Adele Armele Ngo Mouelas’s research while affiliated with University of Yaoundé I and other places

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Publications (5)


Multistability, chaos and hyperchaos in a novel 3D discrete memristive system: microcontroller implementation and cryptography
  • Article

April 2025

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41 Reads

Zeitschrift fur Naturforschung A

Brondelle Falonne Namekong Tatang

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Adele Armele Ngo Mouelas

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Rodrigue Andre Tchamda

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Discrete nonlinear systems involving memristors have been extensively studied in recent years. In this work, we combine the nonlinearity of a discrete memristor with a sinusoidal transformation scheme to construct a new 3D discrete hyperchaotic circuit, which is useful for image encryption. Firstly, we perform a dynamical analysis of the proposed system. Using analytical and numerical tools, we find that the model exhibits rich and complex dynamics, including fixed-point planes, hidden attractors, hyperchaos, periodicity, and multistability. To demonstrate its practical implementation, the proposed oscillator is integrated into a microcontroller-based laboratory setup, and the experimental results closely match the numerical findings. Secondly, hyperchaotic sequences generated by the new model are used to build a simple 8 × 8 S-box for secure image encryption. The hyperchaotic sequences are first used to shuffle the columns and rows of the original image, which is then substituted using the 8 × 8 substitution box and finally encrypted through nonlinear diffusion. To validate the performance of our protocol, we conduct standard security analyses, including correlation coefficient evaluation, pixel change rate, information entropy, time complexity, and key space analysis. The results are in perfect agreement with those in the literature.


Machine Learning for Intrusion Detection in Ad-hoc Networks: Wormhole and Blackhole Attacks Case
  • Article
  • Full-text available

September 2023

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181 Reads

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13 Citations

Cloud Computing and Data Science

This paper addresses the security concerns associated with Mobile Ad-hoc Networks (MANET) and proposes a new method for detecting and preventing attacks using machine learning. The study involved the creation of a MANET with 26 nodes in NetSim (Network Simulator) software, followed by the implementation of wormhole and blackhole attacks. A dataset was generated from the network traffic obtained during the simulations, and a machine-learning model was designed to predict and detect these attacks. The model achieved high sensitivity, accuracy and f1 scores of 99%. The effectiveness of the model was tested by developing a real-time application. This method can be applied to any wireless network and is particularly relevant for companies that use Ad-hoc networks for communication.

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(a) Schematic diagram of the memristive hyperchaotic MLC oscillator. (b) Second-order memristive diode-bridge emulator with parallel LC filter [47]. The following nominal values of the circuit component have been used: Γ(t)=Asin(2πft), R=1.4KΩ, L1=18mH, C1=10nF, L2=25mH, L3=15mH, C2=10nF, G=1/1300, f=7kHz, A∈[0,3.5]V.
Bifurcation diagrams of system (4) and corresponding spectrum of Lyapunov exponent highlighting the rich dynamics in the system. (a): Bifurcation diagram showing the superposition of three sets of data depicting hysteresis and parallel bifurcation branches [44] when varying the control parameter E∈[0,25]. (b) (resp., (c)): Spectrum of the first four largest Lyapunov exponents when increasing (resp., decreasing) E. Other parameters were fixed as α1=1.0763, α2=1.0, β1=1.0888, β2=0.784, β3=1.3066, Ω=0.6157, γ=7.6×10−5 with initial conditions selected as (1,0,0,0,0).
Projection of the attractors in the (x2(τ),x4(τ)) plane showing the route to hyperchaos when varying the bifurcation parameter E. Other parameters are those of figure 2, (a) Symmetric period-1 for E=0, (b) symmetric 2D torus for E=4.5, (c) asymmetric period-4 for E=8.1, (d) symmetric 2D torus for E=8.8, (e) symmetric chaotic attractor for E=10.0, (f) symmetric periodic attractor for E=10.5, (g) symmetric chaotic attractor for E=12.0, (h) symmetric hyperchaotic attractor for E=15.0, (i) asymmetric period-4 for E=17, (c) asymmetric period-1 for E=18.6. Initial conditions were chosen as (1,0,0,0,0).
Zoom on the bifurcation diagrams and corresponding Lyapunov exponents of figure 2 showing the coexisting dynamics in system (4). A wide window of multistability can be noted for 16.72≤E≤23.06. (a) Bifurcation diagrams exploiting hysteresis and parallel bifurcation approaches as in [44]. (b) (resp., (c)) First four largest Lyapunov exponents when increasing (resp., decreasing ) the control parameter E.
(i) Bifurcation diagram of initial condition x1max versus the initial state x6(0); (ii) First three Lyapunov exponents versus initial state x6(0); (iii) Basin of initial conditions in the plane (x1(0),x2(0)) and (iv) the three coexisting phase portraits in plane (x1(τ),x4(τ)). A1 corresponds to symmetric hyperchaotic attractor while A2 and A3 refer respectively to pair of asymmetric chaotic attractors. System parameters are those of figure 4 with E=16.8188.

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Coexistence of hyperchaos with chaos and its control in a diode-bridge memristor based MLC circuit with experimental validation

June 2022

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214 Reads

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20 Citations

This paper reports both the coexistence of chaos and hyperchaos and their control based on a noninvasive temporal feedback method for attractor selection in a multistable non-autonomous memristive Murali-Lakshamanan-Chua (MLC) system. Numerical simulation methods such as bifurcation diagrams, the spectrum of Lyapunov exponents, phase portraits, and cross-section basins of initial states are used to examine several striking dynamical features of the system, including torus, chaos, hyperchaos, and multistability. Of most interest, the rare phenomenon of the coexistence of hyperchaos and chaos has been uncovered based on bifurcation techniques and nonbifurcation scheme like offset boosting. Further analyses based on intermittent feedback-based control in the time domain help to drive the system from the multistable state to a monostable one where only the hyperchaotic attractor survives. Since the attractor's internal dynamics are retained, this control method is non-invasive. At the end of our analyses, the results of both PSpice and that of the microcontroller-based digital calculator of the circuit match perfectly with the numerical investigations.


An Overview of Supervised Machine Learning Methods and Data Analysis for COVID-19 Detection

November 2021

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286 Reads

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25 Citations

Journal of Healthcare Engineering

Methods: Our analysis and machine learning algorithm is based on most cited two clinical datasets from the literature: one from San Raffaele Hospital Milan Italia and the other from Hospital Israelita Albert Einstein São Paulo Brasilia. The datasets were processed to select the best features that most influence the target, and it turned out that almost all of them are blood parameters. EDA (Exploratory Data Analysis) methods were applied to the datasets, and a comparative study of supervised machine learning models was done, after which the support vector machine (SVM) was selected as the one with the best performance. Results: SVM being the best performant is used as our proposed supervised machine learning algorithm. An accuracy of 99.29%, sensitivity of 92.79%, and specificity of 100% were obtained with the dataset from Kaggle (https://www.kaggle.com/einsteindata4u/covid19) after applying optimization to SVM. The same procedure and work were performed with the dataset taken from San Raffaele Hospital (https://zenodo.org/record/3886927#.YIluB5AzbMV). Once more, the SVM presented the best performance among other machine learning algorithms, and 92.86%, 93.55%, and 90.91% for accuracy, sensitivity, and specificity, respectively, were obtained. Conclusion: The obtained results, when compared with others from the literature based on these same datasets, are superior, leading us to conclude that our proposed solution is reliable for the COVID-19 diagnosis.


Extremely rich dynamical behaviors in a simple nonautonomous Jerk system with generalized nonlinearity : hyperchaos, intermittency, offset-boosting and multistability

March 2020

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459 Reads

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23 Citations

International Journal of Dynamics and Control

This paper investigates the extremely rich dynamical behaviors of the simple Jerk system as proposed by Volos et al. (Nonlinear Dyn 89(2):1047–1061, 2017) based on two main modifications: (i) introduction of a periodic sinusoidal external excitation in the system and (ii) generalization of the nonlinear function of the system in the form φk(x)=0.5(exp(kx)exp(x))\varphi _k(x) = 0.5 (\exp (kx)-\exp (-x)) as recently proposed by Kengne group (Negou and Kengne in AEU Int J Electron Commun 90:1–19, 2018). These changes are in origin of the observed rich dynamical behaviors including hyperchaos, chaos, intermittency, offset boosting and coexistence of multiple attractors. All these interesting dynamical behaviors are highlighted using the common dynamical tools such as bifurcation diagrams, spectrum of the Lyapunov exponents, two parameters diagrams, phase portraits and Poincaré sections. To the best of the author’s knowledge, the addition of an external force in the class of Jerk systems is new and has not been discussed earlier (despite the huge amount of related research works) and thus represents an enriching contribution to the understanding of the dynamics of Jerk’s system. The captured laboratory measurements are in perfect agreement with the theoretical analysis.

Citations (4)


... This fluid and adaptive connectivity allows MANETs to function efficiently in environments where traditional network infrastructure is unavailable or impractical. However, the lack of a centralized control mechanism and the inherent openness of wireless communication expose MANETs to a myriad of security threats, including eavesdropping, data modification, impersonation attacks, and various forms of denial-of-service attacks [1]. These ...

Reference:

Development and Evaluation of a Lightweight Encryption Algorithm for Mobile Ad Hoc Networks
Machine Learning for Intrusion Detection in Ad-hoc Networks: Wormhole and Blackhole Attacks Case

Cloud Computing and Data Science

... Since the few past decades, several control techniques have been developed to meet the functional requirements of physical systems. We can mention among others the linear augmentation method [37][38][39] which consists in converging any coexistence towards a periodic dynamics of period-1, the method with the selection [40][41][42] of desired attractor (noise selection), pseudo-forcing methods [43,44], short pulses and harmonic perturbations [45] to name a few. ...

Coexistence of hyperchaos with chaos and its control in a diode-bridge memristor based MLC circuit with experimental validation

... taset. Non-clinical data were analyzed for diagnosing and prognosing COVID-19 patients, with results showing that the Decision Tree model achieved the highest performance accuracy (94.99%), the Support Vector Machine (SVM) demonstrated the highest sensitivity (93.34%), and the Naïve Bayes model recorded the highest specificity (94.30%). Similarly, (Tchagna et. al.,, 2021) applied supervised ML methods to detect COVID-19 patients, focusing on a non-physical diagnostic approach based on symptom data. Their findings indicated that the SVM was the best-performing model, with an accuracy range of 92%-99.29%, sensitivity between 92.79%-93.55%, and specificity ranging from 90.91%-100%. Additionally, (Tena et. a ...

An Overview of Supervised Machine Learning Methods and Data Analysis for COVID-19 Detection

Journal of Healthcare Engineering

... Chaotic systems with four or more dimensions have multiple positive Lyapunov exponents, which means that small perturbations of the system state will diverge exponentially in multiple directions, and the state trajectories are more complex, increasing the unpredictability of the system, and its multi-dimensional complexity, which is superior to the traditional chaotic systems in terms of processing speed, error tolerance, and anti-interference [27][28][29]. In some related technical applications, peripheral devices are often required to modulate the amplitude and position of signals [30,31]. The unique nature of offset-free control, which can effectively change the offset boost of a chaotic system, can effectively change a bipolar signal into a unipolar signal by changing only one constant to achieve the desired level of boost, and the paranoia enhancement property of a chaotic system can play a key role among them [32]. ...

Extremely rich dynamical behaviors in a simple nonautonomous Jerk system with generalized nonlinearity : hyperchaos, intermittency, offset-boosting and multistability

International Journal of Dynamics and Control