Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

ABSTRACTReservoir characterization involves describing different reservoir properties quantitatively using various techniques in spatial variability. Nevertheless, the entire reservoir cannot be examined directly and there still exist uncertainties associated with the nature of geological data. Such uncertainties can lead to errors in the estimation of the ultimate recoverable oil. To cope with uncertainties, intelligent mathematical techniques to predict the spatial distribution of reservoir properties appear as strong tools. The goal here is to construct a reservoir model with lower uncertainties and realistic assumptions. Permeability is a petrophysical property that relates the amount of fluids in place and their potential for displacement. This fundamental property is a key factor in selecting proper enhanced oil recovery schemes and reservoir management. In this paper, a soft sensor on the basis of a feed‐forward artificial neural network was implemented to forecast permeability of a reservoir. Then, optimization of the neural network‐based soft sensor was performed using a hybrid genetic algorithm and particle swarm optimization method. The proposed genetic method was used for initial weighting of the parameters in the neural network. The developed methodology was examined using real field data. Results from the hybrid method‐based soft sensor were compared with the results obtained from the conventional artificial neural network. A good agreement between the results was observed, which demonstrates the usefulness of the developed hybrid genetic algorithm and particle swarm optimization in prediction of reservoir permeability.

A hybrid algorithm based on particle swarm and chemical reaction optimization for multi-object problems

A good exploration ability can ensure that the method jumps out of local optimum in multimodal problems and a good exploitation can ensure an algorithm converge faster to global optimum values. So, this paper proposes a new hybrid sperm swarm optimization and genetic algorithm to obtain global optimal solutions termed HSSOGA which is developed based on the concept of balancing the exploration and exploitation capability by merging Sperm Swarm Optimization (SSO), which has a fast convergence rate, and a Genetic Algorithm (GA) that can explore a search domain efficiently. To ensure that the proposed method delivers good performance, it is evaluated with 11 standard test function problems consisting of 5 unimodal and 6 multimodal functions. The proposed HSSOGA set is compared with conventional GA and SSO methods, as well as with several hybrid methods such as Hybrid Firefly and Particle Swarm Optimization (HFPSO), hybrid Simulated Annealing and Genetic Algorithm (SAGA), Hybrid Particle Swarm Optimization and Genetic Algorithm (HFPSO), hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), and closely related Hybrid Sperm Swarm Optimization and Gravitational Search Algorithm (HSSOGSA). The results are evaluated in terms of each method’s best fitness, mean, standard deviation, and convergence rates. The numerical experiment results show that HSSOGA has better convergence towards the true global optimum values as compared to the conventional and existing hybrid methods in most unimodal and multimodal test function problems.

— In this paper, two optimization algorithms i.e., Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) are combined to produce a hybrid population-based algorithm PSOGSA. The approach of HPSOGSA is to use the assessment of PSO using the social thinking capability and the manipulation of GSA using the local search proficiency. To increase the performance of HPSOGSA, some benchmark functions are used. Two standard database ORL and YALE is used to assess the classification performance of the proposed method. Classification accuracy is compared for diverse number of training samples per class. Further lead of proposed method is confirmed by analyzing percentage improvement in classification accuracy. With limited accessibility of training sample, percentage enhancement is very successful for face recognition applications. Keywords: Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (HPSOGSA). Cite this Article Purvaa Saxena, Gargi Mishra, Apoorva Aggarwal, Ayush Priyadarshi, Nitin Kumar, Rahul. Pattern Recognition Using Hybrid Meta-Heuristic. Journal of Artificial Intelligence Research & Advances. 2019; 6(2): 100–108p.

Particle Swarm Optimization (PSO) technique has proved its ability to deal with very complicated optimization and search problems. Several variants of the original algorithm have been proposed. This paper proposes a novel hybrid PSO – evolutionary algorithm for solving the well known geometrical place problems. Finding the geometrical place could be sometimes a hard task. In almost all situations the geometrical place consists more than one single point. The performance of the newly proposed PSO algorithm is compared with evolutionary algorithms. The main advantage of the PSO technique is its speed of convergence. Also, we propose a hybrid algorithm, combining PSO and evolutionary algorithms. The hybrid combination is able to detect the geometrical place very fast for which the evolutionary algorithms required more time and the conventional PSO approach even failed to find the real geometrical place.

Early detection dramatically increases the survival rate of oral cancer (OC). Artificial intelligence (AI) technology has garnered more attention in the field of diagnostic medicine in present periods. This study set out to assess the available data regarding AI's efficacy in OC diagnosis critically. Artificial intelligence diagnostic accuracy and capacity to detect early phases of OC were highlighted. In this project, performance indicators will be measured and oral cancer will be divided and classified using intelligent computing techniques. The role of oral cancer classification and detection to achieve a high recognition rate while leveraging the best theoretical components of oral cancer images, a newly established region-based Convolutional Neural Network (RCNN-COA) and the Chimp Optimization Algorithm were used to improve a Deep Learning Method. Then, using the recommended Chimp Optimization Algorithm (COA), a region-based convolutional neural network (R-CNN) classifier was trained using the acquired theoretical properties and the innovative image. A comparison of many deep learning and machine learning models' performances has been reported in a study. The findings imply that the deep learning model is capable of managing the problematic task of early oral malignant tumor detection.

The aim of this study is to assess the clinical relevance of the utilization of vital Lugol's iodine staining in detection of oral cancer and dysplastic lesions as well as demarcation of the extent of these lesions. A prospective, cross-sectional, hospital-based study was performed in Khartoum Teaching Dental Hospital, Sudan. Suspicious oral epithelial lesions indicating incisional biopsy were stained with 10% Lugol's iodine solution and were clinically designated as "negative" for dysplasia or neoplasia when no unstained lesion (USL) area was observed, or as "positive" when a USL area is observed. Incisional biopsies involving both the unstained portion and the stained portion were obtained and histologically evaluated for definitive diagnosis. Forty-five biopsies were obtained from 28 patients. Histopathological examination of the specimens confirmed some degree of dysplasia or neoplasia in all clinically positive specimens with oral squamous cell carcinoma (OSCC) being the most common definitive diagnosis (17 patients, 60.7%). Clinical relevance level of vital Lugol's iodine staining in detecting oral cancer and dysplasia was found to be 90.9% (SE=0.05, P=0.05). The findings of this study showed that Lugol's iodine is an easy, safe, and effective method of visualizing oral epithelial lesions and differentiating epithelial carcinoma and dysplasia from other benign mucosal lesions. Despite certain limitations, its use is of great value in detection and diagnosis of oral cancer and dysplasia. Lugol's iodine staining can effectively be used in detection of the dysplastic and malignant superficial lesions of the oral epithelium.

A new and efficient methodology for optimal reactive power and voltage control of distribution networks with distributed generators based on fuzzy adaptive hybrid particle swarm optimisation (PSO) is proposed. The objective is to minimise comprehensive cost, consisting of power loss and operation cost of transformers and capacitors, and subject to constraints such as minimum and maximum reactive power limits of distributed generators, maximum deviation of bus voltages and maximum allowable daily switching operation number. PSO is used to solve the corresponding mixed integer non‐linear programming problem and the hybrid PSO (HPSO) method, consisting of three PSO variants, is presented. In order to mitigate the local convergence problem, fuzzy adaptive inference is used to improve the searching process and the final fuzzy adaptive inference‐based HPSO is proposed. The proposed algorithm is implemented in VC++ 6.0 program language and the corresponding numerical experiments are finished on the modified version of the IEEE 33‐node distribution system with two newly installed distributed generators and eight newly installed capacitors banks. The numerical results prove that the proposed method can search a more promising control schedule of all transformers, all capacitors and all distributed generators with less time consumption, compared with other listed artificial intelligent methods.

A model of wavelet neural network (WNN) using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of Particle Swarm Optimization (PSO) and gradient descent algorithm (GD), and is thus called HGDPSO. The Particle Swarm Optimizer has previously been used to train neural networks and generally met with success. The advantage of the PSO over many of the other optimization algorithms is its relative simplicity and quick convergence. But those particles collapse so quickly that it exits a potentially dangerous property: stagnation, which state would make it impossible to arrive at the global optimum, even a local optimum. HGDPSO was proposed for neural network training to avoid premature and eliminate stagnation in PSO. The effectiveness of the HGDPSO based WNN is demonstrated through the classification of the fault signals in rotating machinery. The simulated results show its feasibility and validity.

In order to increase the precision and effectiveness of power system analysis and fault diagnosis, this study aims to assess the power systems in the energy sector while utilizing artificial intelligence (AI) and environmental social governance (ESG). First, the ESG framework is presented in this study to fully account for the effects of the power system on the environment, society, and governance. Second, to coordinate the operation of various components and guarantee the balance and security of the power system, the CNN-BiLSTM power load demand forecasting model is built by merging convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). Lastly, the particle swarm optimization (PSO) algorithm is used to introduce and optimize the deep belief network (DBN), and a power grid fault diagnostic model is implemented using the PSO technique and DBN. The model’s performance is assessed through experimentation. The outcomes demonstrate how the CNN-BiLSTM algorithm significantly increases forecasting accuracy while overcoming the drawback of just having one dimension of power load data. The values of 0.054, 0.076, and 0.102, respectively, are the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Effective processing of large-scale nonlinear data is achieved in the area of power grid fault diagnosis, resulting in prediction accuracy of 96.22% and prediction time of only 129.94 s. This is clearly better than other algorithms and increases fault prediction efficiency and accuracy. Consequently, the model presented in this study not only produces impressive results in fault diagnosis and load demand forecasting, but also advances the field of power system analysis in the energy industry and offers a significant amount of support for the sustainable and intelligent growth of the energy industry.

Oral cancer is the sixth most frequent type of cancer worldwide, and it is one of the main causes of mortality. Although early detection during precancerous phases can greatly increase survival rates, late-stage diagnoses are the cause of its high mortality rate. Using image analysis of oral lesions, this study suggests an effective diagnosis technique. The lightweight MobileNetV2 CNN architecture is used to classify the texture and color properties that are extracted from photos by exploring five color spaces. This model enhances accuracy while still being appropriate for resource-constrained, mobile applications by fusing deep learning with handmade features. The approach performs well in binary and multi-class classification tasks, differentiating between benign, malignant, and precancerous lesions. Particularly in underprivileged areas, its scalability and affordable design make it perfect for early oral cancer screening. Keywords: Oral Cancer Detection, Pre-Cancerous Lesions, MobileNetV2, Convolutional Neural Network (CNN), Color and Texture Features, Medical Image Classification, Deep Learning, Lightweight Architecture, Real-Time Diagnosis, Resource-Constrained Devices

Novel Artificial Human Optimization Field Algorithms - The Beginning

Oral cancer detection using feature-level fusion and novel self-attention mechanisms

There have been several researches of applying Deep Belief Networks (DBNs) to predict time series data. Most of these works pointed out that DBNs can bring out better prediction accuracy than traditional Artificial Neural Networks. However, one of the main shortcomings of using DBNs in time series prediction concerns with the proper selection of their parameters. In this paper, we investigate the use of Harmony Search algorithm for determining the parameters of DBN in forecasting time series. Experimental results on several synthetic and real world time series datasets revealed that the DBN with parameters selected by Harmony Search performs better than the DBN with parameters selected by Particle Swarm Optimization (PSO) or random method in most of the tested datasets.

Adaptive equalization mitigates the distortions caused by radio channels. The least mean square (LMS) and the recursive least squares (RLS) algorithms are used for such purpose. Recently, particle swarm optimization (PSO) algorithms such as PSO using a linear time decreasing inertia weight (PSO-W) and the PSO using constant constriction factor (PSO-CCF) were shown to be very effective in handling systems having nonlinear behavior. However, these algorithms can be trapped in local minima. This paper presents a new PSO-based algorithm called the hybrid PSO (HPSO) that is capable to handle such problems. The HPSO includes the randomization of particles to improve the search capacity of the swarm, which in turn reduces the probability of being trapped in some local minima. It also adapts the inertia weight assignment to the particles. Extensive simulation results are conducted to confirm the consistency in the performance of the HPSO algorithm in different scenarios. The proposed HPSO secures the minimum steady-state error as compared to LMS and other PSO-based algorithms in both nonlinear and linear channels. Finally, the proposed HPSO algorithm shows a great improvements in Bit Error Rate and convergence rate.