The artificial immune system (AIS) algorithm is a heuristic technique inspired by the biological immune
system. The biological immune system has been proven to be a robust system that defends our body
from any pathogen attacks. This paper presents a hybrid paradigm by implementing the Hopfield neural
network integrated with enhanced AIS for solving a 3-Satisfiability (3-SAT) problem. Fundamentally, a
3-Satisfiability problem is used as an ideal optimisation problem by neural network practitioners in their
research. The core impetus of this study was to compare the performance of artificial immune system
(AIS) algorithm and brute-force search (BFS) algorithm in doing 3-SAT logic programming. Microsoft
Visual C++ 2013 was used as a dynamic platform for training, simulating and testing of the network.
We restricted our analysis to 3-Satisfiability (3-SAT) clauses. The performances of both paradigms were
analysed according to the following measures, namely, global minima ratio, global Hamming distance,
fitness landscape value and computational time. The experimental results successfully depicted the
robustness of the AIS compared to the BFS algorithm. The work presented here has profound implications
for future studies of AIS to solve more complicated NP problems.
There is conflicting evidence on the causal relationship of patent foramen ovale (PFO) in migraine. This review will examine the pathophysiological relevance of PFO in migraine, the epidemiological evidence of PFO causing migraine, and the existing evidence on the effectiveness of closure of PFO on the symptomatology of migraine. From the current available evidence, the role of PFO in migraine is debatable, and interventions such as closure of PFO cannot yet be considered routine treatment of migraine.
Medical diagnosis is the extrapolation of the future course and outcome of a disease and a sign of the likelihood of recovery from that disease. Diagnosis is important because it is used to guide the type and intensity of the medication to be administered to patients. A hybrid intelligent system that combines the fuzzy logic qualitative approach and Adaptive Neural Networks (ANNs) with the capabilities of getting a better performance is required. In this paper, a method for modeling the survival of diabetes patient by utilizing the application of the Adaptive NeuroFuzzy Inference System (ANFIS) is introduced with the aim of turning data into knowledge that can be understood by people. The ANFIS approach implements the hybrid learning algorithm that combines the gradient descent algorithm and a recursive least square error algorithm to update the antecedent and consequent parameters. The combination of fuzzy inference that will represent knowledge in an interpretable manner and the learning ability of neural network that can adjust the membership functions of the parameters and linguistic rules from data will be considered. The proposed framework can be applied to estimate the risk and survival curve between different diagnostic factors and survival time with the explanation capabilities.
Particle swarm optimization (PSO) is employed to investigate the overall performance of a pin fin.The following study will examine the effect of governing parameters on overall thermal/fluid performance associated with different fin geometries, including, rectangular plate fins as well as square, circular, and elliptical pin fins. The idea of entropy generation minimization, EGM is employed to combine the effects of thermal resistance and pressure drop within the heat sink. A general dimensionless expression for the entropy generation rate is obtained by considering a control volume around the pin fin including base plate and applying the conservations equations for mass and energy with the entropy balance. Selected fin geometries are examined for the heat transfer, fluid friction, and the minimum entropy generation rate corresponding to different parameters including axis ratio, aspect ratio, and Reynolds number. The results clearly indicate that the preferred fin profile is very dependent on these parameters.