654 citations · 1.4k across the 23 of their papers we have counts for
26 papers
AI Radiologist: Revolutionizing Liver Tissue Segmentation with Convolutional Neural Networks and a Clinician-Friendly GUI
Ayman Al-Kababji, Faycal Bensaali, Sarada Prasad Dakua +1
Artificial Intelligence (AI) is a pervasive research topic, permeating various sectors and applications. In this study, we harness the power of AI, specifically convolutional neura…
Edge AI for Internet of Energy: Challenges and Perspectives
Yassine Himeur, Aya Nabil Sayed, Abdullah Alsalemi +2
The digital landscape of the Internet of Energy (IoE) is on the brink of a revolutionary transformation with the integration of edge Artificial Intelligence (AI). This comprehensiv…
Federated Learning for Computer Vision
Yassine Himeur, Iraklis Varlamis, Hamza Kheddar +5
Computer Vision (CV) is playing a significant role in transforming society by utilizing machine learning (ML) tools for a wide range of tasks. However, the need for large-scale dat…
Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives
Hafsa Bousbiat, Roumaysa Bousselidj, Yassine Himeur +5
Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. The…
Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization
Hamza Kheddar, Yassine Himeur, Somaya Al-Maadeed +2
Automatic speech recognition (ASR) has recently become an important challenge when using deep learning (DL). It requires large-scale training datasets and high computational and st…
Scheduling Techniques for Liver Segmentation: ReduceLRonPlateau Vs OneCycleLR
Ayman Al-Kababji, Faycal Bensaali, Sarada Prasad Dakua
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 scheduler…
Intelligent edge-based recommender system for internet of energy applications
Aya Sayed, Yassine Himeur, Abdullah Alsalemi +2
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 pr…
Blockchain-based Recommender Systems: Applications, Challenges and Future Opportunities
Yassine Himeur, Aya Sayed, Abdullah Alsalemi +6
Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the a…
Marketability of building energy efficiency systems based on behavioral change: A case study of a novel micro-moments based solution
Yassine Himeur, Abdullah Alsalemi, Faycal Bensaali +5
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 trig…
An IoT-Based Framework for Remote Fall Monitoring
Ayman Al-Kababji, Abbes Amira, Faycal Bensaali +3
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…
Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations
Ayman Al-Kababji, Faycal Bensaali, Sarada Prasad Dakua +1
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…
A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects
Yassine Himeur, Abdullah Alsalemi, Ayman Al-Kababji +5
Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) techn…
Smart non-intrusive appliance identification using a novel local power histogramming descriptor with an improved k-nearest neighbors classifier
Yassine Himeur, Abdullah Alsalemi, Faycal Bensaali +1
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 ef…
Appliance-Level Monitoring with Micro-Moment Smart Plugs
Abdullah Alsalemi, Yassine Himeur, Faycal Bensaali +1
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 appro…
Cloud Energy Micro-Moment Data Classification: A Platform Study
Abdullah Alsalemi, Ayman Al-Kababji, Yassine Himeur +2
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…
The emergence of Explainability of Intelligent Systems: Delivering Explainable and Personalised Recommendations for Energy Efficiency
Christos Sardianos, Iraklis Varlamis, Christos Chronis +5
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…
Reshaping consumption habits by exploiting energy-related micro-moment recommendations: A case study
Christos Sardianos, Iraklis Varlamis, Christos Chronis +5
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 transitio…
Energy Data Visualizations on Smartphones for Triggering Behavioral Change: Novel Vs. Conventional
Ayman Al-Kababji, Abdullah Alsalemi, Yassine Himeur +4
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…
Appliance identification using a histogram post-processing of 2D local binary patterns for smart grid applications
Yassine Himeur, Abdullah Alsalemi, Faycal Bensaali +1
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…
Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives
Yassine Himeur, Khalida Ghanem, Abdullah Alsalemi +2
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, en…
Efficient multi-descriptor fusion for non-intrusive appliance recognition
Yassine Himeur, Abdullah Alsalemi, Faycal Bensaali +1
Consciousness about power consumption at the appliance level can assist user in promoting energy efficiency in households. In this paper, a superior non-intrusive appliance recogni…
Improving in-home appliance identification using fuzzy-neighbors-preserving analysis based QR-decomposition
Yassine Himeur, Abdullah Alsalemi, Faycal Bensaali +1
This paper proposes a new appliance identification scheme by introducing a novel approach for extracting highly discriminative characteristic sets that can considerably distinguish…
Building power consumption datasets: Survey, taxonomy and future directions
Yassine Himeur, Abdullah Alsalemi, Faycal Bensaali +1
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…
Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations
Yassine Himeur, Abdullah Alsalemi, Ayman Al-Kababji +2
Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, a…