An Artificial Intelligence-Based Framework for Automated Information Inquiry from Building Information Models Using Natural Language Processing and Ontology

Building information modeling (BIM), a novel technology in the architectural engineering and construction (AEC) industry, contains various data and information, which is so practical and can be required by many stakeholders during the project\u27s life cycle. For non-technical users with limited or no skill in dealing with BIM software, access to this data can be time-consuming, and tedious. Automating the information extraction from BIM models can efficiently address this need. In this regard, this research proposes an artificial intelligence (AI)-based framework to facilitate information extraction from BIM models. Therefore, the user can ask questions and receive answers from the framework. Utilizing natural language processing (NLP), an ontology database (IfcOWL) and an NLP method [latent semantic analysis (LSA)], the purpose of the user is understood by the framework through syntactic analysis and semantic understanding of the question and answer to the user, based on functions. The results show that the speed of answering the questions in this framework is up to five times faster than the manual while maintaining high accuracy

LEVERAGING NATURAL LANGUAGE PROCESSING FOR AUTOMATED INFORMATION INQUIRY FROM BUILDING INFORMATION MODELS

Building Information Modeling (BIM) is a trending technology in the building industry that can increase efficiency throughout construction. Various practical information can be obtained from BIM models during the project life cycle. However, accessing this information could be tedious and time-consuming for nontechnical users, who might have limited or no knowledge of working with BIM software. Automating the information inquiry process can potentially address this need. This research proposes an Artificial Intelligence-based framework to facilitate accessing information in BIM models. First, the framework uses a support vector machine (SVM) algorithm to determine the user\u27s question type. Simultaneously, it employs natural language processing (NLP) for syntactic analysis to find the main keywords of the user\u27s question. Then it utilizes an ontology database such as IfcOWL and an NLP method (latent semantic analysis (LSA)) for a semantic understanding of the question. The keywords are expanded through the semantic relationship in the ontologies, and eventually, a final query is formed based on keywords and their expanded concepts. A Navisworks API is developed that employs the identified question type and its parameters to extract the results from BIM and display them to the users. The proposed platform also includes a speech recognition module for a more user-friendly interface. The results show that the speed of answering the questions on the platform is up to 5 times faster than the manual use by experts while maintaining high accuracy

New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the Support Vector Machine and Heuristic Algorithms of Imperialist Competition and Genetic

In this paper, two hybrid models are used for timing of the stock markets on the basis of the technical analysis of Japanese Candlestick by Support Vector Machine (SVM) and Heuristic Algorithms of Imperialist Competition and Genetic. In the first model, SVM and Imperialist Competition Algorithm (ICA) are developed for stock market timing in which ICA is used to optimize the SVM parameters. In the second model, SVM is used with Genetic Algorithm (GA) where GA is used for feature selection in addition to SVM parameters optimization. Here the two approaches, Raw-based and Signal-based are devised on the basis of the literature to generate the input data of the model. For a comparison, the Hit Rate is considered as the percentage of correct predictions for periods of 1–6 day. The results show that SVM-ICA performance is better than SVM-GA and most importantly the feed-forward static neural network of the literature as the standard one

Mostafa Malekian: Spirituality, Siyasat-Zadegi and (A)political Self-Improvement

Mostafa Malekian has yet to receive much attention in Western academic literature pertaining to Iranian intellectual life, but inside Iran, he has emerged as a popular public intellectual; seen as both a culmination of and rupture with the project of “religious intellectualism.” Rather than offer a revolutionary and politically engaged vision of Islam, or a “reformist” or “democratic” interpretation of Shi῾ism, his project seeks to integrate what he calls “rationality” (῾aqlaniyat) and “spirituality” (ma᾽naviyat). As Malekian's project has developed, it has broken, in a number of important respects, with mainstream Islam as practiced in Iran, the religious reformist project, and even organized religion as a whole. This article seeks not only to offer one of the first comprehensive analysis of his existential and social thought in English, but also to analyze his project's deep affinities with a pervasive fatigue vis‐à‐vis collective projects of political emancipation and even “politics” tout court, in the latter phases of the “reformist” President Hojjat al‐Islam Seyyed Mohammad Khatami's tenure