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Emotional speaker recognition under real life conditions becomes an urgent need for several applications. This paper proposes a novel approach using multiple feature extraction methods and i-vector modeling technique in order to improve emotional speaker recognition under real conditions. The performance of the proposed approach is evaluated on real condition speech signal (IEMOCAP corpus) under clean and noisy environments using various SNR levels. We examined divers known spectral features in speaker recognition (MFCC, LPCC and RASTA-PLP) and performed combined features called MFCC-SDC coefficients. The feature vectors are then classified using the multiclass Support Vector Machines (SVM). Experimental results illustrate good robustness of the proposed system against talking conditions (emotions) and against real life environment (noise). Besides, results reveal that MFCC-SDC features outperforms the conventional MFCCs.

Helitrons are mobile sequences which belong to the class 2 of eukaryotic transposons. Their specificity resides in their mechanism of transposition: the rolling circle mechanism. They play an important role in remodeling proteomes due to their ability to modify existing genes and introducing new ones. A major difficulty in identifying and classifying Helitron families comes from the complex structure, the unspecified length, and the unbalanced appearance number of each Helitron type. The Helitron’s recognition is still not solved in literature. The purpose of this paper is to characterize and classify Helitron types using spectral features and support vector machine (SVM) classification technique. Thus, the helitronic DNA is transformed into a numerical form using the FCGS2 coding technique. Then, a set of spectral features is extracted from the smoothed Fourier transform applied on the FCGS2 signals. Based on the spectral signature and the classification’s confusion matrix, we demonstrated that some specific classes which do not show similarities, such as HelitronY2 and NDNAX3, are easily discriminated with important accuracy rates exceeding 90%. However, some Helitron types have great similarities such as the following: Helitron1, HelitronY1, HelitronY1A, and HelitronY4. Our system is also able to predict them with promising values reaching 70%.

The Helitrons, an important sub-class of the transposable elements (TEs) class II, have been revealed in diverse eukaryotic genomes. They are mobile elements with great impact on genomic evolution. Till today, there is no systematic classification model of helitrons; that’s why we thought of creating an efficient automatic model to identify these sequences. This paper focuses on the discrimination between helitrons and non-helitrons using the Support Vector Machine (SVM). In this study, we use all the SVM kernels and the higher accuracy rates are obtained by reaching the optimal kernels-parameters (d, c and σ). Further, we introduce two methods to represent the genomic sequences in the form of features to be considered later for the classification task: (i) the temporal and the spectral features extracted from the Frequency Chaos Game Signals order 2 (FCGS2) (ii) the features extracted from the Continuous Wavelet Transform (CWT) applied to the FCGS2 signals. The dataset we used regards two types DNA classes in C.elegans: the helitrons and the repetitive DNA sequences that contain microsatellites and do not form helitrons. The classification results prove that the wavelet energy feature is more effective than the FCGS2 features in the helitron’s recognition system. The performance of our system achieves a high recognition rate (Globally accuracy rate) reaching the value of 92.27%.

Emotion recognition from physiological signals attracted the attention of researchers from different disciplines, such as affective computing, cognitive science and psychology. This paper aims to classify emotional statements using peripheral physiological signals based on arousal-valence evaluation. These signals are the Electrocardiogram, Respiration Volume, Skin Temperature and Galvanic Skin Response. We explored the signals collected in the MAHNOB-HCI multimodal tagging database. We defined the emotion into three different ways: two and three classes using 1-9 discrete self-rating scales and another model using 9 emotional keywords to establish the three defined areas in arousal-valence dimensions. To perform the accuracies, we began by removing the artefacts and noise from the signals, and then we extracted 169 features. We finished by classifying the emotional states using the support vector machine. The obtained results showed that the electrocardiogram and respiration volume were the most relevant signals for human’s feeling recognition task. Moreover, the obtained accuracies were promising comparing to recent related works for each of the three establishments of emotion modeling.

The industrial Supervising Control and Data Acquisition, referred by SCADA system, tends to improve its accuracy in detecting faults. In that, it uses fault diagnosis models based mostly on probabilistic methods with close uncertainties. These models are based on a subjective evaluation by comparing the obtained signal to its reference. Therefore, SCADA precision fault detection varies depending on the operation environment, system design and analysis approach among other factors. The contribution of this research work is to propose a smart strategy that will enrich and enhance failure recognition in SCADA systems by integrating two additional models into the classic technique. The first model is a SOM map reduce simple classifier and the second model is an evolutionary recurrent self-organizing neural filter for final decision-making. This integrated paradigm improves results accuracy and robustness against signal interference. The proposed idea involves best details around any remotely listed defect. This study has been conducted on Simulink-Matlab, through the analysis of multi signals emitted by sensors and received by corresponding antennas.

Enhancing the performance of emotional speaker recognition process has witnessed an increasing interest in the last years. This paper highlights a methodology for speaker recognition under different emotional states based on the mul-ticlass Support Vector Machine (SVM) classifier. We compare two feature extraction methods which are used to represent emotional speech utterances in order to obtain best accuracies. The first method known as traditional Mel-Frequency Cepstral Coefficients (MFCC) and the second one is MFCC combined with Shifted-Delta-Cepstra (MFCC-SDC). Experimentations are conducted on IEMOCAP database using two multiclass SVM ap-proaches: One-Against-One (OAO) and One Against-All (OAA). Obtained results show that MFCC-SDC features outperform the conventional MFCC.

INTRODUCTION: Breast cancer (BC) is the most commonly occurring cancer and the second leading cause for women’s disease death. The BC cases are associated with genital mutations which are inherited from older generations or acquired overtime. If the diagnosis is done at the first stage, effects associated with certain treatments can be limited, costs can be saved and the diagnostic time can be minimized. This can also help specialists target the best treatment to increase the rate of cures. Nevertheless, its discovery in patients is very challenging due to silent symptoms aside from the fact the routine screening is not recommended for women under 40 years old.OBJECTIVES: Several efforts are aimed at the BC early detection using machine and deep learning systems. The proposed algorithms use different data types to distinguish between cancerous and non-cancerous cases; as: mammography, ultrasound and MRI (magnetic resonance imaging) images. Then, different learning tools were applied on this data for the classification task. Despite the classification rates which exceed 90%, the major drawback of all these methods is that they are applicable only after the appearance of the cancerous tumors, which reduces the cure rates.METHODS: We propose a new technique for early breast cancer screening. For the data, we focus on cancerous and non-cancerous SNP (Single Nucleotide Polymorphism) protein sequences of the TP53 gene in chromosome 17. This gene is shown to be linked to different single amino acid mutations on which we will shed light here. The method we propose transforms SNP textual sequences into digital vectors via coding. Then, RGB scalogram images are generated using the continuous wavelet transform. A pretreatment of color coefficients is applied to scalograms aiming at creating four different databases. Finally, a CNN deep learning network is used for the binary classification of cancerous and non-cancerous images.RESULTS: During the validation process, we reached good performance with specificity of 97.84%, sensitivity of 96.45%, an overall accuracy of 95.29% and an equal run time of 12 minutes 3 seconds. These values ensure the efficiency of our method.To enhance more these results, we used the ORB feature detection technique. Consequently, the classification rates have been improved to reach 95.9% as accuracyCONCLUSION: Our method will allow significant savings time and lives by detecting the disease in patients whose genetic mutations are beginning to appear.

INTRODUCTION: Early disease detection plays an important role in medical field especially for cancer disease, which helps doctors in diagnosing and identifying the therapeutic process. Aiming to provide assistance, many biological techniques other than machine and deep learning models were proposed. They were applied on a different type of data such as medical images and clinical data. Despite the efficiency of those techniques, they remain costly and need a lot of execution and preparation time, and resources.OBJECTIVES: In this paper, we present a novel method of disease detection analyzing the genes sequences composition.METHODS: We start by extracting k-mer nucleotides as features from gene sequences with the Frequency Chaos Game Representation (FCGR) technique. Since extracted data are huge, we use a DeepInsight model to extract the most representative k-mers.A combination of a transfer learning model, which is Residual neural Network (ResNet), and a support vector machine (SVM) algorithm is then used then to classify samples into 18 cancer disease types.RESULTS: We achieved an accuracy of 0.98 while choosing FCGR6 in feature extraction, and a combination of ResNet50 and SVM in the multinomial classification step, against an accuracy of 0.97 while using ResNet50 with a fully connected layer and FCGR5.CONCLUSION: Defining the gene sequence alterations helps in the disease detection at early stage. Here, we adopt the FCGR method (that gives the frequency of each k-mer) in defining features of the gene sequences. Then, we use deep learning models to deal with the big number of characteristics and predicting different cancer diseases.

Recognizing human emotions through vocal channel has gained increased attention recently. In this paper, we study how used features, and classifiers impact recognition accuracy of emotions present in speech. Four emotional states are considered for classification of emotions from speech in this work. For this aim, features are extracted from audio characteristics of emotional speech using Linear Frequency Cepstral Coefficients (LFCC) and Mel-Frequency Cepstral Coefficients (MFCC). Further, these features are classified using Hidden Markov Model (HMM) and Support Vector Machine (SVM).

Ameliorating the performances of speech recognition system is a challenging problem interesting recent researchers. In this paper, we compare two extraction methods of Mel Frequency Cepstral Coefficients used to represent stressed speech utterances in order to obtain best performances. The first method known as traditional is based on single window (taper) generally the Hamming window and the second one is a novel technique developed with multitapers instead of a single taper. The extracted features are then classified using the multiclass Support Vector Machines. Experimental results on the SUSAS database have shown that the multitaper MFCC features outperform the conventional MFCCs.