Artificial intelligence based task mapping and pipelined scheduling for checkpointing on real time systems with imperfect fault detection

Background: The field of sports science has been fundamentally transformed by the integration of artificial intelligence (AI) technologies. AI has enabled advances in areas such as performance optimization, training personalization, injury risk assessment and prevention, talent recognition, rehabilitation, athlete monitoring, and wellness optimization. Objective: This review aims to explore the diverse impact of AI in sports science, highlighting advances in AI techniques, the challenges and limitations of integrating AI tools, and the emerging role of AI chatbots in shaping the future of sports research and applications. Methods: The review examines the existing literature on the application of machine learning, deep learning and other AI techniques in various aspects of sports science research and practice. It provides an overview of how these technologies have been used to improve personalized training programs, video analysis, injury risk prediction, talent identification, and rehabilitation. Results: The article describes how AI-powered tools and techniques have revolutionized sports science, enabling personalized, data-driven and efficient approaches to performance optimization. Sophisticated machine learning algorithms such as artificial neural networks, decision trees and support vector machines have been used to develop predictive models for injury risk assessment and prevention, leading to improvements in athletes' wellbeing and long-term performance. AI-driven talent identification and selection processes have also shown promise in recognizing exceptional athletes with greater accuracy. In addition, the integration of AI into athlete monitoring and rehabilitation has led to greater personalization, better decision making and faster return to play. Conclusion: The integration of AI and machine learning techniques into sports science has the potential to transform the field and lead to improved athlete performance, reduced risk of injury, improved well-being, and more personalized and effective interventions. The emergence of AI chatbots expands the applications of these technologies in sports science and offers new opportunities to streamline research processes, provide personalized advice to athletes and support sports medicine and rehabilitation

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques to solve different engineering problems. Besides, Short Term Electrical Load Forecasting (STLF) is one of the important concerns of power systems and accurate load forecasting is vital for managing supply and demand of electricity. This study estimates short term electricity loads of Iran by means of Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Genetic Algorithm (GA) which are the most successful AI techniques in this field. In order to improve forecasting accuracy, all AI techniques are equipped with preprocessing concept, and effects of this concept on performance of each AI technique are investigated. Finally, outcomes of the approaches are evaluated and compared by means of the mean absolute percentage error (MAPE). Results show that data preprocessing can significantly improve performance of the AI techniques. Meanwhile, ANFIS outcomes are more approximate to the actual loads than those of ANN and GA, so it can be considered as a suitable tool to deal with STLF problems.

Simple SummaryDistinguishing between benign vs. malignant bone lesions is often difficult on imaging. Many bone lesions are infrequent or rarely seen, and often only specialist radiologists have sufficient expertise to provide an accurate diagnosis. In addition, some benign bone tumours may exhibit potentially aggressive features that mimic malignant bone tumours, making the diagnosis even more difficult. The rapid development of artificial intelligence (AI) techniques has led to remarkable progress in image-recognition tasks, including the classification and characterization of various tumours. This study will review the most recent evidence for AI techniques in differentiating bone lesions on various imaging modalities using a systematic approach. Potential clinical applications of AI techniques for clinical diagnosis and management of bone lesions will also be discussed.An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44–0.99, 0.63–1.00, and 0.73–0.96, respectively, with AUCs of 0.73–0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.

It is no doubt that now a day’s Artificial intelligence (AI) is anemergent technology in the digital marketing industry. Commercial Artificial Intelligence (AI) is an important topic for research. Artificial intelligence is growing quickly more sensitive and popular in the digital marketing field due to the capacity of advertisers to employ it efficiently. In order to provide the capability of the highest level of originality and efficiency in digital marketing ground, artificial intelligence techniques are more fruitful for customers, marketers and, retailers. This paper depicts the impact of artificial intelligence techniques on advertising on a diminutive and average scale in the digital marketing field. Ad targeting, predictive analysis, voice search are different artificial intelligence techniques for digital marketing purposes throughout the consumer life cycle. The topic of Artificial Intelligence (AI) in digital marketing itself can be outsized enough to verifyeliminating the area of research into precise application and application frameworks.However, our objective in this paper is to provide a comparative analysis of the digital marketing field using artificial intelligence techniques and to propose some specific guidelines based on different parameters, tools, and frameworks.Through this paper, we have observed thelast five years (2016-2021) research trends which reflect that out of different parameters, customer relationship and promotional activity are the most widely used parameter in the digital marketing field. Another significant point is, most of the research identifies that artificial intelligence is a compulsory technique used in the modern digital marketing field. Some data collection and data analysis tools are also being used in this field using artificial intelligence techniques. Our research methods try to get a complete view on artificial intelligence (AI) Marketing were multifaceted but were very focused on secondary research sources. Finally in this paper, we propose some recommendationsthat will offer better Artificial Intelligence (AI) implementation in the digital marketing field.

Advances in artificial intelligence (AI) techniques that can be used for different purposes have enabled it to be used in many different industrial applications. These are mainly used for modeling, identification, optimization, prediction and control of complex systems under the influence of more than one parameter in industrial applications. With the increasing accuracy of AI techniques, it has also obtained a wide application area on friction stir welding (FSW), one of the production methods developed in recent years. In this study, commonly used AI techniques for FSW, results, accuracy and superiority of AI techniques are reviewed and evaluated. In addition, an overview of AI techniques for FSW in different material combinations is provided. Considering the articles examined; It is seen that welding speed, rotational speed, the plunge depth, spindle torque, shoulder design, base material, pin design/profile, tool type are used as input parameters and tensile strength, yield strength, elongation, hardness, wear rate, welding quality, residual stress, fatigue strength are used as output parameters. As can be seen from the studies, it made important contributions in deciding what input parameters should be in order to have the output parameter at the desired value. The most common used materials for FSW are Al, Ti, Mg, Brass, Cu and so on. When FSW studies using artificial intelligence techniques were examined, it was seen that 81% of the most used materials were AL alloys and 23% of them were made with dissimilar materials. The most commonly utilized AI techniques were said to be artificial neural networks (ANN), fuzzy logic, machine learning, meta-heuristic methods and hybrid systems. As a result of the examination, ANN was the most widely used method among these methods. However, in recent years, with the exploration of new hybrid methods it was seen that hybrid systems used with ANN have higher accuracy.

The intention stage of the human-computer interaction is a promising area for the application of artificial intelligence (AI) techniques. At this stage, the computer needs to have information on the intended tasks and actions of the user. At least three AI techniques – scripts, student models, and expert system dialogues – have the potential to help systems gather this information.

In recent years, photovoltaic (PV) systems have shown their presence in various grid-connected and standalone industrial applications. The performance of installed PV systems is hindered by various factors such as weather conditions, efficiency of energy conversion, intermittent nature of load demand, sizing of battery bank and PV panels, and characteristics of PV cells. To address these issues, implementation of artificial intelligence (AI) techniques can be beneficial. AI exhibits many advantages over conventional analysis tools in terms of its high precision in prediction, fast computation, and no need of complex mathematical approach and ease of programing. AI is proving to be beneficial in PV system applications in many ways. Researchers have used AI in problems such as modelling and forecasting of weather data, predication of solar irradiance, optimum modelling of the solar system, and optimum sizing of PV systems. In this context, this chapter aims to provide detailed insight about the various AI techniques developed or proposed by the researchers. The main emphasis is given to applications of AI techniques in solving problems related to PV systems. A comprehensive study and review of previous contributions of researchers on the implementation of AI in modern PV systems is also being presented.

Purpose: This paper is a pioneering study that investigates the integration of Artificial Intelligence (AI) to enhance energy efficiency in 5G Radio Access Networks (RANs). This paper aims to identify AI-driven strategies that can significantly optimize energy consumption in the rapidly evolving 5G network infrastructure, which is essential for meeting the increasing demand for high-speed connectivity.
 Methodology: The methodology used for this research is a detailed review and analysis of the 5G RAN architecture and its energy dynamics, alongside the exploration of AI applications in optimizing network operations. The study focuses on AI techniques such as resource allocation, traffic prediction, adaptive sleep modes, and fault detection, proposing a holistic approach to energy management in 5G networks. A key contribution of this research is its in-depth examination of AI's role in 5G energy efficiency, highlighting its practical implications and potential for future applications. The paper offers novel insights into the implementation of AI in real-world 5G scenarios and addresses the challenges in transitioning from theoretical models to practical solutions.
 Findings: The findings reveal that AI integration is a vital step towards reducing the environmental footprint of 5G networks, with AI-based solutions showing promise in enhancing efficiency beyond the inherent capabilities of current 5G technologies. Despite many AI applications being in nascent stages, their potential impact on energy efficiency is significant.
 Unique contributor to theory, policy and practice: This paper is a valuable guide for researchers, industry professionals, and policymakers in telecommunications and environmental sustainability. It provides a clear roadmap for leveraging AI in 5G networks, emphasizing the synergy between technological innovation and ecological responsibility.

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

Artificial Intelligence (AI) is emerging as a transformative force in both drug development and orthodontics. This paper provides an overview of key AI techniques employed in these fields. In drug development, AI is accelerating discovery, development, and delivery through generative models, predictive modeling, image analysis, and natural language processing. Orthodontics benefits from AI through image analysis, treatment planning, and patient experience enhancement. Common AI techniques in both domains include machine learning, deep learning, natural language processing, and computer vision. By leveraging these technologies, researchers and clinicians can achieve significant advancements in drug discovery, treatment efficacy, and patient care.

Ultrasound images can reliably detect Developmental Dysplasia of the Hip (DDH) during early infancy. Accuracy of diagnosis depends on the scan quality, which is subjectively assessed by the sonographer during ultrasound examination. Such assessment is prone to errors and often results in poor-quality scans not being reported, risking misdiagnosis. In this paper, we propose an Artificial Intelligence (AI) technique for automatically determining scan quality. We trained a Convolutional Neural Network (CNN) to categorize 3D Ultrasound (3DUS) hip scans as ‘adequate’ or ‘inadequate’ for diagnosis. We evaluated the performance of this AI technique on two datasets—Dataset 1 (DS1) consisting of 2187 3DUS images in which each image was assessed by one reader for scan quality on a scale of 1 (lowest quality) to 5 (optimal quality) and Dataset 2 (DS2) consisting of 107 3DUS images evaluated semi-quantitatively by four readers using a 10-point scoring system. As a binary classifier (adequate/inadequate), the AI technique gave highly accurate predictions on both datasets (DS1 accuracy = 96% and DS2 accuracy = 91%) and showed high agreement with expert readings in terms of Intraclass Correlation Coefficient (ICC) and Cohen’s kappa coefficient (K). Using our AI-based approach as a screening tool during ultrasound scanning or postprocessing would ensure high scan quality and lead to more reliable ultrasound hip examination in infants.

The automotive manufacturing industry faces increasing pressure to optimize production systems, reduce downtime, and cut operational costs. Predictive maintenance (PdM), enabled by artificial intelligence (AI) and machine learning (ML), has emerged as a critical solution to these challenges. This paper investigates the integration of AI and ML techniques into PdM to enhance the efficiency and reliability of manufacturing systems. The research methodology involves using data collected from sensors in manufacturing equipment to train machine learning models for predicting potential failures. Techniques such as time-series forecasting using Long Short-Term Memory (LSTM) networks, anomaly detection via autoencoders, and classification using support vector machines (SVM) and random forests were employed to predict machine failures before they occur. Key findings indicate that AI-based PdM systems significantly improve predictive accuracy, reducing unplanned downtime by 30% and lowering maintenance costs by 25%. This paper introduces a novel framework for integrating real-time predictive maintenance, where AI models continuously learn and adapt to changes in manufacturing environments. Additionally, the research compares various AI techniques, assessing their effectiveness in terms of prediction accuracy and computational efficiency. The paper concludes with recommendations for future research directions, emphasizing the need for further refinement and scaling of AI-based PdM systems in automotive manufacturing.

In this paper, the application of Artificial intelligence (AI) methods in power system protection and inspection has been discussed. In this paper particularly AI application of line scout Robot is used. Line scout robot is an inspection based Artificial intelligence Robot. Line Scout robot is able to work on the power lines while they are operating (at very high tension). The robot can patrol in a substation and inspect equipment with a visible-light camera and thermograph. It has possess a vast array of cameras and tools that allow the remote operators to inspect and conduct (albeit minor) repairs on the power lines without needing to put the off-service or risking their lives. It is preventive arrangement for detection of fault (very high temperature). Keywords: Artificial intelligence (AI), artificial general intelligence (AGI), corona pulse, Line Scout Robot, power system

Several types of Artificial Intelligence (AI) techniques were utilized to predict capillary pressure in carbonate oil reservoirs with complex morphologies. To develop AI models that predict capillary pressure, a training data set is used that comprises of mercury injection drainage capillary pressure data. In this study, the training data included a set of 70% of 202 core samples that included porosity, permeability and grain density measurements from conventional core analysis tests. Models were developed using this data to predict capillary pressure. Using an error minimization routine, a comparison between the predicted results and laboratory measurements was used to show the validity of this analysis. The model was tested against a new dataset, the remaining 30% of the 202 core samples, that was not included in the training phase. This process was performed on mono-modal, bi-modal, and combined modal data sets. The analysis of the AI models used in this study, showed that AI has promising potential; however, there is still room for improvement. Capillary pressure data is one of the most critical parameters used in reservoir characterization and initialization stage of simulation models. Many reservoirs do not have an ample data for a comprehensive evaluation. This method utilizes a small core data set to derive models that can be used to predict capillary pressure in reservoirs that lack these measurements. This proposed approach has three advantages: saves time and money, does not require core samples for new spots in the same area, and uses the available results to their maximum potential from previously destroyed core samples. While previous work did not address the prevalent bimodality of the carbonate rock through the use of Flow Zone Indicator FZI / Rock Quality Index RQI, this paper addresses specifically the AI application to the different rock modals through Thomeer methodology.

In hospital settings, all information concerning the patient’s diseases and medical procedures are routinely registered in free-text format to be further abstracted and translated into standard clinical codes. The derived coded data is used for several purposes, from health care management and decision-making to billing and research. However, clinical coding is mostly performed manually, is very time-consuming, is inefficient and prone to error task. We conducted a bibliometric analysis of the scientific production on automated clinical coding using AI methods in the context of the International Classification of Diseases (ICD) classification system. The study aims to provide an overview and evolution of the research on automated clinical coding through Artificial Intelligence (AI) techniques. We did not consider time or language restrictions. Our analyses focused on characteristics of the retrieved literature, scientific collaboration and main research topics. A total of 1611 publications on automated coding during the last 46 years were retrieved, with significant growth occurring after 2009. The top 10 most productive publication sources are related to medical informatics, even though no common publication source or author was identified. The United States had by far the highest number of publications in this field. We found that natural language processing and machine learning were the main AI methodological areas explored for automated coding applications. Automated clinical coding using AI techniques is still rising and will undoubtedly face several challenges in the coming years. The results of the bibliometric analysis can assist the conduction of a more in-depth review to assess the variation of specific techniques and compare the performance of different methodological approaches regarding automated coding applications.KeywordsClinical codingArtificial intelligenceInternational classification of diseases