Developing a reliable and accurate indoor localization system is a crucial step for creating a seamless and interactive user-device experience in nearly all intelligent internet of things (IIoTs) and smart applications. Indoor localization systems based on WiFi fingerprinting have been considered as a promising alternative to model-based approaches owing to their accuracy, low cost, availability, and ease of configuration. However, recent studies have revealed that in complex environments, WiFi fingerprinting techniques are faced with a lot of challenges as the coverage area increases. These challenges include fingerprint spatial uncertainty, instability in the received signal strength indicator (RSSI) and discrepancy in fingerprint distribution. Furthermore, there is frequent need for database upgrades or even recreation whenever there is a change in the architecture of the location. These challenges have questioned the robustness and efficiency of most of the existing schemes. In this paper, we present an indoor localization architecture for complex multi-building multi-floor location prediction and subsequently propose SALLoc (SAE-ALSTM Localization), a WiFi fingerprinting indoor localization scheme based on Stacked Autoencoder (SAE) and Attention-based Long Short-Time Memory (ALSTM) framework. Firstly, stratified sampling technique is used to separate validation set from the entire uneven RSSI training set which ensures that the same proportion of RSSI samples are present in both sets. Secondly, SAE is utilized to select core features and decrease the dimensions of the RSSI samples. Finally, ALSTM is trained to focus on these features to achieve robust location prediction. Extensive investigations were conducted using UJIIndoorLoc, Tampere and UTSIndoorLoc datasets, and the results obtained demonstrated the superiority of the proposed scheme in terms of prediction accuracy, robustness, and generalizations when compared to state-of-the-art methods. The mean localization error (MLE) on UJIIndoorLoc, Tampere and UTSIndoorLoc datasets are 8.28 m, 9.52 m, and 6.48 m respectively. Consequently, it can be concluded that the proposed scheme is accurate and well-suited for large-scale indoor environment location prediction.

The adoption of Electronic Health Record (EHR) and other e-health infrastructures over the years has been characterized by an increase in medical errors. This is primarily a result of the widespread usage of medical acronyms and abbreviations with multiple possible senses (i.e., ambiguous). Artificial Intelligence (AI) technology, particularly Natural Language Processing (NLP), has emerged as a promising solution for addressing the challenge of automatic sense resolution in acronyms. Machine learning (ML) techniques have been utilized efficiently in the development of such systems, gaining increasing attention in recent years. Nevertheless, there is a scarcity of comprehensive studies that investigate the global adoption of Electronic Health Records (EHR), analyze the impact of acronyms usage on medical errors in EHR, and report on the latest trends and advancements in ML-based NLP solutions to disambiguate acronyms in medical practice. In the current study, we present a detailed overview on medical error, its origins, unintended effects, and EHR-related errors as a subclass of clinical error. This study also investigates the adoption of EHR systems in developed and developing nations, and the review concludes with an examination of various artificial intelligence techniques, particularly machine learning algorithms for medical acronym and abbreviation disambiguation in EHRs.

WiFi fingerprinting based on the Received Signal Strength Indicator (RSSI) is a widely used technique for indoor localization. However, achieving the necessary precision for most smart applications has proven difficult due to various challenges, including the time-varying characteristics of indoor RSSI, device heterogeneity, and ambiguity in the positional fingerprints. To address these concerns, Artificial Intelligence (AI) techniques such as Machine Learning (ML) and Deep Learning (DL) have been used to classify and predict indoor locations. While these techniques have shown promising results, they face two significant challenges. First, their complex multilayer architecture requires extensive training iterations. Second, the distribution of the input layer fluctuates as the networks parameters are updated. In this study, we propose a WiFi indoor localization framework based on Recursive Feature Elimination with Cross-Validation (RFECV) and Deep Neural Network with Batch Normalization (DNNBN). The CV process is repeated multiple times to assess the RFEs generalization ability for optimal feature selection, and the BN algorithm is integrated into each layer of the DNN to ensure consistent activation value distribution and stabilization of the training process. Two datasets were used to assess the performance of the proposed RFECV-DNNBN method. The results show that the method addresses the challenges faced by existing AI techniques and outperforms current methods in classification and regression tasks. Our proposed framework achieved 100% accuracy in building classification and 94.69% and 96.39% in floor classification on the UJIIndoorLoc and UTSIndoorLoc datasets, respectively, demonstrating its effectiveness. Additionally, it achieved a Mean Absolute Error (MAE) of 5.08 m and 4.29 m for the respective datasets, highlighting its potential in multi-floored environments.

The use of Artificial Intelligence (AI) algorithms has enhanced WiFi fingerprinting-based indoor localization. However, most existing approaches are limited to 2D coordinate estimation, which leads to significant performance declines in multi-floor environments due to vertical ambiguity and inadequate spatial modeling. This limitation reduces reliability in real-world applications where accurate indoor localization is essential. This study proposes RELoc, a new 3D indoor localization framework that integrates Recursive Feature Elimination with Cross-Validation (RFECV) for optimal Access Point (AP) selection and Extremely Randomized Trees (ERT) for precise 2D and 3D coordinate regression. The ERT hyperparameters are optimized using Bayesian optimization with Optuna’s Tree-structured Parzen Estimator (TPE) to ensure robust, stable, and accurate localization. Extensive evaluation on the SODIndoorLoc and UTSIndoorLoc datasets demonstrates that RELoc delivers superior performance in both 2D and 3D indoor localization. Specifically, RELoc achieves Mean Absolute Errors (MAEs) of 1.84 m and 4.39 m for 2D coordinate prediction on SODIndoorLoc and UTSIndoorLoc, respectively. When floor information is incorporated, RELoc improves by 33.15% and 26.88% over the 2D version on these datasets. Furthermore, RELoc outperforms state-of-the-art methods by 7.52% over Graph Neural Network (GNN) and 12.77% over Deep Neural Network (DNN) on SODIndoorLoc and 40.22% over Extra Tree (ET) on UTSIndoorLoc, showing consistent improvements across various indoor environments. This enhancement emphasizes the critical role of 3D modeling in achieving robust and spatially discriminative indoor localization.