A work point count system coupled with back-propagation for solving double dummy bridge problem

Amalraj, R.; Dharmalingam, M.

doi:10.1016/j.neucom.2015.06.001

Cited by 13 publications

(4 citation statements)

References 14 publications

(36 reference statements)

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“…Artificial neural networks were discussed for measuring the digits of tricks and addressing the DDBP issue [27], [28]. A CNN was developed to evaluate image quality [29].…”

Section: Related Workmentioning

confidence: 99%

Deep Belief Structure Learning for Finger Vein Verification by Solving the Biometric Recognition Model

Muthusamy

2022

Preprint

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Vein authentication is a novel biometric method to authenticate the individuality of a person. The conventional biometric technique employs shape images and exact segments of finger veins for the verification process. To improve the verification accuracy, a novel Anisotropic Filtered Stromberg Feature Transform based on Tucker’s Congruence Deep Belief Structure Learning (AFSFT-TCDBSL) technique is intended. The proposed AFSFT-TCDBSL technique comprises one input, three hidden, and one output layers. The numbers of images are collected in the input, and input images are preprocessed using anisotropic diffusion filtering in the first hidden layer. Finally, the verification process is performed using Tucker’s congruence correlation coefficient (TCCC). Based on the correlation, the verification outputs are getting to the output layer. In this way, accurate finger vein verification is performed with superior accuracy and with a minimum false rate. We performed experimental assessments with different factors, such as PSNR, FVVA, FPR, and CT. The proposed ADFSFT-TCDBSL technique offers better finger-vein verification results than the state-of-the-art methods.

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“…Artificial neural networks were discussed for measuring the digits of tricks and addressing the DDBP issue [27], [28]. A CNN was developed to evaluate image quality [29].…”

Section: Related Workmentioning

confidence: 99%

Deep Belief Structure Learning for Finger Vein Verification by Solving the Biometric Recognition Model

Muthusamy

2022

Preprint

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“…ANN proposes tools where humans are allowed to handle architectural decisions while optimization and traceability features are controlled and performed by networks mechanisms. ANN helps to develop associated links along with optimizing networks [51] [52].…”

Section: ) Artificial Neural Network (Ann)mentioning

confidence: 99%

A Comparative Analysis of Information and Artificial Intelligence Toward National Security

Al-Suqri,

Gillani

2022

IEEE Access

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Information is inevitable when it comes to national security. The information revolution seems to hold the massive potential to strengthen national security against current and upcoming threats and cyberattacks. However, advancements in information accessibility possess innumerable complications for retaining stable national security. One of the preeminent information sources is social media which certainly raises information manipulation factors and destabilizes national security. To accomplish better national security plans, information technology can help countries to identify potential threats, share information securely, and protect mechanisms in them. Artificial Intelligence (AI) is one of the smart areas that robustly facilitates secure information handling to avoid threats and cyber-attacks. It intelligently scrutinizes information available to the public through social media and assists in refraining negative effects on national security. This research article widely focuses on four main analytical milestones; 1) Information available to the public 2) Information affecting national security 3) Risks of cyber-attacks 4) AI as paramount to national security for accomplishing competent information role. Our principal objective is to demystify information accessibilities perspectives for readers to understand the fundamentals of information accessibility and inaccessibility corresponding to national security. To support and manifest our milestones and objectives, a Systematic Literature Review (SLR) is methodologically adapted to draw suitable conclusions and develop a farsighted frame of reference. This paper concludes with AI tool based categorization and domain-specific analysis with area-based limitations to highlight current needs. Above all, this article is a thought-provoking kick-start for many naive social media users that usually avoid information-bearing elements and are victimized by cyber-attacks followed by national security compromises.

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“…Algorithm 1: pseudo code of pruned cascade correlation learning algorithm derived from cascade correlation learning algorithm [7,11,20,27].…”

Section: Apruned Cascade Neural Network Algorithmmentioning

confidence: 99%

“…Dharmalingam (2015) etal [15] was solve double dummy bridge problem in gamming theory to neural network to solve the bridge problem on cascade correlation neural network and Elman neural network with work point count system in Rpropagation, cascade correlation is an superior of ENN method. Both methods are produce the better result [16][17] [20]. Scott Fahlman(1990) etal [22] is a supervised learning architecture which built a minimal multilayer topology, combines two ideas (i.e) cascade architecture , learning algorithm.…”

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confidence: 99%

Pruned Cascade Neural Network Image Classification

Praveenkumar¹,

Dharmalingam²

2019

IJRTE

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In this paper we propose a new model of deep neural network to build in deeper network. The convoluational neural network is one of the leading Image classification problem. The vanishing gradient problem requires us to use small learning rate with gradient descent which needs many small steps to converge and its take long time to proceed . By using GPU we can process more than one dataset (CIFAR-100) in a particular session. To overcome vanishing gradient problem by using the prune cascade correlation neural network learning algorithm compared to the deep cascade learning in CNN architecture. We improve the filter size, to reduce to the problem by training algorithm that trains in the network from bottom to top approach and its performing attain the task for better image classification in Google Net. We reduce the time complexity (training time ), storage capacity can be used pre training algorithm.

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A work point count system coupled with back-propagation for solving double dummy bridge problem

Cited by 13 publications

References 14 publications

Deep Belief Structure Learning for Finger Vein Verification by Solving the Biometric Recognition Model

Deep Belief Structure Learning for Finger Vein Verification by Solving the Biometric Recognition Model

A Comparative Analysis of Information and Artificial Intelligence Toward National Security

Pruned Cascade Neural Network Image Classification

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