Application Of Artificial Intelligence Techniques Research Articles

Artificial Intelligence Techniques for the Photovoltaic System: A Systematic Review and Analysis for Evaluation and Benchmarking

AbstractNovel algorithms and techniques are being developed for design, forecasting and maintenance in photovoltaic due to high computational costs and volume of data. Machine Learning, artificial intelligence techniques and algorithms provide automated, intelligent and history-based solutions for complex scenarios. This paper aims to identify through a systematic review and analysis the role of artificial intelligence algorithms in photovoltaic systems analysis and control. The main novelty of this work is the exploration of methodological insights in three different ways. The first approach is to investigate the applicability of artificial intelligence techniques in photovoltaic systems. The second approach is the computational study and analysis of data operations, failure predictors, maintenance assessment, safety response, photovoltaic installation issues, intelligent monitoring etc. All these factors are discussed along with the results after applying the artificial intelligence techniques on photovoltaic systems, exploring the challenges and limitations considering a wide variety of latest related manuscripts.

Modification Method of Expanded Polystyrene Granular Concrete Based on Computer Data Analysis

Expanded Polystyrene Granular concrete (EPS) is renowned for its exceptional lightweight and thermal insulation properties, yet it is hindered by its low strength. Despite the existence of numerous modification methods, there remains a dearth of quantification and summarization, particularly in the era of Artificial Intelligence (AI). Consequently, this study undertook the quantification of several conventional and novel modifications, uncovering intriguing challenges and discerning future trends in modification techniques. These methods encompass nanomaterials, phase change materials, ceramic concrete, and composite modifications, all aimed at bolstering mechanical properties and introducing novel functionalities to address diverse environmental, energy, and engineering requirements. Challenges encompass intricate processes, high costs, material compatibility issues, nascent processes, and application hurdles. Leveraging the advancements in simulation and AI technology, numerical simulations can meticulously replicate the microstructure of EPS granules and the physical attributes of concrete in numerical models, facilitating accurate prediction of material properties. The application of artificial intelligence techniques also includes utilizing big data analysis to optimize the proportions of various materials in EPS granular concrete, providing a valuable framework for the study of high-performance EPS granular concrete and significantly contributing to the enhancement of concrete performance.

Modeling net ecosystem exchange of CO2 with gated recurrent unit neural networks

The complex forcing mechanisms of biophysical drivers on ecosystem productivity, as well as some unknown relationships, stipulates the application of artificial intelligence techniques in modeling the ecosystem carbon cycle. In particular, various deep learning algorithms have been successfully applied to comprehend temporal relationships, though some issues regarding capturing temporal features for modeling CO2 fluxes across time scales remain unsolved. In this study, a gated recurrent unit (GRU) model was trained for simulating half-hourly net ecosystem exchange of CO2 (NEE) using 12-year continuous flux data (2009–2020) from seven experimental bioenergy crops in southwest Michigan, USA. The fields were historically managed either as corn-soybean rotation agricultural (AGR) or Conservation Reserve Program (CRP) lands and planted to no-till corn (AGR-C and CRP-C), restored-prairie (AGR-Pr and CPR-Pr), switchgrass (AGR-Sw and CRP-Sw) and smooth brome grass (CRP-Ref). We compared NEE simulations for the entire year, growing season, and non-growing season datasets, and analyzed relative importance of biophysical variables on variations of NEE. GRU performed well in simulating NEE at single site, with R2 of 0.89–0.93, 0.85–0.90, and 0.61–0.85 for the annual, growing season, and non-growing season datasets, respectively, which provided comparable accuracy and 6 % of run-time shorter than those of long short-term memory. R2 reached 0.70 and 0.80 across all sites and unique land-cover types. Radiation parameters contributed most to the variability of NEE. Contributions of individual variables appear more complex during the non-growing season and include incoming shortwave radiation, day of year, temperature, Monin-Obukhov stability, wind direction, and soil water content. The six most important forcing variables are consistent with our current understanding, although their ranked importance varied by ecosystem type and modeling scale. These results bring renewed insights in modeling observations at seasonal scales and provide guidance for variable selection to scale up measurements from one site to multiple land-cover types across the landscape.

Artificial Intelligence Islamic Architecture (AIIA): What Is Islamic Architecture in the Age of Artificial Intelligence?

Revisiting the long-debated question: “What is Islamic architecture?”, this research article aims to explore the identity of “Islamic architecture (IA)” in the context of artificial intelligence (AI) as well as the novel opportunities and cultural challenges associated with applying AI techniques, such as the machine learning of Midjourney in the context of IA. It investigates the impact factors of AI technologies on the understanding and interpretation of traditional Islamic architectural principles, especially architectural design processes. This article employs a quantitative research methodology, including the observation of works of artists and architectural designers appearing in the mass media in light of a literature review and critical analysis of scholarly debates on Islamic architecture, spanning from historical perspectives to contemporary discussions. The article argues for the emergence of a continuous paradigm shift from what is commonly known as “postmodern Islamic architecture” (PMIA) into “artificial intelligence Islamic architecture” (AIIA), as coined by the authors of this article. It identifies the following impact factors of AI on IA: (1) particular requirements and sensitivities, inaccuracies, and biases, (2) human touch, unique craftsmanship, and a deep understanding of cultural issues, (3) regional variation, (4) translation, (5) biases in sources, (6) previously used terms and expressions, and (7) intangible values. The significance of this research in digital heritage lies in the fact that there are no pre-existing theoretical publications on the topic of “Islamic architecture in the age of artificial intelligence”, although an extensive set of publications interpreting the question of the definition of Islamic architecture, in general, is found. This article is pivotal in analyzing this heritage-inspired design approach in light of former criticism of the definition of “Islamic architecture”, which could benefit both theorists and practitioners. This theoretical article is the first in a series of two sequential articles in the Buildings journal; the second (practical) article is an analytical evaluation of the Midjourney architectural virtual lab, defining major current limits in AI-generated representations of Islamic architectural heritage.

Application of artificial intelligence techniques in meat processing: A review

AbstractThe field of meat processing plays a critical role in the food industry and has seen increasing adoption of artificial intelligence (AI) technology with rapid technological advancements. AI technology has tremendous potential for enhancing production efficiency and product quality in meat processing. However, further research and exploration are necessary to tackle the challenges posed by the use of AI technology. This article details the implementation of AI technology in meat processing, focusing on carcass classification, automation and intelligent processing, and meat‐quality detection. We aim to provide inspiration to researchers and industry professionals and promote the advancement of AI technology in the meat processing sector.Practical applicationsOur review article showcases the potential industrial applications of artificial intelligence (AI) techniques in the meat processing industry. AI technology can greatly improve production efficiency and product quality in meat processing. By implementing AI algorithms, meat processors can accurately classify carcasses, automate various processing tasks, and detect meat quality with higher accuracy. These advancements can lead to increased profitability and improved food safety in the industry. We hope to provide valuable insights for researchers and industry professionals, encouraging them to further explore and adopt AI technology in the meat processing sector.

Automated Intelligent Platforms for High-Throughput Chemical Synthesis

Automation and high-throughput technique provide a solid technical foundation for realizing the deep fusion of artificial intelligence and chemistry as well as the full utilization of their advantages. In recent years, with the unique advantages of low consumption, low risk, high efficiency, high reproducibility, high flexibility and good versatility, intelligent automated platforms for high-throughput chemical synthesis aroused widespread concerns of synthetic chemists. In this review, the automated high-throughput chemical synthesis, automated high-throughput sample treatment and characterization technique, as well as the application of artificial intelligence technique in chemical synthesis are introduced. The characteristics of the systems and platforms based on these techniques, including the iChemFoundry platform developed in the ZJU-Hangzhou Global Scientific and Technological Innovation Center, are introduced. The intelligent automated platforms for high-throughput chemical synthesis will reshape the thinking mode of traditional disciplines, promote the innovation of disruptive techniques, redefine the rate of chemical synthesis, and innovate the way of material manufacturing.

Applications of Artificial Intelligence Techniques in Healthcare and Medicine

Abstract—Artificial intelligence (AI) is swiftly coming into fitness care and serving foremost roles, from automating drudgery and ordinary responsibilities in scientific exercise to dealing with sufferers and scientific resources. As builders create AI structures to tackle those responsibilities, numerous dangers and demanding situations emerge, along with the hazard of accidents to sufferers from AI gadget errors, the hazard to affected person privateness of information acquisition and AI inference, and more. Potential answers are complicated however contain funding in infrastructure for high-quality, consultant information; collaborative oversight via way of means of each the Food and Drug Administration and different fitness-care actors; and adjustments to scientific schooling with a purpose to put together companies for moving roles in an evolving gadget. Keywords — Dermatology, Radiology, Screening, Psychiatry, Primary care, Disease diagnosis, Telemedicine, Electronic health records.

The application of AI techniques in requirements classification: a systematic mapping

Requirement Analysis is the essential sub-field of requirements engineering (RE). From the last decade, numerous automatic techniques are widely exploited in requirements analysis. In this context, requirements identification and classification is challenging for RE community, especially in context of large corpus and app review. As a consequence, several Artificial Intelligence (AI) techniques such as Machine learning (ML), Deep learning (DL) and transfer learning (TL)) have been proposed to reduce the manual efforts of requirement engineer. Although, these approaches reported promising results than traditional automated techniques, but the knowledge of their applicability in real-life and actual use of these approaches is yet incomplete. The main objective of this paper is to systematically investigate and better understand the role of Artificial Intelligence (AI) techniques in identification and classification of software requirements. This study conducted a systematic literature review (SLR) and collect the primary studies on the use of AI techniques in requirements classification. (1) this study found that 60 studies are published that adopted automated techniques in requirements classification. The reported results indicate that transfer learning based approaches extensively used in classification and yielding most accurate results and outperforms the other ML and DL techniques. (2) The data extraction process of SLR indicates that Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are widely used in selected studies. (3) Precision and Recall are the commonly used metrics for evaluating the performance of automated techniques. This paper revealed that while these AI approaches reported promising results in classification. The applicability of these existing techniques in complex and real-world settings has not been reported yet. This SLR calls for the urge for the close alliance between RE and AI techniques to handle the open issues confronted in the development of some real-world automated system.

Perspectives on Preparedness for Chemical, Biological, Radiological, and Nuclear Threats in the Middle East and North Africa Region: Application of Artificial Intelligence Techniques

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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

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

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.

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.