Bioinformatics and Medicine in the Era of Deep Learning

Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic

Bioinformatics and Medicine in the Era of Deep Learning

Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic

Machine Learning for Data-Driven Smart Grid Applications

The field of Machine Learning grew out of the quest for artificial intelligence. It gives computers the ability to learn statistical patterns from data without being explicitly programmed. These patterns can then be applied to new data in order to make predictions. Machine Learning also allows to automatically adapt to changes in the data without amending the underlying model. We deal every day dozens of times with Machine Learning applications such as when doing a Google search, using spam filters, face detection, speaking to voice recognition software or when sitting in a self-driving car. In recent years, machine learning methods have evolved in the smart grid community. This change towards analyzing data rather than modeling specific problems has lead to adaptable, more generic methods, that require less expert knowledge and that are easier to deploy in a number of use cases. This is an introductory level course to discuss what machine learning is and how to apply it to data-driven smart grid applications. Practical case studies on real data sets, such as load forecasting, detection of irregular power usage and visualization of customer data, will be included. Therefore, attendees will not only understand, but rather experience, how to apply machine learning methods to smart grid data

Detection of Irregular Power Usage using Machine Learning

Electricity losses are a frequently appearing problem in power grids. Non-technical losses (NTL) appear during distribution and include, but are not limited to, the following causes: Meter tampering in order to record lower consumptions, bypassing meters by rigging lines from the power source, arranged false meter readings by bribing meter readers, faulty or broken meters, un-metered supply, technical and human errors in meter readings, data processing and billing. NTLs are also reported to range up to 40% of the total electricity distributed in countries such as India, Pakistan, Malaysia, Brazil or Lebanon. This is an introductory level course to discuss how to predict if a customer causes a NTL. In the last years, employing data analytics methods such as machine learning and data mining have evolved as the primary direction to solve this problem. This course will present and compare different approaches reported in the literature. Practical case studies on real data sets will be included. As an additional outcome, attendees will understand the open challenges of NTL detection and learn how these challenges could be solved in the coming years

Improved Sensor-Based Human Activity Recognition Via Hybrid Convolutional and Recurrent Neural Networks

Non-intrusive sensor-based human activity recognition is utilized in a spectrum of applications including fitness tracking devices, gaming, health care monitoring, and smartphone applications. Deep learning models such as convolutional neural networks (CNNs) and long short-term memory (LSTMs) recurrent neural networks provide a way to achieve human activity recognition accurately and effectively. This project designed and explored a variety of multi-layer hybrid deep learning architectures which aimed to improve human activity recognition performance by integrating local features and was scale invariant with dependencies of activities. We achieved a 94.7% activity recognition rate on the University of California, Irvine public domain dataset for human activity recognition containing 6 activities with a 2-layer CNN-1-layer LSTM hybrid model. Additionally, we achieved an 88.0% activity recognition rate on the University of Texas at Dallas Multimodal Human Activity dataset containing 27 activities with a 4-layer CNN-1-layer LSTM hybrid model. For both datasets, our hybrid models outperformed other deep learning models and traditional machine learning methods