Publications by authors named "Mustafa Khalid"

6 Publications

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Cortico-Hippocampal Computational Modeling Using Quantum-Inspired Neural Networks.

Front Comput Neurosci 2020 5;14:80. Epub 2020 Nov 5.

State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China.

Many current computational models that aim to simulate cortical and hippocampal modules of the brain depend on artificial neural networks. However, such classical or even deep neural networks are very slow, sometimes taking thousands of trials to obtain the final response with a considerable amount of error. The need for a large number of trials at learning and the inaccurate output responses are due to the complexity of the input cue and the biological processes being simulated. This article proposes a computational model for an intact and a lesioned cortico-hippocampal system using quantum-inspired neural networks. This cortico-hippocampal computational quantum-inspired (CHCQI) model simulates cortical and hippocampal modules by using adaptively updated neural networks entangled with quantum circuits. The proposed model is used to simulate various classical conditioning tasks related to biological processes. The output of the simulated tasks yielded the desired responses quickly and efficiently compared with other computational models, including the recently published Green model.
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http://dx.doi.org/10.3389/fncom.2020.00080DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7674175PMC
November 2020

Recent Techniques in Nutrient Analysis for Food Composition Database.

Molecules 2020 Oct 6;25(19). Epub 2020 Oct 6.

Nutrition, Metabolism and Cardiovascular Research Centre, Institute for Medical Research, National Institutes of Health, No.1, Jalan Setia Murni U13/52, Seksyen U13 Setia Alam, Shah Alam 40170, Malaysia.

Food composition database (FCD) provides the nutritional composition of foods. Reliable and up-to date FCD is important in many aspects of nutrition, dietetics, health, food science, biodiversity, plant breeding, food industry, trade and food regulation. FCD has been used extensively in nutrition labelling, nutritional analysis, research, regulation, national food and nutrition policy. The choice of method for the analysis of samples for FCD often depends on detection capability, along with ease of use, speed of analysis and low cost. Sample preparation is the most critical stage in analytical method development. Samples can be prepared using numerous techniques; however it should be applicable for a wide range of analytes and sample matrices. There are quite a number of significant improvements on sample preparation techniques in various food matrices for specific analytes highlighted in the literatures. Improvements on the technology used for the analysis of samples by specific instrumentation could provide an alternative to the analyst to choose for their laboratory requirement. This review provides the reader with an overview of recent techniques that can be used for sample preparation and instrumentation for food analysis which can provide wide options to the analysts in providing data to their FCD.
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http://dx.doi.org/10.3390/molecules25194567DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7582643PMC
October 2020

Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning Paradigms.

Brain Sci 2020 Jul 7;10(7). Epub 2020 Jul 7.

The State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China.

Most existing cortico-hippocampal computational models use different artificial neural network topologies. These conventional approaches, which simulate various biological paradigms, can get slow training and inadequate conditioned responses for two reasons: increases in the number of conditioned stimuli and in the complexity of the simulated biological paradigms in different phases. In this paper, a cortico-hippocampal computational quantum (CHCQ) model is proposed for modeling intact and lesioned systems. The CHCQ model is the first computational model that uses the quantum neural networks for simulating the biological paradigms. The model consists of two entangled quantum neural networks: an adaptive single-layer feedforward quantum neural network and an autoencoder quantum neural network. The CHCQ model adaptively updates all the weights of its quantum neural networks using quantum instar, outstar, and Widrow-Hoff learning algorithms. Our model successfully simulated several biological processes and maintained the output-conditioned responses quickly and efficiently. Moreover, the results were consistent with prior biological studies.
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http://dx.doi.org/10.3390/brainsci10070431DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7407954PMC
July 2020

Green model to adapt classical conditioning learning in the hippocampus.

Neuroscience 2020 02 5;426:201-219. Epub 2019 Dec 5.

The State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China.

Compared with the biological paradigms of classical conditioning, non-adaptive computational models are not capable of realistically simulating the biological behavioural functions of the hippocampal regions, because of their implausible requirement for a large number of learning trials, which can be on the order of hundreds. Additionally, these models did not attain a unified, final stable state even after hundreds of learning trials. Conversely, the output response has a different threshold for similar tasks in various models with prolonged transient response of unspecified status via the training or even testing phases. Accordingly, a green model is a combination of adaptive neuro-computational hippocampal and cortical models that is proposed by adaptively updating the whole weights in all layers for both intact networks and lesion networks using instar and outstar learning rules with adaptive resonance theory (ART). The green model sustains and expands the classical conditioning biological paradigms of the non-adaptive models. The model also overcomes the irregular output response behaviour by using the proposed feature of adaptivity. Further, the model successfully simulates the hippocampal regions without passing the final output response back to the whole network, which is considered to be biologically implausible. The results of the Green model showed a significant improvement confirmed by empirical studies of different tasks. In addition, the results indicated that the model outperforms the previously published models. All the obtained results successfully and quickly attained a stable, desired final state (with a unified concluding state of either "1" or "0") with a significantly shorter transient duration.
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http://dx.doi.org/10.1016/j.neuroscience.2019.11.021DOI Listing
February 2020

Sensor and sensorless fault tolerant control for induction motors using a wavelet index.

Sensors (Basel) 2012 27;12(4):4031-50. Epub 2012 Mar 27.

Department of Electrical Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.

Fault Tolerant Control (FTC) systems are crucial in industry to ensure safe and reliable operation, especially of motor drives. This paper proposes the use of multiple controllers for a FTC system of an induction motor drive, selected based on a switching mechanism. The system switches between sensor vector control, sensorless vector control, closed-loop voltage by frequency (V/f) control and open loop V/f control. Vector control offers high performance, while V/f is a simple, low cost strategy with high speed and satisfactory performance. The faults dealt with are speed sensor failures, stator winding open circuits, shorts and minimum voltage faults. In the event of compound faults, a protection unit halts motor operation. The faults are detected using a wavelet index. For the sensorless vector control, a novel Boosted Model Reference Adaptive System (BMRAS) to estimate the motor speed is presented, which reduces tuning time. Both simulation results and experimental results with an induction motor drive show the scheme to be a fast and effective one for fault detection, while the control methods transition smoothly and ensure the effectiveness of the FTC system. The system is also shown to be flexible, reverting rapidly back to the dominant controller if the motor returns to a healthy state.
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http://dx.doi.org/10.3390/s120404031DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3355397PMC
October 2012

Synthesis of peptide-based polymers by microwave-assisted cycloaddition backbone polymerization.

Biomacromolecules 2007 Feb;8(2):327-30

Department of Medicinal Chemistry and Chemical Biology, and Department of Pharmaceutics, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, P.O. Box 80082, 3508 TB Utrecht, The Netherlands.

The optimized reaction conditions for the Cu(I)-catalyzed N-->C polymerization of azido-phenylalanyl-alanyl-propargyl amide to yield either high molecular weight linear polymers or medium-sized cyclic polymers is described. These reaction conditions will be applied to tailor the synthesis, properties, and structure of biologically relevant peptide-based biopolymers.
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http://dx.doi.org/10.1021/bm061010gDOI Listing
February 2007