Kestrel-based Search Algorithm (KSA) and Long Short Term Memory (LSTM) Network for feature selection in classification of high-dimensional bioinformatics datasets (original) (raw)

IRJET- BIRD SPECIES CLASSIFICATION USING DEEP LEARNING APPROACH

IRJET, 2020

Birds are the warm-blooded vertebrates constituting of class Aves, there are nearly 10 thousand living species of birds in the world with multifarious characteristics and appearances. Bird watching is often considered to be an interesting hobby by human beings in the natural environment. The human knowledge over the species isn't enough to identify a species of bird accurately, as it requires lot of expertise in the field of Ornithology. This paper presents an automated model based on the deep neural networks which automatically identifies the species of a bird given as the test data set. The model was trained and tested for 20 species of birds with the total images 7637 and 1853 images for train and test respectively and the model has shown a promising accuracy of 98% when tested with the test datasets.

Crow search algorithm with time varying flight length Strategies for feature selection

Future Computing and Informatics Journal

Feature Selection (FS) is an efficient technique use to get rid of irrelevant, redundant and noisy attributes in high dimensional datasets while increasing the efficacy of machine learning classification. The CSA is a modest and efficient metaheuristic algorithm which has been used to overcome several FS issues. The flight length (fl) parameter in CSA governs crows' search ability. In CSA, fl is set to a fixed value. As a result, the CSA is plagued by the problem of being hoodwinked in local minimum. This article suggests a remedy to this issue by bringing five new concepts of time dependent fl in CSA for feature selection methods including linearly decreasing flight length, sigmoid decreasing flight length, chaotic decreasing flight length, simulated annealing decreasing flight length, and logarithm decreasing flight length. The proposed approaches' performance is assessed using 13 standard UCI datasets. The simulation result portrays that the suggested feature selection ap...

Optimization Driven Adam-Cuckoo Search-Based Deep Belief Network Classifier for Data Classification

IEEE Access, 2020

Data classification effectively classifies the data based on the labeled class distribution. To classify the data using the imbalanced distribution poses a significant challenge in the class inequity problem. Various data classification methods are developed in the learning framework, but proving better classification accuracy is a significant challenge in the application domain. Therefore, an effective classification method named Adam-Cuckoo search based Deep Belief Network (Adam-CS based DBN) is proposed to perform the classification process. At first, the input data is forwarded to the pre-processing stage, and then the feature selection stage. The wrapper-based feature selection model conducts the search in space with the possible parameters. The operators specify the connectivity between the states and select the features based on their state. The classification is performed using the Deep Belief Network (DBN) classifier such that the multilayer perceptron (MLP) layer of Deep Belief Network (DBN) is trained using the proposed Adam based Cuckoo search (Adam-CS) algorithm. The breeding behavior of cuckoos is integrated with the step size parameter to enhance the accuracy of the classification process. The adaptive learning rate parameter effectively estimates the moments using a sparse gradient. The proposed Adam based Cuckoo search (Adam-CS) algorithm attained better performance using the metrics, such as accuracy, specificity, and sensitivity, with 90% training data. INDEX TERMS Cuckoo search algorithm, Adam optimization algorithm, deep belief network (DBN), deep learning approach, data classification.

Classification of Bird Species Using Deep Learning Techniques

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Nowadays some bird species are not being located often than we used to see them in our childhood and if we do observed them we can't predict the type of bird species as it is very hard to predict as we haven't familiar enough with them. naturally, birds found in numerous situations appear in one-of-a-kind sizes, shapes, coloring, and angles from the human perspective. except, the robust to discover the bird species extra than audio category. additionally, the human capability to apprehend the birds through the photos is more comprehensible. So, this approach makes use of the dataets made by many researchersa and bird enthusiasts for schooling also in addition to testing purposes. through the usage of a convolutional neural network (CNN) basically set of rules a photo that is converted into a greyscale layout to generate an autograph via the usage of tensor-flow, where the multiple nodes of comparison are generated. those different nodes are compared with the traing and testing dataset and a score sheet is received from it. After reading the datasheet describing the accuracy of our proposed trained model, it can predicate the desired species with the aid of using the highest precision we could get. Experimental evaluation at the dataset shows that the algorithm achieves an accuracy of identity among 86% to 87% The experimental observed is accomplished with visual studio book with the usage of a Tensor flow library.

Implementation of Bird Species Detection Algorithm using Deep Learning

ITM Web of Conferences

Automatically identifying what types of the bird is present in the sound recording using the monitor reading. To distinguishing automatic birds based on their sound patterns.This is useful in the field of ornithology for studying bird species and their behavior based on their sound. Proposed method will be used to distinguish birds automatically using different sound processing methods and mechanical learning methods based on their chirping patterns. We propose a sequential model for audio features within a short interval of time. The model will be used Mel Frequency Cepstral Coefficients to extract features from the audio files and presented it in the model. The proposed work classifies the data set containing three species of bird, and outperform support vector machines.

Birds Identification System using Deep Learning

International Journal of Advanced Computer Science and Applications, 2021

Identifying birds is one of challenging role for bird watchers due to the similarity of the birds' forms/image background and the lack of experience for watchers. So, it needs a computer system based images to help birdwatchers in order to identify birds. This study aims at investigating the use of deep learning for birds' identification using convolutional neural network for extracting features from images. The investigation was performed on database contained 4340 images that collected by the paper author from Jordan. The Principal Component Analysis (was applied on layer 6 and 7, as well as on the statistical operations of merging the two layers like: average, minimum, maximum and combine of both layers. The datasets were investigated by the following classifiers: Artificial neural networks, K-Nearest Neighbor, Random Forest, Naïve Bayes and Decision Tree. Whereas, the metrics used in each classifier are: accuracy, precision, recall, and F-Measure. The results of investigation include and not limited to the following, the PCA used on the deep features does not only reduce the dimensionality, and therefore, the training/testing time is reduced significantly, but also allows for increasing the identification accuracy, particularly when using the Artificial Neural Networks classifier. Based on the results of classifiers; Artificial neural networks showed high classification accuracy (70.9908), precision (0.718), recall (0.71) and F-Measure (0.708) compared to other classifiers.

Bird Species Classification And Acoustic Features Selection Based on Distributed Neural Network with Two Stage Windowing of Short-Term Features

ArXiv, 2022

Identification of bird species from audio records is one of the challenging tasks due to the existence of multiple species in the same recording, noise in the background, and long-term recording. Besides, choosing a proper acoustic feature from audio recording for bird species classification is another problem. In this paper, a hybrid method is represented comprising both traditional signal processing and a deep learning-based approach to classify bird species from audio recordings of diverse sources and types. Besides, a detailed study with 34 different features helps to select the proper feature set for classification and analysis in real-time applications. Moreover, the proposed deep neural network uses both acoustic and temporal feature learning. The proposed method starts with detecting voice activity from the raw signal, followed by extracting short-term features from the processed recording using 50 ms (with 25ms overlapping) time windows. Later, the short-term-features are r...

Feature selection using Binary Crow Search Algorithm with time varying flight length

Expert Systems with Applications, 2021

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

IRJET- Bird Classification using Deep Learning

2020

IRJETNow a day some bird species are being found rarely and if found classification of bird species prediction is difficult. Naturally, birds present in various scenarios may be in different sizes, shapes, colors and angles from human perspective. In 21 st century the world is moving towards digitalization and effective monitoring system in every sector. Most of the population uses mobile phones, so it is possible that anyone can capture the bird's image. By using Convolutional neural networks (CNN) algorithm that image is converted into a grey scale format to generate autograph by using Pytorch model, where the multiple nodes of comparison are generated. These different nodes are compared with the testing dataset and score sheet is obtained from it. After analyzing the score sheet It can predict the required bird species.

Rider-chicken optimization dependent recurrent neural network for cancer detection and classification using gene expression data

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2020

One of the deadly diseases prevailing worldwide is cancer. The rigorous symptoms of cancers should be studied properly prior to the diagnosis to save patients life. Thus, an automatic prediction system for classifying cancer using gene expression data is needed. This paper develops a cancer classification and detection method by proposing the Rider Chicken Optimisation algorithm dependent Recurrent Neural Network (RCO-RNN) classifier. At first, pre-processing is done on the gene expression data to fit for the further processes of classification. In gene selection, the genes are selected based on entropy for reducing the dimension. Finally, the selected genes are classified using Recurrent Neural Network (RNN), which is trained by using the proposed Rider Chicken Optimisation (RCO) algorithm, which is the integration of Chicken Swarm Optimisation (CSO), and Rider Optimisation algorithm (ROA). The experimentation is carried out using the Leukaemia database, Small Blue Round Cell Tumour (SBRCT) dataset and Lung Cancer Dataset. The performance of the RCO-RNN is evaluated based on specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV) and accuracy. The proposed method produces the maximal accuracy, sensitivity, PPV, NPV and specificty upto 95%. Which indicates the superiority of the proposed method.