Type-II q-rung neutrosophic interval valued soft sets

M.; G.; T. T.; K.; Aiyared

doi:https://doi.org/10.54216/IJNS.230326

Journal of Intelligent Systems and Internet of Things | 2024

Grey Wolf Optimizer Algorithm for Multi-Objective Optimal Power Flow

This article introduces the Grey Wolf Optimizer (GWO) algorithm, a novel method aimed at tackling the challenges posed by the multi-objective Optimal Power Flow (OPF) problem. Drawing inspiration from the foraging behavior of grey wolves, GWO stands apart from traditional approaches by enhancing initial solutions without relying on gradient data collection from the objective function. In the domain of power system optimization, the OPF problem is widely acknowledged, involving constraints related to generator parameters, valve-point loading, reactive power, and active power. The proposed GWO technique is applied to IEEE 14-bus and 30-bus power systems, targeting four case objectives: minimizing cost with quadratic cost function, minimizing cost with inclusion of valve point, minimizing power loss, and minimizing both cost and losses simultaneously. For the IEEE-14 bus system, which requires meeting a power demand of 259 MW, GWO yields optimal costs of 827.0056 $/hr, 833.4691 $/hr, 1083.2410 $/hr, and 852.2255 $/hr across the four cases. Similarly, for the IEEE-30 bus system aiming to satisfy a demand of 283.4 MW, GWO achieves optimal costs of 801.8623 $/hr, 825.9321 $/hr, 1028.6309 $/hr, and 850.4794 $/hr for the respective cases. These optimal results are then compared with existing research outcomes, highlighting the efficiency and cost-effectiveness of the GWO algorithm when juxtaposed with alternative methods for solving the OPF problem.

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Y. V. Krishna Reddy mail -

R. Sireesha mail -

BP Mishra mail -

Pavithra G. mail -

Soban Badonia mail

link https://doi.org/10.54216/JISIoT.120102

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Enhancing Brain Tumor Detection and Classification using Osprey Optimization Algorithm with Deep Learning on MRI Images

Brain tumors (BT) are abnormal cell growth from the brain or the surrounding cells. It is categorized into 2 major types such as malignant (cancerous) and benign (non-cancerous). Classifying and detecting BTs is critical for knowledge of their mechanisms. Magnetic Resonance Imaging (MRI) is a helpful but time-consuming system, that needs knowledge for manual examination. A new development in Computer-assisted Diagnosis (CAD) and deep learning (DL) allows more reliable BT detection. Typical machine learning (ML) depends on handcrafted features, but DL achieves correct outcomes without such manual extraction. DL methods, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can exposed to optimum outcomes in the domain of medical image analysis, comprising the classification and recognition of BTs in MRI and CT scans. Thus, the study designs an automated BT Detection and Classification using the Osprey Optimization Algorithm with Deep Learning (BTDC-OOADL) method on MRI Images. The BTDC-OOADL technique deeply investigates the MRI for the identification of BT. In the proposed BTDC-OOADL algorithm, the Wiener filtering (WF) model is applied for the elimination of noise. Besides, the BTDC-OOADL algorithm exploits the MobileNetV2 technique for the procedure of feature extractor. In the meantime, the OOA is utilized for the optimum hyperparameter choice of the MobileNetv2 model. Finally, the graph convolutional network (GCN) model can be deployed for the classification and recognition of BT. The experimental outcome of the BTDC-OOADL methodology can be tested under benchmark dataset. The simulation values infer the betterment of the BTDC-OOADL system with recent approaches.

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S. Stephe mail -

V. Nivedita mail -

B. Karthikeyan mail -

K. Nithya mail -

Mohamed Yacin Sikkandar mail

link https://doi.org/10.54216/JISIoT.120103

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Energy Efficient Task Scheduling Strategy using Modified Coot Optimization Algorithm for Cloud Computing

Cloud computing (CC) refers to a current computing method that provides the virtualization of computing services as a utility to Cloud service users. Problems based on ineffective task mapping to cloud resource frequently happen in a cloud atmosphere. Task scheduling (TS), thus, means effective scheduling of rational allocation and computational actions of computing resource in certain limitations in the IaaS cloud network. Job scheduling was to allocate tasks to the most appropriate sources to reach more than one goal. Thus, choosing a suitable work scheduling technique for rising CC resource efficiency, whereas maintaining high quality of service (QoS) assurances, becomes a significant problem that remains to attract interest of researchers. Metaheuristic techniques shown remarkable efficacy in supplying near-optimal scheduling solutions for a complicated large-sized issues. Recently, a rising number of independent scholar has examined the QoS rendered by TS approaches. Therefore, this study develops an Energy Efficient Task Scheduling Strategy using Modified Coot Optimization Algorithm (EETSS-MCOA) for CC environment. The EETSS-MCOA method carries out the derivation of features and MCOA is applied to schedule tasks. In addition, the MCOA algorithm is derived by the combination of adaptive β hill climbing concept with the COA for enhanced task scheduling. The conventional COA is stimulated by the swarming characteristics of birds known as coots. The COA followed two distinct stages of bird movements on water surface. The experimental results of the EETSS-MCOA model are validated on CloudSim tool. The solutions attained by the EETSS-MCOA model are found to be better than the existing algorithms.

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Kandan M. mail -

M. Mutharasu mail -

Siva Satya Sreedhar P. mail -

S. Thenappan mail -

G. Nagarajan mail

link https://doi.org/10.54216/JISIoT.120104

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Intrusion Detection in Software-Defined Networks: Leveraging Deep Reinforcement Learning with Graph Convolutional Networks for Resilient Infrastructure

Protecting Software-Defined Networking (SDN) environments from intrusions and unauthorized access requires a high level of security. Security issues have arisen because of the widespread use of Software-Defined Networking (SDN), especially regarding intrusions that may cause disruptions to network operations by gaining unauthorized access. Intrusion is a danger to an SDN architecture's security, efficacy, and dependability because it involves manipulation or disruption. To improve SDN security through Intrusion Detection Systems (IDS), this study suggests a novel approach that makes use of Graph Convolutional Networks (GCN) and Deep Reinforcement Learning (DRL). The approach, which makes use of the NSL-KDD dataset, shows enhanced performance measures for intrusion detection, such as accuracy (93.8%), recall (93%), F1-score (92%), and precision (94.2%). This work establishes the groundwork for resilient infrastructure against threats and advances the security posture of SDN environments.

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Fuqdan A. Al-Ibraheemi mail -

Firas Hazzaa mail -

Mohanad Sameer Jabbar mail -

Jamal Fadhil Tawfeq mail -

Ravi Sekhar mail -

Pritesh Shah mail -

Sushma Parihar mail

link https://doi.org/10.54216/FPA.150107

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

Multi-criteria group decision making approach based on a new type of neutrosophic vague approach is used to select the shares of the companies for purchase

In this paper, we introduce the neutrosophic vague soft set, a combination of vague and neutrosophic soft sets. With the help of aggregated operations, we discuss neutrosophic vague soft sets. Multi-criteria group decision making can be evaluated effectively using the VIKOR approach. In this approach, the score function is generated by aggregating the VIKOR method to a neutrosophic vague soft approach. With the help of closeness values, alternative solutions are presented as optimal ones. To invest some money into the top five companies on the stock exchange, an investment company intends to purchase shares of the companies. Their investment strategy was to allocate some of their cash in percentages of 30 dollars, 25 dollars, 20 dollars, 15 dollars, and 10 dollars according to the top five ranked companies to minimize this effect.

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K. Raja mail -

P. Maragatha Meenakshi mail -

Abdallah Al-Husban mail -

Abdallah shihadeh mail -

Mowafaq Omar Al-Qadri mail -

N. Rajesh mail -

M. Palanikumar mail

link https://doi.org/10.54216/IJNS.230324

Volume & Issue

Vol. Volume 23 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2024

Measuring non-monetary poverty via machine learning and neutrosophic method: Review

       Poverty is an emerging problem that most economies are facing today. The study is aimed at exploring research conducted on measuring non-monetary poverty via machine learning.  Non-monetary poverty is identified through the following factors: demographics, population, distribution of income, climate, culture, ethnics, and availability of natural and artificial resources. Today, one of the most important aspects of non-monetary poverty measurement is using machine learning for multiple data points other than wealth or income to assess the quality of life of an individual or community. The socioeconomic factors that contribute poverty in emerging nations have also been found using machine learning algorithms. To achieve our goal neutrosophic model and machine learning algorithms were applied. Neutrosophic model used for reviewing the poverty indicators along with ML algorithms.   While exploring the utility of machine learning in our study to measure poverty we will find the answers for the following questions: (1) Why it is important to take into consideration of non-monetary approaches while calculating poverty rate? (2) Which machine learning algorithms were used in poverty measurement? (3) What is the future scope of machine learning applications in poverty prediction? In finding answers for those questions, we have analyzed overall 10 papers which were collected according to exclusion and inclusion criteria and the purpose of the selection according to the content of the paper. During the survey it was found out that machine learning gives sophisticated data for identifying non-monetary reasons of poverty and this survey is first that uses machine learning to non-monetary poverty factors.  

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Durdona Davletova mail -

Ahmed Aziz mail

link https://doi.org/10.54216/IJNS.230322

Volume & Issue

Vol. Volume 23 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2024

Fermatean Shortest Route Problem with Interval Fermatean Neutrosophic Fuzzy Arc Length: Formulation and a Modified Dijkstra’s Algorithm

Dijkstra’s algorithm (DA) is a very popular approach for finding the shortest route (SR) in the shortest route problem (SRP). The SRP becomes a challenging and complex problem in real life scenarios. The Fermatean neutrosophic set is a mathematical model that combines Fermatean sets with neutrosophic sets. It can handle the unclear, ambiguous, inconsistent, confusing, and uncertain information that comes from real-world problems. Decision-makers face difficulty accurately determining the precise membership (MG) and non membership levels due to the lack of appropriate data available. The FNS can handle this problem. In this study, we consider the interval FNS to describe the arc weight of a neutrosophic graph (NG). This SRP is called an interval Fermatean neutrosophic shortest route problem (IFNSRP). A modified DA is presented to solve this IFNSRP in an uncertain environment. The effectiveness of the presented method is illustrated with a numerical instance of a neutrosophic network.

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Arindam Dey mail -

Said Broumi mail -

Ranjan Kumar mail -

Jayanta Pratihar mail

link https://doi.org/10.54216/IJNS.230323

Volume & Issue

Vol. Volume 23 / Iss. Issue 3

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Enhancing Real-Time Malware Analysis with Quantum Neural Networks

The proposed Quantum Neural Networks (QNN) perform better than traditional machine learning models. The escalating complexity of malware poses a significant challenge to cybersecurity, necessitating innovative approaches to keep pace with its rapid evolution. Contemporary malware analysis techniques underscore the urgent need for solutions that can adapt to the dynamic functionalities of evolving malware. In this context, Quantum Neural Networks (QNNs) emerge as a cutting-edge and distinctive approach to malware analysis, promising to overcome the limitations of conventional methods. Our exploration of QNNs focuses on uncovering their valuable applications, particularly in real-time malware research. We meticulously examine the advantages of QNNs in contrast to conventional machine-learning methods employed in malware detection and classification. The proposed QNN showcases its unique capability to handle complex patterns, emphasizing its potential to achieve heightened levels of accuracy. Our contribution extends to introducing a dedicated framework for QNN-based malware analysis, harnessing the formidable computational capabilities of quantum computing for real-time malware analysis. This framework is structured around three pivotal components, Malware Feature Extraction utilizes quantum feature extraction techniques to identify relevant features from malware samples. Malware Classification employs a QNN classifier to categorize malware samples as benign or malicious. Real-Time Analysis enables the instantaneous examination of malware samples by integrating feature extraction and classification within a streaming data pipeline. Our proposed methodology undergoes comprehensive evaluation using a benchmark dataset of malware samples. The Proposed Quantum Neural Networks (QNNs) demonstrated a high accuracy of 0.95, outperforming other quantum models such as Quantum Support Vector Machines (QSVM) and Quantum Decision Trees (QDT), as well as classical models like Random Forest (RF), Support Vector Machines (SVM), and Decision Trees (DT) on the Malware DB dataset. The results affirm the framework's exceptional accuracy rates and low latency, establishing its suitability for real-time malware analysis. These findings underscore the potential for QNNs to revolutionize malware evaluation and strengthen real-time defenses against cyberattacks. While our research demonstrates promising outcomes, further exploration and development in this domain are imperative to fully exploit the extensive viability that QNNs offer for cybersecurity applications.

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Thulasi Bikku mail -

Suresh Babu Chandolu mail -

S. Phani Praveen mail -

Narasimha Rao Tirumalasetti mail -

K. Swathi mail -

U. Sirisha mail

link https://doi.org/10.54216/JISIoT.120105

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

Neutrosophic ideals of several types in UP (BCC)-algebras

Characterizations of (∈,∈)-neutrosophic ideals and (q,∈ &or;q)-neutrosophic ideals are provided. Given special sets, so-called neutrosophic ∈-subsets, neutrosophic q-subsets, and neutrosophic (q,∈ &or;q)-subsets, conditions for the neutrosophic ∈-subsets, neutrosophic q-subsets, and neutrosophic (q,∈ &or;q)-subsets to be ideals are discussed.

groups

V. Rajam mail -

N. Rajesh mail -

Aiyared Iampan mail

link https://doi.org/10.54216/IJNS.230325

Volume & Issue

Vol. Volume 23 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2024