Application Of Artificial Intelligence Algorithms Research Articles

Advances in applications of artificial intelligence algorithms for cancer-related miRNA research.

MiRNAs are a class of small non-coding RNAs, which regulate gene expression post-transcriptionally by partial complementary base pairing. Aberrant miRNA expressions have been reported in tumor tissues and peripheral blood of cancer patients. In recent years, artificial intelligence algorithms such as machine learning and deep learning have been widely used in bioinformatic research. Compared to traditional bioinformatic tools, miRNA target prediction tools based on artificial intelligence algorithms have higher accuracy, and can successfully predict subcellular localization and redistribution of miRNAs to deepen our understanding. Additionally, the construction of clinical models based on artificial intelligence algorithms could significantly improve the mining efficiency of miRNA used as biomarkers. In this article, we summarize recent development of bioinformatic miRNA tools based on artificial intelligence algorithms, focusing on the potential of machine learning and deep learning in cancer-related miRNA research.

Wearable sensors for monitoring vital signals in sports and health: progress and perspective

Purpose This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics. Design/methodology/approach In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices. Findings This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution. Originality/value The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

Scoring PD-L1 Expression in Urothelial Carcinoma: An International Multi-Institutional Study on Comparison of Manual and Artificial Intelligence Measurement Model (AIM-PD-L1) Pathology Assessments.

Assessing programmed death ligand 1 (PD-L1) expression on tumor cells (TCs) using Food and Drug Administration-approved, validated immunoassays can guide the use of immune checkpoint inhibitor (ICI) therapy in cancer treatment. However, substantial interobserver variability has been reported using these immunoassays. Artificial intelligence (AI) has the potential to accurately measure biomarker expression in tissue samples, but its reliability and comparability to standard manual scoring remain to be evaluated. This multinational study sought to compare the %TC scoring of PD-L1 expression in advanced urothelial carcinoma, assessed by either an AI Measurement Model (AIM-PD-L1) or expert pathologists. The concordance among pathologists and between pathologists and AIM-PD-L1 was determined. The positivity rate of ≥ 1%TC PD-L1 was between 20-30% for 8/10 pathologists, and the degree of agreement and scoring distribution for among pathologists and between pathologists and AIM-PD-L1 was similar both scored as a continuous variable or using the pre-defined cutoff. Numerically higher score variation was observed with the 22C3 assay than with the 28-8 assay. A 2-h training module on the 28-8 assay did not significantly impact manual assessment. Cases exhibiting significantly higher variability in the assessment of PD-L1 expression (mean absolute deviation > 10) were found to have patterns of PD-L1 staining that were more challenging to interpret. An improved understanding of sources of manual scoring variability can be applied to PD-L1 expression analysis in the clinical setting. In the future, the application of AI algorithms could serve as a valuable reference guide for pathologists while scoring PD-L1.

Research on Data Fusion and Analysis Based on Artificial Intelligence Algorithm in Sensor Networks

This paper discusses the data fusion and analysis technology based on artificial intelligence algorithm in sensor networks. Firstly, the basic concept, composition and structure of sensor network are summarized, and its characteristics and challenges are analyzed. Then, the basic principle of data fusion technology and its application in sensor networks are introduced, and the basic concept, classification and application of artificial intelligence algorithm in data fusion are described in detail. On this basis, this paper constructs a data fusion framework based on artificial intelligence, and proposes a new data fusion method combining deep learning and machine learning algorithm. This method can effectively improve the accuracy and reliability of sensor data and reduce data redundancy and noise interference. In addition, the thesis also conducts in-depth research on the application scenarios of data fusion and analysis in sensor networks, including Environmental monitoring, intelligent transportation, medical health and other fields. The effectiveness and practicability of the proposed method are verified by experiments and case studies. Experimental results show that the data fusion technology based on artificial intelligence algorithm can significantly improve the data processing ability and analysis accuracy of sensor networks, and provide strong support for related applications. Finally, this paper summarizes the main conclusions of the study, and points out the shortcomings of current research and future prospects. In the future, the data fusion method based on artificial intelligence will be further optimized and improved, and its application in more fields will be expanded, which will provide more powerful support for the development and application of sensor network technology.

Intelligent Classification of Metallographic Based on Improved Deep Residual Efficiency Networks

The recognition of steel microstructure images plays a crucial role in the metallographic analysis process. Although some progress has been made through the application of artificial intelligence algorithms, several challenges remain. First, existing algorithms exhibit weak nonlinear feature extraction capabilities and noticeable limitations. Second, they overlook the intrinsic noise and redundant interference present in microscopic images. To address these issues, this paper investigates the automatic recognition of metallographic tissues by leveraging residual structures in deep neural networks. An enhanced residual network model based on transfer learning is proposed, which utilizes the pre-trained weights from the ImageNet dataset to facilitate learning with small sample data. This network offers higher classification accuracy and higher F1 scores. In addition, a deep residual shrinkage network model based on an attention mechanism is proposed. This model incorporates an attention sub-network into the original residual module and employs a soft threshold function to eliminate redundant features, including noise. The proposed algorithms are evaluated against various convolutional neural networks using 20 types of metallographic test sets. The experimental results showed that both methods have high accuracy rates of 95% and 94.44%, respectively, and F1 scores of 0.9464 and 0.9419. While maintaining the complexity of the model, there has been a significant improvement in accuracy, and the models exhibit strong generalization capabilities. Our research contributes to enhancing production efficiency, strengthening quality control, and improving material performance through computer vision technology.

Short-term load forecasting method based on secondary decomposition and improved hierarchical clustering

In the context of large-scale grid connection of new energy, short-term load forecasting is a vital and challenging task for power system to balance supply and demand. To effectively improve the forecasting accuracy, a new load forecasting method is proposed aiming to mine the characteristics of load data and study the application of artificial intelligence algorithms. In this paper, the seasonal and trend decomposition using loess (STL) method is firstly applied to decompose the load data into the trend, seasonal and residual components and the residual component with the highest complexity is further decomposed by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) approach. Secondly, in order to reduce the number of components, the improved hierarchical clustering technique is proposed to cluster all intrinsic mode functions (IMFs) obtained by CEEMDAN into high-frequency and low-frequency components. Then, different network models are trained to get the prediction results for different components, and the total load prediction value is achieved by stacking all of them. Finally, the national demand dataset of Great Britain in 2021–2022 is used to conduct the ablation and comparative experiments. The mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the proposed method are 2.064% and 724.01 MW, respectively, which verified the effectiveness and advancement of the proposed method.

A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions.

Plants intricately deploy defense systems to counter diverse biotic and abiotic stresses. Omics technologies, spanning genomics, transcriptomics, proteomics, and metabolomics, have revolutionized the exploration of plant defense mechanisms, unraveling molecular intricacies in response to various stressors. However, the complexity and scale of omics data necessitate sophisticated analytical tools for meaningful insights. This review delves into the application of artificial intelligence algorithms, particularly machine learning and deep learning, as promising approaches for deciphering complex omics data in plant defense research. The overview encompasses key omics techniques and addresses the challenges and limitations inherent in current AI-assisted omics approaches. Moreover, it contemplates potential future directions in this dynamic field. In summary, AI-assisted omics techniques present a robust toolkit, enabling a profound understanding of the molecular foundations of plant defense and paving the way for more effective crop protection strategies amidst climate change and emerging diseases.

INFLUENCE OF ARTIFICIAL INTELLIGENCE ON BUSINESS DECISION-MAKING

The paper delves into the influence of artificial intelligence (AI) on business decision-making. By examining this phenomenon's technical, strategic, and ethical dimensions, the study seeks to unravel the implications that artificial intelligence integration brings to decision-making. The study conducted a comprehensive analysis to investigate the perceptions and experiences of individuals regarding integrating artificial intelligence in business decision-making. The study involved a detailed examination of demographic characteristics, artificial intelligence awareness, implementation status, perceived impact on decision-making speed and accuracy and ethical considerations related to bias in artificial intelligence-driven decision-making. The findings show that the gender and age distribution of respondents influence the perception and use of artificial intelligence in business decision-making. And artificial intelligence-driven decisions are dominant in the healthcare sector. Furthermore, artificial intelligence awareness and implementation indicated a generally positive outlook, with significant acknowledgement and familiarity among respondents. There is a positive perception of artificial intelligence making decisions faster with a positive contribution to the accuracy of business decisions. However, there is a record of some biases in artificial intelligence-driven decision-making. This highlights a significant concern in the fair and equitable application of artificial intelligence algorithms. This shows the importance of addressing biases to ensure ethical decision-making. The hypothesis testing sought to ascertain whether the incorporation of artificial intelligence is contingent on the accuracy of business decisions. The chi-square test results indicated insufficient evidence to propose a noteworthy relationship between the integration of artificial intelligence and decision accuracy. This implies that organizations should explore additional factors influencing decision accuracy, recognizing that artificial intelligence integration alone may not be the sole determinant.

A study of data heterogeneity in federated learning

Data-driven artificial intelligence algorithms cannot do without large amounts of training data. However, the characteristics of data privacy and decentralization make constructing large-scale training data costly, which restricts the further application of artificial intelligence algorithms in different downstream fields. To address the above problems, federated learning has gradually attracted more and more research interests in recent years, which aims to utilize decentralized private data for model training while preserving privacy. However, the non-independent and homogeneous distribution of data across devices causes federated learning to face problems such as data imbalance and label bias, which in turn affects the generalization performance of the model. The problem of data heterogeneity has become a major key challenge in federated learning, and this paper aims to explore the impact of data heterogeneity on federated learning and to synthesize recent research results in this area. By analyzing different solution approaches from the aspects of adaptive data distribution, adding regularization terms, contrastive Learning, and multi-task learning, a comprehensive overview is provided for researchers. This paper further summarizes the existing challenges of data heterogeneity in the research field of federated learning and discusses its potential development directions.

The application of artificial intelligence algorithms for the generation of synthetic medical imaging data

The application of artificial intelligence algorithms for the generation of synthetic medical imaging data

A new perspective in radon risk assessment: Mapping the geological hazard as a first step to define the collective radon risk exposure

Radon is a radioactive gas and a major source of ionizing radiation exposure for humans. Consequently, it can pose serious health threats when it accumulates in confined environments. In Europe, recent legislation has been adopted to address radon exposure in dwellings; this law establishes national reference levels and guidelines for defining Radon Priority Areas (RPAs). This study focuses on mapping the Geogenic Radon Potential (GRP) as a foundation for identifying RPAs and, consequently, assessing radon risk in indoor environments. Here, GRP is proposed as a hazard indicator, indicating the potential for radon to enter buildings from geological sources. Various approaches, including multivariate geospatial analysis and the application of artificial intelligence algorithms, have been utilised to generate continuous spatial maps of GRP based on point measurements. In this study, we employed a robust multivariate machine learning algorithm (Random Forest) to create the GRP map of the central sector of the Pusteria Valley, incorporating other variables from census tracts such as land use as a vulnerability factor, and population as an exposure factor to create the risk map. The Pusteria Valley in northern Italy was chosen as the pilot site due to its well-known geological, structural, and geochemical features. The results indicate that high Rn risk areas are associated with high GRP values, as well as residential areas and high population density. Starting with the GRP map (e.g., Rn hazard), a new geological-based definition of the RPAs is proposed as fundamental tool for mapping Collective Radon Risk Areas in line with the main objective of European regulations, which is to differentiate them from Individual Risk Areas.

PIMFP: An accurate tool for the prediction of intramuscular fat percentage in live pigs using ultrasound images based on deep learning

Intramuscular fat (IMF) percentage is the proportion of fat between the muscle fibers of the loin eye, which contributes significantly to the flavor and tenderness of the pork. Traditional IMF percentage is measured after slaughter, which requires a high cost, and the slaughtered pig with high IMF percentage can not be directly retained for breeding. Instead of slaughter, using ultrasound images to predict IMF percentage has been gradually proposed as an alternative measurement. However, due to the high cost of dataset construction and limited feature extraction capability of traditional image analysis methods, there is still a lack of an accurate and robust method based on the large-scale dataset for predicting IMF percentage. Here, to establish an accurate model for non-invasive prediction of IMF percentage in pigs, a total number of 4,552 ultrasound images and their corresponding IMF percentage of 945 pigs are collected in this study. We proposed a general-purpose model, PIMFP, to accurately predict IMF percentage based on the deep convolutional neural network. The PIMFP model comprises three primary modules: image preprocessing, feature extraction, and IMF percentage prediction. The preprocessing procedures for the raw ultrasound images include channel conversion, region of interest selection, and contrast enhancement. The feature extraction module characterizes the preprocessed images using the residual neural network as the backbone to extract features. The image features are then delivered to the IMF percentage prediction module depending on the fully connected layer. Our proposed model exhibits significant advantages over existing models in predictive performance, computational speed, classification ability, robustness, and interpretability. For easier use, we also provide a user-friendly web tool for predicting IMF percentage automatically in a single step. In conclusion, our study demonstrates the power of the PIMFP model for predicting IMF percentage, which has great potential in future breeding and highlights the application of artificial intelligence algorithms in livestock.

Retracted: Application of Artificial Intelligence Algorithm in Relay Protection of Distribution Network

Retracted: Application of Artificial Intelligence Algorithm in Relay Protection of Distribution Network

A Comprehensive Review of Artificial Intelligence Algorithms Applications for Distributed Generation Sizing and Location Optimization to Enhance Efficiency and Reliability Indices in Radial Distribution Systems

The research aimed to understand radial distribution systems by defining them and exploring their strategies, and to explore methods for applying artificial intelligence algorithms in radial distribution systems, It also aimed to clarify types of indicators in radial distribution systems, highlighting the importance of providing a scientific and applied framework for integrating artificial intelligence in the field of energy distribution. The significance of the research lies in presenting a comprehensive and up-to-date approach based on the applications of artificial intelligence algorithms to improve the design and organization of distributed production systems in radial distribution systems. By employing a descriptive and analytical approach, the researcher was able to reach several conclusions, with the most prominent being that the application of artificial intelligence algorithms in radial distribution systems contributes to improving energy efficiency by directing it more accurately and effectively, the ability of artificial intelligence to provide precise data-dependent analyses enhances decision-making processes in radial distribution systems, The integration of artificial intelligence techniques contributes to improving overall efficiency in radial distribution systems, reducing energy losses, and enhancing energy distribution effectively. The research recommends enhancing integration between artificial intelligence techniques and radial distribution systems to ensure effective and smooth interaction, thereby increasing efficiency, Priority should be given to the safety and protection of data, and effective cybersecurity measures should be implemented to address the growing challenges in this context.

Феномен риска в условиях применения алгоритмов искусственного интеллекта

With the advent of large language models, researchers became interested in artificial intelligence algorithms and their application in the activities of enterprises. Such algorithms have already made a significant contribution in the fields of medicine, transport and finance. It is now necessary to understand what side effects artificial intelligence algorithms may cause once they are integrated into the production of goods and services. The purpose of this work is to identify risk factors for the use of algorithms and artificial intelligence systems, assess their impact on the level of risk and offer tools to reduce the level of risk. As a result of the conducted research, the term “artificial intelligence” was clarified; algorithms for analytical, functional, interactive, textual and visual artificial intelligence were identified. The modern artificial intelligence system represents the next wave of enterprise automation. Using the methods of system economic theory and operational theory, the risk factors for the use of artificial intelligence algorithms in the object, process, design and environmental subsystems of the enterprise are classified. Anti-risk management actions are proposed to reduce the likelihood of a risk situation or to compensate for the damage caused. The changes in the activities of large companies in the field of information technology related to the change in the concept of copyright and intellectual property are described. The limitations of the introduction and application of artificial intelligence algorithms associated with the inertia of managers’ thinking and the complexity of restructuring business processes at the enterprise are shown. The necessity of regulation in the field of the introduction of artificial intelligence systems, the definition of clear boundaries and restrictions to ensure the safety of individuals and society as a whole, and their compliance with ethical standards are substantiated.

Drug discovery for COVID-19 and related mutations using artificial intelligence

Researchers and scientists can transform interconnected data into valuable knowledge using computational-based models that can assist in disease diagnosis, inspection, and virus containment thanks to recent developments in the fields of artificial intelligence and machine learning. In this paper, we present a comprehensive analysis of how artificial intelligence and machine learning can contribute in the delivery of effective remedies and the fight against the COVID-19 pandemic, particularly in disease treatment and drug discovery. During the pandemic period, a large number of noteworthy studies were conducted in this direction by numerous academic and research communities from many fields. We explore the theoretical developments and practical applications of artificial intelligence algorithms and machine learning techniques that suggest potential solutions for accelerating the discovery of new drugs as well as repurposing existing ones, not only for COVID-19 but also for other related mutations and future pandemics, which unfortunately are highly predicted.

Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review

In recent years, the widespread application of artificial intelligence algorithms in protein structure, function prediction, and de novo protein design has significantly accelerated the process of intelligent protein design and led to many noteworthy achievements. This advancement in protein intelligent design holds great potential to accelerate the development of new drugs, enhance the efficiency of biocatalysts, and even create entirely new biomaterials. Protein characterization is the key to the performance of intelligent protein design. However, there is no consensus on the most suitable characterization method for intelligent protein design tasks. This review describes the methods, characteristics, and representative applications of traditional descriptors, sequence-based and structure-based protein characterization. It discusses their advantages, disadvantages, and scope of application. It is hoped that this could help researchers to better understand the limitations and application scenarios of these methods, and provide valuable references for choosing appropriate protein characterization techniques for related research in the field, so as to better carry out protein research.

ANN based-model for estimating the boron permeability coefficient as boric acid in SWRO desalination plants using ensemble-based machine learning

This study examines the relationship between input conditions and the prediction of boron rejection in full-scale seawater reverse osmosis (SWRO) desalination plants using ensemble-based machine learning. While reverse osmosis is the dominant desalination technology, limited research has focused on analyzing plant performance under actual operating conditions. To address this gap, we developed and implemented machine learning algorithms to forecast boron permeability coefficient values, which are indicative of boron rejection concentrations in the permeate. Our analysis utilizes data from a SWRO desalination plant in southeast Spain, examining various input variables and their influence on the prediction of these parameters. The results demonstrate that our ensemble-based machine learning approach can predict boron permeability coefficient values with a reasonable margin of error of 1 mgL−1, as evidenced by mean average error (MAE) and mean absolute percentage error (MAPE) values of 7.93·10–8 and 11.8 %, respectively. In conclusion, an innovative application of artificial intelligence algorithms in the field of water purification under real operational conditions has been introduced, thus introducing valuable insights into the use of machine learning algorithms for forecasting boron rejection concentrations in full-scale SWRO desalination plants. The findings lay the foundation for future researches exploring automated and deep-learning methods in water purification.

Application of artificial intelligence algorithms and low-cost sensors to estimate respirable dust in the workplace

The Internet of Things (IoT) and low-cost sensor technology have become common tools for environmental exposure monitoring; however, their application in measuring respirable dust (RD) in the workplace remains limited. This study aimed to develop a predictive model for RD using artificial intelligence (AI) algorithms and low-cost sensors and subsequently assess its validity using a standard sampling approach. Various low-cost sensors were combined into an RD sensor module and mounted on a portable aerosol monitor (GRIMM 11-D) for two weeks. AI algorithms were used to capture data per minute over 14 days to establish predictive RD models. The best-fitting model was validated using an aluminum cyclone equipped with an air pump and polytetrafluoroethylene filters to sample the 8-hour RD for 5 days at an aircraft manufacturing company. This module was continuously monitored for two weeks to evaluate its stability. The RD concentration measured by GRIMM 11-D in a general outdoor environment over two weeks was 28.1 ± 16.1 μg/m3 (range: 2.4–85.3 μg/m3). Among the various established models, random forest regression was observed to have the best prediction capacity (R2 = 0.97 and root mean square error = 2.82 μg/m3) in comparison to the other 19 methods. Field-based validation revealed that the predicted RD concentration (35.9 ± 4.1 μg/m3, range: 32.7–42.9 μg/m3) closely approximated the results obtained by the traditional method (38.1 ± 8.9 μg/m3, range: 28.1–52.5 μg/m3), and a strong positive Spearman correlation was observed between the two (rs = 0.70). The average bias was −2.2 μg/m3 and the precision was 5.8 μg/m3, resulting in an accuracy of 6.2 μg/m3 (94.2 %). Data completeness was 99.7 % during the continuous two-week monitoring period. The developed sensor module of RD exhibited excellent predictive performance and good data stability that can be applied to exposure assessments in occupational epidemiological studies.

Application of Artificial Intelligence Algorithms in Multilayer Perceptron and Elman Networks to Predict Photovoltaic Power Plant Generation

This paper presents the models developed for the short-term forecasting of energy production by photovoltaic panels. An analysis of a set of weather factors influencing daily energy production is presented. Determining the correlation between the produced direct current (DC) energy and the individual weather parameters allowed the selection of the potentially best explanatory factors, which served as input data for the neural networks. The forecasting models were based on MLP and Elman-type networks. An appropriate selection of structures and learning parameters was carried out, as well as the process of learning the models. The models were built based on different time periods: year-round, semi-annual, and seasonal. The models were developed separately for monocrystalline and amorphous photovoltaic modules. The study compared the models with the predicted and measured insolation energy. In addition, complex forecasting models were developed for the photovoltaic system, which could forecast DC and AC energy simultaneously. The complex models were developed according to the rules of global and local modeling. The forecast errors of the developed models were included. The smallest values of the DC energy forecast errors were achieved for the models designed for summer forecasts. The percentage forecast error was 1.95% using directly measured solar irradiance and 5. 57% using predicted solar irradiance. The complex model for summer forecasted the AC energy with an error of 1.86%.