The document describes a study that uses artificial neural networks to model and predict the final costs of Iraqi public school construction projects. Researchers collected data from 65 completed school projects and identified 9 influential parameters to use as inputs for the neural network model, including accepted bid price, estimated cost, contractor rank, and number of bidders. The model was able to predict final costs with a correlation of 91% and accuracy of 99.98% when validated on data not used in training. The study aims to improve early cost estimation for school projects and reduce cost overruns.
The final cost of public school building projects, like other construction projects, is unknown
to the owner till the account closure. Artificial Neural Networks (ANN) is used in an attempt to
predict the final cost of two story (12 classes) school projects under lowest bid system of award
before work starts. A database of (65) school projects records completed in (2007-2012) are used to
develop and verify the ANN model. Based on expert opinions, nine out of eleven parameters are
considered to have the most significant impact on the magnitude of final cost. Hence they are used as
model inputs while the output of the model is going to be the final cost (FC). These parameters are;
accepted bid price, average bid price, estimated cost, contractor rank, supervising engineer
experience, project location, number of bidders, year of contracting, and contractual duration. It was
found that ANN has the ability to predict the final cost for school projects with very good degree of
accuracy having a coefficient of correlation (R) of (91%), and an average accuracy percentage of
(99.98%).
Comparison of Cost Estimation Methods using Hybrid Artificial Intelligence on...IJERA Editor
Cost estimating at schematic design stage as the basis of project evaluation, engineering design, and cost
management, plays an important role in project decision under a limited definition of scope and constraints in
available information and time, and the presence of uncertainties. The purpose of this study is to compare the
performance of cost estimation models of two different hybrid artificial intelligence approaches: regression
analysis-adaptive neuro fuzzy inference system (RANFIS) and case based reasoning-genetic algorithm (CBRGA)
techniques. The models were developed based on the same 50 low-cost apartment project datasets in
Indonesia. Tested on another five testing data, the models were proven to perform very well in term of accuracy.
A CBR-GA model was found to be the best performer but suffered from disadvantage of needing 15 cost drivers
if compared to only 4 cost drivers required by RANFIS for on-par performance.
Funding agencies such as the U.S. National Science Foundation (NSF), U.S. National Institutes of Health (NIH), and the Transportation Research Board (TRB) of The National Academies make their online grant databases publicly available which document a variety of information on grants that have been funded over the past few decades. In this paper, based on a quantitative analysis of the TRB’s Research In Progress (RIP) online database, we explore the feasibility of automatically estimating the appropriate funding level, given the textual description of a transportation research project. We use statistical Text Mining (TM) and Machine Learning (ML) technologies to build this model using the 14,000 or more records of the TRB’s RIP research grants big data. Several Natural Language Processing (NLP) based text representation models such as the Latent Dirichlet Allocation (LDA), Latent Semantic Indexing (LSI) and the Doc2Vec Machine Learning (ML) approach are used to vectorize the project descriptions and generate semantic vectors. Each of these representations is then used to train supervised regression models such as Random Forest (RF) regression. Out of the three latent feature generation models, we found LDA gives the least Mean Absolute Error (MAE) using 300 feature dimensions and RF regression model. However, based on the correlation coefficients, it was found that it is not very feasible to accurately predict the funding level directly from the unstructured project abstract, given the large variations in source agencies, subject areas, and funding levels. By using separate prediction models for different types of funding agencies, funding levels were better correlated with the project abstract.
Construction Management (CM) has to deal with a variety of uncertainties related to Time, Cost, Quality, and Safety, to name a few. Such uncertainties make the entire construction process highly unpredictable. It, therefore, falls under the purview of artificial neural networks (ANNs) in which the given hazy information can be effectively interpreted in order to arrive at meaningful conclusions. This paper reviews the application of ANNs in construction activities related to the prediction of costs, risk, and safety, tender bids, as well as labor and equipment productivity. The review suggests that the ANN’s had been highly beneficial in correctly interpreting inadequate input information. It was seen that most of the investigators used the feed forward back propagation type of the network; however, if a single ANN architecture was found to be insufficient, then hybrid modeling in association with other machine learning tools such as genetic programming and support vector machines were much useful. It was however clear that the authenticity of data and experience of the modeler are important in obtaining good results.
Development Principles of Knowledge Database of Intelligent System for Estima...ITIIIndustries
In the paper, a principles underlying the construction of an intelligent information system estimated results of the dynamic interaction of orbital systems with space debris is presented. It describes the knowledge database model based on these principles which is the synthesis of theoretical and practical information in the field of estimating the high-speed interaction of objects.
The final cost of public school building projects, like other construction projects, is unknown
to the owner till the account closure. Artificial Neural Networks (ANN) is used in an attempt to
predict the final cost of two story (12 classes) school projects under lowest bid system of award
before work starts. A database of (65) school projects records completed in (2007-2012) are used to
develop and verify the ANN model. Based on expert opinions, nine out of eleven parameters are
considered to have the most significant impact on the magnitude of final cost. Hence they are used as
model inputs while the output of the model is going to be the final cost (FC). These parameters are;
accepted bid price, average bid price, estimated cost, contractor rank, supervising engineer
experience, project location, number of bidders, year of contracting, and contractual duration. It was
found that ANN has the ability to predict the final cost for school projects with very good degree of
accuracy having a coefficient of correlation (R) of (91%), and an average accuracy percentage of
(99.98%).
Comparison of Cost Estimation Methods using Hybrid Artificial Intelligence on...IJERA Editor
Cost estimating at schematic design stage as the basis of project evaluation, engineering design, and cost
management, plays an important role in project decision under a limited definition of scope and constraints in
available information and time, and the presence of uncertainties. The purpose of this study is to compare the
performance of cost estimation models of two different hybrid artificial intelligence approaches: regression
analysis-adaptive neuro fuzzy inference system (RANFIS) and case based reasoning-genetic algorithm (CBRGA)
techniques. The models were developed based on the same 50 low-cost apartment project datasets in
Indonesia. Tested on another five testing data, the models were proven to perform very well in term of accuracy.
A CBR-GA model was found to be the best performer but suffered from disadvantage of needing 15 cost drivers
if compared to only 4 cost drivers required by RANFIS for on-par performance.
Funding agencies such as the U.S. National Science Foundation (NSF), U.S. National Institutes of Health (NIH), and the Transportation Research Board (TRB) of The National Academies make their online grant databases publicly available which document a variety of information on grants that have been funded over the past few decades. In this paper, based on a quantitative analysis of the TRB’s Research In Progress (RIP) online database, we explore the feasibility of automatically estimating the appropriate funding level, given the textual description of a transportation research project. We use statistical Text Mining (TM) and Machine Learning (ML) technologies to build this model using the 14,000 or more records of the TRB’s RIP research grants big data. Several Natural Language Processing (NLP) based text representation models such as the Latent Dirichlet Allocation (LDA), Latent Semantic Indexing (LSI) and the Doc2Vec Machine Learning (ML) approach are used to vectorize the project descriptions and generate semantic vectors. Each of these representations is then used to train supervised regression models such as Random Forest (RF) regression. Out of the three latent feature generation models, we found LDA gives the least Mean Absolute Error (MAE) using 300 feature dimensions and RF regression model. However, based on the correlation coefficients, it was found that it is not very feasible to accurately predict the funding level directly from the unstructured project abstract, given the large variations in source agencies, subject areas, and funding levels. By using separate prediction models for different types of funding agencies, funding levels were better correlated with the project abstract.
Construction Management (CM) has to deal with a variety of uncertainties related to Time, Cost, Quality, and Safety, to name a few. Such uncertainties make the entire construction process highly unpredictable. It, therefore, falls under the purview of artificial neural networks (ANNs) in which the given hazy information can be effectively interpreted in order to arrive at meaningful conclusions. This paper reviews the application of ANNs in construction activities related to the prediction of costs, risk, and safety, tender bids, as well as labor and equipment productivity. The review suggests that the ANN’s had been highly beneficial in correctly interpreting inadequate input information. It was seen that most of the investigators used the feed forward back propagation type of the network; however, if a single ANN architecture was found to be insufficient, then hybrid modeling in association with other machine learning tools such as genetic programming and support vector machines were much useful. It was however clear that the authenticity of data and experience of the modeler are important in obtaining good results.
Development Principles of Knowledge Database of Intelligent System for Estima...ITIIIndustries
In the paper, a principles underlying the construction of an intelligent information system estimated results of the dynamic interaction of orbital systems with space debris is presented. It describes the knowledge database model based on these principles which is the synthesis of theoretical and practical information in the field of estimating the high-speed interaction of objects.
Calculation of Reusability Matrices for Object Oriented applicationsIJMERJOURNAL
ABSTRACT: Reusability is one of the major concerns of object oriented applications. Object oriented paradigm is having its own various advantages including reusability. There are a lots of Metrics available for quantitative measure of readabilities of any of the application which is developed using object oriented paradigm. The goals of software metrics are to identify and control essential parameters that affect the parameters related to software development. There are various types of measurements which are required in software development including size of the project, complexities involved, measurement of cohesion and coupling among modules, testability, reusability, effort and resources required etc. This paper presents a practical calculation on some of the reusability metrics which can be used for object oriented applications
An Application of Genetic Algorithm for Non-restricted Space and Pre-determin...drboon
The use of a genetic algorithm is presented to solve a facility layout problem in the situation where there is non-restricted space but the ratio of plant length and width is pre-determined. A two-leveled chromosome is constructed. Six rules are established to translate the chromosome to facility design. An approach of solving a facility layout problem is proposed. A numerical example is employed to illustrate the approach.
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.
A preliminary survey on optimized multiobjective metaheuristic methods for da...ijcsit
The present survey provides the state-of-the-art of research, copiously devoted to Evolutionary Approach
(EAs) for clustering exemplified with a diversity of evolutionary computations. The Survey provides a
nomenclature that highlights some aspects that are very important in the context of evolutionary data
clustering. The paper missions the clustering trade-offs branched out with wide-ranging Multi Objective
Evolutionary Approaches (MOEAs) methods. Finally, this study addresses the potential challenges of
MOEA design and data clustering, along with conclusions and recommendations for novice and
researchers by positioning most promising paths of future research.
Enhancement of student performance prediction using modified K-nearest neighborTELKOMNIKA JOURNAL
The traditional K-nearest neighbor (KNN) algorithm uses an exhaustive search for a complete training set to predict a single test sample. This procedure can slow down the system to consume more time for huge datasets. The selection of classes for a new sample depends on a simple majority voting system that does not reflect the various significance of different samples (i.e. ignoring the similarities among samples). It also leads to a misclassification problem due to the occurrence of a double majority class. In reference to the above-mentioned issues, this work adopts a combination of moment descriptor and KNN to optimize the sample selection. This is done based on the fact that classifying the training samples before the searching actually takes place can speed up and improve the predictive performance of the nearest neighbor. The proposed method can be called as fast KNN (FKNN). The experimental results show that the proposed FKNN method decreases original KNN consuming time within a range of (75.4%) to (90.25%), and improve the classification accuracy percentage in the range from (20%) to (36.3%) utilizing three types of student datasets to predict whether the student can pass or fail the exam automatically.
A fuzzy approach to evaluate suitability of infrastructure projects with safetyeSAT Journals
Abstract
The current project deals with safety management in highways and infrastructure (Buildings and Roads).The research is partly doctrinal and partly empirical in natural. Research tools used is fuzzy logic. The scope research has been to mainly cover highways and infrastructure (Buildings and Roads).The topic of construction includes the process of Highways, Building and society roads, construction and maintenance, including the design of respective construction, contracting, accomplishing, supervision, and maintenance of Highways, Building, society roads and related structures. Our study of project will fulfil the safety requirements by using Fuzzy logic that should be consider before starting highways and infrastructure (Buildings and Roads) and this study will be very helpful in construction industry. as this study will decrease the chances of accidents as well as to save lives. Today so many people lose their lives when traffic has to move through maintenance works and road construction every year also during the construction of Buildings. The cases of construction section injuries and accidental death are predicted to go even higher than now. Construction in highways and infrastructure (Building and Roads) covers various activities, techniques, materials and source of danger therefore because of this conditions the probability of accidents increases every time. The fact is construction industry has the most disappointing record of safety compare to all industrial sectors, with a risk of casualty 5 times higher than several other industry in the world. The higher rate of accidents and deaths in the construction industry compare to all other industries are may be due to the process of the construction work. The factors due to which the rate of accidents are high in construction industry are such as poor planning, lack of safety training, lack of supervision, lack of safety equipment , and lack of incident investigation helps to create more problems in future. Hence, by using Multi criteria decision making by Fuzzy logic will reduce the risk of accidents while construction of highways and infrastructure (Building and Roads).
Keywords: Highway and Infrastructure Safety, Defuzzification, Fuzzy Multi Criteria Decision Making, Expert Opinion, Linguistic Terms and Safety Potential Index
An Iterative Model as a Tool in Optimal Allocation of Resources in University...Dr. Amarjeet Singh
In this paper, a study was carried out to aid in
adequate allocation of resources in the College of Natural
Sciences, TYZ University (not real name because of ethical
issue). Questionnaires were administered to the highranking officials of one the Colleges, College of Pure and
Applied Sciences, to examine how resources were allocated
for three consecutive sessions(the sessions were 2009/2010,
2010/2011 and 2011/2012),then used the data gathered and
analysed to generate contributory inputs for the three basic
outputs (variables)formed for the purpose of the study.
These variables are: 1
x
represents the quality of graduates
produced;
2
x
stands for research papers, Seminars,
Journals articles etc. published by faculties and
3
x
denotes service delivery within the three sessions under study.
Simplex Method of Linear Programming was used to solve
the model formulated.
The Optimization of choosing Investment in the capital markets using artifici...inventionjournals
Optimization is one of crucial items in behavioural sciences. These daystheuse of Meta heuristic has grown considerably in all fields. In this study, we will look for optimization of selection in a portfolio of investment opportunities. We’ve been looking for a selection logic using a meta-heuristic algorithm Called artificial neural networks. The results showed that using artificial neural network algorithm had an optimization in decision-making and selection of investment opportunities. The research is applied one considering the purpose and is looking for developing knowledge in a particular field.
ENSEMBLE REGRESSION MODELS FOR SOFTWARE DEVELOPMENT EFFORT ESTIMATION: A COMP...ijseajournal
As demand for computer software continually increases, software scope and complexity become higher than ever. The software industry is in real need of accurate estimates of the project under development. Software development effort estimation is one of the main processes in software project management. However, overestimation and underestimation may cause the software industry loses. This study determines which technique has better effort prediction accuracy and propose combined techniques that could provide better estimates. Eight different ensemble models to estimate effort with Ensemble Models were compared with each other base on the predictive accuracy on the Mean Absolute Residual (MAR) criterion and statistical tests. The results have indicated that the proposed ensemble models, besides delivering high efficiency in contrast to its counterparts, and produces the best responses for software project effort estimation. Therefore, the proposed ensemble models in this study will help the project managers working with development quality software.
Open from the start - Business e Open Source con Ubuntu - Smau Milano 2010Flavia Weisghizzi
In un mondo in costante fluida modificazione è necessario saper riconoscere nuove strade. L'open source da anni si è affrancato da una dimensione di puro volontariato per diventare strumento di lavoro affidabile e fortemente personalizzabile. L'open source offre un modello alternativo di sviluppo, potente e flessibile, e inoltre un terreno che ha grandi spazi di espansione. Ubuntu, grazie alla sua semplicità, affidabilità e customizzabilità, è porta di accesso privilegiata a questa frontiera, riuscendo a conciliare lo spirito libero dell'open source e lo sguardo rivolto al mondo del business. Dal desktop all'enterprise, dell'ufficio agli applicativi mission critical, dalla ricerca al multimedia, Ubuntu permette di ritagliare su misura soluzioni umane e innovazioni tecnologiche Target: Il talk si rivolge a un pubblico che non ha esperienza nel mondo dell'open source ma è interessato a conoscerne le potenzialità
Calculation of Reusability Matrices for Object Oriented applicationsIJMERJOURNAL
ABSTRACT: Reusability is one of the major concerns of object oriented applications. Object oriented paradigm is having its own various advantages including reusability. There are a lots of Metrics available for quantitative measure of readabilities of any of the application which is developed using object oriented paradigm. The goals of software metrics are to identify and control essential parameters that affect the parameters related to software development. There are various types of measurements which are required in software development including size of the project, complexities involved, measurement of cohesion and coupling among modules, testability, reusability, effort and resources required etc. This paper presents a practical calculation on some of the reusability metrics which can be used for object oriented applications
An Application of Genetic Algorithm for Non-restricted Space and Pre-determin...drboon
The use of a genetic algorithm is presented to solve a facility layout problem in the situation where there is non-restricted space but the ratio of plant length and width is pre-determined. A two-leveled chromosome is constructed. Six rules are established to translate the chromosome to facility design. An approach of solving a facility layout problem is proposed. A numerical example is employed to illustrate the approach.
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.
A preliminary survey on optimized multiobjective metaheuristic methods for da...ijcsit
The present survey provides the state-of-the-art of research, copiously devoted to Evolutionary Approach
(EAs) for clustering exemplified with a diversity of evolutionary computations. The Survey provides a
nomenclature that highlights some aspects that are very important in the context of evolutionary data
clustering. The paper missions the clustering trade-offs branched out with wide-ranging Multi Objective
Evolutionary Approaches (MOEAs) methods. Finally, this study addresses the potential challenges of
MOEA design and data clustering, along with conclusions and recommendations for novice and
researchers by positioning most promising paths of future research.
Enhancement of student performance prediction using modified K-nearest neighborTELKOMNIKA JOURNAL
The traditional K-nearest neighbor (KNN) algorithm uses an exhaustive search for a complete training set to predict a single test sample. This procedure can slow down the system to consume more time for huge datasets. The selection of classes for a new sample depends on a simple majority voting system that does not reflect the various significance of different samples (i.e. ignoring the similarities among samples). It also leads to a misclassification problem due to the occurrence of a double majority class. In reference to the above-mentioned issues, this work adopts a combination of moment descriptor and KNN to optimize the sample selection. This is done based on the fact that classifying the training samples before the searching actually takes place can speed up and improve the predictive performance of the nearest neighbor. The proposed method can be called as fast KNN (FKNN). The experimental results show that the proposed FKNN method decreases original KNN consuming time within a range of (75.4%) to (90.25%), and improve the classification accuracy percentage in the range from (20%) to (36.3%) utilizing three types of student datasets to predict whether the student can pass or fail the exam automatically.
A fuzzy approach to evaluate suitability of infrastructure projects with safetyeSAT Journals
Abstract
The current project deals with safety management in highways and infrastructure (Buildings and Roads).The research is partly doctrinal and partly empirical in natural. Research tools used is fuzzy logic. The scope research has been to mainly cover highways and infrastructure (Buildings and Roads).The topic of construction includes the process of Highways, Building and society roads, construction and maintenance, including the design of respective construction, contracting, accomplishing, supervision, and maintenance of Highways, Building, society roads and related structures. Our study of project will fulfil the safety requirements by using Fuzzy logic that should be consider before starting highways and infrastructure (Buildings and Roads) and this study will be very helpful in construction industry. as this study will decrease the chances of accidents as well as to save lives. Today so many people lose their lives when traffic has to move through maintenance works and road construction every year also during the construction of Buildings. The cases of construction section injuries and accidental death are predicted to go even higher than now. Construction in highways and infrastructure (Building and Roads) covers various activities, techniques, materials and source of danger therefore because of this conditions the probability of accidents increases every time. The fact is construction industry has the most disappointing record of safety compare to all industrial sectors, with a risk of casualty 5 times higher than several other industry in the world. The higher rate of accidents and deaths in the construction industry compare to all other industries are may be due to the process of the construction work. The factors due to which the rate of accidents are high in construction industry are such as poor planning, lack of safety training, lack of supervision, lack of safety equipment , and lack of incident investigation helps to create more problems in future. Hence, by using Multi criteria decision making by Fuzzy logic will reduce the risk of accidents while construction of highways and infrastructure (Building and Roads).
Keywords: Highway and Infrastructure Safety, Defuzzification, Fuzzy Multi Criteria Decision Making, Expert Opinion, Linguistic Terms and Safety Potential Index
An Iterative Model as a Tool in Optimal Allocation of Resources in University...Dr. Amarjeet Singh
In this paper, a study was carried out to aid in
adequate allocation of resources in the College of Natural
Sciences, TYZ University (not real name because of ethical
issue). Questionnaires were administered to the highranking officials of one the Colleges, College of Pure and
Applied Sciences, to examine how resources were allocated
for three consecutive sessions(the sessions were 2009/2010,
2010/2011 and 2011/2012),then used the data gathered and
analysed to generate contributory inputs for the three basic
outputs (variables)formed for the purpose of the study.
These variables are: 1
x
represents the quality of graduates
produced;
2
x
stands for research papers, Seminars,
Journals articles etc. published by faculties and
3
x
denotes service delivery within the three sessions under study.
Simplex Method of Linear Programming was used to solve
the model formulated.
The Optimization of choosing Investment in the capital markets using artifici...inventionjournals
Optimization is one of crucial items in behavioural sciences. These daystheuse of Meta heuristic has grown considerably in all fields. In this study, we will look for optimization of selection in a portfolio of investment opportunities. We’ve been looking for a selection logic using a meta-heuristic algorithm Called artificial neural networks. The results showed that using artificial neural network algorithm had an optimization in decision-making and selection of investment opportunities. The research is applied one considering the purpose and is looking for developing knowledge in a particular field.
ENSEMBLE REGRESSION MODELS FOR SOFTWARE DEVELOPMENT EFFORT ESTIMATION: A COMP...ijseajournal
As demand for computer software continually increases, software scope and complexity become higher than ever. The software industry is in real need of accurate estimates of the project under development. Software development effort estimation is one of the main processes in software project management. However, overestimation and underestimation may cause the software industry loses. This study determines which technique has better effort prediction accuracy and propose combined techniques that could provide better estimates. Eight different ensemble models to estimate effort with Ensemble Models were compared with each other base on the predictive accuracy on the Mean Absolute Residual (MAR) criterion and statistical tests. The results have indicated that the proposed ensemble models, besides delivering high efficiency in contrast to its counterparts, and produces the best responses for software project effort estimation. Therefore, the proposed ensemble models in this study will help the project managers working with development quality software.
Open from the start - Business e Open Source con Ubuntu - Smau Milano 2010Flavia Weisghizzi
In un mondo in costante fluida modificazione è necessario saper riconoscere nuove strade. L'open source da anni si è affrancato da una dimensione di puro volontariato per diventare strumento di lavoro affidabile e fortemente personalizzabile. L'open source offre un modello alternativo di sviluppo, potente e flessibile, e inoltre un terreno che ha grandi spazi di espansione. Ubuntu, grazie alla sua semplicità, affidabilità e customizzabilità, è porta di accesso privilegiata a questa frontiera, riuscendo a conciliare lo spirito libero dell'open source e lo sguardo rivolto al mondo del business. Dal desktop all'enterprise, dell'ufficio agli applicativi mission critical, dalla ricerca al multimedia, Ubuntu permette di ritagliare su misura soluzioni umane e innovazioni tecnologiche Target: Il talk si rivolge a un pubblico che non ha esperienza nel mondo dell'open source ma è interessato a conoscerne le potenzialità
Award Winning Exhibit Company. Exhibitors, join us in making your next exhibit design project a major success. Browse our impressive exhibit design portfolio and trade show booths.For More Information, Visit: http://www.catalystexhibit.com/
Diversity has been a key topic of first CLSxItaly, hold in Milan, on February 26th. Numbers of female participation in FLOSS and tips and ideas to growth female involvement coming from international FLOSS communities.
Welcome CoC - 10 verità e bugie sul Codice di Condotta - Flavia Weisghizzi - ...Flavia Weisghizzi
Il codice di condotta è il cuore del "contratto" di rispetto reciproco necessario in una community, un'azienda o una conferenza. Eppure ancora oggi fa discutere, pochi lo conoscono e ancor meno realtà ne hanno uno. Ne parliamo al Community Leader Summit a Roma.
All About Dev Community - Tips & Tricks for wannabe startupsFlavia Weisghizzi
Are you a startup? Are you looking for a top-notch developer able to code your ideas? Don’t miss our next “Startup Europe & TWIST” webinar! Flavia Weisghizzi, Communication Manager at Codemotion, will introduce you to all the tips and tricks to interact with developers and engage them to work with you to develop your startup!
As former intern of OPW program, I’d like to share my work about FOSS community outreaches, focusing my talk on problems volunteers could encounter in approaching FOSS communities and how to support their outreaches at best, with particular attention on GNOME.
This work was presented at GUADEC 2013 conference, whitin the community outreach tracks.
Stop saying "innovative" - La comunicazione per startup - Smau Milano 2014Flavia Weisghizzi
Riconoscere e definire la propria brand identity e visual identity, definire il target target di comunicazione, trovare i canali giusti con cui parlare ed evitare gli errori più comuni della comunicazione. Il talk è stato presentato a #SMAU Milano 2014 nella track startup e marketing
AN ARTIFICIAL NEURAL NETWORK-BASED APPROACH COUPLED WITH TAGUCHI'S METHOD FOR...IAEME Publication
Nowadays, project duration prediction has become of crucial importance for managers since it points out the expectancy-life of project realization. To this end, the Neural Network-based approach coupled with the Taguchi method is used to predict the necessary time, which allows the fulfillment of the targeted project within the prescribed span without delay. Accordingly, the whole process for modeling the targeted problem is described, in which the modeling and simulation of the activities network are introduced for calculating the total average time of project. Then, the neural network approach is adopted to predict the total time for finishing the considered project within the deadlines, where the neural network’s input variables are composed of success probability, improvement and learning factors. While, the output variable is the total average project duration, which is the critical data during design phase. After that, the well-known Taguchi method is purposefully used to optimize the already obtained target by neural network. Finally, Simulation analysis through MATLAB are used to show the efficiency of the proposed approach regarding the workability of the approach when it comes to estimating the deadline of the targeted project.
A systematic mapping study of performance analysis and modelling of cloud sys...IJECEIAES
Cloud computing is a paradigm that uses utility-driven models in providing dynamic services to clients at all levels. Performance analysis and modelling is essential because of service level agreement guarantees. Studies on performance analysis and modelling are increasing in a productive manner on the cloud landscape on issues like virtual machines and data storage. The objective of this study is to conduct a systematic mapping study of performance analysis and modelling of cloud systems and applications. A systematic mapping study is useful in visualization and summarizing the research carried in an area of interest. The systematic study provided an overview of studies on this subject by using a structure, based on categorization. The results are presented in terms of research such as evaluation and solution, and contribution such as tools and method utilized. The results showed that there were more discussions on optimization in relation to tool, method and process with 6.42%, 14.29% and 7.62% respectively. In addition, analysis based on designs in terms of model had 14.29% and publication relating to optimization in terms of evaluation research had 9.77%, validation 7.52%, experience 3.01%, and solution 10.51%. Research gaps were identified and should motivate researchers in pursuing further research directions.
PREDICTION OF AVERAGE TOTAL PROJECT DURATION USING ARTIFICIAL NEURAL NETWORKS...IAEME Publication
The prediction of project‘s expectancy life is an important issue for entrepreneurs since it helps them to avoid the expiration time of projects. To properly address this issue, Neural Network-based approach, fuzzy logic and regression methods are used to predict the necessary time that can be consumed to put an end to the targeted project. Before applying the three aforementioned approaches, the modeling and simulation of the activities network are introduced for calculating the total average time of project. Then, comparatively speaking, the neural network, fuzzy logic and regression method approach are compared in terms prediction’s accuracy. The generated error from the three methods is compared, namely different types of errors are calculated. Basically, the input variables consist of the probability of success (PS), the coefficient of improvement (Coef_PS) and the coefficient of learning (CofA), while the output variable is the average total duration of the project (DTTm). The Predicted mean square error (MSE) values are purposefully used to compare the three models. Interestingly, the results show that the optimum prediction model is the fuzzy logic model with accurate results. It is noteworthy to say that the application in this paper can be applied on a real case study.
In the present paper, applicability and
capability of A.I techniques for effort estimation prediction has
been investigated. It is seen that neuro fuzzy models are very
robust, characterized by fast computation, capable of handling
the distorted data. Due to the presence of data non-linearity, it is
an efficient quantitative tool to predict effort estimation. The one
hidden layer network has been developed named as OHLANFIS
using MATLAB simulation environment.
Here the initial parameters of the OHLANFIS are
identified using the subtractive clustering method. Parameters of
the Gaussian membership function are optimally determined
using the hybrid learning algorithm. From the analysis it is seen
that the Effort Estimation prediction model developed using
OHLANFIS technique has been able to perform well over normal
ANFIS Model.
Time-Cost Trade-Off Analysis in a Construction Project Problem: Case Studyijceronline
In construction project, cost and time reduction is crucial in today’s competitive market respect. Cost and time along with quality of the project play vital role in construction project’s decision. Reduction in cost and time of projects has increased the demand of construction project in the recent years. Trade-off between different conflicting aspects of projects is one of the challenging problems often faced by construction companies. Time, cost and quality of project delivery are the important aspects of each project which lead researchers in developing time-cost trade-off model. These models are serving as important management tool for overcoming the limitation of critical path methods frequently used by company. The objective of time-cost trade-off analysis is to reduce the original project duration with possible least total cost. In this paper critical path method with a heuristic method is used to find out the crash durations and crash costs. A regression analysis is performed to identify the relationship between the times and costs in order to formulize an optimization problem model. The problem is then solved by Matlab program which yields a least cost of $60937 with duration 129.50 ≈130 days. Applying this approach, the result obtained is satisfactory, which is an indication of usefulness of this approach in construction project problems.
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...ijaia
In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASA’s turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...gerogepatton
In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASA’s turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods
Conceptual Cost Estimate of Libyan Highway Projects Using Artificial Neural N...IJERA Editor
It is well known that decisions at early stages of a construction project have great impact on subsequent project performance. Conceptual cost estimate is a challenging task that is done with limited information at the early stages of a project life where many factors affecting the project costs are still unknown. The objective of this paper is to support decision makers in predicting the conceptual cost of highway construction projects in Libya. Initially, the factors that significantly influence highway construction are identified. Then, an artificial neural network model is developed for predicting the cost. The network is trained and tested with a total of 67 projects historical data. Training of the model is administered via back-propagation algorithm. The model is coded ad implemented using MATLAB® to facilitate its use. An optimization module is also added to the Neural Network model with the objective of minimizing the error of the predicted cost. The model is then validated and the results show better predictions of conceptual cost of highway projects in Libya.
Risk Contingency Evaluation in International Construction Projects (Real Case...IJLT EMAS
Most construction companies operating in the global construction industry would undertake international projects to maximize their profitability through benefitting from the new attractive markets and reducing the dependence upon local markets. As a result of the nature of construction works the company and project's conditions actually include massive risks and uncertainty. So the risk sensitivity of projects costs should be assessed in a realistic manner. The comprehensive risk assessment method was introduced as a decision making supporting tool to be employed for international constructive projects through applying a risk model that will aid the procedures of evaluating risks and prioritizing such projects and assessing risk contingency value. Both the Analytic Hierarchy Process (AHP), applied for evaluating risk factors weight (likelihood), and FUZZY LOGIC approach, applied for evaluating risk factors influence (Risk consequences) employing software aids such as EXECL and MATLAB software, were used for developing the risk model. The reliability of the developed software has been verified by applications on a real construction projects. The proposed methodology and decision support tool have been proved to be reliable for the estimation of cost overrun resulting from risk on basis of actual final reports of projects. Six actual case studies from different countries were chosen to determine the highest risk factors and to implement the designed models, test their results and evaluate risk cost impact. The proposed models result showed that: the highest and lowest risk contingency percentage of 48 % and 16 % were in Project no (5), (6) respectively in Egypt. On the other hand, the projects no (1, 2, 4,7) in Saudi Arabia, UAE, Libya and Jordan, the risk contingency of 29%, 39%, 20% and 28% respectively. The actual results are close to those of the proposed program.
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Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
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HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.