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artificial intelligence techniques for driving safety and vehicle crash prediction

artificial intelligence REVIEW 2016年第3期46卷 351-387页

作者： Halim, Zahid Kalsoom, Rizwana Bashir, Shariq Abbas, Ghulam Ghulam Ishaq Khan Inst Engn Sci & Technol Fac Comp Sci & Engn Topi Pakistan Al Imam Mohammad Ibn Saud Islamic Univ IMSIU Dept Informat Studies Coll Comp & Informat Sci Riyadh Saudi Arabia

Accident prediction is one of the most critical aspects of road safety, whereby an accident can be predicted before it actually occurs and precautionary measures taken to avoid it. For this purpose, accident prediction models are popular in road safety analysis. artificial intelligence (AI) is used in many real world applications, especially where outcomes and data are not same all the time and are influenced by occurrence of random changes. This paper presents a study on the existing approaches for the detection of unsafe driving patterns of a vehicle used to predict accidents. The literature covered in this paper is from the past 10 years, from 2004 to 2014. AI techniques are surveyed for the detection of unsafe driving style and crash prediction. A number of statistical methods which are used to predict the accidents by using different vehicle and driving features are also covered in this paper. The approaches studied in this paper are compared in terms of datasets and prediction performance. We also provide a list of datasets and simulators available for the scientific community to conduct research in the subject domain. The paper also identifies some of the critical open questions that need to be addressed for road safety using AI techniques.

关键词： Road safety Accident prediction artificial intelligence techniques Traffic datasets and simulators

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artificial intelligence techniques for the Puerto Rico Strategy Game 11th

Artificial Intelligence Techniques for the Puerto Rico Strat...

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11th KES International Conference on Agent and Multi-Agent Systems - Technology and Applications (KES-AMSTA)

作者： Drezewski, Rafal Kleczar, Maciej AGH Univ Sci & Technol Dept Comp Sci Krakow Poland Fac Management Finance & Comp Sci Sch Banking & Management Krakow Poland

ISBN: (纸本)9783319593944

It was always a challenging task to create artificial opponents for strategy video games. It is usually quite easy to discover and exploit their weaknesses because their tactics usually do not adapt to changing conditions and to human opponent tactics. In this paper two artificial intelligence techniques for well known Puerto Rico strategy game are proposed. One of them does not rely on any precoded tactics, but tries to dynamically learn and adapt to the changing game environment. Both techniques were compared on the basis of results of games played against each other and also against human expert players.

关键词： Strategy games artificial intelligence techniques Computer games

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Exploring the Use of artificial intelligence techniques to Detect the Presence of Coronavirus Covid-19 Through Speech and Voice Analysis

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IEEE ACCESS 2021年 9卷 65750-65757页

作者： Verde, Laura De Pietro, Giuseppe Ghoneim, Ahmed Alrashoud, Mubarak Al-Mutib, Khaled N. Sannino, Giovanna CNR Natl Res Council Inst High Performance Comp & Networking ICAR I-80131 Naples Italy King Saud Univ Coll Comp & Informat Sci Dept Software Engn Riyadh 11543 Saudi Arabia Menou Univ Math & Comp Sci Dept Fac Sci Shibin Al Kawm 32511 Egypt

The Covid-19 pandemic represents one of the greatest global health emergencies of the last few decades with indelible consequences for all societies throughout the world. The cost in terms of human lives lost is devastating on account of the high contagiousness and mortality rate of the virus. Millions of people have been infected, frequently requiring continuous assistance and monitoring. Smart healthcare technologies and artificial intelligence algorithms constitute promising solutions useful not only for the monitoring of patient care but also in order to support the early diagnosis, prevention and evaluation of Covid-19 in a faster and more accurate way. On the other hand, the necessity to realise reliable and precise smart healthcare solutions, able to acquire and process voice signals by means of appropriate Internet of Things devices in real-time, requires the identification of algorithms able to discriminate accurately between pathological and healthy subjects. In this paper, we explore and compare the performance of the main machine learning techniques in terms of their ability to correctly detect Covid-19 disorders through voice analysis. Several studies report, in fact, significant effects of this virus on voice production due to the considerable impairment of the respiratory apparatus. Vocal folds oscillations that are more asynchronous, asymmetrical and restricted are observed during phonation in Covid-19 patients. Voice sounds selected by the Coswara database, an available crowd-sourced database, have been e analysed and processed to evaluate the capacity of the main ML techniques to distinguish between healthy and pathological voices. All the analyses have been evaluated in terms of accuracy, sensitivity, specificity, F1-score and Receiver Operating Characteristic area. These show the reliability of the Support Vector Machine algorithm to detect the Covid-19 infections, achieving an accuracy equal to about 97%.

关键词： COVID-19 Feature extraction Databases Reliability Pathology Medical services Machine learning algorithms artificial intelligence techniques Covid-19~detection speech analysis voice analysis

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A study on visual features of leaves in plant identification using artificial intelligence techniques

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2019年 156卷 369-377页

作者： Yigit, Enes Sabanci, Kadir Toktas, Abdurrahim Kayabasi, Ahmet Karamanoglu Mehmetbey Univ Dept Elect Elect Engn Engn Fac TR-70100 Karaman Turkey

In this paper, artificial intelligence techniques (AIT) such as artificial neural network, naive bayes algorithm, random forest algorithm, K-nearest neighborhood (KNN) and support vector machine (SVM) are implemented to design an automatic identifier for the plant leaves. For this purpose, data of 637 healthy leaves consisting of 32 different plant species are used. 22 visual features (VF) of each leaf are extracted by using image processing techniques. These 22 VF are considered in 4 groups including dimension (D#6), color (C#6), texture (T#5) and pattern (P#5). In order to investigate the effects of these groups on the classifying performance, 15 possible different combinations from the 4 groups are constituted. The models are then trained via the data of 510 leaves, and their accuracy are tested through the data of 127 leaves. From the results of the test, SVM model with the accuracy of 92.91% is found to be the most successful identifier for combination including all groups. The next best result is achieved with the accuracy of 87.40% for the combination of D#6, C#6 and P#5 groups. Since the most important issue in the classification process is the use of the minimum number of VF, 16 most effective VF on the identification are determined by means of correlation-based feature selection (CFS) method. The best result for these 16 VF is also achieved with the accuracy of 94.49% by the SVM model. Then the performance of the proposed method is tested to identify the diseased and defected leaves. Therefore, 637 healthy and 33 diseased/defected leaves are put together. Randomly selected 536 leaves corresponding to 80% of all leaves are used for training and the remaining 134 leaves are used for testing, and identified with the accuracy of 92.53% by the SVM model. With this study, it is numerically revealed that the P#5 is the most effective feature group. Moreover, it has been determined that the most effective feature in the P#5 group is the feature of edge Fourier tra

关键词： Plant species Identification Image processing technique artificial intelligence techniques Fourier Transform

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Prediction of Density, Surface Tension, and Viscosity of Quaternary Ammonium-Based Ionic Liquids ([N_222(n)]Tf₂N) by Means of artificial intelligence techniques

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JOURNAL OF DISPERSION SCIENCE AND TECHNOLOGY 2014年第12期35卷 1809-1829页

作者： Golzar, Karim Amjad-Iranagh, Sepideh Modarress, Hamid Amirkabir Univ Technol Dept Chem Engn Tehran Iran Amirkabir Univ Technol Dept Chem Tehran Iran

In this work, thermophysical properties of quaternary ammonium-based ionic liquids (ILs) including density, surface tension, and viscosity are produced by two powerful artificial intelligence techniques: genetic function approximation (GFA) and artificial neural network (ANN). In proposed GFA and ANN models, the critical temperature and water content of studied ILs ([N-222(n)] Tf2N with n=5, 6, 8, 10, and 12) as well as operation temperature were given as the input parameters and the density, surface tension, and viscosity were predicted as the output results. The obtained results reveal that the selected input parameters are appropriate for prediction of thermophysical properties of quaternary ammonium-based ILs. In addition, the high statistical quality represented by various criteria and the low prediction errors of the presented models indicate that they can accurately predict the density, surface tension, and viscosity of new ILs without recourse to experimental data.

关键词： artificial intelligence techniques density quaternary ammonium-based ionic liquid ([N-222(n)]Tf2N) surface tension viscosity

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Predicting thermal degradation kinetics of nylon6/feather keratin blends using artificial intelligence techniques

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POLYMER 2012年第11期53卷 2255-2264页

作者： Fazilat, Hakimeh Akhlaghi, Shahin Shiri, Mohammad Ebrahim Sharif, Alireza Amirkbair Univ Technol Dept Comp Sci Math & Comp Sci Fac Tehran Iran Islamic Azad Univ S Tehran Branch Tehran Iran Tarbiat Modares Univ Polymer Engn Grp Dept Chem Engn Tehran Iran

A multi-structured architecture of artificial intelligence techniques including artificial neural network (ANN), adaptive-neuro-fuzzy-interference system (ANFIS) and radial basis function (RBF) were developed to predict thermal degradation kinetics (TDK) of nylon6 (NY6)/feather keratin (FK) blend films. By simultaneous implementation of back-propagation ANN and feed-forward ANFIS modeling on the experimental data obtained from thermogravimetric analysis (TGA) method, thermal degradation behavior of various compositions of NY6/FK blends was successfully predicted with minimum mean square errors (MSE). RBF networks were then trained on the TGA data at one heating rate for predicting analogs information at different heating rates, providing sufficient feed for TDK modeling. According to the comparison made between experimental and predicted kinetic parameters of thermal degradation process calculated from Friedman and Kissinger methods, the proposed prediction effort could effectively contribute to the estimation of precise activation energy (E-a) and reaction order (n) values with least amount of experimental work and most accuracy. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Feather keratin Thermal degradation kinetics artificial intelligence techniques

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Detection of drugs and explosives using neutron computerized tomography and artificial intelligence techniques

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APPLIED RADIATION AND ISOTOPES 2010年第6期68卷 1012-1017页

作者： Ferreira, F. J. O. Crispim, V. R. Silva, A. X. Inst Engn Nucl BR-21945970 Rio De Janeiro Brazil Univ Fed Rio de Janeiro DNC Poli PEN COPPE CT BR-21941972 Rio De Janeiro Brazil

In this study the development of a methodology to detect illicit drugs and plastic explosives is described with the objective of being applied in the realm of public security. For this end, non-destructive assay with neutrons was used and the technique applied was the real time neutron radiography together with computerized tomography. The system is endowed with automatic responses based upon the application of an artificial intelligence technique. In previous tests using real samples, the system proved capable of identifying 97% of the inspected materials. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Detect illicit drugs and explosives Real time neutron radiography artificial intelligence techniques

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Prediction and optimization of fireproofing properties of intumescent flame retardant coatings using artificial intelligence techniques

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FIRE SAFETY JOURNAL 2013年 61卷 193-199页

作者： Arabasadi, Zeinab Khorasani, Manouchehr Akhlaghi, Shahin Fazilat, Hakimeh Gedde, Ulf W. Hedenqvist, Mikael S. Shiri, Mohammad Ebrahim Amirkabir Univ Technol Math & Comp Sci Fac Dept Comp Sci Tehran 158754413 Iran Amirkabir Univ Technol Color & Polymer Res Ctr Tehran 158754413 Iran KTH Royal Inst Technol Sch Chem Sci & Engn SE-10044 Stockholm Sweden Islamic Azad Univ South Tehran Branch Dept Polymer Engn Tehran 17667 Iran

A inulti-structured architecture of artificial intelligence techniques including artificial neural network (ANN), adaptive neuro-fuzzy-inference-system (ANFIS) and genetic algorithm (GA) were developed to predict and optimize the fireproofing properties of a model intumescent flame retardant coating including ammonium polyphosphate, pentaerythritol, melamine, thermoplastic acrylic resin and liquid hydrocarbon resin. By implementing ANN on heat insulation results of coating samples, prepared based on a L-16 orthogonal array, mean fireproofing time (MFPT) values were properly predicted. The predicted data were then proved to be valid through performing closeness examinations on fuzzy inference systems results regarding their experimental counterparts. However, the possible deviations tapped into phenomena like foam detachment and char cracking were alleviated by ANFIS modeling embedded with pertinent fuzzy rules based on the sole and associative practical role of used additives. The contribution of each intumescent coating component on the formulation with optimized fireproofing behavior was then explored using GA modeling. A similar optimization procedure was also conducted using conventional Taguchi experimental design but the GA based optimized intumescent coating was found to exhibit higher MFPT value than that suggested by the Taguchi method. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Intumescent coating Flame retardant artificial intelligence techniques Genetic algorithm

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Dynamic performance using artificial intelligence techniques and educational assessment of nanocomposite structures

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STEEL AND COMPOSITE STRUCTURES 2024年第1期53卷 115-121页

作者： Zengxia, Han Nasihatgozar, M. Shen, X. Xinzhou Teachers Univ Dept Educ Xinzhou Peoples R China Islamic Azad Univ Dept Mech Engn Kashan Branch Kashan Iran Ristab Co Dept Mech Engn Dubai U Arab Emirates

The present paper deals with a comprehensive study about dynamic performance and educational economic assessment of nanocomposite structures, while it focuses on truncated conical shells. Advanced structure dynamic behavior has been analyzed by means of AI techniques, which allow one to predict and optimize their performances with good accuracy for different loading and environmental conditions. The incorporation of the AI method significantly enhances the computational efficiency and is a powerful tool in designing nanocomposites and for their structural analysis. Further, an educational assessment is provided in the context of cost and practicality related to such structures in engineering education. This study showcases the capabilities of AI-enabled methods with regard to cost reduction, improvement of structural efficiency, and enhancement of learning engagement for students through certain practical examples on state-of-the-art nanocomposite technology. The results also confirm a remarkable capability of artificial intelligence regarding the optimization of both dynamic and economic aspects, which could be highly valued for further development of nanocomposite structures.

关键词： artificial intelligence techniques dynamic performance educational nanocomposite structures

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Application of artificial intelligence techniques in automatic hull form generation

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OCEAN ENGINEERING 2001年第12期28卷 1531-1544页

作者： Islam, MM Khondoker, MRH Rahman, CM BUET Dept Comp Sci & Engn Dhaka 1000 Bangladesh BUET Dept Naval Architecture & Marine Engn Dhaka 1000 Bangladesh

The paper describes a methodology for automatic hull form generation of ships with some desired performances using artificial intelligence techniques. The whole implementation Process is divided into three main components. First of all the half-breadth weight matrices are generated that would provide population with pre pre-fitness by examining the relationship of the hull form with the principal dimensions of a number of existing vessels. Secondly, breadth and draft are adjusted using Neural Network Concepts. Breadth, draft, length, displacement and speed of the ship are very related terms and relations among them create some constraints. Neural Networks solve these constraints and adjust the parameters. Finally, Genetic Algorithm is used for searching the exact solution by examining several generations. For this, the algorithms need to measure fitness for every population in every generation. Unfortunately, GA doesn't guarantee fairness of the surface of the hull form, which can't be ignored. So, for every population especially for newborn population fairing techniques would be used. However, being complex geometric shape, hull form surface can't be faired using least-square or other single equation fitting techniques. Fairness is done taking pair-wise points using B-spline functions [CAD 20(1988)]. (C) 2001 Elsevier Science Ltd. All rights reserved.

关键词： automatic hull form generation artificial intelligence techniques neural network genetic algorithm

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