Brain tumor classification in magnetic resonance imaging images using convolutional neural network

Deep learning (DL) is a subfield of artificial intelligence (AI) used in several sectors, such as cybersecurity, finance, marketing, automated vehicles, and medicine. Due to the advancement of computer performance, DL has become very successful. In recent years, it has processed large amounts of data, and achieved good results, especially in image analysis such as segmentation and classification. Manual evaluation of tumors, based on medical images, requires expensive human labor and can easily lead to misdiagnosis of tumors. Researchers are interested in using DL algorithms for automatic tumor diagnosis. convolutional neural network (CNN) is one such algorithm. It is suitable for medical image classification tasks. In this paper, we will focus on the development of four sequential CNN models to classify brain tumors in magnetic resonance imaging (MRI) images. We followed two steps, the first being data preprocessing and the second being automatic classification of preprocessed images using CNN. The experiments were conducted on a dataset of 3,000 MRI images, divided into two classes: tumor and normal. We obtained a good accuracy of 98,27%, which outperforms other existing models

A robust speech enhancement method in noisy environments

Speech enhancement aims to eliminate or reduce undesirable noises and distortions, this processing should keep features of the speech to enhance the quality and intelligibility of degraded speech signals. In this study, we investigated a combined approach using single-frequency filtering (SFF) and a modified spectral subtraction method to enhance single-channel speech. The SFF method involves dividing the speech signal into uniform subband envelopes, and then performing spectral over-subtraction on each envelope. A smoothing parameter, determined by the a-posteriori signal-to-noise ratio (SNR), is used to estimate and update the noise without the need for explicitly detecting silence. To evaluate the performance of our algorithm, we employed objective measures such as segmental SNR (segSNR), extended short-term objective intelligibility (ESTOI), and perceptual evaluation of speech quality (PESQ). We tested our algorithm with various types of noise at different SNR levels and achieved results ranging from 4.24 to 15.41 for segSNR, 0.57 to 0.97 for ESTOI, and 2.18 to 4.45 for PESQ. Compared to other standard and existing speech enhancement methods, our algorithm produces better results and performs well in reducing undesirable noises

Emotion recognition based on the energy distribution of plosive syllables

We usually encounter two problems during speech emotion recognition (SER): expression and perception problems, which vary considerably between speakers, languages, and sentence pronunciation. In fact, finding an optimal system that characterizes the emotions overcoming all these differences is a promising prospect. In this perspective, we considered two emotional databases: Moroccan Arabic dialect emotional database (MADED), and Ryerson audio-visual database on emotional speech and song (RAVDESS) which present notable differences in terms of type (natural/acted), and language (Arabic/English). We proposed a detection process based on 27 acoustic features extracted from consonant-vowel (CV) syllabic units: \ba, \du, \ki, \ta common to both databases. We tested two classification strategies: multiclass (all emotions combined: joy, sadness, neutral, anger) and binary (neutral vs. others, positive emotions (joy) vs. negative emotions (sadness, anger), sadness vs. anger). These strategies were tested three times: i) on MADED, ii) on RAVDESS, iii) on MADED and RAVDESS. The proposed method gave better recognition accuracy in the case of binary classification. The rates reach an average of 78% for the multi-class classification, 100% for neutral vs. other cases, 100% for the negative emotions (i.e. anger vs. sadness), and 96% for the positive vs. negative emotions

Investigating the residential electricity consumption-income nexus in Morocco: a stochastic impacts by regression on population, affluence, and technology analysis

In a comprehensive LMDI-STIRPAT-ARDL framework, this research investigates the residential electricity consumption (REC)-income nexus in Morocco for the period 1990 to 2018. The logarithmic mean Divisia index (LMDI) results show that economic activity and electricity intensity are the leading drivers of Morocco’s REC, followed by population and residential structure. And then, the LMDI analysis was combined with stochastic impacts by regression on population, affluence, and technology (STIRPAT) analysis and the bounds testing approach to search for a long-run equilibrium relationship. The empirical results show that REC, economic growth, urbanization, and electricity intensity are cointegrated. The results further show that there exists a U-shaped relationship between per capita gross domestic product (GDP) and REC: an increase in per capita GDP reduces REC initially; but, after reaching a turning point (the GDPPC level of 17,145.22 Dh), further increases in per capita GDP increase REC. Regarding urbanization, the results reveal that it has no significant impact on Morocco’s REC. The stability parameters of the short and long-term coefficients of residential electricity demand function are tested. The results of these tests showed a stable pattern. Finally, based on the findings mentioned above, policy implications for guiding the country's development and electricity planning under energy and environmental constraints are given

A Miniaturized Patch Antenna Designed and Manufactured Using Slot's Technique for RFID UHF Mobile Applications

In this research work, a novel compact antenna with rectangular slots is presented for radio frequency identification (RFID) handled applications in the ultrahigh frequency (UHF) band that can be manufactured and integrated into RFID readers without difficult. A prototype demonstrating the aforementioned features was constructed and measured. The proposed antenna is fed by 50-Ω coaxial cable and printed on a 1.6mm thick FR4 substrate which has a small size and occupies a volume of 68×66 ×1.6mm3. The patch, the feed-line and ground plane are made of PEC (Perfect Electric Conductor) with a thickness of 0.035 mm. Measured results indicate that the proposed antenna has a good impedance matching characteristic ranging from 889 to 939MHz, which covers the USA RFID-band (902–928MHz), the Chinese RFID-operating-band (920–924.5MHz), and the Korea and Japan RFID-band (917–923.5MHz). These results were achieved by the insertion of slots in the compact structure of the antenna. The electromagnetic simulators HFSS (High Frequency structure simulator) and CST (Computer Simulation Technology) microwave studio were used for the design, modeling and simulation of the antenna. The focus of the study of our antenna was on the parameters of return loss, bandwidth, Voltage Standing Wave Ratio (VSWR), input impedance and gain

Energy use and CO2 emissions of the Moroccan transport sector

In this paper, optimized models based on two different machine learning (ML) methods were developed to forecast the transport energy consumption (TEC) and carbon dioxide (CO2) emissions in Morocco by 2030. More precisely, artificial neural networks (ANN) and support vector regression (SVR) were used for modelling non-linear TEC and CO2 emissions data. This study uses data from 1990 to 2020 and employs various independent parameters, including population, gross domestic product, urbanization rate, evolution of the number of vehicles, and the number of electric vehicle introductions. Four statistical metrics are derived to assess the effectiveness of the ML algorithms used. The forecasts for 2030 were based on six scenarios, including three scenarios for the growth of gross domestic product (GDP) and two scenarios for the evolution of electric cars’ introduction into Moroccan vehicle fleet. The ANN model outputs showed that a decrease in TEC and CO2 emissions is expected until 2030. However, the SVR model predicts outputs values close to those in 2020. The study's results also indicate that: i) TEC and transport CO2 emissions are positively impacted by economic growth in Morocco and ii) electric vehicles will be essential components enabling substantial reductions in overall CO2 emissions in future transport systems

Emotion recognition from syllabic units using k-nearest-neighbor classification and energy distribution

In this article, we present an automatic technique for recognizing emotional states from speech signals. The main focus of this paper is to present an efficient and reduced set of acoustic features that allows us to recognize the four basic human emotions (anger, sadness, joy, and neutral). The proposed features vector is composed by twenty-eight measurements corresponding to standard acoustic features such as formants, fundamental frequency (obtained by Praat software) as well as introducing new features based on the calculation of the energies in some specific frequency bands and their distributions (thanks to MATLAB codes). The extracted measurements are obtained from syllabic units’ consonant/vowel (CV) derived from Moroccan Arabic dialect emotional database (MADED) corpus. Thereafter, the data which has been collected is then trained by a k-nearest-neighbor (KNN) classifier to perform the automated recognition phase. The results reach 64.65% in the multi-class classification and 94.95% for classification between positive and negative emotions

Characterization of Arabic sibilant consonants

The aim of this study is to develop an automatic speech recognition system in order to classify sibilant Arabic consonants into two groups: alveolar consonants and post-alveolar consonants. The proposed method is based on the use of the energy distribution, in a consonant-vowel type syllable, as an acoustic cue. The application of this method on our own corpus reveals that the amount of energy included in a vocal signal is a very important parameter in the characterization of Arabic sibilant consonants. For consonants classifications, the accuracy achieved to identify consonants as alveolar or post-alveolar is 100%. For post-alveolar consonants, the rate is 96% and for alveolar consonants, the rate is over 94%. Our classification technique outperformed existing algorithms based on support vector machines and neural networks in terms of classification rate

Classification of Arabic fricative consonants according to their places of articulation

Many technology systems have used voice recognition applications to transcribe a speaker’s speech into text that can be used by these systems. One of the most complex tasks in speech identification is to know, which acoustic cues will be used to classify sounds. This study presents an approach for characterizing Arabic fricative consonants in two groups (sibilant and non-sibilant). From an acoustic point of view, our approach is based on the analysis of the energy distribution, in frequency bands, in a syllable of the consonant-vowel type. From a practical point of view, our technique has been implemented, in the MATLAB software, and tested on a corpus built in our laboratory. The results obtained show that the percentage energy distribution in a speech signal is a very powerful parameter in the classification of Arabic fricatives. We obtained an accuracy of 92% for non-sibilant consonants /f, χ, ɣ, ʕ, ћ, and h/, 84% for sibilants /s, sҁ, z, Ӡ and ∫/, and 89% for the whole classification rate. In comparison to other algorithms based on neural networks and support vector machines (SVM), our classification system was able to provide a higher classification rate

Backstepping nonlinear control to maximize energy capture in a variable speed wind turbine

We are considering the problem of maximum power point tracking MPPT in wind energy conversion system (WECS). The paper proposes a new control strategy to maximize the wind aerodynamic energy captured in variable speed wind turbine with a separately excited DC-Generator and transformed to the battery through a controlled DC-DC converter. The proposed strategy controls the stip speed ratio via the rotor angular speed to an optimum point at wich the power coefficient is maximal. The controller is designed using the backstepping technique. A formal analysis based on lyapunov stability is developed to describe the control system performances. In addition to closed-loop global asymptotic stability, it is proved that the controller actually meets the MPPT requirement. The above results are confirmed by simulations.