There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Stock-market prediction using machine-learning technique aims at developing effective and efficient models that can provide a better and higher rate of prediction accuracy. Numerous ensemble regressors and classifiers have been applied in stock market predictions, using different combination techniques. However, three precarious issues come in mind when constructing ensemble classifiers and regressors. The first concerns with the choice of base regressor or classifier technique adopted. The second concerns the combination techniques used to assemble multiple regressors or classifiers and the third concerns with the quantum of regressors or classifiers to be ensembled. Subsequently, the number of relevant studies scrutinising these previously mentioned concerns are limited. In this study, we performed an extensive comparative analysis of ensemble techniques such as boosting, bagging, blending and super learners (stacking). Using Decision Trees (DT), Support Vector Machine (SVM) and Neural Network (NN), we constructed twenty-five (25) different ensembled regressors and classifiers. We compared their execution times, accuracy, and error metrics over stock-data from Ghana Stock Exchange (GSE), Johannesburg Stock Exchange (JSE), Bombay Stock Exchange (BSE-SENSEX) and New York Stock Exchange (NYSE), from January 2012 to December 2018. The study outcome shows that stacking and blending ensemble techniques offer higher prediction accuracies (90–100%) and (85.7–100%) respectively, compared with that of bagging (53–97.78%) and boosting (52.7–96.32%). Furthermore, the root means square error (RMSE) recorded by stacking (0.0001–0.001) and blending (0.002–0.01) shows a better fit of ensemble classifiers and regressors based on these two techniques in market analyses compared with bagging (0.01–0.11) and boosting (0.01–0.443). Finally, the results undoubtedly suggest that an innovative study in the domain of stock market direction prediction ought to include ensemble techniques in their sets of algorithms.

/pdf/a-comprehensive-evaluation-of-ensemble-learning-for-stock-14pcf72a0y.pdf

A comprehensive evaluation of ensemble learning for stock-market prediction

A multi-agent system approach to exploit demand-side flexibility in an energy community

Understanding to what extent the emergence of prosumers and prosumagers organized in energy communities can impact the organization and operation of power grids has been one of the major recent research avenues at the European level. In renewable-based communities aiming to reach some level of energy self-sufficiency, a key issue to be addressed is assessing how the presence of end-users playing different roles in the system (self-consuming, producing and trading, performing demand management, etc.) can influence the overall system performance. In this setting, this paper combines Distributed Artificial Intelligence and optimization approaches to assess how prosumagers and consumers pursuing different goals can influence the energy self-sufficiency of a local energy community. The residential demand is accurately modeled, and the agents’ preferences are considered in the modeling to represent a smart community. The results show that although energy community members may have conflicting individual goals, the overall system self-sufficiency can be maximized with economic benefits for all stakeholders, thus illustrating the advantages of energy communities.

https://www.mdpi.com/1996-1073/14/4/989/pdf

Assessing the Influence of Different Goals in Energy Communities’ Self-Sufficiency—An Optimized Multiagent Approach

Efficient market hypothesis in the modern era

Agent architecture has been one of the core components in building an agent application. Agent architecture is considered as the functional brain of an agent in making decision and reasoning to solve problem and achieving goals. This paper reviews some of the existing agent architectures such as logic-based architecture, reactive architecture, BDI architecture, hybrid architecture, cognitive architecture, and semantic architecture. The purpose of this study is to identify distinctive features of the different types of agent architectures and how they are implemented to solve real world problems.

/pdf/agent-architecture-an-overviews-1yvf15wpln.pdf

Agent Architecture: An Overviews

In stock forecasting, the main concern is the accuracy of the stock market prediction. Various methods, techniques and algorithms have been proposed to achieve higher prediction accuracy. One of the popular techniques is Artificial Neural Network (ANN). ANN is a bio-inspired algorithm that tries to simulate the brain’s mechanism which is achieved through simulating a connected artificial neuron network. Although ANN has shown promising results in forecasting, researches have shown that combining multiple neural networks can further improve the forecasting result. The aim of this paper is to compare the performance of a single and multiple homogeneous ANN in forecasting CIMB stock market closing price. The empirical result shows that homogeneous ensemble ANN performs better than a single ANN in predicting the stock market price.

Homogeneous Ensemble FeedForward Neural Network in CIMB Stock Price Forecasting

The nature of computing paradigm has shifted from centralized, static and closed to distributed, dynamic and open due to the advent and popularity of Internet. Multi-agent system (MAS) gained popularity as its characteristic match with this paradigm shift. In order for MAS to interact efficiently and communicate meaningfully, agent communication language (ACL) plays an important role. FIPA-ACL is an ACL developed by FIPA that has become the de facto standard of ACL’s implementation in MAS. Another emerging trend due to Internet is the Semantic Web (SW). Semantic web is an extension of the current World Wide Web, which encodes the content of the web with well-defined meaning to allow it to be processed by machines such as computer (agents). Hence, combining the existing FIPA-ACL with semantic web can bring ACLs to another level to enhance the interoperability in MAS. In this paper, a FIPA-ACL ontology in OWL is proposed to enhance the communication between agents in MAS.

A FIPA-ACL Ontology in Enhancing Interoperability Multi-agent Communication

The proliferation of online auctions has caused the increasing need to monitor and track multiple bids in multiple auctions. As a solution to the problem, an autonomous agent was developed to work in a flexible and configurable heuristic decision making framework that can tackle the problem of bidding across multiple auctions that apply different protocols (English, Vickrey and Dutch). Due to the dynamic and unpredictable nature of online auctions, the agent utilizes genetic algorithm to search for effective solution. Instead of using the conventional genetic algorithm, this paper investigates the application of deterministic dynamic adaptation genetic algorithm and self adaptive genetic algorithm to search for the most effective strategies (offline). An empirical evaluation on the comparison between the effectiveness of self-adaptive genetic algorithm and deterministic dynamic adaptation genetic algorithm for searching the most effective strategies in the online auction environment are discussed in this paper.

Comparing the performance of deterministic dynamic adaptation GA and self adaptive GA in online auctions environment

Agent technology is an emerging software paradigm for developing open, distributed and heterogeneous complex system. There is a need for different software to communicate with one another in order to achieve its task. Therefore, a standard protocol is required. In agent technology, this common protocol is referred to as agent communication language. Agent communication language is a high-level protocol/language that allows agent applications to exchange, parse and understand the meaning of the exchanged content. One of the widely adopted agent communications is FIPA-ACL. An ontology for FIPA-ACL has been developed in previous work. However, the developed ontology does not incorporate any semantic model. Hence, in this paper, SPIN notation is used to model the social semantic of FIPA-ACL as it is able to link the class definition with SPARQL queries to capture rules and constraints to formalize the expected behaviour of classes.

Enforcing Social Semantic in FIPA-ACL Using SPIN

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Kim Soon Gan

Author Tools

Chat about Author