Hela elma Elmannai

École Supérieure des Communications de Tunis | Sup'Com · LTSIRS

Nowadays, Internet of Things, artificial intelligence, cloud computing, and other revolutionary technologies (e.g., edge and fog computing) have become the pillar of smart cities. These latter make users' lives easier, thanks to a wide variety of smart services offered in different dimensions (e.g., smart living, smart mobility, smart economy, smar...

Security in smart cities is a challenging issue in urban environments as they depend upon interconnected technologies and data for effective services. To address security challenges, smart cities implement robust cybersecurity measures, including network monitoring, encryption, and intrusion detection systems. Detecting and mitigating possible secu...

Melanoma skin cancer is primarily characterized by poor prognostic responses. Surgical treatment can achieve advanced cure rate with early melanoma detection. Manual segmentation of suspected lesions aids early melanoma diagnosis. However, the limitations of manual segmentation include low efficiency and a risk of misclassification. Deep learning,...

In this work, a slotted wideband eight-element multiple-input multiple-output (MIMO) antenna system is presented, which covers the N77 (3.2–4.2 GHz) frequency band. The MIMO antennas are printed on a 0.8-mm-thick FR-4 substrate with dimensions of 150 × 75 mm2. The antennas are placed along the length and width of the printed circuit board (PCB). Th...

Network on chip (NoC) is an integrated communication system on chip (SoC), efficiently connecting various intellectual property (IP) modules on a single die. NoC has been suggested as an enormously scalable solution to overcome the communication problems in SoC. The performance of NoC depends on several aspects in terms of area, latency, throughput...

This study proposes a novel method for streaming data compression and encoding protocols based on Generative Adversarial Networks (GAN) and Fuzzy Logic. The concept of GAN and Fuzzy Logic integration creates a different prospect as compared to the existing multimedia streaming data compression and security protocols. This paper proposed a collabora...

The Industrial Internet of Things has gained more attention because of the self-governing nature of system configuration with interoperable application connectivity. In the E-Healthcare environment, it cooperates with medical sensors to capture, examine, analyze, preserve, and document day-to-day transactions of patients in real-time. The integrati...

As e-commerce platforms grow, consumers increasingly purchase clothes online; however, they often need clarification on clothing choices. Consumers and stores interact through the clothing recommendation system. A recommendation system can help customers to find clothing that they are interested in and can improve turnover. This work has two main g...

Feature selection (FS) is an adequate data pre-processing method that reduces the dimensionality of datasets and is used in bioinformatics, finance, and medicine. Traditional FS approaches, however, frequently struggle to identify the most important characteristics when dealing with high-dimensional information. To alleviate the imbalance of explor...

Cloud computing plays an important role in every IT sector. Many tech giants such as Google, Microsoft, and Facebook as deploying their data centres around the world to provide computation and storage services. The customers either submit their job directly or they take the help of the brokers for the submission of the jobs to the cloud centres. Th...

The Internet of Things is emerging as a crucial technology in aiding humans and making their lives easier. Among the human population, a large percentage of people suffer from disabilities resulting in challenges in everyday life particularly people with visual disabilities. While several inventions exist to aid people with blindness in their every...

Since the outbreak of COVID-19, it has seriously endangered the health of human beings. Computer automatic segmentation of COVID-19 X-ray images is an important means to assist doctors in rapid and accurate diagnosis. Therefore, this paper proposes a modified FOA (EEFOA) with two optimization strategies added to the original FOA, including elite na...

The intrinsic and liveness detection behavior of electrocardiogram (ECG) signals has made it an emerging biometric modality for the researcher with several applications including forensic, surveillance and security. The main challenge is the low recognition performance with datasets of large populations, including healthy and heart-disease patients...

Computerized tomography (CT) is of great significance for the localization and diagnosis of liver cancer. Many scholars have recently applied deep learning methods to segment CT images of liver and liver tumors. Unlike natural images, medical image segmentation is usually more challenging due to its nature. Aiming at the problem of blurry boundarie...

Polycystic ovary syndrome (PCOS) has been classified as a severe health problem common among women globally. Early detection and treatment of PCOS reduce the possibility of long-term complications, such as increasing the chances of developing type 2 diabetes and gestational diabetes. Therefore, effective and early PCOS diagnosis will help the healt...

The internet’s future architecture, known as Named Data Networking (NDN), is a creative way to offer content-based services. NDN is more appropriate for content distribution because of its special characteristics, such as naming conventions for packets and methods for in-network caching. Mobility is one of the main study areas for this innovative i...

The demand for the accurate and timely identification of melanoma as a major skin cancer type is increasing daily. Due to the advent of modern tools and computer vision techniques, it has become easier to perform analysis. Skin cancer classification and segmentation techniques require clear lesions segregated from the background for efficient resul...

Although PNLB is generally considered safe, it is still invasive and risky. Pneumothorax, the most common complication of lung puncture, can cause shortness of breath, chest pain, and even life-threatening. Therefore, the auxiliary diagnosis for pneumothorax is of great clinical interest. This paper proposes an ant colony optimizer with slime mould...

Advances in semiconductor technology and wireless sensor networks have permitted the development of automated inspection at diverse scales (machine, human, infrastructure, environment, etc.). However, automated identification of road cracks is still in its early stages. This is largely owing to the difficulty obtaining pavement photographs and the...

In recent decades, deep-learning dependent fruit disease detection and classification techniques have evinced outstanding results in technologically advanced horticulture investigation. Due to the comparatively limited image processing capabilities of edge computing devices, implementing deep learning methods in actual field scenarios is currently...

Falls are critical events among the elderly living alone in their rooms and can have intense consequences, such as the elderly person being left to lie for a long time after the fall. Elderly falling is one of the serious healthcare issues that have been investigated by researchers for over a decade, and several techniques and methods have been pro...

Pedo-spectroscopy has the potential to provide valuable information about soil physical, chemical, and biological properties. Nowadays, we may predict soil properties using VNIR field imaging spectra (IS) such as Prisma satellite data or laboratory spectra (LS). The primary goal of this study is to investigate machine learning models namely Partial...

Breast cancer has replaced lung cancer as the most prevalent malignancy threatening human health. Early breast screening can help improve treatment success and reduce the risk of death. The analysis and diagnosis of breast cancer real images by computer-aided technology is the key link to early diagnosis. High-quality medical segmentation images ca...

With the growing number of cloud users, shared data auditing is becoming increasingly important. However, these schemes have issues with the certificate management. Although there is a certificate-shared auditing scheme, it is ineffective in dealing with dynamic data and protecting data privacy. The verifier cannot access the data content to ensure...

The vast enhancement in the development of the Internet of Vehicles (IoV) is due to the impact of the distributed emerging technology and topology of the industrial IoV. It has created a new paradigm, such as the security-related resource constraints of Industry 5.0. A new revolution and dimension in the IoV popup raise various critical challenges...

If found and treated early, fast-growing skin cancers can dramatically prolong patients’ lives. Dermoscopy is a convenient and reliable tool during the fore-period detection stage of skin cancer, so the efficient processing of digital images of dermoscopy is particularly critical to improving the level of a skin cancer diagnosis. Notably, image seg...

A self-decoupled technique is described that enables the radiating elements in the antenna array to be densely packed for multiple-input multiple-output (MIMO) wireless communications systems. High isolation between the adjacent antenna elements is obtained by fixing the radiating elements in an orthogonal configuration with respects to each other....

By leveraging the Internet, cloud computing allows users to have on-demand access to large pools of configurable computing resources. PaaS (Platform as a Service), IaaS (Infrastructure as a Service), and SaaS (Software as a Service) are three basic categories for the services provided by cloud the computing environments. Quality of service (QoS) me...

High-speed internet has boosted clients’ traffic needs. Content caching on mobile edge computing (MEC) servers reduces traffic and latency. Caching with MEC faces difficulties such as user mobility, limited storage, varying user preferences, and rising video streaming needs. The current content caching techniques consider user mobility and content...

Vehicular edge networks (VENs) connect vehicles to share data and infotainment content collaboratively to improve network performance. Due to technological advancements, data growth is accelerating, making it difficult to always connect mobile devices and locations. For vehicle-to-vehicle (V2V) communication, vehicles are equipped with onboard unit...

The development of high throughput sequencing technologies i.e. Next Generation Sequencing (NGS) is revolutionizing the exploration of cancer. Though sequence datasets are highly complex, mutation can occur randomly in DNA or RNA sequences that can make cells sicker or less fit. The unusual growth and behavior of genes in cells cause cancer. Cancer...

ICU readmission is usually associated with an increased number of hospital death. Predicting readmission helps to reduce such risks by avoiding early discharge, providing appropriate intervention, and planning for patient placement after ICU discharge. Unfortunately, ICU scores such as the simplified acute physiology score (SAPS) and Acute Physiolo...

Recent terrorist attacks have emerged as a formidable menace to global peace and security, giving rise to an acute humanitarian and economic crisis characterized by the loss of numerous lives and the incurring of substantial financial damages. In response to this pressing concern, the scholarly community has introduced a range of AI-driven predicti...

Traditional advertising techniques seek to govern the consumer’s opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers’ actual purchase behaviors. Neuromarketing is the new paradigm of...

Artificial Intelligence (AI) technologies are vital in identifying patients at risk of serious illness by providing an early hazards risk. Myocardial infarction (MI) is a silent disease that has been harvested and is still threatening many lives. The aim of this work is to propose a stacking ensemble based on Convolutional Neural Network model (CNN...

Internet of Things (IoT) devices usage is increasing exponentially with the spread of the internet. With the increasing capacity of data on IoT devices, these devices are becoming venerable to malware attacks; therefore, malware detection becomes an important issue in IoT devices. An effective, reliable, and time-efficient mechanism is required for...

Handling missing values (MVs) and feature selection (FS) are vital preprocessing tasks for many pattern recognition, data mining, and machine learning (ML) applications, involving classification and regression problems. The existence of MVs in data badly affects making decisions. Hence, MVs have to be taken into consideration during preprocessing t...

Heart disease is one of the lethal diseases causing millions of fatalities every year. The Internet of Medical Things (IoMT) based healthcare effectively enables a reduction in death rate by early diagnosis and detection of disease. The biomedical data collected using IoMT contains person-alized information about the patient and this data has serio...

The coronavirus disease pandemic (COVID-19) is a contemporary disease. It first appeared in 2019 and has sparked a lot of attention in the public media and recent studies due to its rapid spread around the world in recent years and the fact that it has infected millions of individuals. Many people have died in such a short time. In recent years, se...

The slime mould algorithm (SMA) has become a classical algorithm applied in many fields since it was presented. Nevertheless, when faced with complex tasks, the algorithm converges slowly and tends to fall into the local optimum. So, there is still room for improvement in the performance of SMA. This work proposes a novel SMA variant (SDSMA), combi...

River streamflow is an essential hydrological parameters for optimal water resource management. This study investigates models used to estimate monthly time-series river streamflow data at two hydrological stations in the USA (Heise and Irwin on Snake River, Idaho). Five diverse types of machine learning (ML) model were tested, support vector machi...

A large volume of high-dimensional genetic data has been produced in modern medicine and biology fields. Data-driven decision-making is particularly crucial to clinical practice and relevant procedures. However, high-dimensional data in these fields increase the processing complexity and scale. Identifying representative genes and reducing the data...

Due to the rapid growth in IT technology, digital data have increased availability, creating novel security threats that need immediate attention. An intrusion detection system (IDS) is the most promising solution for preventing malicious intrusions and tracing suspicious network behavioral patterns. Machine learning (ML) methods are widely used in...

An intrusion detection system, often known as an IDS, is extremely important for preventing attacks on a network, violating network policies, and gaining unauthorized access to a network. The effectiveness of IDS is highly dependent on data preprocessing techniques and classification models used to enhance accuracy and reduce model training and tes...

A compact 5G wideband antenna for body-centric network (BCN) operating on Ka band has been presented in this paper. The design of the antenna consists of a very simple key-shaped radiator patch with a vertical slot for better impedance matching. The antenna was designed and simulated with the help of the Computer Simulation Technology (CST) Microwa...

The Hunger Games Search (HGS) algorithm is a recently proposed population-based optimization algorithm that mimics a common phenomenon of animals searching for food due to hunger stimuli and has a simple and easy-to-understand structure. However, the original HGS still suffers from shortcomings, such as low population diversity and the tendency to...

With the evolution of information technology, the use of internet of things has increased. It affects several areas such as medical field, smart cities, and information systems. In this work, we will use this technological development in the context of health, particularly e-health. We present a platform based on IoMT to allow the monitoring of pat...

The evolution of applications in telecommunication, network, computing, and embedded systems has led to the emergence of the Internet of Things and Artificial Intelligence. The combination of these technologies enabled improving productivity by optimizing consumption and facilitating access to real-time information. In this work, there is a focus o...

Data is the most valuable asset in any rm. As time passes, the data expands at a breakneck speed. A major research issue is the extraction of meaningful information from a complex and huge data source. Clustering is one of the data extraction methods. e basic K-Mean and Parallel K-Mean partition clustering algorithms work by picking random starting...

COVID-19 has remained a threat to world life despite a recent reduction in cases. There is still a possibility that the virus will evolve and become more contagious. If such a situation occurs, the resulting calamity will be worse than in the past if we act irresponsibly. COVID-19 must be widely screened and recognized early to avert a global epide...

The discovery of a new form of corona-viruses in December 2019, SARS-CoV-2, commonly named COVID-19, has reshaped the world. With health and economic issues at stake, scientists have been focusing on understanding the dynamics of the disease, in order to provide the governments with the best policies and strategies allowing them to reduce the span...

Cervical cancer has become the third most common form of cancer in the in-universe, after the widespread breast cancer. Human papillomavirus risk of infection is linked to the majority of cancer cases. Preventive care, the most expensive way of fighting cancer, can protect about 37% of cancer cases. The Pap smear examination is a standard screening...

We propose in this work, a new approach for feature extraction based on deep Self-Organizing Map (SOM) network, named Generalized Unsupervised Deep SOM (G-UDSOM). This work presents an enhancement of the classic unsupervised deep SOM (UDSOM) algorithm in two ways. First, we modify the UDSOM Sub-sampling module in such a way that the image reconstru...

COVID-19 is currently raging worldwide, with more patients being diagnosed every day. It usually is diagnosed by examining pathological photographs of the patient's lungs. There is a lot of detailed and essential information on chest radiographs, but manual processing is not as efficient or accurate. As a result, how efficiently analyzing and proce...

Many real-world classification problems such as fraud detection, intrusion detection, churn prediction, and anomaly detection suffer from the problem of imbalanced datasets. Therefore, in all such classification tasks, we need to balance the imbalanced datasets before building classifiers for prediction purposes. Several data-balancing techniques (...

Learning data analytics improves the learning field in higher education using educational data for extracting useful patterns and making better decisions. Identifying potential at-risk students may help instructors and academic guidance to improve the students' performance and the achievement of learning outcomes. The aim of this research study is...

For the elderly population, falls are a vital health problem especially in the current context of home care for COVID-19 patients. Given the saturation of health structures, patients are quarantined, in order to prevent the spread of the disease. Therefore, it is highly desirable to have a dedicated monitoring system to adequately improve their ind...

A new feature extraction approach is proposed in this paper to improve the classification performance in remotely sensed data. The proposed method is based on a primary sources subset (PSS) obtained by nonlinear transform that provides lower space for land pattern recognition. First, the underlying sources are approximated using multilayer neural n...

Land cover classification has interested recent works especially for deforestation, urban are monitoring and agricultural land use. Traditional classification approaches have limited accuracy especially for non-heterogeneous land cover. Thus, using machine may improve the classification accuracy. The presented paper deals with the land-use scene re...

Breast cancer is one of the foremost reasons of death among women in the world. It has the largest mortality rate compared to the types of cancer accounting for 1.9 million per year in 2020. An early diagnosis may increase the survival rates. To this end, automating the analysis and the diagnosis allows to improve the accuracy and to reduce process...

It is very important to track the current status of land. Many applications like geology and ecology are based on collecting information from sensors about land surfaces. These sensors are either airborne or on satellite. Thus, image processing has prominent importance for the land classification and analysis. Taking advantages from feature extract...

Source separation is to retrieve the origin signals from mixed signals. For linear mixture, the problem consists in generating the separating matrix, having only the observations. In the blind source separation, no prior information about the transformation is available. In our model, the sources are supposed non gaussian and thus independent. Base...

The land cover classification is an important task in geoscience applications. Many methods and implementations are based on multispectral data processing. The presented work aims to benefit from the nonlinear source separation process to enhance land cover identification. The source separation technique aims to provide underlying images and to com...

Pattern recognition for multispectral data aims to identify land cover thematics for environmental monitoring and disaster risk reduction. Multispectral images contain data acquired from different channels within the frequency spectrum. They represent a mixture of latent signals. This paper represents a pattern recognition contribution for remote s...

Major goal of multispectral data analysis is land cover classification and related applications. The dimension drawback leads to a small ratio of the remote sensing training data compared to the number of features. Therefore robust methods should be associated to overcome the dimensionality curse. The presented work proposed a pattern recognition a...

We present a new approach for remote sensing image classification. The methodology combines many related tasks namely non linear source separation, feature extraction, feature fusion and learning classification. Nonlinear source separation is a pre-processing stage that aims to compensate the nonlinear mixing natural phenomenon. Latent signals, cal...

In this paper, we aim to classify remotely sensed images for land characterisation. The major goal is approaching the natural nonlinear mixture for band observation and then dimension reduction by supervised classification. After that, an unsupervised method combining feature extraction and SVM in investigating to discriminate the land cover for SP...

Source separation is relatively a new area of data analysis. The most widely used separation approach's are linear. However, in many realistic cases the process which generates the observations is nonlinear and no information is available about the mixture. In this case, it can be expected to capture the structure of the data better if the data poi...

In this paper, we consider the problem of Blind source separation (BSS) method by taking advantage of the sparse modeling of the hyperspectral images. These images are produced by sensors which provide hundreds of narrow and adjacent spectral bands. The idea behind transform domains is to apply some transformations to illustrate the dataset with a...