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A gas sensor array is used for gas analysis to aid in an inspection. The signals from the sensor array are fed into machine learning models for learning and classification. These signals are characterized by time series fluctuating according to the environment or drift. When an unseen pattern is entered, the classification may be incorrect,…

The classification of static or mobile objects, from a signal or an image containing information as to their structure or their form, constitutes a constant concern of specialists in the electronic field. The remarkable progress made in past years, particularly in the development of neural networks and artificial intelligence systems, has further accentuated this trend.…

With the advent of the Internet of Things Technologies (IOT), smart homes, and smart city applications, E-Nose was created. Almost of gas sensors consisting the electronic nose system suffer from cross sensitivity and lack of selectivity. Coupling smart gas sensors with artificial intelligence algorithms can thus empower conventional gas sensing technologies and increase accuracy in…

Recitation and listening of the Holy Quran with Tajweed is an essential activity as a Muslim and is a part of the faith. In this article, we use a machine learning approach for the Quran Reciter recognition. We use the database of Twelve Qari who recites the last Ten Surah of Quran. The twelve Qari…

Person recognition using thermal imaging, multi-biometric traits, with groups of feature filters and classifiers, is the subject of this paper. These were used to tackle the problems of biometric systems, such as a change in illumination and spoof attacks. Using a combination of, hard and soft-biometric, attributes in thermal facial images. The hard-biometric trait, of…

Currently, one of the challenges of educational institutions is drop-out student issues. Several factors have been found and determined potentially capable to stimulate dropouts. Many researchers have been applied data mining methods to analyze, predict dropout students and also optimize finding dropout variables in advance. The main objective of this study is to find the…

From the collection of supervised machine learning technique, an ensemble procedure is used in Random Forest. In the arena of Data mining, there is an excellent claim for machine learning techniques. Random Forest has tremendous latent of becoming a widespread technique for forthcoming classifiers as its performance has been found analogous with ensemble techniques bagging…

Vowel classification is an essential part of speech recognition. In classical studies, this problem is mostly handled by using spectral domain features. In this study, a novel approach is proposed for vowel classification based on the visual features of speech waveforms. In sound vocalizing, the position of certain organs of the human vocal system such…

Distraction, drowsiness, and fatigue are the main factors of car accidents recently. To solve such problems, an Eye-tracking system based on camera is proposed in this paper. The system detects the driver’s Distraction or sleepiness and gives an alert to the driver as an assistance system. The camera best position is chosen to be on…

Early detection and treatment of cervical cancer is crucial to patients’ recovery with a reported success rate of nearly 100%. Presently, Pap smear test which is a visual inspection of cells collected from the ectocervix is the screening tool mainly used in cancer prevention programs. The Pap smear is relatively easy to handle however, it…

Feature extraction is taking a very vital and essential part of bio-signal processing. We need to choose one of two paths to identify and select features in any system. The most popular track is engineering handcrafted, which mainly depends on the user experience and the field of application. While the other path is feature learning,…

Hereby in this paper, we are going to refer image classification. The main issue in image classification is features extraction and image vector representation. We expose the Bag of Features method used to find image representation. Class prediction accuracy of varying classifiers algorithms is measured on Caltech 101 images. For feature extraction functions we evaluate…

This paper is an extended version of the paper originally presented at the International Conference on Machine Learning and Applications (ICMLA 2016), which proposes the construction of classifiers, based on the application of machine learning techniques, to identify defaulting clients with credit recovery potential. The study was carried out in 3 segments of a Bank’s…

The research in Neuro-Prosthetics is gaining more significance and popularity as the advancement in prosthetics control allows amputees to perform even more tasks. Indeed, the improvement of classification accuracy is a challenge in prosthetics control. In this research, a system is developed in order to improve the multiclass classification rate. Two classifiers namely Artificial Neural…

Classification of EEG signals in self-paced Brain Computer Interfaces (BCI) is an extremely challenging task. The main difficulty stems from the fact that start time of a control task is not defined. Therefore it is imperative to exploit the characteristics of the EEG data to the extent possible. In sensory motor self-paced BCIs, while performing…

Feature extraction is playing a significant role in bio-signal processing. Feature identification and selection has two approaches. The standard method is engineering handcraft which is based on user experience and application area. While the other approach is feature learning that based on making the system identify and select the best features suit the application. The…

In the recent years, the fast development and the exponential utilization of social networks have prompted an expansion of social Computing. In social networks users are interconnected by edges or links, where Facebook, twitter, LinkedIn are most popular social networks websites. Due to the growing popularity of these sites they serve as a target for…

In this paper, we evaluate a list of classifiers in order to use them in the detection of vandalism by focusing on metadata features. Our work is focused on two low resource data sets (Simple English and Albanian) from Wikipedia. The aim of this research is to prove that this form of vandalism detection applied…

This paper analyses the impact of current trend in applying machine learning in detection of vandalism, with the specific aim of analyzing the impact of the class imbalance in Wikipedia articles. The class imbalance problem has the effect that almost all the examples are labelled as one class (legitimate editing); while far fewer examples are…