A Web Application-Based Secured Image Retrieval System With an IoT-Cloud Network

Many encryption and searching techniques have been used, but they did not prove effective to support smart devices in order to provide input image. Therefore, based on these facts, an effective and novel system has been developed in this paper which is based on content-based search concentrated on encrypted images. Four type of features, namely color moment (CM), Gray level co-occurrence matrix (GLCM), hybrid of CM and GLCM, and lastly, a deep belief network (DBN) has been used here. This deep neural network is based on clustering in combination with indexing and the developed model is called as cluster-based deep belief network (CBDBN) in the present work. A web based application has also been developed using Apache Tomcat server and MATLAB engine. Analysis of many parameters like precision, recall, entropy, correlation coefficient, and time has been done here on benchmark datasets, namely WANG and COIL.

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Deep Appearance Model and Crow-Sine Cosine Algorithm-Based Deep Belief Network for Age Estimation

International Journal of Ambient Computing and Intelligence ◽

10.4018/ijaci.2021070109 ◽

2021 ◽

Vol 12 (3) ◽

pp. 185-207

Author(s):

Anjali A. Shejul ◽

Kinage K. S. ◽

Eswara Reddy B.

Keyword(s):

Feature Extraction ◽

Age Estimation ◽

Learning Algorithm ◽

Directional Pattern ◽

Input Image ◽

Deep Belief Network ◽

Appearance Model ◽

Belief Network ◽

Sine Cosine Algorithm ◽

New Feature

Age estimation has been paid great attention in the field of intelligent surveillance, face recognition, biometrics, etc. In contrast to other facial variations, aging variation presents several unique characteristics, which make age estimation very challenging. The overall process of age estimation is performed using three important steps. In the first step, the pre-processing is performed from the input image based on Viola-Jones algorithm to detect the face region. In the second step, feature extraction is done based on three important features such as local transform directional pattern (LTDP), active appearance model (AAM), and the new feature, deep appearance model (Deep AM). After feature extraction, the classification is carried out based on the extracted features using deep belief network (DBN), where the DBN classifier is trained optimally using the proposed learning algorithm named as crow-sine cosine algorithm (CS).

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Brain tumor diagnosis based on discrete wavelet transform, gray-level co-occurrence matrix, and optimal deep belief network

SIMULATION ◽

10.1177/0037549720948595 ◽

2020 ◽

Vol 96 (11) ◽

pp. 867-879

Author(s):

Li Xu ◽

Qi Gao ◽

Nasser Yousefi

Keyword(s):

Brain Tumor ◽

Brain Tumors ◽

Tumor Cells ◽

Deep Belief Network ◽

Tumor Segmentation ◽

Discrete Wavelet ◽

Gray Level ◽

Belief Network ◽

Occurrence Matrix ◽

The Brain

Brain tumors are a group of cancers that originate from different cells of the central nervous system or cancers of other tissues in the brain. Excessive cell growth in the brain is called a tumor. Tumor cells need food and blood to survive. Growth and proliferation of tumor cells in the cranial space, cause strain inside the brain and thus disrupt vital human structures. Therefore, diagnosis in the early stages of brain tumors is crucial. This study introduces a new optimized method for early diagnosis of the brain tumor. The method has five main parts of noise reduction, tumor segmentation, morphology, feature extraction based on wavelet and gray-level co-occurrence matrix, and classification based on an optimized deep belief network. For optimizing the classifier network, an enhanced version of the moth search algorithm is utilized. Simulation results are applied to three different datasets, FLAIR, T1, and T2, and the accuracy results of the presented method are compared with two other metaheuristics, particle swarm optimization and Bat algorithms. The final results showed that the presented technique has good achievements toward the compared methods.

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A Deep Belief Network Classification Approach for Automatic Diacritization of Arabic Text

Applied Sciences ◽

10.3390/app11115228 ◽

2021 ◽

Vol 11 (11) ◽

pp. 5228

Author(s):

Waref Almanaseer ◽

Mohammad Alshraideh ◽

Omar Alkadi

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Language Processing ◽

High Accuracy ◽

Deep Belief Network ◽

Learning Problems ◽

Arabic Text ◽

Belief Network ◽

Benchmark Datasets ◽

Learning Research

Deep learning has emerged as a new area of machine learning research. It is an approach that can learn features and hierarchical representation purely from data and has been successfully applied to several fields such as images, sounds, text and motion. The techniques developed from deep learning research have already been impacting the research on Natural Language Processing (NLP). Arabic diacritics are vital components of Arabic text that remove ambiguity from words and reinforce the meaning of the text. In this paper, a Deep Belief Network (DBN) is used as a diacritizer for Arabic text. DBN is an algorithm among deep learning that has recently proved to be very effective for a variety of machine learning problems. We evaluate the use of DBNs as classifiers in automatic Arabic text diacritization. The DBN was trained to individually classify each input letter with the corresponding diacritized version. Experiments were conducted using two benchmark datasets, the LDC ATB3 and Tashkeela. Our best settings achieve a DER and WER of 2.21% and 6.73%, receptively, on the ATB3 benchmark with an improvement of 26% over the best published results. On the Tashkeela benchmark, our system continues to achieve high accuracy with a DER of 1.79% and 14% improvement.

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Character Recognition Algorithm Based on Fusion Probability Model and Deep Learning

The Computer Journal ◽

10.1093/comjnl/bxaa025 ◽

2020 ◽

Author(s):

Zhijun Liu ◽

Xuefeng Pan ◽

Yuan Peng

Keyword(s):

Character Recognition ◽

Probability Model ◽

Rejection Rate ◽

Recognition System ◽

Recognition Algorithm ◽

Deep Belief Network ◽

Feature Model ◽

Data Set ◽

Belief Network ◽

Occurrence Matrix

Abstract The authors proposed a character recognition algorithm to improve the recognition efficiency and recognition accuracy in character recognition. The algorithm is based on a deep belief network classifier. In the character recognition, a comprehensive feature model is established firstly by combining the histogram Gabor feature, grid level feature and gray level co-occurrence matrix. Subsequently, the deep belief network trains the feature model. Finally, the probability model is used to judge the recognition symbols. The algorithm is tested with 74 k data set and is compared and analyzed from three indexes: false acceptance rate, false rejection rate and accuracy. The data set simulation and comparison with other algorithms indicate that the recognition system based on the probability model and depth learning has higher accuracy and better performance.

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