Artificial Neural Network Algorithm Research Articles

Spatio-temporal evolution of land subsidence and susceptibility zonation of associated ground fissures in the urban area of loess plateau: a case in Xianyang city, China

For over 50 years, the Xianyang city on the Chinese loess plateau, has been deeply affected by land subsidence and its associated ground fissures. In this study, 67 Sentinel-1A images from 2015 to 2022 were analysed to obtain the spatio-temporal evolution of land subsidence. Subsidence centers identified in Yunyang town and Luqiao town exhibit annual subsidence rates of −31 mm/a and −26.7 mm/a, respectively, culminating in total subsidence of −258 mm in Yunyang town and −139 mm in Luqiao town. Moreover, time series analysis revealed that both towns exhibit pronounced seasonal subsidence patterns. It is believed that human cultivation and groundwater overuse, combined with the effects of active faults, have led to uneven land subsidence. Then, the gradient of land subsidence and its potential relation to ground fissure hazards were evaluated. The results highlight a strong spatial correlation between high subsidence gradient areas and dense ground fissure distribution. Therefore, we proposed for the first time to introduce the land subsidence gradient factors for the susceptibility mapping of ground fissures using the artificial neural network algorithm. The susceptibility zonation results show that integrating the gradient factor can improve the rationality of the susceptibility evaluation. The above understanding provides valuable support for disaster prevention and mitigation in urban areas affected by land subsidence and ground fissures in loess regions.

Optimizing removal of antiretroviral drugs from tertiary wastewater using chlorination and AI-based prediction with response surface methodology

Chemical and pharmaceutical chemicals found in water sources create substantial risks to human health and the environment. The presence of pharmaceutical contaminants in water can cause antibiotic resistance development, toxicity to aquatic organisms, and endocrine disruption. Hence, the elimination of chemicals and other contaminants from wastewater prior to its release is a burgeoning concern in the domains of engineering and science. The use of treatment technologies in wastewater treatment plants can remove pharmaceutical contaminants through the oxidation process. However, many traditional wastewater treatment plants lack the advanced monitoring tools required to detect low concentrations of pharmaceuticals. Without the ability to detect these compounds, it's challenging to treat them effectively. The goal of this study was to use Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) algorithms to model and improve how Nevirapine and Efavirenz break down in different chlorination conditions. The RSM analysis revealed statistically significant models (F-values: Nevirapine, pH-t: 108.15, T-t: 76.55, ICC-t: 110.84), indicating a strong correlation between operational parameters (pH, temperature, and initial chlorine concentration) and degradation behavior. The ANN model accurately predicted the degradation of both Nevirapine and Efavirenz under various chlorination conditions, as confirmed by analyzing actual-predicted graphs, residual plots, and Mean Squared Error (MSE) values. The ANN model using ICC-t achieved the highest MOD value of 31.31 % for Nevirapine. The ANN model based on ICC-t yielded a maximum MOD value of 16.06 % for Efavirenz. These findings provide valuable insights into optimizing chlorination processes for better removal of these pharmaceutical contaminants from water.

Back Propagation Artificial Neural Network Based DC Bus Capacitance Identification Method in Three-Phase PWM Rectifier for Charging System of EVs

In order to improve the performance and reliability of the three-phase PWM rectifier in electric vehicle (EV) charging system, it is important to focus on the identification of the DC bus capacitance. The existing methods have the disadvantages such as a low identification accuracy, a high hardware dependency, and high costs. Therefore, the development of new identification methodologies could be the way out of the aforementioned disadvantages, in terms of advanced algorithms. In this paper, a DC bus capacitance identification method based on back propagation (BP) artificial neural network (ANN) algorithm is proposed. The capacitance can be accurately identified in the diode rectification stage, by training the neural network to extract the potential laws in the data, which only includes the Phase-a voltage and current, as well as the DC bus voltage ripple. The regression response is 0.99991. Finally, the experimental results show the accuracy and effectiveness of the proposed method. A simple and effective BP ANN is designed, and it can identify the capacitance at different grid voltages and DC bus loads. It has a high identification accuracy that the maximum identification error is less than 4.5%.

Machine Learning Based Classification for Spam Detection

Electronic Electronic messages, i.e. e-mails, are a communication tool frequently used by individuals or organizations. While e-mail is extremely practical to use, it is necessary to consider its vulnerabilities. Spam e-mails are unsolicited messages created to promote a product or service, often sent frequently. It is very important to classify incoming e-mails in order to protect against malware that can be transmitted via e-mail and to reduce possible unwanted consequences. Spam email classification is the process of identifying and distinguishing spam emails from legitimate emails. This classification can be done through various methods such as keyword filtering, machine learning algorithms and image recognition. The goal of spam email classification is to prevent unwanted and potentially harmful emails from reaching the user's inbox. In this study, Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms are used to classify spam emails and the results are compared. Algorithms with different approaches were used to determine the best solution for the problem. 5558 spam and non-spam e-mails were analyzed and the performance of the algorithms was reported in terms of accuracy, precision, sensitivity and F1-Score metrics. The most successful result was obtained with the RF algorithm with an accuracy of 98.83%. In this study, high success was achieved by classifying spam emails with machine learning algorithms. In addition, it has been proved by experimental studies that better results are obtained than similar studies in the literature.

An optimal framework for natural English translate processing: On the application of artificial neural network and Adam optimization algorithm

Traditional rule-based and statistical methods have limitations when dealing with complex language structures and semantics. In neural network machine translation algorithms, the objective function is usually to improve the accuracy of n-ary words. However, this does not guarantee a more natural and accurate translation. To overcome these challenges, this paper proposes an optimization algorithm for English natural translation processing based on neural networks, which combines Generative Adversarial Network (GAN) and Transformer models. In GAN, the generative model uses the Transformer model to generate false samples, while the discriminative model uses a binary classifier based on convolutional neural networks and attention mechanisms to distinguish between true and false samples. During the training process, reinforcement learning algorithms are added to evaluate and adjust the generated sentences, and the parameters of the generated model are updated. The classification results of the discriminative model are used together with the Bilingual Evaluation Basis Value (BLEU) objective function to evaluate false samples, and the results are fed back to the generating model to guide parameter updates and optimization. Extensive experiments were conducted on a standard English-Chinese machine translation dataset to evaluate our method. Compared with the benchmark model that only uses supervised learning methods, our neural network-based optimization algorithm for English natural translation processing has achieved significant improvements in translation quality. According to statistical comparison, compared with the Transformer model (BLUE = 33.63 and AP = 90%) and the deep learning model based on long-term and short-term memory (BLUE = 30.26 and AP = 83%), the GAN and Transformer models proposed as the best framework exhibit better performance in bilingual evaluation deficiency (BLEU) (34.35) and accuracy (AP = 95%).

A technical perspective on integrating artificial intelligence to solid-state welding

AbstractThe implementation of artificial intelligence (AI) techniques in industrial applications, especially solid-state welding (SSW), has transformed modeling, optimization, forecasting, and controlling sophisticated systems. SSW is a better method for joining due to the least melting of material thus maintaining Nugget region integrity. This study investigates thoroughly how AI-based predictions have impacted SSW by looking at methods like Artificial Neural Networks (ANN), Fuzzy Logic (FL), Machine Learning (ML), Meta-Heuristic Algorithms, and Hybrid Methods (HM) as applied to Friction Stir Welding (FSW), Ultrasonic Welding (UW), and Diffusion Bonding (DB). Studies on Diffusion Bonding reveal that ANN and Generic Algorithms can predict outcomes with an accuracy range of 85 – 99%, while Response Surface Methodology such as Optimization Strategy can achieve up to 95 percent confidence levels in improving bonding strength and optimizing process parameters. Using ANNs for FSW gives an average percentage error of about 95%, but using metaheuristics refined it at an incrementally improved accuracy rate of about 2%. In UW, ANN, Hybrid ANN, and ML models predict output parameters with accuracy levels ranging from 85 to 96%. Integrating AI techniques with optimization algorithms, for instance, GA and Particle Swarm Optimization (PSO) significantly improves accuracy, enhancing parameter prediction and optimizing UW processes. ANN’s high accuracy of nearly 95% compared to other techniques like FL and ML in predicting welding parameters. HM exhibits superior precision, showcasing their potential to enhance weld quality, minimize trial welds, and reduce costs and time. Various emerging hybrid methods offer better prediction accuracy.

Early automated detection system for skin cancer diagnosis using artificial intelligent techniques

Recently, skin cancer is one of the spread and dangerous cancers around the world. Early detection of skin cancer can reduce mortality. Traditional methods for skin cancer detection are painful, time-consuming, expensive, and may cause the disease to spread out. Dermoscopy is used for noninvasive diagnosis of skin cancer. Artificial Intelligence (AI) plays a vital role in diseases’ diagnosis especially in biomedical engineering field. The automated detection systems based on AI reduce the complications in the traditional methods and can improve skin cancer’s diagnosis rate. In this paper, automated early detection system for skin cancer dermoscopic images using artificial intelligent is presented. Adaptive snake (AS) and region growing (RG) algorithms are used for automated segmentation and compared with each other. The results show that AS is accurate and efficient (accuracy = 96%) more than RG algorithm (accuracy = 90%). Artificial Neural networks (ANN) and support vector machine (SVM) algorithms are used for automated classification compared with each other. The proposed system with ANN algorithm shows high accuracy (94%), precision (96%), specificity (95.83%), sensitivity (recall) (92.30%), and F1-score (0.94). The proposed system is easy to use, time consuming, enables patients to make early detection for skin cancer and has high efficiency.

Chaos Game Optimization-Hybridized Artificial Neural Network for Predicting Blast-Induced Ground Vibration

In this study, we introduced the chaos game optimization-artificial neural network (CGO-ANN) model as a novel approach for predicting peak particle velocity (PPV) induced by mine blasting. The CGO-ANN model is compared with other established methods, including the particle swarm optimization-artificial neural network (PSO-ANN), the genetic algorithm-artificial neural network (GA-ANN), single ANN, and the USBM empirical model. The aim is to demonstrate the superiority of the CGO-ANN model for PPV prediction. Utilizing a dataset comprising 180 blasting events from the Tonglushan Copper Mine in China, we investigated the performance of each model. The results showed that the CGO-ANN model outperforms other models in terms of prediction accuracy and robustness. This study highlights the effectiveness of the CGO-ANN model as a promising tool for PPV prediction in mining operations, contributing to safer and more efficient blasting practices.

MDs-NP: a property prediction model construction procedure for naphtha based on molecular dynamics simulation.

In the context of carbon neutrality and carbon peaking, molecular management has become a focus of the petrochemical industry. The key to achieving molecular management is molecular reconstruction, which relies on rapid and accurate calculation of oil properties. Focusing on naphtha, we proposed a novel property prediction model construction procedure (MDs-NP) employing molecular dynamics simulations for property collections and gamma distribution from real analytical data for calculating mole fractions of simulation mixtures. We calculated 348 sets of mixture properties data in the range of 273K to 300K by molecular dynamics simulations. Molecular feature extraction was based on molecular descriptors. In addition to descriptors based on open-source toolkits (RDKit and Mordred), we designed 12 naphtha knowledge (NK) descriptors with a focus on naphtha. Three machine learning algorithms (support vector regression, extreme gradient boosting and artificial neural network) were applied and compared to establish models for the prediction of the density and viscosity of naphtha. Mordred and NK descriptors + support vector regression algorithm achieved the best performance for density. The selected RDKFp and NK descriptors + artificial neural network algorithm achieved the best performance for viscosity. Using ablation studies, T, P_w and CC(C)C are three effective descriptors in NK that can improve the performance of the property prediction models. MDs-NP has the potential to be extended to more properties as well as more-complex petroleum systems. The models from MDs-NP can be used for rapid molecular reconstruction to facilitate construction of data-driven models and intelligent transformation of petrochemical processes.

Detection of Land Use Changes in Forests Using Satellite Image Classification Based on Deep Learning: A Case Study of Sardasht Forests

Awareness of changes in forest areas has always been one of the most crucial environmental considerations globally. Sardasht forests is located in the northern Zagros region and with an area of around 91,117 hectares constitute for 24% of Iran total forests. They play an important role in water supply, soil preservation, climate regulation and overall economic and social balance in the country. Nevertheless, these forests are currently perceived as degraded due to the primary reason of tree cutting for fuel and livestock feed. In this study, remote sensing data of the study area including Landsat 5 TM satellite images from 2002, Landsat 7 ETM images from 2012, Landsat 8 OLI images 2020 and classification algorithms, maximum likelihood and artificial neural network were utilized. To examine the accuracy of each classification, ground truth points were randomly collected using GPS devices and by implementing control points, statistical parameters of error accuracy including Kappa coefficient and overall accuracy were calculated. The results show that the map generated using the artificial neural network algorithm has an overall accuracy of 98.32% with a Kappa coefficient of 0.9781 while the map produced by the maximum likelihood algorithm has an overall accuracy of 92.45% with a Kappa coefficient of 0.901. As a result, the artificial neural network algorithm demonstrates to be a more suitable method for producing land cover maps compared to the maximum likelihood algorithm.

Intelligent algorithm-based maximum power point tracker for an off-grid photovoltaic-powered direct-current irrigation system

Abstract This research aims to enhance the performance of photovoltaic (PV) systems on a 2-fold basis. Firstly, it introduces an advanced deep artificial neural network algorithm for accurate and fast maximum power point tracking, ensuring optimal extraction of electrical power from PV arrays. Secondly, it proposes the use of 96-V, 2.98-kW direct-current (DC) water pumps for farm irrigation, aiming to improve efficiency, reduce cost and complexity, and overcome challenges associated with connecting faraway farm irrigation systems to the utility grid. In this study, it has been demonstrated that the use of DC pumps greatly improves system performance and efficiency by eliminating the need for isolation transformers, power passive filters and inverters, therefore simplifying the architecture of the system. The efficacy of the proposed methodology is confirmed by MATLAB®/Simulink® simulation results, whereby the proposed algorithm attains a mean squared error of 6.5705 × 10–5 and a system efficiency approaching 99.8%, ensuring a steady voltage under varying load conditions.

Multi-decade land cover/land use dynamics and future predictions for Zambia: 2000–2030

Human LULCC is the many driver of environmental changes. Accurate and up-to-date current and predicted information on LULCC is important in land use planning and natural resource management; however, in Zambia, detailed information on LULCC is insufficient. Therefore, this study assessed the dynamics of LULC change (2000–2020) and future projections (2020–2030) for Zambia. The ESA CCI land cover maps, which have been developed from Sentinel-2 images were used in this study. This dataset has a grid spatial resolution of 300 m for the 2000, 2010 and 2020. The 31 ESA CCI Classification were reclassified into ten (10) local Classifications using the r.class module in QGIS 2.18.14. The 2000 and 2010 LULC maps were used to simulate the 2020 LULC scenario using Artificial Neural Network (Multi-layer Perception) algorithms in Modules for Land Use Change Evaluation (MOLUSCE) plugin in QGIS 2.18.14. The 2010 and 2020 maps were used to predict the 2030 LULC classes. The reference 2020 and predicted 2020 LULC maps were used to validate the model. Predicted against observed 2020 LULC map, Kappa (loc) statistic was 0.9869. The 2020 LULC patterns was successfully simulated using ANN-MLP with accuracy level of 95%. LULC classes were predicted for 2030 using the 2010–2020 calibration period. The predicted 2030 LULC types shows an increase in built-up (71.44%) and decrease in cropland (0.73%) with reference to 2020 LULC map. Dense forest (0.19%), grassland (0.85%) and bare land (1.37%) will reduce from 2020–2030. However, seasonally flooded, sparse forest, shrub land, wetland and water body will increase marginally. The largest LULC change is from forest into other LULC types. The insights from this study show that ANN-MLP can be used to predict LULCC, and that the generated information can be employed in land use planning and National Adaptation Plans at regional and national scale.

Design of AI-based 3.84 kW Battery Package Using Backpropagation Artificial Neural Network Algorithm for Cargo Drones

Design of AI-based 3.84 kW Battery Package Using Backpropagation Artificial Neural Network Algorithm for Cargo Drones

Examination of load-deformation characteristics of long-span bridges in harsh natural environments based on real-time updating artificial neural network

Long-span bridges, often exposed to challenging harsh natural environments with severe weather conditions, necessitate real-time examination of load-deformation characteristics to ensure structural integrity and safety. Previous studies have primarily focused on investigating the causes of deformation in bridge structures under different single-load conditions during severe natural disasters, utilizing physics-based, mechanics-based, and data-driven methods. However, these methods cannot achieve fully achieve effective analysis of the real-time effects of multi-factor loads on bridge deformation, particularly in the presence of dynamic and simultaneous loads such as wind or temperature variations. A novel data-driven method is proposed based on a state-of-the-art real-time updating artificial neural networks (ANNs) algorithm to investigate the real-time coupling relationship between multi-loads and bridge deformation, enabling real-time prediction of bridge deformations. Additionally, the real-time characteristics between structural deformation and multi-loads are explained by incorporating SHapley Additive exPlanation (SHAP) in harsh natural environments. The proposed method has been validated on the 1,006-meter Forth Bridge in Scotland, showing high accuracy in real-time displacement prediction. The 9-day testing dataset demonstrated the R2 values for Y and Z direction deformations were found to be 0.98 and 0.87, respectively. The performance metrics for each day indicated that the majority of Y and Z direction deformations had R2 values exceeding 0.8, with RMSE and MAE values below 30 mm. The SHAP analysis revealed that an increase in wind speed leads to intensified Y direction deformation (larger SHAP values), while temperature has a significant impact on Z direction deformation (smaller SHAP values). Moreover, the weight influences of each load on the deformation are not fixed. The study's findings demonstrate that the proposed method enables accurate long-term prediction and assessment, allowing precise monitoring and prevention of abnormal risks in bridges under harsh environmental conditions.

Enhancing Microwave Photonic Interrogation Accuracy for Fiber-Optic Temperature Sensors via Artificial Neural Network Integration

In this paper, an application of an artificial neural network algorithm is proposed to enhance the accuracy of temperature measurement using a fiber-optic sensor based on a Fabry–Perot interferometer (FPI). It is assumed that the interrogation of the FPI is carried out using an optical comb generator realizing a microwave photonic approach. Firstly, modelling of the reflection spectrum of a Fabry–Perot interferometer is implemented. Secondly, probing of the obtained spectrum using a comb-generator model is performed. The resulting electrical signal of the photodetector is processed and is used to create a sample for artificial neural network training aimed at temperature detection. It is demonstrated that the artificial neural network implementation can predict temperature variations with an accuracy equal to 0.018 °C in the range from −10 to +10 °C and 0.147 in the range from −15 to +15 °C.

Enhanced accuracy through machine learning-based simultaneous evaluation: a case study of RBS analysis of multinary materials

We address the high accuracy and precision demands for analyzing large in situ or in operando spectral data sets. A dual-input artificial neural network (ANN) algorithm enables the compositional and depth-sensitive analysis of multinary materials by simultaneously evaluating spectra collected under multiple experimental conditions. To validate the developed algorithm, a case study was conducted analyzing complex Rutherford backscattering spectrometry (RBS) spectra collected in two scattering geometries. The dual-input ANN analysis excelled in providing a systematic analysis and precise results, showcasing its robustness in handling complex data and minimizing user bias. A comprehensive comparison with human supervision analysis and conventional single-input ANN analysis revealed a reduced susceptibility of the dual-input ANN analysis to inaccurately known setup parameters, a common challenge in material characterization. The developed multi-input approach can be extended to a wide range of analytical techniques, in which the combined analysis of measurements performed under different experimental conditions is beneficial for disentangling details of the material properties.

Efficient and Secure Communication in Vanets Using Block Chain

This project describes the design and implementation of a non-contact vibration picker for capturing data from spinning machinery in order to detect bearing faults early. The Hilbert transform is used to denoise the collected vibration signals, and the dataset is then analyzed by Principal Component Analysis (PCA) and Sequential Floating Forward Selection (SFFS) for dimensionality reduction and feature selection, respectively. The most essential attributes are then used to identify and categorize various bearing issues using Support Vector Machines (SVM) and Artificial Neural Network (ANN) algorithms. The entire methodology provides an effective and proactive way for bearing health monitoring and maintenance, emphasizing fast defect identification and leading to significant savings in time, effort, and equipment maintenance costs.

Gaze-based human intention prediction in the hybrid foraging search task

An agent’s ability to predict a human’s intention can facilitate the effectiveness of a human-agent team. The aim of this study was to explore the feasibility of predicting human intention in the hybrid foraging search task using eye gaze data and machine learning. In the hybrid foraging search paradigm, the human performs searches in patches and decides to stay and exploit a patch or to leave and explore another patch. Researchers have modeled the “patch-leaving problem” on the collective level using the marginal value theorem. However, few have attempted to predict the exact moment when the human will leave. In the current study, 40 participants performed the hybrid foraging search task while eye gaze data were collected with an eye-tracker. The leaving intention was associated with larger pupil size, shorter average fixation duration, larger number of fixations, longer saccade amplitude, and faster saccade velocity compared to the searching intention. A cross-subject machine learning model with an artificial neural network algorithm was able to predict whether a participant would leave the current patch with up to 78% accuracy with a 2-second data analysis window. The length of the data analysis window did not significantly affect prediction accuracy. Furthermore, the earlier the behavior prediction was made, the lower the reliability of the prediction. These results demonstrate that eye gaze features and machine learning algorithms are useful in predicting human intention in visual search tasks.

Modeling and optimization of hybrid geothermal-solar energy plant using coupled artificial neural network and genetic algorithm

Avoiding solar thermal energy storage reduces efficiency in hybrid solar-geothermal energy systems, making them impractical. To address this challenge, a synergistic approach involves the integration of these resources in the construction of hybrid power plants. An experiment is conducted with five independent variables: evaporator temperature, separator pressure, inlet pressure of turbine 2, effectiveness of vapor generator 1, and desalination mass ratio. Unlike most energy systems that rely on a single optimization pattern, this study utilizes response surface methodology (RSM) to design and gather data through simulation. Additionally, an artificial neural network (ANN) is employed alongside RSM to establish mappings from independent variables to response variables, including thermal efficiency and levelized cost of product. The selection of objective functions derived from ANN is predicated on their commendable performance, denoted by an R-squared value of 1. Furthermore, a cost function is formulated with the dual aims of maximizing thermal efficiency and minimizing the levelized cost of product. This function is subsequently optimized through the application of genetic algorithms (GAs). The findings elucidate that specific parameter values—namely, a desalination mass ratio of 2.43, separator pressure of 455.77 kPa, effectiveness of vapor generator 1 of 0.82, inlet pressure of turbine 2 of 12000 kPa, and evaporator temperature of −11.51 ℃—conducive to the optimal condition are identified, yielding a thermal efficiency of 30.47% and a levelized cost of product of 13.04 $/GJ. This endeavor is anticipated to furnish an algorithmic framework not only for modeling hybrid plants but also for optimizing electrical power generation processes.

Comparison of Predictive Performance of Data Mining Algorithms in Predicting Tomato Yield with the A Case Study in Igdir

Among the vegetable species in the world, the plant with the most cultivation area is tomato. Increasing tomato yield is important in terms of contributing more to the world economy, producer’s income and human health. With the advancement in software technologies, the importance of data mining algorithms is increasing due to the fact that these algorithms can produce more sophisticated solutions for regression and classification problems. Determining the factors affecting tomato yield and comparing different data mining algorithms on prediction of tomato yield are the purpose of this study. For this purpose, survey study was conducted with the 105 farmers, selected by Simple Random Sampling Method in Igdir province in 2016. Different data mining algorithms including Classification and Regression Tree, Exhaustive CHAID, Chi-Square Automatic Interaction Detector, Artificial Neural Network Algorithm, Multivariate Adaptive Regression Splines and General Linear Model were developed and compared their predictive performance. MARS decision tree has been able to build a model with greatest predictive accuracy, and the others are respectively ANN, GLM, CART, CHAID and Exhaustive CHAID. In the MARS model, number of irrigation , amount of chemical fertilizer , age of farmer , number of seedlings , education level , soil analysis status , sowing region were found statistically significant (P˂0.05). Preferring the MARS model could give an opportunity to detect factors affecting tomato yield and their interactions with higher accuracy. Moreover, results can be easily interpreted and the rules are understandable.