EEG-Based Neonatal Sleep-Wake Classification Using Multilayer Perceptron Neural Network

Abbasi, Saadullah Farooq; Ahmad, Jawad; Tahir, Ahsen; Awais, Muhammad; Chen, Chen; Irfan, Muhammad; Siddiqa, Hafiza Ayesha; Waqas, Abu Bakar; Long, Xi; Yin, Bin; Akbarzadeh, Saeed; Lu, Chunmei; Wang, Laishuan; Chen, Wei

doi:10.1109/access.2020.3028182

Cited by 46 publications

(33 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e constraint satisfaction model established in this paper is a model in multilayer neural network [33][34][35]. Each node in the model represents a hypothesis.…”

Section: Constraint Satisfaction Modelmentioning

confidence: 99%

[Retracted] Optimization of Personnel Placement Scheme and Big Data Analysis Based on Multilayer Variable Neural Network Algorithm

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

People usually use the method of job analysis to understand the requirements of each job in terms of personnel characteristics, at the same time use the method of psychological measurement to understand the psychological characteristics of each person, and then put the personnel in the appropriate position by matching them with each other. With the development of the information age, massive and complex data are produced. How to accurately extract the effective data needed by the industry from the big data is a very arduous task. In reality, personnel data are influenced by many factors, and the time series formed by it is more accidental and random and often has multilevel and multiscale characteristics. How to use a certain algorithm or data processing technology to effectively dig out the rules contained in the personnel information data and explore the personnel placement scheme has become an important issue. In this paper, a multilayer variable neural network model for complex big data feature learning is established to optimize the staffing scheme. At the same time, the learning model is extended from vector space to tensor space. The parameters of neural network are inversed by high-order backpropagation algorithm facing tensor space. Compared with the traditional multilayer neural network calculation model based on tensor space, the multimodal neural network calculation model can learn the characteristics of complex data quickly and accurately and has obvious advantages.

show abstract

“…e constraint satisfaction model established in this paper is a model in multilayer neural network [33][34][35]. Each node in the model represents a hypothesis.…”

Section: Constraint Satisfaction Modelmentioning

confidence: 99%

[Retracted] Optimization of Personnel Placement Scheme and Big Data Analysis Based on Multilayer Variable Neural Network Algorithm

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…(i.e., bagging method and random forest classifier) and boosting strategies (i.e., Adaboost and XGBoost classifier). These methods are trained and evaluated on six datasets, as shown in Table (11). We adopt 10-fold cross-validation for the training phase.…”

Section: Discussion and Resultsmentioning

confidence: 99%

“…In addition, MLP requires a small training set and can be easily implemented [9] [10]. In MLP, each neuron j in the hidden or intermediate layer obtains the sum of its input variables xi after multiplying them by the related connection weights wij and then computes its output y as a sum of products [11]. Mathematically, .…”

Section: Mlpmentioning

confidence: 99%

Sequence Alignment Using Machine Learning-Based Needleman–Wunsch Algorithm

et al. 2021

View full text Add to dashboard Cite

Biological pairwise sequence alignment can be used as a method for arranging two biological sequence characters to identify regions of similarity. This operation has elicited considerable interest due to its significant influence on various critical aspects of life (e.g., identifying mutations in coronaviruses). Sequence alignment over large databases cannot yield results within a reasonable time, power, and cost. heuristic methods, such as FASTA, the BLAST family have been demonstrated to perform 40 times faster than DP-based (e.g., Needleman-Wunsch) techniques they cannot guarantee an optimum alignment result An optimized software platform of a widely used DNA sequence alignment algorithm called the Needleman-Wunsch (NW) algorithm based on a lookup table, is described in this study. This global alignment algorithm is the best approach for identifying similar regions between sequences. This study presents a new application of classical machine learning (ML) to global sequence alignment. Customized ML models are used to implement NW global alignment. An accuracy of 99.7% is achieved when using a multilayer perceptron with the ADAM optimizer, and up to 2912 Giga cell updates per second are realized on two real DNA sequences with a length of 4.1 M nucleotides. Our implementation is valid for RNA/DNA sequences. This study aims to parallelize the computation steps involved in the algorithm to accelerate its performance by using ML algorithms. All datasets used in this study are available from https://ieeedataport.org/documents/dna-sequence-alignment-datasets-based-nw-algorithm. INDEX TERMSBioinformatics, DNA, RNA, Pairwise sequence alignment (PWSA), Needleman-Wunsch (NW) algorithm, Machine learning (ML) algorithms, Multilayer perceptron (MLP), XGBoost algorithm. CONTRIBUTION:This study presented six DNA/RNA sequence alignment datasets for one of the most common alignment algorithms, namely, the Needleman-Wunsch (NW) algorithm. It proposed a fast and parallel implementation of the NW algorithm by using machine learning techniques. This research is an extension and improved version of our previous work [1]. The current implementation achieved 99.7% accuracy by using a multilayer perceptron with the ADAM optimizer and up to 2912 Giga cell updates per second on two real DNA sequences with an of length 4.1 M nucleotides. Our implementation is valid for extremely long sequences by using the divide-and-conquer strategy.

show abstract

“…For the remaining 4, "T5 -6," "F7 -8," "Cz" and "O1 -2" were not recorded. These led to 10 channels included [41]. Note that, in neonates, recognizable EEG patterns during sleep are mostly visible at a postmenstrual age (PMA) of larger than 32 weeks.…”

Section: Data Collectionmentioning

confidence: 99%

A Hybrid DCNN-SVM Model for Classifying Neonatal Sleep and Wake States Based on Facial Expressions in Video

Awais

Long

Yin

et al. 2021

IEEE J. Biomed. Health Inform.

Self Cite

View full text Add to dashboard Cite

published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

show abstract

EEG-Based Neonatal Sleep-Wake Classification Using Multilayer Perceptron Neural Network

Cited by 46 publications

References 40 publications

[Retracted] Optimization of Personnel Placement Scheme and Big Data Analysis Based on Multilayer Variable Neural Network Algorithm

[Retracted] Optimization of Personnel Placement Scheme and Big Data Analysis Based on Multilayer Variable Neural Network Algorithm

Sequence Alignment Using Machine Learning-Based Needleman–Wunsch Algorithm

A Hybrid DCNN-SVM Model for Classifying Neonatal Sleep and Wake States Based on Facial Expressions in Video

Contact Info

Product

Resources

About