Journal of Artificial Intelligence and Technology
The Journal of Artificial Intelligence and Technology is a Gold Open Access and peer-reviewed journal that publishes articles on the theoretical, technological, and experimental aspects of artificial intelligence and related studies. The journal aims to be a high-level international academic journal that provides a prestigious platform for researchers and practitioners in the focused artificial intelligence community. Frequency: Continuous Editor-in-Chief: Yinong Chen, Arizona State University, USA CiteScore 2025: 10.1 | SJR 2025: 0.722 Submission to first decision: 59 days Acceptance to publication: 12 days Acceptance rate: 25% ISSN: 2766-8649 EISSN:27668649 (EI Compendex) © 2025 Intelligence Science and Technology Press Inc.
Intelligence Science and Technology Press
Paper Drone Design Based on Pose Estimation Technology
Paper drones, as a low-cost green new type of drone, have played a role in various fields. However, traditional pose estimation methods have high requirements for cost and environment, and they are not suitable for paper drones. Therefore, in the context of artificial intelligence, a data augmentation method and vision-based pose estimation technology are proposed using deep learning algorithms. This technology is used to achieve accurate pose estimation and effective motion control of paper drones, and the effectiveness of this technology is verified. The experimental results show that the highest accuracy achieved by using data augmentation methods is 96.31%, and it can enhance the generalization performance of the algorithm. When using the technology proposed in the study for pose estimation, the average errors of roll angle, pitch angle, and yaw angle are 0.07°, 0.18°, and 0.31°, respectively. When using the technology proposed in the study for motion control, the flight path is closest to the specified path. Research can effectively improve the pose estimation and motion control performance of paper drones, providing novel methods and ideas for the design of paper drones, which is of great significance for promoting the intelligent development of drone technology.
Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
A Bibliometric-Scoping Review of Machine Learning and Metaheuristics in Optimization
This study adopts a bibliometric-scoping approach to review recent publications introducing metaheuristics (MHs) hybridized with machine learning (ML), referred to as MH-ML and with other MHs (MH-MH) for optimization. Using structured searches on Google Scholar covering January to October 2024, 119 relevant studies are identified through PRISMA-based filtering. Manual analysis is conducted to classify algorithmic combinations, publication trends, country contributions, and application domains. MH-ML research shows uneven emergence, with peaks in March and June, while MH-MH maintains more consistent development. Analysis of lead authorship shows that most publications stem from single-country affiliations, while dual-country cases are less frequent, with UAE–Egypt pairs being the most common. China leads MH-ML publications, whereas India dominates hybrid MH research, particularly in energy forecasting, logistics, and scheduling. Across the other leading countries, energy forecasting, global optimization, logistics, and scheduling emerged as the most common application areas, reflecting shared priorities in optimization research. Among publishers, Elsevier and Springer are the most active, and Cluster Computing (Springer) emerges as a leading venue for MH-MH. Convolutional neural networks and k-means are the most used ML techniques, while genetic algorithms and particle swarm optimization lead among MHs. This review also captures recent hybrid combinations that emerge in the 2024 literature, reflecting the ongoing innovation in MH-ML and MH-MH integration. By highlighting publication trends and regional research patterns, this review offers a timely foundation for assessing the evolution of hybrid optimization techniques and guiding future exploration of learning-based strategies.
Who is Feeding Whom? A Linguistic Inquiry into Human–AI Relationship
The study investigates the evolving linguistic dynamics between humans and artificial intelligence (AI) by asking the central question: Who feeds whom? The research examines two key data sources through a discourse-analytic approach: real-world human–AI interactions and AI-related discourse in public and corporate domains. Six recurring themes emerged: AI as Helper/Assistant, Human as Data Feeder, AI as Expert/Advisor, AI Anthropomorphized, Ambiguous Agency, and Human Deference to AI. This revealed a complex, bidirectional relationship shaped through language. While corporate discourse positions humans as data providers who “feed” and “train” AI, user interactions suggest a reversal, wherein AI increasingly supplies language, advice, and authority that users readily adopt. This linguistic inversion challenges traditional notions of control and authorship. The study also identifies a troubling ambiguity around agency, with users and developers attributing quasi-human traits and decision-making capacity to AI systems. Anthropomorphism and discursive deference further entrench AI as a credible, even empathetic, interlocutor. Language emerges as the mirror and engine of this relationship, shaping user expectations, reinforcing power dynamics, and influencing communicative norms. The findings highlight the urgent need for discursive transparency in AI design, critical literacy in user education, and expanded ethical frameworks that account for language as a form of soft power. Ultimately, this inquiry argues that understanding human–AI relationships requires more than technical literacy; it demands critical awareness of the linguistic structures through which these relationships are constructed, negotiated, and sustained. The metaphorical question—Who is feeding whom?—invites ongoing reflection on power, trust, and authorship in the age of AI.
Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
Bhinneka.Space: A Smart Digital Hub for Indonesia’s Character IP Ecosystem
This study offers both practical and theoretical contributions by presenting the design of an artificial intelligence (AI)-based digital system aimed at enhancing the local intellectual property (IP) ecosystem in Indonesia. Bhinneka.Space is a smart digital hub leveraging AI and cloud computing to connect diverse stakeholders including creators, investors, publishers, merchandising agencies, educational institutions, and government entities within a unified ecosystem. The platform aims to improve collaboration, increase efficiency, promote transparency, and expand monetization opportunities in the character IP industry. The development process uses the Agile Scrum methodology, allowing for iterative and adaptable improvements to the system based on user feedback. Key features include panels with Optimized Character Analytics technology, automated IP valuation, and digital copyright protection mechanisms. In addition, this study analyzes stakeholder roles and discusses barriers to technology uptake, data security concerns, and the need for sustainable monetization models. The findings suggest the Bhinneka. The space has great potential to boost growth in the Indonesian IP sector and strengthen the competitiveness of local IP on the world stage through effective technological integration and mixed cooperation.
YOLOv8-Based Student Behavior Detection in the Classroom: Introducing BAR Attention and Shape IoU Improvements to Enhance Detection Capabilities
Aiming at the problem that it is difficult to monitor students’ behaviors in real time and comprehensively in traditional classroom management, the improved YOLOv8 (you only look once 8) model was studied and utilized to achieve efficient and precise monitoring of students’ classroom behaviors. This model improves the YOLOv8 model by introducing the double-layer routing attention mechanism and the Shape intersection over union (IoU) loss function (LF). The two-layer routing attention mechanism is structured with a coarse-grained, regional-level filtering layer followed by a fine-grained, token-to-token attention layer. The first layer effectively prunes uncorrelated key–value pairs at the region level by constructing sparse region-to-region association graphs. The second layer performs detailed attention calculations within these selectively collected areas. This allows the model to focus its computational resources on features that contain the most information. The results show that the improved YOLOv8 model performs well in all aspects. Compared with other improved algorithms in the field of pose recognition in the past three years, the improved YOLOv8 model exceeds these algorithms by 2.1%, 4%, and 2.3%, respectively, in the mAP@0.5 index and has obvious advantages in the number of parameters at the same time. The ablation experiment shows that the introduction of the efficient multi-scale convolution (EMC) module can increase the average detection accuracy (DA) by 1.08%, the Shape IoU LF can increase the average DA to 95.30%, and the bidirectional attention refinement module can increase the average DA by 0.52%. The improved YOLOv8 model proposed in this study enhances DA and efficiency in student classroom behavior detection tasks. It also provides an effective solution for real-time detection of student behavior in complex classroom environments.
Feature-Optimized Intrusion Detection Based on a Hybrid Spiking Neural Network for the Internet of Things
The intrusion detection system (IDS) has gained significant attention due to its ability to enhance network utilization. However, different types of IDS approaches have been developed in traditional research that concentrate on recognizing intrusions from datasets with the help of classification. This research proposes a Lyrebird Optimization Algorithm (LOA) with Beta Hebbian Learning-based Elite Spike Neural Network (BHLESNN) for IDS classification. The LOA selects optimal features and reduces redundancy because it can explore and exploit the search space, thereby enhancing classifier performance. The usage of the beta function for spike encoding enhances temporal precision and allows a better presentation of dynamic features in network traffic. Furthermore, the network’s capability to learn temporal and spatial patterns makes it efficient in detecting IDS. The metrics, including precision, accuracy, F1-score, and recall, are assessed to show the efficiency of LOA-BHLESNN. The proposed LOA-BHLESNN achieves accuracy of 99.96%, 99.94% and 99.81% for ToN-IoT, BoT-IoT, and IoT-23 datasets, respectively, which is better than Dual Phase Feature Extraction-Conditional Tabular Generative Adversarial Networks(DPFEN-CTGAN).
Enhancing Security in Operational Technology: The Role of Multi-Factor Authentication Against Cyber Threats
As cyber threats continue to increase in sophistication, the security of Operational Technology (OT) environments has become a paramount priority for organizations in various sectors. OT systems, including Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) systems, are vital in the functioning of critical infrastructure but often lack robust security due to legacy security weaknesses. This research discusses the implementation of Multi-Factor Authentication (MFA) as a baseline strategy for enhancing the security of such systems. We recognize the distinct cybersecurity issues of OT environments, especially the use of legacy hardware that does not have contemporary security mechanisms. By suggesting an extensive framework for implementing MFA, this research offers a multi-layered system that incorporates knowledge-based, possession-based, and biometric authentication techniques. We further stress the need for role-based access control, ongoing monitoring, and user training to enhance security mechanisms. Using case studies and real-world examples, we show how MFA can be used to counter unauthorized access and increase system resilience. We present actionable recommendations for organizations wishing to deploy MFA, including mitigation strategies that reduce identified vulnerabilities to acceptable levels for critical infrastructure as a foundation of their cybersecurity approach, with the ultimate goal of safeguarding critical infrastructure and sensitive information in a hyper-connected world. Our research not only adds to the body of knowledge but also acts as a guide to deploying stringent security controls in OT networks.
Diabetes Prediction Using Hybrid Supervised and Unsupervised Techniques Based on PIMA Dataset
Diabetes prediction using machine learning remains challenging due to the limited size and inherent imbalance of available medical datasets. This paper presents a hybrid framework that blends supervised and unsupervised machine learning techniques to improve the accuracy and robustness of early diabetes prediction. The proposed framework integrates clustering, feature selection, and classification to enhance predictive performance and robustness on small-scale medical datasets, specifically the PIMA Indian Diabetes Dataset. Feature selection using Mutual Information minimizes computational complexity while maintaining discriminative power. The unsupervised clustering component groups similar patient records to reduce intra-class variability, improving class separability for the subsequent supervised learning stage. Thirteen classifiers, including Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest (RF), Neural Networks, Adaptive Boosting, Gaussian Naïve Bayesian, Quadratic Discriminant Analysis, Skope Rules, eXtreme Gradient Boosting (XGB), Gradient Boosting, Deep Neural Network, and Logistic Regression, are evaluated to compare model performance under clustered and non-clustered settings. Experimental results show that ensemble-based classifiers, particularly RF and XGB, achieve the highest accuracy, precision, recall, and area under the curve (AUC) scores across two optimized clusters, confirming that integrating clustering and feature selection substantially improves the robustness of diabetes prediction models. The results showed that the proposed framework achieved 88.5% accuracy, 0.836 precision, 0.836 recall, 0.836 f-measure, and 0.874 AUC using a RF, and 88.5% accuracy, 0.838 precision, 0.832 recall, 0.835 f-measure, and 0.873 AUC with the XGB classifier.
Development and Evaluation of a Math mCSCL Platform with Aggregated Content-Based Filtering and Guessing Detection
This study reported the development and evaluation of an intelligent math mobile computer-supported collaborative learning (mCSCL) system for solving fractions. The software included a recommender system and a guessing detector. The aggregated model of a content-based filtering (CBF) algorithm for a group was used to build the recommendation function of the software. The dice coefficient was utilized to determine the dissimilarity of the selected game settings vis-à-vis the problem space PPP. Meanwhile, the guessing detector comprised the Rasch model and computational fluency (CF). CF, in turn, had two components: response time and accuracy. All three indicators had to be flagged as true to classify a student as guessing. Fifty-five Grade 5 students from the elementary departments of four universities in Manila participated in the study. Results showed that the software was able to detect the guessing behavior of each student and provided individualized feedback to those who exhibited guessing. The software also generated recommendations (i.e., game settings), confirming the effectiveness of the CBF and dice coefficient. Content analysis revealed that the software received favorable remarks from the students, implying its relevance to their mathematics learning. However, despite these capabilities, students still exhibited under- and over-practice of certain skills. Limitations of the software were also discussed.
When Time Doesn’t Matter: Investigating the Determinants and Consequences of Showrooming Behavior in the AI-Driven Era
Artificial intelligence (AI), such as augmented reality (AR)-based virtual try-on, has transformed the retail landscape. This technology can enhance perceived usability and immersion, creating a more realistic online shopping experience. However, consumers still combine physical and digital experiences to ensure the best purchasing decisions. Consumers visit physical stores and purchase products online, a process known as showrooming. This phenomenon is an evolving challenge for both retail and academics. This research examines the influence of channel characteristics (antecedents) and situational characteristics (consumers’ time pressure) as a moderating variable on showrooming behavior phenomena and avoidance of regret as consequences. This research surveys a representative sample of 474 Indonesian cosmetics and skincare shoppers. This study uses SmartPLS 4.0 to analyze the data. The results prove that channel characteristics (sales-staff assistance), online channel convenience, and social influence positively affect showrooming intentions. Then, showrooming behavior helps consumers reduce uncertainty, positively impacting avoidance of regret as consequences. By integrating the Theory of Planned Behavior (TPB) and the Stimulus–Organism–Behavior–Consequence (SOBC) framework, this research model provides a comprehensive understanding of how external factors influence consumers’ psychological processes, which then shape their intentions and behaviors. Ultimately, showrooming behavior results in post-purchase consequences. This model integration is expected to analyze showrooming behavior in a more holistic modern retail context. The findings of this study provide practical implications for retailers in formulating more effective strategies for managing consumer behavior in an omnichannel environment.
Applying Image Processing Technology to Improve the Effectiveness of Intelligent Robotic Gloves in the Rehabilitation Treatment
This paper presents the design and development of a robotic glove system—comprising hardware, software, and control algorithms—intended for rehabilitation of patients with paralysis or limited mobility. The hardware integrates both mechanical and electrical components. Mechanically, the system utilizes a commercially available glove equipped with soft rubber joints to improve finger flexibility, and it also employs electric air pumps to deliver the necessary actuation force. The electrical circuitry is custom-designed by the authors. On the software side, the glove features a suite of goal-oriented rehabilitation exercises. Furthermore, leveraging image processing technology, the system includes a mirroring mode, allowing the healthy hand to guide the movements of the impaired hand. The authors also introduce adaptive control algorithms and targeted exercises tailored to varying levels of patient progress. To enhance user engagement—particularly for elderly patients and children—the system incorporates game-based training, reducing the monotony often associated with rehabilitation routines.
Segmentation of Brain Tumor from Magnetic Resonance Imaging Using Handcrafted Features with BOA-Based Transformer
Early brain tumor detection is crucial for improving patients’ prognosis and chances of survival. Physical analysis of brain tumor magnetic resonance imaging (MRI) images is necessary for this task. Consequently, computational techniques are required for more precise tumor diagnosis. However, evaluations of shape, volume, boundaries, size, tumor identification, segmentation, and classification remain challenging. Additionally, characteristics of cancer, such as fuzziness, complex backgrounds, and significant variations in size, shape, and intensity distribution, make accurate segmentation challenging. This work suggests a novel Optimizer-based Semantic-Aware Transformer (OSAT) for brain tumor segmentation in order to address these problems. Moreover, MRI data was manually analyzed to extract features based on texture, intensity, and other factors. The Bonobo optimization algorithm (BOA) improves SAT and increases feature representation learning capabilities with less memory and computational complexity. Several evaluation metrics were used in this work to assess performance on the three Brain Tumor Segmentation (BraTS) challenge datasets including segmentation measures. By enhancing OSAT’s performance with the addition of handcrafted features, a more reliable and broadly applicable solution was also achieved. This study may have significant applications in the field of accurate and efficient brain tumor segmentation. Future studies could examine various feature fusion techniques and incorporate additional imaging modalities to improve the efficacy of the proposed method.
The Development of a Data Protection System in Healthcare Using Deep Learning Models
This research addresses the critical challenge of cybersecurity in healthcare by evaluating the effectiveness of machine learning (ML) and deep learning (DL) models in identifying and mitigating five significant cybersecurity threats: distributed denial-of-service (DDoS), man-in-the-middle (MitM), malware, phishing, and SQL injection. The study integrates a secure hardware–software architecture utilizing WireGuard, a lightweight, modern VPN protocol that establishes encrypted tunnels between network nodes, ensuring robust data integrity, confidentiality, and authenticated communication. Two ML models, support vector machine and random forest, and four DL architectures, dense neural networks (DNNs), convolutional neural networks-long short-term memory (CNN-LSTM), and LSTM-gated recurrent unit (LSTM-GRU), are systematically trained and tested using publicly available datasets specific to each threat category. The experimental outcomes demonstrate exceptional detection capabilities for structured network threats, with DNN and CNN-LSTM achieving accuracies and F1-scores from 95% to 97.6% for DDoS and MitM threats. In malware classification, the performance of DNN and CNN maintains precision and recall above 94%. Phishing and SQL injection attacks have lower classification scores of around 82% for most models. Visual analytics, including accuracy, loss plots, and confusion matrices, provide valuable insights into the convergence behaviors and sensitivity of different architectures, highlighting the strong generalization of DNN and variability in recurrent models. Overall, this research highlights the substantial potential of DL, combined with secure communication technologies like WireGuard, in enhancing healthcare cybersecurity, while also identifying areas for further development and optimization.
My Eye AI: A Hybrid Cloud-Mobile Object Detection System for the Visually Impaired Using YOLOv11, OWL-ViT, and BLIP
My Eye AI is a hybrid cloud-mobile assistive system that delivers real-time object detection and scene description for visually impaired users. The system integrates three AI components: YOLOv11 for object detection, OWL-ViT for zero-shot open-vocabulary recognition, and Bootstrapping Language-Image Pretraining for natural-language scene captioning. Two YOLOv11 variants were trained on custom-curated datasets: the Medium model achieved mAP@0.5 = 0.443 and recall = 0.457, while the X-Large model improved to mAP@0.5 = 0.578 and recall = 0.603—reducing false negatives by 14.6 %. OWL-ViT extended detection to unseen objects with 71.4 % zero-shot accuracy. The cloud-based architecture offloads computation from the smartphone, maintaining low latency while supporting Android and iOS without special hardware. My Eye AI demonstrates measurable improvements in detection accuracy, adaptability, and real-time usability, directly benefiting visually impaired individuals through affordable, accessible mobile deployment.
Integrated Machine Learning Framework for Smart Food Systems Optimization in Aceh, Indonesia
Effective management of smart food systems in Aceh, Indonesia, requires accurate forecasting, assessment of regional vulnerability, and robust supply chain monitoring. This study presents a multistage machine learning (ML) framework integrating predictive modeling, clustering optimization, and classification to support evidence-based food management decisions. Ridge regression projected food commodity prices for 2025–2028, achieving an average mean squared error(MSE) of 43.045.946 and an average root mean squared error (RMSE) of 4.352,238, capturing trends for both staple and high-value commodities. Regional food vulnerability is evaluated using K-means clustering enhanced with simple additive weighting(SAW)-based centroid initialization, which reduces the average number of iterations to 7.5 compared to 8.7 for standard K-means. The SAW-enhanced clustering achieves an average Calinski–Harabasz score of 40.887 and an average Silhouette Score of 0.288, generating three coherent clusters: Food Secure, Food Vulnerable, and Food Insecure. Classification of regional supply chain stability using support vector machines (SVM) with radial basis function (RBF) and sigmoid kernels alongside random forest (RF) demonstrates that SVM-RBF attains 94.59% accuracy, SVM-sigmoid reaches 46%, and RF achieves a mean 10-fold cross-validation accuracy of 98.89% with low variability and F1-scores ranging from 0.985 to 0.995. By integrating predictive, clustering, and classification analyses, this framework provides actionable insights that enable policymakers to anticipate price fluctuations, identify vulnerable districts, and implement targeted interventions, thereby enhancing food security and resilience in Aceh. The proposed methodology highlights the value of combining multiple ML approaches for optimizing smart food systems in regional contexts.
Design and Application of Track and Field Sports Teaching and Training Intensity Monitoring System Based on Wireless Sensor Technology
This study designs and validates a wireless sensor network (WSN) system for the objective, continuous quantification of training intensity in track and field. The system integrates wearable inertial and heart rate sensors, transmitting data via a low-power LoRa (Long Range) network to a cloud platform. Machine learning algorithms classify activities and compute intensity metrics like PlayerLoad™ and Training Impulse (TRIMP). In a 12-week study with 45 university athletes, the system’s intensity metrics show a strong correlation with blood lactate levels (r = 0.92, p < 0.01). The experimental group using the system demonstrates a 15.3% greater improvement in performance times and a 28% reduction in non-functional overreaching compared to the control group, confirming the system’s effectiveness for data-driven training optimization.
SmartGRI Integrating Eckenrode, TOPSIS, and Borda for Reliable Sustainability Assessment Framework Decisions
This study presents SmartGRI, a systematic innovation in the form of a Group Decision Support System (GDSS) specifically designed to improve the accuracy, efficiency, and objectivity of sustainability audits based on the Global Reporting Initiative (GRI) framework. The main novelty of this study lies in integrating the first of its three multi-attribute decision-making (MADM) methods (Eckenrode, TOPSIS, and Borda) into a single collaborative digital audit platform, enabling decision-making based on evidence and consensus from various stakeholders. By involving five independent auditors and 91 GRI indicators across four reporting scenarios, SmartGRI demonstrates high predictive validity (ρ = 0.92) and strong inter-rater reliability (κ =0.84) and reduces audit duration by up to 35%. In theory, this model broadens the scope of decision support systems and sustainability audits through a technological approach that can be replicated in different institutional contexts. In practice, SmartGRI enables the digital transformation of Environmental, Social, and Governance (ESG) audits, helping organizations across all sectors to improve reporting transparency and performance accountability and reduce the risk of greenwashing. Its potential is particularly relevant for developing countries, the public sector, and companies with limited audit capacity. SmartGRI significantly strengthens inclusive, measurable, and participatory governance in global sustainability by providing efficient and adaptive audit solutions.
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by/4.0/
Selective Kernel-DenseNet for Effective Areca Plant Disease Classification
Accurate classification of Arecanut plant disease is essential for crop damage prevention, ensuring healthy yields and sustainable farming. Timely identification of diseases enables farmers to take prompt action to minimize yield losses and improve overall plantation management. However, plant diseases vary in scales from fine details like spots or small lesions to larger areas of discoloration which often leads to inaccurate performance. To address this issue, this research proposes a Selective Kernel-DenseNet (SK-DenseNet) for an efficient and accurate Arecanut plant disease classification. In traditional DenseNet, the selective kernel is incorporated to enhance feature adaptability by enabling the dynamic adjustment of receptive fields. This enhances the model’s ability to capture both large-scale patterns and fine-grained information, resulting in better feature representation. The dense connections in DenseNet assist in reusing these features effectively which enables the model to handle diseases with symptoms at varying scales. The resizing technique is applied to standardize input dimensions, and label encoding is used to convert categorical into numerical data for effective model processing. When compared with existing methods like VGG19-ViT, the proposed SK-DenseNet obtained a high accuracy of 99.99% and 98.92% on PlantVillage and Arecanut datasets, respectively.
CLSTMNet Architecture: A CNN–LSTM-Based Hybrid Deep Learning Model for DDoS Attack Detection and Mitigation in Network Security
Distributed denial-of-service (DDoS) attacks represent one of the most damaging cybersecurity threats to modern network systems. The impact of this attack causes server failure and creates complaints about service inconvenience from users, thus reducing the company’s reputation and trust; more crucially, it is the loss of revenue. Although intrusion detection systems(IDSs) and other conventional security mechanisms have been widely deployed, many advanced DDoS attacks continue to bypass these defenses due to their evolving and complex patterns. This study aims to provide a state-of-the-art strategy to identify denial-of-service (DDoS) attacks more precisely using machine learning (ML) calculations. Creation of a modern deep learning(DL) strategy identifies DDoS attacks more precisely by combining the two best DL calculations and comparing their execution by actualizing them on the most challenging dataset. This research applies a combination strategy of two DL calculations models, convolutional neural network (CNN) and long short-term memory (LSTM). These calculations are actualized on the Network Security Laboratory–Knowledge Discovery and Data Mining (NSL-KDD) dataset, which is considered the most challenging dataset for DDoS attack discovery. The results show that the modern DL strategy created in this consideration outperforms other state-of-the-art strategies in terms of precision and discovery rate. The combination of CNN and LSTM results in superior execution than either calculation alone. This implies that the modern DL strategy created in this consideration is a feasible approach to identify DDoS attacks with high precision.
Improved Feature Selection in Bacteria Detection Using Two-phase Mutation Grey Wolf Optimizer
Traditional bacterial identification methods relying on microscopic and biological analyses are often constrained by high time, cost, and labor requirements. To address these limitations, this study proposes an enhanced bacterial detection framework integrating the Two-phase Mutation Grey Wolf Optimizer (TMGWO) for feature selection. The TMGWO algorithm is employed to identify the most informative feature subsets, minimize redundancy, and improve classification performance. The proposed workflow comprises data collection, preprocessing, segmentation, feature extraction, feature selection, and classification using the K-nearest neighbor (KNN) algorithm. Experimental evaluation demonstrates that incorporating TMGWO improves classification accuracy by 6.14% and reduces execution time by 33.6 seconds compared with models that exclude feature selection. These results confirm that the TMGWO-based approach offers a robust and efficient solution for automated bacterial detection in microscopic imagery.
Validated Swin Transformer-Based Deep Learning Pipeline with Cross-Validation and McNemar’s Test for Multi-Class Hemorrhage Classification in Traumatic Brain Injury CT Scans
Traumatic brain injury (TBI) remains a major global health concern, where rapid and accurate identification of intracranial hemorrhage on computed tomography (CT) scans is essential for improving patient outcomes. Manual radiological assessment, although clinically effective, is time-consuming and subject to inter-observer variability, creating a need for reliable automated diagnostic systems. Most existing artificial intelligence (AI) studies focus on binary hemorrhage detection or limited subtype classification, leaving a gap in robust multi-class intracranial hemorrhage categorization. This study presents a validated deep learning pipeline based on the Swin Transformer—a hierarchical Vision Transformer architecture that employs windowed and shifted multi-head self-attention to capture both local and global contextual information. A balanced dataset of 60,000 CT images across six clinically relevant classes was generated through structured augmentation of a publicly available source, ensuring fairness and improved generalization. The model was optimized using AdamW, cosine annealing scheduling, and label smoothing to enhance stability. The proposed framework achieved a classification accuracy of 82.02%, outperforming the strong baseline EfficientNetV2-S model (80.17%). Statistical significance was established using McNemar’s test (p < 0.0001), and robustness was demonstrated through 5-fold cross-validation. Gradient-weighted Class Activation Mapping (Grad-CAM)++- based interpretability analysis further confirmed that the model consistently highlighted clinically meaningful hemorrhagic regions, reinforcing its diagnostic relevance. These findings establish the Swin Transformer as an effective, interpretable, and statistically validated solution for multi-class intracranial hemorrhage classification, with strong potential to support radiologists in emergency triage and improve clinical decision-making.
CropResMoE-50: A Region-Aware Mixture-of-Experts Framework for Fine-Grained Vehicle Damage Detection and Semi-Automated Annotation
Accurate detection of vehicle damages such as dents, scratches, and cracks is essential for improving the efficiency, consistency, and scalability of insurance claim assessment. Conventional inspection procedures rely heavily on manual evaluation, making them time-consuming, subjective, and costly. To address these limitations, this paper presents a three-stage progression of mixture-of-experts (MoE)–based classification models trained on the CarDD dataset, which contains 4,000 COCO-annotated vehicle damage images. The study begins with a baseline RawMoE model operating on flattened image representations, followed by ResMoE-50, which incorporates ResNet-50 for deep feature extraction. Building upon these foundations, we propose CropResMoE-50, a region-aware hybrid architecture that integrates object-level cropping with scale-specific analysis to enhance spatial localization and classification accuracy. Extensive experiments on the CarDD benchmark, together with comparisons against established baselines including ResNet-50, EfficientNet-B0, and Swin-T, demonstrate that CropResMoE-50 achieves strong performance with favorable computational efficiency (24.8M parameters, 4.13 GFLOPs, and approximately 0.006 s inference latency). The model attains 89.30% test accuracy and average precision scores of 87.70%, 93.45%, and 98.12% for small, medium, and large objects, respectively. To extend practical applicability, a semi-automated labeling pipeline integrating ChromaDB is introduced to support retrieval-augmented learning and pseudo-labeling under uncertainty. Additional validation on a real-world insurance dataset from a real-world insurance company confirms robust generalization, achieving 83.33% accuracy. Overall, the proposed framework offers a scalable, interpretable, and deployment-ready solution for automated vehicle damage assessment.
An ASR Transformer-Based Model for Kannada Speech-to-Text Transcription
This work presents a dialect-aware and noise-robust Kannada automatic speech recognition (ASR) system that bridges the gap between low-resource linguistic contexts and state-of-the-art deep learning models. We design a two-stage approach: (i) a scratch-built convolutional neural network (CNN)–Transformer hybrid trained on curated Kannada speech data with fast Fourier transform-based noise reduction and (ii) fine-tuning OpenAI’s Whisper-small model on a dialect-diverse corpus. The proposed pipeline integrates adaptive noise suppression, subword tokenization, and beam-search decoding to handle agglutinative morphology, speaker variation, and environmental noise. Extensive experiments were conducted on two datasets: the Few-shot Learning Evaluation of Universal Representations of Speech (FLEURS) multilingual Kannada subset and a curated 150-sample, Custom-collected Kannada speech dataset covering both formal and conversational speech. On the FLEURS test set (≈ 2.5 hours), our fine-tuned Whisper model achieves a word error rate (WER) of 0.15, a character error rate (CER) of 0.255, and a BLEU score of 0.912, representing a 53% relative reduction in WER and 36% reduction in CER compared to the scratch CNN–Transformer baseline. For the customized dataset, the fine-tuned Whisper model achieves a WER of 0.2311 and a CER of 0.0453, outperforming Google Speech-to-Text Application Programming Interface (API) by 16.8% (relative WER reduction) and surpassing the scratch transformer by over 70% in WER. We further evaluate robustness under dialectal variation and noisy recordings, providing detailed error analysis and computational efficiency metrics. To our knowledge, this is the first comprehensive evaluation of Whisper fine-tuning for Kannada, demonstrating its viability for real-time, edge-deployable applications in education, accessibility, and public administration.
Teaching in the Age of Generative AI: A Qualitative Analysis of Faculty Identity, Emotion, and Assessment Concerns
The rise of generative AI, particularly large language models like ChatGPT, has profoundly impacted higher education. While debates have centered on academic integrity, curriculum design, and the future of work, less attention has been paid to the affective dimensions of this shift—specifically, how university educators are emotionally experiencing and responding to these transformations. Drawing on affect theory, cognitive dissonance, identity theory, and assessment theory, this study explores the affective experiences of university teachers in response to AI’s integration into teaching and learning. Thematic analysis was conducted using Braun and Clarke’s method, and NVivo 14 was used to support the coding process for 20 faculty members’ in-depth interviews. Three core themes emerged that encapsulate faculty members’ affective experiences with AI integration in higher education. These interconnected themes are as follows: (1) Erosion of Pedagogical Identity, (2) Emotional Disruption and Cognitive Dissonance, and (3) Assessment Anxiety and Distrust. The thematic analysis reflected the profound psychological and emotional challenges educators face asthey navigate the rapid adoption of AI technologies in their teaching practices. Findings suggest that institutional response to AI must engage with the emotional labor and subjectivities of teachers, not just the technological infrastructure of learning. Ignoring the human side of AI adoption risks burnout and resistance. Recommendations made to achieve sustainable solutions include providing psychological support for identity shifts, fostering collaborative policy-making with faculty input, and balancing AI adaptability with trust-building in assessment.
Trusting ChatGPT? When a Subtle Variation in the Prompt Can Significantly Alter the Results
How much can we trust highly complex predictive models like ChatGPT? This study tests if subtle changes in prompt structuring do not produce significant variations in the classification results of sentiment polarity analysis generated by the LLM GPT-4o mini. The model classified 100.000 comments in Spanish on four Latin American presidents as positive, negative, or neutral on 10 occasions, varying the prompts each time. The experimental methodology included exploratory and confirmatory analyses to identify significant discrepancies among classifications. The results reveal that minor modifications to prompts, such as lexical, syntactic, modal, or even their lack of structure, impact the classifications. At times, the model produced undecided responses mixing categories, providing unsolicited explanations, or using languages other than Spanish. Statistical analysis using Chi-square tests confirmed significant differences in most comparisons between prompts, except in one case when linguistic structures were similar. These findings challenge the robustness and trustworthiness of large language models (LLMs) for classification tasks, highlighting their vulnerability to variations in instructions. Moreover, it was evident that the lack of structured grammar in prompts increases the frequency of hallucinations. The discussion underscores that trust in LLMs is based not only on technical performance but also on the social and institutional relationships underpinning their use.
Impact of AI-Powered Solutions on Salesforce User Experience: Leveraging Cloud Technologies
Background: Artificial intelligence intelligence (AI) and cloud computing are reshaping customer relationship management (CRM) systems, with Salesforce leading this transformation through its Einstein AI and cloud-based architecture. However, the actual impact of these innovations on user experience (UX) remains underexplored. Objective: This study investigates how AI-powered features within Salesforce, supported by cloud technologies, influence user satisfaction, ease of use, perceived usefulness, and overall UX across organizational departments. Methods: A quantitative, cross-sectional survey was conducted with 150 Salesforce users from various industries. Data were analyzed using descriptive statistics, correlation analysis, multiple linear regression, and analysis of variance (ANOVA) to assess the relationships between AI usage, system usability, cloud performance, and user satisfaction. Results: Participants rated cloud performance and AI ease of use positively. However, regression analysis revealed a significant negative relationship between perceived usefulness and user satisfaction. Other variables, including AI usage frequency and ease of use, did not significantly predict satisfaction. ANOVA showed no significant departmental differences in user satisfaction. Conclusion: AI and cloud tools offer value to Salesforce users, but satisfaction depends on transparency, usability, and alignment with user expectations. Strategic design and user-centered implementation are key for maximizing UX.
KL Grading-Based Knee Abnormality Classification Using Multi-Modal Deep Learning: KOMMCF – A Fusion of X-Ray Imaging and Clinical Biomarkers
Osteoarthritis of the knee (KOA) is a progressive musculoskeletal ailment that increasingly distresses the mobility of patients and their quality of life. Although the KL-Kellgren & Lawrence grading system is extensively used to stage osteoarthritis, it is challenging to accurately stage early osteoarthritis because of the minute changes that occur in the joint. This study accentuates a three-grade classification of severity of osteoarthritis of the human knee: Normal (KL:0), Early OA (KL:1–2), and Moderate to Severe OA (KL:3–4). We proposed an efficient multi-modal deep learning framework Knee Osteoarthritis Multi-Modal Classification Framework (KOMMCF). The framework integrates X-ray images and fourteen biomarkers obtained from the Osteoarthritis Initiative (OAI). It encompasses advanced X-ray image processing, bilateral difference features, and normalized biomarkers. An ordinal distance-aware loss function is used to address natural order of KL grades. In addition, patient-level Group K-Fold cross-validation is adopted to ensure robust testing and avoid data leakage. Although mainly trained for three-class classification, the framework was also tested for binary classification (Normal vs. Abnormal OA) and five-class classification (KL0–KL4). For the Kaggle-OAI dataset, mean cross-validation accuracies for the 2-class, 3-class, and 5-class problems were 98.67%, 90.58%, and 80.82%, respectively. Corresponding test accuracies were 98.67%, 90.58%, and 81.34%, with macro-average ROC-AUC values of 0.9993, 0.9683, and 0.94. McNemar’s statistical tests confirmed that KOMMCF significantly outperforms ResNet50, VGG16, and MobileNetV2 (p < 0.001). This work demonstrates power of combining imaging and clinical data in an AI-driven approach to knee abnormality classification, potentially supporting early diagnosis and treatment planning.
From Fragmented Knowledge to Semantic Integration: An Ontology Framework for Elderly Research in the Thai Aging Society
This study presents the development of a domain-specific ontology to structure knowledge for aging research in Thailand, in response to the increasing fragmentation across disciplines. The ontology formally represents 10 core domains, including research, policy, health, quality of life, and age-friendly cities, reflecting national research priorities while remaining compatible with established gerontological and semantic web standards. The study adopts a structured ontology engineering methodology adapted from Noy and McGuinness, encompassing scope definition, vocabulary elicitation, class hierarchy design, property specification, constraint formulation, and evaluation. Primary knowledge sources are derived from national research repositories. The resulting ontology comprises 10 top-level classes and 27 subclasses. Quality assessment is conducted through automated evaluation using the Ontology Pitfall Scanner (OOPS!) and expert-based review by three domain specialists, serving as an initial validation of structural soundness and domain coherence rather than a statistically generalizable evaluation. While the ontology provides a semantic foundation that may support ontology-driven retrieval, reasoning, and policy-oriented knowledge integration, empirical performance evaluation is beyond the scope of this study. Overall, the work contributes a transparent and context-sensitive ontology development case that may inform future semantic web applications in aging research.
A Hybrid Ensemble and Explainable AI Framework for Predictive Maintenance of Industrial Equipment
A modern industrial system with its critical machinery is very sensitive to unexpected equipment failure and may experience extensive operation interruption, danger to safety, and cost. The traditional maintenance approaches, reactive and preventative, lack intelligence and flexibility to make predictions of the failure based on real-time information, leading to failures that are expensive to fix or unnecessary maintenance. This paper proposes a hybrid ensemble predictive maintenance(PdM) framework to assist in overcoming these drawbacks by combining potent machine learning (ML) models as classifiers to PdM and SHapley Additive eXplanation (SHAP) framework to make decision-making PdM transparent and interpretable. The suggested approach is trained on actual industrial sensor data comprising multivariate time-series data such as temperature, vibration, voltage, and pressure measurements. Data are preprocessed in a powerful way with the removal of redundancy, label encoding, and scaling. The accuracy, precision, recall, F1-score, and analysis of the confusion matrix are used to evaluate each model. Strikingly, the three ensemble classifiers had 100 percent success in the detection of faults, with SHAP values having obvious key features dictating forecasts. The newness of this method is that it is potentially high-accuracy and interpretable at the same time, which is a respite from deeper or federated learning models, which are typically high-computational-load methods. The study adds a scalable, accurate, and explainable PdM framework that can be part of the new smart manufacturing.
CAAST-Net: Causality-Aware Spiking Transformer with Flask API for EEG-Based Seizure Detection
Epileptic seizure detection from electroencephalogram (EEG) signals is an essential task for real-time neurological monitoring. Traditional models face challenges with interpretability, energy efficacy, and capturing temporal causality in neural data. To address these drawbacks, this manuscript proposes a Causality-Aware Attention with Spiking Transformer Network (CAAST-Net). The frequency-domain features are extracted by fast Fourier transform (FFT) to acquire band power across five canonical EEG bands. Subsequently, the features are normalized using a Standard Scalar and transformed into spike trains by rate coding, which enables biologically inspired processing. The CAAST-Net model includes a linear projection layer, leaky integrate-and-fire (LIF) neurons and causality-aware attention module that ensures temporal consistency by facilitating signals from past to present. This model learns discriminative and temporally predictive EEG patterns with minimized computational overhead. The whole model is deployed through a Flask API for real-time seizure detection. The proposed CAAST-Net obtains a higher accuracy of 98.95% with a CHB-MIT dataset and 99.85% accuracy with a BONN dataset when compared with traditional models.
Evaluating Large Language Models for Educational Measurement Insights from Automated and Human Scoring of Language Exams
This study investigates the use of large language models (LLMs)—ChatGPT-5, Claude Opus 4.1, Gemini Advanced 2.5 Pro, DeepSeek Pro, Qwen-3 Max, and Mistral Le Chat Pro—and a locally fine-tuned LLaMA 3.3 70B Instruct model for automating assessment tasks in language education. Specifically, the study looks to examine LLM capabilities in automating assessments with authentic midterm exam sheets from a “German as a Foreign Language” (GFL) course in three different scenarios: (1) general-purpose LLM with pre-corrected samples giving a score, (2) localized grading using a fine-tuned LLaMA model and reference answer keys, and (3) manual grading with and without a visual overlay technique. Human grading, supported by a structured scoring process, remained nearly perfect in terms of accuracy and reliability, whereas the local model failed because OCR and visual input techniques did not produce usable outputs. These findings reinforce the necessity for domain-specific adaptation, the design of stronger OCR and multimodal workflows, and explainable scoring mechanisms before local AI solutions can reliably contribute to the assessment of language learning tasks in applied educational settings.
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Hybrid Deep Learning Framework for Diabetic Wound Assessment: Integrating Segmentation, Classification, and Explainable AI in Cloud-Based Telemedicine Systems
Diabetic foot ulcers (DFUs) are among the most serious complications of diabetes mellitus and frequently lead to infection, hospitalization, and lower-limb amputation. Early and accurate assessment of wound severity is therefore essential for preventing complications and supporting clinical decision-making, particularly in telemedicine settings. This study proposes a hybrid deep learning framework for automated DFU evaluation that integrates image segmentation, ordinal-aware classification, and visual interpretability. The framework employs a U-Net-based segmentation module to isolate ulcer regions, followed by an EfficientNet-B3 classifier trained with consistent rank logits to grade wound severity according to the Meggitt–Wagner scale. Gradient-weighted Class Activation Mapping (Grad-CAM) visualization is incorporated to highlight discriminative regions that influence model predictions, thereby improving clinical transparency. Experimental evaluation on a dataset of 6,500 annotated DFU images achieved an Intersection over Union of 0.861, a Dice coefficient of 0.924, a classification accuracy of 92.3%, and a macro-F1 score of 0.904, with an average inference time of under 2 s per image. These results demonstrate that the proposed hybrid pipeline enables precise, interpretable, and computationally efficient wound assessment suitable for real-time telemedicine and mobile health applications. The framework provides a scalable and explainable artificial intelligence approach that supports consistent grading and early detection in diabetic foot management.
Leveraging a graph attention network-based academic recommendation framework for Indian higher education institutions
The competitive nature of admission to the top Indian institutes on the basis of the Graduate Aptitude Test in Engineering (GATE) requires intelligent systems in the student–college matching. Conventional recommendation procedures usually do not take into consideration important aspects like cutoff differences and institutional ratings, resulting in minimal impartiality and transparency to the students. Following this approach, this paper suggests a recommendation framework using the graph attention network (GAT), where students and colleges are treated as graph nodes, and compatibility as an edge is represented by GATE scores, category cutoffs, course preferences, and seat availability. The attention mechanism allows adaptive learning of feature relevance, improving the quality of personalized suggestions. The system produces optimal-3 college suggestions to each student and is tested with various ranking and error-based measurements to insist on stability. Experimental analysis underlines the promise of GAT-based models to provide fair, clear, and data-driven solutions, helping students make informed choices and make a contribution to more efficient admission of higher education.
Hyperparameter Optimization in Machine Learning Classification Models: A Case Study in the Retail Industry
It is now important to forecast customer purchase patterns and classify stores appropriately considering the effects of the global pandemic and changing consumer preferences in the retail sector. Machine learning (ML) algorithms can be used to predict customer buying patterns and classify stores. However, conventional ML algorithms are very sensitive to their parameters. Hyperparameter optimization (HPO) is used for tuning the parameters of the model with grid search and random search algorithms and finding the best parameters. The aim of this study is to analyze which HPO approach performs best in determining parameters in ML-based classification algorithms. In this study, for the store classification in the retail sector, gradient boosting classifier, XGBClassifier, and random forest (RF) methods are selected. Model parameters are determined by using two different HPO methods for each model, and performance metrics are compared. In these three classification models, monthly sales amount, population in the store region, GDP per capita, and warehouse size of the stores are used between January 2017 and December 2021. In the analysis that evaluated the pre-pandemic and pandemic periods together in the study, RF is the best prediction algorithm with an accuracy score of 98%. The RF algorithm has an accuracy rate of 99% for both periods separately. Our analyses evaluated pre-pandemic, pandemic, and both periods together. The HPO that gives the best results is the random search tuning. This study focuses on evaluating the comparative performance of grid search and random search HPO techniques across multiple ML models for retail store classification.
CAAF: A Cross-Attention and Adaptive Fusion Framework for Automated Detection and Severity Grading of Fetal Ventriculomegaly in Ultrasound Imaging
Background: Fetal ventriculomegaly (VM), which is defined by abnormal ventricular size, is one of the most common brain malformations detected during fetal screening. Early and precise diagnostic tools are important in predicting neurodevelopment, but ultrasound examination is highly operator-dependent, thereby creating subjectivity in its application. Objective: To design a precise, reliable, and computationally efficient deep learning system for detecting fetal VM cases and grading their severity through ultrasound images. Method: In this paper, we propose a novel deep learning architecture labeled “CAAF” (Cross-Attention and Adaptive Fusion) that leverages two different but equally powerful convolutional models – DenseNet121 and EfficientNet-B4 – which are both augmented with channel-spatial attention and bidirectional cross-attention to allow for information sharing between different representations. The model was trained on a dataset of fetal brain ultrasound images obtained from Roboflow (CC BY 4.0) using a stratified 5-fold cross-validation structure to test its performance on four different classes of VM ranging from normal to severe. Due to the absence of explicit patient identifiers in the publicly available ultrasound dataset, cross-validation was performed at the image level. This limitation is acknowledged, and future work will incorporate patient-wise evaluation when metadata becomes available. The model was then assessed against other state-of-the-art models using accuracy, precision, recall, F1-score, Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) curve, and kappa coefficient. Results: In total, across all 5-fold cross-validation folds, CAAF achieved a mean classification accuracy of 97.14% ± 2.29%, a Cohen’s Kappa of 0.9606 ± 0.0316, a macro F1-score of 0.9896, and a macro-averaged Receiver Operating Characteristic-Area Under the Curve ROC-AUC approaching 1.00, outperforming all baselines, including DenseNet121 with a classification accuracy of 84.9% and a DenseNet–EfficientNet fusion approach with a classification accuracy of 95.7%. In final testing, the model reached a peak accuracy of 98.92% and a test Cohen’s Kappa of 0.9856 with near-perfect sensitivity (1.00) for moderate and severe cases. To ensure generalizability, the framework was further validated on the Zenodo dataset (n = 597), maintaining high predictive stability with a mean confidence of 0.8982 and a low predictive entropy of 0.2611. Gradient Weighted Class Activation Maps were also employed to validate that the newly developed framework consistently localized clinically significant anatomical structures, particularly the lateral ventricles. Conclusion: Thus, by leveraging the benefits of cross-attention-based interaction of features and adaptive fusion in a lightweight model, CAAF is shown to be an effective, stable, and interpretable model in the context of fetal VM detection.
Entropy-Guided Gradient Pruning with Informer for Intrusion Detection System in Internet of Things
The internet of things (IoT) is becoming increasingly significant in computer networks and applications. The existing deep learning (DL)-based intrusion detection system (IDS) suffers from high computational complexity and poor generalization, where irrelevant network flows are due to the inability of the filter. To address this challenge, this research proposes a novel entropy-guided gradient pruning (EGGP) with an Informer for IDS classification. The EGGP eliminates the less impactful and redundant network flows based on entropy and gradients that focus on significant traffic. The EGGP is integrated with the Informer backbone, which utilizes ProbSparse self-attention and sequence distillation while effectively capturing long-term temporal dependencies. The experiments are conducted on the ToN-IoT, BoT-IoT, IoT-23, and CICIDS2019 datasets; the EGGP-Informer outperforms other state-of-the-art methods while maintaining lesser memory usage and inference time. Furthermore, this research includes preprocessing for data quality and class imbalance mitigation using class weights, thereby validating its robustness through an ablation study. Therefore, the EGGP-Informer achieves an accuracy of 99.98% for ToN-IoT, 99.99% for BoT-IoT, 99.96% for IoT-23, and 99.89% for CICIDS2019, demonstrating its scalability to diverse networks and uneven distribution of attacks.
omato Leaf Disease Classification Using Mixed Pooling with Nesterov Accelerated Gradient-Based Convolutional Neural Network
Tomato leaf disease classification plays an important part in improving agricultural efficiency by enabling early and accurate detection of plant diseases. Plant diseases have a crucial effect on leaves, with every disease displaying specific spots characterized by individual colors and locations. The precise classification of tomato plant disease plays a vital part in identifying the rate as well as production quality. Deep learning (DL) has arisen as an important approach for determining plant diseases because of its remarkable performance. Thus, this research proposes the Mixed Pooling with Nesterov Accelerated Gradient-based Convolutional Neural Network (MPNAGCNN) for accurate multi-class classification of tomato leaf diseases. The proposed model integrates mixed pooling to preserve both dominant and subtle disease features, along with the NAG optimizer to achieve faster and more stable convergence. Primarily, this research considers two different benchmark datasets, such as PlantVillage and tomato leaf disease, for evaluating the proposed method. In addition, the different preprocessing approaches, named image resizing and data augmentation, are performed to enhance the classification performance. The experimental results demonstrate that the proposed MPNAGCNN approach reaches better accuracy of 99.95% and 99.98% as compared to the existing methods such as Capsule Neural Network (CapsNet) and DenseNet121. The findings illustrate that the proposed method provides lightweight, effective, and more precise problemsolving for automated tomato leaf disease detection.
Region-Aware Genetic Feature Selection with Demographic Meta-Integration and Ensemble Learning for EEG epilepsy Seizure Detection
Accurate electroencephalogram (EEG)-based seizure detection is important for early epilepsy diagnosis and timely intervention, yet existing methods often trade predictive performance for interpretability. Deep learning models can achieve high accuracy but function as black boxes, limiting clinical trust. Conventional machine learning models are more transparent, but they often ignore neurophysiological structure and patient-specific metadata, which can reduce performance. To address this gap, this study proposes an interpretable framework that combines region-aware genetic algorithm feature selection, demographic meta-integration, and ensemble learning. EEG channels are first grouped into neurophysiological regions, including frontal, central-parietal, temporal, and occipital areas. A genetic algorithm is then applied within each region to identify the most informative channels while preserving clinically meaningful brain topology. The selected EEG features are combined with patient demographic and clinical metadata, including age, gender, medication status, and seizure history, to create a compact feature vector. This feature set is used to train a soft-voting ensemble of RandomForest, ExtraTrees, and XGBoost classifiers. The framework was evaluated on a dataset of 50 drug-resistant epileptic patients and achieved 99.28% accuracy with a very low false-negative rate. In addition to strong predictive performance, the proposed method remains inherently interpretable by indicating which brain regions contribute to seizure prediction, making it suitable for practical clinical deployment.
SmartCPS-ADAPT: An Intrusion Detection System for Cyber-Physical Systems
The rapid growth of cyber-physical systems (CPS) and internet of things Edge-Cloud environments has amplified concerns regarding cyber threats, making intrusion detection systems (IDS) a vital component of network resilience. However, recent studies reveal a research gap in existing IDS frameworks: many fail to adapt to evolving attacks, exhibit imbalanced detection performance, or incur high computational costs. To address this challenge, this work aims to develop a robust deep learning-based classification model that accurately detects cyberattacks in CPS. Hence, this work proposed SmartCPS-ADAPT, which leverages Convolutional Neural Network-Bi-Directional Long Short-Term Memory (CNN–BiLSTM) for spatial-temporal feature extraction, an adaptive extreme gradient boosting (XGBoost) classifier for handling evolving data distributions, and a drift detection mechanism for continuous learning. Experimental evaluation on the CICIoT2023 dataset demonstrates superior performance, achieving 100% accuracy in binary classification and 99.85% accuracy in multi-class classification. The findings confirm that SmartCPS-ADAPT significantly outperforms existing approaches, ensuring reliable detection of diverse cyber attacks. In conclusion, the SmartCPS-ADAPT establishes a highly effective IDS model that addresses adaptability, robustness, and accuracy in CPS security.
The Identification of Risk Organizations with Machine Learning and Deep Learning Models
An organizational audit plays a critical role in identifying risk factors, evaluating resource compliance, and supporting informed managerial decision-making. However, traditional audit approaches are increasingly limited by their manual, time-consuming nature and their inability to process complex, multidimensional organizational data efficiently. To address these limitations, this study proposes an intelligent audit risk assessment framework based on machine learning (ML) and deep learning (DL) models. The framework is evaluated on a real-world dataset comprising 773 organizational units. A multi-stage methodology is applied, including data preprocessing, normalization, feature selection, class balancing, and model development. A range of ML and DL models, including Naive Bayes, support vector machine, decision tree, random forest (RF), XGBoost, dense neural network (DNN), convolutional neural network (CNN), long short-term memory (LSTM), recurrent and hybrid CNN-LSTM, and LSTM-gated recurrent unit (LSTM-GRU) models, are implemented. The performance is evaluated using accuracy, precision, recall, and F1-score. Experimental results show that ensemble models, particularly RF and XGBoost, achieve stable, well-generalized performance with test accuracies up to 0.957 and F1-scores up to 0.958, while a DNN and recurrent neural networks demonstrate competitive performance. Overall, the proposed framework demonstrates the strong practical potential of ML and DL models for an organizational audit.
GraphSkinUQ: A Hybrid Transfer Learning Approach for Feature Extraction and Graph Transformer-Based Uncertainty Quantification for Robust Skin Cancer Classification
The early and reliable diagnosis of skin cancer is critical to mitigating its metastatic potential and progression. However, even highly accurate artificial intelligence (AI) classifiers tend to produce overconfident predictions on ambiguous lesions, increasing the risk of misdiagnosis. Uncertainty quantification (UQ) addresses this challenge by evaluating model confidence and distinguishing definitive classifications from cases requiring clinician review—a cornerstone for safely integrating AI into clinical practice. We propose GraphSkinUQ, a hybrid framework that integrates convolutional neural network (CNN)-based feature extraction and graph-based relational reasoning to model lesion contextual relationships, together with Bayesian Monte Carlo dropout, to simultaneously quantify epistemic uncertainty (model limitations) and aleatoric uncertainty (data noise). Through iterative sampling of the predictive posterior distribution, GraphSkinUQ generates well-calibrated confidence scores, evaluated using average predictive entropy (0.10 bits), Brier score (0.1382), and expected calibration error (ECE). Experimental results show that GraphSkinUQ achieves a Brier score of 0.1382, Receiver Operating Characteristic (ROC) area under the curve (AUC) of 0.954, and average predictive entropy of 0.10, outperforming conventional classifiers in calibration. The framework effectively identifies high-uncertainty cases, allowing clinicians to prioritize ambiguous lesions for additional scrutiny. By bridging AI-driven diagnostics with clinically interpretable confidence metrics, GraphSkinUQ enhances the safety and transparency of automated skin cancer screening, fostering trustworthy and robust human–AI collaboration in clinical decision-making, and aligning machine-driven insights with the nuanced demands of real-world oncology practice.
Evaluating Machine Learning, Deep Learning, and YOLO Models with Preprocessing and Feature Extraction Variations for Melon Leaf Disease Detection
In the idea of creating a digital village system, the digital transformation of the agricultural sector is crucial. A crucial step within the agricultural process is recognizing plant diseases. Automatic identification of plant diseases is essential to streamline agricultural procedures. In light of this, the study aims to assess different machine learning, deep learning, and YOLO (You Only Look Once) techniques for identifying melon diseases through the use of leaf images. In the experiments, data preparation involving preprocessing and feature extraction tasks was conducted using various methods, both manually and automatically through the use of pretrained models like ResNet50 (50-Layer Residual Network). According to the findings, various machine learning techniques utilizing features obtained from a pretrained ResNet50 model achieved optimal outcomes with an accuracy reached up to 100%. The preprocessing set and the extracted features assured the identification of the disease characteristics. Among the techniques, Gradient Boosting, Naïve Bayes, Random Forest, and Support Vector Machine are suggested. Modeling using machine learning with manually set features, Convolutional Neural Networks, ResNet50, and YOLO showed accuracy levels that remain relatively low, ranging from 54.5% to 81.9%. For all subsequent techniques, enhancing preprocessing, further extracting features, improving labeling and bounding box configuration accuracy, managing data imbalance, applying augmentation variations, and thorough parameter tuning could be advantageous.
RAE-NetB7: Disease Classification from Chest X-Rays using Regularization-Aware EfficientNetB7
Coronavirus disease of 2019 (COVID-19) is a global epidemic, impacting millions of people and causing numerous deaths in a short period. However, conventional classification approaches struggle to capture the complex pixel relationships in images due to their inability to handle nonlinearities. To address this issue, this research proposes a Regularization-Aware EfficientNetB7 (RAE-NetB7) approach for disease classification using chest X-ray (CXR) images. The nature of the proposed method allows for dynamic adjustment of regularization strength during training, thereby enhancing the network’s ability to generalize to unseen data. Initially, the benchmark COVID-19 radiography dataset is utilized for classifier’s performance estimation. In the preprocessing step, data augmentation approaches are employed for improving the image counts in an acquired dataset, thereby enhancing the robustness of the classification tasks. Next, Neural Architecture Search Network (NASNet) is utilized to extract meaningful features, and the Dynamic Probabilistic Elitist-based Ant Colony Optimization (DPE-ACO) approach is applied for selecting relevant features. Finally, the RAE-NetB7 approach is proposed for classifying CXR images into different categories such as normal, COVID-19, lung opacity, and viral pneumonia. The proposed method achieves a commendable accuracy of 99.53%, outperforming the COVID-19 Detection using Feature Reuse Residual Blocks and Depth-wise Dilated Convolutional Neural Network(CovidDWNet).
Modeling the Detection of the Prevalence of Epileptic Seizures using Machine Learning
The frequency of epileptic seizures lead to many dimension of time frame from its beginning to its end and can be anticipated when adequate measures are in place to track and tackle it. This study presents a model designed to detect the prevalence of epileptic seizures through the application of machine learning (ML) techniques. The experimental investigation focused on exploring the early detection of epileptic seizure through the use of a dataset sourced from the UCI Machine Learning Repository. The experimental procedure entails gathering data, which is subsequently processed for analysis. Following this, the data are divided into multiple sets designated for training, testing, and validation purposes. The training involved the application of several ML algorithms, including “Logistic Regression,” “K-Nearest Neighbors,” “Decision Trees,” “Random Forest,” “Naive Bayes,” and “XGBoost.” The findings demonstrate that Random Forest and XGBoost attained accuracies of 98.31% and 98.86%, respectively. The results underscore the enhanced capabilities of these two models in comparison to others for identifying epileptic seizure. In comparison to other studies, these findings suggest that the precision of this investigation is remarkably elevated. This guarantees the development of reliable methods for predicting epileptic seizures, subsequently improving patient care and elevating quality of life.
A Multi-Scale CNN–Transformer Fusion Framework with Stain Normalization and Focal Loss for High-Accuracy Multi-Stage Gastric Cancer Diagnosis
Early-stage gastric cancer (GC) diagnosis from histopathological images remains challenging due to subtle morphological variations and inter-slide staining variability. This study proposes a deep learning-based multi-stage GC classification framework that integrates convolutional feature extraction with attention-based contextual modeling. Eight pretrained convolutional neural networks (CNNs) are evaluated, among which DenseNet121 and MobileNetV2 achieve the strongest baseline performance (accuracy ≈ 85.8% and 85.9%, respectively). Building on these results, two novel architectures are developed. The first is an enhanced DenseNet121 model that incorporates multi-path convolution, squeeze-and-excitation(SE) channel recalibration, and attention optimization to capture multi-scale morphological patterns. The second is a Hybrid DenseNet121–Transformer framework that integrates global self-attention with convolutional representations to improve contextual understanding of tissue structures. The models are trained using standardized preprocessing, Macenko stain normalization, extensive data augmentation, and class balancing on a dataset of 7,010 histopathology images representing Normal, Stage I, and Stage II gastric tissues. The proposed hybrid CNN–Transformer framework achieves 90.2% classification accuracy, a macro F1-score of 91.4%, and an Area Under the Curve (AUC) of 0.985, outperforming baseline CNN architectures in stage-wise discrimination. Attention-based visualization highlights diagnostically relevant tissue regions and improves model interpretability. These findings demonstrate that combining multi-scale convolutional representations with Transformer-based global attention provides a robust and interpretable framework for automated GC histopathology analysis.
AI-Based Adaptive Career Guidance for Sustainable Transformation in TVET Education
The transformation of vocational education requires career guidance models that are personalized, adaptive, and capable of responding to rapidly evolving labor market expectations. Existing guidance practices in vocational institutions remain largely general, static, and fragmented, providing limited support for students’ career development and lacking integration of psychological, academic, and contextual data. This study aims to identify the needs of vocational learners and key stakeholders and to develop a conceptual framework for an artificial intelligence-based adaptive career guidance system grounded in established career development theories. Using a Design and Development Research approach, data were collected through surveys with 210 students and through interviews and focus group discussions with guidance counselors and industry representatives. The findings indicate a strong demand for individualized, technology-supported guidance, while counselors and industry partners highlighted challenges related to data fragmentation, limited digital tools, and misalignment between student competencies and workforce requirements. Based on these insights, the study proposes a conceptual framework that integrates multimodal data processing, natural language analysis, psychometric profiling, and adaptive recommendation mechanisms. The framework provides a theoretically coherent and contextually relevant foundation for developing intelligent guidance systems that support more responsive, ethical, and sustainable career development in vocational education.
Investigating the role of using AI and machine learning for demand forecasting in supply chain management
This research investigates the application of artificial intelligence (AI) in demand forecasting within the retail sector as part of supply chain management (SCM). The research aims to develop a framework based on conceptual AI models for better enhancing demand forecasting in SCM. The key focus area of this research is demand forecasting through AI, emphasizing its importance in attaining precision and reliability in predicting demand and enhancing SCM through developing and creating various models. Since demand forecasting is perceived to play a vital role in SCM, researchers examine the application of AI in effectively predicting demand and addressing current SCM issues. There is a focus on providing information to researchers on the application of AI in demand forecasting in SCM, with a deep and in-depth focus on explaining the application and development of AI in predicting demand. The empirical validation in this study is limited to retail data, while the proposed conceptual framework is designed to be extendable to other sectors.
Intelligent Games for UAV Systems: A Survey of Game-Theoretic and AI-Enabled Methods
Game theory provides a mathematical framework for strategic interactions involving conflict and cooperation, while artificial intelligence offers computational tools for learning, inference, and decision-making. Driven by the increasing complexity of autonomous missions, their convergence has fostered intelligent game approaches that are increasingly explored in unmanned aerial vehicle (UAV) systems. This survey reviews theoretical foundations and recent progress, organizing the literature along two complementary dimensions: (i) AI-enhanced game paradigms, including game-theoretic deep reinforcement learning, GAN-style adversarial learning, and large language model–enabled agents; and (ii) advanced game-theoretic models for complex environments, spanning perfect- and imperfect-information settings, differential games, evolutionary games, and mean field games for large-scale swarms. Representative UAV scenarios such as pursuit–evasion, cooperative control, and resource scheduling are summarized to highlight methodological trends and practical considerations. Finally, we outline open problems and future directions, emphasizing key challenges in real-time equilibrium computation, partial observability and communication constraints, sim-to-real gaps, and multi-objective trade-offs in safety–energy–mission performance.