In online advertising, users may be exposed to a range of different advertising campaigns, such as natural search or referral or organic search, before leading to a final transaction. Estimating the contribution of advertising campaigns on the user's journey is very meaningful and crucial. A marketer could observe each customer's interaction with different marketing channels and modify their investment strategies accordingly. Existing methods including both traditional last-clicking methods and recent data-driven approaches for the multi-touch attribution (MTA) problem lack enough interpretation on why the methods work. In this paper, we propose a novel model called DeepMTA, which combines deep learning model and additive feature explanation model for interpretable online multi-touch attribution. DeepMTA mainly contains two parts, the phased-LSTMs based conversion prediction model to catch different time intervals, and the additive feature attribution model combined with shaley values. Additive feature attribution is explanatory that contains a linear function of binary variables. As the first interpretable deep learning model for MTA, DeepMTA considers three important features in the customer journey: event sequence order, event frequency and time-decay effect of the event. Evaluation on a real dataset shows the proposed conversion prediction model achieves 91\% accuracy.

Urban air pollution has become a major environmental problem that threatens public health. It has become increasingly important to infer fine-grained urban air quality based on existing monitoring stations. One of the challenges is how to effectively select some relevant stations for air quality inference. In this paper, we propose a novel model based on reinforcement learning for urban air quality inference. The model consists of two modules: a station selector and an air quality regressor. The station selector dynamically selects the most relevant monitoring stations when inferring air quality. The air quality regressor takes in the selected stations and makes air quality inference with deep neural network. We conduct experiments on a real-world air quality dataset and our approach achieves the highest performance compared with several popular solutions, and the experiments show significant effectiveness of proposed model in tackling problems of air quality inference.

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.

In process mining, process models are extracted from event logs using process discovery algorithms and are commonly assessed using multiple quality dimensions. While the metrics that measure the relationship of an extracted process model to its event log are well-studied, quantifying the level by which a process model can describe the unobserved behavior of its underlying system falls short in the literature. In this paper, a novel deep learning-based methodology called Adversarial System Variant Approximation (AVATAR) is proposed to overcome this issue. Sequence Generative Adversarial Networks are trained on the variants contained in an event log with the intention to approximate the underlying variant distribution of the system behavior. Unobserved realistic variants are sampled either directly from the Sequence Generative Adversarial Network or by leveraging the Metropolis-Hastings algorithm. The degree by which a process model relates to its underlying unknown system behavior is then quantified based on the realistic observed and estimated unobserved variants using established process model quality metrics. Significant performance improvements in revealing realistic unobserved variants are demonstrated in a controlled experiment on 15 ground truth systems. Additionally, the proposed methodology is experimentally tested and evaluated to quantify the generalization of 60 discovered process models with respect to their systems.

Hisham Elamir is a data scientist with expertise in machine learning, deep learning, and statistics. He currently lives and works in Cairo, Egypt. In his work projects, he faces challenges ranging from natural language processing (NLP), behavioral analysis, and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.

Deep learning algorithms trained using artificial intelligence (AI) may help to determine how patients will respond to systemic treatments for non-small cell lung cancer (NSCLC), according to new research published in the journal Clinical Cancer Research. For the study, associate research scientist Laurent Dercle (Department of Radiology, Columbia University Irving Medical Center) and colleagues applied AI to standard-of-care (SoC) computed tomography (CT) scans of advanced NSCLC and trained deep learning algorithms to predict how sensitive tumors would be to three types of systemic treatments. Deep learning is a type of machine learning where algorithms called artificial neural networks learn from large datasets and solve problems in a way that mimics how the human brain works and without requiring human supervision. Dercle says that currently, the way radiologists interpret CT scans of patients with cancer who are receiving systemic therapy is essentially subjective. The purpose of this study was to train cutting-edge AI technologies to predict patients' responses to treatment, allowing radiologists to deliver more accurate and reproducible predictions of treatment efficacy at an early stage of the disease," Currently, to check how NSCLC patients' respond to systemic therapies, radiologists assess differences in the size of existing tumors and in the appearance of new tumors that have formed.

About this Course The Deep Learning Masterclass: Make a Keras Image Classifier Welcome to this epic masterclass on Keras (and so much more) with our #1 data scientist and app developer Nimish Narang, creator of over 20 Mammoth Interactive courses and a top-seller on Eduonix This course was funded by a wildly successful Kickstarter Add To Cart - GET COUPON CODE Anyone can take this course. If you already have experience using PyCharm and running Python files and programs on the interface, you can simply skip ahead to whatever section best suits your needs. Or, you can follow the progression of this meticulously curated course especially designed to take any absolute beginner off the street and make them a data modeler. This course is divided into days, but of course you can learn at your own pace. In Day 2 we teach you all the fundamentals of the Python programming language.

In machine learning, regularization is a approach used to combat high variance -- in other words, the issue of your model learning to reproduce the data, rather than the underlying semantics about your problem. In an analogous way to humans learning, the idea is to construct your homework problems to test and build for knowledge, rather than simply rote learning: for example, learning multiplication tables as opposed to learning how to multiply. This kind of phenomenon is especially prevalent in learning by neural networks -- with great learning capacity comes a large likelihood for memorization, and it is up to us practitioners to guide deep learning models into soaking up our problem, not our data. Many of you will have come across these methods in the past, and may have developed your own intuition for how different regularization methods affect the outcome. For those of you who don't (and even for those who do!) this article provides a visual guide for how neural network parameters are shaped by regularization.

In this post, we're collecting articles that detail how AI, machine learning, and data science are being used in response to the coronavirus. So far it covers predictive and monitoring systems, AI-driven diagnostics, and efforts in the data science community. We hope it will provide a comprehensive, grounded look at exactly where AI technology is being used, and how it is making a difference. In time, we hope it will also provide an overview of how AI technology was employed to understand, predict, and fight the spread of COVID-19. This post will be regularly updated as new developments are reported.

Coronavirus disease has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performances. In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19. Community acquired pneumonia (CAP) and other non-pneumonia CT exams were included to test the robustness of the model.