An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification

Dhindsa, Anaahat; Bhatia, Sanjay; Agrawal, Sunil; Sohi, Balwinder Singh

doi:10.3390/e23020257

Cited by 14 publications

(15 citation statements)

References 78 publications

(65 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Some flaws are identified. Dhindsa et al 11 applied a segmentation algorithm to generate high-quality inputs for machine learning models. Although an accuracy as high as 98% was achieved, the segmentation algorithm incorporated subjective rules, restricting the algorithm to classify more diverse algal species accurately.…”

Section: ■ Discussionmentioning

confidence: 99%

Accurate Classification of Algae Using Deep Convolutional Neural Network with a Small Database

Chen

et al. 2022

ACS EST Water

View full text Add to dashboard Cite

The variations in algal diversity and populations are essential for evaluating aquatic system health. However, manual classification is time-consuming and labor-intensive. As AI has shown its capacity in face identification and would be possible for algal identification, we developed a deep convolutional neural network (CNN) algorithm for the accurate identification and classification of algae. Results showed that a fractional threshold at 0.6 ensured a good balance between precision, recall, and F1_score. Furthermore, the corresponding confusion matrix showed that the lowest probability for classifying algal species was 93.9%, indicating the high classification capacity of the CNN, which was supported by receiver operating characteristics. In contrast, conventional extensive sampling activities for establishing an algal database of publicly available algal images ensured a good training of the CNN, showing the robustness of the CNN. This study proved that the applied CNN can achieve an efficient and accurate algal classification. Therefore, our developed CNN approach is a successful pioneer for building advanced identification and classification systems with broad applications for aquatic system protection.

show abstract

Section: ■ Discussionmentioning

confidence: 99%

Accurate Classification of Algae Using Deep Convolutional Neural Network with a Small Database

Chen

et al. 2022

ACS EST Water

View full text Add to dashboard Cite

show abstract

“…Feature reduction: To reduce the computational complexity of the model for resource-constrained IoT devices and to maximize the performance of the classifier, a common feature selection approach, called Mutual Information (MI) has been utilized for feature selection on the basis of the information value. According to the authors [ 65 , 66 ], The MI between two random variables X and Y can be is defined as:

where,

is the mutual information value for variable X and Y ,

denotes the entropy for variable X and

denotes the conditional entropy for X given Y . The output is denoted as the units of bits.…”

Section: Materials and Methodsmentioning

confidence: 99%

Towards an Effective Intrusion Detection Model Using Focal Loss Variational Autoencoder for Internet of Things (IoT)

Khanam

Ahmedy

Idris

et al. 2022

Sensors

View full text Add to dashboard Cite

As the range of security attacks increases across diverse network applications, intrusion detection systems are of central interest. Such detection systems are more crucial for the Internet of Things (IoT) due to the voluminous and sensitive data it produces. However, the real-world network produces imbalanced traffic including different and unknown attack types. Due to this imbalanced nature of network traffic, the traditional learning-based detection techniques suffer from lower overall detection performance, higher false-positive rate, and lower minority-class attack detection rates. To address the issue, we propose a novel deep generative-based model called Class-wise Focal Loss Variational AutoEncoder (CFLVAE) which overcomes the data imbalance problem by generating new samples for minority attack classes. Furthermore, we design an effective and cost-sensitive objective function called Class-wise Focal Loss (CFL) to train the traditional Variational AutoEncoder (VAE). The CFL objective function focuses on different minority class samples and scrutinizes high-level feature representation of observed data. This leads the VAE to generate more realistic, diverse, and quality intrusion data to create a well-balanced intrusion dataset. The balanced dataset results in improving the intrusion detection accuracy of learning-based classifiers. Therefore, a Deep Neural Network (DNN) classifier with a unique architecture is then trained using the balanced intrusion dataset to enhance the detection performance. Moreover, we utilize a challenging and highly imbalanced intrusion dataset called NSL-KDD to conduct an extensive experiment with the proposed model. The results demonstrate that the proposed CFLVAE with DNN (CFLVAE-DNN) model obtains promising performance in generating realistic new intrusion data samples and achieves superior intrusion detection performance. Additionally, the proposed CFLVAE-DNN model outperforms several state-of-the-art data generation and traditional intrusion detection methods. Specifically, the CFLVAE-DNN achieves 88.08% overall intrusion detection accuracy and 3.77% false positive rate. More significantly, it obtains the highest low-frequency attack detection rates for U2R (79.25%) and R2L (67.5%) against all the state-of-the-art algorithms.

show abstract

“…An additional advantage of the model was that it provided morphological information on the microalgal populations. Anaahat Dhindsa et al designed a new scheme to classify microalgae (Dhindsa et al, 2021). The microalgae images were segmented, and 25 features were extracted by a generalized segmentation algorithm, and then various machine learning algorithms were applied for classification.…”

Section: Microalgae Detection and Classification With Machine Learningmentioning

confidence: 99%

Machine learning for microalgae detection and utilization

Ning

Zhou

2022

Front. Mar. Sci.

View full text Add to dashboard Cite

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.

show abstract

An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification

Cited by 14 publications

References 78 publications

Accurate Classification of Algae Using Deep Convolutional Neural Network with a Small Database

Accurate Classification of Algae Using Deep Convolutional Neural Network with a Small Database

Towards an Effective Intrusion Detection Model Using Focal Loss Variational Autoencoder for Internet of Things (IoT)

Machine learning for microalgae detection and utilization

Contact Info

Product

Resources

About