Fadi Al Machot

Norwegian University of Life Sciences · Department of Mathematical Sciences and Technology (IMT)

A core aspect of advanced driver assistance systems (ADAS) is to support the driver with information about the current environmental situation of the vehicle. Bad weather conditions such as rain might occlude regions of the windshield or a camera lens and therefore affect the visual perception. Hence, the automated detection of raindrops has a sign...

This paper is submitted for publication on journal of NEUROCOMPUTING (NEUCOM-D-17-01233) This paper presents a novel online adaptive neuro-computing framework for a robust time-series forecast. The proposed framework does mimic the human mind's biological two-thinking model. Our mind makes decisions/calculations using a two-connected system. A firs...

In Active and Assisted Living environments (AAL), a major service that can be provided is the automated assessment of old people's well-being. Therefore, activity recognition is required to detect what types of help disabled persons need to support them in their daily life activities. Unfortunately, it is still a difficult task to estimate the size...

One of the main objectives of Active and Assisted Living (AAL) environments is to ensure that elderly and/or disabled people perform/live well in their immediate environments; this can be monitored by among others the recognition of emotions based on non-highly intrusive sensors such as Electrodermal Activity (EDA) sensors. However, designing a lea...

Time series forecasting is critical for various fields such as stock market prediction, weather forecasting, and risk management. Traditional models like ARIMA and SARIMA are limited in capturing non-linear and multivariate dependencies in time series data. Deep learning models such as LSTM and Transformer-based architectures have emerged to addres...

Data and metadata documentation requirements for explainable-AI-ready (XAIR) models and data in physics-based simulation technology are discussed by analysing different perspectives from the literature on two core aspects: First, the scope of the simulation; this category is taken to include subject matter, the objective with which the simulation i...

Growing concerns over the lack of transparency in AI, particularly in high-stakes fields like healthcare and finance, drive the need for explainable and trustworthy systems. While Large Language Models (LLMs) perform exceptionally well in generating accurate outputs, their "black box" nature poses significant challenges to transparency and trust. T...

This paper presents a hybrid methodology that enhances the training process of deep learning (DL) models by embedding domain expert knowledge using ontologies and answer set programming (ASP). By integrating these symbolic AI methods, we encode domain-specific constraints, rules, and logical reasoning directly into the model's learning process, the...

Recent advancements in Human Emotion Recognition (HER) technology can enable active and assisted living systems to respond more intuitively to the emotional needs of users, enhancing their overall quality of life. This chapter presents a comprehensive study using only Electrocardiogram (ECG) data for the recognition of human emotions. We introduce...

Emotion and activity recognition can play an important role in supporting people, especially in the AAL environment. Modeling is one of the important factors that can be used to introduce such support. Metamodel conceptualization provides the basis for designing a modeling framework. Metamodel development is an iterative activity that is commonly e...

Zero-shot learning (ZSL) is a machine learning paradigm that enables models to recognize and classify data from classes they have not encountered during training. This approach is particularly advantageous in recognizing activities where labeled data is limited, allowing models to identify new, unseen activities by leveraging semantic knowledge fro...

In Generalized Zero-Shot Learning (GZSL), we aim to recognize both seen and unseen categories using a model trained only on seen categories. In computer vision, this translates into a classification problem, where knowledge from seen categories is transferred to unseen categories by exploiting the relationships between visual features and available...

This position paper reports on the initial discussions within the Knowledge Graph Alliance's working group on explainable-AI-ready data and metadata principles, which was created in March 2024. At present, we are taking initial steps toward capturing core concepts related to explanation, grounding, reliance, and trust; the scope also extends to pot...

This position paper reports on the requirements analysis within the project Battery Cell Assembly Twin (BatCAT), which develops a digital twin for battery manufacturing. The focus is on the aspects of this work that are at the intersection between semantic web technology and materials science and engineering, specifically, the co-design of the arch...

Floating wind turbines have enormous potential in harnessing high wind speeds in deep ocean locations. Compared with bottom-fixed technology, the latter is limited by the depth to which it can be installed. These turbines are designed to be deployed in deep waters and rely on specialized floaters such as spar buoys, semi-submersibles, and tension l...

Recently, transfer learning has gained popularity in the machine learning community. Transfer Learning (TL) has emerged as a promising paradigm that leverages knowledge learned from one or more related domains to improve prediction accuracy in a target domain with limited data. However, for time series forecasting (TSF) applications, transfer learn...

This paper addresses the issue of reliable and automated analog circuit’s structure recognition (ACSR). First, it presents a comprehensive critical review of the related state-of-the-art. Then, essentially, this study proposes and validates a novel approach for realizing a dependable structure recognition system for analog circuits through a compre...

Automated early detection and classification of paddy diseases help in applying treatment efficiently according to the detected diseases. Early detection also minimises the usage of chemical substances and pesticides and hinders the spread of the disease to healthy crops. On a broader scale, it aids in halting the global spread of diseases. Thus, i...

Neural-symbolic learning, an intersection of neural networks and symbolic reasoning, aims to blend neural networks' learning capabilities with symbolic AI's interpretability and reasoning. This paper introduces an approach designed to improve the performance of neural models in learning reasoning tasks. It achieves this by integrating Answer Set Pr...

Human Activity Recognition (HAR) has become one of the leading research topics of the last decade. As sensing technologies have matured and their economic costs have declined, a host of novel applications, e.g., in healthcare, industry, sports, and daily life activities have become popular. The design of HAR systems requires different time-consumin...

Zero-shot Learning (ZSL) classification categorizes or predicts classes (labels) that are not included in the training set (unseen classes). Recent works proposed different semantic autoencoder (SAE) models where the encoder embeds a visual feature vector space into the semantic space and the decoder reconstructs the original visual feature space....

This chapter presents the Lipidomics Informatics for Life‐Science workflow, which consists of multiple modules for targeted and untargeted computational lipid analyses in mass spectrometry. The modules cover assay design, raw data analysis, standardization of results, visualization, data storage, enrichment, and data integration. We highlight and d...

Citation: Deeb, A.; Ibrahim, A.; Salem, M.; Pichler, J.; Tkachov, S.; Karaj, A.; Al Machot, F.; Kyandoghere, K. A Robust Abstract: Analog mixed-signal (AMS) verification is one of the essential tasks in the development process of modern systems-on-chip (SoC). Most parts of the AMS verification flow are already automated, except for stimuli generati...

The boom seen in artificial intelligence in recent years has led to a revolution in the automotive industry. Numerous automakers around the world, such as Tesla, Toyota, Honda, and BMW, have achieved giant strides in the development of e-autonomous vehicles. Consequently, shared electric automated vehicle mobility (SEAVM) systems, which are a cruci...

Zero-shot Learning (ZSL) classification categorizes or predicts classes (labels) that are not included in the training set (unseen classes). Recent works proposed different semantic autoencoder (SAE) models where the encoder embeds a visual feature vector space into the semantic space and the decoder reconstructs the original visual feature space....

The use of IoT-based Emotion Recognition (ER) systems is in increasing demand in many domains such as active and assisted living (AAL), health care and industry. Combining the emotion and the context in a unified system could enhance the human support scope, but it is currently a challenging task due to the lack of a common interface that is capabl...

Mass spectrometry is a widely used technology to identify and quantify biomolecules such as lipids, metabolites and proteins necessary for biomedical research. In this study, we catalogued freely available software tools, libraries, databases, repositories and resources that support lipidomics data analysis and determined the scope of currently use...

Zero-Shot Learning (ZSL) is related to training machine learning models capable of classifying or predicting classes (labels) that are not involved in the training set (unseen classes). A well-known problem in Deep Learning (DL) is the requirement for large amount of training data. Zero-Shot learning is a straightforward approach that can be applie...

Emotions are an essential part of a person’s mental state and influence her/his behavior accordingly. Consequently, emotion recognition and assessment can play an important role in supporting people with ambient assistance systems or clinical treatments. Automation of human emotion recognition and emotion-aware recommender systems are therefore inc...

Nowadays, digital content like videos, audio and images are widely used as evidence in criminal courts and forensic laboratories. Due to the advanced low-cost and easily available multimedia/communication tools and softwares, manipulation of the content is a no-brain task. Thus, the protection of digital content originality is a challenge for the c...

The current population worldwide extensively uses social media to share thoughts, societal issues, and personal concerns. Social media can be viewed as an intelligent platform that can be augmented with a capability to analyze and predict various issues such as business needs, environmental needs, election trends (polls), governmental needs, etc. T...

Document imaging/scanning approaches are essential techniques for digitalizing documents in various real-world contexts, e [...]

Intelligent sociotechnical systems are gaining momentum in today’s information-rich society, where different technologies are used to collect data from such systems and mine this data to make useful insights about our daily activities [...]

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITSs) are being widely adopted worldwide to improve the efficiency and safety of the transportation system [...]

Most people nowadays depend on the Web as a primary source of information. Statistical studies show that young people obtain information mainly from Facebook, Twitter, and other social media platforms. By relying on these data, people may risk drawing the incorrect conclusions when reading the news or planning to buy a product. Therefore, systems t...

Due to significant advances in sensor technology, studies towards activity recognition have gained interest and maturity in the last few years. Existing machine learning algorithms have demonstrated promising results by classifying activities whose instances have been already seen during training. Activity recognition methods based on real-life set...

It is an important topic in Active and Assisted Living (AAL) research and development to support elderly people suffering from memory impairment in their daily activities. A promising approach to such support is providing memory aids based on knowledge of how the person to be supported usually (i.e., in an unimpaired condition) copes with her/his d...

Machine learning approaches for human emotion recognition have recently demonstrated high performance. However, only/mostly for subject-dependent approaches, in a variety of applications like advanced driver assisted systems, smart homes and medical environments. Therefore, now the focus is shifted more towards subject-independent approaches, which...

Abstract—Providing reminders to elderly people in their home environment, while they perform their daily activities, is considered as a user support activity, and thus a relevant topic in Active and Assisted Living (AAL) research and development. Determining such reminders implies decision-making, since the actions’ flow (behavior) usually involves...

In Active and Assisted Living environments (AAL), one of the major tasks is to make sure that old people or disabled persons do feel well in their environment. Unfortunately, it is still a difficult task to design a learning system or build a machine learning model which can be trained on a group of subjects using physiological sensors and performs...

In Active and Assisted Living (AAL) systems, a major task is to support old people who suffer from diseases such as Dementia or Alzheimer. To provide required support, it is essential to know their Activities of Daily Living (ADL) and support them accordingly. Thus, the accurate recognition of human activities is the foremost task of such an AAL sy...

The aim of the Human Behavior Monitoring and Support (HBMS) project has been to actively assist individuals in activities of daily living and other situations using users’ own episodic knowledge. This knowledge is represented and preserved in HBMS in the HCM, the Human Cognitive Model, expressed in the domain specific modelling language HCM-L. HCM...

This chapter discusses a promising approach for multisensor-based activity recognition in smart homes. The research originated in the domain of active and assisted living, particularly in the field of supporting people in mastering their daily life activities. The chapter proposes (a) a reasoning method based on answer set programming that uses dif...

Emotions play an extremely important role in how we make decisions, in planning, in reasoning, and in other human mental states. The recognition of a driver’s emotions is becoming a vital task for advanced driver assistance systems (ADAS). Monitoring drivers’ emotions while driving offers drivers important feedback that can be useful in preventing...

"This paper is accepted for publication and this is a preprint version". This paper does present a comprehensive concept for a robust and reliable truck detection involving solely one single presence sensor (e.g. an inductive loop, but also any other presence sensor) at a signalized traffic junction. Hereby, two operations modes are distinguished:...

This book draws new attention to domain-specific conceptual modeling by presenting the work of thought leaders who have designed and deployed specific modeling methods. It provides hands-on guidance on how to build models in a particular domain, such as requirements engineering, business process modeling or enterprise architecture. In addition to t...

Activity recognition systems are a large field of research and development, currently with a focus on advanced machine learning algorithms, innovations in the field of hardware architecture, and on decreasing the costs of monitoring while increasing safety. This paper concentrates on the applications of activity recognition systems and surveys thei...

Modeling and modeling methods are crucial for information systems engineering but are seldom seamlessly integrated into all phases of development and operation: Practitioners challenge the benefits of modeling and complain about the confusing variety of concepts with overlapping semantics, symbols and syntactic rules of today’s standardized, “unive...

Emotions play an extremely important role in how we make a decision, planning, reasoning and other human mental states. The recognition of these emotions is becoming a vital task for e-healthcare systems. Using bio-sensors such as Electroencephalogram (EEG) to recognise the mental state of patients that could need a special care offers an important...

Determining the appropriate data window size for online sensor data streams to recognize a specific activity is still a challenging task. In particular, when new sensor events are recorded. This paper proposes a windowing algorithm which presents promising results to recognize complex activities, e.g., in a smart home environment. The underlying ba...

This paper presents a promising approach to enhance multi - sensor based activity recognition in smart homes. The research is originated in the domain of Active and Assisted Living which mainly is about support ing older people to master their daily life activities . The paper proposes (a) a windowing technique which can be used for online sensor s...

Electroencephalogram (EEG) signals play an important role in e-healthcare systems, especially to recognise the mental state of patients that could need a special care. This paper presents an EEG-based emotion recognition approach to detect the emotional state of patients for Ambient Assisted Living (AAL). The proposed approach combines wavelet ener...

Supporting drivers by Advanced Driver Assistance Systems (ADAS) significantly increases road safety. Driver’s emotions recognition is a building block of advanced systems for monitoring the driver’s comfort and driving ergonomics additionally to driver’s fatigue and drowsiness forecasting. This paper presents an approach for driver emotions recogni...

This paper does present and validate the concept of a black-box trained neurocomputing based matrix inversion system. Hereby, the neurocomputing processor is realized by a Cellular Neural Network (CNN) system. First, the high significance of an ultrafast and re-altime matrix inversion function for science and engineering is highlighted. Then we bri...

Meta-modeling platforms that support the automatic generation of modeling tools open a new quality in information systems development for engineers: Emphasis can be put on the design and use of a modeling language that is customized to the particular needs and desired features. This may contribute to strengthen the information-system design phase a...