ANN Architecture Research Articles

C2R: A Novel ANN Architecture for Boosting Indoor Positioning With Scarce Data

C2R: A Novel ANN Architecture for Boosting Indoor Positioning With Scarce Data

Machine learning approaches in non-contact autofluorescence spectrum classification.

Manual surgical resection of soft tissue sarcoma tissue can involve many challenges, including the critical need for precise determination of tumor boundary with normal tissue and limitations of current surgical instrumentation, in addition to standard risks of infection or tissue healing difficulty. Substantial research has been conducted in the biomedical sensing landscape for development of non-human contact sensing devices. One such point-of-care platform, previously devised by our group, utilizes autofluorescence-based spectroscopic signatures to highlight important physiological differences in tumorous and healthy tissue. The following study builds on this work, implementing classification algorithms, including Artificial Neural Network, Support Vector Machine, Logistic Regression, and K-Nearest Neighbors, to diagnose freshly resected murine tissue as sarcoma or healthy. Classification accuracies of over 93% are achieved with Logistic Regression, and Area Under the Curve scores over 94% are achieved with Support Vector Machines, delineating a clear way to automate photonic diagnosis of ambiguous tissue in assistance of surgeons. These interpretable algorithms can also be linked to important physiological diagnostic indicators, unlike the black-box ANN architecture. This is the first known study to use machine learning to interpret data from a non-contact autofluorescence sensing device on sarcoma tissue, and has direct applications in rapid intraoperative sensing.

Comparative analysis of neural network architectures for short-term FOREX forecasting

This document outlines the analysis, design, implementation, and benchmarking of various neural network architectures in a short-term frequency prediction system for the FOREX market. Our objective is to emulate the decision-making process of a human expert (technical analyst) through a system that swiftly adapts to market condition changes, thereby optimizing short-term trading strategies. We have designed and implemented a series of LSTM neural network architectures that take exchange rate values as input to generate short-term market trend forecasts. Additionally, we developed a custom ANN architecture based on simulators for technical analysis indicators. We performed a comparative analysis of the results and came to useful conclusions regarding the suitability of each architecture and the cost in terms of time and computational power to implement them. The ANN custom architecture produces better prediction quality with higher sensitivity using fewer resources and spending less time than LSTM architectures. The ANN custom architecture appears to be ideal for use in low-power computing systems and for use cases that need fast decisions with the least possible computational cost.

A cost-effective ECG monitoring in rural areas: leveraging artificial neural networks for efficient healthcare solutions

Cardiovascular diseases engender serious public health concerns in developing nations since access to specialized medical equipment is often limited and standard treatment expenses can be prohibitive. This study proposes an efficient and relatively affordable electrocardiogram (ECG) monitoring system that reads and analyzes a person's electrocardiogram data to provide affordable and quality healthcare solutions. The device initially extracts features from electrocardiogram records by reading electrical signals in the heart. Extracted data are then analyzed by a trained deep learning model to determine precisely if the heart is in a healthy state or undergoing complexities. Experimental results showed that the fine-tuned ANN architecture outperformed the state-of-the-art architectures in this field with an accuracy of 98.95%. The data can also be sent to specialists through an MQTT server if necessary, allowing for remote diagnosis and treatment. The system is intended to be deployed in countries where rural regions lack access to specialized healthcare equipment and professionals. Additionally, the device is inexpensive and, hence can be made accessible to people with limited affordability.

Validation of Ecology and Energy Parameters of Diesel Exhausts Using Different Fuel Mixtures, Consisting of Hydrogenated Vegetable Oil and Diesel Fuels, Presented at Real Market: Approaches Using Artificial Neural Network for Large-Scale Predictions

Machine learning models have been used to precisely forecast emissions from diesel engines, specifically examining the impact of various fuel types (HVO10, HVO 30, HVO40, HVO50) on the accuracy of emission forecasts. The research has revealed that models with different numbers of perceptrons had greater initial error rates, which subsequently reached a stable state after further training. Additionally, the research has revealed that augmenting the proportion of Hydrogenated Vegetable Oil (HVO) resulted in the enhanced precision of emission predictions. The use of visual data representations, such as histograms and scatter plots, yielded significant insights into the model’s versatility across different fuel types. The discovery of these results is vital for enhancing engine performance and fulfilling environmental regulations. This study highlights the capacity of machine learning in monitoring the environment and controlling engines and proposes further investigation into enhancing models and making real-time predictive adjustments. The novelty of the research is based on the determination of the input interface (a sufficient amount of input parameters, including chemical as well as technical), which characterizes the different regimes of the diesel engine. The novelty of the methodology is based on the selection of a suitable ANN type and architecture, which allows us to predict the required parameters for a wide range of input intervals (different types of mixtures consisting of HVO and pure diesel, different loads, different RPMs, etc.).

Crop Yield Prediction using CNN

Deep learning is a branch of Machine Learning which is completely based on artificial neural networks, as neural networks are going to mimic the human brain so deep learning is also a kind of mimic of the human brain. Farming is the main occupation of India. Crop yield has a direct impact on nation and international economies annually and the yield predicted plays a significant part in the food management and agriculture sector. The task is to build a prediction model for crop production . A prerequisite of intelligent systems has brought artificial neural networks to become a new technology which provides assorted solutions for complex problems in agriculture research. Performance of the agriculture sector mainly hinges on natural forces such as spatio-temporal distribution of rainfall, temperature, climate etc, with the result any deviation of monsoon from the normal pattern brings about numerous fluctuations in area and production. Crop yield has a direct impact on nation and international economies annually and the yield predicted plays a significant part in the food management and agriculture sector. The task is to build a prediction model for crop production. The basic principle of ANN architecture, Data Modeling for Prediction involves four stages namely historical data analysis (Descriptive), Data preprocessing, modeling of Data and Performance Estimation. First classify data based on different attributes. Regression analysis using CNN, it observes the relation between an independent (predictor) and dependent (target) variables. Based on relation training the model will predict crop yield production. application of ANN in predicting crop yield by using various crop performance features as input parameter

EARTHQUAKE PREDICTION USING ARTIFICIAL INTELLIGENCE IN THE FERGHANA DEPRESSION (UZBEKISTAN)

Solving the problem of predicting earthquakes faces difficulties of both theoretical and practical nature. The reason is that the occurrence of earthquakes depends on many factors, which give rise to various anomalies that are used as precursors. However, because of the complexity of the earthquake process and the unavailability of much information about the detailed structure of the Earth's crust, a small number of them can accurately indicate future seismic events. The results of the application of machine learning and deep learning give hope for the possibility of obtaining more accurate information about future strong earthquakes if disparate factors are combined. To determine the most important signs of an earthquake and determine the spatial location of strong earthquakes in a specific seismically active territory of Uzbekistan, namely, in the Fergana depression, the Cora 3, Cora 4, random forest algorithms of machine learning and LSTM, ANN architectures of deep learning were implemented.

Predictive Modeling and Optimization of Plywood Drying: An Artificial Neural Network Approach

Introduction: This investigation delves into the optimization of the plywood drying process through the development of predictive models for output moisture content (MC_Out) and waviness. It focuses on bridging the gap in current methodologies by employing artificial neural networks (ANNs), optimized with genetic algorithms, to enhance prediction accuracy and process efficiency. Materials and Methods: A comprehensive experimental design was employed, analyzing the effects of three wood types (Doncel, Tamburo, and Zapote), two thickness levels, and three drying speeds on MC_Out and waviness. Data collected were subjected to both traditional statistical analysis and ANNs. The ANNs were fine-tuned through genetic algorithms, exploring different network architectures to achieve optimal predictive performance. Results: Statistical models revealed the significant influence of wood type, thickness, and drying speed on MC_Out and waviness, explaining 95.9% and 84.3% of the variations, respectively. The optimized ANN models, however, demonstrated superior accuracy, with the MC_Out model achieving fitted R-squared values of 0.940 and 0.757 for training and validation sets, respectively, thus outperforming traditional models in predicting drying outcomes. Discussion: The study underscores the effectiveness of ANNs in capturing complex nonlinear relationships within the plywood drying data, which traditional statistical models might not fully elucidate. The successful optimization of ANN architecture via genetic algorithms further highlights the potential of machine learning approaches in industrial applications, offering a more precise and reliable method for predicting drying process outcomes. Conclusion: The integration of artificial neural networks, optimized through genetic algorithms, represents a significant advancement in the predictive modeling of plywood drying processes. This approach not only offers enhanced prediction accuracy for key variables such as MC_Out and waviness but also paves the way for more efficient and controlled drying operations, ultimately contributing to the production of higher-quality plywood.

Benchmarking Artificial Neural Network Architectures for High-Performance Spiking Neural Networks.

Organizations managing high-performance computing systems face a multitude of challenges, including overarching concerns such as overall energy consumption, microprocessor clock frequency limitations, and the escalating costs associated with chip production. Evidently, processor speeds have plateaued over the last decade, persisting within the range of 2 GHz to 5 GHz. Scholars assert that brain-inspired computing holds substantial promise for mitigating these challenges. The spiking neural network (SNN) particularly stands out for its commendable power efficiency when juxtaposed with conventional design paradigms. Nevertheless, our scrutiny has brought to light several pivotal challenges impeding the seamless implementation of large-scale neural networks (NNs) on silicon. These challenges encompass the absence of automated tools, the need for multifaceted domain expertise, and the inadequacy of existing algorithms to efficiently partition and place extensive SNN computations onto hardware infrastructure. In this paper, we posit the development of an automated tool flow capable of transmuting any NN into an SNN. This undertaking involves the creation of a novel graph-partitioning algorithm designed to strategically place SNNs on a network-on-chip (NoC), thereby paving the way for future energy-efficient and high-performance computing paradigms. The presented methodology showcases its effectiveness by successfully transforming ANN architectures into SNNs with a marginal average error penalty of merely 2.65%. The proposed graph-partitioning algorithm enables a 14.22% decrease in inter-synaptic communication and an 87.58% reduction in intra-synaptic communication, on average, underscoring the effectiveness of the proposed algorithm in optimizing NN communication pathways. Compared to a baseline graph-partitioning algorithm, the proposed approach exhibits an average decrease of 79.74% in latency and a 14.67% reduction in energy consumption. Using existing NoC tools, the energy-latency product of SNN architectures is, on average, 82.71% lower than that of the baseline architectures.

Artificial Neural Network Applications for Energy Management in Buildings: Current Trends and Future Directions

ANNs have become a cornerstone in efficiently managing building energy management systems (BEMSs) as they offer advanced capabilities for prediction, control, and optimization. This paper offers a detailed review of recent, significant research in this domain, highlighting the use of ANNs in optimizing key energy systems, such as HVAC systems, domestic water heating (DHW) systems, lighting systems (LSs), and renewable energy sources (RESs), which have been integrated into the building environment. After illustrating the conceptual background of the most common ANN architectures for controlling BEMSs, the current work dives deep into relative research applications, thereby exhibiting their methodology and outcomes. By summarizing the numerous impactful applications during 2015–2023, this paper categorizes the predominant ANN-based techniques according to their methodological approach, specific energy equipment, and experimental setups. Grounded in the different perspectives that the integrated studies illustrate, the primary focus of this paper is to evaluate the overall status of ANN-driven control in building energy management, as well as to offer a deep understanding of the prevailing trends at the building level. Leveraging detailed graphical depictions and comparisons between different concepts, future directions, and fruitful conclusions are drawn, and the upcoming innovations of ANN-based control frameworks in BEMSs are highlighted.

Understanding mobile learning continuance after the COVID-19 pandemic: Deep learning-based dual stage partial least squares-structural equation modeling and artificial neural network analysis

The influence of COVID-19 on educational processes has halted physical forms of teaching and learning and initiated online and mobile learning systems in most countries. The provision and usage of online and e-learning systems are becoming the main challenge for many universities during the COVID-19 pandemic. Due to the novelty of this situation, a substantial amount of research has been carried out to investigate the issue of m-learning adoption or acceptance. Nevertheless, little is known about studying to examine the continued use of m-learning, which is still in short supply and calls for further research. Five different theoretical models are integrated into this study to develop an integrated model that overcomes this limitation, including the technology acceptance model, the theory of planned behavior, the expectation-confirmation model, the Delone and McLean Information System Success Model, and the Unified Theory of Acceptance and Utilization of Technology 2. This conceptual framework shows novel relationships between variables by integrating trust, personal innovation, learning value, instructor quality, and course quality. Unlike extant literature, this study utilized a hybrid analysis methodology combining two-stage analysis using partial least squares structural equation modeling (PLS-SEM) and evolving artificial intelligence named deep learning (Artificial Neural Network [ANN]) on 250 usable responses. The sensitivity analysis results revealed that attitude has the most considerable effect on the continued use of m-learning, with 100% normalized importance, followed by perceived usefulness (88%), satisfaction (77%), and habit (61%). This research reveals that a “deep ANN architecture” may determine the non-linear relationships between variables in the theoretical model. Further theoretical and practical implications are also discussed.

Assessing Feed-Forward Backpropagation Artificial Neural Networks for Strain-Rate-Sensitive Mechanical Modeling.

The manufacturing processes and design of metal and alloy products can be performed over a wide range of strain rates and temperatures. To design and optimize these processes using computational mechanics tools, the selection and calibration of the constitutive models is critical. In the case of hazardous and explosive impact loads, it is not always possible to test material properties. For this purpose, this paper assesses the efficiency and the accuracy of different architectures of ANNs for the identification of the Johnson-Cook material model parameters. The implemented computational tool of an ANN-based parameter identification strategy provides adequate results in a range of strain rates required for general manufacturing and product design applications. Four ANN architectures are studied to find the most suitable configuration for a reduced amount of experimental data, particularly for cases where high-impact testing is constrained. The different ANN structures are evaluated based on the model's predictive capability, revealing that the perceptron-based network of 66 inputs and one hidden layer of 30 neurons provides the highest prediction accuracy of the effective flow stress-strain behavior of Ti64 alloy and three virtual materials.

Precision forecasting of spray-dry desulfurization using Gaussian noise data augmentation and k-fold cross-validation optimized neural computing

Perceptron models have become integral tools for pattern recognition and classification problems in engineering fields. This study envisioned implementing artificial neural networks to forecast the performance of a mini-spray dryer for desulfurization activities. This work adopted k-fold cross-validation, a rigorous technique that evaluates model performance across multiple data segments. Several ANN models (21) were trained on data obtained from sulfation conditions, including sulfation temperature (120 °C–200 °C), slurry pH (4–12), stoichiometric ratio (0.5–2.5), slurry solid concentration (6%–14%) as the feed input and sulfur capture as the response. Three hundred synthetic datasets generated using the Gaussian noise data augmentation underwent a 10-fold cross-validation process before simulation on neural networks triggered by the logsig and tansig activation functions. The computation accuracy was further evaluated by altering the number of hidden cells from 2 to 10. The ANN architectures were assessed using statistical metrics such as mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and the coefficient of determination (R 2) techniques. Overall, error estimation suggests cross-validation and data augmentation are critical in efficient neural network generalization. The logsig function trained with 10 hidden cells presented closer data articulation when mapped onto actual values.

Enhancing short-term streamflow forecasting of extreme events: A wavelet-artificial neural network hybrid approach

Accurate short-term streamflow forecasting models are crucial for effective water resource management, enabling timely responses to extreme flood or drought events and mitigating potential socioeconomic damage. This study proposes robust hybrid Wavelet Artificial Neural Network (WANN) models for real-world hydrological applications. Two WANN variants, WANNone and WANNmulti, are proposed for short-term streamflow forecasting of extreme (high and low) flows at eight gauging stations within Brazil's Paraíba do Sul River basin. WANNone directly feeds both the original streamflow data and the decomposed components obtained through an À Trous wavelet transform into the ANN architecture. Conversely, WANNmulti utilizes separate ANNs for the original data, with the final streamflow estimate reconstructed via the inverse wavelet transform of the individual ANN outputs. The performance of these WANN models is then compared against conventional ANN models. In both approaches, Bayesian optimization is employed to fine-tune the hyperparameters within the ANN architecture. The WANN models achieved superior performance for 7-day streamflow forecasts compared to conventional ANN models. WANN models yielded high R2 values (>0.9) and low MAPE (4.8%–14.7%) within the expected RMSE range, demonstrating statistically significant improvements over ANN models (71% and 75% reduction in RMSE and MAPE, respectively, and 69% increase in R2). Further analysis revealed that WANNmulti models generally exhibited superior performance for low extreme flow predictions, while WANNone models achieved the highest accuracy for high extreme flows at most stations. WANN models' strong performance suggests their value for real-time flood warnings, enabling improved decision-making in areas like flood/drought mitigation and urban water planning.

Prediction of the removal of solid suspensions and chemical oxygen demand from a pharmaceutical wastewater plant using a neural network approach

This study aimed to model the removal efficiency of chemical oxygen demand (COD) and solid suspension (SS) from a real pharmaceutical wastewater treatment plant (WWTP) using artificial neural network-multilayer perception (ANN-MLP). The ANN model was developed using experimental data which were collected during four years. The input variables of the neural network are water pH, temperature, SS, and COD. The percentages of removal of COD (CODRE) and of SS (SSRE) are considered as output variables. The Levenberg–Marquardt algorithm was utilized to train ANN. It was found that the ANN architecture has two hidden layers with 8 neurons. The results of ANN models were compared with the measured data based on the correlation coefficient (R2) and mean square error (MSE). It was noticed that the best ANN model provides good accurate results with R2 values of 0.9783 for SSRE and 0.9826 for CODRE, and a value of MSE equal to 1.695 10−3. This study may aid the users to adjust operational parameters in recovering COD and SS in the case of the process treatment of industrial effluents.

Application of an artificial neural network coupled to a genetic algorithm for the production of polyphenols in Parachlorella kessleri grown under mixotrophic conditions

Microalgae are vital sources of high-value compounds, like polyphenols. Boosting polyphenol production requires considering diverse factors and their impacts on synthesis. This study aims to optimise polyphenol yield in microalga Parachlorella kessleri using an artificial neural network coupled with a genetic algorithm (ANN-GA). P. kessleri was grown under mixotrophic conditions and subjected to 13 experimental treatments. Factors studied included glucose, NaNO3, KH2PO4, LED light wavelength, intensity, and photoperiod. Total polyphenol concentration was quantified with Folin-Ciocalteu reagent; the polyphenol profile was obtained via HPLC. A database was constructed for ANN training to predict polyphenol production. Developed models were compared using indicators: coefficient of determination (R2pred), Mean Absolute Error (MAE), and Mean Square Error (MSE). Optimal models were fine-tuned with a genetic algorithm and validated using randomised experiments. The optimal ANN architecture for P. kessleri polyphenol production is: 6 inputs, 4 hidden layers, 6 neurons per layer, one output. This design achieved high prediction accuracy (R2 = 0.93). The genetically algorithm-optimised ANN model has these conditions: 26.60 g/L glucose; 1.42 g/L NaNO3; 0.59 g/L KH2PO4; white light; 1000 lx light intensity; 12:12 photoperiod. In experimental validation, 28 mg gallic acid equivalents per gram dry weight (GAE/g DW) were obtained, surpassing all models. An ANN-GA optimisation procedure was used for the first time for polyphenol production in P. kessleri. The model under mixotrophic conditions showed higher polyphenol production than autotrophic conditions. Polyphenol composition in P. kessleri varied between autotrophic and mixotrophic cultures, indicating inducible polyphenol production. The ANN-GA approach for polyphenol production in P. kessleri proved effective, with potential for maximising other high-value microalgae metabolites.

Forecasting the Tourist Arrival Volumes and Tourism Income with Combined ANN Architecture in the Post COVID-19 Period: The Case of Turkey

Accurate forecasting of tourism demand and income holds paramount importance for both the tourism industry and the national economy. This study aims to address several objectives: (1) specify the best forecasting model in the prediction of tourist arrival volumes and tourism income for Turkey; (2) assess the degree of impact exerted by various determinants on the tourism forecasts; (3) generate forecasts for tourist arrival volumes and tourism income using the most suitable models; and (4) examine potential scenarios illustrating the ramifications of the Russia-Ukraine war on tourist arrival volumes and tourism income. The forecasting models employed in this study encompass a comprehensive set of statistical methods, including ETS, ARIMA, TRAMO-SEATS, X13, X11, STL, Grey, and their combinations with ANN. In the ANN models, exogenous variables such as the global financial crisis, the Turkey-Russia warplane crash crisis, the COVID-19 pandemic, and USD/TRY exchange rates are incorporated. The results unveil the identification of five superior models: ETS, Grey, hybrid ETS-ANN, hybrid Grey-ANN, and hybrid ARIMA-ANN models, which exhibit the lowest MAPE and sMAPE values. Forecasts for the forthcoming quarters are examined under two scenarios: assuming the continuity or cessation of the Russia-Ukraine war. Comparative analysis of the relative effects of exogenous variables indicates that COVID-19 has the most substantial impact on tourist arrival volumes, and tourism income is primarily influenced by the USD/TRY exchange rate.

The ANN Architecture Analysis: A Case Study on Daylight, Visual, and Outdoor Thermal Metrics of Residential Buildings in China

Selecting an appropriate ANN model is crucial for speeding up the process of building performance simulation during the design phase of residential building layouts, particularly when evaluating three or more green performance metrics simultaneously. In this study, daylight, visual, and outdoor thermal metrics were selected as main green performance. To find the suitable ANN model, sensitivity analysis was used to obtain a set of proper parameters applied to the ANN structure. To train the ANN model with a higher predicting accuracy, this paper tested four different scenarios of ANN parameter setups to find some general guidelines about how to set up an ANN model to predict DF, sunlight hours, QuVue and UTCI. The results showed that an ANN model with a combined output variable demonstrated better average prediction accuracy than ANN models with a separated output variable. Having two times the number of training samplings compared to the number of input variables can lead to a high accuracy of prediction. The ideal number of neurons in the hidden layer was approximately 1.5 times the number of input variables. These findings of how to improve the ANN model may provide guidance for modeling an ANN for building performance.

Implementasi Kontrol Nutrisi Dan pH Pada Hidroponik Cerdas Berbasis Arduino Dan JST

This research aims to implement an automated nutrition and pH control system in NFT hydroponic system based on ANN control. NFT hydroponics involves growing plants without soil as a medium. In hydroponics, it is essential to continuously control the nutrient levels and pH of the solution. However, manual control performed by humans continuously is inefficient and time-consuming.The ANN method is used to model and predict the output actuators based on sensor input in the NFT hydroponic system. This ANN architecture consists of several layers with the following number of neurons: input layer 2, first hidden layer 128, second hidden layer 64, and output layer 3, representing multipleoutputs. The ANN training process involves classifying the data samples using various hyperparameters.The research findings demonstrate the ANN classification model successfully applied to control pH and nutrient levels through the predicted output actuators. The pump actuators are activated according to input received from the TDS and pH sensors. Through the variation of hyperparameters, the classification model with a test_size: 0.3, epoch: 400, batch_size: 32, and random_state: 42 provided the best performance in prediction. This ANN classification model achieved the best results in model testing with an accuracy rate: 97.96% from 49 data.

Analysis of heat transfer enhancement of passive methods in tubes with machine learning

This study investigates the efficacy of machine learning techniques and correlation methods for predicting heat transfer performance in a dimpled tube under varying flow conditions, including the presence of nanoparticles. A comprehensive numerical analysis involving 120 cases was conducted to obtain Nusselt numbers and friction factors, considering different dimple depths and velocities for both pure water and water-Al2O3 nanofluid at 1%, 2%, and 3% volume concentrations. Utilizing the data acquired from the numerical simulations, a correlation equation, SVM ANN architectures were developed. The predictive capabilities of the statistical approach, ANN, and SVM models for Nusselt number distribution and friction factor were meticulously assessed through mean average percentage error (MAPE) and correlation coefficients ( R2). The research findings reveal that machine learning techniques offer a highly effective approach for accurately predicting heat transfer performance in a dimpled tube, with results closely aligned with Computational Fluid Dynamics (CFD) simulations. Particularly noteworthy is the superior performance of the ANN model, demonstrating the most precise predictions with an error rate of 2.54% and an impressive R2 value of 0.9978 for Nusselt number prediction. In comparison, the regression model achieved an average error rate of 6.14% with an R2 value of 0.8623, and the SVM model yielded an RMSE value of 2.984% with an R2 value of 0.9154 for Nusselt number prediction. These outcomes underscore the ANN model’s ability to effectively capture complex patterns within the data, resulting in highly accurate predictions. In conclusion, this research showcases the promising potential of machine learning techniques in accurately forecasting heat transfer performance in dimpled tubes. The developed ANN model exhibits notable superiority in predicting Nusselt numbers, making it a valuable tool for enhancing thermal system analyses and engineering design optimization.