Imbalanced data classification problem has always been a popular topic in the field of machine learning research. In order to balance the samples between majority and minority class. Oversampling algorithm is used to synthesize new minority class samples, but it could bring in noise. Pointing to the noise problems, this paper proposed a denoising autoencoder neural network (DAE) algorithm which can not only oversample minority class sample through misclassification cost, but it can denoise and classify the sampled dataset. Through experiments, compared with the denoising autoencoder neural network (DAE) with oversampling process and traditional fully connected neural networks, the results showed the proposed algorithm improves the classification accuracy of minority class of imbalanced datasets.

Process mining sheds new light on the relationship between process models and real-life processes. Process discovery can be used to learn process models from event logs. Conformance checking is concerned with quantifying the quality of a business process model in relation to event data that was logged during the execution of the business process. There exist different categories of conformance measures. Recall, also called fitness, is concerned with quantifying how much of the behavior that was observed in the event log fits the process model. Precision is concerned with quantifying how much behavior a process model allows for that was never observed in the event log. Generalization is concerned with quantifying how well a process model generalizes to behavior that is possible in the business process but was never observed in the event log. Many recall, precision, and generalization measures have been developed throughout the years, but they are often defined in an ad-hoc manner without formally defining the desired properties up front. To address these problems, we formulate 21 conformance propositions and we use these propositions to evaluate current and existing conformance measures. The goal is to trigger a discussion by clearly formulating the challenges and requirements (rather than proposing new measures). Additionally, this paper serves as an overview of the conformance checking measures that are available in the process mining area.

By routing your camera feeds to our AI Engine, you can be informed in just 3 seconds when a firearm is detected in surveillance cameras. Additionally, this AI technology can track shooters in real time, providing shooter location(s) and fast live updates to police, school security and educators. In the wake of school shooting incidents over the past 10 years, people are anxious about creating safe environments. The ability to detect weapons on premises is unfortunately a necessity now. Cameras are already in place at most schools.

Automatic speech recognition systems like those at the core of Alexa convert speech into text, and one of their components is a model that predicts which word will come after a sequence of words. They're typically n-gram based, meaning they suss out the probability of next words given the past n-1 words. But architectures like recurrent neural networks, which are commonly used in speech recognition because of their ability to learn long-range dependencies, are tough to incorporate into real-time systems and often struggle to ingest data from multiple corpora. That's why researchers at Amazon's Alexa research division investigated techniques to make such AI models more practical for speech recognition. In a blog post and accompanying paper ("Scalable Multi Corpora Neural Language Models for ASR") scheduled to be presented at the upcoming Interspeech 2019 conference in Graz, Austria, they claim they can reduce word recognition error rate by 6.2% over the baseline.

It is not surprising that machines are an integral part of our eco-system driven by technology. Reaching a point in technical pinnacle was made easier from the time machine started learning and reasoning even without the intervention of a human being. The world is changing from the models developed by machine learning, artificial intelligence and deep learning which adapt themselves independently to a given scenario. Data being the lifeline of businesses obtaining machine learning training helps in better decision-making for the company to stay ahead of the competition. Machine learning interview questions may pop up from any part of the subject like it may be about algorithms and the theory that works behind it, your programming skills and the ability to work over those algorithms and theory or about your general insights about machine learning and its applicability.

An e xtreme wind speed estimation method that consider s wind hazard climate type s is critical for design wind load calculation for building structure s affected by mixed climate s . However, it is very difficult to obtain wind hazard climate type s from meteorologi cal data records, because they restrict the application of extreme wind speed estimation in mixed climates . This paper first proposes a wind hazard type identification algorithm based on a numerical pattern recognition method that utilizes feature extraction and generalization . Next, it compares six commonly used machine learning models using K - fold cross - validation. Finally, it takes meteorological data from three locations near the southeast coast of China as example s to examine t he algor ithm's performance . Based on classification results, the extreme wind speed s calculated based on mixed wind hazard types is compared with those obtained from conventional methods, and the effects on structural design for different return periods are discus sed . Extreme wind speed; Mixed climates; Data - driven method; Pattern Recognition; Machine Learning; 1. Introduction Wind effects are key factors in structural design, and extreme wind speeds are the starting point . F or flexible structures such as long - span bridges, long - span roofs and high - rise buildings, wind loads are normally the predominant loads. I n order to meet both the ultimate safety and performance requirements of wind - resistant structural design, it s necessary to accurately estimate the extreme wind speed s for different recurrence period s . For significant buildings and infrastructures, it is necessary to estimat e the extreme wind speed through probabilistic methods from local wind speed record s .

This week's opinion piece is from technology influencer and futurist Dr Sarah-Jayne Gratton The latest statistics around breast cancer send a stark reminder of just how important early detection is in combating this brutal disease. With revolutionary strides forward in Artificial Intelligence (AI) all that looks set to change for the better. One of the leading causes of death for cancer patients is a late diagnosis, too often brought about by inferior testing facilities, human factors, such as fatigue and loss of concentration, or by the patients themselves, who put off seeing a specialist due to the fear of what they might discover. But now, thanks to nothing short of revolutionary strides forward in Artificial Intelligence (AI) all that looks set to change for the better. AI is capable of advanced learning using large complex datasets and has the potential to perform tasks such as image interpretation.

Tell me if you've heard this: there is a new, advanced network intrusion device that uses modern super-smart Machine Learning (ML) to root out known and unknown intrusions. The IDS device is so smart it learns what's normal on your network and not, immediately informing you when it sees an anomaly. Or, maybe it's an intrusion prevention system (IPS) that will then block all malicious traffic. This AI-enabled solution boasts 99% accuracy detecting attacks. Even more, it can detect previously unknown attacks. That's an amazing sales pitch, but can we do it?

Aim: To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring. Materials and Methods: The PubMed and MEDLINE databases were searched for articles published before September 2018 using relevant search terms. The search strategy focused on articles applying ML to high-grade glioma biomarkers for treatment response monitoring, prognosis, and prediction. Results: Magnetic resonance imaging (MRI) is typically used throughout the patient pathway because routine structural imaging provides detailed anatomical and pathological information and advanced techniques provide additional physiological detail. Using carefully chosen image features, ML is frequently used to allow accurate classification in a variety of scenarios. Rather than being chosen by human selection, ML also enables image features to be identified by an algorithm. Much research is applied to determining molecular profiles, histological tumour grade, and prognosis using MRI images acquired at the time that patients first present with a brain tumour. Differentiating a treatment response from a post-treatment-related effect using imaging is clinically important and also an area of active study (described here in one of two Special Issue publications dedicated to the application of ML in glioma imaging). Conclusion: Although pioneering, most of the evidence is of a low level, having been obtained retrospectively and in single centres. Studies applying ML to build neuro-oncology monitoring biomarker models have yet to show an overall advantage over those using traditional statistical methods. Development and validation of ML models applied to neuro-oncology require large, well-annotated datasets, and therefore multidisciplinary and multi-centre collaborations are necessary.

AutoML systems are currently rising in popularity, as they can build powerful models without human oversight. They often combine techniques from many different sub-fields of machine learning in order to find a model or set of models that optimize a user-supplied criterion, such as predictive performance. The ultimate goal of such systems is to reduce the amount of time spent on menial tasks, or tasks that can be solved better by algorithms while leaving decisions that require human intelligence to the end-user. In recent years, the importance of other criteria, such as fairness and interpretability, and many others have become more and more apparent. Current AutoML frameworks either do not allow to optimize such secondary criteria or only do so by limiting the system's choice of models and preprocessing steps. We propose to optimize additional criteria defined by the user directly to guide the search towards an optimal machine learning pipeline. In order to demonstrate the need and usefulness of our approach, we provide a simple multi-criteria AutoML system and showcase an exemplary application.