Moulay A. Akhloufi

University of Moncton, Moncton, Canada · Computer Science

Incorrectly diagnosing plant diseases can lead to various undesirable outcomes. This includes the potential for the misuse of unsuitable herbicides, resulting in harm to both plants and the environment. Examining plant diseases visually is a complex and challenging procedure that demands considerable time and resources. Moreover, it necessitates ke...

Wildland fires cause economic and ecological damage with devastating consequences, including loss of life. To reduce these risks, numerous fire detection and recognition systems using deep learning techniques have been developed. However, the limited availability of annotated datasets has decelerated the development of reliable deep learning techni...

Recent advances in the field of large language models (LLMs) underline their high potential for applications in a variety of sectors. Their use in healthcare, in particular, holds out promising prospects for improving medical practices. As we highlight in this paper, LLMs have demonstrated remarkable capabilities in language understanding and gener...

Wildland fires negatively impact forest biodiversity and human lives. They also spread very rapidly. Early detection of smoke and fires plays a crucial role in improving the efficiency of firefighting operations. Deep learning techniques are used to detect fires and smoke. However, the different shapes, sizes, and colors of smoke and fires make the...

Viewed as a significant natural disaster, wildfires present a serious threat to human communities, wildlife, and forest ecosystems. The frequency of wildfire occurrences has increased recently, with the impacts of global warming and human interaction with the environment playing pivotal roles. Addressing this challenge necessitates the ability of f...

Natural events, such as wildfires, pose a serious threat to the human population and cause significant environmental and economic damage. As climate change increases the frequency and intensity of extreme natural events, more efficient solutions are required to mitigate their impacts. One proposed solution is the usage of a swarm of drones and unma...

Unmanned aerial vehicles, also known as drones, have seen increasing interest in recent years. This surge of interest is based on technological advancements, enhanced performance, affordability, and their large array of applications. Despite their utility in various applications, drones could also be used for malicious intent. The increasing concer...

The concept of endorsing AI in embedded systems is growing in all sectors including the development of Accident Avoidance Systems. Although real-time road crash prediction is vital for enhancing road user safety, there has been limited focus on the analysis of real-time crash events within ensemble and deep learning fused systems. The main aim of t...

Deep learning has emerged as a powerful tool for medical image analysis and diagnosis, demonstrating high performance on tasks such as cancer detection. This literature review synthesizes current research on deep learning techniques applied to lung cancer screening and diagnosis. This review summarizes the state-of-the-art in deep learning for lung...

Hospital readmission involves the unplanned emergency admission of patients within 30 days from discharge after the previous admission. According to the Canadian Health Institute (CIHI), 1 in 11 patients were readmitted within 30 days of leaving the hospital in 2021. In the USA, nearly 20% of Medicare patients were readmitted after discharge, where...

Fire accidents cause alarming damage. They result in the loss of human lives, damage to property, and significant financial losses. Early fire ignition detection systems, particularly smoke detection systems, play a crucial role in enabling effective firefighting efforts. In this paper, a novel DL (Deep Learning) method, namely BoucaNet, is introdu...

Unmanned Aerial Vehicles (UAVs) or drones are currently gaining a lot of popularity due to the versatility of this technology and its ability to perform multiple tasks in various industries. However, arbitrary or malicious use of drones can pose a major risk for public and aviation safety. The automated detection and neutralization of malicious dro...

A common consequence of diabetes mellitus called diabetic retinopathy (DR) results in lesions on the retina that impair vision. It can cause blindness if not detected in time. Unfortunately, DR cannot be reversed, and treatment simply keeps eyesight intact. The risk of vision loss can be considerably decreased with early detection and treatment of...

Wildfires are an important natural risk which causes enormous damage to the environment. Many researchers are working to improve firefighting using AI. Various vision-based fire detection methods have been proposed to detect fire. However, these techniques are still limited when it comes to identifying the precise fire’s shape as well as small fire...

Image segmentation is one of the most challenging and difficult tasks in digital image processing. It has many medical applications such as cancerous tumors segmentation, organ segmentation, or abnormalities segmentation. Recent techniques combining convolution-based models and transformers are proposed for automatic medical segmentation tasks. The...

Robots such as drones, ground rovers, underwater vehicles and industrial robots have increased in popularity in recent years. Many sectors have benefited from this by increasing productivity while also decreasing costs and certain risks to humans. These robots can be controlled individually but are more efficient in a large group, also known as a s...

COVID-19,which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is one of the worst pandemics in recent history. The identification of patients suspected to be infected with COVID-19 is becoming crucial to reduce its spread. We aimed to validate and test a deep learning model to detect COVID-19 based on chest X-rays. T...

This paper presents a novel framework for breast cancer detection using mammogram images. The proposed solution aims to output an explainable classification from a mammogram image. The classification approach uses a Case-Based Reasoning system (CBR). CBR accuracy strongly depends on the quality of the extracted features. To achieve relevant classif...

Accurate segmentation of the lungs in CXR images is the basis for an automated CXR image analysis system. It helps radiologists in detecting lung areas, subtle signs of disease and improving the diagnosis process for patients. However, precise semantic segmentation of lungs is considered a challenging case due to the presence of the edge rib cage,...

The simultaneous advances in deep learning and the Internet of Things (IoT) have benefited distributed deep learning paradigms. Federated learning is one of the most promising frameworks, where a server works with local learners to train a global model. The intrinsic heterogeneity of IoT devices, or non-independent and identically distributed (Non-...

X-ray images are the most widely used medical imaging modality. They are affordable, non-dangerous, accessible, and can be used to identify different diseases. Multiple computer-aided detection (CAD) systems using deep learning (DL) algorithms were recently proposed to support radiologists in identifying different diseases on medical images. In thi...

Wildland fires are one of the most dangerous natural risks, causing significant economic damage and loss of lives worldwide. Every year, millions of hectares are lost, and experts warn that the frequency and severity of wildfires will increase in the coming years due to climate change. To mitigate these hazards, numerous deep learning models were d...

The COVID-19 virus has made a huge impact on people’s lives ever since the outbreak happened in December 2019. Unfortunately, the COVID-19 virus has not completely vanished from the world yet, and thus, global agitation is still increasing with mutations and variants of the same. Early diagnosis is the best way to decline the mortality risk associa...

Abstract: Opinion Mining or Sentiment Analysis (SA) is a key component of E-commerce applications where a vast number of reviews are generated by customers. SA operates on aspect level where the views are expressed on a specific aspect of a product and have a big influence on the customers’ choices and businesses’ reputation. Aspect Based Sentiment...

The world has seen an increase in the number of wildland fires in recent years due to various factors. Experts warn that the number of wildland fires will continue to increase in the coming years, mainly because of climate change. Numerous safety mechanisms such as remote fire detection systems based on deep learning models and vision transformers...

To proactively mitigate malware threats, cybersecurity tools, such as anti-virus and anti-malware software, as well as firewalls, require frequent updates and proactive implementation. However, processing the vast amounts of dataset examples can be overwhelming when relying solely on traditional methods. In cybersecurity workflows, recent advances...

Glaucoma is one of the major reasons for visual impairment all across the globe. The recent advancements in machine learning techniques have greatly facilitated ophthalmologists in the early diagnosis of ocular diseases through the employment of automated systems. Several studies have been published lately to address the timely detection of glaucom...

Guided text generation is one of the key issues when it comes to creating human-like artificial intelligence writing machines. Humans can use their writing skills depending on the topic of the text and the pieces of information they want to include. The context and style also play an important role in mediating the engagement level of the press rel...

With the widespread use of deep learning in leading systems, it has become the mainstream in the table detection field. Some tables are difficult to detect because of the likely figure layout or the small size. As a solution to the underlined problem, we propose a novel method, called DCTable, to improve Faster R-CNN for table detection. DCTable ca...

Besides the many advances made in the facial detection and recognition fields, face recognition applied to visual images (VIS-FR) has received increasing interest in recent years, especially in the field of communication, identity authentication, public safety and to address the risk of terrorism and crime. These systems however encounter important...

Opinion mining or sentiment analysis (SA) is a key component of real-world applications for e-commerce organizations, manufacturers, and customers. SA deals with the computational evaluation of people’s views, thoughts, and feelings in the text, whether they are visible or concealed. The Aspect based SA level is becoming one of the most active phas...

Transformer architectures are highly expressive because they use self-attention mechanisms to encode long-range dependencies in the input sequences. In this paper, we present a literature review on Transformer-based (TB) models, providing a detailed overview of each model in comparison to the Transformer’s standard architecture. This survey focuses...

Chest X-ray radiography (CXR) is among the most frequently used medical imaging modalities. It has a preeminent value in the detection of multiple life-threatening diseases. Radiologists can visually inspect CXR images for the presence of diseases. Most thoracic diseases have very similar patterns, which makes diagnosis prone to human error and lea...

Sign language is the native form of expression used by deaf people in the world. With the recognition techniques applied to sign language, a significant need for developing tools to facilitate the accessibility of information to the deaf public has arisen. Little work deals with recognizing Moroccan sign language (MoSL) for the Moroccan deaf commun...

Recent advances in deep learning have enhanced medical imaging research. Breast cancer is the most prevalent cancer among women, and many applications have been developed to improve its early detection. The purpose of this review is to examine how various deep learning methods can be applied to breast cancer screening workflows. We summarize deep l...

Video to text conversion is a vital activity in the field of computer vision. In recent years, deep learning algorithms have dominated automatic text generation in English, but there are a few research works available for other languages. In this paper, we propose a novel encoding-decoding system that generates character-level Arabic sentences from...

Breast thermography is a screening approach for breast cancer detection by measuring the breast skin temperature. Breast cancer is the most common cancer among women and can affect either women or men. Its early diagnosis and treatment reduce deaths and increase survival chances. The use of deep learning algorithms and techniques has made it easier...

The question answering system is frequently applied in the area of natural language processing (NLP) because of the wide variety of applications. It consists of answering questions using natural language. The problem is, in general, solved by employing a dataset that consists of an input text, a query, and the text segment or span from the input te...

Simple Summary The findings of predictive and diagnostic systems in cancer are an intriguing topic for physicians and the oncologic community. Computer-aided decision (CAD) is vital for breast cancer diagnosis. It aids in higher accuracy and early, reliable diagnosis. To achieve such aims, diverse imaging modalities have been used and decision-maki...

The rapid spread of COVID-19 across the globe since its emergence has pushed many countries' healthcare systems to the verge of collapse. To restrict the spread of the disease and lessen the ongoing cost on the healthcare system, it is critical to appropriately identify COVID-19-positive individuals and isolate them as soon as possible. The primary...

Wildfires are a worldwide natural disaster causing important economic damages and loss of lives. Experts predict that wildfires will increase in the coming years mainly due to climate change. Early detection and prediction of fire spread can help reduce affected areas and improve firefighting. Numerous systems were developed to detect fire. Recentl...

The coronavirus pandemic is spreading around the world. Medical imaging modalities such as radiography play an important role in the fight against COVID-19. Deep learning (DL) techniques have been able to improve medical imaging tools and help radiologists to make clinical decisions for the diagnosis, monitoring and prognosis of different diseases....

Disease detection using chest X-ray (CXR) images is one of the most popular radiology methods to diagnose diseases through a visual inspection of abnormal symptoms in the lung region. A wide variety of diseases such as pneumonia, heart failure and lung cancer can be detected using CXRs. Although CXRs can show the symptoms of a variety of diseases,...

Early fundus screening is a cost-effective and efficient approach to reduce ophthalmic disease-related blindness in ophthalmology. Manual evaluation is time-consuming. Ophthalmic disease detection studies have shown interesting results thanks to the advancement in deep learning techniques, but the majority of them are limited to a single disease. I...

COVID-19 is an acute severe respiratory disease caused by a novel coronavirus SARS-CoV-2. After its first appearance in Wuhan (China), it spread rapidly across the world and became a pandemic. It had a devastating effect on everyday life, public health, and the world economy. The use of advanced artificial intelligence (AI) techniques combined with...

Melanoma is considered as one of the world's deadly cancers. This type of skin cancer will spread to other areas of the body if not detected at an early stage. Convolutional Neural Network (CNN) based classifiers are currently considered one of the most effective melanoma detection techniques. This study presents the use of recent deep CNN approach...

Automatically estimating the number of people in unconstrained scenes is a crucial yet challenging task in different real-world applications, including video surveillance, public safety, urban planning, and traffic monitoring. In addition, methods developed to estimate the number of people can be adapted and applied to related tasks in various fiel...

In this paper, we address the problem of forest fires’ early detection and segmentation in order to predict their spread and help with fire fighting. Techniques based on Convolutional Networks are the most used and have proven to be efficient at solving such a problem. However, they remain limited in modeling the long-range relationship between obj...

Inferring human pose from a monocular RGB image remains an interesting field of research in computer vision. It serves as a fundamental key for many real-world applications, including human-computer interaction, animation, and detecting abnormal or illegal human behavior. Despite the considerable progress made in this area during the last decade, t...

With the spread of COVID-19 pandemic worldwide, medical imaging modalities and deep learning can play an important role in the fight against this disease. Recent years have seen the impressive results obtained using deep neural networks in different fields. Radiology is among the medical fields that can benefit from this recent progress and improve...

COVID-19 is an infectious disease, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this research, we firstly present an overview of the main forecasting models to predict the new cases of COVID-19. In this context, we focus on univariate time series models to analyze the dynamic change of this pandemic through ti...

The COVID-19 pandemic continues to spread globally at a rapid pace, and its rapid detection remains a challenge due to its rapid infectivity and limited testing availability. One of the simply available imaging modalities in clinical routine involves chest X-ray (CXR), which is often used for diagnostic purposes. Here, we proposed a computer-aided...

A bstract The novel coronavirus disease 2019 (COVID-19) is disrupting all aspects of our lives as the global spread of the virus continues. In this difficult period, various research projects are taking place to study and analyse the dynamics of the pandemic. In the present work, we firstly present a deep overview of the main forecasting models to...

Nowadays, we are facing a tremendous increase in the number of forest fires around the world. While in 2010, the world had 3.92Gha of forest cover, covering 30% of its land area, in 2019, there was a loss of forest cover of 24.2Mha according to the Global Forest Watch institute. These fires can take different forms depending on the characteristics...

In recent years we have witnessed an increase in cyber threats and malicious software attacks on different platforms with important consequences to persons and businesses. It has become critical to find automated machine learning techniques to proactively defend against malware. Transformers, a category of attention-based deep learning techniques,...

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the...

Wildfires represent a significant natural risk causing economic losses, human death and environmental damage. In recent years, the world has seen an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland fire assistance and fighting. Systems were proposed for the remote dete...

Research on unmanned aerial vehicles is growing as they are becoming less expensive and more available than before. The applications span a large number of areas and include border security, search and rescue, wildlife surveying, firefighting, precision agriculture, structure inspection, surveying and mapping, aerial photography, and recreative app...

Purpose: Diabetic retinopathy (DR) is characterized by retinal lesions affecting people having diabetes for several years. It is one of the leading causes of visual impairment worldwide. To diagnose this disease, ophthalmologists need to manually analyze retinal fundus images. Computer-aided diagnosis systems can help alleviate this burden by autom...

The coronavirus disease (COVID-19) has emerged in Wuhan (China) in December 2019. Despite implementing myriad measures to contain its spread, the whole world is now suffering from this pandemic and find difficulties in forecasting its unknown future. In this paper, we study the dynamic change of this pandemic using sequence modeling with Long Short...

With cancer being one of the main remaining challenges of modern medicine, a lot of effort is put towards oncology research. Since early diagnosis is a highly important factor for the treatment of many types of cancer, screening tests have become a popular research subject. Technical and technological advances have brought down the price of genome...