Faycal Bensaali

Faycal Bensaali
Qatar University · Department of Electrical Engineering

PhD

About

212
Publications
39,683
Reads
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2,546
Citations
Citations since 2016
167 Research Items
2295 Citations
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20162017201820192020202120220200400600
Introduction
Faycal Bensaali is currently a Professor in Electrical Engineering at Qatar University. He took other academic positions at Queen’s University Belfast-UK and the University of Hertfordshire-UK. His research interests are mainly in embedded systems and high performance computing, intelligent systems and connected health.

Publications

Publications (212)
Article
Full-text available
Machine learning and computer vision techniques have grown rapidly in recent years due to their automation, suitability, and ability to generate astounding results. Hence, in this paper, we survey the key studies that are published between 2014 and 2022, showcasing the different machine learning algorithms researchers have used to segment the liver...
Article
Full-text available
In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluat...
Article
Full-text available
The building internet of things (BIoT) is quite a promising concept for curtailing energy consumption, reducing costs, and promoting building transformation. Besides, integrating artificial intelligence (AI) into the BIoT is essential for data analysis and intelligent decision-making. Thus, data-driven approaches to infer occupancy patterns usage a...
Article
Full-text available
Interactive data visualization tools for residential energy data are instrumental indicators for analyzing end user behavior. These visualizations can be used as continuous home feedback systems and can be accessed from mobile devices using touch-based applications. Visualizations have to be carefully selected in order for them to partake in the be...
Article
Smart cities attempt to reach net-zero emissions goals by reducing wasted energy while improving grid stability and meeting service demand. This is possible by adopting next-generation energy systems, which leverage artificial intelligence, the Internet of things (IoT), and communication technologies to collect and analyze big data in real-time and...
Chapter
Machine learning and computer vision techniques have influenced many fields including the biomedical one. The aim of this paper is to investigate the important concept of schedulers in manipulating the learning rate (LR), for the liver segmentation task, throughout the training process, focusing on the newly devised OneCycleLR against the ReduceLRo...
Article
Full-text available
Internet of Things (IoT) devices are becoming popular solutions for smart home and office environments and contribute the most to energy efficiency. The most common implementation of such solutions relies on smart home systems that are hosted on the cloud. They collect data from a multitude of sensors, process it in real-time on the cloud and deliv...
Article
Background: Training is an essential aspect of providing high-quality treatment and ensuring patient safety in any medical practice. Because extracorporeal membrane oxygenation (ECMO) is a complicated operation with various elements, variables, and irregular situations, doctors must be experienced and knowledgeable about all conventional protocols...
Chapter
Full-text available
This chapter provides an introduction to the use of machine learning (ML) for the diagnosis of heart failure (HF). ML is the field responsible for developing methods and tools that can learn and make decisions based on data. The growing number of HF patients and increasing healthcare costs indicate the importance of the early diagnosis of HF for ef...
Article
Full-text available
With the widespread use of Internet of things (IoT), mobile phones, connected devices and artificial intelligence (AI), recommender systems (RSs) have become a booming technology because of their capability to analyze big data and shape users’ habits through well-designed, contextual, and engaging recommendations. Novel generations of RSs have been...
Article
Full-text available
Smart nonintrusive load monitoring (NILM) represents a cost‐efficient technology for observing power usage in buildings. It tackles several challenges in transitioning into a more effective, sustainable, and digital energy efficiency environment. This paper presents a comprehensive review of recent trends in the NILM field, in which we propose a mu...
Preprint
Machine learning and computer vision techniques have influenced many fields including the biomedical one. The aim of this paper is to investigate the important concept of schedulers in manipulating the learning rate (LR), for the liver segmentation task, throughout the training process, focusing on the newly devised OneCycleLR against the ReduceLRo...
Article
Full-text available
Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of various types of user data, including demographics, preferences, social interactions, etc. in order to develop accurate and preci...
Article
Despite the variety of sensors that can be used in a smart home or office setup, for monitoring energy consumption and assisting users to save energy, their usefulness is limited when they are not properly integrated into the daily activities of humans. Energy-saving applications in such environments can benefit from the use of sensors and actuator...
Article
Full-text available
In the above article [1] , the fifth author, Guillaume Alinier, was missing. The author byline should have five authors: Abdullah Alsalemi, Yahya Alhomsi, Fayçal Bensaali, Guillaume Alinier, and Ali Ait Hssain.
Chapter
Full-text available
The detection of anomalous energy usage could help significantly in signaling energy wastage and identifying faulty appliances, especially if the individual power traces are analyzed. To that end, this paper proposes a novel abnormal energy consumption detection approach at the appliance-level using autoencoder and micro-moments. Accordingly, energ...
Article
The emerging field of flexible bio-mechatronic technologies, such as wearable sensors, actuators and robots, are playing more and more crucial roles in the design and development of next generation healthcare and rehabilitation systems that safely and smartly interact with human patients. To create a complex and intelligent healthcare and rehabilit...
Article
Full-text available
Energy efficiency based on behavioral change has attracted increasing interest in recent years, although, solutions in this area lack much needed techno-economic analysis. That is due to the absence of both prospective studies and consumer awareness. To close such gap, this paper proposes the first techno-economic assessment of a behavioral change-...
Article
Due to the ubiquity and maturity of Artificial Intelligence (AI), it became an essential tool in the development real-time Internet of energy (IoE) solutions. Also, since cloud platforms are not being the first implementation choice due to their bandwidth and latency issues that limit data transmission capacity, Edge Computing is becoming a popular...
Article
Simulation plays an important part in enhancing the outcomes of clinical training worldwide. In particular, extracorporeal membrane oxygenation (ECMO) is a life-saving procedure that utilizes a cardiopulmonary bypass circuit to offer short or mid-term respiratory and circulatory assistance to seriously ill patients. After the current Coronavirus (C...
Preprint
Full-text available
Preserving energy in households and office buildings is a significant challenge, mainly due to the recent shortage of energy resources, the uprising of the current environmental problems, and the global lack of utilizing energy-saving technologies. Not to mention, within some regions, COVID-19 social distancing measures have led to a temporary tran...
Article
Preserving energy in households and office buildings is a significant challenge, mainly due to the recent shortage of energy resources, the uprising of the current environmental problems, and the global lack of utilizing energy-saving technologies. Not to mention, within some regions, COVID-19 social distancing measures have led to a temporary tran...
Preprint
Full-text available
Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of various types of user data, including demographics, preferences, social interactions, etc. in order to develop accurate and preci...
Conference Paper
Introduction: Almost two million people worldwide die annually due to hepatic-related diseases. Half of these diseases are attributed to cirrhosis and the other half are related to hepatitis and hepatocellular carcinoma (HCC). The liver is also a metastasis hub from adjacent organs. This research aims to create an accurate high-quality delineation...
Conference Paper
Full-text available
When investigating how people conserve energy, most researchers and decision-makers render a conceptual distinction between prevention (e.g. unplugging devices) and productivity measures. Nevertheless, such a two-dimensional approach is inefficient from both a conceptual and policy standpoint, since it ignores individual differences that influence...
Article
Full-text available
(1) Background: Simulation-based training (SBT) is the practice of using hands-on training to immerse learners in a risk-free and high-fidelity environment. SBT is used in various fields due to its risk-free benefits from a safety and an economic perspective. In addition, SBT provides immersive training unmatched by traditional teaching the interac...
Article
Internet of Energy (IoE) is revolutionizing the building energy industry by introducing numerous innovations that help in data collection, interpretation, and behavioural improvement. Consequently, collecting and analyzing big data is fiercely impacting every field and research area in the context of energy utilization, and therefore, the concept o...
Article
Internet-of-Things (IoT) is an appealing service to revolutionise Smart City (SC) initiatives across the globe. IoT interconnects a plethora of digital devices known as Sensor Nodes (SNs) to the Internet. Due to their high performance and exceptional Quality-of-Service (QoS) Multiprocessor System-on-Chip (MPSoC) computing architectures are gaining...
Chapter
Full-text available
Edge computing is attracting an increasing attention presently even though most of the building energy efficiency solutions are still using cloud computing for gathering, pre-processing and analyzing energy data. However, edge computing still requires more power in order to be used alone to meet the high computation demand of artificial intelligenc...
Article
Full-text available
Simulators for extracorporeal membrane oxygenation (ECMO) have problems of bulky devices and low-fidelity methodologies. Hence, ongoing efforts for optimizing modern solutions focus on minimizing expenses and blending training with the intensive care unit. This is particularly evident following the coronavirus pandemic, where economic resources hav...
Article
Full-text available
Despite many advancements in extracorporeal membrane oxygenation (ECMO), the procedure is still correlated with a high risk of patient complications. Simulation-based training provides the opportunity for ECMO staff to practice on real-life scenarios without exposing ECMO patients to medical errors while practicing. At Hamad Medical Corporation (HM...
Article
Fall detection is a serious healthcare issue that needs to be solved. Falling without quick medical intervention would lower elderly's chances of survival, especially if living alone. Hence, the need is there for developing fall detection algorithms with high accuracy. This paper presents a novel IoT-based system for fall detection that includes a...
Article
Full-text available
Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a mino...
Preprint
There is no denying how machine learning and computer vision have grown in the recent years. Their highest advantages lie within their automation, suitability, and ability to generate astounding results in a matter of seconds in a reproducible manner. This is aided by the ubiquitous advancements reached in the computing capabilities of current grap...
Preprint
Full-text available
Fall detection is a serious healthcare issue that needs to be solved. Falling without quick medical intervention would lower the chances of survival for the elderly, especially if living alone. Hence, the need is there for developing fall detection algorithms with high accuracy. This paper presents a novel IoT-based system for fall detection that i...
Article
Full-text available
Anomaly detection in energy consumption is a crucial step towards developing efficient energy saving systems, diminishing overall energy expenditure and reducing carbon emissions. Therefore, implementing powerful techniques to identify anomalous consumption in buildings and providing this information to end‐users and managers is of significant impo...
Preprint
Full-text available
Non-intrusive load monitoring (NILM) is a key cost-effective technology for monitoring power consumption and contributing to several challenges encountered when transiting to an efficient, sustainable, and competitive energy efficiency environment. This paper proposes a smart NILM system based on a novel local power histogramming (LPH) descriptor,...
Preprint
Full-text available
Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, whi...
Article
Full-text available
Non-intrusive load monitoring (NILM) is a key cost-effective technology for monitoring power consumption and contributing to several challenges encountered when transiting to an efficient, sustainable, and competitive energy efficiency environment. This paper proposes a smart NILM system based on a novel local power histogramming (LPH) descriptor,...
Chapter
Human population are striving against energy-related issues that not only affects society and the development of the world, but also causes global warming. A variety of broad approaches have been developed by both industry and the research community. However, there is an ever increasing need for comprehensive, end-to-end solutions aimed at transfor...
Article
Full-text available
Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, whi...
Chapter
The environmental change and its effects, caused by human influences and natural ecological processes over the last decade, prove that it is now more prudent than ever to transition to more sustainable models of energy consumption behaviors. User energy consumption is inductively derived from the time-to-time standards of living that shape the user...
Chapter
Full-text available
In recent years, the automatic identification of electrical devices through their power consumption signals finds a variety of applications in smart home monitoring and non-intrusive load monitoring (NILM). This work proposes a novel appliance identification scheme and introduces a new feature extraction method that represents power signals in a 2D...
Article
Full-text available
Medical simulators, employed in extracorporeal membrane oxygenation (ECMO), are burdened with costly equipment and low-fidelity methodologies. This dichotomy necessitated a new approach that eliminates high-costs and integrates with the critical care environment. This is especially applicable after the Coronavirus pandemic, where resources and supp...
Article
Full-text available
Internet of Medical Things (IoMT) systems are envisioned to provide high-quality healthcare services to patients in the comfort of their home, utilizing cutting-edge Internet of Things (IoT) technologies and medical sensors. Patient comfort and willingness to participate in such efforts is a prominent factor for their adoption. As IoT technology ha...
Preprint
Full-text available
Human population are striving against energy-related issues that not only affects society and the development of the world, but also causes global warming. A variety of broad approaches have been developed by both industry and the research community. However, there is an ever increasing need for comprehensive, end-to-end solutions aimed at transfor...
Article
Full-text available
Recently, a growing interest has been dedicated towards developing and implementing low-cost energy efficiency solutions in buildings. Accordingly, non-intrusive load monitoring has been investigated in various academic and industrial projects for capturing device-specific consumption footprints without any additional hardware installation. However...
Preprint
Full-text available
In spite of the substantial advance in developing energy-efficient buildings, power demand in the building sector is still remarkably growing due to teleworking and e-learning triggered by the COVID-19 movement restrictions. This is highlighted by the inefficiency of energy saving measures that have recently been set owing to the the marketability...
Article
Full-text available
In the last decade, extended efforts have been poured into energy efficiency. Several energy consumption datasets were henceforth published, with each dataset varying in properties, uses and limitations. For instance, building energy consumption patterns are sourced from several sources, including ambient conditions, user occupancy, weather conditi...
Article
Full-text available
Nowadays, analyzing, detecting, and visualizing abnormal power consumption behavior of householders are among the principal challenges in identifying ways to reduce power consumption. This paper introduces a new solution to detect energy consumption anomalies based on extracting micro-moment features using a rule-based model. The latter is used to...
Preprint
Full-text available
Energy efficiency is a crucial factor in the well-being of our planet. In parallel, Machine Learning (ML) plays an instrumental role in automating our lives and creating convenient workflows for enhancing behavior. So, analyzing energy behavior can help understand weak points and lay the path towards better interventions. Moving towards higher perf...
Preprint
Full-text available
The recent advances in artificial intelligence namely in machine learning and deep learning, have boosted the performance of intelligent systems in several ways. This gave rise to human expectations, but also created the need for a deeper understanding of how intelligent systems think and decide. The concept of explainability appeared, in the exten...
Preprint
Full-text available
The environmental change and its effects, caused by human influences and natural ecological processes over the last decade, prove that it is now more prudent than ever to transition to more sustainable models of energy consumption behaviors. User energy consumption is inductively derived from the time-to-time standards of living that shape the user...
Preprint
Full-text available
Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a mino...
Preprint
Full-text available
This paper conveys the importance of using suitable data visualizations for electrical energy consumption and the effect it carries on reducing said consumption. Data visualization tools construct an important pillar in energy micro-moments, i.e., the concept of providing the right information at the right time in the right way for a specific power...
Preprint
Full-text available
Identifying domestic appliances in the smart grid leads to a better power usage management and further helps in detecting appliance-level abnormalities. An efficient identification can be achieved only if a robust feature extraction scheme is developed with a high ability to discriminate between different appliances on the smart grid. Accordingly,...
Chapter
This paper proposes a new appliance identification scheme by introducing a novel approach for extracting highly discriminative characteristic sets that can considerably distinguish between various appliance footprints. In this context, a precise and powerful characteristic projection technique depending on fuzzy-neighbors-preserving analysis based...
Article
Full-text available
The recent advances in artificial intelligence namely in machine learning and deep learning, have boosted the performance of intelligent systems in several ways. This gave rise to human expectations, but also created the need for a deeper understanding of how intelligent systems think and decide. The concept of explainability appeared, in the exten...