An Integrated Approach to Stage 1 Breast Cancer Detection

Fitzgerald, Jeannie; Ryan, Conor; Medernach, David; Krawiec, Krzysztof

doi:10.1145/2739480.2754761

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…We list a few applications. For example, in climatology for modeling global temperature changes [10], for creating a monitoring network to measure the state of groundwater [11], in healthcare for early detection of breast cancer [12], in genetics for operon prediction [13], and in bioinformatics [14]. Among industrial applications belong a management and reservation system for airlines [15], finding the optimal robot path for revising complex structures [16], solving vehicle route planning problems [17], or optimizing and simplifying design in CAD systems [18].…”

Section: Related Workmentioning

confidence: 99%

The Influence of Genetic Algorithms on Learning Possibilities of Artificial Neural Networks

et al. 2022

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The presented research study focuses on demonstrating the learning ability of a neural network using a genetic algorithm and finding the most suitable neural network topology for solving a demonstration problem. The network topology is significantly dependent on the level of generalization. More robust topology of a neural network is usually more suitable for particular details in the training set and it loses the ability to abstract general information. Therefore, we often design the network topology by taking into the account the required generalization, rather than the aspect of theoretical calculations. The next part of the article presents research whether a modification of the parameters of the genetic algorithm can achieve optimization and acceleration of the neural network learning process. The function of the neural network and its learning by using the genetic algorithm is demonstrated in a program for solving a computer game. The research focuses mainly on the assessment of the influence of changes in neural networks’ topology and changes in parameters in genetic algorithm on the achieved results and speed of neural network training. The achieved results are statistically presented and compared depending on the network topology and changes in the learning algorithm.

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Section: Related Workmentioning

confidence: 99%

The Influence of Genetic Algorithms on Learning Possibilities of Artificial Neural Networks

et al. 2022

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“…Genetic algorithms are mathematical optimization algorithms that solve engineering and other problems by representing the unknown variables as strings of digits that mimic DNA. This biomimetic approach has been hugely successful producing innovations in a range of fields (e.g., ophthalmology [ 31 ], oncology [ 32 ], engineering [ 33 ], and economics [ 34 ]); however, such work is rarely framed with any reference to the natural world or sustainability. Since 2010, there have been over 338,000 articles and books mentioning the approach, yet only 6700 (1.9%) mentioning sustainability and even fewer (0.8%) mentioning the word nature (results from Google Scholar, ) ignoring citations and patents.…”

Section: Epistemological Influences In Transdisciplinary Fields Ofmentioning

confidence: 99%

Navigating the Tower of Babel: The Epistemological Shift of Bioinspired Innovation

2020

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The disparity between disciplinary approaches to bioinspired innovation has created a cultural divide that is stifling to the overall advancement of the approach for sustainable societies. This paper aims to advance the effectiveness of bioinspired innovation processes for positive benefits through interdisciplinary communication by exploring the epistemological assumptions in various fields that contribute to the discipline. We propose that there is a shift in epistemological assumptions within bioinspired innovation processes at the points where biological models derived from reductionist approaches are interpreted as socially-constructed design principles, which are then realized in practical settings wrought with complexity and multiplicity. This epistemological shift from one position to another frequently leaves practitioners with erroneous assumptions due to a naturalistic fallacy. Drawing on examples in biology, we provide three recommendations to improve the clarity of the dialogue amongst interdisciplinary teams. (1) The deliberate articulation of epistemological perspectives amongst team members. (2) The application of a gradient orientation towards sustainability instead of a dichotomous orientation. (3) Ongoing dialogue and further research to develop novel epistemological approaches towards the topic. Adopting these recommendations could further advance the effectiveness of bioinspired innovation processes to positively impact social and ecological systems.

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“…The gene in GA takes on a value from an alphabet of sizeB ∈ C ) from each string or chromosome then consists of a series of n genes resulting in a solution space of size B ( . The binary alphabet {0, 1} is the most common and generally most effective string which means each gene takes on a possible value of 0 or 1: GA has established itself to be extremely effective in problems such as software testing [167], [170], medical diagnosis [68], [121], airline booking [13], seismic vibration [161], mechatronics and robot [76] and load-balancing [238], networks [110]. The simple algorithm of GA and flow chart for chromosome population are shown in algorithm 2 and figure 11 respectively.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

“…The mark sign of "√" is used to show where there exist tapped areas and the null shows that there is need for more attention from researchers. [75], [97], [113], [178], [197], [221], [222] [139], [152], [154], [156], [173], [208] [64], [93], [120], [131], [183] 5 Data mining [43], [65], [66], [116], [148], [160], [166], [188], [228] [74], [104], [123], [146], [206], [211] [35], [68], [103], [121], [142], [148], [161], [184], [186], [190], [216] 13…”

Section: Applications Of Soft Computingmentioning

confidence: 99%

A Holistic Review of Soft Computing Techniques

Philip¹,

Mom²,

Igwue³

2017

ACM

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Due to notable technological convergence that brought about exponential growth in computer world, SoftComputing (SC) has played a vital role with automation capability features to new levels of complex applications. In this research paper, the authors reviewed journals related to the subject matter with the aim of striking a convincing balance between a system that is capable of tolerance to uncertainty, imprecision, approximate reasoning and partial truth to achieve tractability, robustness, economy of communication, high machine intelligence quotient (MIQ), low cost solution and better rapport with reality to conventional techniques. This paper gives an insight on four major consortiums of SC that sprang from the concept of cybernetics, explores and reviews the different techniques, methodologies; application areas and algorithms are formulated to give an idea on how these computing techniques are applied to create intelligent agents to solve a variety of problems. The mechanisms highlighted can serve as an inspiration platform and awareness to new and old researchers that are not or fully grounded in this unique area of research and to create avenue in order to fully embrace the techniques in research communities.

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An Integrated Approach to Stage 1 Breast Cancer Detection

Cited by 5 publications

References 25 publications

The Influence of Genetic Algorithms on Learning Possibilities of Artificial Neural Networks

The Influence of Genetic Algorithms on Learning Possibilities of Artificial Neural Networks

Navigating the Tower of Babel: The Epistemological Shift of Bioinspired Innovation

A Holistic Review of Soft Computing Techniques

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