AI-based Methods Research Articles

Multimodal deep learning-based diagnostic model for BPPV

BackgroundBenign paroxysmal positional vertigo (BPPV) is a prevalent form of vertigo that necessitates a skilled physician to diagnose by observing the nystagmus and vertigo resulting from specific changes in the patient’s position. In this study, we aim to explore the integration of eye movement video and position information for BPPV diagnosis and apply artificial intelligence (AI) methods to improve the accuracy of BPPV diagnosis.MethodsWe collected eye movement video and diagnostic data from 518 patients with BPPV who visited the hospital for examination from January to March 2021 and developed a BPPV dataset. Based on the characteristics of the dataset, we propose a multimodal deep learning diagnostic model, which combines a video understanding model, self-encoder, and cross-attention mechanism structure.ResultOur validation test on the test set showed that the average accuracy of the model reached 81.7%, demonstrating the effectiveness of the proposed multimodal deep learning method for BPPV diagnosis. Furthermore, our study highlights the significance of combining head position information and eye movement information in BPPV diagnosis. We also found that postural and eye movement information plays a critical role in the diagnosis of BPPV, as demonstrated by exploring the necessity of postural information for the diagnostic model and the contribution of cross-attention mechanisms to the fusion of postural and oculomotor information. Our results underscore the potential of AI-based methods for improving the accuracy of BPPV diagnosis and the importance of considering both postural and oculomotor information in BPPV diagnosis.

Reinforcement-Learning-Based Virtual Inertia Controller for Frequency Support in Islanded Microgrids

As the world grapples with the energy crisis, integrating renewable energy sources into the power grid has become increasingly crucial. Microgrids have emerged as a vital solution to this challenge. However, the reliance on renewable energy sources in microgrids often leads to low inertia. Renewable energy sources interfaced with the network through interlinking converters lack the inertia of conventional synchronous generators, and hence, need to provide frequency support through virtual inertia techniques. This paper presents a new control algorithm that utilizes the reinforcement learning agents Twin Delayed Deep Deterministic Policy Gradient (TD3) and Deep Deterministic Policy Gradient (DDPG) to support the frequency in low-inertia microgrids. The RL agents are trained using the system-linearized model and then extended to the nonlinear model to reduce the computational burden. The proposed system consists of an AC–DC microgrid comprising a renewable energy source on the DC microgrid, along with constant and resistive loads. On the AC microgrid side, a synchronous generator is utilized to represent the low inertia of the grid, which is accompanied by dynamic and static loads. The model of the system is developed and verified using Matlab/Simulink and the reinforcement learning toolbox. The system performance with the proposed AI-based methods is compared to conventional low-pass and high-pass filter (LPF and HPF) controllers.

Experimental analysis of forming forces in incremental forming and its prediction by AI-based methods

Single point incremental forming (SPIF) is an emerging approach to manufacture the user-specific parts of greater strength economically without involving dedicated tools and dies. In this study, peak deforming loads have been measured and estimated in order to determine the size and capacity of forming machinery for manufacturing components, using the SPIF process, for designed process conditions on AA7075-O sheets. Estimation of maximum deforming loads has been accomplished with the help of hybrid artificial neural network (HANN) approach by taking experimental load values as the input dataset. The load values, estimated by HANN approach, have been compared with the load values estimated by two state-of-art regression approaches, that is, Support Vector Machine (SVM) and Gaussian Process Regression (GPR). Results show that the regression methods (SVM and GPR) are outperformed by the HANN model with an accuracy of 99.80%. HANN model estimated force values very close to experimental results. The forming forces were found to be greater when the flat-end tools were used as a forming agent in place of hemispherical-end tool. The proposed study would help the researchers and scientists to assess the capacity the machines used in SPIF process to prepare a particular sheet-part and to prevent the failure of hardware during the SPIF process.

Artificial Intelligence as A Complementary Tool for Clincal Decision-Making in Stroke and Epilepsy.

Neurology is a quickly evolving specialty that requires clinicians to make precise and prompt diagnoses and clinical decisions based on the latest evidence-based medicine practices. In all Neurology subspecialties-Stroke and Epilepsy in particular-clinical decisions affecting patient outcomes depend on neurologists accurately assessing patient disability. Artificial intelligence [AI] can predict the expected neurological impairment from an AIS [Acute Ischemic Stroke], the possibility of ICH [IntraCranial Hemorrhage] expansion, and the clinical outcomes of comatose patients. This review article informs readers of artificial intelligence principles and methods. The article introduces the basic terminology of artificial intelligence before reviewing current and developing AI applications in neurology practice. AI holds promise as a tool to ease a neurologist's daily workflow and supply unique diagnostic insights by analyzing data simultaneously from several sources, including neurological history and examination, blood and CSF laboratory testing, CNS electrophysiologic evaluations, and CNS imaging studies. AI-based methods are poised to complement the other tools neurologists use to make prompt and precise decisions that lead to favorable patient outcomes.

Nanocomposite ceramic membranes as novel tools for remediation of textile dye waste water – A review of current applications, machine learning based modeling and future perspectives

Textile effluent treatment has gained significant attention due to the carcinogenic effects of the dye pollutants present and their enhanced resistance to degradation. Porous ceramic membranes have gained growing interest for dye remediation due to various unique characteristics, including resistance to fungal invasion, adverse chemical conditions, and high temperature. The current study explores applications of ceramic membrane technology, including membranes like MF, UF, and NF, for dye remediation from textile effluent. The best-performing nanocomposite membranes for eliminating azo dyes from textile effluent and efficiencies achieved are reported as Clay-alumina (99%), yttria-stabilized-zirconia (99%), nano TiO2-bentonite UF (95%), and tight TiO2 UF (100%). Ceramic nanocomposites with TiO2 and Al2O3 active layer NF (99%), Ceramic Hybrid hollow fiber LNF (99.3%). Loose nanofiltration (LNF) ceramic membranes have demonstrated outstanding targeted separation performance for the separation of dyes, making them affirm the effective recoveries and recurrent use of high-value-added constituents amongst all the membrane approaches examined. The potential of ceramic membranes for future research and development, including membrane production and mechanism, are discussed. In addition, the implementation of AI-based methods and Machine learning (ML) algorithms are discussed in relation to predicting membrane filtration performance and fouling tendencies and dye removal processes, maximizing opportunities in this field.

Recommendations for using artificial intelligence in clinical flow cytometry.

Flow cytometry is a key clinical tool in the diagnosis of many hematologic malignancies and traditionally requires close inspection of digital data by hematopathologists with expert domain knowledge. Advances in artificial intelligence (AI) are transferable to flow cytometry and have the potential to improve efficiency and prioritization of cases, reduce errors, and highlight fundamental, previously unrecognized associations with underlying biological processes. As a multidisciplinary group of stakeholders, we review a range of critical considerations for appropriately applying AI to clinical flow cytometry, including use case identification, low and high risk use cases, validation, revalidation, computational considerations, and the present regulatory frameworks surrounding AI in clinical medicine. In particular, we provide practical guidance for the development, implementation, and suggestions for potential regulation of AI-based methods in the clinical flow cytometry laboratory. We expect these recommendations to be a helpful initial framework of reference, which will also require additional updates as the field matures.

Deep Learning for Dyslexia Detection: A Comprehensive CNN Approach with Handwriting Analysis and Benchmark Comparisons

Dyslexia is a complex learning disorder that affects neurological nerves in the brain and makes reading and writing difficult; therefore, early diagnosis for effective interventions becomes important. This study demonstrates how quickly dyslexia can be identified by introducing an advanced convolutional neural network model developed for detecting dyslexia through image-based handwriting analysis. The need for early identification is informed by the fact that dyslexia may, in certain cases, lead to poor academic performance and emotional imbalance among learners. This method of using deep learning outperforms all other established conventional methods due to inherent sensitivity in classifying handwritings of dyslexics from those of normal individuals. The artificial intelligence (AI)-supported technology has the highest training accuracy of 99.5% proving its ability to capture subtle features related to the presence of dyslexic tendencies. Consequently, it records a testing accuracy of 96.4%, thereby confirming its efficacy under practical circumstances. In addition, the model also shows a good F1-score of 96 which indicates that it can achieve a balanced precision versus recall trade-off unlike other state-of-the-art approaches. The obtained results of the proposed methodology were compared with those of previous state–of-the-art approaches, and it has been observed that the proposed study provides better outcomes. These detailed performance indicators point toward the potential usefulness of AI-based methods in identifying dyslexia thus informing appropriate interventions on time and targeted assistance to the patients suffering from this disease.

Approach of Automated Robot Arrangement in Manufacturing Cells

Implementing industrial robots in manufacturing cells for automation requires specialized knowledge. Trajectory planning and end-effector design significantly increase the required effort. The trajectory planning is iterative and time-consuming and also depends on the robot base placement, which is why the automation of robot base placement is being explored. This paper presents a novel approach to automate the robot base positioning demonstrated on the example of the Central Research Center 1153 (CRC 1153) Tailored Forming. Accordingly, a three-step approach is developed: First, an accurate 3D environment has to be created to define the forging cell structure, otherwise problems may occur during the transition to the real-world application. Next, the robot’s position and trajectory are calculated and optimized using a proprietary algorithm. If there is no arrangement that allows collision-free trajectory planning, another algorithm is applied that extends the robot’s workspace by adjusting the arrangement of the robot’s flange and the tool center point (TCP) of the end effector. Finally, AI-based methods are used to equip the end-effector and the robot with customized coupling elements depending on the TCP adjustment performed in the previous step. As a result of this work, a framework for automated robot placement in manufacturing cells using optimization algorithms and AI-based methods is presented. This increases the efficiency of automated manufacturing cell design and minimizes the dependency of the result’s quality on the engineer’s experience.

RETRACTED: 60 Predicting chorioamnionitis using AI-based methods: a retrospective cohort study

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/policies/article-withdrawal). This meeting abstract has been retracted at the request of the authors. The team determined further analysis is warranted before the formal presentation of the results.

A Platform Architecture for Data- and AI-supported human-centred Zero Defect Manufacturing for Sustainable Production

The challenge for manufacturing companies lies in efficiently adapting to economic, ecological, and social sustainability while maintaining a competitive edge in the global market, despite the rapid advancements in information, communication, and resource-efficient production processes utilizing robotics and AI-based methods. The key question is: How can knowledge regarding feasibility and implementation possibilities be effectively transferred?In our paper, we propose a platform architecture designed to facilitate knowledge exchange and transfer between technology providers and the manufacturing industry. The platform facilitates the dissemination of innovative methods and technologies, improving collaboration and operational efficiency It will also serve as a repository for insights and experiences from technology implementation, making this knowledge accessible for internal and industry-wide use. The goal is to create a sustainable ecosystem for continuous improvement and competitive advantage in manufacturing, by matching technologies and methodologies to specific production needs using algorithms and tracking sustainability metrics.

A Systematic Literature Review on AI-Based Methods and Challenges in Detecting Zero-Day Attacks

A Systematic Literature Review on AI-Based Methods and Challenges in Detecting Zero-Day Attacks

Deep feature analysis, classification with AI-driven gastrointestinal diagnostics

Several AI-based methods have substantially progressed the area of medical image and video-based diagnostics, which encompasses radiography, pathology, endoscopy, and the categorization of gastrointestinal (GI) diseases. When it comes to classifying numerous GI disorders, the majority of prior research that relies solely on spatial cues performs poorly. While some prior research has made use of temporal features trained on a 3D convolution neural network, these studies have focused on a very small subset of the gastrointestinal system and have used very few classes. To address these concerns, we introduce an all-inclusive AI-based system for classifying different GI illnesses using endoscopic recordings. This system can extract spatial and temporal data concurrently, leading to improved classification performance. For temporal variables, we employ a long short-term memory model; for spatial features, we employ two independent residual networks in cascade mode.

A Framework for Monitoring Stability of Tailings Dams in Realtime Using Digital Twin Simulation and Machine Learning

Tailings dam failures cause catastrophic impact on the environment and surrounding communities. Incidences of failure in the recent past have caused industrialists and researchers to seek innovative ways for proactively managing their safety and disaster mitigation. Given Industry 4.0 technologies now available, researchers are looking to develop digital tools for cost-effective, realtime monitoring of tailings dams. However, published literature indicates that a reliable framework is still lacking. This paper proposes a framework for developing a data-driven system for monitoring tailings dam stability and early warning detection. The framework relies upon digital twin simulation and machine-learning (ML) techniques, and comprises four main components: realtime data collection, digital twin modelling, ML-based early detection and prediction, and intelligence-driven decision-support. Sensors gather real-time geophysical data from monitored structure, and the digital twin uses this data to simulate dam behaviour. ML algorithms analyse the data and simulations to enable early detection of instability and failure prediction. Literature suggests that digital twin and ML-based approaches may have advantages over traditional monitoring techniques and other AI-based methods. The paper concludes with a discussion of the framework's limitations, opportunities for improvement, and potential for application in mining and geotechnical engineering. The paper serves as a basis for model development and future research.

A Comparative Evaluation of AI-Based Methods and Traditional Approaches for Air Quality Monitoring: Analyzing Pros and Cons

A Comparative Evaluation of AI-Based Methods and Traditional Approaches for Air Quality Monitoring: Analyzing Pros and Cons

The hybrid of BERT and deep learning models for Indonesian sentiment analysis

<span>Artificial intelligence (AI) is one example of how data science innovation has advanced quickly in recent years and has greatly improved human existence. Neural networks, which are a type of machine learning model, are a fundamental component of deep learning in the field of AI. Deep learning models can carry out feature extraction and classification tasks in a single design because of their numerous neural network layers. Modern machine learning algorithms have been shown to perform worse than this model on tasks including text classification, audio recognition, imaginary, and pattern recognition. Deep learning models have outperformed AI-based methods in sentiment analysis and other text categorization tasks. Text data can originate from a number of places, including social media. Sentiment analysis is the computational examination of textual expressions of ideas and feelings. This study employs the convolutional neural network (CNN), long-short term memory (LSTM), CNN-LSTM, and LSTM-CNN models in a deep learning framework using bidirectional encoder representations from transformers (BERT) data representation to assess the performance of machine learning. The implementation of the model utilises YouTube discussion data pertaining to political films associated with the Indonesian presidential election of 2024. Confusion metrics, including as accuracy, precision, and recall, are then used to analyse the model’s performance.</span>

Automated lung disease detection, classification and prediction using RNN framework

Lung diseases are widespread throughout the globe. This group of illnesses includes chronic obstructive pulmonary disease, pneumonia, asthma, TB, fibrosis, and others. The earliest possible diagnosis of lung illness is crucial. Numerous image processing and artificial intelligence models have been created with this goal in mind. Several types of research have been initiated around the world since the arrival of the novel Covid-19 for its reliable estimation. The earlier respiratory disease pneumonia is linked to Covid-19 because several patients died as a result of severe chest congestion (pneumonic condition). Medical experts find it pulmonary illnesses caused by pneumonia and Covid-19 are difficult to differentiate. Chest CT-Scan imaging is the most accurate approach for predicting lung disease. Recently, a number of academics reported using AI-based methods to classify medical images using training data from CT scans. Deep learning is a very effective technique for understanding difficult cognitive difficulties, and more and more challenges are using and evaluating it. Recurrent neural network method, a deep learning system that can accurately detect COVID from CT-scan pictures, was employed in this study. Detect various lung illnesses like pneumonia and TB by using Multi-class RNN. The experimental findings demonstrate the suggested approach increases the precision of disease prediction and also gives information on the diagnoses of the illnesses under study.

MSMTSeg: Multi-Stained Multi-Tissue Segmentation of Kidney Histology Images via Generative Self-Supervised Meta-Learning Framework.

Accurately diagnosing chronic kidney disease requires pathologists to assess the structure of multiple tissues under different stains, a process that is timeconsuming and labor-intensive. Current AI-based methods for automatic structure assessment, like segmentation, often demand extensive manual annotation and focus on single stain domain. To address these challenges, we introduce MSMTSeg, a generative self-supervised meta-learning framework for multi-stained multi-tissue segmentation in renal biopsy whole slide images (WSIs). MSMTSeg incorporates multiple stain transform models for style translation of inter-stain domains, a self-supervision module for obtaining pre-trained models with the domain-specific feature representation, and a meta-learning strategy that leverages generated virtual data and pre-trained models to learn the domain-invariant feature representation across multiple stains, thereby enhancing segmentation performance. Experimental results demonstrate that MSMTSeg achieves superior and robust performance, with mDSC of 0.836 and mIoU of 0.718 for multiple tissues under different stains, using only one annotated training sample for each stain. Our ablation study confirms the effectiveness of each component, positioning MSMTSeg ahead of classic advanced segmentation networks, recent few-shot segmentation methods, and unsupervised domain adaptation methods. In conclusion, our proposed few-shot cross-domain technology offers a feasible and cost-effective solution for multi-stained renal histology segmentation, providing convenient assistance to pathologists in clinical practice. The source code and conditionally accessible data are available at https://github.com/SnowRain510/MSMTSeg.

A Comparative Analysis of Advanced Machine Learning Algorithms to diagnose Parkinson's Disease

Parkinson's disease (PD) is a prevalent and debilitating neurological disorder affecting millions worldwide, necessitating early and precise diagnosis for effective management and therapy. Recent breakthroughs in artificial intelligence (AI) applications within the medical field have showcased promising potential in disease identification and diagnosis. This study presents a comprehensive analysis of cutting-edge AI-based methods for the identification of Parkinson's disease. This paper encompasses extensive testing on substantial datasets containing both PD patients and healthy individuals, aiming to assess the efficacy of AI models. Performance metrics such as sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC-ROC) are employed to evaluate model performance rigorously. This paper is not only reviewing the state-of-the-art AI techniques for Parkinson's disease identification but also introduces novel contributions in the field. Our results demonstrate the potential of AI in achieving early and accurate PD diagnosis, offering hope for improved patient care and treatment outcomes. Furthermore, this study paves the way for future research and development in the domain of AI-driven healthcare, with the ultimate goal of enhancing the quality of life for individuals suffering from Parkinson's disease and similar neurodegenerative conditions.

The progression of human posture concept and advances in postural assessment techniques

This narrative review delves into the historical and evolutionary aspects of human bipedal upright posture, tracing its development from anthropological perspectives to modern postural analysis methods. It examines the multifaceted factors influencing the evolution of upright posture in human ancestors, challenging the notion of a singular cause. The review highlights various environmental and social hypotheses, such as the need for gathering food, self-defense, or tool use, which may have contributed to this significant evolutionary shift. The concept of posture has been interpreted differently across historical periods, reflecting the prevailing cultural and scientific understanding. From Aristotle’s view of upright posture as a divine gift to Johann Gottfried Herder’s perspective of it as a distinguishing human characteristic from animals, and to the more contemporary views of Henry and Florence Kendall, the evolution of the concept mirrors the changing paradigms in human thought. The development of methods for evaluating posture is also a key focus. The review traces the journey from the introduction of the plumb line in the 1850s, a simple yet effective tool for assessing spinal asymmetries, to the advent of the Adams test and the scoliometer, which revolutionized scoliosis diagnosis. Today, advanced techniques like rasterstereography and AI-based methods offer sophisticated, non-invasive means for detailed posture analysis, minimizing errors and maximizing efficiency. The review emphasizes the ongoing importance of posture in the context of overall health, underscoring the adage of a healthy mind in a healthy body. It illustrates how the study of posture, from its evolutionary roots to modern analytical tools, remains a vital aspect of understanding human health and well-being.

A Fuel Cell’s Big Brother: Artificial Intelligence for Monitoring Fuel Cells

The operation of fuel cell stacks on test benches today is typically monitored by constantalarm threshold values for selected operating conditions. However, due to the wide range ofoperating conditions of the stacks, this type of monitoring only works for extreme maximumand minimum operating conditions. Wide setting ranges for thresholds prevent the detectionof minor faults, whereas too narrow limits will interrupt the tests unnecessarily. A particularchallenge is the monitoring of faults that either do not result in a directly measured responsefrom the fuel-cell stack or that only become noticeable with a time delay. The continuousimprovement of fuel cell stacks over the last years necessarily requires much-improvedmonitoring methods, especially for durability tests with an operating time of thousands ofhours. For this reason, a novel monitoring concept using AI-based methods for the operationof PEM fuel cells on test benches was developed and is being presented.Firstly, machine-learning based mechanisms for monitoring the operating conditions as setand controlled by the test bench are shown. Due to the cyclic operation during durabilitytests, the operating conditions set at a load point can be compared to the past operatingconditions at the same load point. This proceeding allows the early and accurate detection offaults caused by the test bench and thereby ensures the usability of the measured data aswell as an early alarm in case of problems.In addition, a digital twin based method for monitoring the condition of the fuel cell stack ispresented. The deep-learning based digital twin calculates a probabilistic prediction of theexpected voltage of the fuel cell stack based on the current and past operating conditions.The comparison of expected and measured cell voltage taking the model’s confidence intoaccount enables the early and precise detection of unforeseen events such as contaminationand thus averts consequential damage to the fuel cell stack. In contrast to physical models,the digital twin represents a data driven model-ling approach for fuel cells.The presented methods are applied to real testing data to demonstrate the detection of faultsduring the operation of fuel cell stacks on test benches that would have remainedundiscovered by today’s monitoring mechanisms. It shows that the data driven digital twin isable to predict the fuel cell’s stack voltage with an accuracy of 2.5 mV over 1000 h of unseendata. Figure 1