Artificial Intelligence and Robotics Commons^™

Multi-Strategy Hybrid Abc For Microarray High-Dimensional Feature Selection, Chuandong Qin, Baosheng Li, Baole Han

Journal of System Simulation

Abstract: Traditional feature selection approaches have major limitations for high-dimensional microarrays, and it is difficult to accurately and efficiently propose the best feature subset. To address this problem, a multi-strategy hybrid artificial bee colony (ABC) algorithm based on wrapper is proposed, which mixes chaotic opposition-based learning strategy, elite guidance strategy, and Mantegna Lévy distribution strategy, and proposes two new search strategies in the employed and onlooker bee phases respectively. A new objective function is proposed for the microarray high-dimensional feature selection problem, which balances the optimal performance of the model with the minimization of the feature subset …

Application Of Digital Twin Model In Grinding Of Bearing Rings, Hongbin Liu, Zhiqiang Shen, Yize Wang, Ming Qiu, Wenrong Lin

Journal of System Simulation

Abstract: The digital twin model can effectively promote the virtual-real interaction between the actual product and the product model. Aiming at the grinding force generated in the grinding process of spherical roller bearings, this paper constructs the bearing ring raceway grinding process by performing dynamics, contact algorithm modeling and rigid-flexible coupling treatment on the components of the grinding work area. The digital twin model completes the virtual mapping of the grinding work area of the bearing ring in the digital space. The model is used to analyze and test the process parameters such as the grinding wheel linear speed and …

Multi-Media Energy Planning Optimization Of Steel Based On Improved Moea/D, Hongcai Ouyang, Dinghui Wu, Junyan Fan, Jing Wang

Journal of System Simulation

Abstract: To address the problems of multi-media iron and steel energy planning model with more variables, complex constraints and high difficulty in model solving, an improved MOEA/D (decomposition-based multi-objective evolutionary algorithm) based on adaptive neighborhood is proposed to realize multi-media energy planning optimization. Considering the characteristics of TOU price and the buffer effect of gas holder, the objective function to minimize operation cost and total energy consumption is constructed. And the model constraints are designed such as energy supply and demand balance. The decoding method based on energy production and consumption rules is designed to determine the target value. The …

Integrated Soft Sensor Modeling Of Fermentation Process Based On Transfer Component Analysis, Yuesheng Zhou, Weili Xiong

Journal of System Simulation

Abstract: The Penicillin fermentation process is an uncertain and multi-stage process. There are different working conditions among different batch fermentation processes, and the distribution of process data is not necessarily the same, which degrades the performance of the traditional soft sensing model. Combined with the transfer learning strategy and Gaussian mixture model, a multi-model ensemble soft sensor modeling method based on transfer component analysis is proposed. In this method, the transfer component analysis is used to get the shared feature mapping matrix between samples, and adapt the edge probability distribution of labeled dataset and unlabeled dataset; the modeling data are …

Impacts Of Cutting-Edge Artificial Intelligence On Economic Research Paradigm, Yongmiao Hong, Shouyang Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

No abstract provided.

A Data Augmented Method For Plant Disease Leaf Image Recognition Based On Enhanced Gan Model Network, Mingyuan Xin, Ling Weay Ang, Sellappan Palaniappan

Journal of Informatics and Web Engineering

The identification of plant disease leaves based on deep learning is the key to control the development and spread of plant diseases. In this paper, the existing problems of traditional classification and recognition of plant disease leaves and the limitations of deep learning-based plant disease leaf training are analysed. An enhanced GAN model network based on the Wasserstein GAN loss function has been developed to address the limited training images of plant disease leaves. The self-attention layer is added into the self-encoding structure of the generating network. The effectiveness of data generated by the encoder is increased after the self-attention …

Elderly And Smartphone Apps: Case Study With Lightweight Mysejahtera, Ciu Yung Seek, Shih Yin Ooi, Ying Han Pang, Sook Ling Lew, Xin Yun Heng

Journal of Informatics and Web Engineering

The outbreak of Covid-19 in the past 2 years has made the usage of contact-tracing app almost mandatory in Malaysia. Though the usage is seeming to be simple, but one interesting phenomenon can be observed in Malaysia is that most of the senior citizen are found not using smartphones regularly. While check-in through MySejahtera (the primary contract-tracing app used in Malaysia) for business, premises, and transports has made compulsory, it does bring a lot of inconveniences to the elderly. Many elderlies start to learn how to use smartphone and government has also taking initiative by providing free or low-cost smartphone …

The Impact Of The Telecommunication Industry As A Moderator On Poverty Alleviation And Educational Programmes To Achieve Sustainable Development Goals In Developing Countries, S.K.C Ruklani Wickramasinghe, Kamal Abd Razak

Journal of Informatics and Web Engineering

The purpose of this study is to investigate the influence of the telecommunications industry's contribution to sustainable development in developing nations. A detailed literature study is conducted for this purpose, and a conceptual framework is offered. To validate the proposed conceptual framework, we conducted multiple case studies involving companies in the telecommunications industry from various countries. These findings improved the suggested conceptual framework produced for this study. However, despite the contributions of the telecommunications industry to the Sustainable Development Goals, no study has been conducted to examine how they have benefited poverty reduction and educational programmes in Sri Lanka. The …

A Swot Analysis With A Digital Transformation: A Case Study For Hospitals In The Pharmaceutical Supply Chain, Nguyen Huu Khanh Quan, Harwindar Singh, Thai Hong Thuy Khanh, Premkumar Rajagopal

Journal of Informatics and Web Engineering

The pharmaceutical business meets all requirements for a complicated and tightly regulated sector, including multi-stakeholder participation. The objective is to study digital transformation strategies in hospitals to improve the safety and convenience of pharmaceutical services when providing healthcare services. In this study, a SWOT analysis is performed to identify strengths, weaknesses, opportunities, and threats with a digital transformation case study for hospitals in the pharmaceutical supply chain in Ho Chi Minh City, Vietnam. Information source for SWOT analysis of published articles and reports in the following aspects: Digital transformation environment of hospitals in the global pharmaceutical supply chain; new technology …

Enhancing Mimo Capacity Through Space-Time Coding: Analysis And Design Framework, Lijun Han, Ling Weay Ang, Sellappan Palaniappan, Jing Wang

Journal of Informatics and Web Engineering

Space-time coding combines time and space to generate codewords, transmitting signals in both time and space domains. This leads to not only diversity and coding gains but also reduces the impact of multipath fading, resulting in high spectral efficiency. This paper examines the challenges in implementing space-time coding to enhance the capacity of MIMO systems. It analyzes the principle, design objectives, and criteria of space-time coding, providing a basic design framework, based on the space-time coding (STC) system model. .STC and MIMO have proven to be effective in improving system capacity, reliability, and overall performance in wireless communication. They have …

Robust Image Watermarking With Quaternion Fractional-Order Polar Harmonic-Fourier Moments Based On Wavelet Transformation: Resistance Against Rotation Attacks, Wang Jing, Ling Weay Ang, Sellappan Palaniappan, Bing He

Journal of Informatics and Web Engineering

This study presents a zero-watermarking algorithm that can resist rotation attacks. The algorithm uses quaternion fractional-order polar harmonic-Fourier moments (QFr-PHFMs) based on wavelet-transformation. First, the wavelet-transformation is applied to each component of the host image, which is in RGB three-channel color. The low-frequency sub-bands of each component are then extracted and represented using quaternion algebra. Multiple QFr-PHFMs are calculated, and the invariants of the QFr-PHFMs are utilized to establish the watermark system. The watermark extraction process is also simplified. The detection of the image requires a two-level wavelet transformation, followed by the calculation of multiple invariant moments of the low-frequency …

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 …

Conversations With Chatgpt About C Programming: An Ongoing Study, James C. Davis, Yung-Hsiang Lu, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

AI (Artificial Intelligence) Generative Models have attracted great attention in recent years. Generative models can be used to create new articles, visual arts, music composition, even computer programs from English specifications. Among all generative models, ChatGPT is becoming one of the most well-known since its public announcement in November 2022. GPT means {\it Generative Pre-trained Transformer}. ChatGPT is an online program that can interact with human users in text formats and is able to answer questions in many topics, including computer programming. Many computer programmers, including students and professionals, are considering the use of ChatGPT as an aid. The quality …

Applications Of Generative Adversarial Networks In Single Image Datasets, Dylan E. Cramer

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

One of the main difficulties faced in most generative machine learning models is how much data is required to train it, especially when collecting a large dataset is not feasible. Recently there have been breakthroughs in tackling this issue in SinGAN, with its researchers being able to train a Generative Adversarial Network (GAN) on just a single image with a model that can perform many novel tasks, such as image harmonization. ConSinGAN is a model that builds upon this work by concurrently training several stages in a sequential multi-stage manner while retaining the ability to perform those novel tasks.

Deep Reinforcement Learning Based Optimization Techniques For Energy And Socioeconomic Systems, Salman Sadiq Shuvo

USF Tampa Graduate Theses and Dissertations

Optimization, which refers to making the best or most out of a system, is critical for an organization's strategic planning. Optimization theories and techniques aim to find the optimal solution that maximizes/minimizes the values of an objective function within a set of constraints. Deep Reinforcement Learning (DRL) is a popular Machine Learning technique for optimization and resource allocation tasks. Unlike the supervised ML that trains on labeled data, DRL techniques require a simulated environment to capture the stochasticity of real-world complex systems. This uncertainty in future transitions makes the planning authorities doubt real-world implementation success. Furthermore, the DRL methods have …

Observing Human Mobility Internationally During Covid-19, Shane Allcroft, Mohammed Metwaly, Zachery Berg, Isha Ghodgaonkar, Fischer Bordwell, Xinxin Zhao, Xinglei Liu, Jiahao Xu, Subhankar Chakraborty, Vishnu Banna, Akhil Chinnakotla, Abhinav Goel, Caleb Tung, Gore Kao, Wei Zakharov, David A. Shoham, George K. Thiruvathukal, Yung-Hsiang Lu

Computer Science: Faculty Publications and Other Works

This article analyzes visual data captured from five countries and three U.S. states to evaluate the effectiveness of lockdown policies for reducing the spread of COVID-19. The main challenge is the scale: nearly six million images are analyzed to observe how people respond to the policy changes.

Solving Fjssp With A Genetic Algorithm, Michael John Srouji

Computer Science and Software Engineering

The Flexible Job Shop Scheduling Problem is an NP-Hard combinatorial problem. This paper aims to find a solution to this problem using genetic algorithms, and discuss the effectiveness of this. Initially, I did exploratory work on whether neural networks would be effective or not, and found a lot of trade offs between using neural networks and chromosome sequencing. In the end, I decided to use chromosome sequencing over neural networks, due to the scope of my problem being on a small scale rather than on a large scale.

Therefore, the genetic algorithm was implemented using chromosome sequencing. My chromosomes were …

Modeling Daily Fantasy Basketball, Martin Jiang

Master's Theses

Daily fantasy basketball presents interesting problems to researchers due to the extensive amounts of data that needs to be explored when trying to predict player performance. A large amount of this data can be noisy due to the variance within the sport of basketball. Because of this, a high degree of skill is required to consistently win in daily fantasy basketball contests. On any given day, users are challenged to predict how players will perform and create a lineup of the eight best players under fixed salary and positional requirements. In this thesis, we present a tool to assist daily …

Learning And Understanding User Interface Semantics From Heterogeneous Networks With Multimodal And Positional Attributes, Meng Kiat Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

User interfaces (UI) of desktop, web, and mobile applications involve a hierarchy of objects (e.g., applications, screens, view class, and other types of design objects) with multimodal (e.g., textual and visual) and positional (e.g., spatial location, sequence order, and hierarchy level) attributes. We can therefore represent a set of application UIs as a heterogeneous network with multimodal and positional attributes. Such a network not only represents how users understand the visual layout of UIs but also influences how users would interact with applications through these UIs. To model the UI semantics well for different UI annotation, search, and evaluation tasks, …

The Use Of Artificial Intelligence To Detect Students Sentiments And Emotions In Gross Anatomy Reflections, Krzysztof J. Rechowicz, Carrie A. Elzie

VMASC Publications

Students' reflective writings in gross anatomy provide a rich source of complex emotions experienced by learners. However, qualitative approaches to evaluating student writings are resource heavy and timely. To overcome this, natural language processing, a nascent field of artificial intelligence that uses computational techniques for the analysis and synthesis of text, was used to compare health professional students' reflections on the importance of various regions of the body to their own lives and those of the anatomical donor dissected. A total of 1365 anonymous writings (677 about a donor, 688 about self) were collected from 132 students. Binary and trinary …