Securing and Optimizing Wireless Sensor Military Networks: A Hybrid KNN-Decision Tree Model for Anomaly Detection and False Alarm Reduction

Anushri Narendra Pathak; Arvind R. Yadav

doi:https://doi.org/10.54216/FPA.200109

Journal of Artificial Intelligence and Metaheuristics | 2026

An Intelligent Learning Analytics Framework for Predicting AI Proficiency Using BER-SC-Optimized MLP

The fast adoption of artificial intelligence (AI) and machine learning (ML) in the sphere of higher education has become a catalyst that requires more precise and data-driven models that could assess the level of proficiency and interest in new technologies in students. This work is driven by the fact that there are currently no personalized and analytically rigorous frameworks for assessing the level of AI literacy among college students and proposes an optimized learning analytics model that hyperparameters a Multilayer Perceptron (MLP) by utilizing the Binary Al-Biruni Earth Radius with Sine Cosine Algorithm (BER-SC) metaheuristic algorithm to close the performance gap between human and artificial intelligence. The suggested BER-SC + MLP hybrid was created and evaluated using a sample consisting of 258 student responses from Grand Canyon University, which encompassed AI knowledge and usage behavior as well as career interest. The model achieved an accuracy of 0.9312 after optimization, compared to the MLP’s pre-optimization accuracy (0.8996), and a significant decrease in Mean Squared Error (MSE = 0.0002808). The results demonstrate that BER-SC is far superior to nontraditional optimization techniques in terms of convergence efficiency and prediction accuracy. The contribution of the study is that it created a scalable and interpretable, and high-performing educational data modeling framework that has the potential to support adaptive educational learning analytics and institutional decision-making. The results suggest that intelligent optimization added to the ML-based assessment systems has a high possibility of enhancing the quality, efficiency, and feasibility of AI-based education and allowing more adaptable, more affordable, and more individually focused learning settings.

groups

Hessa Al-Junaid mail

link https://doi.org/10.54216/JAIM.110108

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2026

Global Crop Yields Forecasting Using Informer Architecture Optimized with Football-Inspired Metaheuristic

Accurate forecasting of agricultural production is essential for managing food supply chains, guiding policy decisions, and building resilience against climate variability. However, modeling long-range, country-level crop production remains challenging due to temporal complexity, nonlinear dependencies, and the need for highly generalizable prediction systems. This study addresses these challenges by developing a hybrid forecasting framework that combines deep learning architectures with metaheuristic hyperparameter optimization. A global dataset spanning tomato and potato production from 1961 to 2021 was used to evaluate multiple forecasting models, including Informer, N-BEATS, LogTrans, N-HITS, EALSTM, TST, and LSTM. The Informer model achieved the best baseline performance (RMSE = 0.0799; NSE = 0.9070) and was selected for optimization. A comparative analysis was conducted using several metaheuristic algorithms, with particular focus on the Football Optimization Algorithm (FbOA), a novel strategy inspired by cooperative team dynamics. FbOA delivered the highest gains across all evaluated metrics, reducing RMSE to 0.00109, MSE to 1.19×10−6, and increasing NSE to 0.9260, R2 to 0.9600, and the correlation coefficient r to 0.9550. These results confirmed that metaheuristic tuning substantially enhances the forecasting capability of deep models, particularly when guided by domain-inspired search logic. The proposed framework demonstrates strong potential for integration into real-time, AI-driven agricultural decision support systems, offering scalable solutions for food security planning, climate-smart agriculture, and long-term sustainability forecasting.

groups

Toufik Mzili mail

link https://doi.org/10.54216/JAIM.110207

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new

Journal of Neutrosophic and Fuzzy Systems | 2025

HyperFuzzy Graph and HyperFuzzy HyperGraph

Fuzzy sets, intuitionistic fuzzy sets, neutrosophic sets, plithogenic sets, and other uncertainty handling frameworks are the subject of intensive daily research. Analogous investigations have been pursued in the contexts of graphs, hypergraphs, and superhypergraphs. In this paper, we introduce new definitions of the hyperfuzzy hypergraph and superhyperfuzzy hypergraph, which extend the notion of the fuzzy hypergraph. We also revisit and refine the concepts of the hyperfuzzy graph and the superhyperfuzzy graph.

groups

Takaaki Fujita mail -

Prem Kumar Singh mail

link https://doi.org/10.54216/JNFS.100101

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2025

A Short Contribution to the Classification of the Group of Units of the Rings (NCR)_(Z_pq ), (NCR)_(Z_(2^n ) ) and NCRZ_(p^2 )

In this paper, we study the group of units problem of three different non-commutative logical extensions rings, where we classify the group of units of the rings (NCR)_(Z_pq ), (NCR)_(Z_(2^n ) )and (NCR)_(Z_(p^2 ) )as semi direct products of well-known abelian groups as the following: U(N⊂R)_(Z_pq )&cong;(Z_(p-1)×Z_(q-1) )&prop;[(Z_p×Z_q )&prop;(Z_(p-1)×Z_(q-1)), U(NCR)_(Z_(2^n ) )&cong;(Z_2×Z_(2^(n-2)))&prop;(Z_(2^n )&prop;(Z_2×Z_(2^(n-2)))),   U(N⊂R)_(Z_(p^2 ) )&cong;Z_(p^2-p)&prop;(Z_(p^2 )&prop;Z_(p^2-p)).

groups

Lee Xu mail -

Olalekan Joosati mail

link https://doi.org/10.54216/GJMSA.0120103

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new

Pure Mathematics for Theoretical Computer Science | 2025

HyperWeighted Graph, SuperHyperWeighted Graph, and MultiWeighted Graph

A weighted graph is a graph in which each edge is assigned a numerical value (weight), typically representing cost, distance, or intensity. In this paper, we revisit and further explore three generalizations of weighted graphs: the Hyperweighted Graph, the Superhyperweighted Graph, and the MultiWeighted Graph. These advanced structures were initially introduced in.10 Our objective is to enhance understanding and broaden awareness of their theoretical foundations and potential applications through renewed analysis and formal refinement

groups

Takaaki Fujita mail

link https://doi.org/10.54216/PMTCS.050103

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

Pure Mathematics for Theoretical Computer Science | 2025

Neighborhood HyperRough Set and Neighborhood SuperHyperRough Set

Fuzzy sets,20 rough sets,14 intuitionistic fuzzy sets,3 neutrosophic sets,15 soft sets,13 hesitant fuzzy set,17 plithogenic sets,16 and other uncertainty-handling frameworks have been the focus of intensive and ongoing research. Rough set theory provides a mathematical framework for approximating subsets through lower and upper approximations defined by equivalence relations, effectively capturing uncertainty in classification and data analysis.5, 10 Building upon these foundational concepts, further generalizations such as Hyperrough Sets8 and Superhyperrough Sets have been introduced. In this paper, we investigate the concepts of Neighborhood Hyperrough Sets and Neighborhood Superhyperrough Sets. These models extend the classical Neighborhood Rough Set framework by incorporating the structural richness of Hyperrough Sets and Superhyperrough Sets.

groups

Takaaki Fujita mail

link https://doi.org/10.54216/PMTCS.050104

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

Journal of Sustainable Development and Green Technology | 2025

Ultra-Accurate CO2 Emission Forecasting for the Cement Industry Using FbOA-Optimized Neural NODE Models

The cement sector is a linchpin of global infrastructure and is also one of the world’s most significant industrial sources of CO2 emissions, accounting for about 7-8% of anthropogenic emissions. The proper prediction of cementgenerated emissions is thus essential for designing mitigation strategies, planning industrial transitions, and evaluating progress toward carbon-neutrality goals. This paper proposes a new time-series forecasting model that combines Neural Ordinary Differential Equations (NODE) with the Football Optimization Algorithm (FbOA) to enable automated, data-driven hyperparameter optimization. The performance of NODE is compared with Seq2Seq and ConvLSTM models for global CO2 emis-sions from cement production in baseline settings, and subsequently metaheuristically optimized using FbOA, PSO, MVO, WOA, and GA. The baseline experiments demonstrate that NODE, with an MSE of 0.00745, RMSE of 0.0863, MAE of 0.0515, and high levels of agreement (NSE = 0.91, WI = 0.905), outperforms both Seq2Seq and ConvLSTM. Upon hyperparameter optimization, the FbOA + NODE combination achieves significant performance improvement, with MSE of 3.95×10−7 , RMSE of 6.28×10−3 , and MAE of 3.42 × 10−4 , r = 0.977, R2 = 0.973, NSE = 0.975 and WI = 0.98. Competing optimizers (PSO, MVO, WOA, GA) also improve NODE’s performance, and across all important metrics, they are consistently below FbOA. The findings indicate that integrating NODE and FbOA yields an accurate, stable, and computationally inexpensive model for predicting cement-associated CO2 emissions, offering a potential avenue for data-driven climate and industrial planning.

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Omnia M. Osama mail -

Marwa M. Eid mail -

El-Sayed M. El-Rabaie mail

link https://doi.org/10.54216/JSDGT.050105

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

A Statistical Neutrosophic Analysis to Measure WhatsApp's Effect on Academic Performance

There has been an unending debate about the effect of WhatsApp on students’ performance globally. This paper seeks to contribute to this debate by investigating the extent of WhatsApp usage and its’ effect on Uttarakhands’ post-graduate students’ academic performance. Estimation tools such as simple descriptive statistics, the difference in difference, and ordinary least square regression analyses were applied to a survey of 250 post-graduate students. At the top of the study, we found that most MBA students in India use WhatsApp during academic activity, connect with their professor via WhatsApp, and spend between 1 – 2 hours each day on WhatsApp. We also found a significant difference between the GPAs of students who are connected with their professors and those who are not connected with their professors. Again, we found a low level of addiction to WhatsApp but severe threats to circulating and withholding information by post-graduate students. It was also discovered that student connection with the professors via WhatsApp and spending 3 – 5 hours on WhatsApp increases ones’ academic performance. Therefore, we recommend that; school management put policies that will promote a positive and healthy relationship between professors and students, primarily via WhatsApp. The Indian Ministry of Information should enact laws that frond on sending false information on social media and possible punishment. Finally, we recommend that school management institutions have strict policies to prevent students from using WhatsApp during academic activity

groups

Prayas Sharma mail -

Ashish Kumar Singh mail -

Benedict Afful Jr. mail -

Bharti Agrawal mail -

Gopal Kumar Gupta mail

link https://doi.org/10.54216/IJNS.260327

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Design and Implementation of an Automated Certificate Generation System for Higher Education Institutions

Certificate generation systems play an important role in higher education institutions because they prepare certified students for the job market and induce organizational efficiency. In this context, the College of Education for Pure Science / Ibn Al-Haitham (CEPSIH) does not, however, have its own electronic system. This point motivates us to conduct this monitoring situation as a case. We designed and implemented the Ibn al-Haitham Certificate System (IHCS) as an automated certificate generation database system at the CEPSIH. This case study aims to put this system in a real educational environment into a valuable context, where the successfully implemented system meets the objectives of CEPSIH, illustrates the major bottlenecks, the overwhelming challenges, and the negative impact resulting from a time-consuming, manual certificate issue process at the CEPSIH. The authorized staff members will generate certificates using IHCS database server with a user-friendly interface. Users (or the student graduators) will go through online panel and register before logging into the IHCS. Registered users authenticate themselves, after which new user accounts can be used to request the graduation certificate from IHCS database and then generated it automatically. To implement the IHCS, it was necessary to collect data from paper records and old Excel sheets which belong to more than 35000 graduates since 1980s. The collected data should be converted to CSV files which we designed in particular form in order to be imported to the IHCS database. Data verification and validation are conducted in specific manners within IHCS to ensure that all stored data are correct without any errors and meet certain standards of the CEPSIH. All graduate information stored in the IHCS database are encrypted by AIS algorithm using encryption key of 256 bit.

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Mohammed Kamal Nsaif mail -

Abdullah A. Rashid mail -

Haifaa J. Muhasin mail -

Wisam A. Shukur mail -

Amna Y. Muhammad mail -

Firas A. Abdullatif mail

link https://doi.org/10.54216/JCIM.160210

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Securing and Optimizing Wireless Sensor Military Networks: A Hybrid KNN-Decision Tree Model for Anomaly Detection and False Alarm Reduction

In applications related to military operations, Wireless Sensor Military Networks (WSMNs) aid a critical function by deploying a distributed group of sensor nodes. Such sensor networks lift the overall effectiveness of military activities by situational alertness and permitting instantaneous decision-making processes. This deployment also rises noteworthy challenges, namely scalability, energy efficiency, and security vulnerabilities. Ensuring the accessibility, trustfulness and confidentiality of the data sensed by sensor nodes is prime important challenge. It could lead to disastrous consequences on the military field. Looking into this shortfall, ongoing research is mainly targeted at obtaining advanced solutions to such challenges, such as secure and energy-efficient routing algorithms. However, one of the considerable challenges in WSNs is anomaly detection and the existence of false alarms. This can affect the dependability and effectiveness of the system. The ongoing research in this field focuses on exploring the condition of WSMN, mainly their applications, challenges, and future directions. Authors propose an adaptive and hybrid Machine Learning (ML) approach to reduce false alarms and anomaly detection along considering mutual authentication system. ML approaches offer reliable solutions by improving the data classification accuracy and detection of anomalies. These algorithms have better capability to distinguish between normal and abnormal events, which ultimately reduces false triggers. The authors propose a hybrid approach of k-Nearest Neighbors (KNN) and Decision Tree (DT), which results in a powerful method for improved classification accurateness and robustness in WSN. The effectiveness of KNN in local decision-making and better clear interpretability of Decision Tree to handle feature interactions are combined together in this strategy, to increase overall performance.

groups

Anushri Narendra Pathak mail -

Arvind R. Yadav mail

link https://doi.org/10.54216/FPA.200109

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new