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Social media are increasingly being used as sources in mainstream news coverage. However, since news is so rapidly updating it is very easy to fall into the trap of believing everything as truth. Spam content usually refers to the information that goes viral and skews users' views on subjects. To this end, this paper introduces a new approach for detecting spam tweets using Cost-Sensitive Classifier...
In recent years, analysis of remote sensing imagery is imperatives in the domain of environmental and climate monitoring primarily for the application of detecting and managing a natural disaster. Satellite imagery or aerial imagery is beneficial because it can widely capture the condition of the surface ground and provides a massive amount of information in a piece of satellite imagery. Since obtaining...
Tor is an anonymous Internet communication system based on the second generation of onion routing network protocol. Using Tor is really difficult to trace the users Internet activity: this is the reason why the usage of Tor is intended in order to protect the privacy of users, their freedom and the ability to conduct confidential communications without being monitored. Tor is even more used by cyber...
Radio frequency interference (RFI) is electromagnetic interference (EMI) from signals in the radio frequencies of the electromagnetic spectrum. RFI reduces the sensitivity of radio telescope and produces artefacts in the observed data. We present the result of applying machine learning techniques to detect confidently man made RFI. We confirm that not all the features selected to characterise RFI...
In this paper, we use machine learning for profiling authors of online textual media. We are interested in determining the gender and age of an author. We use two different approaches, one where the features are learned from raw data and one where features are manually extracted.We are interested in understanding how well author profiling works in the wild and therefore we have tested our models on...
Traditional machine learning approaches are based on the premise that the training and testing samples come from a common probability distribution. Transfer learning refers to situations where this assumption does not necessarily hold. Integrating biological data measured on diverse platforms is a major challenge. Transfer learning is a natural candidate for achieving such integration. In this paper,...
Recurrent neural networks with various types of hidden units have been used to solve a diverse range of problems involving sequence data. Two of the most recent proposals, gated recurrent units (GRU) and minimal gated units (MGU), have shown comparable promising results on example public datasets. In this paper, we introduce three model variants of the minimal gated unit which further simplify that...
Epilepsy is defined as a collection of symptoms and clinical signs are emerging due to intermittent brain dysfunction, which occur due to loose or excessive abnormal electrical discharges of neurons in paroxysmal with various etiologies. In this article the implemented software detection of disease epilepsy, characteristics which will represent in the detection of epilepsy and not epilepsy are from...
Genetic mutations are the first warning to the onset of lung cancer. The ability to early predict these mutations could open the door for a targeted treatment options for lung cancer patients. Three top candidate genes previously reported to have the highest frequency of lung cancer mutations. Each gene is encoded as a symbolic sequence of four letters. A novel method for gene representation is introduced...
Multimedia semantic concept detection is one of the major research topics in multimedia data analysis in recent years. Disaster information management needs the assistance of multimedia data analysis to better utilize those disasterrelated information, which has been widely shared by people through the Internet. In this paper, a Feature Affinity based Multiple Correspondence Analysis and Decision...
Among dependency parsing algorithms available in MALTParser and MSTParser, the best accuracy for parsing Indonesian language is achieved by Chu-Liu-Edmonds algorithm. This is due to the long distance relation between head and dependent in Indonesian sentences. Most of inaccuracy parsing results is caused by the non-verb sentence root score where there are many cases in Indonesian sentence having a...
Committees of multilayer neural networks were used to estimate the appropriate surface area and thickness of RF absorbing material needed to achieve a desired quality factor (Q) inside a reverberation chamber. The networks were trained with Bayesian Regularization to avoid overfitting. Monte Carlo cross-validation was used to develop confidence bounds on the accuracy of the network committees.
Color is one of the attributes that play a role in identifying specific objects, color processing including the extraction of information about the spectral properties of the object's surface and look for the best similarity of a set of descriptions which have been known to do an introduction. Therefore, the classification is needed right fuji apples to obtain good quality fruit. Fuzzy model is one...
With the evolution of large computer data, every corner of the society is filled with a variety of text information. Indeed, large data information that need manage by people has been unable to meet the rapid development of society. Therefore, the technology of efficient management and accurate positioning of vast quantities of text information has become a hot topic in the research community. In...
This work aims to investigate the use of deep neural network to detect commercial hobby drones in real-life environments by analyzing their sound data. The purpose of work is to contribute to a system for detecting drones used for malicious purposes, such as for terrorism. Specifically, we present a method capable of detecting the presence of commercial hobby drones as a binary classification problem...
Fully automated defect detection and classification of automobile components are crucial for solving quality and efficiency problems for automotive manufacturers, due to the rising wage, production costs and warranty claims. However, metrological deviations in form still represent unsolved problems using state-of-the-art techniques, especially for forged or casted components with complex geometry...
The rapidly increasing number of elderly people has led to the development of in-home assistive robots for assisting and monitoring elderly people in their daily life. To these ends, indoor scene and human activity recognition is fundamental. However, image processing is an expensive process, in computational, energy, storage and pricing terms, which can be problematic for consumer robots. For this...
This research proposes a reliable machine learning based computational solution for human detection. The proposed model is specifically applicable for illumination-variant natural scenes in big data video frames. In order to solve the illumination variation problem, a new feature set is formed by extracting features using histogram of gradients (HoG) and linear phase quantization (LPQ) techniques,...
In machine learning applications, there are scenarios of having no labeled training data, due to the data being rare or too expensive to obtain. In these cases, it is desirable to use readily available labeled data, that is similar to, but not the same as, the domain application of interest. Transfer learning algorithms are used to build high-performance classifiers, when the training data has different...
Visual inspection process for weld defects still manually operated by human vision, so the result of the test still highly subjective. In this research, the visual inspection process will be done through image processing on the image sequence to make data accuracy more better. CNN as one of the image processing technique can determine the feature automatically which is suitable for this problem in...
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