Advanced Machine Learning Methods Research Articles

A Comprehensive Review of DeepFake Detection Using Advanced Machine Learning and Fusion Methods

Recent advances in Generative Artificial Intelligence (AI) have increased the possibility of generating hyper-realistic DeepFake videos or images to cause serious harm to vulnerable children, individuals, and society at large with misinformation. To overcome this serious problem, many researchers have attempted to detect DeepFakes using advanced machine learning techniques and advanced fusion techniques. This paper presents a detailed review of past and present DeepFake detection methods with a particular focus on media-modality fusion and machine learning. This paper also provides detailed information on available benchmark datasets in DeepFake detection research. This review paper addressed the 67 primary papers that were published between 2015 and 2023 in DeepFake detection, including 55 research papers in image and video DeepFake detection methodologies and 15 research papers on identifying and verifying speaker authentication. This paper offers lucrative information on DeepFake detection research and offers a unique review analysis of advanced machine learning and modality fusion that sets it apart from other review papers. This paper further offers informed guidelines for future work in DeepFake detection utilizing advanced state-of-the-art machine learning and information fusion models that should support further advancement in DeepFake detection for a sustainable and safer digital future.

Principles based accounting standards, audit fees and going concern: evidence using advanced machine learning

PurposeThe current study uses an advanced machine learning method and aims to investigate whether auditors perceive financial statements that are principles-based as less risky. More specifically, this study aims to explore the association between principles-based accounting standards and audit pricing and between principles-based accounting standards and the likelihood of receiving a going concern opinion.Design/methodology/approachThe study uses an advanced machine-learning method to understand the role of principles-based accounting standards in predicting audit fees and going concern opinion. The study also uses multiple regression models defining audit fees and the probability of receiving going concern opinion. The analyses are complemented by additional tests such as economic significance, firm fixed effects, propensity score matching, entropy balancing, change analysis, yearly regression results and controlling for managerial risk-taking incentives and governance variables.FindingsThe paper provides empirical evidence that auditors charge less audit fees to clients whose financial statements are more principles-based. The finding suggests that auditors perceive financial statements that are principles-based less risky. The study also provides evidence that the probability of receiving a going-concern opinion reduces as firms rely more on principles-based standards. The finding further suggests that auditors discount the financial numbers supplied by the managers using rules-based standards. The study also reveals that the degree of reliance by a US firm on principles-based accounting standards has a negative impact on accounting conservatism, the risk of financial statement misstatement, accruals and the difficulty in predicting future earnings. This suggests potential mechanisms through which principles-based accounting standards influence auditors’ risk assessments.Research limitations/implicationsThe authors recognize the limitation of this study regarding the sample period. Prior studies compare rules vs principles-based standards by focusing on the differences between US generally accepted accounting principles (GAAP) and international financial reporting standards (IFRS) or pre- and post-IFRS adoption, which raises questions about differences in cross-country settings and institutional environment and other confounding factors such as transition costs. This study addresses these issues by comparing rules vs principles-based standards within the US GAAP setting. However, this limits the sample period to the year 2006 because the measure of the relative extent to which a US firm is reliant upon principles-based standards is available until 2006.Practical implicationsThe study has major public policy suggestions as it responds to the call by Jay Clayton and Mary Jo White, the former Chairs of the US Securities and Exchange Commission (SEC), to pursue high-quality, globally accepted accounting standards to ensure that investors continue to receive clear and reliable financial information globally. The study also recognizes the notable public policy implications, particularly in light of the current Chair of the International Accounting Standards Board (IASB) Andreas Barckow’s recent public statement, which emphasizes the importance of principles-based standards and their ability to address sustainability concerns, including emerging risks such as climate change.Originality/valueThe study has major public policy suggestions because it demonstrates the value of principles-based standards. The study responds to the call by Jay Clayton and Mary Jo White, the former Chairs of the US SEC, to pursue high-quality, globally accepted accounting standards to ensure that investors continue to receive clear and reliable financial information as business transactions and investor needs continue to evolve globally. The study also recognizes the notable public policy implications, particularly in light of the current Chair of the IASB Andreas Barckow’s recent public statement, which emphasizes the importance of principles-based standards and their ability to address sustainability concerns, including emerging risks like climate change. The study fills the gap in the literature that auditors perceive principles-based financial statements as less risky and further expands the literature by providing empirical evidence that the likelihood of receiving a going concern opinion is increasing in the degree of rules-based standards.

Few-Shot Classification with Meta-Learning for Urban Infrastructure Monitoring Using Distributed Acoustic Sensing.

This paper studies an advanced machine learning method, specifically few-shot classification with meta-learning, applied to distributed acoustic sensing (DAS) data. The study contributes two key aspects: (i) an investigation of different pre-processing methods for DAS data and (ii) the implementation of a neural network model based on meta-learning to learn a representation of the processed data. In the context of urban infrastructure monitoring, we develop a few-shot classification framework that classifies query samples with only a limited number of support samples. The model consists of an embedding network trained on a meta dataset for feature extraction and is followed by a classifier for performing few-shot classification. This research thoroughly explores three types of data pre-processing, that is, decomposed phase, power spectral density, and frequency energy band, as inputs to the neural network. Experimental results show the efficient learning capabilities of the embedding model when working with various pre-processed data, offering a range of pre-processing options. Furthermore, the results demonstrate outstanding few-shot classification performance across a large number of event classes, highlighting the framework's potential for urban infrastructure monitoring applications.

Examining urban delivery service user profiles and determinants of drone delivery adoption in Ghana considering usage before and after the COVID-19 pandemic

The demand for urban delivery services (UDS) in low- and middle-income countries (LMICs) has been growing due to the spike in e-commerce activities. This phenomenon has led to the search for more innovative UDS modes, such as drones, as they provide fast, safer, and eco-friendly services. Although researchers have examined the perceptions regarding the adoption of drone delivery, there are many unknowns about the intentions to use this futuristic delivery mode. By employing survey data obtained through an online survey conducted in Ghana, this study aims to gain a deeper understanding of the current UDS user profiles and investigate their intentions to adopt drone deliveries in the future, considering the lived experiences of traditional UDS usage pre/post the COVID-19 pandemic. Regarding UDS user profiling, the study applied an advanced unsupervised machine learning method known as cluster correspondence analysis to simultaneously identify critical clusters in the data as well as key factor associations for each group. The findings demonstrated that most people in Ghana who seldom used the traditional UDS usually experienced poor service levels. Females living in the capital of Ghana who are frequent users of UDS mainly experienced acceptable service levels. Using an ordered probit regression model, the study identified that variables such as males, people who desired contactless UDS, and frequent UDS users at work or school during the COVID-19 pandemic were more inclined to embrace drone deliveries when introduced. Conversely, individuals who frequently used UDS services at home, those living in large cities, and those with lower income levels expressed reluctance toward drone delivery services. This study augments the urban logistics literature in LMICs by offering insightful suggestions in the areas of education, enhanced marketing strategies, and delivery optimization, which are instrumental for informing policy decisions. Government-related transportation bodies, private logistics firms, and trade unions can collaborate and adopt the recommendations provided to improve urban delivery services and to pave the way for integrating drone delivery services in the near future.

Dispersion engineering of metalenses

Optical dispersion, the variation of the speed of light with frequency in a material, presents significant challenges in modern optical systems, including chromatic aberration and pulse signal distortion. Traditional approaches to dispersion engineering of an optical lens system require the use of a set of sub-lenses of opposite dispersion properties, largely increasing the overall lens thickness. Ultrathin metasurfaces offer unprecedented control over optical wavefronts with advanced functionalities. Developing achromatic metalenses has thereby emerged as a timely research topic for metasurface research. This Perspective article provides a comprehensive overview of dispersion engineering methods in metalenses, including the use of 2D and 3D meta-atoms fabricated from planar lithography and 3D laser lithography methods, respectively. We compare key figures of merit of achromatic metalenses developed for different wavelength ranges and discuss recent inverse design of large-scale achromatic metalenses. We believe advanced machine learning methods as well as hybrid nanofabrication of diffractive metalenses, refractive lenses, and metamaterials-like spaceplates could offer promising avenues for overcoming current challenges and eventually push ultrathin achromatic optics to practical applications in optics-related fields.

Detection for melanoma skin cancer through ACCF, BPPF, and CLF techniques with machine learning approach

Intense sun exposure is a major risk factor for the development of melanoma, an abnormal proliferation of skin cells. Yet, this more prevalent type of skin cancer can also develop in less-exposed areas, such as those that are shaded. Melanoma is the sixth most common type of skin cancer. In recent years, computer-based methods for imaging and analyzing biological systems have made considerable strides. This work investigates the use of advanced machine learning methods, specifically ensemble models with Auto Correlogram Methods, Binary Pyramid Pattern Filter, and Color Layout Filter, to enhance the detection accuracy of Melanoma skin cancer. These results suggest that the Color Layout Filter model of the Attribute Selection Classifier provides the best overall performance. Statistics for ROC, PRC, Kappa, F-Measure, and Matthews Correlation Coefficient were as follows: 90.96% accuracy, 0.91 precision, 0.91 recall, 0.95 ROC, 0.87 PRC, 0.87 Kappa, 0.91 F-Measure, and 0.82 Matthews Correlation Coefficient. In addition, its margins of error are the smallest. The research found that the Attribute Selection Classifier performed well when used in conjunction with the Color Layout Filter to improve image quality.

Rapid Permeability Upscaling of Digital Porous Media via Physics‐Informed Neural Networks

AbstractSubsurface processes are important in solving many of the grand challenges facing our society today, including the sustainable extraction of hydrocarbons, the permanent geological sequestration of carbon dioxide, and the seasonal storage of renewable energy underground. Permeability characterization of underground rocks is the critical first step in understanding and engineering these processes. While recent advances in machine learning methods have enabled fast and efficient permeability prediction of digital rock samples, their practical use remains limited since they can only accommodate subsections of the digital rock samples, which is often not representative of properties at the core‐scale. Here, we derive a novel analytical solution that approximates the effective permeability of a three‐dimensional (3D) digital rock consisting of 2 × 2 × 2 anisotropic cells based on the physical analogy between Darcy’s law and Ohm’s law. We further develop physics‐informed neural network (PINN) models that incorporate the analytical solution and subsequently demonstrate that the PINN equipped with the physics‐informed module achieves excellent accuracy, even when used to upscale previously unseen samples over multiple levels of upscaling. Our work elevates the potential of machine learning models such as 3D convolutional neural network for rapid, end‐to‐end digital rock analysis at the core‐scale.

Markerless motion capture technology to quantify functional performance in dementias: a systematic review

AbstractBackgroundQuantifying functional performance objectively and unobtrusively could offer vital insights for clinical decision making on the level of support required by people living with dementia and other neurodegenerative conditions. Markerless motion capture (MMC) is more suitable for deployment in clinical and real‐world settings because single sensor MMC systems are less expensive and less complex compared to marker‐based systems. The aim of this study was to complete a systematic review of published literature on the use of MMC with full‐body tracking in people with dementias.MethodWe systematically searched for relevant articles using the search terms motion capture, motion analysis and movement analysis combined with dementia, mild cognitive impairment (MCI) and Parkinson’s disease (PD) which yielded a total of 1103 results. Inclusion criteria were MMC and full‐body tracking. Studies evaluating marker‐based motion capture, body‐worn sensors or Inertial Measurement Units, virtual reality, kinetics and interventions like DBS or exercises were excluded. 134 studies were included for full article screening out of which 17 eligible studies that met the selection criteria were included in the review.ResultMMC devices used in the reviewed studies included Microsoft Kinect (n = 15) and unspecified 2D motion camera (n = 2). Patient groups included in the studies were PD (n = 12), dementia (n = 4) and MCI (n = 1). Functional performance components evaluated were gait (n = 16), sit to stand (n = 2), turning (n = 1) and stepping in place (n = 1). About half the studies used advanced machine learning methods whereas the other half used descriptive statistical methods. The lack of standardisation in the measures used prevented us from comparing and synthesizing study results.ConclusionAlthough MMC has the potential to be used for quantifying functional performance in people with dementia, MCI and PD, there appears to be significant variation in the methods used in its evaluation. Although features of mobility were measured, none of the reviewed studies analysed activities of daily living tasks, a key component of functional performance. Standardising the process for extracting features and assessing accuracy would enable more effective validation of real‐world impact. Further research is required in people with neurodegenerative conditions for MMC to be routinely used in clinical and real‐world applications.

Predicting the Effect of Proton Beam Therapy Technology on Pulmonary Toxicities for Patients With Locally Advanced Lung Cancer Enrolled in the Proton Collaborative Group Prospective Clinical Trial

This study aimed to predict the probability of grade ≥2 pneumonitis or dyspnea within 12 months of receiving conventionally fractionated or mildly hypofractionated proton beam therapy for locally advanced lung cancer using machine learning. Demographic and treatment characteristics were analyzed for 965 consecutive patients treated for lung cancer with conventionally fractionated or mildly hypofractionated (2.2-3 Gy/fraction) proton beam therapy across 12 institutions. Three machine learning models (gradient boosting, additive tree, and logistic regression with lasso regularization) were implemented to predict Common Terminology Criteria for Adverse Events version 4 grade ≥2 pulmonary toxicities using double 10-fold cross-validation for parameter hyper-tuning without leak of information. Balanced accuracy and area under the curve were calculated, and 95% confidence intervals were obtained using bootstrap sampling. The median age of the patients was 70 years (range, 20-97), and they had predominantly stage IIIA or IIIB disease. They received a median dose of 60 Gy in 2 Gy/fraction, and 46.4% received concurrent chemotherapy. In total, 250 (25.9%) had grade ≥2 pulmonary toxicity. The probability of pulmonary toxicity was 0.08 for patients treated with pencil beam scanning and 0.34 for those treated with other techniques (P = 8.97e-13). Use of abdominal compression and breath hold were highly significant predictors of less toxicity (P = 2.88e-08). Higher total radiation delivered dose (P = .0182) and higher average dose to the ipsilateral lung (P = .0035) increased the likelihood of pulmonary toxicities. The gradient boosting model performed the best of the models tested, and when demographic and dosimetric features were combined, the area under the curve and balanced accuracy were 0.75 ± 0.02 and 0.67 ± 0.02, respectively. After analyzing performance versus the number of data points used for training, we observed that accuracy was limited by the number of observations. In the largest analysis of prospectively enrolled patients with lung cancer assessing pulmonary toxicities from proton therapy to date, advanced machine learning methods revealed that pencil beam scanning, abdominal compression, and lower normal lung doses can lead to significantly lower probability of developing grade ≥2 pneumonitis or dyspnea.

Radiomics of pericardial fat: a new frontier in heart failure discrimination and prediction

ObjectivesTo use pericardial adipose tissue (PAT) radiomics phenotyping to differentiate existing and predict future heart failure (HF) cases in the UK Biobank.MethodsPAT segmentations were derived from cardiovascular magnetic resonance (CMR) studies using an automated quality-controlled model to define the region-of-interest for radiomics analysis. Prevalent (present at time of imaging) and incident (first occurrence after imaging) HF were ascertained using health record linkage. We created balanced cohorts of non-HF individuals for comparison. PyRadiomics was utilised to extract 104 radiomics features, of which 28 were chosen after excluding highly correlated ones (0.8). These features, plus sex and age, served as predictors in binary classification models trained separately to detect (1) prevalent and (2) incident HF. We tested seven modeling methods using tenfold nested cross-validation and examined feature importance with explainability methods.ResultsWe studied 1204 participants in total, 297 participants with prevalent (60 ± 7 years, 21% female) and 305 with incident (61 ± 6 years, 32% female) HF, and an equal number of non-HF comparators. We achieved good discriminative performance for both prevalent (voting classifier; AUC: 0.76; F1 score: 0.70) and incident (light gradient boosting machine: AUC: 0.74; F1 score: 0.68) HF. Our radiomics models showed marginally better performance compared to PAT area alone.Increased PAT size (maximum 2D diameter in a given column or slice) and texture heterogeneity (sum entropy) were important features for prevalent and incident HF classification models.ConclusionsThe amount and character of PAT discriminate individuals with prevalent HF and predict incidence of future HF.Clinical relevance statementThis study presents an innovative application of pericardial adipose tissue (PAT) radiomics phenotyping as a predictive tool for heart failure (HF), a major public health concern. By leveraging advanced machine learning methods, the research uncovers that the quantity and characteristics of PAT can be used to identify existing cases of HF and predict future occurrences. The enhanced performance of these radiomics models over PAT area alone supports the potential for better personalised care through earlier detection and prevention of HF.Key Points•PAT radiomics applied to CMR was used for the first time to derive binary machine learning classifiers to develop models for discrimination of prevalence and prediction of incident heart failure.•Models using PAT area provided acceptable discrimination between cases of prevalent or incident heart failure and comparator groups.•An increased PAT volume (increased diameter using shape features) and greater texture heterogeneity captured by radiomics texture features (increased sum entropy) can be used as an additional classifier marker for heart failure.

An explainable artificial intelligence framework for risk prediction of COPD in smokers

BackgroundSince the inconspicuous nature of early signs associated with Chronic Obstructive Pulmonary Disease (COPD), individuals often remain unidentified, leading to suboptimal opportunities for timely prevention and treatment. The purpose of this study was to create an explainable artificial intelligence framework combining data preprocessing methods, machine learning methods, and model interpretability methods to identify people at high risk of COPD in the smoking population and to provide a reasonable interpretation of model predictions.MethodsThe data comprised questionnaire information, physical examination data and results of pulmonary function tests before and after bronchodilatation. First, the factorial analysis for mixed data (FAMD), Boruta and NRSBoundary-SMOTE resampling methods were used to solve the missing data, high dimensionality and category imbalance problems. Then, seven classification models (CatBoost, NGBoost, XGBoost, LightGBM, random forest, SVM and logistic regression) were applied to model the risk level, and the best machine learning (ML) model’s decisions were explained using the Shapley additive explanations (SHAP) method and partial dependence plot (PDP).ResultsIn the smoking population, age and 14 other variables were significant factors for predicting COPD. The CatBoost, random forest, and logistic regression models performed reasonably well in unbalanced datasets. CatBoost with NRSBoundary-SMOTE had the best classification performance in balanced datasets when composite indicators (the AUC, F1-score, and G-mean) were used as model comparison criteria. Age, COPD Assessment Test (CAT) score, gross annual income, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), anhelation, respiratory disease, central obesity, use of polluting fuel for household heating, region, use of polluting fuel for household cooking, and wheezing were important factors for predicting COPD in the smoking population.ConclusionThis study combined feature screening methods, unbalanced data processing methods, and advanced machine learning methods to enable early identification of COPD risk groups in the smoking population. COPD risk factors in the smoking population were identified using SHAP and PDP, with the goal of providing theoretical support for targeted screening strategies and smoking population self-management strategies.

Cloud-based Near Real-Time Multiclass Interruption Recognition and Classification using Ensemble and Deep Learning

Due to speedy development in internet facilities, detecting intrusions in a real-time cloud environment is challenging via traditional methods. In this case, advanced machine or deep learning methods can be efficiently used in anomaly or intrusion detection. Thus, the present study focuses on designing and developing the intrusion detection scheme using an ensemble learning-based random forest method and deep convolutional neural networks in a near real-time cloud atmosphere. The proposed models were tested on CSE-CICIDS2018 datasets in Python (Anaconda 3) environment. The proposed models achieved 97.73 and 99.91 per cent accuracy using random forest and deep convolutional neural networks, respectively. The developed models can be effectively utilised in real-time cloud datasets to detect intrusions.

Prediction model for real-world survival in men with castration-resistant prostate cancer and bone metastases in the United States: A real-world database study.

507 Background: Bone metastases (bm) are common in men with castration-resistant prostate cancer (CRPC) and are associated with poor prognosis. Therefore, identifying prognostic factors for survival is key to aid decision-making. While several models exist for predicting survival in men with mCRPC, most were derived using randomized controlled trial (RCT) data, which may have limited application to real-world (RW) populations and utilized traditional linear methods. We aimed to develop and validate a prediction model of real-world survival (rwOS) in men with bmCRPC using a national RW electronic medical record (EMR) database with traditional and advanced machine learning (ML) methods. Methods: A retrospective cohort of men diagnosed with bmCPRC between 2010 and 2021 was identified using the Optum EMR. rwOS was identified as documented evidence of death in the EMR derived through either the Social Security Death Index or EMR-reported death date. Independent variables were demographic (age, race), clinical conditions (Charlson comorbidity index, pain, prior phases of PC), PC-related medications, and laboratory parameters during a 6-month baseline period. A dynamic model was used to predict survival at 1, 2, and 3 years after diagnosis of bmCRPC using traditional (logistic, Cox regression) and ML (light gradient boosting (LGB)) models. Training, calibration, and validation were performed by splitting the data and utilizing 5-fold cross-validation on the training sample and calibration plots. Diagnostic performances were evaluated using area-under-the-curve (AUC), precision-recall curve (PRC), and calibration curves across three models. Results: The study cohort included 4,097 men with a median age of 76 years (interquartile range: 68-82), predominantly white (81.2%), and residing in the Midwest (48.2%). Over a median follow-up period of 19.2 months, 2,220 (54.2%) men died with median rwOS of 31.0 months (95% CI: 29.6-32.9). The AUC for Cox, logistic and LGB models at 1 year were well within the acceptable range of (≥0.7.0) 0.78, 0.79, and 0.81 respectively. Similar findings were observed at 2 and 3-year landmark analyses with marginal improvement in model performance with LGB. Top predictors of rwOS were consistent across all time points in the LGB models and included laboratory parameters (prostate-specific antigen level, alkaline phosphatase, albumin, hemoglobin), age, presence of pain, transition from mHSPC to mCRPC, surgical castration, and baseline use of androgen receptor inhibitors. Conclusions: Study findings highlight consistency in the predictors of rwOS in a large cohort of men with bmCRPC using a national EMR database. ML-based models predicted rwOS with modest performance improvement compared to traditional models and could provide rwOS prediction to aid treatment decision-making.

Optimization of RF to alloy elastic modulus prediction based on cuckoo algorithm

In recent years, there has been an explosive rise in the combination of density-functional theory (DFT) computation and machine learning in materials science research. In this paper, a cuckoo algorithm-optimized random forest (CS-RF) based alloy elastic modulus prediction model is proposed using density-functional theory. 320 alloy samples are used as test materials to calculate elastic modulus using the density-functional theory method, and the calculated raw data are normalized to establish a cuckoo algorithm-optimized random-forest model to predict alloy elastic modulus. The experiments show that for predicting the elastic modulus of alloys, CS-RF also has obvious advantages in computational efficiency with improved prediction accuracy. In conclusion, CS-RF is an advanced machine learning (ML) method for predicting the elastic modulus of alloys, which can provide a scientific reference for metal elastic modulus prediction.

Squamous Cell Carcinoma of the Bladder: Analysis of Treatments and Outcomes.

Squamous cell carcinoma (SCC) of the bladder is a rare disease composing 2-5% of all bladder cancers. Currently, no consensus exists regarding treatment recommendations for this rare malignancy, although established regimens include various permutations of chemotherapy, radiation, laser treatments, immunotherapies, and surgeries, ranging from pelvic exenteration to local excision. The present study aims to analyze outcomes of the aforementioned treatments to guide clinical decision-making for patients with the disease. Patients with bladder SCC diagnosed between 2000 and 2018 were reviewed utilizing data from the Surveillance, Epidemiology, and End Results Registry (SEER) program. Data was analyzed using SPSS and Python. Examined variables included presence of metastases at diagnosis, surgery, radiotherapy, and chemotherapy reception. Five-year overall survival (OS) curves were analyzed using Kaplan-Meier probability stratified by treatment modality. A total of 5,635 patients with bladder SCC were identified; the most abundantly reported demographics were white race (86%), patient age > 70 years (61%), and male sex (63%). Median survival was 13 months and was significantly decreased in patients treated with chemotherapy or radiation (median survival of nine months or 12 months, respectively). Patients treated with radiation and surgery saw a decreased 10-year survival rate of about 10% if no chemotherapy was administered; this difference narrowed to about a 4% decrease in patients treated with chemotherapy. All radical surgical procedures (to include partial and total cystectomy, and total and posterior exenteration) resulted in a statistically significant increase in survival, except for patients that underwent radical cystectomy including anterior exenteration (p = 0.101). Patients that underwent surgery saw an increased median survival of 15 months. Heatmap analysis demonstrated a significant correlation between tumor size and distant metastases (r = 0.898). Preliminary unsupervised cluster analysis identified two distinct patient subgroups characterized most strongly by race and treatment modality used. Bladder SCC carries a high mortality burden, with a median survival time of 13 months after diagnosis. Based on this study's analysis, radical surgery may be the most effective treatment for this disease. This study also sets a precedent for future research, as more advanced machine learning methods (including cluster and binary tree analysis, as was done in this analysis) can be used to identify the most explanatory variables related to improved patient outcomes.

Sentiment Analysis in Online Product Reviews: Mining Customer Opinions for Sentiment Classification

Online product reviews have become a valuable resource for consumers seeking detailed information and making informed choices. The process of automatically extracting sentiment or opinions from these reviews heavily relies on sentiment analysis, a branch of Natural Language Processing (NLP). This research article focuses on sentiment categorization in online product evaluations, utilizing innovative techniques for mining consumer opinions. The project aims to establish a robust framework for sentiment analysis that accurately classifies emotions expressed in these reviews. The proposed system incorporates advanced deep learning and machine learning methods to enhance data classification and extract fine-grained sentiment information. The study addresses the unique challenges of sentiment analysis in the context of online product evaluations, including polarity changes, sarcasm, and domain-specific sentiment expressions, which often pose significant obstacles to precise sentiment classification. The approach combines feature engineering and deep learning techniques, extracting lexical, syntactic, and semantic features such as part-of-speech tags, n-grams, sentiment lexicons, and word embeddings from the review texts. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are employed as sophisticated neural network architectures to leverage these features, creating robust representations and capturing contextual information. The suggested architecture is extensively evaluated on a large dataset of online product reviews, demonstrating superior performance in sentiment categorization compared to existing approaches. The evaluation encompasses various sentiment classes, measuring metrics like accuracy, recall, and F1-score, and assessing the framework's adaptability to different product domains. The study showcases the effectiveness of advanced machine learning and deep learning algorithms in sentiment categorization, advancing the field of sentiment analysis for online product evaluations. Businesses can gain valuable insights into customer sentiment and make well-informed decisions regarding product enhancements and marketing strategies by leveraging the proposed framework

Catalytic reactivity descriptors of metal‐nitrogen‐doped carbon catalysts for electrocatalysis

AbstractMetal‐nitrogen‐doped carbon material have sparked enormous attentions as they show excellent electrocatalytic performance and provide a prototype for mechanistic understandings of electrocatalytic reactions. Researchers spare no effort to find catalytic reactivity “descriptor”, which is correlated with catalytical properties and could be utilized for guiding the rational design of high‐performance catalysts. In recent years, benefited from the development of computational technology, theoretical calculation came into being as a powerful tool to understand catalytic mechanisms from an atomic level as well as to accelerate the process of finding a catalytic reactivity descriptor and promoting the development of effective catalysts. In the present review, we provide the latest theoretical research toward energetic and electronic descriptors for metal‐nitrogen‐doped carbon (M‐N‐C) materials, which have shown excellent electrocatalytic performance and provide a prototype for the mechanistic understanding of electrocatalytic reactions. This review uses density functional theory calculation and the most advanced machine learning method to describe the exploration of four kinds of electrocatalytic reaction descriptors, namely oxygen reduction reaction, carbon dioxide reduction reaction, hydrogen evolution reaction, and nitrogen reduction reaction. The aim of this review is to inspire the future design of high‐efficiency M‐N‐C catalysts by providing in‐depth insights into the electrocatalytic activity of these materials.

Deep Learning and Sustainability in Agriculture: A Systematic Review

Agriculture plays a pivotal role in the global economy, and with the increasing human population, there is substantial pressure to enhance agricultural productivity while minimizing environmental impacts. Digital agriculture and precision farming have emerged as innovative fields leveraging data-intensive approaches to optimize agricultural operations. However, there is a gap in understanding how machine learning (ML) techniques can be effectively integrated across various agricultural domains, including crop management, livestock monitoring, soil analysis, and water management. To address this gap, this study provides a comprehensive review of the application of ML techniques in different agricultural domains. By analyzing publications from 2016 to 2023, we identify key trends, challenges, and future directions for ML in agriculture. The methodology involves categorizing 40 papers from top reputable journals and conferences into four main domains: crop, livestock, soil, and water, and examining the specific ML techniques applied in each domain. Our results show that crop management is the most researched domain, with significant use of machine learning, deep learning, ensemble learning, and transfer learning techniques. Livestock monitoring and soil analysis also employ advanced ML methods, though they face unique challenges related to data quality and model generalizability. Water management benefits from ML for optimizing resource use but encounters difficulties in data integration and scalability. The significance of this study lies in its holistic analysis of ML applications in agriculture, highlighting the most effective techniques and identifying critical challenges that need to be addressed. By providing insights into current research trends and potential future directions, this review serves as a valuable resource for researchers and practitioners aiming to enhance agricultural productivity through advanced ML technologies.

Thin, soft, wearable system for continuous wireless monitoring of artery blood pressure

Continuous monitoring of arterial blood pressure (BP) outside of a clinical setting is crucial for preventing and diagnosing hypertension related diseases. However, current continuous BP monitoring instruments suffer from either bulky systems or poor user-device interfacial performance, hampering their applications in continuous BP monitoring. Here, we report a thin, soft, miniaturized system (TSMS) that combines a conformal piezoelectric sensor array, an active pressure adaptation unit, a signal processing module, and an advanced machine learning method, to allow real wearable, continuous wireless monitoring of ambulatory artery BP. By optimizing the materials selection, control/sampling strategy, and system integration, the TSMS exhibits improved interfacial performance while maintaining Grade A level measurement accuracy. Initial trials on 87 volunteers and clinical tracking of two hypertension individuals prove the capability of the TSMS as a reliable BP measurement product, and its feasibility and practical usability in precise BP control and personalized diagnosis schemes development.

Enhanced wave overtopping simulation at vertical breakwaters using machine learning algorithms.

Accurate prediction of wave overtopping at sea defences remains central to the protection of lives, livelihoods, and infrastructural assets in coastal zones. In addressing the increased risks of rising sea levels and more frequent storm surges, robust assessment and prediction methods for overtopping prediction are increasingly important. Methods for predicting overtopping have typically relied on empirical relations based on physical modelling and numerical simulation data. In recent years, with advances in computational efficiency, data-driven techniques including advanced Machine Learning (ML) methods have become more readily applicable. However, the methodological appropriateness and performance evaluation of ML techniques for predicting wave overtopping at vertical seawalls has not been extensively studied. This study examines the predictive performance of four ML techniques, namely Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Support Vector Machines-Regression (SVR), and Artificial Neural Network (ANN) for overtopping discharge at vertical seawalls. The ML models are developed using data from the EurOtop (2018) database. Hyperparameter tuning is performed to curtail algorithms to the intrinsic features of the dataset. Feature Transformation and advanced Feature Selection methods are adopted to reduce data redundancy and overfitting. Comprehensive statistical analysis shows superior performance of the RF method, followed in turn by the GBDT, SVR, and ANN models, respectively. In addition to this, Decision Tree (DT) based methods such as GBDT and RF are shown to be more computationally efficient than SVR and ANN, with GBDT performing simulations more rapidly that other methods. This study shows that ML approaches can be adopted as a reliable and computationally effective method for evaluating wave overtopping at vertical seawalls across a wide range of hydrodynamic and structural conditions.