علي عبدالكريم حبيب الرماحي

This paper introduces a method for fake accounts detection on social media through machine learning using several classification algorithms and sophisticated feature extraction. Preprocessing steps included dataset integration, feature normalization, entropy-based feature extraction, behavioral ratio computation, and class imbalance handling using SMOTE and SMOTEENN. Ensemble learning models, including LightGBM, CatBoost, XGBoost, and Logistic Regression, were trained on the training dataset and subsequently evaluated on the unseen testing dataset to assess their classification performance. Models were evaluated using accuracy, precision, recall, F1 score, and confusion matrices. SHAP explanation displayed the feature contributions. Experimental results demonstrated the effectiveness of the proposed approach, achieving the highest predictive accuracy of 99.71% and a balanced F1-score of 99.70%. These findings demonstrate excellent discriminative performance in distinguishing real accounts from the fake accounts and generated the following implications. Mixtures of feature selection, resampling strategies, and explainable AI can clearly enhance fake account detection. The proposed framework provides a scalable and effective solution for identifying fraudulent social media accounts and enhancing trust and security across online platforms.

This paper explores the combined effect of diverse datasets and advanced feature extraction techniques on the performance of email spam detection using unsupervised clustering algorithms Fuzzy C-Means and K-Means. Leveraging the Enron Email Dataset and the SMS Spam Collection Dataset, we extract both traditional features (TF-IDF) and novel attributes (punctuation count, capital letter usage, and body length) that reflect spam characteristics. Clustering is applied to group emails based on these features, and the generated cluster labels are subsequently used to train supervised classifiers, including K-Nearest Neighbours, Naive Bayes, Support Vector Machines, and XGBoost. Comparative analysis reveals that clustering significantly enhances classification accuracy by uncovering latent structures within high-dimensional data. Moreover, our results highlight the strengths and limitations of Fuzzy C-Means versus K-Means in this context. The study demonstrates that integrating unsupervised clustering with rich feature engineering contributes to more robust and efficient spam detection, paving the way for future hybrid spam filtering models. © The Author(s) 2025.

Article history: The use of digital technologies in agriculture has become very important to ensure the protection of trees from disease and limit their development, which leads to increased production, so the paper proposes a modified analytical model to analyze the data and graphical parts of the leaves of fruit trees using priority fuzzy C-means (PFCM). ) based on the proposed distance scale to obtain a clustering with a less error rate and fairly close to accuracy for the purpose of monitoring the development of diseases of fruit trees, by classifying the diseases and medications needed for each disease, a database was created containing large samples of data and images, where the results of Analysis of previous studies that analyzes of large amounts of data give accurate results. The proposed method was used in smart gardens with large areas and we got the desired results.

The article examines the composition and purpose of the elements of the automated complex “Smart Garden”, designed to develop software and hardware solutions for building an intelligent management system for intensive garden objects. The structure of an intelligent monitoring and control system for an automated complex is given. The choice of a wireless standard and a network for collecting information from geographically distributed sensors of the monitoring and control system is substantiated. The option of constructing an automated workstation for the operator-technologist of the automated complex “Smart Garden” is being considered.

A patient who has fully recovered from COVID-19 can help patients currently fighting infection by donating plasma. Because it is an infection killer, the plasma now contains antibodies against COVID-19. These antibodies provided the immune system with one way to fight the virus when it was sick, so plasma can be used to fight diseases. Therefore, this paper monitoring recovering patients based on the clustering of data and classifying them using fuzzy hierarchical clustering to reach the plasma as soon as possible.

An approach to the optimal allocation of resources of a network information system by its elements is proposed. Two probabilistic resource allocation models and a fuzzy model have been developed.