An improved cuckoo search based extreme learning machine for medical data classification

Mohapatra, Puspanjali; Chakravarty, S.; Dash, P.K.

doi:10.1016/j.swevo.2015.05.003

Cited by 172 publications

(69 citation statements)

References 71 publications

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“…The recognition accuracy figure: in each diagram, the data that located in the third row and the third column is total identification accuracy of the network [8] .…”

Section: Assessment Criteria Of Recognition Resultsmentioning

confidence: 99%

An Empty Nester Recognition Model Based On a Feed Forward Neural Network

Sun

Feng

Xia

2015

2015 Second International Conference on Soft Computing and Machine Intelligence (ISCMI)

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With the trend of aging of society, the number of empty nesters is rising, which has become a social problem that cannot be ignored. In this paper, two empty nester recognition models were presented based on the analysis of calling list and user information table .Based on the normal data ,the empty nesters and their children's number can be identified by a recognition function. When the properties of a user are not adequate, recognition function cannot be applied to identify it. Feed forward neural network algorithm is used to solve this problem. The recognition accuracy rate can reach 73.3% after training the network. Based on the research, certain approaches can be made to improve the situations, which is beneficial to the development of a harmonious society.

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“…The recognition accuracy figure: in each diagram, the data that located in the third row and the third column is total identification accuracy of the network [8] .…”

Section: Assessment Criteria Of Recognition Resultsmentioning

confidence: 99%

An Empty Nester Recognition Model Based On a Feed Forward Neural Network

Sun

Feng

Xia

2015

2015 Second International Conference on Soft Computing and Machine Intelligence (ISCMI)

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“…In the past few years, ELM has been regarded as an effective solution for various applications, like active recognition [39], speech emotion recognition [40], and medical data classification [41], to name a few. With the fast developments in big data and distributed systems, there are also literatures dedicated to making the algorithm adaptive to large scale datasets [42] or Map-Reduce framework [43,44].…”

Section: Extreme Learning Machinementioning

confidence: 99%

Weighted Domain Transfer Extreme Learning Machine and Its Online Version for Gas Sensor Drift Compensation in E‐Nose Systems

Ma¹,

Luo²,

Qin³

et al. 2018

Wireless Communications and Mobile Computing

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Machine learning approaches have been widely used to tackle the problem of sensor array drift in E-Nose systems. However, labeled data are rare in practice, which makes supervised learning methods hard to be applied. Meanwhile, current solutions require updating the analytical model in an offline manner, which hampers their uses for online scenarios. In this paper, we extended Target Domain Adaptation Extreme Learning Machine (DAELM T) to achieve high accuracy with less labeled samples by proposing a Weighted Domain Transfer Extreme Learning Machine, which uses clustering information as prior knowledge to help select proper labeled samples and calculate sensitive matrix for weighted learning. Furthermore, we converted DAELM T and the proposed method into their online learning versions under which scenario the labeled data are selected beforehand. Experimental results show that, for batch learning version, the proposed method uses around 20% less labeled samples while achieving approximately equivalent or better accuracy. As for the online versions, the methods maintain almost the same accuracies as their offline counterparts do, but the time cost remains around a constant value while that of offline versions grows with the number of samples.

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“…In 2014, Zhu et al, created an intelligent system for lung cancer diagnosis using an innovative genetic algorithm based feature choice technique [20]. In 2015, Mohapatra et al, created a better cuckoo search based extreme learning machine model for classifying medical data to diagnose medical problems [21]. Bonze et al, in 2016, developed a sophisticated SNP diseases selecting and organizing by hybrid association rule mining and artificial neural network which is based on evolutionary algorithms [6].…”

Section: Related Workmentioning

confidence: 99%

A Cognitive Inference Approach for Developing Medical Diagnostic Expert Systems

Chandiok¹,

Chaturvedi²

2018

IJCA

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Cognitive approaches are nowadays the most popular and widely used means of computing information due to its human-like sensing, comprehending, and action. The cognitive system can handle diverse data, uses modern technologies like natural language processing, machine learning, semantics, and decision support. It can have human interaction behavior with contextual data handling. The decisions are also not made on fixed rules, but human-like weight based judgments. Expert systems (ES's) are intelligent software tools that use acquired knowledge from experts in a specific domain to offer assistance to its users over a dialog or a query talks conducted between the user and the ES application software. This research work is for implementing a cognitive approach for determining diagnosis decisions providing expert system services. Cognitive strategies frequently denoted as "Human-like intelligent Computing Method" using restricted processing, storing and displaying skills. This paper presents a new diagnosis problem resolving model grounded in the investigation of the relationships among symptoms and illnesses in the form of certainty and severity elements. The article also acquaints with a modified knowledge representation appropriate for a cognitive system with limited structured data handling capabilities using XML language.The proposed model thrived and tested in the domain of medicals like fever (Flu, Pneumonia, and Cold Fever) using real knowledge base acquired by the mayo clinic. The earned consequences establish the strength and competence of the planned model. General Terms

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An improved cuckoo search based extreme learning machine for medical data classification

Cited by 172 publications

References 71 publications

An Empty Nester Recognition Model Based On a Feed Forward Neural Network

An Empty Nester Recognition Model Based On a Feed Forward Neural Network

Weighted Domain Transfer Extreme Learning Machine and Its Online Version for Gas Sensor Drift Compensation in E‐Nose Systems

A Cognitive Inference Approach for Developing Medical Diagnostic Expert Systems

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