Application Of Artificial Intelligence Techniques Research Articles

Application of artificial intelligence techniques for predicting the flyrock, Sungun mine, Iran

Application of artificial intelligence techniques for predicting the flyrock, Sungun mine, Iran

بناء مقياس لإدارة المنظمات الرياضية عالية الأداء باستخدام تقنيات الذكاء الاصطناعي

تناولت الدراسة الحالية واحدة من أهم المشاكل التي تعاني منها المنظمات الرياضية عالية الاداء وهي عدم الاستفادة القصوى من تطبيقات تقنيات الذكاء الاصطناعي، فلقد تمثلت مشكلة البحث الأساسية في معرفة أثر تطبيق تقنيات الذكاء الاصطناعي في ادارة المنظمات الرياضية عالية الأداء وما مدى مساهمة تطبيقات تقنيات الذكاء الاصطناعي في تشخيص ومعالجة المعوقات الادارية والتقنية في اقسام النشاط الرياضي والمدرسي في المديريات العامة للتربية في محافظة بغداد للتوجه نحو الاداء العالي وفي هذا السياق، فلقد تمثل هدف الدراسة الرئيس في بناء مقياس للاطار المفاهيمي لتطبيقات تقنيات الذكاء الاصطناعي في المنظمات الرياضية عالية الاداء -اقسام النشاط الرياضي والمدرسي في المديريات العامة للتربية في محافظة بغداد أنموذجاً. ولغرض تحقيق أهداف الدراسة اتبعت الباحثتان المنهج الوصفي بالأسلوب التحليلي لملامته لطبيعة المشكلة كذلك القيام بدراسة استطلاعيه غير مقننه في اقسام النشاط الرياضي والمدرسي وكانت الاستبانة هي الأداة المستخدمة في ذلك، وتوصلت الدراسة إلى بناء مقياس رصين لتطبيقات تقنيات الذكاء الاصطناعي في المنظمات الرياضية عالية الاداء ومكون من خمس محاور وهي النظم الخبيرة والواقع الافتراضي والوكلاء الاذكياء وتقنيات المعلومات واتمتة العمليات وقد جاء اختيار حجم العينة مساويا للمجتمع لتلام طبيعة البحث، وقد تم اثبات صدق وموثوقية هذا المقياس من خلال الوسائل الإحصائية كمعامل الفا كرو نباخ بمقدار 89,7، لقد احتوت محاور المقياس الخمسة على خمسة وعشرون عبارة، ولقد تراوحت الأهمية النسبية لهذه العبارات ما بين (63-84)%، وكانت اعلى قيمه للأهمية النسبية تبلغ 84% لعباره الثانية من محور النظم الخبيرة وهي (تعمل النظم الخبيرة كخبير استشاري للإدارة العليا في اقسام النشاط الرياضي والمدرسي لتسهم في اتخاذ القرارات الصحيحة)، بينما كانت اقل قيمة للأهمية النسبية لعبارة الأولى من محور الواقع الافتراضي وهي (استخدام الواقع الافتراضي في اقسام النشاط الرياضي والمدرسي يسهم في اكتساب مهارات متنوعة للموظفين ويعمل على تطوير الاداء الاداري والتقني)

Economic Sustainability of Building and Construction Projects Based on Artificial Intelligence Techniques

Artificial intelligence (AI) has been shown to be an effective replacement for conventional modelling approaches. AI is a subfield of computer science that develops software and tools that mimic human intelligence. AI offers advantages over traditional methods for handling ambiguous circumstances. In addition, AI-based solutions can successfully replace testing when identifying engineering design parameters, saving a lot of time and resources. AI can also increase computer efficiency, decrease mistake rates, and speed up decision-making. Recently, there has been a lot of interest in machine learning (ML), a new area of cutting-edge intelligent methods for use in structural engineering. Consequently, this work presents a study on the economic management of building and construction projects based on creating ML techniques. It begins with an overview of the value of applying AI techniques in building and construction industry. The analysis of the prediction of reinforced concrete’s compressive strength while taking cost into account is then done using empirical data based on a case study. Accordingly, the findings showed that the support vector regression (SVR) and k-Nearest Neighbour (KNN) intelligence techniques are helpful in the construction business for controlling the strength of concrete based on sustainable cost reduction.

Rise of Artificial Intelligence in Business and Industry

The ongoing development of business and the most recent advances in artificial intelligence (AI) allow for the many business practices to be improved by the capacity to establish new forms of collaboration, which is a significant competitive advantage. This rapidly developing technology enables to offer brand services and even some new forms of business interactions with consumers and personnel. The digitalization of AI concurrently emphasized for businesses that they need concentrate on their present strategies while also routinely and early pursuing new chances in the market. Not only in business but also in different industry sectors, Al techniques are being used and revolutionized different industry sectors. This review focuses on the application of AI techniques in business and different industries.

Seismic Risk Regularization for Urban Changes Due to Earthquakes: A Case of Study of the 2023 Turkey Earthquake Sequence

Damage identification soon after a large-magnitude earthquake is a major problem for early disaster response activities. The faster the damaged areas are identified, the higher the survival chances of inhabitants. Current methods for damage identification are based on the application of artificial intelligence techniques using remote sensing data. Such methods require a large amount of high-quality labeled data for calibration and/or fine-tuning processes, which are expensive in the aftermath of large-scale disasters. In this paper, we propose a novel semi-supervised classification approach for identifying urban changes induced by an earthquake between images recorded at different times. We integrate information from a small set of labeled data with information from ground motion and fragility functions computed on large unlabeled data. A relevant consideration is that ground motion and fragility functions can be computed in real time. The urban changes induced by the 2023 Turkey earthquake sequence are reported as an evaluation of the proposed method. The method was applied to the interferometric coherence computed from C-band synthetic aperture radar images from Sentinel-1. We use only 39 samples labeled as changed and 9000 unlabeled samples. The results show that our method is able to identify changes between images associated with the effects of an earthquake with an accuracy of about 81%. We conclude that the proposed method can rapidly identify affected areas in the aftermath of a large-magnitude earthquake.

Matlab-Based Design Consideration of Series ZVS Single-Ended Resonant DC-DC Converter

The paper presents a model-based design consideration of a series single-ended transistor resonant DC-DC converter with zero voltage switching (ZVS). A characteristic of this converter is that it is highly efficient due to the resonant nature of electromagnetic processes in the power circuit and operation with soft commutations. The manuscript proposes that the determination of some of the circuit elements of the device be carried out with optimization procedures based on the application of artificial intelligence techniques. For this purpose, an objective function is used with additional constraints, such as equalities and inequalities, for both the optimization parameters and the state variables. The use of the proposed method is justified in cases where there is no methodology for the design of the specific power electronic device or there is, but it is too complicated to apply. This is usually due to the increasing complexity of power circuits and their possible modes of operation and the inevitable assumptions and limitations in the analysis and the relevant methodologies based on that analysis. In this way, a natural combination, complement and development of classical design methods with innovative ones based on the application of artificial intelligence techniques is carried out.

Application of artificial intelligence techniques for modeling, optimizing, and controlling desalination systems powered by renewable energy resources

Utilizing various renewable energy resources (RERs) for powering desalination plants is an encouraging choice, specifically in arid and remote areas where conventional resources are unavailable or costly. Various RERs were used to derive desalination systems such as solar, wind, geothermal, etc. Reverse osmosis, multi-stage flash, and multi-effect distillation are the most used desalination technologies in connection with various RES. Some RES/desalination technologies combinations could be cost-efficient and reliable if appropriately designed. However, the unpredictable load demands and the intermittency nature of the RERs make designing such systems difficult. Various integrated scenarios are proposed in such systems, such as PV combinations with wind, battery energy storage systems, fuel cells, electrolyzers, etc. As a result, determining the optimum configuration using traditional methods is difficult. Implementing intelligent techniques that can integrate all working and design parameters involved in the various possible scenarios is critical for finding the optimal operating conditions. This work discusses and summarizes various artificial intelligence (AI) techniques to enhance RES-powered desalination systems. The implementation of various forecasting models, optimization algorithms, and control systems in designing and operating RER-powered desalination systems was analyzed. Finally, future research recommendations for further improving the current technology were included.

Application of artificial intelligence technique in optimization and prediction of the stability of the walls against wind loads in building design

The present study is done to perform the optimal design of the structural components of the buildings against the unwanted wind load exerted on their outer face. To this end, the case study of the research is the outer wall made of a one-floor building modeled as a rectangular plate with only one free edge and three clamped ones. It is assumed that the wall is a sandwich plate whose core is made of auxetic material and dace-sheets are reinforced with nanoparticles of graphene platelets (GPL). Differential equations governing the system’s motion are obtained within the background of the plate’s shear-deformation theories. The stability analysis of the sandwich wall is performed based on the application of artificial intelligence (AI) methods optimized with an innovative optimization approach to gain a high level of accuracy. To determine the stability information of the system at the train points, the differential quadrature approach (DQA) is applied as the solver of differential equations of motion. The accuracy of the methods used in this paper is examined and verified by comparing the results with those acquired in the articles published previously. The results obtained in this study provide very useful information about the stability response of lightweight building components through AI-based solutions.

Development and evaluation of Artificial Intelligence techniques for IoT data quality assessment and curation

Nowadays, data is becoming the new fuel for economic wealth and creation of novel and profitable business models. Multitude of technologies are contributing to an abundance of information sources which are already the baseline for multi-millionaire services and applications. Internet of Things (IoT), is probably the most representative one. However, for an economy of data to actually flourish there are still several critical challenges that have to be overcome. Among them, data quality can become an issue when data come from heterogeneous sources or have different formats, standards and scale. Improving data quality is of utmost importance for any domain since data are the basis for any decision-making system and decisions will not be accurate if they are based on inadequate low-quality data. In this paper we are presenting a solution for assessing several quality dimensions of IoT data streams as they are generated. Additionally, the solution described in the paper actually improves the quality of data streams by curating them through the application of Artificial Intelligence techniques. The approach followed in our work has been to append data quality information as metadata linked to each individual piece of curated data. We have leveraged linked-data principles and integrated the developed AI-based IoT data curation mechanisms within a Data Enrichment Toolchain (DET) that employs the NGSI-LD standard to harmonize and enrich heterogeneous data sources. Furthermore, we have evaluated our design under experimental research conditions, achieving a robust compromise between functionality and overhead. Besides, it demonstrates a stable and scalable performance.

Special Issue on Application of Artificial Intelligence Techniques in Production Engineering

Artificial intelligence (AI) techniques have been behind disruptive innovations in every industry. Based on AI techniques, large amounts of data can be converted into actionable insights and predictions. Manufacturers have frequently faced different kinds of challenges, such as unexpected machinery failures or defective product deliveries. Still, the adoption of AI techniques is expected to improve operational efficiency, enable the launch of new products, customize product designs, and plan future financial actions. Recently, manufacturers have been using AI techniques to improve the quality of their products, achieve greater speed and visibility across supply chains, and optimize inventory management. Given that the attention and interest in AI techniques has been growing rapidly, it is time that the current state of the art of their practical applications be presented. The main aim of this special issue is to bring together the latest AI research and practical case studies of AI techniques in production engineering. This special issue features 10 papers related to not only operation automation but also sophisticated skill transfer in manufacturers. Their subjects cover various advancements, such as failure diagnosis, product estimation, process planning, operation planning, and workpiece fixturing in the area of machining. Moreover, the authors boldly strive to apply AI technologies even to complex systems in manufacturing fields such as laser-assisted incremental forming, injection molded direct joining, and parts assembling. We thank the authors for their interesting papers submitted for this special issue, and we are sure that both general readers and specialists will find the information the authors provide both interesting and informative. Moreover, we deeply appreciate the reviewers for their incisive efforts. Without these contributions, this special issue would not have been possible. We truly hope that this special issue triggers further research on AI techniques in production engineering.

Performance Evaluation of an Improved ANFIS Approach Using Different Algorithms to Predict the Bonding Strength of Glulam Adhered by Modified Soy Protein–MUF Resin Adhesive

Despite studies on the potential replacement of synthetic resins by bio-based adhesives such as proteins in recent years, there is still no reliable method for estimating the strength of wood products made using the combined parameters in the literature. This limitation is due to the nonlinear relationship between strength and the combined components. In the present research, the application of artificial intelligence techniques was studied to predict the bonding strength of glulam adhered by protein containing different ratios of MUF (melamine–urea–formaldehyde) resin with different F-to-U/M molar ratios at different press temperatures. For this purpose, the ANFIS artificial intelligence model was used as basic mode or combined with ant colony optimization (ACOR), particle swarm optimization (PSO), differential evaluation (DE) and genetic algorithms (GA) to develop an optimal trained model to predict the bonding strength of glulam based on experimental results. Comparison of the obtained results with the experimental results showed the ability of the above methods to estimate the bonding strength of glulam in a reliable manner. Although the basic ANFIS alone and in combination with other algorithms was not able to achieve an ideal performance prediction to estimate bonding strength, the combination of GA and ANFIS offered an excellent ability compared to the combination of other algorithms combined with ANFIS. Hence, the developed ANFIS-GA model is introduced as the best prediction technique to solve bonding strength problems of laminated products. In addition, using the developed optimal model, a precise attempt was made to show the nature of the parameters used to produce glulam and determine the optimum limit.

Review of artificial intelligence techniques in green/smart buildings

The numerous benefits offered by green and/or intelligent buildings have led to an increase in their construction. In parallel, various artificial intelligence (AI) techniques have been recognized as relevant for these types of buildings to rationalize, optimize and innovate their operation. The amalgamation of green and smart buildings with AI techniques holds remarkable potential to ensure greater structural stability and minimize negative ecological impacts on the environment and society. Although research on the application of AI of green and smart buildings exists, our understanding of the utility and benefits of its integration remain fragmented. To address this gap, this investigation aims to examine the relevance and benefits of the convergence of AI techniques in green and smart buildings. Using Web of Science and Scopus databases, this study used bibliometric analysis to present an exhaustive review of the field’ publication productivity, most influential articles and most relevant actors (i.e., authors, research centers, and countries). Additionally, using content analysis, this investigation sheds light on the field’ cognitive structure, which is based on five thematic clusters. The study concludes with a temporal keyword analysis focusing on the Triple Bottom Line benefits obtained through the application of AI techniques in green or smart buildings. The most noteworthy contribution of this study is that it is the first study that performs a bibliometric analysis on Artificial Intelligence Techniques in green and smart Buildings, which is an important endeavor to help interested scholars and professionals to gain a scientific overview of existing research in the field.

B-SMART: A reference architecture for artificially intelligent autonomic smart buildings

The pervasive application of artificial intelligence and machine learning algorithms is transforming many industries and aspects of the human experience. One very important industry trend is the move to convert existing human dwellings to smart buildings, and to create new smart buildings. Smart buildings aim to mitigate climate change by reducing energy consumption and associated carbon emissions. To accomplish this, they leverage artificial intelligence, big data, and machine learning algorithms to learn and optimize system performance. These fields of research are currently very rapidly evolving and advancing, but there has been very little guidance to help engineers and architects working on smart buildings apply artificial intelligence algorithms and technologies in a systematic and effective manner. In this paper we present B-SMART: the first reference architecture for autonomic smart buildings. B-SMART facilitates the application of artificial intelligence techniques and technologies to smart buildings by decoupling conceptually distinct layers of functionality and organizing them into an autonomic control loop. We also present a case study illustrating how B-SMART can be applied to accelerate the introduction of artificial intelligence into an existing smart building.

Application of artificial intelligence techniques in the intensive care unit

Intensive care unit deals with data that are dynamic in nature like real time measurement of health condition to laboratory test data that are continuously changes accordingly with time. Artificial intelligence (AI’s) potential ability to perform complex pattern analyses using large volumes of data. Generated pattern discovers the new symptoms of the disease in the Intensive care units (ICUs), helps the doctors to prescribe the new drug discovery which is helpful to intelligent use. Currently research work has been focused in the ICU making more efficient clinical workflow by generation of high-risk patterns from improved high volumes of data. Emerging area of AI in the ICU includes mortality prediction, uses of powerful sensors, new drug discovery, prediction of length of stay and legal role in uses of drugs for severity of disease. This review focuses latest application of AI drugs and other relevant issues for the ICU.

Application of artificial intelligence techniques in modeling attenuation behavior of ionization radiation: a review

Application of artificial intelligence techniques in modeling attenuation behavior of ionization radiation: a review

Preventing Keratoconus through Eye Rubbing Activity Detection: A Machine Learning Approach

Keratoconus is a non-inflammatory disease of the eyes diagnosed in more than 1/2000 people, making it significantly common. Among others, eye rubbing has been identified as a risk factor for the development of keratoconus. The severity of the disease strongly depends on the frequency and force of eye rubbing. Vast research efforts have focused on diagnosing keratoconus through the application of artificial intelligence techniques over optical coherence tomography images and corneal measurements. However, to the best of the authors’ knowledge, no studies have been conducted which provide an eye rubbing detection and alert mechanism for keratoconus prevention. This study intends to help close this research gap. An inertial measurement unit that is dedicated to collecting hand motion data and machine learning techniques are jointly employed for the early detection of potential problems and complications. Four conventional classification methods (support vector machines, decision trees, random forest, and XGBoost) were evaluated and compared. All methods attain high-quality accuracy results, with SVMs, RF, and XGBoost slightly outperforming DTs. As the results reveal, the performance of all methods is remarkable, allowing the integration of such a solution in wearable devices such as smartwatches to be considered for the early detection of eye rubbing and keratoconus prevention.

Protection scheme for multi-terminal HVDC system with superconducting cables based on artificial intelligence algorithms

This paper presents the development of a novel data-driven fault detection and classification scheme for DC faults in multi-terminal HVDC transmission system which incorporates superconducting cables and modular multi-level converters. As the deployment of superconducting cables for bulk power transmission from remote renewable generation is progressively increasing in the future energy grids, many fault-related challenges have been raised (i.e., fault detection, protection sensitivity/stability). In this context, the applications of Artificial Intelligence techniques have started to be considered as a powerful tool for the development of robust fault management solutions. The proposed artificial intelligence-based method utilizes local current and voltage measurements to detect and classify all types of faults on the DC cables and DC buses, without the requirement of measurements exchange among different DC substations. The performance of the proposed scheme has been assessed through detailed transient simulation analysis and the results confirmed its effectiveness against a wide range of fault conditions (i.e., various fault types, fault locations and fault resistances). Furthermore, the feasibility of the developed scheme for real-time implementation has been validated using real-time software in the loop testing. The results revealed that the proposed algorithm can correctly, and within a very short period of time (i.e. less than 2 ms) detect and classify the faults within the protected zone and concurrently remain stable during external faults. Additionally, the generalization capability of the algorithm has been verified against influencing factors such as the addition of noise, highlighting the robustness of the presented scheme.

Sugarcane Disease Detection Using CNN-Deep Learning Method: An Indian Perspective

Agriculture produce especially sugarcane crop is no exception to diseases as compared to the other crops. Sugarcane diseases can be mitigated more successfully if they are identified properly and in the early stages of the diseases. Disease prone sugarcane crop directly affects the production quality and quantity. Sugarcane infections are a cause of worry for the farmers because they can wipe out the entire crop field, causing financial loss. Researchers are working on applying Artificial Intelligence (AI) techniques, like Machine Learning (ML) and Deep Learning (DL), to analyse the agricultural data (yield prediction, selling price forecasting, climate, and soil quality etc.) and prevent crop damage due to various reasons, diseases being one of them. Sugarcane producing farmers are required to be enriched with real-time data analysis using various computational techniques along with managing huge datasets. Deep neural network which includes Convolutional Neural Network (CNN) is a modern technique for agricultural disease detection. Hence, this paper presents the feasibility study and the effectiveness of DL based CNN algorithm in the disease detection of crops with special reference to selective four diseases of sugarcane crop in India. The study was prompted by the rapid evolution of sugarcane disease classes and farmers' lack of disease diagnostic and recognition skills. To solve this challenge, deep learning and computer vision are adopted. By categorizing sugarcane images into two groups: healthy and unhealthy/diseased (with disease type), the trained model is able to fulfill its goal. Using a simple CNN with four discrete classes, the analysis shows an accuracy of 98.69% for sugarcane disease detection. Further to guide the farmers, a web-based application is developed for sugarcane crop disease detection and implemented to fetch and monitor the data about the diseases. The paper also puts forth a few future research areas such as: (i) the user can enter the feedback which can be tuned with the model for accurately predicting the sugarcane crop diseases and dynamic database update; (ii) the effect of disease detection can be combined with agricultural productivity enhancement and price forecasting using AI tools and techniques, thereby aiding the farmers in effective decision making.

A Systematic Review of Application Progress on Machine Learning-Based Natural Language Processing in Breast Cancer over the Past 5 Years.

Artificial intelligence (AI) has been steadily developing in the medical field in the past few years, and AI-based applications have advanced cancer diagnosis. Breast cancer has a massive amount of data in oncology. There has been a high level of research enthusiasm to apply AI techniques to assist in breast cancer diagnosis and improve doctors' efficiency. However, the wise utilization of tedious breast cancer-related medical care is still challenging. Over the past few years, AI-based NLP applications have been increasingly proposed in breast cancer. In this systematic review, we conduct the review using preferred reporting items for systematic reviews and meta-analyses (PRISMA) and investigate the recent five years of literature in natural language processing (NLP)-based AI applications. This systematic review aims to uncover the recent trends in this area, close the research gap, and help doctors better understand the NLP application pipeline. We first conduct an initial literature search of 202 publications from Scopus, Web of Science, PubMed, Google Scholar, and the Association for Computational Linguistics (ACL) Anthology. Then, we screen the literature based on inclusion and exclusion criteria. Next, we categorize and analyze the advantages and disadvantages of the different machine learning models. We also discuss the current challenges, such as the lack of a public dataset. Furthermore, we suggest some promising future directions, including semi-supervised learning, active learning, and transfer learning.

The application of artificial intelligence techniques in credit card fraud detection: a quantitative study

Credit card fraud is a major problem that has caused several challenges for practitioners in the accounting and finance industry due to a large number of daily transactions as well as the difficulties encountered in identifying fraudulent transactions. The purpose of this study is to investigate the application of artificial intelligence techniques as a fraud detection mechanism that can effectively and efficiently detect credit card fraud and identify fraudulent financial transactions. The data was acquired from 100 respondents across the accounting and finance industry and analysed using SPSS. Researcher analysed the data using regression analysis, Pearson correlation coefficient, and reliability analysis. Findings revealed that the three artificial intelligence techniques machine learning, data mining, and fuzzy logic have a significant positive relationship with credit card fraud detection. However, fuzzy logic was discovered to be the least utilized by experts due to its low accuracy/precision in comparison with machine learning and data mining. Based on these findings, our study concludes that the application of artificial intelligence techniques provides experts with better accuracy and efficiency in detecting fraudulent transactions. Therefore, it is recommended that fraud examiners, auditors, accountants, bankers, and organizations should implement and apply artificial intelligence techniques in order to spot anomalies faster and identify fraudulent financial transactions effectively and efficiently.