Survey of Texture Based Image Processing and Analysis with Differential Fractional Calculus Methods

Ganga, M.; Janakiraman, N.; Sivaraman, Arun Kumar; Balasundaram, A.; Vincent, Rajiv; Rajesh, M.

doi:10.1109/icscan53069.2021.9526439

Cited by 11 publications

(5 citation statements)

References 30 publications

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“…A trust management protocol [12] was proposed to analyze the group communication systems where selfish nodes exist, and system survivability was essential to mission execution. In this approach, the tradeoff between a node's welfare, such as energy conservation for improving the network lifetime, and global welfare, such as achieving the given mission with enough service availability, was considered.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Reputation Based Trust Mechanism for Mitigating Attacks in MANET

S.¹,

Vijayabhasker²

2023

Intelligent Automation &Amp; Soft Computing

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Mobile Ad-hoc Networks (MANET) usage across the globe is increasing by the day. Evaluating a node's trust value has significant advantages since such network applications only run efficiently by involving trustable nodes. The trust values are estimated based on the reputation values of each node in the network by using different mechanisms. However, these mechanisms have various challenging issues which degrade the network performance. Hence, a novel Quality of Service (QoS) Trust Estimation with Black/Gray hole Attack Detection approach is proposed in this research work. Initially, the QoS-based trust estimation is proposed by using a Fuzzy logic scheme. The trust value of each node is estimated by using each node's reputation values which are determined based on the fuzzy membership function values and utilizing QoS parameters such as residual energy, bandwidth, node mobility, and reliability. This mechanism prevents only the black hole attack in the network during transmission. But, the gray hole attacks are not identified which in turn increases the packet drop rate significantly. Hence, the gray hole attack is also detected based on the Kullback-Leibler (KL) divergence method used for estimating the statistical measures. Additional QoS metrics are considered to prevent the gray hole attack, such as packet loss, packet delivery ratio, and delay for each node. Thus, the proposed mechanism prevents both black hole and gray hole attacks simultaneously. Finally, the simulation results show that the effectiveness of the proposed mechanism compared with the other trust-aware routing protocols in MANET.

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Section: Related Workmentioning

confidence: 99%

Fuzzy Reputation Based Trust Mechanism for Mitigating Attacks in MANET

S.¹,

Vijayabhasker²

2023

Intelligent Automation &Amp; Soft Computing

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“…The researchers studied the use of Naive Bayes (NB) and Support Vector Machine for machine learningbased sentiment classification of movie reviews (SVM) [8][9][10][11][12]. Sentiment Analysis is a two-class classification problem comprising Positive and Negative classes; this kind of study may be used to classify textual information and feature selection affects classifier performance.…”

Section: Related Workmentioning

confidence: 99%

An Ensemble Based Approach for Sentiment Classification in Asian Regional Language

Shelke¹,

Lee²,

Samanta³

et al. 2023

Computer Systems Science and Engineering

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In today's digital world, millions of individuals are linked to one another via the Internet and social media. This opens up new avenues for information exchange with others. Sentiment analysis (SA) has gotten a lot of attention during the last decade. We analyse the challenges of Sentiment Analysis (SA) in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which we first produced an annotated dataset composed of Marathi text acquired from microblogging websites such as Twitter. We also choose domain experts to manually annotate Marathi microblogging posts with positive, negative, and neutral polarity. In addition, to show the efficient use of the annotated dataset, an ensemble-based model for sentiment analysis was created. In contrast to others machine learning classifier, we achieved better performance in terms of accuracy for ensemble classifier with 10-fold cross-validation (cv), outcomes as 97.77%, f-score is 97.89%.

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“…Similarly, for data classification, decisions are made based on a set of features. Here, many structural or pattern authentication tasks are first used by the pathologist, and then the same features are used in the automated classification [13,14]. However, it can also be a difficult task.…”

Section: Figurementioning

confidence: 99%

Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

Arunachalam¹,

Janakiraman²,

Rashid³

et al. 2022

Computers, Materials &Amp; Continua

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In this research work, we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma (SS) is the cell structure for cancer. Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform. Subsequently, the structure features (SFs) such as Principal Components Analysis (PCA), Independent Components Analysis (ICA) and Linear Discriminant Analysis (LDA) were extracted from this subband image representation with the distribution of wavelet coefficients. These SFs are used as inputs of the Support Vector Machine (SVM) classifier. Also, classification of PCA + SVM, ICA + SVM, and LDA + SVM with Radial Basis Function (RBF) kernel the efficiency of the process is differentiated and compared with the best classification results. Furthermore, data collected on the internet from various histopathological centres via the Internet of Things (IoT) are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices. Due to this, the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration. Consequently, these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell (SSC) histopathological imaging databases. The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics (ROC) curve, and significant differences in classification performance between the techniques are analyzed. The combination of LDA + SVM technique has been proven to be essential for intelligent SS cancer detection in the future, and it offers excellent classification accuracy, sensitivity, specificity.

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Survey of Texture Based Image Processing and Analysis with Differential Fractional Calculus Methods

Cited by 11 publications

References 30 publications

Fuzzy Reputation Based Trust Mechanism for Mitigating Attacks in MANET

Fuzzy Reputation Based Trust Mechanism for Mitigating Attacks in MANET

An Ensemble Based Approach for Sentiment Classification in Asian Regional Language

Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

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