RideNN: A New Rider Optimization Algorithm-Based Neural Network for Fault Diagnosis in Analog Circuits

Binu, D.; Kariyappa, B S

doi:10.1109/tim.2018.2836058

Cited by 259 publications

(157 citation statements)

References 31 publications

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“…Here, the pixel map will be optimally generated for the watermarking and the image embedding using the proposed classifier. The proposed Moth-flamerider optimization algorithm-based Neural network (MF-ROA-based NN) classifier will be the integration of the standard RideNN [14] and moth-flame optimization (MFA) [15] such that the proposed classifier will generate the optimal map prediction based on the fitness measure, such as wavelet coefficient, energy, entropy, loop coefficient, and standard deviation, respectively. Then, the generated optimal pixel map will be forwarded to the embedding phase, where the watermark image will be embedded in the video.…”

Section: Proposed Methodologymentioning

confidence: 99%

A Robust Watermarking Method for an Authentication of Video Surveillance Applications

Mali,

Agilandeeswari

2019

IJEAT

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The problem of authenticating the video content is the major role of any automated video surveillance systems (AVS). This research work delivers an approach to authenticate the surveillance video which can be used by attorneys to prove their clients using Digital Watermarking. The digital watermark gives an assurance that the provided video surveillance frame is not tampered. In this paper, we proposed a robust video watermarking approach for an authentication of video surveillance applications. The digital watermark is embedded into the Discrete Wavelet domain of the identified key frames using holoentropy. Here, the pixel map will be optimallygenerated for the watermarking and the image embedding using the proposed classifier using Moth – flame - Rider optimization based Neural Network (MF-ROA-based NN) will generate the optimal map prediction based on the fitness measure, such as wavelet coefficient, energy, entropy, loop coefficient, and standard deviation, respectively. This optimal map is used for both embedding and extraction. The experimental results proves that the proposed systems can authenticate the surveillance video frames against various attacks when compared to the existing systems.

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Section: Proposed Methodologymentioning

confidence: 99%

A Robust Watermarking Method for an Authentication of Video Surveillance Applications

Mali,

Agilandeeswari

2019

IJEAT

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“…The ROA is a unique optimization algorithm, [13] which contemplates a few rider groups and can achieve global optimal solutions by speeding up the convergence rate.…”

Section: Literature Surveymentioning

confidence: 99%

“…The coordinate selector K is selected based on the on-time probability. 12The coordinate selector is given by the Eqn.13 (13) Overtaker update:…”

Section: Bypass Rider Updatementioning

confidence: 99%

A QoS-Oriented Novel Optimization Schemes for Web Service Composition for Improved Healthcare

Vaitheki.¹,

Kumar²,

Joseph*³

2019

IJITEE

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Due to massive increase in Web service in order to provide users an improved service it is necessary to develop a dynamic approach based on multi constraint Quality-of-Service (QoS) driven web service composition model to recommend suitable services to intended users is a big challenge. QoS plays a prominent role in selecting a Web Service. QoS is mainly determined by several non-functional parameters like availability, reliability, robustness, integrity, accessibility, interoperability, accuracy and security. It is evidenced based on experimentation that the proposed Improved Rider Optimization scheme achieves near optimal solution where group of riders racing towards a target location and attains adaptability and scalability by making a more confident web service selection with QoS prediction.

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“…Finally, the selected unique features are further processed in the master node, where the big data classification is done effectively using the proposed FABC+CFFRideNN approach. The NN classifier is an effective classifier and is employed for classification and the learning in the classifier is enabled using the proposed CFFRide algorithm, which is the integration of CFFO [31], and ROA [32]. Fig.…”

Section: Spark Architecture-based Big Data Classification In Basementioning

confidence: 99%

Spark Architecture and Fractional Artificial Bee Colony-Chaotic Fruitfly RideNN for Big data Classification in Internet of Things

Yousir*¹

2019

IJEAT

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The typical Internet of Things (IoT) device gathers a huge amount of data specifically termed as big data framework, which transfers the collected data from the sensing layer to the information processing layer. Various big data classification methods are adopted in the industrial applications, and smart cities, but accurately classifying the data in the IoT network poses a challenging task in the research community. Therefore, an effective big data classification model using spark-based architecture is proposed in this research. The big data classification is performed at the master node using the proposed Fractional Artificial Bee Colony- Chaotic Fruitfly Rider Optimization Algorithm (FABC-CFFRideNN). The concept of fictional computing is adopted by the rider optimization algorithm (ROA) to update the position of rider groups based on success rate and the foraging behavior of fruit flies along with the rider parameters is used to enhance to performance of data classification using the proposed CFFRideNN classifier. Moreover, the proposed Fractional Artificial Bee Colony- Chaotic Fruitfly Rider Optimization Algorithm attained better performance using the metrics, namely accuracy, specificity, and sensitivity with the values of 95.382%, 95.81%, and 98.824% for training percentage without node velocity.

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RideNN: A New Rider Optimization Algorithm-Based Neural Network for Fault Diagnosis in Analog Circuits

Cited by 259 publications

References 31 publications

A Robust Watermarking Method for an Authentication of Video Surveillance Applications

A Robust Watermarking Method for an Authentication of Video Surveillance Applications

A QoS-Oriented Novel Optimization Schemes for Web Service Composition for Improved Healthcare

Spark Architecture and Fractional Artificial Bee Colony-Chaotic Fruitfly RideNN for Big data Classification in Internet of Things

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