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

Marwa M.; Rokaia M.

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

International Journal of Neutrosophic Science | 2023

New algebraic extension of interval valued Q-neutrosophic normal subbisemirings of bisemirings

In this research article, we introduce the notions of interval valued Q-neutrosophic subbisemirings (IVQNSSBSs), level sets of an IVQNSSBS and interval valued Q-neutrosophic normal subbisemirings (IVQNSNSBSs) of bisemirings. Let Y ⃗ be an interval valued Q-neutrosophic set (IVQNS set) in a bisemiring 〆. Prove that Y ⃗ is an IVQNSSBS of S if and only if all nonempty level set Ξ(t,s) ⃗ is a subbisemiring (SBS) of S for t, s ∈ D[0, 1]. Let Y ⃗ be an IVQNSSBS of a bisemiring 〆 and V ⃗ be the strongest interval valued Qneutrosophic relation of 〆. Prove that Y ⃗ is an IVQNSSBS of S if and only if V ⃗ is an IVQNSSBS of 〆 × 〆. We illustrate homomorphic image of IVQNSSBS is an IVQNSSBS. Prove that homomorphic preimage of IVQNSSBS is an IVQNSSBS. Examples are given to demonstrate our findings.

groups

M. Palanikumar mail -

K. Arulmozhi mail -

Aiyared Iampan mail -

Said Broumi mail

link https://doi.org/10.54216/IJNS.200109

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

Prospects for Applied Mathematics and Data Analysis | 2023

Application of Integral Operator Generated by Touchard Polynomials to Certain Subclasses of Harmonic Functions

Let SH denote the class of functions f = h + g which are harmonic univalent and sense-preserving in the unite disk U = {z : |z| < 1} where h(z) = z +P∞ k=2 akzk, g(z) =∞Pk=1 bkzk (|b1| < 1). In this paper we establish connections between various subclasses of harmonic univalent functions by applying certain integral operator involving the Touchard Polynomials.

groups

Khalifa AlShaqsi mail

link https://doi.org/10.54216/PAMDA.010102

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2023

An Efficient and Secured Triple-Layered Wireless Sensor Network with Machine Learning Techniques

Replacement of physical labor and repetitive tasks by the agents is an attractive issue in the Smart Environment (SE). SE is distinguished by its ability to be controlled from a distance, to facilitate the connection between devices through middleware, to gather and share data from sensors, to improve the intelligence of devices, and to make decisions. To be effective, SE design must make use of information and networks that already exist in the actual world. Effective SE design is complicated by several difficulties, including monitoring, data collecting, assessment, evaluation, prediction of important data, and meaningful presentation. For SE, the most important step is gathering information from a variety of sensors in various locations. Wireless sensor networks provide an underlying architecture for the coordinated collection of data from many sensors that have common characteristics (WSN). An essential aspect of sensor networks is their inability to function in the currently complicated environment for wireless network security. In the realm of remote sensor businesses, cryptology is an essential part of safety measures. Several of the prevalent cryptographic methods have significant flaws that prevent them from being fully reliable. In this paper, we provide a unified, three-stage cryptographic procedure that combines public-key and secret-key techniques for maximum security. Due to consideration of Public-key management and high degree of security, Rijndael Encryption Approach (REA), Horst Feistel's Encryption Approach (HFEA), and the more sophisticated Rivest-Shamir-Adleman (e-RSA). Time spent in both execution and decoding of the suggested approach was utilized to rank the quality of displays. The suggested set of rules uses a single evaluation boundary or computation time, which is different from the methodologies used before. Low Encryption Time (LET) and Low Unscrambling Time (LDT) values of 1.12 and 1.26 were observed on texts ranging in size from 6 to 184 MB, respectively. Comparisons show that the suggested hybrid form is 2.9% more efficient than AES+RSA, 1.36 times more efficient than ECC+RSA+MD-5, 1.36 times more efficient than AES+ECC, and 1.36 times more efficient than AES+ECC+RSA+MD-5.

groups

Reem Atassi mail -

Aditi Sharma mail

link https://doi.org/10.54216/IJWAC.060201

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2023

Cyber Attack Detection in Wireless Adhoc Network using Artificial Intelligence

A wireless sensor network, also known as a WSN, is made up of thousands of minuscule sensor nodes that are connected to one another in order to monitor, track, and organize data collected in an unattended environment in the most prominent location. Due to its one-of-a-kind qualities, it has, the wireless sensor network is gaining traction in a variety of sectors and put to use in a wide range of applications, including surveillance, healthcare, and industry. These networks exposed to a variety of security flaws and major threats because of their dynamic design and deployment in an unsupervised environment. Cybercriminals prey on individuals who utilize the internet as well as organizations in order to get sensitive information. The hackers were able to access critical data on the company's systems, such as login information, credit card details, and bank account numbers. Phishing attacks are a sort of cyberattack in which hackers trick internet users into believing their websites are authentic in order to collect the users' private information. The purpose of these attacks is to steal this information. Malware assaults begin with the covert installation of malicious software on corporate servers or user PCs via the use of the internet. The attackers then continue to steal every piece of information that kept on the targeted server or computer. Malware used in an ever-increasing number of attacks these days. An incursion into a network is a kind of attack in which the perpetrator seeks to take possession of all of the network's resources. Approaches based on heuristic analysis and visual resemblance used, regardless of whether they are blacklisted or whitelisted.

groups

Mahmoud A. Zaher mail -

Nabil M. Eldakhly mail

link https://doi.org/10.54216/IJWAC.060202

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2023

A Review on Software Fault Detection Mechanisms and Fault Prevention Mechanisms in Networks

It is possible to improve software quality by anticipating fault location through the utilization of software metrics within fault prediction models in network. This article provides a comprehensive literature review on the topic of software fault forecasting. The paper also seeks to identify software metrics and evaluate how applicable those metrics are to the process of software fault prediction. It is recommended that additional research be conducted on large industrial software systems to identify metrics that are more pertinent for the industry and to find an answer to the question of which metrics should be employed in a particular setting.

groups

Preeti Baderiya mail -

Chetan Gupta mail -

Shivendra Dubey mail

link https://doi.org/10.54216/IJWAC.060203

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2023

FLC-NET: Federated Lightweight Network for Early Discovery of Malware in Resource-constrained IoT

In the past few years, billions of Internet of Things (IoT) devices that lacked adequate security procedures were created and deployed, and more of these devices are on the way as a result of the development of Beyond 5G technologies. Because of their susceptibility to malware, there is a pressing need for reliable methods that can identify infected IoT devices within networks. Precise and early identification of IoT malware is inevitable to achieve IoT security. Nevertheless, prevailing studies of IoT malware detection mostly support certain platforms, need complicated deep learning (DL) models to achieve efficiency, and are centrally trained on the device. The purpose of this study is to introduce a new Federated Learning (FL) Framework, which has been given the name FLC-NET, in order to train numerous distributed edge devices to identify malware cooperatively. After the malware binaries have been encoded into image representations using FLC-NET, a lightweight convolutional network known as LC-NET is introduced to model these malware patterns directly from the image data without any data engineering being required. Because of its lightweight design, LC-NET is suited for use in devices with limited resource availability. After that, sophisticated adversarial training will be offered on FLC-NET in order to collect defensive knowledge against adversarial samples from a variety of clients who will be participating. The FLC-NET is experimentally evaluated on the public malware dataset, and it is demonstrated efficient (Accuracy: 96.1%, f1-score: 95.5), effective, scalable, and resistant to adversarial attacks.

groups

Denis A. Pustokhin mail -

Irina V. Pustokhina mail

link https://doi.org/10.54216/IJWAC.060204

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2022

Effective Drive an Autonomous Vehicle, The Environment Characteristics Are Extracted Via Intelligent Image Processing

With the development of image handling technology, computerized technology, and the theory of image preparation, it has become clear that image processing is a crucial area of computer application. It is frequently used in many logical and designing applications, such as remote detection, medicine, meteorology, exchanges, and so on.  However, with the swift development of picture preparation technology, it is becoming more and more important to precisely and successfully evaluate the quality of a picture.  Recently, image quality evaluation has grown in importance as a study area in the field of developing picture data, which has attracted a lot of attention from academics.  The importance of picture quality primarily takes into account two aspects: picture loyalty and picture coherence.  picture quality directly depends on depending on the optical characteristics of the imaging equipment, image contrast, instrument clamor, and other factors.  It may provide checking intentions to depict gaining, handling, and various connections through quality assessment.  The evaluation of image quality assessment has become one of the essential breakthroughs of picture data designing to create a meaningful assessment of all components of picture preparation.  People have needed to learn picture loyalty and the understandability of the quantitative estimation strategy using the picture a lot framework plan as the assessment premise for a very long time, but one of the people on the human visual characteristics is still not fully understood, in particular the description methods of psychological characteristics in human vision is also difficult to learn the quantitative evaluation of image quality, so, extensive investigation is required.

groups

M. Sumithra mail -

G. Naveen Sundar mail -

B. Buvaneswari mail -

K. Sridharan mail -

V. D. Ambeth Kumar mail

link https://doi.org/10.54216/JISIoT.070104

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new

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

Details open_in_new

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.

groups

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

Details open_in_new

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.

groups

Marwa M. Eid mail -

Rokaia M. Zaki mail

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

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

Details open_in_new