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An offset-free MPC formulation for nonlinear systems using adaptive integral controller

Hermansson AW, Syafiie S

ISA Trans, 2019 Aug;91:66-77.
PMID: 30782432 DOI: 10.1016/j.isatra.2019.01.037

This paper investigates a novel offset-free control scheme based on a multiple model predictive controller (MMPC) and an adaptive integral action controller for nonlinear processes. Firstly, the multiple model description captures the essence of the nonlinear process, while keeping the MPC optimization linear. Multiple models also enable the controller to deal with the uncertainty associated with changing setpoint. Then, a min-max approach is utilized to counter the effect of parametric uncertainty between the linear models and the nonlinear process. Finally, to deal with other uncertainties, such as input and output disturbances, an adaptive integral action controller is run in parallel to the MMPC. Thus creating a novel offset-free approach for nonlinear systems that is more easily tuned than observer-based MPC. Simulation results for a pH-controller, which acts as an example of a nonlinear process, are presented to demonstrate the usefulness of the technique compared to using an observer-based MPC.
Fulltext Wearable IoT Smart-Log Patch: An Edge Computing-Based Bayesian Deep Learning Network System for Multi Access Physical Monitoring System

Manogaran G, Shakeel PM, Fouad H, Nam Y, Baskar S, Chilamkurti N, et al.

Sensors (Basel), 2019 Jul 09;19(13).
PMID: 31324070 DOI: 10.3390/s19133030

According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents' physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology.
Adaptive neuro-fuzzy inference system for breath phase detection and breath cycle segmentation

Palaniappan R, Sundaraj K, Sundaraj S

Comput Methods Programs Biomed, 2017 Jul;145:67-72.
PMID: 28552127 DOI: 10.1016/j.cmpb.2017.04.013

BACKGROUND: The monitoring of the respiratory rate is vital in several medical conditions, including sleep apnea because patients with sleep apnea exhibit an irregular respiratory rate compared with controls. Therefore, monitoring the respiratory rate by detecting the different breath phases is crucial.
OBJECTIVES: This study aimed to segment the breath cycles from pulmonary acoustic signals using the newly developed adaptive neuro-fuzzy inference system (ANFIS) based on breath phase detection and to subsequently evaluate the performance of the system.
METHODS: The normalised averaged power spectral density for each segment was fuzzified, and a set of fuzzy rules was formulated. The ANFIS was developed to detect the breath phases and subsequently perform breath cycle segmentation. To evaluate the performance of the proposed method, the root mean square error (RMSE) and correlation coefficient values were calculated and analysed, and the proposed method was then validated using data collected at KIMS Hospital and the RALE standard dataset.
RESULTS: The analysis of the correlation coefficient of the neuro-fuzzy model, which was performed to evaluate its performance, revealed a correlation strength of r = 0.9925, and the RMSE for the neuro-fuzzy model was found to equal 0.0069.
CONCLUSION: The proposed neuro-fuzzy model performs better than the fuzzy inference system (FIS) in detecting the breath phases and segmenting the breath cycles and requires less rules than FIS.

Matched MeSH terms: Neural Networks (Computer)*
A novel dengue fever (DF) and dengue haemorrhagic fever (DHF) analysis using artificial neural network (ANN)

Ibrahim F, Taib MN, Abas WA, Guan CC, Sulaiman S

Comput Methods Programs Biomed, 2005 Sep;79(3):273-81.
PMID: 15925426

Dengue fever (DF) is an acute febrile viral disease frequently presented with headache, bone or joint and muscular pains, and rash. A significant percentage of DF patients develop a more severe form of disease, known as dengue haemorrhagic fever (DHF). DHF is the complication of DF. The main pathophysiology of DHF is the development of plasma leakage from the capillary, resulting in haemoconcentration, ascites, and pleural effusion that may lead to shock following defervescence of fever. Therefore, accurate prediction of the day of defervescence of fever is critical for clinician to decide on patient management strategy. To date, no known literature describes of any attempt to predict the day of defervescence of fever in DF patients. This paper describes a non-invasive prediction system for predicting the day of defervescence of fever in dengue patients using artificial neural network. The developed system bases its prediction solely on the clinical symptoms and signs and uses the multilayer feed-forward neural networks (MFNN). The results show that the proposed system is able to predict the day of defervescence in dengue patients with 90% prediction accuracy.

Matched MeSH terms: Neural Networks (Computer)*
Response surface methodology and artificial neural network for remediation of acid orange 7 using TiO2-P25: optimization and modeling approach

Zulfiqar M, Chowdhury S, Omar AA, Siyal AA, Sufian S

Environ Sci Pollut Res Int, 2020 Sep;27(27):34018-34036.
PMID: 32557068 DOI: 10.1007/s11356-020-09674-4

The primary responsibility for continuously discharging toxic organic pollutants into water bodies and open environments is the increase in industrial and agricultural activities. Developing economical and suitable methods to continuously remove organic pollutants from wastewater is highly essential. The aim of the present research was to apply response surface methodology (RSM) and artificial neural networks (ANNs) for optimization and modeling of photocatalytic degradation of acid orange 7 (AO7) by commercial TiO2-P25 nanoparticles (TNPs). Dose of TNPs, pH, and AO7 concentration were selected as investigated parameters. RSM results reveal the reflective rate of AO7 removal of ~ 94.974% was obtained at pH 7.599, TNP dose of 0.748 g/L, and AO7 concentration of 28.483 mg/L. The resulting quadratic model is satisfactory with the highest coefficient of determination (R2) between the predicted and experimental data (R2 = 0.98 and adjusted R2 = 0.954). On the other hand, ANNs were successfully employed for modeling of AO7 degradation process. The proposed ANN model was absolutely fitted with experimental results producing the highest R2. Furthermore, root mean square error (RMSE), mean average deviation (MAD), absolute average relative error (AARE), and mean square error (MSE) were examined more to compare the predictive capabilities of ANN and RSM models. The experimental data was well fitted into pseudo-first-order and pseudo-second-order kinetics with more accuracy. Thermodynamic parameters, namely enthalpy, entropy, Gibbs' free energy, and activation energy, were also evaluated to suggest the nature of the degradation process. The increase of temperature was analyzed to be more suitable for the fast removal of AO7 over TNPs. Graphical abstract.

Matched MeSH terms: Neural Networks (Computer)*
Automated EEG-based screening of depression using deep convolutional neural network

Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H, Subha DP

Comput Methods Programs Biomed, 2018 Jul;161:103-113.
PMID: 29852953 DOI: 10.1016/j.cmpb.2018.04.012

In recent years, advanced neurocomputing and machine learning techniques have been used for Electroencephalogram (EEG)-based diagnosis of various neurological disorders. In this paper, a novel computer model is presented for EEG-based screening of depression using a deep neural network machine learning approach, known as Convolutional Neural Network (CNN). The proposed technique does not require a semi-manually-selected set of features to be fed into a classifier for classification. It learns automatically and adaptively from the input EEG signals to differentiate EEGs obtained from depressive and normal subjects. The model was tested using EEGs obtained from 15 normal and 15 depressed patients. The algorithm attained accuracies of 93.5% and 96.0% using EEG signals from the left and right hemisphere, respectively. It was discovered in this research that the EEG signals from the right hemisphere are more distinctive in depression than those from the left hemisphere. This discovery is consistent with recent research and revelation that the depression is associated with a hyperactive right hemisphere. An exciting extension of this research would be diagnosis of different stages and severity of depression and development of a Depression Severity Index (DSI).

Matched MeSH terms: Neural Networks (Computer)*
Artificial neural network based epileptic detection using time-domain and frequency-domain features

Srinivasan V, Eswaran C, Sriraam N

J Med Syst, 2005 Dec;29(6):647-60.
PMID: 16235818

Electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The long-term EEG recordings of an epileptic patient obtained from the ambulatory recording systems contain a large volume of EEG data. Detection of the epileptic activity requires a time consuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy have emerged in recent years. This paper discusses an automated diagnostic method for epileptic detection using a special type of recurrent neural network known as Elman network. The experiments are carried out by using time-domain as well as frequency-domain features of the EEG signal. Experimental results show that Elman network yields epileptic detection accuracy rates as high as 99.6% with a single input feature which is better than the results obtained by using other types of neural networks with two and more input features.

Matched MeSH terms: Neural Networks (Computer)*
Fulltext Forecasting ozone concentration levels using Box-Jenkins ARIMA modelling and artificial neural networks: a comparative study

Norhashidah Awang, Ng, Kar Yong, Soo, Yin Hoeng

MATEMATIKA, 2017;33(2):119-130.
MyJurnal

An accurate forecasting of tropospheric ozone (O3) concentration is benefi-
cial for strategic planning of air quality. In this study, various forecasting techniques are
used to forecast the daily maximum O3 concentration levels at a monitoring station
in the Klang Valley, Malaysia. The Box-Jenkins autoregressive integrated movingaverage
(ARIMA) approach and three types of neural network models, namely, backpropagation
neural network, Elman recurrent neural network and radial basis function
neural network are considered. The daily maximum data, spanning from 1 January
2011 to 7 August 2011, was obtained from the Department of Environment, Malaysia.
The performance of the four methods in forecasting future values of ozone concentrations
is evaluated based on three criteria, which are root mean square error (RMSE),
mean absolute error (MAE) and mean absolute percentage error (MAPE). The findings
show that the Box-Jenkins approach outperformed the artificial neural network
methods.
A new hybrid intelligent system for accurate detection of Parkinson's disease

Hariharan M, Polat K, Sindhu R

Comput Methods Programs Biomed, 2014 Mar;113(3):904-13.
PMID: 24485390 DOI: 10.1016/j.cmpb.2014.01.004

Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP is becoming a useful tool to differentiate them from healthy controls. Several dysphonia features, feature reduction/selection techniques and classification algorithms were proposed by researchers in the literature to detect PD. In this paper, hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection (SBS), and classification using three supervised classifiers such as least-square support vector machine (LS-SVM), probabilistic neural network (PNN) and general regression neural network (GRNN). PD dataset was used from University of California-Irvine (UCI) machine learning database. The strength of the proposed method has been evaluated through several performance measures. The experimental results show that the combination of feature pre-processing, feature reduction/selection methods and classification gives a maximum classification accuracy of 100% for the Parkinson's dataset.
Fulltext Artificial Neural Networks (ANNs) and Response Surface Methodology (RSM) Approach for Modelling the Optimization of Chromium (VI) Reduction by Newly Isolated Acinetobacter radioresistens Strain NS-MIE from Agricultural Soil

Ram Talib NS, Halmi MIE, Abd Ghani SS, Zaidan UH, Shukor MYA

Biomed Res Int, 2019;2019:5785387.
PMID: 31240217 DOI: 10.1155/2019/5785387

Numerous technologies and approaches have been used in the past few decades to remove hexavalent chromium (Cr[VI]) in wastewater and the environment. However, these conventional technologies are not economical and efficient in removing Cr(VI) at a very low concentration (1-100 ppm). As an alternative, the utilization of bioremediation techniques which uses the potential of microorganisms could represent an effective technique for the detoxification of Cr(VI). In this study, we reported a newly isolated bacterium identified as Acinetobacter radioresistens sp. NS-MIE from Malaysian agricultural soil. The chromate reduction potential of strain NS-MIE was optimized using RSM and ANN techniques. The optimum condition predicted by RSM for the bacterium to reduce hexavalent chromium occurred at pH 6, 10 g/L ppm of nutrient broth (NB) concentration and 100 ppm of chromate concentration while the optimum condition predicted by ANN is at pH 6 and 10 g/L of NB concentration and of 60 ppm of chromate concentration with chromate reduction (%) of 75.13 % and 96.27 %, respectively. The analysis by the ANN model shows better prediction data with a higher R2 value of 0.9991 and smaller average absolute deviation (AAD) and root mean square error (RMSE) of 0.33 % and 0.302 %, respectively. Validation analysis showed the predicted values by RSM and ANN were close to the validation values, whereas the ANN showed the lowest deviation, 2.57%, compared to the RSM. This finding suggests that the ANN showed a better prediction and fitting ability compared to the RSM for the nonlinear regression analysis. Based on this study, A. radioresistens sp. NS-MIE exhibits strong potential characteristics as a candidate for the bioremediation of hexavalent chromium in the environment.

Matched MeSH terms: Neural Networks (Computer)*
A tree-based multiclassification of breast tumor histopathology images through deep learning

Murtaza G, Abdul Wahab AW, Raza G, Shuib L

Comput Med Imaging Graph, 2021 04;89:101870.
PMID: 33545489 DOI: 10.1016/j.compmedimag.2021.101870

Worldwide, the burden of cancer is drastically increasing over the past few years. Among all types of cancers in women, breast cancer (BrC) is the main cause of unnatural deaths. For early diagnosis, histopathology (Hp) imaging is a gold standard for positive and detailed (at tissue level) diagnosis of breast tumor (BrT) compared to mammogram images. A large number of studies used BrT Hp images to solve binary or multiclassification problems using high computational resources. However, classification models' performance may be compromised due to the high correlation among various types of BrT in Hp images, which raises the misclassification rate. Thus, this paper aims to develop a tree-based BrT multiclassification model via deep learning (DL) to extract discriminative features to solve the multiclassification problem with better performance using less computational resources. The main contributions of this work are to create an ensemble, tree-based DL model that is pre-trained on the BreakHis dataset, and implementation of a misclassification reduction algorithm. The ensemble, tree-based DL model, extracts discriminative BrT features from Hp images. The target dataset (i.e., Bioimaging challenge 2015 breast histology) is small in size; thus, to avoid overfitting of the proposed model, pretraining is performed on the BreakHis dataset. Whereas, misclassification reduction algorithm is implemented to enhance the performance of the classification model. The experimental results show that the proposed model outperformed the existing state-of-the-art baseline studies. The achieved classification accuracy is ranging from 87.50 % to 100 % for four subtypes of BrT. Thus, the proposed model can assist doctors as the second opinion in any healthcare centre.
Trends in digital image processing of isolated microalgae by incorporating classification algorithm

Chong JWR, Khoo KS, Chew KW, Ting HY, Show PL

Biotechnol Adv, 2023;63:108095.
PMID: 36608745 DOI: 10.1016/j.biotechadv.2023.108095

Identification of microalgae species is of importance due to the uprising of harmful algae blooms affecting both the aquatic habitat and human health. Despite this occurence, microalgae have been identified as a green biomass and alternative source due to its promising bioactive compounds accumulation that play a significant role in many industrial applications. Recently, microalgae species identification has been conducted through DNA analysis and various microscopy techniques such as light, scanning electron, transmission electron, and atomic force -microscopy. The aforementioned procedures have encouraged researchers to consider alternate ways due to limitations such as costly validation, requiring skilled taxonomists, prolonged analysis, and low accuracy. This review highlights the potential innovations in digital microscopy with the incorporation of both hardware and software that can produce a reliable recognition, detection, enumeration, and real-time acquisition of microalgae species. Several steps such as image acquisition, processing, feature extraction, and selection are discussed, for the purpose of generating high image quality by removing unwanted artifacts and noise from the background. These steps of identification of microalgae species is performed by reliable image classification through machine learning as well as deep learning algorithms such as artificial neural networks, support vector machines, and convolutional neural networks. Overall, this review provides comprehensive insights into numerous possibilities of microalgae image identification, image pre-processing, and machine learning techniques to address the challenges in developing a robust digital classification tool for the future.
Fulltext Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features

Mahmud SMH, Goh KOM, Hosen MF, Nandi D, Shoombuatong W

Sci Rep, 2024 Feb 05;14(1):2961.
PMID: 38316843 DOI: 10.1038/s41598-024-52653-9

DNA-binding proteins (DBPs) play a significant role in all phases of genetic processes, including DNA recombination, repair, and modification. They are often utilized in drug discovery as fundamental elements of steroids, antibiotics, and anticancer drugs. Predicting them poses the most challenging task in proteomics research. Conventional experimental methods for DBP identification are costly and sometimes biased toward prediction. Therefore, developing powerful computational methods that can accurately and rapidly identify DBPs from sequence information is an urgent need. In this study, we propose a novel deep learning-based method called Deep-WET to accurately identify DBPs from primary sequence information. In Deep-WET, we employed three powerful feature encoding schemes containing Global Vectors, Word2Vec, and fastText to encode the protein sequence. Subsequently, these three features were sequentially combined and weighted using the weights obtained from the elements learned through the differential evolution (DE) algorithm. To enhance the predictive performance of Deep-WET, we applied the SHapley Additive exPlanations approach to remove irrelevant features. Finally, the optimal feature subset was input into convolutional neural networks to construct the Deep-WET predictor. Both cross-validation and independent tests indicated that Deep-WET achieved superior predictive performance compared to conventional machine learning classifiers. In addition, in extensive independent test, Deep-WET was effective and outperformed than several state-of-the-art methods for DBP prediction, with accuracy of 78.08%, MCC of 0.559, and AUC of 0.805. This superior performance shows that Deep-WET has a tremendous predictive capacity to predict DBPs. The web server of Deep-WET and curated datasets in this study are available at https://deepwet-dna.monarcatechnical.com/ . The proposed Deep-WET is anticipated to serve the community-wide effort for large-scale identification of potential DBPs.
Deep Deterministic Learning for Pattern Recognition of Different Cardiac Diseases through the Internet of Medical Things

Iqbal U, Wah TY, Habib Ur Rehman M, Mujtaba G, Imran M, Shoaib M

J Med Syst, 2018 Nov 05;42(12):252.
PMID: 30397730 DOI: 10.1007/s10916-018-1107-2

Electrocardiography (ECG) sensors play a vital role in the Internet of Medical Things, and these sensors help in monitoring the electrical activity of the heart. ECG signal analysis can improve human life in many ways, from diagnosing diseases among cardiac patients to managing the lifestyles of diabetic patients. Abnormalities in heart activities lead to different cardiac diseases and arrhythmia. However, some cardiac diseases, such as myocardial infarction (MI) and atrial fibrillation (Af), require special attention due to their direct impact on human life. The classification of flattened T wave cases of MI in ECG signals and how much of these cases are similar to ST-T changes in MI remain an open issue for researchers. This article presents a novel contribution to classify MI and Af. To this end, we propose a new approach called deep deterministic learning (DDL), which works by combining predefined heart activities with fused datasets. In this research, we used two datasets. The first dataset, Massachusetts Institute of Technology-Beth Israel Hospital, is publicly available, and we exclusively obtained the second dataset from the University of Malaya Medical Center, Kuala Lumpur Malaysia. We first initiated predefined activities on each individual dataset to recognize patterns between the ST-T change and flattened T wave cases and then used the data fusion approach to merge both datasets in a manner that delivers the most accurate pattern recognition results. The proposed DDL approach is a systematic stage-wise methodology that relies on accurate detection of R peaks in ECG signals, time domain features of ECG signals, and fine tune-up of artificial neural networks. The empirical evaluation shows high accuracy (i.e., ≤99.97%) in pattern matching ST-T changes and flattened T waves using the proposed DDL approach. The proposed pattern recognition approach is a significant contribution to the diagnosis of special cases of MI.
Fulltext Prediction of the Influential Factors on Eating Behaviors: A Hybrid Model of Structural Equation Modelling-Artificial Neural Networks

Kheirollahpour MM, Danaee MM, Merican AFAF, Shariff AAAA

ScientificWorldJournal, 2020;2020:4194293.
PMID: 32508538 DOI: 10.1155/2020/4194293

The importance of eating behavior risk factors in the primary prevention of obesity has been established. Researchers mostly use the linear model to determine associations among these risk factors. However, in reality, the presence of nonlinearity among these factors causes a bias in the prediction models. The aim of this study was to explore the potential of a hybrid model to predict the eating behaviors. The hybrid model of structural equation modelling (SEM) and artificial neural networks (ANN) was applied to evaluate the prediction model. The SEM analysis was used to check the relationship of the emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) and their effect on different categories of eating behavior patterns (EBP). In the second step, the input and output required for ANN analysis were obtained from SEM analysis and were applied in the neural network model. 340 university students participated in this study. The hybrid model (SEM-ANN) was conducted using multilayer perceptron (MLP) with feed-forward network topology. Moreover, Levenberg-Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The tangent/sigmoid function was used for the input layer, while the linear function was applied for the output layer. The coefficient of determination (R2) and mean square error (MSE) were calculated. Using the hybrid model, the optimal network happened at MLP 3-17-8. It was proved that the hybrid model was superior to SEM methods because the R2 of the model was increased by 27%, while the MSE was decreased by 9.6%. Moreover, it was found that BSC, BAS, and EES significantly affected healthy and unhealthy eating behavior patterns. Thus, a hybrid approach could be suggested as a significant methodological contribution from a machine learning standpoint, and it can be implemented as software to predict models with the highest accuracy.

Matched MeSH terms: Neural Networks (Computer)*
Fulltext Multistrategy self-organizing map learning for classification problems

Hasan S, Shamsuddin SM

Comput Intell Neurosci, 2011;2011:121787.
PMID: 21876686 DOI: 10.1155/2011/121787

Multistrategy Learning of Self-Organizing Map (SOM) and Particle Swarm Optimization (PSO) is commonly implemented in clustering domain due to its capabilities in handling complex data characteristics. However, some of these multistrategy learning architectures have weaknesses such as slow convergence time always being trapped in the local minima. This paper proposes multistrategy learning of SOM lattice structure with Particle Swarm Optimisation which is called ESOMPSO for solving various classification problems. The enhancement of SOM lattice structure is implemented by introducing a new hexagon formulation for better mapping quality in data classification and labeling. The weights of the enhanced SOM are optimised using PSO to obtain better output quality. The proposed method has been tested on various standard datasets with substantial comparisons with existing SOM network and various distance measurement. The results show that our proposed method yields a promising result with better average accuracy and quantisation errors compared to the other methods as well as convincing significant test.

Matched MeSH terms: Neural Networks (Computer)*
Neuro-fuzzy method for predicting the viability of stem cells treated at different time-concentration conditions

Bindal P, Bindal U, Lin CW, Kasim NHA, Ramasamy TSAP, Dabbagh A, et al.

Technol Health Care, 2017 Dec 04;25(6):1041-1051.
PMID: 28800347 DOI: 10.3233/THC-170922

Dental stem cells isolated for human dental pulp are an excellent source for regenerative medicine and dentistry. Simulation of clinical scenario is one of the crucial challenges for evaluation of the efficacy of DPSCs in various regenerative therapies. In this study we evaluated the viability of DPSCs after treatment with artificial bacterial lipopolysaccharides (LPS) as the main component responsible for inducing inflammatory response in majority of the inflammatory conditions in clinical scenario. Although a number of studies have previously treated stem cells with LPS from bacteria, however the accuracy level of the outcome was not established. Here we have analyzed the outcome using adaptive neuro-fuzzy inferences system (ANFIS) to predict the viability of human DPSCs after treatment with bacterial LPS.

Matched MeSH terms: Neural Networks (Computer)*
Fulltext Morphological diagnosis of hematologic malignancy using feature fusion-based deep convolutional neural network

Yadav DP, Kumar D, Jalal AS, Kumar A, Singh KU, Shah MA

Sci Rep, 2023 Oct 09;13(1):16988.
PMID: 37813973 DOI: 10.1038/s41598-023-44210-7

Leukemia is a cancer of white blood cells characterized by immature lymphocytes. Due to blood cancer, many people die every year. Hence, the early detection of these blast cells is necessary for avoiding blood cancer. A novel deep convolutional neural network (CNN) 3SNet that has depth-wise convolution blocks to reduce the computation costs has been developed to aid the diagnosis of leukemia cells. The proposed method includes three inputs to the deep CNN model. These inputs are grayscale and their corresponding histogram of gradient (HOG) and local binary pattern (LBP) images. The HOG image finds the local shape, and the LBP image describes the leukaemia cell's texture pattern. The suggested model was trained and tested with images from the AML-Cytomorphology_LMU dataset. The mean average precision (MAP) for the cell with less than 100 images in the dataset was 84%, whereas for cells with more than 100 images in the dataset was 93.83%. In addition, the ROC curve area for these cells is more than 98%. This confirmed proposed model could be an adjunct tool to provide a second opinion to a doctor.
Wavelet neural networks applied to pulping of oil palm fronds

Zainuddin Z, Wan Daud WR, Pauline O, Shafie A

Bioresour Technol, 2011 Dec;102(23):10978-86.
PMID: 21996481 DOI: 10.1016/j.biortech.2011.09.080

In the organosolv pulping of the oil palm fronds, the influence of the operational variables of the pulping reactor (viz. cooking temperature and time, ethanol and NaOH concentration) on the properties of the resulting pulp (yield and kappa number) and paper sheets (tensile index and tear index) was investigated using a wavelet neural network model. The experimental results with error less than 0.0965 (in terms of MSE) were produced, and were then compared with those obtained from the response surface methodology. Performance assessment indicated that the neural network model possessed superior predictive ability than the polynomial model, since a very close agreement between the experimental and the predicted values was obtained.
Fulltext Optogenetic control of iPS cell-derived neurons in 2D and 3D culture systems using channelrhodopsin-2 expression driven by the synapsin-1 and calcium-calmodulin kinase II promoters

Lee SY, George JH, Nagel DA, Ye H, Kueberuwa G, Seymour LW

J Tissue Eng Regen Med, 2019 Mar;13(3):369-384.
PMID: 30550638 DOI: 10.1002/term.2786

Development of an optogenetically controllable human neural network model in three-dimensional (3D) cultures can provide an investigative system that is more physiologically relevant and better able to mimic aspects of human brain function. Light-sensitive neurons were generated by transducing channelrhodopsin-2 (ChR2) into human induced pluripotent stem cell (hiPSC) derived neural progenitor cells (Axol) using lentiviruses and cell-type specific promoters. A mixed population of human iPSC-derived cortical neurons, astrocytes and progenitor cells were obtained (Axol-ChR2) upon neural differentiation. Pan-neuronal promoter synapsin-1 (SYN1) and excitatory neuron-specific promoter calcium-calmodulin kinase II (CaMKII) were used to drive reporter gene expression in order to assess the differentiation status of the targeted cells. Expression of ChR2 and characterisation of subpopulations in differentiated Axol-ChR2 cells were evaluated using flow cytometry and immunofluorescent staining. These cells were transferred from 2D culture to 3D alginate hydrogel functionalised with arginine-glycine-aspartate (RGD) and small molecules (Y-27632). Improved RGD-alginate hydrogel was physically characterised and assessed for cell viability to serve as a generic 3D culture system for human pluripotent stem cells (hPSCs) and neuronal cells. Prior to cell encapsulation, neural network activities of Axol-ChR2 cells and primary neurons were investigated using calcium imaging. Results demonstrate that functional activities were successfully achieved through expression of ChR2- by both the CaMKII and SYN1 promoters. The RGD-alginate hydrogel system supports the growth of differentiated Axol-ChR2 cells whilst allowing detection of ChR2 expression upon light stimulation. This allows precise and non-invasive control of human neural networks in 3D.

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