Close range photogrammetry network design is referred to the process of placing a set of
cameras in order to achieve photogrammetric tasks. The main objective of this paper is tried to find
the best location of two/three camera stations. The genetic algorithm optimization and Particle
Swarm Optimization are developed to determine the optimal camera stations for computing the three
dimensional coordinates. In this research, a mathematical model representing the genetic algorithm
optimization and Particle Swarm Optimization for the close range photogrammetry network is
developed. This paper gives also the sequence of the field operations and computational steps for this
task. A test field is included to reinforce the theoretical aspects.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
The International Journal of Engineering and Science (The IJES)theijes
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.
MULTI-OBJECTIVE ENERGY EFFICIENT OPTIMIZATION ALGORITHM FOR COVERAGE CONTROL ...ijcseit
Many studies have been done in the area of Wireless Sensor Networks (WSNs) in recent years. In this kind of networks, some of the key objectives that need to be satisfied are area coverage, number of active sensors and energy consumed by nodes. In this paper, we propose a NSGA-II based multi-objective algorithm for optimizing all of these objectives simultaneously. The efficiency of our algorithm is demonstrated in the simulation results. This efficiency can be shown as finding the optimal balance point among the maximum coverage rate, the least energy consumption, and the minimum number of active nodes while maintaining the connectivity of the network
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
The International Journal of Engineering and Science (The IJES)theijes
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.
MULTI-OBJECTIVE ENERGY EFFICIENT OPTIMIZATION ALGORITHM FOR COVERAGE CONTROL ...ijcseit
Many studies have been done in the area of Wireless Sensor Networks (WSNs) in recent years. In this kind of networks, some of the key objectives that need to be satisfied are area coverage, number of active sensors and energy consumed by nodes. In this paper, we propose a NSGA-II based multi-objective algorithm for optimizing all of these objectives simultaneously. The efficiency of our algorithm is demonstrated in the simulation results. This efficiency can be shown as finding the optimal balance point among the maximum coverage rate, the least energy consumption, and the minimum number of active nodes while maintaining the connectivity of the network
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Analysis of data is an important task in data managements systems. Many mathematical tools are used in data analysis. A new division of data management has appeared in machine learning, linear algebra, an optimal tool to analyse and manipulate the data. Data science is a multi-disciplinary subject that uses scientific methods to process the structured and unstructured data to extract the knowledge by applying suitable algorithms and systems. The strength of linear algebra is ignored by the researchers due to the poor understanding. It powers major areas of Data Science including the hot fields of Natural Language Processing and Computer Vision. The data science enthusiasts finding the programming languages for data science are easy to analyze the big data rather than using mathematical tools like linear algebra. Linear algebra is a must-know subject in data science. It will open up possibilities of working and manipulating data. In this paper, some applications of Linear Algebra in Data Science are explained.
Optimization of Mechanical Design Problems Using Improved Differential Evolut...IDES Editor
Differential Evolution (DE) is a novel evolutionary
approach capable of handling non-differentiable, non-linear
and multi-modal objective functions. DE has been consistently
ranked as one of the best search algorithm for solving global
optimization problems in several case studies. This paper
presents an Improved Constraint Differential Evolution
(ICDE) algorithm for solving constrained optimization
problems. The proposed ICDE algorithm differs from
unconstrained DE algorithm only in the place of initialization,
selection of particles to the next generation and sorting the
final results. Also we implemented the new idea to five versions
of DE algorithm. The performance of ICDE algorithm is
validated on four mechanical engineering problems. The
experimental results show that the performance of ICDE
algorithm in terms of final objective function value, number
of function evaluations and convergence time.
Reoptimization techniques for solving hard problemsJhoirene Clemente
Unless P=NP, we cannot obtain a polynomial-time algorithm solving hard combinatorial problems. One practical approach in solving this kind of problem is to relax the condition of always finding the optimal solution for an instance and settle for “good enough” solutions. The kind of algorithms which are guaranteed to obtain a solution with a certain quality are called approximative algorithms. However, not all hard problems are approximable, i.e., we can obtain a polynomial-time algorithm that can guarantee the goodness of the solution for a problem.
In this lecture, we will present the concept of reoptimization. In this approach, given an instance I of some problem Π, an optimal solution OPT for Π in I, and a modified instance I' resulting from a local perturbation of I, we wish to use OPT in order to solve Π in I'. With this additional information, reoptimization may help to improve the approximability of the problem or the running time of the solution to it. In fact, we can obtain a polynomial-time approximation scheme (PTAS) for a reoptimization variant of a problem given that the unmodified problem is approximable.
In this paper fuzzy VRPTW with an uncertain travel time is considered. Credibility theory is used to model
the problem and specifies a preference index at which it is desired that the travel times to reach the
customers fall into their time windows. We propose the integration of fuzzy and ant colony system based
evolutionary algorithm to solve the problem while preserving the constraints. Computational results for
certain benchmark problems having short and long time horizons are presented to show the effectiveness of
the algorithm. Comparison between different preferences indexes have been obtained to help the user in
making suitable decisions
A HYBRID COA/ε-CONSTRAINT METHOD FOR SOLVING MULTI-OBJECTIVE PROBLEMSijfcstjournal
In this paper, a hybrid method for solving multi-objective problem has been provided. The proposed method is combining the ε-Constraint and the Cuckoo algorithm. First the multi objective problem transfers into a single-objective problem using ε-Constraint, then the Cuckoo optimization algorithm will optimize the problem in each task. At last the optimized Pareto frontier will be drawn. The advantage of
this method is the high accuracy and the dispersion of its Pareto frontier. In order to testing the efficiency of the suggested method, a lot of test problems have been solved using this method. Comparing the results of this method with the results of other similar methods shows that the Cuckoo algorithm is more suitable for solving the multi-objective problems.
OPTIMAL GLOBAL THRESHOLD ESTIMATION USING STATISTICAL CHANGE-POINT DETECTIONsipij
Aim of this paper is reformulation of global image thresholding problem as a well-founded statistical
method known as change-point detection (CPD) problem. Our proposed CPD thresholding algorithm does
not assume any prior statistical distribution of background and object grey levels. Further, this method is
less influenced by an outlier due to our judicious derivation of a robust criterion function depending on
Kullback-Leibler (KL) divergence measure. Experimental result shows efficacy of proposed method
compared to other popular methods available for global image thresholding. In this paper we also propose
a performance criterion for comparison of thresholding algorithms. This performance criteria does not
depend on any ground truth image. We have used this performance criterion to compare the results of
proposed thresholding algorithm with most cited global thresholding algorithms in the literature.
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
MEDICAL DIAGNOSIS CLASSIFICATION USING MIGRATION BASED DIFFERENTIAL EVOLUTION...cscpconf
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
Performance Comparision of Machine Learning AlgorithmsDinusha Dilanka
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Analysis of data is an important task in data managements systems. Many mathematical tools are used in data analysis. A new division of data management has appeared in machine learning, linear algebra, an optimal tool to analyse and manipulate the data. Data science is a multi-disciplinary subject that uses scientific methods to process the structured and unstructured data to extract the knowledge by applying suitable algorithms and systems. The strength of linear algebra is ignored by the researchers due to the poor understanding. It powers major areas of Data Science including the hot fields of Natural Language Processing and Computer Vision. The data science enthusiasts finding the programming languages for data science are easy to analyze the big data rather than using mathematical tools like linear algebra. Linear algebra is a must-know subject in data science. It will open up possibilities of working and manipulating data. In this paper, some applications of Linear Algebra in Data Science are explained.
Optimization of Mechanical Design Problems Using Improved Differential Evolut...IDES Editor
Differential Evolution (DE) is a novel evolutionary
approach capable of handling non-differentiable, non-linear
and multi-modal objective functions. DE has been consistently
ranked as one of the best search algorithm for solving global
optimization problems in several case studies. This paper
presents an Improved Constraint Differential Evolution
(ICDE) algorithm for solving constrained optimization
problems. The proposed ICDE algorithm differs from
unconstrained DE algorithm only in the place of initialization,
selection of particles to the next generation and sorting the
final results. Also we implemented the new idea to five versions
of DE algorithm. The performance of ICDE algorithm is
validated on four mechanical engineering problems. The
experimental results show that the performance of ICDE
algorithm in terms of final objective function value, number
of function evaluations and convergence time.
Reoptimization techniques for solving hard problemsJhoirene Clemente
Unless P=NP, we cannot obtain a polynomial-time algorithm solving hard combinatorial problems. One practical approach in solving this kind of problem is to relax the condition of always finding the optimal solution for an instance and settle for “good enough” solutions. The kind of algorithms which are guaranteed to obtain a solution with a certain quality are called approximative algorithms. However, not all hard problems are approximable, i.e., we can obtain a polynomial-time algorithm that can guarantee the goodness of the solution for a problem.
In this lecture, we will present the concept of reoptimization. In this approach, given an instance I of some problem Π, an optimal solution OPT for Π in I, and a modified instance I' resulting from a local perturbation of I, we wish to use OPT in order to solve Π in I'. With this additional information, reoptimization may help to improve the approximability of the problem or the running time of the solution to it. In fact, we can obtain a polynomial-time approximation scheme (PTAS) for a reoptimization variant of a problem given that the unmodified problem is approximable.
In this paper fuzzy VRPTW with an uncertain travel time is considered. Credibility theory is used to model
the problem and specifies a preference index at which it is desired that the travel times to reach the
customers fall into their time windows. We propose the integration of fuzzy and ant colony system based
evolutionary algorithm to solve the problem while preserving the constraints. Computational results for
certain benchmark problems having short and long time horizons are presented to show the effectiveness of
the algorithm. Comparison between different preferences indexes have been obtained to help the user in
making suitable decisions
A HYBRID COA/ε-CONSTRAINT METHOD FOR SOLVING MULTI-OBJECTIVE PROBLEMSijfcstjournal
In this paper, a hybrid method for solving multi-objective problem has been provided. The proposed method is combining the ε-Constraint and the Cuckoo algorithm. First the multi objective problem transfers into a single-objective problem using ε-Constraint, then the Cuckoo optimization algorithm will optimize the problem in each task. At last the optimized Pareto frontier will be drawn. The advantage of
this method is the high accuracy and the dispersion of its Pareto frontier. In order to testing the efficiency of the suggested method, a lot of test problems have been solved using this method. Comparing the results of this method with the results of other similar methods shows that the Cuckoo algorithm is more suitable for solving the multi-objective problems.
OPTIMAL GLOBAL THRESHOLD ESTIMATION USING STATISTICAL CHANGE-POINT DETECTIONsipij
Aim of this paper is reformulation of global image thresholding problem as a well-founded statistical
method known as change-point detection (CPD) problem. Our proposed CPD thresholding algorithm does
not assume any prior statistical distribution of background and object grey levels. Further, this method is
less influenced by an outlier due to our judicious derivation of a robust criterion function depending on
Kullback-Leibler (KL) divergence measure. Experimental result shows efficacy of proposed method
compared to other popular methods available for global image thresholding. In this paper we also propose
a performance criterion for comparison of thresholding algorithms. This performance criteria does not
depend on any ground truth image. We have used this performance criterion to compare the results of
proposed thresholding algorithm with most cited global thresholding algorithms in the literature.
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
MEDICAL DIAGNOSIS CLASSIFICATION USING MIGRATION BASED DIFFERENTIAL EVOLUTION...cscpconf
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
Performance Comparision of Machine Learning AlgorithmsDinusha Dilanka
In this paper Compare the performance of two
classification algorithm. I t is useful to differentiate
algorithms based on computational performance rather
than classification accuracy alone. As although
classification accuracy between the algorithms is similar,
computational performance can differ significantly and it
can affect to the final results. So the objective of this paper
is to perform a comparative analysis of two machine
learning algorithms namely, K Nearest neighbor,
classification and Logistic Regression. In this paper it
was considered a large dataset of 7981 data points and 112
features. Then the performance of the above mentioned
machine learning algorithms are examined. In this paper
the processing time and accuracy of the different machine
learning techniques are being estimated by considering the
collected data set, over a 60% for train and remaining
40% for testing. The paper is organized as follows. In
Section I, introduction and background analysis of the
research is included and in section II, problem statement.
In Section III, our application and data analyze Process,
the testing environment, and the Methodology of our
analysis are being described briefly. Section IV comprises
the results of two algorithms. Finally, the paper concludes
with a discussion of future directions for research by
eliminating the problems existing with the current
research methodology.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Prediction of Euro 50 Using Back Propagation Neural Network (BPNN) and Geneti...AI Publications
Modeling time series is often associated with the process forecasts certain characteristics in the next period. One of the methods forecasts that developed nowadays is using artificial neural network or more popularly known as a neural network. Use neural network in forecasts time series can be a good solution, but the problem is network architecture and the training method in the right direction. One of the choices that might be using a genetic algorithm. A genetic algorithm is a search algorithm stochastic resonance based on how it works by the mechanisms of natural selection and genetic variation that aims to find a solution to a problem. This algorithm can be used as teaching methods in train models are sent back propagation neural network. The application genetic algorithm and neural network for divination time series aim to get the weight optimum. From the training and testing on the data index share price euro 50 obtained by the RMSE testing 27.8744 and 39.2852 RMSE training. The weight or parameters that produced by has reached an optimum level in second-generation 1000 with the best fitness and the average 0.027771 the fitness of 0.0027847.Model is good to be used to give a prediction that is quite accurate information that is shown by the close target with the output.
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
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.
Artificial Intelligence in Robot Path Planningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Genetic Algorithm on Optimization Test FunctionsIJMERJOURNAL
ABSTRACT: Genetic Algorithms (GAs) have become increasingly useful over the years for solving combinatorial problems. Though they are generally accepted to be good performers among metaheuristic algorithms, most works have concentrated on the application of the GAs rather than the theoretical justifications. In this paper, we examine and justify the suitability of Genetic Algorithms in solving complex, multi-variable and multi-modal optimization problems. To achieve this, a simple Genetic Algorithm was used to solve four standard complicated optimization test functions, namely Rosenbrock, Schwefel, Rastrigin and Shubert functions. These functions are benchmarks to test the quality of an optimization procedure towards a global optimum. We show that the method has a quicker convergence to the global optima and that the optimal values for the Rosenbrock, Rastrigin, Schwefel and Shubert functions are zero (0), zero (0), -418.9829 and -14.5080 respectively
Parallel and distributed genetic algorithm with multiple objectives to impro...khalil IBRAHIM
we argue that the timetabling problem reflects the problem of scheduling university courses, So you must specify the range of time periods and a group of instructors for a range of lectures to check a set of constraints and reduce the cost of other constraints ,this is the problem called NP-hard, it is a class of problems that are informally, it’s mean that necessary operations to solve the problem will increase exponentially and directly proportional to the size of the problem, The construction of timetable is the most complicated problem that was facing many universities, and increased by size of the university data and overlapping disciplines between colleges, and when a traditional algorithm (EA) is unable to provide satisfactory results, a distributed EA (dEA), which deploys the population on distributed systems, it also offers an opportunity to solve extremely high dimensional problems through distributed coevolution using a divide-and-conquer mechanism, Further, the distributed environment allows a dEA to maintain population diversity, thereby avoiding local optima and also facilitating multi-objective search, by employing different distribution models to parallelize the processing of EAs, we designed a genetic algorithm suitable for Universities environment and the constraints facing it when building timetable for lectures.
Feature selection in high-dimensional datasets is
considered to be a complex and time-consuming problem. To
enhance the accuracy of classification and reduce the execution
time, Parallel Evolutionary Algorithms (PEAs) can be used. In
this paper, we make a review for the most recent works which
handle the use of PEAs for feature selection in large datasets.
We have classified the algorithms in these papers into four main
classes (Genetic Algorithms (GA), Particle Swarm Optimization
(PSO), Scattered Search (SS), and Ant Colony Optimization
(ACO)). The accuracy is adopted as a measure to compare the
efficiency of these PEAs. It is noticeable that the Parallel Genetic
Algorithms (PGAs) are the most suitable algorithms for feature
selection in large datasets; since they achieve the highest accuracy.
On the other hand, we found that the Parallel ACO is timeconsuming
and less accurate comparing with other PEA.
Applications and Analysis of Bio-Inspired Eagle Strategy for Engineering Opti...Xin-She Yang
Applications and Analysis of Bio-Inspired Eagle Strategy for Engineering Optimization
Similar to COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPTIMIZATION FOR DESIGN THE CLOSE RANGE PHOTOGRAMMETRY NETWORK (20)
Submission Deadline: 30th September 2022
Acceptance Notification: Within Three Days’ time period
Online Publication: Within 24 Hrs. time Period
Expected Date of Dispatch of Printed Journal: 5th October 2022
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...IAEME Publication
White layer thickness (WLT) formed and surface roughness in wire electric discharge turning (WEDT) of tungsten carbide composite has been made to model through response surface methodology (RSM). A Taguchi’s standard Design of experiments involving five input variables with three levels has been employed to establish a mathematical model between input parameters and responses. Percentage of cobalt content, spindle speed, Pulse on-time, wire feed and pulse off-time were changed during the experimental tests based on the Taguchi’s orthogonal array L27 (3^13). Analysis of variance (ANOVA) revealed that the mathematical models obtained can adequately describe performance within the parameters of the factors considered. There was a good agreement between the experimental and predicted values in this study.
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURSIAEME Publication
The study explores the reasons for a transgender to become entrepreneurs. In this study transgender entrepreneur was taken as independent variable and reasons to become as dependent variable. Data were collected through a structured questionnaire containing a five point Likert Scale. The study examined the data of 30 transgender entrepreneurs in Salem Municipal Corporation of Tamil Nadu State, India. Simple Random sampling technique was used. Garrett Ranking Technique (Percentile Position, Mean Scores) was used as the analysis for the present study to identify the top 13 stimulus factors for establishment of trans entrepreneurial venture. Economic advancement of a nation is governed upon the upshot of a resolute entrepreneurial doings. The conception of entrepreneurship has stretched and materialized to the socially deflated uncharted sections of transgender community. Presently transgenders have smashed their stereotypes and are making recent headlines of achievements in various fields of our Indian society. The trans-community is gradually being observed in a new light and has been trying to achieve prospective growth in entrepreneurship. The findings of the research revealed that the optimistic changes are taking place to change affirmative societal outlook of the transgender for entrepreneurial ventureship. It also laid emphasis on other transgenders to renovate their traditional living. The paper also highlights that legislators, supervisory body should endorse an impartial canons and reforms in Tamil Nadu Transgender Welfare Board Association.
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURSIAEME Publication
Since ages gender difference is always a debatable theme whether caused by nature, evolution or environment. The birth of a transgender is dreadful not only for the child but also for their parents. The pain of living in the wrong physique and treated as second class victimized citizen is outrageous and fully harboured with vicious baseless negative scruples. For so long, social exclusion had perpetuated inequality and deprivation experiencing ingrained malign stigma and besieged victims of crime or violence across their life spans. They are pushed into the murky way of life with a source of eternal disgust, bereft sexual potency and perennial fear. Although they are highly visible but very little is known about them. The common public needs to comprehend the ravaged arrogance on these insensitive souls and assist in integrating them into the mainstream by offering equal opportunity, treat with humanity and respect their dignity. Entrepreneurship in the current age is endorsing the gender fairness movement. Unstable careers and economic inadequacy had inclined one of the gender variant people called Transgender to become entrepreneurs. These tiny budding entrepreneurs resulted in economic transition by means of employment, free from the clutches of stereotype jobs, raised standard of living and handful of financial empowerment. Besides all these inhibitions, they were able to witness a platform for skill set development that ignited them to enter into entrepreneurial domain. This paper epitomizes skill sets involved in trans-entrepreneurs of Thoothukudi Municipal Corporation of Tamil Nadu State and is a groundbreaking determination to sightsee various skills incorporated and the impact on entrepreneurship.
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONSIAEME Publication
The banking and financial services industries are experiencing increased technology penetration. Among them, the banking industry has made technological advancements to better serve the general populace. The economy focused on transforming the banking sector's system into a cashless, paperless, and faceless one. The researcher wants to evaluate the user's intention for utilising a mobile banking application. The study also examines the variables affecting the user's behaviour intention when selecting specific applications for financial transactions. The researcher employed a well-structured questionnaire and a descriptive study methodology to gather the respondents' primary data utilising the snowball sampling technique. The study includes variables like performance expectations, effort expectations, social impact, enabling circumstances, and perceived risk. Each of the aforementioned variables has a major impact on how users utilise mobile banking applications. The outcome will assist the service provider in comprehending the user's history with mobile banking applications.
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONSIAEME Publication
Technology upgradation in banking sector took the economy to view that payment mode towards online transactions using mobile applications. This system enabled connectivity between banks, Merchant and user in a convenient mode. there are various applications used for online transactions such as Google pay, Paytm, freecharge, mobikiwi, oxygen, phonepe and so on and it also includes mobile banking applications. The study aimed at evaluating the predilection of the user in adopting digital transaction. The study is descriptive in nature. The researcher used random sample techniques to collect the data. The findings reveal that mobile applications differ with the quality of service rendered by Gpay and Phonepe. The researcher suggest the Phonepe application should focus on implementing the application should be user friendly interface and Gpay on motivating the users to feel the importance of request for money and modes of payments in the application.
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINOIAEME Publication
The prototype of a voice-based ATM for visually impaired using Arduino is to help people who are blind. This uses RFID cards which contain users fingerprint encrypted on it and interacts with the users through voice commands. ATM operates when sensor detects the presence of one person in the cabin. After scanning the RFID card, it will ask to select the mode like –normal or blind. User can select the respective mode through voice input, if blind mode is selected the balance check or cash withdraw can be done through voice input. Normal mode procedure is same as the existing ATM.
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...IAEME Publication
There is increasing acceptability of emotional intelligence as a major factor in personality assessment and effective human resource management. Emotional intelligence as the ability to build capacity, empathize, co-operate, motivate and develop others cannot be divorced from both effective performance and human resource management systems. The human person is crucial in defining organizational leadership and fortunes in terms of challenges and opportunities and walking across both multinational and bilateral relationships. The growing complexity of the business world requires a great deal of self-confidence, integrity, communication, conflict and diversity management to keep the global enterprise within the paths of productivity and sustainability. Using the exploratory research design and 255 participants the result of this original study indicates strong positive correlation between emotional intelligence and effective human resource management. The paper offers suggestions on further studies between emotional intelligence and human capital development and recommends for conflict management as an integral part of effective human resource management.
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMYIAEME Publication
Our life journey, in general, is closely defined by the way we understand the meaning of why we coexist and deal with its challenges. As we develop the "inspiration economy", we could say that nearly all of the challenges we have faced are opportunities that help us to discover the rest of our journey. In this note paper, we explore how being faced with the opportunity of being a close carer for an aging parent with dementia brought intangible discoveries that changed our insight of the meaning of the rest of our life journey.
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...IAEME Publication
The main objective of this study is to analyze the impact of aspects of Organizational Culture on the Effectiveness of the Performance Management System (PMS) in the Health Care Organization at Thanjavur. Organizational Culture and PMS play a crucial role in present-day organizations in achieving their objectives. PMS needs employees’ cooperation to achieve its intended objectives. Employees' cooperation depends upon the organization’s culture. The present study uses exploratory research to examine the relationship between the Organization's culture and the Effectiveness of the Performance Management System. The study uses a Structured Questionnaire to collect the primary data. For this study, Thirty-six non-clinical employees were selected from twelve randomly selected Health Care organizations at Thanjavur. Thirty-two fully completed questionnaires were received.
Living in 21st century in itself reminds all of us the necessity of police and its administration. As more and more we are entering into the modern society and culture, the more we require the services of the so called ‘Khaki Worthy’ men i.e., the police personnel. Whether we talk of Indian police or the other nation’s police, they all have the same recognition as they have in India. But as already mentioned, their services and requirements are different after the like 26th November, 2008 incidents, where they without saving their own lives has sacrificed themselves without any hitch and without caring about their respective family members and wards. In other words, they are like our heroes and mentors who can guide us from the darkness of fear, militancy, corruption and other dark sides of life and so on. Now the question arises, if Gandhi would have been alive today, what would have been his reaction/opinion to the police and its functioning? Would he have some thing different in his mind now what he had been in his mind before the partition or would he be going to start some Satyagraha in the form of some improvement in the functioning of the police administration? Really these questions or rather night mares can come to any one’s mind, when there is too much confusion is prevailing in our minds, when there is too much corruption in the society and when the polices working is also in the questioning because of one or the other case throughout the India. It is matter of great concern that we have to thing over our administration and our practical approach because the police personals are also like us, they are part and parcel of our society and among one of us, so why we all are pin pointing towards them.
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...IAEME Publication
The goal of this study was to see how talent management affected employee retention in the selected IT organizations in Chennai. The fundamental issue was the difficulty to attract, hire, and retain talented personnel who perform well and the gap between supply and demand of talent acquisition and retaining them within the firms. The study's main goals were to determine the impact of talent management on employee retention in IT companies in Chennai, investigate talent management strategies that IT companies could use to improve talent acquisition, performance management, career planning and formulate retention strategies that the IT firms could use. The respondents were given a structured close-ended questionnaire with the 5 Point Likert Scale as part of the study's quantitative research design. The target population consisted of 289 IT professionals. The questionnaires were distributed and collected by the researcher directly. The Statistical Package for Social Sciences (SPSS) was used to collect and analyse the questionnaire responses. Hypotheses that were formulated for the various areas of the study were tested using a variety of statistical tests. The key findings of the study suggested that talent management had an impact on employee retention. The studies also found that there is a clear link between the implementation of talent management and retention measures. Management should provide enough training and development for employees, clarify job responsibilities, provide adequate remuneration packages, and recognise employees for exceptional performance.
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...IAEME Publication
Globally, Millions of dollars were spent by the organizations for employing skilled Information Technology (IT) professionals. It is costly to replace unskilled employees with IT professionals possessing technical skills and competencies that aid in interconnecting the business processes. The organization’s employment tactics were forced to alter by globalization along with technological innovations as they consistently diminish to remain lean, outsource to concentrate on core competencies along with restructuring/reallocate personnel to gather efficiency. As other jobs, organizations or professions have become reasonably more appropriate in a shifting employment landscape, the above alterations trigger both involuntary as well as voluntary turnover. The employee view on jobs is also afflicted by the COVID-19 pandemic along with the employee-driven labour market. So, having effective strategies is necessary to tackle the withdrawal rate of employees. By associating Emotional Intelligence (EI) along with Talent Management (TM) in the IT industry, the rise in attrition rate was analyzed in this study. Only 303 respondents were collected out of 350 participants to whom questionnaires were distributed. From the employees of IT organizations located in Bangalore (India), the data were congregated. A simple random sampling methodology was employed to congregate data as of the respondents. Generating the hypothesis along with testing is eventuated. The effect of EI and TM along with regression analysis between TM and EI was analyzed. The outcomes indicated that employee and Organizational Performance (OP) were elevated by effective EI along with TM.
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...IAEME Publication
By implementing talent management strategy, organizations would have the option to retain their skilled professionals while additionally working on their overall performance. It is the course of appropriately utilizing the ideal individuals, setting them up for future top positions, exploring and dealing with their performance, and holding them back from leaving the organization. It is employee performance that determines the success of every organization. The firm quickly obtains an upper hand over its rivals in the event that its employees having particular skills that cannot be duplicated by the competitors. Thus, firms are centred on creating successful talent management practices and processes to deal with the unique human resources. Firms are additionally endeavouring to keep their top/key staff since on the off chance that they leave; the whole store of information leaves the firm's hands. The study's objective was to determine the impact of talent management on organizational performance among the selected IT organizations in Chennai. The study recommends that talent management limitedly affects performance. On the off chance that this talent is appropriately management and implemented properly, organizations might benefit as much as possible from their maintained assets to support development and productivity, both monetarily and non-monetarily.
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...IAEME Publication
Banking regulations act of India, 1949 defines banking as “acceptance of deposits for the purpose of lending or investment from the public, repayment on demand or otherwise and withdrawable through cheques, drafts order or otherwise”, the major participants of the Indian financial system are commercial banks, the financial institution encompassing term lending institutions. Investments institutions, specialized financial institution and the state level development banks, non banking financial companies (NBFC) and other market intermediaries such has the stock brokers and money lenders are among the oldest of the certain variants of NBFC and the oldest market participants. The asset quality of banks is one of the most important indicators of their financial health. The Indian banking sector has been facing severe problems of increasing Non- Performing Assets (NPAs). The NPAs growth directly and indirectly affects the quality of assets and profitability of banks. It also shows the efficiency of banks credit risk management and the recovery effectiveness. NPA do not generate any income, whereas, the bank is required to make provisions for such as assets that why is a double edge weapon. This paper outlines the concept of quality of bank loans of different types like Housing, Agriculture and MSME loans in state Haryana of selected public and private sector banks. This study is highlighting problems associated with the role of commercial bank in financing Small and Medium Scale Enterprises (SME). The overall objective of the research was to assess the effect of the financing provisions existing for the setting up and operations of MSMEs in the country and to generate recommendations for more robust financing mechanisms for successful operation of the MSMEs, in turn understanding the impact of MSME loans on financial institutions due to NPA. There are many research conducted on the topic of Non- Performing Assets (NPA) Management, concerning particular bank, comparative study of public and private banks etc. In this paper the researcher is considering the aggregate data of selected public sector and private sector banks and attempts to compare the NPA of Housing, Agriculture and MSME loans in state Haryana of public and private sector banks. The tools used in the study are average and Anova test and variance. The findings reveal that NPA is common problem for both public and private sector banks and is associated with all types of loans either that is housing loans, agriculture loans and loans to SMES. NPAs of both public and private sector banks show the increasing trend. In 2010-11 GNPA of public and private sector were at same level it was 2% but after 2010-11 it increased in many fold and at present there is GNPA in some more than 15%. It shows the dark area of Indian banking sector.
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...IAEME Publication
An experiment conducted in this study found that BaSO4 changed Nylon 6's mechanical properties. By changing the weight ratios, BaSO4 was used to make Nylon 6. This Researcher looked into how hard Nylon-6/BaSO4 composites are and how well they wear. Experiments were done based on Taguchi design L9. Nylon-6/BaSO4 composites can be tested for their hardness number using a Rockwell hardness testing apparatus. On Nylon/BaSO4, the wear behavior was measured by a wear monitor, pinon-disc friction by varying reinforcement, sliding speed, and sliding distance, and the microstructure of the crack surfaces was observed by SEM. This study provides significant contributions to ultimate strength by increasing BaSO4 content up to 16% in the composites, and sliding speed contributes 72.45% to the wear rate
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...IAEME Publication
The majority of the population in India lives in villages. The village is the back bone of the country. Village or rural industries play an important role in the national economy, particularly in the rural development. Developing the rural economy is one of the key indicators towards a country’s success. Whether it be the need to look after the welfare of the farmers or invest in rural infrastructure, Governments have to ensure that rural development isn’t compromised. The economic development of our country largely depends on the progress of rural areas and the standard of living of rural masses. Village or rural industries play an important role in the national economy, particularly in the rural development. Rural entrepreneurship is based on stimulating local entrepreneurial talent and the subsequent growth of indigenous enterprises. It recognizes opportunity in the rural areas and accelerates a unique blend of resources either inside or outside of agriculture. Rural entrepreneurship brings an economic value to the rural sector by creating new methods of production, new markets, new products and generate employment opportunities thereby ensuring continuous rural development. Social Entrepreneurship has the direct and primary objective of serving the society along with the earning profits. So, social entrepreneurship is different from the economic entrepreneurship as its basic objective is not to earn profits but for providing innovative solutions to meet the society needs which are not taken care by majority of the entrepreneurs as they are in the business for profit making as a sole objective. So, the Social Entrepreneurs have the huge growth potential particularly in the developing countries like India where we have huge societal disparities in terms of the financial positions of the population. Still 22 percent of the Indian population is below the poverty line and also there is disparity among the rural & urban population in terms of families living under BPL. 25.7 percent of the rural population & 13.7 percent of the urban population is under BPL which clearly shows the disparity of the poor people in the rural and urban areas. The need to develop social entrepreneurship in agriculture is dictated by a large number of social problems. Such problems include low living standards, unemployment, and social tension. The reasons that led to the emergence of the practice of social entrepreneurship are the above factors. The research problem lays upon disclosing the importance of role of social entrepreneurship in rural development of India. The paper the tendencies of social entrepreneurship in India, to present successful examples of such business for providing recommendations how to improve situation in rural areas in terms of social entrepreneurship development. Indian government has made some steps towards development of social enterprises, social entrepreneurship, and social in- novation, but a lot remains to be improved.
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...IAEME Publication
Distribution system is a critical link between the electric power distributor and the consumers. Most of the distribution networks commonly used by the electric utility is the radial distribution network. However in this type of network, it has technical issues such as enormous power losses which affect the quality of the supply. Nowadays, the introduction of Distributed Generation (DG) units in the system help improve and support the voltage profile of the network as well as the performance of the system components through power loss mitigation. In this study network reconfiguration was done using two meta-heuristic algorithms Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA) to enhance power quality and voltage profile in the system when simultaneously applied with the DG units. Backward/Forward Sweep Method was used in the load flow analysis and simulated using the MATLAB program. Five cases were considered in the Reconfiguration based on the contribution of DG units. The proposed method was tested using IEEE 33 bus system. Based on the results, there was a voltage profile improvement in the system from 0.9038 p.u. to 0.9594 p.u.. The integration of DG in the network also reduced power losses from 210.98 kW to 69.3963 kW. Simulated results are drawn to show the performance of each case.
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...IAEME Publication
Manufacturing industries have witnessed an outburst in productivity. For productivity improvement manufacturing industries are taking various initiatives by using lean tools and techniques. However, in different manufacturing industries, frugal approach is applied in product design and services as a tool for improvement. Frugal approach contributed to prove less is more and seems indirectly contributing to improve productivity. Hence, there is need to understand status of frugal approach application in manufacturing industries. All manufacturing industries are trying hard and putting continuous efforts for competitive existence. For productivity improvements, manufacturing industries are coming up with different effective and efficient solutions in manufacturing processes and operations. To overcome current challenges, manufacturing industries have started using frugal approach in product design and services. For this study, methodology adopted with both primary and secondary sources of data. For primary source interview and observation technique is used and for secondary source review has done based on available literatures in website, printed magazines, manual etc. An attempt has made for understanding application of frugal approach with the study of manufacturing industry project. Manufacturing industry selected for this project study is Mahindra and Mahindra Ltd. This paper will help researcher to find the connections between the two concepts productivity improvement and frugal approach. This paper will help to understand significance of frugal approach for productivity improvement in manufacturing industry. This will also help to understand current scenario of frugal approach in manufacturing industry. In manufacturing industries various process are involved to deliver the final product. In the process of converting input in to output through manufacturing process productivity plays very critical role. Hence this study will help to evolve status of frugal approach in productivity improvement programme. The notion of frugal can be viewed as an approach towards productivity improvement in manufacturing industries.
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENTIAEME Publication
In this paper, we investigated a queuing model of fuzzy environment-based a multiple channel queuing model (M/M/C) ( /FCFS) and study its performance under realistic conditions. It applies a nonagonal fuzzy number to analyse the relevant performance of a multiple channel queuing model (M/M/C) ( /FCFS). Based on the sub interval average ranking method for nonagonal fuzzy number, we convert fuzzy number to crisp one. Numerical results reveal that the efficiency of this method. Intuitively, the fuzzy environment adapts well to a multiple channel queuing models (M/M/C) ( /FCFS) are very well.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
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.
2. Comparison Between The Genetic Algorithms Optimization and Particle Swarm Optimization For
Design The Close Range Photogrammetry Network, Hossam El-Din Fawzy, Journal Impact Factor
(2015): 9.1215 (Calculated by GISI) www.jifactor.com
www.iaeme.com/ijciet.asp 148 editor@iaeme.com
Photogrammetric measurement operations attempt to satisfy, in an optimal manner, several
objectives as precision, reliability and economy. The ZOD and SOD are greatly simplified in
comparison to geodetic networks for which the four stages were originally developed. Indeed FOD,
the design of network configuration or the sensor placementtask needs to be comprehensively
addressed for photogrammetric projects. The close range photogrammetric network design is the
process of optimizing a network configuration in terms of the accuracy of object-points. This design
stage must provide an optimal imaging geometry and convergence angle for each set of points placed
over a complex object.
2. HEURISTIC OPTIMIZATION ALGORITHMS
Optimization has been an active area of research for several decades. As As many real world
optimization problems become increasingly complex, better optimization algorithms are always
needed. Recently, meta-heuristic global optimization algorithms have become a popular choice for
solving complex and intricate problems, which are otherwise difficult to solve by traditional methods
[12]. The objective of optimization isto seeks values for a set of parameters that maximize or
minimize objective functions subject to certain constraints [13].
Choices of values for the set of Parameters that satisfy all constraints are called a feasible
solution. Feasible solutions with objective function value(s) as good as the values of any other
feasible solutions are called optimal solutions [13]. In order to use optimization successfully, we
must first determine an objective through which we can measure the performance of the system
under study. The objective relies on certain characteristics of the system, called variable or
unknowns. The goal is to find a set of values of the variable that result in the best possible solution to
an optimization problem within a reasonable time limit. The optimization algorithms come from
different areas and are inspired by different techniques. But they all share some common
characteristics. They are iterative; they all begin with an initial guess of the optimal values of the
variables and generate a sequence of improved estimates until they converge to a solution. The
strategy used to move from one potential solution to the next is what distinguishes one algorithm
from another [12].
Figure1: pie chart of the publication distribution of meta-heuristic algorithms
Broadly speaking, optimization algorithms can be placed in two categories: the conventional
or deterministic methods and the modern heuristics or stochastic methods. Conventional methods
adopt the deterministic approach. During the optimization process, any solutions found are assumed
to be exact and the computation for next set of solutions completely depends on the previous
solutions found. That’s why conventional methods are also known as deterministic optimization
3. Comparison Between The Genetic Algorithms Optimization and Particle Swarm Optimization For
Design The Close Range Photogrammetry Network, Hossam El-Din Fawzy, Journal Impact Factor
(2015): 9.1215 (Calculated by GISI) www.jifactor.com
www.iaeme.com/ijciet.asp 149 editor@iaeme.com
methods. In addition, these methods involve certain assumptions about the formulation of the
objective functions and constraint functions. Conventional methods include algorithms such as linear
programming, non linear programming, dynamic programming, Newton’s method and others. In the
past few decades several global optimization algorithms have been developed that are based on the
nature inspired analogy. These are mostly population based meta-heuristics also called general
purpose algorithms because of their applicability to a wide range of problems. Some popular global
optimization algorithms include Evolution Strategies (ES), Evolutionary Programming (EP), Genetic
Algorithms (GA), Artificial Immune System (AIS), Tabu Search (TS), Ant Colony Optimization
(ACO), Particle Swarm Optimization (PSO), Harmony Search (HS) algorithm, Bee Colony
Optimization (BCO), Gravitational Search Algorithm (GSA), etc [12].
Figure 1 shows the distribution of publications which applied the meta-heuristics methods to
solve the optimization problem. This survey is based on ISI Web of Knowledge databases and
included most of the papers that have been published during the past decade. Figure 1 shows that the
GA and PSO are the most popular algorithms among the others.
3. CLASSIFICATION OF HEURISTIC ALGORITHMS
A vast literature exists on heuristic for solving an impressive array of problems as mentioned
in the introduction of this paper and, more recently, a number of studies have reported on the success
of such techniques for solving difficult problems in all key areas of computer science. The two most
predominant and successful classes or directions are Evolutionary Algorithms and Swarm based
Algorithms which are inspired by the natural evolution and collective behaviour in animals
respectively. There are several algorithms with the same basic concepts of Evolutionary Algorithms
and Swarm based Algorithms are prevailed in figure 2.
Figure 2: Classification of meta-heuristic algorithms
4. Comparison Between The Genetic Algorithms Optimization and Particle Swarm Optimization For
Design The Close Range Photogrammetry Network, Hossam El-Din Fawzy, Journal Impact Factor
(2015): 9.1215 (Calculated by GISI) www.jifactor.com
www.iaeme.com/ijciet.asp 150 editor@iaeme.com
In this paper, we will give a concise introduction to the theory of some popular and well
known global optimization algorithms.
4. GENETIC ALGORITHMS (GA)
Genetic algorithmis one of the most popular types of Evolutionary algorithms. To be more
precise, it constitutes a computing model for simulating natural and genetic selection that is attached
to the biological evolution described in Darwin’s Theory which was first issued by Holland [6],[7].
In this computing model a population of abstract representations (call chromosomes or the genotype
of the genome) of candidate solutions (called individuals, creatures, or phenotypes) to an
optimization problem could result in better solutions, which are traditionally represented in binary
form as strings comprising of 0 and 1 s with fixed length, but other kinds of encoding are also
possible which include real-values and order chromosomes. The program then will assign the proper
number of bits and the coding. Being a member of the family of evolutionary computation, the first
step of GA is population initialization which is usually done stochastically. The GA usually uses
three simple operators called selection, recombination (usually called crossover) and mutation.
Selection is the step of a genetic algorithm in which a certain number of individuals is chosen from
the current population for later breeding (recombination or crossover); the choosing rate is normally
proportional to individual's fitness value. There are several general selection techniques. Tournament
selection and fitness proportionality selection (also known as roulette-wheel selection) consider all
given individuals. Other methods only choose those individuals with a fitness value greater than a
given arbitrary constant. Crossover and mutation taken together is called reproduction. They are
analogous to biological crossover and mutation respectively [12].
The most important operator in GA is crossover which refers to the recombination of genetic
information during sexual reproduction. The child shares in common with it parents many
characteristics. Therefore, in Gas, the offspring has an equal chance of receiving any given gene
from either one parent because the parents 'chromosomes are combined randomly. To date, there are
many crossover techniques for organisms which use different data structures to store themselves,
such like One-point crossover, two-point crossover, Uniform Crossover as well as Half Uniform
Crossover. The probabilities for crossovers vary according to the problem. Generally speaking,
values between 60 and 80% are typical for one-point crossover as well as two-point crossover.
Uniform crossovers work well with slightly lower probabilities on the other hand. The probability
could also be altered during evolution. So a higher value might initially be attributed to the crossover
probability. Then it is decreased linearly until the end of the run, ending with a value of one half or
two thirds of the initial value [10]. Additionally for real-value cases, the crossover operation could be
expressed as:
211 )1( xxy λλ −+=
1221 )1( xxy λλ −+= ........... (1)
Where y1 and y2 are two descendants created by two given parents x1andx2, and λis random
number between 0 and 1.
Mutation is the stochastic flipping of chromosome bits that occurs each generation which is
used to maintain genetic diversity from one generation of a population of chromosomes to the next.
Mutation occurs step by step until the entire population has been covered. Again, the coding used is
decisive. In the case of binary chromosomes, it simply flips the bit while in real-valued
chromosomes a noise parameter N [0, F] is added to the value at that position. σ could be chosen in
5. Comparison Between The Genetic Algorithms Optimization and Particle Swarm Optimization For
Design The Close Range Photogrammetry Network, Hossam El-Din Fawzy, Journal Impact Factor
(2015): 9.1215 (Calculated by GISI) www.jifactor.com
www.iaeme.com/ijciet.asp 151 editor@iaeme.com
such way that it decreases in time, e.g., )1(/1 t+=σ , where t is the number of current iteration. The
probability of mutation is normally kept as a low constant value for the entire runoff the GA, such
like 0.001. Despite of these evolutionary operators, in some cases a strategy called "elites" is used,
where the best individual is directly copied to the next generation without undergoing any of
the genetic operators. In one single interaction, new parents are selected for each child and the
process continues until a proper size of individuals for the new population is reached. This process
ultimately results in the population of the new generation differing from the current one. Generally
the average fitness of the population should be improved by this procedure since only the best
organisms from the last generation are selected for breeding [12].
The implementation of the genetic algorithm is described as follows [14]:
Step 1: Initialization. The algorithm starts with a set of solutions (represented by chromosomes)
called population. A set of chromosomes is randomly generated. Each chromosome is composed of
genes.
Step 2: Evaluation. For every chromosome, its fitness value is calculated. Each chromosome’s
fitness value is analyzed one by one. Compared with the existing best fitness value, if one
chromosome can generate better fitness, renew the values of the defined vector and variable with this
chromosome and its fitness value; otherwise, keep their values unchanged.
Step 3: Selection. Population selection within the algorithm utilizes the principle of survival of the
fittest, which is based on the Darwinian's concept of Natural Selection [6],[7]. A random number
generator is employed to generate random numbers whose values are between 0 and 1.
Step 4: Crossover. The crossover operation is one of the most important operations in GA. The basic
idea is to combine some genes from different chromosomes. A GA recombines of bit strings by
copying segments from chromosomes pairs.
Step 5: Mutation. Some useful genes are not generated during the initial step. This difficulty can be
overcome by using the mutation approach. The basic mutation operator randomly generates a
number as the crossover position and then changes the value of this gene randomly.
Step 6: Stopping criteria. Steps 2-5 are repeated until the predefined number of generations has been
reached. The optimal solution can be generated after termination [12].
Where all these steps are shown in figure 3
Figure 1 A graphical representation of genetic algorithm
5. PARTICLE SWARM OPTIMIZATION (PSO)
Particle swarm optimization (PSO) is a computational intelligence oriented, stochastic,
population-based global optimization technique proposed by Kennedy and Eberhartin 1995 [4]. It is
inspired by the social behaviour of bird flocking searching for food. PSO has been extensively
6. Comparison Between The Genetic Algorithms Optimization and Particle Swarm Optimization For
Design The Close Range Photogrammetry Network, Hossam El-Din Fawzy, Journal Impact Factor
(2015): 9.1215 (Calculated by GISI) www.jifactor.com
www.iaeme.com/ijciet.asp 152 editor@iaeme.com
applied to many engineering optimization areas due to its unique searching mechanism, simple
concept, computational efficiency, and easy implementation. In PSO, the term ―particles refers to
population members which are mass-less and volume-less (or with an arbitrarily small mass or
volume) and are subject to velocities and accelerations towards a better mode of behaviour. Each
particle in the swarm represents a solution in a high-dimensional space with four vectors, its current
position; best position found so far, the best position found by its neighbourhood so far and its
velocity and adjusts its position in the search space based on the best position reached by itself
(pbest) and on the best position reached by its neighbourhood (gbest) during the search process [11].
In each iteration, each particle updates its position and velocity as follows:
i
k
i
k
i
k vxx 11 ++ += (2)
)()( 22111
i
k
g
k
i
k
i
k
i
k
i
k xprcxprcvv −+−+=+ (3)
Where,
i
kx represents Particle position
i
kv represents Particle velocity
i
kp represents personal best position
g
kp represents global best position
C1, C2represents Particle position
r1, r2represents Particle position
Steps in PSO algorithm can be briefed as below:
1) Initialize the swarm by assigning a random position in the problem space to each particle.
2) Evaluate the fitness function for each particle.
3) For each individual particle, compare the particle‘s fitness value with its pbest. If the current
value is better than the pbest value, then set this value as the pbest and the current particle‘s
position, xi, as pi.
4) Identify the particle that has the best fitness value. The value of its fitness function is identified
as guest and its position as pg.
5) update the velocities and positions of all the particles using (1) and (2).
6) Repeat steps 2–5 until a stopping criterion is met (e.g., maximum number of iterations or a
sufficiently good fitness value). [4]
Figure 4: A graphical representation of PSO particle updating position
7. Comparison Between The Genetic Algorithms Optimization and Particle Swarm Optimization For
Design The Close Range Photogrammetry Network, Hossam El-Din Fawzy, Journal Impact Factor
(2015): 9.1215 (Calculated by GISI) www.jifactor.com
www.iaeme.com/ijciet.asp 153 editor@iaeme.com
1
1
11109
8765
11109
4321
+++
+++
−∆+=
+++
+++
−∆+=
ZLYLXL
LZLYLXL
yyg
ZLYLXL
LZLYLXL
xxf
−−−−
+++++=∆ yxpxrprkrkrkxx 2
22
1
6
3
4
2
2
1 2)2()(
)2(22)( 2
21
6
3
4
2
2
1
−−−−
+++++=∆ yrpyxprkrkrkyy
6. MATHEMATICAL MODEL FOR CLOSE RANGE PHOTOGRAMMETRY NETWORK
DESIGN
The photogrammetric three dimension coordinate determination is based on the co-linearity
equation which simply states that object point, camera projective centre and image point lie on a
straight line. The determination of the three dimension coordinates from a definite point is achieved
through the intersection of two or more straight lines. Abdel Aziz and Karara proposed a simple
method for close range photogrammetric data reduction with non- metric cameras; it establishes the
direct linear transformation (DLT) between the two-dimensional coordinates, and the corresponding
object- space coordinates. The Direct Linear Transformation (DLT) between a point (X, Y, Z) in
object space and its corresponding image space coordinates (x, y) can be established by the linear
fractional equations [3]:
(4)
Where:
L1, L2 L3…L11 are the transformation parameters
X, Y and Z are the object space coordinates
(5)
Where:
x-
= x – xo y-
= y – yo r2
= (x – xo)2
+ (y – yo)2
(6)
x, y are image coordinates
p1and p2 are two asymmetric parameters for decentring distortion
k1 ,k2 and k3 arethree symmetric parameters for radial distortion
r is the radial distance from the principal point [2]
Equation 4 results from the equation of the central perspective in a trivial manner; Δx, Δy are
systematic deformations of the image, i.e. deviations from the central perspective. Equation 4 can be
solved directly for the 11 transformation parameters (L1, L2 L3 …L11) if there are at least six points
in the image whose object-space coordinates are known. Equation 1 is rewritten to serve in a least
squares formulation relating known control points to image coordinate measurements [1]:
vx = L1 X + L2 Y + L3 Z + L4 – xX L9 – xY L10 – xZ L11– Δx (7)
vy = L5 X + L6 Y + L7 Z + L8– yX L9 – yY L10 – y Z L11– Δy
After (L1 to L11, k1, k2, k3, p1, p2) parameters of each stereo pair of photos become available,
it can be computed the object space coordinate (X, Y, Z) of any points appear in each photos of
stereo pair, by applying the DLT equations with additional parameters. By rearranging these
equations it can be represented in matrices forms (for m photos) as shown bellow.
8. Comparison Between The Genetic Algorithms Optimization and Particle Swarm Optimization For
Design The Close Range Photogrammetry Network, Hossam El-Din Fawzy, Journal Impact Factor
(2015): 9.1215 (Calculated by GISI) www.jifactor.com
www.iaeme.com/ijciet.asp 154 editor@iaeme.com
)3,2(
"
8
"
4
1"1
8
1"1
4
)1,3(
)3,2(711
"
610
"
59
"
311
"
210
"
19
"
1
7
1
11
1"1
6
1
10
1"1
5
1
9
1"
1
3
1
11
1"1
2
1
10
1"1
1
1
9
1"
:
:
:::
:::
m
m
i
m
m
i
m
i
i
I
I
I
m
mmm
i
mmm
i
mmm
i
mmm
i
mmm
i
mmm
i
iii
iii
L
L
L
L
Z
Y
X
LLyLLyLLy
LLxLLxLLx
LLyLLyLLy
LLxLLxLLx
−
−
−
−
=
×
−−−
−−−
−−−
−−−
η
µ
η
µ
(8)
Where: = + ∆ , = + Δ
7. ASSESSMENT OF ACCURACY
There are two different methods can be used to evaluate accuracy: one can evaluate accuracy
by using check measurements and determining from these check measurements the value of
appropriate accuracy criteria; and one can use accuracy predictors. In this study, check
measurements will be used to evaluate accuracy [9].
In this study, we consider n (i = 1,2...n) check points in the studied object that is points whose
true coordinates are known but not used in the photogrammetric computations. Then if Xit,Yit and Zit
are the true coordinates of the check points, and Xiph,Yiph and Ziph its photogrammetric coordinates,
an estimation of the MRXYZ spatial residual is
)()()(
1 222
1
itiphitiphit
n
iph ZZXYXX
n
MRXYZ −+−+−= ∑
(9)
Analogous quantities can be estimated for three axes:
The X- direction: )(
1 2
1
it
n
iph XX
n
MRX −= ∑
The Y-direction: )(
1 2
1
itiph
n
XY
n
MRY −= ∑
The Z-direction: )(
1 2
1
itiph
n
ZZ
n
MRZ −= ∑
8. THE TEST FIELD AND DISCUSSION
The photogrammetric test field at the surveying laboratory (Civil Engineering Department,
Faculty of Engineering, Kafrelsheikh University, Egypt) was used. The three dimension coordinates
of 40 well distributed ground control points (15 points) and check point (25 points) with varying
heights were measured using a Multi-Station Intersection as shown in figure 5. The Multi-Station
Intersection system consists of three Sokkia Reflector less Total Station (SET330RK) and one
processing computer unit, which is connected to them. After an initialization step, three operators
simultaneously measure the vertical angles and horizontal directions of the targeted point. The
system calculates the three dimensions coordinates (by Multi-Station intersection) and the precision
values in real-time. The average precision values of the ground control points and check points are
9. Comparison Between The Genetic Algorithms Optimization and Particle Swarm Optimization For
Design The Close Range Photogrammetry Network, Hossam El-Din Fawzy, Journal Impact Factor
(2015): 9.1215 (Calculated by GISI) www.jifactor.com
www.iaeme.com/ijciet.asp 155 editor@iaeme.com
±0.2, ±0.4 and ±0.2 mm for X, Y and Z axes, respectively [8]. Two programs were carried out using
Matlab programs to design the close range photogrammetry network, one using the genetic algorithm
optimization and the other with particle swarm optimization. Programs have been designed for
computation of the optimal outer orientation for three camera station. The test field was
photographed from optimal camera stations outing of the genetic algorithm optimization and the
particle swarm optimization. All photographs were taken using high resolution CCD camera (Nikon
D 3100) as shown in figure 6. The cameras settings such as zoom factor, focus, white balance etc.
were kept constant during the test procedure.
Figure 5: The 3D Test Field
Figure 6: The Nikon D 3100 digital camera
Another Matlab program has been designed for computation of the spatial coordinates (X, Y,
Z) of the n checkpoints, the maximum and minimum residual in the X, Y and Z-direction, the
maximum and minimum spatial differences among the n checkpoints and the variance-covariance
matrix of the parameters. It is to be mentioned that the determinations of the residuals have been
carried out for two cases: 1-DLT from optimal camera stations outing of the genetic algorithm
optimization, 2-DLT from optimal camera stations outing of the particle swarm optimization. The
estimated accuracy and standard deviations (SD) for the space coordinates will also be presented in
tabular form.
10. Comparison Between The Genetic Algorithms Optimization and Particle Swarm Optimization For
Design The Close Range Photogrammetry Network, Hossam El-Din Fawzy, Journal Impact Factor
(2015): 9.1215 (Calculated by GISI) www.jifactor.com
www.iaeme.com/ijciet.asp 156 editor@iaeme.com
Table (5): Statistics for the evaluated
standard deviations of the 3D
coordinates, as extracted from the
evaluated variance-covariance matrix,
associated with different cameras.
Std (δ x)mm Case - 1 Case - 2
Max 4.664077 3.785348
Min 0.780686 0.598918
Std (δ y)mm Case - 1 Case - 2
Max 15.1931 11.78741
Min 4.171262 3.60388
Std (δ z)mm Case - 1 Case - 2
Max 5.629424 4.575657
Min 1.11616 1.07935
Table (4): Statistics for the obtained 3D
coordinate differences associated with
different camera stations at the used
checkpoints (in mm)
(δ X) Case - 1 Case - 2
Max 11.43163 8.613751
Min 1.4388 1.140868
(δ Y) Case - 1 Case - 2
Max 12.22304 12.02975
Min 1.928755 1.493896
(δ Z) Case - 1 Case - 2
Max 6.489615 5.554176
Min 0.983443 0.799878
Pos. Case - 1 Case - 2
Max 15.28432 12.77278
Min 2.328068 1.865873
Case–1: DLT from optimal camera stations outing of the genetic algorithm optimization
Case–2: DLT from optimal camera stations outing of the particle swarm optimization.
Using insight into Tables 4, 5 some interesting points is noted:
- In the X, Y and Z direction, the best accuracy has been obtained, when case-2 is used (DLT
from optimal camera stations outing of the particle swarm optimization)
- According to the obtained results, the minimum position error is provided by case-2, when
DLT from optimal camera stations outing of the particle swarm optimization is used.
Using insight into Table 4 one can notice that, there is an improvement in the standard
deviation of the individual point coordinates, when case-2 (DLT from optimal camera stations outing
of the particle swarm optimization) is used
9. CONCLUSIONS
The genetic algorithm optimization and the particle swarm optimization have been used to
design the close range photogrammetry network and the obtained accuracy is discussed and
presented. Based on the experimental results, it can be seen that case-2 (DLT from optimal camera
stations outing of the particle swarm optimization) provides good results for the X, Y and Z
coordinates when compared of case-1 (DLT from optimal camera stations outing of the genetic
algorithm optimization)
From all of the above discussions, the following Advantages for particle swarm optimization
over Genetic Algorithm optimization can be drawn:
- Particle swarm optimization is easier to implement and there are fewer parameters to adjust.
- Particle swarm optimization has a more effective memory capability than Genetic Algorithm.
- particle swarm optimization is more efficient in maintaining the diversity of the swarm, since
all the particles use the information related to the most successful particle in order to improve
themselves, whereas in Genetic algorithm, the worse solutions are discarded and only the new ones
are saved; i.e. in Genetic Algorithm the population evolve around a subset of the best individuals.