Publications Scientifiques
Permanent URI for this communityhttps://dspace.univ-boumerdes.dz/handle/123456789/10
Browse
4 results
Search Results
Item Artificial Intelligence Based Detection of COVID-19 Pneumonia Using CT Scan and X-ray Images: A Comparative study(Institute of Electrical and Electronics Engineers Inc, 2023) Ilyas, Muhammad; Cherifi, DalilaAccording to a new study, a computer program that was trained to see patterns by analyzing thousands of chest X-rays was able to predict with up to 95% accuracy which patients with coronavirus disease (COVID-19) would develop life-threatening complications within four days. In order to quickly identify patients with COVID-19 whose condition is most likely to deteriorate, hospital physicians and radiologists require tools like our program.Unfortunately, we are fighting one of the worst epidemics ever known to mankind called COVID-2019, a coronavirus-derived pathogen. We see ground-glass opacity in the chest X-ray and CT scan images as a result of fibrosis in the lungs when the virus has reached the lungs. The artificial intelligence techniques can be used to identify and quantify the infection because of the significant differences between infected and non-infected X-ray images. A classification model for interpreting chest X-rays and CT scan images is proposed, which may lead to improved COVID-19 diagnosis. Classifying the chest X-rays into three categories, normal, viral pneumonia, and COVID-19, is our method of classification. Additionally, COVID-19 using CT scan images has higher classification accuracy as compared to x-ray images.Item Classification of Left/Right Hand and Foot Movements from EEG using Machine Learning Algorithms(Institute of Electrical and Electronics Engineers Inc, 2023) Cherifi, Dalila; Berghouti, Baha Eddine; Boubchir, LarbiIn recent years, there has been growing interest in utilizing Electroencephalography (EEG) data and machine learning techniques to develop innovative solutions for individuals with disabilities. The ability to accurately classify hands and foot motion based on EEG signals holds great potential for enabling individuals to regain control and functionality of their disabled parts, improving their quality of life and independence. Making a better solution than the traditional ones that often require physical contact or can be challenging to operate. In our study, we have focused on hands (right/left) and foot motion disabilities, using supervised Machine Learning algorithms for the classification of EEG data related to left/right hand and foot movements; aiming to reach accurate results that can contribute to providing a solution for people with this kind of motion disabilities. Three supervised machine learning algorithms are considered for the EEG classification, namely Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), using Common Spatial Patterns (CSP) algorithm and logarithm of the variance (logvar) for feature extraction. In our experiments, we adopted these algorithms to classify the Motor Imagery EEG dataset for hands and foot movements given in BCI Competition IV. The data we used went through different steps before fitting into the models such as filtering, feature extraction, and discrimination. We achieved significant success in accurately classifying hand movements in the initial experiment, attaining an impressive classification accuracy of up to 97.5% with SVM and LDA. Furthermore, in the multi-classification task involving both hand (right/left) and foot movements, KNN and SVM classifiers yielded commendable results up to 87%. These models can be further used and developed, where a hardware implementation will be done as a further work for this study.Item Performance Evaluation of Machine Learning Algorithms for Smart Irrigation Systems(Institute of Electrical and Electronics Engineers Inc, 2024) Yahia, Amina; Menasri, Wahiba; Cherifi, Dalila; Gacemi, AbderrezakSmart irrigation systems have revolutionized the farming industry by utilizing modern innovations to optimize water usage and handle water scarcity issues. The performance of two machine learning algorithms, Decision Tree and Support Vector Machines (SVM), in categorizing irrigation status in smart irrigation systems is evaluated in this research. The goal is to detect whether or not specified areas or plants have been watered, which is critical for proper irrigation management. Sensors in smart irrigation systems detect environmental data such as temperature, humidity, soil moisture, and rainfall. The obtained data is processed using machine learning methods to classify the irrigation status. For training and evaluation, this study makes use of a large dataset from a real-world smart irrigation system. The results demonstrate the effectiveness of both the Decision Tree and SVM algorithms. Decision Tree excels in terms of precision and recall, allowing for the accurate detection of hydrated and non-watered areas or plants. SVM achieves good accuracy and F1-score, allowing for a complete evaluation of irrigation status. These findings promote smart irrigation systems by emphasizing the importance of machine learning methods for accurate irrigation status classification. The findings help stakeholders choose appropriate algorithms for efficient water management and support sustainable agriculture practices.Item EEG signal feature extraction and classification for epilepsy detection(Slovene Society Informatika, 2022) Cherifi, Dalila; Falkoun, Noussaiba; Ouakouak, Ferial; Boubchir, Larbi; Nait-Ali, AmineEpilepsy is a neurological disorder of the central nervous system, characterized by sudden seizures caused by abnormal electrical discharges in the brain. Electroencephalogram (EEG) is the most common technique used for Epilepsy diagnosis. Generally, it is done by the manual inspection of the EEG recordings of active seizure periods (ictal). Several techniques have been proposed throughout the years to automate this process. In this study, we have developed three different approaches to extract features from the filtered EEG signals. The first approach was to extract eight statistical features directly from the time-domain signal. In the second approach, we have used only the frequency domain information by applying the Discrete Cosine Transform (DCT) to the EEG signals then extracting two statistical features from the lower coefficients. In the last approach, we have used a tool that combines both time and frequency domain information, which is the Discrete Wavelet Transform (DWT). Six different wavelet families have been tested with their different orders resulting in 37 wavelets. The first three decomposition levels were tested with every wavelet. Instead of feeding the coefficients directly to the classifier, we summarized them in 16 statistical features. The extracted features are then fed to three different classifiers k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Artificial Neural Network (ANN) to perform two binary classification scenarios: healthy versus epileptic (mainly from interictal activity), and seizure-free versus ictal. We have used a benchmark database, the Bonn database, which consists of five different sets. In the first scenario, we have taken six different combinations of the available data. While in the second scenario, we have taken five combinations. For Epilepsy detection (healthy vs epileptic), the first approach performed badly. Using the DCT improved the results, but the best accuracies were obtained with the DWT-based approach. For seizure detection, the three methods performed quite well. However, the third method had the best performance and was better than many state-of-the-art methods in terms of accuracy. After carrying out the experiments on the whole EEG signal, we separated the five rhythms and applied the DWT on them with the Daubechies7 (db7) wavelet for feature extraction. We have observed that close accuracies to those recorded before can be achieved with only the Delta rhythm in the first scenario (Epilepsy detection) and the Beta rhythm in the second scenario (seizure detection)