Use Of Artificial Intelligence Techniques Research Articles

Intelligent Adaptation Mechanism of Full-Order Luenberger Observer based on Fuzzy Logic Applied to Direct Control by Flux Orientation without Speed Sensor for Doubly Fed Induction Motor

The present work focused on the control by flow orientation without mechanical sensors on the doubly fed induction motor by the use of artificial intelligence techniques. Our main contribution lies in the development of control methodologies to improve control by flux orientation, for this, we acted on the type and control of inverters by the use of multilevel inverters and then on the observation technique of rotor speed based on the high gain Luenberger observer and improvements to the speed adaptation mechanism using fuzzy logic. Simulation tests of the proposed improvement approach were carried out at the end of this work to verify the behavior of this system in the face of different types of training.

Reshaping 3PL Operations: Machine Learning Approaches to Mitigate and Manage Damage Parameters

In the third-party logistics (3PL) environment, it is very important to reduce damage parameters, increase operational efficiency and reduce costs. This study aims to develop strategies for reshaping 3P operations by analyzing the parameters involved in damage control with machine learning. The logistics sector is gradually growing in the world and the potential of the sector is better understood over time. Damage to products in the logistics sector, especially during transportation and storage, not only causes financial losses but also affects customer productivity and operational efficiency. With the use of artificial intelligence techniques, it is possible to determine consumer expectations, predict damage losses, and develop innovative strategies by applying machine learning algorithms. At the same time, options such as driverless vehicles, robots used in storage and shelves, and the easy use of big data within the system, which have emerged with artificial intelligence, minimize errors in the logistics sector. Thanks to the use of artificial intelligence in the logistics sector, businesses are more efficient. This study includes an estimation study in the field of error parameters for the logistics service sector with machine learning methods. In the application, real data of a 3PL company for the last 5 years is used. For the success of 3PL companies, warehousing and undamaged delivery of products are of great importance. The fewer damaged products they send, the more they increase their value. The company examined in the study kept its damage data and wanted it to be analyzed so that it could take precautions accordingly and follow a more profitable path. For this reason, the study focuses on data on errors and damages. This study shows what kind of problems can occur in such a company and how the 3PL company can evaluate the problems to increase customer service quality and cost efficiency.

Artificial intelligence for education and its emphasis on assessment and adversity quotient: a review

PurposeThe purpose of this study was to analyze importance of artificial intelligence (AI) in education and its emphasis on assessment and adversity quotient (AQ).Design/methodology/approachThe study utilizes a systematic literature review of over 141 journal papers and psychometric tests to evaluate AQ. Thematic analysis of quantitative and qualitative studies explores domains of AI in education.FindingsResults suggest that assessing the AQ of students with the help of AI techniques is necessary. Education is a vital tool to develop and improve natural intelligence, and this survey presents the discourse use of AI techniques and behavioral strategies in the education sector of the recent era. The study proposes a conceptual framework of AQ with the help of assessment style for higher education undergraduates.Originality/valueResearch on AQ evaluation in the Indian context is still emerging, presenting a potential avenue for future research. Investigating the relationship between AQ and academic performance among Indian students is a crucial area of research. This can provide insights into the role of AQ in academic motivation, persistence and success in different academic disciplines and levels of education. AQ evaluation offers valuable insights into how individuals deal with and overcome challenges. The findings of this study have implications for higher education institutions to prepare for future challenges and better equip students with necessary skills for success. The papers reviewed related to AI for education opens research opportunities in the field of psychometrics, educational assessment and the evaluation of AQ.

Analysis of seedling images to evaluate the physiological potential of soybean seeds

The use of artificial intelligence techniques collaborate in laboratory analysis of seeds. In this sense, the objective of this work was to verify the efficiency of the Ground Eye® equipment in evaluating the vigor of soybean seeds and the agronomic performance of soybean varieties 74I77 RSF IPRO and NT 1478SP. For this, experiments were carried out to analyze the germination of soybean seeds. The variables analyzed by the analyst and Ground Eye® equipment were the following: number of normal, intermediate, weak and abnormal seedlings. The tetrazolium, vigor and seedling emergence test under field conditions for soybean cultivars were also performed. For seedling emergence in the field, tetrazolium test and vigor there was no statistical difference between soybean cultivars. The Ground Eye Series L equipment showed a lower number of normal seedlings, number of intermediate seedlings, number of weak seedlings and a higher number of vigorous seedlings when compared to the analyzes carried out manually. Based on the results, the cultivar NT 1478SP showed better performance and may be promising in the field. The Ground Eye L Series equipment is an efficient tool for checking the vigor of soybean seedlings, helping the analyst to perform the tests more quickly.

Artificial intelligence techniques as an indicator of knowledge management and information Monitoring in higher education in Algeria: a field study at the University of Tamanghasset

The roles of higher education institutions are based on managing and monitoring access to knowledge and its sharing among individuals with the aim of development and innovation to serve scientific, cultural and economic development, to perform the roles, higher education institutions respond to the requirements of modern technical growth in their fields of activity by overcoming the difficulties that hinder relations between those involved in their activities, and perhaps artificial intelligence techniques is one of the modern technologies that contribute to the tasks of managing access to knowledge and monitoring its sharing among individuals. The field study aims to highlight the level of knowledge management and information monitoring among educational staff in higher education in Algeria by measuring their use of artificial intelligence techniques in teaching and scientific research activities, a descriptive analytical approach was used on a sample consisting of 70 professors distributed among five colleges at the University of Tamanghasset (College of Humanities and Social Sciences 18 professors, College of Legal and Political Sciences 13 professors, College of Economics and Management Sciences 14 professors, College of Science and Technology 13 professors, College of Arts and Languages 12 professors). The study concluded that knowledge management and information monitoring in higher education is a vital activity within the scientific and technological environment in the University of Tamanghasset that contributes to enriching the practices and uses of artificial intelligence techniques with a moderate positive correlation, the study recommended the necessity of conducting field studies and questionnaires to identify more comprehensive level that includes the administrative side and the student side with including the institution's research centers and laboratories and organizations surrounding university in the process, in addition conducting analytical studies to data and statistics related to the use of artificial intelligence techniques and their negative impact on various educational and research aspects, while working to strengthen information systems based on artificial intelligence.

Potential Applications of Explainable Artificial Intelligence to Actuarial Problems

Explainable artificial intelligence (XAI) is a group of techniques and evaluations that allows users to understand artificial intelligence knowledge and increase the reliability of the results produced using artificial intelligence. XAI can assist actuaries in achieving better estimations and decisions. This study reviews the current literature to summarize XAI in common actuarial problems. We proposed a research process based on understanding the type of AI used in actuarial practice in the financial industry and insurance pricing and then researched XAI implementation. This study systematically reviews the literature on the need for implementation options and the current use of explanatory artificial intelligence (XAI) techniques for actuarial problems. The study begins with a contextual introduction outlining the use of artificial intelligence techniques and their potential limitations, followed by the definition of the search equations used in the research process, the analysis of the results, and the identification of the main potential fields for exploitation in actuarial problems, as well as pointers for potential future work in this area.

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.

Modified Fuzzy Neural Network Approach for Academic Performance Prediction of Students in Early Childhood Education

Modern education relies heavily on educational technology, which provides students with unique learning opportunities and enhances their ability to learn. For many years now, computers and other technological tools have been an integral part of education. However, compared to other educational levels, the incorporation of educational technology in early childhood education is a more recent trend. It is because of this that materials and procedures tailored to young children must be created, implemented, and studied. The use of artificial intelligence techniques in educational technology resources has resulted in better engagement for students. Early childhood special education students' academic achievement is predicted using a Modified Fuzzy Neural Network (MFNN). Before constructing the classifier, the dataset had to be preprocessed to remove any extraneous information. As a follow-up, this study will put to the test an organized approach to the implementation of customized fuzzy neural networks for the prediction of academic achievement in early childhood settings. Considerations for the analysis of academic achievement in early childhood education are discussed in this article, including recommendations for the implementation of proposed modified fuzzy neural networks. In terms of evaluation metrics such as Precision, recall, accuracy, and the F1 coefficient, the proposed model outperforms conventional machine-learning (ML) techniques.

A proposed Fedathi teaching sequence and the use of artificial intelligence techniques applied to the teaching of optical networks in telecommunications engineering

The Teaching physics through the traditional lecture method has proven to be a significant challenge for students. It is important to note that students often perceive learning physics as a complex task. This difficulty is often associated with the maintenance of traditional teaching methods, which place students in a passive position, as mere spectators, passive recipients of the knowledge transmitted. On the other hand, artificial intelligence has become increasingly present in our society and promises to revolutionize the way we interact with our world. Based on this discussion, this work presents a proposal for a Fedathi didactic sequence for teaching Physics through a simulation of optical networks using artificial intelligence.

International and Constitutional Efforts to Protect the Environment Through the Use of Artificial Intelligence Techniques

The purpose of the study is to demonstrate the role of artificial intelligence in the ability to accelerate global efforts to protect the environment and conserve natural resources by monitoring air pollution and energy emissions. Monitoring attacks on forest areas, as well as describing the role of the constitutions of the world's countries, including the Jordanian Constitution, by adding constitutional texts addressing environmental protection using artificial intelligence technologies. The importance of this research lies in demonstrating international and constitutional efforts to protect the environment using artificial intelligence technologies by accelerating global and local efforts to protect the environment, conserve natural resources, and use digital tools to monitor air pollution and energy emissions. And monitoring attacks on forest areas, as well as describing the role of countries' constitutions, including the Jordanian Constitution, by adding constitutional texts addressing environmental protection using artificial intelligence technologies.

Design control and management of intelligent and autonomous nanorobots with artificial intelligence for Prevention and monitoring of blood related diseases

The effective management of human bloodstream remains to be the prime focus for the clinicians over years and it impose greater challenges when it comes to real-time solution. In particular managing hypoxemia and detection of blood clots is most pertinent. One major challenge faced is the existence of limited training data generated from real-world scenarios. On the other hand, creating an efficient model is often time consuming and expensive. This paper focusses on effective convergence of artificial intelligence and nanorobotics technologies to design and implement autonomous intelligent nanorobots to deal with blood related diseases. The major contribution of the research is two-fold, first we define an efficient architecture of the nanorobotics system with appropriate design parameter. Next, we develop a novel semi-supervised learning model using stochastic gradient descent method and kernel space that efficiently control and manage the nanorobots and helps in earlier prognosis and treatment of blood related diseases. The proposed model is novel and efficient as it enables working at nanoscale, providing resourceful understanding on physical and chemical properties associated with human body. The use of artificial intelligence techniques further makes the system to work more intelligently and independently. COSMOL with integrated MATLAB environment is used for experimental setup and simulation. MNIST dataset is compared with online RP tree method and other conventional batch related techniques. The performance analysis is compared based on performance, error rates and risk related factors. The proposed approach provides significant improvement in terms of performance with minimal error rate and improved accuracy measures.

AI-enhanced adsorption modeling: Challenges, applications, and bibliographic analysis

Inorganic and organic contaminants, such as fertilisers, heavy metals, and dyes, are the primary causes of water pollution. The field of artificial intelligence (AI) has received significant interest due to its capacity to address challenges across various fields. The use of AI techniques in water treatment and desalination has recently shown useful for optimising processes and dealing with the challenges of water pollution and scarcity. The utilization of AI in the water treatment industry is anticipated to result in a reduction in operational expenditures through the lowering of procedure costs and the optimisation of chemical utilization. The predictive capabilities of artificial intelligence models have accurately assessed the efficacy of different adsorbents in removing contaminants from wastewater. This article provides an overview of the various AI techniques and how they can be used in the adsorption of contaminants during the water treatment process. The reviewed publications were analysed for their diversity in journal type, publication year, research methodology, and initial study context. Citation network analysis, an objective method, and tools like VOSviewer are used to find these groups. The primary issues that need to be addressed include the availability and selection of data, low reproducibility, and little proof of uses in real water treatment. The provision of challenges is essential to ensure the prospective success of AI associated with technologies. The brief overview holds importance to everyone involved in the field of water, encompassing scientists, engineers, students, and stakeholders.

Use of Artificial Intelligence Techniques in Characterization of Vibration Signals for Application in Agri-Food Engineering

Use of Artificial Intelligence Techniques in Characterization of Vibration Signals for Application in Agri-Food Engineering

The use of artificial intelligence in orthodontics

The application of Artificial Intelligence (AI) in orthodontics is very diverse and ranges from the identification of anatomical and pathological structures of the human dentition to support complex decision-making in orthodontic treatment planning. Its application has grown significantly in recent years, as reflected by the exponential increase in the number of scientific publications on the integration of artificial intelligence into everyday clinical practice. In many cases, AI can be seen as a valuable tool whose algorithms help dentists and clinicians analyze data from multiple sources of information. The purpose of this paper was to analyze current views on the use of artificial intelligence techniques and models in orthodontics based on a literature review. The scientific publications of various scientometric databases (PubMed, Scopus, Google Scolar, Web of Science, etc.) over the past 5 years were processed. Artificial intelligence is one of the most promising tools due to its high accuracy and efficiency. Given the current scientific dynamics in the field of AI, it can be assumed that AI will become an integral part of diagnostics and treatment planning in the near future. Practicing dentists will be able to use it as an additional tool to reduce their workload. However, this requires close cooperation of commercial AI products with the scientific community, further research, including randomized clinical trials, to test and integrate this concept in dental practice. Modern artificial intelligence is excellent at utilizing structured knowledge and gaining insights from huge amounts of data. However, it is not able to create associations like the human brain and is only partially capable of making complex decisions in a clinical situation. In turn, the efficiency of AI is achieved only when unbiased training data and a properly designed and trained algorithm are used. Keywords: dentistry, diagnostic, machine learning, cephalometry.

Methods for using Bing's AI-powered search engine for data extraction for a systematic review.

Data extraction is a time-consuming and resource-intensive task in the systematic review process. Natural language processing (NLP) artificial intelligence (AI) techniques have the potential to automate data extraction saving time and resources, accelerating the review process, and enhancing the quality and reliability of extracted data. In this paper, we propose a method for using Bing AI and Microsoft Edge as a second reviewer to verify and enhance data items first extracted by a single human reviewer. We describe a worked example of the steps involved in instructing the Bing AI Chat tool to extract study characteristics as data items from a PDF document into a table so that they can be compared with data extracted manually. We show that this technique may provide an additional verification process for data extraction where there are limited resources available or for novice reviewers. However, it should not be seen as a replacement to already established and validated double independent data extraction methods without further evaluation and verification. Use of AI techniques for data extraction in systematic reviews should be transparently and accurately described in reports. Future research should focus on the accuracy, efficiency, completeness, and user experience of using Bing AI for data extraction compared with traditional methods using two or more reviewers independently.

Artificial intelligence (AI) in engineering and technology

From traditional civil engineering and aerospace engineering to large portions of electrical, electronics, and computer engineering, and most importantly, the medical and clinical sciences, artificial intelligence (AI) is one of the most critical and useful tools for research. The human brain’s ability to interpret sensory data, solve issues, learn from past experiences and tests, store and retrieve data, etc., is where the current discipline starts. In particular, the power sector is on the cusp of inevitable change as a result of numerous restructuring efforts, and the power science community requires artificial intelligence resources for effective planning, operation, and control of the power system. Almost every AI method is logically conceived of as an optimization or decision-making problem. Power utilities can benefit from these AI methods because they provide novel approaches to efficient evaluation, effective management, and astute decision-making. We have learned more about the design process and created higher-quality goods and artifacts thanks in large part to the widespread use of AI techniques and approaches over the past few decades. When these disciplines work together, they produce cutting-edge architectures that can solve a wider range of design problems at once.

Predicting bankruptcy using artificial intelligence: The case of the engineering industry

Bankruptcy prediction is a powerful early-warning tool and plays a crucial role in various aspects of financial and business management. It is vital for safeguarding investments, maintaining financial stability, making informed credit decisions, and contributing to the overall health of the economy. This paper aims to develop bankruptcy prediction models for the Slovak engineering industry and to compare their effectiveness. Predictions are generated using the classical logistic regression (LR) method as well as artificial intelligence (AI) techniques (artificial neural networks (ANN) and support vector machines (SVM)). Research sample consists of 825 businesses operating in the engineering industry (Manufacture of machinery and equipment n.e.c.; Manufacture of motor vehicles, trailers and semi-trailers; Manufacture of other transport equipment). The selection of eight financial indicators is grounded in prior research and existing literature. The results show high accuracy for all used methods. The SVM outcomes indicate a level of accuracy on the test set that is nearly indistinguishable from that of the ANN model. The use of AI techniques demonstrates their effective predictive capabilities and holds a significant position within the realm of tools for forecasting bankruptcy.

Artificial Intelligence (AI) Techniques for Intelligent Control Systems in Mechanical Engineering

In mechanical engineering, control systems are essential to the dependable and effective operation of mechanical equipment and processes. The advantages of integrating AI into control systems include energy savings, greater product quality, increased process effectiveness, and adaptive and predictive control. Various AI approaches, including machine learning methods like decision trees, neural networks, and support vector machines, are extensively studied for system identification, modelling, and adaptive control. AI techniques enhance these applications' control performance, energy efficiency, problem discovery, and maintenance. The methodology uses metaheuristic techniques for creating intelligent control systems, such as simulated annealing, ant colony optimisation, and harmony search. With the help of these algorithms, the solution space is efficiently searched for ideal control strategies while avoiding local optima. The importance of evaluating the learned control algorithm on a different dataset or running experiments to determine its performance is also underlined. Performance assessment benchmarks for tracking error, settling time, overshoot, and energy efficiency are provided in the results section. This paper investigates the use of AI techniques in mechanical engineering intelligent control systems. The paper also offers areas for additional research in intelligent control systems and identifies research directions for future advancements.

Resource orchestration in Indian ethnic entrepreneurial enterprises through generation change in Malaysia

Ethnic entrepreneurial enterprises are continuously evolving, especially when generations change. As these changes take place, resources are also orchestrated differently. However, research gap exists on how resources are orchestrated in ethnic entrepreneurial enterprises through generational change. We answer this question by adopting a qualitative approach based on data from eleven ethnic entrepreneurial enterprises that have experienced generational succession. The data was then analysed by adopting a novel approach of artificial intelligence. Our results suggest that the orchestration in class and ethnic resources has equipped the later generation ethnic entrepreneurs with capabilities to expand and develop their ethnic entrepreneurial enterprises. We emphasize the importance of orchestrating resources in ethnic entrepreneurial enterprises for product innovation, market growth and business development as generations change. The use of artificial intelligence technique enables underlying patterns in ethnic entrepreneurship to be discovered, which assist practitioners in making the best decisions concerning entrepreneurial efforts. This study invites entrepreneurs to comprehend the importance of orchestrating resources for entrepreneurial decision-making in business expansion and development, especially in ethnic entrepreneurial enterprises. With novelty in the methodological application, we extend a cordial invitation to erudite scholars to apply artificial intelligence technique within qualitative research to achieve precision and nuances.

Fabrication and investigation of agricultural monitoring system with IoT & AI

Artificial intelligence (AI) can be used in a variety of fields and has the potential to alter how we currently view farming. Due to its emphasis on effectiveness and usability artificial intelligence has the largest impact on agriculture of all industries. We highlight the automation-supporting technologies such as Artificial Intelligence (AI), Machine Learning, and Long-Range (LoRa) technology which provides data integrity and protection. We also offer a structure for smart farming that depends on the location of data processing after a comprehensive investigation of numerous designs. As part of our future study we have divided the unresolved difficulties in smart agriculture into two categories such as networking issues and technology issues. Artificial Intelligence and Machine Learning are examples of technologies whereas the Moderate Resolution Imaging Spectroradiometer satellite and LoRa are used for all network-related jobs. The goal of the research is to deploy a network of sensors throughout agricultural fields to gather real-time information on a variety of environmental factors including temperature, humidity, soil moisture and nutrient levels. The seamless data transmission and communication made possible by these sensors’ integration with Internet of Things technologies. With the use of AI techniques and algorithms the gathered data is examined. The technology may offer practical insights and suggestions for improving agricultural practices because the AI models are trained to spot patterns, correlations, and anomalies in the data. We are also focusing on indoor farming by supplying Ultra Violet radiation and artificial lighting in accordance with plant growth. When a pest assault is detected using AI and LoRa even in poor or no network coverage area and notifies the farmer’s mobile in any part of the world. The irrigation system is put to the test with various plants at various humidity and temperature levels in both dry and typical situations. To keep the water content in those specific regions soil moisture sensors are used.