Efficacy Of Artificial Neural Network Research Articles

Unveiling surgical expertise through machine learning in a novel VR/AR spinal simulator: A multilayered approach using transfer learning and connection weights analysis

BackgroundVirtual and augmented reality surgical simulators, integrated with machine learning, are becoming essential for training psychomotor skills, and analyzing surgical performance. Despite the promise of methods like the Connection Weights Algorithm, the small sample sizes (small number of participants (N)) typical of these trials challenge the generalizability and robustness of models. Approaches like data augmentation and transfer learning from models trained on similar surgical tasks address these limitations. ObjectiveTo demonstrate the efficacy of artificial neural network and transfer learning algorithms in evaluating virtual surgical performances, applied to a simulated oblique lateral lumbar interbody fusion technique in an augmented and virtual reality simulator. DesignThe study developed and integrated artificial neural network algorithms within a novel simulator platform, using data from the simulated tasks to generate 276 performance metrics across motion, safety, and efficiency. Innovatively, it applies transfer learning from a pre-trained ANN model developed for a similar spinal simulator, enhancing the training process, and addressing the challenge of small datasets. SettingMusculoskeletal Biomechanics Research Lab; Neurosurgical Simulation and Artificial Intelligence Learning Centre, McGill University, Montreal, Canada. ParticipantsTwenty-seven participants divided into 3 groups: 9 post-residents, 6 senior and 12 junior residents. ResultsTwo models, a stand-alone model trained from scratch and another leveraging transfer learning, were trained on nine selected surgical metrics achieving 75 % and 87.5 % testing accuracy respectively. ConclusionsThis study presents a novel blueprint for addressing limited datasets in surgical simulations through the strategic use of transfer learning and data augmentation. It also evaluates and reinforces the application of the Connection Weights Algorithm from our previous publication. Together, these methodologies not only enhance the precision of performance classification but also advance the validation of surgical training platforms.

Effect of foaming agents and dilution ratio on physicochemical and bioactive compounds profile of dried palmyra pulp

The study was carried out to observe the effect of glycerol monostearate (1.0 %, 2.0 %, and 3.0 %), soy protein isolate (0.5 %, 1.0 %, and 1.5 %) and dilution ratio (1:1, 1:1.5) on foam characteristics at whipping time of 4 min. All the samples were incorporated with 0.5 % methyl cellulose (MC) as foam stabiliser and 5 % maltodextrin (MD) as drying aid. The effect of different drying air temperatures (60 °C and 70 °C) at foam thickness of 4 mm on phytochemical, biochemical and functional quality attributes was studied. The efficacy of artificial neural network (ANN) on foam-mat drying kinetics was tested. Foam expansion of the palmyra pulp increased to 108 % when added with 2 % GMS and diluted to 1:1.5. The increase was even more up to 138 % when the same diluted pulp was added with 1.5 % SPI. On the contrary, the foam retention and the foam stability were reduced at a higher dilution ratio. GMS (2 %) or SPI (1 %), each diluted at a 1:1 ratio, exhibited better foaming and colour characteristics. Drying was faster in pulp foamed with SPI (1 %) than in pulp incorporated with GMS (2 %). Non-foamed pulp showed lower drying rates and produced a compact sheet-like mat with a relatively harder and sticky texture. Effective moisture diffusivity values ranged from 1.820 × 10−8 to 2.721 × 10−8 (m2/min) and increased with an increase in the drying temperature. The tansig transfer function of ANN modelling could best predict the moisture ratio with drying time for all the foamed and non-foamed samples. Quality analysis showed that the palmyra pulp foamed with 0.5 % MC, 5 %, MD, and 1 % SPI, diluted at a 1:1 ratio, whipped for 4 min, and dried at 70 °C with foam thickness of 4 mm could produce the powder with best quality attributes. The developed powder was found to possess 79.341 ± 0.998 mg GAE/100 g of total phenolic content, 72.669 ± 0.964 % of antioxidant activity, 60.526 ± 0.975 mg/100 g of ascorbic acid content, 35.287 ± 0.973 % of reducing sugar, 15661 ± 192 µg/100 g of β-carotene with water absorption and hygroscopicity of 2.374 ± 0.827 g/g and 38.487 ± 1.020 % respectively.

Adsorption of diazinon from aqueous solution using metal organic framework and functionalized graphene: Comparison of BBD, ANN models

In the present study, metal organic frameworks (MOFs) and aminated graphitic carbonaceous structure (ACS-RGO) through chemical synthesis prepared by a simple precipitation method and used for diazinon removal. Several techniques such as XRD , FESEM and FTIR were applied for identification of MOF-5 and ACS-RGO. Also, response surface methodology (RSM) was employed in this work to look at the effectiveness of diazinon adsorption. To forecast pesticide removal, we applied artificial neural network (ANN) and Box-Behnken Design (BBD) models. For the ANN model, a sensitivity analysis was also performed. The effect of independent variables like solution pH, various concentrations of diazinon, MOFs and ACS-RGO adsorbent dose and contact time were assessed to find out the optimum conditions. Based on the model prediction, the optimal condition for adsorption ACS-RGO and MOF-5 were determined to be pH 6.6 and 6.6, adsorbent dose of 0.59 and 0.906 g/L, and mixing time of 52.15 and 36.96 min respectively. These conditions resulted in 96.69% and 80.62% diazinon removal using ACS-RGO and MOF-5, respectively. Isotherm studies proved the adsorption of ACS-RGO and MOF-5 following the Langmuir isotherm model for diazinon removal. Diazinon removal followed by the pseudo-second and Pseudo-first order kinetics model provides a better fit for analyzing the kinetic data associated with pesticide adsorption for ACS-RGO and MOF-5, respectively. Based on the obtained results, the predicted values for the efficiency of diazinon removal with the ANN and BBD were similar (R2=0.98). Therefore, two models were able to predict diazinon removal by ACS-RGO and MOF-5.

Laboratory variables-based artificial neural network models for predicting fatty liver disease: A retrospective study.

The efficacy of artificial neural network (ANN) models employing laboratory variables for predicting fatty liver disease (FLD) remains inadequately established. The study aimed to develop ANN models to precisely predict FLD. Of 12,058 participants undergoing the initial FLD screening, 7,990 eligible participants were included. A total of 6,309 participants were divided randomly into the training (4,415 participants, 70%) and validation (1,894 participants, 30%) sets for developing prediction models. The performance of ANNs was additionally tested in the testing set (1,681 participants). The area under the receiver operating characteristic curve (AUROC) was employed to assess the models' performance. The 18-variable, 11-variable, 3-variable, and 2-variable models each achieved robust FLD prediction performance, with AUROCs over 0.92, 0.91, and 0.89 in the training, validation, and testing, respectively. Although slightly inferior to the other three models in performance (AUROC ranges: 0.89-0.92 vs 0.91-0.95), the 2-variable model showed 80.3% accuracy and 89.7% positive predictive value in the testing. Incorporating age and gender increased the AUROCs of the resulting 20-variable, 13-variable, 5-variable, and 4-variable models each to over 0.93, 0.92, and 0.91 in the training, validation, and testing, respectively. Implementation of the ANN models could effectively predict FLD, with enhanced predictive performance via the inclusion of age and gender.

Stacked vector multi-source lithologic classification utilizing Machine Learning Algorithms: Data potentiality and dimensionality monitoring

Machine Learning Algorithms (MLAs) have recently introduced considerable lithologic mapping. Thus, this study scrutinizes the efficacy of Artificial Neural Network (ANN), Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM) over hybrid datasets including optical (Sentinel 2, ASTER, Landsat OLI and Earth-observing 1 Advanced Land Imager (ALI)), radar (Sentinel 1 and ALOS PALSAR), DEMs and their derivatives (Slope, and Aspect). The study aims to (1) monitor the effect of data dimensionality in enhancing categorization accuracy. (2) disclose the most efficient MLA and most powerful dataset in labeling rock units accurately. (3) highlight the impact of embedding topographical and radar data in lithologic classification. (4) outline the best relation between the number of training pixels and number of utilized bands, in delivering reliable allocation. To achieve these aims, we selected training and testing pixels meticulously, in concordance with a recently published geological map of the study area. We adopted a stacked vector approach for handling the implemented multi-sensor data. Results show that diversifying information sources raised the classification accuracy by approximately 10% for each classifier. SVM and MLC are much better than ANN. Slope is better than aspect and both are less qualified when compared to DEM. Sentinel 1 (C-band) and ALOS PALSAR (L-band) effects are not so different whatever the implemented polarization. Landsat OLI is less qualified in lithologic classification when compared to Sentinel 2, ASTER and ALI. The utilized training pixels should be at least 30N for (N) channels submitted to the classifiers.

An artificial neural network model for glioma grading using image information

To explore the feasibility and efficacy of artificial neural network for differentiating high-grade glioma and low-grade glioma using image information. Methods: A total of 130 glioma patients with confirmed pathological diagnosis were selected retrospectively from 2012 to 2017. Forty one imaging features were extracted from each subjects based on 2-dimension magnetic resonance T1 weighted imaging with contrast-enhancement. An artificial neural network model was created and optimized according to the performance of feature selection. The training dataset was randomly selected half of the whole dataset, and the other half dataset was used to verify the performance of the neural network for glioma grading. The training-verification process was repeated for 100 times and the performance was averaged. Results: A total of 5 imaging features were selected as the ultimate input features for the neural network. The mean accuracy of the neural network for glioma grading was 90.32%, with a mean sensitivity at 87.86% and a mean specificity at 92.49%. The area under the curve of receiver operating characteristic curve was 0.9486. Conclusion: As a technique of artificial intelligence, neural network can reach a relatively high accuracy for the grading of glioma and provide a non-invasive and promising computer-aided diagnostic process for the pre-operative grading of glioma.

Ecosystem modeling using artificial neural networks: An archaeological tool

Prediction of past Normalized Difference Vegetation Index (paleo-NDVI) in Valle de Ambato (Catamarca, Argentina) in the periods of 550–650 and 1550–1650CE was carried out to test the efficacy of Artificial Neural Network (ANN) to predict past environments for Archaeology. This work shows that both subtropical Yunga and xerophytic Chaqueña vegetations respond in contrasting fashion to changes in climate forcings. To predict the past an ANN perceptron multilayer model was used. Modern NDVI data and Tree-Ring data were obtained from NOAA-Paleoclimate, and other public sources. These data were used to train the model. Real data and predictions were close (Pearson correlation 0.83–0.90) and warranted the following step, hindcasting. Important paleo-NDVI fluctuations lasting 15 to 20years were identified in both periods under study. The paleo-NDVI fluctuations in the earlier period were probably related to the unidentified eruption of 583. The fluctuations in the later period appear related to the eruption of 1600 of the Huaynaputina volcano (SW Peru). These findings suggest that the model accurately identified vegetation fluctuations in response to changes in the volcanic forcing. Hence, the ANNs may be considered as apt tools for modeling past environments in support of archaeology.

Efficacy of Artificial Neural Network based Decision Support System for Career Counseling

Objectives: This paper presents the use of machine learning technique in order to eliminate the components affecting the human decision making process. To assist decision making in career counseling, an Artificial Intelligence model was implemented using Artificial Neural Network (ANN) in MATLAB for predicting vocational stream of pursuit based on the behavioral characteristic of the beneficiary. Methods/Statistical Analysis: The Differential Aptitude Test (DAT) battery; and Scientific Knowledge and Aptitude Test (SKAT) were used to assess an individual’s specific abilities in different areas. The training data set for the ANN model was procured in the form of normalized scores based on occupational/vocational profiles as given by the authors of DAT battery and SKAT. The trained ANN was tested with normalized stanine scores of 100 tenth class studentsraised through cluster random sampling technique for predicting a vocational stream of pursuit. In order to evaluate its accuracy and efficiency, three techniques of classification were employed. The unclassified data was classified by using Discriminant function analysis, ANN and two classifications were obtained from the trained counselors. Findings: The classification result obtained from the above mentioned techniques were compared and was found that the ANN system and Discriminant function analysis agreed approximately by 91% over all the test cases. The results of the statistical method support the classification made by the ANN. The two counsellors were in agreement with ANN’s classification output by approximately 81%. However, the counsellors disagreed with each other’s prediction approximately by 27% over all the test cases. The experimental results support the hypothesis that the proposed machine learning technique performed better than the prediction made by counsellors. Application/Improvements: In the Indian scenario, the developed machine learning system may be used as a standalone system in places where there is the paucity of counsellorsor can assist in the career decision making provided by human beings.

AI tools in medical image analysis: efficacy of ANN for oestrogen receptor status assessment in immunohistochemical staining of breast cancer

Evaluating oestrogen receptor status in immunohistochemical staining of breast cancer is so complicated. This process is done subjectively and is so much time consuming. In fact, the studied images present many characteristics such as the non uniformity in the intensity of the organic tissue and the cells, and also the variability of the size and the form of cells which make their processing so difficult. So, given all these complexities, conventional methods are unable to solve the problem. In this work, we study the ability of artificial intelligence as a modern and an unconventional technique to automatically classify breast cancer cells. This step is considered as primary in assessing oestrogen receptor status. Three intelligent techniques are presented, applied and compared: fuzzy c-means (FCM), artificial neural network (ANN) and genetic algorithm (GA). The statistical analysis demonstrates the efficacy of the artificial neural network by recording an average rate of sensitivity, specificity and acc...

Artificial Neural Network Based Model for Forecasting Sugar Cane Production

Problem statement: The global need for alternative energy source has n ecessitated the exploration of vast organic agricultural products w ith a view of processing them for the production of ethanol in commercial quantity. To ascertain a sust ainable production of ethanol from processed sugar cane, a predictive model based on non-linearity nat ure of its production is imperative. This is due to unavailability of sufficient reliable data and the wide yield fluctuation that was not well dispersed over time. Approach: This study employed heuristic technique to develop an Artificial Neural Network (ANN) model to forecast sugar cane production in Nigeria. The input data set used includes the socio- economic and agro-climatic factors affecting sugar cane production while the output is the actual suga r cane output for the period covering 1920-2005. Vari ous hidden layers and processing elements were tested giving rise to different Artificial Neural N etwork (ANN) models. The performance of the ANN models were measured using the Mean Squared Error (MSE), Normalized Mean Squared Error (NMSE), correlation coefficient (r), Akaike's Infor mation Criterion (AIC) and Minimum Description Length (MDL). The contributions of the inputs to th e outputs were determined to know how variation of the input variables affected the output. Results: The 85.70% accuracy result of the best ANN model of 2-hidden layer network of 4 Processing Ele ments (PEs) indicated the efficacy of Artificial Neural Network in accurate prediction. Conclusion: The developed ANN based model fits well real data and can be used for predicting purpose with a high accuracy.

New neural multiprocess memory model for adaptively regulating associative learning

This article describes the development, operation, and behaviour of the neural multiprocess memory model ( NMMM). The NMMM is a new artificial synaptic multistage memory system that replaces the single associative long-term memory that normally modulates the efficacy of artificial neural network ( ANN) element inputs. The NMMM enhances learning in an ANN element by supporting multiple time scales in the acquisition, retention, and recall of encoded associative memory traces, and by appropriately modulating the learning rate based upon previous learning experience over many trials. Presented computer simulation results indicate that the NMMM on its own supports spontaneous regression and recovery behaviour and U-shaped memory retention, and that when functioning as part of the associative conditioning element ( ACE), the NMMM's new adaptive associability mechanism is capable of supporting both negatively accelerated and sigmoidal acquisition curves, latent inhibition, learned irrelevance, the partial reinforcement effect, and accelerated learning following alternating acquisition-extinction training sessions.