Algebraic structures such as distributive, associativity and boundedness properties via tangent neutrosophic set acting generalized weighted averaging and geometric

Aiyared; Murugan; T. T.

doi:https://doi.org/10.54216/IJNS.250417

Fusion: Practice and Applications | 2025

Optimized Machine Learning Framework for SMS Spam Detection and Classification:A Comparative Evaluation

This paper presents an optimized framework for detecting SMS spam using advanced machine learning algorithms and natural language processing (NLP) techniques. Two datasets, the Filtering Mobile Phone Spam Dataset and the SMS Spam Collection Dataset, were utilized to evaluate the performance of various classifiers, including Multinomial Naive Bayes, K-Nearest Neighbors, Support Vector Classifier, Decision Trees, and AdaBoost. The methodology encompasses comprehensive data preprocessing steps, such as tokenization, stopword removal, and text normalization, followed by feature extraction using TF-IDF and Bag-of-Words models. The classifiers’ performances were evaluated using accuracy, precision, recall, and F1-score, alongside cross-validation techniques. Results indicate that Support Vector Classifier and AdaBoost consistently achieved superior accuracy in distinguishing between spam and ham messages. The study underscores the importance of data preprocessing and model optimization in enhancing spam detection accuracy, offering valuable insights for improving SMS filtering systems in cybersecurity applications.

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Firas Zawaideh mail -

Qusay Bsoul mail -

Ala Alzoubi mail -

Nardine T. Botros mail -

Moaz T. Fawzy mail -

Diaa Salama AbdElminaam mail -

Nour Mostafa mail

link https://doi.org/10.54216/FPA.180112

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

A novel Q-neutrosophic soft under interval matrix setting and its applications

Decision-making theory serves as an effective framework to guide decision-makers in solving problems. One notable application of this theory is in the medical field, where it aids doctors in analyzing patient data to determine whether a patient is infected. To enhance this theory with more adaptable mathematical methods, we propose an expanded approach based on previously introduced matrixes of Q-neutrosophic soft under an Interval-valued setting (IV-Q-NSM). This represents a new finding of existing mathematical tools to address the two-dimensional uncertainty prevalent in various life domains. This work explores several algebraic properties and matrix operations associated with IV-Q-NSM. Subsequently, we introduce a new methodology for decision-making (DM) in medical diagnosis selection problems. This approach aims to provide a more flexible and comprehensive framework for evaluating complex medical data and improving diagnostic accuracy.

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Ayman Hazaymeh mail -

Yousef Al-Qudah mail -

Faisal Al-Sharqi mail -

Anwar Bataihah mail

link https://doi.org/10.54216/IJNS.250413

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

Details open_in_new

Fusion: Practice and Applications | 2025

Heart Failure Early Prediction Using Machine And Deep Learning Algorithm

In this article, we use machine learning approaches to give a thorough investigation into the prediction of cardiac illnesses and strokes. The Stroke Prediction Dataset and the Heart Failure Prediction Dataset are the two datasets that we use. Our objective is to maximize accuracy and minimize Mean Absolute Error (MAE) and Mean Squared Error (MSE) in order to enhance predictive performance. We use a variety of machine learning methods, such as Random Forests, Naive Bayes, Decision Trees, and k-Nearest Neighbors (KNN). We also use Artificial Neural Networks (ANN) and Multi-Layer Perceptrons (MLP) as deep learning models. We use oversampling approaches to rectify the imbalance in classes. For hyperparameter tweaking, we also use Grid Search and k-Fold Cross Validation. Our goal is to deliver valuable insights into early detection and preventive measures through comprehensive testing and assessment for prevention of strokes and heart diseases.

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Lamis F. Al-Qora’n mail -

Qusay Bsoul mail -

Firas Zawaideh mail -

Ala Alzoubi mail -

Silvyras Sayed mail -

Raghad W. Bsoul mail -

Diaa Salama AbdElminaam mail -

Nour Mostafa mail

link https://doi.org/10.54216/FPA.180113

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

An Approach to Develop a Model to Detect the Phosphorus and Potassium Deficiency in Paddy Crop on Agriculture Farm Using DIP & ML

Excessive use of fertilizers harms the environment and disrupts plant habitats, while also raising costs for farmers. Proper timing and amounts of nutrients are crucial for plant health and environmental balance. The greenness of rice leaves indicates their chlorophyll and nutrient levels. Agronomy studies show rice plants need 10 nutrients, including primary ones like Nitrogen (N), Phosphorus (P), and Potassium (K), and secondary ones like Iron (Fe), Manganese (Mn), Copper (Cu), Zinc (Zn), Boron (B), Molybdenum (Mo), and Chlorine (Cl). Leaf nitrogen concentration (LNC) is highly correlated with chlorophyll content. There are several tools on LEAF+ to measure it, such as leaf color (LCC), SPAD, chlorophyll or nitrogen. Since these tools are cost-effective and not available to all farmers, LCC offers farmers the ability to estimate plant nitrogen needs in real-time for efficient fertilizer use and increased rice yield. Notable innovation in agriculture is the Leaf Color Chart (LCC), developed by Japanese experts. It measures chlorophyll levels in rice plants and aids in nitrogen management without harming the plant. Today, LCC is used globally to improve production efficiency and optimize nitrogen application rates. The remaining 2 major nutrients potassium and phosphorus can also be measured by experimentally expanding the available database of LCC, as has been done in the two models developed in this research paper.

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Mohammad Arif Ali Usmani mail -

Ausaf Ahmad mail

link https://doi.org/10.54216/FPA.180114

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

A Comprehensive Review of Arabic and English Sentiment Analysis in BBC and SANAD News

News agencies connect global events to local communities. It plays a pivotal role in influencing public opinion. Thus, the necessity arises to recognize news article’s sentiment. The purpose of this paper is to analyze sentiment for English and Arabic news articles in terms of positivity, negativity, or neutrality. Analyzing the articles of Arabic and English news can be challenging from the perspective of morphology. In this paper, we introduce 4 Machine Learning methods, including Logistic Regression (LR), k Nearest Neighbors (KNN), Random Forests (RF) and Naive Bayes (NB), with the TF-IDF as the feature extraction. The study was validated using 2 data sets (BBC, SANAD Arabic news), and two learning models (Hold out and 10-fold cross-validation). The evaluation was based on; Accuracy (ACC), Precision (PREC), Recall (REC), F1-score (F1), and The Matthews Correlation Coefficient (MCC) where it shows an outstanding performance for ML on a 10-fold strategy. The experiments provided in the paper indicated that the proposed ML models achieved the best results.

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Hassan Al-Sukhni mail -

Qusay Bsoul mail -

Sharaf Alzoubi mail -

Fadi yassin Salem Al jawazneh mail -

Dalia Ehab Abdelaziz mail -

Hisham Mohamed Gamel mail -

Diaa Salama AbdElminaam mail

link https://doi.org/10.54216/FPA.180115

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

New Keys for Cloud Resource Provisioning Optimization Method in Multi-Tier Style

Resource provisioning is regarded as a crucial technology in the cloud-computing environment. Nonetheless, the primary challenge associated with the cloud involves ensuring resource availability while improving throughput, balancing loads, and optimizing execution time. There are two types of provisioning methodologies in a cloud environment: single-tier and multi-tier. This paper presents a novel method that combines hybrid metaheuristic optimization techniques, specifically Ant Colony Optimization (ACO) and Firefly Algorithm (FA), referred to as ACOF. This study presented an implementation of dynamic resource provisioning in a multi-tier cloud architecture. The results obtained from the proposed method demonstrate an enhancement in resource provisioning compared to other studies. Indeed, the ACOF algorithm demonstrates a reduced execution time for resource provisioning compared to alternative algorithms. Furthermore, ACOF algorithms have the potential to decrease implementation time by up to 13.2% in comparison to the execution time of alternative methods.

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Omer K. Jasim Mohammad mail

link https://doi.org/10.54216/JISIoT.150115

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Intelligent Enhancement of Biometric Verification Using Deep Learning Technology

Biometric verification has grown into critical to privacy across areas such as finance and safe accessing services. The present study addresses the utilization of techniques for deep learning, namely convolutional neural networks (CNNs), to boost both the precision and dependability of biometric authentication. Researchers explore the effectiveness of these algorithms on collections containing genuine and forged banknote photos, taking into account information collecting obstacles such as operator condition changes and ambient conditions. The novelty shows an incredible proficiency in classification of 100%, with clarity, recall, and F1-scores of 1.00 across the two categories, demonstrating that the representation is excellent at discerning amongst legitimate and replica materials. Further, researchers investigate the effects of different design variables on efficiency and precision. This investigation provides important insights into merging deep learning with biometric data, laying the basis for future safe authorization developments.

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Maha A. Al-Bayati mail

link https://doi.org/10.54216/FPA.180116

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Enhanced Entity Recognition of Islamic Hadiths based-on Hybrid LSTM and AraBERT Model

This paper focuses on the training, evaluation and development of named entity recognition (NER) models designed for Islamic hadiths in Arabic Utilizing the Hadith Noor dataset, the study uses the BIO (Basic, In, Out) tagging scheme to classify words or tokens in NER tasks and the segmentation of the text into individual tokens. The right-skewed distribution revealed by examining the lengths of the Islamic hadiths revealed a right-skewed distribution, indicating that shorter texts are more common. Texts less than 100 words were most prevalent, followed by texts between 100 and 200 words, while texts longer than 200 words were rare. The dataset identifies eight types of entities, such as common names among narrators and locations. The study by training the three models AraBERT, LSTM and the hybrid model AraBERT-LSTM on Arabic text processing respectively, the hybrid model showed a performance, efficiency and accuracy of 0.981, outperforming the rest of the models, confirming its worth and reliability in NER tasks for natural language in Arabic, especially Islamic hadiths, which opens the way for exploring further investigations for future research in natural language processing.

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Wessam Lahmod Nados mail -

Behrooz Minaei Bidgoli mail -

Sayyed Sauleh Eetemadi mail -

Mohammad Ebrahim Shenasa mail -

Seyyed Ali Hosseini mail

link https://doi.org/10.54216/FPA.180117

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Efficient Deployment Approach in WSNs Using Heuristic Technique

Several researchers have paid attention to designing deployment algorithms in WSNs. In fact, there are many different ways to deploy sensors in sensors' fields. Selecting one of them mainly is based on the application for which WSN design. However, two main factors should be considered when designing a deployment approach in WSN: coverage and connectivity. In this paper, we present a genetic algorithm (GA) to enhance the sensor deployment in WSNs while concurrently improving the coverage and connectivity rate. The most popular deployment approach is to deploy sensor nodes randomly in the field. Although this approach is simple and easy, it may not achieve good results. In the proposed GA algorithm, the metaheuristic algorithm is used to deploy sensors. Simulations demonstrate that GA achieves a good deployment result compared to other research papers by ensuring maximum network coverage and connectivity rate by achieving efficient coverage and connectivity.

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Noor Ali Abbas mail -

Muhammed Abaid Mahdi mail -

Mahdi Abed Salman mail

link https://doi.org/10.54216/JCIM.150224

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025