Proposal of an Automated Schema to Generate a Complex Password by Employing the Genetic Algorithm | Chapter 3 | Science and Technology - Recent Updates and Future Prospects Vol. 8

 

Due to the occurred increasing in information sharing, internet popularization, E-commerce transactions, and data transferring, security and authenticity become an important and necessary subject. During recent years and yet now the terms of security, data integrity, authenticity are very concern, whereas in order to authenticate users of any service that is worked online, the authentication of password is used. In this paper an automated schema was proposed to generate a strong and complex password which is based on entering initial data such as text (meaningful and simple information or not), with the concept of encoding it, then employing the Genetic Algorithm by using its operations crossover and mutation to generated different data from the entered one. The generated password is non-guessable and can be used in many and different applications and internet services like social networks, secured system, distributed systems, and online services. The proposed password generator achieved diffusion, randomness, and confusions, which are very necessary, required and targeted in the resulted password, in addition to the notice that the length of the generated password differs from the length of initial data, and any simple changing and modification in the initial data produces more and clear modification in the generated password. The generated password cannot be remembered, so that the application of such password is as a master password, which is saved in a file after it was generated and it is used in user specific configuration data for access the server, also it can be distributed among the users who share information and data, it can also be used for barcode generation for commercial field. The proposed work was done using visual basic programing language.

 

Author(s) Details

Sura Jasim Mohammed
Information Technology College, University of Babylon, Iraq.

Safa Saad Al-Murieb
Information Technology College, University of Babylon, Iraq.

 

Please see the link:- https://doi.org/10.9734/bpi/strufp/v8/903

A Hybrid Approach to Support Robotic Polishing Process Planning | Chapter 11 | Research and Developments in Engineering Research Vol. 8

 Currently, machinelike polishing and manual labour are used to complete the finishing stages of the produce of moulds. This stage of the procedure is not only ultimate expensive, but it is likewise currently experience a shortage of knowledgeable people. The production industry has anticipated towards robotic electronics to assist the polishing process in order to address these questions and serve the fuller needs of Industry 4.0. One factor that needs expected considered when automating the polishing process is the order of machined physiognomy that need to be treated. The research presented in this place examines the preparation and optimisation of the polishing process for the production of moulds and is a component of a more considerable project that aims to automate 80% of the existent human process. This chapter names an optimisation technique for done or made by machine polishing process sequencing that aims to simultaneously placate polishing sequence tests and minimize polishing period. A hybrid approach joining both hereditary algorithms and analytical hierarchic processes is proposed established the specific traits of polishing process sequencing. A multi-objective fitness function is delineated using AHP, containing the calculation of polishing occasion and the evaluation of polishing process rules. The projected process sequencing has been favorably demonstrated on test piece instances.

Author(s) Details:

K. Wang,
Bristol Robotics Laboratory (BRL), School of Engineering, University of the West of England, Cold Harbour Lane Bristol, United Kingdom.

L. Ding,
Bristol Robotics Laboratory (BRL), School of Engineering, University of the West of England, Cold Harbour Lane Bristol, United Kingdom.

F. Dailami,
Bristol Robotics Laboratory (BRL), School of Engineering, University of the West of England, Cold Harbour Lane Bristol, United Kingdom.

J. Matthews,
Bristol Robotics Laboratory (BRL), School of Engineering, University of the West of England, Cold Harbour Lane Bristol, United Kingdom.

Please see the link here: https://stm.bookpi.org/RADER-V8/article/view/11981

A Thin and Efficient Invisibility Cloak Designed by Using Genetic Algorithm from Layers of Ordinary Dielectric Materials | Chapter 11 | Fundamental Research and Application of Physical Science Vol. 9

 The objective concerning this work is to evolve a thin and efficient transmission cloak for concealing objects larger than the wavelengths of occurrence radiation by using multi-tier coating calm of ordinary dielectrics, instead of meta- and plasmonic fabrics. The genetic treasure-based optimization process is used to decide the parameters of the cloak coatings, and the fitness function is the alternate of the total scattering cross-width of the disguised target, that is obtained from the solution of the Helmholtz equating. The proposed cloak explains better cloaking efficiency than does a similarly judge metamaterial cloak designed by utilizing the transformation optics connections.

Author(s) Details:

Xiaohui Wang,
Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, 49931, USA.

Elena Semouchkina,
Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, 49931, USA.

Please see the link here: https://stm.bookpi.org/FRAPS-V9/article/view/11768

Design Analysis of Electrical System Onshore Wind Farms Using Genetic Algorithm: A Case Study | Chapter 1 | Research and Developments in Engineering Research Vol. 1

 An addition methods established historical invention (GA) is presented, place the main elements of a wind farm and key mechanics requirements are secondhand as recommendation limits and the energetic order design of the wind farm is optimised in terms of two together result cost and structure movement. A approximate study of an existent wind farm design is acted including the application of ancestral algorithms (GAs) for answer growth. A legitimate wind farm in Brazil is secondhand in the reasoning, accompanying the within energetic network configuration delineated apiece common plan of energetic construction for judge networks accompanying underground cables directly engrossed in the ground. In the expression of the GA methods, the necessary financing in construction and current strength misfortunes are delineated as objective functions in the optimisation process, and the business-related calculation of the wire is secondhand. Simulations are acted for network optimisation, and the results are executed accompanying the project answer approaching farm. The influence of short circuits on network judge is more analysed. The results show the economics of an within combine the exercise of the the earth's features of the projected optimisation plan of R$168,905.32. By alone judging the decline in the losses and finance, it maybe noticed that the saving realized is on account of functional costs, accompanying a decline of R $ 233,504.78. In the studied case, the avoid judge was not considerably troubled apiece width of the within network cord.

Author(s) Details:

Paulo Roberto Duailibe Monteiro,
Electrical Engineering Department, Fluminense Federal University – UFF, Brazil.

Thiago Trezza Borges,
Electrical Engineering Department, Fluminense Federal University – UFF, Brazil.

Andre Fernando Schiochet,
Brazilian Petroleum Company- Petrobras, Brazil.

Please see the link here: https://stm.bookpi.org/RADER-V1/article/view/10086

Influence of Crossover Probability on Performance of Genetic Algorithm in Scheduling of Parallel Machines| Chapter 3 | Techniques and Innovation in Engineering Research and Technology Vol. 5

 In this division, an investigation of the influence of Crossover Probability on Genetic Algorithm (GA) conduct for the bi-criteria objective function to acquire the best resolution in a rational time in organizing of parallel machines is studied. A curious model for reducing the assigned work imbalance on the machines seeing work-in-process material is developed. The imitation on a proposed historical treasure was carried out accompanying a crossover probability of 0.4 to 0.95 (accompanying a step of 0.05) and 0.97, and it was discovered that the results were gathering for the crossover probability of 0.6 accompanying a computing opportunity of 3.41 seconds. The submitted algorithm assists the conclusion maker in analysing the objective function accompanying the computational time.

Author(s) Details:

B. V. Raghavendra,
Department of Mechanical Engineering, JSS Academy of Technical Education, Bengaluru – 560060, India.

Dayananda K. Pai,
Department of Aeronautical and Automobile Engineering, Manipal Institute of Technology, Manipal – 576104, India.

Please see the link here: https://stm.bookpi.org/TAIERT-V5/article/view/9054

Computation of Optimal Cross Section of Gravity Dams Using Genetic Algorithms | Chapter 1 | Current Overview on Science and Technology Research Vol. 6

 The construction of gravity dams uses an interactive method involving preliminary layout of the structure followed by stability and stress studies. In this study, we present a method to define the optimal cross-sectional area of ​​weight dams determined using genetic algorithms. To solve the optimization task (minimization of dumb costs), an optimization routine based on genetic algorithms (GA) was implemented in a Microsoft Excel spreadsheet. It proved adequate and the GA parameters were optimized in a parametric study. Using the parameters found in the parametric study, we determined the top width of the heavy wall and compared it with the slope-based optimization method (traditional method). The agreement between the two approaches was excellent. For the optimal dam cross-section, the ratio of dam base to dam height is ~0.85 and the ratio of dam crest width to dam height is ~0.13. This computational approach allows the calculation of the optimal top width for a wide range of gravity dam heights.

Author(s) Details:

Farzin Salmasi,
Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.

John Abraham,
University of St. Thomas, Minnesota, School of Engineering 2115 Summit Avenue St. Paul, Minnesota 55105, USA.

Please see the link here: https://stm.bookpi.org/COSTR-V6/article/view/8411

Genetic Algorithm and Support Vector Machine for DNS Tunneling Detection: A Hybrid Method Approach | Chapter 10 | Novel Research Aspects in Mathematical and Computer Science Vol. 5

In order to find the best characteristics that may maximise the detection of DNS tunnelling, this research suggests a hybrid technique of genetic algorithm feature selection approach with the support vector machine classifier. Corporations are increasingly spending a lot of money developing web applications as online commerce expands. On the other hand, such dangers can leave companies open to future attacks. One of these dangers is DNS tunnelling, which sends dangerous information through the domain name protocol. As a result, confidential data would be exposed and violated. Machine learning has been the subject of several studies to develop a detecting technique. The strategies used by authors comprised a wide range of characteristics, such as domain length, Bytes, content, DNS traffic volume, hostnames per domain, location, and domain history are all factors. Evidently, there is a critical need for feature selection tasks to be supported in order to discover the finest features. A benchmark dataset for DNS tunnelling was used to assess the proposed method. The new method surpassed the classic SVM by receiving an F-measure of 0.946, proving that it was superior.

Author (s) Details:

Fuqdan A. Al-Ibraheemi,
College of Dentistry, University of Al-Ameed, Iraq.

Sattar Al-Ibraheemi,
Education Ministry, Iraq.

Haleh Amintoosi,
Faculty of Engineering, Ferdowsi University of Mashhad, Iran.

Please see the link here: https://stm.bookpi.org/NRAMCS-V5/article/view/7487

Different Challenges in Energy- Efficient Cloud Security: A Brief Review | Chapter 09 | Research Developments in Science and Technology Vol. 6

 Cloud computing is the most popular research topic nowadays because of its capacity to lower processing costs while increasing scalability and flexibility for computer services. Cloud computing is internet-based computing that shares resources and delivers data to apps to provide users with on-demand services. Cloud computing is one of the fastest-growing IT fields in the business sector. The security of cloud computing has become a key roadblock to its expansion. Cloud computing security is a set of rules, processes, controls, roles, permissions, and technology that all work together to keep cloud-based systems and data safe. Cloud computing security has become a hot subject in academia and industry because cloud users must keep their data safe in the cloud. Users may lose trust in the cloud due to a lack of security. A review of cloud computing security is the subject of this research. All tiers of SaaS (Software as a Service), PaaS (Platform as a Service), and IaaS (Infrastructure as a Service), as well as deployment strategies, are included in the study (Private, Public, Hybrid, and Community Cloud). The major goal of this research is to investigate and comprehend existing genetic algorithms and two-factor authentication in the context of cloud computing security, as well as to identify gaps through a literature review.

Author(S) Details

Poonam Kumari
Department of Computer Science and Engineering, Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India.

Meeta Singh
Department of Computer Science and Engineering, Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India.


View Book:- https://stm.bookpi.org/RDST-V6/article/view/7015

Study on Application of Optimized GA-BPNN Algorithm in English Teaching Quality Evaluation System| Chapter 2 | Current Research in Language, Literature and Education Vol. 5

The evaluation of teaching quality is a complicated and fuzzy nonlinear process involving numerous aspects and variables, making it difficult to construct a mathematical model, and the old approach of assessing teaching quality is no longer entirely competent. An enhanced GA-BPNN technique based on genetic algorithm (GA) and backpropagation neural network (BPNN) is suggested to evaluate teaching quality effectively and precisely. To begin, an index system for evaluating teaching quality is constructed, and a questionnaire is designed to gather data using the index system. The model parameters are then optimised to create an English teaching quality evaluation system. The GA-BPNN method has an average assessment accuracy of 98.56 percent, which is 13.23 percent greater than the BPNN model and 5.85 percent higher than the optimised BPNN model, according to the simulation. The comparative findings suggest that the GA-BPNN algorithm may produce fair and scientific results when evaluating teaching quality. The GA-BPNN strategy described in this paper searches locally near the global optimal solution, successfully overcoming the slow convergence speed of the conventional approach as well as the problem of being easily local limited to the minimum.

Author(s) Details:

Yaowu Zhu,
Editorial Office of the Journal, Anhui Vocational College of City Management, Hefei 230011, China and  Institute of Psychology CAS, Beijing 100101, China.

Please see the link here: https://stm.bookpi.org/CRLLE-V5/article/view/6717

Modeling and Parameters Identification of PEM Fuel Cell for Application to a Hybrid Vehicle System | Chapter 5 | Research Trends and Challenges in Physical Science Vol.8

 In this chapter, we are interested in the research of fuel cell instrumentation, particularly that of the proton exchange membrane type PEM, by measuring complex impedance in order to predict faults and avoid serious stack problems such as drowning or reversal phenomena. As a result, this paper examines the vehicle's model description, with a focus on the Powertrain's design and modelling. The fuel cell vehicle's power system includes a fuel cell, a DC/DC converter, a three-phase inverter, and a PM synchronous motor. Our goal is to reduce the amount of hydrogen used by a Proton Exchange Membrane (PEM) Fuel Cell. To achieve this purpose, the system is regulated by an Energy Management Strategy (EMS), which minimises fuel cell power demand transitions and so enhances the system's longevity. For the NEDC, the suggested energy management strategy is assessed (New European Driving Cycle). The obtained findings show that the proposed energy management technique is effective in reducing hydrogen use.

Author(s) Details:

Mohamed Sélmene Ben Yahia,
Faculty of Sciences of Tunis, University of Tunis El Manar, Laboratory of Research in Energy Efficiency Application and Renewable Energy LAPER, Tunis, Tunisia.


Wahib Andari,
Faculty of Sciences of Tunis, University of Tunis El Manar, Laboratory of Research in Energy Efficiency Application and Renewable Energy LAPER, Tunis, Tunisia.


Hatem Allagui,
Faculty of Sciences of Tunis, University of Tunis El Manar, Laboratory of Research in Energy Efficiency Application and Renewable Energy LAPER, Tunis, Tunisia.


Abdelkader Mami,
Faculty of Sciences of Tunis, University of Tunis El Manar, Laboratory of Research in Energy Efficiency Application and Renewable Energy LAPER, Tunis, Tunisia.

Please see the link here: https://stm.bookpi.org/RTCPS-V8/article/view/6011 

Assessment of a Genetic Algorithm Based Security Constrained Economic Dispatch Approach for LMP Calculation | Chapter 2 | Novel Perspectives of Engineering Research Vol. 4

 An effective transmission pricing strategy is essential to handle transmission difficulties and create correct economic signals in restructured power markets. Energy costs might fluctuate across the network due to transmission line restrictions. Generator bids, load levels, and transmission network constraints all influence these prices. In the energy market, locational marginal pricing (LMP) is a prominent strategy for establishing nodal prices and managing transmission congestion. This chapter explains how to apply a Genetic Algorithm (GA)-based security constrained economic dispatch (SCED) approach to evaluate LMPs at all buses while reducing overall system fuel cost for a restricted transmission system, both with and without system losses. The EEE 14 bus system, the 75-bus Indian power system, and the New England 39-bus system all utilise the proposed GA-based SCED concept. The acquired results are compared to traditional Linear Programming-based DCOPF using Power World Simulator. Fixed and linear bids are examined for generators. The load is assumed to be inelastic. In all of the scenarios tested, the suggested GA-based SCED for LMP calculation has been proved to be relatively simple, reliable, and efficient. Furthermore, adopting GA to optimise generator redispatch results in a reduction in overall generation fuel costs.

Author(S) Details

M. Murali
Department of E.E.E, K.S.R.M. College of Engineering (Autonomous), Kadapa, Andhra Pradesh, India.

View Book:- https://stm.bookpi.org/NPER-V4/article/view/5102

Optimal Collector Grid Design for Wind Farm Based on Genetic Algorithm | Chapter 7 | New Approaches in Engineering Research Vol. 15

 The use of a genetic algorithm to optimise the collector grid arrangement of wind farms is presented in this study (GA). The goal is to create a radial arrangement that connects a group of wind generators to the substation in the most efficient way possible, while minimising operational and construction expenses, lowering loss, and therefore contributing to long-term sustainability. The investment in construction and the present value of energy losses are used as the goal function in the collector grid optimization procedure. This paper also examines the many factors that can be used in the design of the collector grid for a wind farm. The results indicate the best collector grid layout and demonstrate that the collector grid design is responsive to the objective function requirements.

Author(S) Details

P. R. Duailibe Monteiro
Department of Electrical Engineering and Post-Graduate Program in Industrial Assembly, Fluminense Federal University, Rua Passo da Pátria, Niterói, CEP 24210-240, Brasil.

T. T. Borges
Department of Electrical Engineering, Fluminense Federal University, Rua Passo da Pátria, 156, Niterói, CEP 24210-240, Brasil.

A. F. Schiochet
Petróleo Brasileiro S.A., Rua Henrique Valadares, 28, Rio de Janeiro, CEP 20231-030, Brasil.

View Book:- https://stm.bookpi.org/NAER-V15/article/view/4027

Novel and Efficient Hybrid Model for Classification of Heart Disease | Chapter 3 | Current Approaches in Science and Technology Research Vol. 9

 Propose an effective cardiac disease categorization method that can predict disease early on and cut death rates. The study used a hybrid intelligence model of Genetic Algorithm (GA) and Support Vector Machine (SVM) for prediction, and the Cleveland dataset from the UCI machine learning library was used. SVM and GA were used to predict coronary artery disease by maximising the hyper parameters of SVM: ‘C' and ‘gamma.' Implementing meta-heuristics improved the performance of heart disease classification and resulted in a 91 percent accuracy when compared to SVM without GA. In terms of accuracy, a method of optimising SVM parameters using GA outperforms SVM and SVM with k-cross validation for predicting heart disorders. It points in the direction of making machine learning algorithms more efficient.

Author (S) Details

Mittal Desai
CMPICA, Charotar Univerity of Science and Technology (CHARUSAT), Gujarat, India.

Atul Patel
CMPICA, Charotar Univerity of Science and Technology (CHARUSAT), Gujarat, India.

View Book :- https://stm.bookpi.org/CASTR-V9/article/view/1975

Research on Automatic Crack Detection for Concrete Infrastructures Using Image Processing and Deep Learning | Chapter 6 | Current Approaches in Science and Technology Research Vol. 3

 Automatic crack detection is a critical task in the generation of a crack map for existing concrete infrastructure inspection. This paper describes an automatic crack detection and classification method based on a genetic algorithm (GA) for optimizing image processing technique parameters (IPTs). Under various complex photometric conditions, the crack detection results of concrete infrastructure surface images remain noise pixels. Following that, a deep convolution neural network (CNN) method is used to automatically classify crack candidates and non-crack candidates. Furthermore, the proposed method is compared to state-of-the-art crack detection methods. The experimental results validate the reasonable accuracy in practice. The final goal was to create a crack map, which necessitated automatic pixel-level accuracy.

Author(s) Details

Cuong Nguyen Kim
Faculty of Highway & Bridge, Mien Trung of Civil Engineering, Vietnam.

Kei Kawamura
Graduate School of Science & Technology for Innovation, Yamaguchi University, Japan.

Hideaki Nakamura
Graduate School of Science & Technology for Innovation, Yamaguchi University, Japan.

Amir Tarighat
Department of Civil Engineering, Shahid Rajaee Teacher Training University, Iran.

View Book :- https://stm.bookpi.org/CASTR-V3/article/view/1411

An Approach of Optimization Techniques for History Matching and Production Forecasting | Chapter 11 | New Ideas Concerning Science and Technology Vol. 12

 Petroleum as a natural resource is depleting year after year, necessitating effective management of the resource and its reservoir. Reservoir modelling and production forecasting are critical inputs in this scenario for effective management. The spatial distribution of reservoir properties describing the reservoir and associated output profiles is difficult to estimate since naturally occurring reservoirs are extremely heterogeneous and nonlinear in nature. In terms of reservoir properties and history matching, an accurate model constructed with the aid of data obtained from the reservoir, can lead to more effective reservoir management, and such models can be created using mathematical modelling and optimization techniques. This chapter covers a variety of optimization strategies that can be used for output forecasting and background matching. Simulated Annealing (SA), Scatter Search (SS), Neighborhood algorithm (NA), Particle Swarm Optimization (PSO), and Ant Colony Optimization are examples of gradient-based and non-gradient-based optimization techniques (ACO), The application of Ensemble Kalman Filters (EnKF) and Genetic Algorithms (GA) to reservoir output history matching and efficiency. The chapter also goes into recent developments and adaptations of these techniques.

Author (s) Details

Giridhar Vadicharla
Department of Chemical Engineering, University of Petroleum and Energy Studies, Dehradun, India

Pushpa Sharma
Department of Petroleum Engineering and Earth Sciences, University of Petroleum and Energy Studies, Dehradun, India

View Book :- https://stm.bookpi.org/NICST-V12/article/view/782

Estimation of Electric Power Demand Using Socioeconomic Variables: A Case of Indian Electricity Sector | Chapter 14 | New Ideas Concerning Science and Technology Vol. 1

 The production and industrial growth of any country needs accurate forecasting of energy demand. In this research, per capita electricity demand is forecast using the Genetic Algorithm. As input data, socio-economic variables such as population, Gross Domestic Product, exports, and imports were used. Linear, quadratic and exponential models are used to forecast capacity. Performance parameters, including the Root Mean Square Error, R2 and modified R2, are determined for the models. For three separate potential scenarios, electricity demand is projected for the period 2017-2035. A strong agreement with the actual data with a high correlation coefficient (R2 = 99.45 percent) and low Root Mean Square Error ( RMSE = 0.1455) is shown by the projected demand for electricity. This method can, therefore, be used in India as an alternative energy forecasting technique. 

Author(s) Details

Dr. Sanjeet Singh
Decision Sciences Area, Indian Institute of Management Lucknow, Prabandh Nagar, IIM Road, Lucknow-226013, India.

K. R. Ramakrishnan

ZS Associates India Private Limited, Pune, India.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/308

Exploring Optimum Solutions for Management of House Hold Data with Population Census in Indian Context | Chapter 14 | Emerging Trends in Engineering Research and Technology Vol. 4

The study discusses the population census in identified states and its influence in neighborhood states. A fitting function will be generated from the identified data which will be processed using genetic algorithm to find the most probable state which influences population among the identified set. In continuation to that we can consider the House hold data in different states as rows and different types of House Holds like Good, Livable and Dilapidated as columns as input to the GenAlgo function. The problem is initialized with a fitness function and mutation function relevant to the Household problem. The work starts with data frame that is passed back to fitfun and mutfun to enable them to take advantage of any additional data viable for them to perform their proposed functions. The idea here is to have put together a data frame containing the Good number of households and Livable households of the population to identify the best performers of states in development activities. This work tries to map population growth with development activities like House hold data in finding a balance in growth among different states in India. 

Author(s) Details

Addepalli V. N. Krishna
School of Engineering and Technology, CHRIST (Deemed to be University), Bengaluru-560074, India.

View Book :-  http://bp.bookpi.org/index.php/bpi/catalog/book/180