Nurshazneem Roslan
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Nurshazneem Roslan
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Nurshazneem, Roslan
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Roslan, Nurshazneem
Roslan, N.
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Scopus Author ID
57204426200
Researcher ID
HKF-0060-2023

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  • Publication
    Artificial bee colony for curve reconstruction using quartic bézier
    (Little Lion Scientific, 2022-03-31)
    Nur' Afifah Rusdi
    ;
    Zainor Ridzuan Yahya
    ;
    Wan Zuki Azman Wan Muhamad
    ;
    Nurshazneem Roslan
    This work presents the use of Artificial Bee Colony Algorithm (ABC) for curve reconstruction using Quartic Bézier. Quartic Bézier curve is rarely used by the researchers in the application of medical images. Therefore, by increasing the degree of the Bézier curve, a better curve with small error can be obtain. The process of curve reconstruction involved was boundary and corner point detection of the medical image, parameterization and curve reconstruction by using ABC. By applying these processes, the fitted Quartic Bézier is obtained. The Sum Square Error (SSE) is used to record the error between the fitted Quartic Bézier curve with the original image. The results of SSE is recorded after the process is repeated 10 times with the average error of 3.4463e03 . Because the final output of the fitted curve resembles the original image, the suggested method can be considered as an option method for curve reconstruction applications. ABC algorithm is an interesting algorithm that can be explored in more detail and can be applied in various problems.
      3  10
  • Publication
    2-SAT discrete Hopfield neural networks optimization via Crow search and fuzzy dynamical clustering approach
    ( 2024)
    Caicai Feng
    ;
    Saratha Sathasivam
    ;
    Nurshazneem Roslan
    ;
    Muraly Velavan
    Within the swiftly evolving domain of neural networks, the discrete Hopfield-SAT model, endowed with logical rules and the ability to achieve global minima of SAT problems, has emerged as a novel prototype for SAT solvers, capturing significant scientific interest. However, this model shows substantial sensitivity to network size and logical complexity. As the number of neurons and logical complexity increase, the solution space rapidly contracts, leading to a marked decline in the model's problem-solving performance. This paper introduces a novel discrete Hopfield-SAT model, enhanced by Crow search-guided fuzzy clustering hybrid optimization, effectively addressing this challenge and significantly boosting solving speed. The proposed model unveils a significant insight: its uniquely designed cost function for initial assignments introduces a quantification mechanism that measures the degree of inconsistency within its logical rules. Utilizing this for clustering, the model utilizes a Crow search-guided fuzzy clustering hybrid optimization to filter potential solutions from initial assignments, substantially narrowing the search space and enhancing retrieval efficiency. Experiments were conducted with both simulated and real datasets for 2SAT problems. The results indicate that the proposed model significantly surpasses traditional discrete Hopfield-SAT models and those enhanced by genetic-guided fuzzy clustering optimization across key performance metrics: Global minima ratio, Hamming distance, CPU time, retrieval rate of stable state, and retrieval rate of global minima, particularly showing statistically significant improvements in solving speed. These advantages play a pivotal role in advancing the discrete Hopfield-SAT model towards becoming an exemplary SAT solver. Additionally, the model features exceptional parallel computing capabilities and possesses the potential to integrate with other logical rules. In the future, this optimized model holds promise as an effective tool for solving more complex SAT problems. </abstract>
      25  2
  • Publication
    Conditional random k satisfiability modeling for k =1,2 (CRAN2SAT) with non-monotonic Smish activation function in discrete Hopfield neural network
    ( 2024)
    Nurshazneem Roslan
    ;
    Saratha Sathasivam
    ;
    Farah Liyana Azizan
    <abstract> <p>The current development of logic satisfiability in discrete Hopfield neural networks (DHNN)has been segregated into systematic logic and non-systematic logic. Most of the research tends to improve non-systematic logical rules to various extents, such as introducing the ratio of a negative literal and a flexible hybrid logical structure that combines systematic and non-systematic structures. However, the existing non-systematic logical rule exhibited a drawback concerning the impact of negative literal within the logical structure. Therefore, this paper presented a novel class of non-systematic logic called conditional random <italic>k</italic> satisfiability for <italic>k</italic> = 1, 2 while intentionally disregarding both positive literals in second-order clauses. The proposed logic was embedded into the discrete Hopfield neural network with the ultimate goal of minimizing the cost function. Moreover, a novel non-monotonic Smish activation function has been introduced with the aim of enhancing the quality of the final neuronal state. The performance of the proposed logic with new activation function was compared with other state of the art logical rules in conjunction with five different types of activation functions. Based on the findings, the proposed logic has obtained a lower learning error, with the highest total neuron variation <italic>TV</italic> = 857 and lowest average of Jaccard index, <italic>JSI</italic> = 0.5802. On top of that, the Smish activation function highlights its capability in the DHNN based on the result ratio of improvement <italic>Zm</italic> and <italic>TV</italic>. The ratio of improvement for Smish is consistently the highest throughout all the types of activation function, showing that Smish outperforms other types of activation functions in terms of <italic>Zm</italic> and <italic>TV.</italic> This new development of logical rule with the non-monotonic Smish activation function presents an alternative strategy to the logic mining technique. This finding will be of particular interest especially to the research areas of artificial neural network, logic satisfiability in DHNN and activation function.</p> </abstract>
      1  19
  • Publication
    Logic mining with hybridized 3-satisfiability fuzzy logic and harmony search algorithm in Hopfield neural network for Covid-19 death cases
    ( 2024)
    Farah Liyana Azizan
    ;
    Saratha Sathasivam
    ;
    Nurshazneem Roslan
    ;
    Ahmad Deedat Ibrahim
    <abstract> <p>Since the beginning of the Covid-19 infections in December 2019, the virus has emerged as the most lethally contagious in world history. In this study, the Hopfield neural network and logic mining technique merged to extract data from a model to provide insight into the link between factors influencing the Covid-19 datasets. The suggested technique uses a 3-satisfiability-based reverse analysis (3SATRA) and a hybridized Hopfield neural network to identify the relationships relating to the variables in a set of Covid-19 data. The list of data is to identify the relationships between the key characteristics that lead to a more prolonged time of death of the patients. The learning phase of the hybridized 3-satisfiability (3SAT) Hopfield neural network and the reverse analysis (RA) method has been optimized using a new method of fuzzy logic and two metaheuristic algorithms: Genetic and harmony search algorithms. The performance assessment metrics, such as energy analysis, error analysis, computational time, and accuracy, were computed at the end of the algorithms. The multiple performance metrics demonstrated that the 3SATRA with the fuzzy logic metaheuristic algorithm model outperforms other logic mining models. Furthermore, the experimental findings have demonstrated that the best-induced logic identifies important variables to detect critical patients that need more attention. In conclusion, the results validate the efficiency of the suggested approach, which occurs from the fact that the new version has a positive effect.</p> </abstract>
      2  22
  • Publication
    Hybridised network of fuzzy logic and a genetic algorithm in solving 3-satisfiability hopfield neural networks
    (MDPI, 2023)
    Farah Liyana Azizan
    ;
    Saratha Sathasivam
    ;
    Majid Khan Majahar Ali
    ;
    Nurshazneem Roslan
    ;
    Caicai Feng
    This work proposed a new hybridised network of 3-Satisfiability structures that widens the search space and improves the effectiveness of the Hopfield network by utilising fuzzy logic and a metaheuristic algorithm. The proposed method effectively overcomes the downside of the current 3-Satisfiability structure, which uses Boolean logic by creating diversity in the search space. First, we included fuzzy logic into the system to make the bipolar structure change to continuous while keeping its logic structure. Then, a Genetic Algorithm is employed to optimise the solution. Finally, we return the answer to its initial bipolar form by casting it into the framework of the hybrid function between the two procedures. The suggested network’s performance was trained and validated using Matlab 2020b. The hybrid techniques significantly obtain better results in terms of error analysis, efficiency evaluation, energy analysis, similarity index, and computational time. The outcomes validate the significance of the results, and this comes from the fact that the proposed model has a positive impact. The information and concepts will be used to develop an efficient method of information gathering for the subsequent investigation. This new development of the Hopfield network with the 3-Satisfiability logic presents a viable strategy for logic mining applications in future.
      1  11