STUDY OF SCALABLE DEEP NEURAL NETWORK FOR WILDLIFE ANIMAL RECOGNITION AND IDENTIFICATION
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STUDY OF SCALABLE DEEP NEURAL NETWORK FOR WILDLIFE ANIMAL RECOGNITION AND IDENTIFICATION
Chapter one
Background of the study: 1.0 Introduction.
The challenge of identifying and recognising animals from photographs has long persisted since there is no single method that provides a stable and efficient answer in all scenarios.
Several researchers used long-standing traditional approaches for its implementation, but the problem remains unresolved because the task involves collecting a large volume of images, which is primarily done manually
with images that may have poor quality, which can affect the speed of classification and accuracy even for domain experts. Furthermore, processing big image sets is time-consuming, labor-intensive, and expensive due to the large amount of data collected.
In recent years, there has been a lot of interest in adopting deep neural network-based algorithms for image processing, namely animal detection and identification. However, how scalable the network is structured determines how much performance it can improve.
Scalability is frequently characterised in machine learning as the impact that even minor changes in the size of network parameters such as network layers and training sets have on an algorithm’s computational performance (accuracy, memory allocation, and processing speed).
So the question is to strike a balance, or to put it another way, to arrive at an acceptable answer quickly and effectively. This is a critical challenge, especially for real-time applications dealing with big datasets and computational problems that require speedy prototyping.
To cope with a huge dataset, it is necessary to minimise training time and memory space while maintaining accuracy; unfortunately, most proposed deep learning algorithms do not provide a reasonable trade-off between them.
To address the concerns raised above, we intend to optimise floating points by converting them to fixed points, which will reduce memory complexity and result in faster network processing.
In this study, the convolutional neural network architecture will be employed for animal identification and prediction, while stochastic gradient descent will be used to optimise the network’s parameters (i.e., weights and biases) via error backpropagation with momentum and adaptive learning rate.
Network layers and nodes in each hidden layer will be added through systematic experimentation and intuition, with a robust test to back it up.
1.1 Concept of Deep Learning
Deep learning is a subset of machine learning that is not new in the field of informatics and predictive analytics. However, it has recently gained popularity as neuroscientists, psychologists, engineers, economists, and AI workers strive to investigate their learning potential.
Deep learning techniques are a collection of algorithms that attempt to represent data with extreme abstractions via a replica architecture with tortuous construction.
It is one of the many branches of machine learning approaches based on the concept of learning representations of raw data, such as the intensity per pixel value of a data or sections of a given figure in a more abstract manner.
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Deep learning is a subset of machine learning that uses several layers and nonlinear processing units to extract features. It has been depicted in various ways.
ii. Are based on unsupervised learning of various data representations, with hierarchical representations produced when higher-level characteristics are extracted from lower-level features.
iii. The learned numerous levels of representation correlate to distinct levels of abstraction.
1.2 Definition of Learning.
The definition of learning is a difficult issue to address when developing deep learning objectives. Learning is mostly conceptual, and those who have attempted to give it meaning (psychologists, philosophers, etc.) have only shown one of the many facets of the intricate procedure.
However, there are some perspectives on learning that have gained popularity, primarily among those who have worked tirelessly to disseminate the concept, and these frequently provide a legitimate understanding of the process. Some examples are as follows:
i. There is a system that can manipulate information from its surroundings and improve itself.
ii. The system has multiple means to change its current state, and the information produced can typically take many forms.
iii. The system can remember and recollect what it has encountered.
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1.3 Scalability in Machine Learning
Over time, scalability has been more integrated into deep learning. This is due to the likelihood of performance attributes being impacted as soon as possible; most deep neural networks are heavily involved with the massive quantity of the dataset.
Scalability, as defined in machine learning, refers to the impact of changing system parameters on an algorithm’s performance characteristics. Its methods could include increasing the number of nodes, network levels, and hidden layers through methodical experimentation and/or intuition.
This is done to ensure faster processing with large datasets while maintaining some performance features like (accuracy, memory allocation) and reducing network complexity.
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