Neural Network Machine Learning Research Articles

Polymer-Unit Graph: Advancing Interpretability in Graph Neural Network Machine Learning for Organic Polymer Semiconductor Materials.

The graph representation of complex materials plays a crucial role in the field of inorganic and organic materials investigations for developing data-centric materials science, such as those using graph neural networks (GNNs). However, the currently prevalent GNN models are primarily employed for investigating periodic crystals and organic small molecule data, yet they still encounter challenges in terms of interpretability and computational efficiency when applied to polymer monomers and organic macromolecules data. There is still a lack of graph representation of organic polymers and macromolecules specifically tailored for GNN models to explore the structural characteristics. The Polymer-unit Graph, a novel coarse-grained graph representation method introduced in study, is dedicated to expressing and analyzing polymers and macromolecules. By incorporating the Polymer-unit Graph into the GNN models and analyzing the organic semiconductor (OSC) materials database, it becomes possible to uncover intricate structure-property relationships involving branched-chain engineering, fluoridation substitution, and donor-acceptor combination effects on the elementary structure of OSC polymers. Furthermore, the Polymer-unit Graph enables visualizing the relationship between target properties and polymer units while reducing training time by an impressive 98% and minimizing molecular graph representation models. In conclusion, the Polymer-unit Graph successfully integrates the concept of Polymer-unit into the field of GNNs, enabling more accurate analysis and understanding of organic polymers and macromolecules.

Application of Machine Learning Modeling for the Upstream Oil and Gas Industry Injury Rate Prediction

Introduction: Yearly, the International Labor Organization report indicates many workplace accident occurrences. The degree of the happenings depends on the workplace environment setting and the incident regulatory measures implemented. By the nature of its work environment, the oil and gas upstream sector is susceptible to high incident rates. In the current fierce business competition and practices, improving productivity, quality, and other processes, such as Safety, is vital. Implementing well-designed safety procedures is the key to managing and reducing the risk level of workplace incidents. Methods: Recently, the application of Machine learning (ML) modeling for accident/injury prediction has been reported in the construction, mining, transport, and health sectors. Likewise, the objective of this paper was to implement three machine-learning-based models to predict injury rates in a drilling operation. The petroleum safety authority of Norway provided the datasets. First, the dataset was pre-processed, and then the selected features and target dataset were used for the modeling. Finally, the model prediction and performance accuracy analysis were performed. Results: Results showed that multivariable regression (MVR), Random Forest (RF), and Artificial Neural Network (ANN) machine learning algorithms-based models predict the test data with R2 values of 0.9576, 0.793, and 0.97036, respectively. Conclusion: As the common saying goes, 'prevention is better than cure.' For this, implementing methods such as improved work processes and Health, Safety, and Environment (HSE) mitigation procedures, workplace injuries, and accidents allow for reducing the risk level of workplace injuries. The application of integrated machine learning tools, along with carefully built-in workplace accident database implementation, will provide early detection and possible remedial precautions that can be taken to prevent workplace injuries/accidents/fatalities. However, extensive research and development are required to deploy the method in real life. Combining Machine Learning modeling and carefully designed safety measures is vital for successful and robust predictive tools.

Deep Potential fitting and mechanical properties study of MgAlSi alloy

MgAlSi alloy materials have the main properties of light weight and high strength, good electrical and thermal conductivity and corrosion resistance, and have various applications in the industrial field, making an important contribution to the realization of lightweight and high performance needs. In order to be able to predict the material properties of MgAlSi alloys with a high degree of accuracy, this paper develops for the first time an interatomic potential function for MgAlSi alloys based on a neural network machine learning approach. The effectiveness of the developed machine learning potentials is verified by analyzing the problems encountered during the training process and the errors of the finally obtained potential functions, and comparing some of the radial distribution functions, coordination numbers, and predictions of properties such as the equation of state, lattice constants, shear modulus and bulk modulus with those of AIMD. It is found that the performance error of the deep potential model is basically kept in the same order of magnitude as that of DFT calculations, the computational speed can be up to nearly a thousand times that of DFT, and the computational cost is linearly related to the atomic number, which is well suited for large-scale molecular dynamics simulations, and it will provide a promising solution for accurate large-scale molecular dynamics simulations.

Abstract 7662: Convolutional neural network algorithms and hematoxylin-and-eosin-stained images predict clinical outcomes in high-grade serous ovarian cancer patients with advanced age and stage

Abstract Ovarian cancer is the deadliest among gynecologic cancers. It is partly due to the mild symptoms in early stages and relatively high diagnostic age (about 63-year-old median age). These factors contribute to the short chemotherapy response time and survival time with about 18- and 36-months median progression-free survival and overall survival duration, respectively. The advancements of convolutional neural network (CNN) machine learning algorithms provide opportunities for the development of cost- and time-effective models which is urgently needed for ovarian cancer patients, especially for those with elevated risk. A CNN model was trained with hematoxylin and eosin (H&E) stained tumor whole slide images (WSIs) from ovarian cancer patients with advanced stages. 773 WSIs of 335 patients from TCGA-OV dataset were separated randomly for training and testing cohort. The model was then applied on testing cohort with advanced stage and age patients for H&E survival score. The c-index of the predicted risk on overall survival (OS) and progression-free survival (PFS) were 0.66 and 0.63, respectively. The log-rank p-values of patients between low and high risk were 0.00137 and 0.00138, respectively. The model was then further evaluated with an external dataset. The c-index of the predicted risk were 0.64 and 0.72 for OS and PFS, respectively. And the log-rank test between patients with low and high risk achieved p-values 0.017 and 0.0047 for OS and PFS, respectively. In conclusion, a novel prognostic CNN model was developed for ovarian cancer patients with advanced age and stage for better disease management. Citation Format: Chun Wai Ng, Kwong-kwok Wong, Berrett Lawson, Sammy Ferri-Borgogno, Samuel Mok. Convolutional neural network algorithms and hematoxylin-and-eosin-stained images predict clinical outcomes in high-grade serous ovarian cancer patients with advanced age and stage [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7662.

An asynchronous wireless network for capturing event-driven data from large populations of autonomous sensors

Networks of spatially distributed radiofrequency identification sensors could be used to collect data in wearable or implantable biomedical applications. However, the development of scalable networks remains challenging. Here we report a wireless radiofrequency network approach that can capture sparse event-driven data from large populations of spatially distributed autonomous microsensors. We use a spectrally efficient, low-error-rate asynchronous networking concept based on a code-division multiple-access method. We experimentally demonstrate the network performance of several dozen submillimetre-sized silicon microchips and complement this with large-scale in silico simulations. To test the notion that spike-based wireless communication can be matched with downstream sensor population analysis by neuromorphic computing techniques, we use a spiking neural network machine learning model to decode prerecorded open source data from eight thousand spiking neurons in the primate cortex for accurate prediction of hand movement in a cursor control task.

Applied Deep learning approaches on canker effected leaves to enhance the detection of the disease using Image Embedding and Machine learning Techniques

Canker, a disease that causes considerable financial losses in the agricultural business, is a small deep lesion that is visible on the leaves of many plants, especially citrus/apple trees. Canker detection is critical for limiting its spread and minimizing harm. To address this issue, we describe a computer vision-based technique that detects Canker in citrus leaves using image embedding and machine learning (ML) algorithms. The major steps in our proposed model include image embedding, and machine learning model training and testing. We started with preprocessing and then used image embedding techniques like Inception V3 and VGG 16 to turn the ROIs into feature vectors that retained the relevant information associated with Canker leaf disease, using the feature vectors acquired from the embedding stage, we then train and evaluate various ML models such as support vector machines (SVM), Gradient Boosting, neural network, and K Nearest Neighbor. Our experimental results utilizing a citrus leaf picture dataset show that the proposed strategy works. With Inception V3 as the image embedder and neural network machine learning model we have obtained an accuracy of 95.6% which suggests that our approach is effective in canker identification. Our method skips traditional image processing techniques that rely on by hand features and produces results equivalent to cutting-edge methods that use deep learning models. Finally, our proposed method provides a dependable and efficient method for detecting Canker in leaves. Farmers and agricultural specialists can benefit greatly from early illness diagnosis and quick intervention to avoid disease spread as adoption of such methods can significantly reduce the losses incurred by farmers and improve the quality of agricultural produce.

A convolutional neural network machine learning based navigation of underwater vehicles under limited communication

This paper proposes navigation of multiple autonomous underwater vehicles (AUVs) by employing machine learning approach for wide area surveys in underwater environment. Wide area survey in underwater environment is affected by low data rate.We consider two AUVs moving in formation through clustering followed by selection of optimal path that is affected by low data rate and limited acoustical underwater communication. A state compression approach using machine learning based acoustical localization and communication (ML-ALOC) is proposed to overcome the low data rate issue in which AUV states are approximated by Hierarchical clustering followed by an optimal selection approach using Convolutional Neural Network (CNN). The performance of the proposed state compression algorithm is compared with particle state compression algorithm based on K-Means clustering at each iteration followed by Akaike information criterion (AIC) pursuing extensive simulations, in which two AUVs navigate through trajectory. It is observed from the simulations that the proposed ML-ALOC system provides better estimates when compared with acoustical localization and communication (ALOC) system using particle clustering for state compression scheme.

Investigation in optimization of process parameters in turning of mild steel using response surface methodology and modified deep neural network

The turning operation is a traditional and conventional process for machining cylindrical materials to achieve a desired shape. During machining in the turning process, the accuracy and quality of the end product mainly depend on its process parameters. Therefore, this study attempts to achieve a quality product from a conventional lathe machine by optimizing its process parameters for En2-BS970 mild steel. The experiments were designed with the help of response surface methodology (RSM). The most influential turning parameters, such as feed rate, spindle speed, cutting fluid flow rate, and cutting angle, are investigated experimentally. In addition, the machining responses, namely material removal rate (MRR), surface roughness (SR), cutting force (CF), and cutting time (CT), have been optimized using the RSM numerical optimization method. Furthermore, a deep neural network (DNN) machine learning prediction model based on the whale optimization algorithm (WOA) is developed for this experiment in order to predict turning performances for En2-BS970 material. The predicted DNN results showed an accuracy of about 90% compared to experimental results, indicating that the implementation of WOA significantly optimized the DNN weights during training. The RSM optimized responses are obtained as 14.608 mm3/min for MRR, 0.7504 µm for SR, 442.94 N for CF, and 2.48 seconds for CT during the turning operation with input settings of 0.24 mm/rev, 466.535 rpm, 0.075 ml/s and 60.18° for feed rate, spindle speed, cutting fluid flow rate and cutting edge angle respectively.

Alkali resistance prediction and degradation mechanism of basalt fiber: Integrated with artificial neural network machine learning model

Basalt fiber is commonly regarded as an alkaline corrosion-resistant material, capable of being transformed into various products (such as basalt fiber roving, chopped yarn and composite reinforcement). However, the main composition of the basalt fiber network skeleton consists of Si ∼ O bonds, which can still react with ∼OH in the alkaline environment. Nevertheless, there are differing viewpoints on the alkaline corrosion of basalt fibers within literatures. Therefore, this study aims to select three representative types of basalt fibers and compare them with two alkali-resistant glass fibers and one regular glass fiber. The attenuation mechanism of basalt fibers were investigated through orthogonal experiments, practical application scenarios and microscopic characterization methods. Additionally, we employed the Weibull model to analyze failure probabilities after erosion on basalt fibers while also applying a machine learning artificial neural network (ANN) model for predicting their alkali resistance. The results indicated that the tensile strength of basalt fiber tended to decrease and the alkali resistance of basalt fiber between alkali-resistant glass fiber and regular glass fiber in four alkaline solutions. The Weibull model also confirms that erosion increases failure probability of basalt fiber and glass fiber. Moreover, Si ∼ O, which forms the network reacted with OH-, resulting in a reduction in surface Si and Al elements content. It became possible to predict the alkali resistance of basalt fiber accurately by establishing an ANN model. Our study provide a theoretical foundation for predicting the lifespan of basalt fibers under corrosive conditions while offering guidance for enhancing their alkali resistance.

Metapopulation networks unlock the effects of landscape fragmentation on agricultural pests and natural predators

In agricultural landscapes, patches containing diverse species can serve as nodes, and species diffusion among these patches can serve as edges, forming metapopulation networks. However, the impact of landscape fragmentation on these networks and consequences for keystone crop species remain unclear. Here, we investigated thrips (pests) and lacewings (natural predators) in mango orchards and surrounding habitats across 15 landscapes. Using a machine learning neural network, we built metapopulation networks for thrips and lacewings based on the relationship between patch properties and species abundance. We evaluated these networks with metrics like clustering coefficient, strength, and path length. Furthermore, we analyzed the effects of landscape fragmentation, as indicated by edge density and landscape Shannon diversity, on network structures and the subsequent effect on species abundance using piecewise structural equation modeling. The results show that the neural network effectively constructed the metapopulation network, accurately predicting lacewing and thrips abundance in various patches with an accuracy rate exceeding 80%. Positive correlations were observed between edge density and network strength for both species. However, a positive relationship between landscape Shannon diversity and network path length was identified only for lacewings. Edge density had a greater impact on lacewings and thrips compared to landscape Shannon diversity, with a negative effect on lacewings and a positive effect on thrips. Edge density affected lacewing abundance both directly and indirectly through network strength. However, no network structure mediated the effect of landscape fragmentation on thrips abundance. Our conclusion highlights that the effects of landscape fragmentation on species are primarily driven by edge effects. These effects were clarified by the metapopulation network structure, which varied depending on the species. The findings provide new perspectives on metapopulation networks to unlock the landscape fragmentation effects.

A Spatially Explicit Uncertainty Analysis of the Air‐Sea CO2 Flux From Observations

AbstractIn order to understand the oceans role as a global carbon sink, we must accurately quantify the amount of carbon exchanged at the air‐sea interface. A widely used machine learning neural network product, the SOM‐FFN, uses observations to reconstruct a monthly, 1° × 1° global CO2 flux estimate. However, uncertainties in neural network and interpolation techniques can be large, especially in seldom‐sampled regions. Here, we present a three‐dimensional (latitude, longitude, time) gridded product for our SOM‐FFN observational data set consisting of uncertainties (pCO2 mapping, transfer velocity, wind) and biases (pCO2 mapping). We find that polar regions are dominated by uncertainty from gas exchange transfer velocity, with an average 48.7% contribution. In contrast, for subtropical regions, wind product choice contributes an average 50.0%. Regions with fewer observations correlate with higher uncertainty and biases, illustrating the importance of maintaining and expanding existing measurements.

The Application of Machine Learning to Quasar and Seyfert Classification

Machine learning can be utilized to classify spectra flagged as Active Galactic Nuclei (AGNs) belonging to Seyferts or Quasars, expediting data collection and aiding in analyzing the AGN types. While many properties of Seyferts and Quasars can be used as feature points in training a machine learning model, one relatively available property with high information density is the spectra of the AGN types. This paper aims to describe the training and results of a K-Nearest Neighbors and a Dense Neural Network machine learning model built to classify AGNs as Seyfert type 1s, Seyfert type 2s, or Quasars.

Prediction of emergency department revisits among child and youth mental health outpatients using deep learning techniques

BackgroundThe proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent unmet mental health needs. Accurate ED revisit prediction could aid early intervention and ensure efficient healthcare resource allocation. We examine the potential increased accuracy and performance of graph neural network (GNN) machine learning models compared to recurrent neural network (RNN), and baseline conventional machine learning and regression models for predicting ED revisit in electronic health record (EHR) data.MethodsThis study used EHR data for children and youth aged 4–17 seeking services at McMaster Children’s Hospital’s Child and Youth Mental Health Program outpatient service to develop and evaluate GNN and RNN models to predict whether a child/youth with an ED visit had an ED revisit within 30 days. GNN and RNN models were developed and compared against conventional baseline models. Model performance for GNN, RNN, XGBoost, decision tree and logistic regression models was evaluated using F1 scores.ResultsThe GNN model outperformed the RNN model by an F1-score increase of 0.0511 and the best performing conventional machine learning model by an F1-score increase of 0.0470. Precision, recall, receiver operating characteristic (ROC) curves, and positive and negative predictive values showed that the GNN model performed the best, and the RNN model performed similarly to the XGBoost model. Performance increases were most noticeable for recall and negative predictive value than for precision and positive predictive value.ConclusionsThis study demonstrates the improved accuracy and potential utility of GNN models in predicting ED revisits among children and youth, although model performance may not be sufficient for clinical implementation. Given the improvements in recall and negative predictive value, GNN models should be further explored to develop algorithms that can inform clinical decision-making in ways that facilitate targeted interventions, optimize resource allocation, and improve outcomes for children and youth.

Effect of Machine Learning Algorithms on Prediction of In-Cylinder Combustion Pressure of Ammonia–Oxygen in a Constant-Volume Combustion Chamber

Road vehicles, particularly cars, are one of the primary sources of CO2 emissions in the transport sector. Shifting to unconventional energy sources such as solar and wind power may reduce their carbon footprints considerably. Consequently, using ammonia as a fuel due to its potential benefits, such as its high energy density, being a carbon-free fuel, and its versatility during storage and transportation, has now grabbed the attention of researchers. However, its slow combustion speed, larger combustion chamber requirements, ignition difficulties, and limited combustion stability are still major challenges. Therefore, authors tried to analyze the combustion pressure of ammonia in a constant-volume combustion chamber across different equivalence ratios by adopting a machine learning approach. While conducting the analysis, the experimental values were assessed and subsequently utilized to predict the induced combustion pressure in a constant-volume combustion chamber across various equivalence ratios. In this research, a two-step prediction process was employed. In the initial step, the Random Forest algorithm was applied to assess the combustion pressure. Subsequently, in the second step, artificial neural network machine learning algorithms were employed to pinpoint the most effective algorithm with a lower root-mean-square error and R2. Finally, Linear Regression illustrated the lowest error in both steps with a value of 1.0, followed by Random Forest.

Abstract TP14: SocialBit: A Novel Smartwatch Sensor to Detect Social Interaction Frequency in Stroke Survivors

Introduction: Stroke survivors experience high levels of social isolation which hinder their rehabilitation and well-being. A gap in the field that impedes intervention development is a lack of real-time social interaction measurement tailored for this population. In response, we developed SocialBit, a smartwatch-based machine learning algorithm to discreetly and passively detect social interactions through privacy-preserving acoustic analysis. In this observational study, we estimated the accuracy of SocialBit to detect social interactions compared to human observers in stroke survivors. Methods: SocialBit was built as a neural network machine learning algorithm using features from YAMNet and a Transformer classifier. It was then trained and tested in stroke survivors for up to 8 days in hospital. Independently, human observers tallied the presence or absence of social interactions every minute to establish the ground truth. SocialBit performance of detecting social interactions was compared against the ground truth. Results: We analyzed 1137.7 hours of ambient audio data in 90 patients within 14 days of stroke onset. 16% of patients had aphasia, 25% reported symptoms of clinical depression, and 28% reported moderate to severe loneliness. SocialBit had an accuracy of 0.83 (SD = 0.2), sensitivity of 0.86 (SD = 0.2), specificity of 0.81 (SD = 0.02), and an area under the curve (AUC) of 0.90 (SD = 0.2) (Figure 1). Conclusion: SocialBit accurately detected the frequency of social interactions in stroke survivors in a hospital setting. These results will drive future planned studies to examine SocialBit's performance in patients’ home communities. SocialBit shows the potential to be the first real-time social sensor to quantify social interactions and social isolation in stroke survivors.

Hydro-chemical based assessment of groundwater vulnerability in the Holocene multi-aquifers of Ganges delta

Determining the degree of high groundwater arsenic (As) and fluoride (F−) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F− concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F− contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination.

Balancing urban energy considering economic growth and environmental sustainability through integration of renewable energy

This paper delves into the critical intersection of urban energy management, economic growth, and environmental sustainability through the integration of renewable energy sources in smart urban homes. The escalating demand for power has created an energy scarcity, necessitating innovative solutions to balance supply and consumption. In this context, the transition from traditional electric grids to smart grids (SG) is gaining prominence. To harness the potential of smart urban homes, particularly in the context of renewable energy, solar panels on rooftops have become indispensable. Smart grid technologies enable users to engage in demand response strategies, offering incentives and financial benefits for flexible device usage. This study introduces a novel optimization algorithm, the Polar Bear Optimization Algorithm (PBOA), designed to schedule domestic device usage patterns efficiently. To evaluate the effectiveness of PBOA, this paper conducts comparative analyses with other optimization algorithms such as Differential Evolution (DE) and Particle Swarm Optimization Algorithm (PSOA). Energy cost metrics, including Critical Peak Price (CPP) and Real-Time Price (RTP), are employed to assess cost reduction strategies. The primary focus of this study is to minimize energy costs and reduce the Peak to Average Ratio (PAR), thereby promoting economic growth and environmental sustainability in smart urban homes. It should be noted that neural network (NN) machine learning technique has been used for forecasting the weather and renewable energies output power.

Using automl technologies to solve monitoring problems

Purpose: to substantiate the relevance of using the approach and technologies of automated machine learning (AutoML) to solve monitoring problems. The methodology for using this approach was described using the example of problems of monitoring animals on aerial photographs. Materials and methods: the article describes the architecture of a software application for automated machine learning of convolutional neural networks based on a genetic algorithm, as well as the methodology for its use in solving monitoring problems. Results: testing (validation) of the presented system was carried out using the example of solving problems of animal recognition on aerial photographs. Conclusions: the advantages and disadvantages, implementation features, areas of application of the AutoML approach and technologies are shown.

WDM C-band four channel demultiplexer using cascaded multimode interference on SiN strip waveguide structure

Back reflection challenges significantly constrain the efficiency of optical communication networks employing dense wavelength division multiplexing (DWDM) technology, particularly those based on Silicon (Si) Multimode Interference (MMI) waveguides. To mitigate this limitation, we present a novel 1×4 optical demultiplexer design using MMI within a Silicon-Nitride (SiN) strip waveguide configuration, operating within the C-band spectrum. Our design was optimized using AI-enhanced RSoft-CAD simulations that combined the Beam Propagation Method (BPM) and Finite-Difference Time-Domain (FDTD) techniques, integrated with convolutional neural network (CNN) machine learning algorithms. The simulation results demonstrate that the proposed device efficiently transmits four channels with 10 nm spacing in the C-band, showing low power loss ranging from 1.98-2.35 dB, a broad bandwidth of 7.68-8.08 nm, and excellent crosstalk suppression between 20.8-23.4 dB. Leveraging the low refractive index of SiN, we achieve ultra-low back reflection of 40.58 dB without requiring specialized angled MMI designs, which are often necessary in Si-based MMI technology. Consequently, this SiN-based MMI demultiplexer provides an effective solution for DWDM systems, enabling high data transmission rates with minimal back reflection in optical communication networks.

Utilizing machine learning to enhance performance of thin-film solar cells based on Sb2(S x Se1-x )3: investigating the influence of material properties.

Antimony chalcogenides (Sb2(S x Se1-x )3) have drawn attention as a potential semiconducting substance for heterojunction photovoltaic (PV) devices due to the remarkable optoelectronic properties and wide range of bandgaps spanning from 1.1 to 1.7 eV. In this investigation, SCAPS-1D simulation software is employed to design an earth abundant, non-toxic, and cost-effective antimony sulfide-selenide (Sb2(S,Se)3)-based thin-film solar cell (TFSC), where tungsten disulfide (WS2) and cuprous oxide (Cu2O) are used as an electron transport layer (ETL) and hole transport layer (HTL), respectively. The PV performance parameters such as power conversion efficiency, open-circuit voltage (V oc), short-circuit current (J sc), and fill factor (FF) are assessed through adjustments in material properties including thickness, acceptor concentration, bulk defect density of the absorber, defect state of absorber/ETL and HTL/absorber interfaces, operating temperature, work function of the rear electrode, and cell resistances. This analysis aims to validate their collective impact on the overall efficiency of the designed Ni/Cu2O/Sb2(S,Se)3/WS2/FTO/Al TFSC. The optimized physical parameters for the Sb2(S,Se)3 TFSC lead to impressive PV outputs with an efficiency of 30.18%, V oc of 1.02 V, J sc of 33.65 mA cm-2, and FF of 87.59%. Furthermore, an artificial neural network (ANN) machine learning (ML) algorithm predicts the optimal PCE by considering five semiconductor parameters: absorber layer thickness, bandgap, electron affinity, electron mobility, and hole mobility. This model, which has an approximate correlation coefficient (R 2) of 0.999, is able to predict the data with precision. This numerical analysis provides valuable data for the fabrication of an environmentally friendly, economical, and incredibly non-toxic efficient heterojunction TFSC.