We propose SmartEscape, a real-time, dynamic, intelligent and user-specific evacuation system with a mobile interface for emergency cases such as fire. Unlike past work, we explore dynamically changing conditions and calculate a personal route for an evacuee by considering his/her individual features. SmartEscape, which is fast, low-cost, low resource-consuming and mobile supported, collects various environmental sensory data and takes evacuees’ individual features into account, uses an artificial neural network (ANN) to calculate personal usage risk of each link in the building, eliminates the risky ones, and calculates an optimum escape route under existing circumstances. Then, our system guides the evacuee to the exit through the calculated route with vocal and visual instructions on the smartphone. While the position of the evacuee is detected by RFID (Radio-Frequency Identification) technology, the changing environmental conditions are measured by the various sensors in the building. Our ANN (Artificial Neural Network) predicts dynamically changing risk states of all links according to changing environmental conditions. Results show that SmartEscape, with its 98.1% accuracy for predicting risk levels of links for each individual evacuee in a building, is capable of evacuating a great number of people simultaneously, through the shortest and the safest route.

Foot pain, particularly caused by heel spurs and Sever's disease, significantly impacts mobility and daily activities for many people. These diseases are traditionally diagnosed by orthopedic specialists using X‐ray images of the lateral foot. In certain situations, the absence of specialists requires the adoption of AI‐based methods; however, the lack of a dataset hinders the use of AI for the preliminary diagnosis of these diseases. Therefore, this study first presents a novel dataset consisting of 3956 annotated lateral foot X‐ray images and uses the original capsule network (CapsNet) to automatically detect and classify heel bone diseases. The low accuracy of 73.99% of CapsNet due to the low extraction feature layers led us to search for a new model. For this reason, this paper also proposes a new enhanced capsule network (HeCapsNet) by adjusting the features extraction layers, adding extra convolutional layers, using “he normal” kernel initializer instead of “normal” and utilizing the “same” padding scheme to perform better with medical images. Evaluating the performance of the proposed model, higher accuracy rates are achieved, including 97.29% for balanced data, 94.19% for imbalanced data, area under the curve (AUC) of 98.69%, and a fivefold cross‐validation accuracy of 95.77%. We then compared our proposed model with state‐of‐the‐art modified CapsNet models using various datasets (MNIST, Fashion‐MNIST, CIFAR10, and brain tumor). HeCapsNet performed similarly to modified CapsNets on relatively simple non‐medical datasets such as MNIST and Fashion‐MNIST, but performed better on more complex medical datasets.

Conditions affecting the heel bone, such as heel spurs and sever's disease, pose significant challenges to patients' daily activities. While orthopedic and traumatology doctors rely on foot X-rays for diagnosis, there is a need for more AI-based detection and classification of these conditions. Therefore, this study addresses this need by proposing MedcapsNet, a novel hybrid capsule model combining modified DenseNet201 with a capsule network, designed to accurately detect and classify heel bone diseases utilizing lateral heel x-ray foot images. We conducted a comprehensive series of experiments on the proposed hybrid architecture with several datasets, including the Heel dataset, Breast BreaKHis v1, HAM10000 skin cancer dataset, and Jun Cheng Brain MRI dataset. The first experiment evaluates the proposed model for heel diseases, while the other experiments evaluate the model on a range of medical datasets to demonstrate its performance over existing studies. On the heel dataset, MedCapsNet achieves an accuracy of 96.38%, AUC of 98.35% without data augmentation, cross-validation accuracy of 95.69%, and AUC of 98.87%. The proposed model, despite employing a fixed architecture and hyperparameters, outperformed other models across four distinct datasets, including MRI, X-ray, and microscopic images with various diseases. This is notable because different types of medical image datasets typically require different architectures and hyperparameters to achieve optimal performance.

Objective: This study investigated the predictability of the Posterior Canal Being Paroxysmal Positional Vertigo (PC‐BPPV) using vertigo/dizziness features and medical history in machine learning. Secondly, this study aimed to develop a mobile application using the model that predicts PC‐BPPV with the highest accuracy rate.Methods: This study retrospectively analyzed the medical records of patients who presented to the Audiology and Balance Clinic with complaints of dizziness or vertigo between 04/01/2021 and 09/16/2023. Patients' diagnoses, demographic information, medical history, and dizziness/vertigo characteristics were used in 8 different machine learning models. A mobile application was developed with the model with the highest accuracy.Results: The study included data from 280 patients. Age, symptom onset time, duration of symptoms, dizziness type, triggering factors, and auditory symptom status were the distinguishing factors for PC‐BPPV. Using these features, the Random Forest algorithm predicted PC‐BPPV with 96.43% accuracy. The accuracy rates of other algorithms were between 89.28% and 94.64%.Conclusion: Dizziness/vertigo characteristics and medical history can be effectively utilized in machine learning to predict BPPV with high accuracy. The mobile application developed using this algorithm underscores the potential of artificial intelligence platforms to contribute to vestibular science in the telemedicine field.Level of Evidence: Level 2.

Doğal afetler ve insan kaynaklı tahribatlar sebebi ile zarar gören tarihi yapılar ilk haline en yakın bir görünüme kavuşacak şekilde restore edilmektedir. Aynı zamanda mimarlık öğrencileri bu yapılar üzerinde restorasyon süreçlerini öğrenmek için araştırmalar yapmakta ve bilgi toplamaktadır. Fakat erişebilirliği kısıtlayan pandemi, hastalık, tehlike ve olumsuz hava gibi koşullar öğrencilerin bu yapılara erişmelerini engellemektedir. Bu çalışma, mimarlık öğrencilerinin restorasyon derslerinde tarihi yapıları inceleme sırasında yaşadıkları erişim zorluklarını Sanal Gerçeklik (SG) teknolojisinden faydalanarak gidermeyi ve özellikle öğrencilere uzaktan erişimle, restorasyon dersindeki ölçme tekniklerini sanal ortamda uygulamalı olarak öğretmeyi amaçlamaktadır. Çalışma kapsamında, restorasyon öncesi veri toplama işlemi tamamlanmış olan Tarihi Safranbolu Tabakhane Binası gerçek ölçüleri ile modellenmiş ve sanal gerçeklik ortamına aktarılmıştır. Öğrencilerin bu binada etkileşimli olarak beraber çalışabilmeleri için geleneksel ölçme araçları olan metre, su hortumu, şakül ve yazı tahtasının sanal versiyonları sisteme entegre edilmiştir. Böylece mimari restorasyon alanında literatürde yer alan diğer çalışmalardan farklı olarak, birden fazla öğrencinin fiziksel olarak farklı ortamlarda olmalarına rağmen aynı ortamdaymış gibi eş zamanlı olarak birbirleriyle etkileşimde bulunduğu, sesli ve görsel iletişim kurabildikleri sanal bir eğitim ortamı oluşturulmuştur. Kullanıcı deneyi sonrası yapılan anketlerde, öğrencilerin geliştirilen yönteme genellikle olumlu bakış açısı sergiledikleri ve anketteki toplam 14 ifadenin puanlamasında sistemin %88,62’lik bir puan ortalamasına ulaştığı gözlemlenmiştir. Sonuçlar, sanal gerçekliğin yüz yüze eğitime ulaşımın olmadığı durumlarda, restorasyon derslerine yönelik uygulamalı bir eğitim fırsatı sunduğunu ortaya koymaktadır.