GIAO C-H COSY Simulations Merged with Artificial Neural Networks Pattern Recognition Analysis. Pushing the Structural Validation a Step Forward.

Whose Balance Sheet Is This? Neural Networks for Bankss Pattern Recognition

This study describes the implementation of Artificial Neural Network Pattern Recognition using Back-Propagation algorithm which can perform cardiovascular health pre-diagnosis of a patient through a generated blood pressure profile. The proponents gathered data from institutions that conduct Exercise Stress Testing, specifically the Treadmill Stress Test. The data gathered were age, gender, height, weight, blood pressure and heart rate readings and is considered as blood pressure condition risk factor. They compose the 47 input parameters of the network and was then divided into two — the training data and the testing data. This was put into a database created using Microsoft Excel. The back-propagation neural network model gives an accurate pre-diagnosis. The trained system gives an acceptable pre-diagnosis in reference to the given diagnosis by the attending practitioner that facilitated the collection of data.

A thin-film CMOS MEMS thermal sensor has been designed, fabricated and tested with the addition of through-membrane isolating holes. These holes have been shown to enhance the discrimination towards gases with differing thermal conductivity in the presence of flow. Using three on-membrane resistors as inputs, linear statistical methods alongside Artificial Neural Network pattern recognition techniques have been investigated for decoupling the two parameters of thermal conductivity and flow rate using a single sensor. In addition to this, the addition of the membrane holes increases the sensitivity towards flow rate by 10 times and the sensitivity towards thermal conductivity by 2 times. This sensor design coupled with well-known post-processing techniques will enable a new generation of multi-parameter sensing solutions.

Error diagnosis and detection have become important in modern production due to the importance of spinning equipment. Artificial neural network pattern recognition methods are widely utilized in rotating equipment fault detection. These methods often need a large quantity of sample data to train the model; however, sample data (especially fault samples) are uncommon in engineering. Preliminary work focuses on dimensionality reduction for big data sets using semisupervised methods. The rotary machine’s polar coordinate signal is used to build a GAN network structure. ANN and tiny samples are utilized to identify DCGAN model flaws. The time-conditional generative adversarial network is proposed for one-dimensional vibration signal defect identification under data imbalance. Finally, auxiliary samples are gathered under similar conditions, and CCNs learn about target sample characteristics. Convolutional neural networks handle the problem of defect identification with small samples in different ways. In high-dimensional data sets with nonlinearities, low fault type recognition rates and fewer marked fault samples may be addressed using kernel semisupervised local Fisher discriminant analysis. The SELF method is used to build the optimum projection transformation matrix from the data set. The KNN classifier then learns low-dimensional features and detects an error kind. Because DCGAN training is unstable and the results are incorrect, an improved deep convolutional generative adversarial network (IDCGAN) is proposed. The tests indicate that the IDCGAN generates more real samples and solves the problem of defect identification in small samples. Time-conditional generation adversarial network data improvement lowers fault diagnosis effort and deep learning model complexity. The TCGAN and CNN are combined to provide superior fault detection under data imbalance. Modeling and experiments demonstrate TCGAN’s use and superiority.

Online detection technology research on agricultural product quality deterioration

This article aimed to determine the effectiveness of (Machine Learning, Artificial Neural Networks, Pattern Recognition and Data Analysis, and Fire Dynamics Simulations) in improving the results of arson investigations in the United Arab Emirates (UAE). The research relied on the descriptive method. The study community consisted of all members of the teams specialized in fire investigations. The research sample was taken using a simple random method that included (74) individuals from the research community. The researcher relied on the questionnaire as the main instrument for collecting data from the study sample. The results of the study showed that artificial intelligence techniques (Machine Learning, Artificial Neural Networks, Pattern Recognition and Data Analysis, and Fire Dynamics Simulations) can be effective in improving the results of arson investigations. The study recommended providing training programs to investigators on how to make full use of AI techniques in the investigation process.

Arcing fault identification using combined Gabor Transform-neural network for transmission lines

A pattern recognition artificial neural network method for random fatigue loading life prediction

Integration of modern remote sensing technologies for faster utility mapping and data extraction

In case of a carbonate reservoir, conventional techniques such as artificial neural networks and pattern recognition may not perform well in predicting the reservoir potential zones. This paper proposes a new implementation to highlight the anomalous zones of a carbonate reservoir using grey theory. The input is a 3D seismic volume for a square area which contains eight drilled wells of varying producing quality. The grey analysis is carried out for the zone of interest on a trace-by-trace basis. The result is validated on the reservoir main producing layer by utilizing the wells production classification information.

Intelligent ground vehicles require automated steering control systems. The complexity of the automated steering systems has increased as the concurrent data receiving, processing, decision-making and monitoring all happen simultaneously in super-fast sampling rates. The research reported in this paper focuses on the neural network training and machine learning approach for an automated steering system. The essential information of the steering controller was trained using artificial neural network (ANN) and pattern recognition algorithm approaches. The objective is to investigate the design of ANN training and deep machine learning on collected data from sensors of steering wheel angular position, speed, steering column torque, and ego vehicle speed and generating acceptable steering commands for the vehicle. The results of the research showed that the proposed network control system had trained and validated more than 96.5% steering system behavior patterns and adapted large random disturbances of the steering controller commands. It is, therefore, necessary to develop artificial intelligence methodologies in automated steering systems of autonomous vehicles with neural network representing the main topology blocks of the control system architecture and utilize ANN abstraction in the control system of autonomous vehicle steering control system.

With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.

Breast cancer has an important incidence in women worldwide. Early diagnosis of this illness plays a key role in decreasing its mortality and improves its prognosis. Currently, mammography is considered as the standard examination for detection of breast cancer. However, the identification of breast abnormalities and the classification of masses on mammographic images are not trivial tasks for dense breasts, and is a challenge for artificial intelligence and pattern recognition. This work presents preliminary results of automatic classification of mammographies by texture characterization based mainly on the Haralick's descriptors. We implement an artificial neural network (ANN) for classification in three classes: normal, benign and cancer using leave one out technique. The set of images for training and testing the ANN, are taken from the Digital Database for Screening Mammography (DDSM). Results show that the percentage of correct classification occurs in average for 84.72% of the data set.

Ecological Applications of Fuzzy Logic.- Ecological Applications of Qualitative Reasoning.- Ecological Applications of Non-supervised Artificial Neural Networks.- Ecological Applications of Genetic Algorithms.- Ecological Applications of Evolutionary Computation.- Ecological Applications of Adaptive Agents.- Bio-Inspired Design of Computer Hardware by Self-Replicating Cellular Automata.- Prediction and Elucidation of Stream Ecosystems.- Development and Application of Predictive River Ecosystem Models Based on Classification Trees and Artificial Neural Networks.- Modelling Ecological Interrelations in Running Water Ecosystems with Artificial Neural Networks.- Non-linear Approach to Grouping, Dynamics and Organizational Informatics of Benthic Macroinvertebrate Communities in Streams by Artificial Neural Networks.- Elucidation of Hypothetical Relationships between Habitat Conditions and Macroinvertebrate Assemblages in Freshwater Streams by Artificial Neural Networks.- Prediction and Elucidation of River Ecosystems.- Prediction and Elucidation of Population Dynamics of the Blue-green Algae Microcystis aeruginosa and the Diatom Stephanodiscus hantzschii in the Nakdong River-Reservoir System (South Korea) by a Recurrent Artificial Neural Network.- An Evaluation of Methods for the Selection of Inputs for an Artificial Neural Network Based River Model.- Utility of Sensitivity Analysis by Artificial Neural Network Models to Study Patterns of Endemic Fish Species.- Prediction and Elucidation of Lake and Marine Ecosystems.- A Comparison between Neural Network Based and Multiple Regression Models for Chlorophyll-a Estimation.- Artificial Neural Network Approach to Unravel and Forecast Algal Population Dynamics of Two Lakes Different in Morphometry and Eutrophication.- Hybrid Evolutionary Algorithm for Rule Set Discovery in Time-Series Data to Forecast and Explain Algal Population Dynamics in Two Lakes Different in Morphometry and Eutrophication.- Multivariate Time Series Prediction of Marine Zooplankton by Artificial Neural Networks.- Classification of Fish Stock-Recruitment Relationships in Different Environmental Regimes by Fuzzy Logic with Bootstrap Re-sampling Approach.- Computational Assemblage of Ordinary Differential Equations for Chlorophyll-a Using a Lake Process Equation Library and Measured Data of Lake Kasumigaura.- Classification of Ecological Images at Micro and Macro Scale.- Identification of Marine Microalgae by Neural Network Analysis of Simple Descriptors of Flow Cytometric Pulse Shapes.- Age Estimation of Fish Using a Probabilistic Neural Network.- Pattern Recognition and Classification of Remotely Sensed Images by Artificial Neural Networks.

Artificial Neural Networks (ANN) are computational models inspired by the human brain, capable of recognizing patterns and making predictions. Scale Conjugate Gradient (SCG) algorithm is an efficient training method for ANN that accelerates the learning process and improves output accuracy. However, conventional ANN training methods often struggle with slow convergence and can be less accurate when analyzing complex, high-dimensional data such as Electroencephalogram (EEG) signals. Furthermore, the precise classification of subtle neural pattern changes induced by Radiofrequency (RF) exposure remains a significant challenge. SCG improves the learning process of ANNs by speeding up the adjustment of their internal weights, helping the network learn faster and more accurately from large data sets. This study aims to improve the classification of RF neural data patterns using SCG. EEG neural data was captured in sessions before, during and after RF exposure. Power Asymmetry Ratio (PAR) was used for feature extraction. The data involved 96 subjects, were split into 70:30 ratio for training and testing in ANN modelling. The SCG algorithm was integrated, initialized with one hidden layer of 10 neurons. Parameter adjustments were made to optimize convergence, potentially involving multiple layers for model refinement. The results show that RF exposure in During session produces significantly distinct neural patterns, enabling the highest ANN classification accuracy.