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 © 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.