Kamal Transform for Neutrosophic Initial Value Problem

Azal J.; Huda A.; Sahar M.

doi:https://doi.org/10.54216/IJNS.260105

Journal of Intelligent Systems and Internet of Things | 2025

A New Descriptor Based on Machine Learning for Intrusion Detection in Wireless Sensor Networks WNSs

Wireless sensor networks have become a vital component of the infrastructure for many modern applications. With the increasing use of wireless sensor networks, the challenges facing these networks in the field of security are escalating and growing, and with the rapid advancement of wireless communication technology, these networks are exposed to increasing, complex and continuous threats. Our research is characterized by innovation in the field of security technology to enhance protection, repel attacks and detect intrusions, among these innovations are intrusion detection systems based on machine learning as a creative and new solution. In this research, we highlight the effectiveness of different machine learning algorithms, such as supervised and unsupervised learning, in detecting anomalies and intrusions within wireless sensor networks, as our goal focuses on enhancing the security of wireless sensor networks (WSNs) by adopting intrusion detection systems (IDS) based on machine learning techniques. In this context, with a focus on using the WSN-DS dataset. The results of this research showed that machine-learning models could improve the security efficiency of wireless sensor networks by achieving accuracy ranging from 91% to 99.7% and testing time ranging from 0.006 to 0.1249, which enhances the ability to effectively retrieve and detect threats in real time.

groups

Esraa Saleh Alomari mail -

Oday Ali Hassen mail -

Wisam Makki Salim mail -

Selvakumar Manickam mail -

Nur Azman Abu mail

link https://doi.org/10.54216/JISIoT.160115

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Multi-objective Optimization in Satellite-Assisted UAVs

Nowadays, Vehicular communication is used in intelligent transmission applications. The number of vehicles used in a particular region has numerously increased energy consumption, computation delay, and computation overhead. In this paper, Multi-Objective Optimization in Satellite Assisted UAVs (MO-SAUVs) is proposed under an improved Ant Colony Optimization (IACO) algorithm. The procedures that are considered for the process of MO are optimal logistics distribution, path prediction-based pheromone deposition, and evaporation. Using this method, effective region selection for the UAVs is performed which leads to improving the network energy efficiency by decreasing energy consumption and delay. The simulation is performed in NS2 and the proposed MO-SAUAVs method is compared with the TA-SAUAVs method and PL-SAUAVs method according to different parameters. The results show that the proposed MO-SAUAVs method achieves lower computation delay (70ms to 110ms), higher energy efficiency (6% to 16%), lower energy consumption (7% to 14%), and packets lower computation overhead (500 packets to 700) when we were compared with TA-SAUAVs and PL-SAUAVs.

groups

Mohammed Ahmed Jubair mail -

Shaima Miqdad Mohamed Najeeb mail -

Kifaa Hadi Thanoon mail -

Mujahid Hamood Hilal Alzakwani mail -

Fatima Hashim Abbas mail -

Rabei Raad Ali mail

link https://doi.org/10.54216/JISIoT.160116

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

International Journal of BIM and Engineering Science | 2025

Concrete Waste Management Based on BIM for Syrian Buildings

This study focuses on the management of concrete waste from demolished buildings. It is a crucial issue globally and particularly in Syria due to the significant amounts of concrete waste resulting from the long war and the February 6, 2023 earthquake. The research aims to promote sustainability and resource conservation in the Syrian construction sector by introducing a method for managing demolition waste using Building Information Modeling (BIM) technology. A case study was conducted on a residential building in the old city of Homs that was demolished due to the war. The building was modeled using the Revit software, and mathematical modeling was applied to calculate and manage the demolition waste related to the building's structural frame. The study revealed potential economic savings of up to 4.2% of the total cost of the building's concrete framework through recycling the structural frame waste (coarse aggregate + fine aggregate only). Furthermore, the study estimated the financial returns that could be realized from managing demolished concrete waste across the entire Homs Governorate.

groups

Mohammed Hasan mail -

Lama Saoud mail

link https://doi.org/10.54216/IJBES.100207

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Enhancing Security in Cloned Nodes: An Intelligent Framework for Attack Detection and Mitigation using Deep Learning with Optimization Algorithm in Wireless Sensor Networks

Wireless Sensor Network (WSN) signifies a state-of-the-art technology that combines energy-effective sensors with wireless transmission services enabling prompt surveillance and data collecting from the nearby environments. Owing to the intrinsic features of WSNs, they face numerous challenges of security that range from resource-based attacks, like computational overload or energy depletion, to interception, eavesdropping, and tampering. With the hacked data, the attackers can replicate the same sensors and use clones in the corresponding WSNs. This kind of cloning of the sensors, which is comprised of the WSN, is called a clone attack. Since the replicated sensors formed by the attackers have parallel keys and information, therefore the clone attacks have become a great attack for WSN. To defend WSNs against cyberattacks, machine learning (ML) and deep learning (DL) were applied to classify malicious and normal traffic. This study designs an Attack Detection and Mitigation using Deep Learning with an Optimization Algorithm in Wireless Sensor Networks (ADMDL-OAWSN). The main objective of the ADMDL-OAWSN system is to improve security in cloned nodes for the cyberattack detection model. In the primary step, the data pre-processing employs the StandardScalar method to transform input data into a suitable format. Next, the proposed ADMDL-OAWSN model designs a crayfish optimization algorithm (COA) for the subset of the feature selection (FS) to pick the most related features from an input dataset. For the attack classification process, the convolutional neural network and bi-directional gated recurrent unit with attention mechanism (CNN-BiGRU-A) technique have been exploited. At last, the parameter tuning of the CNN-BiGRU-A is applied by the design of the secretary wolf bird optimization (SeWBO) algorithm. Extensive experiments have been conducted to validate the results of the ADMDL-OAWSN system. The simulation results revealed that the ADMDL-OAWSN system emphasized furtherance when compared to other recent systems

groups

P. Kalvikkarasi mail -

K. Selvakumar mail

link https://doi.org/10.54216/FPA.190115

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Robustness of Ensemble Deep Learning Model with Zebra Optimization Algorithm for Weather-Related Disaster Detection System Using Remote Sensing Images

Weather monitoring is a vital challenge in dissimilar areas of applications such as military missions, higher precision agriculture, outdoor entertainment and recreation, industrial manufacture, and logistics. The most vital application is natural weather disaster monitoring. Weather change has made stronger an occurrence of natural disasters all over the world. More extreme climate events have been experienced for the past few years, like lower and higher temperatures, sturdy winds in humid cyclones, heavy rains, and intensified lack. Therefore, at present, remote sensing imagery (RSI) analysis is necessary in the field of ecological and weather monitoring mainly for the application of identifying and handling a natural climate disaster. To upsurge the accuracy of detection, machine learning (ML) and deep learning (DL) systems were applied to enhance the efficacy of removing features and help to perceive large-scale losses like landslides, earthquakes, and floods. In this manuscript, we design and develop a Weather Disaster Detection Model Using Zebra Optimization Algorithm with Ensemble Learning on Remote Sensing Images (WDDZOA-ELRSI) technique. The proposed WDDZOA-ELRSI model's main intention is to improve the detection model of weather disasters using state-of-the-art DL methods. Initially, the bilateral filter (BF) method is employed in the image pre-processing stage to eliminate the unwanted noise from input data. Furthermore, the feature extraction method executes GoogleNet technique to transform raw data into a reduced set of relevant features. For the classification process, the ensemble of deep learning models such as conditional variational autoencoder (CVAE), graph convolutional network (GCN), and Elman recurrent neural network (ERNN) have been deployed. Eventually, the zebra optimization algorithm (ZOA)-based hyperparameter tuning procedure has been achieved to improve the detection outcomes of ensemble models. The simulation analysis of the WDDZOA-ELRSI system is verified on a benchmark image dataset and the outcomes were evaluated under numerous measures. The simulation outcome emphasized the enhancement of the WDDZOA-ELRSI model in the weather disaster detection process

groups

Daniel Arockiam mail -

Azween Abdullah mail -

Valliappan Raju mail

link https://doi.org/10.54216/FPA.190117

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Fixed point results in ωt-distance mappings for Geraghty type contractions

In this study, we establish fixed point theorems for Pωt-contractions within b-metric spaces by utilizing ωtdistance mappings. Subsequently, we demonstrate fixed point results pertaining to nonlinear contraction conditions of the Geraghty type, again employing ωt-distance mappings in the context of a complete b-metric space. Additionally, we bolster our findings with appropriate examples to illustrate the applicability of our results.

groups

Ammar Al-tawil mail -

Ayman. A Hazaymeh mail -

Anwar Bataihah mail

link https://doi.org/10.54216/IJNS.260101

Volume & Issue

Vol. Volume 26 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Lexicographic Approach for Integer Programming Problem under Triangular Neutrosophic Fuzzy Environment and it’s Application

Linear programming is an effective way in mathematical programming for solving optimization problems with linear objectives and linear constraints. There is determinant and indeterminant information in the actual world. As a result, the indeterminate problem is veritable and must be considered in the optimization problem,To handle this situation the neutrosophic theory is formed from extension of fuzzy set theory and is a helpful tool for dealing with inconsistent, indeterminate, and incomplete information.In this paper, we examine the coefficient of single valued triangular neutrosophic numbers to solve the neutrosophic integer programming problem.The neutrosophic integer programming problem are formulated with highest truth membership (T), indeterminancy membership and falsity membership function. The neutrosophic objective function involving a neutrosophic number, and then constructs a neutrosophic integer programming problem technique to handle neutrosophic optimization.In this paper we propose a strategy by using lexicographic approach in fractional dual algorthim to obtaining the basic solution and optimal solution as single valued neutrosophic triangular numbers.To gauge the efficacy of the model we solved few examples.

groups

Yuvashri P. mail -

Saraswathi A. mail -

broumi said mail

link https://doi.org/10.54216/IJNS.260102

Volume & Issue

Vol. Volume 26 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Subfamilies of analytic functions associated with Rabotnov function

The aim of this paper is to investigate various subfamilies of analytic functions to find inclusion properties, and necessary and sufficient conditions for the Rabotnov function to be in these subfamilies. Furthermore, several corollaries will be implied from our main results.

groups

Ahlam Fallatah mail -

Tariq Al-Hawary mail -

Mourad Oqla Massa’deh mail -

Feras Yousef mail

link https://doi.org/10.54216/IJNS.260103

Volume & Issue

Vol. Volume 26 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Extended Refined Neutrosophic Vector Spaces, Subspaces and their Application

The objective of this paper is to present a perspective of Refined Neutrosophic Vector Space (r-NVS), Sub spaces and some basic operations on Refined Neutrosophic Sets such as Algebraic sum and Algebraic product. Further some basic propositions, lemma and examples are presented. Finally an application on Refined Neutrosophic Vector Space is presented in the field of e-Commerce buyer oriented product (Smart Phones) ranking to illustrate the advantage of representing r-NVS.

groups

T. Sathinathan mail -

J. Aldring mail -

S. John Borg mail -

D. Ajay mail

link https://doi.org/10.54216/IJNS.260104

Volume & Issue

Vol. Volume 26 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025