scholarly journals Algorithmic Governance and the International Politics of Big Tech

2021 ◽  
pp. 1-12
Author(s):  
Swati Srivastava
Keyword(s):  
Machine Learning ◽  
International Politics ◽  
Supervised Machine Learning ◽  
Classification Algorithms ◽  
Global Power ◽  
Model Predictions ◽  
Critical Scrutiny ◽  
Interdisciplinary Scholarship ◽  
Massive Databases ◽  
Mass Surveillance

Big technology companies like Facebook, Google, and Amazon amass global power through classification algorithms. These algorithms use unsupervised and semi-supervised machine learning on massive databases to detect objects, such as faces, and to process texts, such as speech, to model predictions for commercial and political purposes. Such governance by algorithms—or “algorithmic governance”—has received critical scrutiny from a vast interdisciplinary scholarship that points to algorithmic harms related to mass surveillance, information pollution, behavioral herding, bias, and discrimination. Big Tech’s algorithmic governance implicates core IR research in two ways: (1) it creates new private authorities as corporations control critical bottlenecks of knowledge, connection, and desire; and (2) it mediates the scope of state–corporate relations as states become dependent on Big Tech, Big Tech circumvents state overreach, and states curtail Big Tech. As such, IR scholars should become more involved in the global research on algorithmic governance.

scholarly journals Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Emmanuel P. Mwanga ◽  
Elihaika G. Minja ◽  
Emmanuel Mrimi ◽  
Mario González Jiménez ◽  
Johnson K. Swai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Infrared Spectroscopy ◽  
Human Blood ◽  
Supervised Machine Learning ◽  
Classification Algorithms ◽  
Clinical Settings ◽  
Mid Infrared ◽  
Blood Spots ◽  
Model Training

Abstract Background Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots. Methods Filter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in 12 wards in southeastern Tanzania in 2018/19. The DBS were scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra in the range 4000 cm−1 to 500 cm−1. The spectra were cleaned to compensate for atmospheric water vapour and CO2 interference bands and used to train different classification algorithms to distinguish between malaria-positive and malaria-negative DBS papers based on PCR test results as reference. The analysis considered 296 individuals, including 123 PCR-confirmed malaria positives and 173 negatives. Model training was done using 80% of the dataset, after which the best-fitting model was optimized by bootstrapping of 80/20 train/test-stratified splits. The trained models were evaluated by predicting Plasmodium falciparum positivity in the 20% validation set of DBS. Results Logistic regression was the best-performing model. Considering PCR as reference, the models attained overall accuracies of 92% for predicting P. falciparum infections (specificity = 91.7%; sensitivity = 92.8%) and 85% for predicting mixed infections of P. falciparum and Plasmodium ovale (specificity = 85%, sensitivity = 85%) in the field-collected specimen. Conclusion These results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in human DBS. The approach could have potential for rapid and high-throughput screening of Plasmodium in both non-clinical settings (e.g., field surveys) and clinical settings (diagnosis to aid case management). However, before the approach can be used, we need additional field validation in other study sites with different parasite populations, and in-depth evaluation of the biological basis of the MIR signals. Improving the classification algorithms, and model training on larger datasets could also improve specificity and sensitivity. The MIR-ML spectroscopy system is physically robust, low-cost, and requires minimum maintenance.

scholarly journals Supervised Machine Learning Classification Algorithms for Detection of Fracture Location in Dissimilar Friction Stir Welded Joints

2021 ◽  
Vol 15 (58) ◽  
pp. 242-253
Author(s):  
Akshansh Mishra ◽  
Apoorv Vats
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Classification Algorithms ◽  
Accuracy Score ◽  
Friction Stir ◽  
Machine Learning Classification ◽  
Fracture Location ◽  
Svm Algorithm ◽  
The Given

Machine Learning focuses on the study of algorithms that are mathematical or statistical in nature in order to extract the required information pattern from the available data. Supervised Machine Learning algorithms are further sub-divided into two types i.e. regression algorithms and classification algorithms. In the present study, four supervised machine learning-based classification models i.e. Decision Trees algorithm, K- Nearest Neighbors (KNN) algorithm, Support Vector Machines (SVM) algorithm, and Ada Boost algorithm were subjected to the given dataset for the determination of fracture location in dissimilar Friction Stir Welded AA6061-T651 and AA7075-T651 alloy. In the given dataset, rotational speed (RPM), welding speed (mm/min), pin profile, and axial force (kN) were the input parameters while Fracture location is the output parameter. The obtained results showed that the Support Vector Machine (SVM) algorithm classified the fracture location with a good accuracy score of 0.889 in comparison to the other algorithms.

scholarly journals USING MACHINE LEARNING AS A TOOL TO HELP GUIDE UNDECLARED/UNDECIDED FIRST-YEAR ENGINEERING STUDENTS TOWARDS A DISCIPLINE

2019 ◽  
Author(s):  
Shashank Iyer ◽  
Chirag Variawa
Keyword(s):  
Machine Learning ◽  
Engineering Students ◽  
Supervised Machine Learning ◽  
First Year ◽  
Classification Algorithms ◽  
Specific Class ◽  
K Nearest Neighbors ◽  
Machine Learning Classification ◽  
University Of Toronto ◽  
Qualitative Survey

Supervised Machine Learning classification algorithms are used to analyze the potential inclination of the undecided/undeclared first-year engineering students.  The data exploration task is possible by building a dataset that comprises of questions based on significant attributes.  These attributes hover around different disciplines of engineering being offered at the University of Toronto. This qualitative survey is distributed to upperclassmen students (3rd, 4th year and graduate students, N = 54) and undecided first-year engineering students (N = 29) Multi-class classification is a technique that is used to categorize the data into two or more classes, in this case, the different disciplines of engineering at University of Toronto. The dataset that is built, based on the answers provided by the upperclassmen, is programmed into different classification algorithms such as Logistic Regression, KNN (K-nearest neighbors), Decision Tree and Random Forest classifier. The algorithms are compared so as to identify the most appropriate one that can determine the specific class label of the upperclassmen based on the answers provided in the qualitative survey.  The accuracy of the various algorithms is an indicator of the favorable algorithm that can serve as a tool to suggest the potential majors that could be pursued by the undecided/undeclared students. Moreover, the answers given by the upperclassmen is visually analyzed for identifying the patterns of inclination of the students belonging to different disciplines of engineering.  

scholarly journals Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis

2019 ◽  
Author(s):  
Emmanuel P. Mwanga ◽  
Elihaika G. Minja ◽  
Emmanuel Mrimi ◽  
Mario González Jiménez ◽  
Johnson K. Swai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Infrared Spectroscopy ◽  
Human Blood ◽  
Supervised Machine Learning ◽  
Classification Algorithms ◽  
Clinical Settings ◽  
Test Results ◽  
Mid Infrared ◽  
Blood Spots

AbstractBackgroundEpidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study shows that mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots.MethodsFilter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in twelve wards in south-eastern Tanzania in 2018/19. The DBS were scanned using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra in the range, 4000 cm-1 to 500 cm−1. The spectra were cleaned to compensate for atmospheric water vapor and CO2 interference bands and used to train different classification algorithms to distinguish between malaria-positive and malaria-negative DBS papers based on PCR test results as reference. The analysis considered 296 individuals, including 123 PCR-confirmed malaria-positives and 173 negatives. Model training was done using 80% of the dataset, after which the best-fitting model was optimized by bootstrapping of 80/20 train/test stratified splits. The trained models were evaluated by predicting Plasmodium falciparum positivity in the 20% validation set of DBS.ResultsLogistic regression was the best-performing model. Considering PCR as reference, the models attained overall accuracies of 92% for predicting P. falciparum infections (specificity = 91.7%; sensitivity = 92.8%) and 85% for predicting mixed infections of P. falciparum and P. ovale (specificity = 85%, sensitivity = 85%) in the field-collected specimen.ConclusionThese results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in dried human blood spots. The approach could have potential for rapid and high-throughput screening of Plasmodium infections in both non-clinical settings (e.g. field surveys) and clinical settings (diagnosis to aid case management). However, full utility will require further advances in classification algorithms, field validation of this technology in other study sites and an in-depth evaluation of the biological basis of the observed test results. Training the models on larger datasets could also improve specificity and sensitivity of the technique. The MIR-ML spectroscopy system is robust, low-cost, and requires minimum maintenance.

scholarly journals Heart Disease Prediction using Supervised Machine Learning Algorithms

2020 ◽  
Vol 9 (4) ◽  
pp. 148-151
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Classification Algorithms ◽  
Disease Detection ◽  
Disease Prediction ◽  
Benchmark Datasets ◽  
Complicated Task

Generally, the most complicated task in the healthcare field is the diagnosis of the disease itself. The diagnosis phase in disease detection is usually the most time-consuming task and is prone to most of the errors. Such complications can be effectively handled if the disease detection process is well automated by incorporating effective machine learning algorithms trained with some benchmark datasets. It should also be noted that huge amounts of data that are acquired from Heart Specialization Hospitals are being wasted every year. In this paper, various classification algorithms have been used to train the machine to diagnose heart disease. By a comparative study of various learning models, we have identified the appropriate learning model for the heart disease dataset. Initially, the work will begin with an overview of various machine learning algorithms followed by the algorithmic comparison.

scholarly journals For Acute Pancreatitis using Supervised Machine Learning Algorithms

2020 ◽  
pp. 242-244
Keyword(s):  
Machine Learning ◽  
Acute Pancreatitis ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Classification Algorithms ◽  
Reliable Prediction ◽  
Test Dataset ◽  
Machine Learning Methods ◽  
Diverse Perspective ◽  
Breathing Problem

The data present in healthcare industry is very huge and delicate which requires to be managed watchfully. There are multiple fatal diseases which grow rapidly all over the world pancreatitis is one among them. Medical professionals want a reliable prediction system to diagnose Pancreatitis. Getting useful information out of the data which has been examined using diverse perspective and various machine learning methods and grouping the required information is a bit difficult task. When various data mining methods are applied on a huge and accessible data which will definitely provide us with the required information to the users. Pancreatitis contributes to Infection, Kidney failure, Breathing problem, Diabetes, Malnutrition, Pancreatic cancer. So, mining the Pancreatitis data in efficient way is a crucial concern. An outcome feature has to be predicted using a dataset where the outcome may contain only two constants that is either 1 or 0. 0 refers to the sufferer having Acute Pancreatitis and 1 refers to the sufferer may have chronic pancreatitis. Thus, an outcome feature with exemplary accuracy has to be predicted using the test dataset and classification algorithms. In order to realize this data is very necessary and then diverse classification techniques can be experimented. Then a finest model can be preferred which gives the maximum accuracy among all others.

Rumor Detection on Twitter Using a Supervised Machine Learning Framework

2018 ◽  
Vol 8 (3) ◽  
pp. 1-13 ◽  
Author(s):  
Hardeo Kumar Thakur ◽  
Anand Gupta ◽  
Ayushi Bhardwaj ◽  
Devanshi Verma
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Supervised Machine Learning ◽  
Classification Algorithms ◽  
Learning Framework ◽  
Online Social Media ◽  
Rumor Propagation ◽  
Multiple Classification ◽  
The Government ◽  
Rumor Detection

This article describes how a rumor can be defined as a circulating unverified story or a doubtful truth. Rumor initiators seek social networks vulnerable to illimitable spread, therefore, online social media becomes their stage. Hence, this misinformation imposes colossal damage to individuals, organizations, and the government, etc. Existing work, analyzing temporal and linguistic characteristics of rumors seems to give ample time for rumor propagation. Meanwhile, with the huge outburst of data on social media, studying these characteristics for each tweet becomes spatially complex. Therefore, in this article, a two-fold supervised machine-learning framework is proposed that detects rumors by filtering and then analyzing their linguistic properties. This method attempts to automate filtering by training multiple classification algorithms with accuracy higher than 81.079%. Finally, using textual characteristics on the filtered data, rumors are detected. The effectiveness of the proposed framework is shown through extensive experiments on over 10,000 tweets.

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

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