Your search

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.

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.

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.

Stock market forecasting is an essential factor in the daily operations of many companies and individuals. However, the complex and nonlinear nature of the stock market and the unpredictable variations in factors affecting stock prices present significant challenges in accurate forecasting. To address this, we employ four model-based metaheuristic search algorithms (MHs), namely the Crow Search Algorithm (CSA), Particle Swarm Optimizer (PSO), Gray Wolf Optimizer (GWO), and Dandelion Optimizer (DO), to estimate the parameters of stock market prices models. The data utilized in our experiments are extracted from the widely recognized stock index of Standard & Poor's 500 (S&P 500), that serves as a representative benchmark for the United States stock market. Our findings demonstrate that the CSA outperforms other MHs by providing the best combination of parameters for modeling stock market prices. The optimized parameters for the CSA model yielded Variance-Account-For (VAF) values of 97.846% in the training set and 93.483% in the testing set. This suggests that CSA offers promising capabilities for enhancing the accuracy and effectiveness of stock market forecasting models. © (2024), (Research Institute of Intelligent Computer Systems). All rights reserved.

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.

Epidermolysis bullosa acquisita (EBA) represents a big challenge as a rare skin disorder, with no established markers for early detection for patients. Moreover, as a rare disease, it is extremely difficult to acquire good number of patient sample to diagnose accurately with high confidence. EBA has many biomarkers very similar to other bullosa diseases and needs specific clinical expertise to detect it using immunofluorescence microscopy. In this study, we introduce a deep learningbased method, EBAnet, that leveraged Convolutional Neural Network (CNN) based model for the detection of EBA based on Direct immunofluorescence (DIF) microscopy image. The proposed EfficientNet-based model achieved 97.3% sensitivity, 96.1% precision, and 96.7% accuracy in distinguishing EBA from other class and outperformed the existing model for the same purpose. GradCAM based class activation map also highlighted the important region of the DIF images that was focused by the proposed model leveraging the explainability of the model. We believe, EBAnet will add value in the early and accurate detection of EBA, addressing a critical need in clinical practice.

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.

Obstructive Sleep Apnea (OSA) is a prevalent health issue affecting 10-25% of adults in the United States (US) and is associated with significant economic consequences. Machine learning methods have shown promise in improving the efficiency and accessibility of OSA diagnoses, thus reducing the need for expensive and challenging tests. A comparative analysis of Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosting (GB), Gaussian Naive Bayes (GNB), Random Forest (RF), and K-Nearest Neighbors (KNN) algorithms was conducted to predict Obstructive Sleep Apnea (OSA). To improve the predictive accuracy of these models, Random Oversampling was applied to address the imbalance in the dataset, ensuring a more equitable representation of the minority class. Patient demographics, including age, sex, height, weight, BMI, neck circumference, and gender, were employed as predictive features in the models. The RFC provided outstanding training and testing accuracies of 87% and 65%, respectively, and a Receiver Operating Characteristic (ROC) score of 87%. The GBC and SVM classifiers also demonstrated good performance on the test dataset. The results of this study show that machine learning techniques may be effectively used to diagnose OSA, with the Random Forest Classifier demonstrating the best results.

Background: In the United States, chronic obstructive pulmonary disease (COPD) is a significant cause of mortality. As far as we know, it is a chronic, inflammatory lung condition that cuts off airflow to the lungs. Many symptoms have been reported for such a disease: breathing problems, coughing, wheezing, and mucus production. Patients with COPD might be at risk, since they are more susceptible to heart disease and lung cancer. Methods: This study reviews COPD diagnosis utilizing various machine learning (ML) classifiers, such as Logistic Regression (LR), Gradient Boosting Classifier (GBC), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Random Forest Classifier (RFC), K-Nearest Neighbors Classifier (KNC), Decision Tree (DT), and Artificial Neural Network (ANN). These models were applied to a dataset comprising 1603 patients after being referred for a pulmonary function test. Results: The RFC has achieved superior accuracy, reaching up to 82.06% in training and 70.47% in testing. Furthermore, it achieved a maximum F score in training and testing with an ROC value of 0.0.82. Conclusions: The results obtained with the utilized ML models align with previous work in the field, with accuracies ranging from 67.81% to 82.06% in training and from 66.73% to 71.46% in testing.

Meta-heuristic optimization algorithms have become widely used due to their outstanding features, such as gradient-free mechanisms, high flexibility, and great potential for avoiding local optimal solutions. This research explored the grey wolf optimizer (GWO) to find the ideal configuration for a six-element Yagi–Uda antenna. The GWO algorithm adjusted the lengths of the antenna wires and the spacings between them. The goal was to maximize the antenna’s ability to transmit signals (throughput gain). Optimal antenna selection relies on various parameters, including gain, bandwidth, impedance matching, frequency, side-lobe levels, etc. The optimization of a six-element Yagi–Uda antenna presents a challenging engineering design problem due to its multimodal and nonlinear nature. Achieving optimal performance hinges on the intricate interplay between the lengths of the constituent elements and the spacing configurations. To this end, a multiobjective function was adopted to design this antenna. The performance of several meta-heuristic algorithms, including genetic algorithms, biogeography-based optimization, simulated annealing, and grey wolf optimizer, was compared. The GWO-based approach has performed better than its competitors. This optimized antenna design based on GWO reported a gain of 14.21 decibel. Therefore, the GWO-based method optimizes antennas that can be further investigated for other antenna design problems.

Across three online studies, we examined the relationship between the Fear of Missing Out (FoMO) and moral cognition and behavior. Study 1 (N = 283) examined whether FoMO influenced moral awareness, judgments, and recalled and predicted behavior of first-person moral violations in either higher or lower social settings. Study 2 (N = 821) examined these relationships in third-person judgments with varying agent identities in relation to the participant (agent = stranger, friend, or someone disliked). Study 3 (N = 604) examined the influence of recalling activities either engaged in or missed out on these relationships. Using the Rubin Causal Model, we created hypothetical randomized experiments from our real-world randomized experimental data with treatment conditions for lower or higher FoMO (median split), matched for relevant covariates, and compared differences in FoMO groups on moral awareness, judgments, and several other behavioral outcomes. Using a randomization-based approach, we examined these relationships with Fisher Tests and computed 95% Fisherian intervals for constant treatment effects consistent with the matched data and the hypothetical FoMO intervention. All three studies provide evidence that FoMO is robustly related to giving less severe judgments of moral violations. Moreover, those with higher FoMO were found to report a greater likelihood of committing moral violations in the past, knowing people who have committed moral violations in the past, being more likely to commit them in the future, and knowing people who are likely to commit moral violations in the future.

Establishing an optimal datacenter selection policy within the cloud environment is paramount to maximize the performance of the cloud services. Service broker policy governs the selection of datacenters for user requests. In our research, we introduce an innovative approach incorporating the genetic algorithm with service broker policy to assist cloud services in identifying the most suitable datacenters for specific userbases. The effectiveness of our proposed genetic algorithm was rigorously evaluated through experiments conducted on CloudAnalyst platform. The results clearly indicate that our proposed algorithm surpasses existing service broker policies and previous research works done in this field in terms of reducing response time and data processing time. The results analysis validates its efficacy and potential for enhancing cloud service performance and reducing the cost of overall cloud infrastructure.

This study developed a framework for predicting usability factors through an understanding of how cognitive traits relate to human interaction with a computer system. Specifically, this study examined the relationship of field-independence, spatial visualization, logical reasoning, and integrative reasoning to interaction process and outcome. The research hypothesis was tested through correlation to determine the relationships among variables. As a post hoc analysis, multiple regression analysis was used to examine the predictive power of four cognitive variables on interaction outcome. The results of the study emphasize the importance of considering cognitive variables as important predictors to human interaction process and outcome. © 2024 IEEE.

Scheduling periodic real-time tasks on multiple periodic resources is an emerging research issue in the real-time scheduling community and has drawn increased attention over the last few years. This paper studies a sub-category of the scheduling problem which focuses on scheduling a periodic task on multiple periodic resources where none of these resources have sufficient capacity to support the task. Instead of splitting the task into sub-tasks, which is not always practical in real systems, we integrate resources together to jointly support the task. First, we develop a method to integrate two fixed but arbitrary pattern periodic resources into an equivalent periodic resource. Second, for two periodic resources with unknown but fixed resource occurrence patterns, we give the lower and upper bounds of the available time provided by an integrated periodic resource within a period. Third, we present theoretical and empirical analysis on the schedulability of a non-splittable periodic task on two periodic resources and their integrated periodic resource.

In this paper, we address the resource and virtual machine instance hour minimization problem for directed-acyclic-graph based deadline constrained applications deployed on computer clouds. The allocated resources and instance hours on computer clouds must: (1) guarantee the satisfaction of a deadline constrained application's end-to-end deadline; (2) ensure that the number of virtual machine (VM) instances allocated to the application is minimized; (3) under the allocated number of VM instances, determine application execution schedule that minimizes the application's makespan; and (4) under the decided application execution schedule, determine a VM operation schedule, i.e., when a VM should be turned on or off, that minimizes total VM instance hours needed to execute the application. We first give lower and upper bounds for the number of VM instances needed to guarantee the satisfaction of a deadline constrained application's end-to-end deadline. Based on the bounds, we develop a heuristic algorithm called minimal slack time and minimal distance (MSMD) algorithm that finds the minimum number of VM instances needed to guarantee the application's deadline and schedules tasks on the allocated VM instances so that the application's makespan is minimized. Once the application execution schedule and the number of VM instances needed are determined, the proposed VM instance hour minimization (IHM) algorithm is applied to further reduce the instance hours needed by VMs to complete the application's execution. Our experimental results show that the MSMD algorithm can guarantee applications' end-to-end deadlines with less resources than the HEFT [32], MOHEFT [16], DBUS [9], QoS-base [40] and Auto-Scaling [25] heuristic scheduling algorithms in the literature. Furthermore, under allocated resources, the MSMD algorithm can, on average, reduce an application's makespan by 3.4 percent of its deadline. In addition, with the IHM algorithm we can effectively reduce the application's execution instance hours compared with when IHM is not applied.