Papers by Yousef Farhaoui
Big data mining and analytics, Jun 1, 2024
Big data has the ability to open up innovative and groundbreaking prospects for the electrical gr... more Big data has the ability to open up innovative and groundbreaking prospects for the electrical grid, which also supports to obtain a variety of technological, social, and financial benefits. There is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies, along with data processing and advanced tools. The main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information security. The original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI mechanisms. Here, an AdaBelief Exponential Feature Selection (AEFS) technique is used to efficiently handle the input huge datasets from the smart grid for boosting security. Then, a Kernel based Extreme Neural Network (KENN) technique is used to anticipate security vulnerabilities more effectively. The Polar Bear Optimization (PBO) algorithm is used to efficiently determine the parameters for the estimate of radial basis function. Moreover, several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network (AEFS-KENN) big data security framework. The results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5% with precision and AUC of 99% for all smart grid big datasets used in this study.
Intelligent and converged networks, Mar 1, 2024
Due to the dynamic nature and node mobility, assuring the security of Mobile Ad-hoc Networks (MAN... more Due to the dynamic nature and node mobility, assuring the security of Mobile Ad-hoc Networks (MANET) is one of the difficult and challenging tasks today. In MANET, the Intrusion Detection System (IDS) is crucial because it aids in the identification and detection of malicious attacks that impair the network's regular operation. Different machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of MANET. However, it still has significant flaws, including increased algorithmic complexity, lower system performance, and a higher rate of misclassification. Therefore, the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning models. Here, the minmax normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields, which increases the overall intrusion detection performance of classifier. Then, a novel Adaptive Marine Predator Optimization Algorithm (AOMA) is implemented to choose the optimal features for improving the speed and intrusion detection performance of classifier. Moreover, the Deep Supervise Learning Classification (DSLC) mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training operations. During evaluation, the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.
Salud, Ciencia y Tecnología - Serie de Conferencias, Mar 11, 2024
In the dynamic and often volatile world of the cryptocurrency market, accurately predicting futur... more In the dynamic and often volatile world of the cryptocurrency market, accurately predicting future market movements is crucial for making informed trading decisions. While manual trading involves traders making subjective judgments based on market observations, the development of algorithmic trading systems, incorporating Machine Learning and Deep Learning, has introduced a more systematic approach to trading. These systems often employ technical analysis and machine learning techniques to analyze historical price data and generate trading signals. This study delves into a comparative analysis of two charting techniques, Heikin-Ashi and alternate candlestick patterns, in the context of forecasting single-step future price movements of cryptocurrency pairs. Utilizing a range of time windows (1 day, 12 hours, 8 hours, ..., 5 minutes) and various regression algorithms (Huber regressor, k-nearest neighbors regressor, Light Gradient Boosting Machine, linear regression, and random forest regressor), the study evaluates the effectiveness of each technique in forecasting future price movements. The primary outcomes of the research indicate that the application of ensemble learning methods to the alternate candlestick patterns consistently surpasses the performance of Heikin-Ashi candlesticks across all examined time windows. This suggests that alternate candlestick patterns provide more reliable information for predicting short-term price movements. Additionally, the study highlights the varying behavior of Heikin-Ashi candlesticks over different time windows.
Data & metadata, Dec 20, 2023
Diabetes is a persistent medical condition that arises when the pancreas loses its ability to pro... more Diabetes is a persistent medical condition that arises when the pancreas loses its ability to produce insulin or when the body is unable to utilize the insulin it generates effectively. In today's world, diabetes stands as one of the most prevalent and, unfortunately, one of the deadliest diseases due to certain complications. Timely detection of diabetes plays a crucial role in facilitating its treatment and preventing the disease from advancing further. In this study, we have developed a diabetes prediction model by leveraging a variety of machine learning classification algorithms, including K-Nearest Neighbors (KNN), Naive Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest, and Logistic Regression, to determine which algorithm yields the most accurate predictive outcomes. we employed the famous PIMA Indians Diabetes dataset, comprising 768 instances with nine distinct feature attributes. The primary objective of this dataset is to ascertain whether a patient has diabetes based on specific diagnostic metrics included in the collection. In the process of preparing the data for analysis, we implemented a series of preprocessing steps. The evaluation of performance metrics in this study encompassed accuracy, precision, recall, and the F1 score. The results from our experiments indicate that the K-nearest neighbors' algorithm (KNN) surpasses other algorithms in effectively differentiating between individuals with diabetes and those without in the PIMA dataset.
Indonesian Journal of Electrical Engineering and Computer Science, Jan 31, 2024
Monitoring fetal health during pregnancy ensures safe delivery and the newborn's well-being. The ... more Monitoring fetal health during pregnancy ensures safe delivery and the newborn's well-being. The fetal electrocardiogram (fetal ECG) is a valuable tool for assessing fetal cardiac health, but interpretation of ECG data can be challenging due to its complexity and variability. In this work, we explore the application of machine learning, particularly random forest, to predict and analyze fetal ECGs. With its ability to manage large datasets and provide precise insights, random forest is a promising solution for this challenge. By comparing our random forest-based approach with other standard machine learning techniques such as artificial neural network (ANN), support vector machines (SVM), and recurrent neural networks (RNN), we observed that our solution outperformed these methods in accuracy, robustness, and reliability. This article details the methodology used, the implementation of the algorithm, as well as the comparative results obtained. Emphasis is placed on the benefits of random forest in this specific medical context, highlighting its potential as a future tool for fetal ECG prediction. Ultimately, our research suggests a shift toward random forestbased solutions for more efficient and accurate analysis of fetal ECGs, with direct implications for clinical practice and fetal well-being.
Skin cancer is a medical condition characterized by abnormal growth of skin cells. This occurs wh... more Skin cancer is a medical condition characterized by abnormal growth of skin cells. This occurs when the DNA within these skin cells becomes damaged. In addition, it is a prevalent form of cancer that can result in fatalities if not identified in its early stages. A skin biopsy is a necessary step in determining the presence of skin cancer. However, this procedure requires time and expertise. In recent times, artificial intelligence and deep learning algorithms have exhibited superior performance compared with humans in visual tasks. This result can be attributed to improved processing capabilities and the availability of vast datasets. Automated classification driven by these advancements has the potential to facilitate the early identification of skin cancer. Traditional diagnostic methods might overlook certain cases, whereas artificial intelligence-powered approaches offer a broader perspective. Transfer learning is a widely used technique in deep learning, involving the use of pre-trained models. These models are extensively implemented in healthcare, especially in diagnosing and studying skin lesions. Similarly, convolutional neural networks (CNNs) have recently established themselves as highly robust autonomous feature extractors that can achieve excellent accuracy in skin cancer detection because of their high potential. The primary goal of this study was to build deep-learning models designed to perform binary classification of skin cancer into benign and malignant categories. The tasks to resolve are as follows: partitioning the database, allocating 80% of the images to the training set, assigning the remaining 20% to the test set, and applying a preprocessing procedure to the images, aiming to optimize their suitability for our analysis. This involved augmenting the dataset and resizing the images to align them with the specific requirements of each model used in our research; finally, building deep learning models to enable them to perform the classification task. The methods used are a CNNs model and two transfer learning models, i.e., Visual Geometry Group 16 (VGG16) and Visual Geometry Group 19 (VGG19). They are applied to dermoscopic images from the International Skin Image Collaboration Archive (ISIC) dataset to classify skin lesions into two classes and to conduct a comparative analysis. Our results indicated that the VGG16 model outperformed the others, achieving an accuracy of 87% and a loss of 38%. Additionally, the VGG16 model demonstrated the best recall, precision, and F1-score. Comparatively, the VGG16 and VGG19 models displayed superior performance in this classification task compared with the CNN model. Conclusions. The significance of this study stems from the fact that deep learning-based clinical decision support systems have proven to be highly beneficial, offering valuable recommendations to dermatologists during their diagnostic procedures.
Big Data Mining and Analytics
Cloud computing (CC) is a novel technology that has made it easier to access network and computer... more Cloud computing (CC) is a novel technology that has made it easier to access network and computer resources on demand such as storage and data management services. In addition, it aims to strengthen systems and make them useful. Regardless of these advantages, cloud providers suffer from many security limits. Particularly, the security of resources and services represents a real challenge for cloud technologies. For this reason, a set of solutions have been implemented to improve cloud security by monitoring resources, services, and networks, then detect attacks. Actually, intrusion detection system (IDS) is an enhanced mechanism used to control traffic within networks and detect abnormal activities. This paper presents a cloud-based intrusion detection model based on random forest (RF) and feature engineering. Specifically, the RF classifier is obtained and integrated to enhance accuracy (ACC) of the proposed detection model. The proposed model approach has been evaluated and validated on two datasets and gives 98.3% ACC and 99.99% ACC using Bot-IoT and NSL-KDD datasets, respectively. Consequently, the obtained results present good performances in terms of ACC, precision, and recall when compared to the recent related works.
Big Data Mining and Analytics
The development of hand gesture recognition systems has gained more attention in recent days, due... more The development of hand gesture recognition systems has gained more attention in recent days, due to its support of modern human-computer interfaces. Moreover, sign language recognition is mainly developed for enabling communication between deaf and dumb people. In conventional works, various image processing techniques like segmentation, optimization, and classification are deployed for hand gesture recognition. Still, it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption, increased false positives, error rate, and misclassification outputs. Hence, this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques. During image segmentation, skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion. Then, the Heuristic Manta-ray Foraging Optimization (HMFO) technique is employed for optimally selecting the features by computing the best fitness value. Moreover, the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate. Finally, an Adaptive Extreme Learning Machine (AELM) based classification technique is employed for predicting the recognition output. During results validation, various evaluation measures have been used to compare the proposed model's performance with other classification approaches.
Big Data Mining and Analytics
This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdomina... more This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdominal lead. It is based on the Convolutional Neural Network (CNN) combined with advanced mathematical methods, such as Independent Component Analysis (ICA), Singular Value Decomposition (SVD), and a dimension-reduction technique like Nonnegative Matrix Factorization (NMF). Due to the highly disproportionate frequency of the fetus's heart rate compared to the mother's, the timescale representation clearly distinguishes the fetal electrical activity in terms of energy. Furthermore, we can disentangle the various components of fetal ECG, which serve as inputs to the CNN model to optimize the actual FECG signal, denoted by FECGr, which is recovered using the SVD-ICA process. The findings demonstrate the efficiency of this innovative approach, which may be deployed in real-time.
Big Data Mining and Analytics
Industrial Internet of Things (IIoT) represents the expansion of the Internet of Things (IoT) in ... more Industrial Internet of Things (IIoT) represents the expansion of the Internet of Things (IoT) in industrial sectors. It is designed to implicate embedded technologies in manufacturing fields to enhance their operations. However, IIoT involves some security vulnerabilities that are more damaging than those of IoT. Accordingly, Intrusion Detection Systems (IDSs) have been developed to forestall inevitable harmful intrusions. IDSs survey the environment to identify intrusions in real time. This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security. We combine Isolation Forest (IF) with Pearson's Correlation Coefficient (PCC) to reduce computational cost and prediction time. IF is exploited to detect and remove outliers from datasets. We apply PCC to choose the most appropriate features. PCC and IF are applied exchangeably (PCCIF and IFPCC). The Random Forest (RF) classifier is implemented to enhance IDS performances. For evaluation, we use the Bot-IoT and NF-UNSW-NB15-v2 datasets. RF-PCCIF and RF-IFPCC show noteworthy results with 99.98% and 99.99% Accuracy (ACC) and 6.18 s and 6.25 s prediction time on Bot-IoT, respectively. The two models also score 99.30% and 99.18% ACC and 6.71 s and 6.87 s prediction time on NF-UNSW-NB15-v2, respectively. Results prove that our designed model has several advantages and higher performance than related models.
Big Data Mining and Analytics
Date palm production is critical to oasis agriculture, owing to its economic importance and nutri... more Date palm production is critical to oasis agriculture, owing to its economic importance and nutritional advantages. Numerous diseases endanger this precious tree, putting a strain on the economy and environment. White scale Parlatoria blanchardi is a damaging bug that degrades the quality of dates. When an infestation reaches a specific degree, it might result in the tree's death. To counter this threat, precise detection of infected leaves and its infestation degree is important to decide if chemical treatment is necessary. This decision is crucial for farmers who wish to minimize yield losses while preserving production quality. For this purpose, we propose a feature extraction and machine learning (ML) technique based framework for classifying the stages of infestation by white scale disease (WSD) in date palm trees by investigating their leaflets images. 80 gray level co-occurrence matrix (GLCM) texture features and 9 hue, saturation, and value (HSV) color moments features are extracted from both grayscale and color images of the used dataset. To classify the WSD into its four classes (healthy, low infestation degree, medium infestation degree, and high infestation degree), two types of ML algorithms were tested; classical machine learning methods, namely, support vector machine (SVM) and k-nearest neighbors (KNN), and ensemble learning methods such as random forest (RF) and light gradient boosting machine (LightGBM). The ML models were trained and evaluated using two datasets: the first is composed of the extracted GLCM features only, and the second combines GLCM and HSV descriptors. The results indicate that SVM classifier outperformed on combined GLCM and HSV features with an accuracy of 98.29%. The proposed framework could be beneficial to the oasis agricultural community in terms of early detection of date palm white scale disease (DPWSD) and assisting in the adoption of preventive measures to protect both date palm trees and crop yield.
Big Data Mining and Analytics
Human Action Recognition (HAR) attempts to recognize the human action from images and videos. The... more Human Action Recognition (HAR) attempts to recognize the human action from images and videos. The major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments. A novel action descriptor is proposed in this study, based on two independent spatial and spectral filters. The proposed descriptor uses a Difference of Gaussian (DoG) filter to extract scale-invariant features and a Difference of Wavelet (DoW) filter to extract spectral information. To create a composite feature vector for a particular test action picture, the Discriminant of Guassian (DoG) and Difference of Wavelet (DoW) features are combined. Linear Discriminant Analysis (LDA), a widely used dimensionality reduction technique, is also used to eliminate duplicate data. Finally, a closest neighbor method is used to classify the dataset. Weizmann and UCF 11 datasets were used to run extensive simulations of the suggested strategy, and the accuracy assessed after the simulations were run on Weizmann datasets for five-fold cross validation is shown to perform well. The average accuracy of DoG + DoW is observed as 83.6635% while the average accuracy of Discrinanat of Guassian (DoG) and Difference of Wavelet (DoW) is observed as 80.2312% and 77.4215%, respectively. The average accuracy measured after the simulation of proposed methods over UCF 11 action dataset for five-fold cross validation DoG + DoW is observed as 62.5231% while the average accuracy of Difference of Guassian (DoG) and Difference of Wavelet (DoW) is observed as 60.3214% and 58.1247%, respectively. From the above accuracy observations, the accuracy of Weizmann is high compared to the accuracy of UCF 11, hence verifying the effectiveness in the improvisation of recognition accuracy.
Big Data Mining and Analytics
The Internet of Things (IoT) is currently reflected in the increase in the number of connected ob... more The Internet of Things (IoT) is currently reflected in the increase in the number of connected objects, that is, devices with their own identity and computing and communication capacities. IoT is recognized as one of the most critical areas for future technologies, gaining worldwide attention. It applies to many areas, where it has achieved success, such as healthcare, where a patient is monitored using nodes and lightweight sensors. However, the powerful functions of IoT in the medical field are based on communication, analysis, processing, and management of data autonomously without any manual intervention, which presents many difficulties, such as energy consumption. However, these issues significantly slow down the development and rapid deployment of this technology. The main causes of wasted energy from connected objects include collisions that occur when two or more nodes send data simultaneously and the leading cause of data retransmission that occurs when a collision occurs or when data are not received correctly due to channel fading. The distance between nodes is one of the factors influencing energy consumption. In this article, we have proposed direct communication between nodes to avoid collision domains, which will help reduce data retransmission. The results show that the distribution can ensure the performance of the system under general conditions compared to the centralization and to the existing works.
Big Data Mining and Analytics
Solar radiation is capable of producing heat, causing chemical reactions, or generating electrici... more Solar radiation is capable of producing heat, causing chemical reactions, or generating electricity. Thus, the amount of solar radiation at different times of the day must be determined to design and equip all solar systems. Moreover, it is necessary to have a thorough understanding of different solar radiation components, such as Direct Normal Irradiance (DNI), Diffuse Horizontal Irradiance (DHI), and Global Horizontal Irradiance (GHI). Unfortunately, measurements of solar radiation are not easily accessible for the majority of regions on the globe. This paper aims to develop a set of deep learning models through feature importance algorithms to predict the DNI data. The proposed models are based on historical data of meteorological parameters and solar radiation properties in a specific location
Big Data Mining and Analytics
Artificial Intelligence (AI) is based on algorithms that allow machines to make decisions for hum... more Artificial Intelligence (AI) is based on algorithms that allow machines to make decisions for humans. This technology enhances the users' experience in various ways. Several studies have been conducted in the field of education to solve the problem of student orientation and performance using various Machine Learning (ML) algorithms. The main goal of this article is to predict Moroccan students' performance in the region of Guelmim Oued Noun using an intelligent system based on neural networks, one of the best data mining techniques that provided us with the best results.
Advances in intelligent systems and computing, 2021
Advances in Intelligent Systems and Computing, 2021
Session Initiation Protocol (SIP) can be defined as an application layer protocol that was develo... more Session Initiation Protocol (SIP) can be defined as an application layer protocol that was developed in order to initiate, modify, and stop a multimedia session through internet network. In recent decades, SIP is coming more and more popular as it is adopted by many typical applications such as telephony over IP (ToIP). Due to its popularity, SIP has some security problems that we have to be resolve. In this paper, we concentrate our efforts on SIP malformed messages attack detection. Therefore, we propose our algorithm that can determine whether the received message is normal or abnormal. The simulation results confirm that our algorithm is efficient and can detect the attack in a minimum time.
Studies in Big Data, 2019
Starting from the fact that an achievement by the company of unfavorable or insufficient results ... more Starting from the fact that an achievement by the company of unfavorable or insufficient results would be a waste of resources damaging for the company itself as well as for its financial backers and for the economy in general, the current leaders of the companies are aware of the added value that the implementation of a marketing information system can bring through the applications that are integrated to it including groupware, workflows and datamining. Indeed automatisms are able to stimulate the productivity, the dynamism, the flexibility of the employees by favoring a culture of network work and also the adoption of new managerial practices which will be beneficial for the company which is exposed permanently to a constantly changing competitive environment. Finally, and in order to achieve a satisfactory level of performance in terms of productivity, reliability, reduction of deadlines and more particularly of the governance of human, material and financial resources, the computerization process required by the implementation of a marketing information system must review the division of tasks, roles and relationships between the various actors. It must be said that the installation of the marketing information system also tends to reduce the dependence of the administrative staff on supervisors, since it is no longer necessary to call on them at any time to obtain information or to solve a given problem. In return, their dependence increases with respect to the central IT administrator.
Big Data and Networks Technologies, 2019
In the last decade, Session Initiation Protocol (SIP) is the most popular application layer proto... more In the last decade, Session Initiation Protocol (SIP) is the most popular application layer protocol created in order to manage multimedia sessions over IP protocol. SIP is not used only by telephony over IP (ToIP), but it can be used also by other in line application such as instant message, video conferences, and others. Since SIP inherits the security threats of IP which are added to SIP owner problems, the security of SIP services must be enhanced. Recently, we have designed a new SIP authentication protocol. Then, we have proved theoretically that our protocol is secured against various attacks. In this paper, we use the API JAIN SIP to implement our protocol. Therefore, we have tested developed applications in local area network. The obtained results confirm that our proposed protocol is efficient when it is compared with some implemented protocols.
show limits on intrusion detection/attacks by the fact that most web vulnerabilities are specific... more show limits on intrusion detection/attacks by the fact that most web vulnerabilities are specifically for specific applications may be developed in-house by companies. Behavioral methods are therefore an interesting approach in this area. An IPS (Intrusion Prevention System) is a tool that is used to enhance the security level. We present here the secure IPS architecture web server. We will also discuss measures that define the effectiveness of our IPS and very recent work of standardization and homogenization of our IPS platform. The approach relies on preventive mechanisms: it is then to develop devices capable of preventing any action that would result in a violation of the security policy. However, experience and results show that it is impossible to build a fully secure system for technical or practical reasons.
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Papers by Yousef Farhaoui