Abdelhamid Bouchachia

Abdelhamid Bouchachia

Bournemouth University

H-index: 32

Europe-United Kingdom

About Abdelhamid Bouchachia

Abdelhamid Bouchachia Information

University

Bournemouth University

Position

United Kingdom

Citations(all)

6401

Citations(since 2020)

4011

Cited By

4063

hIndex(all)

32

hIndex(since 2020)

22

i10Index(all)

64

i10Index(since 2020)

36

Email

University Profile Page

Bournemouth University

Abdelhamid Bouchachia Skills & Research Interests

Machine Learning

Artificial Intelligence

Data Science

Top articles of Abdelhamid Bouchachia

Event Detection for Non-intrusive Load Monitoring using Tukey s Fences

The primary objective of non-intrusive load monitoring (NILM) techniques is to monitor and track power consumption within residential buildings. This is achieved by approximating the consumption of each individual appliance from the aggregate energy measurements. Event-based NILM solutions are generally more accurate than other methods. Our paper introduces a novel event detection algorithm called Tukey's Fences-based event detector (TFED). This algorithm uses the fast Fourier transform in conjunction with the Tukey fences rule to detect variations in the aggregated current signal. The primary benefit of TFED is its superior ability to accurately pinpoint the start times of events, as demonstrated through simulations. Our findings reveal that the proposed algorithm boasts an impressive 99% accuracy rate, surpassing the accuracy of other recent works in the literature such as the Cepstrum and GOF statistic-based analyses, which only achieved 98% accuracy.

Authors

Sidi Mohammed Kaddour,Mohamed Lehsaini,Abdelhamid Bouchachia

Journal

arXiv preprint arXiv:2402.17809

Published Date

2024/2/27

Unified embedding and clustering

This paper investigates the problem of treating embedding and clustering simultaneously to uncover data structure reliably by constraining manifold embedding through clustering. Conversely, most existing methods perform embedding sequentially, followed by clustering, which leads to a clustering that sometimes pushes data towards a direction induced by embedding. Instead, we perform them simultaneously through an original formulation, which allows for preserving the data’s original structure in the embedding space and producing a better clustering assignment. To achieve this goal, we introduce a novel algorithm that unifies manifold embedding and clustering (UEC). The proposed UEC algorithm is based on a bi-objective loss function that combines data embedding and clustering, which is optimised using three different ways: (1) Comma Variant, (2) Plus Variant, and (3) Light Plus Variant. The …

Authors

Mebarka Allaoui,Mohammed Lamine Kherfi,Abdelhakim Cheriet,Abdelhamid Bouchachia

Published Date

2023/10/11

Randomising the Simple Recurrent Network: a lightweight, energy-efficient RNN model with application to forecasting problems

Multi-variate time-series (MTS) forecasting is the prediction of future for a sequence of data. The process of analysing obtained data can benefit the community financially and securely, for instance observing stock exchange trends and predicting malicious attacks whenabout. MTS forecasting models face many problems including data and model complexity, energy constraints and computational cost. These problems could affect budget allocation, latency and carbon emission. Recurrent neural networks are one of these models, which are known for their computational complexity due to slow learning process which requires more energy to train. Contributing to green AI, in this paper, we propose a competitive and energy-efficient lightweight recurrent neural network based on a hybrid neural architecture that combines Random Neural Network (RaNN) and Simple Recurrent Network (SRN), namely Random Simple …

Authors

Mohammed Elmahdi Khennour,Abdelhamid Bouchachia,Mohammed Lamine Kherfi,Khadra Bouanane

Journal

Neural Computing and Applications

Published Date

2023/9

A Survey on Ambient Sensor-Based Abnormal Behaviour Detection for Elderly People in Healthcare

With advances in machine learning and ambient sensors as well as the emergence of ambient assisted living (AAL), modeling humans’ abnormal behaviour patterns has become an important assistive technology for the rising elderly population in recent decades. Abnormal behaviour observed from daily activities can be an indicator of the consequences of a disease that the resident might suffer from or of the occurrence of a hazardous incident. Therefore, tracking daily life activities and detecting abnormal behaviour are significant in managing health conditions in a smart environment. This paper provides a comprehensive and in-depth review, focusing on the techniques that profile activities of daily living (ADL) and detect abnormal behaviour for healthcare. In particular, we discuss the definitions and examples of abnormal behaviour/activity in the healthcare of elderly people. We also describe the public ground-truth datasets along with approaches applied to produce synthetic data when no real-world data are available. We identify and describe the key facets of abnormal behaviour detection in a smart environment, with a particular focus on the ambient sensor types, datasets, data representations, conventional and deep learning-based abnormal behaviour detection methods. Finally, the survey discusses the challenges and open questions, which would be beneficial for researchers in the field to address.

Authors

Yan Wang,Xin Wang,Damla Arifoglu,Chenggang Lu,Abdelhamid Bouchachia,Yingrui Geng,Ge Zheng

Journal

Electronics

Published Date

2023/3/24

Scaling up stochastic gradient descent for non-convex optimisation

Stochastic gradient descent (SGD) is a widely adopted iterative method for optimizing differentiable objective functions. In this paper, we propose and discuss a novel approach to scale up SGD in applications involving non-convex functions and large datasets. We address the bottleneck problem arising when using both shared and distributed memory. Typically, the former is bounded by limited computation resources and bandwidth whereas the latter suffers from communication overheads. We propose a unified distributed and parallel implementation of SGD (named DPSGD) that relies on both asynchronous distribution and lock-free parallelism. By combining two strategies into a unified framework, DPSGD is able to strike a better trade-off between local computation and communication. The convergence properties of DPSGD are studied for non-convex problems such as those arising in statistical modelling and machine …

Authors

Saad Mohamad,Hamad Alamri,Abdelhamid Bouchachia

Journal

Machine Learning

Published Date

2022/11

Iterative ridge regression using the aggregating algorithm

In this paper, regularised regression for sequential data is investigated and a new ridge regression algorithm is proposed. It uses the Aggregating Algorithm (AA) to devise an iterative version of ridge regression (IRR). This algorithm is called AAIRR. A competitive analysis is conducted to show that the guarantee on the performance of AAIRR is better than that of the known online ridge regression algorithms. Moreover, an empirical study is carried out on real-world datasets to demonstrate the superior performance over those state-of-the-art algorithms.

Authors

Waqas Jamil,Abdelhamid Bouchachia

Journal

Pattern Recognition Letters

Published Date

2022/6/1

Fuzzy Classifiers

The present chapter discusses fuzzy classification with a focus on rule-based classification systems (FRS). The chapter consists of two parts. The first part deals with type-1 fuzzy rule-based classification systems (FRCS). It introduces the steps of building such systems before overviewing their optimality quality in terms of performance, completeness, consistency, compactness, and transparency/interpretability. The second part discusses incremental and online FRCS providing insight into both online type-1 and type-2 FRCS both at the structural and functional levels.

Authors

Hamid Bouchachia

Published Date

2022

An information theory approach to aesthetic assessment of visual patterns

The question of beauty has inspired philosophers and scientists for centuries. Today, the study of aesthetics is an active research topic in fields as diverse as computer science, neuroscience, and psychology. Measuring the aesthetic appeal of images is beneficial for many applications. In this paper, we will study the aesthetic assessment of simple visual patterns. The proposed approach suggests that aesthetically appealing patterns are more likely to deliver a higher amount of information over multiple levels in comparison with less aesthetically appealing patterns when the same amount of energy is used. The proposed approach is evaluated using two datasets; the results show that the proposed approach is more accurate in classifying aesthetically appealing patterns compared to some related approaches that use different complexity measures.

Authors

Abdullah Khalili,Hamid Bouchachia

Journal

Entropy

Published Date

2021/1/27

Detection of dementia-related abnormal behaviour using recursive auto-encoders

Age-related health issues have been increasing with the rise of life expectancy all over the world. One of these problems is cognitive impairment, which causes elderly people to have problems performing their daily activities. Detection of cognitive impairment at an early stage would enable medical doctors to deepen diagnosis and follow-up on patient status. Recent studies show that daily activities can be used to assess the cognitive status of elderly people. Additionally, the intrinsic structure of activities and the relationships between their sub-activities are important clues for capturing the cognitive abilities of seniors. Existing methods perceive each activity as a stand-alone unit while ignoring their inner structural relationships. This study investigates such relationships by modelling activities hierarchically from their sub-activities, with the overall goal of detecting abnormal activities linked to cognitive impairment. For this purpose, recursive auto-encoders (RAE) and their linear vs. greedy and supervised vs. semi-supervised variants are adopted to model the activities. Then, abnormal activities are systematically detected using RAE’s reconstruction error. Moreover, to apply RAEs for this problem, we introduce a new sensor representation called raw sensor measurement (RSM) that captures the intrinsic structure of activities, such as the frequency and the order of sensor activations. As real-world data are not accessible, we generated data by simulating abnormal behaviour, which reflects on cognitive impairment. Extensive experiments show that RAEs can be used as a decision-supporting tool, especially when the training set is not labelled to …

Authors

Damla Arifoglu,Yan Wang,Abdelhamid Bouchachia

Journal

Sensors

Published Date

2021/1/2

Smart-Cover: A real time sitting posture monitoring system

Prolonged asymmetrical sitting is common and can exacerbate musculoskeletal back pain and spinal deformities. Monitoring sitting posture can help maintain correct posture and prevent health problems. Currently posture is assessed by expert clinicians using subjective visual observation. More objective methods involve the use of a gold-standard motion capture system in Laboratories, which is expensive and not widely available. We develop a Smart-Cover, an automatic system to provide real time visualization and information about sitting posture. A Sitting Pressure Sensor (SPS) is built using Velostat, conductive fabric and foam to collect pressure distribution information within the seat surface. The data are collected from 10 healthy young subjects where each subject sits for 30 min and are transferred to a cloud server using Internet-of-Things (IoT). A rule-based classifier is used to provide timely notification to …

Authors

Arif Reza Anwary,Deniz Cetinkaya,Michael Vassallo,Hamid Bouchachia

Journal

Sensors and Actuators A: Physical

Published Date

2021/1/1

2021 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 32

2021 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 32 Page 1 5753 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. 32, NO. 12, DECEMBER 2021 This index covers all technical items—papers, correspondence, reviews, etc.—that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author’s name. The primary entry includes the coauthors’ names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author’s name, the …

Authors

A Abbas,M Abbasi,MM Abdelsamea,A Abusorrah,E Adeli,RK Agarwal,M Ahishali,CK Ahn,O Akbilgic,T Akilan,VHCd Albuquerque,JH Alcantara,P Alet,MG Allen,G An,S An,A Ananthakrishnan,DT Anderson,S Ando,KK Ang,SR Arashloo,H Asgharnezhad,AM Atto,M Avolio,VI Avrutskiy,M Awais,D Ba,S Bae,L Bai,Y Bai,G Baier,V Bajaj,K Bandara,F Bao,H Bao,Y Bao,P Barnaghi,L Behera,H Beigy,C Bergmeir,E Beyazit,J Bi,J Bian,Z Bian,FM Bianchi,MT Bin Iqbal,JM Bioucas-Dias,RR Bisset,P Bizopoulos,P Blunsom,A Bouchachia,OF Bourahla,AP Braga,P Bremer,X Bu,A Buck,GT Buzzard,J Cai,Q Cai,Z Cai,GC Calafiore,J Calvo-Zaragoza,C Cao,D Cao,J Cao

Journal

IEEE Transactions on Neural Networks and Learning Systems

Published Date

2021/12

Detection of Abnormal Activities Stemming from Cognitive Decline Using Deep Learning

The rapid increase in the population of elderly people will demand the development of new analytic tools since elderly people suffer from the consequences of cognitive impairment. This affects the cognitive abilities of elderly people and causes problems with learning and memory, which makes it difficult to have an independent life for elderly people. If the indicators of cognitive impairment could be detected at an early stage, early treatment and further diagnosis could be applied. Thus, in this chapter, a method is presented to track the daily life activities of elderly people at a smart-home and it detects the abnormal behaviour, which might arise from the consequences of cognitive decline. This chapter presents a three-step methodology for this purpose: (1) A method is proposed to simulate the abnormal behaviour of elderly people suffering from cognitive decline since it is difficult to collect real-world data. (2) Deep …

Authors

Damla Arifoglu,Abdelhamid Bouchachia

Published Date

2021/8/3

Deep online hierarchical dynamic unsupervised learning for pattern mining from utility usage data

While most non-intrusive load monitoring (NILM) work has focused on supervised algorithms, unsupervised approaches can be more interesting and practical. Specifically, they do not require labelled training data to be acquired from the individual appliances and can be deployed to operate on the measured aggregate data directly. We propose a fully unsupervised novel NILM framework based on Dynamic Bayesian hierarchical mixture model and Deep Belief network (DBN). The deep network learns, in unsupervised fashion, low-level generic appliance-specific features from the raw signals of the house utilities usage, then the hierarchical Bayesian model learns high-level features representing the consumption patterns of the residents captured by the correlations among the low-level features. The temporal ordering of the high-level features is captured by the Dynamic Bayesian Model. Using this architecture, we …

Authors

Saad Mohamad,Abdelhamid Bouchachia

Journal

Neurocomputing

Published Date

2020/5/21

Monitoring of prolonged and asymmetrical posture to improve sitting behavior

Prolonged and asymmetrical sitting has been known to cause severe health problems like musculoskeletal pain, low back pain and spinal deformity. These in turn increase the cost of medical care. If sitting posture could be measured in a user friendly manner it will be possible to take steps to prevent deterioration in the patient's health, reducing the cost of medical care. To meet this goal, we develop and implement a low cost portable automatic sitting posture monitoring system. A pressure sensing system is constructed to measure different sitting postures (active, static or prolonged and asymmetry). The pressure sensors are placed in six different ergonomic locations on a chair. Sitting information is acquired from a healthy office worker for 10 days and stored in a cloud server through Internet of Things (IoT). A smart IoT end point device application is designed and developed to provide real time posture visualization …

Authors

Arif Reza Anwary,Michael Vassallo,Hamid Bouchachia

Published Date

2020/10/26

Competitive regularised regression

Regularised regression uses sparsity and variance to reduce the complexity and over-fitting of a regression model. The present paper introduces two novel regularised linear regression algorithms: Competitive Iterative Ridge Regression (CIRR) and Online Shrinkage via Limit of Gibbs Sampler (OSLOG) for fast and reliable prediction on “Big Data” without making distributional assumption on the data. We use the technique of competitive analysis to design them and show their strong theoretical guarantee. Furthermore, we compare their performance against some neoteric regularised regression methods such as Online Ridge Regression (ORR) and the Aggregating Algorithm for Regression (AAR). The comparison of the algorithms is done theoretically, focusing on the guarantee on the performance on cumulative loss, and empirically to show the advantages of CIRR and OSLOG.

Authors

Waqas Jamil,Abdelhamid Bouchachia

Journal

Neurocomputing

Published Date

2020/5/21

Artificial intelligence and health in Nepal

The growth in information technology and computer capacity has opened up opportunities to deal with much and much larger data sets than even a decade ago. There has been a technological revolution of big data and Artificial Intelligence (AI). Perhaps many readers would immediately think about robotic surgery or self-driving cars, but there is much more to AI. This Short Communication starts with an overview of the key terms, including AI, machine learning, deep learning and Big Data.

Authors

Alexander van Teijlingen,Tell Tuttle,Hamid Bouchachia,Brijesh Sathian,Edwin van Teijlingen

Journal

Nepal Journal of Epidemiology

Published Date

2020/9

Long short-term memory networks based fall detection using unified pose estimation

Falls are one of the major causes of injury and death among elderly globally. The increase in the ageing population has also increased the possibility of re-occurrence of falls. This has further added social and economic burden due to the higher demand for the caretaker and costly treatments. Detecting fall accurately, therefore, can save lives as well as reduce the higher cost by reducing the false alarm. However, recognising falls are challenging as they involve pose translation at a greater speed. Certain activities such as abruptly sitting down, stumble and lying on a sofa demonstrate strong similarities in action with a fall event. Hence accuracy in fall detection is highly desirable. This paper presents a Long Short-Term Memory (LSTM) based fall detection using location features from the group of available joints in the human body. The result from the confusion matrix suggests that our proposed model can detect fall …

Authors

Kripesh Adhikari,Hamid Bouchachia,Hammadi Nait-Charif

Published Date

2020/1/31

Competitive normalized least-squares regression

Online learning has witnessed an increasing interest over the recent past due to its low computational requirements and its relevance to a broad range of streaming applications. In this brief, we focus on online regularized regression. We propose a novel efficient online regression algorithm, called online normalized least-squares (ONLS). We perform theoretical analysis by comparing the total loss of ONLS against the normalized gradient descent (NGD) algorithm and the best off-line LS predictor. We show, in particular, that ONLS allows for a better bias-variance tradeoff than those state-of-the-art gradient descent-based LS algorithms as well as a better control on the level of shrinkage of the features toward the null. Finally, we conduct an empirical study to illustrate the great performance of ONLS against some state-of-the-art algorithms using real-world data.

Authors

Waqas Jamil,Abdelhamid Bouchachia

Journal

IEEE Transactions on Neural Networks and Learning Systems

Published Date

2020/8/5

Asynchronous stochastic variational inference

Stochastic variational inference (SVI) employs stochastic optimization to scale up Bayesian computation to massive data. Since SVI is at its core a stochastic gradient-based algorithm, horizontal parallelism can be harnessed to allow larger scale inference. We propose a lock-free parallel implementation for SVI which allows distributed computations over multiple slaves in an asynchronous style. We show that our implementation leads to linear speed-up while guaranteeing an asymptotic ergodic convergence rate while the number of slaves is bounded by (T is the total number of iterations). The implementation is done in a high-performance computing environment using message passing interface for python (MPI4py). The empirical evaluation shows that our parallel SVI is lossless, performing comparably well to its counterpart serial SVI with linear speed-up.

Authors

Saad Mohamad,Abdelhamid Bouchachia,Moamar Sayed-Mouchaweh

Published Date

2020

Hybrid Fault Prognosis for Excitation Capacitors of Self-Excited Induction Generator for Wind Energy Applications

This paper presents a new fault prognosis approach applied to wind turbine system based on self-excited induction generator (SEIG) for offshore and isolated areas. This generator is very sensitive to wind speed variation and excitation source. The SEIG is excited by a capacitor bank with an appropriate value to ensure the good operating of the production system. Capacitor bank faults are usually related to chemical aging, electrical and thermal stress conditions. These abnormalities can affect one or more properties of the system, which can lead to failures or even complete breakdown of the production system. Specifically, in this paper, we propose a saturated flux model for the SEIG and develop a hybrid monitoring method that detects faults occurrence gradually and estimates the remaining useful life (RUL). Such monitoring method applies data mining techniques in order to identify and track the faults using only useful data that captures the dynamics of the degradation. Moreover, to deploy efficient maintenance schedules, RUL is estimated by exploiting wind speed (variable and max speed) information. The proposed hybrid fault prognosis method is tested under variable excitation capacitors degradation scenarios. The obtained results confirm the robustness and accuracy of the proposed method.

Authors

Massinissa Derbal,Houari Toubakh,Moamar Sayed-Mouchaweh,Abdelhamid Bouchachia

Journal

PHM Society European Conference

Published Date

2020/7/18

Online Gaussian LDA for unsupervised pattern mining from utility usage data

Non-intrusive load monitoring (NILM) aims at separating a whole-home energy signal into its appliance components. Such method can be harnessed to provide various services to better manage and control energy consumption (optimal planning and saving). NILM has been traditionally approached from signal processing and electrical engineering perspectives. Recently, machine learning has started to play an important role in NILM. While most work has focused on supervised algorithms, unsupervised approaches can be more interesting and of practical use in real case scenarios. Specifically, they do not require labelled training data to be acquired from individual appliances and the algorithm can be deployed to operate on the measured aggregate data directly. In this paper, we propose a fully unsupervised NILM framework based on Bayesian hierarchical mixture models. In particular, we develop a new …

Authors

Saad Mohamad,Abdelhamid Bouchachia

Published Date

2020/12/14

Online active learning for human activity recognition from sensory data streams

Human activity recognition (HAR) is highly relevant to many real-world domains like safety, security, and in particular healthcare. The current machine learning technology of HAR is highly human-dependent which makes it costly and unreliable in non-stationary environment. Existing HAR algorithms assume that training data is collected and annotated by human a prior to the training phase. Furthermore, the data is assumed to exhibit the true characteristics of the underlying distribution. In this paper, we propose a new autonomous approach that consists of novel algorithms. In particular, we adopt active learning (AL) strategy to selectively query the user/resident about the label of particular activities in order to improve the model accuracy. This strategy helps overcome the challenge of labelling sequential data with time dependency which is highly time-consuming and difficult. Because of the changes that may affect …

Authors

Saad Mohamad,Moamar Sayed-Mouchaweh,Abdelhamid Bouchachia

Journal

Neurocomputing

Published Date

2020/5/21

Online Bayesian shrinkage regression

The present work introduces an original and new online regression method that extends the shrinkage via limit of Gibbs sampler (SLOG) in the context of online learning. In particular, we theoretically show how the proposed online SLOG (OSLOG) is obtained using the Bayesian framework without resorting to the Gibbs sampler or considering a hierarchical representation. Moreover, in order to define the performance guarantee of OSLOG, we derive an upper bound on the cumulative squared loss. It is the only online regression algorithm with sparsity that gives logarithmic regret. Furthermore, we do an empirical comparison with two state-of-the-art algorithms to illustrate the performance of OSLOG relying on three aspects: normality, sparsity and multicollinearity showing an excellent achievement of trade-off between these properties.

Authors

Waqas Jamil,Abdelhamid Bouchachia

Journal

Neural Computing and Applications

Published Date

2020/12

Abdelhamid Bouchachia FAQs

What is Abdelhamid Bouchachia's h-index at Bournemouth University?

The h-index of Abdelhamid Bouchachia has been 22 since 2020 and 32 in total.

What are Abdelhamid Bouchachia's top articles?

The articles with the titles of

Event Detection for Non-intrusive Load Monitoring using Tukey s Fences

Unified embedding and clustering

Randomising the Simple Recurrent Network: a lightweight, energy-efficient RNN model with application to forecasting problems

A Survey on Ambient Sensor-Based Abnormal Behaviour Detection for Elderly People in Healthcare

Scaling up stochastic gradient descent for non-convex optimisation

Iterative ridge regression using the aggregating algorithm

Fuzzy Classifiers

An information theory approach to aesthetic assessment of visual patterns

...

are the top articles of Abdelhamid Bouchachia at Bournemouth University.

What are Abdelhamid Bouchachia's research interests?

The research interests of Abdelhamid Bouchachia are: Machine Learning, Artificial Intelligence, Data Science

What is Abdelhamid Bouchachia's total number of citations?

Abdelhamid Bouchachia has 6,401 citations in total.

What are the co-authors of Abdelhamid Bouchachia?

The co-authors of Abdelhamid Bouchachia are Joao Gama, Mykola Pechenizkiy, Hermann Hellwagner, Jose de Jesus Rubio, Indrė Žliobaitė, Waqas Jamil.

    Co-Authors

    H-index: 73
    Joao Gama

    Joao Gama

    Universidade do Porto

    H-index: 50
    Mykola Pechenizkiy

    Mykola Pechenizkiy

    Technische Universiteit Eindhoven

    H-index: 43
    Hermann Hellwagner

    Hermann Hellwagner

    Alpen-Adria-Universität Klagenfurt

    H-index: 36
    Jose de Jesus Rubio

    Jose de Jesus Rubio

    Instituto Politécnico Nacional

    H-index: 36
    Indrė Žliobaitė

    Indrė Žliobaitė

    Helsingin yliopisto

    H-index: 18
    Waqas Jamil

    Waqas Jamil

    University of Sindh

    academic-engine

    Useful Links