Development of a Scaled Conjugate Gradient Algorithm for Significant RF Neural Signal Processing

ANN and ANFIS models for performance evaluation of a vertical ground source heat pump system

Mosquito-borne diseases, such as dengue, Zika, and chikungunya, pose significant public health threats, particularly in tropical regions like Malaysia. Aedes aegypti and Aedes albopictus are primary vectors of these diseases, with their developmental stages being highly sensitive to environmental factors. While temperature is a well-known driver of mosquito biology, the potential influence of anthropogenic factors such as radio frequency (RF) exposure remains underexplored. This study investigates the combined effects of temperature and RF exposure on the developmental stages of these mosquito species to provide insights into their population dynamics and inform vector control strategies. A factorial experimental design was employed, incorporating four temperature conditions (20 °C, 25 °C, 30 °C, and 35 °C) and three RF exposure levels (900 MHz, 18 GHz, and a control group with no RF exposure). The developmental durations for hatching, larval, pupation, and adult emergence stages were monitored daily under controlled laboratory conditions. Data were analyzed using a quadratic response surface model to evaluate the main effects and interactions between temperature and RF exposure. Temperature emerged as the dominant factor influencing developmental durations, with optimal conditions observed at 30–32 °C. RF exposure, particularly at 18 GHz, acted as a secondary modulating factor, accelerating developmental stages under certain temperature conditions. Ae. aegypti exhibited greater sensitivity to temperature changes compared to Ae. albopictus, which displayed higher adaptability and resilience to environmental variations. Interaction effects were most evident at intermediate temperatures (25–30 °C), where RF exposure synergistically reduced developmental durations. However, extreme RF exposure levels and suboptimal temperatures prolonged developmental periods. This study highlights the critical role of temperature in mosquito development while identifying RF exposure as a potential modulator under specific conditions. The findings underscore the importance of considering both environmental and anthropogenic factors in vector management strategies. Future research should explore the molecular mechanisms underlying these interactions to refine predictive models and enhance vector control efforts in rapidly urbanizing regions.

Angiotensin I converting enzyme (ACE) gene, as a component of Renin-Angiotensin System (RAS), regulates blood pressure as it converts somatic isozyme Angiotensin I into physiologically active peptide Angiotensin II and simultaneously brakes down bradykinins. Over 100 polymorphisms are reported for ACE gene. Most of these polymorphisms having no phenotypic effect relay the attention towards polymorphisms based on insertions (I) or deletions (D) of a 287 bp Alu repeat sequence in 16th intron. There are three possible genotypes for the stated polymorphism: DD, DI or II. There have been a lot of studies searching for direct associations between ACE polymorphisms and performance phenotypes along different sports requiring power or endurance. The previous experiments are based on the performance criteria but direct associations of ACE polymorphisms are not fully understood until today. We believe different approaches may aid scientist to plot the big picture. A sample population of 101 individuals from Bosnia and Herzegovina contributed to sample pool of the initial project. Buccal swabs from 101 samples were collected along with a phenotypic structure and environmental characteristic survey which was filled by each individual himself/herself. Genotypes of the individuals were obtained after isolation, amplification and gel electrophoresis of biological samples collected as buccal swabs. A total of 165 artificial neural network (ANN) models were developed considering the input parameters, possible genotype outputs, applied algorithm and sample size. The aim of developing various ANNs was to validate a possible ACE polymorphism genotype prediction algorithm based on phenotypic and environmental characteristics of individuals, in other words, without any biological testing. A two-layered feed-forward network, with sigmoid hidden neurons was designed to perform the classification of input data. Trainscg (Scaled Conjugate Gradient) activation function was used in hidden layer since classification of data was non-linear. All ANN models were trained with scaled conjugate gradient backpropagation. ANN models differing in the parameters has shown different accuracy in the results. Most outstanding result was observed in the ANN build composed of 2 distinctive layers with 500 neurons in the first and 3 neurons in the second layer. Trained with 70% of samples and verified with 15% of samples and validated with an additional 50 samples. Training set was composed of the following subject parameters; gender, eye color, hair color, height, weight, presence of hypertension in family and presence of cardiovascular diseases in family. The highest prediction accuracy was obtained as 86,6 % training score, 78,6 % testing score and 80,2 % overall score in genotype prediction for ACE polymorphisms. With further development of data collection and high resolution analysis, overall score could be boosted. Also, phenotypical data can be applied as markers of genotypes in ACE polymorphisms.

The aim of this paper is to establish a reliable model that provides the best fit to the specific behavior of the flow stresses of the 10%Cr steel alloy at the time of hot deformation. Modified Johnson–Cook and strain-compensated Arrhenius-type (phenomenological models), in addition to two Artificial Neural Network (ANN) models were established with the view toward investigating their stress prediction performances. The ANN models were trained using Scaled Conjugate Gradient (SCG) and Levenberg–Marquardt (LM) algorithms. The prediction accuracy of the established models was evaluated using the following well-known statistical parameters: (a) correlation coefficient (R), (b) Average Absolute Relative Error (AARE), (c) Root Mean Squared Error (RMSE), and Relative Error (RE). The results showed that both of the modified Johnson–Cook and strain-compensated Arrhenius models could not competently predict the flow behavior. On the contrary, the results indicated that the two proposed ANN models precisely predicted the flow stress values and that the LM-trained ANN provided a superior performance over the SCG-trained model, as it yielded an RMSE of as low as 0.441 MPa.

This article introduces a new way of using a fibre Bragg grating (FBG) sensor for detecting the presence and number of occupants in the monitored space in a smart home (SH). CO2 sensors are used to determine the CO2 concentration of the monitored rooms in an SH. CO2 sensors can also be used for occupancy recognition of the monitored spaces in SH. To determine the presence of occupants in the monitored rooms of the SH, the newly devised method of CO2 prediction, by means of an artificial neural network (ANN) with a scaled conjugate gradient (SCG) algorithm using measurements of typical operational technical quantities (indoor temperature, relative humidity indoor and CO2 concentration in the SH) is used. The goal of the experiments is to verify the possibility of using the FBG sensor in order to unambiguously detect the number of occupants in the selected room (R104) and, at the same time, to harness the newly proposed method of CO2 prediction with ANN SCG for recognition of the SH occupancy status and the SH spatial location (rooms R104, R203, and R204) of an occupant. The designed experiments will verify the possibility of using a minimum number of sensors for measuring the non-electric quantities of indoor temperature and indoor relative humidity and the possibility of monitoring the presence of occupants in the SH using CO2 prediction by means of the ANN SCG method with ANN learning for the data obtained from only one room (R203). The prediction accuracy exceeded 90% in certain experiments. The uniqueness and innovativeness of the described solution lie in the integrated multidisciplinary application of technological procedures (the BACnet technology control SH, FBG sensors) and mathematical methods (ANN prediction with SCG algorithm, the adaptive filtration with an LMS algorithm) employed for the recognition of number persons and occupancy recognition of selected monitored rooms of SH.

Turkey has sufficient solar radiation and radiation period for solar thermal applications since it lies in a sunny belt between 36° and 42° N latitudes. The yearly average solar radiation is 3.6 kWh/m2day, and the total yearly radiation period is ∼2610 h. This study investigates the estimation of solar resources in Turkey using artificial neural networks (ANNs). Scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM) learning algorithms and logistic sigmoid transfer function were used in the network. In order to train the neural network, meteorological data for last three years (2000–2002) from 17 stations (namely cities) spread over Turkey were used as training (11 stations) and testing (6 stations) data. These cities selected can give a general idea about Turkey. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, and mean temperature) is used in input layer of network. Solar radiation is in output layer. The maximum mean absolute percentage error was found to be less than 6.7% and R2 values to be about 99.8937% for the testing stations. However, these values were found to be 2.41% and 99.99658% for the training stations. The results indicate that the ANN model seems promising for evaluating solar resource posibilities at the places where there are no monitoring stations in Turkey. The results on the testing stations indicate a relatively good agreement between the observed and the predicted values.

The modulation of color temperature of illumination always use the red or yellow LED to adjust the white LED. The research was to use the Artificial Neural Network to estimate the chromaticity coordinates of 3x2 LED array mixing by red and yellow LEDs. There are four algorithm methods: Levenberg-Marquardt (LM), Broyden Fletch Goldfard Shan no (BFGS), Fletcher-Reeves Conjugate Gradient (FRCG) and Scaled Conjugate Gradient (SCG) to train the learning function. The modulation color temperature was control at 2700K. The result showed that the artificial neural network model has a good and stable predictive ability by FRCG method.

Automatic detection and accurate classification of skin cancer has been achievable with the help of machine learning techniques. Now-a-days, skin cancer has posed to be a dreadful disease mostly seen in the western region of the world. The current work focuses on the automated identification of skin cancer. The proposed method initially deals with pre-processing steps like normalization and image enhancement. Texture based different features of the digital skin images are extracted by using Gray Level Co-occurrence Matrices (GLCM). Then classification is performed based on the extracted features using Support Vector Machine (SVM), Ensemble and Artificial Neural Network (ANN). To train the ANN, Scaled conjugate gradient (SCG) algorithm has been adopted. This paper compares the performance of these three classifiers in terms of some parameters namely accuracy, precision, recall and f-measure. The experimental results show the effectiveness of ANN-SCG classifier. The ANN-SCG model provides 84.21% accuracy, 83.33% precision, 71.42% f-measure and 76.91% recall which is superior to the SVM and Ensemble classifiers.

The increasing need for efficient CO 2 capture methods has led to the exploration of NaOH-modified nanoclay montmorillonite as an adsorbent. This study utilizes response surface methodology (RSM) and artificial neural networks (ANN) for modelling and optimizing CO 2 adsorption. Data from previous experiments were used to develop the models. RSM employed a central composite design (CCD) and was evaluated using analysis of variance (ANOVA), while ANN models were created with various training methods (Levenberg–Marquardt, Bayesian regularization, and scaled conjugate gradient). The ANN model using Bayesian regularization (R 2 = 0.98719; MSE = 0.00049) demonstrated the best predictive accuracy. ANOVA revealed that NaOH concentration, pressure, and temperature significantly affected CO 2 adsorption capacity. Sensitivity analysis confirmed NaOH concentration as the most influential variable. Optimization results indicated that maximum CO 2 adsorption (72.873 mg/g) occurs at 35 °C, 9 bar pressure, 5 mol/L acid concentration, and 30% w/w NaOH. This study effectively applies RSM-CCD and ANN models for optimizing CO 2 adsorption with NNM adsorbent.

Performance prediction of a solar thermal energy system using artificial neural networks

Effect of radiofrequency exposure on body temperature: Real-time monitoring in normal rats

The effect of GSM-like electromagnetic fields with the resting electroencephalogram (EEG) alpha band activity was investigated in a double-blind cross-over experimental paradigm, testing the hypothesis that pulsed but not continuous radio frequency (RF) exposure would affect alpha activity, and the hypothesis that GSM-like pulsed low frequency fields would affect alpha. Seventy-two healthy volunteers attended a single recording session where the eyes open resting EEG activity was recorded. Four exposure intervals were presented (sham, pulsed modulated RF, continuous RF, and pulsed low frequency) in a counterbalanced order where each exposure lasted for 20 min. Compared to sham, a suppression of the global alpha band activity was observed under the pulsed modulated RF exposure, and this did not differ from the continuous RF exposure. No effect was seen in the extremely low frequency condition. That there was an effect of pulsed RF that did not differ significantly from continuous RF exposure does not support the hypothesis that "pulsed" RF is required to produce EEG effects. The results support the view that alpha is altered by RF electromagnetic fields, but suggest that the pulsing nature of the fields is not essential for this effect to occur.

Prediction of first lactation 305-day milk yield in Karan Fries dairy cattle using ANN modeling

Accurately determining rock strength characteristics, such as uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and Los Angeles Abrasion (LAA), through conventional methods is both time-consuming and resource-intensive. To address this challenge, this study develops efficient artificial neural network (ANN) models optimized with Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) algorithms to predict UCS, BTS, and LAA from petrographic properties, advancing civil engineering applications. A total of 100 dolerite samples were analyzed to assess their strength and petrographic characteristics, with the ANNs trained on the three aforementioned algorithms. The results indicate that the BR model achieved the highest accuracy, with a correlation coefficient (R) of 0.9999 and a root mean square error (RMSE) of 0.3164. The LM model also demonstrated strong performance with an R-value of 0.9997 and an RMSE of 0.8619. The BR and LM models significantly outperformed the SCG model, which had an R-value of 0.9954 and an RMSE of 2.398. Sensitivity analysis identified plagioclase and chlorite as the most influential factors in predicting rock mechanical parameters. The effectiveness of the BR and LM techniques highlights their potential to offer substantial time and cost savings in rock strength prediction.

The fly ash-slag geopolymer is regarded as one of the new green cementitious materials that can replace cement, but it is difficult to predict its mechanical properties by conventional methods. Therefore, in the present study, the back propagation (BP) artificial neural network technique is used to predict the compressive strength of the fly ash-slag geopolymer. In this paper, data from the published literature were collected as the training set and the experimental results from laboratory experiments were used as the test set. Eight input parameters were determined, as follows: the percentage of fly ash, the percentage of slag, the water–cement ratio, the curing age, the modulus of alkali activator, the mass ratio of NaOH to Na2SiO3 and the moles of Na2O and SiO2 in the alkali activator. Three multilayer artificial neural network models were constructed using the Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG) algorithms to compare the prediction accuracy of the compressive strength of the fly ash-slag geopolymer paste at different ages (3, 7, and 28 d). It was concluded that the training set error of the BR–BP neural network was the smallest. Ultimately, the hyperparameter optimization of the BR–BP neural network was carried out to compare the training set and the test set errors before and after the optimization, and the results show that the BR–BP neural network model with hyperparameter optimization had the highest prediction accuracy.