Hybrid Artificial Intelligence Research Articles

AI-Driven Open Source Intelligence in Cyber Defense: A Double-edged Sword for National Security

This study explores the dual implications of Artificial Intelligence (AI)-driven Open Source Intelligence (OSINT) in enhancing cyber defense capabilities. Using publicly available datasets, including IBM X-Force breach metrics, MITRE ATT&CK adversarial tactics, GDPR privacy violations, AI-driven phishing incidents, and case-specific data from the Colonial Pipeline ransomware attack and Russia-Ukraine conflict, the research employs multivariate regression, logistic regression, and K-Means clustering. The findings indicate that AI investments improve detection time (-0.68), accuracy (+2.09), and resolution rates (+1.55) with statistical significance (p < 0.001). However, risks associated with algorithmic opacity, weak regulatory frameworks, and reactive AI systems pose ethical and operational challenges. Clustering reveals variability in AI applications, with optimized systems achieving 95.2% detection rates and 5.5-hour response times. Recommendations include investing in scalable tools, strengthening regulations, fostering public-private collaborations, and enhancing reactive AI oversight. The results highlight AI’s transformative potential in cyber defense while emphasizing the need for ethical and regulatory alignment. Future directions include testing these models in diverse operational environments to validate effectiveness and exploring hybrid AI approaches to balance proactive and reactive capabilities, ensuring robust and adaptive defense mechanisms.

AI For Defect Detection in Additive Manufacturing: Applications In Renewable Energy And Biomedical Engineering

Defect detection in Additive Manufacturing (AM) is a critical aspect of ensuring product quality, particularly in industries such as renewable energy and biomedical engineering, where reliability and precision are paramount. This study conducted a systematic review of 152 peer-reviewed articles, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, to analyze the adoption of Artificial Intelligence (AI) techniques in defect detection within AM processes. The review revealed that machine learning (ML) and deep learning (DL) techniques, such as Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), are widely employed for identifying common defects like porosity, delamination, and dimensional inaccuracies. Hybrid AI models, integrating ML and DL, demonstrated superior performance in detecting complex, multi-dimensional defects across various AM applications. Additionally, the integration of multimodal data, including thermal imaging, acoustic signals, and optical measurements, was found to improve defect detection rates by an average of 22%, enhancing the robustness and accuracy of AI models. The study also identified significant challenges, including dataset scarcity and annotation inconsistencies, which limit the generalizability and scalability of AI solutions. Comparative analyses further highlighted the distinct advantages of tailored AI approaches for specific applications, with renewable energy and biomedical engineering being key focus areas. This review underscores the transformative potential of AI in advancing defect detection in AM, providing a comprehensive understanding of its capabilities, challenges, and implications for high-stakes manufacturing industries.

An Intelligent Approach for Early and Accurate Predication of Cardiac Disease Using Hybrid Artificial Intelligence Techniques.

This study proposes a new hybrid machine learning (ML) model for the early and accurate diagnosis of heart disease. The proposed model is a combination of two powerful ensemble ML models, namely ExtraTreeClassifier (ETC) and XGBoost (XGB), resulting in a hybrid model named ETCXGB. At first, all the features of the utilized heart disease dataset were given as input to the ETC model, which processed it by extracting the predicted probabilities and produced an output. The output of the ETC model was then added to the original feature space by producing an enriched feature matrix, which is then used as input for the XGB model. The new feature matrix is used for training the XGB model, which produces the final result that whether a person has cardiac disease or not, resulting in a high diagnosis accuracy for cardiac disease. In addition to the proposed model, three other hybrid DL models, such as convolutional neural network + recurrent neural network (CNN-RNN), convolutional neural network + long short-term memory (CNN-LSTM), and convolutional neural network + bidirectional long short-term memory (CNN-BLSTM), were also investigated. The proposed ETCXGB model improved the prediction accuracy by 3.91%, while CNN-RNN, CNN-LSTM, and CNN-BLSTM enhanced the prediction accuracy by 1.95%, 2.44%, and 2.45%, respectively, for the diagnosis of cardiac disease. The simulation outcomes illustrate that the proposed ETCXGB hybrid ML outperformed the classical ML and DL models in terms of all performance measures. Therefore, using the proposed hybrid ML model for the diagnosis of cardiac disease will help the medical practitioner make an accurate diagnosis of the disease and will help the healthcare society decrease the mortality rate caused by cardiac disease.

Text‐to‐Haptics: Enhancing Multisensory Storytelling through Emotionally Congruent Midair Haptics

In multisensory storytelling, the integration of touch, sound, speech, and visual elements plays a crucial role in enhancing the narrative immersion and audience engagement. In light of this, this article presents a scalable and intelligent hybrid artificial intelligence (AI) method that uses emotional text analysis for deciding when and what midair haptics to display alongside audiovisual content generated by latent stable diffusion methods. Then, a user study involving 40 participants is described, the results of which suggest that the proposed approach enhances the audience level of engagement as they experience a short AI‐generated multisensory (audio–visual–haptic) story.

Application of Carrera Unified Formulation and Hybrid AI-Composition with CNN and ReliefF feature selection: presenting some useful suggestions for improving the stability of football and sport equipment via advanced nanocomposites

This study explores the dynamic deflection and vibrational analysis of a spherical shell, modeled as a football game ball, reinforced with graphene platelet nanocomposites (GPLs). The analysis leverages the Carrera Unified Formulation (CUF) for accurate and efficient modeling of the mechanical behavior of the shell under dynamic loads. CUF’s flexibility in adapting to complex geometries and material properties is utilized to represent the heterogeneous reinforcement of GPLs within the spherical shell structure. To enhance the reliability of the computational results, a hybrid artificial intelligence (AI) framework is implemented for result verification. This framework integrates Convolutional Neural Networks (CNNs) for spatial data representation with ReliefF feature selection to identify and prioritize influential variables. The hybrid AI system ensures robust predictive modeling, addressing the high-dimensional nature of the problem domain. The study also delves into the implications of graphene reinforcement on the ball’s performance, focusing on factors such as deformation under load, vibrational response, and stability thresholds. The results indicate that GPL reinforcement significantly improves the dynamic stability and stiffness of the spherical shell. Comparative analyses validate the efficiency of the CUF-based computational approach through mathematics benchmarks and AI-verified predictions. This interdisciplinary work highlights the potential of combining advanced computational mechanics, nanomaterials, and AI-driven verification in optimizing dynamic stability for applications in sports engineering and beyond.

Enhancing Sensitivity of Point-of-Care Thyroid Diagnosis via Computational Analysis of Lateral Flow Assay Images Using Novel Textural Features and Hybrid-AI Models.

Lateral flow assays are widely used in point-of-care diagnostics but face challenges in sensitivity and accuracy when detecting low analyte concentrations, such as thyroid-stimulating hormone biomarkers. This study aims to enhance assay performance by leveraging textural features and hybrid artificial intelligence models. A modified Gray-Level Co-occurrence Matrix, termed the Averaged Horizontal Multiple Offsets Gray-Level Co-occurrence Matrix, was utilised to compute the textural features of the biosensor assay images. Significant textural features were selected for further analysis. A deep learning Convolutional Neural Network model was employed to extract features from these textural features. Both traditional machine learning models and hybrid artificial intelligence models, which combine Convolutional Neural Network features with traditional algorithms, were used to categorise these textural features based on the thyroid-stimulating hormone concentration levels. The proposed method achieved accuracy levels exceeding 95%. This pioneering study highlights the utility of textural aspects of assay images for accurate predictive disease modelling, offering promising advancements in diagnostics and management within biomedical research.

Improved convergence speed using hybrid AI for TD EM modeling in power electronics

This paper presents a time-domain (TD) approach based on hybrid artificial intelligence (AI) to speed up convergence of radiating sources characterization in power electronics. To obtain a representative equivalent model of device under test, a dedicated optimization framework has been developed in TD using a particle swarm optimization (PSO) toolbox. In addition, for elementary feature extraction, a pseudo-Zernike moment invariant (PZMI) descriptor has been defined. Finally, with the aim of identifying remaining dipole parameters and classification problems, artificial neural networks (ANN) have been implemented. A coupling of TD electromagnetic (EM) inverse method based on a PSO algorithm along with PZMI and ANN application has been investigated and applied to a real test case. Experimental measurements have been conducted using the near-field scanning technique above an alternating current (AC)/direct current (DC) converter. Obtained results are discussed based on a comparison between measured and estimated EM field distributions using both the hybrid AI method and a conventional TD inverse method based on genetic algorithms (GA) only. This study confirms that, compared with those given by non-hybrid method, the proposed algorithm further improves the convergence speed while maintaining high accuracy. Hence, the present work offers an impressive perspective for radiated emissions characterization using hybrid AI algorithms.

A novel energy efficient data gathering algorithm for wireless sensor networks using artificial intelligence

Energy efficiency is challenging task in wireless sensor network (WSN), it is the main barrier in extending network lifespan. In WSN, maximum energy is wasted during data gathering, hence energy efficient algorithms using artificial intelligence can be designed, that preserves energy while data gathering. Thus, our proposed methodology, A novel energy efficient data gathering algorithm using artificial intelligence for wireless sensor networks (NDGAI), uses novel artificial intelligence algorithms and addresses issue of energy consumption while gathering data. In our proposed work, mobile element is utilized to gather information from sensor nodes in the clusters, formed using amended-expectation-maximization. Each cluster should have a cluster leader and a virtual-point. These cluster leaders are formed utilizing fuzzy logic technique. Virtual-points are formed in the range of cluster leader, only when cluster leader has data. The mobile element reaches virtual point by taking the optimal path, that determined by the hybrid artificial intelligence algorithms, such as artificial-bee-colony (ABC) technique and particle swarm optimization (PSO) algorithms. Thus, by properly performing clustering, cluster leader selection, virtual-point selection and optimal path determination, lead to improved network lifetime and energy saving while gathering the data. Results are simulated and compared with scalable gridbased data gathering algorithm for environmental monitoring wireless sensor networks (SGBDN) and proposed algorithm performs better.

Integrative Computational Intelligence Techniques for Insomnia and Sleep Stage Recognition from ECG Data

Abstract—Insomnia is a common sleep disorder that can lead to serious health problems, including cardiovascular issues. Early and accurate detection is essential for effective treatment, but traditional diagnostic methods are often costly, time-consuming, and prone to human error. To address these challenges, this paper introduces a novel hybrid artificial intelligence (AI) approach for automatic insomnia detection using electrocardiogram (ECG) signals. The method utilizes heart rate variability (HRV) and power spectral density (PSD) analysis in various classification scenarios, including subject- based classification (normal vs. insomnia), sleep stage-based classification (REM vs. wake), and a combined classification approach using both subject and sleep stage features. Ensemble learning techniques, such as random forest (RF) and decision tree (DT) classifiers, are used in the first two scenarios, while linear discriminant analysis (LDA) is applied in the combined classification. The approach includes ECG signal collection, HRV feature extraction, PSD estimation, and classification. Evaluation using the publicly available PhysioNet dataset shows strong performance across all scenarios, with high sensitivity, specificity, and accuracy. The combined classification scenario, in particular, achieves the highest detection accuracy with LDA. Keywords—HRV Extraction, Sleep Stage, Ensemble, LDA

Enhancing pile-bearing capacity estimation through hybrid artificial intelligence models using K-nearest neighbors approach augmented with northern goshawk and beluga whale optimization techniques

Enhancing pile-bearing capacity estimation through hybrid artificial intelligence models using K-nearest neighbors approach augmented with northern goshawk and beluga whale optimization techniques

Python-Based Hybrid Ai Models For Real-Time Grid Stability Analysis In Solar Energy Networks

The growing reliance on renewable energy, particularly solar power, presents significant challenges to grid stability due to the variability and intermittency of energy generation. Hybrid Artificial Intelligence (AI) models have emerged as a transformative solution, integrating multiple AI techniques to address these challenges effectively. This study systematically reviewed 130 peer-reviewed articles, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a transparent and rigorous process. The review highlights how hybrid AI models, by combining neural networks, reinforcement learning, evolutionary algorithms, and advanced architectures like transformers, achieve superior performance in predictive modeling, fault detection, load balancing, and scalability. Predictive modeling for solar power variability saw up to a 30% improvement in mean absolute error (MAE), while fault detection systems exhibited enhanced precision and recall, diagnosing multiple simultaneous faults in real time. Hybrid AI models also excel in load balancing and demand forecasting, reducing energy wastage by 20% and enabling dynamic resource allocation. Furthermore, their scalability and adaptability make them ideal for large-scale, distributed energy networks, addressing the complexities of modern smart grids. Despite these advancements, persistent challenges such as real-time data integration, standardization, and deployment barriers remain. The study underscores the need for future research to address these limitations through innovations like federated learning and decentralized AI frameworks. This comprehensive review demonstrates the critical role of hybrid AI in advancing grid stability, optimizing renewable energy integration, and paving the way for sustainable, resilient energy infrastructures.

Artificial intelligence and machine learning at various stages and scales of process systems engineering

AbstractWe review the utility and application of artificial intelligence (AI) and machine learning (ML) at various process scales in this work, from molecules and reactions to materials to processes, plants, and supply chains; furthermore, we highlight whether the application is at the design or operational stage of the process. In particular, we focus on the distinct representational frameworks employed at the various scales and the physics (equivariance, additivity, injectivity, connectivity, hierarchy, and heterogeneity) they capture. We also review AI techniques and frameworks important in process systems, including hybrid AI modelling, human‐AI collaborations, and generative AI techniques. In hybrid AI models, we emphasize the importance of hyperparameter tuning, especially in the case of physics‐informed regularization. We highlight the importance of studying human‐AI interactions, especially in the context of automation, and distinguish the features of human‐complements‐AI systems from those of AI‐complements‐human systems. Of particular importance in the AI‐complements‐human framework are model explanations, including rule‐based explanation, explanation‐by‐example, explanation‐by‐simplification, visualization, and feature relevance. Generative AI methods are becoming increasingly relevant in process systems engineering, especially in contexts that do not belong to ‘big data’, primarily due to the lack of high quality labelled data. We highlight the use of generative AI methods including generative adversarial networks, graph neural networks, and large language models/transformers along with non‐traditional process data (images, audio, and text).

Machine Learning Based Decision Support System for the Diagnosis of Breast Cancer

Breast cancer is among the most prevalent diseases encountered among women worldwide. Early diagnosis of breast cancer is crucial for the treatment of the disease. Detecting the disease at an early stage prevents deaths resulting from the condition. Recently, computer-aided systems have been developed to ensure early-stage diagnosis and accuracy of breast cancer. Computer-aided systems developed with machine learning approaches significantly contribute to the process of diagnosing breast cancer. The aim of this study is to propose a new classification system based on machine learning algorithms developed for the diagnosis of breast cancer. In this study, sub-data sets were created by reducing features, and data cleaning processes were applied. After these procedures, stages such as feature selection and feature extraction were applied. In this study, classification processes such as Ensemble, k- Nearest Neighbors (kNN), Support Vector Machines (SVMs), and Hybrid Artificial Intelligence were used in line with machine learning. With the obtained results, a Breast Cancer diagnosis algorithm was created. Performance evaluation criteria such as accuracy rate, specificity, sensitivity, kappa number and F-Measure were applied to the created algorithms. In the results obtained in this study, the highest accuracy rate was found to be 99.3% with the Ensemble method, the highest specificity rate was 98.7% with the Ensemble method, and the highest sensitivity rate was found to be 100% with many methods. In light of these results, it was observed that the machine learning algorithms used in this study, implemented in the Matlab environment, were effective. Consequently, it was proven that higher accuracy, specificity, and sensitivity rates can be found with different machine learning techniques. This also demonstrates that the study in our article is a reliable one in detecting diseased and healthy individuals in the diagnosis of breast cancer, showing that it is a more applicable and feasible study in the healthcare field.

Hybrid Artificial Intelligence Strategies for Drone Navigation

Objective: This paper describes the development of hybrid artificial intelligence strategies for drone navigation. Methods: The navigation module combines a deep learning model with a rule-based engine depending on the agent state. The deep learning model has been trained using reinforcement learning. The rule-based engine uses expert knowledge to deal with specific situations. The navigation module incorporates several strategies to explain the drone decision based on its observation space, and different mechanisms for including human decisions in the navigation process. Finally, this paper proposes an evaluation methodology based on defining several scenarios and analyzing the performance of the different strategies according to metrics adapted to each scenario. Results: Two main navigation problems have been studied. For the first scenario (reaching known targets), it has been possible to obtain a 90% task completion rate, reducing significantly the number of collisions thanks to the rule-based engine. For the second scenario, it has been possible to reduce 20% of the time required to locate all the targets using the reinforcement learning model. Conclusions: Reinforcement learning is a very good strategy to learn policies for drone navigation, but in critical situations, it is necessary to complement it with a rule-based module to increase task success rate.

Artificial Intelligence in Logistics Optimization with Sustainable Criteria: A Review

In recent years, the integration of artificial intelligence (AI) into logistics optimization has gained significant attention, particularly concerning sustainability criteria. This article provides an overview of the diverse AI models and algorithms employed in logistics optimization, with a focus on sustainable practices. The discussion covers several techniques, including generative models, machine learning methods, metaheuristic algorithms, and their synergistic combinations with traditional optimization and simulation methods. By employing AI capabilities, logistics stakeholders can enhance decision-making processes, optimize resource utilization, and minimize environmental impacts. Moreover, this paper identifies and analyzes prominent challenges within sustainable logistics, such as reducing carbon emissions, minimizing waste generation, and optimizing transportation routes while considering ecological factors. Furthermore, the paper explores emerging trends in AI-driven logistics optimization, such as the integration of real-time data analytics, blockchain technology, and autonomous systems, which hold immense potential for enhancing efficiency and sustainability. Finally, the paper outlines future research directions, emphasizing the need for further exploration of hybrid AI approaches, robust optimization frameworks, and scalable solutions that accommodate dynamic and uncertain logistics environments.

Biomass chemical exergy estimation using novel intelligent tool: application to biorefinery sustainability

ABSTRACT In response to the growing importance of sustainable energy and environmental considerations in biorefinery technologies, this study introduces a novel hybrid artificial intelligence approach for predicting biomass chemical exergy. The proposed least-squares support vector machine method based on an imperialist competitive algorithm (ICA-LSSVM) combines composition and energy analysis methods to model the chemical energy of biomass accurately. Extensive data were collected to train the model, resulting in a high-precision prediction with a coefficient of determination (R2) of 0.999 and a root mean square error (RMSE) of 30.3. The main point of this study is the development of this accurate model to determine the chemical energy, and also different forms of comparison are used to prove this issue. The sensitivity analysis highlights the direct impact of carbon (C%), hydrogen (H%), and oxygen (O%) percentages on biomass chemical exergy, while nitrogen (N%) and sulfur (S%) exhibit an inverse relationship. The model outperforms existing correlations in the literature, emphasizing its accuracy and practicality for biomass chemical exergy prediction, and contributing to both laboratory and industrial applications. The study emphasizes the model’s real-data reliance and suggests further exploration with an expanded dataset for enhanced industrial applicability.

Stacking Ensemble Artificial Intelligence Model for Heart Disease Diagnosis

Personalized treatment plans, predictive analytics, and artificial intelligence (AI)-driven diagnostics are becoming more and more popular as a way to improve decision-making, expedite operations, and improve patient care. But there are still a number of substantial barriers to overcome, which includes but not limited to issues with user adoption, trust, prejudice, and fairness brought on by resistance from healthcare providers and a lack of confidence in the system's recommendations. To overcome these challenges and realize the full potential of AI-driven solutions, the system's accuracy and safety through meticulous testing and validation of AI algorithms becomes indispensable. This research provides a hybrid AI model that blends three base models with a Meta model in order to diagnose heart disease effectively. The essence is to revalidate the existing AI diagnostic models for cardiac diseases diagnostics and to tackle the concerns impeding the full utilization of the available AI diagnostic system. The study makes use of each model's advantages by merging these various and complimentary algorithms into a stacking ensemble model to create a diagnostic system that is more potent. Using publicly available heart disease data, the model performs remarkably well; it achieves 89% accuracy, 85% recall (sensitivity), 92% specificity, and 89% precision. This hybrid model's performance and proven efficacy are expected to boost trust in the system's recommendations and encourage broader implementation in clinical practice.

Hybrid Artificial Intelligence Techniques for Enhanced Electricity Outage Prediction and Management in Distribution Networks

Hybrid Artificial Intelligence Techniques for Enhanced Electricity Outage Prediction and Management in Distribution Networks

Towards a neuro-symbolic cycle for human-centered explainability

Deep learning is being very successful in supporting humans in the interpretation of complex data (such as images and text) for critical decision tasks. However, it still remains difficult for human experts to understand how such results are achieved, due to the “black box” nature of the deep models used. In high-stake decision making scenarios such as the interpretation of medical imaging for diagnostics, such a lack of transparency still hinders the adoption of these techniques in practice. In this position paper we present a conceptual methodology for the design of a neuro-symbolic cycle to address the need for explainability and confidence (including trust) of deep learning models when used to support human experts in high-stake decision making, and we discuss challenges and opportunities in the implementation of such cycle as well as its adoption in real world scenarios. We elaborate on the need to leverage the potential of hybrid artificial intelligence combining neural learning and symbolic reasoning in a human-centered approach to explainability. We advocate that the phases of such a cycle should include i) the extraction of knowledge from a trained network to represent and encode its behaviour, ii) the validation of the extracted knowledge through commonsense and domain knowledge, iii) the generation of explanations for human experts, iv) the ability to map human feedback into the validated representation from i), and v) the injection of some of this knowledge in a non-trained network to enable knowledge-informed representation learning. The holistic combination of causality, expressive logical inference, and representation learning, would result in a seamless integration of (neural) learning and (cognitive) reasoning that makes it possible to retain access to the inherently explainable symbolic representation without losing the power of the deep representation. The involvement of human experts in the design, validation and knowledge injection process is crucial, as the conceptual approach paves the way for a new human–ai paradigm where the human role goes beyond that of labeling data, towards the validation of neural-cognitive knowledge and processes.

A hybrid deep learning skin cancer prediction framework

Skin cancer, a critical health concern, necessitates accurate early detection and classification to mitigate its impact. However, the limited availability of medical datasets and the challenge of optimizing learnable parameters in deep learning strategies for medical images pose significant hurdles. To address these challenges, we propose an innovative solution—a hybrid artificial intelligence (AI) framework for skin cancer prediction. This framework consists of two pivotal steps: firstly, a comprehensive skin cancer dataset is curated by combining diverse public datasets encompassing multiple disease types. This facilitates robust weight tuning of Convolutional Neural Networks (CNNs) by enhancing the diversity of the learning process. Subsequently, the optimization of deep learning is meticulously executed through the artificial Bee Colony (ABC) strategy, effectively mitigating the potential adverse effects of initial parameter randomness on AI model performance. Rigorous evaluation of the hybrid framework against eight prominent CNN models, including DenseNet121, InceptionResNetV2, and others, underscores its superior predictive capabilities. The optimized hybrid framework achieves exceptional results, boasting an accuracy of 93.04%, recall of 92.0%, precision of 93.0%, and an impressive F1-score of 93.12%. In summary, our proposed hybrid solution demonstrates a substantial advancement in multi-disease skin cancer classification. It outperforms existing AI models and holds promise as a pragmatic smart solution with far-reaching implications.