Finding new similarities measures for Type-II Diophantine neutrosophic interval valued soft sets and its basic operations

Sharifah Sakinah Syed; Nasreen; Murugan

doi:https://doi.org/10.54216/IJNS.250145

International Journal of Neutrosophic Science | 2025

Development of Neutrosophic Cognitive Maps (NCM) for the Evaluation and Ranking of the Main Causes of the Appearance of Fruit Fly Pests

The development of Neutrosophic Cognitive Maps (NCM) for the evaluation and ranking of the main causes of the appearance of fruit fly pests represents a significant advance in the field of agriculture and entomology  ̣This innovative approach allows for a holistic and integrated view of the complex and often interdependent factors that contribute to the proliferation of these destructive pests  ̣Using neutrosophic theory, which incorporates degrees of truth, falsehood, and indeterminacy, NCMs offer a powerful tool for identifying and prioritizing critical variables  ̣In this way, a more nuanced and precise understanding of the phenomenon is facilitated, enabling the design of more effective and sustainable management strategies  ̣The methodology applied in the construction of the NCM is characterized by its ability to manage the uncertainty and ambiguity inherent to ecological and agricultural systems  ̣Through the participation of experts and the analysis of empirical data, maps can be outlined that reflect the real complexity of the problem  ̣These maps not only highlight direct causes, such as weather conditions and poor agricultural practices, but also address underlying and systemic factors  ̣Thus, the use of NCM provides a robust conceptual framework for informed decision making, improving the efficiency of interventions and contributing significantly to crop protection and global food security.

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Emerson Javier Jácome-Mogro mail -

Pablo Morales mail -

Cristian Jiménez-Jácome mail -

Dilfuza Abidova mail

link https://doi.org/10.54216/IJNS.250143

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Enhancing Stock Market Trend Prediction Using Explainable Artificial Intelligence and Multi-source Data

Determining the trend of the stock market is a complex task influenced by numerous factors like fundamental variables, company performance, investor behavior, sentiments expressed in social media, etc. Although machine learning models support predicting stock market trends using historical or social media data, reliance on a single data source poses a serious challenge. This study introduces a novel Explainable artificial intelligence (XAI) to address a binary classification problem wherein the objective is to predict the trend of the stock market, utilizing an integration of multiple data sources. The dataset includes trading data, news and Twitter sentiment, and technical indicators. Sentiment analysis and the Natural Language Toolkit are utilized to extract the qualitative information from social media data. Technical indicators, or quantitative characteristics, are therefore generated from trade data. The technical indicators are fused with the stock sentiment features to predict the future stock market trend. Finally, a machine learning model is employed for upward or downward stock trend predictions. The proposed model in this study incorporates XAI to interpret the results. The presented model is evaluated using five bank stocks, and the results are promising, outperforming other models by reporting a mean accuracy of 90.14%. Additionally, the proposed model is explainable, exposing the rationale behind the classifier and furnishing a complete set of interpretations for the attained outcomes.

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John Ranjith mail -

Kumar Chandar S mail

link https://doi.org/10.54216/FPA.160211

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Enhancing Cybersecurity: Detecting Hidden Information in Spatial Domain Images Using Convolutional Neural Networks

Steganography involves concealing hidden messages inside various types of media, whereas steganalysis is the process of identifying the presence of steganography. Convolutional neural networks (CNN), a type of neural network that outperformed previously proposed machine learning-based methods when introduced, are among the models used for deep learning. While CNN-based methods may yield satisfactory results, they face challenges in terms of classification accuracy and network training stability. The present research introduces a CNN structure to increase hidden data detection and spatial domain image training reliability. The suggested method includes pre-processing, feature extraction, and classification. Evaluation of performance is conducted on datasets Break Our Steganographic System Base (BOSSbase-.01) and Break Our Watermarking System (BOWS2) with three adaptive steganography algorithms. Wavelet Obtained Weights (WOW), Spatial Universal Wavelet Relative Distortion (S-UNIWARD), and Highly Undetectable steGO (HUGO) operating at low payload capacities of 0.2 and 0.4 bits per pixel (bpp). The experimental results surpass the accuracy and network stability of prior publications. Training accuracy ranges from 91% to 94%, and testing accuracy ranges from 74.8% to 86.65%.

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Akram Mshet mail -

Huda Tayyeh mail

link https://doi.org/10.54216/JCIM.150101

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Optimized and Comprehensive Fake Review Detection based on Harris Hawks optimization integrated with Machine Learning Techniques

Fake review detection, often known as spam review detection, is a crucial aspect of natural language processing. It involves extracting valuable information from text documents obtained from various sources. Various methodologies, such as simple rule-based approaches, lexicon-based methods, and advanced machine learning algorithms, have been extensively employed with diverse classifiers to provide accurate detection of fake reviews. Nevertheless, review classification based on lexicons continues to face challenges in achieving high accuracies, mostly because of the need for domain-specific comprehensive dictionaries. Furthermore, machine learning-driven review detection also addresses the limitations in accuracy caused by the uncertainty of features in social data. In order To address the problem of accuracy, one effective approach is to carefully choose the most optimal set of features and minimize the number of features used. The Objective of the research paper is to select a small subset of features out of the thousands of features for accurate classification of spam review detection. Therefore, a good feature selection method is needed in order to speed up the processing rate and predictive accuracy. This paper, Harris Hawks Optimization (HHO), is utilized for feature selection in sentiment analysis tasks. The performance of the selected feature subsets was evaluated using SVM, X-GBoost, ETC classifiers. Experimental results on tweet reviews for the airline dataset demonstrated superior sentiment classification capabilities, achieving an accuracy of 0.9435% with SVM and 0.9607%, 0.9635% for X-Boost, ETC, respectively.

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Zahraa Fadhel mail -

Hussien Attia mail -

Yossra Hussain Ali mail

link https://doi.org/10.54216/JCIM.150102

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

AFCP Data Security Model for EHR Data Using Blockchain

The problem of data security in EHR is deeply concerning, as well as the methods used in session, feature, service, rule, and access restriction models. However, they fail to achieve higher security performance, which degrades the trust of data owners. To handle this issue, an efficient Adaptive Feature Centric Polynomial (AFCP) data security model is described here. The proposed method can be adapted to enforce security on any kind of data. The AFCP scheme classifies the features of EHR data under different categories based on their importance in being identified from the data taxonomy. By maintaining different categories of data encryption schemes and keys, the model selects a specific key for a unique feature with the use of the polynomial function. The method is designed to choose a dynamic polynomial function in the form of m(x) n, where the values of m and n are selected in a dynamic way. The method generates a blockchain according to the feature values and adapts the cipher text generated by applying a polynomial function to data encryption. The same has been reversed to produce the original EHR data by reversing the operation. The method enforces the Healthy Trust Access Restriction scheme in restricting malicious access. By adapting the AFCP model, the security performance is improved by up to 98%, and access restriction performance is improved by up to 97%. The proposed method increases the access restriction performance in the ratio of 19%, 16%, and 11% to HCA-ECC, EHRCHAIN, and PCH methods. Similarly, security performance is increased by 17% 13%, and 11% to HCA-ECC, EHRCHAIN, and PCH methods.

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D. Selvaraj mail -

J. Jeno Jasmine mail -

R. Ramani mail -

D. Dhinakaran mail -

G. Prabaharan mail

link https://doi.org/10.54216/JCIM.150103

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Detecting Zero-day Polymorphic Worms Using Honeywall

A polymorphic worm is a kind of worm that can change its payload in every infection attempt, so it can evade the Intrusion Detection Systems (IDSs) and perform illegal activities that lead to high losses. These worms can mutate as they spread across the network, causing most of the existing IDSs to carry out the polymorphic worm’s detection with high levels of both false positives and false negatives. In this paper, we propose a double-honeynet system that can detect polymorphic worm instances automatically. The Double-honeynet system is a hybrid system with both Network-based and Host-based mechanisms. This allows us to collect polymorphic worm instances at the network-level and host-level, which reduces the false positives and false negatives dramatically. The experimental deployment of a Double-honeynet network over a seven-day period successfully collected instances of various polymorphic worms, including 3511 Allaple, 3228 Conficker, 2817 Blaster, and 2452 Sasser worms. By utilizing, the Honeywall's Walleye interface; we were able to analyze the data and simulate the detection of these worms by generating new signatures, which were not previously recorded, demonstrating the system's capability to detect zero-day polymorphic threats. Analysis of Blaster worm instances revealed significant similarities in their payloads due to exe headers, indicating the necessity of preprocessing to remove these headers before signature generation, although the generation of signatures is beyond the scope of this study.

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Mohssen Mohammed mail -

Mohamed Abdalla Nour mail -

Mohamed Elhoseny mail

link https://doi.org/10.54216/JCIM.150104

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2024

Fusion of Artificial Intelligence Based Deep Learning Model for Product Reviews on E-Commerce Environment

The emergence of e-commerce is introduced in the golden era. E-commerce product reviews are comments generated by customers of online shopping to estimate the service and product qualities having purchased; these remarks aid users in identifying the facts of the product. The sentiment polarity of e-commerce product analyses is the optimal method to get consumer opinions on a service or product. Hence, sentiment analysis (SA) of product remarks on e-commerce platforms is much more influential.  Deep learning (DL) analysis of online consumer feedback can identify user behavior toward a sustainable future. Artificial intelligence (AI) can acquire perceptions from product evaluations to develop efficient products. The main challenge is that numerous ethical products do not satisfy customers’ expectations owing to the gap among users’ expectations and their perception of sustainable products. This paper focuses on the design of the Fusion of Artificial Intelligence Deep Learning Model for Product Reviews on E-Commerce (FAIDLM-PREC) model. The main intention of FAIDLM-PREC method is to appropriately distinguish the dissimilar types of sentiments that occur in the e-commerce reviews.  Initially, data preprocessing is executed to increase the product review quality with Glove based word embedding method. For product reviews classification, the FAIDLM-PREC approach evolves fusion of dual DL methods namely Bidirectional Long Short‐Term Memory (Bi-LSTM) and gated recurrent unit (GRU) methods. Eventually, the parameters relevant to the two DL methods are perfectly modified utilizing the Archimedes optimization algorithm (AOA). An extensive experiment of the FAIDLM-PREC technique was conducted utilizing customer review database and outcomes indicated that the FAIDLM-PREC technique highlighted betterment over other recent methods to several measures.

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Nasser Nammas Albogami mail

link https://doi.org/10.54216/FPA.160212

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Pure Mathematics for Theoretical Computer Science | 2024

On the Assessment Restricted Liu Estimator for Dealing with Multi-Collinearity Problem

In this paper, we concentrate on comparing the restricted least squares with restricted Liu estimator  based on (MSE) criterion in the existence of multi-collinearity. In addition, we find the best estimation in many different cases with some related numerical examples.

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Noor Edin Rabeh mail

link https://doi.org/10.54216/PMTCS.040105

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

A Plithogenic Statistical Approach to Assessing the Effects of Ginger Powder as a Growth Promoter

In a world where efficiency and sustainability in poultry production are crucial, the need arises to find natural additives that enhance the growth of broiler chickens  ̣Recent research has put ginger powder under the microscope, evaluating its impact as a growth promoter through a detailed analysis of plithogenic statistics  ̣This study not only focuses on the quantitative aspects of weight gain and improved feed conversion, but also on the qualitative effects that this additive may have on the general health and well-being of the birds  ̣ The methodology used involves a rigorous and multifaceted approach, integrating biological and nutritional variables, which allows a deep and holistic understanding of the benefits of ginger powder in poultry farming  ̣Preliminary results suggest that ginger powder could be a viable alternative to synthetic growth promoters, showing significant improvement in growth parameters of broilers  ̣ However, plithogenic analysis reveals complex nuances that require careful interpretation, as variations in bird response indicate that factors such as dosage and administration time are crucial to maximizing benefits  ̣ This finding opens a range of possibilities for future research and practical applications, pointing towards more natural and sustainable poultry production  ̣ Additionally, it raises important questions about the integration of herbal supplements into animal diets, inviting a broader debate about science and ethics in the food industry. 

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Lucía Monserrath Silva Déley mail -

Dorian Michael Lisintuña Montaguano mail -

Jaime Iván Acosta Velarde mail -

Blanca Mercedes Toro Molina mail -

Blanca Jeaneth Villavicencio Villavicencio mail -

Edilberto Chacón Marcheco mail

link https://doi.org/10.54216/IJNS.250144

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025