A New Initialization Approach in Particle Swarm Optimization for Global Optimization Problems

Bangyal, Waqas Haider; Hameed, Abdul; Alosaimi, Wael; Alyami, Hashem

doi:10.1155/2021/6628889

Cited by 53 publications

(20 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the proposed DKNet model is an efficient recognition system. In future, performnce of DKNet can be improved by tuning its hyper-parameters using metaheuristic techniques such as bat algorithm [50], particle swarm optimization [49], [51], etc.…”

Section: Discussionmentioning

confidence: 99%

DKNet: Deep Kuzushiji Characters Recognition Network

Singh

Aravinda²,

Kaur

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Kuzushiji, a cursive writing style, had been extensively utilized in Japan for over a thousand years starting from the 8 th century. In 1900, Kuzushiji was not included in regular school curricula due to the change in the Japanese writing system. Nowadays Japanese natives are unable to read historical books that were written using Kuzushiji language. Therefore, libraries and museums have decided to build digital copies of the documents and books that were written in Kuzushiji language. Due to a limited number of trained experts, researchers have utilized machine and deep learning models to convert historical documents and books into a modern script that can be easily read by human beings. However, the existing deep learning techniques suffer from over-fitting and gradient vanishing problems. To overcome these problems, an efficient deep Kuzushiji characters recognition network (DKNet) is proposed. Initially, to remove noise from the training images, a trilateral joint filter is applied. Contrast limited adaptive histogram equalization (CLAHE) is then applied to enhance the visibility of filtered images. Thereafter, a pre-trained MobileNet is utilized to extract the features of Kuzushiji characters. MobileNet's final layers are removed, including the fully connected layer and softmax. The flatten layer is then applied to the input. A fully connected classification layer is then used with Rectified linear units (ReLUs) and dropouts. Dropouts are used to generalize the model, thus preventing the over-fitting problem. Finally, the softmax activation function is employed to provide the recognition results. To test the proposed model, actual documents are first segmented by using the proposed Maximally stable extremal regions (MSERs) and convexhull-based segmentation approach. Segmented characters are then recognized using the trained DKNet. Extensive comparative analyses reveal that DKNet achieves better performance than the competitive models in terms of various performance metrics. An efficient Application Programming Interface (API) is also designed for Japanese Kuzushiji ancient heritage character recognition to help the end-users. INDEX TERMSMaximally stable extremal regions, Deep Learning Models, Kuzushiji character recognition, MobileNet, InceptionNet, ResNet I. INTRODUCTIONK UZUSHIJI, a cursive writing style, had been extensively utilized in Japan for over a thousand years starting from the 8 th century. But, in 1900, Kuzushiji was not included in regular school curricula due to the change in Japanese writing system. Nowadays Japanese natives are unable to read historical books that were written using Kuzushiji language. Therefore, libraries and museums have decided to build digital copies of the documents and books that were written in Kuzushiji language. But, due to a limited number of trained experts, researchers have utilized machine and deep learning models to convert historical documents and

show abstract

Section: Discussionmentioning

confidence: 99%

DKNet: Deep Kuzushiji Characters Recognition Network

Singh

Aravinda²,

Kaur

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…There are 22 layers in GoogleNet, and it can be used for multiple purposes like face recognition, object finding, image processing, etc. [58,59].…”

Section: Googlenetmentioning

confidence: 99%

Analyzing RNA-Seq Gene Expression Data Using Deep Learning Approaches for Cancer Classification

et al. 2022

Self Cite

View full text Add to dashboard Cite

Ribonucleic acid Sequencing (RNA-Seq) analysis is particularly useful for obtaining insights into differentially expressed genes. However, it is challenging because of its high-dimensional data. Such analysis is a tool with which to find underlying patterns in data, e.g., for cancer specific biomarkers. In the past, analyses were performed on RNA-Seq data pertaining to the same cancer class as positive and negative samples, i.e., without samples of other cancer types. To perform multiple cancer type classification and to find differentially expressed genes, data for multiple cancer types need to be analyzed. Several repositories offer RNA-Seq data for various cancer types. In this paper, data from the Mendeley data repository for five cancer types are analyzed. As a first step, RNA-Seq values are converted to 2D images using normalization and zero padding. In the next step, relevant features are extracted and selected using Deep Learning (DL). In the last phase, classification is performed, and eight DL algorithms are used. Results and discussion are based on four different splitting strategies and k-fold cross validation for each DL classifier. Furthermore, a comparative analysis is performed with state of the art techniques discussed in literature. The results demonstrated that classifiers performed best at 70–30 split, and that Convolutional Neural Network (CNN) achieved the best overall results. Hence, CNN is the best DL model for classification among the eight studied DL models, and is easy to implement and simple to understand.

show abstract

“…However, these approaches cannot be applied in our formulation, since our model allows routing cycles that can cover multiple planning cycles. Other heuristic solution approaches include the iterative refinement algorithm [35], gradient search [36], probability search algorithm [37], modified controlled bat algorithm [38], particle swarm optimization [39,40], and bundle-based relaxation [41]. The performances of these algorithms are not suitable for solving large-scale instances because either the solution quality cannot meet the requirement, or they fail to obtain solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

An Iterative Backbone Algorithm for Service Network Design Problems

2022

View full text Add to dashboard Cite

Service network design problems arise at airlines, trucking companies, and railroads wherever there is a need to determine cost-minimizing routes and schedules, given resource availability and service constraints. In recent years, the application of consolidation-based service network design in the express service has attracted lots of academic attention due to the rapid growth of the express industry. This paper studies the consolidation-based service network design problem, which jointly determines the commodity flow, vehicle dispatching, and fleet sizing. We propose a mixed-integer optimization model to address the problem and design an efficient iterative backbone algorithm to solve large-scale real-world problems. The numerical results of large-scale instances confirmed that the solution obtained by our proposed algorithm is better than that of the primal model, and the running time taken is less than half that of the general solution approach. The computational study confirmed the effectiveness and efficiency of the proposed algorithm.

show abstract

A New Initialization Approach in Particle Swarm Optimization for Global Optimization Problems

Cited by 53 publications

References 35 publications

DKNet: Deep Kuzushiji Characters Recognition Network

DKNet: Deep Kuzushiji Characters Recognition Network

Analyzing RNA-Seq Gene Expression Data Using Deep Learning Approaches for Cancer Classification

An Iterative Backbone Algorithm for Service Network Design Problems

Contact Info

Product

Resources

About