Search results for "Intelligence"
showing 10 items of 6959 documents
Morse Description and Geometric Encoding of Digital Elevation Maps
2004
Two complementary geometric structures for the topographic representation of an image are developed in this work. The first one computes a description of the Morse-topological structure of the image, while the second one computes a simplified version of its drainage structure. The topographic significance of the Morse and drainage structures of digital elevation maps (DEMs) suggests that they can been used as the basis of an efficient encoding scheme. As an application, we combine this geometric representation with an interpolation algorithm and lossless data compression schemes to develop a compression scheme for DEMs. This algorithm achieves high compression while controlling the maximum …
Khmer character recognition using artificial neural network
2014
Character Recognition has become an interesting and a challenge topic research in the field of pattern recognition in recent decade. It has numerous applications including bank cheques, address sorting and conversion of handwritten or printed character into machine-readable form. Artificial neural network including self-organization map and multilayer perceptron network with the learning ability could offer the solution to character recognition problem. In this paper presents Khmer Character Recognition (KCR) system implemented in Matlab environment using artificial neural networks. The KCR system described the utilization of integrated self-organization map (SOM) network and multilayer per…
Data Mining in Cancer Research [Application Notes
2010
This article is not intended as a comprehensive survey of data mining applications in cancer. Rather, it provides starting points for further, more targeted, literature searches, by embarking on a guided tour of computational intelligence applications in cancer medicine, structured in increasing order of the physical scales of biological processes.
Improving the k-NCN classification rule through heuristic modifications
1998
Abstract This paper presents an empirical investigation of the recently proposed k-Nearest Centroid Neighbours ( k -NCN) classification rule along with two heuristic modifications of it. These alternatives make use of both proximity and geometrical distribution of the prototypes in the training set in order to estimate the class label of a given sample. The experimental results show that both alternatives give significantly better classification rates than the k -Nearest Neighbours rule, basically due to the properties of the plain k -NCN technique.
A new shape-oriented classification method for UV/VIS-spectra
1996
A new shape-oriented classification method is described. It is shown, how shapes of UV/VIS-spectra can be classified and coded and how a classification technique can be used to improve database search operations for pre-selections or even shape-oriented identifications.
Information Technology Issues in Finland
2020
Information technology (IT) industry is essential in Finland because of its significant export contribution, extensive workforce, and research and innovation contributions. This chapter highlights key issues in this important industry. For instance, IT reliability and efficiency are the top issues necessary for the Finnish IT industry’s competitiveness in a global context. Furthermore, business intelligence and analytics tools, techniques and skills are central to the Finnish IT industry. The industry has a very experienced workforce that is however aging and thus there is a need for training of young personnel to join the industry. Generally, the Finnish IT workers are satisfied with their…
Impacts of Emotional Intelligence and Leadership on Motivation in the German Hotel Industry
2016
This paper aims to clarify the role of leadership and emotional intelligence in the motivational process of employees in the hotel industry. A review of relevant literature is done before a tentative model is presented to experts from the fields of leadership, emotional intelligence and the hotel industry to get their view on the constructed relations. After having been approved by the experts, the model has been tested with structuring equation modelling (SEM) based on a quantitative research among employees and leaders of the hotel industry in Germany. The positive relation among leadership and motivation as well as the moderating effect of emotional intelligence could be substantiated.
Algorithms, Artificial Intelligence and Automated Decisions about Workers and the Risks of Discrimination: The Necessary Collective Governance of Dat…
2018
Big data, algorithms and artificial intelligence currently allow entrepreneurs to process information about their employees in a far more efficient manner and at a much lower cost than has been the case until now. This makes it possible to profile workers automatically and even allows technology itself to replace human resources supervisors and managers and to make decisions that have legal effects on the employees (recruitment, promotion, dismissals, etc.). This entails great risks of discrimination by the technology in command, as well as the defencelessness of the worker, who is unaware of the reasons underlying such a decision. This study analyses the guarantees established in the exist…
Concept Drift Detection Using Online Histogram-Based Bayesian Classifiers
2016
In this paper, we present a novel algorithm that performs online histogram-based classification, i.e., specifically designed for the case when the data is dynamic and its distribution is non-stationary. Our method, called the Online Histogram-based Naïve Bayes Classifier (OHNBC) involves a statistical classifier based on the well-established Bayesian theory, but which makes some assumptions with respect to the independence of the attributes. Moreover, this classifier generates a prediction model using uni-dimensional histograms, whose segments or buckets are fixed in terms of their cardinalities but dynamic in terms of their widths. Additionally, our algorithm invokes the principles of info…
Handling local concept drift with dynamic integration of classifiers : domain of antibiotic resistance in nosocomial infections
2006
In the real world concepts and data distributions are often not stable but change with time. This problem, known as concept drift, complicates the task of learning a model from data and requires special approaches, different from commonly used techniques, which treat arriving instances as equally important contributors to the target concept. Among the most popular and effective approaches to handle concept drift is ensemble learning, where a set of models built over different time periods is maintained and the best model is selected or the predictions of models are combined. In this paper we consider the use of an ensemble integration technique that helps to better handle concept drift at t…