Separation Axioms on Bipolar Hypersoft Topological Spaces

Sagvan Y.; Baravan A.

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

Journal of Artificial Intelligence and Metaheuristics | 2022

Survey on Design of Digital FIR Filters using Optimization Models

As the discipline of Digital Signal Processing develops, digital filters play an increasingly vital role in modern technology (DSP). The FIR filter, which stands for "finite impulse response," is the most common type of filter. As a result of its versatility, FIR filters find widespread application in many fields, including image filtering, frequency modulation, precision arithmetic, and many more. For this reason, digital FIR filters are designed using various optimization techniques. Using various optimization strategies yields the best results when optimizing for different filter coefficients (concerning control parameters, dependence, premature convergence, etc.). They're advantageous due to several factors, including their straightforward implementation, low error function, high-quality searching ability, and rapid convergence. In this paper, we have covered the topic of designing efficient digital filters for signal, image, and video processing using various optimization techniques.

groups

Mohamed Saber mail -

Mohamed E. Ghoneim mail -

Sunil Kumar mail

link https://doi.org/10.54216/JAIM.020102

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

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Journal of Artificial Intelligence and Metaheuristics | 2022

Energy Efficiency Modeling Using Whale Optimization Algorithm and Ensemble Model

machinery enterprises can benefit greatly from including energy efficiency models into their energy management and conservation efforts. Due to a lack of theoretical formulations, this paper integrates machining parameters and configuration parameters into energy efficiency models, with ML methods applied to increase generality. A three-year data set from a manufacturing facility serves as the basis for a comparison examination of two scenarios, with an emphasis on evaluating forecast precision, stability, and computing efficiency. To estimate future energy efficiency in Scenario 1, only cross-sectional data is utilized, completely discounting the wear and tear on spindle motors and cutting tools. In this study, we use five error measures to compare and contrast three classic ML algorithms: artificial neural networks, support vector regression, and Gaussian process regression. In Case 2, we build the a voting ensemble model in a more realistic setting, taking into account the dynamic characteristics of the aging spindle motor and tool wear. It is clear from the comparison that all of the Case 1 models experience performance erosion, but the proposed voting ensemble model is able to produce a sustainable increase in accuracy.

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Adel Oubelaid mail -

M. Y. Shams mail -

Mostafa Abotaleb mail

link https://doi.org/10.54216/JAIM.020103

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

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Journal of Artificial Intelligence and Metaheuristics | 2022

Classification of Student Performance Based on Ensemble Optimized Using Dipper Throated Optimization

Forecasting student performance, sorting students into groups according to their strengths, and working to improve future test scores are all crucial for any institution in today's competitive world. It is important to give students ample notice before a school year begins if they are to be coached to improve their grades by focusing on a certain subject area. Examining this can helps a school significantly reduce its dropout rate. This analysis predicts how well students will do in a given course based on how they did in previous, similar courses. Discovering previously unknown relationships among vast stores of data is the goal of data mining. Insights and forecasts might be gained from these recurring structures. The term "education data mining" describes the assortment of data mining programs used in the educational sector. The primary focus of these tools is on analyzing the information gathered from classrooms and educators. Potential applications of this research include classification and forecasting. It looks into several machine learning methods, including Naive Bayes, ID3, C4.5, and SVM. The experimental analysis uses data collection containing UCI machinery students' grades and other outcomes. Accuracy and error rate are two metrics used to evaluate algorithms.

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Marwa M. Eid mail -

Rokaia M. Zaki mail

link https://doi.org/10.54216/JAIM.020104

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

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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.

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Khadija Shazly mail -

Nima Khodadadi mail

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

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

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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

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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

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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

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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.

groups

Kaviyarasu M. mail -

Rajeshwari M. mail

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

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

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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.

groups

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