Rapid advances of hardware-based technologies during the past decades have opened up new possibilities for Life scientists to gather multimodal data in various application domains (e.g., Omics, Bioimaging, Medical Imaging, and [Brain/Body]-Machine Interfaces), thus generating novel opportunities for development of dedicated data intensive machine learning techniques. Overall, recent research in Deep learning (DL), Reinforcement learning (RL), and their combination (Deep RL) promise to revolutionize Artificial Intelligence. The growth in computational power accompanied by faster and increased data storage and declining computing costs have already allowed scientists in various fields to apply these techniques on datasets that were previously intractable for their size and complexity. This review article provides a comprehensive survey on the application of DL, RL, and Deep RL techniques in mining Biological data. In addition, we compare performances of DL techniques when applied to different datasets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.
Machine learning and deep learning have provided us with an exploration of a whole new research era. As more data and better computational power become available, they have been implemented in various fields. The demand for artificial intelligence in the field of health informatics is also increasing and we can expect to see the potential benefits of artificial intelligence applications in healthcare. Deep learning can help clinicians diagnose disease, identify cancer sites, identify drug effects for each patient, understand the relationship between genotypes and phenotypes, explore new phenotypes, and predict infectious disease outbreaks with high accuracy. In contrast to traditional models, its approach does not require domain-specific data pre-process, and it is expected that it will ultimately change human life a lot in the future. Despite its notable advantages, there are some challenges on data (high dimensionality, heterogeneity, time dependency, sparsity, irregularity, lack of label) and model (reliability, interpretability, feasibility, security, scalability) for practical use. This article presents a comprehensive review of research applying deep learning in health informatics with a focus on the last five years in the fields of medical imaging, electronic health records, genomics, sensing, and online communication health, as well as challenges and promising directions for future research. We highlight ongoing popular approaches' research and identify several challenges in building deep learning models.
Future buildings will offer new convenience, comfort, and efficiency possibilities to their residents. Changes will occur to the way people live as technology involves into people's lives and information processing is fully integrated into their daily living activities and objects. The future expectation of smart buildings includes making the residents' experience as easy and comfortable as possible. The massive streaming data generated and captured by smart building appliances and devices contains valuable information that needs to be mined to facilitate timely actions and better decision making. Machine learning and big data analytics will undoubtedly play a critical role to enable the delivery of such smart services. In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics. This survey also reviews the current trends and challenges faced in the development of smart building services.
Click on the blue word above the attention of the medical profession, every day there are material! When it comes to Dr. Siddartha (Siddhartha Mukherjee), perhaps a lot of people are unfamiliar with his name. But a lot of people are familiar with his two book, "the king of all diseases: cancer," and "genes: Intimate History.". The former allows Dr. Mukherjee to get a non fiction Pulitzer prize, while the latter was recommended as the best book of 2016 by Mr. Bill Gate. Recently, Dr. Mukherjee in "New York guest" (The New Yorker) published a long article, the unique perspective of a doctor, artificial intelligence survey in recent years the impact for medicine. The seven story is he in this long article record, outlines the future doctors and artificial intelligence, harmonious coexistence. The author Dr. Mukherjee is a doctor, but also a good writer. One night in November 2016, a 54 year old woman in New York, Bronx (Bronx) was sent to the emergency room at the Columbia University (Columbia University) medical center because of a severe headache. She told the emergency room doctor that his vision was blurred and his left hand was numb. The doctor arranged for CT. A few months later, on January, one of the 4 radiologists huddled in front of a computer on the third floor of the hospital, the room was dark and windowless, with only the screen light, which seemed to be filtered by the sea. She's training them to read CT. "Once the brain shows death and gray, it's easy to diagnose a stroke," Dr. Lignelli-Dipple said. The key is to diagnose a stroke before most nerve cells die." A stroke is usually caused by a blockage or bleeding of the blood vessel. The radiologist has about 45 minutes of window time so that the doctor can intervene in time to dissolve the clot. "Imagine you're in the emergency room right now," continued Dr. Lignelli-Dipple. "Every minute, a part of the brain dies.