New Learning Approach for High-Load Traffic Optimization SDN

Mohammad; Hassan Mohamed; Basma Rashid Mahdi

doi:https://doi.org/10.54216/JISIoT.170118

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 affect in improving the 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

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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

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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

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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.

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Anushri Narendra Pathak mail -

Arvind R. Yadav mail

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

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

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Fusion: Practice and Applications | 2025

A Personalized Tourism Recommendation Framework Based on Artificial Intelligence and Multi-Modal Data Fusion

In recent years, the tourism industry has increasingly embraced advanced technologies to deliver highly personalized travel experiences. This paper proposes the development of an AI-powered Personalized Tourism Recommendation System (PTRS), to be piloted in Samarkand, Uzbekistan—a city renowned for its rich cultural and historical heritage. The system leverages artificial intelligence techniques alongside multi-source data fusion to generate dynamic and context-aware travel recommendations. By integrating diverse data sources—including user preferences, weather conditions, seasonal trends, and geographic factors—the system provides adaptive recommendations tailored to individual tourist profiles. A combination of recommendation algorithms, such as cosine similarity, Pearson correlation, and matrix factorization, is employed to optimize the accuracy and relevance of suggestions. Performance evaluation is conducted using standard metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R²), and Mean Squared Error (MSE). The results underscore the effectiveness of incorporating AI and data fusion in enhancing smart tourism systems, paving the way for more intelligent and user-centric travel experiences in culturally rich destinations like Samarkand.

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Gozal Absalamova mail -

Kamalov Shukhrat mail -

Diyora Absalamova mail -

Tengelova Farangiz mail -

Nematova Farangiz mail

link https://doi.org/10.54216/FPA.200110

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Hybrid Multi-Descriptor and Deep Belief Network Model for Acute Lymphoblastic Leukaemia Diagnosis

The nature of images can differ in texture, contrast, illumination, noise levels, and structural patterns. The descriptor suitable for one image may not be optimal for another. Therefore, this paper proposes a new hybrid diagnostic model that combines multi-descriptor feature extraction with a Deep Belief Network. It is used to classify Acute Lymphoblastic Leukaemia. The proposed model consists of two phases: feature extraction and classification. Three descriptors, Histogram of Oriented Gradients, Scale-Invariant Feature Transform, and Convolutional Neural Network are employed in the feature extraction phase. Each descriptor captures different aspects of the image using distinct computational techniques. The Deep Belief Network was trained on each group of features individually. Three trained Deep Belief Network were produced with each data extract by descriptors. The membership function between the training set and the test data determines which DBN will be selected. The model was tested and evaluated on the 10,661 Leukaemia images of the C-NMC_Leukaemia dataset. It consists of two classes of images: 7272 images of Leukaemia cancer and 3389 of the Benign. Experimental results showed that the proposed model achieved an accuracy outperforming several recent methods. The accuracy of the proposed model reaches 96.87%, while the best accuracy of the recent works is 94.91%.

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Saif Ali Abd Alradha Alsaidi mail -

Ali Hakem Alsaeedi mail -

Hussein Al-Khamees mail -

Riyadh Rahef Nuiaa Al Ogaili mail -

Zaid Abdi Alkareem Alyasseri mail -

Mazin Abed Mohammed mail

link https://doi.org/10.54216/FPA.200111

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Optimizing Random Forest for Handwritten Digit Recognition Through Hyper-parameter Tuning

The significant increase in the volume of recently released records and multimedia news that is available presents fresh issues for pattern-recognition and machine-learning, particularly in addressing the longstanding issue of recognizing handwritten digits. Handwriting-recognition is a captivating area of research due to the uniqueness of each individual's handwriting style. It involves a computer's ability that automatically identify and comprehend handwritten (digit or character). Hyper parameters play a crucial role in the performance of machine learning algorithms, directly influencing the training process and significantly affecting the resulting model's performance. This work introduce a general automated hyper parameter tuning mechanics were used to optimize the random forest parameters, which are: grid- random search and Bayesian optimization applying on MNIST digit database (images) that have already been pre-processed. These proposed methods successfully identify optimal hyper parameters across a wide variety of ML models, taking into consideration the time cost of the search. This work shows the effectiveness and efficiency of used techniques, crucial for real-world applications. The results of this study show an accuracy rate of 99.3% for the Grid Search model, 98.8% for the Random Search model, and 96.0% for Bayesian Optimization on random forest algorithm.

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Yaqeen Saad Ali mail -

Rihab Hazim Qasim mail -

Sura Mahroos Searan mail -

Othman Mohammed Jasim mail -

Ibaa Sadoon Jabbar Alzubaydı mail

link https://doi.org/10.54216/FPA.200112

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Anomaly Detection in Satellite Imagery Using Deep Autoencoders

This study affords a deep autoencoder-primarily based framework for anomaly detection in multispectral satellite tv for pc imagery, addressing vital challenges in environmental monitoring and disaster response. Utilizing datasets from Sentinel-2, Landsat-eight, and MODIS, the version employs a hybrid loss function (MSE+MS-SSIM) and spatial attention mechanisms to discover and localize anomalies consisting of wildfires, floods, and urban encroachment. Experimental outcomes display superior overall performance (F1-Score: 0.84, AUC-ROC: 0.93) compared to PCA and Isolation Forest baselines, with precise anomaly localization demonstrated thru errors heatmaps and IoU metrics. The framework’s integration with early warning structures highlights its capability for actual-time applications, although boundaries in managing seasonal versions and occasional-decision information underscore the want for future paintings in multi-modal fusion and semi-supervised studying. This study advances scalable solutions for sustainable land control and emergency response, leveraging open-supply satellite data for global accessibility.

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Ayat Jasim Mohammed mail -

Ali Raheem Khraibet mail -

Huda Lafta Majeed mail -

Oday Ali Hassen mail

link https://doi.org/10.54216/FPA.200113

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2025

New Learning Approach for High-Load Traffic Optimization SDN

Due to the Internet's growing importance in our lives, Software-Defined Networking (SDN) networks have experienced high load traffic issues. Thus, network load has increased, lowering quality of service (Qos) performance. Modern networked systems depend on communication channels to transmit data between sources and destinations.  High traffic loads exacerbate packet distribution inefficiencies, causing network congestion in specific channels, compromising these communication channels. Congestion delays packet delivery and generates significant packet loss, reducing network dependability and efficiency. Communication channels' improper packet allocation along accessible paths is the fundamental issue. Some paths are overcrowded during peak traffic, while others are underused.   Bottlenecks slow packet transit and increase packet loss due to this imbalance. Current packet distribution techniques don't adapt effectively to dynamic traffic, resulting in poor network performance. Current traffic management solutions often rely on load balancing algorithms, but these methods may not adequately account for the dynamic and unpredictable nature of high-load traffic. This paper introduces Adaptive Load Balancing using Reinforcement Learning (ALBRL), which uses Q-learning and deep reinforcement learning to distribute traffic in real time in SDNs with high traffic loads. This model uses more network-specific indicators including packet loss ratio, latency, Jitter, and traffic pattern history to improve decision-making. ALBRL outperformed static routing and Q-learning with 15.34(ms) average delay, 2.11(ms) jitter, and 7.89% packet loss ratio.

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Mohammad Khalid mail -

Hassan Mohamed Muhi-Aldeen mail -

Basma Rashid Mahdi Alhamdani mail

link https://doi.org/10.54216/JISIoT.170118

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new