Automatic Vision Based Classification System Using DNN and SVM Classifiers

Durairajah, Vickneswari; Gobee, Suresh; Muneer, Amgad

doi:10.1109/crc.2018.00011

Cited by 21 publications

(7 citation statements)

References 14 publications

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“…In addition, it utilises the advantages of Adagrad [67] to perform well in environments with sparse gradients, but it struggles with non-convex optimization of neural networks. It also use the advantage of Root Mean Square Propagation (RMSprop) [68] to address some of Adagrad's shortcomings and to perform well in online settings. Utilizing averages causes this method to converge to the bare minimum more quickly.…”

Section: Adam Optimizermentioning

confidence: 99%

A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data

Muneer¹,

Taib²,

Fati³

et al. 2022

Computers, Materials &Amp; Continua

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Anomaly detection in high dimensional data is a critical research issue with serious implication in the real-world problems. Many issues in this field still unsolved, so several modern anomaly detection methods struggle to maintain adequate accuracy due to the highly descriptive nature of big data. Such a phenomenon is referred to as the "curse of dimensionality" that affects traditional techniques in terms of both accuracy and performance. Thus, this research proposed a hybrid model based on Deep Autoencoder Neural Network (DANN) with five layers to reduce the difference between the input and output. The proposed model was applied to a real-world gas turbine (GT) dataset that contains 87620 columns and 56 rows. During the experiment, two issues have been investigated and solved to enhance the results. The first is the dataset class imbalance, which solved using SMOTE technique. The second issue is the poor performance, which can be solved using one of the optimization algorithms. Several optimization algorithms have been investigated and tested, including stochastic gradient descent (SGD), RMSprop, Adam and Adamax. However, Adamax optimization algorithm showed the best results when employed to train the DANN model. The experimental results show that our proposed model can detect the anomalies by efficiently reducing the high dimensionality of dataset with accuracy of 99.40%, F1-score of 0.9649, Area Under the Curve (AUC) rate of 0.9649, and a minimal loss function during the hybrid model training.

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Section: Adam Optimizermentioning

confidence: 99%

A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data

Muneer¹,

Taib²,

Fati³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

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“…CNNs are designed to handle learning problems involving high dimensional input data with complex spatial structures such as image classification [55], pattern recognition [56], amino acid sequence prediction [54,57], and time series failure signals. CNNs attempt to learn hierarchical filters that can transform large input data to accurate class labels using minimal trainable parameters.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis

et al. 2021

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Accurately predicting the remaining useful life (RUL) of the turbofan engine is of great significance for improving the reliability and safety of the engine system. Due to the high dimension and complex features of sensor data in RUL prediction, this paper proposes four data-driven prognostic models based on deep neural networks (DNNs) with an attention mechanism. To improve DNN feature extraction, data are prepared using a sliding time window technique. The raw data collected after normalizing is simply fed into the suggested network, requiring no prior knowledge of prognostics or signal processing and simplifying the proposed method's applicability. In order to verify the RUL prediction ability of the proposed DNN techniques, the C-MAPSS benchmark dataset of the turbofan engine system is validated. The experimental results showed that the developed long short-term memory (LSTM) model with attention mechanism achieved accurate RUL prediction in both scenarios with a high degree of robustness and generalization ability. Furthermore, the proposed model performance outperforms several state-of-the-art prognosis methods, where the LSTM-based model with attention mechanism achieved an RMSE of 12.87 and 11.23 for FD002 and FD003 subset of data, respectively.

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“…Preparations are often made in alcoholic beverages and/or honey or in other materials by steeping or heating herbal material. Herbal preparations consisting of one or more herbs are the finished herbal preparations [10]. The word "herbal product mixing" can also be used if more than one herb is used.…”

Section: Introductionmentioning

confidence: 99%

A Group Labelled Classification Model for Accurate Medical Plant Detection Used in Drug Preparation

Thella¹,

Ulagamuthalvi²

2021

RIA

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The use of medical plants in the preparation of medicines has been increased in recent years. Medical plants are an essential component in the production of medicinal products. Medicines are made from root powder or plant leaves. When the herbal medicine is reduced to powder, more experience is required to determine the medicinal product through pharmacognoses. Inaccurate medical plants can cause patients serious health problems. For standardization and quality control of medical drugs the correct identification of the powder shape of medical plants is important. Medical plants are currently classified using a chemical leaf-based assessment, physical assessment and biological assessment. In medicine industry it is extremely necessary to identify the right medicinal plants for the preparation of a medicine. Its leaves form, color and texture are the key features needed to recognize a medicinal plant. In hierarchical clustering technology, the coefficient of inconsistency is used to generate natural clusters. Intra class differences can be seen with the amount of clusters obtained for plant organisms. The aggregate of the corresponding vectors of each sample of a cluster is calculated for one cluster representation. In terms of its leaf samples, therefore, the multiple members of the valued interval type are used to represent the plants in an effective way. The proposed model performs classification of leaf features using group labelled clustering model and then perform locking of labelling. This paper considers a Group Labelled Classification (GLC) Model that examines feature on the front and back of a green leaf, along with morphological characteristics, to achieve a specific optimal combination of features that optimize the recognition rate. The proposed model efficiently extracts the relevant features only from the medical leaf for accurate medical leaf detection. The proposed model is compared with the traditional methods and the results show that the proposed model performance is better.

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Automatic Vision Based Classification System Using DNN and SVM Classifiers

Cited by 21 publications

References 14 publications

A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data

A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data

Data-Driven Deep Learning-Based Attention Mechanism for Remaining Useful Life Prediction: Case Study Application to Turbofan Engine Analysis

A Group Labelled Classification Model for Accurate Medical Plant Detection Used in Drug Preparation

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