Use Of Artificial Intelligence Techniques Research Articles

The Relationship Between Using Artificial Intelligence Technologies and the Effectiveness of Human Resources Practices (Recruitment and Selection) in Small and Medium Enterprises.

This study aims to explore the relationship between using artificial intelligence technologies and the effectiveness of human resources practices (recruitment and selection) in small and medium companies. The study used descriptive survey approach where the data was collected by a questionnaire. The findings of the study showed a positive relationship between the use of artificial intelligence techniques and the effectiveness of recruitment and selection. The study recommended the need to update and develop artificial intelligence techniques used in human resource management and the need to educate and train staff in the methods of artificial intelligence and its uses in human resources management practices. The study also recommended conducting future studies on the relationship between the use of artificial intelligence techniques and other human resource practices such as: (training, development, replacement).

DETERMINATION OF CONSTRUCTION PROJECT DURATION WITH ARTIFICIAL INTELLIGENCE

Aim: With Industry 4.0, the period of digitalization has begun in the construction industry, as in many other industries. The use of artificial intelligence techniques or algorithms instead of traditional methods to solve the problems experienced in construction projects and increase the risk of the project has increased in recent years. These studies have shown that artificial intelligence techniques or algorithms give successful results in solving problems in construction projects. This study investigates the applications of artificial intelligence techniques on construction project duration and demonstrates the use of the Gene Expression Programming method in estimating construction project duration. Method: In this study, analyzes were made with Gene Expression Programming (GEP) technique to estimate the project duration by using the variables of the number of floors, floor area and total construction area of 71 construction projects (60 training and 11 test data) built between 2011-2016. Results: The determination coefficient (R2) for this study was calculated as 0.78 for the training set and as 0.72 for the testing set. Conclusion: In this study, it has been seen that the ability of the created algorithm to estimate the construction time with limited data provides partially acceptable performance.

Use of reinforcement learning algorithms to optimize control strategies for single machine systems

The stability of power systems is critical to ensuring reliable and efficient operation of electrical grids. In recent years, there has been a growing interest in the use of artificial intelligence techniques, such as reinforcement learning, to improve the stability of single machine systems. Reinforcement learning is a machine learning approach that enables agents to learn optimal control policies through trial and error. In this paper, we explore the use of reinforcement learning algorithms to optimize control strategies for single machine systems. We demonstrate how these algorithms can be used to identify the best control actions to take in real-time to prevent system instability. The challenges and limitations of using reinforcement learning in power system applications are discussed and recommendations are provided for future research in this area. Our results show that reinforcement learning has great potential for improving the stability of single machine systems and can be a valuable tool for power system operators and engineers.

Compounding process optimization for recycled materials using machine learning algorithms

The sustainable manufacturing of goods is one of the factors to minimize natural resource depletion and CO2 emissions. In the last decade a big effort has been done to transition from linear economy to circular economy. This transition requires to implement re-manufacturing processes into the current industrial manufacturing framework, replacing the sourcing of raw materials by re-manufacturing technologies. However, this transition is very challenging since it requires the transformation of the companies and more specially their processes, from traditional to circular. To speed up this transformation, the use of tools provided by the 4th industrial revolution are crucial. In particular, the use of artificial intelligence techniques enables the optimization of the re-manufacturing processes and make those optimizations available to all the stakeholders. This paper presents an optimization system for re-manufacturing of recycled fiber through compounding processes with materials that come from composite waste or end of life of products. The proposed approach has been trained with the data collected from several experiments carried out with a compounding machine under different specifications, fiber reinforcement grades, and output material properties. The system will allow to set up a compounding machine for different types of reinforced plastics needless of setting point experiments. The algorithms have been tested with previously unseen scenarios and they have proved to be efficient for giving the optimal material characteristics.

Role of artificial intelligence in supply chain management

The term “supply chain” refers to a network of facilities that includes a variety of companies. To minimize the entirecost of the supply chain, these entities must collaborate. This research focuses on the use of artificial intelligencetechniques in supply chain management. It includes supply chain management examples like demand forecastingand supply forecasting, text analytics, pricing planning, and more to help companies improve their processes,lower costs and risk, and boost revenue. It gives us a quick rundown of all the key principles of economics andhow to comprehend and use them effectively.

End-To-End Strategic Planning Model Proposal with Artificial Intelligence

Organizations want to manage the future by being positioned correctly in the future with strategic planning, which is a part of strategic management. Strategic planning, which is a proactive approach, is a long-term type of planning and a case of forecasting based on data. It also contains many data that are complex, uncertain and linguistic, without statistical history. Real-life challenges, like strategic planning, are dynamic, unpredictable, and multi-criteria. It is known that the use of artificial intelligence techniques in such problems gives optimum results and minimizes human errors. At the same time, the use of multi-criteria-decision-making methods together with artificial intelligence techniques provides accurate and effective results to the decision maker. Artificial intelligence (AI) applications, which is the phenomenon of today, continue to enter all areas of our lives and its usage area is rapidly spreading. AI techniques are also used in the stages of strategic planning. There are applications of artificial intelligence (fuzzy logic) with SWOT analysis, which is a very common method to determine the current situation in strategic planning. Moreover, the number of models offering solutions with artificial intelligence techniques at all stages of planning from the very beginning to the end is low. In this study, a model proposal is presented for the preparation of end-to-end strategic planning with AI techniques. In the proposed model, strategic planning was examined in four stages as analyses of current situation, strategic concepts, assessment and evaluation. (Strategic planning was examined in four stages in the proposed model: current situation analyses, strategic concepts, assessment, and evaluation.) AI techniques are suggested for each stage. The proposed AI techniques have been chosen from among the most used techniques in the literature. The methods used are of 3 types. The first type is fuzzy logic (FL) which is one of the AI techniques, expert systems (ES), artificial neural networks (ANN), and genetic algorithms (GA). The second type is the combination of delphi technique used in data collection and multi-criteria decision-making methods, which is the decision-making methods, and FL. Fuzzy delphi and fuzzy multi-criteria-decision-making methods. The third type is the use of other AI techniques with FL. Fuzzy ES, fuzzy ANN. The aim of this study is to provide a perspective to organizations and experts that make strategic planning.

Machine Learning Techniques for Anxiety Disorder

In recent years, artificial intelligence based applications have been improved and used to improve the timing, sensitivity and quality of diagnosis of psychiatric diseases. We aim to review the existing literature on the use of artificial intelligence techniques in the assessment of subjects with anxiety disorder. We searched databases about DSM-5 (Diagnostic and Statistical Manual of Mental Disorders) one of the main categories of anxiety disorders; Separation Anxiety Disorder, Generalized Anxiety Disorder, Panic Disorder and Social Anxiety Disorder between 2015-2021. We identified 30 different techniques on these works. Comparisons have been made with more than one algorithm in the studies. The Random Forest Algorithm has been seen in the most used machine learning method among these algorithms. In addition, the best accuracy performance has been observed in the Random Forest Algorithm. This article critically analyzes these recent research studies on anxiety. Considering the clinical heterogeneity of the data obtained from anxiety patients, we conclude that artificial intelligence techniques can provide important information to clinicians and researchers in areas such as diagnosis, personalized treatment, and prognosis.

The Effectiveness of Utilising Modern Artificial Intelligence Techniques and Initiatives to Combat COVID-19 in South Korea: A Narrative Review

All nations are facing significant challenges and difficulties in the face of an intense pandemic that controls human life, and many things have changed in life, which led to the doubling of the global economy and the difficulty of life for many of the world's inhabitants. At present, the nations are seeking to control the spread of the epidemic in them, as every country uses plans to prevent this pandemic. This article presents South Korea's experience in employing modern artificial intelligence techniques in facing COVID-19. Besides, the use of artificial intelligence techniques and their great role in analysing images of people with COVID-19 disease are discussed, and how they are used to save many lives of the victims. The data obtained in this article is from a group of studies, social networking sites, and news on TV channels in the first period of 2020, which are collected and summarised in this article. This study summarises the role of South Korea in facing the pandemic through the application of artificial intelligence techniques to detect the disease, tracking the injured through GPS and how an intelligent quarantine is established, diagnosing and examining the damaged remotely, applying an electronic platform to publish information about how to avoid infection and the use of robots to assist diagnose disease and use drones for sterilisation purposes.

Deep Learning Applied to Ligand-Based De Novo Drug Design.

In the latest years, the application of deep generative models to suggest virtual compounds is becoming a new and powerful tool in drug discovery projects. The idea behind this review is to offer an updated view on de novo design approaches based on artificial intelligent (AI) algorithms, with a particular focus on ligand-based methods. We start this review by reporting a brief overview of the most relevant de novo design approaches developed before the use of AI techniques. We then describe the nowadays most common neural network architectures employed in ligand-based de novo design, together with an up-to-date list of more than 100 deep generative models found in the literature (2017-2020). In order to show how deep generative approaches are applied into drug discovery context, we report all the now available studies in which generated compounds have been synthetized and their biological activity tested. Finally, we discuss what we envisage as beneficial future directions for further application of deep generative models in de novo drug design.

Development of mobile aircraft control system model with additive technologies

The article describes the parameter evaluation of mobile spacecraft control systems and identifies their main advantages in comparison with stationary assets. The model and the mobile spacecraft control system mock-up using additive technologies were developed with existing methods for prototyping complex technical systems. This model performs experimental operations of the prototype of the mobile control system, accompanies the spacecraft in the area of its radio visibility, simulates the implementation of technological cycles of spacecraft control, evaluates the parameters of the target equipment, analyzes the characteristics of the antenna system and the layout, conducts experiments and eliminates abnormal situations. It also optimizes the spacecraft testing based on the use of artificial intelligence techniques.

Learning the Use of Artificial Intelligence in Heterogeneous Catalysis

We describe the use of artificial intelligence techniques in heterogeneous catalysis. This description is intended to give readers some clues for the use of these techniques in their research or industrial processes related to hydrodesulfurization. Since the description corresponds to supervised learning, first of all, we give a brief introduction to this type of learning, emphasizing the variables X and Y that define it. For each description, there is a particular emphasis on highlighting these variables. This emphasis will help define them when one works on a new application. The descriptions that we present relate to the construction of learning machines that infer adsorption energies, surface areas, adsorption isotherms of nanoporous materials, novel catalysts, and the sulfur content after hydrodesulfurization. These learning machines can predict adsorption energies with mean absolute errors of 0.15 eV for a diverse chemical space. They predict more precise surface areas of porous materials than the BET technique and can calculate their isotherms much faster than the Monte Carlo method. These machines can also predict new catalysts by learning from the catalytic behavior of materials generated through atomic substitutions. When the machines learn from the variables associated with a hydrodesulfurization process, they can predict the sulfur content in the final product.

Process intensification 4.0: A new approach for attaining new, sustainable and circular processes enabled by machine learning

This paper reviews system-level transformations converging into the next generation of Process Intensification strategies defined as PI4.0. Process Intensification 4.0 uses data-driven algorithms to understand other physical and chemical processes that improve equipment design, predictive control, and optimization. Following this, an overview of the use of Artificial Intelligence techniques, particularly Machine Learning for the acceleration of equipment design, process optimization, and streamlining, is presented. This work will highlight and discuss the emerging framework of the integration between Circular Chemistry, Industry 4.0, and Process Intensification and how the data obtained from this integration is at the core of the next generation of Process Intensification strategies. This is supported by a discussion of different cases that apply data-driven models enabled by Machine Learning as a mean to enhance an intensified system (product synthesis, equipment or methods).

Application of approaches to artificial intelligence and machine training in cyber security

Of interest is how machine learning is used in cybersecurity for both security and criminal activities, incl. and for cyberattacks targeting machine-learning models. The aim of the development is to show how machine learning, can be applied in the context of security in terms of both protection and attack, as well as the potential threats posed to machine learning models. Results were obtained using analytical descriptive methodology based on literature sources, examining the use of artificial intelligence techniques and machine learning to increase cybersecurity, as well as the most important areas of application that improve cybersecurity. On the other hand, the features and applications of artificial intelligence and machine learning in the service of cybercriminals are considered. Regarding the contribution part, an in-depth and extensive analysis of the features and applications of artificial intelligence and machine learning from two points of view (cybersecurity and cybercrime) has been made.

Predicting fracture outcomes from clinical registry data using artificial intelligence supplemented models for evidence-informed treatment (PRAISE) study protocol.

Distal radius (wrist) fractures are the second most common fracture admitted to hospital. The anatomical pattern of these types of injuries is diverse, with variation in clinical management, guidelines for management remain inconclusive, and the uptake of findings from clinical trials into routine practice limited. Robust predictive modelling, which considers both the characteristics of the fracture and patient, provides the best opportunity to reduce variation in care and improve patient outcomes. This type of data is housed in unstructured data sources with no particular format or schema. The "Predicting fracture outcomes from clinical Registry data using Artificial Intelligence (AI) Supplemented models for Evidence-informed treatment (PRAISE)" study aims to use AI methods on unstructured data to describe the fracture characteristics and test if using this information improves identification of key fracture characteristics and prediction of patient-reported outcome measures and clinical outcomes following wrist fractures compared to prediction models based on standard registry data. Adult (16+ years) patients presenting to the emergency department, treated in a short stay unit, or admitted to hospital for >24h for management of a wrist fracture in four Victorian hospitals will be included in this study. The study will use routine registry data from the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), and electronic medical record (EMR) information (e.g. X-rays, surgical reports, radiology reports, images). A multimodal deep learning fracture reasoning system (DLFRS) will be developed that reasons on EMR information. Machine learning prediction models will test the performance with/without output from the DLFRS. The PRAISE study will establish the use of AI techniques to provide enhanced information about fracture characteristics in people with wrist fractures. Prediction models using AI derived characteristics are expected to provide better prediction of clinical and patient-reported outcomes following distal radius fracture.

The Automated Vacuum Waste Collection Optimization Problem

One of the most challenging problems on modern urban planning and one of the goals to be solved for smart city design is that of urban waste disposal. Given urban population growth, and that the amount of waste generated by each of us citizens is also growing, the total amount of waste to be collected and treated is growing dramatically (EPA 2011), becoming one sensitive issue for local governments. A modern technique for waste collection that is steadily being adopted is automated vacuum waste collection. This technology uses air suction on a closed network of underground pipes to move waste from the collection points to the processing station, reducing greenhouse gas emissions as well as inconveniences to citizens (odors, noise, . . . ) and allowing better waste reuse and recycling. This technique is open to optimize energy consumption because moving huge amounts of waste by air impulsion requires a lot of electric power. The described problem challenge here is, precisely, that of organizing and scheduling waste collection to minimize the amount of energy per ton of collected waste in such a system via the use of Artificial Intelligence techniques. This kind of problems are an inviting opportunity to showcase the possibilities that AI for Computational Sustainability offers.

Artificial Intelligence Revolutionises Weather Forecast, Climate Monitoring and Decadal Prediction

Artificial Intelligence (AI) is an explosively growing field of computer technology, which is expected to transform many aspects of our society in a profound way. AI techniques are used to analyse large amounts of unstructured and heterogeneous data and discover and exploit complex and intricate relations among these data, without recourse to an explicit analytical treatment of those relations. These AI techniques are unavoidable to make sense of the rapidly increasing data deluge and to respond to the challenging new demands in Weather Forecast (WF), Climate Monitoring (CM) and Decadal Prediction (DP). The use of AI techniques can lead simultaneously to: (1) a reduction of human development effort, (2) a more efficient use of computing resources and (3) an increased forecast quality. To realise this potential, a new generation of scientists combining atmospheric science domain knowledge and state-of-the-art AI skills needs to be trained. AI should become a cornerstone of future weather and climate observation and modelling systems.

A reinforcement learning-based framework for disruption risk identification in supply chains

Risk management is one of the critical activities which needs to be done well to ensure supply chain activities operate smoothly. The first step in risk management is risk identification, in which the risk manager identifies the risk events of interest for further analysis. The timely identification of risk events in the risk identification step is crucial for the risk manager to be proactive in managing the supply chain risks in its operations. Undertaking this step manually, however, is tedious and time-consuming. With the increased sophistication and capability of advanced computing algorithms, various eminent supply chain researchers have called for the use of artificial intelligence techniques to increase efficiency and efficacy when performing their tasks. In this paper, we demonstrate how reinforcement learning, which is one of the recent artificial intelligence techniques, can assist risk managers to proactively identify the risks to their operations. We explain the working of our proposed Reinforcement Learning-based approach for Proactive Risk Identification (RL-PRI) and its various steps. We then show the performance accuracy of RL-PRI in identifying the risk events of interest by comparing its output with the risk events which are manually identified by professional risk managers.

Activating the Use of Artificial Intelligence Techniques in Higher Education

Activating the Use of Artificial Intelligence Techniques in Higher Education

Playable Experiences at AIIDE 2017

This paper describes the accepted entries to the fifth Playable Experiences track to be held at the AIIDE conference. The Playable Experiences track showcases complete works that make use of artificial intelligence techniques as an integral part of the player experience.

A Review on the use of Artificial Intelligence Techniques in Brain MRI Analysis

Over the past 20 years, the global research going on in Artificial Intelligence in applications in medication is a venue internationally, for medical trade and creating an energetic research community. The Artificial Intelligence in Medicine magazine has posted a massive amount. This paper gives an overview of the history of AI applications in brain MRI analysis to research its effect at the wider studies discipline and perceive de-manding situations for its destiny. Analysis of numerous articles to create a taxonomy of research subject matters and results was done. The article is classed which might be posted between 2000 and 2018 with this taxonomy. Analyzed articles have excessive citations. Efforts are useful in figuring out popular studies works in AI primarily based on mind MRI analysis throughout specific issues. The biomedical prognosis was ruled by way of knowledge engineering research in its first decade, whilst gadget mastering and records mining prevailed thereafter. Together these two topics have contributed a lot to the latest medical domain.