Adaptive PSO Based Association Rule Mining Technique for Software Defect Classification Using ANN

Dhanalaxmi, B.; Naidu, G. Apparao; Anuradha, K.

doi:10.1016/j.procs.2015.02.041

Cited by 93 publications

(30 citation statements)

References 11 publications

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“…The recommended approach for detecting normal and abnormal instances in thermograms is designed in four key phases. These stages are as follows [10][11][12][13][14][15][16]…”

Section: Methodsmentioning

confidence: 99%

Breast Thermograms Asymmetry Analysis using Gabor filters

Yadlapalli¹,

Reddy²,

Sunitha³

et al. 2021

E3S Web Conf.

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Women are far more likely than males to acquire breast cancer, and current research indicates that this is entirely avoidable. It is also to blame for higher death rates among younger women compared to older women in nearly all developing nations. Medical imaging modalities are continuously in need of development. A variety of medical techniques have been employed to detect breast cancer in women. The most recent studies support mammography for breast cancer screening, although its sensitivity and specificity remain suboptimal, particularly in individuals with thick breast tissue, such as young women. As a result, alternative modalities, such as thermography, are required. Digital Infrared Thermal Imaging (DITI), as it is known, detects and records temperature changes on the skin’s surface. Thermography is well-known for its non-invasive, painless, cost-effective, and high recovery rates, as well as its potential to identify breast cancer at an early stage. Gabor filters are used to extract the textural characteristics of the left and right breasts. Using a support vector machine, the thermograms are then classified as normal or malignant based on textural asymmetry between the breasts (SVM). The accuracy achieved by combining Gabor features with an SVM classifier is around 84.5 percent. The early diagnosis of cancer with thermography enhances the patient’s chances of survival significantly since it may detect the disease in its early stages.

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“…The recommended approach for detecting normal and abnormal instances in thermograms is designed in four key phases. These stages are as follows [10][11][12][13][14][15][16]…”

Section: Methodsmentioning

confidence: 99%

Breast Thermograms Asymmetry Analysis using Gabor filters

Yadlapalli¹,

Reddy²,

Sunitha³

et al. 2021

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…The proposed software fault prediction method is based on data mining techniques and Particle Swarm Optimization (PSO) (Jiang et al, 2011). The PSO algorithm chooses powerful program features for the fault prediction process to minimize processing time (Dhanalaxmi et al, 2015). The Remarkable machine topographies are classified as contestants for imperfection extrapolation using data mining practices, such as Association Law Mining, Decision Tree and Naïve Bayes Classification.…”

Section: Software Error Prediction With Dm and Psomentioning

confidence: 99%

System Error Estimate using Combination of Classification and Optimization Technique

Alattas¹

2021

Journal of Computer Science

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“…In future, they intend to use AI based solution in the smart cities use cases. In [18][19][20][21][22] authors have been proposed image transformation and classification techniques used to classify features of the real time autonomous and electric vehicles images and also provided details about real time datasets.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence (AI) Framework for Multi-Modal Learning and Decision Making towards Autonomous and Electric Vehicles

Ramesh¹,

Praveen²

2021

E3S Web Conf.

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An electric vehicle with autonomous driving is a possibility provided technology innovations in multi-disciplinary approach. Electric vehicles leverage environmental conditions and are much desired in the contemporary world. Another great possibility is to strive for making the vehicle to drive itself (autonomous driving) provided instructions. When the two are combined, it leads to a different dimension of environmental safety and technology driven driving that has many pros and cons as well. It is still in its infancy and there is much research to be carried out. In this context, this paper is aimed at building an Artificial Intelligence (AI) framework that has dual goal of “monitoring and regulating power usage” and facilitating autonomous driving with technology-driven and real time knowledge required. A methodology is proposed with multiple deep learning methods. For instance, deep learning is used for localization of vehicle, path planning at high level and path planning for low level. Apart from this, there is reinforcement learning and transfer learning to speed up the process of gaining real time business intelligence. To facilitate real time knowledge discovery from given scenarios, both edge and cloud resources are appropriately exploited to benefit the vehicle as driving safety is given paramount importance. There is power management module where modular Recurrent Neural Network is used. Another module known as speed control is used to have real time control over the speed of the vehicle. The usage of AI framework makes the electronic and autonomous vehicles realize unprecedented possibilities in power management and safe autonomous driving. Key words: Artificial Intelligence Autonomous Driving Recurrent Neural Network Transfer Learning

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Adaptive PSO Based Association Rule Mining Technique for Software Defect Classification Using ANN

Cited by 93 publications

References 11 publications

Breast Thermograms Asymmetry Analysis using Gabor filters

Breast Thermograms Asymmetry Analysis using Gabor filters

System Error Estimate using Combination of Classification and Optimization Technique

Artificial Intelligence (AI) Framework for Multi-Modal Learning and Decision Making towards Autonomous and Electric Vehicles

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