Inspire Intelligence Journal (ISSN: 3110-942X) welcomes high-quality submissions that advance the understanding, design, implementation, and societal impact of Artificial Intelligence. Whether rooted in machine learning, autonomous robotics, or emerging AI, Inspire Intelligence seeks to reflect the interdisciplinary and rapidly evolving nature of the field. We are particularly interested in research that demonstrates originality, scientific rigor, and practical relevance. Inspire Intelligence welcomes the submission of a variety of paper types, including original research articles, review papers, short communications, case studies, technical notes, and visionary perspective articles related to all areas of Artificial Intelligence. Click to get more details about the Journal Tracks and Topics. Abstract & Indexing: + Dimensions indexing by Digital Science + Semantic Scholar OpenAIRE: Open Access Infrastructure for Research in Europe + Crossref + Scilit + Google Scholar
Resilience in Humanitarian Supply Chains: A Review of AI-Driven Big Data Analytics Applications
Humanitarian supply chains face unprecedented challenges due to escalating global crises, with approximately 279 million people requiring assistance in 2022 alone. The traditional reactive approaches to humanitarian logistics are becoming ineffective at managing the increasingly complex and large-scale disaster responses. In this paper, we analyzed the coupling of artificial intelligence (AI) and big data analytics (BDA) as transformative levers for improving the resilience of humanitarian supply chain operations. With a focus on disaster forecasting, resource allocation, and stakeholder coordination in humanitarian contexts, we study how AI-driven predictive modelling and BDA will change the narrative. This paper identified several significant implementation barriers, including data quality issues, infrastructure limitations, and ethical concerns related to algorithmic bias and privacy. Theoretical frameworks for using AI-BDA for humanitarian logistics exist, but examples of real-world effectiveness are limited. The paper aims to fill this research gap by evaluating some AI-BDA techniques focusing on time-series forecasting, early warning systems, logistic optimization, and real-time monitoring in humanitarian settings and offering evidence-based recommendations for implementation. The strategic integration of these technologies may be leveraged to transition from the reactive to the more proactive humanitarian response models, thereby improving aid delivery efficiency by up to 30% during a crisis. We conclude with practical guidelines for humanitarian organizations exploring the use of AI-BDA capacity building and appropriate data governance frameworks for AI-BDA adoption.
Copyright (c) 2026 Emmanuel Ahatsi, Oludolapo Olanrewaju (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Machine Learning Pipeline Integrating Real-time Multimodal Biosignal Sensor Data for Early Detection of Postpartum Hemorrhage
Postpartum hemorrhage (PPH) is one of the leading causes of maternal deaths worldwide, especially in low-resource regions where early detection and timely intervention are a challenge. Current methods detecting PPH include visual estimation of blood loss and are often inaccurate, leading to delays in diagnosis and treatment of PPH. This paper will propose an innovative machine learning pipeline that integrates real-time data from wearable biosignal sensors in conjunction with specific electronic medical records (EMRs) to predict the risk of PPH early on. Using non-invasive, real-time physiological monitoring, personal EMR data, and ML algorithms, this system will improve predictive accuracy, improving maternal health outcomes in areas where immediate access to medical care and necessary resources can be limited.
Copyright (c) 2026 Yanishka Gahlot, Said Baadel (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Multi-Phase CNN-ViT-Wavelet Fusion with Attention for Robust Kidney Stone Detection from Ultrasound Images
Accurate detection of kidney stones in ultrasound images is hindered by low contrast, speckle noise, and operator-dependent variability. The objective of this study is to develop a robust multi-phase model that integrates the local, global, and frequency-domain features to enable a reliable and generalizable kidney stone classification. Three convolutional neural networks (ResNet50, DenseNet121, and EfficientNet-B0) and vision transformer variants (ViT-Base, Swin-Transformer, and DeiT-Small) were independently trained on a primary renal ultrasound dataset. The dataset was split patient-wise in a 70-15-15 manner; extensive data augmentation, normalization, and five-fold cross-validation were implemented to avoid data leakage and ensure the model's robustness. The best-performing CNN and ViT features were fused at the feature level with an attention mechanism and classified using ML classifiers, among which XGBoost demonstrated the optimal performance. Next, a discrete wavelet transform (DWT) branch was incorporated to acquire complementary frequency information for further enhancing the discriminative capability. The multi-phase framework achieved 97.9%, 97.8%, and 0.997 for accuracy, F1-score, and AUC, respectively, on the internal dataset. Similarly, it obtained 94.3%, 94.0%, and 0.970 for accuracy, F1-score, and AUC on the external renal ultrasound dataset. These results demonstrate a robust generalization backed up by Grad-CAM and attention maps. The Multi-Phase Framework (MPF) offers a consistent, generalizable, and fully automated method for kidney stone detection in ultrasound images, supporting improved diagnostic performance.
Copyright (c) 2026 Faizan Ahmad, Uzair Ishtiaq, Malik M. Ali Shahid (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Generative AI-Based Privacy-Preserving Next-Gen Student Support and Mental Health Navigation System
The increasing demand for accessible, 24/7 student support services in higher education presents a significant challenge, particularly in the domain of mental health. Generative AI and Large Language Models (LLMs) offer a scalable solution, yet their deployment is fraught with a critical dilemma between the performance and safety of proprietary models and the data privacy afforded by open-source alternatives. This paper presents a comparative study of four leading LLMs (open-source Llama 3 70B and DeepSeek R1; proprietary GPT-4o Mini and Gemini 2.5 Flash) for a university-specific student support chatbot at Thompson Rivers University. Using a custom knowledge base and a novel six-dimensional evaluation framework (Factual Accuracy, Contextual Relevance, Completeness, Tone & Empathy, Practical Utility, and Safety & Risk Management), we analyzed over 200 model responses to 50+ real-world student queries. The results reveal that while open-source models demonstrate parity in factual accuracy for academic questions, a critical performance gap exists in the human-centric attributes of safety, empathy, and utility, particularly in response to sensitive mental health queries. The study concludes that current open-source models, in their base form, are not yet suitable for unmonitored, student-facing deployment in high-stakes scenarios. We propose a hybrid deployment model as a pragmatic and responsible path forward for universities seeking to balance innovation with student welfare and data sovereignty.
Copyright (c) 2026 Gursahib Singh, Jaspreet Kaur, Runna Alghazo (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Inaugural Editorial of the Inspire Intelligence First Issue Publication
Artificial Intelligence (AI) has rapidly evolved into a key technology that affects decision-making in healthcare, analytics, and operational systems. Machine learning and deep learning have advanced significantly, enabling predictive modeling frameworks to learn from large, complex, and messy real-world data. You can use these frameworks for tasks such as predicting disease risk, diagnosing medical images, and forecasting operations [1]. These advancements have shown significant improvements in accuracy and efficiency, especially through time-series analysis and ensemble learning methods that identify temporal dependencies and diverse data patterns. AI-powered clinical decision support systems have shown promise in healthcare by improving the accuracy of diagnoses, streamlining care pathways, and allowing for earlier intervention. Deep learning models used in medical imaging and healthcare analytics are now getting close to or even better than expert-level performance in some tasks. But as these systems become more complicated, people are worried about transparency, accountability, and clinical trust, especially in high-stakes situations [2]. As a result, Explainable Artificial Intelligence (XAI) and model interpretability have become important tools for responsible AI use [3]. Interpretability methods help ensure that AI outputs are useful, can be checked, and are in line with what experts in the field know. In addition to technical performance, the increasing focus on human-centered AI underscores the necessity to address workforce adaptation, usability, and well-being as intelligent systems transform professional roles and organizational frameworks. Current research underscores that sustainable AI innovation relies not solely on predictive capabilities but also on interpretability, real-world applicability, and human-centered design [4]. The first contribution in this issue addresses a critical yet underexplored problem in intensive care (ICU) practice: the high frequency of phlebotomy required for patient monitoring and its substantial contribution to iatrogenic anemia, a condition affecting most ICU patients. Conventional blood sampling methods are still primarily reactive and do not have the ability to predict the total number of blood samples needed during an ICU stay. The authors use Long Short-Term Memory (LSTM) architecture to model the temporal dynamics of patient data available at ICU admission, including physiological measurements, laboratory values, and demographic characteristics, to overcome this limitation. A comprehensive framework for predicting blood draw frequency as a longitudinal outcome is presented in this work. The model's capacity to capture heterogeneity in patient trajectories is improved by the introduction of a novel loss function intended to handle variable-length, multi-output sequences. The outcomes support the viability of proactive phlebotomy planning by showing encouraging predictive performance on actual ICU data. According to the study's findings, incorporating temporal deep learning models into ICU workflows may improve patient outcomes by reducing needless blood draws, personalizing monitoring strategies, and reducing avoidable complications like iatrogenic anemia. The second contribution in this issue tackles the ongoing difficulty of correctly differentiating benign from malignant breast lesions. This task is crucial for early diagnosis and better clinical outcomes, but it is still constrained by the limitations of conventional imaging modalities. While digital mammography (DM) continues to be the clinical standard, its predominantly structural information may restrict the performance of deep learning–based classifiers. The authors use a comparative deep learning approach to overcome this constraint, training a ResNet-18 convolutional neural network on both DM and contrast-enhanced spectral mammography (CESM), which offers complementary functional imaging data. The study's methodology includes rigorous evaluation using stratified cross-validation, blind testing, and SHapley Additive exPlanations (SHAP) model interpretability analysis, which evaluates both predictive performance and the clinical plausibility of model decisions. The findings show that CESM performs significantly better than DM in the classification of malignant lesions, achieving higher AUC values across several evaluation metrics and generating more targeted and anatomically significant attribution maps. These results imply that the accuracy and interpretability of CNN-based models are improved by the additional functional information in CESM. The study acknowledges the ongoing value of DM as a contrast-free screening tool when CESM is unavailable but concludes that CESM-integrated AI systems hold significant promise for dependable clinical deployment, particularly in detecting malignancies. The third contribution acknowledges the limitations of depending solely on clinical or genetic data and tackles the persistent public health challenge of accurately identifying people at risk of diabetes using scalable and accessible data sources. Using extensive surveys and clinical data from the National Health and Nutrition Examination Survey (NHANES), the authors use a machine learning-based approach to address this issue. They build a binary classification framework based on reported diagnoses and objective plasma glucose measurements. The analysis produces a small set of 16 variables that strike a balance between model complexity and practicality by prioritizing clinically relevant and interpretable predictors through systematic feature selection. In addition to an ensemble approach intended to capitalize on complementary model strengths, several individual machine learning models are assessed. Although Gradient Boosting, the best individual learner with a strong AUC of 0.84, outperformed the ensemble approach, decision-threshold optimization significantly improved recall for diabetic cases. The results show that survey-derived data can support reliable disease risk modeling and they show how well tree-based models predict diabetes. The research concludes that employing machine learning methodologies on population-level data can facilitate the early identification of diabetes and inform targeted public health initiatives for disease prevention and management. The fourth contribution illustrated the importance of predictive modeling in the fields of public health and healthcare, where trade-offs between practical deployment, interpretability, and accuracy are crucial. Contribution 4 covers the operational context of last-mile logistics. Contribution 4 primarily discusses the challenge of producing accurate demand forecasts in settings characterized by erratic consumer behavior and strict operational limitations. Inaccurate forecasts can result in inefficiencies, higher expenses, and detrimental effects on sustainability. To overcome this difficulty, the authors use a comparative modeling approach, comparing a modified transformer-based architecture (GPT-2) that can capture intricate and nonlinear demand patterns with a transparent, additive time-series model (Prophet). The models are evaluated in several areas, such as forecast accuracy, robustness to volatility, computational efficiency, and sustainability considerations, using real-world delivery data and systematic hyperparameter optimization. The findings show that while GPT-2 demonstrates better adaptability in extremely volatile environments, albeit with higher resource requirements, Prophet performs robustly in stable demand scenarios because of its interpretability and low computational overhead. To provide a practical framework for integrating AI-driven forecasting solutions in last-mile delivery systems, the study concludes that no single model is always best. Instead, model selection should be guided by operational context, balancing predictive performance with explainability and resource constraints. The fifth contribution in this issue draws attention to the frequently disregarded human effects of AI integration, which complement the other contributions that concentrate on using AI to optimize performance and decision-making. The main issue discussed is the increasing psychological strain that companies are placing on workers as they implement AI technologies and demand ongoing skill development without corresponding changes to workload or institutional support. The study takes a conceptual and analytical approach to investigate the relationship between technological change, learning demands, and workplace well-being instead of using a technical modeling framework. The study emphasizes how inadequate adaptation time and ongoing performance pressures worsen detrimental mental health outcomes by combining observations on stress, anxiety, burnout, and decreased motivation. The analysis underscores that the application of AI-driven changes within organizational structures, rather than AI itself, is the root cause of these difficulties. The study emphasizes that human-centered considerations are crucial for the long-term success of AI-enabled workplaces by concluding that sustainable adoption of AI requires intentional workload management, encouraging training environments, and organizational policies that prioritize employee well-being alongside productivity. These studies demonstrate the breadth of AI applications and the common challenges that accompany their deployment, including interpretability, contextual suitability, and human impact. They demonstrate the necessity of developing AI solutions that are not only accurate and efficient but also transparent, domain-sensitive, and sustainable, thereby ensuring that technological progress translates into meaningful and responsible real-world benefits. We thank the authors of these articles. We anticipate these articles will add positively to the current body of knowledge. Data Availability Statement Not applicable. Funding This work was supported without any funding. Conflicts of Interest The author declares no conflicts of interest. Ethical Approval and Consent to Participate Not applicable. List of Contributions: 1. Al Mallah, R., & Quintero, A. (2026). Toward improved ICU care: Phlebotomy frequency using deep learning. Inspire Intelligence, 1(1), 1-9. 2. Acosta-Jiménez, S., Mendoza-Mendoza, M. M., Galván-Tejada, C. E., Celaya-Padilla, J. R. D.-C., & Galván-Tejada, J. I. (2026). Interpretable deep learning for breast lesion classification: A SHAP-based comparison of CESM and digital mammography. Inspire Intelligence, 1(1), 10-28. 3. Mabkhout, H., & Benhra, J. (2026). Prophet and GPT-2 algorithms for demand forecasting in last-mile delivery: A comparative analysis and optimization. Inspire Intelligence, 1(1), 29-41. 4. Patel, P. (2026). Enhancing diabetes prediction through ensemble machine learning models on survey-based and clinical data. Inspire Intelligence, 1(1), 42-51. 5. Ranjan, B., Sivanesan, I., & Bernie, S. B. (2026). Artificial intelligence and anxiety: The human price of adapting to a smarter workplace. Inspire Intelligence, 1(1), 52-67. References [1]. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., … Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://www.nature.com/articles/s41591-018-0316-z [2]. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://www.nature.com/articles/s41591-018-0300-7 [3]. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?” Explaining the predictions of any classifier. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144. https://doi.org/10.1145/2939672.2939778 [4]. Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., … Horvitz, E. (2019). Guidelines for human–AI interaction. Proceedings of the CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3290605.3300233
Copyright (c) 2026 Ghazanfar Latif (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Artificial Intelligence and Anxiety: The Human Price of Adapting to a Smarter Workplace
Artificial Intelligence is changing the way people work, which means that people need to keep learning new things and improving their skills. But this change often happens without lowering the amount of work that employees already have to do, which puts a lot of stress on them mentally. As employees try to balance their learning needs with their daily tasks, they face problems like increased stress, anxiety, and feelings of not being good enough. Limited time for adaptation and the pressure to stay competitive make symptoms of burnout, fatigue, and lack of motivation worse. This paper examines the human toll of these competing demands. It stresses the need to deal with workload management and support systems in order to make long-lasting ways for workers to adapt to AI-driven environments. The findings of this study show that using AI makes things more efficient, but it also makes technostress, skill insecurity, and job polarization worse. Digital skill gaps and cognitive overload make anxiety and burnout worse, especially in workplaces that change quickly. The study underscores the essential importance of organizational communication, ongoing skill enhancement, and supportive policies, stressing that sustainable AI integration must reconcile technological progress with employee welfare and social equity.
Enhancing Diabetes Prediction through Ensemble Machine Learning Models on Survey-Based and Clinical Data
Machine learning (ML) has emerged as a significant asset in bioinformatics, particularly for early disease prediction, by leveraging various data types ranging from genetic markers to survey data. This research concentrates on diabetes prediction through the application of machine learning algorithms to data sourced from the National Health and Nutrition Examination Survey (NHANES). We built a binary classification system by looking at data from more than 5,500 non-pregnant adults. This system used reported diagnoses and plasma glucose levels to assign diabetes labels. Feature selection focused on important factors like age, waist circumference, and cholesterol, which led to a more focused set of 16 features. We trained five separate machine learning models, including Logistic Regression, K-Nearest Neighbors, Random Forests, Gradient Boosting, and Support Vector Machines. Then, to make the predictions more accurate, we combined them into an ensemble model. The ensemble did not perform better than the Gradient Boosting model, which had an AUC of 0.84. However, changing the decision threshold made it easier for diabetic patients to remember things. This study shows how useful survey data can be for predictive modeling for disease detection. It also shows how this information could be used in real life for public health programs that aim to manage and prevent diabetes.
Copyright (c) 2026 Inspire Intelligence https://creativecommons.org/licenses/by-nc-nd/4.0
Prophet and GPT-2 Algorithms for Demand Forecasting in Last-Mile Delivery: A Comparative Analysis and Optimization
Demand forecasting is a central issue in last-mile logistics, where operational performance depends on a delicate balance between prediction accuracy and interpretability of results. This study offers a rigorous comparison between two radically different modeling approaches: Prophet, a transparent additive model well-suited to decomposable time series, and GPT-2, a modified transformer capable of capturing complex and nonlinear demand dynamics. Using real-world delivery data, we conduct a thorough evaluation of both models based on several criteria: forecast accuracy (MAE/RMSE), robustness to demand volatility, computational efficiency, and sustainability impact. To ensure a fair comparison, hyperparameter optimization is systematically conducted using Optuna, ensuring optimal configurations for each model. The results reveal that Prophet excels in stable demand environments, thanks to its ease of interpretation and regularity, while GPT-2 demonstrates a marked adaptability to unpredictable variations, at the cost of higher computational costs. The article concludes with practical recommendations for logistics stakeholders, proposing tailored selection criteria (accuracy, explainability, resource constraints) based on the specificities of operational environments. This work aims to bring theoretical advances in predictive modeling closer to the realities of last-mile logistics, providing a concrete decision-making framework for implementing forecasting solutions based on artificial intelligence.
Interpretable Deep Learning for Breast Lesion Classification: A SHAP-Based Comparison of CESM and Digital Mammography
Accurate detection of malignant breast lesions is fundamental for timely intervention and improved patient outcomes. Digital Mammography (DM) remains the standard imaging modality in most clinical settings. In contrast, Contrast-Enhanced Spectral Mammography (CESM) provides functional imaging data, which has the potential to improve the performance of deep learning algorithms in lesion detection. Materials and Methods: The study compares a ResNet-18 CNN trained on DM versus CESM for breast-lesion classification, uses 80% stratified 3-fold cross-validation, evaluates the best model on a held-out 20% blind test, and assesses interpretability with SHapley Additive exPlanations (SHAP) attribution maps. Results: On the blind test set, CESM achieved superior overall classification performance, with an AUC of 0.890 for malignant lesions compared to 0.790 for DM. The micro- and macro-averaged AUCs are 0.83 and 0.77 for CESM, respectively, compared to 0.68 and 0.68 for DM. SHAP analysis revealed that CESM yielded more focused and anatomically aligned model attributions, particularly in malignant cases. DM produced more diffuse, yet still lesion-centered, attribution patterns. Conclusion: CESM substantially improves CNN-based diagnostic accuracy and interpretability in breast lesion classification, with the most significant benefit observed in the detection of malignant lesions, which is the most clinically significant category. Although it is still challenging to classify benign lesions, DM can help identify malignancies without the need for contrast, making it useful for screening when CESM is not available. These results show the value of CESM-enhanced Artificial Intelligence (AI) systems and explainability tools for dependable use in clinics.
Toward Improved ICU Care - Phlebotomy Frequency Using Deep Learning
Frequent phlebotomies in Intensive Care Units (ICUs) are crucial for patient monitoring but pose significant challenges, including complications such as iatrogenic anemia, which affects up to 70% of ICU patients. Traditional blood sampling methods do not sufficiently mitigate anemia or improve blood draw frequency. Recent advancements in Artificial Intelligence (AI) offer promising opportunities to enhance healthcare practices in the ICU, especially by analyzing diverse patient data, such as vital signs, blood test results, and demographics. However, existing AI models have predominantly focused on predicting isolated blood test results, rather than providing a holistic solution to forecast blood draw needs throughout a patient's ICU stay. This study addresses this gap by developing a Long Short-Term Memory (LSTM) model to predict blood draw frequency upon ICU admission, using key temporal, quantitative, and patient-specific features. Trained on data extracted from a real-world database, the model demonstrates promising performance, particularly by incorporating a novel loss function in the field to improve predictions of variable multi-output sequence lengths. This research introduces the first predictive model for ICU blood draw frequency, shifting the approach from reactive to proactive care. It underscores the potential of AI to personalize ICU practices, reduce unnecessary blood draws, and improve patient outcomes.