Randomised Alpha-Cut Fuzzy Logic Hybrid Model in Solving 3-Satisfiability Hopfield Neural Network

This paper proposes an innovative approach to improve the performance of 3SAT logic programming in the Hopfield neural network. The merged structures of the 3SAT and Hopfield network have specific weaknesses, one of which is that, at times, the system attained local minimum solutions rather than global minimum solutions. A new model of integration randomised alpha-cut fuzzy logic with 3SAT in the Hopfield network is built to convey information more effectively. 3SAT and fuzzy logic can work together to solve Hopfield networks' combinatorial optimisation issues. Procedures of fuzzifying and defuzzifying the neurons might ease the computational burden of determining the correct neuron states. Until the proper neuron state is established, unsatisfied neuron clauses will be modified using a randomised alpha-cut approach in the defuzzifier step. An incorporated design built a random approach to select the alpha-cut values of 0.1, 0.25, and 0.5. At this point, a fuzzy value switches into a crisp output back through the defuzzifier process. Based on the results obtained, the proposed hybrid strategy effectively improves the indicators of RMSE, SSE, MAE, MAPE, global minima and total computational time. A computer-generated data set was used to measure how well the hybridised techniques performed. The performance of the proposed network was trained and validated using Matlab 2020b. The results are significant because this model significantly affects how successfully Hopfield networks merged with fuzzy logic can tackle the 3SAT challenges. The obtained data and ideas will help to create novel approaches to data collection for upcoming logic programming exploration

Relationship between Mathematics Diagnostic Test and Mathematics Final Assessment Among Pre-University Students Based on Gender

Diagnostic testing is considered as an important device that can be used by educators to identify students' strong and weak points in Mathematics subjects as it is often used as a preliminary evaluation of basic Mathematics skills of students. Educators must be able to detect the students' mathematical capacity earlier before delivering new content to them. This article purposes are to identify the achievement of pre-university students in Mathematics diagnostic test and Mathematics final assessment based on their gender, the differences between the gender of the students with their achievement in Mathematics diagnostic test, and the correlation between the students' Mathematics diagnostic test and their achievement in pre-university Mathematics final assessment. The findings demonstrated that students of both genders, who accomplished good results in their Mathematics diagnostic test, did similarly good in their final assessment. Also, students who carried out badly in their Mathematics diagnostic test did poorly in their final assessment. From this diagnostic result, it facilitates the educator to provide the students with the best teaching method and material required

Logic mining with hybridized 3-satisfiability fuzzy logic and harmony search algorithm in Hopfield neural network for Covid-19 death cases

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

DeepFinder : An integration of feature-based and deep learning approach for DNA motif discovery

We propose an improved solution to the three-stage DNA motif prediction approach. The three-stage approach uses only a subset of input sequences for initial motif prediction, and the initial motifs obtained are employed for site detection in the remaining input subset of non-overlaps. The currently available solution is not robust because motifs obtained from the initial subset are represented as a position weight matrices, which results in high false positives. Our approach, called DeepFinder, employs deep learning neural networks with features associated with binding sites to construct a motif model. Furthermore, multiple prediction tools are used in the initial motif prediction process to obtain a higher number of positive hits. Our features are engineered from the context of binding sites, which are assumed to be enriched with specificity information of sites recognized by transcription factor proteins. DeepFinder is evaluated using several performance metrics on ten chromatin immunoprecipitation (ChIP) datasets. The results show marked improvement of our solution in comparison with the existing solution. This indicates the effectiveness and potential of our proposed DeepFinder for large-scale motif analysis. © 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group

Interaction and Development of Breast Cancer Cells with Immune Response Using First Order ODE

Breast cancer arises when cells develop uncontrollably in the breast to form tumour cells. The risk of having breast cancer rises as a woman continues to age. Thus, details about the early stages of cancer progression can help a woman make early treatment choices, preventing them from being diagnosed with these harmful cancers. It is believed that cytotoxic T lymphocytes (CTLs) act as effector cells to eradicate cancer cells. CTLs and tumor cells were discovered to be around a "predator-prey" relationship, with CTL acting as the predator along with tumor cells acting as the prey. In this paper, we examined steady-state solutions for two numerical differentiation using the Jacobian matrix. We will also examine the stability region of breast cancer cells in two different phases to describe its progress in different types of the human body at various phases. Also, compare tumour cells population development in the duration of interphase and metaphase in the presence and the absence of immune response, which is dependent on the CTL population, will be observed by applying Fourth Order Runge Kutta (RK4) method. We can see that the Runge-Kutta method is an important method for approximate solutions to ordinary systems with known initial conditions. We achieved populations value of tumour cells throughout interphase and mitosis as well as the population of the immune system using the method. Based on our observations, we draw the conclusion that persons with a higher immune system are more likely to be able to fight cancer for for a period of time than cancer sufferers with a poor immune system. We may observe that the cancer cells were reduced in a shorter period of time with a high immune response population compared to other lower CTL values

Hybridised Network of Fuzzy Logic and a Genetic Algorithm in Solving 3-Satisfiability Hopfield Neural Networks

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

Innovative Classroom Strategy: Impact on Students’ Mathematics Motivation, Anxiety and Achievement in Pre-University Studies

One aspect that influences mathematics achievement is students' mathematics motivation, which is closely related to their mathematics anxiety. This study aims to incorporate a classroom intervention strategy using a brain-based teaching approach (BBTA) with technological tools to improve students' mathematics performance in pre-university studies. BBTA was used in the classroom to increase students’ mathematics interest and minimise their mathematics anxiety to increase mathematics performance. Two hundred and six (206) pre-university students were exposed to both BBTA and conventional instructions during their Statistic lessons. Questionnaire comprises of motivation and anxiety-related questions as well as pre and post mathematics tests were administered to these students. Based on the findings, students with low anxiety appeared to have more self-confidence when studying mathematics, which simultaneously improved their examination results. These two elements are critical in students' learning of mathematics because students who have low levels of anxiety and high levels of motivation in learning mathematics attain high achievement in mathematics

Prediction of Drug Concentration in Human Bloodstream using AdamsBashforth-Moulton Method

Pharmaceutical drugs are chemicals intended to avoid, assess, heal, or cure a disease. It is also commonly referred to as medication. When medicine is taken, it gets absorbed into the bloodstream, spreads throughout the body, and achieves its maximum concentration. Following this, the medication level gradually decreases as it is removed from the body. The drug concentration according to the time can be predicted using mathematical concepts and pharmacokinetic models. The compartmental model is a fundamental type of model used in pharmacokinetics. The number of compartments required to describe the drug's action in the body is one-compartment, twocompartment, and multicompartment. These models can forecast medication concentrations in the body over time. This paper will focus on the one-compartment model and Adams Bashforth-Moulton method. Adams Method is one of the linear multistep techniques applied to solve numerical ordinary differential equations that contain the predictor method (Adams Bashforth) and corrector method (Adams Moulton). The integrated development environment used for the computation and graphing is MATLAB. The expected result of this report is that we can predict the concentration of the chosen drugs over time and how long a particular person needs to wait before donating blood safely

Study of transmission of tuberculosis by SIR model using Runge-Kutta method

This project is conducted to see the prediction of the transmission of the tuberculosis disease's trend with demography and without demography. It is carried out by the SIR model with the Runge-Kutta fourth-order technique using mathematical modelling to analyse Tuberculosis transmission. Furthermore, this project examines the Tuberculosis disease prediction performance of the two SIR models by comparing the data and also to predict the future trend of Tuberculosis transmission in Malaysia in the year 2021 by calculating its incidence rate for each 100 thousand people. We discovered that combining the SIR Model with demography improves the prediction of Tuberculosis disease spread. We also discovered that the higher the transmission rate, the lower the incidence rate per 100 thousand people, and the higher the incidence rate per 100 thousand people, the lower the recovery rate. As a result, it is acceptable to argue that these variables play a significant impact in determining epidemic growth rates