An Optimization Model for Stock Market Direction Prediction

Mingzhong; N

doi:https://doi.org/10.54216/AJBOR.060102

Journal of Artificial Intelligence and Metaheuristics | 2022

Credit Card Clients Classification Using Hybrid Guided wheel with Particle Swarm Optimized for Voting Ensemble

Credit card use is rapidly increasing as a result of the widespread availability of these cards, the ease of making electronic transfers, and the ubiquity of online shopping. But credit card debt poses a serious risk to businesses and governments alike, not to mention individual savers and investors. Consequently, the need for efficient, timely, and reliable ways to anticipate credit card risk has grown. In this study, we offer a framework that combines three classifiers, namely, support vector machines, multilayer perceptron and decision trees, to improve the network's accuracy. The proposed strategy is shown to be very competitive with others through simulation.

groups

Khadija Shazly mail -

Nima Khodadadi mail

link https://doi.org/10.54216/JAIM.020105

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2023

Metaheuristic Optimized Ensemble Model for Classification of SMS Spam in Computer Networks

By use of electronic communication, we are able to communicate a message to the recipient. In this digital age, a collaboration between several people is possible thanks to a variety of digital technologies. This interaction may take place in a variety of media formats, including but not limited to text, images, sound, and language. Today, a person's primary means of communication is their smart gadget, most commonly a cell phone. Spam is another side effect of our increasingly text-based modes of communication. We received a bunch of spam texts on our phones, and we know they're not from anyone we know. The vast majority of businesses nowadays use spam texts to advertise their wares, even when recipients have explicitly requested not to receive such messages. As a rule, there are many more spam emails than genuine ones. We apply text classification approaches to define short messaging service (SMS) and spam filtering in this study, which effectively categorizes messages. In this paper, we use "machine learning algorithms" and metaheuristic optimization to determine what percentage of incoming SMS messages are spam. This is why we used the optimized models to evaluate and contrast many classification strategies for gathering data.

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Mohamed Saber mail -

El-Sayed M. El-Kenawy mail -

Abdelhameed Ibrahim mail -

Marwa M. Eid mail -

Abdelaziz A. Abdelhamid mail

link https://doi.org/10.54216/IJWAC.060205

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2021

Virtual Machine Placement in Cloud Computing: Challenges, Research Gaps, and Future

Cloud computing provides various types of services to users. The goal of virtual machine placement (VMP) is to map the best physical machine to a virtual machine. With the help of Virtual Machine Placement, we can reduce cost, maximize resource utilization, reduced energy consumption of data centers in cloud environments. The focus of Virtual Machine Placement is to saving of power, quality of service. In this paper, we have reviewed various placements techniques used in cloud computing. At last, we have also studied various challenges for virtual machine placement in cloud computing. The main motive of various types of Virtual Machine Placement algorithms have to reduced energy consumption and minimize cost by maximizing utilization of various resources in the cloud platform. For further study, the researcher should focus on these challenges for the best virtual machine placement in a cloud environment. In this paper, we critically examine the techniques, challenges, and research gaps in virtual placements in cotext with Cloud Computing. Cloud computing, placement of virtual machines becomes major problems. For finding the solution to the problem we can use the various virtual machine placement algorithms. The main motive is to reduce consumption of energy, maximum resource utilization, minimizing cost factors used for virtual to the physical machine mapping in the cloud environment. For selecting the best algorithm various optimization methods are used. With these different optimization methods, we can analyze different algorithms. There is a great scope of improvement in existing systems of virtual placements to make them more energy-efficient, more reliable, and fault-tolerant. Redundancy in cloud downloading can be made more intelligent and minimized for duplicate data while downloading and uploading. 

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Puneet Kaushal mail -

Subash Chander mail -

Vijay Kumar Sinha mail

link https://doi.org/10.54216/IJWAC.030202

Volume & Issue

Vol. Volume 3 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2021

Nψ#0 α and Nψ#1 α-spaces in Neutrosophic Topological Spaces

In this paper, we have introduced the concept of nk#Nα (n1 ) by using CLN α({n1}) where n1∈ N via Nα-open sets. Also we have introduced the spaces called Nψα# 0-space and Nψα# 1-space.

groups

P. Basker mail -

broumi said mail

link https://doi.org/10.54216/IJNS.160101

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2023

Direct Product of Neutrosophic h-ideal in INK-Algebra

In this paper, “we first define the belief of direct product from neutrosophic sets in INK algebras, neutrosophic set, neutrosophic h-ideals, neutrosophic INK-subalgebra and direct product of neutrosophic h-ideals in INK algebras. Let's prove some theorems that show that there is some connection between these principles. Finally, we define the INK subalgebra of the INK algebra and then offer the ideal theorem approximately the connection between its pix and the direct product from the neutrosophic h-ideals.

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Kaviyarasu M. mail -

Rajeshwari M. mail

link https://doi.org/10.54216/IJNS.200110

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2023

Neutrosophic MCDM Approach for Performance Evaluation and Recommendation of Best Players in Sports League

In this era of the commercialization of sports, various sports leagues are organized across the globe. At the end of the Series, players are awarded for their performances. These awards are decided by human experts or are based on just one performance indicator. However, human decisions are subjective and error-prone, and decisions based on just one criterion are incomplete and inconsistent. This paper identifies the decision-making problem in sports. It proposes a Neutrosophic TOPSIS approach for performance evaluation and recommendation of the best batsman and bowler of the Series. The approach is well-structured, robust, and efficient in handling vagueness, inconsistency, indeterminacy, and imprecision in real-life problems. We present a case study using the data of IPL 2021. In the case study, we calculate the ranks of the players using neutrosophic TOPSIS with two objective weight calculation methods. Then we evaluate and compare the obtained rank lists using Kendal Tau (). The values of  for bowling-ranked lists is 0.83 and for batting-ranked lists is 0.72, which are impressive and prove the efficiency and effectiveness of the proposed approach. We believe that the proposed approach can be applied to identify and recommend the best resources in other domains of life.

groups

Khalid Anwar mail -

Aasim Zafar mail -

Arshad Iqbal mail

link https://doi.org/10.54216/IJNS.200111

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2023

Separation Axioms on Bipolar Hypersoft Topological Spaces

According to its definition, a topological space could be a highly unexpected object. There are spaces (indiscreet space) which have only two open sets: the empty set and the entire space. In a discrete space, on the other hand, each set is open. These two artificial extremes are very rarely seen in actual practice. Most spaces in geometry and analysis fall somewhere between these two types of spaces. Accordingly, the separation axioms allow us to say with confidence whether a topological space contains a sufficient number of open sets to meet our needs. To this end, we use bipolar hypersoft (BHS) sets (one of the efficient tools to deal with ambiguity and vagueness) to define a new kind of separation axioms called BHS Ti-space (i = 0, 1, 2, 3, 4). We show that ee BHS Ti-space (i = 1,2) implies BHS Ti−1-space; however, the converse is false, as shown by an example. e For i = 0, 1, 2, 3, 4, we prove that BHS Ti -space is hypersoft (HS) Ti -space and we present a condition so that eee HS Ti-space is BHS Ti-space. Moreover, we study that a BHS subspace of a BHS Ti-space is a BHS Ti-space for i = 0,1,2,3.

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Sagvan Y. Musa mail -

Baravan A. Asaad mail

link https://doi.org/10.54216/IJNS.200112

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2023

Benchmarking Machine Learning for Sentimental Analysis of Climate Change Tweets in Social Internet of Things

Climate change has become one of the most critical problems threatening our world, gaining increased attention in either academia or industry. Climate change has been demonstrated as the major barrier in the way of sustainable development strategy in the 2030 Agenda. Nowadays, the Social Internet of Things (SIoT) has paved new ways for public deliberations and has transformed the communication of global issues such as climate change. Thus, sentiment analysis of SIoT media streams can offer great help in improving the mitigation and adaptation to climate change. Machine learning (ML) is demonstrating great success in a wide range of SIoT applications. However, training ML algorithms for sentimental analysis of climate change is notoriously hard as it suffers from feature engineering issues, information squashing, unbalancing, and curse-of-dimensionality, which bounds their possible power for modeling social awareness of climate change. Besides, the absence of a standard benchmark with reasonable and dependable experimentations brings a practically intractable difficulty to the evaluation of the efficiency of new solutions. In this regard, this study introduces the first reasonable and reproducible benchmark devoted to evaluating the potential of ML algorithms in identifying users’ opinions about climate change. Moreover, a novel taxonomy is presented for categorizing the existing ML algorithms, exploring their optimal hyperparameter, and unifying their elementary settings. Inclusive experiments are then performed on real Twitter data with different families of ML algorithms. To promote further study, a detailed analysis is provided for the state of the field to uncover the open research challenges and promising future directions.

groups

Irina V. Pustokhina mail -

Denis A. Pustokhin mail

link https://doi.org/10.54216/FPA.100102

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2023

Machine learning for False Information Detection in Social Internet of Things

By capitalizing on object relationships and local navigability, the social internet of things (SIoT) is one of the burgeoning paradigms that could solve the technical challenges of conventional IoT. Because of this paradigm's capacity to combine conventional IoT with social media, it is possible to create smart objects and services with greater utility than those created using conventional IoT infrastructures. In recent years, scholars have become interested in SIoT, leading to a plethora of works examining various mechanisms for providing services and technologies within this context. In this vein, we present a comprehensive review of recent research covering important aspects of SIoT. In this research, we give a detailed justification for the function of several machine learning paradigms and provide a practical application of it to unexamined concerns relating to erroneous data and other social IoT. First, we give a classification of false news detection approaches and an analysis of these techniques. Second, the potential uses for detecting fake news are examined at length, including how it might be applied to the areas of fake profile detection, traffic management, bullying detection, etc . We also suggested a detailed review of the possibilities of machine learning algorithms for detecting bogus news and intervening in social networks. In our paper, we introduce categories of fake news detection methods providing a comparison between these methods. After that, the promising applications for false news detection are extensively discussed in terms of fake account detection, bot detection, bullying detection, and the security and privacy of SIoT. After all, A thorough discussion of the potential of machine learning approaches for fake news detection and interventions in SIoT networks along with the state-of-the-art challenges, opportunities, and future search prospects. This article seeks for aiding the readers and researchers in explaining the motive and role of the different machine learning paradigms to offer them a comprehensive realization of so far unexplored issues related to false information and other scenarios of SIoT networks.

groups

Mahmoud M. Ismail mail -

Nihal N. Mostafa mail -

Esmeralda Kazia mail -

Ibrahim Elhenawy mail

link https://doi.org/10.54216/FPA.100103

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

American Journal of Business and Operations Research | 2022

An Optimization Model for Stock Market Direction Prediction

Stock market direction prediction becomes an essential task in the business sector. The inherent volatile behavior of stock markets worldwide makes the prediction process difficult. The improvement in the prediction accuracy of the stock market direction prediction helps to avoid the risks involved in the investment process. In this aspect, this study designs a swallow swarm optimization (SSO) with a fuzzy support vector machine (FSVM) model for stock market direction prediction. The proposed SSO-FSVM model encompasses preprocessing, feature extraction, FSVM, and SSO based parameter tuning. The usage of the SSO algorithm to fine-tune the parameters involved in the FSVM model helps to significantly improve the overall predictive performance. To validate the improved performance of the SSO-FSVM model, a wide range of experiments were carried out using two benchmark datasets. The experimental outcomes reported the betterment of the SSO-FSVM model over the recent approaches in terms of several evaluation metrics. 

groups

Mingzhong Liu mail -

N Metawa mail

link https://doi.org/10.54216/AJBOR.060102

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new