An auxiliary classification diagnosis software development of cervical cancer medical data based on various artificial neural networks

Artificial neural networks (ANN) assume a significant part in numerous clinical imaging applications. Cervical cancer ranks the 4th dangerous women cancers in less developed countries due to insufficient trained staffs and medical procedures. The location of cervical malignancy cells utilizes ANN for characterizing the typical and unusual cells in the cervix wall of the uterus. Cervical malignancy location is exceptionally difficult on the grounds that this disease happens with no manifestations. The order between the typical,unusual and malignant cells produces exact outcomes than other manual screening techniques.The ANN utilizes a few models for a simple and precise identification of cervical cells. The main aim of artificial neural networks is to supply right information at a right time. Hence we implement artificial neural techniques with collected data Analysis,to improve the life of an individual and to decrease the death rate of the society respectively.

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Risk Management in Software Development using Artificial Neural Networks

International Journal of Computer Applications ◽

10.5120/16468-6155 ◽

2014 ◽

Vol 93 (19) ◽

pp. 22-28

Author(s):

Amrita Gandhi ◽

Ajit Naik ◽

Kapil Thakkar ◽

Manisha Gahirwal

Keyword(s):

Neural Networks ◽

Risk Management ◽

Artificial Neural Networks ◽

Software Development ◽

Artificial Neural

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Auto Encoder and Deep Auto Encoders Based Deep Learning on Medical Data

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d8205.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 3832-3835

Keyword(s):

Neural Networks ◽

Feature Extraction ◽

Artificial Neural Networks ◽

Deep Learning ◽

Clustering Algorithms ◽

Medical Data ◽

Machine Learning Techniques ◽

Knowledge Generation ◽

Generation Process ◽

Artificial Neural

In rapid growth of medical informatics, patient data need to be organized and used for medical diagnosis and other uses such as disease prediction and drug discovery. There are many more traditional methods used for text based information such as K-NN, K-Means and other clustering algorithms, but image based medical data (or) signals based medical data is needed. So there is a need of new approaches for efficient classification and knowledge generation process. Artificial neural network based methods are mostly suited for deep learning, since there are many more approaches available in artificial neural networks. Deep learning and Machine learning techniques requires efficient pattern or feature extraction and pattern identification. Auto encoders and deep auto encoders works based on artificial neural networks and most suitable multimodal data feature extraction and identification. In this paper we have to show deep learning methods such as auto encoder and deep auto encoders for classifying multimodal medical data.

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The Role of Neural Networks and Metaheuristics in Agile Software Development Effort Estimation

10.4018/978-1-6684-2408-7.ch014 ◽

2022 ◽

pp. 306-328

Author(s):

Anupama Kaushik ◽

Devendra Kumar Tayal ◽

Kalpana Yadav

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Software Development ◽

Statistical Tests ◽

Agile Software Development ◽

Effort Estimation ◽

Software Development Effort ◽

Artificial Neural ◽

Agile Software

In any software development, accurate estimation of resources is one of the crucial tasks that leads to a successful project development. A lot of work has been done in estimation of effort in traditional software development. But, work on estimation of effort for agile software development is very scant. This paper provides an effort estimation technique for agile software development using artificial neural networks (ANN) and a metaheuristic technique. The artificial neural networks used are radial basis function neural network (RBFN) and functional link artificial neural network (FLANN). The metaheuristic technique used is whale optimization algorithm (WOA), which is a nature-inspired metaheuristic technique. The proposed techniques FLANN-WOA and RBFN-WOA are evaluated on three agile datasets, and it is found that these neural network models performed extremely well with the metaheuristic technique used. This is further empirically validated using non-parametric statistical tests.

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