project we do is successful. That's why we have perfected our process after years of experience.
Our real-world applications prove we have all it takes to get the job done.
Deploy microservices on docker and kubernetes clusters as well as provision the technology stacks for easy versioning and replication with architecture that enables the rapid, frequent and reliable delivery of large, complex applications.
Develop Rest API with CRUD operations having authorizations in various model applications such as sentiment analytics, object character recognition etc. while using Swagger UI to visually render documentation and interact with the API’s resources.
At BigCodeGen LLC, our large-scale data processing and analytics solutions offer state of the art methodologies that leverage AI and ML frameworks like Spark, Keras, and Tensorflow while deploying these containerized applications on kubernetes clusters in-cloud and on-premise.
Big data requires big infrastructure. We build scalable ELT, ETL, AI & ML data pipelines for organizations interested in monetizing the ever increasing amounts of data generated by their customers, products (e.g. IoT devices), or services.
Our Process is pivoted by Key Performance Indicators(KPIs) that are tailored to the clients' requirements. This includes building big data pipelines that support field engineers in the oil and gas sector to healthcare clinicians in need of accurate predictions to support their various decisions and operations.
Our team of expert data analysts, scientists and engineers are everly on ground to answer core business questions and provide solutions to complex big data engineering problems while optimizing the resources at their disposal. Our success stories define us. We are excited about starting a partnership with your organization.
Our Mobility as a Service Application for real-time traffic prediction provides the system and method for predicting congested road-vehicles traffic on a given roadway within a region.
In particular, the computer implemented method utilize real time traffic images from traffic cameras for the input of data and utilizes computer processing and machine learning to model a predictive level of congestion within a category of low congestion, medium congestion, or high congestion. By implementing machine learning in the comparison of exemplary images and administrator review, the computer processing system and method steps can predict a more efficient real time congestion prediction over time.
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In recent times, the unprecedented surge in Coronavirus disease 2019 (COVID-19) due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to several attempts at understanding and containing the outbreak of the pandemic as well as to ultimately eradicate it.
Steps taken so far include encouraging the wearing of face masks and shields, municipality restrictions such as work-from-home orders, the development of vaccines by health research institutions among others. It is widely believed that the main mode of transmission of the virus is from human to human.
We present the multi-class classification and modeling of the hospitalization status of COVID-19 patients by using both machine learning and compartmental mathematical models focusing on critical factors like hospital stay-days(SDs) and admission type based on severity of illness. Two key machine learning algorithms-the decision tree and random forest, are deployed in our analyses.
Cancer is a deadly disease that has gained a reputation as a global health concern. Further, lung cancer has been widely reported as the most deadly cancer type globally, while colon cancer comes second. Meanwhile, early detection is one of the primary ways to prevent lung and colon cancer fatalities.
To aid the early detection of lung and colon cancer, we propose a computer-aided diagnostic approach that employs a Deep Learning (DL) architecture to enhance the detection of these cancer types from Computed Tomography (CT) images of suspected body parts. Our experimental dataset (LC25000) contains 25000 CT images of benign and malignant lung and colon cancer tissues.
We used weights from a pre-trained DL architecture for computer vision, EfficientNet, to build and train a lung and colon cancer detection model. EfficientNet is a Convolutional Neural Network architecture that scales all input dimensions such as depth, width, and resolution at the same time. Our research findings showed detection accuracies of 99.63%, 99.50%, and 99.72% for training, validation, and test sets, respectively.
