Engineering Commons^™

Revolutionizing Feature Selection: A Breakthrough Approach For Enhanced Accuracy And Reduced Dimensions, With Implications For Early Medical Diagnostics, Shabia Shabir Khan, Majid Shafi Kawoosa, Bonny Bannerjee, Subhash C. Chauhan, Sheema Khan

Research Symposium

Background: The system's performance may be impacted by the high-dimensional feature dataset, attributed to redundant, non-informative, or irrelevant features, commonly referred to as noise. To mitigate inefficiency and suboptimal performance, our goal is to identify the optimal and minimal set of features capable of representing the entire dataset. Consequently, the Feature Selector (Fs) serves as an operator, transforming an m-dimensional feature set into an n-dimensional feature set. This process aims to generate a filtered dataset with reduced dimensions, enhancing the algorithm's efficiency.

Methods: This paper introduces an innovative feature selection approach utilizing a genetic algorithm with an ensemble crossover operation …

An Artificial Neural Network Approach To Predicting Formation Stress In Multi-Stage Fractured Marcellus Shale Horizontal Wells Based On Drilling Operations Data, Moudhi Alawadh

Graduate Theses, Dissertations, and Problem Reports

The distribution of the anisotropic minimum horizontal stress, both in horizontal and vertical directions, is necessary for effective hydraulic fracture treatment design in Marcellus Shale horizontal wells. Typically, the minimum horizontal stress can be estimated sonic logs. However, sonic log data is not commonly available for the horizontal Marcellus shale wells due to the complexity and cost. The objective of this research is to predict the anisotropic minimum horizontal stress by utilizing drilling parameters including depth, weight-on-bit (WOB), revolution per minute (RPM), standpipe pressure, torque, pump flow rate, and the rate of penetration (ROP). More specifically, artificial neural network (ANN) …

Microstrip Antenna Design Using Cst Optimized By Neural Network Algorithm, Hadeer. A Shoeab, Mohamed A.Mohamed, Marzouk El Said A, Ahmed. A. Kabeel

Mansoura Engineering Journal

In this paper, a general design procedure is suggested for the microstrip antennas using artificial neural networks and this is demonstrated using the rectangular patch geometry. The model was analyzed for 1733 data sets of input output parameters. 1300 samples for training and 433 samples for testing and 1500 epoch, learning rate from (0.003 to 0.005). Python was used to create and implement the ANN algorithm model. The mean error in detection of resonance frequencies (return loss peaks) was 0.144GHz on train set, and 0.116GHz on test set. The outputs of the radial basis function are optimized by varying the …

Prediction Of Temperature Difference Across Thermoacoustic Stack Through Artificial Neural Network Technique, Anas A. Rahman, Xiaoqing Zhang

Future Engineering Journal

This study involved the application of artificial neural network (ANN) as a new approach for thermoacoustic refrigerators to predict the temperature difference across the stack under some operating conditions. One ANN model for a standing wave thermoacoustic refrigerator, had been developed based on the experimental data from other literature. Temperature difference across the stack was chosen as a response to the input parameters, mean pressure and frequency in the proposed ANN model. A multi-layer feed-forward neural network with a back propagation algorithm had been proposed for predicting the temperature difference across the stack of the thermoacoustic refrigerator. This proposed ANN …

Minimization Of The Weld Distortion By Weld Sequence Optimization Using Artificial Intelligence, Jeyaganesh Devaraj

Theses

The application of dissimilar metal welding processes is increasing nowadays in the automobile, aerospace, marine industry as they not only serve for joint welding for different metals but also assist in repairing and reworking in a simplified manner. Study on optimization of weld parameter to weld a structure is important to have control over the distortion, mass deposition, tensile strength, etc. The present research reports the development and implementation of the Genetic algorithm integrated Artificial Neural Network (GANN) based weld sequence optimization for reducing deformation of dissimilar metal joining using the hot-encode technique. Gas Metal Arc Welding (GMAW) is used …

Multi-Resolution Spatio-Temporal Change Analyses Of Hydro-Climatological Variables In Association With Large-Scale Oceanic-Atmospheric Climate Signals, Kazi Ali Tamaddun

UNLV Theses, Dissertations, Professional Papers, and Capstones

The primary objective of the work presented in this dissertation was to evaluate the change patterns, i.e., a gradual change known as the trend, and an abrupt change known as the shift, of multiple hydro-climatological variables, namely, streamflow, snow water equivalent (SWE), temperature, precipitation, and potential evapotranspiration (PET), in association with the large-scale oceanic-atmospheric climate signals. Moreover, both observed datasets and modeled simulations were used to evaluate such change patterns to assess the efficacy of the modeled datasets in emulating the observed trends and shifts under the influence of uncertainties and inconsistencies. A secondary objective of this study was to …

Investigation Of Variability In Cognitive State Assessment Based On Electroencephalogram-Derived Features, Samantha Lokelani Crossen

Browse all Theses and Dissertations

To implement adaptive aiding in modern aviation systems there is a need for accurate and reliable classification of cognitive workload. Using electroencephalogram (EEG)-derived features, it has been reported that an Artificial Neural Network (ANN) can achieve 95% or higher classification accuracy on the same day for an individual operator, but only 70% or less on a different day. To gain a further insight into this discrepancy, data from a previous study was utilized to study the classification variability. The EEG-derived features were first calculated by spectral power estimation. The variability was then analyzed by performing cognitive workload classification in which …

Embedded Neural Network For Fire Classification Using An Array Of Gas Sensors, Shishir Bashyal, Ganesh K. Venayagamoorthy, Bandana Paudel

Electrical and Computer Engineering Faculty Research & Creative Works

Fire is one of the most common hazards in US households. In 2006 alone, 2705 people were killed due to fire in building structures. 74% of the deaths result from fires in homes with no smoke alarms or no working smoke alarms while surveys report that 96% of all homes have at least one smoke alarm. This study discusses the development of a fire sensing system that is not only capable of detecting fire in its early stage but also of classifying the fire based on the smell of the smoke in the environment. By using an array of sensors …