So you think that robots are best suited for menial jobs? You couldn't be far from wrong. The research in artificial intelligence (AI) has moved to an all-new level with scientists at the University of Adelaide in Australia announcing that robots can predict mortality. By analysing CT scans from 48 patients, the deep learning algorithms could predict whether they would die within five years with 69% accuracy, which is broadly similar to the scores from human diagnosticians, the paper says. It will open up new avenues for the application of AI in medical image analysis, offering hope for early detection of serious illness that requires specific medical interventions.

Measurement error in the observed values of the variables can greatly change the output of various causal discovery methods. This problem has received much attention in multiple fields, but it is not clear to what extent the causal model for the measurement-error-free variables can be identified in the presence of measurement error with unknown variance. In this paper, we study precise sufficient identifiability conditions for the measurement-errorfree causal model and show what information of the causal model can be recovered from observed data. In particular, we present two different sets of identifiability conditions, based on the second-order statistics and higher-order statistics of the data, respectively. The former was inspired by the relationship between the generating model of the measurement-errorcontaminated data and the factor analysis model, and the latter makes use of the identifiability result of the over-complete independent component analysis problem.

This post is a summary of 3 different posts about outlier detection methods. One of the challenges in data analysis in general and predictive modeling in particular is dealing with outliers. There are many modeling techniques which are resistant to outliers or reduce the impact of them, but still detecting outliers and understanding them can lead to interesting findings. We generally define outliers as samples that are exceptionally far from the mainstream of data.There is no rigid mathematical definition of what constitutes an outlier; determining whether or not an observation is an outlier is ultimately a subjective exercise. There are several approaches for detecting Outliers.

Simple Decision Tree is an Excel Add-in created by Thomas Seyller. The Add-in is released under the terms of GPL v3 with additional permissions. Thomas created this Add-in for the Stanford Decisions and Ethics Center and open-sourced it for the Decision Professionals Network. This software has been extensively used to teach Decision Analysis at Stanford University.

Scikit-Learn is a popular library for Machine Learning in python programming language. If you want to test your knowledge with just a few lines of code, scikit-learn is what you need. From Linear and Logistic Regression to SVM and KNN, you name and scikit-learn has it. You will often need to prepare and transform your data in a form that is suitable for scikit-learn to use for training the models. Pandas is an awesome library for python which can be used for this purpose.

Machine learning techniques are increasingly demonstrating success in image-based diagnosis, disease detection and disease prognosis. To reduce operator dependency and get better diagnostic accuracy, a computer aided diagnositic (CAD) system is a valuable and beneficial means for breast tumor detection and classification, fetal development and growth, Brain functioning, skin lesions and Lungs diseases [1]. Image denoising using machine learning techniques plays important role in the various application area of medical imaging such as pre-processing (noise removal from Ultrasound (US) images, segmentation (MRI of brain tumors and lung infections using X-rays), Computer aided diagnosis (CAD) for breast cancer, fetus development and many more). Further, denoising of medical images using data mining methods are analyzed. This paper focuses on the review of various denoising methods along with machine learning approaches to develop a systematic decision for diagnosing and prediction for medical images. The representation of the machine learning i.e. based on various numbers of methods which focuses on prediction, based on known properties learned from the training data has been considered.

When discussing classifiers, decision trees are often thought of as easily interpretable models when compared to numerous more complex classifiers, especially those of the blackbox variety. And this is generally true. This is especially true of rather comparatively simple models created from simple data. This is much-less true of complex decision trees crafted from large amounts of (high-dimensional) data. Even otherwise straightforward decision trees which are of great depth and/or breadth, consisting of heavy branching, can be difficult to trace.

Plants monitor their surrounding environment and control their physiological functions by producing an electrical response. We recorded electrical signals from different plants by exposing them to Sodium Chloride (NaCl), Ozone (O3) and Sulfuric Acid (H2SO4) under laboratory conditions. After applying pre-processing techniques such as filtering and drift removal, we extracted few statistical features from the acquired plant electrical signals. Using these features, combined with different classification algorithms, we used a decision tree based multi-class classification strategy to identify the three different external chemical stimuli. We here present our exploration to obtain the optimum set of ranked feature and classifier combination that can separate a particular chemical stimulus from the incoming stream of plant electrical signals. The paper also reports an exhaustive comparison of similar feature based classification using the filtered and the raw plant signals, containing the high frequency stochastic part and also the low frequency trends present in it, as two different cases for feature extraction. The work, presented in this paper opens up new possibilities for using plant electrical signals to monitor and detect other environmental stimuli apart from NaCl, O3 and H2SO4 in future.

Diagnosing disease is one of the more labor-intensive aspects of the healthcare system. It also happens to be one that is particularly well-suited to being performed by machine learning algorithms. While work in this area is in its early stages, the technology is evolving rapidly and appears poised to transform diagnostic medicine. Thanks largely to the huge volumes of data collected from patients, medical diagnostics is an ideal domain for machine learning. Much of the diagnostic data is image-based, such as X-rays, MRI scans, and ultrasound imagery, but can also include things like genomic profiles, epidemiological data, blood tests, biopsy results, and even medical research papers.

Facebook uses machine learning and ranking models to deliver the best experiences across many different parts of the app, such as which notifications to send, which stories you see in News Feed, or which recommendations you get for Pages you might want to follow. To surface the most relevant content, it's important to have high-quality machine learning models. We look at a number of real-time signals to determine optimal ranking; for example, in the notifications filtering use case, we look at whether someone has already clicked on similar notifications or how many likes the story corresponding to a notification has gotten. Because we perform this every time a new notification is generated, we want to return the decision for sending notifications as quickly as possible. More complex models can help improve the precision of our predictions and show more relevant content, but the trade-off is that they require more CPU cycles and can take longer to return results.