Enhanced Particle Swarm Optimization-Based Models And Their Application To License Plate Recognition

Model pengecaman corak memainkan peranan yang penting dalam banyak aplikasi dunia sebenar seperti pengesanan teks dan pengecaman objek. Pelbagai kaedah termasuk model Kecerdikan Berkomputer (CI) telah dibangunkan untuk menangani masalah pengecaman corak berasaskan imej. Tertumpu kepada model CI, penyelidikan ini mempersembah model berasaskan pengoptimuman kawanan zarah (PSO) yang cekap serta aplikasinya untuk pengecaman lesen plat. Pertama, model Pengoptimuman Kawanan Zarah Memetik berasaskan pengukuhan pembelajaran yang baharu (RLMPSO) diperkenalkan. Masalah pengoptimuman penanda aras digunakan untuk menilai prestasi RLMPSO, dan kaedah bootstarp digunakan untuk menilai keputusan secara statistik. Kedua, RLMPSO disepadukan dengan mesin Penyokong Vektor Kabur (FSVM) untuk merumuskan model RLMPSO-FSVM yang cekap. Secara khusus, RLMPSO-FSVM terdiri daripada gabungan pengelas linear FSVM yang dibina menggunakan RLMPSO untuk melaksanakan penalaan parameter, pemilihan ciri, serta pemilihan contoh latihan. Untuk menilai prestasi model RLMPSO-FSVM yang dicadangkan, pangkalan data imej penanda aras digunakan. Ketiga, model dua-peringkat RLMPSO-FSVM dicipta untuk mempertingkatkan lagi kecekapan. Ia mengandungi peringkat pengecaman global dan peringkat pengesahan tempatan. Peningkatan model RLMPSO turut diperkenalkan dengan memasukkan operasi carian tambahan. Model RLMPSO yang (ERLMPSO) dipertingkatkan terdiri daripada tiga lapisan, iaitu lapisan global dengan empat operasi carian, lapisan tempatan dengan satu operasi carian, dan lapisan berasaskan komponen dengan dua belas operasi carian. Akhir sekali, model dua-peringkat ERLMPSO-FSVM yang dicadangkan telah digunapakai dalam masalah Pengecaman Plat Lesen Kereta Malaysia (VLPR) yang sebenar. Kadar pengecaman setinggi 98.1% telah diperoleh. Keputusan ini mengesahkan keberkesanan model dua-peringkat ERLMPSO-FSVM yang dicadangkan dalam menangani masalah pengecaman plat lesen. ________________________________________________________________________________________________________________________ Pattern recognition models play an important role in many real-world applications such as text detection and object recognition. Numerous methodologies including Computational Intelligence (CI) models have been developed in the literature to tackle image-based pattern recognition problems. Focused on CI models, this research presents efficient Particle Swarm Optimization (PSO)-based models and their application to license plate recognition. Firstly, a new Reinforcement Learningbased Memetic Particle Swarm Optimization (RLMPSO) model is introduced. To assess the performance of RLMPSO, benchmark optimization problems are employed, and the bootstrap method is used to quantify the results statistically. Secondly, RLMPSO is integrated with the Fuzzy Support Vector Machine (FSVM) to formulate an efficient RLMPSO-FSVM model. Specifically, RLMPSO-FSVM comprises an ensemble of linear FSVM classifiers that are constructed using RLMPSO to perform parameter tuning, feature selection, as well as training sample selection. To evaluate the performance of the proposed RLMPSO-FSVM model, a benchmark image database is employed. Thirdly, to further improve efficiency, a two-stage RLMPSO-FSVM model is devised. It consists of a global recognition stage and a local verification stage. In addition, enhancement of the RLMPSO model is introduced by incorporating additional search operations. The enhanced RLMPSO model (i.e. ERLMPSO) comprises three layers, namely, a global layer with four search operations, a local layer with one search operation, and a component-based layer with twelve search operations. Finally, the proposed two-stage ERLMPSOFSVM model is applied to a real-world Malaysian vehicle license plate recognition (VLPR) task. A high recognition rate of 98.1% has been achieved, confirming the effectiveness of the proposed two-stage ERLMPSO-FSVM model in tackling the license plate recognition problem

A vision-based machine learning method for barrier access control using vehicle license plate authentication

Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications

Bead geometry modeling on uneven base metal surface by fuzzy systems for multi-pass welding

This paper presents a modeling method of weld bead profiles deposited on uneven base metal surfaces and its application in multi-pass welding. The robotized multi-pass tungsten inert gas welding requires precise positioning of the weld beads to avoid welding defects and achieve the desirable welding join since the weld bead shapes depend on the surface of the previously deposited beads. The proposed model consists of fuzzy systems to estimate the coefficients of the profile function. The characteristic points of the trapezoidal membership functions in the rule bases are tuned by the Bacterial Memetic Algorithm during supervised training. The fuzzy systems are structured as multiple-input-single-output systems, where the inputs are the welding process variables and the coefficients of the shape functions of the segments underlying the modeled bead; the outputs are the coefficients of the bead shape function. Each segment surface is approximated by a second-order polynomial function defined in the weld bead’s local coordinate system. The model is developed from empirical data collected from single and multi-pass welding. The performance of the proposed model is compared with a multiple linear regression model. During the experimental validation, first, the individual beads are evaluated by comparing the estimated coefficients of the profile function and other bead characteristics (bead area, width, contact angles, and position of the toe points) with the measurements, and the estimations of a multiple linear regression model. Second, the sequential placement of the weld beads is evaluated while filling a straight Vgroove by comparing the estimated bead characteristics with the measurements and calculating the accumulated error of the filled groove cross-section. The results show that the proposed model provides a good estimation of the bead shapes during deposition on uneven base metal surfaces and outperforms the regression model with low error in both validation cases. Furthermore, it is experimentally validated that the derived bead characteristics provide a suitable measure to identify locations sensitive to welding defects

An innovative metaheuristic strategy for solar energy management through a neural networks framework

Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development

Intelligent Leukaemia Diagnosis with Bare-Bones PSO based Feature Optimization

In this research, we propose an intelligent decision support system for acute lymphoblastic leukaemia (ALL) diagnosis using microscopic images. Two Bare-bones Particle Swarm Optimization (BBPSO) algorithms are proposed to identify the most significant discriminative characteristics of healthy and blast cells to enable efficient ALL classification. The first BBPSO variant incorporates accelerated chaotic search mechanisms of food chasing and enemy avoidance to diversify the search and mitigate the premature convergence of the original BBPSO algorithm. The second BBPSO variant exhibits both of the abovementioned new search mechanisms in a subswarm-based search. Evaluated with the ALL-IDB2 database, both proposed algorithms achieve superior geometric mean performances of 94.94% and 96.25%, respectively, and outperform other metaheuristic search and related methods significantly for ALL classification

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications

An efficient emotion classification system using EEG

Emotion classification via Electroencephalography (EEG) is used to find the relationships between EEG signals and human emotions. There are many available channels, which consist of electrodes capturing brainwave activity. Some applications may require a reduced number of channels and frequency bands to shorten the computation time, facilitate human comprehensibility, and develop a practical wearable. In prior research, different sets of channels and frequency bands have been used. In this study, a systematic way of selecting the set of channels and frequency bands has been investigated, and results shown that by using the reduced number of channels and frequency bands, it can achieve similar accuracies. The study also proposed a method used to select the appropriate features using the Relief F method. The experimental results of this study showed that the method could reduce and select appropriate features confidently and efficiently. Moreover, the Fuzzy Support Vector Machine (FSVM) is used to improve emotion classification accuracy, as it was found from this research that it performed better than the Support Vector Machine (SVM) in handling the outliers, which are typically presented in the EEG signals. Furthermore, the FSVM is treated as a black-box model, but some applications may need to provide comprehensible human rules. Therefore, the rules are extracted using the Classification and Regression Trees (CART) approach to provide human comprehensibility to the system. The FSVM and rule extraction experiments showed that The FSVM performed better than the SVM in classifying the emotion of interest used in the experiments, and rule extraction from the FSVM utilizing the CART (FSVM-CART) had a good trade-off between classification accuracy and human comprehensibility

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available