Neural Network-based Approach Research Articles

Transcending Linguistic Boundaries: A Technical Exploration of The Evolution and Future Trajectory of Corpus-Based Machine Translation

This review paper presents a comprehensive examination of the evolution, current state, and future perspectives of machine translation (MT). The field of MT, encompassing the automatic translation of text from one language to another, has undergone significant transformations from rule-based methods to statistical models, and more recently, to neural network-based approaches. By synthesizing current research and technological progress, this paper aims to provide a detailed understanding of the dynamic landscape of machine translation and its future directions. In today's globalized world, language barriers remain a significant obstacle to accessing information. Relying solely on human translators often fails to meet the growing demand for translation services, which is why Machine Translation (MT) tools are becoming increasingly popular. These tools have the potential to address this issue effectively. As a result, research in MT is expanding rapidly, and new paradigms are continually being developed. This paper presents a systematic literature review to identify the most widely used MT systems, their architectures, the quality assessment methods used to evaluate them, and which systems deliver the best results.

Convolutional Neural Networks for Local Component Number Estimation from Time–Frequency Distributions of Multicomponent Nonstationary Signals

Frequency-modulated (FM) signals, prevalent across various applied disciplines, exhibit time-dependent frequencies and a multicomponent nature necessitating the utilization of time-frequency methods. Accurately determining the number of components in such signals is crucial for various applications reliant on this metric. However, this poses a challenge, particularly amidst interfering components of varying amplitudes in noisy environments. While the localized Rényi entropy (LRE) method is effective for component counting, its accuracy significantly diminishes when analyzing signals with intersecting components, components that deviate from the time axis, and components with different amplitudes. This paper addresses these limitations and proposes a convolutional neural network-based (CNN) approach for determining the local number of components using a time–frequency distribution of a signal as input. A comprehensive training set comprising single and multicomponent linear and quadratic FM components with diverse time and frequency supports has been constructed, emphasizing special cases of noisy signals with intersecting components and differing amplitudes. The results demonstrate that the estimated component numbers outperform those obtained using the LRE method for considered noisy multicomponent synthetic signals. Furthermore, we validate the efficacy of the proposed CNN approach on real-world gravitational and electroencephalogram signals, underscoring its robustness and applicability across different signal types and conditions.

Deep Learning-Based Optimal Scheduling Scheme for Distributed Wind Power Systems

For maintenance of distributed wind power networks, it remains important to realize intelligent operation scheduling strategies for wind power equipments according to their working status. As a consequence, this paper proposes a deep learning-based optimal scheme for distributed wind power networks. First of all, an adaptive status assessment model is constructed to identify time-varying operation status for unit components. Then, based on the predicted operation risk of unit components, a preventive maintenance decision model is formulated to realize flexible decision-making of maintenance tasks. Finally, a dynamic maintenance task scheduling model based on extreme learning machine (ELM) neural network is designed. The ELM neural network-based scheduling approach is expected to use a historical strategy library to assist in revising realtime voltage control strategy. Also, we conduct some experiments to evaluate the performance of the proposed method through simulation modeling. The obtained results show that real-time voltage control accuracy for wind power networks with an incomplete observation area is improved.

A Deep Convolutional Neural Network-Based Approach for Visual Search & Recommendation of Grocery Products

A Deep Convolutional Neural Network-Based Approach for Visual Search & Recommendation of Grocery Products

CVG-Net: novel transfer learning based deep features for diagnosis of brain tumors using MRI scans

Brain tumors present a significant medical challenge, demanding accurate and timely diagnosis for effective treatment planning. These tumors disrupt normal brain functions in various ways, giving rise to a broad spectrum of physical, cognitive, and emotional challenges. The daily increase in mortality rates attributed to brain tumors underscores the urgency of this issue. In recent years, advanced medical imaging techniques, particularly magnetic resonance imaging (MRI), have emerged as indispensable tools for diagnosing brain tumors. Brain MRI scans provide high-resolution, non-invasive visualization of brain structures, facilitating the precise detection of abnormalities such as tumors. This study aims to propose an effective neural network approach for the timely diagnosis of brain tumors. Our experiments utilized a multi-class MRI image dataset comprising 21,672 images related to glioma tumors, meningioma tumors, and pituitary tumors. We introduced a novel neural network-based feature engineering approach, combining 2D convolutional neural network (2DCNN) and VGG16. The resulting 2DCNN-VGG16 network (CVG-Net) extracted spatial features from MRI images using 2DCNN and VGG16 without human intervention. The newly created hybrid feature set is then input into machine learning models to diagnose brain tumors. We have balanced the multi-class MRI image features data using the Synthetic Minority Over-sampling Technique (SMOTE) approach. Extensive research experiments demonstrate that utilizing the proposed CVG-Net, the k-neighbors classifier outperformed state-of-the-art studies with a k-fold accuracy performance score of 0.96. We also applied hyperparameter tuning to enhance performance for multi-class brain tumor diagnosis. Our novel proposed approach has the potential to revolutionize early brain tumor diagnosis, providing medical professionals with a cost-effective and timely diagnostic mechanism.

Enhanced spectrum prediction using deep learning models with multi-frequency supplementary inputs

Recently, the rapid progress of deep learning techniques has brought unprecedented transformations and innovations across various fields. While neural network-based approaches can effectively encode data and detect underlying patterns of features, the diverse formats and compositions of data in different fields pose challenges in effectively utilizing these data, especially for certain research fields in the early stages of integrating deep learning. Therefore, it is crucial to find more efficient ways to utilize existing datasets. Here, we demonstrate that the predictive accuracy of the network can be improved dramatically by simply adding supplementary multi-frequency inputs to the existing dataset in the target spectrum predicting process. This design methodology paves the way for interdisciplinary research and applications at the interface of deep learning and other fields, such as photonics, composite material design, and biological medicine.

A recompression supercritical CO2 cycle using heat source storage to achieve low-frequency reactor regulation: Analysis and optimization of load capacity under safe compressor operation

This study focuses on proposing a heat source storage mode for the indirect recompression supercritical CO2 cycle (RSCC) coupled with a sodium-cooled fast reactor, aiming to achieve low-frequency regulation of the reactor and enhance the load capacity of system. A deep neural network-based turbomachinery performance prediction approach is employed to assess the system's part-load and overload performance under several typical control strategies. The turbine inlet temperature of the system under all control strategies except the intermediate heat exchanger (IHX) bypass rises with decreasing load. The system's part-load performance is positively correlated with its corresponding overload power. The findings reveal that the adjustable load ranges of the heat source storage tanks under inventory, turbine bypass, turbine throttle, and IHX bypass controls are 10%–156.10%, 10%–156.10%, 30%–147.30%, and 10%–146.88%, respectively, along with the safe compressor operation. As the near-choke operation of the compressor limits the rise in overload power, a turbine fitted with variable inlet guide vanes is tried. The optimization results demonstrate that the largest overload power of the system can reach 161.18 % without reducing the thermal efficiency. This study provides a heuristic insight into the variable load operation for indirect nuclear-powered Brayton cycles under low-frequency reactor regulation.

Assessing Viscosity in Sustainable Deep Eutectic Solvents and Cosolvent Mixtures: An Artificial Neural Network-Based Molecular Approach.

Deep eutectic solvents (DESs) are gaining recognition as environmentally friendly solvent alternatives for diverse chemical processes. Yet, designing DESs tailored to specific applications is a resource-intensive task, which requires an accurate estimation of their physicochemical properties. Among them, viscosity is crucial, as it often dictates a DES's suitability as a solvent. In this study, an artificial neural network (ANN) is introduced to accurately describe the viscosity of DESs and their mixtures with cosolvents. The ANN utilizes molecular parameters derived from σ-profiles, computed using the conductor-like screening model for the real solvent segment activity coefficient (COSMO-SAC). The data set comprises 1891 experimental viscosity measurements for 48 DESs based on choline chloride, encompassing 279 different compositions, along with 1618 data points of DES mixtures with cosolvents as water, methanol, isopropanol, and dimethyl sulfoxide, covering a wide range of viscosity measurements from 0.3862 to 4722 mPa s. The optimal ANN structure for describing the logarithmic viscosity of DESs is configured as 9-19-16-1, achieving an overall average absolute relative deviation of 1.6031%. More importantly, the ANN shows a remarkable extrapolation capacity, as it is capable of predicting the viscosity of systems including solvents (ethanol) and hydrogen bond donors (2,3-butanediol) not considered in the training. The ANN model also demonstrates an extensive applicability domain, covering 94.17% of the entire database. These achievements represent a significant step forward in developing robust, open source, and highly accurate models for DESs using molecular descriptors.

Artificial neural network-based approach for prediction of nanomechanical properties of anodic coating on additively manufactured Al–10Si–Mg alloy

Nowadays, anodic coating on additively manufactured (AM) or 3D printed Al–10Si–Mg alloy are used for various components in spacecraft such as antenna feeds, wave guides, structural brackets, collimators, thermal radiators etc. In this study, artificial neural network (ANN) and power law-based models are developed from experimental nanoindentation data for predicting elastic modulus and hardness of anodized AM Al–10Si–Mg at any desired loads. Data from nanoindentation experiments conducted on plan- and cross-sections of anodized coating on AM Al–10Si–Mg alloy was considered for modeling. Apart from nanomechanical properties, load and displacement curves were predicted using Python software from ANN and the Power law model of nanoindentation. It is observed that the ANN model of 50 mN nanoindentation experimental data can accurately predict the loading pattern at any desired load below 50 mN. Elastic modulus and hardness of anodized AM Al–10Si–Mg computed from ANN and the power law model of the unloading curve are also comparable with the values obtained from Weibull distribution analysis reported elsewhere. The derived models were also used to predict nanomechanical properties at 25 and 35 mN, for which no experimental data was available. The computed hardness of plan section of the anodic coating is 3.99 and 4.02 GPa for 25 and 35 mN, respectively. The computed hardness of cross-section of the anodic coating of is 7.16 and 6.61 GPa for 25 and 35 mN, respectively. Thus, the ANN and Power law model of nanoindentation can predict elastic modulus and hardness at different loads by conducting the minimum number of experiments. The novel approach to predict nanomechanical properties using ANN resulted in determining realistic and design specific data on hardness and modulus of the anodized coating on AM Al–10Si–Mg alloy.

Identification of linear flat outputs using neural networks—Examples of two-degree-of-freedom underactuated mechanical systems

This paper proposes a neural networks-based approach of finding flat output of linearized underactuated mechanical systems (UMS). Given that differential flatness and controllability are equivalent for linear systems, the problem is equivalent to finding the Brunovsky canonical form of linearized UMSs. We use a two degree-of-freedom (2DOF) system to illustrate the theoretical development. The proposed method identifies the local flat output of nonlinear mechanical systems from the measurements only, without a detailed mathematical model. The identification allows us to combine the well-known active disturbance rejection control (ADRC) and differential flatness control. A time-domain direct identification (TDDI) algorithm and its variant based on the algebraic method are proposed for flat output identification (FOID). The neural networks for implementing the TDDI algorithm called FOID-NN are created to evaluate flat output candidates and to use the flat output to reconstruct the system states in terms of a linear mapping. The neural networks are trained with the loss functions defined by reconstruction errors. Two special layers, namely tracking differentiator (TD) and algebraic layer, are inserted in the FOID-NN to handle noisy signal differentiations. Simulations of a cart–pole system and a stable 2DOF nonlinear UMS are carried out to show the range of applications of the FOID-NN. The experimental data of an underactuated rotary crane are used to validate the identified flat output.

Abstract PO2-14-03: Novel Metrics of HER2 Heterogeneity in HER2-Positive and HER2-Low Breast Cancer via High Dimensional Multiplexed Immunofluorescence Spatial Profiling

Abstract Background: Categorizing breast cancer HER2/ERBB2 expression as “positive” or “negative” is no longer sufficient, with evidence that treatment response and outcomes are associated with HER2 “low” status and HER2 intratumoral heterogeneity. We hypothesized that interrogating HER2 heterogeneity (HER2het) across multiple spatial resolutions would more accurately capture HER2 diversity and be associated with clinical outcomes. Methods: We interrogated tumor cell and microenvironmental features by profiling 1,113,204 single cells in tissue sections from 171 HER2+/HER2low cancers via custom 25-marker high dimensional multiplexed immunofluorescence (HDmIF) using NeoGenomics MultiOmyx, with adjacent section HER2 immunohistochemistry (IHC). We developed novel metrics to concurrently: 1) interrogate HER2 heterogeneity at four spatial resolutions; 2) use machine learning to translate HER2 IF to IHC, termed ‘HAIQu’ (HER2 Automated Immunofluorescence Quantification), scoring HER2 IF expression according to ASCO/CAP guidelines; 3) delineate HER2 signaling phenotypes at the tumor cell level based on six HER2-related proteins; 4) evaluate immunophenotype of 23 immune cell types. We evaluated the association of these novel HER2het metrics with patient clinicopathologic features, recurrence-free survival (RFS), overall survival (OS), and diverse antibody markers representing tumor cell intrinsic processes and tumor-immune microenvironment (TME). Results: 1166 regions of interest were analyzed from 208 unique tumors profiled. Median follow-up from diagnosis was 143 months and 98.9% (n=183/185) received HER2-directed therapy in the (neo)adjuvant or metastatic setting. Our HAIQu scoring system effectively translated adjacent section HER2 IF to IHC with 97.9% concordance between HAIQu and clinical IHC scoring. Single-cell phenotypic analyses of 392,984 HER2+/PanCK+ tumor cells’ concurrent expression of six HER2-positive breast cancer related proteins (HER2, HER3, EGFR, pAKT, ER, KI67) using an unsupervised neural network-based self-organizing map approach resulted in 7 HER2 signaling cell phenotypes. Most patient samples are dominated by a single cell cluster but, intriguingly, Cluster 1 cells (EGFR-low) predominate in tumors with high HER2 cell membrane heterogeneity (ANOVA p=0.003). Evaluation of immunophenotype in hormone receptor-negative, HER2+ tumors, demonstrated significant association with immune cluster and recurrence-free survival (RFS; log-rank p=0.024) with zero RFS events among immune-high versus median survival of only 53.5mo among immune low-PDL1 low tumors. A multivariable Cox proportional hazards model including single cell HER2-heterogeneity (only significant metric on univariate), receptor subtype, and immunophenotype cluster demonstrated significant association with RFS (overall model log-rank p=0.005) and each significantly contributed to the model (all p< 0.05). Conclusions: We present novel metrics of HER2 heterogeneity via HDmIF, which offer detailed characterization of the diversity of HER2 expression in a large, clinically-annotated cohort with long-term follow-up. Identification of a strong association between immunophenotype and RFS supports further investigation of the highly immune activated subsets of ER-/HER2+ breast cancer. Strong correspondence of HER2 IF and IHC and our HAIQu methodology offers a pathway to translation of HER2het metrics to clinical practice. Table 1. Association of HER2 Heterogeneity Metrics with Recurrence-Free Survival Citation Format: David Tallman, Anna Juncker-Jensen, Harry Nunns, Kevin Gallagher, Heather LeFebvre, Karen Yamamoto, Katharine Collier, Mark Vater, Ava Strahan, Mathew Cherian, Ashley Pariser, Preeti Sudheendra, Bhuvaneswari Ramaswamy, Margaret Gatti-Mays, Ainura Kyshtoobayeva, Zaibo Li, Daniel Stover, Kai Johnson. Novel Metrics of HER2 Heterogeneity in HER2-Positive and HER2-Low Breast Cancer via High Dimensional Multiplexed Immunofluorescence Spatial Profiling [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-14-03.

Classical vs. neural network-based PCA approaches for lossy image compression: Similarities and differences

The paper describes three lossy data compression techniques based on the principal component analysis (PCA), which are compared using the image compression task. The presented approach uses both classical PCA method based on the eigen-decomposition of the image data covariance matrix and two different neural network structures. The first neural structure is a two-layer feed-forward network with supervised learning acting as the so-called autoencoder, and the second one is a single-layered network with an unsupervised learning method commonly known as the generalized Hebbian algorithm. For each compression method mentioned above, the influence of the number of image segments (frames) and the number of eigenvalues/eigenvectors on the compression ratio and the image quality are examined using three different gray-scale test images. The paper also addresses a vital implementation issue regarding selecting appropriate data types to represent the compressed data. The paper’s main conclusion is that the classical PCA method outperforms its neural counterparts, both in terms of the decompressed image quality and the time required to perform the compression procedure. The positive aspect of using neural networks as a tool for PCA-based lossy data compression is that they do not require calculating the correlation matrix explicitly and thus can be used in online data acquisition schemes.

Spatiotemporal dynamics analysis and parameter optimization of a network epidemic-like propagation model based on neural network method

In this paper, a reaction-diffusion model is established to study the dynamic behavior of rumor propagation. Firstly, we consider the existence of the positive equilibrium points. Then, we perform a stability analysis to study the conditions for the occurrence of Turing instability. Secondly, we use multiscale analysis to derive the expression of the amplitude equation. In the process of numerical simulation, the reality is considered. It shows that controlling the spread rate of rumor and the number of new Internet users have a great effect on curbing the spread of online rumor. Furthermore, it is proved that the analysis of amplitude equation plays a decisive role in the formation of Turing patterns. We also discuss the phenomenon of Turing patterns when the network structure changes and verify the rationality of the model by Monte Carlo method. Finally, we consider two methods based on statistical principle and convolutional neural network severally to identify the parameters of the reaction-diffusion system with Turing instability by using stable patterns. The statistical principle-based method offers superior accuracy, whereas the convolutional neural network-based approach significantly reduces recognition time and cuts down time costs.

Reducing Gait Asymmetry in Transfemoral Amputees — A Neural Network-Based Approach

Gait asymmetry between intact and prosthetic limbs is one of the factors that affect the long-term health of an amputee. Existing prosthetic devices have fixed controllers that cannot adapt to changes in user gait, thereby contributing to this asymmetry. In this paper, we use the stance period of both limbs to measure the asymmetry in gait. We propose a neural network-based control strategy that can adapt to changing gait in real time and help achieve near-natural gait.

Tuning the Proportional-Integral-Derivative Control Parameters of Unmanned Aerial Vehicles Using Artificial Neural Networks for Point-to-Point Trajectory Approach.

Nowadays, trajectory control is a significant issue for unmanned micro aerial vehicles (MAVs) due to large disturbances such as wind and storms. Trajectory control is typically implemented using a proportional-integral-derivative (PID) controller. In order to achieve high accuracy in trajectory tracking, it is essential to set the PID gain parameters to optimum values. For this reason, separate gain values are set for roll, pitch and yaw movements before autonomous flight in quadrotor systems. Traditionally, this adjustment is performed manually or automatically in autotune mode. Given the constraints of narrow orchard corridors, the use of manual or autotune mode is neither practical nor effective, as the quadrotor system has to fly in narrow apple orchard corridors covered with hail nets. These reasons require the development of an innovative solution specific to quadrotor vehicles designed for constrained areas such as apple orchards. This paper recognizes the need for effective trajectory control in quadrotors and proposes a novel neural network-based approach to tuning the optimal PID control parameters. This new approach not only improves trajectory control efficiency but also addresses the unique challenges posed by environments with constrained locational characteristics. Flight simulations using the proposed neural network models have demonstrated successful trajectory tracking performance and highlighted the superiority of the feed-forward back propagation network (FFBPN), especially in latitude tracking within 7.52745 × 10-5 RMSE trajectory error. Simulation results support the high performance of the proposed approach for the development of automatic flight capabilities in challenging environments.

Cross-project concurrency bug prediction using domain-adversarial neural network

In recent years, software bug prediction has shown to be effective in narrowing down the potential bug modules and boosting the efficiency and precision of existing testing and analysis tools. However, due to its non-deterministic nature and low presence, concurrency bug labeling is a challenging task, which limits the implementation of within-project concurrency bug prediction. This paper proposes DACon, a Domain-Adversarial neural network-based cross-project Concurrency bug prediction approach to tackle this problem by leveraging information from another related project. By combining a set of designed concurrency code metrics with widely used sequential code metrics, DACon uses SMOTE (Synthetic Minority Over-sampling TEchnique) and domain-adversarial neural network to mitigate two challenges including the severe class imbalance between concurrency bug-prone samples and concurrency bug-free samples, and shift between source and target distribution during bug prediction implementation. Our evaluation on 20 pair-wise groups of experiments constructed from 5 real-world projects indicates that cross-project concurrency bug prediction is feasible, and DACon can effectively predict concurrency bugs across different projects.

AI and Neural Network-Based Approach for Paddy Disease Identification and Classification

The purpose of this work is to use the artificial intelligence features of the ResNet50 architecture to provide a novel method of paddy disease identification. Farmers face numerous problems in raising paddy as its yield is affected by various factors like changing biodiversity, environment, weather pests, and disease. Traditional methods combined with smart farming, innovation, tools, and technology are needed for the mass production of food Here we develop a model using a convolutional neural network, ResNet50 that identifies disease in paddy leaf. The proposed model paddy disease identification model will give more precise results. The paddy disease identification model may be transformed into TensorFlow Lite (TFLite), which can be used for Android phones and drone applications, among other things. The Paddy model in this article obtained a training accuracy of almost 99% and a test accuracy of 92.83% when it was trained on 13,876 well-defined datasets. The loss function of 0.0014 at 100 epochs demonstrated that the model was effectively trained using ResNet50.

Neural networks-based damage detection for symmetrical structures considering modal feature similarity

Symmetric structures are commonly used in various fields. To ensure the safety of these structures, it is crucial to employ appropriate damage detection methods. Among the methods used for structural health detection, the combination of structural dynamics and neural networks based on finite element models has gained popularity due to its non-destructive nature. This study presents a neural network-based damage detection approach for symmetrical structures, taking into account the similarity of modal features. By considering the interference of modal feature similarity, the proposed method effectively detects damage in symmetrical structures. In this approach, the rates of mode shape change before and after damage are used as inputs to the neural network, as they are found to be less sensitive to modeling errors compared to the mode shapes themselves. The effectiveness and adaptability of the method are validated through a numerical case analysis of a symmetrical load-bearing base plate used in walkers for patients with spinal cord injuries.

A machine learning-driven approach to predicting thermo-elasto-hydrodynamic lubrication in journal bearings

Traditional methods of evaluating the performance of journal bearings, for example thermal-elastic-hydrodynamic- lubrication theory, are limited to simplified conditions that often fail to accurately model real-world components. Numerical models that include additional phenomena such as cavitation and fully coupled effects like deformation, temperature, pressure and viscosity can be more accurate but require a large amount of computational overhead, making analysis slower and more costly. To address this limitation, a novel machine learning-driven approach is developed to predict the 2D distribution of surface deformation, film thickness, temperature, and pressure across the bearing surface as a function of design variables such as load and speed. The training dataset, generated using a fully coupled Reynolds’ Equation solver implemented in OpenFOAM, contains a significantly extended range of conditions than in previous studies with approximately 39000000 points encompassing 4925 different test cases. Modelled bearing speeds range from 2000 to 10000rpm, while load values are varied between 1 and 30kN. Predicting surface deformation, film thickness, temperature and pressure across the bearing surface results in a mean absolute percentage error below 0.4% or better. The work also demonstrates that the trained models have a strong ability to generalise the prediction beyond the original training data range with only a 1% error at up to 200% of the highest trained speed. This work also demonstrates that machine learning-based processes are a practical alternative to physics-based numerical modelling, especially in cases where rapid performance evaluation is desired as real-time calculation is possible with significantly reduced computational cost. This has the potential to enable development of rapid design optimisation tools and real-time performance monitoring at high resolution and with low latency. Using consumer hardware, it is found that the neural network-based approach is faster than the existing numerical modelling technique by a factor of over 10000, enabling real-time predictions of lubrication systems.

Neural Network-Based Approach for Failure and Life Prediction of Electronic Components under Accelerated Life Stress

Researchers worldwide have been focusing on accurately predicting the remaining useful life of electronic devices to ensure reliability in various industries. This has been made possible by advancements in artificial intelligence (AI), machine learning, and Internet of Things (IoT) technologies. However, accurately forecasting device life with minimal data sets, especially in industrial applications, remains a challenge. This paper aims to address this challenge by utilizing machine learning algorithms, specifically BP, XGBOOST, and KNN, to predict device reliability with limited data. The remaining life dataset of electronic components is obtained through simulation for training and testing the algorithms, and the experimental results show that the algorithms achieve a certain level of accuracy, with the error rates being as follows: BP algorithm, 0.01–0.02%; XGBOOST algorithm, 0.01–0.02%; and KNN algorithm, 0–0.07%. By benchmarking these algorithms, the study demonstrates the feasibility of deploying machine learning models for device life prediction with acceptable accuracy loss, and highlights the potential of AI algorithms in predicting the reliability of electronic devices.