Microsoft Azure Machine Learning Studio Research Articles

Forecasting Order Delays with Artificial Intelligence-Based Applications

The digitalization trend in the world in the last two decades has begun to transform business processes. Departing from the traditional business approach, the use of technology in every field has changed business processes and, accordingly, the approach of managers. On the other hand, cloud computing, the internet of things, sensors and instant notification systems through these components brought by the last industrial revolution have transformed supply chains into a smart structure. Now, the information that a product sold by a retailer, which is the last link of a supply chain, is out of stock is transmitted to the raw material supplier in the first link of the chain. In this way, a perfect supply chain structure is formed and operations are carried out in lean working principles. The products prepared according to the reduction of the retailer's stocks are continuously supplied, which shows flawless operation. On the other hand, through digital components such as sensors and the Internet of Things, each step in workflows is instantly converted into data and stored in virtual environments called cloud computing. At this point, besides the problem of data storage and security, the main problem is to process the data in a meaningful way. Analyzing big data generated by AI-based components and systems is also possible with AI-based systems. Methods called machine learning have been developed for this situation and their application area is increasing day by day. In the light of this information, within the scope of the study, forecasts of order quantity delays for future periods using historical order data from customers for the products produced by an enterprise were analyzed using machine learning algorithms. The results of the analysis made through the Microsoft Azure Machine Learning Studio platform will contribute to the use of digital tools in the sector as well as increasing the application examples of machine learning. According to the results obtained as a result of the analyzes, recommendations were developed for the enterprise. Finally, suggestions for the application of machine learning in the field of business administration are presented.

AN ADAPTIVE DECISION-MAKING SUPPORT MODEL IN THE MANAGEMETN OF ENGINEERING INFRASTRUCTURE RECONSTRUCTION PROGRAMS AND PROJECTS

Reconstruction of engineering infrastructure has become an important topic for Ukraine since the beginning of the full-scale invasion of russian federation in 2022. Standard approach to implementation of programs and projects is inefficient for turbulent environments and therefore the use adaptive approach is relevant. The concept of adaptive management has been analyzed. Means and instruments of adaptive management were analyzed. It was defined that different means and instruments of adaptive management are relevant for different program implementation phases. For the delivery phase these are learning and forecasting, for the closure phase – analysis of obtained experience for more efficient implementation of the next programs, for the definition phase – instruments that can ensure the further implementation of adaptivity to the management processes. An adaptive decision-making support model concept is proposed for adaptive engineering infrastructure reconstruction programs and projects management. The model is based on machine learning methods and can be used for program architecture and project work structure development and management. In this case the decision-making consists in choosing the optimal composition and configuration of the system that is the reconstruction object from among the available alternatives by predicting the values of the parameters of its elements in order to minimize the costs of implementing the program or the project. The model is created with Microsoft Azure Machine Learning Studio, the user interface is created in Microsoft Excel. The distinguishing features of the model are adaptivity due to the use of machine learning methods, possibility of scaling of the model to ensure its application to different system levels and presence of post-processing instruments for different use cases including calculation of additional parameter values, parameter values dependency graphs construction etc. The dataset for the model consists of several parameter categories that characterize the system modelled: technical and technological parameters, environmental parameters, energy efficiency parameters, energy security parameters, economic parameters, operational safety parameters. Keywords: adaptive program management, adaptive project management, decision support systems, machine learning.

Sentiment analysis of epidemiological surveillance reports on COVID-19 in Greece using machine learning models.

The present research deals with sentiment analysis performed with Microsoft Azure Machine Learning Studio to classify Facebook posts on the Greek National Public Health Organization (EODY) from November 2021 to January 2022 during the pandemic. Positive, negative and neutral sentiments were included after processing 300 reviews. This approach involved analyzing the words appearing in the comments and exploring the sentiments related to daily surveillance reports of COVID-19 published on the EODY Facebook page. Moreover, machine learning algorithms were implemented to predict the classification of sentiments. This research assesses the efficiency of a few popular machine learning models, which is one of the initial efforts in Greece in this domain. People have negative sentiments toward COVID surveillance reports. Words with the highest frequency of occurrence include government, vaccinated people, unvaccinated, telephone communication, health measures, virus, COVID-19 rapid/molecular tests, and of course, COVID-19. The experimental results disclose additionally that two classifiers, namely two class Neural Network and two class Bayes Point Machine, achieved high sentiment analysis accuracy and F1 score, particularly 87% and over 35%. A significant limitation of this study may be the need for more comparison with other research attempts that identified the sentiments of the EODY surveillance reports of COVID in Greece. Machine learning models can provide critical information combating public health hazards and enrich communication strategies and proactive actions in public health issues and opinion management during the COVID-19 pandemic.

Easy-to-use machine learning system for the prediction of IDH mutation and 1p/19q codeletion using MRI images of adult-type diffuse gliomas.

Adult-type diffuse gliomas are divided into Astrocytoma, IDH-mutant, Oligodendroglioma, IDH-mutant and 1p/19q-codeleted and Glioblastoma, IDH-wildtype based on the IDH mutation, and 1p/19q codeletion status. To determine the treatment strategy for these tumors, pre-operative prediction of IDH mutation and 1p/19q codeletion status might be effective. Computer-aided diagnosis (CADx) systems using machine learning have been noted as innovative diagnostic methods. However, it is difficult to promote the clinical application of machine learning systems at each institute because the support of various specialists is essential. In this study, we established an easy-to-use computer-aided diagnosis system using Microsoft Azure Machine Learning Studio (MAMLS) to predict these statuses. We constructed an analysis model using 258 adult-type diffuse glioma cases from The Cancer Genome Atlas (TCGA) cohort. Using MRI T2-weighted images, the overall accuracy, sensitivity, and specificity for the prediction of IDH mutation and 1p/19q codeletion were 86.9%, 80.9%, and 92.0%, and 94.7%, 94.1%, and 95.1%, respectively. We also constructed an reliable analysis model for the prediction of IDH mutation and 1p/19q codeletion using an independent Nagoya cohort including 202 cases. These analysis models were established within 30min. This easy-to-use CADx system might be useful for the clinical application of CADx in various institutes.

Using machine learning to predict venous thromboembolism in patients with uterine cancer (081)

<b>Objectives:</b> Gynecologic cancers are traditionally associated with a high risk of venous thromboembolism (VTE). Recent ASCO clinical practice guidelines have expanded recommendations for pharmacological thromboprophylaxis in high-risk patients; however, high risk is not well defined for the endometrial cancer population. Both the Caprini and the Khorana scores are clinically validated to assess the risk of VTE - Caprini developed for surgical patients and Khorana for cancer patients undergoing chemotherapy. We sought to develop a better tool to assess endometrial cancer patients at high risk for VTE. <b>Methods:</b> A single-institution retrospective cohort study was performed from January 2016 through January 2020. All patients with uterine cancer were screened, and the patients with complete clinical information were included. VTE was evaluated from diagnosis through 90 days post-diagnosis. Both the Caprini and the Khorana scores were calculated for each patient. High-risk Caprini score was defined as ≥ 9 based on prior studies in cancer patients, whereas high-risk Khorana score was defined as ≥ 3. In addition to variables included in the risk assessment tools, additional variables included race/ethnicity, tumor grade, stage, and histology. Microsoft Azure Machine Learning Studio was utilized to develop a machine learning (ML) algorithm to predict VTE. Five different ML models were assessed, including random forest, extreme random trees (ERT), logistic regression, XGBoost classifier, and support vector machine. Data were split into 70% for ML training and 30% for validation. Models were analyzed based on classification accuracy, AUC, and sensitivity and specificity. <b>Results:</b> A total of 1000 patients were included. The majority of patients were White (93.5%), obese (mean BMI of 37.8 kg/m2) with an average age of 61.2 years. Most tumors were grade 1 (62.7%), endometrioid histology (79.3%), and stage I (76.4%). Most patients underwent a hysterectomy (95.4%). A minority received neoadjuvant (4.0%) and adjuvant chemotherapy (13.1%), whereas 269 patients received radiation (26.9%). Only 18 patients (1.8%) were diagnosed with a VTE within 90 days of diagnosis. The Khorana score had a sensitivity of 38.9% and a specificity of 80.1% for VTE. The Caprini score had a sensitivity of 66.7% and a specificity of 61.0%. Combining the features of the Khorana and Caprini scores, the best-performing ML algorithm had an accuracy of 67.3%, AUC of 83.8%, and the sensitivity improved to 100%, retaining specificity at 66.8% (ERT). To further improve the model, histology and grade were included, which performed better as a random forest model with an accuracy of 75.7%, AUC of 84.8%, sensitivity 100%, and specificity of 75.3%. Lastly, we added stage and race/ethnicity to the model (random forest model), which improved the accuracy to 83.7%, AUC 89.4%, sensitivity 100%, and specificity to 83.4%. In the final model, tumor grade and stage were the most weighted for importance, followed by age, platelet count, histology, and hemoglobin. <b>Conclusions:</b> The use of ML to enhance VTE prediction in endometrial cancer is promising. Using the same parameters as clinically validated tools, we were able to greatly improve prediction, and by adding clinically relevant variables (histology, grade, stage, race/ethnicity), we were able to further improve the model with 100% sensitivity and 83.4% specificity for VTE within 90 days of diagnosis. Using ML, VTE thromboprophylaxis could be more individualized for patients at the highest risk for VTE and minimize the potential risks associated with unnecessary anticoagulation.

Data mining of the healthcare system based on the machine learning model developed in the Microsoft azure machine learning studio

This article presents data mining, which is based on the methods of mathematical statistics and machine learning, describes the features of applying regression analysis methods in the machine learning systems. The developed machine learning model includes the regression analysis modules based on the Bayesian linear, artificial neural network, decision tree, decision forest, and linear regressions. In the process of applying this machine learning model, using the mentioned algorithms, the corresponding regression models were constructed and their comparative analysis was performed, the results were analyzed. The results obtained indicate the feasibility of using data mining in the medical research using machine learning systems. The presented methods can serve as a basis for strategic development of a new directions of the medical data processing and decision-making in this field. We have identified the prospects for further research aimed at applying data mining methods to the healthcare system, namely, clustering, classification, anomaly detection.

Developing a Predictive Health Care System for Diabetes Diagnosis as a Machine Learning-Based Web Service

Background:&#x0D; Diabetes is one of the dangerous and silent illnesses that cause sudden death. It can occur at any time and may cause great injury to the organs of the body or damage them completely. So, we must investigate this disease at the beginning of its appearance and before it gets hard to treat. With the fast advancement of Machine Learning (ML), these approaches enhanced the efficiency of decision processes in a wide range of applications, including medical diagnostics.&#x0D; Materials and Methods:&#x0D; In this paper, we chose a medical application field and used supervised machine learning algorithms to construct a high-accuracy prediction model for diabetes in humans at an early stage, before it progresses to the point of morbidity or fatality. The suggested model can extract hidden knowledge from diabetes-related data gathered from the Kaggle machine learning repository. We utilize Microsoft Azure ML Studio to model these ML algorithms.&#x0D; Results:&#x0D; This study will benefit the health industry by offering users an online tool (i.e., web service) that allows them to input data and receive results that predict whether or not the person has diabetes. As a result, prior knowledge and ongoing monitoring of their diabetic health state will lower the risk of complications, morbidity, and death caused by this illness. After running numerous experiments with the classifier models to evaluate the proposed system, several performance indicators, including Recall, Precision, Accuracy, and f1-score, are measured for comparison. Based on the classification output, it was determined that Decision Forest is a better strategy and produces better results than the other ML approaches.&#x0D; Conclusion:&#x0D; The suggested system's key contribution is to improve healthcare quality, minimize hospitalizations, and lower the high expenditures of healthcare and drugs.

Establishment of a prediction tool for ocular trauma patients with machine learning algorithm.

To predict final visual acuity and analyze significant factors influencing open globe injury prognosis. Prediction models were built using a supervised classification algorithm from Microsoft Azure Machine Learning Studio. The best algorithm was selected to analyze the predicted final visual acuity. We retrospectively reviewed the data of 171 patients with open globe injury who visited the Pusan National University Hospital between January 2010 and July 2020. We then applied cross-validation, the permutation feature importance method, and the synthetic minority over-sampling technique to enhance tool performance. The two-class boosted decision tree model showed the best predictive performance. The accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve were 0.925, 0.962, 0.833, 0.893, and 0.971, respectively. To increase the efficiency and efficacy of the prognostic tool, the top 14 features were finally selected using the permutation feature importance method: (listed in the order of importance) retinal detachment, location of laceration, initial visual acuity, iris damage, surgeon, past history, size of the scleral laceration, vitreous hemorrhage, trauma characteristics, age, corneal injury, primary diagnosis, wound location, and lid laceration. Here we devise a highly accurate model to predict the final visual acuity of patients with open globe injury. This tool is useful and easily accessible to doctors and patients, reducing the socioeconomic burden. With further multicenter verification using larger datasets and external validation, we expect this model to become useful worldwide.

High Availability of Datacenter in Karachi, Pakistan

The datacenter is the core infrastructure of today's world. Every data center should have many resources and applications that are running for several decades or even more. Many failures happen in the physical datacenter in on-premises environments. In this research paper evaluating the datacenter by having the dataset provided by Premier Systems (Pvt.) Ltd. This dataset is having all the failures and datacenter related issues from Jan-2016 to Dec-2019 in Karachi, Pakistan. This research performed the Linear Regression via the Microsoft Azure Machine Learning Studio for the machine learning model. It would allow us to know which fault will more and which is not for the concern requirement. This experiment would have the Feature Engineering feature in Microsoft Azure Machine Learning Studio, which will automatically apply the filters required. After knowing which the central issue are related to any physical data center in Karachi. This research allows to handle the required precaution in datacenters of Karachi, Pakistan.

Ubiquitous Health Technology Management (uHTM)

The COVID-19 pandemic increased the need for distributed and ubiquitous health technology management. The eminent risk of Sars-CoV-2 contamination when visiting a health care establishment requires an efficient allocation of the technical team. The equipment problems should be quickly identified and fixed to keep the facility working at its full condition. This article presents a solution to perform remote real-time analysis of primary health care technology behavior, detecting and diagnosing the failures to create predictive maintenance plans. The project uses feature engineering to adapt regular machine learning algorithms to multiclass classification of time series data. The methodology was applied to a dental air compressor. It includes data collection, analysis, and exhibition. The model verified the IBM Watson and the Microsoft Azure Machine Learning Studio with the algorithms of neural networks, logistic regression, decision jungle, and decision forest, which was the most suitable one. The transformation performed in the data considered the influence of time in the read values to obtain a more efficient result in the platform. The solution integrated data collected by the sensors with the cloud using an Internet of Things architecture, a web service, and python scripts to exhibit the outcomes on the computer screen. Therefore, the model performs notification and identification of health technology failures, supporting the decision-making process of ubiquitous management in clinical engineering.

Study of Forecasting and Estimation Methodology of Oilfield Development Cost Based on Machine Learning

Due to the oil price fluctuations in recent decades, international and national oil companies have developed programs of strategically oriented development and assets optimization. Many companies have also promptly promoted opportunities for oilfield joint ventures. Therefore, a fast and accurate assessment methodology of oilfield assets, including planning and costs assessment, is expected to be proposed. Oilfield development cost assessment can be dynamically affected by a number of factors, including oilfield internal indexes and macroeconomic indexes. Based on a machine learning algorithm and combining mathematical and statistical methodology, the Microsoft Azure machine learning studio has been used for modeling the oilfield development cost. The proposed method has adopted three algorithms: a neural network, a boosted decision tree, and a decision forest. Results showed that the boosted decision tree and decision forest algorithms can achieve PFI ranking with stable training results. The results of the machine training model have been analyzed, and they showed that the permutation feature importance (PFI) model can provide reasonable scientific and technical support for oil companies and help to attain more effective and accurate estimation and prediction of oilfield assets.

Improving Farm Management Information Systems with Data Mining

Over the past several years, farm enterprises have grown in size substantially while their number has steadily declined. As the size of their farms grow more and more farmers are deploying information systems, commonly called as Farm Management Information Systems (FMIS), to manage the day to day activities of their farms. The deployment of FMIS enable farmers to capture detailed data that can potentially be analysed by data mining tools to provide valuable information for optimizing the farm enterprises. However, data mining is generally not a common feature of many FMIS. In order to evaluate the suitability of data mining for use in FMIS, we performed two case studies using data captured in FMIS and applying data mining. Microsoft Azure Machine Learning Studio is chosen because it provides a simple drag-and-drop visual interface that can be used by farm domain experts. We addressed two common problems in dairy farming: calving prediction of dairy cows and prediction of lactation value of milking cows. In both cases we built data mining models and run experiments and our results in both cases indicate that the required data is available from FMIS and data mining techniques provides acceptable performance. We also showed that farm domain experts can easily use a user-friendly and drag-and-drop data mining tools with minimal initial training. Based on the insight from the two case studies and literature study, we identified several decision problems that can be addressed with data mining such as heat prediction and lameness prediction.

Development of Predictive Models in Patients with Epiphora Using Lacrimal Scintigraphy and Machine Learning.

We developed predictive models using different programming languages and different computing platforms for machine learning (ML) and deep learning (DL) that classify clinical diagnoses in patients with epiphora. We evaluated the diagnostic performance of these models. Between January 2016 and September 2017, 250 patients with epiphora who underwent dacryocystography (DCG) and lacrimal scintigraphy (LS) were included in the study. We developed five different predictive models using ML tools, Python-based TensorFlow, R, and Microsoft Azure Machine Learning Studio (MAMLS). A total of 27 clinical characteristics and parameters including variables related to epiphora (VE) and variables related to dacryocystography (VDCG) were used as input data. Apart from this, we developed two predictive convolutional neural network (CNN) models for diagnosing LS images. We conducted this study using supervised learning. Among 500 eyes of 250 patients, 59 eyes had anatomical obstruction, 338 eyes had functional obstruction, and the remaining 103 eyes were normal. For the data set that excluded VE and VDCG, the test accuracies in Python-based TensorFlow, R, multiclass logistic regression in MAMLS, multiclass neural network in MAMLS, and nuclear medicine physician were 81.70%, 80.60%, 81.70%, 73.10%, and 80.60%, respectively. The test accuracies of CNN models in three-class classification diagnosis and binary classification diagnosis were 72.00% and 77.42%, respectively. ML-based predictive models using different programming languages and different computing platforms were useful for classifying clinical diagnoses in patients with epiphora and were similar to a clinician's diagnostic ability.

On Featured IoT for Smart Public Transportation with Cloud-based Machine Learning

With rapid development of science and technology and the accelerated pace of urbanization, smart city has been also widely spread across our country. By Internet of Things, we put forward a kind innovative way of real-time air pollution monitoring into public city transportation vehicle. We take information about the surrounding environment through sensors and upload it directly to the internet. This paper fosters the advantages of the cloud-based computing frameworks (such as Microsoft Azure Machine Learning Studio) and presents a practical work-flow well-suited for the standard machine learning tasks.

Predictive Analytics and Modeling Employing Machine Learning Technology: The Next Step in Data Sharing, Analysis, and Individualized Counseling Explored With a Large, Prospective Prenatal Hydronephrosis Database

ObjectiveTo explore the potential value of utilizing a commercially available cloud-based machine learning platform to predict surgical intervention in infants with prenatal hydronephrosis (HN). Materials and MethodsA prospective prenatal HN database was uploaded into Microsoft Azure Machine Learning Studio. Probabilistic principal component analysis was employed for data imputation. Multiple clinical variables were included in two-class decision jungle and neural network for model training, using surgical intervention as the primary outcome. Models were scored and evaluated after a 70/30 split of the data. ResultsA total of 557 entries were included. The optimized model (decision jungle) achieved an area under the curve of 0.9, accuracy of 0.87, and precision of 0.80, employing a threshold of 0.5 to predict surgery. Average time to train, score and evaluate the model was 5 seconds. The predictive model was deployed as a web service in 35 seconds, generating a unique API key for app and webpage development. Individualized prediction based on the included variables was deployed as a web-based and batch execution Excel file in less than one minute. ConclusionThis cloud-based ML technology allows easy building, deployment, and sharing of predictive analytics solutions. Using prenatal HN as an example, we propose an opportunity to address contemporary challenges with data analysis, reporting a creative solution that moves beyond the current standard.

Computer based sport talent identification

This paper presents the concept of the computer decision support system for talent identification in sport. In this concept the use of two methods was assumed: pattern recognition based on multicriteria optimization and machine learning supervised classification algorithm: decision forest. The data for sport dyscyplin patterns has obtained from publication (Santos, Dawson, Matias et al. 2014). This data also has been used to generate test data sets to research purposes. The researches were carried out in author’s application and in the cloud environment Microsoft Azure Machine Learning Studio. The results show that both methods can be used with success to talent identification in sport.

Classification of large-scale fundus image data sets: a cloud-computing framework.

Large medical image data sets with high dimensionality require substantial amount of computation time for data creation and data processing. This paper presents a novel generalized method that finds optimal image-based feature sets that reduce computational time complexity while maximizing overall classification accuracy for detection of diabetic retinopathy (DR). First, region-based and pixel-based features are extracted from fundus images for classification of DR lesions and vessel-like structures. Next, feature ranking strategies are used to distinguish the optimal classification feature sets. DR lesion and vessel classification accuracies are computed using the boosted decision tree and decision forest classifiers in the Microsoft Azure Machine Learning Studio platform, respectively. For images from the DIARETDB1 data set, 40 of its highest-ranked features are used to classify four DR lesion types with an average classification accuracy of 90.1% in 792 seconds. Also, for classification of red lesion regions and hemorrhages from microaneurysms, accuracies of 85% and 72% are observed, respectively. For images from STARE data set, 40 high-ranked features can classify minor blood vessels with an accuracy of 83.5% in 326 seconds. Such cloud-based fundus image analysis systems can significantly enhance the borderline classification performances in automated screening systems.

AG-MIC: Azure-Based Generalized Flow for Medical Image Classification

Medical image-based research requires heavy computational workload associated with image analysis and collaborative device independent platforms to incorporate expert opinions from multiple institutions. Cloud-based resources such as Microsoft Azure Machine Learning Studio (MAMLS) provide such a platform that is conducive to the medical-image-based data analysis. This paper fosters the advantages of the cloud-based computing frameworks (such as MAMLS) and presents a practical work-flow well-suited for the standard machine learning tasks seen in medical image research viz., binary classification, multi-class learning, regression and so on. The proposed automated generalized workflow allows medical researchers/practitioners to focus on data inferencing rather than dealing with the intricate details of predictive modeling, such as feature and model selection. The scalable architecture of the proposed flow utilizes the MAMLS framework to processes data sets that require partial core storage space in the virtual machine to one complete core storage space in a common flow. Also, the proposed flow invokes multiple feature ranking and predictive models in parallel for automated selection and parameterization of the optimal data model. The performance of the proposed flow is bench-marked on 14 public data sets and four local medical image data sets (~0.12 MB–1.22 GB) using a single common flow, while ensuring better (~ 8% improvement) or atleast similar generalization capability with respect to existing works.