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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.

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.

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.

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.

Metaheuristic methods have demonstrated their utility in tackling global optimization problems with and without constraints. However, existing...

We investigate the viability of the capacitive swipe gesture as a biometric modality. While the regular swipe gesture and the capacitive image have been widely explored in biometric literature, the capacitive swipe gesture is fairly new in this line of research. To our knowledge, only one recent study has explored the capacitive swipe gesture, and demonstrated its promise. However, that study is limited by a number of factors, such as using a very small data set in the experiments, collecting data in a single session, allowing the same impostor in both training and testing phases of authentication models, etc. In our paper, we address all these limitations, and rigorously explore the capacitive swipe gesture by creating a new large data set. Additionally, we develop a new technique to preprocess capacitive swipe gesture data, and demonstrate its effectiveness by comparing with existing techniques. A large set of experiments with four machine learning classifiers and two swipe directions prove that the capacitive swipe gesture can be effectively used for user authentication in smartphones.

Lung Adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the two main histology subtypes of non-small cell lung cancer (NSCLC) with 70% of total Lung Cancer. In this article we proposed an ensemble-based model for the identification of subtypes of NSCLC using methylation data. Proposed Random Forest-based model along with out of bag (OOB) error based feature selection technique identified the top ten most important CpG sites that are highly differentiator between LUSC and LUAD subtypes of NSCLC with an accuracy, precision and F1 Score of \(97\%\) . The proposed model outperformed the other existing models for the same purpose with huge margin of 12%. Pathway analysis of the proposed 10 CpG sites revealed different pathways for LUAD and LUSC associated genes, LUAD-associated genes primarily participated in TP53, PTEN, GLP-1, Incretin regulation, and apoptosis. Conversely, LUSC-associated genes were predominantly involved in pathways for platelet degranulation, serine biosynthesis, and Nephrin family interaction.

Retinal Detachment (RD) is one of the major problems with retinal disorder patients. Till to date there existing no confirmatory sign or marker on retina for the early detection of RD. Therefore, patients may have sudden RD at any time of their life. Moreover, it is completely dependent upon the subjective judgement of ophthalmologist to make the final diagnostic decision on RD. To support the decision making process for the ophthalmologist, in this article we proposed RDNet, a SqueezeNet architecture based deep learning model for the early detection of RD. We used publicly available dataset of 1017 images covering rhegmatogenous RD and control group. The proposed model built on this image set achieved 97.55% sensitivity, 99.26% specificity and 98.23% accuracy in detecting RD. The proposed model outperformed the existing models for the same purpose with the highest area under the ROC curve (AUC) of 0.995. We believe our model will support the early detection of RD in clinical setup and assist the ophthalmologist in identifying RD at its early stage.

Photovoltaic systems have proven to be one of the most widely used renewable energies and the best replacement for conventional energy. Yet, their non-linear nature remains a challenge when it comes to extracting maximum power from photovoltaic modules. Therefore, in this work, a nonlinear PID controller has been used to meet the requirements of the photovoltaic system. In addition, to improve system performance and response, metaheuristic search algorithms were introduced into the tuning process of both the NPID controller and conventional PID controller parameters in order to compare them. The use of Artificial Intelligence to fine-tune the controller parameters will enable the optimum values of proportional, integral, derivative and nonlinear gains to be determined as system condition change. Finally, a comparison between the algorithms applied is conducted in terms of efficiency, rise time, settling time and overshoot as well as the overall system stability.

Diabetes, affecting more than 500 million individuals worldwide, is the most widespread non-communicable disease, globally. The early identification and effective management of diabetes are crucial for controlling its spread. Currently, the HbA1c test is the gold standard for the detection of diabetes with high confidence. But this is an invasive, expensive pathology test. Therefore, alternative non-invasive and inexpensive methods have been proposed in the literature for the early detection of diabetes.

Artificial intelligence (AI) is a distinct area of computer science that enables machines to handle and interpret complex data effectively. In recent years, there has been a dramatic uptick in studies devoted to AI, with many focusing on healthcare and medical research. This article delves deep into the potential of AI in several areas of healthcare, including the diagnosis and treatment of diseases. In recent years, Machine learning (ML) and deep learning (DL) have emerged as the most widely used artificial intelligence technologies in the healthcare industry. Moreover, this research demonstrates the crucial significance of progressing AI technologies, namely generative AI and large language models (LLMs), highlighting their revolutionary influence on healthcare. Finally, we highlight upcoming innovations and offer profound insights into the significant ethical, medical, and technological challenges associated with AI in healthcare. © 2025 Nova Science Publishers, Inc. All rights reserved.