Cost-Effective Machine Learning for Automatically Processing Bibliographic Metadata

Recent advances in the fields of artificial intelligence and machine learning have paved a way in solving the unsolved problems embarking into a new dimension, especially, when there is increase in complexity of molecules. Reports have shown the necessity to employ these techniques to address the environmental problems. Herein we report the CO2 sequestration process by means of artificial intelligence (AI) and machine learning (ML) tools. The AI and ML approaches adopted enhance the accuracy of the results and at the same time give scope to explore new strategies in understanding the CO2 sequestration process. Herein we considered the reported active compounds observed in traditional medicinal plants like Oscimum, Azadiracta, Psidium and Ficus leaves and Curcuma and, their interactions with CO2. The crystal structures of the active compounds, collected from NCBI portal, are used for all the calculations. To understand the probable interactions of CO2 with active components AI tool IBMRXN was used and the properties of molecules are evaluated. ML techniques are employed using density functional theory method. Keeping in view the complexity of the molecules, optimization of the molecules is carried out at M062X/6-31G(d) level of theory. HOMO-LUMO energy gaps and binding energies are calculated at M062X/6-311+G(d,p)//M062X/6-31G(d) level of theory.

Anaemia is the term for low red blood cell numbers or a deficiency of hemoglobin, the protein that carries oxygen in red blood cells. This results in a reduction of oxygen-carrying capacity of blood to various tissues in the body, & lead to symptoms like weakness and exhaustion. There are various forms of anaemia, and they all have unique reasons. The main purpose of the review is to outline the types of anaemia and highlight the potential of artificial intelligence (AI) and machine learning (ML) tools in detection. There is high accuracy in identifying anaemia by using AI and ML tools that include analyzing the laboratory parameters and medical imaging. There are various Machine learning tools to identify anaemia in the past and treat anaemia by using AI tools. Promising approaches for effective anemia identification and treatment are offered by AI-assisted methods. By incorporating these advancements into clinical practice, the burden of undetected or improperly managed anemia could be lessened, and patient outcomes could be improved.

The accounting and auditing profession is undergoing a significant transformation driven by the rapid advancement and incorporation of artificial intelligence (AI) and machine learning (ML) tools. These innovative technologies have the potential to automate mundane tasks, improve data analysis capabilities, and enhance decision-making processes, fundamentally changing how accounting and auditing professionals carry out their responsibilities. While these advancements present challenges and opportunities, they also require a reevaluation of skills, knowledge, and ethical considerations for the profession to flourish in this evolving environment. This paper investigates the expectations of accounting and auditing students regarding the impact of artificial intelligence (AI) and machine learning (ML) tools on their future professions. Data was collected through a structured survey questionnaire completed by a sample of 98 students. Statistical methods, including regression analysis, were used to analyze the collected data. The results indicate that students are generally optimistic about the potential of these technologies to enhance efficiency, accuracy, and decision-making in accounting and auditing. However, they also express apprehensions about job security and the necessity for continuous learning to adapt to technological advancements.This research contributes to the literature by offering insights into the expectations of future professionals in the accounting and auditing domain and emphasizing the significance of preparing students for the evolving technological landscape. The study’s findings underline the importance of aligning educational programs with the demands of AI and ML integration to ensure the readiness of future practitioners.

Simple SummaryIn this paper, the authors show that artificial intelligence (AI) and machine learning (ML) are useful approaches to integrate multifactorial data and helpful for personalized prediction. In detail, compared to PD-L1 for advanced non-small cell lung cancer (NSCLC), ML tools predicted better responder (R) and non-responder (NR) patients to immunotherapy (IO). It was also able to indirectly foresee OS and PFS of R and NR patients. Given the high incidence of NSCLC, and the absence of reliable biomarkers to predict the response to IO other than PD-L1, the authors believe this research may be of great interest to anyone involved in thoracic oncology. Furthermore, given the growing interest from the scientific community in AI and ML, the authors believe that this manuscript could represent a fascinating topic to anyone who needs to exploit the enormous potential of these tools in the treatment of cancer.(1) Background: In advanced non-small cell lung cancer (aNSCLC), programmed death ligand 1 (PD-L1) remains the only biomarker for candidate patients to immunotherapy (IO). This study aimed at using artificial intelligence (AI) and machine learning (ML) tools to improve response and efficacy predictions in aNSCLC patients treated with IO. (2) Methods: Real world data and the blood microRNA signature classifier (MSC) were used. Patients were divided into responders (R) and non-responders (NR) to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. (3) Results: One-hundred sixty-four out of 200 patients (i.e., only those ones with PD-L1 data available) were considered in the model, 73 (44.5%) were R and 91 (55.5%) NR. Overall, the best model was the linear regression (RL) and included 5 features. The model predicting R/NR of patients achieved accuracy ACC = 0.756, F1 score F1 = 0.722, and area under the ROC curve AUC = 0.82. LR was also the best-performing model in predicting patients with long survival (24 months OS), achieving ACC = 0.839, F1 = 0.908, and AUC = 0.87. (4) Conclusions: The results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to select NSCLC patients as candidates for IO.

Artificial intelligence (AI) and machine learning (ML) tools play a significant role in the recent evolution of smart systems. AI solutions are pushing towards a significant shift in many fields such as healthcare, autonomous airplanes and vehicles, security, marketing customer profiling and other diverse areas. One of the main challenges hindering the AI potential is the demand for high-performance computation resources. Recently, hardware accelerators are developed in order to provide the needed computational power for the AI and ML tools. In the literature, hardware accelerators are built using FPGAs, GPUs and ASICs to accelerate computationally intensive tasks. These accelerators provide high-performance hardware while preserving the required accuracy. In this work, we present a systematic literature review that focuses on exploring the available hardware accelerators for the AI and ML tools. More than 169 different research papers published between the years 2009 and 2019 are studied and analysed.

Artificial intelligence (AI) and machine learning (ML) tools have been used across all spectrums of the drug development process. The US Food and Drug Administration has accepted over 100 submissions that cite the use of AI that has assisted with clinical trial recruitment, drug discovery, and dose optimization. With these insights in mind, health authorities expect the use of AI and ML tools in regulatory processes and is working to provide meaningful oversight. In medical writing, AI and ML sources have been essential to the writing process by formatting documents, designing study protocols, analyzing manuscripts, and communicating complex scientific information to lay audiences. The technological advances of AI and ML are rapidly evolving, and there is indeed a place for such technologies in the medical writer’s resource toolbox.

Artificial intelligence (AI) is lowering the period and cost of the medication research and discovery process Artificial Intelligence is a revolution of medical research in pharmaceutical companies. In a review article, we give a summary of the many artificial intelligence tools of machine learning (ML) and deep learning (DL) techniques that will be used in drug research and discovery in the future. AI techniques and tools are more specifically designed or better programmed to mimic the operations of the human brain. AI is frequently used in drug discovery for de novo drug creation, virtual screening, reaction prediction, and de novo protein design. In addition, the application of AI and techniques of AI. ML is a disease diagnosis, de novo drug design, drug prediction for diseases, and big data prediction using ANN, CNN, and SNN, as well as deep learning. Furthermore, the function, applications, and methods of AI. Technological hurdles also face the contemporary XAI, and “low level” molecular representations (such as SMILES strings) that are useful for machine learning and AI tools are ‘deep chem’ in drug development several cutting-edge methods referred to as Knowledge Base Systems (KBS). The AI-based nanorobots are drug discovery on creating implantable nanorobots for the targeted delivery of medications and genes, factors including sustained release, dose modification, and control release need to be taken into consideration. Finally recent development of ML and DL techniques and AI models are more useful in the drug development and drug discovery process.

The Indian Human Resource (HR) market is undergoing a significant transformation with the advent of Artificial Intelligence (AI) and Machine Learning (ML) tools. This research paper explores the current state of the Indian HR market, the integration of AI and ML in HR practices, and the resultant impact on growth and development. Through comprehensive analysis and case studies, this paper highlights the opportunities and challenges of adopting AI and ML in HR and provides strategic recommendations for HR professionals in India to leverage these technologies for sustainable growth.

Sensors are essential to collect information from the environment. Smart sensing technology can use artificial intelligence (AI) and machine learning (ML) techniques to enhance the process of information acquisition. AI and ML techniques in smart sensors can improve the process of collection of data and reduce the exchange of data within the networks significantly. The attributes of remote sensor systems bring numerous difficulties. Moreover, the number of interconnected gadgets generate huge amounts of data. These require smart processing and examination to build up a more significant level of information and outcomes in better dynamic, estimating, and solid administration of sensors. So, in this article, a comprehensive review of AI- and ML-based applications has been done to enhance the utility of smart sensor network in different aspects, and discussion about feature selection and dimension reduction with AI and ML tools is also depicted.

Subsea tiebacks are a principal building block of deepwater developments. Operators seek to continuously enhance operational efficiencies and tieback distances while reducing capital, operating expenditures, and emissions. Rapid advances have been made in remote operations, subsea equipment capabilities and standardization. However, significant additional improvements in subsea tieback operational efficiencies are achievable by leveraging the steep decline in data storage and processing costs, the massive increase in processing power and high-speed internet along with the availability of proven Artificial Intelligence (AI) and Machine Learning (ML) tools. Subsea tieback operational efficiency improvements are bottlenecked by human operator ability to process and respond in a timely manner to the overwhelming quantity of data collected by modern subsea monitoring and sensor technologies. This paper will address specific areas where the proven ability of AI and ML tools to assimilate large quantities of data, together with bespoke algorithms, can provide real-time, targeted recommendations to unlock the following improvements in subsea tieback operational efficiencies: Enhanced Oil Recovery: With real-time detection and analyses of changes and anomalies in production flow, AI can use Model Predictive Control (MPC) or Hybrid AI-Physics Models to optimize production rates, riser base gas lift, and gas or water injection systems. Predictive Maintenance: By ingesting and analyzing real-time sensor and Autonomous Underwater Vehicle (AUV)/Remote Operated Vehicle (ROV) data, AI algorithms can:Improve performance and reliability of subsea boosting systems (multiphase pumps, power generation and conditioning).Increase operational uptime and service life by anticipating potential failures in subsea infrastructure. Operational Efficiencies: AI can process vast amounts of data from disparate sources to support decision-making related to:Monitoring and predicting flow assurance challenges while suggesting targeted mitigations.Maintaining optimum production rates and flow conditions from wellhead to production manifold.Providing a holistic view of complex multi-tieback systems to facilitate decision-making targeting total asset performance. By combining data gathered from multiple sources (SCADA, PLC, Camera DVR, etc.) with bespoke algorithms, AI can provide diagnostic, prescriptive, intelligent insights; accelerate positive interventions; increase ultimate recovery while reducing downtime, power requirements, and emissions. The offshore oil and gas industry is embracing AI to make operations safer and more efficient. This paper will show how an integrated, holistic and targeted approach of incorporating AI into subsea tiebacks will enable the industry to immediately and inexpensively attain new levels of operational excellence, cost-effectiveness, and environmental sustainability.

Technological advances in omics evaluation, bioinformatics, and artificial intelligence have made us rethink ways to improve patient outcomes. Collective quantification and characterization of biological data including genomics, epigenomics, metabolomics, and proteomics is now feasible at low cost with rapid turnover. Significant advances in the integration methods of these multiomics data sets by machine learning promise us a holistic view of disease pathogenesis and yield biomarkers for disease diagnosis and prognosis. Using machine learning tools and algorithms, it is possible to integrate multiomics data with clinical information to develop predictive models that identify risk before the condition is clinically apparent, thus facilitating early interventions to improve the health trajectories of the patients. In this review, we intend to update the readers on the recent developments related to the use of artificial intelligence in integrating multiomic and clinical data sets in the field of perinatology, focusing on neonatal intensive care and the opportunities for precision medicine. We intend to briefly discuss the potential negative societal and ethical consequences of using artificial intelligence in healthcare. We are poised for a new era in medicine where computational analysis of biological and clinical data sets will make precision medicine a reality. IMPACT: Biotechnological advances have made multiomic evaluations feasible and integration of multiomics data may provide a holistic view of disease pathophysiology. Artificial Intelligence and machine learning tools are being increasingly used in healthcare for diagnosis, prognostication, and outcome predictions. Leveraging artificial intelligence and machine learning tools for integration of multiomics and clinical data will pave the way for precision medicine in perinatology.

Artificial Intelligence (AI) tools can revolutionize literature review practices by transforming the research landscape towards more efficient and reliable review processes. While conducting literature can be challenging and time-consuming, there is a plethora of AI powered tools which uncover potential solutions to the challenge. AI tools may reduce the time spent on repetitive tasks, allowing scholars to focus more on critical analysis and interpretation. Due to the rising abundance of AI tools, it is difficult to decide which AI tools are best for individual research problems or projects. While concerns exist around the ethical and quality consequences of using AI. The study aims to explore the usage of AI tools on the systematics literature review process, specifically focusing on their effectiveness in various stages and ethical concerns. IEEE and MDPI Journal papers from 2020 to 2024 were reviewed using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RobotReviewer, Covidence and EPPI-Reviewer are AI tools commonly used. These AI tools are designed to support different aspects of the systematic literature review process by offering capabilities such as problem formulation, literature search, inclusion screening and quality assessment. AI tools demonstrate improved effectiveness of literature searches, screening processes and data extraction. Language and content presentation, incorrect citation and plagiarism, grammar and spelling errors may be ren when utilizing AI. Concerns related to data quality, biases, and the need for human oversight were identified.

Objective: To analyze the quality of artificial intelligence (AI) and machine learning (ML) tools developed for facial aesthetic surgery. Data Sources: Medline, Embase, CINAHL, Central, Scopus, and Web of Science databases were searched in February 2024. Study Selection: All original research in adults undergoing facial aesthetic surgery was included. Pilot reports, case reports, case series (n < 5), conference proceedings, letters (except research letters and brief reports), and editorials were excluded. Main Outcomes and Measures: Facial aesthetic surgery procedures employing AI and ML tools to measure improvements in diagnostic accuracy, predictive outcomes, precision patient counseling, and the scope of facial aesthetic surgery procedures where these tools have been implemented. Results: Out of 494 initial studies, 66 were included in the qualitative analysis. Of these, 42 (63.6%) were of "good" quality, 20 (30.3%) were of "fair" quality, and 4 (6.1%) were of "poor" quality. Conclusion: AI improves diagnostic accuracy, predictive capabilities, patient counseling, and facial aesthetic surgery treatment planning.

The simulation of human intelligence by machines, particularly computer systems, is known as artificial intelligence (AI) and a subfield of artificial intelligence and computer science called machine learning (ML) employs data and algorithms to develop model is capable of carrying out specific tasks. Artificial intelligence and machine learning tools transformed talent acquisition by automating tasks, reducing bias, improving candidate experience, and providing data-driven insights for better decision-making. Implementing technologies can give organizations a competitive edge in attracting and retaining top talent however it is essential to balance automation with human insight to ensure fairness and ethical practices throughout the hiring process. Even though artificial intelligence and machine learning have promise to improve the talent acquisition process, there are several drawbacks and challenges that organizations need to consider and address to ensure fairness, accuracy, and a positive candidate experience. Balancing the benefits of automation with the need for human judgment and ethics is crucial in using technologies effectively. This study is both exploratory and descriptive with the salient purpose of gaining familiarity and accurately portraying the current position of artificial intelligence and machine learning-enabled talent acquisition processes in organizations. It explores the significance and utility of artificial intelligence and machine learning in the talent acquisition process, identifies different machine learning algorithms that can be used and their functionality, simultaneously evaluates the impact, and elaborates on the drawbacks and challenges of its application. The present study finds that the application of technology is in the growth stage and there are still doubts and barriers to its adoption, in the end, it provides suggestions and recommendations to different stakeholders of the talent acquisition process.

The widespread deployment of artificial intelligence (AI) and machine learning tools has created a shift in knowledge culture. The marginalisation of slower, more traditional modes of engagement for quantifiable data easily parsed by mathematical algorithms has resulted in prioritising proprietary or opaque datasets (knowledge) explicitly constructed with measurable parameters. Well-documented concerns persist regarding the narrow range of human data used by algorithmic tools, data that arguably encapsulates the many failures of human society. The inevitable result of the use and priority of this data, alongside very particular notions of value and what is valuable, is a replication of many of the foibles of our history as a species.Cultural practice in general necessitates the communication of what drives our hopes and underlies our experiences. In algorithmic times we can see that this kind of communication supports some of the many critiques of AI and machine learning already extant in activist circles. Through investigating some of the theoretical backgrounds of this resistance, this article uses the first iteration of HEXORCISMOS’S SEMILLA AI project and the resulting album release as one of the many possible ways in which we might use machine learning and AI tools alongside very deliberate and uplifting models of community and community building.