https://doi.org/10.1016/j.apsb.2022.02.002

Why 90% of clinical drug development fails and how to improve it?

Genetic Programming and Data Structures: Genetic Programming+Data Structures=Automatic Programming. William B. Langdon, Kluwer Academic Publishers, Boston, USA, 1998

The cloud computing systems are sorts of shared collateral structure which has been in demand from its inception. In these systems, clients are able to access existing services based on their needs and without knowing where the service is located and how it is delivered, and only pay for the service used. Like other systems, there are challenges in the cloud computing system. Because of a wide array of clients and the variety of services available in this system, it can be said that the issue of scheduling and, of course, energy consumption is essential challenge of this system. Therefore, it should be properly provided to users, which minimizes both the cost of the provider and consumer and the energy consumption, and this requires the use of an optimal scheduling algorithm. In this paper, we present a two-step hybrid method for scheduling tasks aware of energy and time called Genetic Algorithm and Energy-Conscious Scheduling Heuristic based on the Genetic Algorithm. The first step involves prioritizing tasks, and the second step consists of assigning tasks to the processor. We prioritized tasks and generated primary chromosomes, and used the Energy-Conscious Scheduling Heuristic model, which is an energy-conscious model, to assign tasks to the processor. As the simulation results show, these results demonstrate that the proposed algorithm has been able to outperform other methods.

/pdf/multi-objective-hybrid-genetic-algorithm-for-task-scheduling-3t54z6nszm.pdf

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Thank you for reading blondie24 playing at the edge of ai. As you may know, people have search hundreds times for their chosen readings like this blondie24 playing at the edge of ai, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some infectious virus inside their computer. blondie24 playing at the edge of ai is available in our book collection an online access to it is set as public so you can download it instantly. Our books collection hosts in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the blondie24 playing at the edge of ai is universally compatible with any devices to read.

Blondie24 Playing At The Edge Of Ai

Humans' perception of visual complexity is often regarded as one of the key principles of aesthetic order, and is intimately related to the physiological, neurological and, possibly, psychological characteristics of the human mind For these reasons, creating accurate computational models of visual complexity is a demanding task Building upon on previous work in the field (Forsythe et al, 2011; Machado et al, 2015) we explore the use of Machine Learning techniques to create computational models of visual complexity For that purpose, we use a dataset composed of 800 visual stimuli divided into five categories, describing each stimulus by 329 features based on edge detection, compression error and Zipf's law In an initial stage, a comparative analysis of representative state-of-the-art Machine Learning approaches is performed Subsequently, we conduct an exhaustive outlier analysis We analyze the impact of removing the extreme outliers, concluding that Feature Selection Multiple Kernel Learning obtains the best results, yielding an average correlation to humans' perception of complexity of 071 with only twenty-two features These results outperform the current state-of-the-art, showing the potential of this technique for regression

Visual complexity modelling based on image features fusion of multiple kernels.

Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.

A review on machine learning approaches and trends in drug discovery.

Artificial intelligence applied to conceptual design. A review of its use in architecture

In this article, we perform an exhaustive analysis of the use of Artificial Neural Networks and Deep Learning in the Visual Arts. We begin by introducing changes in Artificial Intelligence over the years and examine in depth the latest work carried out in prediction, classification, evaluation, generation, and identification through Artificial Neural Networks for the different Visual Arts. While we highlight the contributions of photography and pictorial art, there are also other uses for 3D modeling, including video games, architecture, and comics. The results of the investigations discussed show that the use of Artificial Neural Networks in the Visual Arts continues to evolve and have recently experienced significant growth. To complement the text, we include a glossary and table with information about the most commonly employed image datasets.

Artificial Neural Networks and Deep Learning in the Visual Arts: a review

An important topic in evolutionary art is the development of systems that can mimic the aesthetics decisions made by human begins, e.g., fitness evaluations made by humans using interactive evolution in generative art. This paper focuses on the analysis of several datasets used for aesthetic prediction based on ratings from photography websites and psychological experiments. Since these datasets present problems, we proposed a new dataset that is a subset of DPChallenge.com. Subsequently, three different evaluation methods were considered, one derived from the ratings available at DPChallenge.com and two obtained under experimental conditions related to the aesthetics and quality of images. We observed different criteria in the DPChallenge.com ratings, which had more to do with the photographic quality than with the aesthetic value. Finally, we explored learning systems other than state-of-the-art ones, in order to predict these three values. The obtained results were similar to those using state-of-the-art procedures.

/pdf/avoiding-the-inherent-limitations-in-datasets-used-for-5bjfigtyec.pdf

Avoiding the Inherent Limitations in Datasets Used for Measuring Aesthetics When Using a Machine Learning Approach

Automatic prediction of the aesthetic value of images has received increasing attention in recent years. This is due, on the one hand, to the potential impact that predicting the aesthetic value has on practical applications. Even so, it remains a difficult task given the subjectivity and complexity of the problem. An image aesthetics assessment system was developed in recent years by our research group. In this work, its potential to be applied in commercial tasks is tested. With this objective, a set of three portals and three real estate agencies in Spain were taken as case studies. Images of their websites were taken to build the experimental dataset and a validation method was developed to test their original order with another proposed one according to their aesthetic value. So, in this new order, the images that have the high aesthetic score by the AI system will occupy the first positions of the portal. Relevant results were obtained, with an average increase of 52.54% in the number of clicks on the ads, in the experiment with Real Estate portals. A statistical analysis prove that there is a significant difference in the number of clicks after selecting the images with the AI system.

/pdf/validation-of-an-aesthetic-assessment-system-for-commercial-1gqgii7i.pdf

Nereida Rodriguez-Fernandez

Papers

A review on machine learning approaches and trends in drug discovery.

Artificial intelligence applied to conceptual design. A review of its use in architecture

Artificial Neural Networks and Deep Learning in the Visual Arts: a review

Avoiding the Inherent Limitations in Datasets Used for Measuring Aesthetics When Using a Machine Learning Approach

Validation of an Aesthetic Assessment System for Commercial Tasks