Once again machine learning bug bit me, and after a long delay I wrote about Decision Trees in my book Introduction To DataScience. I am looking to write about K-nearest neighbors next. Even though I have a work where the client hasn't yet given me a unnecessary trouble yet (which has not been the case for years now, and it looks like I have entered dream land or something), work is taking time, and I have to tend to what gives me bread first. But learning Data Science from scratch is my goal, and I will achieve it. I would be very happy if people can read it and mail their feedback to [email protected], no matter where in the world I am, mail works if I have the internet, and I can take corrective measures and possibly answer you.

In urban areas, global navigation satellite system (GNSS) signals are often reflected or blocked by buildings, thus resulting in large positioning errors. In this study, we proposed a machine learning approach for global positioning system (GPS) multipath detection that uses dual antennas. A machine learning model that could classify GPS signal reception conditions was trained with several GPS measurements selected as suggested features. We applied five features for machine learning, including a feature obtained from the dual antennas, and evaluated the classification performance of the model, after applying four machine learning algorithms: gradient boosting decision tree (GBDT), random forest, decision tree, and K-nearest neighbor (KNN). It was found that a classification accuracy of 82%-96% was achieved when the test data set was collected at the same locations as those of the training data set. However, when the test data set was collected at locations different from those of the training data, a classification accuracy of 44%-77% was obtained.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. The decision tree is one of the most powerful and important algorithms present in supervised machine learning.

Could artificial intelligence (AI) assessment have comparable diagnostic accuracy to clinician assessment for fracture detection? In a recently published meta-analysis of 42 studies, the study authors noted 92 percent sensitivity and 91 percent specificity for AI in comparison to 91 percent sensitivity and 92 percent specificity for clinicians based on internal validation test sets. For the external validation test sets, clinicians had 94 percent specificity and sensitivity in comparison to 91 percent specificity and sensitivity for AI, according to the study. In essence, the study authors found no statistically significant differences between AI and clinician diagnosis of fractures. "The results from this meta-analysis cautiously suggest that AI is noninferior to clinicians in terms of diagnostic performance in fracture detection, showing promise as a useful diagnostic tool," wrote Dominic Furniss, DM, MA, MBBCh, FRCS(Plast), a professor of plastic and reconstructive surgery in the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences at the Botnar Research Centre in Oxford, United Kingdom., and colleagues.

Hospitals and private practices are seeing an increase in patients. They said that, however, is not a bad thing. Patients are returning for diagnostic procedures, surgeries, and health screenings that were postponed due to the COVID-19 pandemic. "During the early part of the pandemic, our patients volume really decreased dramatically," said Adam Trybus, chief radiologist at 611 MRI in Altoona. "Patients were postponing routine care. They were only coming in for emergencies."

A China Eastern flight carrying 132 people crashed Monday. A domestic Chinese flight with 132 passengers plummeted into the mountains of southern China on Monday, likely leaving all passengers dead and investigators launching a probe into the cause. Chinese President Xi Jinping has instructed the country's emergency services to "organize a search and rescue" operation and "identify the causes" of the Boeing 737-800 crashing, according to state media. Former chairman of the National Transportation Safety Board Jim Hall told Fox News Digital on Monday that it would be "irresponsible" to speculate what caused the crash so soon after the incident, but described how the NTSB carries out investigations into major commercial crashes. This screen grab taken from video from The Paper and received via AFPTV on March 21, 2022 shows ambulances turning off onto a side road upon arrival after a China Eastern reportedly crashed in Teng County in Wuzhou City, Guangxi province.

Decision Tree algorithm is one of the most powerful algorithms in machine learning and data science. Decision Tree algorithm is one of the most powerful algorithms in machine learning and data science. It is very commonly used by data scientists and machine learning engineers to solve business problem and explain that to your customers easily.

Localizing the root cause of network faults is crucial to network operation and maintenance. However, due to the complicated network architectures and wireless environments, as well as limited labeled data, accurately localizing the true root cause is challenging. In this paper, we propose a novel algorithm named NetRCA to deal with this problem. Firstly, we extract effective derived features from the original raw data by considering temporal, directional, attribution, and interaction characteristics. Secondly, we adopt multivariate time series similarity and label propagation to generate new training data from both labeled and unlabeled data to overcome the lack of labeled samples. Thirdly, we design an ensemble model which combines XGBoost, rule set learning, attribution model, and graph algorithm, to fully utilize all data information and enhance performance. Finally, experiments and analysis are conducted on the real-world dataset from ICASSP 2022 AIOps Challenge to demonstrate the superiority and effectiveness of our approach.

Linear structural equation models represent direct causal effects as directed edges and confounding factors as bidirected edges. An open problem is to identify the causal parameters from correlations between the nodes. We investigate models, whose directed component forms a tree, and show that there, besides classical instrumental variables, missing cycles of bidirected edges can be used to identify the model. They can yield systems of quadratic equations that we explicitly solve to obtain one or two solutions for the causal parameters of adjacent directed edges. We show how multiple missing cycles can be combined to obtain a unique solution. This results in an algorithm that can identify instances that previously required approaches based on Gr\"obner bases, which have doubly-exponential time complexity in the number of structural parameters.

Authors implemented an artificial intelligence (AI)–based detection tool for intracranial hemorrhage (ICH) on noncontrast CT images into an emergent workflow, evaluated its diagnostic performance, and assessed clinical workflow metrics compared with pre-AI implementation. The finalized radiology report constituted the ground truth for the analysis, and CT examinations (n 4450) before and after implementation were retrieved using various keywords for ICH. Diagnostic performance was assessed, and mean values with their respective 95% CIs were reported to compare workflow metrics (report turnaround time, communication time of a finding, consultation time of another specialty, and turnaround time in the emergency department). Although practicable diagnostic performance was observed for overall ICH detection with 93.0% diagnostic accuracy, 87.2% sensitivity, and 97.8% negative predictive value, the tool yielded lower detection rates for specific subtypes of ICH (eg, 69.2% [74 of 107] for subdural hemorrhage and 77.4% [24 of 31] for acute subarachnoid hemorrhage). Common false-positive findings included postoperative and postischemic defects (23.6%, 37 of 157), artifacts (19.7%, 31 of 157), and tumors (15.3%, 24 of 157).