Integration of Information Technology in a Compact Neural Network Model for Real-Time Monitoring of Seagrass

Atyaf Sami

doi:https://doi.org/10.54216/JCIM.170104

International Journal of Neutrosophic Science | 2026

An Enhanced Risk Prediction Framework for Blockchain-based Financial Transactions Using Interval Neutrosophic Covering Rough Sets with Heuristic Search

The most efficient device for modelling uncertainty in decision-making issues is the neutrosophic set (NS) and its add-ons, such as NS of complex, interval, and interval complex. An efficient device for establishing uncertainty in decision-making by inserting three grades of truth, indeterminacy, and falsehood of an established statement. Recently, financial globalization has significantly expanded various methods for enhancing service quality using advanced resources. The practical application of the blockchain (BC) model enables stakeholders concerned about the hazard and return prediction models of economic products. To explore the application of deep learning (DL) in processing financial trading data, a neural network (NN) and DL data are utilized. Absolute stock indices and financial data are utilized for analyzing the efficiency of these models in financial prediction and analysis. This paper presents an Enhanced Risk Prediction Framework for Financial Transactions System Using Interval Neutrosophic Covering Rough Sets (ERPFFTS-INCRS) model. The aim is to develop an effective risk prediction model that enhances the reliability and security of BC financial transactions under uncertain conditions, utilizing neutrosophic logic. Initially, the z-score standardization method is used to clean, transform, and organize raw data into a structured and meaningful format. Furthermore, the ERPFFTS-INCRS method implements the INCRS method for the financial classification process. Finally, the hyperparameter selection for the INCRS model is performed by implementing the Elephant Herding Optimisation (EHO) algorithm. The experimental evaluation of the ERPFFTS-INCRS approach is examined under the metaverse financial transactions (MFT) dataset. The comparison analysis of the ERPFFTS-INCRS approach revealed a superior accuracy value of 98.77% compared to existing methods.

groups

Elvir Akhmetshin mail -

Ilyos Abdullayev mail -

Erkin Shodiev mail -

Samariddin Makhmudov mail -

Gavkhar Khidirova mail -

K. Shankar mail

link https://doi.org/10.54216/IJNS.270111

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2026

A Neutrosophic-AI Model for Spatiotemporal Analysis of Land Parcel Transactions

This paper proposes a novel hybrid framework that integrates Neutrosophic Logic with Artificial Intelligence (AI) for robust spatiotemporal modeling of urban land parcel transactions. The approach captures the uncertainty, inconsistency, and incompleteness often found in public land auction data through the application of neutrosophic triplets, defined by degrees of truth, indeterminacy, and falsity. Using longitudinal transaction records from Tashkent, the model transforms raw data into neutrosophic representations and feeds them into a Long Short-Term Memory (LSTM) network for forecasting. The enriched feature space enhances interpretability and prediction accuracy across administrative zones. Experimental evaluations demonstrate the superiority of the proposed Neutrosophic-AI model over conventional methods in terms of forecasting precision and uncertainty handling. This study offers a foundational contribution to neutrosophic-based urban analytics and supports transparent digital governance frameworks.

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Tanvir Mahmoud Hussein mail -

Tojiyev Rakhmatilla mail -

Danish Ather mail -

Rubina Liyakat Khan mail -

Tiyas Sarkar mail -

Manik Rakhra mail

link https://doi.org/10.54216/IJNS.270112

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2026

Neutrosophic of Capacitated and Uncapacitated Stochastic Facility Location Problems

Facility location problems assigned for determining the location of different types of facilities as factories, warehouses, hospitals,…, etc. It also helps to find the quantity of products and goods delivered to customers from the assigned facilities. As in other fields, uncertainty occurs in facility location problems, when the cost, time and other information seem in deterministic and unknown. The uncertainty in facility location problems promoted scientists to apply robust optimization such as stochastic techniques for solving complex locations problems. However, in stochastic problems some uncertain parameters need highly approaches such as neutrosophic sets, which is an extension of fuzzy sets to tackle the stochastic parameters. In this paper, a neutrosophic approaches based on neutrosophic sets applied for solving capacitated and uncapacitated stochastic facility location problems. The normal and neutrosophic models designed and some applications illustrated for testing the neutrosophic stochastic facility location problems in two cases capacitated and uncapacitated facilities. The result various for the two different situations and shows that decision maker therefore offers flexibility of various solutions when applying the neutrosophic case under different situations.

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Hajem Ati Daham mail -

Husam Jasim Mohammed mail

link https://doi.org/10.54216/IJNS.270113

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2026

A Neutrosophic Decision-Support Framework for Adaptive Learning Pathways in Digital Education Platforms

Personalized learning pathways in digital education platforms have become essential for addressing the unique needs and behaviors of individual learners. However, traditional adaptive systems often fail to account for the uncertainty, ambiguity, and inconsistency inherent in educational data. This paper proposes a novel neutrosophic decision-support framework that models learner profiles using truth (T), indeterminacy (I), and falsity (F) scores derived from student interaction and performance data. Utilizing the Open University Learning Analytics Dataset (OULAD), we compute neutrosophic learner vectors based on assessment outcomes, engagement patterns, and virtual learning environment (VLE) activity. A rule-based decision engine then recommends adaptive learning pathways—ranging from remedial to advanced—by interpreting the T/I/F distributions through a neutrosophic logic framework. Experimental results demonstrate that the proposed model enhances pathway assignment accuracy and provides better support for learners with incomplete or uncertain data compared to traditional fuzzy and crisp models. The neutrosophic approach also ensures interpretability and flexibility, making it well-suited for real-world educational platforms aiming to achieve adaptive learning at scale.

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Tanvir Mahmoud Hussein mail -

Priyanka Sharma mail -

Aastha Budhiraja mail -

Anshu Sharma mail -

Tojiyev Rakhmatilla mail -

Sonia Setia mail

link https://doi.org/10.54216/IJNS.270114

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2026

Fuzzy Reliability Estimation for Benktander Distribution

The fuzzy reliability estimate for the Benktander distribution, a model appropriate for heavy-tailed data, is investigated in this work. By adding membership functions and α-cuts, we extend the Benktander distribution to a fuzzy framework and compute its probability density function and reliability function. The fuzzy reliability is estimated using two methods: maximum likelihood and Bayesian approaches. The Bayesian method uses special loss functions, gamma priors, and squared error. The effectiveness of these estimators is examined in a simulated study using varying sample sizes and parameter values. The findings show that, especially for smaller samples, Bayesian techniques—in particular, the cautious Bayes estimator—perform better in terms of accuracy and stability than maximum likelihood estimation. The results emphasize how crucial it is to choose suitable prior distributions and loss functions while doing reliability analysis.

groups

Naser Odat mail

link https://doi.org/10.54216/IJNS.270115

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

The Impact of Digital Transformation, AI, and IoT on Employee Collaboration and Communication in Organizational Citizenship Behavior: A Comparative Study of Work Models

This study investigates the impact of Digital Transformation (DT), Artificial Intelligence (AI), Internet of Things (IoT), Employee Collaboration (EC), and Communication on Organizational Citizenship Behavior (OCB) across different work models—Hybrid, Remote, and In-Office. A structured questionnaire was developed and administered to employees in the IT industry in Hyderabad to collect data. The major findings indicate that Digital Transformation, AI, IoT, and Employee Collaboration significantly enhance OCB across various work models. Conversely, Communication alone does not significantly affect OCB within different work settings. The integration of advanced digital tools, AI, IoT, and collaborative technologies is crucial for fostering positive employee behaviors, which are less achievable through communication alone. The study underscores the importance of leveraging digital transformation, AI, and IoT to optimize organizational outcomes, particularly when implementing diverse work models.

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Marri Madhavi mail -

Sudha Vemaraju mail

link https://doi.org/10.54216/JISIoT.180124

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2026

Identify and Remove Duplicated Records Using Q-gram and Statistical Techniques from the Data Warehouse

There are several real-world uses for the duplication system or record linkage. In order to help the system make the best judgments, it appears in a broad area of recognizing similar data, joining online papers in the wide web, detecting plagiarism, and allowing several applications to enter it. To improve the financial interest and applicability of logistics project, routing is crucial. The following is the issue with this study: Because duplicate receipts contain the same significant change in data restrictions and limitations, and the data change itself is minor, the duplicate record data is ambiguous to other redacted records that are reassembled with the same customer. The purpose of this study is to use statistical techniques and the Q-gram to discover the best method for the detection and removal of duplicate records. We propose the following goals to help achieve that goal: Reduce the size of the data warehouse (DW) by providing a data warehouse free of duplicates. Decrease the amount of time spent looking for the (DW) and improve the DSS. The approach is divided into two stages: first, identify similarity records based on Q-gram similarity; second, determine whether classification records may be improved by statistical methods. The percentage threshold of 0.68 has been determined. It goes through a statistical process that decides whether this record is duplicated if the key ratio similarity is surpassed. The accuracy of the suggested work is 79%.

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Sura Mahroos mail -

Rihab Hazim mail -

Yaqeen Saad mail -

Nadia Mohammed mail

link https://doi.org/10.54216/JCIM.170101

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2026

Clustering and Classification of IoT-Based Environmental Data Using Machine Learning Techniques

In this study, we present an integrated approach to IoT-based environmental data analysis using a collection of unsupervised-learning techniques. We employed KMeans clustering in particular to identify natural groupings in environmental and behavioral features such as air quality, noise level, temperature, stress level, sleeping hours, and mood score. We then trained a Decision Tree classifier to predict and interpret cluster membership from raw sensor readings. The data of more than 30,000 observations in indoor school environments has multifaceted relationships between environmental factors and psychological well-being. KMeans consistently detected three environmental-behavioral states, and the Decision Tree classifier performed 87% classification accuracy, which indicated extremely high predictability power in addition to interpretability. The results indicated that sleep duration, air, and stress were the main factors for cluster discrimination. The hybrid model introduces the potential of observing real-time environmental and mental states for applications in smart cities. The approach is scalable, interpretable, and usable in IoT settings for proactivity-enabled wellness management.

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Ali Subhi Alhumaima mail -

Waleed Khalid Al-Zubaidi mail -

El-Sayed M. El-Kenawy mail -

Marwa M. Eid mail

link https://doi.org/10.54216/JCIM.170102

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2026

A Novel Hybrid CNN-LSTM Framework for Robust DDoS Attack Detection and Classification

Distributed Denial of Service (DDoS) assaults could be the most prevalent and impactful cybersecurity threats, aiming to disrupt networking services and stop legitimate users from getting access to the service. This paper presents a novel hybrid deep learning framework that employs Convolutional Neural Networks (CNN) for spatial feature extraction and Long Short-Term Memory (LSTM) networking to get long-term dependencies within network traffic. In the experiments on the CIC-DDoS-2019 database, a good classification performance of the proposed model is achieved with accurateness of 99.63%, preciseness of 99.24%, recall of 99.22%, F1 score of 99.22%, and Micro-AUC of 99.71%, surpassing traditional machine learning models such as LGBM, DNN, and standalone CNN and LSTM. In addition, Fuzzy Logic was implemented for risk management using three risk categories low, medium, and high .The findings uncovered that the proposed hybrid CNN-LSTM model gives the best evaluation metrics, despite the complexity and imbalance of the dataset classes. This is due to the capability of the model to combine special and non-permanent features out of the data. The proposed model also is proven to support integration in the whole system including time detection, blocking and alerting, such that it is considered a powerful system for network security.

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Ammar O. K. Al-Hasani mail -

Islam R. Abdelmaksoud mail -

Amira Rezk mail

link https://doi.org/10.54216/JCIM.170103

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2026

Integration of Information Technology in a Compact Neural Network Model for Real-Time Monitoring of Seagrass

Monitoring seagrass ecosystems offers critical insights into water quality, which is essential for maintaining aquatic biodiversity. Real-time monitoring, however, is hindered by various challenges, including coral reef degradation, habitat deterioration, fishing impacts, seagrass dredging risks, and complex coastal management issues. To overcome these barriers, this study presents an improved neural network model enhanced by Information Technology (IT) and Artificial Intelligence Neural Networks (AINN). Specifically, a recurrent neural network (RNN) has been utilized to address fishing pressures and habitat issues by evaluating sediment stability within seagrass areas. Additionally, a modular neural network (MNN), leveraging IT support, effectively analyzed coral reef deterioration to promote ecological sustainability. A convolutional neural network (CNN) was further implemented to enhance risk assessment and facilitate optimal seagrass growth conditions, thus improving real-time monitoring accuracy. Results indicated that this integrated IT-based neural network significantly surpassed traditional CNN methods, achieving superior performance in seagrass monitoring and coastal ecosystem management.

groups

Atyaf Sami Noori mail

link https://doi.org/10.54216/JCIM.170104

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new