Search Results for author:
Found 76 papers, 9 papers with code
Q-FOX Learning: Breaking Tradition in Reinforcement Learning
The results indicate that Q-FOX has played an essential role in HP tuning for RL algorithms to effectively solve different control tasks.
Detection of Auditory Brainstem Response Peaks Using Image Processing Techniques in Infants with Normal Hearing Sensitivity
Findings: Image processing techniques were able to detect 1, 3, and 5 waves in the diagnosis field with accuracy (0. 82), (0. 98), and (0. 98), respectively, and its precision for waves 1, 3, and 5, were respectively (0. 32), (0. 97) and (0. 87).
Discovering the Power of Artificial Cardiac Conduction System (ACCS): Harmony in Bio-inspired Metaheuristic
This work proposes a novel bio-inspired metaheuristic called Artificial Cardiac Conduction System (ACCS) inspired by the human cardiac conduction system.
Offline Handwriting Signature Verification: A Transfer Learning and Feature Selection Approach
Handwritten signature verification poses a formidable challenge in biometrics and document authenticity.
Equitable and Fair Performance Evaluation of Whale Optimization Algorithm
It is essential that all algorithms are exhaustively, somewhat, and intelligently evaluated.
Balancing exploration and exploitation phases in whale optimization algorithm: an insightful and empirical analysis
Obtaining robust results in any algorithm is strongly dependent on how to balance between these two modes.
Multi-Transfer Learning Techniques for Detecting Auditory Brainstem Response
To address these issues, this study proposed deep-learning models using the transfer-learning (TL) approach to extract features from ABR testing and diagnose HL using support vector machines (SVM).
VAPI: Vectorization of Algorithm for Performance Improvement
Vectorization is a technique for converting an algorithm, which operates on a single value at a time to one that operates on a collection of values at a time to execute rapidly.
BCDDO: Binary Child Drawing Development Optimization
A Binary Child Drawing Development Optimization (BCDDO) is suggested for choosing the wrapper features in this study.
GOOSE Algorithm: A Powerful Optimization Tool for Real-World Engineering Challenges and Beyond
The GOOSE algorithm is benchmarked on 19 well-known benchmark test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), and fitness dependent optimizer (FDO).
Current Studies and Applications of Krill Herd and Gravitational Search Algorithms in Healthcare
As a consequence of this, ground-breaking research has been conducted in a variety of domains, including synthetic immune functions, neural networks, the intelligence of swarm, as well as computing of evolutionary.
CDDO-HS:Child Drawing Development Optimization Harmony Search Algorithm
Harmony search (HS) is a highly competitive algorithm relative to other prevalent metaheuristic algorithms, as its exploration phase performance on unimodal benchmark functions is outstanding.
Leo: Lagrange Elementary Optimization
Global optimization problems are frequently solved using the practical and efficient method of evolutionary sophistication.
Evolving Deep Neural Network by Customized Moth Flame Optimization Algorithm for Underwater Targets Recognition
This chapter proposes using the Moth Flame Optimization (MFO) algorithm for finetuning a Deep Neural Network to recognize different underwater sonar datasets.
Multi objective Fitness Dependent Optimizer Algorithm
This paper proposes the multi objective variant of the recently introduced fitness dependent optimizer (FDO).
Awareness requirement and performance management for adaptive systems: a survey
The primary objective of this research is to summarize current advances in awareness requirements for adaptive strategies based on an examination of state-of-the-art methods described in the literature.
Fitness Dependent Optimizer with Neural Networks for COVID-19 patients
However, it is found that FDO has the longest runtime compared to the other algorithms.
Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image
It also illustrates how produced medical images may be utilized to enrich medical data, improve clinical decisions, and boost the performance of the conventional neural network (CNN) for medical image diagnosis.
Kurdish Handwritten Character Recognition using Deep Learning Techniques
From the experimental results, it is clear that the proposed deep learning model is performing well and is comparable to the similar model of other languages' handwriting recognition systems.
Heart Attack Classification System using Neural Network Trained with Particle Swarm Optimization
A real and novel dataset that has been recorded on the disease is used.
Deep Learning for Size and Microscope Feature Extraction and Classification in Oral Cancer: Enhanced Convolution Neural Network
Conclusion: The proposed system focuses on the accurate classification of oral cancer cells of different anatomical locations from the CLE images.
A Novel Enhanced Convolution Neural Network with Extreme Learning Machine: Facial Emotional Recognition in Psychology Practices
The simulation results show significant improvements in accuracy and processing time, making the model suitable for the video analysis process.
A novel solution of deep learning for enhanced support vector machine for predicting the onset of type 2 diabetes
The proposed system consists of a deep learning technique that uses the Support Vector Machine (SVM) algorithm along with the Radial Base Function (RBF) along with the Long Short-term Memory Layer (LSTM) for prediction of onset of Type 2 Diabetes.
Deep Learning Neural Network for Lung Cancer Classification: Enhanced Optimization Function
The aim of this work is to increase the overall prediction accuracy along with reducing processing time by using multispace image in pooling layer of convolution neural network.
Harmony Search: Current Studies and Uses on Healthcare Systems
The popularity of HS urges us to provide a comprehensive survey of the literature on HS and its variants on health systems, analyze its strengths and weaknesses, and suggest future research directions.
Improved Fitness Dependent Optimizer for Solving Economic Load Dispatch Problem
Moreover, the authors have enhanced a novel variant of Fitness Dependent Optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for Fitness Dependent Optimizer.
An Improved Deep Convolutional Neural Network by Using Hybrid Optimization Algorithms to Detect and Classify Brain Tumor Using Augmented MRI Images
In this paper, an improvement in deep convolutional learning is ensured by adopting enhanced optimization algorithms, Thus, Deep Convolutional Neural Network (DCNN) based on improved Harris Hawks Optimization (HHO), called G-HHO has been considered.
Fitness Dependent Optimizer for IoT Healthcare using Adapted Parameters: A Case Study Implementation
Ultimately, the target of this chapter's enhancement is to adapt the IoT healthcare framework based on FDO to spawn effective IoT healthcare applications for reasoning out real-world optimization, aggregation, prediction, segmentation, and other technological problems.
A New Lagrangian Problem Crossover: A Systematic Review and Meta-Analysis of Crossover Standards
The proposed crossover standards result for 100 generations of parent chromosomes are compared to the BX and SBX standards, which are the communal real-coded crossover standards.
Medical Dataset Classification for Kurdish Short Text over Social Media
The percentage ratio of the negative class is 55% while the positive class is 45%.
Deep Learning Neural Networks for Emotion Classification from Text: Enhanced Leaky Rectified Linear Unit Activation and Weighted Loss
The proposed solution improves accuracy and reduces the processing time of text emotion classification.
Deep Learning for Sleep Stages Classification: Modified Rectified Linear Unit Activation Function and Modified Orthogonal Weight Initialisation
Methodology: The proposed system used a modified Orthogonal Convolutional Neural Network and a modified Adam optimisation technique to improve the sleep stage classification accuracy and reduce the gradient saturation problem that occurs due to sigmoid activation function.
Current Studies and Applications of Shuffled Frog Leaping Algorithm: A Review
An overview of the SFLA is first conducted, followed by the algorithm's most recent modifications and hybridizations.
Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems
The results proved the ability of the proposed work in providing a set of non-dominated solutions, and that the algorithm outperformed the other participated algorithms in most of the cases.
A comprehensive review and evaluation on text predictive and entertainment systems
Besides, the evaluation of the prediction systems will be discussed.
Using Fitness Dependent Optimizer for Training Multi-layer Perceptron
The experimental results of this approach are affirmed by comparing with the Back-Propagation algorithm (BP) and some evolutionary models such as FDO with cascade MLP (FDO-CMLP), Grey Wolf Optimizer (GWO) combined with MLP (GWO-MLP), modified GWO combined with MLP (MGWO-MLP), GWO with cascade MLP (GWO-CMLP), and modified GWO with cascade MLP (MGWO-CMLP).
ANA: Ant Nesting Algorithm for Optimizing Real-World Problems
ANA computes the rate of change differently as it uses previous, current solutions, fitness values during the optimization process to generate weights by utilizing the Pythagorean theorem.
Generative Adversarial Network (GAN) and Enhanced Root Mean Square Error (ERMSE): Deep Learning for Stock Price Movement Prediction
It seems that the proposed system concentrates on minimizing the root mean square error and processing time and improving the direction prediction accuracy, and provides a better result in the accuracy of the stock index.
A Novel Cluster Detection of COVID-19 Patients and Medical Disease Conditions Using Improved Evolutionary Clustering Algorithm Star
Third, an operational framework was proposed to rate the effectiveness of iECA* against other algorithms in the datasets analysed, and the results indicated that iECA* exhibited the best performance in clustering all medical datasets.
Artificial Intelligence Algorithms for Natural Language Processing and the Semantic Web Ontology Learning
In turn, an experiment was conducted by applying 385 sample corpora from Wikipedia on the two frameworks to examine the reduction of formal context size, which leads to yield concept lattice and concept hierarchy.
A Novel Solution of an Elastic Net Regularization for Dementia Knowledge Discovery using Deep Learning
Methodology: The proposed system consists of Convolutional Neural Network (CNN) to enhance the accuracy of classification and prediction by using Elastic Net Regularization.
Chaotic Fitness Dependent Optimizer for Planning and Engineering Design
The results prove that CFDO has better capability to solve the problem.
Mixed Reality using Illumination-aware Gradient Mixing in Surgical Telepresence: Enhanced Multi-layer Visualization
The aim of this work is to propose a solution to improve the visualization in the final merged video by producing globally consistent videos when the intensity of illumination in the input source and target video varies.
An Improved BAT Algorithm for Solving Job Scheduling Problems in Hotels and Restaurants
Based on the achieved performance metrics, the proposed MBA establishes better global search ability and convergence than the original BA and other approaches.
Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star
Generating formal context may lead to a time-consuming process, so formal context size reduction is required to remove uninterested and erroneous pairs, taking less time to extract the concept lattice and concept hierarchies accordingly.
A Review-based Taxonomy for Secure Health Care Monitoring: Wireless Smart Cameras
The proposed model has been developed for access control to data through the authentication of users in healthcare to reduce data manipulation or theft.
Deep Learning for Breast Cancer Classification: Enhanced Tangent Function
Methodology: The proposed system consists of a Deep Convolutional Neural Network (DCNN) that helps in enhancing and increasing the accuracy of the classification process.
Child Drawing Development Optimization Algorithm based on Child's Cognitive Development
Hand pressure width, length and golden ratio of the child's drawing are tuned to attain better results.
COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network
Six different deep CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and MobileNetv2) were used to investigate three different classification schemes: two-class classification (Normal vs COVID-19); three-class classification (Normal, COVID-19, and Other CVDs), and finally, five-class classification (Normal, COVID-19, MI, AHB, and RMI).
Real-Time COVID-19 Diagnosis from X-Ray Images Using Deep CNN and Extreme Learning Machines Stabilized by Chimp Optimization Algorithm
Real-time detection of COVID-19 using radiological images has gained priority due to the increasing demand for fast diagnosis of COVID-19 cases.
Evolving Deep Convolutional Neural Network by Hybrid Sine-Cosine and Extreme Learning Machine for Real-time COVID19 Diagnosis from X-Ray Images
Therefore, to cope with this problem and maintain network reliability, the sine-cosine algorithm was utilized to tune the ELM's parameters.
Dynamic Cat Swarm Optimization Algorithm for Backboard Wiring Problem
The original Cat Swarm Optimization suffers from the shortcoming of 'premature convergence', which is the possibility of entrapment in local optima which usually happens due to the off-balance between exploration and exploitation phases.
Deep Learning for Vision-Based Fall Detection System: Enhanced Optical Dynamic Flow
This research aims to propose a vision-based fall detection system that improves the accuracy of fall detection in some complex environments such as the change of light condition in the room.
A Novel Solution of Using Mixed Reality in Bowel and Oral and Maxillofacial Surgical Telepresence: 3D Mean Value Cloning algorithm
The purpose of the proposed solution is to decrease the processing time and enhance the accuracy of merged video by decreasing the overlay and visualization error and removing occlusion and artefacts.
A Novel Visualization System of Using Augmented Reality in Knee Replacement Surgery: Enhanced Bidirectional Maximum Correntropy Algorithm
This has led to improve the processing time as well.
A New K means Grey Wolf Algorithm for Engineering Problems
One of the common metaheuristic optimization algorithms is called Grey Wolf Optimization (GWO).
Critical Analysis: Bat Algorithm based Investigation and Application on Several Domains
Purpose: The purpose of this study is to provide the reader with a full study of the Bat Algorithm, including its limitations, the fields that the algorithm has been applied, versatile optimization problems in different domains, and all the studies that assess its performance against other meta-heuristic algorithms.
A Multi-disciplinary Ensemble Algorithm for Clustering Heterogeneous Datasets
We notice that most of these techniques deterministically define a cluster based on the value of the attributes, distance, and density of homogenous and single-featured datasets.
An Improved Simulation Model for Pedestrian Crowd Evacuation
This method enables the developed model to find a more appropriate evacuation area design, among others regarding safety due to selecting the best exit door location among many suggested locations.
A new evolutionary algorithm: Learner performance based behavior algorithm
It is proved that the algorithm has a great ability to deal with the large optimization problems comparing to the DA, GA, and PSO.
Next word prediction based on the N-gram model for Kurdish Sorani and Kurmanji
The N-gram model has been used for next word prediction to reduce the amount of time while typing in the Kurdish language.
An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment
By increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services.
A Novel Hybrid GWO with WOA for Global Numerical Optimization and Solving Pressure Vessel Design
Grey Wolf Optimization (GWO) is a very competitive algorithm comparing to other common metaheuristic algorithms as it has a super performance in the exploitation phase while it is tested on unimodal benchmark functions.
A survey on dragonfly algorithm and its applications in engineering
Moreover, to examine the ability of the algorithm to optimize large-scale optimization problems CEC-C2019 benchmarks were utilized.
Improved Fitness-Dependent Optimizer Algorithm
Moreover, in determining the weights, the FDO uses the weight factor (wf), which is zero in most cases and one in only a few cases.
Cat Swarm Optimization Algorithm -- A Survey and Performance Evaluation
This paper presents an in-depth survey and performance evaluation of the Cat Swarm Optimization (CSO) Algorithm.
A Simulation Model for Pedestrian Crowd Evacuation Based on Various AI Techniques
The emergency behavior and evacuation efficiency of each pedestrian were evaluated by coupling his or her speed with various elements, such as environment, pedestrian distribution and familiarity with the exits.
Operational Framework for Recent Advances in Backtracking Search Optimisation Algorithm: A Systematic Review and Performance Evaluation
The first framework is a general framework to depict the main extensions of BSA, whereas the second is an operational framework to present the expansion procedures of BSA to guide the researchers who are working on improving it.
Dragonfly Algorithm and its Applications in Applied Science -- Survey
In general, in this survey the strong and weak points of the algorithm are discussed.
A Comprehensive Study on Pedestrians' Evacuation
Human beings face threats because of unexpected happenings, which can be avoided through an adequate crisis evacuation plan, which is vital to stop wound and demise as its negative results.
Multiagent Systems Physics and Society
Investigating the effect of competitiveness power in estimating the average weighted price in electricity market
This paper evaluates the impact of the power extent on price in the electricity market.
Donkey and Smuggler Optimization Algorithm: A Collaborative Working Approach to Path Finding
These are the Smuggler and Donkeys.
Fitness Dependent Optimizer: Inspired by the Bee Swarming Reproductive Process
Furthermore, the results are statistically tested with the Wilcoxon rank-sum test to show the significance of the results.
Lecturer Performance System Using Neural Network with Particle Swarm Optimization
In this paper, Particle Swarm Optimization Neural Network is used to assess performance of lecturers in more fruitful way and also to enhance the accuracy of recognition system.
A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer
Identifying university students' weaknesses results in better learning and can function as an early warning system to enable students to improve.
A Systematic and Meta-analysis Survey of Whale Optimization Algorithm
Then, the statistical results of WOA modifications and hybridizations are established and compared with the most common optimization algorithms and WOA.
