Learning-based Approach Research Articles

Blockchain-Assisted Secure Energy Trading in Electricity Markets: A Tiny Deep Reinforcement Learning-Based Stackelberg Game Approach

Blockchain-Assisted Secure Energy Trading in Electricity Markets: A Tiny Deep Reinforcement Learning-Based Stackelberg Game Approach

Tailoring nonsurgical therapy for elderly patients with head and neck squamous cell carcinoma: A deep learning-based approach.

To assess deep learning models for personalized chemotherapy selection and quantify the impact of baseline characteristics on treatment efficacy for elderly head and neck squamous cell carcinoma (HNSCC) patients who are not surgery candidates. A comparison was made between patients whose treatments aligned with model recommendations and those whose did not, using overall survival as the primary metric. Bias was addressed through inverse probability treatment weighting (IPTW), and the impact of patient characteristics on treatment choice was analyzed via mixed-effects regression. Four thousand two hundred seventy-six elderly HNSCC patients in total met the inclusion criteria. Self-Normalizing Balanced individual treatment effect for survival data model performed best in treatment recommendation (IPTW-adjusted hazard ratio: 0.74, 95% confidence interval [CI], 0.63-0.87; IPTW-adjusted risk difference: 9.92%, 95% CI, 4.96-14.90; IPTW-adjusted the difference in restricted mean survival time: 16.42 months, 95% CI, 10.83-21.22), which surpassed other models and National Comprehensive Cancer Network guidelines. No survival benefit for chemoradiotherapy was seen for patients not recommended to receive this treatment. Self-Normalizing Balanced individual treatment effect for survival data model effectively identifies elderly HNSCC patients who could benefit from chemoradiotherapy, offering personalized survival predictions and treatment recommendations. The practical application will become a reality with further validation in clinical settings.

Deep Learning-Based Approaches Using Medical Imaging for Therapy Response Prediction in Breast Cancer: A Systematic Literature Review

Deep Learning-Based Approaches Using Medical Imaging for Therapy Response Prediction in Breast Cancer: A Systematic Literature Review

RL-based mobile edge computing scheme for high reliability low latency services in UAV-aided IIoT networks

The prevailing adoption of Internet of Things paradigm is giving rise to a wide range of use cases in various vertical industries including remote health, industrial automation, and smart agriculture. However, the realization of such use cases is mainly challenged due to their stringent service requirements of high reliability and low latency. This challenge grows further when the service entails processing collected data for informed decision making. In this work, we consider a field of industrial Internet of Things devices that generate computational tasks and are covered by a nearby base station equipped with an edge server. The edge server offers fast processing to the devices’ tasks to help in meeting their latency requirement. Due to statistical wireless variability, the task data may not be correctly delivered in time for processing. To this end, we utilize an unmanned aerial vehicle as a supplemental edge server that tailors its trajectory and flies closer to the IIoT devices to ensure a highly reliable task delivery based on the given task reliability constraints. We formulate the problem as a Markov Decision Process, and propose a deep reinforcement learning-based approach using proximal policy optimization to optimize the unmanned aerial vehicle trajectory and scheduling devices to offload their data for processing. We present simulation results for various system scenarios to illustrate the effectiveness of the proposed solution as compared to several baseline approaches.

Partial least squares regression based rapid quantification of intracellular biopolymers from a Sudan black absorption assay

Rapid quantitation of intracellular polyhydroxyalkanoate (PHA) is essential for monitoring and possibly intervening in ongoing bioprocesses. The traditional multistep protocol involving cell separation, lysis, organic solvent extraction, and purification of intracellular polymers is time intensive. In this study, indirect Sudan black (ISB) adsorption data combined with culture optical density and intracellular protein measurements were used to develop partial least squares (PLS) regression models to predict the intracellular PHA concentration in Cupriavidus necator. ISB analysis supported the hypothesized partitioning of Sudan black into residual biomass, PHA, and abiotic compartments. The PLS regression model developed with autoscaled data offered excellent fit on cross-validation (R2CV of 0.987) and independent prediction (R2P of 0.994) sets. The method limit of detection for the PLS model (0.036 g l−1) was reasonably low to track biopolymer accumulation in microbial culture, even in early growth phase. The conservative estimate suggests a significantly shorter processing time with the machine learning-based approach (190 min) than with the traditional protocol (940 min) while retaining analytical interchangeability.

Discovery of alkaline laccases from basidiomycete fungi through machine learning-based approach

BackgroundLaccases can oxidize a broad spectrum of substrates, offering promising applications in various sectors, such as bioremediation, biomass fractionation in future biorefineries, and synthesis of biochemicals and biopolymers. However, laccase discovery and optimization with a desirable pH optimum remains a challenge due to the labor-intensive and time-consuming nature of the traditional laboratory methods.ResultsThis study presents a machine learning (ML)-integrated approach for predicting pH optima of basidiomycete fungal laccases, utilizing a small, curated dataset against a vast metagenomic data. Comparative computational analyses unveiled the structural and pH-dependent solubility differences between acidic and neutral-alkaline laccases, helping us understand the molecular bases of enzyme pH optimum. The pH profiling of the two ML-predicted alkaline laccase candidates from the basidiomycete fungus Lepista nuda further validated our computational approach, showing the accuracy of this comprehensive method.ConclusionsThis study uncovers the efficacy of ML in the prediction of enzyme pH optimum from minimal datasets, marking a significant step towards harnessing computational tools for systematic screening of enzymes for biotechnology applications.Graphical

Physics-Informed Machine Learning for Calibrating Macroscopic Traffic Flow Models

Well-calibrated traffic flow models are fundamental to understanding traffic phenomena and designing control strategies. Traditional calibration has been developed based on optimization methods. In this paper, we propose a novel physics-informed, learning-based calibration approach that achieves performances comparable to and even better than those of optimization-based methods. To this end, we combine the classical deep autoencoder, an unsupervised machine learning model consisting of one encoder and one decoder, with traffic flow models. Our approach informs the decoder of the physical traffic flow models and thus induces the encoder to yield reasonable traffic parameters given flow and speed measurements. We also introduce the denoising autoencoder into our method so that it can handle not only with normal data but also corrupted data with missing values. We verified our approach with a case study of Interstate 210 Eastbound in California. It turns out that our approach can achieve comparable performance to the-state-of-the-art calibration methods given normal data and outperform them given corrupted data with missing values. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference. Funding: This study was supported by the National Science Foundation [Grant CMMI-1949710] and the C2SMART Research Center, a Tier 1 University Transportation Center.

Support Vector Machine based Data Hacking Prediction using PMU Data

As global reliance on power systems grows due to increasing energy demands and modern consumption patterns, maintaining the stability and reliability of the power grid has become crucial. Power systems are complex and nonlinear, and their operations are continuously evolving, making it difficult and expensive to ensure stability. Traditionally, power systems are designed to handle a single outage at a time. However, recent years have seen several significant blackouts, each originating from a single failure, which have been extensively reported. These reports are vital for mitigating operational risks by strengthening systems against identified high-risk scenarios. While extensive research has been conducted on these blackouts, cyber- attacks introduce a new dimension of risk. The advent of Phasor Measurement Units (PMUs) has enabled centralized monitoring of power system data, allowing for more effective fault and cyber-attack detection.This paper proposes a machine learning-based approach to detecting cyber-attacks using PMU data. Given the complexity and volume of power system data, traditional mathematical and statistical methods are challenging to implement. Instead, a Support Vector Classification (SVC) algorithm is used for binary classification, distinguishing between 'attack' and 'normal' states. The algorithm is trained on PMU data and evaluated using metrics such as the AUC-ROC curve and confusion matrix, achieving an 82% AUC- ROC score, demonstrating its effectiveness in identifying cyber- attacks.

Vulnerabilities and Security Patches Detection in OSS: A Survey

Over the past decade, Open Source Software (OSS) has experienced rapid growth and widespread adoption, attributed to its openness and editability. However, this expansion has also brought significant security challenges, particularly introducing and propagating software vulnerabilities. Despite the use of machine learning and formal methods to tackle these issues, there remains a notable gap in comprehensive surveys that summarize and analyze both vulnerability detection (VD) and security patch detection (SPD) in OSS. This article seeks to bridge this gap through an extensive survey that evaluates 127 technical studies published between 2014 and 2023, structured around the Vulnerability-Patch life cycle. We begin by delineating the six critical events that constitute the Vulnerability-Patch life cycle, leading to an in-depth exploration of the Vulnerability-Patch Ecosystem. We then systematically review the databases commonly used in VD and SPD, and analyze their characteristics. Subsequently, we examine existing VD methods, focusing on traditional and deep learning-based approaches. Additionally, we organize current security patch identification methods by kernel type and discuss techniques for detecting the presence of security patches. Based on our comprehensive review, we identify open research questions and propose future research directions that merit further exploration.

A Method for All-Weather Unstructured Road Drivable Area Detection Based on Improved Lite-Mobilenetv2

This paper presents an all-weather drivable area detection method based on deep learning, addressing the challenges of recognizing unstructured roads and achieving clear environmental perception under adverse weather conditions in current autonomous driving systems. The method enhances the Lite-Mobilenetv2 feature extraction module and integrates a pyramid pooling module with an attention mechanism. Moreover, it introduces a defogging preprocessing module suitable for real-time detection, which transforms foggy images into clear ones for accurate drivable area detection. The experiments adopt a transfer learning-based training approach, training an all-road-condition semantic segmentation model on four datasets that include both structured and unstructured roads, with and without fog. This strategy reduces computational load and enhances detection accuracy. Experimental results demonstrate a 3.84% efficiency improvement compared to existing algorithms.

AxonFinder: Automated segmentation of tumor innervating neuronal fibers.

Neurosignaling is increasingly recognized as a critical factor in cancer progression, where neuronal innervation of primary tumors contributes to the disease's advancement. This study focuses on segmenting individual axons within the prostate tumor microenvironment, which have been challenging to detect and analyze due to their irregular morphologies. We present a novel deep learning-based approach for the automated segmentation of axons, AxonFinder, leveraging a U-Net model with a ResNet-101 encoder, based on a multiplexed imaging approach. Utilizing a dataset of whole-slide images from low-, intermediate-, and high-risk prostate cancer patients, we manually annotated axons to train our model, achieving significant accuracy in detecting axonal structures that were previously hard to segment. Our analysis includes a comprehensive assessment of axon density and morphological features across different CAPRA-S prostate cancer risk categories, providing insights into the correlation between tumor innervation and cancer progression. Our paper suggests the potential utility of neuronal markers in the prognostic assessment of prostate cancer in aiding the pathologist's assessment of tumor sections and advancing our understanding of neurosignaling in the tumor microenvironment.

A boosted deep learning-based approach for near real-time response estimation of structures under ground motion excitations

This article presents an improved Convolutional Neural Network (CNN)-based approach to predict the vibration response of structures under seismic events without installing sensors at different stories. To do so, three different benchmark buildings, including a Single Degree of Freedom (SDOF) system, a two-dimensional three-story steel moment-framed structure, and a three-dimensional three-story steel structure, were used to validate the proposed framework. A suite of 111 ground motion records, which was originally developed by the SAC project and FEMA P695 instruction, was used to generate a generalized dataset. These records were uniformly scaled to 18 different peak ground accelerations, from 0.05 g to 1.7 g, to ensure the generalization of the dataset in terms of seismic intensity. A strengthened CNN was introduced to provide a prediction model capable of estimating the acceleration, velocity, and displacement histories of the buildings under earthquake records. This network receives not only the ground motion records as the input attribute but also catches additional features such as spectral accelerations at 0.2s and 1.0s as RGB values of colourful images. Results indicate that the proposed algorithm is reliable in estimating the response histories of the buildings under linear and nonlinear conditions.

Enhancing emergency competencies in healthcare professionals via murder mystery games: An innovative gamification learning-based approach

BackgroundEnhancing the emergency competencies of healthcare professionals is essential for ensuring patient safety, optimizing emergency response efficiency, and fostering effective team collaboration. However, traditional simulation-based methods often struggle to accurately replicate real-life emergencies, resulting in outcomes that may not fully reflect actual performance, thereby undermining their effectiveness in training and developing the critical skills needed for emergency situations. ObjectiveThis study evaluated the effectiveness of using murder mystery games (MMGs) as a gamified learning method to enhance the emergency competencies of healthcare professionals. MethodsTwelve scripts of emergency scenarios were developed for the MMGs, and five assessment scales were established, covering emergency response, scenario decision-making, team collaboration, emotional support, and human care. Questionnaire data were analyzed between the experimental and control groups using Chi-square tests for five dimensions and nineteen indicators of emergency competencies. ResultsThe performance of the experimental group in emergency response and emotional support was significantly higher than that of the control group (P<0.001). The experimental group also showed notable excellence in scenario decision-making, team collaboration, and human care (P<0.005). ConclusionsEmergency capabilities can be significantly enhanced through murder mystery games, providing robust support for improving the quality of medical services.

Adversarial attacks on reinforcement learning agents for command and control

Given the recent impact of deep reinforcement learning in training agents to win complex games such as StarCraft and DoTA (Defense Of The Ancients)—there has been a surge in research for exploiting learning-based techniques for professional wargaming, battlefield simulation, and modeling. Real-time strategy games and simulators have become a valuable resource for operational planning and military research. However, recent work has shown that such learning-based approaches are highly susceptible to adversarial perturbations. In this paper, we investigate the robustness of an agent trained for a command and control task in an environment that is controlled by an active adversary. The C2 agent is trained on custom StarCraft II maps using the state-of-the-art RL algorithms—Asynchronous Advantage Actor Critic (A3C) and proximal policy optimization (PPO). We empirically show that an agent trained using these algorithms is highly susceptible to noise injected by the adversary and investigate the effects these perturbations have on the performance of the trained agent. Our work highlights the urgent need to develop more robust training algorithms especially for critical arenas like the battlefield.

Automated condylar seating assessment using a deep learning-based three-step approach

ObjectivesIn orthognatic surgery, one of the primary determinants for reliable three-dimensional virtual surgery planning (3D VSP) and an accurate transfer of 3D VSP to the patient in the operation room is the condylar seating. Incorrectly seated condyles would primarily affect the accuracy of maxillary-first bimaxillary osteotomies as the maxillary repositioning is dependent on the positioning of the mandible in the cone-beam computed tomography (CBCT) scan. This study aimed to develop and validate a novel tool by utilizing a deep learning algorithm that automatically evaluates the condylar seating based on CBCT images as a proof of concept.Materials and methodsAs a reference, 60 CBCT scans (120 condyles) were labeled. The automatic assessment of condylar seating included three main parts: segmentation module, ray-casting, and feed-forward neural network (FFNN). The AI-based algorithm was trained and tested using fivefold cross validation. The method’s performance was evaluated by comparing the labeled ground truth with the model predictions on the validation dataset.ResultsThe model achieved an accuracy of 0.80, positive predictive value of 0.61, negative predictive value of 0.9 and F1-score of 0.71. The sensitivity and specificity of the model was 0.86 and 0.78, respectively. The mean AUC over all folds was 0.87.ConclusionThe innovative integration of multi-step segmentation, ray-casting and a FFNN demonstrated to be a viable approach for automating condylar seating assessment and have obtained encouraging results.Clinical relevanceAutomated condylar seating assessment using deep learning may improve orthognathic surgery, preventing errors and enhancing patient outcomes in maxillary-first bimaxillary osteotomies.

Quantifying gender differences in orbital morphology with large MRI datasets

PurposeTo investigate gender differences in orbital morphology using large MRI datasets. MethodsUsing a deep learning-based approach, the orbit and eyeball were automatically segmented from high resolution 3D MRI images of the IXI and OASIS3 datasets. Orbital and eyeball morphological parameters, including orbital volume, eyeball volume, effective orbital volume (EOV, defined as the orbital cavity volume excluding the eyeball), and coronal orbital dimensions and shape, were quantitatively assessed. The volume index was defined as the ratio of orbital volume to eyeball volume. ResultsThis study included 1926 subjects with a mean age of 63.9 ​± ​15.3 years. The mean volumes of the eyeball and orbit were 7.1 ​± ​1.0 ​ml and 25.9 ​± ​3.5 ​ml, respectively. Significant gender differences (all P ​< ​0.001) were observed in the following parameters (males versus females): orbital volume (28.3 ​± ​3.0 ​ml versus 24.0 ​± ​2.7 ​ml), EOV (25.1 ​± ​3.0 ​ml versus 21.1 ​± ​2.6 ​ml), eyeball volume (7.3 ​± ​1.0 ​ml versus 6.9 ​± ​1.0 ​ml), volume index (3.9 ​± ​0.6 versus 3.5 ​± ​0.5), orbital depth (40.0 ​± ​3.1 ​mm versus 37.4 ​± ​2.9 ​mm), coronal orbital height (40.8 ​± ​3.0 ​mm versus 38.4 ​± ​2.4 ​mm), coronal orbital width (38.0 ​± ​1.9 ​mm versus 36.6 ​± ​1.7 ​mm) and coronal orbital area (1292.5 ​± ​97.1 ​mm2 versus 1177.9 ​± ​89.7 ​mm2). ConclusionsThis study employed deep learning to analyze a large dataset of 3D head MRI scans, achieving accurate and objective measurements of orbital morphology. We identified significant gender differences in orbital parameters, with males generally having larger structures. Additionally, we established a normative database for orbital dimensions, providing a valuable resource for future research on orbital disorders and potentially improving clinical diagnosis and treatment.

Mineral classification with X-ray absorption spectroscopy: A deep learning-based approach

The current suite of algorithms used in intelligent mineral sorting equipment is largely generic, lacking the necessary adaptability and the capability for simultaneous non-destructive structural detection and quantitative analysis. To address this, we have developed two specialized algorithms based on X-ray absorption spectroscopy: the Trans-LSTM algorithm based on Transformer and Long Short-Term Memory(LSTM), utilizing knowledge distillation for rough mineral sorting, and the RNN-overhead-xgboost algorithm based on Recurrent Neural Network(RNN) for fine mineral sorting. We collected 3000 X-ray absorption spectra from 15 types of minerals with similar appearances or compositions. We compared the performance of these proposed algorithms with three other general-purpose algorithms for mineral spectral classification. Our study specifically examined the impact of Trans-LSTM on rough mineral sorting and the effect of RNN-overhead-xgboost on fine mineral sorting. In the rough sorting stage, the Trans-LSTM model demonstrated a prediction time of just 0.0171 s per sample, maintaining a classification accuracy of 93.49%, thereby ensuring high precision with high efficiency. During the fine sorting stage, the RNN-overhead-xgboost algorithm significantly improved sorting accuracy to 99.21%, highlighting its effectiveness in achieving precise sorting. These findings underscore the potential of the Trans-LSTM and RNN-overhead-xgboost algorithms to enhance the adaptability and accuracy of intelligent mineral sorting systems, meeting the specific demands of different stages in mineral production.

Sensing perceived urban stress using space syntactical and urban building density data: A machine learning-based approach

Human well-being is an essential criterion in achieving smart and sustainable cities. Given the significant influence of stress on individuals physical and mental health, various approaches have been proposed to examine the subjective experience of stress induced by the urban built environment and its effects on human well-being. Nevertheless, conducting assessments on a large scale continues to be a significant obstacle, particularly in today's context of rapid urbanization. This study utilized advancements in Machine Learning (ML) to develop a method for measuring perceived stress by analyzing urban building density, space syntactic characteristics, and visual features of the built environment. Through the utilization of ML models, a predictive approach has been developed that can capture the perceived stress levels of urban dwellers. The results are verified with public survey data, with R2 reaching 0.698 obtained by evaluating the mean stress scores of 25 districts in Seoul city. The findings demonstrate that the proposed approach can effectively measure perceived stress, enabling urban planners to analyze the spatial pattern of perceived stress and the influence of the built environment on this perception. This work expands current approaches, which concentrate solely on parks, open spaces, or streetscapes, by developing a more comprehensive predictive model for measuring perceived stress levels in various urban areas.

Evaluation of the prostate cancer and its metastases in the [ 68 Ga]Ga-PSMA PET/CT images: deep learning method vs. conventional PET/CT processing.

This study demonstrates the feasibility and benefits of using a deep learning-based approach for attenuation correction in [ 68 Ga]Ga-PSMA PET scans. A dataset of 700 prostate cancer patients (mean age: 67.6 ± 5.9 years, range: 45-85 years) who underwent [ 68 Ga]Ga-PSMA PET/computed tomography was collected. A deep learning model was trained to perform attenuation correction on these images. Quantitative accuracy was assessed using clinical data from 92 patients, comparing the deep learning-based attenuation correction (DLAC) to computed tomography-based PET attenuation correction (PET-CTAC) using mean error, mean absolute error, and root mean square error based on standard uptake value. Clinical evaluation was conducted by three specialists who performed a blinded assessment of lesion detectability and overall image quality in a subset of 50 subjects, comparing DLAC and PET-CTAC images. The DLAC model yielded mean error, mean absolute error, and root mean square error values of -0.007 ± 0.032, 0.08 ± 0.033, and 0.252 ± 125 standard uptake value, respectively. Regarding lesion detection and image quality, DLAC showed superior performance in 16 of the 50 cases, while in 56% of the cases, the images generated by DLAC and PET-CTAC were found to have closely comparable quality and lesion detectability. This study highlights significant improvements in image quality and lesion detection capabilities through the integration of DLAC in [ 68 Ga]Ga-PSMA PET imaging. This innovative approach not only addresses challenges such as bladder radioactivity but also represents a promising method to minimize patient radiation exposure by integrating low-dose computed tomography and DLAC, ultimately improving diagnostic accuracy and patient outcomes.

Evaluation of tooth development stages with deep learning-based artificial intelligence algorithm

BackgroundThis study aims to evaluate the performance of a deep learning system for the evaluation of tooth development stages on images obtained from panoramic radiographs from child patients.MethodsThe study collected a total of 1500 images obtained from panoramic radiographs from child patients between the ages of 5 and 14 years. YOLOv5, a convolutional neural network (CNN)-based object detection model, was used to automatically detect the calcification states of teeth. Images obtained from panoramic radiographs from child patients were trained and tested in the YOLOv5 algorithm. True-positive (TP), false-positive (FP), and false-negative (FN) ratios were calculated. A confusion matrix was used to evaluate the performance of the model.ResultsAmong the 146 test group images with 1022 labels, there were 828 TPs, 308 FPs, and 1 FN. The sensitivity, precision, and F1-score values of the detection model of the tooth stage development model were 0.99, 0.72, and 0.84, respectively.ConclusionsIn conclusion, utilizing a deep learning-based approach for the detection of dental development on pediatric panoramic radiographs may facilitate a precise evaluation of the chronological correlation between tooth development stages and age. This can help clinicians make treatment decisions and aid dentists in finding more accurate treatment options.