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ASPAD dynamic simulation and artificial neural network for atenolol adsorption in GGSWAC packed bed column

Goh KZ, Ahmad AA, Ahmad MA

Environ Sci Pollut Res Int, 2024 Jan;31(1):1158-1176.
PMID: 38038911 DOI: 10.1007/s11356-023-31177-1

This study aimed to assess the dynamic simulation models provided by Aspen adsorption (ASPAD) and artificial neural network (ANN) in understanding the adsorption behavior of atenolol (ATN) on gasified Glyricidia sepium woodchips activated carbon (GGSWAC) within fixed bed columns for wastewater treatment. The findings demonstrated that increasing the bed height from 1 to 3 cm extended breakthrough and exhaustion times while enhancing adsorption capacity. Conversely, higher initial ATN concentrations resulted in shorter breakthrough and exhaustion times but increased adsorption capacity. Elevated influent flow rates reduced breakthrough and exhaustion times while maintaining constant adsorption capacity. The ASPAD software demonstrated competence in accurately modeling the crucial exhaustion points. However, there is room for enhancement in forecasting breakthrough times, as it exhibited deviations ranging from 6.52 to 239.53% when compared to the actual experimental data. ANN models in both MATLAB and Python demonstrated precise predictive abilities, with the Python model (R2 = 0.985) outperforming the MATLAB model (R2 = 0.9691). The Python ANN also exhibited superior fitting performance with lower MSE and MAE. The most influential factor was the initial ATN concentration (28.96%), followed by bed height (26.39%), influent flow rate (22.43%), and total effluent time (22.22%). The findings of this study offer an extensive comprehension of breakthrough patterns and enable accurate forecasts of column performance.
Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia

Jumin E, Basaruddin FB, Yusoff YBM, Latif SD, Ahmed AN

Environ Sci Pollut Res Int, 2021 Jun;28(21):26571-26583.
PMID: 33484461 DOI: 10.1007/s11356-021-12435-6

Reliable and accurate prediction model capturing the changes in solar radiation is essential in the power generation and renewable carbon-free energy industry. Malaysia has immense potential to develop such an industry due to its location in the equatorial zone and its climatic characteristics with high solar energy resources. However, solar energy accounts for only 2-4.6% of total energy utilization. Recently, in developed countries, various prediction models based on artificial intelligence (AI) techniques have been applied to predict solar radiation. In this study, one of the most recent AI algorithms, namely, boosted decision tree regression (BDTR) model, was applied to predict the changes in solar radiation based on collected data in Malaysia. The proposed model then compared with other conventional regression algorithms, such as linear regression and neural network. Two different normalization techniques (Gaussian normalizer binning normalizer), splitting size, and different input parameters were investigated to enhance the accuracy of the models. Sensitivity analysis and uncertainty analysis were introduced to validate the accuracy of the proposed model. The results revealed that BDTR outperformed other algorithms with a high level of accuracy. The funding of this study could be used as a reliable tool by engineers to improve the renewable energy sector in Malaysia and provide alternative sustainable energy resources.
Groundwater level forecasting with machine learning models: A review

Boo KBW, El-Shafie A, Othman F, Khan MMH, Birima AH, Ahmed AN

Water Res, 2024 Mar 15;252:121249.
PMID: 38330715 DOI: 10.1016/j.watres.2024.121249

Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Accurate forecasting of groundwater level (GWL) is essential for effective management of this vital resource, but it remains a complex and challenging task. In recent years, there has been a notable increase in the use of machine learning (ML) techniques to model GWL, with many studies reporting exceptional results. In this paper, we present a comprehensive review of 142 relevant articles indexed by the Web of Science from 2017 to 2023, focusing on key ML models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support vector regression (SVR), evolutionary computing (EC), deep learning (DL), ensemble learning (EN), and hybrid-modeling (HM). We also discussed key modeling concepts such as dataset size, data splitting, input variable selection, forecasting time-step, performance metrics (PM), study zones, and aquifers, highlighting best practices for optimal GWL forecasting with ML. This review provides valuable insights and recommendations for researchers and water management agencies working in the field of groundwater management and hydrology.
Frequency analysis of capnogram signals to differentiate asthmatic and non-asthmatic conditions using radial basis function neural networks

Kazemi M, Bala Krishnan M, Aik Howe T

Iran J Allergy Asthma Immunol, 2013 Sep;12(3):236-46.
PMID: 23893807

In this paper, the method of differentiating asthmatic and non-asthmatic patients using the frequency analysis of capnogram signals is presented. Previously, manual study on capnogram signal has been conducted by several researchers. All past researches showed significant correlation between capnogram signals and asthmatic patients. However all of them are just manual study conducted through the conventional time domain method. In this study, the power spectral density (PSD) of capnogram signals is estimated by using Fast Fourier Transform (FFT) and Autoregressive (AR) modelling. The results show the non-asthmatic capnograms have one component in their PSD estimation, in contrast to asthmatic capnograms that have two components. Furthermore, there is a significant difference between the magnitude of the first component for both asthmatic and non-asthmatic capnograms. The effectiveness and performance of manipulating the characteristics of the first frequency component, mainly its magnitude and bandwidth, to differentiate between asthmatic and non-asthmatic conditions by means of receiver operating characteristic (ROC) curve analysis and radial basis function (RBF) neural network were shown. The output of this network is an integer prognostic index from 1 to 10 (depends on the severity of asthma) with an average good detection rate of 95.65% and an error rate of 4.34%. This developed algorithm is aspired to provide a fast and low-cost diagnostic system to help healthcare professional involved in respiratory care as it would be possible to monitor severity of asthma automatically and instantaneously.

Matched MeSH terms: Neural Networks (Computer)*
Emotional speech acoustic model for Malay: iterative versus isolated unit training

Mustafa MB, Ainon RN

J Acoust Soc Am, 2013 Oct;134(4):3057-66.
PMID: 24116440 DOI: 10.1121/1.4818741

The ability of speech synthesis system to synthesize emotional speech enhances the user's experience when using this kind of system and its related applications. However, the development of an emotional speech synthesis system is a daunting task in view of the complexity of human emotional speech. The more recent state-of-the-art speech synthesis systems, such as the one based on hidden Markov models, can synthesize emotional speech with acceptable naturalness with the use of a good emotional speech acoustic model. However, building an emotional speech acoustic model requires adequate resources including segment-phonetic labels of emotional speech, which is a problem for many under-resourced languages, including Malay. This research shows how it is possible to build an emotional speech acoustic model for Malay with minimal resources. To achieve this objective, two forms of initialization methods were considered: iterative training using the deterministic annealing expectation maximization algorithm and the isolated unit training. The seed model for the automatic segmentation is a neutral speech acoustic model, which was transformed to target emotion using two transformation techniques: model adaptation and context-dependent boundary refinement. Two forms of evaluation have been performed: an objective evaluation measuring the prosody error and a listening evaluation to measure the naturalness of the synthesized emotional speech.
Fulltext Multiple adaptive neuro-fuzzy inference system with automatic features extraction algorithm for cervical cancer recognition

Al-batah MS, Isa NA, Klaib MF, Al-Betar MA

Comput Math Methods Med, 2014;2014:181245.
PMID: 24707316 DOI: 10.1155/2014/181245

To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy.
Fulltext A novel clustering algorithm for mobile ad hoc networks based on determination of virtual links' weight to increase network stability

Karimi A, Afsharfarnia A, Zarafshan F, Al-Haddad SA

ScientificWorldJournal, 2014;2014:432952.
PMID: 25114965 DOI: 10.1155/2014/432952

The stability of clusters is a serious issue in mobile ad hoc networks. Low stability of clusters may lead to rapid failure of clusters, high energy consumption for reclustering, and decrease in the overall network stability in mobile ad hoc network. In order to improve the stability of clusters, weight-based clustering algorithms are utilized. However, these algorithms only use limited features of the nodes. Thus, they decrease the weight accuracy in determining node's competency and lead to incorrect selection of cluster heads. A new weight-based algorithm presented in this paper not only determines node's weight using its own features, but also considers the direct effect of feature of adjacent nodes. It determines the weight of virtual links between nodes and the effect of the weights on determining node's final weight. By using this strategy, the highest weight is assigned to the best choices for being the cluster heads and the accuracy of nodes selection increases. The performance of new algorithm is analyzed by using computer simulation. The results show that produced clusters have longer lifetime and higher stability. Mathematical simulation shows that this algorithm has high availability in case of failure.

Matched MeSH terms: Neural Networks (Computer)*
Determination of DPPH free radical scavenging activity: application of artificial neural networks

Musa KH, Abdullah A, Al-Haiqi A

Food Chem, 2016 Mar 1;194:705-11.
PMID: 26471610 DOI: 10.1016/j.foodchem.2015.08.038

A new computational approach for the determination of 2,2-diphenyl-1-picrylhydrazyl free radical scavenging activity (DPPH-RSA) in food is reported, based on the concept of machine learning. Trolox standard was mix with DPPH at different concentrations to produce different colors from purple to yellow. Artificial neural network (ANN) was trained on a typical set of images of the DPPH radical reacting with different levels of Trolox. This allowed the neural network to classify future images of any sample into the correct class of RSA level. The ANN was then able to determine the DPPH-RSA of cinnamon, clove, mung bean, red bean, red rice, brown rice, black rice and tea extract and the results were compared with data obtained using a spectrophotometer. The application of ANN correlated well to the spectrophotometric classical procedure and thus do not require the use of spectrophotometer, and it could be used to obtain semi-quantitative results of DPPH-RSA.

Matched MeSH terms: Neural Networks (Computer)*
Fulltext Forecasting stock prices changes using long-short term memory neural network with symbolic genetic programming

Li Q, Kamaruddin N, Yuhaniz SS, Al-Jaifi HAA

Sci Rep, 2024 Jan 03;14(1):422.
PMID: 38172568 DOI: 10.1038/s41598-023-50783-0

This study introduces an augmented Long-Short Term Memory (LSTM) neural network architecture, integrating Symbolic Genetic Programming (SGP), with the objective of forecasting cross-sectional price returns across a comprehensive dataset comprising 4500 listed stocks in the Chinese market over the period from 2014 to 2022. Using the S&P Alpha Pool Dataset for China as basic input, this architecture incorporates data augmentation and feature extraction techniques. The result of this study demonstrates significant improvements in Rank Information coefficient (Rank IC) and IC information ratio (ICIR) by 1128% and 5360% respectively when it is applied to fundamental indicators. For technical indicators, the hybrid model achieves a 206% increase in Rank IC and an impressive surge of 2752% in ICIR. Furthermore, the proposed hybrid SGP-LSTM model outperforms major Chinese stock indexes, generating average annualized excess returns of 31.00%, 24.48%, and 16.38% compared to the CSI 300 index, CSI 500 index, and the average portfolio, respectively. These findings highlight the effectiveness of SGP-LSTM model in improving the accuracy of cross-sectional stock return predictions and provide valuable insights for fund managers, traders, and financial analysts.

Matched MeSH terms: Neural Networks (Computer)*
Fulltext Determination of important topographic factors for landslide mapping analysis using MLP network

Alkhasawneh MSh, Ngah UK, Tay LT, Mat Isa NA, Al-batah MS

ScientificWorldJournal, 2013;2013:415023.
PMID: 24453846 DOI: 10.1155/2013/415023

Landslide is one of the natural disasters that occur in Malaysia. Topographic factors such as elevation, slope angle, slope aspect, general curvature, plan curvature, and profile curvature are considered as the main causes of landslides. In order to determine the dominant topographic factors in landslide mapping analysis, a study was conducted and presented in this paper. There are three main stages involved in this study. The first stage is the extraction of extra topographic factors. Previous landslide studies had identified mainly six topographic factors. Seven new additional factors have been proposed in this study. They are longitude curvature, tangential curvature, cross section curvature, surface area, diagonal line length, surface roughness, and rugosity. The second stage is the specification of the weight of each factor using two methods. The methods are multilayer perceptron (MLP) network classification accuracy and Zhou's algorithm. At the third stage, the factors with higher weights were used to improve the MLP performance. Out of the thirteen factors, eight factors were considered as important factors, which are surface area, longitude curvature, diagonal length, slope angle, elevation, slope aspect, rugosity, and profile curvature. The classification accuracy of multilayer perceptron neural network has increased by 3% after the elimination of five less important factors.

Matched MeSH terms: Neural Networks (Computer)*
Fulltext Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry-A Systematic Review

Ahmed N, Abbasi MS, Zuberi F, Qamar W, Halim MSB, Maqsood A, et al.

Biomed Res Int, 2021;2021:9751564.
PMID: 34258283 DOI: 10.1155/2021/9751564

Objective: The objective of this systematic review was to investigate the quality and outcome of studies into artificial intelligence techniques, analysis, and effect in dentistry.
Materials and Methods: Using the MeSH keywords: artificial intelligence (AI), dentistry, AI in dentistry, neural networks and dentistry, machine learning, AI dental imaging, and AI treatment recommendations and dentistry. Two investigators performed an electronic search in 5 databases: PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). The English language articles reporting on AI in different dental specialties were screened for eligibility. Thirty-two full-text articles were selected and systematically analyzed according to a predefined inclusion criterion. These articles were analyzed as per a specific research question, and the relevant data based on article general characteristics, study and control groups, assessment methods, outcomes, and quality assessment were extracted.
Results: The initial search identified 175 articles related to AI in dentistry based on the title and abstracts. The full text of 38 articles was assessed for eligibility to exclude studies not fulfilling the inclusion criteria. Six articles not related to AI in dentistry were excluded. Thirty-two articles were included in the systematic review. It was revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making. Artificial intelligence appeared as a reliable modality to enhance future implications in the various fields of dentistry, i.e., diagnostic dentistry, patient management, head and neck cancer, restorative dentistry, prosthetic dental sciences, orthodontics, radiology, and periodontics.
Conclusion: The included studies describe that AI is a reliable tool to make dental care smooth, better, time-saving, and economical for practitioners. AI benefits them in fulfilling patient demand and expectations. The dentists can use AI to ensure quality treatment, better oral health care outcome, and achieve precision. AI can help to predict failures in clinical scenarios and depict reliable solutions. However, AI is increasing the scope of state-of-the-art models in dentistry but is still under development. Further studies are required to assess the clinical performance of AI techniques in dentistry.
Fulltext The potential of artificial neural network (ANN) in optimizing media constituents of citric acid production by solid state bioconversion

Ricca, R.N., Jami, Mohammed Saedi, Alam, Md. Zahangir

International Food Research Journal, 2012;19(2):491-497.
MyJurnal

This work aims at optimizing the media constituents for citric acid production from oil palm empty fruit bunches (EFB) as renewable resource using artificial neural networks (ANN) approach. The bioconversion process was done through solid state bioconversion using Aspergillus niger. ANN model was built using MATLAB software. A dataset consists of 20 runs from our previous work was used to develop ANN. The predictive and generalization ability of ANN and the results of RSM were compared. The determination coefficients (R2-value) for ANN and RSM models were 0.997 and 0.985, respectively, indicating the superiority of ANN in capturing the non-linear behavior of the system. Validation process was done and the maximum citric acid production (147.74 g/kg-EFB) was achieved using the optimal solution from ANN which consists of 6.1% sucrose, 9.2% mineral solution and 15.0% inoculum.
Fulltext Explainable AI in Scene Understanding for Autonomous Vehicles in Unstructured Traffic Environments on Indian Roads Using the Inception U-Net Model with Grad-CAM Visualization

Kolekar S, Gite S, Pradhan B, Alamri A

Sensors (Basel), 2022 Dec 10;22(24).
PMID: 36560047 DOI: 10.3390/s22249677

The intelligent transportation system, especially autonomous vehicles, has seen a lot of interest among researchers owing to the tremendous work in modern artificial intelligence (AI) techniques, especially deep neural learning. As a result of increased road accidents over the last few decades, significant industries are moving to design and develop autonomous vehicles. Understanding the surrounding environment is essential for understanding the behavior of nearby vehicles to enable the safe navigation of autonomous vehicles in crowded traffic environments. Several datasets are available for autonomous vehicles focusing only on structured driving environments. To develop an intelligent vehicle that drives in real-world traffic environments, which are unstructured by nature, there should be an availability of a dataset for an autonomous vehicle that focuses on unstructured traffic environments. Indian Driving Lite dataset (IDD-Lite), focused on an unstructured driving environment, was released as an online competition in NCPPRIPG 2019. This study proposed an explainable inception-based U-Net model with Grad-CAM visualization for semantic segmentation that combines an inception-based module as an encoder for automatic extraction of features and passes to a decoder for the reconstruction of the segmentation feature map. The black-box nature of deep neural networks failed to build trust within consumers. Grad-CAM is used to interpret the deep-learning-based inception U-Net model to increase consumer trust. The proposed inception U-net with Grad-CAM model achieves 0.622 intersection over union (IoU) on the Indian Driving Dataset (IDD-Lite), outperforming the state-of-the-art (SOTA) deep neural-network-based segmentation models.
Role of genetic algorithms and artificial neural networks in predicting the phase behavior of colloidal delivery systems

Agatonovic-Kustrin S, Alany RG

Pharm Res, 2001 Jul;18(7):1049-55.
PMID: 11496944

PURPOSE: A genetic neural network (GNN) model was developed to predict the phase behavior of microemulsion (ME), lamellar liquid crystal (LC), and coarse emulsion forming systems (W/O EM and O/W EM) depending on the content of separate components in the system and cosurfactant nature.
METHOD: Eight pseudoternary phase triangles, containing ethyl oleate as the oil component and a mixture of two nonionic surfactants and n-alcohol or 1,2-alkanediol as a cosurfactant, were constructed and used for training, testing, and validation purposes. A total of 21 molecular descriptors were calculated for each cosurfactant. A genetic algorithm was used to select important molecular descriptors, and a supervised artificial neural network with two hidden layers was used to correlate selected descriptors and the weight ratio of components in the system with the observed phase behavior.
RESULTS: The results proved the dominant role of the chemical composition, hydrophile-lipophile balance, length of hydrocarbon chain, molecular volume, and hydrocarbon volume of cosurfactant. The best GNN model, with 14 inputs and two hidden layers with 14 and 9 neurons, predicted the phase behavior for a new set of cosurfactants with 82.2% accuracy for ME, 87.5% for LC, 83.3% for the O/W EM, and 91.5% for the W/O EM region.
CONCLUSIONS: This type of methodology can be applied in the evaluation of the cosurfactants for pharmaceutical formulations to minimize experimental effort.

Matched MeSH terms: Neural Networks (Computer)*
Inactivation of fungal spores from clinical environment by silver bio-nanoparticles; optimization, artificial neural network model and mechanism

Noman E, Al-Gheethi A, Saphira Radin Mohamed RM, Talip B, Othman N, Hossain S, et al.

Environ Res, 2022 03;204(Pt A):111926.
PMID: 34461120 DOI: 10.1016/j.envres.2021.111926

The present study aimed to assess the efficiency of silver bio-nanoparticles (Ag-NPs) in inactivating of the Aspergillus fumigatus, A. parasiticus and A. flavus var. columnaris and A. aculeatus spores. The AgNPs were synthesized in secondary metabolic products of Penicillium pedernalens 604 EAN. The inactivation process was optimized by response surface methodology (RSM) as a function of Ag NPs volume (1-10 μL/mL); time (10-120 min); pH (5-8); initial fungal concentrations (log10) (3-6). The artificial neural network (ANN) model was used to understand the behavior of spores for the factors affecting inactivation process. The best conditions to achieved SAL 10-6 of the fungal spores were recorded with 3.46 μl/mL of AgNPs, after 120 min at pH 5 and with 6 log of initial fungal spore concentrations, at which 5.99 vs. 6.09 (SAL 10-6) log reduction was recorded in actual and predicted results respectively with coefficient of 87.00%. The ANN revealed that the timehas major contribution in the inactivation process compare to Ag NPs volume. The fungal spores were totally inactivated (SAL 10-6, 6 log reduction with 99.9999%) after 110 min of the inactivation process, 10 min more was required to insure the irreversible inactivation of the fungal spores. The absence of protease and cellulase enzymes production confirm the total inactivation of the fungal spores. FESEM analysis revealed that the AgNPs which penetrated the fungal spores leading to damage and deform the fungal spore morphology. The AFM analysis confirmed the total spore surface damage. The bands in the range of the Raman spectroscopy from 1300 to 1600 cm-1 in the inactivated spores indicate the presence of CH3, CH2 and the deformation of lipids released outside the spore cytoplasm. These finding indicate that the AgNPs has high potential as a green alternative inactivation process for the airborne fungal spores.
Cephalexin removal by a novel Cu-Zn bionanocomposite biosynthesized in secondary metabolic products of Aspergillus arenarioides EAN603 with pumpkin peels medium: Optimization, kinetic and artificial neural network models

Al-Gheethi A, Noman E, Saphira Radin Mohamed RM, Talip B, Vo DN, Algaifi HA

J Hazard Mater, 2021 10 05;419:126500.
PMID: 34214856 DOI: 10.1016/j.jhazmat.2021.126500

The present study aimed to investigate the removal efficiency of cephalexin (CFX) by a novel Cu-Zn bionanocomposite biosynthesized in the secondary metabolic products of Aspergillus arenarioides EAN603 with pumpkin peels medium (CZ-BNC-APP). The optimization study was performed based on CFX concentrations (1, 10.5 and 20 ppm); CZ-BNC-APP dosage (10, 55 and 100 mg/L); time (10, 55 and 100 min), temperature (20, 32.5 and 45 °C). The artificial neural network (ANN) model was used to understand the CFX behavior for the factors affecting removal process. The CZ-BNC-APP showed an irregular shape with porous structure and size between 20 and 80 nm. The FTIR detected CC, C-O and OH groups. ANN model revealed that CZ-BNC-APP dosage exhibited the vital role in the removal process, while the removal process having a thermodynamic nature. The CFX removal was optimized with 12.41 ppm CFX, 60.60 mg/L of CZ-BNC-APP, after 97.55 min and at 35 °C, the real maximum removal was 95.53% with 100.52 mg g-1 of the maximum adsorption capacity and 99.5% of the coefficient. The adsorption of CFX on CZ-BNC-APP was fitted with pseudo-second-order model and both Langmuir and Freundlich isotherms models. These findings revealed that CZ-BNC-APP exhibited high potential to remove CFX.
Multiclass tumor identification using combined texture and statistical features

Latif G, Bashar A, Awang Iskandar DNF, Mohammad N, Brahim GB, Alghazo JM

Med Biol Eng Comput, 2023 Jan;61(1):45-59.
PMID: 36323980 DOI: 10.1007/s11517-022-02687-w

Early detection and diagnosis of brain tumors are essential for early intervention and eventually successful treatment plans leading to either a full recovery or an increase in the patient lifespan. However, diagnosis of brain tumors is not an easy task since it requires highly skilled professionals, making this procedure both costly and time-consuming. The diagnosis process relying on MR images gets even harder in the presence of similar objects in terms of their density, size, and shape. No matter how skilled professionals are, their task is still prone to human error. The main aim of this work is to propose a system that can automatically classify and diagnose glioma brain tumors into one of the four tumor types: (1) necrosis, (2) edema, (3) enhancing, and (4) non-enhancing. In this paper, we propose a combined texture discrete wavelet transform (DWT) and statistical features based on the first- and second-order features for the accurate classification and diagnosis of multiclass glioma tumors. Four well-known classifiers, namely, support vector machines (SVM), random forest (RF), multilayer perceptron (MLP), and naïve Bayes (NB), are used for classification. The BraTS 2018 dataset is used for the experiments, and with the combined DWT and statistical features, the RF classifier achieved the highest average accuracy whether for separated modalities or combined modalities. The highest average accuracy of 89.59% and 90.28% for HGG and LGG, respectively, was reported in this paper. It has also been observed that the proposed method outperforms similar existing methods reported in the extant literature.
Fulltext Intelligent diagnostic model for malaria parasite detection and classification using imperative inception-based capsule neural networks

Madhu G, Mohamed AW, Kautish S, Shah MA, Ali I

Sci Rep, 2023 Aug 17;13(1):13377.
PMID: 37591916 DOI: 10.1038/s41598-023-40317-z

Malaria is an acute fever sickness caused by the Plasmodium parasite and spread by infected Anopheles female mosquitoes. It causes catastrophic illness if left untreated for an extended period, and delaying exact treatment might result in the development of further complications. The most prevalent method now available for detecting malaria is the microscope. Under a microscope, blood smears are typically examined for malaria diagnosis. Despite its advantages, this method is time-consuming, subjective, and requires highly skilled personnel. Therefore, an automated malaria diagnosis system is imperative for ensuring accurate and efficient treatment. This research develops an innovative approach utilizing an urgent, inception-based capsule network to distinguish parasitized and uninfected cells from microscopic images. This diagnostic model incorporates neural networks based on Inception and Imperative Capsule networks. The inception block extracts rich characteristics from images of malaria cells using a pre-trained model, such as Inception V3, which facilitates efficient representation learning. Subsequently, the dynamic imperative capsule neural network detects malaria parasites in microscopic images by classifying them into parasitized and healthy cells, enabling the detection of malaria parasites. The experiment results demonstrate a significant improvement in malaria parasite recognition. Compared to traditional manual microscopy, the proposed system is more accurate and faster. Finally, this study demonstrates the need to provide robust and efficient diagnostic solutions by leveraging state-of-the-art technologies to combat malaria.
Fulltext A Study on Regional GDP Forecasting Analysis Based on Radial Basis Function Neural Network with Genetic Algorithm (RBFNN-GA) for Shandong Economy

Zhang Q, Abdullah AR, Chong CW, Ali MH

Comput Intell Neurosci, 2022;2022:8235308.
PMID: 35126503 DOI: 10.1155/2022/8235308

Gross domestic product (GDP) is an important indicator for determining a country's or region's economic status and development level, and it is closely linked to inflation, unemployment, and economic growth rates. These basic indicators can comprehensively and effectively reflect a country's or region's future economic development. The center of radial basis function neural network and smoothing factor to take a uniform distribution of the random radial basis function artificial neural network will be the focus of this study. This stochastic learning method is a useful addition to the existing methods for determining the center and smoothing factors of radial basis function neural networks, and it can also help the network more efficiently train. GDP forecasting is aided by the genetic algorithm radial basis neural network, which allows the government to make timely and effective macrocontrol plans based on the forecast trend of GDP in the region. This study uses the genetic algorithm radial basis, neural network model, to make judgments on the relationships contained in this sequence and compare and analyze the prediction effect and generalization ability of the model to verify the applicability of the genetic algorithm radial basis, neural network model, based on the modeling of historical data, which may contain linear and nonlinear relationships by itself, so this study uses the genetic algorithm radial basis, neural network model, to make, compare, and analyze judgments on the relationships contained in this sequence.

Matched MeSH terms: Neural Networks (Computer)*
Fulltext International Trade Path with Multi-Polarization based on Multidirectional Mutation Genetic Algorithm Enabled Neural Network

Zhang Q, Chong CW, Abdullah AR, Ali MH

Comput Intell Neurosci, 2021;2021:1370180.
PMID: 34691167 DOI: 10.1155/2021/1370180

At present, the development speed of international trade cannot catch up with the economic development speed, and the insufficient development speed of international trade will directly affect the rapid development of national economy. In order to solve the problem of international trade, the overall optimal scheduling of trade vehicles and the optimal planning of trade transportation path are very important to improve enterprise services, reduce enterprise costs, increase enterprise benefits, and enhance enterprise competitiveness. The second development of the program is based on the programming interface provided by Baidu map. This paper proposes a neural network algorithm for genetic optimization of multiple mutations, which overcomes the shortcoming of traditional genetic algorithm population "ten" character distribution by mixing multiple coding methods, and enhances the local search ability of genetic algorithm by introducing a new large-mutation small-range search population. The example application shows that the optimization method can realize the optimization of international trade path under real road conditions and greatly improve the work efficiency of actual trade.

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