Computer Engineering Commons^™

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Enhanced Neurologic Concept Recognition Using A Named Entity Recognition Model Based On Transformers, Sima Azizi, Daniel B. Hier, Donald C. Wunsch

Chemistry Faculty Research & Creative Works

Although Deep Learning Has Been Applied to the Recognition of Diseases and Drugs in Electronic Health Records and the Biomedical Literature, Relatively Little Study Has Been Devoted to the Utility of Deep Learning for the Recognition of Signs and Symptoms. the Recognition of Signs and Symptoms is Critical to the Success of Deep Phenotyping and Precision Medicine. We Have Developed a Named Entity Recognition Model that Uses Deep Learning to Identify Text Spans Containing Neurological Signs and Symptoms and Then Maps These Text Spans to the Clinical Concepts of a Neuro-Ontology. We Compared a Model based on Convolutional Neural Networks …

Tau Kinetics In Alzheimer's Disease, Daniel B. Hier, Sima Azizi, Matthew S. Thimgan, Donald C. Wunsch

Chemistry Faculty Research & Creative Works

The Cytoskeletal Protein Tau is Implicated in the Pathogenesis of Alzheimer's Disease Which is Characterized by Intra-Neuronal Neurofibrillary Tangles Containing Abnormally Phosphorylated Insoluble Tau. Levels of Soluble Tau Are Elevated in the Brain, the CSF, and the Plasma of Patients with Alzheimer's Disease. to Better Understand the Causes of These Elevated Levels of Tau, We Propose a Three-Compartment Kinetic Model (Brain, CSF, and Plasma). the Model Assumes that the Synthesis of Tau Follows Zero-Order Kinetics (Uncorrelated with Compartmental Tau Levels) and that the Release, Absorption, and Clearance of Tau is Governed by First-Order Kinetics (Linearly Related to Compartmental Tau Levels). …

Schizophrenia Classification Using Resting State Eeg Functional Connectivity: Source Level Outperforms Sensor Level, Sima Azizi, Daniel B. Hier, Donald C. Wunsch

Chemistry Faculty Research & Creative Works

Disrupted Functional and Structural Connectivity Measures Have Been Used to Distinguish Schizophrenia Patients from Healthy Controls. Classification Methods based on Functional Connectivity Derived from EEG Signals Are Limited by the Volume Conduction Problem. Recorded Time Series at Scalp Electrodes Capture a Mixture of Common Sources Signals, Resulting in Spurious Connections. We Have Transformed Sensor Level Resting State EEG Times Series to Source Level EEG Signals Utilizing a Source Reconstruction Method. Functional Connectivity Networks Were Calculated by Computing Phase Lag Values between Brain Regions at Both the Sensor and Source Level. Brain Complex Network Analysis Was Used to Extract Features and …

Subsumption Reduces Dataset Dimensionality Without Decreasing Performance Of A Machine Learning Classifier, Donald C. Wunsch, Daniel B. Hier

Chemistry Faculty Research & Creative Works

When Features in a High Dimension Dataset Are Organized Hierarchically, There is an Inherent Opportunity to Reduce Dimensionality. Since More Specific Concepts Are Subsumed by More General Concepts, Subsumption Can Be Applied Successively to Reduce Dimensionality. We Tested Whether Sub-Sumption Could Reduce the Dimensionality of a Disease Dataset Without Impairing Classification Accuracy. We Started with a Dataset that Had 168 Neurological Patients, 14 Diagnoses, and 293 Unique Features. We Applied Subsumption Repeatedly to Create Eight Successively Smaller Datasets, Ranging from 293 Dimensions in the Largest Dataset to 11 Dimensions in the Smallest Dataset. We Tested a MLP Classifier on All …

In Situ Electron Microscopy Of Plasmon-Mediated Nanocrystal Synthesis, Peter Sutter, Ying Li, Christos Argyropoulos, Eli A. Sutter

Department of Electrical and Computer Engineering: Faculty Publications

Chemical processes driven by nonthermal energy (e.g., visible light) are attractive for future approaches to energy conversion, synthesis, photocatalysis, and so forth. The growth of anisotropic metal nanostructures mediated by excitation of a localized surface plasmon resonance (LSPR) is a prototype example of such a reaction. Important aspects, notably the growth mechanism and a possible role of plasmonic “hot spots” within the metal nanostructures, remain poorly understood. Here, we use in situ electron microscopy to stimulate and image the plasmon-mediated growth of triangular Ag nanoprisms in solution. The quantification of the time-dependent evolution of the lateral size and thickness of …

Light-Activated Photocurrent Degradation And Self-Healing In Perovskite Solar Cells, Wanyi Nie, Jean-Christophe Blancon, Amanda J. Neukirch, Kannatassen Appavoo, Hsinhan Tsai, Manish Chhowalla, Muhammad A. Alam, Matthew Y. Sfeir, Claudine Katan, Jacky Even, Sergei Tretiak, Jared J. Crochet, Gautam Gupta, Aditya D. Mohite

Publications and Research

Solution-processed organometallic perovskite solar cells have emerged as one of the most promising thin-film photovoltaic technology. However, a key challenge is their lack of stability over prolonged solar irradiation. Few studies have investigated the effect of light soaking on hybrid perovskites and have attributed the degradation in the optoelectronic properties to photochemical or field-assisted ion migration. Here we show that the slow photocurrent degradation in thin-film photovoltaic devices is due to the formation of light-activated meta-stable deep-level trap states. However, the devices can self-heal completely by resting them in the dark for <1 min or the degradation can be completely prevented by operating the devices at 0°C. We investigate several physical mechanisms to explain the microscopic origin for the formation of these trap states, among which the creation of small polaronic states involving localized cooperative lattice strain and molecular orientations emerges as a credible microscopic mechanism requiring further detailed studies.

Energy-Efficient Computational Chemistry: Comparison Of X86 And Arm Systems, Kristopher Keipert, Gaurav Mitra, Vaibhav Sunriyal, Sarom S. Leang, Masha Sosonkina, Alistair P. Rendell, Mark S. Gordon

Computational Modeling & Simulation Engineering Faculty Publications

The computational efficiency and energy-to-solution of several applications using the GAMESS quantum chemistry suite of codes is evaluated for 32-bit and 64-bit ARM-based computers, and compared to an x86 machine. The x86 system completes all benchmark computations more quickly than either ARM system and is the best choice to minimize time to solution. The ARM64 and ARM32 computational performances are similar to each other for Hartree-Fock and density functional theory energy calculations. However, for memory-intensive second-order perturbation theory energy and gradient computations the lower ARM32 read/write memory bandwidth results in computation times as much as 86% longer than on the …

Distributed Approach For Peptide Identification, Naga V K Abhinav Vedanbhatla

Masters Theses & Specialist Projects

A crucial step in protein identification is peptide identification. The Peptide Spectrum Match (PSM) information set is enormous. Hence, it is a time-consuming procedure to work on a single machine. PSMs are situated by a cross connection, a factual score, or a probability that the match between the trial and speculative is right and original. This procedure takes quite a while to execute. So, there is demand for enhancement of the performance to handle extensive peptide information sets. Development of appropriate distributed frameworks are expected to lessen the processing time.

The designed framework uses a peptide handling algorithm named C-Ranker, …

Online Learning In A Chemical Perceptron, Peter Banda, Christof Teuscher, Matthew R. Lakin

Computer Science Faculty Publications and Presentations

Autonomous learning implemented purely by means of a synthetic chemical system has not been previously realized. Learning promotes reusability and minimizes the system design to simple input-output specification. In this article we introduce a chemical perceptron, the first full-featured implementation of a perceptron in an artificial (simulated) chemistry. A perceptron is the simplest system capable of learning, inspired by the functioning of a biological neuron. Our artificial chemistry is deterministic and discrete-time, and follows Michaelis-Menten kinetics. We present two models, the weight-loop perceptron and the weight-race perceptron, which represent two possible strategies for a chemical implementation of linear integration and …

Data Mining Of Protein Databases, Christopher Assi

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Data mining of protein databases poses special challenges because many protein databases are non-relational whereas most data mining and machine learning algorithms assume the input data to be a relational database. Protein databases are non-relational mainly because they often contain set data types. We developed new data mining algorithms that can restructure non-relational protein databases so that they become relational and amenable for various data mining and machine learning tools. We applied the new restructuring algorithms to a pancreatic protein database. After the restructuring, we also applied two classification methods, such as decision tree and SVM classifiers and compared their …

A Dipolar Coupling Based Strategy For Simultaneous Resonance Assignment And Structure Determination Of Protein Backbones, Fang Tian, Homayoun Valafar, James H. Prestegard

Faculty Publications

A new approach for simultaneous protein backbone resonance assignment and structure determination by NMR is introduced. This approach relies on recent advances in high-resolution NMR spectroscopy that allow observation of anisotropic interactions, such as dipolar couplings, from proteins partially aligned in field ordered media. Residual dipolar couplings are used for both geometric information and a filter in the assembly of residues in a sequential manner. Experimental data were collected in less than one week on a small redox protein, rubredoxin, that was 15N enriched but not enriched above 1% natural abundance in 13C. Given the acceleration possible with partial 13C …