Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

An Introduction to MultiAgent Systems.

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Linear And Nonlinear Programming

Thirty-five years since its introduction into clinical use, valproate (valproic acid) has become the most widely prescribed antiepileptic drug (AED) worldwide. Its pharmacological effects involve a variety of mechanisms, including increased γ-aminobutyric acid (GABA)-ergic transmission, reduced release and/or effects of excitatory amino acids, blockade of voltage-gated sodium channels and modulation of dopaminergic and serotoninergic transmission.

Pharmacological and therapeutic properties of valproate: a summary after 35 years of clinical experience.

This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

/pdf/online-short-term-solar-power-forecasting-1ljgpbshkb.pdf

Online Short-term Solar Power Forecasting

A review of edge computing reference architectures and a new global edge proposal

Today’s globalized and highly competitive world market has broadened the spectrum of requirements in all the sectors of the agri-food industry. This paper focuses on the dairy industry, on its need to adapt to the current market by becoming more resource efficient, environment-friendly, transparent and secure. The Internet of Things (IoT), Edge Computing (EC) and Distributed Ledger Technologies (DLT) are all crucial to the achievement of those improvements because they allow to digitize all parts of the value chain, providing detailed information to the consumer on the final product and ensuring its safety and quality. In Smart Farming environments, IoT and DLT enable resource monitoring and traceability in the value chain, allowing producers to optimize processes, provide the origin of the produce and guarantee its quality to consumers. In comparison to a centralized cloud, EC manages the Big Data generated by IoT devices by processing them at the network edge, allowing for the implementation of services with shorter response times, and a higher Quality of Service (QoS) and security. This work presents a platform oriented to the application of IoT, Edge Computing, Artificial Intelligence and Blockchain techniques in Smart Farming environments, by means of the novel Global Edge Computing Architecture, and designed to monitor the state of dairy cattle and feed grain in real time, as well as ensure the traceability and sustainability of the different processes involved in production. The platform is deployed and tested in a real scenario on a dairy farm, demonstrating that the implementation of EC contributes to a reduction in data traffic and an improvement in the reliability in communications between the IoT-Edge layers and the Cloud.

An intelligent Edge-IoT platform for monitoring livestock and crops in a dairy farming scenario

Integrating hardware agents into an enhanced multi-agent architecture for Ambient Intelligence systems

The significance that ambient intelligence (AmI) has acquired in recent years requires the development of innovative solutions. In this sense, the development of AmI-based systems requires the creation of increasingly complex and flexible applications. One of the most important aspects in AmI is the use of context-aware technologies in order to perceive stimuli from both the users and the environment. Thus, the information obtained must be managed by intelligent and self-adaptable technologies in order to provide an adequate interaction between the users and their environment. Agents and multi-agent systems are one of these technologies. The agents have characteristics such as autonomy, reasoning, reactivity, social abilities and pro-activity which make them appropriate for developing dynamic and distributed systems based on AmI, as they possess the capability of adapting themselves to the users and environmental characteristics. This paper presents several case studies where agents and context-aware technologies have been implemented to build AmI-based systems. These case studies expand the possibilities of AmI and get closer to its vision.

/pdf/agents-and-ambient-intelligence-case-studies-zk10goso08.pdf

Agents and ambient intelligence: case studies

The Internet of Things (IoT) has become one of the most widely research paradigms, having received much attention from the research community in the last few years. IoT is the paradigm that creates an internet-connected world, where all the everyday objects capture data from our environment and adapt it to our needs. However, the implementation of IoT is a challenging task and all the implementation scenarios require the use of different technologies and the emergence of new ones, such as Edge Computing (EC). EC allows for more secure and efficient data processing in real time, achieving better performance and results. Energy efficiency is one of the most interesting IoT scenarios. In this scenario sensors, actuators and smart devices interact to generate a large volume of data associated with energy consumption. This work proposes the use of an Edge-IoT platform and a Social Computing framework to build a system aimed to smart energy efficiency in a public building scenario. The system has been evaluated in a public building and the results make evident the notable benefits that come from applying Edge Computing to both energy efficiency scenarios and the framework itself. Those benefits included reduced data transfer from the IoT-Edge to the Cloud and reduced Cloud, computing and network resource costs.

https://www.mdpi.com/1424-8220/19/15/3353/pdf

Ricardo S. Alonso

Papers

A review of edge computing reference architectures and a new global edge proposal

An intelligent Edge-IoT platform for monitoring livestock and crops in a dairy farming scenario

Integrating hardware agents into an enhanced multi-agent architecture for Ambient Intelligence systems

Agents and ambient intelligence: case studies

Edge Computing, IoT and Social Computing in Smart Energy Scenarios.