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iGluK-Deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions

Naseer S, Ali RF, Khan YD, Dominic PDD

J Biomol Struct Dyn, 2022;40(22):11691-11704.
PMID: 34396935 DOI: 10.1080/07391102.2021.1962738

Lysine glutarylation is a post-translation modification which plays an important regulatory role in a variety of physiological and enzymatic processes including mitochondrial functions and metabolic processes both in eukaryotic and prokaryotic cells. This post-translational modification influences chromatin structure and thereby results in global regulation of transcription, defects in cell-cycle progression, DNA damage repair, and telomere silencing. To better understand the mechanism of lysine glutarylation, its identification in a protein is necessary, however, experimental methods are time-consuming and labor-intensive. Herein, we propose a new computational prediction approach to supplement experimental methods for identification of lysine glutarylation site prediction by deep neural networks and Chou's Pseudo Amino Acid Composition (PseAAC). We employed well-known deep neural networks for feature representation learning and classification of peptide sequences. Our approach opts raw pseudo amino acid compositions and obsoletes the need to separately perform costly and cumbersome feature extraction and selection. Among the developed deep learning-based predictors, the standard neural network-based predictor demonstrated highest scores in terms of accuracy and all other performance evaluation measures and outperforms majority of previously reported predictors without requiring expensive feature extraction process. iGluK-Deep:Computational Identification of lysine glutarylationsites using deep neural networks with general Pseudo Amino Acid Compositions Sheraz Naseer, Rao Faizan Ali, Yaser Daanial Khan, P.D.D DominicCommunicated by Ramaswamy H. Sarma.
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.
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.
Water quality modelling using principal component analysis and artificial neural network

Ibrahim A, Ismail A, Juahir H, Iliyasu AB, Wailare BT, Mukhtar M, et al.

Mar Pollut Bull, 2023 Feb;187:114493.
PMID: 36566515 DOI: 10.1016/j.marpolbul.2022.114493

The study investigates the latent pollution sources and most significant parameters that cause spatial variation and develops the best input for water quality modelling using principal component analysis (PCA) and artificial neural network (ANN). The dataset, 22 water quality parameters were obtained from Department of Environment Malaysia (DOE). The PCA generated six significant principal component scores (PCs) which explained 65.40 % of the total variance. Parameters for water quality variation are mainlyrelated to mineral components, anthropogenic activities, and natural processes. However, in ANN three input combination models (ANN A, B, and C) were developed to identify the best model that can predict water quality index (WQI) with very high precision. ANN A model appears to have the best prediction capacity with a coefficient of determination (R2) = 0.9999 and root mean square error (RMSE) = 0.0537. These results proved that the PCA and ANN methods can be applied as tools for decision-making and problem-solving for better managing of river quality.
Water quality assessment and analysis of spatial patterns and temporal trends

Gazzaz NM, Yusoff MK, Juahir H, Ramli MF, Aris AZ

Water Environ Res, 2013 Aug;85(8):751-66.
PMID: 24003601

This study investigated relationships of a water quality index (WQI) with multiple water quality variables (WQVs), explored variability in water quality over time and space, and established linear and non-linear models predictive of WQI from raw WQVs. Data were processed using Spearman's rank correlation analysis, multiple linear regression, and artificial neural network modeling. Correlation analysis indicated that from a temporal perspective, the WQI, temperature, and zinc, arsenic, chemical oxygen demand, sodium, and dissolved oxygen concentrations increased, whereas turbidity and suspended solids, total solids, nitrate nitrogen (NO3-N), and biochemical oxygen demand concentrations decreased with year. From a spatial perspective, an increase with distance of the sampling station from the headwater was exhibited by 10 WQVs: magnesium, calcium, dissolved solids, electrical conductivity, temperature, NO3-N, arsenic, chloride, potassium, and sodium. At the same time, the WQI; Escherichia coli bacteria counts; and suspended solids, total solids, and dissolved oxygen concentrations decreased with distance from the headwater. Lastly, regression and artificial neural network models with high prediction powers (81.2% and 91.4%, respectively) were developed and are discussed.
Fulltext Vision-Based Apple Classification for Smart Manufacturing

Ismail A, Idris MYI, Ayub MN, Por LY

Sensors (Basel), 2018 Dec 10;18(12).
PMID: 30544660 DOI: 10.3390/s18124353

Smart manufacturing enables an efficient manufacturing process by optimizing production and product transaction. The optimization is performed through data analytics that requires reliable and informative data as input. Therefore, in this paper, an accurate data capture approach based on a vision sensor is proposed. Three image recognition methods are studied to determine the best vision-based classification technique, namely Bag of Words (BOW), Spatial Pyramid Matching (SPM) and Convolutional Neural Network (CNN). The vision-based classifiers categorize the apple as defective and non-defective that can be used for automatic inspection, sorting and further analytics. A total of 550 apple images are collected to test the classifiers. The images consist of 275 non-defective and 275 defective apples. The defective category includes various types of defect and severity. The vision-based classifiers are trained and evaluated according to the K-fold cross-validation. The performances of the classifiers from 2-fold, 3-fold, 4-fold, 5-fold and 10-fold are compared. From the evaluation, SPM with SVM classifier attained 98.15% classification accuracy for 10-fold and outperformed the others. In terms of computational time, CNN with SVM classifier is the fastest. However, minimal time difference is observed between the computational time of CNN and SPM, which were separated by only 0.05 s.
Valorization of groundnut shell via pyrolysis: Product distribution, thermodynamic analysis, kinetic estimation, and artificial neural network modeling

Hai A, Bharath G, Daud M, Rambabu K, Ali I, Hasan SW, et al.

Chemosphere, 2021 Nov;283:131162.
PMID: 34157626 DOI: 10.1016/j.chemosphere.2021.131162

Pyrolysis of agricultural biomass is a promising technique for producing renewable energy and effectively managing solid waste. In this study, groundnut shell (GNS) was processed at 500 °C in an inert gas atmosphere with a gas flow rate and a heating rate of 10 mL/min and 10 °C/min, respectively, in a custom-designed fluidized bed pyrolytic-reactor. Under optimal operating conditions, the GNS-derived pyrolytic-oil yield was 62.8 wt.%, with the corresponding biochar (19.5 wt.%) and biogas yields (17.7 wt.%). The GC-MS analysis of the GNS-based bio-oil confirmed the presence of (trifluoromethyl)pyridin-2-amine (18.814%), 2-Fluoroformyl-3,3,4,4-tetrafluoro-1,2-oxazetidine (16.23%), 5,7-dimethyl-1H-Indazole (11.613%), N-methyl-N-nitropropan-2-amine (6.5%) and butyl piperidino sulfone (5.668%) as major components, which are used as building blocks in the biofuel, pharmaceutical, and food industries. Furthermore, a 2 × 5 × 1 artificial neural network (ANN) architecture was developed to predict the decomposition behavior of GNS at heating rates of 5, 10, and 20 °C/min, while the thermodynamic and kinetic parameters were estimated using a non-isothermal model-free method. The Popescu method predicted activation energy (Ea) of GNS biomass ranging from 111 kJ/mol to 260 kJ/mol, with changes in enthalpy (ΔH), Gibbs-free energy (ΔG), and entropy (ΔS) ranging from 106 to 254 kJ/mol, 162-241 kJ/mol, and -0.0937 to 0.0598 kJ/mol/K, respectively. The extraction of high-quality precursors from GNS pyrolysis was demonstrated in this study, as well as the usefulness of the ANN technique for thermogravimetric analysis of biomass.

Matched MeSH terms: Neural Networks (Computer)*
Utilizing hybrid functional fuzzy wavelet neural networks with a teaching learning-based optimization algorithm for medical disease diagnosis

Majeed Alneamy JS, A Hameed Alnaish Z, Mohd Hashim SZ, Hamed Alnaish RA

Comput Biol Med, 2019 09;112:103348.
PMID: 31356992 DOI: 10.1016/j.compbiomed.2019.103348

Accurate medical disease diagnosis is considered to be an important classification problem. The main goal of the classification process is to determine the class to which a certain pattern belongs. In this article, a new classification technique based on a combination of The Teaching Learning-Based Optimization (TLBO) algorithm and Fuzzy Wavelet Neural Network (FWNN) with Functional Link Neural Network (FLNN) is proposed. In addition, the TLBO algorithm is utilized for training the new hybrid Functional Fuzzy Wavelet Neural Network (FFWNN) and optimizing the learning parameters, which are weights, dilation and translation. To evaluate the performance of the proposed method, five standard medical datasets were used: Breast Cancer, Heart Disease, Hepatitis, Pima-Indian diabetes and Appendicitis. The efficiency of the proposed method is evaluated using 5-fold cross-validation and 10-fold cross-validation in terms of mean square error (MSE), classification accuracy, running time, sensitivity, specificity and kappa. The experimental results show that the efficiency of the proposed method for the medical classification problems is 98.309%, 91.1%, 91.39%, 88.67% and 93.51% for the Breast Cancer, Heart Disease, Hepatitis, Pima-Indian diabetes and Appendicitis datasets, respectively, in terms of accuracy after 30 runs for each dataset with low computational complexity. In addition, it has been observed that the proposed method has efficient performance compared with the performance of other methods found in the related previous studies.

Matched MeSH terms: Neural Networks (Computer)*
Fulltext Using satellite-measured relative humidity for prediction of Metisa plana's population in oil palm plantations: A comparative assessment of regression and artificial neural network models

Ruslan SA, Muharam FM, Zulkafli Z, Omar D, Zambri MP

PLoS One, 2019;14(10):e0223968.
PMID: 31626637 DOI: 10.1371/journal.pone.0223968

Metisa plana (Walker) is a leaf defoliating pest that is able to cause staggering economical losses to oil palm cultivation. Considering the economic devastation that the pest could bring, an early warning system to predict its outbreak is crucial. The state of art of satellite technologies are now able to derive environmental factors such as relative humidity (RH) that may influence pest population's fluctuations in rapid, harmless, and cost-effective manners. This study examined the relationship between the presence of Metisa plana at different time lags and remote sensing (RS) derived RH by using statistical and machine learning approaches. Metisa plana census data of cumulated larvae instar 1, 2, 3, and 4 were collected biweekly in 2014 and 2015 in an oil palm plantation in Muadzam Shah, Pahang, Malaysia. Relative humidity values derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were apportioned to 6 time lags; 1 week (T1), 2 weeks (T2), 3 week (T3), 4 weeks (T4), 5 week (T5) and 6 weeks (T6) and paired with the respective census data. Pearson's correlation was carried out to analyse the relationship between Metisa plana and RH at different time lags. Regression analyses and artificial neural network (ANN) were also conducted to develop the best prediction model of Metisa plana's outbreak. The results showed relatively high correlations, positively or negatively, between the presences of Metisa plana with RH ranging from 0.46 to 0.99. ANN was found to be superior to regression models with the adjusted coefficient of determination (R2) between the actual and predicted Metisa plana values ranging from 0.06 to 0.57 versus 0.00 to 0.05. The analysis on the best time lags illustrated that the multiple time lags were more influential on the Metisa plana population than the individual time lags. The best Metisa plana prediction model was derived from T1, T2 and T3 multiple time lags modelled using the ANN algorithm with R2 value of 0.57, errors below 1.14 and accuracies above 93%. Based on the result of this study, the elucidation of Metisa plana's landscape ecology was possible with the utilization of RH as the predictor variable in consideration of the time lag effects of RH on the pest's population.

Matched MeSH terms: Neural Networks (Computer)*
Fulltext Using neural networks and just nine patient-reportable factors of screen for AMI

Bulgiba AM, Fisher MH

Health Informatics J, 2006 Sep;12(3):213-25.
PMID: 17023409 DOI: 10.1177/1460458206066665

The study investigated the effect of different input selections on the performance of artificial neural networks in screening for acute myocardial infarction (AMI) in Malaysian patients complaining of chest pain. We used hospital data to create neural networks with four input selections and used these to diagnose AMI. A 10-fold cross-validation and committee approach was used. All the neural networks using various input selections outperformed a multiple logistic regression model, although the difference was not statistically significant. The neural networks achieved an area under the ROC curve of 0.792 using nine inputs, whereas multiple logistic regression achieved 0.739 using 64 inputs. Sensitivity levels of over 90 per cent were achieved using low output threshold levels. Specificity levels of over 90 per cent were achieved using threshold levels of 0.4-0.5. Thus neural networks can perform as well as multiple logistic regression models even when using far fewer inputs.

Matched MeSH terms: Neural Networks (Computer)*
Fulltext Using cascade CNN-LSTM-FCNs to identify AI-altered video based on eye state sequence

Saealal MS, Ibrahim MZ, Mulvaney DJ, Shapiai MI, Fadilah N

PLoS One, 2022;17(12):e0278989.
PMID: 36520851 DOI: 10.1371/journal.pone.0278989

Deep learning is notably successful in data analysis, computer vision, and human control. Nevertheless, this approach has inevitably allowed the development of DeepFake video sequences and images that could be altered so that the changes are not easily or explicitly detectable. Such alterations have been recently used to spread false news or disinformation. This study aims to identify Deepfaked videos and images and alert viewers to the possible falsity of the information. The current work presented a novel means of revealing fake face videos by cascading the convolution network with recurrent neural networks and fully connected network (FCN) models. The system detection approach utilizes the eye-blinking state in temporal video frames. Notwithstanding, it is deemed challenging to precisely depict (i) artificiality in fake videos and (ii) spatial information within the individual frame through this physiological signal. Spatial features were extracted using the VGG16 network and trained with the ImageNet dataset. The temporal features were then extracted in every 20 sequences through the LSTM network. On another note, the pre-processed eye-blinking state served as a probability to generate a novel BPD dataset. This newly-acquired dataset was fed to three models for training purposes with each entailing four, three, and six hidden layers, respectively. Every model constitutes a unique architecture and specific dropout value. Resultantly, the model optimally and accurately identified tampered videos within the dataset. The study model was assessed using the current BPD dataset based on one of the most complex datasets (FaceForensic++) with 90.8% accuracy. Such precision was successfully maintained in datasets that were not used in the training process. The training process was also accelerated by lowering the computation prerequisites.

Matched MeSH terms: Neural Networks (Computer)*
Using autoencoders for mammogram compression

Tan CC, Eswaran C

J Med Syst, 2011 Feb;35(1):49-58.
PMID: 20703586 DOI: 10.1007/s10916-009-9340-3

This paper presents the results obtained for medical image compression using autoencoder neural networks. Since mammograms (medical images) are usually of big sizes, training of autoencoders becomes extremely tedious and difficult if the whole image is used for training. We show in this paper that the autoencoders can be trained successfully by using image patches instead of the whole image. The compression performances of different types of autoencoders are compared based on two parameters, namely mean square error and structural similarity index. It is found from the experimental results that the autoencoder which does not use Restricted Boltzmann Machine pre-training yields better results than those which use this pre-training method.

Matched MeSH terms: Neural Networks (Computer)*
Use of artificial neural networks to predict drug dissolution profiles and evaluation of network performance using similarity factor

Peh KK, Lim CP, Quek SS, Khoh KH

Pharm Res, 2000 Nov;17(11):1384-8.
PMID: 11205731

PURPOSE: To use artificial neural networks for predicting dissolution profiles of matrix-controlled release theophylline pellet preparation, and to evaluate the network performance by comparing the predicted dissolution profiles with those obtained from physical experiments using similarity factor.
METHODS: The Multi-Layered Perceptron (MLP) neural network was used to predict the dissolution profiles of theophylline pellets containing different ratios of microcrystalline cellulose (MCC) and glyceryl monostearate (GMS). The concepts of leave-one-out as well as a time-point by time-point estimation basis were used to predict the rate of drug release for each matrix ratio. All the data were used for training, except for one set which was selected to compare with the predicted output. The closeness between the predicted and the reference dissolution profiles was investigated using similarity factor (f2).
RESULTS: The f2 values were all above 60, indicating that the predicted dissolution profiles were closely similar to the dissolution profiles obtained from physical experiments.
CONCLUSION: The MLP network could be used as a model for predicting the dissolution profiles of matrix-controlled release theophylline pellet preparation in product development.

Matched MeSH terms: Neural Networks (Computer)*
Use of a Neural Net to Model the Impact of Optical Coherence Tomography Abnormalities on Vision in Age-related Macular Degeneration

Aslam TM, Zaki HR, Mahmood S, Ali ZC, Ahmad NA, Thorell MR, et al.

Am J Ophthalmol, 2018 Jan;185:94-100.
PMID: 29101008 DOI: 10.1016/j.ajo.2017.10.015

PURPOSE: To develop a neural network for the estimation of visual acuity from optical coherence tomography (OCT) images of patients with neovascular age-related macular degeneration (AMD) and to demonstrate its use to model the impact of specific controlled OCT changes on vision.
DESIGN: Artificial intelligence (neural network) study.
METHODS: We assessed 1400 OCT scans of patients with neovascular AMD. Fifteen physical features for each eligible OCT, as well as patient age, were used as input data and corresponding recorded visual acuity as the target data to train, validate, and test a supervised neural network. We then applied this network to model the impact on acuity of defined OCT changes in subretinal fluid, subretinal hyperreflective material, and loss of external limiting membrane (ELM) integrity.
RESULTS: A total of 1210 eligible OCT scans were analyzed, resulting in 1210 data points, which were each 16-dimensional. A 10-layer feed-forward neural network with 1 hidden layer of 10 neurons was trained to predict acuity and demonstrated a root mean square error of 8.2 letters for predicted compared to actual visual acuity and a mean regression coefficient of 0.85. A virtual model using this network demonstrated the relationship of visual acuity to specific, programmed changes in OCT characteristics. When ELM is intact, there is a shallow decline in acuity with increasing subretinal fluid but a much steeper decline with equivalent increasing subretinal hyperreflective material. When ELM is not intact, all visual acuities are reduced. Increasing subretinal hyperreflective material or subretinal fluid in this circumstance reduces vision further still, but with a smaller gradient than when ELM is intact.
CONCLUSIONS: The supervised machine learning neural network developed is able to generate an estimated visual acuity value from OCT images in a population of patients with AMD. These findings should be of clinical and research interest in macular degeneration, for example in estimating visual prognosis or highlighting the importance of developing treatments targeting more visually destructive pathologies.

Matched MeSH terms: Neural Networks (Computer)*
Fulltext Urban Vegetation Mapping from Aerial Imagery Using Explainable AI (XAI)

Abdollahi A, Pradhan B

Sensors (Basel), 2021 Jul 11;21(14).
PMID: 34300478 DOI: 10.3390/s21144738

Urban vegetation mapping is critical in many applications, i.e., preserving biodiversity, maintaining ecological balance, and minimizing the urban heat island effect. It is still challenging to extract accurate vegetation covers from aerial imagery using traditional classification approaches, because urban vegetation categories have complex spatial structures and similar spectral properties. Deep neural networks (DNNs) have shown a significant improvement in remote sensing image classification outcomes during the last few years. These methods are promising in this domain, yet unreliable for various reasons, such as the use of irrelevant descriptor features in the building of the models and lack of quality in the labeled image. Explainable AI (XAI) can help us gain insight into these limits and, as a result, adjust the training dataset and model as needed. Thus, in this work, we explain how an explanation model called Shapley additive explanations (SHAP) can be utilized for interpreting the output of the DNN model that is designed for classifying vegetation covers. We want to not only produce high-quality vegetation maps, but also rank the input parameters and select appropriate features for classification. Therefore, we test our method on vegetation mapping from aerial imagery based on spectral and textural features. Texture features can help overcome the limitations of poor spectral resolution in aerial imagery for vegetation mapping. The model was capable of obtaining an overall accuracy (OA) of 94.44% for vegetation cover mapping. The conclusions derived from SHAP plots demonstrate the high contribution of features, such as Hue, Brightness, GLCM_Dissimilarity, GLCM_Homogeneity, and GLCM_Mean to the output of the proposed model for vegetation mapping. Therefore, the study indicates that existing vegetation mapping strategies based only on spectral characteristics are insufficient to appropriately classify vegetation covers.

Matched MeSH terms: Neural Networks (Computer)*
Understanding consumer participation in managing ICT waste: Findings from two-staged Structural Equation Modeling-Artificial Neural Network approach

Najmi A, Kanapathy K, Aziz AA

Environ Sci Pollut Res Int, 2021 Mar;28(12):14782-14796.
PMID: 33219501 DOI: 10.1007/s11356-020-11675-2

For environmental management, the role of consumers is extremely important in the settings of reverse logistics. Though it is a manufacturer's extended responsibility to handle the waste however by becoming the supplier of the end of life products, consumers' participation needed to be encouraged and hence require proper attention. For the said purpose, the present study is conducted whereby crucial determinants of consumer reversing behavior were identified and analyzed by the help of a unique two-staged methodology of partial least square-structural equation modeling and artificial neural network. The data comprised of 746 collected by the survey from ICT users whereby the findings reported to have significant relationships of return intention and reversing behavior with their determinants. Moreover, the aforementioned unique methodology helps in generating more robust results as findings from ANN reported to have moral norm as most important variable which according to PLS-SEM was second most significant construct, whereas attitude was found to be second most important as per ANN which according to PLS-SEM is the most significant construct. Nevertheless, the study offers insights which contributes in the literature of environmental management, reverse logistics, and consumer behavior. Lastly, based on the findings, the managerial implications and recommendations are accordingly discussed.
Fulltext Tuning attention based long-short term memory neural networks for Parkinson's disease detection using modified metaheuristics

Cuk A, Bezdan T, Jovanovic L, Antonijevic M, Stankovic M, Simic V, et al.

Sci Rep, 2024 Feb 21;14(1):4309.
PMID: 38383690 DOI: 10.1038/s41598-024-54680-y

Parkinson's disease (PD) is a progressively debilitating neurodegenerative disorder that primarily affects the dopaminergic system in the basal ganglia, impacting millions of individuals globally. The clinical manifestations of the disease include resting tremors, muscle rigidity, bradykinesia, and postural instability. Diagnosis relies mainly on clinical evaluation, lacking reliable diagnostic tests and being inherently imprecise and subjective. Early detection of PD is crucial for initiating treatments that, while unable to cure the chronic condition, can enhance the life quality of patients and alleviate symptoms. This study explores the potential of utilizing long-short term memory neural networks (LSTM) with attention mechanisms to detect Parkinson's disease based on dual-task walking test data. Given that the performance of networks is significantly inductance by architecture and training parameter choices, a modified version of the recently introduced crayfish optimization algorithm (COA) is proposed, specifically tailored to the requirements of this investigation. The proposed optimizer is assessed on a publicly accessible real-world clinical gait in Parkinson's disease dataset, and the results demonstrate its promise, achieving an accuracy of 87.4187 % for the best-constructed models.
Fulltext Tropospheric Ozone Formation Estimation in Urban City, Bangi, Using Artificial Neural Network (ANN)

Abdul Aziz FAB, Abd Rahman N, Mohd Ali J

Comput Intell Neurosci, 2019;2019:6252983.
PMID: 31239836 DOI: 10.1155/2019/6252983

Due to the rapid development of economy and society around the world, the most urban city is experiencing tropospheric ozone or commonly known as ground-level air pollutants. The concentration of air pollutants must be identified as an early precaution step by the local environmental or health agencies. This work aims to apply the artificial neural network (ANN) in estimating the ozone concentration forecast in Bangi. It consists of input variables such as temperature, relative humidity, concentration of nitrogen dioxide, time, UVA and UVB rays obtained from routine monitoring, and data recorded. Ten hidden layer is utilized to obtain the optimized ozone concentration, which is the output layer of the ANN framework. The finding showed that the meteorology condition and emission patterns play an important part in influencing the ozone concentration. However, a single network is sufficient enough to estimate the concentration despite any circumstances. Thus, it can be concluded that ANN is able to give reliable and satisfactory estimations of ozone concentration for the following day.

Matched MeSH terms: Neural Networks (Computer)*
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 Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques

Illias HA, Chai XR, Abu Bakar AH, Mokhlis H

PLoS One, 2015;10(6):e0129363.
PMID: 26103634 DOI: 10.1371/journal.pone.0129363

It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

Matched MeSH terms: Neural Networks (Computer)*

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