A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model

Engineering with Computers - Recycled aggregate concrete is used as an alternative material in construction engineering, aiming to environmental protection and sustainable development. However, the...     

Hybrid artificial intelligence approach based on metaheuristic and machine learning for slope stability assessment: A multinational data analysis

Slope stability assessment is a critical research area in civil engineering. Disastrous consequences of slope collapse necessitate better tools for predicting their occurrences. This research proposes a hybrid Artificial Intelligence (AI) for slope stability assessment based on metaheuristic and machine learning. The contribution of this study to the body of knowledge is multifold. First, advantages of the Firefly Algorithm (FA) and the Least Squares Support Vector Classification (LS-SVC) are combined to establish an integrated slope prediction model. Second, an inner cross-validation with the operating characteristic curve computation is embedded in the training process to reliably construct the machine learning model. Third, the FA, an effective and easily implemented metaheuristic, is employed to optimize the model construction process by appropriately selecting the LS-SVM's hyper-parameters. Finally, a dataset that contains 168 real cases of slope evaluation, recorded in various countries, is used to establish and confirm the proposed hybrid approach. Experimental results demonstrate that the new hybrid AI model has achieved roughly 4% improvement in classification accuracy compared with other benchmark methods.     

A hybrid artificial intelligence model for river flow forecasting

A hybrid hydrologic estimation model is presented with the aim of performing accurate river flow forecasts without the need of using prior knowledge from the experts in the field. The problem of predicting stream flows is a non-trivial task because the various physical mechanisms governing the river flow dynamics act on a wide range of temporal and spatial scales and almost all the mechanisms involved in the river flow process present some degree of nonlinearity. The proposed system incorporates both statistical and artificial intelligence techniques used at different stages of the reasoning cycle in order to calculate the mean daily water volume forecast of the Salvajina reservoir inflow located at the Department of Cauca, Colombia. The accuracy of the proposed model is compared against other well-known artificial intelligence techniques and several statistical tools previously applied in time series forecasting. The results obtained from the experiments carried out using real data from years 1950 to 2006 demonstrate the superiority of the hybrid system.     

Prospects for process selection using artificial intelligence

On problem facing modern industry is the lack of a skilled labor force to produce machined parts as has been done in the past. In the near future, this problem may become acute for a number of manufacturing tasks. One such task is process planning. Since process planning requires intelligent reasoning and considerable experiential knowledge, almost all existing computer aided process planning systems require a significant amount of supervision by an experienced human being.There is some prospect that “expert computer system” techniques from the field of Artificial Intelligence may be successfully used to automate (at least partially) several of the reasoning activities involved with process planning. This paper discusses some current prospects for automating a process planning task known as process selection. These ideas are currently being considered for use int he Automated Manufacturing Research Facility project at the U.S. National Bureau of Standards, and steps are being taken to implement them in an expert computer system.     

A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.

     

A novel decomposition-based ensemble model for short-term load forecasting using hybrid artificial neural networks

Highly accurate short-term load forecasting (STLF) is essential in the operation of power systems. However, the existing predictive methods cannot achieve an effective balance between prediction accuracy and computational cost. Furthermore, the prediction residual is rarely used to improve the predictive accuracy in STLF. This paper proposes a novel decomposition-based ensemble model for the STLF task. First, an optimized empirical wavelet transform (OEWT) is developed to rationally decompose the STLF load by combining the approximate entropy method with the empirical wavelet transform. Particularly, OEWT improves both prediction accuracy and computational cost in STLF. Second, a new hybrid machine learning method (named master learner) is proposed by rationally combining long short-term memory networks (LSTMs) with broad learning system (BLS) in STLF, effectively strengthening the predictive accuracy without significantly increasing the computational cost. Third, a residual learning model (named residual learner) is developed in the master learner to extract the effective predictive information from residual results, further improving the prediction accuracy in STLF. Fourth, an auxiliary learner is proposed by introducing another BLS to connect the input and output of the proposed model, enhancing the predictive robustness. The proposed decomposition-based ensemble model is compared with state-of-the-art and traditional models in STLF. Experimental results show that the model not only has high predictive accuracy and robustness but also low computational cost.

     

Field equations for incompressible non-viscous fluids using artificial intelligence

In this article, we introduce new field equations for incompressible non-viscous fluids, which can be treated similarly to Maxwell’s electromagnetic equations based on artificial intelligence algorithms. Lagrangian and Hamiltonian formulations are used to arrive at field equations that are solved using convolutional neural networks. Four linear differential equations, which describe the two fields, namely, the dynamic pressure and the vortex fields, are derived, and these can be used in place of Euler’s equation. The only assumption while deriving this equation is that the dynamic pressure and vortex fields obey the superposition principle. The important finding to be noted is that Euler’s fluid equations can be converted into field equations analogous to Maxwell’s electromagnetic equations. We solve the flow problem for laminar flow past a cylinder, sphere, and cone in two dimensions similar to the conduction in a uniform electric field and arrive at closed-form expressions. These closed-form expressions, which are obtained for the potentials of fluid flow, are similar to the streamline potential functions in the case of fluid dynamics.

     

Flang: A language for artificial intelligence

Translated from Kibernetika i Sistemnyi Analiz, No. 5, pp. 50–67, September–October, 1993.     

Improving the reliability of artificial intelligence planningsystems by analyzing their failure recovery

As planning technology improves, artificial intelligence planners are being embedded in increasingly complicated environments: ones that are particularly challenging even for human experts. Consequently, failure is becoming both increasingly likely for these systems (due to the difficult and dynamic nature of the new environments) and increasingly important to address (due to the systems' potential use on real world applications). The paper describes the development of a failure recovery component for a planner in a complex simulated environment and a procedure (called failure recovery analysis) for assisting programmers in debugging that planner. The failure recovery design is iteratively enhanced and evaluated in a series of experiments. Failure recovery analysis is described and demonstrated on an example from the Phoenix planner. The primary advantage of these approaches over existing approaches is that they are based on only a weak model of the planner and its environment, which makes them most suitable when the planner is being developed. By integrating them, failure recovery and failure recovery analysis improve the reliability of the planner by repairing failures during execution and identifying failures due to bugs in the planner and failure recovery itself     

Closed schedulers: a novel technique for analyzing asynchronous protocols

Summary Analyzing distributed protocols in various models often involves a careful analysis of the set ofadmissible runs, for which the protocols should behave correctly. In particular, the admissible runs assumed by at-resilient protocol are runs which are fair for all but at mostt processors. In this paper we defineclosed sets of runs, and suggest a technique to prove impossibility results fort-resilient protocols, by restricting the corresponding sets of admissible runs to smaller sets, which are closed, as follows: For each protocolPR and for each initial configurationc, the set of admissible runs ofPR which start fromc defines a tree in a natural way: the root of the tree is the empty run, and each vertex in it denotes a finite prefix of an admissible run; a vertexu in the tree has a sonv iffv is also a prefix of an admissible run, which extendsu by one atomic step.The tree of admissible runs described above may contain infinite paths which are not admissible runs. A set of admissible runs isclosed if for every possible initial configurationc, each path in the tree of admissible runs starting fromc is also an admissible run. Closed sets of runs have the simple combinatorial structure of the set of paths of an infinite tree, which makes them easier to analyze. We introduce a unified method for constructing closed sets of admissible runs by using a model-independent construction of closedschedulers, and then mapping these schedulers to closed sets of runs. We use this construction to provide a unified proof of impossibility of consensus protocols. Ronit Lubitch received her B.Sc. degree in Mathematics and Computer Science from Tel Aviv University in 1989, and her Master degree in Computer Science from the Technion, in 1993. From 1992 she is working in Graffiti Software Industries, which expertise in the design and development of advanced photo realistic rendering, and animation software systems. Shlomo Moran received his B.Sc. and Ph.D. degrees in Mathematics from the Technion in 1975 and 1979 resp. In 1979–1981 he was at the University of Minnesota as a visiting research specialist. In 1981 he joined the Computer Science Department at the Technion, where he is now a full professor. In 1985–1986 he visited at IBM T.J. Watson Research Center. In 1992–1993 he visited at AT&T Bell Laboratories and in Centrum voor Wiskunde en Informatica, Amsterdam. His research interests include distributed computing, Combinatorics and Graph Theory, and Complexity Theory.A preliminary extended version of this paper appeared in the Proceedings of 6-th International Workshop on Distributed Algorithms, Haifa, November 1992This work was supported in part by the Technion V.P.R. fund. Part of this research was conducted while this author was visiting at AT&T Bell Labs at Murray Hill and at CWI, Amsterdam     

A framework for artificial intelligence applications software development

Despite the promise and prominence of artificial intelligence, successful widely-based applications have been rare. Much of previous artificial intelligence work has concentrated on the demonstration of techniques rather than the development of tools that can be used in systems design, analysis and operation. A systematic program of knowledge compilation in artificial intelligence is proposed where extensible function libraries can be used by professionals in various fields and non-programming specialists in developing artificial intelligence applications.     

Cancer diagnosis using artificial intelligence: a review

Artificial intelligence (AI) is the usage of scientific techniques to simulate human intellectual skills and to tackle complex medical issues involving complicated genetic defects such as cancer. The rapid expansion of AI in the past era has paved the way to optimum judgment-making by superior intellect, where the human brain is constrained to manage large information in a limited period. Cancer is a complicated ailment along with several genomic variants. AI-centred systems carry enormous potential in detecting these genomic alterations and abnormal protein communications at a very initial phase. The contemporary biomedical study is also dedicated to bringing AI expertise to hospitals securely and ethically. AI-centred support to diagnosticians and doctors can be the big surge ahead for the forecast of illness threat, identification, diagnosis, and therapies. The applications related to AI and Machine Learning (ML) in the identification of cancer and its therapy possess the potential to provide therapeutic support for quicker planning of a novel therapy for each person. Through the utilization of AI- based methods, scientists can work together in real-time and distribute their expertise digitally to possibly cure billions. In this review, the focus was on the study of linking biology with AI and describe how AI-centred support could assist oncologists in accurate therapy. It is essential to identify new biomarkers that inject drug defiance and discover medicinal goals to improve medication methods. The advent of the “next-generation sequencing” (NGS) programs resolves these challenges and has transformed the prospect of “Precision Oncology” (PO). NGS delivers numerous medical functions which are vital for hazard prediction, initial diagnosis of infection, “Sequence” identification and “Medical Imaging” (MI), precise diagnosis, “biomarker” detection, and recognition of medicinal goals for innovation in medicine. NGS creates a huge repository that requires specific “bioinformatics” sources to examine the information that is pertinent and medically important. The malignancy diagnostics and analytical forecast are improved with NGS and MI that provide superior quality images via AI technology. Irrespective of the advancements in technology, AI faces a few problems and constraints, and the clinical application of NGS continues to be authenticated. Through the steady progress of invention and expertise, the prospects of AI and PO look promising. The purpose of this review was to assess, evaluate, classify, and tackle the present developments in cancer diagnosis utilizing AI methods for breast, lung, liver, skin cancer, and leukaemia. The research emphasizes in what way cancer identification, the treatment procedure is aided by utilizing AI with supervised, unsupervised, and deep learning (DL) methods. Numerous AI methods were assessed on benchmark datasets with respect to “accuracy”, “sensitivity”, “specificity”, and “false-positive” (FP) metrics. Lastly, challenges along with future work were discussed.

     

A voice-activated robot with artificial intelligence

In this paper, a voice activated robot arm with intelligence is presented. The robot arm is controlled with natural connected speech input. The language input allows a user to interact with the robot in terms which are familiar to most people. The advantages of speech activated robots are hands-free and fast data input operations. The proposed robot is capable of understanding the meaning of natural language commands. After interpreting the voice commands a series of control data for performing a tasks are generated. Finally the robot actually performs the task. Artificial Intelligence techniques are used to make the robot understand voice commands and act in the desired mode. It is also possible to control the robot using the keyboard input mode.     

SNAP: A VLSI architecture for artificial intelligence processing

The organization and operation of a semantic network array processor (SNAP) are described. The architecture consists of an array of identical cells each containing a content addressable memory, microprogram control, and communication unit. Each cell is dedicated to one node of the semantic network and its associated relations. The array can perform global associative functions under the supervision of an outside controller. In addition, each-cell is equipped with the necessary logic to perform individual functions. A set of primitive instructions was carefully chosen. Some of the applications discussed include pattern search operations, production systems, and inferences. A LISP simulator was developed for this architecture, and some simulation results are presented.     

A novel solution for maze traversal problems using artificial neural networks

In this paper we have addressed the problem of finding a path through a maze of a given size. The traditional ways of finding a path through a maze employ recursive algorithms in which unwanted or non-paths are eliminated in a recursive manner. Neural networks with their parallel and distributed nature of processing seem to provide a natural solution to this problem. We present a biologically inspired solution using a two level hierarchical neural network for the mapping of the maze as also the generation of the path if it exists. For a maze of size S the amount of time it takes would be a function of S (O(S)) and a shortest path (if more than one path exists) could be found in around S cycles where each cycle involves all the neurons doing their processing in a parallel manner. The solution presented in this paper finds all valid paths and a simple technique for finding the shortest path amongst them is also given. The results are very encouraging and more applications of the network setup used in this report are currently being investigated. These include synthetic modeling of biological neural mechanisms, traversal of decision trees, modeling of associative neural networks (as in relating visual and auditory stimuli of a given phenomenon) and surgical micro-robot trajectory planning and execution.     

Designing new hybrid artificial intelligence model for CFST beam flexural performance prediction

Engineering with Computers - A substantial number of experimental studies have reported on the flexural performance of concrete-filled steel tube (CFST) beams. Due to the problem complexity,...     

A macro-economic model to forecast remittances based on Monte-Carlo simulation and artificial intelligence

A computer system based on Monte-Carlo simulation and fuzzy logic has been designed, developed and tested to: (i) identify covariates that influence remittances received in a specific country and (ii) explain their behavior throughout the time span involved. The resulting remittance model was designed theoretically, identifying the variables which determined remittances and their dependence relationships, and then developed into a computer cluster. This model aims to be global and is useful for assessing the long term evolution of remittances in scenarios where a rich country is the host (United States of America) while a poor country is the where the migrant is from (El Salvador). By changing the socio-economic characteristics of the countries involved, experts can analyze new socio-economic frameworks to obtain useful conclusions for decision-making processes involving development and sustainability.