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Even though the various features of satirical language have been studied in computational linguistics, most of the research works have relied on the performance of the single machine learning algorithm. However, the implicit traits embedded in the language demand more certain, precise and accurate combination powers of an individual algorithm. In this study, we analyzed the performance of emotion-based...
In this paper, we try to recognize negative emotions (sadness and disgust) of human affecting driving by using physiological signals that are commonly used to deal with human emotions. To do this, emotional stimuli are used to induce sadness and disgust, and emotion recognition is performed based on the feature vector extracted from the physiological signals collected on the induced emotion by a stimulus...
Despite widespread use of commercial anti-virus products, the number of malicious files detected on home and corporate computers continues to increase at a significant rate. Recently, anti-virus companies have started investing in machine learning solutions to augment signatures manually designed by analysts. A malicious file's determination is often represented as a hierarchical structure consisting...
With the development of the aviation industry and the improvement of people's living standard, more and more people choose aircraft as their way of travel, but the airline adjusts the price according to the revenue management in real time. The purpose of this paper is to design different decision-making tools from the customer's perspective, and to provide customers with the relevant information needed...
One of the most discussed schedule in Brazil is Education, and with it comes a lot of problems to solve. This project use of artificial intelligence, but precisely in the area of Machine Learning, to enable a solution, in relation to student monitoring. The research has done with students of technical high school education at the Federal Institute of Gois — Brazil, to find a method which would be...
Advances in highly multi-parametric measurements by mass cytometry have made possible the accurate detection of acute myeloid leukemia (AML) cells in complex cell populations. However, current informatics methods bottlenecks data processing by being labor-intensive, time-consuming, and prone to user bias. To address these problems, major efforts have been made to automate the detection of AML cells...
Network security has become a very important issue and attracted a lot of study and practice. To detect or prevent network attacks, a network intrusion detection (NID) system may be equipped with machine learning algorithms to achieve better accuracy and faster detection speed. One of the major advantages of applying machine learning to network intrusion detection is that we don't need expert knowledge...
Software Defined Networking (SDN) is a new promising networking concept which has a centralized control over the network and separates the data and control planes. This new approach provides abstraction of lower-level functionality and allows the network administrators to initialize, control, change, and manage network behavior programmatically. The centralized control, being the major advantage of...
Classification of different tumor type are of great significance in problems cancer prediction. Choosing the most relevant qualities from huge microarray expression is very important. It is a most explored subject in bioinformatics because of its hugeness to move forward humans understanding of inherent causing cancer mechanism. In this paper, we aim to classify leukaemia cells. Our approach relies...
The application of various statistical machine learning methods for the identification of bi-heterocyclic drugs that are based on the THz spectra is presented. A comparison of classification efficiency with six algorithms (LDA, QDA, SVM, Naive Bayes, KNN with Euclidean metrics and the cosine similarity) is shown and a complete THz system allowing for the identification of drugs with an efficiency...
This paper describes a new approach to building the query based relevance sets (qrels) or relevance judgments for a test collection automatically without using any human intervention. The methods we describe use supervised machine learning algorithms, namely the Naïve Bayes classifier and the Support Vector Machine (SVM). We achieve better Kendall's tau and Spearman correlation results between the...
Based on the spectral data from SDSS, Kernel Support Vector Machines (K-SVM) is applied to classify quasars from other celestial body. Firstly, the basic theory of the SVM(Support Vector Machine) with relaxation factor and kernel function is introduced. Then, the main parameters are designed and selected. Finally, the method is applied to the classification and identification of the quasars. The classification...
This paper presents a new pruning extreme learning machine (N-PELM) algorithm which can generate a compact single-hidden-layer neural network (SLNN) by automatically pruning the number of hidden nodes while keep high accuracy. The proposed N-PELM algorithm initializes a SLNN by using extreme learning machine (ELM) algorithm given superfluous number of hidden nodes. The following part consists of two...
This paper presents the development and implementation of a cloud detection algorithm for Proba-V. Accurate and automatic detection of clouds in satellite scenes is a key issue for a wide range of remote sensing applications. With no accurate cloud masking, undetected clouds are one of the most significant sources of error in both sea and land cover biophysical parameter retrieval. The objective of...
Stereotypic behaviours are present in both human and nonhuman primates. Usually, these behaviours are a welfare indicator. However, the stereotypic behaviours may be also a symptom of some mental disorder in the humans. A specific case is Autism Spectrum Disorder (ASD). The individuals with ASD may exhibit stereotypic behaviours through some gestures. The classic stereotyped gestures of autism are:...
Control and monitoring of asthma progress is highly important for patient's quality of life and healthcare management. Emerging tools for self-management of various chronic diseases have the potential to support personalized patient guidance. This work presents the design aspects of the myAirCoach decision support system, with focus on the assessment of three machine learning approaches as support...
We consider a declarative framework for machine learning where concepts and hypotheses are defined by formulas of a logic over some “background structure”. We show that within this framework, concepts defined by first-order formulas over a background structure of at most polylogarithmic degree can be learned in polylogarithmic time in the “probably approximately correct” learning sense.
Smart materials are of growing interest in research as well as industry due to its wide range of possible applications. Combined with a mass production enabled manufacturing of these materials, its application is further increased. A current research project addresses the integration of piezo ceramic elements into metal sheets to develop a smart touch-alike input device. The aim is to develop a low-cost...
In order to improve the performance of the base classifier in the process of AdaBoost algorithm and simplify the complexity of the whole ensemble learning system, this paper presents a SVM ensemble method based on an improved iteration process of Adaboost algorithm. The improved Adaboost algorithm is added with methods of adding sample selection and feature selection in its iterative process in order...
In order to enhance and/or improve recovery after stroke, rehabilitation needs to start early and be monitored by continuous and recurrent long-term interventions in the clinic and home setting. The elderly is a high risk stroke group with advancing age, resulting in increasing demand of strengthened resource of hospitals and physiotherapist. The residential rehabilitation for stroke patients would...
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