On Neutrosophic of BE-Algebra

Marrwa A.; Dunia A.; Majid M.

doi:https://doi.org/10.54216/IJNS.250218

International Journal of Advances in Applied Computational Intelligence | 2024

Realtime Traffic Enhancement using Intelligent Route Optimization for Dynamic Logistics

Software defect prediction is a technique that may foretell when and where software errors will manifest. It should be the aim of every software development project to provide a product devoid of bugs. Software defect prediction (SDP) is a crucial aspect of software repair that involves predicting potential code locations for problems. Software of excellent quality need to be bug-free. Software metrics are assessments of the program or its needs that are either quantitative or qualitative in nature. The Lévy flying patterns of various birds and fruit flies, together with the flight patterns of some cuckoo species, served as inspiration for Cuckoo Search (CS), a population-based algorithm that was developed relatively recently. Computer science satisfies the requirements for global convergence. Among the many supervised learning methods that do not need parameters, KNN stands out. This study provides a social metaphorical overview of Stochastic Diffusion Search (SDS) to show how SDS distributes resources. Using a new probabilistic approach, SDS solved the problems of best-fit pattern recognition and matching. Using interactions amongst basic agents, SDS is a distributed computing paradigm that employs multiagent population-based global search and optimization. An optimization approach that combines CS and SDS methods is suggested in this work. This hybridization proposal seeks to improve the cuckoo bird's search strategy for the ideal host nest by using the global search strategy solution of the SDS algorithm. So, to find the best spot for the cuckoo egg, the SDS approach would be used. One possible explanation for PC2's superior performance when compared to other classifiers is its greater recall values. Specifically, KNN outperforms Radial Bias Neural Network (2.20% improvement) and Naive Bayes (7.54% improvement) classifiers.

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Ahmed Abdelaziz mail -

Alia N. Mahmoud Nova mail

link https://doi.org/10.54216/IJAACI.060208

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Enhancing Stock Price Prediction Using Mutual Information, PCA, and LSTM: A Deep Learning Approach

The stock price exhibits quick and extremely nonlinear fluctuations in the financial market. A prominent worry among scholars and investors is the correct prediction of short-term stock prices and the corresponding upward and downward trends. Financial organizations have successfully incorporated machine learning and deep learning techniques to anticipate time series data accurately. Nevertheless, the precision of these models' predictions still needs improvement. Most current studies employ single prediction algorithms that cannot overcome intrinsic limitations. This paper proposes a methodology that utilizes the MUTUAL, principal component analysis (PCA), and Long Short-Term Memory (LSTM) model to accurately simulate and predict the variations in stock prices. The technology is utilized for the three global stock market datasets: TSLA, S&P500, and NASDAQ. The highest level of improvement achieved is a correlation of 99%. Furthermore, there is a reduction in error for the metrics MSE, MAPE, and RMSE, with improvements of 0.0001, 0.009, and 0.01 correspondingly.

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Zinah Kareem Mansoor mail -

Ali Yakoob Al-Sultan mail

link https://doi.org/10.54216/FPA.170114

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Leveraging LSTM and Attention for High-Accuracy Credit Card Fraud Detection

The increasing use of credit cards, especially for online payments, has led to a significant increase in fraud involving credit card payment technologies. Financial companies must enhance fraud detection systems to mitigate significant losses. This study introduces a methodology for developing a credit card fraud detection system that uses the Synthetic Minority Oversampling Technique (SMOTE) to address an imbalanced dataset problem and an attention layer to identify important features in the input sequence, two long short-term memory (LSTM) layers modeling long-run dependencies within a sequence of transactions, a dropout layer that neglects values lower than 0.3, and two dense layers, which allows enhancing the accuracy of prediction of fraudulent transactions. When implemented, the proposed system achieves an accuracy of 0.9434% on the IEEE dataset, 0.9850% on the Banksim dataset, and 0.9757% on the European dataset. This methodology shows improvements in fraud detection, emphasizing its ability to enhance financial security systems and reduce misclassification in credit card transactions.

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Ola Imran Obaid mail -

Ali Yakoob Al-Sultan mail

link https://doi.org/10.54216/FPA.170115

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Enhancing Network Performance in Wireless Sensor and Anonymous Networks

In Wireless Sensor Networks (WSN), congestion control plays a crucial role as the traffic load surpasses the capacity of each major channel. The WSN constrained resources must be taken in consideration while devising such strategies to get the best throughput. Various factors are contributed in the congestion; the primary factor is the over flowing buffer, packet loss, reduce network throughput and loss of energy. This research, studies path load distribution in novel networks, including anonymous communication. Initially there is a chance that the public Wi-current Fi approach will result in notable imbalances. We next modify an optimal path-selection algorithm and use flow level visualization to show that this results in a substantially improved network load balance. Web-based Congestion Control (WCC) needs to make it possible to give WCC channel flows a distinct quality of service (QoS) in order to overcome this difficulty.

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Zaynab Saeed Hameed mail -

Mohammed Arif Nadhom Obaid Al-agar mail -

Israa Ali Al-Neami mail

link https://doi.org/10.54216/FPA.170116

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Face Detection and Localization in Video Using HOG with CNN

Face detection is important in computer vision and image processing, particularly in surveillance, security systems, video analytics, and facial recognition applications. However, face detection algorithms face challenges like position variations, lighting fluctuations, size and resolution differences, facial expressions, and background clutter. This research aims to develop a system that achieves high accuracy in detecting and localizing faces using local descriptors and spatial feature extraction techniques, specifically the Histogram of Oriented Gradients method (HOG). Using videos from the YouTube Face database, features were extracted from frames and trained using a convolutional neural network (CNN). The HOG technique achieved a 94% accuracy rate and good localization compared to CNN without feature extraction.

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Faqeda Hassen Kareem mail -

Mohammed Abdullah Naser mail

link https://doi.org/10.54216/FPA.170117

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Sentimental Analysis to Predict Stock Market Using in Neutrosophic Time Series

This study delves into the innovative use of sentiment analysis in conjunction with neutrosophic time series to forecast stock market trends in various contexts. By meticulously analyzing financial news and social media data, sentiment scores are derived and subsequently integrated into a neutrosophic time series model. This model is uniquely adept at managing uncertainty and indeterminacy, providing a robust framework for prediction. The findings indicate that this integrated approach significantly enhances predictive accuracy and reliability over traditional time series models. This research presents a novel methodology for tackling the intrinsic unpredictability of stock markets, offering a more reliable tool for investors and analysts across diverse financial environments. Additionally, by incorporating sentiment scores from a wide range of sources, the model captures a comprehensive view of market sentiment, reflecting the collective mood and opinions of investors. This comprehensive approach ensures that the predictions are not only accurate but also reflective of real-time market dynamics. Finally, this work highlights the possibility of merging sentiment analysis with sophisticated modeling approaches to change stock market prediction, as well as providing a promising avenue for future financial forecasting research.

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Saravanaraj .S .S mail -

Vediyappan Govindan mail -

Said Broumi mail -

Haewon Byeon mail

link https://doi.org/10.54216/IJNS.250215

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

On the Irreversible k-Threshold Conversion Number for Some Graph Products and Neutrosophic Graphs

An irreversible k-threshold conversion process on a graph G=(V,E) is an iterative process that studies the spread of a one way change (from state 0 to 1) on V(G). The process begins by choosing a set S_0&sube;V. For each step t(t=1,2,…,), S_t is obtained from S_(t-1) by adjoining all vertices that have at least k neighbors in S_(t-1). We call S_0 the seed set of the k-threshold conversion process and if S_t=V(G) for some t≥0, then S_0 is called an irreversible k-threshold conversion set (IkCS) of G. The k-threshold conversion number of G (denoted by (c_k (G)) is the minimum cardinality of all the IkCSs of G. In this paper, we study IkCSs of toroidal grids and the tensor product of two paths. We determine c_2 (C_3×C_n )  and we present upper and lower bounds for c_2 (C_m×C_n) for m,n≥3. We also determine c_2 (P_2×P_n ),c_2 (P_3×P_n ) and present an upper bound for c_2 (P_m×P_n) when m,n>3. Then we determine c_3 (P_m×P_n) for m=2,3,4 and arbitrary n. Finally, we determine c_4 (P_m×P_n) for arbitrary m,n. . Also, we study the same concepts over some neutrosophic graphs with suggestions for future neutrosophic and fuzzy generalizations.

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Ahmad A. Abubaker mail -

Raed Hatamleh mail -

Khaled Matarneh mail -

Abdallah Al-Husban mail

link https://doi.org/10.54216/IJNS.250216

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

On the Numerical Solutions for Some Neutrosophic Singular Boundary Value Problems by Using (LPM) Polynomials

The main goal of this work is to study the effect of applying Lagrange's polynomials on finding the numerical solutions of many different neutrosophic boundary value problems, where we use those polynomials to solve three different neutrosophic boundary value problems numerically, and we present many numerical tables to compare the accuracy of the solutions obtained by Lagrange's polynomials with other famous methods such as Adomian's method.

groups

Ahmad A. Abubaker mail -

Raed Hatamleh mail -

Khaled Matarneh mail -

Abdallah Al-Husban mail

link https://doi.org/10.54216/IJNS.250217

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Enhancing Anomaly Detection in Industrial Control Systems through Supervised Learning and Explainable Artificial Intelligence

This paper addresses industrial control security (ICS) security, focusing on utilizing intrusion detection systems (IDS) to protect ICS networks. It suggests the use of a Measurement Intrusion Detection System (MIDS) over a Network Intrusion Detection System (NIDS), directly analyzing measurement data to detect unseen activities. Training MIDS requires a labeled dataset of various attacks, and a hardware-in-the-loop (HIL) system is used for safer attack simulations. The main aim is to assess MIDS performance through machine learning (ML) on this dataset. Explainable artificial intelligence (XAI) is integrated for transparency in decision-making. Various ML models, such as random forest, achieve high accuracy in detecting anomalies, notably stealthy attacks, with a receiver operating curve (ROC) of 0.9999 and an accuracy of 0.9795. This highlights the importance of machine learning in securing ICS, supported by XAI's explanatory power.

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Dhruv G. Bhatt mail -

Parshad U. Kyada mail -

Rajkumar Singh Rathore mail -

M. K. Nallakaruppan mail -

Faisal Mohammed alotaibi mail -

Rutvij H. Jhaveri mail

link https://doi.org/10.54216/JCIM.150125

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

On Neutrosophic of BE-Algebra

The BE-Algebra was presented by Kim in 2007. After that, several authors studied this type of logic concept in algebra. In this paper, we introduce more properties and remarks of BE-Algebra. Note that (A,*,1) is called BE-algebra if   ∀ a ∈A, b, c ∈A: collect a*a=1, a*1=1, 1*a=a and a*(b*c)=b*(a*c). In addition, a Neutrosophic BE-filter FI subset of the Neutrosophic BE-algebra is Neutrosophic BE-algebra AI is Neutrosophic BE-subalgebra AI.  Some new results and the criterion to determine some properties of BE-algebra and several relationships with another algebra namely Hibert algebras (H-algebra).

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Marrwa A. Salih mail -

Dunia A. Jarwan mail -

Majid M. Abed mail

link https://doi.org/10.54216/IJNS.250218

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

Details open_in_new