Advanced Artificial Intelligence Methods Research Articles

BIMBOT-(Artificial intelligence applied to BIM design)

BIMBOT es un asistente de diseño inteligente para la industria AEC. Sus herramientas se ejecutan sobre un software de modelado BIM y producen varias soluciones de diseño con modelos BIM optimizados. Funciona con el uso de métodos avanzados de Inteligencia Artificial (optimización soft computing y Machine Learning) y es compatible con bases de datos NoSQL. Contempla varias etapas: La definición por el usuario de restricciones / prioridades establecidas ejecuta un proceso de diseño generativo impulsado por varios métodos de IA. Éste crea diferentes soluciones en modelos BIM almacenados y refinados a partir de un catálogo de objetos inteligentes. Con ello, los usuarios pueden interactuar importando modelos BIM con diseños propuestos, crearlos o editarlos in situ y recibir asistencia de una serie de métricas configurables que dan calidad al diseño de acuerdo con las preferencias iniciales. Así, obtenemos un Modelo BIM completo como resultado del proceso iterativo. Finalmente, el entrenamiento continuo de los algoritmos mejorará la eficiencia en futuros diseños. BIMBOT está concebido para extender las habilidades de los diseñadores a través del desarrollo de software BIM, permitiéndoles ser más productivos en tareas complejas del proceso de diseño. BIMBOT está financiado por el programa europeo Eureka / Eurostars (E! 12863).

Applying Natural Language Processing and Hierarchical Machine Learning Approaches to Text Difficulty Classification

For decades, educators have relied on readability metrics that tend to oversimplify dimensions of text difficulty. This study examines the potential of applying advanced artificial intelligence methods to the educational problem of assessing text difficulty. The combination of hierarchical machine learning and natural language processing (NLP) is leveraged to predict the difficulty of practice texts used in a reading comprehension intelligent tutoring system, iSTART. Human raters estimated the text difficulty level of 262 texts across two text sets (Set A and Set B) in the iSTART library. NLP tools were used to identify linguistic features predictive of text difficulty and these indices were submitted to both flat and hierarchical machine learning algorithms. Results indicated that including NLP indices and machine learning increased accuracy by more than 10% as compared to classic readability metrics (e.g., Flesch-Kincaid Grade Level). Further, hierarchical outperformed non-hierarchical (flat) machine learning classification for Set B (72%) and the combined set A + B (65%), whereas the non-hierarchical approach performed slightly better than the hierarchical approach for Set A (79%). These findings demonstrate the importance of considering deeper features of language related to text difficulty as well as the potential utility of hierarchical machine learning approaches in the development of meaningful text difficulty classification.

The Relationship between Artificial Intelligence and Internet of Things: A quick review

Internet Of Things (IOT) is a network of various devices that are connected over the internet, and they can collect and exchange data with each other. These IOT devices generate a lot of data that needs to be collected and mined for actionable results through use artificial intelligence (AI) to manage huge data flows and storage in the IOT network. In this paper we briefly discussed about what IOT is, what AI is, Algorithm of AI, Challenge AI with IOT, application of artificial intelligence system in the IOT. The self-optimizing network and software defined network are parts of the important parameters in the Artificial Intelligence IoT System. This paper provides a general discussion about importance of the IoT in different applications. The paper covers different applications of IoT and shows the relationship between AI and IoT. The role of the AI in IoT applications is extensively discussed. In the future work, we are planning to work on improving the performance of IoT applications using advanced AI methods and algorithms such as Machine Learning and Deep Learning.

EMULSION: Transparent and flexible multiscale stochastic models in human, animal and plant epidemiology.

Stochastic mechanistic epidemiological models largely contribute to better understand pathogen emergence and spread, and assess control strategies at various scales (from within-host to transnational scale). However, developing realistic models which involve multi-disciplinary knowledge integration faces three major challenges in predictive epidemiology: lack of readability once translated into simulation code, low reproducibility and reusability, and long development time compared to outbreak time scale. We introduce here EMULSION, an artificial intelligence-based software intended to address those issues and help modellers focus on model design rather than programming. EMULSION defines a domain-specific language to make all components of an epidemiological model (structure, processes, parameters…) explicit as a structured text file. This file is readable by scientists from other fields (epidemiologists, biologists, economists), who can contribute to validate or revise assumptions at any stage of model development. It is then automatically processed by EMULSION generic simulation engine, preventing any discrepancy between model description and implementation. The modelling language and simulation architecture both rely on the combination of advanced artificial intelligence methods (knowledge representation and multi-level agent-based simulation), allowing several modelling paradigms (from compartment- to individual-based models) at several scales (up to metapopulation). The flexibility of EMULSION and its capability to support iterative modelling are illustrated here through examples of progressive complexity, including late revisions of core model assumptions. EMULSION is also currently used to model the spread of several diseases in real pathosystems. EMULSION provides a command-line tool for checking models, producing model diagrams, running simulations, and plotting outputs. Written in Python 3, EMULSION runs on Linux, MacOS, and Windows. It is released under Apache-2.0 license. A comprehensive documentation with installation instructions, a tutorial and many examples are available from: https://sourcesup.renater.fr/www/emulsion-public.

MACHINE LEARNING BASED CROP CYCLE MAPPING USING MULTI TEMPORAL SPACE DATASETS

Abstract. The Indo-Gangetic region is an important source of food grain produced in India. With advent of advanced crop management practices, there has been a recent increase in production. This could be due to improved yield and/or multiple crops per year. Understanding cropping patterns is, therefore, a fundamental requirement to strategize food security in the country. The long running space based earth observation programmes, provides a powerful tool to study the change in vegetation, through use of indices, synoptically and temporally. This study proposes a machine learning based technique to identify distributed (pixel-wise) cropping cycles, based on the temporal trend in normalized difference vegetation index (NDVI). The technique is applied to several time series NDVI data sets (OCM, MODIS and GIMMS) and results are discussed. There is tremendous scope to further extend this study to predict drought conditions, change in cropping pattern, and estimate production statistics, by using advanced artificial intelligence methods (such as deep learning).

Artificial Intelligence for Breast MRI in 2008-2018: A Systematic Mapping Review.

The purpose of this study is to review literature from the past decade on applications of artificial intelligence (AI) to breast MRI. In June 2018, a systematic search of the literature was performed to identify articles on the use of AI in breast MRI. For each article identified, the surname of the first author, year of publication, journal of publication, Web of Science Core Collection journal category, country of affiliation of the first author, study design, dataset, study aim(s), AI methods used, and, when available, diagnostic performance were recorded. Sixty-seven studies, 58 (87%) of which had a retrospective design, were analyzed. When journal categories were considered, 36% of articles were identified as being included in the radiology and imaging journal category. Contrast-enhanced sequences were used for most AI applications (n = 50; 75%) and, on occasion, were combined with other MRI sequences (n = 8; 12%). Four main clinical aims were addressed: breast lesion classification (n = 36; 54%), image processing (n = 14; 21%), prognostic imaging (n = 9; 13%), and response to neoadjuvant therapy (n = 8; 12%). Artificial neural networks, support vector machines, and clustering were the most frequently used algorithms, accounting for 66%. The performance achieved and the most frequently used techniques were then analyzed according to specific clinical aims. Supervised learning algorithms were primarily used for lesion characterization, with the AUC value from ROC analysis ranging from 0.74 to 0.98 (median, 0.87) and with that from prognostic imaging ranging from 0.62 to 0.88 (median, 0.80), whereas unsupervised learning was mainly used for image processing purposes. Interest in the application of advanced AI methods to breast MRI is growing worldwide. Although this growth is encouraging, the current performance of AI applications in breast MRI means that such applications are still far from being incorporated into clinical practice.

Non-obvious correlations to disease management unraveled by Bayesian artificial intelligence analyses of CMS data.

Given the availability of extensive digitized healthcare data from medical records, claims and prescription information, it is now possible to use hypothesis-free, data-driven approaches to mine medical databases for novel insight. The goal of this analysis was to demonstrate the use of artificial intelligence based methods such as Bayesian networks to open up opportunities for creation of new knowledge in management of chronic conditions. Hospital level Medicare claims data containing discharge numbers for most common diagnoses were analyzed in a hypothesis-free manner using Bayesian networks learning methodology. While many interactions identified between discharge rates of diagnoses using this data set are supported by current medical knowledge, a novel interaction linking asthma and renal failure was discovered. This interaction is non-obvious and had not been looked at by the research and clinical communities in epidemiological or clinical data. A plausible pharmacological explanation of this link is proposed together with a verification of the risk significance by conventional statistical analysis. Potential clinical and molecular pathways defining the relationship between commonly used asthma medications and renal disease are discussed. The study underscores the need for further epidemiological research to validate this novel hypothesis. Validation will lead to advancement in clinical treatment of asthma & bronchitis, thereby, improving patient outcomes and leading to long term cost savings. In summary, this study demonstrates that application of advanced artificial intelligence methods in healthcare has the potential to enhance the quality of care by discovering non-obvious, clinically relevant relationships and enabling timely care intervention.

What's the Name of the Game? Formal Specification of Artificial Intelligence Games

Artificial intelligence games are a very interesting tool for teaching Artificial Intelligence techniques. Competitors write programs for agents, which are supposed to complete a given task or fight against other agents. In order to achieve the best performance, programs may have to use advanced Artificial Intelligence methods. In this paper, we present a framework to build artificial intelligence games, using Abstract State Machines (ASM) for the specification of the rules of the games. Choosing ASM, we expect that the competitors will be able to understand clearly the semantics of the rules. The framework includes a compiler for an ASM-based language, allows complete control of the order of execution of agents and easy integration with graphical libraries.

Application of artificial intelligence techniques to a well defined clinical problem: jaundice diagnosis

Jaundice is a very common medical condition, in which pathophysiological knowledge has been quite well assessed and clinical features are usually well known. However, incorrect conclusions are sometimes reached in medical practice which can lead to serious implications. Thus, jaundice diagnosis appears as one of the medical situations which might be substantially improved by computer assistance. The present study is aimed at describing and discussing in what way a computer program supporting medical decision making in jaundiced patients can be developed on the basis of advanced Artificial Intelligence methods. To this extent methodological problems concerned with the organization and the formalization of medical knowledge have been outlined with some detail. The resulting expert system is expected to become a well assessed and potentially useful tool for both medical decision making and medical education.