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.

Transformer-based Large Language Models (LLMs), which have recently gained popularity, have significantly impacted the various fields of natural language processing. One of them is text clustering, which involves categorizing the huge texts produced by today’s digital world into meaningful groups. LLMs enable text clustering with a more semantic and contextualized approach than traditional methods. One such model is Sentence-BERT (SBERT), which has been modified to detect semantic similarity between texts. Before being clustered, texts need to be transformed into numerical text embeddings. SBERT-based models have shown promise in generating meaningful sentence embeddings. However, they face limitations when dealing with long texts that exceed their maximum token limit. In this context, this study proposes two distinct methods to overcome these limitations and enhance the performance of SBERT models for clustering long text. The proposed methods are combined with various SBERT models, and their combinations are compared to the existing default method on three datasets containing lengthy texts. This study evaluates the impact of these methods on the models and their contributions to clustering performance. The findings indicate that the proposed methods exhibit a higher clustering performance of up to 14% than the default method in text clustering. Additionally, this study provides valuable insights into the text clustering performance of SBERT models, offering practical implications for further research and applications.

Clustering is a crucial technique in both research and practical applications of data mining. It has traditionally functioned as a pivotal analytical technique, facilitating the organization of unlabeled data to extract meaningful insights. The inherent complexity of clustering challenges has led to the development of a variety of clustering algorithms. Each of these algorithms is tailored to address specific data clustering scenarios. In this context, this paper provides a thorough analysis of clustering techniques in data mining, including their challenges and applications in various domains. It also undertakes an extensive exploration of the strengths and limitations characterizing distinct clustering methodologies, encompassing distance-based, hierarchical, grid-based, and density-based algorithms. Additionally, it explains numerous examples of clustering algorithms and their empirical results in various domains, including but not limited to healthcare, image processing, text and document clustering, and the field of big data analytics.