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Computational Intelligence Research Lab dumped with WikiTeam tools.

Computational Intelligence Research Lab dumped with WikiTeam tools.

Computational Intelligence Research Lab dumped with WikiTeam tools.

The construction of high-rise buildings all overthe world and India is overgrowing. Steel has more advantages in the modern world. Itprovidesan innovativeframe system, easy assembly, a high weight-toweightratio,differentstrengths, and a more extensive section range. The high-rise structures are environmentally friendly, which iswhy steel is used in high-risebuildingsworldwide. So far, designers have only considered gravity when planning buildings. Earthquakes, wind, and lateral forces havebeen addedto the design. The challenge is to find the economic structure system of high-rise buildings in the Indian scenario. Thisdocument covers various structural steel systems: moment stabilization frame system, composite frame system, roof rack system with stabilizingbelt,shear wall frame system, frame tube system. When designing multi-storey buildings, truss systems, clusterpiping systems for high-rise buildings, lateral loads (wind or seismic loads) are mainly responsible for demolition, which usually determines structural systems for high-rise buildings. Tomake the drift asa minimum, the beams, and columns to been larged. In a building with a small number of floors, the lateral load rarely affects the increase in the building and the increase in size. Considering the live and dead loads, the component structure is an option for possible rearrangement of the structure. In other side load resistance systems under study, the side displacement in the torque frame is the highest. The lateral displacement of the double frame is the smallest, and the lateral displacement of the sliding wall system is slightly higher than that of the double system. 

The construction of high-rise buildings all overthe world and India is overgrowing. Steel has more advantages in the modern world. Itprovidesan innovativeframe system, easy assembly, a high weight-toweightratio,differentstrengths, and a more extensive section range. The high-rise structures are environmentally friendly, which iswhy steel is used in high-risebuildingsworldwide. So far, designers have only considered gravity when planning buildings. Earthquakes, wind, and lateral forces havebeen addedto the design. The challenge is to find the economic structure system of high-rise buildings in the Indian scenario. Thisdocument covers various structural steel systems: moment stabilization frame system, composite frame system, roof rack system with stabilizingbelt,shear wall frame system, frame tube system. When designing multi-storey buildings, truss systems, clusterpiping systems for high-rise buildings, lateral loads (wind or seismic loads) are mainly responsible for demolition, which usually determines structural systems for high-rise buildings. Tomake the drift asa minimum, the beams, and columns to been larged. In a building with a small number of floors, the lateral load rarely affects the increase in the building and the increase in size. Considering the live and dead loads, the component structure is an option for possible rearrangement of the structure. In other side load resistance systems under study, the side displacement in the torque frame is the highest. The lateral displacement of the double frame is the smallest, and the lateral displacement of the sliding wall system is slightly higher than that of the double system. 

The construction of high-rise buildings all overthe world and India is overgrowing. Steel has more advantages in the modern world. Itprovidesan innovativeframe system, easy assembly, a high weight-toweightratio,differentstrengths, and a more extensive section range. The high-rise structures are environmentally friendly, which iswhy steel is used in high-risebuildingsworldwide. So far, designers have only considered gravity when planning buildings. Earthquakes, wind, and lateral forces havebeen addedto the design. The challenge is to find the economic structure system of high-rise buildings in the Indian scenario. Thisdocument covers various structural steel systems: moment stabilization frame system, composite frame system, roof rack system with stabilizingbelt,shear wall frame system, frame tube system. When designing multi-storey buildings, truss systems, clusterpiping systems for high-rise buildings, lateral loads (wind or seismic loads) are mainly responsible for demolition, which usually determines structural systems for high-rise buildings. Tomake the drift asa minimum, the beams, and columns to been larged. In a building with a small number of floors, the lateral load rarely affects the increase in the building and the increase in size. Considering the live and dead loads, the component structure is an option for possible rearrangement of the structure. In other side load resistance systems under study, the side displacement in the torque frame is the highest. The lateral displacement of the double frame is the smallest, and the lateral displacement of the sliding wall system is slightly higher than that of the double system. 

The construction of high-rise buildings all overthe world and India is overgrowing. Steel has more advantages in the modern world. Itprovidesan innovativeframe system, easy assembly, a high weight-toweightratio,differentstrengths, and a more extensive section range. The high-rise structures are environmentally friendly, which iswhy steel is used in high-risebuildingsworldwide. So far, designers have only considered gravity when planning buildings. Earthquakes, wind, and lateral forces havebeen addedto the design. The challenge is to find the economic structure system of high-rise buildings in the Indian scenario. Thisdocument covers various structural steel systems: moment stabilization frame system, composite frame system, roof rack system with stabilizingbelt,shear wall frame system, frame tube system. When designing multi-storey buildings, truss systems, clusterpiping systems for high-rise buildings, lateral loads (wind or seismic loads) are mainly responsible for demolition, which usually determines structural systems for high-rise buildings. Tomake the drift asa minimum, the beams, and columns to been larged. In a building with a small number of floors, the lateral load rarely affects the increase in the building and the increase in size. Considering the live and dead loads, the component structure is an option for possible rearrangement of the structure. In other side load resistance systems under study, the side displacement in the torque frame is the highest. The lateral displacement of the double frame is the smallest, and the lateral displacement of the sliding wall system is slightly higher than that of the double system. 

A Mars precision landing requires a landed footprint of no more than 100 meters. Obstacles to reducing the landed footprint include trajectory dispersions due to initial atmospheric entry conditions such as entry angle, parachute deployment height, environment parameters such as wind, atmospheric density, parachute deployment dynamics, unavoidable injection error or propagated error from launch, etc. Computational Intelligence (CI) techniques such as Artificial Neural Nets and Particle Swarm Optimization have been shown to have great success with other control problems. The research period extended previous work on investigating applicability of the computational intelligent approaches. The focus of this investigation was on Particle Swarm Optimization and basic Neural Net architectures. The research investigating these issues was performed for the grant cycle from 5/15/01 to 5/15/02. Matlab 5.1 and 6.0 along with NASA's POST were the primary computational tools.

A Mars precision landing requires a landed footprint of no more than 100 meters. Obstacles to reducing the landed footprint include trajectory dispersions due to initial atmospheric entry conditions such as entry angle, parachute deployment height, environment parameters such as wind, atmospheric density, parachute deployment dynamics, unavoidable injection error or propagated error from launch, etc. Computational Intelligence (CI) techniques such as Artificial Neural Nets and Particle Swarm Optimization have been shown to have great success with other control problems. The research period extended previous work on investigating applicability of the computational intelligent approaches. The focus of this investigation was on Particle Swarm Optimization and basic Neural Net architectures. The research investigating these issues was performed for the grant cycle from 5/15/01 to 5/15/02. Matlab 5.1 and 6.0 along with NASA's POST were the primary computational tools.

A Mars precision landing requires a landed footprint of no more than 100 meters. Obstacles to reducing the landed footprint include trajectory dispersions due to initial atmospheric entry conditions such as entry angle, parachute deployment height, environment parameters such as wind, atmospheric density, parachute deployment dynamics, unavoidable injection error or propagated error from launch, etc. Computational Intelligence (CI) techniques such as Artificial Neural Nets and Particle Swarm Optimization have been shown to have great success with other control problems. The research period extended previous work on investigating applicability of the computational intelligent approaches. The focus of this investigation was on Particle Swarm Optimization and basic Neural Net architectures. The research investigating these issues was performed for the grant cycle from 5/15/01 to 5/15/02. Matlab 5.1 and 6.0 along with NASA's POST were the primary computational tools.

A Mars precision landing requires a landed footprint of no more than 100 meters. Obstacles to reducing the landed footprint include trajectory dispersions due to initial atmospheric entry conditions such as entry angle, parachute deployment height, environment parameters such as wind, atmospheric density, parachute deployment dynamics, unavoidable injection error or propagated error from launch, etc. Computational Intelligence (CI) techniques such as Artificial Neural Nets and Particle Swarm Optimization have been shown to have great success with other control problems. The research period extended previous work on investigating applicability of the computational intelligent approaches. The focus of this investigation was on Particle Swarm Optimization and basic Neural Net architectures. The research investigating these issues was performed for the grant cycle from 5/15/01 to 5/15/02. Matlab 5.1 and 6.0 along with NASA's POST were the primary computational tools.

This article is from Sensors (Basel, Switzerland) , volume 14 . Abstract Tactile sensing helps robots interact with humans and objects effectively in real environments. Piezoelectric polymer sensors provide the functional building blocks of the robotic electronic skin, mainly thanks to their flexibility and suitability for detecting dynamic contact events and for recognizing the touch modality. The paper focuses on the ability of tactile sensing systems to support the challenging recognition of certain qualities/modalities of touch. The research applies novel computational intelligence techniques and a tensor-based approach for the classification of touch modalities; its main results consist in providing a procedure to enhance system generalization ability and architecture for multi-class recognition applications. An experimental campaign involving 70 participants using three different modalities in touching the upper surface of the sensor array was conducted, and confirmed the validity of the approach.

The increase of power demand is a crucial issue in the power system community in many parts of the world. Malaysia has also witnessed the familiar scenario due to the current development throughout the country has invited the urgency of increase in the power supply. Since Malaysia practices vertical system; where the electricity is supplied by only one utility, load management is an important issue so that the delivery of electricity is implemented without discrimination. Parallel Computational Intelligence will be developed which can alleviate and avoid all the unsolved issues, highlighting the weakness of current schemes. Parallel Computational Intelligence is developed to manage the optimal load in making sure the system maintains the stability condition, within the voltage limits. This paper presents evolutionary programming (EP) technique for optimizing the voltage profile. In this study, 3 algorithms which are Gaussian, Cauchy and Parallel EP were developed to solve optimal load management problem on IEEE 26-bus Reliability Test System (RTS). Results obtained from the study revealed that the application of Parallel EP has significantly reduced the time for the optimization process to complete. 

The increase of power demand is a crucial issue in the power system community in many parts of the world. Malaysia has also witnessed the familiar scenario due to the current development throughout the country has invited the urgency of increase in the power supply. Since Malaysia practices vertical system; where the electricity is supplied by only one utility, load management is an important issue so that the delivery of electricity is implemented without discrimination. Parallel Computational Intelligence will be developed which can alleviate and avoid all the unsolved issues, highlighting the weakness of current schemes. Parallel Computational Intelligence is developed to manage the optimal load in making sure the system maintains the stability condition, within the voltage limits. This paper presents evolutionary programming (EP) technique for optimizing the voltage profile. In this study, 3 algorithms which are Gaussian, Cauchy and Parallel EP were developed to solve optimal load management problem on IEEE 26-bus Reliability Test System (RTS). Results obtained from the study revealed that the application of Parallel EP has significantly reduced the time for the optimization process to complete. 

The increase of power demand is a crucial issue in the power system community in many parts of the world. Malaysia has also witnessed the familiar scenario due to the current development throughout the country has invited the urgency of increase in the power supply. Since Malaysia practices vertical system; where the electricity is supplied by only one utility, load management is an important issue so that the delivery of electricity is implemented without discrimination. Parallel Computational Intelligence will be developed which can alleviate and avoid all the unsolved issues, highlighting the weakness of current schemes. Parallel Computational Intelligence is developed to manage the optimal load in making sure the system maintains the stability condition, within the voltage limits. This paper presents evolutionary programming (EP) technique for optimizing the voltage profile. In this study, 3 algorithms which are Gaussian, Cauchy and Parallel EP were developed to solve optimal load management problem on IEEE 26-bus Reliability Test System (RTS). Results obtained from the study revealed that the application of Parallel EP has significantly reduced the time for the optimization process to complete. 

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This book "Computational Intelligence" is to understand the various characteristics of Intelligent agents and their search strategies. It contributes an impression towards representing knowledge in solving AI problems, reasoning, natural language understand, computer vision, automatic programming and machine learning. It provides a preliminary study to design and implement the different ways of software agents for problem solving.

The Traffic and Transportation system is big problem in the world. So business intelligence in vehicle and transportation system solve this problem and solution with the help of new technologies. In the computational intelligence in vehicle and transportation system used computer electrical and electronic conversion technology management. Akshay Shrikant Nehre "Business Intelligence (Computational Intelligence in Vehicle and Transportation System)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30226.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/30226/business-intelligence-computational-intelligence-in-vehicle-and-transportation-system/akshay-shrikant-nehre

Computational Intelligence 1996: Volume 12 , Issue Index. Digitized from IA1532821-01 . Previous issue: sim_computational-intelligence_1995-11_11_4 . Next issue: sim_computational-intelligence_1996-02_12_1 .

Recently, fuzzy adaptive resonance theory mapping (ARTMAP) neural networks are applied to solving complex problems due to their plasticity-stability capability and resonance property. An imbalanced dataset occurs when there is the presence of one class containing a greater number of instances than other classes. It is skewed representation of data. Many standard algorithms have failed in mitigating imbalanced dataset problems. There are four paradigms used-data level, algorithm level, cost-sensitive, and ensemble method in solving imbalanced dataset problems. Here we put forward a method to solve the imbalanced dataset problem by a brain-neuron framework and an ensemble of a special type of artificial neural network (ANN) called fuzzy ARTMAP thereafter we applied a clustering algorithm known as fuzzy C-means clustering to handle missing value and also propose to make fuzzy ARTMAP cost-sensitive. Results indicate 100% accuracy in classification.

Recently, fuzzy adaptive resonance theory mapping (ARTMAP) neural networks are applied to solving complex problems due to their plasticity-stability capability and resonance property. An imbalanced dataset occurs when there is the presence of one class containing a greater number of instances than other classes. It is skewed representation of data. Many standard algorithms have failed in mitigating imbalanced dataset problems. There are four paradigms used-data level, algorithm level, cost-sensitive, and ensemble method in solving imbalanced dataset problems. Here we put forward a method to solve the imbalanced dataset problem by a brain-neuron framework and an ensemble of a special type of artificial neural network (ANN) called fuzzy ARTMAP thereafter we applied a clustering algorithm known as fuzzy C-means clustering to handle missing value and also propose to make fuzzy ARTMAP cost-sensitive. Results indicate 100% accuracy in classification.

Recently, fuzzy adaptive resonance theory mapping (ARTMAP) neural networks are applied to solving complex problems due to their plasticity-stability capability and resonance property. An imbalanced dataset occurs when there is the presence of one class containing a greater number of instances than other classes. It is skewed representation of data. Many standard algorithms have failed in mitigating imbalanced dataset problems. There are four paradigms used-data level, algorithm level, cost-sensitive, and ensemble method in solving imbalanced dataset problems. Here we put forward a method to solve the imbalanced dataset problem by a brain-neuron framework and an ensemble of a special type of artificial neural network (ANN) called fuzzy ARTMAP thereafter we applied a clustering algorithm known as fuzzy C-means clustering to handle missing value and also propose to make fuzzy ARTMAP cost-sensitive. Results indicate 100% accuracy in classification.

Recently, fuzzy adaptive resonance theory mapping (ARTMAP) neural networks are applied to solving complex problems due to their plasticity-stability capability and resonance property. An imbalanced dataset occurs when there is the presence of one class containing a greater number of instances than other classes. It is skewed representation of data. Many standard algorithms have failed in mitigating imbalanced dataset problems. There are four paradigms used-data level, algorithm level, cost-sensitive, and ensemble method in solving imbalanced dataset problems. Here we put forward a method to solve the imbalanced dataset problem by a brain-neuron framework and an ensemble of a special type of artificial neural network (ANN) called fuzzy ARTMAP thereafter we applied a clustering algorithm known as fuzzy C-means clustering to handle missing value and also propose to make fuzzy ARTMAP cost-sensitive. Results indicate 100% accuracy in classification.

Recently, fuzzy adaptive resonance theory mapping (ARTMAP) neural networks are applied to solving complex problems due to their plasticity-stability capability and resonance property. An imbalanced dataset occurs when there is the presence of one class containing a greater number of instances than other classes. It is skewed representation of data. Many standard algorithms have failed in mitigating imbalanced dataset problems. There are four paradigms used-data level, algorithm level, cost-sensitive, and ensemble method in solving imbalanced dataset problems. Here we put forward a method to solve the imbalanced dataset problem by a brain-neuron framework and an ensemble of a special type of artificial neural network (ANN) called fuzzy ARTMAP thereafter we applied a clustering algorithm known as fuzzy C-means clustering to handle missing value and also propose to make fuzzy ARTMAP cost-sensitive. Results indicate 100% accuracy in classification.

Recently, fuzzy adaptive resonance theory mapping (ARTMAP) neural networks are applied to solving complex problems due to their plasticity-stability capability and resonance property. An imbalanced dataset occurs when there is the presence of one class containing a greater number of instances than other classes. It is skewed representation of data. Many standard algorithms have failed in mitigating imbalanced dataset problems. There are four paradigms used-data level, algorithm level, cost-sensitive, and ensemble method in solving imbalanced dataset problems. Here we put forward a method to solve the imbalanced dataset problem by a brain-neuron framework and an ensemble of a special type of artificial neural network (ANN) called fuzzy ARTMAP thereafter we applied a clustering algorithm known as fuzzy C-means clustering to handle missing value and also propose to make fuzzy ARTMAP cost-sensitive. Results indicate 100% accuracy in classification.

Recently, fuzzy adaptive resonance theory mapping (ARTMAP) neural networks are applied to solving complex problems due to their plasticity-stability capability and resonance property. An imbalanced dataset occurs when there is the presence of one class containing a greater number of instances than other classes. It is skewed representation of data. Many standard algorithms have failed in mitigating imbalanced dataset problems. There are four paradigms used-data level, algorithm level, cost-sensitive, and ensemble method in solving imbalanced dataset problems. Here we put forward a method to solve the imbalanced dataset problem by a brain-neuron framework and an ensemble of a special type of artificial neural network (ANN) called fuzzy ARTMAP thereafter we applied a clustering algorithm known as fuzzy C-means clustering to handle missing value and also propose to make fuzzy ARTMAP cost-sensitive. Results indicate 100% accuracy in classification.

Recently, fuzzy adaptive resonance theory mapping (ARTMAP) neural networks are applied to solving complex problems due to their plasticity-stability capability and resonance property. An imbalanced dataset occurs when there is the presence of one class containing a greater number of instances than other classes. It is skewed representation of data. Many standard algorithms have failed in mitigating imbalanced dataset problems. There are four paradigms used-data level, algorithm level, cost-sensitive, and ensemble method in solving imbalanced dataset problems. Here we put forward a method to solve the imbalanced dataset problem by a brain-neuron framework and an ensemble of a special type of artificial neural network (ANN) called fuzzy ARTMAP thereafter we applied a clustering algorithm known as fuzzy C-means clustering to handle missing value and also propose to make fuzzy ARTMAP cost-sensitive. Results indicate 100% accuracy in classification.

Recently, fuzzy adaptive resonance theory mapping (ARTMAP) neural networks are applied to solving complex problems due to their plasticity-stability capability and resonance property. An imbalanced dataset occurs when there is the presence of one class containing a greater number of instances than other classes. It is skewed representation of data. Many standard algorithms have failed in mitigating imbalanced dataset problems. There are four paradigms used-data level, algorithm level, cost-sensitive, and ensemble method in solving imbalanced dataset problems. Here we put forward a method to solve the imbalanced dataset problem by a brain-neuron framework and an ensemble of a special type of artificial neural network (ANN) called fuzzy ARTMAP thereafter we applied a clustering algorithm known as fuzzy C-means clustering to handle missing value and also propose to make fuzzy ARTMAP cost-sensitive. Results indicate 100% accuracy in classification.

Recently, fuzzy adaptive resonance theory mapping (ARTMAP) neural networks are applied to solving complex problems due to their plasticity-stability capability and resonance property. An imbalanced dataset occurs when there is the presence of one class containing a greater number of instances than other classes. It is skewed representation of data. Many standard algorithms have failed in mitigating imbalanced dataset problems. There are four paradigms used-data level, algorithm level, cost-sensitive, and ensemble method in solving imbalanced dataset problems. Here we put forward a method to solve the imbalanced dataset problem by a brain-neuron framework and an ensemble of a special type of artificial neural network (ANN) called fuzzy ARTMAP thereafter we applied a clustering algorithm known as fuzzy C-means clustering to handle missing value and also propose to make fuzzy ARTMAP cost-sensitive. Results indicate 100% accuracy in classification.

Recently, fuzzy adaptive resonance theory mapping (ARTMAP) neural networks are applied to solving complex problems due to their plasticity-stability capability and resonance property. An imbalanced dataset occurs when there is the presence of one class containing a greater number of instances than other classes. It is skewed representation of data. Many standard algorithms have failed in mitigating imbalanced dataset problems. There are four paradigms used-data level, algorithm level, cost-sensitive, and ensemble method in solving imbalanced dataset problems. Here we put forward a method to solve the imbalanced dataset problem by a brain-neuron framework and an ensemble of a special type of artificial neural network (ANN) called fuzzy ARTMAP thereafter we applied a clustering algorithm known as fuzzy C-means clustering to handle missing value and also propose to make fuzzy ARTMAP cost-sensitive. Results indicate 100% accuracy in classification.

Recently, fuzzy adaptive resonance theory mapping (ARTMAP) neural networks are applied to solving complex problems due to their plasticity-stability capability and resonance property. An imbalanced dataset occurs when there is the presence of one class containing a greater number of instances than other classes. It is skewed representation of data. Many standard algorithms have failed in mitigating imbalanced dataset problems. There are four paradigms used-data level, algorithm level, cost-sensitive, and ensemble method in solving imbalanced dataset problems. Here we put forward a method to solve the imbalanced dataset problem by a brain-neuron framework and an ensemble of a special type of artificial neural network (ANN) called fuzzy ARTMAP thereafter we applied a clustering algorithm known as fuzzy C-means clustering to handle missing value and also propose to make fuzzy ARTMAP cost-sensitive. Results indicate 100% accuracy in classification.

Recently, fuzzy adaptive resonance theory mapping (ARTMAP) neural networks are applied to solving complex problems due to their plasticity-stability capability and resonance property. An imbalanced dataset occurs when there is the presence of one class containing a greater number of instances than other classes. It is skewed representation of data. Many standard algorithms have failed in mitigating imbalanced dataset problems. There are four paradigms used-data level, algorithm level, cost-sensitive, and ensemble method in solving imbalanced dataset problems. Here we put forward a method to solve the imbalanced dataset problem by a brain-neuron framework and an ensemble of a special type of artificial neural network (ANN) called fuzzy ARTMAP thereafter we applied a clustering algorithm known as fuzzy C-means clustering to handle missing value and also propose to make fuzzy ARTMAP cost-sensitive. Results indicate 100% accuracy in classification.

This study investigates whether multi-dimensional vector-space representations of children's drawings encode rich latent information. Specifically, we are interested in whether there are 1) qualitatively or quantitatively different conclusions about the relationship between drawings and intelligence or (2) a relationship between different aspects of drawings and different components of intelligence at different ages.

Advanced Computational Intelligence Paradigms in Healthcare 5

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This technical report is the Final Deliverable for Grant No.: N00244-15-1-0051 from the Naval Postgraduate School (NPS) with funding via the NAVSUP Fleet Logistics Center San Diego, on the topic of Advancing Human-Machine Symbiosis Using Hybrid Methods for Collaborative Problem-Solving. This effort was a basic and applied research study program involving thorough reviews and assessments of applicable state of the art research and methods, and the development of a detailed functional design for an advanced-capability intelligence analysis computer-based support system. The technical areas studied involved principles of argumentation including especially computational support aspects for enabling argumentation-based analysis, story-based analysis, uncertainty aspects of analysis in an open-world environment, human-machine symbiosis, hard and soft information fusion, and computational methods for narrative development. Our effort cumulates in a top-level functional design of a notional prototype capability for providing computational support to a hybrid argumentation plus story-based analysis capability. This research was further vetted in the presentation and writing of a paper on the project at the 13th International Conference on Distributed Computing and Artificial Intelligence (DCAI'16) in Seville, Spain in June of 2016

The work reported in this document is concerned with the efficient use of computers in materials research and in applications of the results of that research. Emphasis is on the development of computational methodologies which can facilitate the innovative design of materials and of materials processing, for high performance materials and for composite materials structures. Basic advances have been made in three areas of adaptive computing: in establishing the practice of functional-link neural-net computing for learning models of material behavior, in developing a parallel processing evolutionary search paradigm for optimization and in exploring various of establishing and using associative memories. This report also describes how these ovations in conceptual and theoretical matters can be used to effect in dealing with materials processing and design tasks. Applications include in-situ - real-time interpretation of ellipsometry data, optimal formulation of material composition, control of nonlinear dynamic systems, memory-based design and the implementation of visual displays for multi-dimensional data.

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This paper aims at identifying emerging computational intelligence trends for the design and modeling of complex biometric-enabled infrastructure and systems. Biometric-enabled systems are evolving towards deep learning and deep inference using the principles of adaptive computing, – the front tides of the modern computational intelligence domain. Therefore, we focus on intelligent inference engines widely deployed in biometrics. Computational intelligence applications that cover a wide spectrum of biometric tasks using physiological and behavioral traits are chosen for illustration. We highlight the technology gaps that must be addressed in future generations of biometric systems. The reported approaches and results primarily address the researchers who work towards developing the next generation of intelligent biometric-enabled systems.

This paper aims at identifying emerging computational intelligence trends for the design and modeling of complex biometric-enabled infrastructure and systems. Biometric-enabled systems are evolving towards deep learning and deep inference using the principles of adaptive computing, – the front tides of the modern computational intelligence domain. Therefore, we focus on intelligent inference engines widely deployed in biometrics. Computational intelligence applications that cover a wide spectrum of biometric tasks using physiological and behavioral traits are chosen for illustration. We highlight the technology gaps that must be addressed in future generations of biometric systems. The reported approaches and results primarily address the researchers who work towards developing the next generation of intelligent biometric-enabled systems.

This paper aims at identifying emerging computational intelligence trends for the design and modeling of complex biometric-enabled infrastructure and systems. Biometric-enabled systems are evolving towards deep learning and deep inference using the principles of adaptive computing, – the front tides of the modern computational intelligence domain. Therefore, we focus on intelligent inference engines widely deployed in biometrics. Computational intelligence applications that cover a wide spectrum of biometric tasks using physiological and behavioral traits are chosen for illustration. We highlight the technology gaps that must be addressed in future generations of biometric systems. The reported approaches and results primarily address the researchers who work towards developing the next generation of intelligent biometric-enabled systems.

The amount of crimes committed online has undoubtedly increased as more people use the internet for e-commerce and other financial transactions. Machine learning algorithms have been created to detect payment fraud in online purchasing in order to address the issue. This study performs a thorough comparative examination of different metaheuristic optimizations as hyperparameter tuning methods; these are particle swarm optimization (PSO) and genetic algorithm (GA). They are used to optimize the receiver operating characteristic (ROC) area under the curve (AUC) of the three machine learning algorithms, namely X-gradient boost, random forest classifier, and light gradient boost machine. Since the study's data are unbalanced, the determined metrics were ROC AUC. PSO offers consistent conditions for finding the best solution, according to our experiment. Without the inclusion of population annihilation strategies, PSO can achieve the greatest results in various situations which are different from GA, a consistent condition for finding the best solution, according to our experiment. Without the inclusion of population annihilation strategies, PSO can achieve the greatest results in various situations. The findings indicate that random forest classifier provided the highest ROC AUC value both before and after the hyperparameter tuning process, with a score of 88.69% attained while utilizing PSO.

The amount of crimes committed online has undoubtedly increased as more people use the internet for e-commerce and other financial transactions. Machine learning algorithms have been created to detect payment fraud in online purchasing in order to address the issue. This study performs a thorough comparative examination of different metaheuristic optimizations as hyperparameter tuning methods; these are particle swarm optimization (PSO) and genetic algorithm (GA). They are used to optimize the receiver operating characteristic (ROC) area under the curve (AUC) of the three machine learning algorithms, namely X-gradient boost, random forest classifier, and light gradient boost machine. Since the study's data are unbalanced, the determined metrics were ROC AUC. PSO offers consistent conditions for finding the best solution, according to our experiment. Without the inclusion of population annihilation strategies, PSO can achieve the greatest results in various situations which are different from GA, a consistent condition for finding the best solution, according to our experiment. Without the inclusion of population annihilation strategies, PSO can achieve the greatest results in various situations. The findings indicate that random forest classifier provided the highest ROC AUC value both before and after the hyperparameter tuning process, with a score of 88.69% attained while utilizing PSO.

Tri 1 2015/2016

Tri 1 2015/2016

Computational Intelligence 1995: Volume 11 , Issue Index. Digitized from IA1532821-01 . Previous issue: sim_computational-intelligence_1985-1993_1-9_cumulative-index . Next issue: sim_computational-intelligence_1995-02_11_1 .