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The rapid spread of SARS-CoV-2 threatens global public health and impedes the operation of healthcare systems. Several studies have been conducted to confirm SARS-CoV-2 infection and examine its risk factors. To produce more effective... more
The rapid spread of SARS-CoV-2 threatens global public health and impedes the operation of healthcare systems. Several studies have been conducted to confirm SARS-CoV-2 infection and examine its risk factors. To produce more effective treatment options and vaccines, it is still necessary to investigate biomarkers and immune responses in order to gain a deeper understanding of disease pathophysiology. This study aims to determine how cytokines influence the severity of SARS-CoV-2 infection. We measured the plasma levels of 48 cytokines in the blood of 87 participants in the COVID-19 study. Several Classifiers were trained and evaluated using Machine Learning and Deep Learning to complete missing data, generate synthetic data, and fill in any gaps. To examine the relationship between cytokine storm and COVID-19 severity in patients, the Shapley additive explanation (SHAP) and the LIME (Local Interpretable Model-agnostic Explanations) model were applied. Individuals with severe SARS-Co...
The economic prosperity of a country is highly dependent on agriculture. The use of technology in agriculture has greatly contributed to the economic prosperity of industrialized countries and is crucial for the growth of emerging... more
The economic prosperity of a country is highly dependent on agriculture. The use of technology in agriculture has greatly contributed to the economic prosperity of industrialized countries and is crucial for the growth of emerging countries. One major challenge in agriculture is the detection and control of plant diseases, which can greatly affect food production and population well-being. Plant illnesses have a substantial effect on plant productivity and quality. The detection of various types of diseases in plants with bare eyes is time consuming and a difficult task with little precision. Mainly our primary concern is tomato crops. The economic demand for tomatoes has grown dramatically over time. The complicated task of controlling tomato infection requires ongoing care during the crop cycle and consumes a considerable amount of the total cost of production. To classify tomato diseases, we made the use of the pretrained deep neural networks and automation, which are crucial for...
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Context-aware system (CAS) is a system that can understand the context of a given situation and either share this context with other systems for their response or respond by itself. In surgery, these systems are intended to assist... more
Context-aware system (CAS) is a system that can understand the context of a given situation and either share this context with other systems for their response or respond by itself. In surgery, these systems are intended to assist surgeons enhance the scheduling productivity of operating rooms (OR) and surgical teams, and promote a comprehensive perception and consciousness of the OR. Furthermore, the automated surgical tool classification in medical images is a real-time computerized assistance to the surgeons in conducting different operations. Moreover, deep learning has embroiled in every facet of life due to the availability of large datasets and the emergence of convolutional neural networks (CNN) that have paved the way for the development of different image related processes. The aim of this paper is to resolve the problem of unbalanced data in the publicly available Cholec80 laparoscopy video dataset, using multiple data augmentation techniques. Furthermore, we implement a ...
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Forecasting air pollution is crucial as it not only affects the physical health of people but also provides guidance for pollution control. Particulate Matter with a diameter of less than $2.5\mu m$ (PM2.5) is one of the major... more
Forecasting air pollution is crucial as it not only affects the physical health of people but also provides guidance for pollution control. Particulate Matter with a diameter of less than $2.5\mu m$ (PM2.5) is one of the major contributors to air pollution which can cause acute and chronic effects on human health. Forecasting PM2.5 involves various meteorological factors as well as the influence of historical data. Therefore, the prediction of the surface PM2.5 concentration is of great importance for the protection of human health. This study uses machine learning models, namely Decision Tree (DT), Random Forest (RF), and Gradient Boosting Regressor (GBR) to predict PM2.5 hourly scale concentrations, using meteorological data and PM2.5 concentrations from adjacent stations. The dataset was collected in the city of Beijing in China as a study region. The experiments have shown that the gradient boosting regressor model achieves higher predictive precision than the other models proposed for estimating hourly PM2.5 concentrations with an $R^{2}$ value varying between 0.9 and 0.97. This work offers a promising and affordable approach to robustly predict PM2.5 concentrations.
Cardiovascular diseases had been for a long time one of the essential medical problems. As indicated by the World Health Association, heart ailments are at the highest point of ten leading reasons for death. Correct and early... more
Cardiovascular diseases had been for a long time one of the essential medical problems. As indicated by the World Health Association, heart ailments are at the highest point of ten leading reasons for death. Correct and early identification is a vital step in rehabilitation and treatment. To diagnose heart defects, it would be necessary to implement a system able to predict the existence of heart diseases. In the current article, our main motivation is to develop an effective intelligent medical system based on machine learning techniques, to aid in identifying a patient’s heart condition and guide a doctor in making an accurate diagnosis of whether or not a patient has cardiovascular diseases. Using multiple data processing techniques, we address the problem of missing data as well as the problem of imbalanced data in the publicly available UCI Heart Disease dataset and the Framingham dataset. Furthermore, we use machine learning to select the most effective algorithm for predictin...
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Laparoscopic surgery also know as minimally invasive surgery (MIS), is a type of surgical procedure that allows a surgeon to examine the organs inside of the abdomen without having to make large incisions in the skin. It unifies the... more
Laparoscopic surgery also know as minimally invasive surgery (MIS), is a type of surgical procedure that allows a surgeon to examine the organs inside of the abdomen without having to make large incisions in the skin. It unifies the competence and skills of highly trained surgeons with the power and precision of machines. Furthermore, surgical instruments are inserted through the abdomen with the help of a laparoscope, which is a tube with a high-intensity light and a high-resolution camera at the end. In addition, recorded videos from this type of surgery have become a steadily more important information source. However, MIS videos are often very long, thereby, navigating through these videos is time and effort consuming. The automatic identification of tool presence in laparoscopic videos leads to detecting what tools are used at each time in surgery and helps in the automatic recognition of surgical workflow. The aim of this paper is to predict surgical tools from laparoscopic vi...
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Research Interests:
With the increasing use of credit cards in electronic payments, financial institutions and service providers are vulnerable to fraud, costing huge losses every year. The design and the implementation of efficient fraud detection system is... more
With the increasing use of credit cards in electronic payments, financial institutions and service providers are vulnerable to fraud, costing huge losses every year. The design and the implementation of efficient fraud detection system is essential to reduce such losses. However, machine learning techniques used to detect automatically card fraud do not consider fraud sequences or behavior changes which may lead to false alarms. In this paper, we develop a credit card fraud detection system that employs Long Short-Term Memory (LSTM) networks as a sequence learner to include transaction sequences. The proposed approach aims to capture the historic purchase behavior of credit card holders with the goal of improving fraud detection accuracy on new incoming transactions. Experiments show that our proposed model gives strong results and its accuracy is quite high.
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An intrusion detection system (IDS) is a device or software application that monitors a network for malicious activity or policy violations. It scans a network or a system for a harmful activity or security breaching. IDS protects... more
An intrusion detection system (IDS) is a device or software application that monitors a network for malicious activity or policy violations. It scans a network or a system for a harmful activity or security breaching. IDS protects networks (Network-based intrusion detection system NIDS) or hosts (Host-based intrusion detection system HIDS), and work by either looking for signatures of known attacks or deviations from normal activity. Deep learning algorithms proved their effectiveness in intrusion detection compared to other machine learning methods. In this paper, we implemented deep learning solutions for detecting attacks based on Long Short-Term Memory (LSTM). PCA (principal component analysis) and Mutual information (MI) are used as dimensionality reduction and feature selection techniques. Our approach was tested on a benchmark data set, KDD99, and the experimental outcomes show that models based on PCA achieve the best accuracy for training and testing, in both binary and mul...
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Phishing is referred as an attempt to obtain sensitive information, such as usernames, passwords, and credit card details (and, indirectly, money), for malicious reasons, by disguising as a trustworthy entity in an electronic... more
Phishing is referred as an attempt to obtain sensitive information, such as usernames, passwords, and credit card details (and, indirectly, money), for malicious reasons, by disguising as a trustworthy entity in an electronic communication [1]. Hackers and malicious users, often use Emails as phishing tools to obtain the personal data of legitimate users, by sending Emails with authentic identities, legitimate content, but also with malicious URL, which help them to steal consumer's data. The high dimensional data in phishing context contains large number of redundant features that significantly elevate the classification error. Additionally, the time required to perform classification increases with the number of features. So extracting complex Features from phishing Emails requires us to determine which Features are relevant and fundamental in phishing detection. The dominant approaches in phishing are based on machine learning techniques; these rely on manual feature engineering, which is time consuming. On the other hand, deep learning is a promising alternative to traditional methods. The main idea of deep learning techniques is to learn complex features extracted from data with minimum external contribution [2]. In this paper, we propose new phishing detection and prevention approach, based first on our previous spam filter [3] to classify textual content of Email. Secondly it's based on Autoencoder and on Denoising Autoencoder (DAE), to extract relevant and robust features set of URL (to which the website is actually directed), therefore the features space could be reduced considerably, and thus decreasing the phishing detection time.
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Over the past few decades, due to human activities, industrialization, and urbanization, air pollution has become a life-threatening factor in many countries around the world. Among air pollutants, Particulate Matter with a diameter of... more
Over the past few decades, due to human activities, industrialization, and urbanization, air pollution has become a life-threatening factor in many countries around the world. Among air pollutants, Particulate Matter with a diameter of less than $$2.5 \mu m$$ 2.5 μ m ($$PM_{2.5}$$ P M 2.5 ) is a serious health problem. It causes various illnesses such as respiratory tract and cardiovascular diseases. Hence, it is necessary to accurately predict the $$PM_{2.5}$$ P M 2.5 concentrations in order to prevent the citizens from the dangerous impact of air pollution beforehand. The variation of $$PM_{2.5}$$ P M 2.5 depends on a variety of factors, such as meteorology and the concentration of other pollutants in urban areas. In this paper, we implemented a deep learning solution to predict the hourly forecast of $$PM_{2.5}$$ P M 2.5 concentration in Beijing, China, based on CNN-LSTM, with a spatial-temporal feature by combining historical data of pollutants, meteorological data, and $$PM_{2....
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Research Interests:
Research Interests:
Cardiovascular diseases had been for a long time one of the essential medical problems. As indicated by the World Health Association, heart ailments are at the highest point of ten leading reasons for death. Correct and early... more
Cardiovascular diseases had been for a long time one of the essential medical problems. As indicated by the World Health Association, heart ailments are at the highest point of ten leading reasons for death. Correct and early identification is a vital step in rehabilitation and treatment. To diagnose heart defects, it would be necessary to implement a system able to predict the existence of heart diseases. In the current article, our main motivation is to develop an effective intelligent medical system based on machine learning techniques, to aid in identifying a patient’s heart condition and guide a doctor in making an accurate diagnosis of whether or not a patient has cardiovascular diseases. Using multiple data processing techniques, we address the problem of missing data as well as the problem of imbalanced data in the publicly available UCI Heart Disease dataset and the Framingham dataset. Furthermore, we use machine learning to select the most effective algorithm for predictin...
Context-aware system (CAS) is a system that can understand the context of a given situation and either share this context with other systems for their response or respond by itself. In surgery, these systems are intended to assist... more
Context-aware system (CAS) is a system that can understand the context of a given situation and either share this context with other systems for their response or respond by itself. In surgery, these systems are intended to assist surgeons enhance the scheduling productivity of operating rooms (OR) and surgical teams, and promote a comprehensive perception and consciousness of the OR. Furthermore, the automated surgical tool classification in medical images is a real-time computerized assistance to the surgeons in conducting different operations. Moreover, deep learning has embroiled in every facet of life due to the availability of large datasets and the emergence of convolutional neural networks (CNN) that have paved the way for the development of different image related processes. The aim of this paper is to resolve the problem of unbalanced data in the publicly available Cholec80 laparoscopy video dataset, using multiple data augmentation techniques. Furthermore, we implement a ...
Research Interests:
As credit card becomes the most popular payment mode particularly in the online sector, the fraudulent activities using credit card payment technologies are rapidly increasing as a result. For this end, it is obligatory for financial... more
As credit card becomes the most popular payment mode particularly in the online sector, the fraudulent activities using credit card payment technologies are rapidly increasing as a result. For this end, it is obligatory for financial institutions to continuously improve their fraud detection systems to reduce huge losses. The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data, using attention mechanism and LSTM deep recurrent neural networks. The proposed model, compared to previous studies, considers the sequential nature of transactional data and allows the classifier to identify the most important transactions in the input sequence that predict at higher accuracy fraudulent transactions. Precisely, the robustness of our model is built by combining the strength of three sub-methods; the uniform manifold approximation and projection (UMAP) for selecting the most useful predictive features, the Long Short Term Memo...
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The purpose of this study is to develop and test machine learning-based models for COVID-19 severity prediction. COVID-19 test samples from 337 COVID-19 positive patients at Cheikh Zaid Hospital were grouped according to the severity of... more
The purpose of this study is to develop and test machine learning-based models for COVID-19 severity prediction. COVID-19 test samples from 337 COVID-19 positive patients at Cheikh Zaid Hospital were grouped according to the severity of their illness. Ours is the first study to estimate illness severity by combining biological and non-biological data from patients with COVID-19. Moreover the use of ML for therapeutic purposes in Morocco is currently restricted, and ours is the first study to investigate the severity of COVID-19. When data analysis approaches were used to uncover patterns and essential characteristics in the data, C-reactive protein, platelets, and D-dimers were determined to be the most associated to COVID-19 severity prediction. In this research, many data reduction algorithms were used, and Machine Learning models were trained to predict the severity of sickness using patient data. A new feature engineering method based on topological data analysis called Uniform ...
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Rapid development of internet and network technologies has led to considerable increase in number of attacks. Intrusion detection system is one of the important ways to achieve high security in computer networks. However, it have curse of... more
Rapid development of internet and network technologies has led to considerable increase in number of attacks. Intrusion detection system is one of the important ways to achieve high security in computer networks. However, it have curse of dimensionality which tends to increase time complexity and decrease resource utilization. To improve the ability of detecting anomaly intrusions, a combined algorithm is proposed based on Weighted Fuzzy C-Mean Clustering Algorithm (WFCM) and Fuzzy logic. Decision making is performed in two stages. In the first stage, WFCM algorithm is applied to reduce the input data space. The reduced dataset is then fed to Fuzzy Logic scheme to build the fuzzy sets, membership function and the rules that decide whether an instance represents an anomaly or not.
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Research Interests:
The increasing volume of emails has led to the emergence of problems caused by unsolicited email, commonly referred to as Spam. One of the most commonly presentation used in Spam Filter is the BoW (Bag-of-words). However, this approach... more
The increasing volume of emails has led to the emergence of problems caused by unsolicited email, commonly referred to as Spam. One of the most commonly presentation used in Spam Filter is the BoW (Bag-of-words). However, this approach has a number of weaknesses, mainly the fact that the word order is lost; hence different emails can have the same representation since the same words are used, and it ignores the relationship between words, which can lead to poor performance. This paper proposes a new Spam filter based on PV-DM (Paragraph Vector-Distributed Memory) in order to overcome the limitations of the BoW representation.