Meta Model Research Articles

Proof‐of‐concept of an information visualization classification approach based on their fine‐grained features

AbstractThe misinformation problem affects the development of the society. Misleading content and unreliable information overwhelm social networks and media. In this context, the use of data visualizations to support news and stories is increasing. The use of misleading visualizations both intentionally or accidentally influence in the audience perceptions, which usually are not visualization and domain experts. Several factors influence o accurately tag a visualization as confusing or misleading. In this paper, we present a machine learning approach to detect if an information visualization can be potentially confusing and misunderstood based on the analytic task it tries to support. This approach is supported by fine‐grained features identified through domain engineering and meta modelling on the information visualization and dashboards domain. We automatically generated visualizations from a tri‐variate dataset through the software product line paradigm and manually labelled them to obtain a training dataset. The results support the viability of the proposal as a tool to support journalists, audience and society in general, not only to detect confusing visualizations, but also to select the visualization that supports a previous defined task according to the data domain.

Uncertainty and sensitivity analysis of cooling and heating loads for building energy planning

It has become crucial to investigate the uncertainty and sensitivity of building loads (peak cooling load, peak heating load, annual cooling demand and annual heating demand) for meeting the risk assessment of building energy planning. Therefore, a new Monte Carlo (MC) method based on building performance simulation (BPS) is proposed to solve the problem of building loads forecasting at planning phase. Furthermore, the sensitivity of building loads is examined using two global sensitivity analysis (GSA) methods, including meta modeling method based on tree Gaussian process (TGP) and regression method based on standard regression coefficient (SRC). Finally, a case study of office building is conducted. The results show that the MC method constructed by the combination of R language platform and EnergyPlus software can generate models rapidly and simulate accurately building loads. Note that it is necessary to assess the stability of results as a function of sample size from uncertainty analysis in applying the MC method into building loads assessment. The TGP-based GSA method is applicable to identify and analyze key variables affecting building loads. It is recommended that at least two inherently different GSA methods should be applied to provide robust sensitivity results. Moreover, this study also provides insight on building energy planning and energy conservation design according to the results of uncertainty and sensitivity analysis for case study.

Artificial intelligence assisted technoeconomic optimization scenarios of hybrid energy systems for water management of an isolated community

Water is an essential resource demanded worldwide and it is quite debatable owing to the economic, political, and energy characteristics of any region. Off-grid water filtration plants are an alternative for communities where transportation of freshwater becomes a real challenge due to a lack of infrastructure for the water potabilization processes. For such potable water filtration plants, hybrid renewable energy systems (HRES) can be a viable solution to meet their energy demand meanwhile providing a sustainable water solution.The main contribution of this work is the unique methodology, which starts with a sizing procedure of various hybrid energy systems using a commercial software “Hybrid Optimization of Multiple Energy Resources (HOMER)” and spreadsheet algorithms, followed by a “Non-dominating Sorting Genetic Algorithm II (NSGA-II)” based multiobjective optimization. Single-objective optimization scenarios contain photovoltaic installation capacity, wind turbines, diesel generators, and battery energy storage systems including Pb-acid (Lead-acid), Li-ion (Lithium-ion), and AGM (Absorbent Glass Mat) technologies as design variables to maximize the cost of electricity or net-present-cost. Multiobjective optimization also involved environmental (CO2 emissions i.e. carbon dioxide emissions) and water cost indices as an additional packet to single-objective optimization scenarios. Afterward, a multicriteria decision-making tool using “The Order of Preference by Similarity to Ideal Solution (TOPSIS)” is applied on the Pareto front to attain the final optimization results. The analysis is further explored in depth by generating digital twins (surrogate or meta model) of HRES data using artificial intelligence techniques (artificial neural network and group-method-of-data-handling). Furthermore, calculus and statistical sensitivity analysis assist in the identification of the significant variables in the design procedure. In summary, the technical contribution of this work can be divided into two sections. The first one is the design of a hybrid energy system for the water management of an isolated community of the indigenous Mayan region of Yucatan, Mexico, which has never been considered before. Secondly, the technical contribution is related to the usage of environmental emissions as an objective function, which is not considered in the traditional design of hybrid energy systems by the software HOMER. Environmental emission as an objective function is not considered while designing a hybrid energy system in commerical softwares like HOMER, in fact, HOMER provides a list of environmental impacts but it is a secondary outcome as a result of technoeconomic optimization.Analysis of results between HOMER pro and spreadsheet has shown conformity, reporting that the optimal case consists of a photovoltaic system, diesel generator, and Li-ion technology of battery storage with capacities of ∼17 kW, ∼5kW, and 44–48 kWh, respectively, corresponding to a net present cost ranging from 70,000 United States Dollars (USD) to 79,000 USD and a cost of electricity ranging from 0.205 to 0.229 USD/kWh. The achievements obtained with multiobjective optimization indicate that the cost of electricity and net present cost can be further reduced by 0.86 % and 0.73 %, respectively, at a decrement of only 0.4% of the renewable fraction as compared to the single objective optimization scenario. It is concluded that multiobjective optimization provides an add-in feature to HOMER by using environmental emissions as an objective function.The design procedure and adapted methodology can be useful to promote sustainable development in the statewide context and can provide a scientific justification to national energy policymakers.

Crack nucleation from non-metallic inclusions in aluminum alloys described by peridynamics simulations

Conventional engineering methods oftentimes have challenges in the quantification of crack nucleation processes from manufacturing defects that are relevant for engineering component lifing. We present peridynamic simulation framework for the description of the crack nucleation process from cluster of non-metallic inclusions in aluminum alloys. Our non-local simulation framework characterizes crack nucleation process as multiple micro-crack nucleation events from individual inclusions, and eventually one micro-crack dominates. We define individual stages of the crack nucleation process, i.e., nucleation, micro-crack, technical, and crack initiation, that allow a quantification and meta model development of both the individual stages and the entire crack nucleation process.

Aerodynamic robustness optimization and design exploration of centrifugal compressor impeller under uncertainties

Aerodynamic robustness optimization of centrifugal compressor impeller under multiple uncertainties is an arduous task, due to high dimension, meta modelling workload and trial and error optimization, and an automatic optimization process is often treated as a black box, the underlying mechanism are often not well understood and explored. This paper aims to provide an efficient robustness optimization procedure and explore the underlying physical mechanism for centrifugal compressor impeller under operational and geometric uncertainties. A novel approach is proposed for the multi-objective aerodynamic robustness optimization and design exploration of a centrifugal compressor impeller. A neural-network-based Kriging model is constructed and integrated into the aerodynamic robustness optimization The correlation between the design variables and performance parameters are explored from optimization and directly visualized by self-organizing mapping. Compared to the initial impeller, the average pressure ratio of the optimized impeller increases by 9.3%, the average isentropic efficiency increases by 6.7%, the standard deviations of pressure ratio and efficiency decrease by 7.5% and 15.4% respectively, and the acoustic power level decreases by 11dB. The neural-network-based Kriging model exhibits preferable accuracy for uncertain approximation modeling. The data visualization and interpretation facilitate designers to perform efficient design optimizations. The proposed approach supports design explorations for different applications of turbomachinery.

Crashworthiness analysis and optimization of bionic corrugated sandwich structures

To improve the crashworthiness of thin-walled sandwich structures, a series of bionic corrugated sandwich structures with triangular elements (the height-to-width ratios of the elements are defined as γ) were designed inspired by the structures of shrimp chelas, including single-layer, double-layer and three-layer corrugated sandwich structures (γ1, γ2 and γ3 denotes the height-to-width ratios of single-layer, double-layer and three-layer elements, respectively). To analyze the deformation and mechanical response of the bionic thin-walled structures, the finite element method was adopted based on LS-DYNA and HyperMesh. By taking the initial peak load Fp and specific energy absorption Es as crashworthiness indexes, the influences of γ on the crashworthiness of the corrugated sandwich structures were discussed. The crashworthiness of the single-layer sandwich structures becomes worse gradually when the parameters γ exceed a certain value. For the double-layer sandwich structures, the influences of the parameters γ2 in the lower layers on the crashworthiness is greater than that of the parameters γ1 in the upper layers.. The Fp decreases by 37.8% with the increase of γ2, which means the greater γ2 of the lower layer is beneficial to improve the crashworthiness of the structure. For the three-layer sandwich structures, the influence of γ on crashworthiness indexes was investigated by range and variance analysis methods. The results show that the γ3 has the most significant influence on Es, and the significance level reaches 0.1. Finally, the optimal parameters of the bionic corrugated sandwich structures were obtained by using the multi-objective particle swarm optimization method based on a polynomial regression (PR) meta model. The optimal results show that the crashworthiness of the single-layer corrugated sandwich structures improves with the increase of γ. For the double-layer corrugated sandwich structures, The γ of the lower layer affects the crashworthiness more significantly than the γ of the upper layer. The three-layer corrugated sandwich structure with lower γ has higher Es. The optimal dimensions of the single-layer, double-layer and three-layer structures are γ = 0.8; γ1 = 0.5 and γ2 = 1.2; γ1 = 0.6, γ2 = 0.6 and γ3 = 0.9, respectively. The above results are helpful for the design of the thin-walled sandwich structures.

Identifying elevated risk for future pain crises in sickle-cell disease using photoplethysmogram patterns measured during sleep: A machine learning approach.

Transient increases in peripheral vasoconstriction frequently occur in obstructive sleep apnea and periodic leg movement disorder, both of which are common in sickle cell disease (SCD). These events reduce microvascular blood flow and increase the likelihood of triggering painful vaso-occlusive crises (VOC) that are the hallmark of SCD. We recently reported a significant association between the magnitude of vasoconstriction, inferred from the finger photoplethysmogram (PPG) during sleep, and the frequency of future VOC in 212 children with SCD. In this study, we present an improved predictive model of VOC frequency by employing a two-level stacking machine learning (ML) model that incorporates detailed features extracted from the PPG signals in the same database. The first level contains seven different base ML algorithms predicting each subject's pain category based on the input PPG characteristics and other clinical information, while the second level is a meta model which uses the inputs to the first-level model along with the outputs of the base models to produce the final prediction. Model performance in predicting future VOC was significantly higher than in predicting VOC prior to each sleep study (F1-score of 0.43 vs. 0.35, p-value <0.0001), consistent with our hypothesis of a causal relationship between vasoconstriction and future pain incidence, rather than past pain leading to greater propensity for vasoconstriction. The model also performed much better than our previous conventional statistical model (F1 = 0.33), as well as all other algorithms that used only the base-models for predicting VOC without the second tier meta model. The modest F1 score of the present predictive model was due in part to the relatively small database with substantial imbalance (176:36) between low-pain and high-pain subjects, as well as other factors not captured by the sleep data alone. This report represents the first attempt ever to use non-invasive finger PPG measurements during sleep and a ML-based approach to predict increased propensity for VOC crises in SCD. The promising results suggest the future possibility of embedding an improved version of this model in a low-cost wearable system to assist clinicians in managing long-term therapy for SCD patients.

Exploring Factors for Predicting Anxiety Disorders of the Elderly Living Alone in South Korea Using Interpretable Machine Learning: A Population-Based Study.

This epidemiological study aimed to develop an X-AI that could explain groups with a high anxiety disorder risk in old age. To achieve this objective, (1) this study explored the predictors of senile anxiety using base models and meta models. (2) This study presented decision tree visualization that could help psychiatric consultants and primary physicians easily interpret the path of predicting high-risk groups based on major predictors derived from final machine learning models with the best performance. This study analyzed 1558 elderly (695 males and 863 females) who were 60 years or older and completed the Zung’s Self-Rating Anxiety Scale (SAS). We used support vector machine (SVM), random forest, LightGBM, and Adaboost for the base model, a single predictive model, while using XGBoost algorithm for the meta model. The analysis results confirmed that the predictive performance of the “SVM + Random forest + LightGBM + AdaBoost + XGBoost model (stacking ensemble: accuracy 87.4%, precision 85.1%, recall 87.4%, and F1-score 85.5%)” was the best. Also, the results of this study showed that the elderly who often (or mostly) felt subjective loneliness, had a Self Esteem Scale score of 26 or less, and had a subjective communication with their family of 4 or less (on a 10-point scale) were the group with the highest risk anxiety disorder. The results of this study imply that it is necessary to establish a community-based mental health policy that can identify elderly groups with high anxiety risks based on multiple risk factors and manage them constantly.

Decomposition and optimization of linear structures using meta models

Monolithic optimization of large mechanical systems can be expensive and cumbersome. Drivers of computational cost and integration effort are, e.g., the size of the design problem and the number of different components, models, and disciplines. Distributed optimization schemes decompose large problems into smaller subproblems; however, they typically require intense coordination effort. This paper proposes an approach for complete decoupling by decomposing a monolithic optimization into independent optimization subproblems that can be solved without need for coordination. This is accomplished by sampling the space of component performance, here represented by eigenvalues and eigenvectors of stiffness matrices, and establishing meta models that map the relevant component performance values onto feasibility and mass estimates. The optimization procedure consists of two steps: First, a system optimization problem is solved by assigning stiffness requirements to components that are approximately feasible and mass-optimal. Second, the component optimization problems are solved independently of each other such that stiffness requirements are satisfied. As information on feasibility and mass is provided during system optimization by meta models, the approach will be referred to as informed decomposition. The effectiveness of the approach is demonstrated by minimizing the mass of a simple two-component linear structure subject to a requirement on total stiffness. This is done for three different component models, a beam with constant cross-section, a beam with varying cross-sections, and an arbitrary 2-dimensional body, using parametric and topology optimization, respectively. The approach produces results that are at most 1 % heavier than the results obtained by monolithic optimization.

End-to-end deep meta modelling to calibrate and optimize energy consumption and comfort

In this paper, we propose a new end-to-end methodology to optimize energy performance and thermal comfort in office buildings, without any renovation work. The process is decomposed into three steps: metamodel training, model calibration and optimization. We introduce and train a metamodel on thousands of weather and building settings scenarios, using samples from a physical simulation model. Its much faster computation time allows for the calibration of two weakly instrumented buildings with a derivative free optimization procedure. Using historic data from these buildings, we estimate up to 60 unknown parameters defined by energy managers, such as heat capacity, window area or exposition. Energy consumptions are finally minimized while maintaining a target thermal comfort using the Pareto front provided by a multi-objective optimization algorithm. Our approach allows the computation of the entire calibration-optimization pipeline on several types of buildings. Moreover, the numerical experiments illustrate how it may ensure a significant gain in energy efficiency, up to almost 10%, while being computationally much more appealing than simulation programs.

Multi-objective optimization of the multi-story residential building with passive design strategy in South Korea

In this study, the multi-objective optimization of passive design strategy was conducted for multi-story residential buildings in South Korea. The performance of passive design was analyzed in terms of energy, environmental, and economic metrics, considering the life cycle performance. Various passive design factors were considered to better reflect the actual phenomenon, and a novel simulation modeling method which includes building model simplification and meta model was developed for an accurate and fast calculation of optimization. The sensitivity analysis was conducted to decide critical design factors in optimization and showed that the most influential design factors are airtightness, occupants, and window-to-wall ratio. Throughout optimization, the energy, environmental impact, and economic feasibility in multi-story residential building can be improved by 52.7%, 39.5%, and 36.9%, respectively. The uncertainty of optimal solution was analyzed though the uncertainty analysis. If the utility cost increases and energy system efficiency improves in the future, the optimal passive design solution will not change significantly in energy and economic feasibility, however it may become more critical in the environmental impact.

A Meta Model Used for Reviewing Spirituality at Workplace

This paper attempted to introduce the model used for reviewing spirituality at the workplace based on empirical studies relevant to the workplace spirituality. The paper presents simply Meta model of workplace spirituality with short snap of authors, key findings, basic model and propositions based on the reviewed literature. The paper claims that Meta model is the foundation for the further research that gives baseline for introducing research works. The majority of the empirical studies concluded that people engaged in the organization express their spirituality in the workplace. Similarly, workplace spirituality has close connection with the different facets of work life. The paper recommends further researches in the area of spirituality specific to organizational work-settings.

The Process of Modeling Information System Architectures with IHE.

IHE has defined more than 200 integration profiles in order to improve the interoperability of application systems in healthcare. These profiles describe how standards should be used in particular use cases. These profiles are very helpful but their correct use is challenging, if the user is not familiar to the specifications. Therefore, inexperienced modelers of information systems quickly lose track of existing IHE profiles. In addition, the users of these profiles are often not aware of rules that are defined within these profiles and of dependencies that exist between the profiles. There are also modelers that do not notice the differences between the implemented actors, because they do not know the optional capabilities of some actors. The aim of this paper is therefore to describe a concept how modelers of information systems can be supported in the selection and use of IHE profiles and how this concept was prototypically implemented in the "Three-layer Graph-based meta model" modeling tool (3LGM2 Tool). The described modeling process consists of the following steps that can be looped: defining the use case, choosing suitable integration profiles, choosing actors and their options and assigning them to application systems, checking for required actor groupings and modeling transactions. Most of these steps were implemented in the 3LGM2 Tool. Further implementation effort and evaluation of our approach by inexperienced users is needed. But after that our tool should be a valuable tool for modelers planning healthcare information system architectures, in particular those based on IHE.

Meta-regression framework for energy consumption prediction in a smart city: A case study of Songdo in South Korea

Nowadays the concept of smart city has gained in popularity in urban studies. A smart city collects diverse information to monitor and analyze urban systems, such as energy management. It is crucial for smart cities to monitor energy efficiency to be sustainable. In this study, we search to expose the possibilities offered by the energy data of Songdo, a South Korean smart city. First, we have highlighted the ability of Songdo to generate energy data. Second, we used those data to predict its evolution. As a result, we develop a short-term stacking ensemble model for energy consumption in Songdo, focusing on a three-months-ahead prediction problem. To obtain this result, first we design a baseline regressors for the prediction, second, we apply a three-combination of each best model of the base regressors, and finally, a weighted meta-regression model was applied using meta-XGBoost. We call the resulting model stacking ensemble model. The proposed stacking ensemble model combines the best ensemble networks to improve performance prediction, yielding an R2 value of 97.89 %. The results support the effectiveness of the ensemble networks, which use Artificial Neural Networks (ANN), CatBoost and Gradient Boosting. This study also shows that the weighted meta model outperforms several machine learning models in terms of R2, MAE and RSME.

Dam failure peak outflow prediction through GEP-SVM meta models and uncertainty analysis

Abstract Accurate prediction of a breached dam's peak outflow is a significant factor for flood risk analysis. In this study, the capability of Support Vector Machine and Kernel Extreme Learning Machine as kernel-based approaches and Gene Expression Programming method was assessed in breached dam peak outflow prediction. Two types of modeling were considered. First, only dam reservoir height and volume at the failure time were used as the input combinations (state 1). Then, soil characteristics were added to input combinations to investigate particularly the impact of soil characteristics (state 2). Results showed that the use of only soil characteristics did not lead to a desired accuracy; however, adding soil characteristics to input combinations (state 2) improved the models' accuracy up to 40%. The outcome of the applied models was also compared with existing empirical equations and it was found the applied models yielded better results. Sensitivity analysis results showed that dam height had the most important role in the peak outflow prediction, while the strength parameters did not have significant impacts. Furthermore, for assessing the best-applied model dependability, uncertainty analysis was used and the results indicated that the SVM model had an allowable degree of uncertainty in peak outflow modelling.

DNN2: A hyper-parameter reinforcement learning game for self-design of neural network based elasto-plastic constitutive descriptions

This contribution presents a meta-modeling framework that employs artificial intelligence to design a neural network that replicates the path-dependent constitutive responses of composite materials sampled by a numerical testing procedure of Representative Volume Elements (RVE). A Deep Reinforcement Learning (DRL) combinatorics game is invented to automatically search for the optimal set of hyper-parameters from a decision tree. Besides the typical hyper-parameters for ANN training, such as the network topology, the size and composition of the considered training data are incorporated as additional hyper-parameters to help investigate the amount of data necessary for training and validation. The proposed meta modeling framework is able to identify hyper-parameter configurations with a weighted trade-off between prediction accuracy and computational cost. The capabilities and limitations of the introduced framework are shown and discussed via several numerical examples. Moreover, the possibility of transferring the gained knowledge of hyper-parameters among different RVE is explored in numerical experiments.

Meta Learning for Low-Resource Molecular Optimization.

The goal of molecular optimization (MO) is to discover molecules that acquire improved pharmaceutical properties over a known starting molecule. Despite many recent successes of new approaches for MO, these methods were typically developed for particular properties with rich annotated training examples. Thus, these approaches are difficult to implement in real scenes where only a small amount of pharmaceutical data is usually available due to the expense and significant effort required for the data collection. Here, we propose a new approach, Meta-MO, for molecular optimization with a handful of training samples based on the well-recognized first-order meta-learning algorithms. By using a set of meta tasks with rich training samples, Meta-MO trains a meta model through the meta-learning optimization and adapts the learned model to new low-resource MO tasks. Meta-MO was shown to consistently outperform several pretraining and multitask training procedures, providing an average improvement in the success rate of 4.3% on a large-scale bioactivity data set with diverse target variations. We also observed that Meta-MO resulted in the best performing models across fine-tuning sets with only dozens of samples. To the best of our knowledge, this is the first study to apply meta learning to MO tasks. More importantly, such a strategy could be further extended to many low-resource scenarios in real-world drug design.

Stacked KNN with hard voting predictive approach to assist hiring process in IT organizations

Effective candidates screening is critical for any IT firm as it impacts the future growth and productivity of that firm. Currently majority of these firms follow a manual approach of hiring employees which is more prone to errors and time consuming. Prime purpose of the research is to develop an intelligent predictive model to decide upon candidate’s suitability for an applied It based job. A sample size of 13,168 instances with 19 attributes of job seekers data are used for study. An ensemble predictive meta model approach is presented in the research where a stacked KNN (K-Nearest Neighbours) algorithm combined with hard voting approach is employed. The idea is to use a single unified predictive model in place of separate classification models by considering the predicted class with maximum votes against each class label. The proposed approach comprises two functional modules. ‘Stacked KNN Learner Module’ involves the use of five variants of KNN which include 1-NN, 3-NN, 5-NN, 7-NN and 9-NN. Individual prediction of the job seeker data from these variants of KNN algorithm are input to the ‘Hard Voting Ensemble Predictive unit’ which eventually aggregates majority of predicted class votes determined for each class label to generate the final output predicted class label on basis of maximum votes. The developed ensemble meta model is successfully implemented using python programming language and its performance evaluation is done using various metrics. The implemented model generated a 96.96% prediction accuracy rate. The specificity, sensitivity and f-score value recorded was 96.36%, 96.06% and 96.26% respectively. Mean Absolute Error (MAE) and Root mean Square Error (RMSE) value observed with our proposed meta model was 0.0048 and 0.0102 respectively. A comparative analysis of the proposed model was done by varying the data sample size and it gave a consistent performance throughout. It was observed that proposed ensemble model gave an impressive 96.96% mean accuracy rate and the overall performance of other variants was consistent. Performance of proposed meta model was also compared with some existing techniques like C.45, CART and K-Means. It outperformed other techniques in terms of accurately predicting the class label of candidates applying for job. This ensemble meta model can be of great help for growing IT firms and assist the organization unit in hiring right and deserving candidates in future. Proposed work can act as a decision making framework in multi sector units with enormous work force to streamline performance appraisal process thereby enabling in hiring right people for right job.

Manuscript Exchange Common Approach and Journal Article Tag Suite (JATS) compatibility: a case study utilizing the JATS Compatibility Meta Model

The National Information Standards Organization (NISO) Manuscript Exchange Common Approach (MECA) project is a cross-organization industry initiative to develop a common approach to manuscript transfer that can be adopted across the scholarly publishing industry. MECA establishes a vocabulary set that includes transfer, review, and manifest models. These models are designed to work with different article XML schemas, including the latest NISO Journal Article Tag Suite (JATS) standard (v1.2). In order to avoid conflicts between these project vocabularies and the JATS, we reviewed the MECA vocabularies against the NISO JATS Compatibility Meta Model (v0.7). This paper describes the review and analysis of the MECA schemas against the JATS Meta Model, how we documented the analysis, and the recommendations we made to resolve issues revealed by the analysis. It includes the documentation we produced to communicate the results of the analysis and what actions we took to move forward with the project, including both changes to the schemas and requests for changes in the JATS. We hope sharing our experiences with this process will help others who are trying to do the same.

A Predictive Meta Model for Forecasting Stock Price using Time Series Data

Stock market prediction through time series is a challenging as well as an interesting research areafor the finance domain, through which stock traders and investors can find the right time to buy/sell stocks. However, various algorithms have been developed based on the statistical approach to forecast the time series for stock data, but due to the volatile nature and different price ranges of the stock price one particular algorithm is not enough to visualize the prediction. This study aims to propose a model that will choose the preeminent algorithm for that particular company’s stock that can forecastthe time series with minimal error. This model can assist a trader/investor with or without expertise in the stock market to achieve profitable investments. We have used the Stock data from Stock Exchange Bangladesh, which covers 300+ companies to train and test our system. We have classified those companies based on the stock price range and then applied our model to identify which algorithm suites most for a particular range of stock price. Comparative forecasting results of all algorithms in diverse price ranges have been presented to show the usefulness of this Predictive Meta Model