Application Of Artificial Intelligence Techniques Research Articles

The Use of Artificial Intelligence (AI) is Customer Service Process in the Pharmaceutical Industry: A Case Study in Public Laboratories

The Consumer Service (SAC) is an important communication and relationship channel with the customer. This study sought automated data collection and analysis techniques to trace profiles and strategies that are part of the SAC 4.0 model in official laboratories A and B. The method consisted of document analysis of standardized procedures and database research using the keywords SAC, artificial intelligence, and pharmaceutical industry. The results showed that laboratories A and B use the SAC 2.0 model and do not apply AI techniques. Evidence shows that pharmaceutical industries in Brazil use the SAC 4.0 model. It is concluded that the AI tools in the SAC contribute to improvements and efficiency in the process.

A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring

The study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, it is vulnerable to biases, which have adverse effects on organizations and the broader society. This research aims to analyze case studies on AI hiring to demonstrate both successful implementations and instances of bias. It also seeks to evaluate the impact of algorithmic bias and the strategies to mitigate it. The basic design of the study entails undertaking a systematic review of existing literature and research studies that focus on artificial intelligence techniques employed to mitigate bias in hiring. The results demonstrate that the correction of the vector space and data augmentation are effective natural language processing (NLP) and deep learning techniques for mitigating algorithmic bias in hiring. The findings underscore the potential of artificial intelligence techniques in promoting fairness and diversity in the hiring process with the application of artificial intelligence techniques. The study contributes to human resource practice by enhancing hiring algorithms’ fairness. It recommends the need for collaboration between machines and humans to enhance the fairness of the hiring process. The results can help AI developers make algorithmic changes needed to enhance fairness in AI-driven tools. This will enable the development of ethical hiring tools, contributing to fairness in society.

Effective Application of Artificial Intelligence Techniques in Security Risk Assessment and Dependency Analysis of Open Source Components

Abstract With the wide application of open source software, the security of open source components has become a non-negligible problem in software development. In this paper, based on the research on deep learning algorithms, the deep reinforcement learning algorithm DQN is proposed, and the DQN-LightGBM model is constructed by combining LightGBM classifiers to achieve better mining performance. This model introduces the attention mechanism and BiLSTM network, and the TextACBL vulnerability identification algorithm is proposed. In the end, the performance of the open source component vulnerability mining model is evaluated to investigate its impact on vulnerability identification, risk assessment, and dependency analysis of open source components. The results show that the performance of DQN-LightGBM model is above 0.9 in accuracy, checking accuracy, checking completeness, AUC value, and F1, and the mining completion time is only 54s, which is the best. The accuracy of TextACBL model in identifying the types of vulnerabilities is above 94%, and the vulnerability danger levels on the seven items measured are high, low, high, high, and high risk, respectively, medium risk, high risk, and medium risk. The differences in the global graph features of the identified normal and vulnerability samples are obvious, and there are frequent dependencies in the probability graphs of the opcodes of the vulnerability samples. This study allows for the assessment of security risks of open source components and the comprehensive, accurate, and efficient conduct of dependency analysis.

Vibration-based novelty detection using autoencoders: application to KW51 bridge

In recent years an increasing attention has been given to the application of Artificial Intelligence techniques within the context of Structural Health Monitoring (SHM) of Civil Engineering structures. Multiple advantages compared to traditional techniques based on Operational Modal Analysis (OMA) are achieved, such as a relatively low computational efforts and possible insensitivity to environmental and operational variability (EOV). The present paper proposes an autoencoder-based approach to automatically handle EOVs and, at the same time, to detect structural changes. The proposed procedure is based on the use of an autoencoder (AE) model to reconstruct the measurement data collected during continuous dynamic monitoring. In more details, an AE network is trained using the time series simultaneously collected by all the available channels during a reference period, in which the structure is supposed to be in healthy condition under normal EOV. Through the training procedure, the internal bottleneck layer of AE is supposed to learn how the variation of measured data is affected by the normal EOV. Subsequently, the trained AE is used to reconstruct the data collected from unknown scenarios, providing a reconstruction error (between the measured and the reconstructed signals) that increases as soon as the monitored system departs from its healthy condition. The application of the AE-based procedure is exemplified to the benchmark KW51 bridge (a steel bowstring railway bridge in Leuven, Belgium), showing that the AE network, trained simultaneously with all the available channels, is capable of detecting the structural changes due to a retrofitting performed on the bridge, under changing environmental and operational conditions.

Application of Artificial Intelligence in Predicting Oil Production Based on Water Injection Rate

The utilization of artificial intelligence (AI) has become imperative across various domains, including the oil and gas industry, which covers several fields, including reservoirs, drilling, and production. In oil and gas production, conventional methods, such as reservoir simulation, are used to predict the oil production rate. This simulation requires comprehensive data, so each process step takes a long time and is expensive. AI is urgently needed and can be a solution in this case. This research aims to apply AI techniques to forecast oil production rates based on water injection rates from two injection wells. Three wells are connected with a direct line drive pattern. Three different AI methods were applied, including multiple linear polynomial regression (PR), multiple linear regression (MLR), and artificial neural networks (ANN) in constructing oil production rate prediction models. Actual field data of 1180 data are used, including water injection rate data from two injection wells and oil production history data from one production well. The dataset has been split randomly into 80% training and 20% allocated for testing subsets. The training data is used to build predictive models, while the testing data is used to validate model performance. Comparative analysis selects the model with the lowest root mean square error (RMSE) and the highest R^2 test value. Results demonstrate that the ANN model achieves the smallest Root Mean Square Error (RMSE) of 0.142 and the highest R^2 test value of 16.2%, outperforming the PR and MLR methods. The ANN prediction model provides a rapid and efficient approach to estimating oil production rates.

Application of Artificial Intelligence in Predicting Oil Production Based on Water Injection Rate

The utilization of artificial intelligence (AI) has become imperative across various domains, including the oil and gas industry, which covers several fields, including reservoirs, drilling, and production. In oil and gas production, conventional methods, such as reservoir simulation, are used to predict the oil production rate. This simulation requires comprehensive data, so each process step takes a long time and is expensive. AI is urgently needed and can be a solution in this case. This research aims to apply AI techniques to forecast oil production rates based on water injection rates from two injection wells. Three wells are connected with a direct line drive pattern. Three different AI methods were applied, including multiple linear polynomial regression (PR), multiple linear regression (MLR), and artificial neural networks (ANN) in constructing oil production rate prediction models. Actual field data of 1180 data are used, including water injection rate data from two injection wells and oil production history data from one production well. The dataset has been split randomly into 80% training and 20% allocated for testing subsets. The training data is used to build predictive models, while the testing data is used to validate model performance. Comparative analysis selects the model with the lowest root mean square error (RMSE) and the highest R^2 test value. Results demonstrate that the ANN model achieves the smallest Root Mean Square Error (RMSE) of 0.142 and the highest R^2 test value of 16.2%, outperforming the PR and MLR methods. The ANN prediction model provides a rapid and efficient approach to estimating oil production rates.

Application of Artificial Intelligence Techniques to Detect Fake News: A Review

With the rapid growth of social media platforms and online news consumption, the proliferation of fake news has emerged as a pressing concern. Detecting and combating fake news has become crucial in ensuring the accuracy and reliability of information disseminated through social media. Machine learning plays a crucial role in fake news detection due to its ability to analyze large amounts of data and identify patterns and trends that are indicative of misinformation. Fake news detection involves analyzing various types of data, such as textual or media content, social context, and network structure. Machine learning techniques enable automated and scalable detection of fake news, which is essential given the vast volume of information shared on social media platforms. Overall, machine learning provides a powerful tool for detecting and preventing the spread of fake news on social media. This review article provides an extensive analysis of recent advancements in fake news detection. The chosen articles cover a wide range of approaches, including data mining, deep learning, natural language processing (NLP), ensemble learning, transfer learning, and graph-based techniques.

Application of Robotics, Artificial Intelligence and Deep Learning in Modern Agriculture Technology: A Review

In order to determine their potential impact in the field of agriculture, the proposed work aims to review the various artificial intelligence (AI) techniques, with a focus on expert systems, robots designed specifically for agriculture, and sensors technology for data collection and transmission. These techniques include fuzzy logic (FL), artificial neural network (ANN), genetic algorithm (GA), particle swarm optimisation (PSO), artificial potential field (APF), simulated annealing (SA), deep learning. The application of AI techniques and robots in cultivation, monitoring, and harvesting is not highlighted in any literature, making it difficult to compare each one simultaneously based on popularity and usefulness while also understanding how each contributes to the agricultural industry. With knowledge of the extent of AI engaged and the robots used, this paper compares three crucial agricultural phases: cultivation, monitoring, and harvesting. The current study offers a comprehensive analysis of over 200 publications that cover the use of automation in agriculture as of 1960 and 2021. It draws attention to the unmet research needs for developing intelligent, self-governing agricultural systems. The frequency of various AI, robotics and deep learning techniques for particular applications in the agriculture industry round out the article.

Artificial Intelligence: new data and new models in credit risk management

During the last decade, the increase in computational capacity, the consolidation of new data processing methodologies and the availability of access to new information concerning both individuals and organizations, aided by the widespread internet usage, has increased the development and implementation of artificial intelligence (AI) within companies. The application of AI techniques in the banking sector attracts wide interest as the extraction of information from data is inherent to banks. As matter of fact, for many years now models play a crucial role in several banks processes and are strictly regulated when they drive capital measurement processes. Among banks’ risk models a special role is played by credit ones, as they manage the most relevant risk banks face and are often used in regulatory relevant processes. The new AI techniques, coupled with the usage of novel data, mostly unstructured ones related to borrowers’ behaviors, allow for an improvement of the accuracy of credit risk models, that so far relied on structured internal and external data. This paper takes inspiration from the Position Paper Aifirm 33/2022 and its English published translation (Locatelli, Pepe, Salis (eds), 2022. The paper is focused on literature review regarding the most common AI models in use in credit risk management, also adding a regulatory perspective due to the specific regime banking models are subject when they are used for regulatory purposes. Furthermore, the exploration of forthcoming challenges and future advancements considers a managerial perspective. It aims to uncover how credit risk managers can leverage the new AI toolbox and novel data to enhance the credit risk models’ predictive power, without overlooking the intrinsic problems associated with the interpretability of the results.

Applications of artificial intelligence and machine learning in pharmaceutical domain: A comprehensive review

Artificial Intelligence (AI) is transforming the medical sector by enhancing diagnosis accuracy, improving treatment outcomes, optimizing healthcare workflows and advancing medical research. Its ability to analyze complex data and assist healthcare professionals has the potential to revolutionize patient care and drive innovation in the field of medicine. Its ability to analyze the amounts of data, identify patterns and make predictions has significantly advanced these fields, leading to improved healthcare outcomes and fostering new avenues for medical research and development. In this review, various applications of AI techniques in healthcare, such as drug design, disease diagnosis, personalized treatment, gene editing and medical education. AI plays a crucial role in disease diagnosis by analyzing medical images, genomic data and patient records, were discussed. AI is transforming medical education by providing personalized and interactive learning experiences. AI algorithms can efficiently analyze large genomic datasets, identifying potential target genes and predicting the outcomes of genetic modifications.

Application of Artificial Intelligence Techniques to Predict Strip Foundation Capacity Near Slope Surfaces

Application of Artificial Intelligence Techniques to Predict Strip Foundation Capacity Near Slope Surfaces

Role of environmental specificity in CASP results

BackgroundRecently, significant progress has been made in the field of protein structure prediction by the application of artificial intelligence techniques, as shown by the results of the CASP13 and CASP14 (Critical Assessment of Structure Prediction) competition. However, the question of the mechanism behind the protein folding process itself remains unanswered. Correctly predicting the structure also does not solve the problem of, for example, amyloid proteins, where a polypeptide chain with an unaltered sequence adopts a different 3D structure.ResultsThis work was an attempt at explaining the structural variation by considering the contribution of the environment to protein structuring. The application of the fuzzy oil drop (FOD) model to assess the validity of the selected models provided in the CASP13, CASP14 and CASP15 projects reveals the need for an environmental factor to determine the 3D structure of proteins. Consideration of the external force field in the form of polar water (Fuzzy Oil Drop) and a version modified by the presence of the hydrophobic compounds, FOD-M (FOD-Modified) reveals that the protein folding process is environmentally dependent. An analysis of selected models from the CASP competitions indicates the need for structure prediction as dependent on the consideration of the protein folding environment.ConclusionsThe conditions governed by the environment direct the protein folding process occurring in a certain environment. Therefore, the variation of the external force field should be taken into account in the models used in protein structure prediction.

Real-time detection of wheel-rail conditions in a monorail vehicle-track nonlinear system based on vehicle vibration signals

This paper aims to propose a real-time detection method of wheel-rail conditions in a monorail vehicle-track nonlinear system based on vehicle vibration signals. Monorail rubber tires exhibit strong nonlinearity and three-dimensional elasticity, resulting in highly coupled vertical and lateral vibrations. As a result, traditional methods applied in railway system such as transfer function analysis are not suitable for back-calculation of road irregularities for this nonlinear system. Iterative simulation with numerous parameters is time-consuming, thus this paper proposes a parallel simulation approach fused with the genetic algorithm to shorten the calculation time and facilitate big data processing. The multi-rigid body model’s simulation result can closely match the test data by intelligently modifying the vehicle parameters. This method overcomes the transfer function’s limitations in nonlinear systems and the significant errors introduced by the simplified mathematical derivation method. It also overcomes the shortcoming of significant errors in the mathematical derivation method and disperses errors caused by simplifying the multi-rigid body dynamics and ensures calculation accuracy. Additionally, this paper highlights the application of artificial intelligence techniques in intelligent wheel eccentricity detection. It improves the traditional algorithm to shorten the calculation time and benefit big data processing.

Examining energy use efficiency and conducting an environmental life cycle assessment through the application of artificial intelligence: A case study on the production of cumin and fennel

This study aims to examine the energy use efficiency and conduct an environmental life cycle assessment (LCA) of the production of cumin and fennel using artificial intelligence (AI). The energy consumption for fennel and cumin is 34814.81 MJ ha−1 and 26214.17 MJ ha−1, respectively. The energy ratio for cumin is 0.66, indicating a substantial energy consumption per unit of output energy. Specifically, it takes 22.36 MJ of energy to produce 1 kg of cumin. Both crops exhibit a negative net energy balance. The results of the LCA analysis revealed three key findings when comparing the environmental emissions of cumin and fennel. Firstly, in the context of cumin cultivation, the environmental emissions related to human health were found to be 0.15 DALY. Secondly, there were no significant variations in the environmental emissions of medicinal plants when considering ecosystems. Finally, fennel production exhibited 237.77 USD2013 environmental publications in the resource category. It is worth noting that a substantial portion of the emissions in all three categories of medicinal plants can be attributed to direct emissions from crop cultivation. Additionally, nitrogen plays a significant role in emissions after the direct emissions occur. In comparison to fennel, cultivating cumin is recommended as it helps decrease environmental emissions and minimizes harm to water, air, and soil. The ANN model achieved a determination coefficient greater than 0.93 for four factors related to cumin cultivation in test mode. Similarly, the determination coefficient for output energy in fennel cultivation was found to be 0.852. However, the results indicate that the ANFIS test model outperformed the ANN model in terms of performance and accuracy. The ANFIS model exhibited an average detection coefficient of 0.95 for cumin cultivation and 0.93 for fennel cultivation, highlighting its superior performance compared to the ANN model. The application of AI techniques provides valuable insights into the production of cumin and fennel, facilitating informed decision-making towards more sustainable practices in the spice industry.

Application of AI-Based Techniques on Moody’s Diagram for Predicting Friction Factor in Pipe Flow

The friction factor is a widely used parameter in characterizing flow resistance in pipes and open channels. Recently, the application of machine learning and artificial intelligence (AI) has found several applications in water resource engineering. With this in view, the application of artificial intelligence techniques on Moody’s diagram for predicting the friction factor in pipe flow for both transition and turbulent flow regions has been considered in the present study. Various AI methods, like Random Forest (RF), Random Tree (RT), Support Vector Machine (SVM), M5 tree (M5), M5Rules, and REPTree models, are applied to predict the friction factor. While performing the statistical analysis (root-mean-square error (RMSE), mean absolute error (MAE), squared correlation coefficient (R2), and Nash–Sutcliffe efficiency (NSE)), it was revealed that the predictions made by the Random Forest model were the most reliable when compared to other AI tools. The main objective of this study was to highlight the limitations of artificial intelligence (AI) techniques when attempting to effectively capture the characteristics and patterns of the friction curve in certain regions of turbulent flow. To further substantiate this behavior, the conventional algebraic equation was used as a benchmark to test how well the current AI tools work. The friction factor estimates using the algebraic equation were found to be even more accurate than the Random Forest model, within a relative error of ≤±1%, in those regions where the AI models failed to capture the nature and variation in the friction factor.

Artificial intelligence in the diagnosis of thyroid cancer: Recent advances and future directions

The diagnosis and management of thyroid cancer is fraught with challenges despite the advent of innovative diagnostic, surgical, and chemotherapeutic modalities. Challenges like inaccuracy in prognostication, uncertainty in cytopathological diagnosis, trouble in differentiating follicular neoplasms, intra-observer and inter-observer variability on ultrasound imaging preclude personalised treatment in thyroid cancer. Artificial intelligence (AI) is bringing a paradigm shift to the healthcare, powered by quick advancement of the analytic techniques. Several recent studies have shown remarkable progress in thyroid cancer diagnostics based on AI-assisted algorithms. Application of AI techniques in thyroid ultrasonography and cytopathology have shown remarkable impro-vement in sensitivity and specificity over the traditional diagnostic modalities. AI has also been explored in the development of treatment algorithms for indeterminate nodules and for prognostication in the patients with thyroid cancer. The benefits of high repeatability and straightforward implementation of AI in the management of thyroid cancer suggest that it holds promise for clinical application. Limited clinical experience and lack of prospective validation studies remain the biggest drawbacks. Developing verified and trustworthy algorithms after extensive testing and validation using prospective, multi-centre trials is crucial for the future use of AI in the pipeline of precision medicine in the management of thyroid cancer.

Analysis Indonesia’s Export Value Forecasting to G20 Countries Using Long Short-Term Memory Neural Network Method

Export is one of the most important ways for the country to generate income, which can have an impact on the country's economic stability. This research aims to forecast the value of Indonesian exports to G20 member countries. The Long Short-Term Memory method is used in this research to examine historical data on Indonesian exports from the previous 16 years. Experimental results show that the LSTM Neural Network method has the ability to predict the value of Indonesian exports to G20 member countries with a sufficient level of accuracy. The predictions generated by the model provide insight into trends and fluctuations in the value of exports in the future. The results of this study provide insight into the potential application of artificial intelligence techniques in economic and trade analysis. The results demonstrate that the LSTM model is capable of producing relatively accurate predictions, with an average score of Root Mean Square Error (RMSE) on training data is 0.10 and on testing data is 0.13, as well as graphs of prediction results demonstrating that the LSTM model can capture patterns and trends from Indonesia's export data to G20 countries. According to the prediction results, the highest export value to China is expected to be $6,100,000 in the 200th month (or in the year 2039), while the lowest export value to Mexico is expected to be $27,000 in the 135th month (or in the year 2034).

Difficulties and possible mistakes in applying AI techniques in industrial control systems by new generation of engineers.

Nowadays, Artificial Intelligence (AI) is making its entrance in absolutely all fields and only the economic level of the promoting states is responsible for the percentage of the insertion of this new technology. The economic aspect is not directly connected with the AI because there is already a wide range of open-source software’s and AI analysis models but is strictly connected with the digitalization level of the country and/or company that wants to follow this trend. This article proposes a subjective analysis of some situation that young engineers can face by early use of dedicated software’s for data analysis rise from different systems.

Artificial intelligence in estimating fractional flow reserve: a systematic literature review of techniques

BackgroundFractional Flow Reserve (FFR) is the gold standard for the functional evaluation of coronary arteries, which is effective in selecting patients for revascularization, avoiding unnecessary procedures, and reducing treatment costs. However, its use is limited due to invasiveness, high cost, and complexity. Therefore, the non-invasive estimation of FFR using artificial intelligence (AI) methods is crucial.ObjectiveThis study aimed to identify the AI techniques used for FFR estimation and to explore the features of the studies that applied AI techniques in FFR estimation.MethodsThe present systematic review was conducted by searching five databases, PubMed, Scopus, Web of Science, IEEE, and Science Direct, based on the search strategy of each database.ResultsFive hundred seventy-three articles were extracted, and by applying the inclusion and exclusion criteria, twenty-five were finally selected for review. The findings revealed that AI methods, including Machine Learning (ML) and Deep Learning (DL), have been used to estimate the FFR.ConclusionThis study shows that AI methods can be used non-invasively to estimate FFR, which can help physicians diagnose and treat coronary artery occlusion and provide significant clinical performance for patients.

Risk prediction model for cannabis use with artificial intelligence approach

ABSTRACT Background Identifying the most important predictors of substance use is crucial for developing effective prevention policies. Traditional statistical methods have some limitations in this regard. To address these limitations, the researchers utilized artificial intelligence (AI) methods to identify the top 10 most important predictors of cannabis use in Finland. Objective The objective of this study was to apply AI techniques to identify the key predictors of cannabis use in Finland. Specifically, the researchers aimed to determine the top 10 most important features related to cannabis use from a dataset consisting of 3229 observations and 313 questionnaire items, with 48 selected for preprocessing. Methods The researchers employed the recursive feature elimination (RFE) method as part of their AI analysis. This technique was used on 60 processed variables, following the application of missing data imputation, resampling, and scaling techniques. The RFE method allowed the researchers to narrow down the 60 variables to the top 10 most important features associated with cannabis use. Results The AI models developed using the selected features were able to predict cannabis use with a remarkable accuracy of 96% for the previous 12 months. The results of the study revealed that the social settings of individuals played the most significant role in predicting cannabis use in the context of Finland. Conclusions In conclusion, this study demonstrated the effectiveness of AI-based approaches in identifying the most critical predictors of cannabis use in Finland. The research highlighted that social settings had the highest impact on cannabis use in this setting. Moreover, the study showcased the potential of AI methods not only for identifying key risk indicators among various factors but also for optimizing the utilization of limited public resources when devising prevention strategies. These findings can be valuable for shaping targeted and efficient prevention policies to address cannabis use in Finland.