Relevant bibliographies by topics / Artificial intelligent techniques

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Artificial intelligent techniques'

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Published: 19 February 2023

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Journal articles on the topic "Artificial intelligent techniques"

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Hashmi, Abdul Wahab, Harlal Singh Mali, Anoj Meena, Irshad Ahamad Khilji, Mohammad Farukh Hashmi, and Siti Nadiah binti Mohd Saffe. "Artificial intelligence techniques for implementation of intelligent machining." Materials Today: Proceedings 56 (2022): 1947–55. http://dx.doi.org/10.1016/j.matpr.2021.11.277.

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Saleem, Nada, Baydaa Khaleel, and Shahbaa Khaleel. "Artificial Intelligent Techniques with Watermarking." AL-Rafidain Journal of Computer Sciences and Mathematics 6, no. 2 (July 1, 2009): 229–66. http://dx.doi.org/10.33899/csmj.2009.163810.

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Pappas, Marios, and Athanasios Drigas. "Incorporation of Artificial Intelligence Tutoring Techniques in Mathematics." International Journal of Engineering Pedagogy (iJEP) 6, no. 4 (November 24, 2016): 12. http://dx.doi.org/10.3991/ijep.v6i4.6063.

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Intelligent Tutoring Systems incorporate Artificial Intelligence techniques, in order to imitate a human tutor. These expert systems are able to assess student’s proficiency, to provide solved examples and exercises for practice in each topic, as well as to provide immediate and personalized feedback to learners. The present study is a systematic review that evaluates the contribution of the Intelligent Tutoring Systems developed so far, to Mathematics Education, representing some of the most representative studies of the last decade.
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Ibrahim, Laheeb, and Ibrahim Saleh. "Face Recognition using Artificial Intelligent Techniques." AL-Rafidain Journal of Computer Sciences and Mathematics 6, no. 2 (July 1, 2009): 211–27. http://dx.doi.org/10.33899/csmj.2009.163809.

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Sundhararajan, Mahalingam, Xiao-Zhi Gao, and Hamed Vahdat Nejad. "Artificial intelligent techniques and its applications." Journal of Intelligent & Fuzzy Systems 34, no. 2 (February 27, 2018): 755–60. http://dx.doi.org/10.3233/jifs-169369.

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Long, Teng, Zhangbing Zhou, Gerhard Hancke, Yang Bai, and Qi Gao. "A Review of Artificial Intelligence Technologies in Mineral Identification: Classification and Visualization." Journal of Sensor and Actuator Networks 11, no. 3 (August 29, 2022): 50. http://dx.doi.org/10.3390/jsan11030050.

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Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine capable of responding in a manner similar to human intelligence. Research in this area includes robotics, language recognition, image identification, natural language processing, and expert systems. In recent years, the availability of large datasets, the development of effective algorithms, and access to powerful computers have led to unprecedented success in artificial intelligence. This powerful tool has been used in numerous scientific and engineering fields including mineral identification. This paper summarizes the methods and techniques of artificial intelligence applied to intelligent mineral identification based on research, classifying the methods and techniques as artificial neural networks, machine learning, and deep learning. On this basis, visualization analysis is conducted for mineral identification of artificial intelligence from field development paths, research hot spots, and keywords detection, respectively. In the end, based on trend analysis and keyword analysis, we propose possible future research directions for intelligent mineral identification.
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Kumar, Avinash, Abhishek Kumar, and Arun Prasad Burnwal. "CORRELATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES WITH SOFT COMPUTING IN VARIOUS AREAS." International Journal of Students' Research in Technology & Management 5, no. 4 (November 27, 2017): 58–65. http://dx.doi.org/10.18510/ijsrtm.2017.548.

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Artificial Intelligence (AI) is a part of computer science concerned with designing intelligent computer systems that exhibit the characteristics used to associate with intelligence in human behavior. Basically, it define as a field that study and design of intelligent agents. Traditional AI approach deals with cognitive and biological models that imitate and describe human information processing skills. This processing skills help to perceive and interact with their environment. But in modern era developers can build system that assemble superior information processing needs of government and industry by choosing from large areas of mature technologies. Soft Computing (SC) is an added area of AI. It focused on the design of intelligent systems that process uncertain, imprecise and incomplete information. It applied in real world problems frequently to offer more robust, tractable and less costly solutions than those obtained by more conventional mathematical techniques. This paper reviews correlation of artificial intelligence techniques with soft computing in various areas.
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Thanh, Cong Truong, and Ivan Zelinka. "A Survey on Artificial Intelligence in Malware as Next-Generation Threats." MENDEL 25, no. 2 (December 20, 2019): 27–34. http://dx.doi.org/10.13164/mendel.2019.2.027.

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Recent developments in Artificial intelligence (AI) have a vast transformative potential for both cybersecurity defenders and cybercriminals. Anti-malware solutions adopt intelligent techniques to detect and prevent threats to the digital space. In contrast, cybercriminals are aware of the new prospects too and will probably try to use it in their activities. This survey aims at providing an overview on the way artificial intelligence can be used to power a malicious program that is: intelligent evasion techniques, autonomous malware, AI against itself, and applying bio-inspired computation and swarm intelligence.
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Khaleel, Shahbaa, Baydaa Khaleel, and Alaa khaleel. "Image Compression Based on Artificial Intelligent Techniques." AL-Rafidain Journal of Computer Sciences and Mathematics 6, no. 3 (September 1, 2009): 75–109. http://dx.doi.org/10.33899/csmj.2009.163839.

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Roy, Sanjiban Sekhar, and V. Madhu Viswanatham. "Classifying Spam Emails Using Artificial Intelligent Techniques." International Journal of Engineering Research in Africa 22 (February 2016): 152–61. http://dx.doi.org/10.4028/www.scientific.net/jera.22.152.

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Spam emails have become an increasing difficulty for the entire web-users.These unsolicited messages waste the resources of network unnecessarily. Customarily, machine learning techniques are adopted for filtering email spam. This article examines the capabilities of the extreme learning machine (ELM) and support vector machine (SVM) for the classification of spam emails with the class level (d). The ELM method is an efficient model based on single layer feed-forward neural network, which can choose weights from hidden layers,randomly. Support vector machine is a strong statistical learning theory used frequently for classification. The performance of ELM has been compared with SVM. The comparative study examines accuracy, precision, recall, false positive, true positive.Moreover, a sensitivity analysis has been performed by ELM and SVM for spam email classification.
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Dissertations / Theses on the topic "Artificial intelligent techniques"

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Liu, Huixiang. "Intelligent search techniques for large software systems." Thesis, University of Ottawa (Canada), 2002. http://hdl.handle.net/10393/6422.

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There are many tools available today to help software engineers search in source code systems. It is often the case, however, that there is a gap between what people really want to find and the actual query strings they specify. This is because a concept in a software system may be represented by many different terms, while the same term may have different meanings in different places. Therefore, software engineers often have to guess as they specify a search, and often have to repeatedly search before finding what they want. To alleviate the search problem, this thesis describes a study of what we call intelligent search techniques as implemented in a software exploration environment, whose purpose is to facilitate software maintenance. We propose to utilize some information retrieval techniques to automatically apply transformations to the query strings. The thesis first introduces the intelligent search techniques used in our study, including abbreviation concatenation and abbreviation expansion. Then it describes in detail the rating algorithms used to evaluate the query results' similarity to the original query strings. Next, we describe a series of experiments we conducted to assess the effectiveness of both the intelligent search methods and our rating algorithms. Finally, we describe how we use the analysis of the experimental results to recommend an effective combination of searching techniques for software maintenance, as well as to guide our future research.
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Kostias, Aristotelis, and Georgios Tagkoulis. "Development of an Artificial Intelligent Software Agent using Artificial Intelligence and Machine Learning Techniques to play Backgammon Variants." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-251923.

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Artificial Intelligence has seen enormous progress in many disciplines in the recent years. Particularly, digitalized versions of board games require artificial intelligence application due to their complex decision-making environment. Game developers aim to create board game software agents which are intelligent, adaptive and responsive. However, the process of designing and developing such a software agent is far from straight forward due the nature and diversity of each game. The thesis examines and presents a detailed procedure of constructing a software agent for backgammon variants, using temporal difference, artificial neural networks and backpropagation. Different artificial intelligence and machine learning algorithms used in board games, are overviewed and presented. Finally, the thesis describes the development and implementation of a software agent for the backgammon variant called Swedish Tables and evaluates its performance.
Artificiell intelligens har sett enorma framsteg inom många discipliner de senare åren. Speciellt, digitaliserade brädspel kräver implementering av Artificiell intelligens då deras beslutfattande logik är väldigt komplex. Dataspelutvecklarnas syfte och mål är att skapa programvaror som är intelligenta, adaptiva och lyhörda. Dock konstruktionsoch utvecklingsprocess för att kunna skapa en sådan mjukvara är långtifrån att vara faställd, mest på grund av diversitet av naturen av varje spel. Denna avhandlingen forskar och föreslår en detaljerad procedur för att bygga en "Software Agent" för olika slags Backagammon, genom att använda AI neurala nätvärk och back-propagation metoder. Olika artificiell intelligensoch maskininlärningsalgoritmer som används i brädspel forskas och presenteras. Slutligen denna avhandling beskriver implementeringen och utvecklingen av ett mjukvaru agent för en backgammonvariant, närmare bestämt av "Svenska Tabeller" samt utvärderar dess prestanda.
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Angeli, Chrissanthi. "Intelligent fault detection techniques for an electro-hydraulic system." Thesis, University of Sussex, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262693.

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Yang, Jianhua. "Intelligent data mining using artificial neural networks and genetic algorithms : techniques and applications." Thesis, University of Warwick, 2010. http://wrap.warwick.ac.uk/3831/.

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Data Mining (DM) refers to the analysis of observational datasets to find relationships and to summarize the data in ways that are both understandable and useful. Many DM techniques exist. Compared with other DM techniques, Intelligent Systems (ISs) based approaches, which include Artificial Neural Networks (ANNs), fuzzy set theory, approximate reasoning, and derivative-free optimization methods such as Genetic Algorithms (GAs), are tolerant of imprecision, uncertainty, partial truth, and approximation. They provide flexible information processing capability for handling real-life situations. This thesis is concerned with the ideas behind design, implementation, testing and application of a novel ISs based DM technique. The unique contribution of this thesis is in the implementation of a hybrid IS DM technique (Genetic Neural Mathematical Method, GNMM) for solving novel practical problems, the detailed description of this technique, and the illustrations of several applications solved by this novel technique. GNMM consists of three steps: (1) GA-based input variable selection, (2) Multi- Layer Perceptron (MLP) modelling, and (3) mathematical programming based rule extraction. In the first step, GAs are used to evolve an optimal set of MLP inputs. An adaptive method based on the average fitness of successive generations is used to adjust the mutation rate, and hence the exploration/exploitation balance. In addition, GNMM uses the elite group and appearance percentage to minimize the randomness associated with GAs. In the second step, MLP modelling serves as the core DM engine in performing classification/prediction tasks. An Independent Component Analysis (ICA) based weight initialization algorithm is used to determine optimal weights before the commencement of training algorithms. The Levenberg-Marquardt (LM) algorithm is used to achieve a second-order speedup compared to conventional Back-Propagation (BP) training. In the third step, mathematical programming based rule extraction is not only used to identify the premises of multivariate polynomial rules, but also to explore features from the extracted rules based on data samples associated with each rule. Therefore, the methodology can provide regression rules and features not only in the polyhedrons with data instances, but also in the polyhedrons without data instances. A total of six datasets from environmental and medical disciplines were used as case study applications. These datasets involve the prediction of longitudinal dispersion coefficient, classification of electrocorticography (ECoG)/Electroencephalogram (EEG) data, eye bacteria Multisensor Data Fusion (MDF), and diabetes classification (denoted by Data I through to Data VI). GNMM was applied to all these six datasets to explore its effectiveness, but the emphasis is different for different datasets. For example, the emphasis of Data I and II was to give a detailed illustration of how GNMM works; Data III and IV aimed to show how to deal with difficult classification problems; the aim of Data V was to illustrate the averaging effect of GNMM; and finally Data VI was concerned with the GA parameter selection and benchmarking GNMM with other IS DM techniques such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Evolving Fuzzy Neural Network (EFuNN), Fuzzy ARTMAP, and Cartesian Genetic Programming (CGP). In addition, datasets obtained from published works (i.e. Data II & III) or public domains (i.e. Data VI) where previous results were present in the literature were also used to benchmark GNMM’s effectiveness. As a closely integrated system GNMM has the merit that it needs little human interaction. With some predefined parameters, such as GA’s crossover probability and the shape of ANNs’ activation functions, GNMM is able to process raw data until some human-interpretable rules being extracted. This is an important feature in terms of practice as quite often users of a DM system have little or no need to fully understand the internal components of such a system. Through case study applications, it has been shown that the GA-based variable selection stage is capable of: filtering out irrelevant and noisy variables, improving the accuracy of the model; making the ANN structure less complex and easier to understand; and reducing the computational complexity and memory requirements. Furthermore, rule extraction ensures that the MLP training results are easily understandable and transferrable.
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Ebada, Adel. "Intelligent techniques-based approach for ship manoeuvring simulations and analysis artificial neural networks application /." [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=984707166.

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Yang, Ao. "Artificial Intelligent Techniques in Residential Water End-use Studies for Optimized Urban Water Management." Thesis, Griffith University, 2018. http://hdl.handle.net/10072/382672.

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In the urban water planning and management industry, end-use water consumption monitoring is a primary tool for water demand management and source substitution. Numerous residential end-use consumption studies have been carried out worldwide in the last two decades. With the rapid development of intelligent technology, the traditional time-consuming process for water flow data disaggregation has been replaced by a smart water metering system with advanced analysis. However, the existing water flow trace analysis system cannot accurately disaggregate all categories of residential water end-use events. In response to this issue, this research focused on developing new techniques, which can improve the autonomous categorisation accuracy of the residential water flow disaggregation process. A rigorous research method was adopted to achieve the above-mentioned research objectives and included the following two stages: (1) review and testing of pattern recognition techniques; and (2) software development. This study employed the extensive South-east Queensland (SEQ) Residential Water End Use Study dataset to undertake the development of the intelligent and autonomous water end-use recognition technique. Due to the array of objectives, methods, and results, this thesis has been structured around two refereed journal publications produced during the MPhil study. Two themes emerged from the research, namely: (1) development of hybrid intelligent model for mechanised water end-use analysis; and (2) optimising water end-use analysis process with Self-organising maps and K-Means clustering. The application of many sophisticated intelligent techniques has been attempted in order to tackle this complex problem. In the first stage, the original application of Dynamic Time Warping (DTW) algorithm was found to be ineffective due to settings of the threshold value. Through further investigation into the existing database, Artificial Bee Colony (ABC) and K-Medoids algorithm were selected. In this stage, this technique was applied to assist in finding toilet events in an artificially mixed data. 95.71% accuracy for correctly classified mechanical events was achieved when tested on 136 mixed events from different categories. The performance of the selected algorithms have been compared against previously reported approaches, with the technique and accuracy comparisons presented in a refereed journal paper. While the ABC and K-Medoids approach to clustering flow data into water end-use categories was suitable for mechanical end-use categories, it was less effective for other behaviourally influenced categories. Further exploration of various water flow data clustering techniques was required in order to discover a more suitable approach for the preliminary clustering of flow data into all of the water end-use categories. This prompted the undertaking of the research activities for the second journal paper described as follows. The study continued with the development of a hybrid technique in the second stage. Self-organising maps (SOM) and K-means algorithms were applied to the existing software Autoflow through pre-grouping of water end-use events in order to improve the accuracy. The verification on two datasets (i.e., (1) over 100,000 single events, and (2) 30 independent homes), resulted in an improvement in water end-use categorisation accuracy, when compared to the original technique employed in Autoflow, for each residential end-use category. Accuracy improvements were particularly noticeable for the mechanical water end-use event categories (i.e., washing machine, toilet, and evaporative cooler). The research outcomes have implications for researchers and the water industry. For researchers, the revised Autoflow v3.1 developed in this study is more accurate than previous versions reported in the literature. The novel hybrid pattern recognition approach and the associated algorithms employed in this latest Autoflow v3.1 version can be adapted for a range of pattern recognition problems. For the water industry, an accurate and autonomous water end-use analysis software tool has a range of implications, including, providing bottom-up data for demand forecasting and infrastructure planning, evidence-based water demand management, and end-use level customer feedback phone and web-based applications.
Thesis (Masters)
Master of Philosophy (MPhil)
School of Eng & Built Env
Science, Environment, Engineering and Technology
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Hasan, Irfan. "Machine learning techniques for automated knowledge acquisition in intelligent knowledge-based systems." Instructions for remote access. Click here to access this electronic resource. Access available to Kutztown University faculty, staff, and students only, 1991. http://www.kutztown.edu/library/services/remote_access.asp.

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Thesis (M.S.)--Kutztown University of Pennsylvania, 1991.
Source: Masters Abstracts International, Volume: 45-06, page: 3187. Abstract precedes thesis as [2] preliminary leaves. Typescript. Includes bibliographical references (leaves 102-104).
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Wong, Kam Cheung. "Intelligent methods of power system components monitoring by artificial neural networks and optimisation using evolutionary computing techniques." Thesis, University of Sunderland, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285580.

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Jarvis, Matthew P. "Applying machine learning techniques to rule generation in intelligent tutoring systems." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0429104-112724.

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Thesis (M.S.)--Worcester Polytechnic Institute.
Keywords: Intelligent Tutoring Systems; Model Tracing; Machine Learning; Artificial Intelligence; Programming by Demonstration. Includes bibliographical references.
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Raja, Muhammad Nouman Amjad. "Load-settlement investigation of geosynthetic-reinforced soil using experimental, analytical, and intelligent modelling techniques." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2021. https://ro.ecu.edu.au/theses/2455.

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During the past five decades, numerous studies have been conducted to investigate the load-settlement behaviour of geosynthetic-reinforced soil. The main advantage of reinforced soil foundations are the increase in the bearing capacity and decrease in the settlement. Whereas, for pavement foundation design, the strength of the subgrade soil is often measured in terms of California bearing ratio (CBR). The researchers have suggested various methods to improve the quality of geosynthetic-reinforced foundations soils. In the recent past, the wraparound geosynthetic reinforcement technique has been proposed to strengthen the foundation soil effectively. However, there are several research gaps in the area; for example, there has been no analytical solution for estimating the ultimate bearing capacity of wraparound reinforced foundations, and there has been no evaluation of this technique under repeated loading conditions. Similarly, for planar geosynthetic-reinforced soil foundations, the prediction of load-settlement behaviour also requires more attention. The advent of artificial intelligence (AI) based modelling techniques has made many traditional approaches antiquated. Despite this, there is limited research on using AI techniques to derive mathematical expressions for predicting the load-settlement behaviour of reinforced soil foundations or the strength of reinforced subgrade soil. This research is undertaken to examine the load-settlement behaviour of geosynthetic-reinforced foundation soils using experimental, analytical, and intelligent modelling methods. For this purpose, extensive laboratory measurements, analytical, numerical and AI-based modelling and analysis have been conducted to: (i) derive theoretical expression to estimate the ultimate bearing capacity of footing resting on soil bed strengthened by wraparound reinforcement technique; (ii) using detail experimental study, present the effectiveness of wraparound reinforcement for improving the load-settlement characteristics of sandy soil under repeated loading conditions; (iii) to build the executable artificial intelligence-based or computationally intelligent soft computing models and converting them into simple mathematical equations for estimating the (a) ultimate bearing capacity of reinforced soil foundations; (b) settlement at peak footing loads; (c) strength (California bearing ratio) of geosynthetic-reinforced subgrade soil; and (iv) to examine and predict the settlement of geosynthetic-reinforced soil foundations (GRSF) under service loading condition using novel hybrid approach, that is, finite element modelling (FEM) and AI modelling. In the analytical phase, a theoretical expression has been developed for estimating the ultimate bearing capacity of strip footing resting on soil bed reinforced with the geosynthetic layer having the wraparound ends. The wraparound ends of the geosynthetic reinforcement are considered to provide the shearing resistance at the soil-geosynthetic interface as well as the passive resistance due to confinement of soil by the geosynthetic reinforcement. The values of ultimate load-bearing capacity determined by using the developed analytical expression have predicted values closer to the model studies reported in the literature, with a difference in the range of 0% to 25% with an average difference of 10%. In the experimental phase, model footing load tests have been conducted on strip footing resting on a sandy soil bed reinforced with geosynthetic in wraparound and planar forms under monotonic and repeated loadings. The geosynthetic layers were laid according to the reinforcement ratio to minimise the scale effect. The effect of repeated load amplitude and the number of cycles, and the effect of reinforcement parameters, such as number of layers, reinforcement width, lap-length ratio and planar width of wraparound, were investigated, and their potential effect on the load-settlement behaviour has been studied. The wraparound reinforced model has shown about 45% lower average total settlement than the unreinforced model. In comparison, the double-layer reinforced model has about 41% at the cost of twice the material and 1.5 times the occupied land width ratio. Additionally, for lower settlement levels (s/B ≤ 5%), the wraparound geotextile with a smaller occupied land width ratio (bp/B = 3.5) has performed well in comparison to the wraparound with a slightly larger occupied land width ratio (bp/B = 4). However, the wraparound with occupied width ratio of 4 provides more stability to the foundation soil for higher settlement levels. The performance of the fully wrapped model (bp/B = 2.8) is more similar to that of the planar double-layer reinforced model (b/B = 4); however, it is noted that even the fully wrapped model outperforms the planar single-layer reinforced model with the same amount of geotextile and 50% less occupied land width For data analytic methods, first historical data has been collected to build the various machine learning (ML) models, and then detailed comparison has been presented among the ML-based models and with other available theoretical methods. A comprehensive study was conducted for each model to choose its structure, optimisation, and tuning of hyperparameters and its interpretation in the form of mathematical expressions. The forecasting strength of the models was assessed through a cross-validation approach, rigorous statistical testing, multi-criteria approach, and external validation process. The traditional statistical indices such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percent deviation (MAPD); along with several other modern model performance indicators, were utilised to evaluate the accuracy of the developed models. For ultimate bearing capacity (UBC) estimation, the performance of the extreme learning machine (ELM) and TreeNet models has shown a good degree of prediction accuracy in comparison with traditional methods over the test dataset. However, the overall performance of the ELM model (R2 = 0.9586, MAPD=12.8%) was better than that of the TreeNet model (R2 = 0.9147, MAPD =17.2%). Similarly, for settlement estimation at peak footing loads, multivariate adaptive regression splines (MARS) modelling technique has outperformed (R2 = 0.974, RMSE = 1.19 mm, and MAPD = 7.19%) several other robust AI-based models, namely ELM, support vector regression (SVR), Gaussian process regression (GPR), and stochastic gradient boosting trees (SGBT). For CBR, the competency and reliability of the several intelligent models such as artificial neural network (ANN), least median of squares regression (LMSR), GPR, elastic net regularization regression (ENRR), lazy K-star (LKS), M5 model trees, alternating model trees (AMT), and random forest (RF). Among all the intelligent modelling techniques, ANN (R2 = 0.944, RMSE = 1.74, and MAE = 1.27) and LKS (R2 = 0.955, RMSE = 1.52, and MAE = 1.04) has achieved the highest ranking score of 35 and 40, respectively, in predicting the CBR of geosynthetic-reinforced soil. Moreover, for UBC and settlement at peak footing loads, new model footing load tests, and for strength of reinforced subgrade soil, new CBR tests were also conducted to verify the predictive veracity of the developed AI-based models. For predicting the settlement behaviour of GRSF under various service loads, an integrated numerical-artificial intelligence approach was utilised. First, the large-scale footing load tests were simulated using the FEM technique. At the second stage, a detailed parametric study was conducted to find the effect of footing-, geosynthetic- and soil strength- parameters on the settlement of GRSF under various service loads. Afterward, a novel evolutionary artificial intelligence model, that is, grey-wolf optimised artificial neural network (ANN-GWO), was developed and translated to the simple mathematical equation for estimating the load-settlement behaviour of GRSF. The results of this study indicate that the proposed ANN-GWO model predict the settlement of GRSF with high accuracy for training (RMSE = 0.472 mm, MAE = 0.833, R2 = 0.982), and testing (RMSE = 0.612 mm, MAE = 0.363, R2 = 0.962,) dataset. Furthermore, the predictive veracity of the model was verified by detailed and rigorous statistical testing and against several independent scientific studies as reported in the literature. This work is practically valuable for understanding and predicting the load-settlement behaviour of reinforced soil foundations and applies to traditional planar geosynthetic-reinforced and as well as recently developed wraparound geosynthetic-reinforced foundation soil technique. For wraparound reinforced soil foundations, the analytical expression will be helpful in the estimation of ultimate bearing capacity, and experimental study shows the beneficial effects of such foundations systems in terms of enhancement in bearing capacity and reduction in the settlement, and economic benefits in terms of saving land area and amount of geosynthetic, under repeated loading conditions. Moreover, the developed AI-based models and mathematical expressions will be helpful for the practitioners in predicting the strength and settlement of reinforced soil in an effective and intelligent way and will be beneficial in the broader understanding of embedding the intelligent modelling techniques with geosynthetic-reinforced soil (GRS) technology for the automation in construction projects.
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Books on the topic "Artificial intelligent techniques"

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S, Wiak, Krawczyk Andrzej, Doležel Ivo, and International Symposium on Electromagnetic Fields in Electrical Engineering. (2007 : Prague), eds. Intelligent computer techniques in applied electromagnetics. Berlin: Springer, 2008.

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Muhammad, Sarfraz, ed. Computer-aided intelligent recognition techniques and applications. Chichester, West Sussex, England: John Wiley, 2005.

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Intelligent multimedia communication: Techniques and applications. Berlin: Springer, 2010.

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Kołodziej, Joanna. Advances in Intelligent Modelling and Simulation: Artificial Intelligence-Based Models and Techniques in Scalable Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Sarfraz, Muhammad. Computer-Aided Intelligent Recognition Techniques and Applications. New York: John Wiley & Sons, Ltd., 2005.

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Köppen, Mario. Intelligent Computational Optimization in Engineering: Techniques and Applications. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Daradoumis, Thanasis. Intelligent Adaptation and Personalization Techniques in Computer-Supported Collaborative Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Chountas, Panagiotis, Petrouniass Ilias, and Kacprzyk Janusz, eds. Intelligent techniques and tools for novel system architectures. Berlin: Springer, 2008.

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Ruano, António E. New Advances in Intelligent Signal Processing. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Paul, Casasent David, Society of Photo-optical Instrumentation Engineers., Carnegie-Mellon University. Center for Optical Data Processing., Institute of Electrical and Electronics Engineers. Philadelphia Section., and IEEE Industrial Electronics Society, eds. Intelligent robots and computer vision VIII: Algorithms and techniques, 6-10 November 1989, Philadelphia, Pennsylvania. Bellingham, Wash., USA: The Society, 1990.

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Book chapters on the topic "Artificial intelligent techniques"

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Laskari, Naveen Kumar, and Suresh Kumar Sanampudi. "Design Artificial Intelligence Course Contents Using Artificial Intelligent Techniques." In ICICCT 2019 – System Reliability, Quality Control, Safety, Maintenance and Management, 592–99. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8461-5_68.

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Fonseca, Camilo Isaza, Octavio J. Salcedo Parra, and Brayan S. Reyes Daza. "Intelligent Road Design Using Artificial Intelligence Techniques." In Mobile, Secure, and Programmable Networking, 166–77. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67807-8_13.

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Akerkar, Rajendra, and Priti Srinivas Sajja. "Artificial Neural Network." In Intelligent Techniques for Data Science, 125–55. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29206-9_5.

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Pham, D. T., and E. Tacgin. "Techniques for Intelligent Computer-Aided Design." In Artificial Intelligence in Design, 5–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-74354-2_1.

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Stacey, Deborah. "Intelligent systems architecture: Design techniques." In Artificial Neural Networks for Intelligent Manufacturing, 17–38. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-0713-6_2.

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Rafique, Imsal, Mudasir Dilawar, Amina Umer, and Muhammad Ahmad Hassan. "Classification of Cardiotocography Data for Fetal Health Using Feature Selection Techniques." In Artificial Intelligence in Intelligent Systems, 34–44. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77445-5_4.

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Plemenos, Dimitri, and George Miaoulis. "1 Intelligent Techniques for Computer Graphics." In Artificial Intelligence Techniques for Computer Graphics, 1–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-85128-8_1.

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El Gourari, Abdelali, Mustapha Raoufi, and Mohammed Skouri. "Formulating Quizzes Questions Using Artificial Intelligent Techniques." In Networking, Intelligent Systems and Security, 535–47. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3637-0_38.

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Nasiri, Ehsan, Mariofanna Milanova, and Ardalan Nasiri. "Masked Face Detection Using Artificial Intelligent Techniques." In New Approaches for Multidimensional Signal Processing, 3–34. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8558-3_1.

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Suresh, Yeresime, M. Sriraksha, C. Chaitra, G. Chaitra, and S. Jayateertha. "Stock Market Prediction Using Artificial Intelligent Techniques." In Innovations in Computer Science and Engineering, 347–54. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8987-1_37.

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Conference papers on the topic "Artificial intelligent techniques"

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Sun Yuanyuan, Guo Lili, and Wang Yongming. "Artificial intelligence and learning techniques in intelligent fault diagnosis." In 2015 4th International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2015. http://dx.doi.org/10.1109/iccsnt.2015.7490841.

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Stottler, Richard, Bonnie Schwartz, and Randy Jensen. "Intelligent Pilot Intent Analysis System Using Artificial Intelligence Techniques." In Infotech@Aerospace 2012. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2012. http://dx.doi.org/10.2514/6.2012-2506.

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Yang, Xiaohua, and Yuqi Li. "Runoff Simulation Using Artificial Intelligent Techniques." In 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE). IEEE, 2012. http://dx.doi.org/10.1109/rsete.2012.6260721.

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Monteiro, Juarez, Roger Granada, Rafael C. Pinto, and Rodrigo C. Barros. "Beating Bomberman with Artificial Intelligence." In XV Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/eniac.2018.4430.

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Artificial Intelligence (AI) seeks to bring intelligent behavior for machines by using specific techniques. These techniques can be employed in order to solve tasks, such as planning paths or controlling intelligent agents. Some tasks that use AI techniques are not trivially testable, since it can handle a high number of variables depending on their complexity. As digital games can provide a wide range of variables, they become an efficient and economical means for testing artificial intelligence techniques. In this paper, we propose a combination of a behavior tree and a Pathfinding algorithm to solve a maze-based problem using the digital game Bomberman of the Nintendo Entertainment System (NES) platform. We perform an analysis of the AI techniques in order to verify the feasibility of future experiments in similar complex environments. Our experiments show that our intelligent agent can be successfully implemented using the proposed approach.
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Bonnet, Christine, Helene Taterode, Jacques Kouloumdjian, and Mohand-Said Hacid. "Using artificial intelligence techniques for intelligent simulation in memory re-education." In the third international conference. New York, New York, USA: ACM Press, 1990. http://dx.doi.org/10.1145/98894.98941.

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Machin, Mirialys, Julio A. Sanguesa, Piedad Garrido, and Francisco J. Martinez. "On the use of artificial intelligence techniques in intelligent transportation systems." In 2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW). IEEE, 2018. http://dx.doi.org/10.1109/wcncw.2018.8369029.

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Trifonov, Roumen, Radoslav Yoshinov, Galya Pavlova, and Georgi Tsochev. "Artificial neural network intelligent method for prediction." In MATHEMATICAL METHODS AND COMPUTATIONAL TECHNIQUES IN SCIENCE AND ENGINEERING. Author(s), 2017. http://dx.doi.org/10.1063/1.4996678.

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Maina, Elizaphan M., Robert O. Oboko, and Peter W. Waiganjo. "Extending moodle grouping functionality using artificial intelligent techniques." In 2017 IEEE AFRICON. IEEE, 2017. http://dx.doi.org/10.1109/afrcon.2017.8095455.

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Upadhyay, Ved Prakash, Subhash Panwar, and Ramchander Merugu. "Protein Sequence Structure Prediction Using Artificial Intelligent Techniques." In the International Conference. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2979779.2979887.

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Usha, S., M. Karthik, V. Hariharan, P. Riddhi, A. Kishok, and R. Mohan Krishna. "Robotic Trash Collector Boat Using Artificial Intelligent Techniques." In 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC). IEEE, 2022. http://dx.doi.org/10.1109/icaaic53929.2022.9793092.

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Reports on the topic "Artificial intelligent techniques"

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Lehner, Paul E., James R. McIntyre, Leonard Adelman, Kermit Gates, Peter Luster, Matthew Probus, and Michael L. McDonnel. Combining Decision Analysis and Artificial Intelligence Techniques. An Intelligent Aid for Estimating Enemy Courses of Action. Fort Belvoir, VA: Defense Technical Information Center, August 1985. http://dx.doi.org/10.21236/ada159846.

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Fishcer, Mark, Robert Whyte, and Nicholas Straguzzi. Application of Artificial Intelligence Techniques to Exterior Ballistics. Fort Belvoir, VA: Defense Technical Information Center, December 1989. http://dx.doi.org/10.21236/ada214381.

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Davis, Wayne J., and Albert T. Jones. Artificial intelligence techniques in real-time production scheduling. Gaithersburg, MD: National Institute of Standards and Technology, 1989. http://dx.doi.org/10.6028/nist.ir.88-3891.

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Henke, Andrea L., and Timothy P. Maher. Artificial Intelligence Techniques for Parts Obsolescence Prediction. Phase 1. Fort Belvoir, VA: Defense Technical Information Center, March 1995. http://dx.doi.org/10.21236/ada292185.

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Stottier, Richard H. Artificial Intelligence Techniques for Flight Test Planning. Phase 1. Fort Belvoir, VA: Defense Technical Information Center, March 1995. http://dx.doi.org/10.21236/ada293962.

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Richards, Robert, Jason Chrenka, and Marvin Thordsen. Artificial Intelligence Technique for Pilot Approach Decision Aid Logic (PADAL) System. Fort Belvoir, VA: Defense Technical Information Center, March 2001. http://dx.doi.org/10.21236/ada389405.

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SAINI, RAVINDER, AbdulKhaliq Alshadid, and Lujain Aldosari. Investigation on the application of artificial intelligence in prosthodontics. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2022. http://dx.doi.org/10.37766/inplasy2022.12.0096.

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Review question / Objective: 1. Which artificial intelligence techniques are practiced in dentistry? 2. How AI is improving the diagnosis, clinical decision making, and outcome of dental treatment? 3. What are the current clinical applications and diagnostic performance of AI in the field of prosthodontics? Condition being studied: Procedures for desktop designing and fabrication Computer-aided design (CAD/CAM) in particular have made their way into routine healthcare and laboratory practice.Based on flat imagery, artificial intelligence may also be utilized to forecast the debonding of dental repairs. Dental arches in detachable prosthodontics may be categorized using Convolutional neural networks (CNN). By properly positioning the teeth, machine learning in CAD/CAM software can reestablish healthy inter-maxillary connections. AI may assist with accurate color matching in challenging cosmetic scenarios that include a single central incisor or many front teeth. Intraoral detectors can identify implant placements in implant prosthodontics and instantly input them into CAD software. The design and execution of dental implants could potentially be improved by utilizing AI.
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Hofer, Martin, Tomas Sako, Arturo Martinez Jr., Mildred Addawe, Joseph Bulan, Ron Lester Durante, and Marymell Martillan. Applying Artificial Intelligence on Satellite Imagery to Compile Granular Poverty Statistics. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200432-2.

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This study outlines a computational framework to enhance the spatial granularity of government-published poverty estimates, citing data from the Philippines and Thailand. Computer vision techniques were applied on publicly available medium resolution satellite imagery, household surveys, and census data from the two countries. The results suggest that even using publicly accessible satellite imagery, predictions generally aligned with the distributional structure of government-published poverty estimates after calibration. The study further examines the robustness of the resulting estimates to user-specified algorithmic parameters and model specifications.
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Rudd, Ian. Leveraging Artificial Intelligence and Robotics to Improve Mental Health. Intellectual Archive, July 2022. http://dx.doi.org/10.32370/iaj.2710.

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Artificial Intelligence (AI) is one of the oldest fields of computer science used in building structures that look like human beings in terms of thinking, learning, solving problems, and decision making (Jovanovic et al., 2021). AI technologies and techniques have been in application in various aspects to aid in solving problems and performing tasks more reliably, efficiently, and effectively than what would happen without their use. These technologies have also been reshaping the health sector's field, particularly digital tools and medical robotics (Dantas & Nogaroli, 2021). The new reality has been feasible since there has been exponential growth in the patient health data collected globally. The different technological approaches are revolutionizing medical sciences into dataintensive sciences (Dantas & Nogaroli, 2021). Notably, with digitizing medical records supported the increasing cloud storage, the health sector created a vast and potentially immeasurable volume of biomedical data necessary for implementing robotics and AI. Despite the notable use of AI in healthcare sectors such as dermatology and radiology, its use in psychological healthcare has neem models. Considering the increased mortality and morbidity levels among patients with psychiatric illnesses and the debilitating shortage of psychological healthcare workers, there is a vital requirement for AI and robotics to help in identifying high-risk persons and providing measures that avert and treat mental disorders (Lee et al., 2021). This discussion is focused on understanding how AI and robotics could be employed in improving mental health in the human community. The continued success of this technology in other healthcare fields demonstrates that it could also be used in redefining mental sicknesses objectively, identifying them at a prodromal phase, personalizing the treatments, and empowering patients in their care programs.
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Danner, William F. The use of artificial intelligence programming techniques for communication between incompatible building information systems. Gaithersburg, MD: National Bureau of Standards, 1987. http://dx.doi.org/10.6028/nbs.ir.87-3529.

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