In this paper, the significance of supply chain management in healthcare logistics is emphasized by addressing the challenges of fluctuating demand and controlled drug production. To overcome these issues, the proposed system monitors the quantity of manufactured drugs through a combination of centralized and individual hub establishments that manage product distribution efficiently. An Artificial Intelligence (AI)-based automatic product clustering mechanism is integrated to analyze demand expectations and associated risk factors. The clustered products are then systematically arranged, and the internal connectivity between local suppliers is optimized to ensure minimal demand imbalance. Furthermore, to enhance the stability of the healthcare supply chain, proportional connections among hubs are evaluated, enabling data-driven and optimized decision-making. The performance of the proposed AI model is validated using four case studies, demonstrating its capability to achieve high connectivity and scalability. The model can be implemented in real time with a minimal operational cost of approximately 1,635 USD, confirming its practicality and cost-effectiveness.

The significance of Biomedical Natural Language Processing (Bio-NLP) has escalated in the current era, owing to the rapid expansion of biomedical data. Biomedical Named Entity Recognition (Bio-NER) is a fundamental task in Bio-NLP that involves the identification and categorization of named entities within biomedical text documents. During the early days, various traditional, machine learning, and deep learning-based methods were proposed to solve the Bio-NER task. However, each of these methods has its own limitations and drawbacks. With the advent of transformer-based pretrained language models such as Bidirectional Encoder Representations from Transformers (BERT), Bio-NER methods advanced significantly through fine-tuning approaches. These approaches leverage the learned weights of pre-trained language models as a starting point for Bio-NER. BioBERT is a specialized adaptation of BERT in the biomedical domain, which learns word representations from both left and right contexts. In addition, PubMedBERT shares the same architecture as BERT; however, unlike BioBERT, it is trained from scratch on PubMed abstracts, ensuring domain-specific pretraining for improved biomedical text understanding. To explore the importance of context information, we propose an effective method for Bio-NER using BioBERT and PubMedBERT models by incorporating more context information during fine-tuning (referred to as CI-BioBERT and CI-PubMedBERT). We perform a comprehensive analysis that encompasses models enriched with diverse forms of context information, including self-sentence and cross-sentence contexts: (i) previous sentence only, (ii) next sentence only, (iii) both previous and next sentences, (iv) next sentences up to the maximum length, and (v) previous sentences up to the maximum length. Further, the effectiveness of the proposed model is experimentally analyzed on the NCBI disease, BC5CDR disease, and BC5CDR chemical datasets under various hyperparameter settings. The experimental results reveal that incorporating contextual information improves the performance of Bio-NER. Since ensemble learning can further enhance performance, we employed ensemble learning to combine predictions from the top three performing models based on contextual information. The experimental results demonstrate that the proposed approach outperforms competitive methods in terms of the F1 score and Recall.

In this research, we assess the impact of training One-Class Classification (OCC) algorithms on the majority class versus the minority class, by employing highly imbalanced large and big datasets. It is worth highlighting that the accessibility of class data can be a major hurdle in training models. Depending on the situation, it may be possible to obtain one class within a reasonable time frame while not obtaining another. Our study centers around detecting fraudulent activities using the Credit Fraud Detection Dataset and a dataset derived from Medicare Part B, Part D, and the Durable Medical Equipment, Prosthetics, Orthotics and Supplies (DMEPOS) data, combined with the labels of the List of Excluded Individuals and Entities (LEIE) records. The Credit Card Fraud Detection Dataset, with its real-world transactional data and pronounced class disparity, serves as a benchmark for fraud detection. Notably, it stands as the only large-scale public dataset tailored for credit card fraud analysis. In Part B, Part D, and DMEPOS, researchers can investigate national trends and patterns based on Medicare big data. For our experiments, we use One-Class Gaussian Mixture Model (GMM), One-Class Adversarial Nets (OCAN), and One-Class Support Vector Machine (SVM). Their classification performance is evaluated with the Area Under the Precision-Recall Curve (AUPRC) and Area Under the Receiver Operating Characteristic Curve (AUC). Given the challenges posed by imbalanced datasets, our results indicate that strategically focusing training on the majority class yields better results.

Osteoporosis is a progressive skeletal disorder characterized by decreased Bone Mineral Density (BMD) and structural deterioration, leading to an increased risk of fractures. Traditional diagnostic methods, such as Dual-Energy X-ray Absorptiometry (DXA) and the Fracture Risk Assessment Tool (FRAX), often fail to detect osteoporosis at an early stage due to their limited sensitivity and accessibility. Recent advancements in Artificial Intelligence (AI), Deep Learning (DL), and Machine Learning (ML) have shown significant potential in improving osteoporosis detection. However, existing models face challenges related to data privacy, explainability, computational complexity, and generalizability across diverse populations. This study introduces Federated Graph-based Multimodal Osteoporosis Diagnosis (FGMOD), a novel AI-driven framework that integrates Federated Learning (FL) and Graph Neural Networks (GNNs) for enhanced osteoporosis risk assessment. Unlike traditional DL models that rely on centralized training, FL ensures privacy-preserving learning across multiple hospitals and institutions. Simultaneously, GNNs model trabecular bone connectivity, offering deeper insights into microarchitectural changes in osteoporotic bones. Multimodal data fusion, incorporating structured clinical data, DXA scans, and biochemical markers, significantly improves prediction accuracy and interpretability. The proposed FGMOD model was validated on a real-world dataset comprising 10 000 patient records, 5000 DXA scans, and biochemical profiles. Comparative evaluation with state-of-the-art AI models demonstrated superior performance, achieving 94.2% accuracy, 93.4% precision, 93.1% recall, and a ROC-AUC score of 0.96. Unlike CNN-based models, which rely heavily on high-resolution images, FGMOD effectively extracts clinically relevant features from graph-based bone connectivity analysis, ensuring robust and interpretable predictions. Furthermore, the use of SHAP-based feature importance analysis provides clinicians with an explainable AI decision-making process, enhancing trust in the model’s recommendations. Performance comparisons against CNN, Gradient Boosting, Random Forest, and Transfer Learning models revealed that FGMOD outperformed all baselines, particularly in privacy-aware federated training, osteoporosis risk stratification, and real-time inference on edge AI devices. Additionally, FGMOD reduced inference latency to 45 ms per sample, making it suitable for real-time clinical deployment. FGMOD represents a significant advancement in AI-powered osteoporosis detection, addressing key challenges such as privacy, generalizability, and interpretability. Future research should focus on expanding FL collaborations across global hospitals, integrating wearable sensor data for continuous bone health monitoring, and enhancing GNN-based osteoporosis modeling for faster inference on mobile healthcare platforms. This study paves the way for clinically reliable, privacy-preserving, and scalable AI solutions in osteoporosis risk assessment and early detection.

The [Formula: see text]-plex is a relaxation of the classical clique structure, which is usually used to characterize the cohesiveness of a subgroup in social networks. In this paper, we propose a new notion called the [Formula: see text]-plex influence to depict the outward connection of a [Formula: see text]-plex. The influence of a [Formula: see text]-plex is defined as the number of outside vertices adjacent to the vertices of the [Formula: see text]-plex. With the new notion, we can distinguish different [Formula: see text]-plexes and find more pivotal subgroups in networks. We propose an exact algorithm to find the [Formula: see text]-plex with the maximum influence. The algorithm implements a branch-and-bound approach, in which we integrate a novel upper bound and an effective preprocessing strategy. We conducted experiments to evaluate the performance of the algorithm and compared it with the general mixed integer linear programming approach. The results show that both the preprocessing strategy and the upper bound can effectively reduce the search space, and the proposed branch-and-bound algorithm can solve the problem in massive graphs effectively and has a significant performance advantage over the general mixed integer linear programming approach.

The purpose of this paper is to give a brief demonstration on how deontic logic can be used to help the design of robots capable of choice, equipped with artificial intelligence, by providing a framework that will help maintain ethically sound behavior. We begin by presenting an overview of the potential applications of robots and the expansion of their use in various areas of our society as well as the ethical concerns artificial intelligence and robots raise. Then, we give a quick introduction to deontic logic, highlighting its key concepts and explaining what it offers to the field of ethics. In the third part of the paper, we present our own approach to deontic logic, based on common sense reasoning. The fourth part includes three short applications of our common sense deontic logic approach in the field of artificial intelligence and robotics. These applications illustrate how deontic logic can be used to guide robots in making morally sound decisions, using examples from the health sector. In the final section, we have the conclusions of our paper as well as our limitations and plans for future research.

Visual question answering (VQA) research has garnered increasing attention in recent years. It is considered a visual Turing test because it requires a computer to respond to textual questions based on an image. Expertise in computer vision, natural language processing, knowledge understanding, and reasoning is required to solve the problem of VQA. Most techniques employed for VQA consist of models that are developed to learn the combination of image and question features along with the expected answer. The techniques chosen for image and question feature extraction and combining the features change with each model. This method of teaching a model of the question–answer pattern is ineffective for queries that involve counting and reasoning. This approach also requires considerable resources and large datasets for the training. The general VQA datasets feature a restricted number of items as responses to counting questions ([Formula: see text]), and the distribution of the answers is not uniform. To investigate these issues in VQA, we created synthetic datasets that could be modified to adjust the number of objects in the image and the amount of occlusion. Specifically, a zero-shot learning VQA system was devised for counting-related questions that provide answers by analyzing the output of an object detector and the query keywords. Using synthetic datasets, our model generated 100% correct results. Testing on the benchmark datasets task directed image understanding challenge (TDIUC) and TallyQA-simple indicated that the proposed model matched the performance of the learning-based baseline models. This methodology can be used efficiently for counting VQA questions confined to certain domains when the number of items to be counted is significant.

Agriculture is considered the leading field around the world, which is also the backbone of India. Agriculture is in a flawed state because the temperature changes, along with their uncertainty, cause huge damage to the crops during the manufacturing process. So, the appropriate prediction of crop expansion plays a vital role in the management of crop growth. This prediction can enhance the federated industries to make their sustainability toward the occupation. Recently, the farmers have not selected suitable crops for their cultivation based on soil factors. This makes a negative impact on crop yield, and thus, the Indian farmers can suffer from severe losses besides the monetary front. Hence, the optimal crop recommendation model has to consider different parameters of the soil for forecasting the best crop for cultivation, which increases crop growth and crop production. Thus, this research work explores a new crop recommendation model for precision agriculture intending to promote crop yield and alleviate the loss to farmers. Initially, this research work gathers the standard data regarding the agricultural parameters of some areas. Then, the deep features using an autoencoder, and statistical features are gathered along with the Principal Component Analysis (PCA)-based features. Next, all three sets of features are fused and fed to the developed Adaptive Henry Gas Solubility Optimization (AHGSO) for selecting the optimal features. Finally, the chosen optimal features are fed to the recommendation stage, where a Gated Recurrent Unit with Ridge Classifier (GRU-RC) is suggested for getting the precise outcome regarding the recommended crop suitable to that agricultural parameter. Here, the optimal solutions are attained by tuning the parameters of GRU and ridge classifier with the same I-HGSO. At last, the results obtained from the hybrid method can be considered more efficient.

Automatically detecting unusual behavior in a crowded environment greatly improves public safety. Unusual behaviors are those that depend on the rules established in the environment under consideration and cannot be properly described. This paper proposes a new deep feature-based crowd anomaly detection method. Priorly, the input image is preprocessed using the Weiner filtering method. Subsequently, AlexNet, VGGnet, and ResNet-based deep features are extracted. During this process, all three models were optimally tuned. For optimization, a new hybrid optimization method called Hybrid COOT and Bald Eagle with Bernoulli Map Evaluation (HCBEBME) is introduced in this work. This improves the performance of extracting features from the input. Finally, based on the proposed feature set, anomalies are detected by the hybrid detection model that combines LSTM and Bi-GRU models, respectively. Finally, the performance of the proposed model is validated over the conventional models. The detection accuracy of the suggested approach is 96.59%, whereas the minimal accuracy scores for the other methods BRCASO, GNNN, CNN-BILSTM, LSTM, BIGRU, BILSTM, CNN, and RNN are 93.63%, 79.16%, 87.57%, 73.85%, 70.59%, 77.08%, 81.44%, and 84.66% respectively.