Amir Mosavi

Integration of Machine Learning and Optimization for Robot Learning

Learning ability in Robotics is acknowledged as one of the major challenges facing artificial intelligence. Although in the numerous areas within Robotics machine learning (ML) has long identified as a core technology, recently Robot learning, in particular, has been witnessing major challenges due to the theoretical advancement at the boundary between optimization and ML. In fact the integration of ML and optimization reported to be able to dramatically increase the decision-making quality and learning ability in decision systems. Here the novel integration of ML and optimization which can be applied to the complex and dynamic contexts of Robot learning is described. Furthermore with the aid of an educational Robotics kit the proposed methodology is evaluated.

Reviewing the Novel Machine Learning Tools for Materials Design

Computational materials design is a rapidly evolving field of challenges and opportunities aiming at development and application of multi-scale methods to simulate, predict and select innovative materials with high accuracy. Today the latest advancements in machine learning, deep learning, internet of things (IoT), big data, and intelligent optimization have highly revolutionized the computational methodologies used for materials design innovation. Such novelties in computation enable the development of problem-specific solvers with vast potential applications in industry and business. This paper reviews the state of the art of technological advancements that machine learning tools, in particular, have brought for materials design innovation. Further via presenting a case study the potential of such novel computational tools are discussed for the virtual design and simulation of innovative materials in modeling the fundamental properties and behavior of a wide range of multi-scale materials design problems.

Learning and Intelligent Optimization for Material Design Innovation

Learning and intelligent optimization (LION) techniques enable problem-specific solvers with vast potential applications in industry and business. This paper explores such potentials for material design innovation and presents a review of the state of the art and a proposal of a method to use LION in this context. The research on material design innovation is crucial for the long-lasting success of any technological sector and industry and it is a rapidly evolving field of challenges and opportunities aiming at development and application of multi-scale methods to simulate, predict and select innovative materials with high accuracy. The LION way is proposed as an adaptive solver toolbox for the virtual optimal design and simulation of innovative materials to model the fundamental properties and behavior of a wide range of multi-scale materials design problems.

Predicting the Future Using Web Knowledge: State of the Art Survey

Accurate prediction models can potentially transform businesses, organizations, governments, and industries. Data-driven prediction methods and applications have recently become very popular. One of the novel method of building prediction models is to use data-driven methods and knowledge discovery on the web contents. This includes the news and media as well as social networks contents. This method uses advanced technologies of big data, machine learning, deep learning and intelligent optimization for finding patterns in big data to build prediction models. This article presents a state of the art survey on the latest technological advancements, novel methods, and applications in developing prediction models.

Industrial Applications of Big Data: State of the Art Survey

Big data analytics has become an important tool for the progress and success of a wide range of businesses and industries. Its diversity and flexibility offer a steady increasing scope for the several applications to stay competitive in the market. For that, big data approach provides several advantages such as advanced analytics, intelligent optimization, informed decision making, large-scale modeling, and accurate predictions. Due to the numerous advantages, it has been particularly possible to find more accurate and feasible solutions for the current engineering problems. Hence, the impact of big-data analytics in the engineering realm and applications is increasing more than ever. This article presents a survey to investigate how engineering community has adopted big data technologies to stay competitive. To conduct the investigation a state of the art survey of the academic literature on the big data applications to engineering is presented.

Review on the Usage of the Multiobjective Optimization Package of modeFrontier in the Energy Sector

The multiobjective optimization (MOO) software package of modeFrontier has recently become popular within industries, academics and research communities. Today, universities as well as research institutes are using modeFrontier optimization toolboxes for teaching and research proposes around the world. One of the reason behind the popularity of the package, is the way it utilizes the available resources in an efficient and integrated manner and providing multidimensional post-processing tools. The user-friendly design optimization environment of modeFrontier integrates various optimization methods with the major computer aided engineering codes and commercial numerical analysis tools. Among the wide range of applications of modeFrontier, the energy sector, particularly, has been highly benefiting from the advancements in design optimization. This article presents the state of the art survey of the novel applications of modeFrontier in this realm.

A Hybrid Machine Learning Approach for Daily Prediction of Solar Radiation

In this paper, we present a Cluster-Based Approach (CBA) that utilizes the support vector machine (SVM) and an artificial neural network (ANN) to estimate and predict the daily horizontal global solar radiation. In the proposed CBA-ANN-SVM approach, we first conduct clustering analysis and divided the global solar radiation data into clusters, according to the calendar months. Our approach aims at maximizing the homogeneity of data within the clusters, and the heterogeneity between the clusters. The proposed CBA-ANN-SVM approach is validated and the precision is compared with ANN and SVM techniques. The mean absolute percentage error (MAPE) for the proposed approach was reported lower than those of ANN and SVM.

Flood Prediction Using Machine Learning, Literature Review

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods have highly contributed in the advancement of prediction systems providing better performance and cost effective solutions. Due to the vast benefits and potential of ML, its popularity has dramatically increased among hydrologists. Researchers through introducing the novel ML methods and hybridization of the existing ones have been aiming at discovering more accurate and efficient prediction models. The main contribution is to demonstrate the state of the art of ML models in flood prediction and give an insight over the most suitable models. The literature where ML models are benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed have been particularly investigated to provide an extensive overview on various ML algorithms usage in the field. The performance comparison of ML models presents an in-depth understanding about the different techniques within the framework of a comprehensive evaluation and discussion. As the result, the paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported the most effective strategy in improvement of the ML methods. This survey can be used as a guideline for the hydrologists as well as climate scientists to assist them choosing the proper ML method according to the prediction task conclusions.

A Hybrid Neuro-Fuzzy Algorithm for Prediction of Reference Evapotranspiration

In this study, a hybrid algorithm of adaptive neuro fuzzy inference system (ANFIS), particle swarm optimization (PSO) and principle component analysis (PCA) is utilized to predict the reference evapotranspiration (ET0). The accuracy of the computational model is evaluated using four statistical tests including Pearson correlation coefficient (r), mean square error (MSE), root mean-square error (RMSE), and coefficient of determination (R2). The results show that the ET0 can be estimated with an acceptable accuracy trough combination of PCA and ANFIS. Moreover, the result indicated that the ANFIS model can be simplified via reducing dimensionality of the input data.

Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network

ABSTRACT Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production data-driven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R2) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalization ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results.