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This paper presents an inference algorithm for knowledge bases with a rule cluster structure. The research includes the study of the efficiency of inference, measured by the number of cases in which the inference was successful. Finding a rule ...

Nowadays data streams from different sources, like blockchain‐based and traditional financial transactions, social networks, and interconnected Internet of Things (IoT) devices, are becoming increasingly large in volume and the need to recognize ...

Evaluating the health of a peach tree based on the characteristics of its leaves is a common practice in botany and agriculture. The length, width, area, and perimeter of peach leaves represent their basic physical characteristics, which can be used ...

Clustering multidimensional points is a fundamental data mining task, with applications in many fields, such as astronomy, neuroscience, bioinformatics, and computer vision. The goal of clustering algorithms is to group similar objects together. Density-...

The increasing number of objectives poses a great challenge upon many-objective optimization algorithms (MaOOAs) when solving many-objective optimization problems (MaOOPs), since it is rather difficult to obtain well-distributed solutions with tight ...

Modular robots are defined as autonomous kinematic machines with variable morphology. They are composed of several thousands or even millions of modules that are able to coordinate to behave intelligently. Clustering the modules in modular robots has many ...

Nowadays, omics techniques have been widely used to study cancer and other related problems, but there are many cancer subtypes in a certain type of cancer which are unclear or even completely unknown, and thus unsupervised learning are only suitable ...

In Wireless Sensor Networks (WSNs), effective transmission with acceptable degradation in the power of sensor nodes is a key challenge. In a large network, holdup is bound to occur in communicating superfluous data. The aforementioned issues namely, ...

Density-based clustering algorithms are commonly adopted when arbitrarily shaped clusters exist. Usually, they do not need to know the number of clusters in prior, which is a big advantage. Conventional density-based approaches such as DBSCAN, utilize ...

Big data analysis requires the presence of large computing powers, which is not always feasible. And so, it became necessary to develop new clustering algorithms capable of such data processing. This study proposes a new parallel clustering algorithm ...

At the present time, a great interest has been shown by the research and the industrial community concerning smart grid communications where important technical advances have arisen as a consequence. Concretely, one of the most important goals of RF ...

The primary challenge in template-free protein structure prediction is controlling the quality of computed tertiary structures, also known as decoys. While research on how to do so is highly active, the main rule of thumb is to generate as many decoys ...

With the rapid growth of social media platform, hotspot detection has become an increasing important issue in the We media era. As for researchers, the emergence of all kinds of public opinion crises is one of the greatest challenges. In order to make a ...

In 2010 Durda, Caron, and Buchanan published a paper in INFOR: Information systems and Operational Research, entitled: An application of operational research to computational linguistics: Word ambiguity. In this article the authors developed “a new ...

Digital forensics faces several challenges in examining and analyzing data due to an increasing range of technologies at people's disposal. The investigators find themselves having to process and analyze many systems manually (e.g. PC, laptop, ...

In this paper, a novel approach for the analysis of the movement evolution in patients with Parkinson's disease is presented. The system offers the capabilities of detecting significant degradations in the motor-skills of the patients according to the ...

Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. The literature presents several approaches to identifying the emergence of ...

Vehicular CrowdSensing (VCS) is an emerging solution designed to remotely collect data from smart vehicles. It enables a dynamic and large-scale phenomena monitoring just by exploring the variety of technologies which have been embedded in modern cars. ...

Twitter is a popular microblogging platform, with 310 million monthly active users as of the first quarter of 2016. It is a rapidly growing microblogging platform where people share opinions, news on any topic of their interest. More than 7000 tweets are ...

It is a promising vision to exploit white spaces, that is, vacant VHF and UHF TV channels, to meet the rapidly growing demand for wireless data services in both outdoor and indoor scenarios. While most prior works have focused on outdoor white space, ...