Bio-Inspired Requirements Variability Modeling with use Case ijseajournal
Background.Feature Model (FM) is the most important technique used to manage the variability through products in Software Product Lines (SPLs). Often, the SPLs requirements variability is by using variable use case modelwhich is a real challenge inactual approaches: large gap between their concepts and those of real world leading to bad quality, poor supporting FM, and the variability does not cover all requirements modeling levels. Aims. This paper proposes a bio-inspired use case variability modeling methodology dealing with the above shortages.
Method. The methodology is carried out through variable business domain use case meta modeling,
variable applications family use case meta modeling, and variable specific application use case generating.
Results. This methodology has leaded to integrated solutions to the above challenges: it decreases the gap
between computing concepts and real world ones. It supports use case variability modeling by introducing
versions and revisions features and related relations. The variability is supported at three meta levels
covering business domain, applications family, and specific application requirements.
Conclusion. A comparative evaluation with the closest recent works, upon some meaningful criteria in the
domain, shows the conceptual and practical great value of the proposed methodology and leads to
promising research perspectives
BIO-INSPIRED REQUIREMENTS VARIABILITY MODELING WITH USE CASE mathsjournal
Background.Feature Model (FM) is the most important technique used to manage the variability through
products in Software Product Lines (SPLs). Often, the SPLs requirements variability is by using variable
use case modelwhich is a real challenge inactual approaches: large gap between their concepts and those of
real world leading to bad quality, poor supporting FM, and the variability does not cover all requirements
modeling levels.
Using Model-Driven Engineering for Decision Support Systems Modelling, Implem...CSCJournals
Following the principle of everything is object, software development engineering has moved towards the principle of everything is model, through Model Driven Engineering (MDE). Its implementation is based on models and their successive transformations, which allow starting from the requirements specification to the code’s implementation. This engineering is used in the development of information systems, including Decision-Support Systems (DSS). Here we use MDE to propose an DSS development approach, using the Multidimensional Canonical Partitioning (MCP) design approach and a design pattern. We also use model’s transformation in order to obtain not only implementation codes, but also data warehouse feeds.
Requirements Variability Specification for Data Intensive Software ijseajournal
Nowadays, the use of feature modeling technique, in software requirements specification, increased the variation support in Data Intensive Software Product Lines (DISPLs) requirements modeling. It is considered the easiest and the most efficient way to express commonalities and variability among different
products requirements. Several recent works, in DISPLs requirements, handled data variability by different models which are far from real world concepts. This,leaded to difficulties in analyzing, designing, implementing, and maintaining this variability. However, this work proposes a software requirements
specification methodology based on concepts more close to the nature and which are inspired from genetics. This bio-inspiration has carried out important results in DISPLs requirements variability specification with feature modeling, which were not approached by the conventional approaches.The feature model was enriched with features and relations, facilitating the requirements variation management, not yet considered in the current relevant works.The use of genetics-based methodology
seems to be promising in data intensive software requirements variability specification
REQUIREMENTS VARIABILITY SPECIFICATION FOR DATA INTENSIVE SOFTWARE mathsjournal
Nowadays, the use of feature modeling technique, in software requirements specification, increased the
variation support in Data Intensive Software Product Lines (DISPLs) requirements modeling. It is
considered the easiest and the most efficient way to express commonalities and variability among different
products requirements. Several recent works, in DISPLs requirements, handled data variability by different
models which are far from real world concepts. This,leaded to difficulties in analyzing, designing,
implementing, and maintaining this variability. However, this work proposes a software requirements
specification methodology based on concepts more close to the nature and which are inspired from
genetics. This bio-inspiration has carried out important results in DISPLs requirements variability
specification with feature modeling, which were not approached by the conventional approaches.The
feature model was enriched with features and relations, facilitating the requirements variation
management, not yet considered in the current relevant works.The use of genetics-based m
This document discusses online feature selection (OFS) for data mining applications. It addresses two tasks of OFS: 1) learning with full input, where the learner can access all features to select a subset, and 2) learning with partial input, where only a limited number of features can be accessed for each instance. Novel algorithms are presented for each task, and their performance is analyzed theoretically. Experiments on real-world datasets demonstrate the efficacy of the proposed OFS techniques for applications in computer vision, bioinformatics, and other domains involving high-dimensional sequential data.
Multidirectional Product Support System for Decision Making In Textile Indust...IOSR Journals
This document proposes a multidirectional rank prediction algorithm (MDRP) for decision making in the textile industry using collaborative filtering methods. MDRP learns asymmetric similarities between users, items, ratings, and sellers simultaneously through matrix factorization to overcome data sparsity and scalability issues. The algorithm was tested on textile datasets and analyzed product and user preferences. Results showed MDRP provided more accurate recommendations than existing similarity learning and collaborative filtering methods. MDRP allows effective decision making for multiple entities with multiple attributes.
This document summarizes a research article that proposes using continuous hidden Markov models (CHMMs) with a change point detection algorithm for online adaptive bearings condition assessment. The approach aims to (1) estimate the initial number of CHMM states and parameters from historical data and (2) update the state space and parameters during monitoring to adapt to changes. Compared to existing techniques, the proposed approach improves HMM training, detects unknown states earlier, and better represents degradation processes with unknown conditions by changing the CHMM structure.
Bio-Inspired Requirements Variability Modeling with use Case ijseajournal
Background.Feature Model (FM) is the most important technique used to manage the variability through products in Software Product Lines (SPLs). Often, the SPLs requirements variability is by using variable use case modelwhich is a real challenge inactual approaches: large gap between their concepts and those of real world leading to bad quality, poor supporting FM, and the variability does not cover all requirements modeling levels. Aims. This paper proposes a bio-inspired use case variability modeling methodology dealing with the above shortages.
Method. The methodology is carried out through variable business domain use case meta modeling,
variable applications family use case meta modeling, and variable specific application use case generating.
Results. This methodology has leaded to integrated solutions to the above challenges: it decreases the gap
between computing concepts and real world ones. It supports use case variability modeling by introducing
versions and revisions features and related relations. The variability is supported at three meta levels
covering business domain, applications family, and specific application requirements.
Conclusion. A comparative evaluation with the closest recent works, upon some meaningful criteria in the
domain, shows the conceptual and practical great value of the proposed methodology and leads to
promising research perspectives
BIO-INSPIRED REQUIREMENTS VARIABILITY MODELING WITH USE CASE mathsjournal
Background.Feature Model (FM) is the most important technique used to manage the variability through
products in Software Product Lines (SPLs). Often, the SPLs requirements variability is by using variable
use case modelwhich is a real challenge inactual approaches: large gap between their concepts and those of
real world leading to bad quality, poor supporting FM, and the variability does not cover all requirements
modeling levels.
Using Model-Driven Engineering for Decision Support Systems Modelling, Implem...CSCJournals
Following the principle of everything is object, software development engineering has moved towards the principle of everything is model, through Model Driven Engineering (MDE). Its implementation is based on models and their successive transformations, which allow starting from the requirements specification to the code’s implementation. This engineering is used in the development of information systems, including Decision-Support Systems (DSS). Here we use MDE to propose an DSS development approach, using the Multidimensional Canonical Partitioning (MCP) design approach and a design pattern. We also use model’s transformation in order to obtain not only implementation codes, but also data warehouse feeds.
Requirements Variability Specification for Data Intensive Software ijseajournal
Nowadays, the use of feature modeling technique, in software requirements specification, increased the variation support in Data Intensive Software Product Lines (DISPLs) requirements modeling. It is considered the easiest and the most efficient way to express commonalities and variability among different
products requirements. Several recent works, in DISPLs requirements, handled data variability by different models which are far from real world concepts. This,leaded to difficulties in analyzing, designing, implementing, and maintaining this variability. However, this work proposes a software requirements
specification methodology based on concepts more close to the nature and which are inspired from genetics. This bio-inspiration has carried out important results in DISPLs requirements variability specification with feature modeling, which were not approached by the conventional approaches.The feature model was enriched with features and relations, facilitating the requirements variation management, not yet considered in the current relevant works.The use of genetics-based methodology
seems to be promising in data intensive software requirements variability specification
REQUIREMENTS VARIABILITY SPECIFICATION FOR DATA INTENSIVE SOFTWARE mathsjournal
Nowadays, the use of feature modeling technique, in software requirements specification, increased the
variation support in Data Intensive Software Product Lines (DISPLs) requirements modeling. It is
considered the easiest and the most efficient way to express commonalities and variability among different
products requirements. Several recent works, in DISPLs requirements, handled data variability by different
models which are far from real world concepts. This,leaded to difficulties in analyzing, designing,
implementing, and maintaining this variability. However, this work proposes a software requirements
specification methodology based on concepts more close to the nature and which are inspired from
genetics. This bio-inspiration has carried out important results in DISPLs requirements variability
specification with feature modeling, which were not approached by the conventional approaches.The
feature model was enriched with features and relations, facilitating the requirements variation
management, not yet considered in the current relevant works.The use of genetics-based m
This document discusses online feature selection (OFS) for data mining applications. It addresses two tasks of OFS: 1) learning with full input, where the learner can access all features to select a subset, and 2) learning with partial input, where only a limited number of features can be accessed for each instance. Novel algorithms are presented for each task, and their performance is analyzed theoretically. Experiments on real-world datasets demonstrate the efficacy of the proposed OFS techniques for applications in computer vision, bioinformatics, and other domains involving high-dimensional sequential data.
Multidirectional Product Support System for Decision Making In Textile Indust...IOSR Journals
This document proposes a multidirectional rank prediction algorithm (MDRP) for decision making in the textile industry using collaborative filtering methods. MDRP learns asymmetric similarities between users, items, ratings, and sellers simultaneously through matrix factorization to overcome data sparsity and scalability issues. The algorithm was tested on textile datasets and analyzed product and user preferences. Results showed MDRP provided more accurate recommendations than existing similarity learning and collaborative filtering methods. MDRP allows effective decision making for multiple entities with multiple attributes.
This document summarizes a research article that proposes using continuous hidden Markov models (CHMMs) with a change point detection algorithm for online adaptive bearings condition assessment. The approach aims to (1) estimate the initial number of CHMM states and parameters from historical data and (2) update the state space and parameters during monitoring to adapt to changes. Compared to existing techniques, the proposed approach improves HMM training, detects unknown states earlier, and better represents degradation processes with unknown conditions by changing the CHMM structure.
This document describes an empirical study that compared the effectiveness of two variability management approaches for software product lines (SPLs) at the UML class level: PLUS and SMarty. The study found that PLUS was more effective at identifying and representing variabilities in class models. Based on participant feedback, guidelines were improved for SMarty and a new experiment is planned to evaluate the updated SMarty approach against PLUS. The results provide evidence that PLUS is currently more effective but further studies are needed to generalize findings and potentially improve SMarty's effectiveness.
IRJET - Student Pass Percentage Dedection using Ensemble LearninngIRJET Journal
This document discusses using ensemble learning methods to predict student pass rates. It begins with an abstract describing ensemble learning and its applications. It then provides background on strengthening the STEM workforce and using prediction modeling in educational data mining. The methodology section describes using decision trees, logistic regression, nearest neighbors, neural networks, naive Bayes, and support vector machines as base classifiers in an ensemble model to predict student enrollment in STEM courses. The results show the J48 decision tree algorithm correctly classified 84% of instances, outperforming naive Bayes and CART. The conclusion is that ensemble models can better categorize factors affecting student choice to enroll in STEM by combining multiple classification techniques.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
The document proposes a Response Aware Probabilistic Matrix Factorization (RAPMF) framework to address limitations in existing collaborative filtering recommendation systems. RAPMF incorporates users' response patterns into probabilistic matrix factorization by modeling responses as a Bernoulli distribution for observed ratings and a step function for unobserved ratings. This allows marginalizing missing responses. The authors also develop a mini-batch implementation of RAPMF to reduce computational costs from O(N×M) to O(B2) for mini-batches of B users and items. Experimental evaluation on synthetic and real-world datasets demonstrates the merits of RAPMF, including improved performance and reduced training time compared to other methods.
Multiagent Systems are autonomous intelligent systems. In many academic institutions student
admissions are performed after generating merit lists. Generation of merit lists is preceded by
manual scrutiny of admission forms. This manual scrutiny is a knowledge-intensive, tedious and
error-prone task. In this paper the design, implementation and testing of Multiagent System for
Scrutiny of Admission Forms (MASAF) using Automatic Knowledge Capture is presented.
MASAF consists of three agents namely: Form agent, Record agent, and Scrutiny agent. These
three agents, using ontology, cooperatively fulfill the goal of highlighting the discrepancies
in filled forms. MASAF has been tested by scrutinizing about 1000 forms and all of
discrepancies found were correct as verified by human scrutinizer. Thus it can be concluded
that using Multiagent system for scrutiny of forms can reduce human intervention, improve
performance in terms of speed and accuracy. The system can be enhanced to automatically
correct the discrepancies in forms.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes a study that aimed to identify value co-creation attributes that influence the UTM Institutional Repository (UTM IR), an e-service application. The study used interviews with UTM IR providers and users to collect data on the e-service based on the DART model of value co-creation. The DART model examines dialogue, access, risk, and transparency between customers and providers. Interview responses were coded according to the DART building blocks. A gap analysis of the coded provider and user responses identified attributes influencing the UTM IR from a value co-creation perspective. The findings aimed to help evaluate the UTM IR e-service based on customer and provider value co-creation.
Unsupervised Feature Selection Based on the Distribution of Features Attribut...Waqas Tariq
Since dealing with high dimensional data is computationally complex and sometimes even intractable, recently several feature reductions methods have been developed to reduce the dimensionality of the data in order to simplify the calculation analysis in various applications such as text categorization, signal processing, image retrieval, gene expressions and etc. Among feature reduction techniques, feature selection is one the most popular methods due to the preservation of the original features. However, most of the current feature selection methods do not have a good performance when fed on imbalanced data sets which are pervasive in real world applications. In this paper, we propose a new unsupervised feature selection method attributed to imbalanced data sets, which will remove redundant features from the original feature space based on the distribution of features. To show the effectiveness of the proposed method, popular feature selection methods have been implemented and compared. Experimental results on the several imbalanced data sets, derived from UCI repository database, illustrate the effectiveness of our proposed methods in comparison with the other compared methods in terms of both accuracy and the number of selected features.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Fuzzy Rule Base System for Software Classificationijcsit
This document describes a fuzzy rule-based system for classifying Java applications using object-oriented metrics. Key features of the system include automatically extracting OO metrics from source code, a configurable set of fuzzy rules, and classifying software at both the application and class level. The system is designed to address limitations of existing OO metric tools by providing an automated, unified analysis and classification without requiring complex post-processing methods. The document outlines the system design, including subsystems for the fuzzy rules engine and extracting OO metrics, and defines membership functions and fuzzy rules for classification.
A practical approach for model based slicingIOSR Journals
This document presents a methodology for model-based slicing of UML sequence diagrams to extract submodels. The methodology involves:
1. Generating a sequence diagram from requirements and converting it to XML.
2. Parsing the XML with a DOM parser to extract message information.
3. Slicing the message information based on a slicing criteria, such as a variable, to extract relevant messages.
4. Converting the sliced messages back into a simplified sequence diagram fragment focused on the slicing criteria.
The methodology aims to address the difficulty of visualizing and testing large, complex software models by extracting a relevant submodel based on a slicing criteria, making the model easier to understand and test.
Estimation of Functional Size of a Data Warehouse System using COSMIC FSM Methodidescitation
It is not easy to measure the functional size of
Data Warehouse System. Data Warehouse system is not
traditional system and it can be easily measured using FSM
(Functional Size Measurement) Method. In this paper we have
shown with the help of a case study to measure the functional
size of the Data Warehouse System using COSMIC
FSMmethod. We will explore the use of COSMIC in sizing
Data Warehouse Systems.
The document proposes an integrated approach for selecting the optimal combination of multiple flexible manufacturing systems (FMS). It first identifies both qualitative and quantitative attributes for evaluating FMS alternatives. Grey systems theory is used to handle uncertain qualitative data, while simulation modeling provides objective data. A goal programming model is formulated to prioritize objectives like cost, work in process, quality, etc. Finally, a genetic algorithm solves the multi-FMS combination problem by finding the combination that best meets the objectives. The proposed approach aims to facilitate complex decision making for selecting an optimal set of interrelated FMS alternatives.
This document summarizes a research paper on developing a feature-based product recommendation system. It begins by introducing recommender systems and their importance for e-commerce. It then describes how the proposed system takes basic product descriptions as input, recognizes features using association rule mining and k-nearest neighbor algorithms, and outputs recommended additional features to improve the product profile. The paper evaluates the system's performance on recommending antivirus software features. In under 3 sentences.
DEA-Based Benchmarking Models In Supply Chain Management: An Application-Orie...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/data-envelopment-analysis/
Data Envelopment Analysis (DEA) is a mathematical methodology for benchmarking a group of entities in a group. The inputs of a DEA model are the resources that the entity consumes, and the outputs of the outputs are the desired outcomes generated by the entity, by using the inputs. DEA returns important benchmarking metrics, including efficiency score, reference set, and projections. While DEA has been extensively applied in supply chain management (SCM) as well as a diverse range of other fields, it is not clear what has been done in the literature in the past, especially given the domain, the model details, and the country of application. Also, it is not clear what would be an acceptable number of DMUs in comparison to existing research. This paper follows a recipe-based approach, listing the main characteristics of the DEA models for supply chain management. This way, practitioners in the field can build their own models without having to perform detailed literature search. Further guidelines are also provided in the paper for practitioners, regarding the application of DEA in SCM benchmarking.
In this paper we have focused on an efficient feature selection method in classification of audio files.
The main objective is feature selection and extraction. We have selected a set of features for further
analysis, which represents the elements in feature vector. By extraction method we can compute a
numerical representation that can be used to characterize the audio using the existing toolbox. In this
study Gain Ratio (GR) is used as a feature selection measure. GR is used to select splitting attribute
which will separate the tuples into different classes. The pulse clarity is considered as a subjective
measure and it is used to calculate the gain of features of audio files. The splitting criterion is
employed in the application to identify the class or the music genre of a specific audio file from
testing database. Experimental results indicate that by using GR the application can produce a
satisfactory result for music genre classification. After dimensionality reduction best three features
have been selected out of various features of audio file and in this technique we will get more than
90% successful classification result.
Content Based Image Retrieval for Unlabelled ImagesIOSR Journals
Abstract: Recently, content-based image retrieval has become hot topic and the techniques of content-based
image retrieval have been achieved good development. Content-based image retrieval systems were introduced
to address the problems associated with text-based image retrieval. In this paper, basic components of contentbased
image retrieval system are introduced here. Images are classified as lablled and unlablled images. Here
survey on content based image retrieval given with some Image retrieval methods based on unlabelled data like
D-EM, SVM, Relevance Feedback, Semi-Supervised/Active Learning, Transductive Learning, Bootstrapping
SVM, Active learning, SSMIL and Label propagation Methods are presented in this paper. Comparison of these
all methods is also presented in this paper.
Keywords: Image retrieval, CBIR, Unlabelled images, SVM
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Projection pursuit Random Forest using discriminant feature analysis model fo...IJECEIAES
A major and demand issue in the telecommunications industry is the prediction of churn customers. Churn describes the customer who attrites from the current provider to competitors searching for better service offers. Companies from the Telco sector frequently have customer relationship management offices it is the main objective in how to win back defecting clients because preserve long-term customers can be much more beneficial than gain newly recruited customers. Researchers and practitioners are paying great attention to developing a robust customer churn prediction model, especially in the telecommunication business by proposed numerous machine learning approaches. Many approaches of Classification are established, but the most effective in recent times is a tree-based method. The main contribution of this research is to predict churners/non-churners in the Telecom sector based on project pursuit Random Forest (PPForest) that uses discriminant feature analysis as a novelty extension of the conventional Random Forest for learning oblique Project Pursuit tree (PPtree). The proposed methodology leverages the advantage of two discriminant analysis methods to calculate the project index used in the construction of PPtree. The first method used Support Vector Machines (SVM) while, the second method used Linear Discriminant Analysis (LDA) to achieve linear splitting of variables during oblique PPtree construction to produce individual classifiers that are robust and more diverse than classical Random Forest. It is found that the proposed methods enjoy the best performance measurements e.g. Accuracy, hit rate, ROC curve, Lift, H-measure, AUC. Moreover, PPForest based on LDA delivers effective evaluators in the prediction model.
This document discusses the use of mathematical programming to optimize supply chain management. It begins with an introduction to mathematical programming and its applications in supply chain management. It then presents a generic mixed-integer programming model for supply chain configuration that aims to minimize total costs. The model includes constraints related to demand fulfillment, facility flows, capacity, material availability and open facilities. The document discusses common modifications to the generic model, such as incorporating international factors, inventory, transportation and policies. It provides two case studies that apply the generic model to analyze costs for different companies. The conclusion states that mathematical programming allows comparison of costs between products and optimization of production costs and systems.
This document discusses the use of mathematical programming to optimize supply chain management. It begins with an introduction to mathematical programming and its applications in supply chain management. It then describes a generic mixed-integer programming model for supply chain configuration that aims to minimize total costs. The model includes constraints related to demand fulfillment, facility flows, capacity, material availability and open facilities. The document also discusses common modifications to the generic model, such as incorporating international factors, inventory, transportation and policies. It provides two case studies that apply the generic model to analyze different companies' supply chain costs.
The document discusses modeling performance for distributed real-time process control systems early in development. It proposes representing individual system elements like sensors and actuators as periodic processes and using Model Driven Architecture to develop functional models prior to UML models. This allows performance to be animated or calculated early based on individual element models, helping establish performance requirements before implementation.
Handwritten Text Recognition Using Machine LearningIRJET Journal
This document discusses a system for handwritten text recognition using machine learning. It proposes using both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to recognize handwritten text. CNNs are used for feature extraction from images while RNNs model the sequential nature of handwriting. The system collects data, preprocesses it, trains a model using CNNs and RNNs, and then uses the model to generate recognized text output with high accuracy. Potential applications of this handwritten text recognition system include document digitization, banking, education, and more.
This document describes an empirical study that compared the effectiveness of two variability management approaches for software product lines (SPLs) at the UML class level: PLUS and SMarty. The study found that PLUS was more effective at identifying and representing variabilities in class models. Based on participant feedback, guidelines were improved for SMarty and a new experiment is planned to evaluate the updated SMarty approach against PLUS. The results provide evidence that PLUS is currently more effective but further studies are needed to generalize findings and potentially improve SMarty's effectiveness.
IRJET - Student Pass Percentage Dedection using Ensemble LearninngIRJET Journal
This document discusses using ensemble learning methods to predict student pass rates. It begins with an abstract describing ensemble learning and its applications. It then provides background on strengthening the STEM workforce and using prediction modeling in educational data mining. The methodology section describes using decision trees, logistic regression, nearest neighbors, neural networks, naive Bayes, and support vector machines as base classifiers in an ensemble model to predict student enrollment in STEM courses. The results show the J48 decision tree algorithm correctly classified 84% of instances, outperforming naive Bayes and CART. The conclusion is that ensemble models can better categorize factors affecting student choice to enroll in STEM by combining multiple classification techniques.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
The document proposes a Response Aware Probabilistic Matrix Factorization (RAPMF) framework to address limitations in existing collaborative filtering recommendation systems. RAPMF incorporates users' response patterns into probabilistic matrix factorization by modeling responses as a Bernoulli distribution for observed ratings and a step function for unobserved ratings. This allows marginalizing missing responses. The authors also develop a mini-batch implementation of RAPMF to reduce computational costs from O(N×M) to O(B2) for mini-batches of B users and items. Experimental evaluation on synthetic and real-world datasets demonstrates the merits of RAPMF, including improved performance and reduced training time compared to other methods.
Multiagent Systems are autonomous intelligent systems. In many academic institutions student
admissions are performed after generating merit lists. Generation of merit lists is preceded by
manual scrutiny of admission forms. This manual scrutiny is a knowledge-intensive, tedious and
error-prone task. In this paper the design, implementation and testing of Multiagent System for
Scrutiny of Admission Forms (MASAF) using Automatic Knowledge Capture is presented.
MASAF consists of three agents namely: Form agent, Record agent, and Scrutiny agent. These
three agents, using ontology, cooperatively fulfill the goal of highlighting the discrepancies
in filled forms. MASAF has been tested by scrutinizing about 1000 forms and all of
discrepancies found were correct as verified by human scrutinizer. Thus it can be concluded
that using Multiagent system for scrutiny of forms can reduce human intervention, improve
performance in terms of speed and accuracy. The system can be enhanced to automatically
correct the discrepancies in forms.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes a study that aimed to identify value co-creation attributes that influence the UTM Institutional Repository (UTM IR), an e-service application. The study used interviews with UTM IR providers and users to collect data on the e-service based on the DART model of value co-creation. The DART model examines dialogue, access, risk, and transparency between customers and providers. Interview responses were coded according to the DART building blocks. A gap analysis of the coded provider and user responses identified attributes influencing the UTM IR from a value co-creation perspective. The findings aimed to help evaluate the UTM IR e-service based on customer and provider value co-creation.
Unsupervised Feature Selection Based on the Distribution of Features Attribut...Waqas Tariq
Since dealing with high dimensional data is computationally complex and sometimes even intractable, recently several feature reductions methods have been developed to reduce the dimensionality of the data in order to simplify the calculation analysis in various applications such as text categorization, signal processing, image retrieval, gene expressions and etc. Among feature reduction techniques, feature selection is one the most popular methods due to the preservation of the original features. However, most of the current feature selection methods do not have a good performance when fed on imbalanced data sets which are pervasive in real world applications. In this paper, we propose a new unsupervised feature selection method attributed to imbalanced data sets, which will remove redundant features from the original feature space based on the distribution of features. To show the effectiveness of the proposed method, popular feature selection methods have been implemented and compared. Experimental results on the several imbalanced data sets, derived from UCI repository database, illustrate the effectiveness of our proposed methods in comparison with the other compared methods in terms of both accuracy and the number of selected features.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Fuzzy Rule Base System for Software Classificationijcsit
This document describes a fuzzy rule-based system for classifying Java applications using object-oriented metrics. Key features of the system include automatically extracting OO metrics from source code, a configurable set of fuzzy rules, and classifying software at both the application and class level. The system is designed to address limitations of existing OO metric tools by providing an automated, unified analysis and classification without requiring complex post-processing methods. The document outlines the system design, including subsystems for the fuzzy rules engine and extracting OO metrics, and defines membership functions and fuzzy rules for classification.
A practical approach for model based slicingIOSR Journals
This document presents a methodology for model-based slicing of UML sequence diagrams to extract submodels. The methodology involves:
1. Generating a sequence diagram from requirements and converting it to XML.
2. Parsing the XML with a DOM parser to extract message information.
3. Slicing the message information based on a slicing criteria, such as a variable, to extract relevant messages.
4. Converting the sliced messages back into a simplified sequence diagram fragment focused on the slicing criteria.
The methodology aims to address the difficulty of visualizing and testing large, complex software models by extracting a relevant submodel based on a slicing criteria, making the model easier to understand and test.
Estimation of Functional Size of a Data Warehouse System using COSMIC FSM Methodidescitation
It is not easy to measure the functional size of
Data Warehouse System. Data Warehouse system is not
traditional system and it can be easily measured using FSM
(Functional Size Measurement) Method. In this paper we have
shown with the help of a case study to measure the functional
size of the Data Warehouse System using COSMIC
FSMmethod. We will explore the use of COSMIC in sizing
Data Warehouse Systems.
The document proposes an integrated approach for selecting the optimal combination of multiple flexible manufacturing systems (FMS). It first identifies both qualitative and quantitative attributes for evaluating FMS alternatives. Grey systems theory is used to handle uncertain qualitative data, while simulation modeling provides objective data. A goal programming model is formulated to prioritize objectives like cost, work in process, quality, etc. Finally, a genetic algorithm solves the multi-FMS combination problem by finding the combination that best meets the objectives. The proposed approach aims to facilitate complex decision making for selecting an optimal set of interrelated FMS alternatives.
This document summarizes a research paper on developing a feature-based product recommendation system. It begins by introducing recommender systems and their importance for e-commerce. It then describes how the proposed system takes basic product descriptions as input, recognizes features using association rule mining and k-nearest neighbor algorithms, and outputs recommended additional features to improve the product profile. The paper evaluates the system's performance on recommending antivirus software features. In under 3 sentences.
DEA-Based Benchmarking Models In Supply Chain Management: An Application-Orie...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/data-envelopment-analysis/
Data Envelopment Analysis (DEA) is a mathematical methodology for benchmarking a group of entities in a group. The inputs of a DEA model are the resources that the entity consumes, and the outputs of the outputs are the desired outcomes generated by the entity, by using the inputs. DEA returns important benchmarking metrics, including efficiency score, reference set, and projections. While DEA has been extensively applied in supply chain management (SCM) as well as a diverse range of other fields, it is not clear what has been done in the literature in the past, especially given the domain, the model details, and the country of application. Also, it is not clear what would be an acceptable number of DMUs in comparison to existing research. This paper follows a recipe-based approach, listing the main characteristics of the DEA models for supply chain management. This way, practitioners in the field can build their own models without having to perform detailed literature search. Further guidelines are also provided in the paper for practitioners, regarding the application of DEA in SCM benchmarking.
In this paper we have focused on an efficient feature selection method in classification of audio files.
The main objective is feature selection and extraction. We have selected a set of features for further
analysis, which represents the elements in feature vector. By extraction method we can compute a
numerical representation that can be used to characterize the audio using the existing toolbox. In this
study Gain Ratio (GR) is used as a feature selection measure. GR is used to select splitting attribute
which will separate the tuples into different classes. The pulse clarity is considered as a subjective
measure and it is used to calculate the gain of features of audio files. The splitting criterion is
employed in the application to identify the class or the music genre of a specific audio file from
testing database. Experimental results indicate that by using GR the application can produce a
satisfactory result for music genre classification. After dimensionality reduction best three features
have been selected out of various features of audio file and in this technique we will get more than
90% successful classification result.
Content Based Image Retrieval for Unlabelled ImagesIOSR Journals
Abstract: Recently, content-based image retrieval has become hot topic and the techniques of content-based
image retrieval have been achieved good development. Content-based image retrieval systems were introduced
to address the problems associated with text-based image retrieval. In this paper, basic components of contentbased
image retrieval system are introduced here. Images are classified as lablled and unlablled images. Here
survey on content based image retrieval given with some Image retrieval methods based on unlabelled data like
D-EM, SVM, Relevance Feedback, Semi-Supervised/Active Learning, Transductive Learning, Bootstrapping
SVM, Active learning, SSMIL and Label propagation Methods are presented in this paper. Comparison of these
all methods is also presented in this paper.
Keywords: Image retrieval, CBIR, Unlabelled images, SVM
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Projection pursuit Random Forest using discriminant feature analysis model fo...IJECEIAES
A major and demand issue in the telecommunications industry is the prediction of churn customers. Churn describes the customer who attrites from the current provider to competitors searching for better service offers. Companies from the Telco sector frequently have customer relationship management offices it is the main objective in how to win back defecting clients because preserve long-term customers can be much more beneficial than gain newly recruited customers. Researchers and practitioners are paying great attention to developing a robust customer churn prediction model, especially in the telecommunication business by proposed numerous machine learning approaches. Many approaches of Classification are established, but the most effective in recent times is a tree-based method. The main contribution of this research is to predict churners/non-churners in the Telecom sector based on project pursuit Random Forest (PPForest) that uses discriminant feature analysis as a novelty extension of the conventional Random Forest for learning oblique Project Pursuit tree (PPtree). The proposed methodology leverages the advantage of two discriminant analysis methods to calculate the project index used in the construction of PPtree. The first method used Support Vector Machines (SVM) while, the second method used Linear Discriminant Analysis (LDA) to achieve linear splitting of variables during oblique PPtree construction to produce individual classifiers that are robust and more diverse than classical Random Forest. It is found that the proposed methods enjoy the best performance measurements e.g. Accuracy, hit rate, ROC curve, Lift, H-measure, AUC. Moreover, PPForest based on LDA delivers effective evaluators in the prediction model.
This document discusses the use of mathematical programming to optimize supply chain management. It begins with an introduction to mathematical programming and its applications in supply chain management. It then presents a generic mixed-integer programming model for supply chain configuration that aims to minimize total costs. The model includes constraints related to demand fulfillment, facility flows, capacity, material availability and open facilities. The document discusses common modifications to the generic model, such as incorporating international factors, inventory, transportation and policies. It provides two case studies that apply the generic model to analyze costs for different companies. The conclusion states that mathematical programming allows comparison of costs between products and optimization of production costs and systems.
This document discusses the use of mathematical programming to optimize supply chain management. It begins with an introduction to mathematical programming and its applications in supply chain management. It then describes a generic mixed-integer programming model for supply chain configuration that aims to minimize total costs. The model includes constraints related to demand fulfillment, facility flows, capacity, material availability and open facilities. The document also discusses common modifications to the generic model, such as incorporating international factors, inventory, transportation and policies. It provides two case studies that apply the generic model to analyze different companies' supply chain costs.
The document discusses modeling performance for distributed real-time process control systems early in development. It proposes representing individual system elements like sensors and actuators as periodic processes and using Model Driven Architecture to develop functional models prior to UML models. This allows performance to be animated or calculated early based on individual element models, helping establish performance requirements before implementation.
Handwritten Text Recognition Using Machine LearningIRJET Journal
This document discusses a system for handwritten text recognition using machine learning. It proposes using both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to recognize handwritten text. CNNs are used for feature extraction from images while RNNs model the sequential nature of handwriting. The system collects data, preprocesses it, trains a model using CNNs and RNNs, and then uses the model to generate recognized text output with high accuracy. Potential applications of this handwritten text recognition system include document digitization, banking, education, and more.
A Hierarchical Feature Set optimization for effective code change based Defec...IOSR Journals
This document summarizes research on using support vector machines (SVMs) for software defect prediction. It analyzes 11 datasets from NASA projects containing code metrics and defect information for modules. The researchers preprocessed the data by removing duplicate/inconsistent instances, constant attributes, and balancing the datasets. They used SVMs with 5-fold cross validation to classify modules as defective or non-defective, achieving an average accuracy of 70% across the datasets. The researchers conclude SVMs can effectively predict defects but note earlier studies using the NASA data may have overstated capabilities due to insufficient data preprocessing.
Analyzing the solutions of DEA through information visualization and data min...Gurdal Ertek
Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the solutions of basic DEA models. The paper formally shows how the solutions of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, Smart DEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides a DEA solution which is consistent with the framework and is ready-to-analyze with data mining tools, through a table-based structure. The developed framework is tested and applied in a real world project for bench marking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework.
http://research.sabanciuniv.edu.
Machine Learning Approaches to Predict Customer Churn in Telecommunications I...IRJET Journal
This project aimed to develop machine learning models to predict customer churn in the telecommunications industry. Four algorithms were evaluated - logistic regression, support vector machine, decision tree, and random forest. Logistic regression performed best with an accuracy of 79.25% and AUC score of 84.08%. The models analyzed customer attribute data to identify patterns and predict churn, helping telecom companies understand churn reasons and develop retention strategies. The results provide insights to improve customer experience and reduce costly customer churn.
The document discusses visual data mining techniques and their application to spatial data analysis. It proposes the CubeView system which uses a data cube structure to support data mining and visualization of large spatial datasets. CubeView allows selective visualization through spatial outlier detection algorithms that identify suspiciously deviating observations in the data. The system was applied to traffic data from road sensor networks to enable analysis of traffic patterns and outliers.
IRJET - Customer Churn Analysis in Telecom IndustryIRJET Journal
This document discusses using machine learning techniques like logistic regression to analyze customer data and predict customer churn in the telecom industry. It proposes a system to build a churn prediction model using logistic regression on historical customer data to identify high-risk customers. The system would have options to view results, perform training and testing on new data, and analyze performance. It would also include a recommender system to recommend suitable plans for identified churn customers based on their usage patterns. The results show the model can predict churn with 80% accuracy and identify similar customers who may also churn.
Visual data mining combines traditional data mining methods with information visualization techniques to explore large datasets. There are three levels of integration between visualization and automated mining methods - no/limited integration, loose integration where methods are applied sequentially, and full integration where methods are applied in parallel. Different visualization methods exist for univariate, bivariate and multivariate data based on the type and dimensions of the data. The document describes frameworks and algorithms for visual data mining, including developing new algorithms interactively through a visual interface. It also summarizes a document on using data mining and visualization techniques for selective visualization of large spatial datasets.
IRJET-Scaling Distributed Associative Classifier using Big DataIRJET Journal
This document discusses scaling a distributed associative classifier using big data. It proposes using a distributed associative classifier approach to build a decision tree classifier and compare its scalability to a random forest classifier. The distributed associative classifier is implemented using Apache Hadoop and Apache Pig on a Bluetooth travel sensor dataset sized at 1.8GB with 126 features. The goal is to demonstrate the distributed associative classifier is more scalable than the random forest approach for large datasets.
The document is a request for fully solved SMU MBA assignments from Spring 2014. It provides contact information for students to send their semester and specialization to obtain the assignments. It notes that sample assignments can be found in blog archives or by searching. The document then provides several MBA assignments related to software engineering, database management systems, computer networks, and other topics. Students are to answer the questions and provide explanations and examples.
A Machine learning based framework for Verification and Validation of Massive...IRJET Journal
This document presents a machine learning based framework for verification and validation of massive scale image data. It discusses the challenges of managing and analyzing large image datasets. The proposed framework uses techniques like data augmentation, feature extraction and selection, decision trees, cross-validation and test cases to systematically manage massive image data and validate machine learning algorithms and systems. It uses Cell Morphology Analysis (CMA) as a case study to demonstrate how the framework can verify and validate large datasets, software systems and algorithms. The effectiveness of the framework is shown through its application to CMA, which involves classifying cell images using machine learning.
Capella Based System Engineering Modelling and Multi-Objective Optimization o...MehdiJahromi
This document proposes using the Capella modeling tool and ARCADIA framework to model and optimize a distributed avionics system. Specifically, it will develop a simplified model of a Distributed Integrated Modular Avionics (DIMA) system in Capella, extract parameters to specify an optimization problem, and evaluate different cost functions to optimize tasks allocation and hardware placement for the DIMA architecture. The goal is to demonstrate how model-based systems engineering tools can help automate and improve the design of complex avionics systems.
Integrating profiling into mde compilersijseajournal
Scientific computation requires more and more performance in its algorithms. New massively parallel
architectures suit well to these algorithms. They are known for offering high performance and power
efficiency. Unfortunately, as parallel programming for these architectures requires a complex distribution
of tasks and data, developers find difficult to implement their applications effectively. Although approaches
based on source-to-source intends to provide a low learning curve for parallel programming and take
advantage of architecture features to create optimized applications, programming remains difficult for
neophytes. This work aims at improving performance by returning to the high-level models, specific
execution data from a profiling tool enhanced by smart advices computed by an analysis engine. In order to
keep the link between execution and model, the process is based on a traceability mechanism. Once the
model is automatically annotated, it can be re-factored aiming better performances on the re-generated
code. Hence, this work allows keeping coherence between model and code without forgetting to harness the
power of parallel architectures. To illustrate and clarify key points of this approach, we provide an
experimental example in GPUs context. The example uses a transformation chain from UML-MARTE
models to OpenCL code.
Comparative Study of Enchancement of Automated Student Attendance System Usin...IRJET Journal
This document discusses developing an automated student attendance system using facial recognition and deep learning algorithms. It begins with an overview of how facial recognition can be used to take attendance accurately and efficiently. It then describes the methodology, which involves using a convolutional neural network (CNN) to detect and recognize faces. Dimensionality reduction techniques like principal component analysis (PCA) and linear discriminant analysis (LDA) are also used to improve recognition accuracy. The goal is to build a system that can identify students in real-time with a high degree of accuracy, even in varying lighting conditions. It aims to automate the entire attendance tracking process for both students and teachers.
This document describes a distributed implementation of a multi-objective evolutionary algorithm called EMO using the Offspring framework. Offspring allows rapid deployment and execution of evolutionary algorithms on distributed computing environments like enterprise clouds. The key points are:
1. Offspring is a plug-in based framework that makes it easy to distribute evolutionary algorithms on enterprise clouds with minimal coding effort.
2. A distributed implementation of EMO was developed as an Offspring plug-in by defining a coordination strategy to distribute serial executions across nodes and apply migrations between iterations.
3. Preliminary results show Offspring can leverage cloud computing power to solve large multi-objective optimization problems in a reasonable time by distributing the computation load of evolutionary algorithms.
IRJET - Finger Vein Extraction and Authentication System for ATMIRJET Journal
This document summarizes a research paper on a finger vein extraction and authentication system for ATMs. The system uses repeated line tracking during feature extraction to improve the analysis of 256 pixels in finger vein images. During preprocessing, images undergo binarization, edge detection to isolate the finger region of interest, and enhancement. Features are then extracted using the repeated line tracking before classification with support vector machines. The system was tested on images from 30 subjects and achieved a peak signal to noise ratio of 78.1443 for identification, demonstrating its potential for biometric authentication applications like ATMs.
Cloud service ranking with an integration of k-means algorithm and decision-m...IJECEIAES
The present research focuses on ranking cloud services by using the k-means algorithm with multi-criteria decision-making (MCDM) approaches that are the prime factor in the decision-making process and have been used to choose cloud services. The tools offered by MCDM can solve almost any decision-making problem. When faced with a selection challenge in the cloud environment, the trusted party would need to weigh the client’s choice against a predetermined list of criteria. There is a wide range of approaches to evaluating the quality of cloud services. The deep learning model has been considered a branch of artificial intelligence that assesses datasets to perform training and testing and makes decisions accordingly. This paper presents a concise overview of MCDM approaches and discusses some of the most commonly used MCDM methods. Also, a model based on deep learning with the k-means algorithm based decision-making trial and evaluation laboratory (kDE-MATEL) and analytic network process (ANP) is proposed as k-means algorithm based decision-making trial and evaluation laboratory with analytic network process (kD-ANP) for selecting cloud services. The proposed model uses the k-means algorithm and gives different levels of priority and weight to a set of criteria. A traditional model is also compared with a proposed model to reflect the efficiency of the proposed approach.
The document proposes a federated learning approach for decentralized traffic flow prediction to address privacy and latency issues. Federated learning allows multiple nodes to build a shared machine learning model without sharing local datasets. The authors describe federated learning and its advantages like data security, diversity, and efficiency. They discuss applications and related work using federated learning for traffic prediction. The proposed approach uses a federated averaging algorithm to train a model across decentralized nodes holding local traffic data, aiming to accurately predict traffic flow while preserving data privacy.
The presentation in detail covers the Glycemic index and glycemic load of various kinds of food. The standard calculation of Glycemic index and GLycemic load.
Moreover, it covers the food processing effects that can alter the glycemic load and glycemic index like gelatinization, retrogradation, cooking, annealing, etc.
Nutraceutical and Health Supplement Regulation FSSAIRAJAT GOEL
This document discusses Food for Special Dietary Uses (FSDU) in the Indian market. It provides information on different types of FSDU products for conditions like weight management, pregnancy/lactation, diabetes, blood pressure, renal issues, and for kids and adults. It also discusses FSDU proprietary food and how it differs from medicinal products. The document outlines labeling requirements for FSDU according to the gazette, including identifying information, target groups, storage instructions, and warning statements. It also proposes amendments to the regulations regarding FSDU for sportsperson.
About Village Adoption Program which is unique to NIFTEM, and similar to many extension programs done by agricultural universities. The presentation is report of the work which the team has done in Maharashtra
National Institute of Food Technology Entrepreneurship and Management hosted an event on food processing, food loss, and food waste. Mahatma Gandhi once said "to a man with an empty stomach, food is God", highlighting the importance of addressing food waste and loss. Learnings from the Eat Lancet report show that food production is the largest driver of environmental change and a "Great Food Transformation" is needed. Reducing food wastage by 50% across the supply chain in accordance with UN Sustainable Development Goals is a top priority. India wastes 670 lakh tonnes of food worth Rs.92,000 crore annually and ranks 7th globally in food wastage. Various stakeholders must work
This document discusses various methods of food drying. It begins by explaining the basic principles of drying, including vaporization and diffusion of moisture from the food surface and interior. It then covers the advantages of drying foods, such as long shelf life and ease of storage and transport. Various drying methods are described, including contact, vacuum, freeze and solar drying. The document provides details on each method and discusses their advantages and limitations. It also covers traditional sun drying and newer technologies like microwave, osmotic and superheated steam drying.
The document discusses various profitability ratios that can be used to analyze a company's ability to generate profits. It defines key profitability ratios like gross profit ratio, net profit ratio, operating profit ratio, return on assets, return on equity, return on capital employed, earnings per share, dividend payout ratio, and provides the formulas to calculate each ratio. The document also discusses various turnover or activity ratios like inventory turnover ratio, debtors turnover ratio, creditors turnover ratio, fixed assets turnover ratio, and current assets turnover ratio that measure how efficiently a company utilizes its assets and collects cash.
1. DSS PRESENTATION
MODEL SELECTION AND
SEQUENCING IN DECISION
SUPPORT SYSTEMS
GROUP MEMBERS:
116101-PUJA PUNIA
116102-RAHUL YADAV
116103-RAHUL KUMAR MEENA
116104-RAHUL SINGH RAJPUT
116105-RAJAT GOEL
2. ABSTRACT
A crucial problem confronting the users of decision
support systems(DSS) is the identification of an
appropriate model or a sequence of models that may
be used to solve a particular problem.
If a single model in the model base cannot satisfy
user requirements, the solution procedure seeks to
obtain a string of models such that some
performance measure is minimized.
3. INTRODUCTION
A complementary trend is the popularity of decision support
systems (DSS), where the intended functionality of these systems
is to assist decision makers' solve semi-structured and un-
structured problems that require extensive usage of
computational models.
The high costs of developing and maintaining models further
reinforce the importance of model management in organizations.
A need to maintain consistency, integrity, non-redundancy, and
flexibility and the need to enhance the availability of models on
an organization-wide basis, has led to the evolution of software
known as model management systems (MMS).
4. INTRODUCTION CONTD.
The MMS component of a DSS provides decision support by
abstracting procedural and technical aspects of model
implementation and making these invisible to the non-technical
user .
Two fundamental concerns faced by a user in interacting with a
DSS are: what is the appropriate model to use for a particular
problem, and if such a model does not exist as a single entity,
how can the models defined in the model base be combined to
solve the problem?
These concerns are particularly relevant when complex decision
processes require the utilization of several models in sequence.
The model linking procedure must not violate the fundamental
objective underlying MMS that of relieving the user from
procedural and technical details.
5. CONTD.
This paper develops a procedure for model
sequencing that permits the construction of ad
hoc model chains. Knowledge is extracted from a
specialist and stored in the model base, allow- ing
the user's interaction with the DSS to be limited to
conceptualizing the problem in terms of output
requirements.
6. MODEL SELECTION AND
INTEGRATION
Model selection deals with locating an appropriate model in the
model base that maps into the problem posed by the decision
maker.
If this search process identifies a subset of the model space
that is potentially applicable, i.e. more than one model is able
to satisfy the user's output requirement, then the MMS chooses
to select and execute a single model from this subset.
The model integration problem concerns itself with finding a
target set of models to be combined and executed, if, in fact,
no single model is able to provide the requisite output.
7. CONTD.
Blanning proposed a model representation scheme where model inputs
and outputs are treated as tuples in a relational table, with each tuple
containing information about a specific variable, the models to which it
is input, and the models that estimate it.
Geoffrion describes a structured modelling approach to model
representation where models are represented in canonical form.
Structured modelling is an extension of graph-based approaches and the
framework uses a "hierarchically organized, partitioned and attributed
acyclic graph to represent a model or a model class“.
The construction of composite models allows input ports to represent
the input requirements of the model and output ports to represent the
outputs produced. These input/output relationships are used to
construct higher-level composite models.
8. CONTD.
The use of information in our approach is parsimonious,
as are our assumptions regarding the knowledge
possessed by the user of the MMS (the only information
required by the approach are model inputs and
outputs).
The model sequencing procedure provides the user
with an optimal sequence of models, where the
criterion for optimality can be varied if so desired.
9. PROCEDURE
The manner in which the problem is modelled is influenced by the
availability of input data. The objective of MMS is to assist the user
select an appropriate model (or a model string) from the model
base. This selection may be based on either
(i) desired output variables
(ii) both desired output variables and input variables that can be supplied.
The model sequencing procedure described here operates on an
existing model base. Two assumptions are made regarding the
model base: (i) it contains a large number of models represented
using perhaps different formalisms, and (ii) these models have been
created by knowledgeable users or specialists and have passed some
type of quality assurance test to ensure their validity.
10. 1. MODEL BASE CATALOGS
Three primary catalogs are maintained in the
system, one each for models (MODCAT), out- put
variables (OUTCAT) and input variables (INCAT).
MODCAT contains the name of the model, a brief
description of its scope, and assumptions underlying
its use, such as linearity and independence.
The catalog of outputs is annotated with a
description of the procedure by which the output is
computed.
11. COMPONENTS OF THE
MODEL SELECTOR
The model selector software consists of two primary modules
called DIALOG and SEARCH. The former provides a user-
friendly interface for a non-technical user and uses the
catalogs to ascertain if a single stored model can satisfy a
specific user need.
SEARCH consists of a 0/1 integer programming formulation that
seeks to obtain an optimal path to the required outputs.
The DIALOG component. DIALOG is menu- driven and prompts
the user to supply required output variables and associated
objectives.
12. 3. ITERATION BETWEEN
SEARCH AND DIALOG
DIALOG analyses the final solution obtained from
SEARCH and uses the catalogs to display the list of
model names, input variable names, and the associated
cost of model execution.
The primary objective now is to seek another model
string that does not require this input variable. In this
fashion, DIALOG and SEARCH are executed iteratively to
obtain an acceptable model string.
Iteration between SEARCH and DIALOG may also occur if
either the user finds the objective(s) of the model(s)
selected by the system to be inappropriate, or, the cost
of executing the model string is unacceptable.
13. CONTD.
For example, if the user does not specify an objective a
priori, and the system selects an estimation model to
compute the value of a certain variable, the user has
the flexibility of requesting SEARCH to find another
model with an optimization objective.
The approach described combines management science
techniques and basic user interface design principles to
remove procedural and technical obstacles in the
utilization of models by end-users.
14. A PROTOTYPE SYSTEM
Prototype system has been applied to a model
base.
The menu driven DIALOG component is
programmed in GWBASIC and the integer
program is solved using LINDO.
15. ASCERTAINING COSTS
The cost parameters, and the policies used for
obtaining them, can be classified into two distinct
Classes.
Method employed for estimating the dollar value.
1. In some instances the estimates used are specific
to the computer system (and the associated
charge-back rules)where the model base is
installed.
2. For the prototype developed, all models are stored
and executed on the IBM 3090/600E at Louisiana
State University (LSU), Baton Rouge. Thus, various
cost parameters were estimated based on the
charging algorithm employed by LSU's System
Network Computer Center.
16. CONTD.
(I)Model execution
CPU usage costs are computed as the product of
actual CPU time (in seconds) and a factor F
(S/second).
The value of F varies depending on the time of
the day.
For prototype system, it was assumed that all
models will be executed during "demand time"
(i.e. between 8 am and 5 pm).
After a model had been debugged and validated,
average CPU usage time was calculated by
executing each model with historic (or
representative) data.
17. (a)Primary inputs available in
the database
cost was obtained by employing the
university's charging algorithm for
performing I/O operations.
Cost parameters were calculated by
multiplying the average number of I/Os
required by the model and a charge factor
I ($ per 100 I/Os).
Standard tape mounting charges used by
the university were incorporated for
obtaining the cost of inputs that required
reading data from magnetic tapes.
18. (b)Primary inputs supplied by
decision maker
Some primary inputs (e.g. expected
growth rate) must be supplied by the
decision maker, based on his/her
experience or intuition, the cost of these
inputs were arbitrarily set to a low value.
19. C)Primary inputs available from
'outside‘ sources
Primary inputs available from 'outside‘ sources fall into two
major categories.
1.Inputs are obtainable from commercial on-line databases.
The cost associated with this type of input is based on the
connect time to the on-line database.
These inputs can be further classified into two classes:
(a) input data that can be stored for future use, and
(b) up-to-date, current data that must be down-loaded from
the database each time the input is required.
20. CONCLUSION
Model manipulation continues to be an important
issue for both academics and practitioners.
Model management systems attempt to enhance
utilization by effectively shielding non-expert users
from internal representations of models in
computers.
Our research has addressed one respect of model
management that concerns itself with model
selection and sequencing.
In this paper we have described mechanisms for
achieving model selection and sequencing that are
independent of model implementation and storage.