Mourad Oussalah

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Verified

Mourad verified their affiliation via an institutional email.

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• Msc, PhD
• Prof at University of Oulu

Breast cancer is the most prevalent form of tumor in women, and this is the leading cause of mortality in women. Accurately detecting and classifying breast cancer is crucial for effective treatment and diagnosis preparation. Internet of Things (IoT) wearable devices (smart bra) are considered one of the best methods for early detection and, thereb...

Sarcoidosis is a multisystem granulomatous disease with an unknown cause distinguished by the development of noncaseating granulomas in implicated organs. In patients with sarcoidosis, ventricular arrhythmias, and atrioventricular blocks can be deadly and result in sudden death. Clinically cardiac sarcoidosis affects five percent of sarcoidosis pat...

Accurately estimating snow hydrology parameters, including snow coverage mapping and snow depth, plays a significant role in comprehending water resource dynamics, flood forecasting, and environmental management in regions influenced by snow cover. These parameters are critical for hydrological models that simulate snowmelt and runoff, which are es...

Food recommendation systems have become increasingly popular due to the proliferation of online food service websites. Accordingly, the ratings assigned by users are one of the most important resources in these systems. However, users generally express their opinions about a few foods, which results in data sparsity. Furthermore, food recommendatio...

Passive non-invasive sensing signals from wearable devices and smartphones are typically collected continuously without user input. This passive and continuous data collection makes these signals suitable for moment-by-moment monitoring of health-related outcomes, disease diagnosis, and prediction modeling. A growing number of studies have utilized...

Standard evaluation of automated text summarization (ATS) methods relies on manually crafted golden summaries. With the advances in Large Language Models (LLMs), it is legitimate to question whether these models can now potentially complement or replace human-crafted summaries. This study examines the effectiveness of several language models (LMs)...

Disaster management and detection from satellite imagery and different sources are crucial yet challenging tasks. In this work, we introduce SECA-Net, a compact deep learning architecture designed to enhance disaster image classification. Employing a cascade attention mechanism, SECA-Net refines feature maps to improve accuracy significantly, achie...

Face presentation attack detection (PAD) plays a pivotal role in securing face recognition systems against spoofing attacks. Although great progress has been made in designing face PAD methods, developing a model that can generalize well to unseen test domains remains a significant challenge. Moreover, due to the different types of spoofing attacks...

In recent years citizen science emerged as apromising technology in environmental science and hy-drology with the potential to overcome the lack of in-situ measurements and create efficient ecosystems. Thispaper provides an up-to-date systematic literature reviewof applications of citizen science technology in water qual-ity monitoring and estimati...

Accurately estimating snow hydrology parameters, including snow coverage mapping and snow depth, plays a significant role in comprehending water resource dynamics, flood forecasting, and environmental management in regions influenced by snow cover. These parameters are critical for hydrological models that simulate snowmelt and runoff, which are es...

Accurately estimating snow hydrology parameters, including snow coverage mapping and snow depth, plays a significant role in comprehending water resource dynamics, flood forecasting, and environmental management in regions influenced by snow cover. These parameters are critical for hydrological models that simulate snowmelt and runoff, which are es...

Food recommendation systems have become pivotal in offering personalized suggestions, enabling users to discover recipes in line with their tastes. However, despite the existence of numerous such systems, there are still unresolved challenges. Much of the previous research predominantly lies on users’ past preferences, neglecting the significant as...

Background Traditionally, existing studies assessing the health associations of accelerometer-measured movement behaviors have been performed with few averaged values, mainly representing the duration of physical activities and sedentary behaviors. Such averaged values cannot naturally capture the complex interplay between the duration, timing, and...

In recent years, human physical activity recognition has increasingly attracted attention from different research fields such as healthcare, computer-human interaction, lifestyle monitoring, and athletics. Deep learning models have been extensively employed in developing physical activity recognition systems. To improve these models, their hyperpar...

The aim of this study is to explore how parenthood and birth rate is manifested in Finnish society and citizens as revealed by automated mining of news articles from Finland News API. Several levels of analysis were conducted using natural language processing and text mining techniques to unfold relevant insights from the collected News API. This i...

This paper presents a novel approach to spoken language identification in Arabic, English, Spanish, and German languages using ensemble learning techniques. The study compares the performance of two well-known ensemble learning algorithms (Random Forest and XGBoost). Next, a Stacking Ensemble method with Logistic Regression is used as a meta-model,...

Bangladesh is one of the countries struggling to prevent road accidents, which is a global cause for concern. An early warning system that indicates road conditions can contribute to the prevention task. For this purpose, a deep-learning based approach using a Convolutional Neural Network (CNN) to learn from random road images the safety factor is...

Query‐focused multi‐document summarization (Qf‐MDS) is a sub‐task of automatic text summarization that aims to extract a substitute summary from a document cluster of the same topic and based on a user query. Unlike other summarization tasks, Qf‐MDS has specific research challenges including the differences and similarities across related document...

Food recommendation systems play a crucial role in promoting personalized recommendations designed to help users find food and recipes that align with their preferences. However, many existing food recommendation systems have overlooked the important aspect of healthy-food and nutritional value of recommended foods, thereby limiting their effective...

Conventional feature extraction techniques in the face anti-spoofing domain either analyze the entire video sequence or focus on a specific segment to improve model performance. However, identifying the optimal frames that provide the most valuable input for the face anti-spoofing remains a challenging task. In this paper, we address this challenge...

Due to the growing availability of face anti-spoofing databases, researchers are increasingly focusing on video-based methods that use hundreds to thousands of images to assess their impact on performance. However, there is no clear consensus on the exact number of frames in a video required to improve the performance of face anti-spoofing tasks. I...

Face presentation attacks, also known as spoofing attacks, pose a significant threat to biometric systems that rely on facial recognition systems, such as access control systems, mobile payments, and identity verification systems. To prevent spoofing, several video-based methods have been presented in the literature that analyze facial motion in su...

In the present study, we review the methods and approaches used for uncertainty handling in hydrological forecasting of streamflow, floods, and snow. This review has six thematic sections: (1) general trends in accounting uncertainties in hydrological forecasting, (2) sources of uncertainties in hydrological forecasting, (3) methods used in the stu...

Objective: Although machine learning techniques have been repeatedly used for activity prediction from wearable devices, accurate classification of 24-hour activity behaviour categories from accelerometry data remains a challenge. We developed and validated a deep learning-based framework for classifying 24-hour activity behaviours from wrist-worn...

This study advocates a multi-criteria approach to improve the streamflow predictions in a data-scarce catchment of Chennai metropolitan city of India using the Soil Water and Assessment Tool (SWAT). The remotely sensed evapotranspiration (ET) data, groundwater recharge estimation, and parameter regionalisation were used to improve model prediction....

Access to spatiotemporal distribution of precipitation is needed in many hydrological applications. However, gauges often have spatiotemporal gaps. To mitigate this, we considered three main approaches: (i) using remotely sensing and reanalysis precipitation products; (ii) machine learning-based approaches; and (iii) a gap-filling software explicit...

This study aimed to examine the associations of sedentary time, and substituting sedentary time with physical activity and sleep, with cardiometabolic health markers while accounting for a full 24 hours of movement and non‐movement behaviors, cardiorespiratory fitness (CRF), and other potential confounders. The participants were 4,585 members of th...

Food recommendation systems aim to provide recommendations according to a user's diet, recipes, and preferences. These systems are deemed useful for assisting users in changing their eating habits towards a healthy diet that aligns with their preferences. Most previous food recommendation systems do not consider the health and nutrition of foods, w...

Hybrid recommender systems utilize advanced algorithms capable of learning heterogeneous sources of data and generating personalized recommendations for users. The data can range from user preferences (e.g., ratings or reviews) to item content (e.g., description or category). Prior studies in the field of recommender systems have primarily relied o...

Face presentation attack detection (PAD) plays a pivotal role in securing face recognition systems against spoofing attacks. Although great progress has been made in designing face PAD methods, developing a model that can generalize well to an unseen test domain remains a significant challenge. Moreover, due to different types of spoofing attacks,...

Text classification emerged as one of the main applications of natural language processing tasks such as topical analysis, sentiment analysis, and news classification where several deep learning models, as well as multi-classifier systems, have been put forward. This challenge is even more stressed when using low-resource languages. In this study,...

Over the past few years, the number and volume of data sources in healthcare databases has grown exponentially. Analyzing these voluminous medical data is both opportunity and challenge for knowledge discovery in health informatics. In the last decade, social network analysis techniques and community detection algorithms are being used more and mor...

This paper tackles the problem of sentiment analysis in the Arabic language where a new deep learning model has been put forward. The proposed model uses a hybrid bidirectional gated recurrent unit (BiGRU) and bidirectional long short-term memory (BiLSTM) additive-attention model where the Bidirectional GRU/LSTM reads the individual sentence input...

Due to its ease and popularity, social media has recently become an essential source of data for researchers and various stakeholder groups that seek a reliable assessment of their policies based on a comprehensive understanding of users' feedback and inputs, which are reflected in their posts and discussions. In this study, we investigate the issu...

Abstract Without deploying face anti‐spoofing countermeasures, face recognition systems can be spoofed by presenting a printed photo, a video, or a silicon mask of a genuine user. Thus, face presentation attack detection (PAD) plays a vital role in providing secure facial access to digital devices. Most existing video‐based PAD countermeasures lack...

Food recommendation systems have been increasingly developed in online food services to make recommendations to users according to their previous diets. Although unhealthy diets may cause challenging diseases such as diabetes, cancer, and premature heart diseases, most of the developed food recommendation systems neglect considering health factors...

The research focus in remote sensing scene image classification has been recently shifting towards deep learning (DL) techniques. However, even the state-of-the-art deep-learning-based models have shown limited performance due to the inter-class similarity and the intra-class diversity among scene categories. To alleviate this issue, we propose to...

p>social media became a fertile soil for various threats, extremism, and radicalization. This challenged policy-makers, researchers and practitioners. Preventing such extreme activities from happening becomes an ultimate priority at local and global scale. This paper introduces a new intertwine between radicalization and natural language processing...

p>social media became a fertile soil for various threats, extremism, and radicalization. This challenged policy-makers, researchers and practitioners. Preventing such extreme activities from happening becomes an ultimate priority at local and global scale. This paper introduces a new intertwine between radicalization and natural language processing...

In the era of web 2.0, social media has reshaped several industries nowadays, putting citizen’s view at the heart of their strategy and business model. This paper put forward a new approach to examine car parking industry ecosystem from social media perspective as revealed by the structure and insights inferred from hashtags network analysis. Start...

Medical image captioning is a very challenging task that has been rarely addressed in the literature on natural image captioning. Some existing image captioning techniques exploit objects present in the image next to the visual features while generating descriptions. However, this is not possible for medical image captioning when one requires follo...

In food diet communication domain, images convey important information to capture users' attention beyond the traditional ingredient content, making it crucial to influence user-decision about the relevancy of a given diet. By using a deep learning-based image clustering method, this paper proposes an Explainable Food Recommendation system that use...

We propose a new method for COVID-19 screening from cough sound, which is based on the extraction of Low-Level Descriptors from cough sound and make use of a Stacked Autoencoder to extract some specific non-linear features, and then, utilize Random Forest an ensemble learning technique to build a Machine Learning model that classifies a cough sound...

Snow depth estimation is an important parameter that guides several hydrological applications and climate change prediction. Despite advances in remote sensing technology and enhanced satellite observations, the estimation of snow depth at local scale still requires improved accuracy and flexibility. The advances in ubiquitous and wearable technolo...

Automatically understanding the content of medical images and delivering accurate descriptions is an emerging field of artificial intelligence that combines skills in both computer vision and natural language processing fields. Medical image captioning is involved in various applications related to diagnosis, treatment, report generation and comput...

p>The bag-of-words (BoW) model is one of the most popular representation methods for image classification. However, the lack of spatial information, the intra-class diversity, and the inter-class similarity among scene categories impair its performance in the remote-sensing domain. To alleviate these issues, this paper proposes to explore the spati...

Gene expression data have become increasingly important in machine learning and computational biology over the past few years. In the field of gene expression analysis, several matrix factorization-based dimensionality reduction methods have been developed. However, such methods can still be improved in terms of efficiency and reliability. In this...

Without deploying face anti-spoofing countermeasures, face recognition systems can be spoofed by presenting a printed photo, a video, or a silicon mask of a genuine user. Thus, face presentation attack detection (PAD) plays a vital role in providing secure facial access to digital devices. Most existing video-based PAD countermeasures lack the abil...

Face presentation attack detection (PAD) plays an important role in defending face recognition systems against presentation attacks. The success of PAD largely relies on supervised learning that requires a huge number of labeled data, which is especially challenging for videos and often requires expert knowledge. To avoid the costly collection of l...

Purpose: This study aimed to identify and characterize joint profiles of sedentary time and physical activity among adults and investigate how these profiles are associated with markers of cardiometabolic health. Methods: The participants included 3,702 of the Northern Finland Birth Cohort 1966 at age 46 years, who wore a hip-worn accelerometer...

Community detection is one of the primary problems in social network analysis and this problem has more challenges in attributed social networks. The purpose of community detection in attributed social networks is to discover communities with not only homogeneous node properties but also adherent structures. Although community detection has been ex...

Nowadays, microarray data processing is one of the most important applications in molecular biology for cancer diagnosis. A major task in microarray data processing is gene selection, which aims to find a subset of genes with the least inner similarity and most relevant to the target class. Removing unnecessary, redundant, or noisy data reduces the...

This paper investigates car parking users’ behaviors from social media perspective using social network based analysis of online communities revealed by mining the associated hashtags in Twitter. We propose a new interpretable community detection approach for mapping user’s car parking behavior by combining Clique, K-core and Girvan–Newman communit...

Cross-border trade barriers introduced by national authorities to protect local business and labor force cause substantial damage to international economical actors. Therefore, identifying such barriers beyond regulator’s audit reporting is of paramount importance. This paper contributes towards this goal by proposing a novel approach that uses nat...

In this letter a smart parking application based on Internet of things paradigm have been demonstrated. The application that uses PlacePod sensors, LoRaWan network and an Android mobile app is implemented at University car parking to deliver real-time services to drivers. The feasibility and soundness of the theoretical concept underpinning the dev...

This study advocates a multi-criteria approach to improve the streamflow predictions in a data-scarce catchment of Chennai metropolitan city of India using the Soil Water and Assessment Tool (SWAT). The remotely sensed evapotranspiration (ET) data, groundwater recharge estimation and parameter regionalization were used to improve model prediction....

Purpose Mobile phone-based human activity recognition (HAR) consists of inferring user’s activity type from the analysis of the inertial mobile sensor data. This paper aims to mainly introduce a new classification approach called adaptive k-nearest neighbors (AKNN) for intelligent HAR using smartphone inertial sensors with a potential real-time imp...

Food recommender-systems are considered an effective tool to help users adjust their eating habits and achieve a healthier diet. This paper aims to develop a new hybrid food recommender-system to overcome the shortcomings of previous systems, such as ignoring food ingredients, time factor, cold start users, cold start food items and community aspec...

COVID-19 pandemic has fueled the interest in artificial intelligence tools for quick diagnosis to limit virus spreading. Over 60% of people who are infected complain of a dry cough. Cough and other respiratory sounds were used to build diagnosis models in much recent research. We propose in this work, an augmentation pipeline which is applied on th...

Several Artificial Intelligence-based models have been developed for cancer prediction. In spite of the promise of artificial intelligence, there are very few models which bridge the gap between traditional human-centered prediction and the potential future of machine-centered cancer prediction. In this study, an efficient and effective model is de...

p>COVID-19 is a rapidly spreading viral disease and has affected over 100 countries worldwide. The numbers of casualties and cases of infection have escalated particularly in countries with weakened healthcare systems. Recently, reverse transcription-polymerase chain reaction (RT-PCR) is the test of choice for diagnosing COVID-19. However, current...

Several Artificial Intelligence-based models have been developed for COVID-19 disease diagnosis. In spite of the promise of artificial intelligence, there are very few models which bridge the gap between traditional human-centered diagnosis and the potential future of machine-centered disease diagnosis. Under the concept of human-computer interacti...