1. Introduction

FECG reflects electrical activity of fetal heart. It can be used to discover Fetal Heart Rate (FHR) and multiple pregnancies and to establish if the fetus is in distress. It is also useful in function parameter analysis of heart for the prevention of neonatal diseases [1]. The two methods used for obtaining FECG are the use of electrodes applied on the scalp of the fetus and through non-invasive recording by placing electrode on mother’s abdomen. Invasive recordings achieve better quality but the process is regretfully difficult and only applicable during labor [2]. In contrast, the recording by noninvasive means has the advantage of being simple, noninvasive and can be applied throughout gestational period. That is why medical workers and pregnant women have deeply welcomed it [3].

The major drawback of noninvasive FECG (NI-FECG) recording is contamination with various forms of noise like maternal ECG (MECG), electromyogram (EMG), baseline wandering, power line interference, etc.[4], [5].

Research is ongoing on methods of extracting FECG from AECG. MECG is the most influential interference noise signal present in the AECG which has been suppressed using various techniques such as adaptive filters [4], [5] and wavelet transform [6]-[9] among other methods [9], [7]. Two things characterize adaptive filters. First, the type of unit sample response based on which we have Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters. Second, the optimization algorithm used. Gradient-based optimization algorithms are popular, but they have the problem of convergence at local minimum for multimodal error surfaces [10]. Attempts to solve the problem of local minima and achieve global optimum solution has led many researchers to introduce the use of global optimization techniques for adaptive filter optimization such as Genetic Algorithm (GA) [11], Simulated Annealing (SA) [12], Tabu Search (TS) [13], Differential Evolution (DE) [14], Particle Swarm Optimization (PSO) [15], Ant-colony (ACO) [16], Artificial intelligence [17] Modified firefly and modified ABC algorithms [18].

In this work, LMS algorithm with its exploitation ability, robustness, ease of implementation, low computational complexity and unbiased convergence [19] is combined with MFO which has high global search (exploration) capability. MFO has the additional advantages of simplicity, flexibility and ease of implementation. Due to these advantages, it has been successfully applied in many optimization applications [20]. Some of these include scheduling [21], classification [22], power energy [23], medical [24], and image processing [25]. However, from available literature, this is the first time MFO is being used for FECG Extraction.

2. Fetal ECG Monitoring

Figure 1 illustrates the method of FECG monitoring. Two electrodes are placed on the body of a pregnant woman; one each on her chest and abdomen. Three signals are recorded by the electrode placed on the abdomen. One of them is the corrupted version of the maternal ECG (MECG) signal, i.e. . The second is FECG signal  and the third is noise from various sources denoted as .  Equations (1) to (3) describe the relationship between these signals.

MECG as recorded by the abdominal electrode is corrupted due to the nonlinear transformation it undergoes as it travels from the chest to the abdomen.

Figure 1: Fetal ECG Monitoring

3. Proposed Extraction Algorithm

The proposed method of adaptive filtering as depicted in Figure 2 consists of two adaptation algorithms; the LMS alone and LMS/MFO. These algorithms (implemented one after the other) were used to update the weights of Finite Impulse Response Filter based on the error signal (desired signal). Here the noisy abdominal signal is given as input and a thoracic electrode output signal is given as reference input to the filter. Since the signal of interest is the fetal heartbeat, maternal heart beat is considered as the interference noise to the signal. The primary signal is the fetal electrocardiogram measured from the abdominal electrode. It consists of mainly maternal heartbeat which dominates the fetal heartbeat and some residual measurement noises. The reference signal is taken from the chest of the mother through thoracic electrode which consists of actual maternal heart beat with some additive noise. Adaptation algorithm here uses the error signal as its input and updates the filter coefficients based on the parameters defined to the filter. The output obtained by the filter should be approximately equal to the noise i.e. the maternal heart beat in the primary signal so that the error signal obtained should be fetal heart signal. The error signal obtained finally corresponds to the fetal heart beat signal but corrupted with residual noises which are in turn eliminated by filtering them out adaptively.

3.1. Configuration of the Adaptive Filter

The configuration of the adaptive filter is shown in Figure. 3. The filter is shown to be operating in time domain.

Figure 2: Block diagram of proposed algorithm

Figure 3: Adaptive filter structure

In Figure 3,  is the primary signal while the primary noise is , and the two are uncorrelated.  is the reference signal and it is strongly correlated with  but uncorrelated with . The output error  is given by

The error signal serves as the input to the adaptation algorithm [26]-[28] which subsequently adjusts the adaptive filter coefficient vector    depending on some measurements standard. The adaptation process is geared towards minimizing the error signal (in this case the fetal ECG).

 According to [26]-[28], the mean square error (MSE) is a measure that shows how effectively a system adapts to a specified solution. The final value of MSE can be used to decide on proficiency of the adaptation algorithm. Consequently, a least value signifies the efficiency of the algorithm and in contrast, a high value normally signifies the inability of the algorithm to model the specified system or the initial state of the adaptive filter is inappropriate starting point to ensure the adaptive filter convergence. The mean square error is given by

where E[.] indicates expectation operation. For noise cancellation, the goal is to have a system output best fit in the least squares sense of the signal s(n) . The mean square of error is of interest and can be obtained as follows

Since  is uncorrelated with both  and

Adapting the filter minimizes the mean square of error. This has no effect on the input signal .

If the means square error is minimized, output of the filter,  will be a best least square match to  We can rewrite (4) as

Minimization of the mean square error makes e(n) to be the most suitable match of the signal s(n) in the least squares sense. This is achieved by adaptive filtering with no a priori information about the signal and noise statistic.

3.2. Least Mean Square Algorithm

The popularity of LMS as an adaptive filter algorithm is due to its simplicity, robustness, ease of implementation, low computational complexity, stability and unbiased convergence as reported by [19]. From Figure 3

where  is a vector representation of the adaptive filter weights and  is a vector of the corresponding delayed values of x(n) as depicted in Figure 3. That is,

where µ is called the step-size, w(n) represents the new coefficient values for the next time interval, x(n) is the filter (reference) input signal, y(n) represent the filtered output, s(n) is the desired response and e(n) is the error function. The convergence of  to the optimum solution and the convergence speed depend on the value of step-size parameter µ.

3.3. Moth-Flame Optimization Algorithm (MFO)

Moth-flame optimization (MFO) was initiated by Mirjalili in 2015 [29]. It is among the latest metaheuristic population-based methods. It is an optimization design to mimic the behavior of moth, a type of butterfly species flying in night towards a moon in a straight line known as transverse orientation for navigation. However, turn to spiral motion when encountered with shorter light source. Consequently, this led to the formulation of an interestingly new optimization algorithm. Additionally, MFO was designed to achieve exploration and exploitation goal as it combines population-based algorithm and local search strategy. Also, to prevent the solution being trapped in local minima, the optimal solutions were maintained in each repetition as reported in [29]. The algorithm uses the explorative as well as exploitative search to get an optimized solution.

 In the introduced MFO, a moth is considered to be a candidate solution and the position of moth at different search space as problem’s variables. Hence, the moth can fly in hyper dimensional space or 3D dimensions and changing their position vectors. Since MFO is population-based, matrix representation of the swarm of moths can be made as in (18)

    We can evaluate the fitness of each moth by means of the fitness function. The fitness is stored in a matrix as in (19)

     The matrix of (20) stores moths best positions in each dimension as flames, while the generated corresponding fitness values are stored as in (21).

      The spiral movement of the moth around the flame is represented in (22)

here,    indicate the spiral motion,  is a constant associated with the shape of the spiral movement.  is a random variable used to select the position of a moth from the flame  represents the closest position while  represents the farthest position of the moth. Equation (22) is used to update the position of the moths with the best moth position updating the flame position. Furthermore, the number of flames is decreased in each iteration using (23)

where ceil,     and    denote round to whole number, the current iteration number and the total number of iterations respectively. MFO process is elucidated in Table 1.

MFO algorithm is illustrated in Table 1. The next stage describes the implementation   of MFO in the proposed work.

Table 1: The MFO Process [29]

3.4. MFO Adaptive Filtering

It is noteworthy that we are using the moth flame optimization algorithm to optimize the LMS adaptive filtering algorithm. The moths here represent the filter coefficients to be updated. This means for each filter coefficient ( ), there is a moth and the position of the filter coefficient is considered as the position of the moth. This is because the moth is taken to be candidate solution in the search space and when the moth changes position, the filter coefficient is adjusted. The process continues repeatedly until the best coefficients are obtained as in MFO process in Table 1. The number of dimensions,  since the filter coefficients ( ) would be placed in a real world environment.  The position matrix representing the moth based on filter coefficient ( ) is given by (24).

here, n is a number representing the length of the coefficient vector ( ) in the network. The fitness value of the current filter coefficient ( ) is generated by using its distance from the destination. Equation (25) is used to generate the said fitness value.

The flame matrix gives the position matrix of the best neighbor filter coefficient ( ) to be selected in the route by any filter coefficient ( ) given by (27) and corresponding fitness value matrix generated using the (25) is given in the (28).

The position of the filter coefficient ( )  to be selected next in the route is given by the (29).

The new position obtained using the (12) need not be the exact position of the filter coefficient ( ), so the filter coefficient ( ) nearest to the updated position is to be selected, so the (12) is applied to generate the matrix in (30).

For the updated matrix   to be generated, a quick sort can be performed on matrix . The algorithm would then select or  according to the fitness value. In other words, filter coefficients ( ) with lower fitness values (which are better) are selected. The estimation error is thus decreased to minimal value.

4. Simulation, Results and Discussion

4.1. Generation of Synthetic Signals

A sampling frequency of 4 KHz is used to simulate the maternal ECG shown in Figure 4. Other important parameters for the signal are the approximate heart rate of 89 bmp (beats per minute) and the peak voltage of 3.5 mV.

Figure 4: Maternal Heartbeat Signal

Figure 5: Fetal Heartbeat Signal (Targeted Signal)

The FHR is normally higher than the maternal heart rate, with the former ranging from 120 bpm to 160 bpm. For the simulated FECG signal the heart rate is 139 bpm and the peak voltage is 0.25 mV. [30]

The signal obtained from the abdominal electrode is a noisy signal and is shown in Figure 6. This is to be filtered and given as input to the adaptive noise canceller.

The signal recorded by the thoracic electrode is given as reference signal [30] as shown in Figure 7. This is given as reference input to the adaptive noise canceller.

Figure 6: Abdominal ECG Signal

Figure 7: Measured Maternal Electrocardiogram (Reference) Signal

4.2. Simulation using Synthetic Signals

Figure 8 is the result obtained by training the adaptive noise canceller with LMS algorithm. In fetal ECG extraction QRS complex detection is important so that R-R interval and R peaks can properly be identified. In this case two R peaks indicated by arrows are poorly extracted that may hinder proper monitoring of fetal heart status.

Figure 9 to Figure 13: The results show that with moth population between 20 and 80, the first p-waves are missing or not clearly defined. However, all the waves were recovered and R-R interval clearly seen when the moth population was increased to 100. Also, the expected FHR was equally recovered. The FHR was recovered using (31).

Figure 8: Result obtained by LMS

where  is the R-R  interval and t is the time interval.

Figure 9:  LMS/MFO result at 100 moth pop.

Figure 10: LMS/MFO result at 80 moth pop.

Figure 11: LMS/MFO result at 60 moth pop.

Figure 12: LMS/MFO result at 40 moth pop.

Figure 13:  LMS/MFO result at 20 moth pop.

Table 2 shows the results of magnitude of Mean Square Error (MSE) obtained at different moth population (between 20 and 100) at a fixed number of iterations. Table 3 shows the SNR before the application of the proposed technique and after. The SNR is calculated using (34).

SNR= 10 log₁₀ (P_s / P_n)                 (34)

where Ps is the signal power, in this case the power of fetal ECG signal  which was obtained using  and  is the noise power which represents the power of both deformed maternal ECG and white Gaussian noise. It was obtained using

The result in Table 2 shows that the MSE is low with high population of moth (i.e. 100). The obtained mean square error values show that system has converted to a solution. Table 3 indicates a decrease of MSE and increase of SNR when the proposed algorithm was applied as required.

Table 2: Mean Square Error (MSE) Values at different Moth Population and fixed iteration number

Table 3: SNR and MSE for LMS and proposed LMS/MFO

Table 4 gives performance comparison of the proposed algorithm with conventional LMS and some existing methods. The proposed method shows improvement in SNR and decrease in MSE.

Table 4: comparison of MSE and SNR

4.3. Analysis Using Real ECG Signal

To establish the efficacy of the proposed approach a real abdominal ECG and thoracic ECG data were obtained from Physionet developed by MIT-BIH. Figure 14 and Figure 15 are the abdominal and thoracic ECG signals respectively. After the application of the proposed algorithm, Fetal ECG was extracted as in Figure 16. The R peaks that are used to determine the FHR were efficiently localized.

Figure 14: Real abdominal ECG

Figure 15: Real thoracic ECG

Figure 16: Extracted fetal ECG

4.4. Performance Comparison with Existing Techniques

The results obtained using proposed LMS/MFO extraction method was compared with selected existing methods and the result is summarized in Table 4.

According to [34], actual comparison of the existing techniques for FECG extraction is still difficult due to the differences in dataset, evaluation techniques, etc used by individual researchers. It is thus easier to use specific criteria like improvement in SNR, computational complexity, etc. the following rather subjective criteria were proposed.

Overall performance: This has to do with the robustness of the technique. Performance can be classified as low, medium or high.

Low: Performance is classified as low when the technique is not suitable for FECG extraction but may be used to eliminate specific types of noise like power line interference, baseline wandering, etc. Low performance techniques are primarily capable of NI-FECG preprocessing.

Medium: In this case, techniques are suitable for advanced preprocessing. They are capable of removing most noise types in NI-FECG preprocessing.  These include EMG, myopotentials, motion artifacts, among others. Medium performance techniques partly remove MECG thereby enabling the detection of FQRS (Fetal QRS) and consequently the determination of FHR.

High: High performance techniques are the strongest in terms of NI-FECG processing. A high performance technique supplies information on FHR and FECG morphology (PR, QT, ST, etc.).

Improvement in SNR: Like with overall performance, techniques are categorized into three (i.e. low, medium and high) in terms of SNR improvement. It is noteworthy that SNR accurately verifies proficiency of a technique with respect to some reference value. However, when it comes to clinical applications the use of SNR may not be very helpful. Techniques that show very high performance in the improvement of SNR may not be good in FQRS detection.

Computational Complexity: This is an assessment of the computational requirements of a particular technique. Computational complexity requirements may be high, medium or low. Of course, low is the best.

FHR (R-R): This is an assessment of the credibility of a technique under investigation in terms of its ability to determine FHR based on R-R interval. Four classes are identified, viz. inaccurate, moderately accurate and very accurate.

Inaccurate: This is when a technique is not capable of effectively removing artifacts and noise for proper detection of the R-R interval. When an inaccurate technique is used to process NI-FECG, the result cannot be used for monitoring FHR.

Moderately accurate: This is when a technique effectively removes most common noises, thus enabling the detection of R-R interval. Of course, the noises are not effectively removed resulting in undetected and false-detected complexes. For moderately accurate techniques, maximum sensitivity is 80%, maximum PPV (positive predictive value) is 90%, maximum accuracy is 80% and probability of correct detection of beats (F1) is 85%.

Accurate: These techniques permit precise FHR detection. Maximum sensitivity is 85%, maximum PPV, 95%, maximum accuracy, 85% and maximum F1, 95%.

Very accurate: This class of techniques permits a very precise detection of FHR. For very accurate techniques, the maximum sensitivity is 95%, maximum PPV is 95%, maximum accuracy is 95% and maximum F1 is 95%. For values above this (say 99% PPV) a technique is still considered very accurate.

Morphological analysis (T/QRS; QT): This is a measure of the effectiveness of a technique being investigated for an in-depth analysis of the morphology of FECG. Three classes are identified.

Insufficient: When the quality of FECG extracted by a technique is not sufficient for morphological analysis.

Moderately accurate: This is when a technique allows for morphological analysis. The problem, in this case, is that SNR, age of gestation, position of fetus, etc. significantly affect the efficacy of the method except for some tested real data. Thus, for monitoring of T/QRS or QT interval, a moderately accurate technique cannot be used.

Promising: A promising technique has a very high potential of being used for morphological analysis.

Table 4: Comparison with some existing methods

5. Conclusion

A new algorithm for FECG extraction has been presented. The algorithm which combines MFO algorithm and LMS adaptive filtering has two inputs, viz. the abdominal signal which is the major signal of interest and the thoracic signal which serves as the reference input.

The hybrid algorithm has been shown to be better than the LMS adaptive algorithm. Results have shown improvement in the detection of R-peaks of fetal ECG much better with 100 moth population. The superiority of the new approach has been shown quantitatively by MSE and SNR calculation.

 A decrease of mean square error of 0.0215 was observed with the proposed algorithms and improvement of signal to noise ratio of 10.28 was also observed.

 Comparison with existing methods shows better performance of the proposed approach.

Further research should focus on applying this method for multifetal ECG separation and the use of statistical method of analysis e.g. anova method for comparison of result.

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8. Yehan Kodithuwakku, Chanuka Bandara, Ashan Sandanayake, R.A.R Wijesinghe, Velmanickam Logeeshan, "Smart Healthcare Kit for Domestic Purposes", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 3, pp. 170–177, 2023. doi: 10.25046/aj080319
9. Saidi Mohamed, Habib Cherif, Othman Hasnaoui, Jamel Belhadj, "Design and Comparative Analysis of Hybrid Energy Systems for Grid-Connected and Standalone Applications in Tunisia: Case Study of Audiovisual Chain", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 3, pp. 144–153, 2023. doi: 10.25046/aj080316
10. Orapin Channumsin, Jirapun Pimpol, Tattaya Pukkalanun, Worapong Tangsrirat, "Tunable Resistorless Phase Shifter Realization with a Single VDGA", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 3, pp. 136–143, 2023. doi: 10.25046/aj080315
11. Temsamani Khallouk Yassine, Achchab Said, Laouami Lamia, Faridi Mohammed, "Hybrid Discriminant Neural Networks for Performance Job Prediction", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 2, pp. 116–122, 2023. doi: 10.25046/aj080213
12. Abdulrasaq Jimoh, Samson Oladayo Ayanlade, Emmanuel Idowu Ogunwole, Dolapo Eniola Owolabi, Abdulsamad Bolakale Jimoh, Fatina Mosunmola Aremu, "Metaheuristic Optimization Algorithm Performance Comparison for Optimal Allocation of Static Synchronous Compensator", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 1, pp. 116–124, 2023. doi: 10.25046/aj080114
13. Jabrane Slimani, Abdeslam Kadrani, Imad EL Harraki, El hadj Ezzahid, "Long-term Bottom-up Modeling of Renewable Energy Development in Morocco", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 129–145, 2022. doi: 10.25046/aj070515
14. Ampavathina Sowjanya, Damera Vakula, "Metamaterial-Inspired Compact Single and Multiband Filters", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 4, pp. 92–97, 2022. doi: 10.25046/aj070412
15. Afsah Sharmin, Farhat Anwar, S M A Motakabber, Aisha Hassan Abdalla Hashim, "A Secure Trust Aware ACO-Based WSN Routing Protocol for IoT", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 95–105, 2022. doi: 10.25046/aj070311
16. Ilhem Bouchriha, Ali Ben Ghanem, Khaled Nouri, "Optimization of the Sliding Mode Control (SMC) with the Particle Swarm Optimization (PSO) Algorithm for Photovoltaic Systems Based on MPPT", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 1, pp. 100–106, 2022. doi: 10.25046/aj070110
17. Arakelyan Edik, Kosoy Anatoliy, Andryushin Alexander, Mezin Sergey, Yagupova Yulia, Leonov Maxim, Pashchenko Fedor, "Problems of Increasing the Intelligence of Algorithms for Optimal Distribution of the Current Load on the Combined Heat and Power Plant and Ways to Solve Them", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 369–374, 2021. doi: 10.25046/aj060542
18. Liang Chen, Mo-How Herman Shen, "A New Topology Optimization Approach by Physics-Informed Deep Learning Process", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 233–240, 2021. doi: 10.25046/aj060427
19. Abdulkarim Saleh Masoud Ali, Rozmie Razif Othman, Yasmin Mohd Yacob, Haitham Saleh Ali Ben Abdelmula, "An Efficient Combinatorial Input Output-Based Using Adaptive Firefly Algorithm with Elitism Relations Testing", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 223–232, 2021. doi: 10.25046/aj060426
20. Hathairat Ketmaneechairat, Maleerat Maliyaem, Chalermpong Intarat, "Kamphaeng Saen Beef Cattle Identification Approach using Muzzle Print Image", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 110–122, 2021. doi: 10.25046/aj060413
21. Junho Chang, Mustafa Melih Pelit, Masaki Yamakita, "SLIP-SL: Walking Control Based on an Extended SLIP Model with Swing Leg Dynamics", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 84–91, 2021. doi: 10.25046/aj060309
22. Van-Truong Nguyen, Anh-Tu Nguyen, Viet-Thang Nguyen, Huy-Anh Bui, Xuan-Thuan Nguyen, "Real-time Target Human Tracking using Camshift and LucasKanade Optical Flow Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 907–914, 2021. doi: 10.25046/aj0602103
23. Tamer Saraçyakupoğlu, "Usage of Additive Manufacturing and Topology Optimization Process for Weight Reduction Studies in the Aviation Industry", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 815–820, 2021. doi: 10.25046/aj060294
24. Athanasios Tziouvaras, Georgios Dimitriou, Michael Dossis, Georgios Stamoulis, "Frequency Scaling for High Performance of Low-End Pipelined Processors", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 763–775, 2021. doi: 10.25046/aj060288
25. Susanto Kumar Ghosh, Mohammad Rafiqul Islam, "Convolutional Neural Network Based on HOG Feature for Bird Species Detection and Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 733–745, 2021. doi: 10.25046/aj060285
26. Yu-Chun Huang, "Analyze Performance of Enterprise Supervision System by Game Theory-Take the case of Tatung Management Rights Competition as Example", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 689–693, 2021. doi: 10.25046/aj060279
27. : Mourad Fariss, Naoufal El Allali, Hakima Asaidi, Mohamed Bellouki, "An Improved Approach for QoS Based Web Services Selection Using Clustering", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 616–621, 2021. doi: 10.25046/aj060270
28. Khalil Ibrahim Mohammad Abuzanouneh, Khalil Hamdi Ateyeh Al-Shqeerat, "Development and Improvement of Web Services Selections using Immigrants Scheme of Multi-Objective Genetic Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 412–422, 2021. doi: 10.25046/aj060248
29. Amany Khalil, Osama Tolba, Sherif Ezzeldin, "Design Optimization of Open Office Building Form for Thermal Energy Performance using Genetic Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 254–261, 2021. doi: 10.25046/aj060228
30. Abdelaziz Lberni, Malika Alami Marktani, Abdelaziz Ahaitouf, Ali Ahaitouf, "Multi-Objective Design of Current Conveyor using Optimization Algorithms", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 154–160, 2021. doi: 10.25046/aj060218
31. Sk. Md. Masudul Ahsan, Aminul Islam, "Visual Saliency Detection using Seam and Color Cues", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 139–153, 2021. doi: 10.25046/aj060217
32. Abba Suganda Girsang, Antoni Wibowo, Jason, Roslynlia, "Comparison between Collaborative Filtering and Neural Collaborative Filtering in Music Recommendation System", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1215–1221, 2021. doi: 10.25046/aj0601138
33. Natchanai Roongmuanpha, Taweepol Suesut, Worapong Tangsrirat, "Electronically Tunable Triple-Input Single-Output Voltage-Mode Biquadratic Filter Implemented with Single Integrated Circuit Package", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1120–1127, 2021. doi: 10.25046/aj0601125
34. Subash Pokharel, Aleksandar Dimitrovski, "Ferromagnetic Core Reactor Modeling and Design Optimization", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 810–818, 2021. doi: 10.25046/aj060190
35. Antonio Vitale, Federico Corraro, Nicola Genito, Luca Garbarino, Leopoldo Verde, "An Innovative Angle of Attack Virtual Sensor for Physical-Analytical Redundant Measurement System Applicable to Commercial Aircraft", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 698–709, 2021. doi: 10.25046/aj060176
36. Jesus Aguila-Leon, Cristian Chiñas-Palacios, Carlos Vargas-Salgado, Elias Hurtado-Perez, Edith Xio Mara Garcia, "Particle Swarm Optimization, Genetic Algorithm and Grey Wolf Optimizer Algorithms Performance Comparative for a DC-DC Boost Converter PID Controller", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 619–625, 2021. doi: 10.25046/aj060167
37. Theerayod Wiangtong, Sethakarn Prongnuch, Suchada Sitjongsataporn, "Stochastic Behaviour Analysis of Adaptive Averaging Step-size Sign Normalised Hammerstein Spline Adaptive Filtering", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 577–586, 2021. doi: 10.25046/aj060162
38. Hendro Arieyanto, Andry Chowanda, "Classification of Wing Chun Basic Hand Movement using Virtual Reality for Wing Chun Training Simulation System", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 250–256, 2021. doi: 10.25046/aj060128
39. Pham Hong Thinh, Tran Thi Thanh Huyen, Nguyen Ngoc Quang, Pham Ngoc Nam, Truong Cong Thang, Nguyen Viet Hung, Truong Thu Huong, "QoE-aware Bandwidth Allocation for Multiple Video Streaming Players over HTTP and SDN", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 184–199, 2021. doi: 10.25046/aj060121
40. Broderick Crawford, Ricardo Soto, Gino Astorga, José Lemus-Romani, Sanjay Misra, Mauricio Castillo, Felipe Cisternas-Caneo, Diego Tapia, Marcelo Becerra-Rozas, "Balancing Exploration-Exploitation in the Set Covering Problem Resolution with a Self-adaptive Intelligent Water Drops Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 134–145, 2021. doi: 10.25046/aj060115
41. Poonam Ghuli, Manoj Kartik R, Mohammed Amaan, Mridul Mohta, N Kruthik Bhushan, Poonam Ghuli, Shobha G, "Recommendation System for SmartMart-A Virtual Supermarket", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1408–1413, 2020. doi: 10.25046/aj0506170
42. Hoang Xuan Thinh, Pham Van Dong, Tran Ve Quoc, "A Study on the Tool Wear in Milling Process of the Gleason Spiral Bevel Gear", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1402–1407, 2020. doi: 10.25046/aj0506169
43. Ragam Rajagopal, K. Palanisamy, S. Paramasivam, "PV Integrated Recursive Least Mean Square Estimation Based Shunt Active Power Filter", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1171–1177, 2020. doi: 10.25046/aj0506141
44. Adewale Opeoluwa Ogunde, Mba Obasi Odim, Oluwabunmi Omobolanle Olaniyan, Theresa Omolayo Ojewumi, Abosede Oyenike Oguntunde, Michael Adebisi Fayemiwo, Toluwase Ayobami Olowookere, Temitope Hannah Bolanle, "The Design of a Hybrid Model-Based Journal Recommendation System", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1153–1162, 2020. doi: 10.25046/aj0506139
45. Muhammed Mansoor C. B., Hanumantha Rao G., Rekha S., "Low Power Fast Settling Switched Capacitor PTAT Current Reference Circuit for Low Frequency Applications", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 865–870, 2020. doi: 10.25046/aj0506103
46. Luisella Balbis, "Optimal Irrigation Strategy using Economic Model Predictive Control", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 781–787, 2020. doi: 10.25046/aj050693
47. Yohei Yamauchi, Mitsuyuki Saito, "Adaptive Identification Method of Vehicle Model for Autonomous Driving Robust to Environmental Disturbances", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 710–717, 2020. doi: 10.25046/aj050685
48. Sethakarn Prongnuch, Suchada Sitjongsataporn, "Performance Analysis and Enhancement of Spline Adaptive Filtering based on Adaptive Step-size Variable Leaky Least Mean Square Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 642–651, 2020. doi: 10.25046/aj050678
49. Pearl Keitemoge, Daniel Tetteh Narh, "Effective Application of Information System for Purchase Process Optimization", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 594–605, 2020. doi: 10.25046/aj050673
50. Radouane Ourhdir, Mohammed Rachidi, "An Adaptive Nonlinear Sensorless Controller of Doubly Fed Induction Generator Driven By Wind Turbine", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 489–496, 2020. doi: 10.25046/aj050658
51. Sergey Alekseevich Serebryansky, Alexander Vladimirovich Barabanov, "To the Question of Multi-Criteria Optimization of Aircraft Components in Order to Optimize its Life Cycle", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 408–415, 2020. doi: 10.25046/aj050649
52. Athraa Ali Kadhem, Noor Izzri Abdul Wahab, Ahmed Abdalla, "The Contribution of Wind Energy Capacity on Generation Systems Adequacy Reliability using Differential Evolution Optimization Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 331–340, 2020. doi: 10.25046/aj050640
53. Simona Kirilova Filipova-Petrakieva, "Applications of the Heuristic Optimization Approach for Determining a Maximum Flow Problem Based on the Graphs’ Theory", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 175–184, 2020. doi: 10.25046/aj050621
54. Ihsan Mizher Baht, Petre Marian Nicolae, Ileana Diana, Nameer Baht, "Analysis of Green Building Effect on Micro grid Based on Potential Energy Savings and BIM", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 30–35, 2020. doi: 10.25046/aj050604
55. Vijay Yadav, Ujjwal Gewali, Suman Khatri, Shree Ram Rauniyar, Aman Shakya, "Need of E-Recruitment System for Universities: Case of Pulchowk Campus, Nepal", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 902–912, 2020. doi: 10.25046/aj0505110
56. Halima Housny, El Ayachi Chater, Hassan El Fadil, "Multi Closed-loop Adaptive Neuro-Fuzzy Inference System for Quadrotor Position Control", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 526–535, 2020. doi: 10.25046/aj050565
57. Mehdi Zhar, Omar Bouattane, Lhoussain Bahatti, "New Algorithm for the Development of a Musical Words Descriptor for the Artificial Composition of Oriental Music", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 434–443, 2020. doi: 10.25046/aj050554
58. Imad El Hajjami, Bachir Benhala, Hamid Bouyghf, "Shape Optimization of Planar Inductors for RF Circuits using a Metaheuristic Technique based on Evolutionary Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 426–433, 2020. doi: 10.25046/aj050553
59. Agus Junaidi, Rahmaniar, Rudi Salman, Joni Safrin Rambey, Baharuddin, "Modelling and Simulation of Reduce Harmonic Distortion in Non-linear Loads", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 364–369, 2020. doi: 10.25046/aj050545
60. Dina Fitria Murad, Rosilah Hassan, Wahiza Wahi, Bambang Dwi Wijanarko, "A User-Item Collaborative Filtering System to Predict Online Learning Outcome", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 117–121, 2020. doi: 10.25046/aj050516
61. Sushma Padubidri Shivaprasad, Sreemannarayanay Kulkarni, "Low Power Bulk Driven Series Parallel OTA for Low Frequency Applications", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 69–73, 2020. doi: 10.25046/aj050510
62. Rand Talib, Alexander Rodrigues, Nabil Nassif, "Energy Recovery Equipment and Control Strategies in Various Climate Regions", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 47–53, 2020. doi: 10.25046/aj050407
63. Pham Van Bach Ngoc, Bui Trung Thanh, "Dynamics Model and Design of SMC-type-PID Control for 4DOF Car Motion Simulator", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 557–562, 2020. doi: 10.25046/aj050369
64. Ayyoub El Berbri, Adil Saadi, Seddik Bri, "Design and Optimization of Dual-Band Branch-Line Coupler with Stepped-Impedance-Stub for 5G Applications", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 355–360, 2020. doi: 10.25046/aj050346
65. J. Vijay Fidelis, E. Karthikeyan, "Estimation of Influential Parameter Using Gravitational Search Optimization Algorithm for Soccer", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 340–348, 2020. doi: 10.25046/aj050344
66. Jesuretnam Josemila Baby, James Rose Jeba, "A Hybrid Approach for Intrusion Detection using Integrated K-Means based ANN with PSO Optimization", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 317–323, 2020. doi: 10.25046/aj050341
67. Noraziah Adzhar, Yuhani Yusof, Muhammad Azrin Ahmad, "A Review on Autonomous Mobile Robot Path Planning Algorithms", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 236–240, 2020. doi: 10.25046/aj050330
68. Vasiliy Olonichev, Boris Staroverov, Maxim Smirnov, "Dynamic Objects Parameter Estimation Program for ARM Processors Based Adaptive Controllers", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 34–40, 2020. doi: 10.25046/aj050305
69. Kapil Kumar Gupta, Namrata Dhanda, Upendra Kumar, "A Novel Hybrid Method for Segmentation and Analysis of Brain MRI for Tumor Diagnosis", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 16–27, 2020. doi: 10.25046/aj050303
70. Ricardo Simões Santos, António João Pina da Costa Feliciano Abreu, Joaquim José Rodrigues Monteiro, "Using Metaheuristics-Based Methods to Provide Sustainable Market Solutions, Suitable to Consumer Needs", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 399–410, 2020. doi: 10.25046/aj050252
71. Hind El Hassani, Nour- Eddine Boutammachte, Sanae El Hassani, "Optimization of Low Temperature Differential Stirling Engine Regenerator Design", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 272–279, 2020. doi: 10.25046/aj050235
72. Abderrahim Bajit, Mohammed Nahid, Ahmed Tamtaoui, Mohammed Benbrahim, "A Psychovisual Optimization of Wavelet Foveation-Based Image Coding and Quality Assessment Based on Human Quality Criterions", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 225–234, 2020. doi: 10.25046/aj050229
73. Halima Begum, Muhammed Mazharul Islam, "A Study on the Effects of Combining Different Features for the Recognition of Handwritten Bangla Characters", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 197–203, 2020. doi: 10.25046/aj050225
74. Nguyen Tuan Anh, Hoang Thang Binh, Tran The Tran, "Optimization of the Stabilizer Bar by Using Total Scores Method", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 431–435, 2020. doi: 10.25046/aj050155
75. Temitayo Olayemi Olowu, Mohamadsaleh Jafari, Arif Sarwat, "A Multi-Objective Voltage Optimization Technique in Distribution Feeders with High Photovoltaic Penetration", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 377–385, 2019. doi: 10.25046/aj040648
76. Madallah Alruwaili, "Multi-Stage Enhancement Approach for Image Dehazing", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 343–352, 2019. doi: 10.25046/aj040644
77. Mohd Razif Idris, Imad Mokhtar Mosrati, "Optimization of the Electrical Discharge Machining of Powdered Metallurgical High-Speed Steel Alloy using Genetic Algorithms", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 255–262, 2019. doi: 10.25046/aj040632
78. Jalal Benallal, Lekbir Cherif, Mohamed Chentouf, Mohammed Darmi, Rachid Elgouri, Nabil Hmina, "A New Wire Optimization Approach for Power Reduction in Advanced Technology Nodes", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 140–146, 2019. doi: 10.25046/aj040617
79. Andrei Panteleev, Valentin Panovskiy, "Application of Open-Source Optimization Library “Extremum” to the Synthesis of Feedback Control of a Satellite", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 23–29, 2019. doi: 10.25046/aj040503
80. Houcine Marouani, Amin Sallem, Mondher Chaoui, Pedro Pereira, Nouri Masmoudi, "Multiple-Optimization based-design of RF Integrated Inductors", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 574–584, 2019. doi: 10.25046/aj040468
81. M. Monica Subashini, Abhinav Deshpande, Ramani Kannan, "Study and Implementation of Various Image De-Noising Methods for Traffic Sign Board Recognition", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 545–560, 2019. doi: 10.25046/aj040466
82. Jaya V. Gaitonde, Rajesh B. Lohani, "Material, Structural Optimization and Analysis of Visible-Range Back-Illuminated OPFET photodetector", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 485–502, 2019. doi: 10.25046/aj040459
83. Mohamed Hamada, Abdulsalam Latifat Ometere, Odu Nkiruka Bridget, Mohammed Hassan, Saratu Yusuf Ilu, "A Fuzzy-Based Approach and Adaptive Genetic Algorithm in Multi-Criteria Recommender Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 449–457, 2019. doi: 10.25046/aj040454
84. Olfa Jedda, Ali Douik, "Optimal Discrete-time Sliding Mode Control for Nonlinear Systems Subject to Input Constraints", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 141–146, 2019. doi: 10.25046/aj040417
85. Darya Denisenko, Nikolay Prokopenko, Nikolay Butyrlagin, "All-Pass RC-Filters Architecture with Independent Adjustment of the Main Parameters Based on Differential Difference Amplifiers", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 65–72, 2019. doi: 10.25046/aj040409
86. Trinh Luong Mien, "An Adaptive Fuzzy-Sliding Mode Controller for The Bridge Crane", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 164–170, 2019. doi: 10.25046/aj040322
87. Ethmane Isselem Arbih Mahmoud, Mohamed Maaroufi, Abdel Kader Mahmoud, Ahmed Yahfdhou, "Optimization of Statcom in a Nouakchott Power System", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 333–339, 2019. doi: 10.25046/aj040242
88. Uttam S. Satpute, Diwakar R. Joshi, Shruti Gunaga, "Frequency-Based Design of Electric System for Off-shore Wind Power Plant (OWPP)", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 153–161, 2019. doi: 10.25046/aj040220
89. Gabriel Dämmer, Sven Gablenz, Alexander Hildebrandt, Zoltan Major, "Design of an Additively Manufacturable Multi-Material Light-Weight Gripper with integrated Bellows Actuators", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 23–33, 2019. doi: 10.25046/aj040204
90. Nikolay Starostin, Konstantin Mironov, "Strategies of the Level-By-Level Approach to the Minimal Route", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 268–281, 2019. doi: 10.25046/aj040126
91. Samuel Oludare Bamgbose, Xiangfang Li, Lijun Qian, "Trajectory Tracking Control Optimization with Neural Network for Autonomous Vehicles", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 217–224, 2019. doi: 10.25046/aj040121
92. Hiroyuki Yamamoto, Tomohiro Hayashida, Ichiro Nishizaki, Shinya Sekizaki, "Hypervolume-Based Multi-Objective Reinforcement Learning: Interactive Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 93–100, 2019. doi: 10.25046/aj040110
93. Viorel Lupu, "Web Authentication: no Password; Listen and Touch", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 84–92, 2019. doi: 10.25046/aj040109
94. David Kondru, Mehmet Celenk, Xiaoping A. Shen, "State Estimation based Echolocation Bionics and Image Processing based Target Pattern Recognition", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 73–83, 2019. doi: 10.25046/aj040108
95. Masahiro Kanazaki, Yusuke Yamada, Masaki Nakamiya, "Multi-Objective Path Optimization of a Satellite for Multiple Active Space Debris Removal Based on a Method for the Travelling Serviceman Problem", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 479–488, 2018. doi: 10.25046/aj030656
96. Aye Min, Zin Mar Kyu, "MRI images Enhancement and Brain Tumor Segmentation", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 339–346, 2018. doi: 10.25046/aj030642
97. Chika Yinka-Banjo, Babatunde Opesemowo, "Metaheuristics for Solving Facility Location Optimization Problem in Lagos, Nigeria", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 319–323, 2018. doi: 10.25046/aj030639
98. Mohammad Harun Rashid, Lixin Tao, "Parallelizing Combinatorial Optimization Heuristics with GPUs", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 265–280, 2018. doi: 10.25046/aj030635
99. Saori Nakagawa, Takeshi Yamasaki, Hiroyuki Takano, Isao Yamaguchi, "Guidance Law Based on Line-of-Sight Rate Information Considering Uncertain Modeled Dynamics", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 195–203, 2018. doi: 10.25046/aj030626
100. Wiem Zouari, Ines Alaya, Moncef Tagina, "A Comparative Study of a Hybrid Ant Colony Algorithm MMACS for the Strongly Correlated Knapsack Problem", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 1–22, 2018. doi: 10.25046/aj030601