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Modeling lipase activity aids researchers in optimizing features such as temperature, pH, and substrate concentration to boost enzyme performance. This is essential in biotechnology for progressing the productivity and yield of processes such as fermentation, biodiesel production, and bioremediation. Fermentation is a highly complex, multivariable, and non-linear biotechnological process that produces bioactive materials. This study leverages artificial neural networks (ANN) to predict lipase activity in batch fermentation processes, addressing the inherent challenges in weight learning optimization often encountered with traditional algorithms like Backpropagation (BP). Several metaheuristic algorithms were employed to optimize the Multilayered Perceptron (MLP) structure and weights, including moth-frequency optimization (MFO), Particle Swarm Optimization (PSO), Dandelion Optimizer Algorithm (DO), Crow Search Algorithm (CSA), and Salp Swarm Algorithm (SSA) to overcome these limitations. Among the tested algorithms, MFO emerged as the most effective approach, achieving superior performance in weight learning with the best fitness value (i.e., mean square error (MSE)) of 0.6006. MFO-optimized ANN models deliver the most accurate predictions for lipase activity, highlighting their potential as a powerful tool for advancing industrial fermentation process optimization. © 2025 IEEE.

The rapid growth of technology has brought about many advantages, but has also made networks more susceptible to security threats. Intrusion Detection Systems (IDS) play a vital role in protecting computer networks against malicious activities. Given the dynamic and constantly evolving nature of cyber threats, these systems must continuously adapt to maintain their effectiveness. Machine Learning (ML) methods have gained prominence as effective tools for constructing IDS that offer both high accuracy and efficiency. This study conducts a performance assessment of several machine learning classifiers, including Random Forests (RF), Decision Trees (DT), and Support Vector Machines (SVM), in addressing multiclass intrusion detection as a means to counter cybersecurity threats. The NSL-KDD dataset, which includes various network attacks, served as the basis for our experimental evaluation. The research explores two classification scenarios: a five-class and a three-class model, analyzing their impact on detection performance. The results demonstrate that RF consistently achieves the highest accuracy (85.42%) on the three-class scenario testing set, highlighting its effectiveness in handling patterns and non-linear relationships within the intrusion data. Furthermore, reducing the classification complexity (three classes vs. five classes) significantly improves model generalization, as evidenced by the reduced performance gap between training and testing data. Friedman’s rank test and Holm’s post-hoc analysis were applied to ensure statistical rigor, confirming that RF outperforms DT and SVM in all evaluation metrics. These findings establish RF as the most robust classifier for intrusion detection and underscore the importance of simplifying classification tasks for improved IDS performance. © (2025), (Science Publications). All rights reserved.

Abstract Metaheuristic methods have demonstrated their utility in tackling global optimization problems with and without constraints. However, existing state-of-the-art (SOTA) algorithms often suffer from limitations such as premature convergence, inefficient exploration-exploitation balance, and poor adaptability to complex discrete optimization problems like Team Formation (TF). The Golden Eagle Optimizer (GEO) algorithm is a promising metaheuristic that addresses some of these challenges by effectively managing its hunting spiral motion using two control parameters: cruise (exploration) and attack (exploitation). Despite its strengths, the standard GEO algorithm requires modifications to handle the discrete and multi-objective nature of the TF problem effectively. This paper proposes an amended version of GEO, called AGEO, which integrates specialized operators to enhance its performance in TF scenarios. A skillful TF aims to form teams of experts with complementary skills in social networks (SN) while optimizing multiple objectives, including minimizing communication costs, maximizing the similarity score between team members, and achieving minimal team cardinality. AGEO preserves GEO’s powerful exploitation and exploration mechanisms while introducing tailored operator strategies to overcome the challenges inherent in TF. The AGEO undergoes testing on several well-established benchmark datasets, including Universiti Malaysia Pahang (UMP), Internet Movie Database (IMDB), Association for Computing Machinery (ACM), and Database Systems & Logic Programming (DBLP). Additionally, a comparative study against SOTA metaheuristic algorithms such as Particle Swarm Optimization (PSO), Butterfly Optimization Algorithm (BOA), Crow Search Algorithm (CSA), and Jaya Algorithm demonstrates AGEO’s superior performance in forming highly optimized teams with the least communication cost, lowest team cardinality, and highest similarity score.

Traditional brain tumor diagnosis and classification are time-consuming and heavily reliant on radiologist expertise. The ever-growing patient population generates vast data, rendering existing methods expensive and inefficient. Deep Learning (DL) is a promising approach for developing automated systems to diagnose or segment brain tumors with high accuracy in less time. Within Deep Learning, Convolutional Neural Networks (CNNs) are potent tools for image classification tasks. This is achieved through a series of specialized layers, including convolution layers that identify patterns within images, pooling layers that summarize these patterns, fully connected layers that ultimately classify the image, and a feedforward layer to produce the output class. This study employed a CNN to classify brain tumors in T1-weighted contrast-enhanced images with various image resolutions, including 30×30, 50×50, 70×70, 100×100, and 150×150 pixels. The model successfully distinguished between three tumor types: glioma, meningioma, and pituitary. The CNN's impressive accuracy on training data reached up to 86.38% for image resolution (30×30) and 94.64% for higher resolution (150×150). This indicates its potential as a valuable tool in real-world brain tumor classification tasks. © 2025 IEEE.

The increasing volume of suspicious emails, commonly known as spam, has created a critical need for more reliable and robust anti-spam filters. These suspicious emails can be dangerous and can lead to the loss of personal information, underscoring the necessity for an effective spam filtering system. The application of machine learning methods has enhanced system security and improved the detection of suspicious messages. This research evaluates the effectiveness of seven machine learning algorithms for classifying suspicious email messages: random forest, support vector machine, artificial neural network, decision tree, gradient boosting classifier, and k-nearest neighbor. The primary focus of this evaluation is the accuracy achieved by each algorithm in identifying spam emails. Our analysis revealed that the random forest algorithm outperformed the other evaluated algorithms in terms of accuracy for spam email classification, achieving a remarkable 95%. The accuracy percentages of the various methods ranged from 88% to 93%. Copyright 2025. The Korean Institute of Information Scientists and Engineers.

Coronary heart disease (CHD) is the leading global cause of death, making early detection essential. While coronary angiography is the diagnostic gold standard, its invasive nature poses risks, and non-invasive symptom-based methods often lack accuracy. Machine learning-powered computer-aided diagnostic systems can effectively address challenges in clinical decisionmaking. This work presents an Evolutionary Strategy-optimized Support Vector Machine (ES-SVM) model for classifying CHD based on non-invasive test results and patient characteristics. Using the Coronary Heart Disease dataset, the proposed ESSVM demonstrated significant precision and F1-scores, as well as the accuracy of the proposed model. The results indicate that SVM performance can be significantly enhanced through evolutionary hyperparameter tuning, resulting in a reliable, noninvasive diagnostic tool for initial CAD screening and supporting early intervention techniques. © 2025 IEEE.