A Framework to Classify Clinical Data Using a Genetic Algorithm and Artificial Flora-Optimized Neural Network

The research paper is an introduction to the artificial neural networks and wireless network by using sensor technology. There are myriads types of neural networks which are used in computer network and wireless network technology. Neural networks explain and give the proper demonstration for the applications of neural networks and wireless network. The artificial network and wireless network have strong connection and can be investigated and explained properly. There are also some models which are called mathematical models which can explain and demonstrate the artificial and wireless networks in detail. There are many organizations which are using artificial neural networks by using sensor technology for wide range of purpose. A new technology ZigBee introduced in artificial networks and wireless networks by evolving the new protocol system based on meter reading. Artificial neural networks and wireless network along database management system have great importance in revolutionizing the world in artificial and wireless technology by using different wireless sensors, RF chip, and fusion of RFID system along with nodes of ZigBee technology.

The Sixth International Symposium on Neural Networks (ISNN 2009)

The Brain Emotional Learning (BEL) is a novel bio-inspired machine learning approach mentioned as a new class of artificial neural network (ANN). The artificial emotional neural network (AENN) is one of the BEL methods which used the genetic algorithm (GA) to compute proper weights, weights of the amygdala (AMYG), orbitofrontal cortex (OFC) weights and a bias value. AENN trained by GA has been reported that it could produce low error rates. However, AENN still has more rooms to enhance its prediction of performance, especially generalization and the prediction of performance. Therefore, this paper aims to propose a new training method for AENN. The JAYA optimization algorithm optimized the weights and the biases of AENN. Two new proposed models are named as AENN-Max-JAYA and AENN-Mean-JAYA. Their names are according to the way of selecting the additional expanded feature which obtained through either the max or the average of input patterns, respectively. From the experimental results for wind power forecasting dataset, the proposed methods proved that the results are better in generalization performance and give lower error rates which compared to the comparative AENN models and traditional ANNs.

This paper presents an investigation of an artificial neural network (ANN) to predict the flutter characteristics of a simple wing that under went multiple weight variation changes. The weight variations were used to simulate the addition of non-structural weight or structural repair weight. The test case wing had 24 designated repair areas where repair weights were added as lumped masses. Weight and location of the repairs were randomly generated to increase the sample size and help the generalization of the ANN. The ANN was evaluated for both the flutter speed and flutter frequency for multiple levels of structural damping. The number of test cases required to train the neural network was also evaluated for both the error between the input and output results as well as the generalization of the neural networks.

NOVEL STAGING TOOL FOR LOCALIZED PROSTATE CANCER: A PILOT STUDY USING GENETIC ADAPTIVE NEURAL NETWORKS

This paper focuses on the Genetic Algorithm learning paradigm applied to train the ANNs for balancing the cart-pole balancing system. The studied system is a classic control problem namely “cart-pole” problem. We will apply the unconventional techniques Artificial Neural Network, Genetic Algorithm and Fuzzy Logic to a classic control problem "cart-pole”. In this paper we have tried to train the Artificial Neural Network (ANN) with using Genetic Algorithms (program is written in MATLAB) which is compared with the output obtained using the Artificial Neural Network Toolbox provided in MATLAB. In proposed approach we have used both ANNs and Genetic Algorithm to get more optimal solution. Here we applied the approach for the Fuzzy logic technique to design a Fuzzy Logic Controllers (FLC) using ANNs and Genetic Algorithm (GA). The Fuzzy rules which are needed to control the problem will be framed with the combination of Artificial Neural Networks and Genetic Algorithm. It has been found that such a searching technique converges intelligently and much faster than conventional learning means. Performance of the presented neural network training using the genetic algorihtms is much better and providing more accurate results.

Artificial networks can be used to identify hydrogen nuclear magnetic resonance (1H-NMR) spectra of complex oligosaccharides. Feed-forward neural networks with back-propagation of errors can distinguish between spectra of oligosaccharides that differ by only one glycosyl residue in twenty. The artificial neural networks use features of the strongly overlapping region of the spectra (hump region) as well as features of the resolved regions of the spectra (structural reporter groups) to recognize spectra and efficiently recognized 1H-NMR spectra even when the spectra were perturbed by minor variations in their chemical shifts. Identification of spectra by neural network-based pattern recognition techniques required less than 0.1 second. It is anticipated that artificial neural networks can be used to identify the structures of any complex carbohydrate that has been previously characterized and for which a 1H-NMR spectrum is available.

Artificial neural network and regression coupled genetic algorithm to optimize parameters for enhanced xylitol production by Debaryomyces nepalensis in bioreactor

Since the last decade, several studies have shown the ability of Artificial Neural Networks (ANNs) in modeling of rainfall-runoff process. From methodological viewpoint, ANN belongs to more general paradigm, i.e., soft computing or computational intelligence, in which independent methodologies, mostly Fuzzy Logic (FL), ANN, and Genetic Algorithms (GAs), are combined together in order to provide an intelligent behavior in computational frameworks. Consequently, in the context of rainfall-runoff modeling, this question rises whether hybridization of ANNs with other soft computing-related methodologies improves the overall performance of modeling or not. In this study, based on the idea of structure and/or parameter identification of ANNs with GAs, the evolutionary neural networks modeling paradigm is examined for describing the rainfall-runoff process. One of the benchmark data set of current literature, i.e., Leaf River basin (near Collins, Mississippi) data set, is used for simulation. The results show that on the one hand, the overall accuracy is improved; but one the other hand, in evolutionary neural modeling, the computational time is increased significantly. Hence the modeler may be faced with a trade-off problem between accuracy and computational difficulties which may have different importance in a particular rainfall-runoff problem.

Throughout history, the development of artificial intelligence, especially artificial neural networks, has been continuously influenced by a deeper understanding of the brain. This influence includes the development of the neocognitron, considered a precursor to convolutional neural networks. The emerging field of NeuroAI posits that leveraging neuroscience knowledge could significantly advance AI by imbuing networks with enhanced capabilities. Unlike the human brain, which features a variety of morphologically and functionally distinct neurons, artificial neural networks typically rely on a homogeneous neuron model. In the human brain, the diversity of neurons facilitates a wide range of intelligent behaviors. Given that artificial networks aim to mimic the human brain, incorporating a diversity of neuron models could address key challenges in artificial intelligence, such as efficiency, interpretability, and memory capacity. This perspective begins by examining the basics of biological neuronal diversity and how biological neurons transmit and process information. We then explore research efforts to design novel neuron models for artificial networks and discuss the potential benefits of neuronal diversity, including applications in several critical areas. Finally, we address the challenges and future directions for integrating neuronal diversity into artificial networks, highlighting its potential to enrich NeuroAI.

این مطالعه به منظور بررسی پیش بینی شاخص های عملکرد تولیدی در مرغان تخم گذار با استفاده از شبکه های عصبی مصنوعی و رگرسیون غیرخطی چندگانه انجام شد. بررسی بر روی اطلاعات چهار دوره متوالی پرورش در یک واحد پرورش مرغ تخم گذار صورت گرفت. روش های داده-کاوی شامل رگرسیون خطی و غیر خطی، شبکه عصبی پرسپترون سه لایه، شبکه عصبی پرسپترون چهار لایه و شبکه عصبی با تابع پایه ای شعاعی بود. در این مدل ها از متغیرهای سن گله، میزان خوراک مصرفی و فصل تولید به عنوان متغیر پیشگو و شاخص های عملکرد تولیدی شامل درصد تخم-گذاری، وزن توده ای تخم مرغ تولیدی و ضریب تبدیل غذایی به عنوان متغیر پاسخ استفاده شد. نتایج نهایی رگرسیون های خطی نشان داد که برای تمامی متغیرهای وابسته مورد مطالعه متغیر مستقل سن گله معنی دار می باشد. بنابراین رگرسیون غیر خطی شاخص های عملکرد تولیدی در مقابل سن برای مقایسه با شبکه های عصبی مختلف مورد بررسی قرار گرفت و برای مقایسه کلیه مدل ها از ضریب تعیین (R2) و میانگین قدر مطلق خطا (MAE) استفاده شد. نتایج نشان داد بین شبکه های عصبی مصنوعی مختلف مورد مطالعه، شبکه با تابع پایه ای شعاعی بهتر از سایر مدل های در پیش‌بینی شاخص های عملکرد تولیدی مرغان تخم گذار عمل می کند.

Artificial neural network (ANN) based prediction and optimization of an organic Rankine cycle (ORC) for diesel engine waste heat recovery

In recent times, research efforts have been directed towards hybridization of various intelligent methodologies in order to solve complex industrial problems. At the same time these research efforts have been undertaken to develop a better understanding of the human information processing system. Today, one can find a number of applications involving hybridization of intelligent methodologies like knowledge based systems, fuzzy systems, genetic algorithms, and case based reasoning with artificial neural networks. The central methodology of many hybrid systems has been artificial neural networks. In fact, 2/3rd of the applications involving intelligent hybrid systems use neural networks. Broadly, in these applications neural networks are either used as the primary problem solving entity, or are used in conjunction with other intelligent methodology/ies which have a distinct and separate role to play in the problem solving process. The former are categorized as fusion and/or transformation based approaches, and the later are categorized as combination approaches. However, fusion and/or transformation based approaches center around not only neural networks but also center around the other other methodology, namely, genetic algorithms. Similarly, the combination approaches can involve intelligent methodologies other than neural networks also. The goal of this chapter is to provide an overview to the reader about fusion and transformation based synergies with neural network and genetic algorithms as the primary problem solving entities. In this direction, this chapter looks at the neuro-symbolic systems, neuro-fuzzy systems, genetic-neuro systems, and genetic-fuzzy systems.

Objective: The main objective of this paper is to improve the intrusion detection accuracy of neural networks by hybridized with Jordan network, applying novel Lyapunov function and changing input values as fuzzy values using fuzzy logic. Methods: In this research, the intrusion detection of NSL KDD dataset is carry out by neural network first . The performance of neural networks mainly depends on the parameters like number of hidden layer, no of node in the hidden layer and the no of epoch. Selecting the proper parameters values and weight initialization are the main difficulty in neural network. To overcome this issue a hybrid network by combining neural network with Jordan network is proposed which is highly sensitive to weight convergence. In hybrid network Lyapunov function is used to achieve the stability of equilibrium between two networks. But in this work a novel Lyapunov function is proposed to achieve global robust stability in hybrid network. A novel Lyapunov function uses a class of general activation functions which are not to be differentiable, bounded or monotonically nondecreasing. A set of criteria are derived to guarantee the existence, uniqueness and global robust stability of the equilibrium of hybrid networks with time delays. Then the learning ability of hybrid network is improved by using fuzzy logic. The hybrid network improved by a novel Lyapunov and fuzzy logic is called as Improved Fuzzy Hybrid Jordan network and Artificial Neural Network (IHFJANN). Findings: Artificial Neural Network (ANN), Hybrid Jordan network and Artificial Neural Network (HJANN) and Improved Hybrid Fuzzy Jordan network and Artificial Neural Network (IHFJ ANN) are applied on NSL KDD training dataset to lean the type of available attacks. NSL training dataset contains 2500 instances and 38 attributes where as testing dataset contains 995 instances and 38 attributes. The learned model of three classifiers is used to predict the classes in test dataset. The performance measures are evaluated in terms of accuracy, precision, recall and f-measure values. Improvement: The classification accuracy of Artificial Neural Network (ANN), Hybrid Jordan network and Artificial Neural Network (HJANN) and Improved Hybrid Fuzzy Jordan network and Artificial Neural Network (IHFJANN) are 72%, 80% and 84% respectively. Accuracy is increased by 12% in IHFJANN than the ANN and 4% than the HJANN. The precision value of ANN, HJANN and IHFJANN are 0.46, 0.54 and 0.57 respectively. Precision value is increased by 0.9 in IHFJANN than the ANN and 0.3 than the HJANN. The recall value of ANN, HJANN and IHFJANN are 0.73, 0.81 and 0.84 respectively. Recall value is increased by 0.9 in IHFJANN than the ANN and 0.3 than the HJANN. The F-measure value of ANN, HJANN and IHFJANN are 0.58, 0.65 and 0.68 respectively. F-measure value is increased by 0.10 in IHFJANN than the ANN and 0.3 than the HJANN. The results proved that the proposed IHFJANN provides better performance than ANN and HJANN.

Rule extraction based on PROMETHEE-assisted multi-objective genetic algorithm for generating interpretable neural networks