Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments

Rani, Pooja; Kotwal, Shallu; Manhas, Jatinder; Sharma, Vinod; Sharma, Sparsh

doi:10.1007/s11831-021-09639-x

Cited by 71 publications

(34 citation statements)

References 136 publications

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“… Pattern recognition: plays a crucial role by identifying certain patterns within the data, such as movement patterns, to offer insights into crowd behavior [20]. Image recognition is employed to identify specific objects or features within an image, such as identifying individuals or detecting signs of overcrowding [21].…”

Section: Vision-basedmentioning

confidence: 99%

Advancements in Crowd-Monitoring System: A Comprehensive Analysis of Systematic Approaches and Automation Algorithms: State-of-the-Art

Ameen¹,

Stone²

2023

IJNSA

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Growing apprehensions surrounding public safety have captured the attention of numerous governments and security agencies across the globe. These entities are increasingly acknowledging the imperative need for reliable and secure crowd-monitoring systems to address these concerns. Effectively managing human gatherings necessitates proactive measures to prevent unforeseen events or complications, ensuring a safe and well-coordinated environment. The scarcity of research focusing on crowd monitoring systems and their security implications has given rise to a burgeoning area of investigation, exploring potential approaches to safeguard human congregations effectively. Crowd monitoring systems depend on a bifurcated approach, encompassing vision-based and non-vision-based technologies. An in-depth analysis of these two methodologies will be conducted in this research. The efficacy of these approaches is contingent upon the specific environment and temporal context in which they are deployed, as they each offer distinct advantages. This paper endeavors to present an in-depth analysis of the recent incorporation of artificial intelligence (AI) algorithms and models into automated systems, emphasizing their contemporary applications and effectiveness in various contexts.

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Section: Vision-basedmentioning

confidence: 99%

Advancements in Crowd-Monitoring System: A Comprehensive Analysis of Systematic Approaches and Automation Algorithms: State-of-the-Art

Ameen¹,

Stone²

2023

IJNSA

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“…Machine learning algorithms based on data-driven models are very advantageous in dealing with different types of unstructured data (Rani et al, 2021). Much progress has been made in introducing machine learning algorithms in microalgae detection and classification work.…”

Section: Microalgae Detection and Classification With Machine Learningmentioning

confidence: 99%

“…First of all, there are very few datasets available for machine learning algorithms in microalgae process. Even though some datasets have been widely used in some specific areas, they still face the problem of over-fitting (Rani et al, 2021). Secondly, when the performance of a single machine learning algorithm is limited, multiple algorithms can be coupled to build a hybrid model.…”

Section: Prospectmentioning

confidence: 99%

Machine learning for microalgae detection and utilization

Ning

Zhou

2022

Front. Mar. Sci.

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Microalgae are essential parts of marine ecology, and they play a key role in species balance. Microalgae also have significant economic value. However, microalgae are too tiny, and there are many different kinds of microalgae in a single drop of seawater. It is challenging to identify microalgae species and monitor microalgae changes. Machine learning techniques have achieved massive success in object recognition and classification, and have attracted a wide range of attention. Many researchers have introduced machine learning algorithms into microalgae applications, and similarly significant effects are gained. The paper summarizes recent advances based on various machine learning algorithms in microalgae applications, such as microalgae classification, bioenergy generation from microalgae, environment purification with microalgae, and microalgae growth monitor. Finally, we prospect development of machine learning algorithms in microalgae treatment in the future.

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“…The popularization of EMS is one of the important development directions of fishery monitoring in the future, so it is of great significance to identify ship behavior based on EMS data. In video and image recognition, the current common processing schemes include traditional computer vision processing schemes, support vector machine classification schemes (Hamdan, 2021), and convolutional neural network processing schemes (Rani et al, 2021). The convolutional neural network has the most excellent performance in video recognition tasks in various fields.…”

Section: Introductionmentioning

confidence: 99%

Developing machine learning methods for automatic recognition of fishing vessel behaviour in the Scomber japonicus fisheries

et al. 2023

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IntroductionWith a higher degree of automation, fishing vessels have gradually begun adopting a fishing monitoring method that combines human and electronic observers. However, the objective data of electronic monitoring systems (EMS) has not yet been fully applied in various fishing boat scenarios such as ship behavior recognition.MethodsIn order to make full use of EMS data and improve the accuracy of behaviors recognition of fishing vessels, the present study proposes applying popular deep learning technologies such as convolutional neural network, long short-term memory, and attention mechanism to Chub mackerel (Scomber japonicus) fishing vessel behaviors recognition. The operation process of Chub mackerel fishing vessels was divided into nine kinds of behaviors, such as “pulling nets”, “putting nets”, “fish pick”, “reprint”, etc. According to the characteristics of their fishing work, four networks with different convolutional layers were designed in the pre-experiment. And the feasibility of each network in behavior recognition of the fishing vessels was observed. The pre-experiment is optimized from the perspective of the data set and the network. From the standpoint of the data set, the size of the optimized data set is significantly reduced, and the original data characteristics are preserved as much as possible. From the perspective of the network, different combinations of pooling, long short-term memory(LSTM) network, and attention(including CBAM and SE) are added to the network, and their effects on training time and recognition effect are compared.ResultsThe experimental results reveal that the deep learning methods have outstanding performance in behaviors recognition of fishing vessels. The LSTM and SE module combination produced the most apparent optimization effect on the network, and the optimized model can achieve an F1 score of 97.12% in the test set, surpassing the classic ResNet, VGGNet, and AlexNet.DiscussionThis research is of great significance to the management of intelligent fishery vessels and can promote the development of electronic monitoring systems for ships.

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Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments

Cited by 71 publications

References 136 publications

Advancements in Crowd-Monitoring System: A Comprehensive Analysis of Systematic Approaches and Automation Algorithms: State-of-the-Art

Advancements in Crowd-Monitoring System: A Comprehensive Analysis of Systematic Approaches and Automation Algorithms: State-of-the-Art

Machine learning for microalgae detection and utilization

Developing machine learning methods for automatic recognition of fishing vessel behaviour in the Scomber japonicus fisheries

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