Artificial Intelligence Algorithms Research Articles

Artificial Intelligence And Its Implications In Health Care: A Textual Analysis

This all-inclusive research paper plunges into the transformative nature of artificial intelligence (AI) in healthcare, emphasizing its ability to change patient care and administrative activities. The investigation examines systematically how AI technologies are creating major improvements towards patients’ outcomes and enhanced operational efficiency within the healthcare systems. From diagnostics to personalized treatment plans and administrative tasks, AI is being integrated using sophisticated algorithms in numerous aspects of healthcare delivery. One major focus is improving diagnostic accuracy. AI-backed diagnostic tools such as image recognition software as well as predictive models have outperformed traditional methods in medical condition identification and diagnosis. For instance, artificial intelligence algorithms show exceptional skills in analysing medical images resulting to earlier detection as well as accurate diagnoses of diseases like cancer, cardiovascular conditions among others neurological disorders. These enhancements not only allow for timely intervention but they significantly reduce instances where diagnosis goes wrong hence enhancing patient prognosis while reducing health costs drastically. The ability to customize personalized therapy is yet another essential aspect of AI in healthcare. With such information as genetic data, medical records and lifestyle factors, and other patient information, AI systems can develop individualized treatment plans. It is for this reason that personalization of treatments enhances their efficiency while reducing the risk of any adverse effects as well as ensuring patients’ adherence to prescribed therapies. Besides, it is important to note that AI-driven predictive models are helpful in identifying individuals with a higher probability of developing specific conditions thereby allowing interventions aimed at preventing diseases and bettering overall population health. AI has also transformed administrative tasks within hospitals besides its clinical applications. For example, AI driven automation is now used to streamline scheduling of appointments or billing processes or even storage of patient records which are normally considered routine administrative works. In addition, through these means there is reduced burden on administrative duties for healthcare workers who continue to perform them with increased precision and timeliness. The net effect is that time and resources can be redirected towards more direct patient care thus improving service quality across the board’s entire system

Artificial Intelligence Smartphone Application for Detection of Simulated Skin Changes: An In Vivo Pilot Study.

The development of artificial intelligence (AI) is rapidly expanding, showing promise in the dermatological field. Skin checks are a resource-heavy challenge that could potentially benefit from AI-tool assistance, particularly if provided in widely available AI solutions. A novel smartphone application(app)-based AI system, "SCAI," was developed and trained to recognize spots in paired images of skin, pursuing identification of new skin lesions. This pilot study aimed to investigate the feasibility of the SCAI-app to identify simulated skin changes in vivo. The study was conducted in a controlled setting with healthy volunteers and standardized, simulated skin changes (test spots), consisting of customized 3-mm adhesive spots in three colors (black, brown, and red). Each volunteer had a total of eight test spots adhered to four areas on back and legs. The SCAI-app collected smartphone- and template-guided standardized images before and after test spot application, using its backend AI algorithms to identify changes between the paired images. Twenty-four volunteers were included, amounting to a total of 192 test spots. Overall, the detection algorithms identified test spots with a sensitivity of 92.0% (CI: 88.1-95.9) and a specificity of 95.5% (CI: 95.0-96.0). The SCAI-app's positive predictive value was 38.0% (CI: 31.0-44.9), while the negative predictive value was 99.7% (CI: 99.0-100). This pilot study showed that SCAI-app could detect simulated skin changes in a controlled in vivo setting. The app's feasibility in a clinical setting with real-life skin lesions remains to be investigated, where the challenge with false positives in particular needs to be addressed.

A Personalized Teaching System for College English Based on Big Data and Artificial Intelligence

In the ultra-modern digital age, the schooling sector is experiencing a transformative shift driven by the convergence of massive records and artificial Intelligence (AI). This paper offers a singular initiative—a ”personalised teaching machine for university English” that harnesses the electricity of these technologies to revolutionise how English language practice is delivered at the college level. The system is designed to address the various ways of gaining knowledge of the desires of students and adapting dynamically to their talent degrees, styles, and possibilities. The centre of this device lies in its capability to collect, technique, and analyse extensive amounts of records associated with students’ language acquisition journey. By leveraging massive statistics techniques, the machine captures and interprets college students’ interactions with direct materials, assignments, exams, and peer interactions. Concurrently, the latest AI algorithms, which include herbal Language Processing (NLP) and gadget learning (ML), are employed to create a responsive and smart learning environment. The system tailors mastering pathways for personal college students, ensuring they get hold of content material and sporting events aligned with their cutting-edge scalability stages. Ordinary formative checks are performed to gauge college students’ progress, taking into consideration timely interventions and changes in teaching techniques. This progressive customised coaching device for university English now not only enhances language proficiency but additionally promotes self-directed getting-to-know and empowers educators with information-pushed insights. The amalgamation of large facts and AI promises to reshape English language education’s panorama, paving the way for an extra personalised, green, and effective pedagogical method in university settings. In conclusion, this study illuminates the transformative ability of harnessing massive records and AI in schooling, with precise relevance to language instruction. The machine is a pioneering model for personalised, adaptive, and information-centric teaching methodologies in higher training.

Artificial intelligence in action: Improving breast disease management through surgical robotics and remote monitoring

ObjectivesThis article aims to discuss the transformative impact of artificial intelligence (AI) on the identification and management of breast diseases, with a specific focus on breast cancer. Materials and methodsHealthcare providers have integrated AI technologies such as surgical robots and remote monitoring systems to improve the accuracy and efficiency of breast disease diagnosis and treatment. ResultsSurgical robots equipped with AI algorithms provide real-time guidance to surgeons, analyze imaging data, and ensure precise procedures for better outcomes in breast cancer surgeries. AI-driven remote monitoring systems allow for close monitoring of patient health data, disease progression prediction, and personalized treatment recommendations. ConclusionsThe incorporation of AI technology into healthcare practices for breast diseases has revolutionized patient care delivery, enhancing outcomes, promoting patient engagement, and improving overall quality of care. These advancements offer personalized and tailored healthcare strategies that meet the unique needs of each patient, reshaping the healthcare industry.

Blood Vessel Segmentation and Classification of Diabetic Retinopathy with Machine Learning-Based Ensemble Model

The incidence of diabetes has increased in recent times due to factors such as obesity and genetic predisposition. Diabetes wears out the eye vessels over time. Diabetic retinopathy (DR) is a serious disease that leads to vision problems. DR can be diagnosed by specialists who examine the fundus images of the eye at regular intervals. With 537 million diabetics in 2021, this method can be time-consuming, costly and inadequate. Artificial intelligence algorithms can provide fast and cost-effective solutions for DR diagnosis. In this study, the noise of blood vessels in fundus images was eliminated using the LinkNet-RCB7 model, and diabetic retinopathy was categorized into five classes using a machine learning-based ensemble model. Artificial intelligence-based classification training using images as input takes a long time and requires high resource requirements such as Random Access Memory (RAM) and Graphics Processing Unit (GPU). By using Gray Level Cooccurrence Matrix (GLCM) attributes in the classification phase, a lower resource requirement was aimed for. A Dice coefficient of 85.95% was achieved for the segmentation of blood vessels in the Stare dataset, in addition to 97.46% accuracy for binary classification and 96.10% accuracy for classifying DR into five classes in the dataset APTOS 2019.

The use of artificial intelligence, numerical algorithms and expert approach in selected nephrology problems

Background: The integration of AI, numerical algorithms, and expert approaches in nephrology shows promise for enhancing patient care. Traditionally, nephrology relied on clinician expertise, but the growing volume of data requires innovative methods. AI, fueled by vast datasets, can uncover patterns and improve diagnostic accuracy and treatment optimization. Numerical algorithms enable computational modeling of renal physiology and disease progression, aiding in treatment prediction and optimization. Expert systems codify nephrologists' knowledge into decision support tools, aiding in diagnosis and treatment selection. Embracing these technologies can revolutionize nephrological practice, improving patient outcomes.Aim of the study: The aim of the research is to review scientific works on the use of artificial intelligence (AI), numerical algorithms, and expert algorithms in diagnosis, decision support and prediction of selected disease states and issues related to nephrology.Material and methods: The study rigorously retrieved relevant publications from esteemed academic databases, Scopus and PubMed, and screened them based on predefined criteria. Through keyword-based searches, it analyzed the intersection of nephrology and artificial intelligence, offering insights into emerging trends and advancements. This approach facilitated a comprehensive exploration of the integration of artificial intelligence in nephrology research, paving the way for future developments in the field.Results: Artificial intelligence in nephrology and medicine uses various technologies such as machine learning, deep learning and natural language processing to improve diagnosis, treatment and patient care. Analyzes clinical, laboratory, imaging and genetic data to accelerate the identification of kidney diseases. By integrating health information systems and research data, artificial intelligence enables pattern recognition, outcome prediction and treatment optimization, ultimately having the potential to permanently improve patient care.Conclusions: Numerical algorithms, artificial intelligence, and expert algorithms show promise in nephrology, improving patient care and research.

Comprehensive risk management and safety strategies in radiation use in medical imaging

Radiation use in medical imaging, while essential for accurate diagnosis and treatment planning, poses inherent risks that require comprehensive risk management and safety strategies. This review discusses the critical aspects of managing these risks in clinical settings, focusing on patient safety, staff protection, and environmental considerations. A major challenge in medical imaging is balancing the diagnostic benefits of radiation against potential harms, particularly in sensitive populations such as children and pregnant women. Comprehensive risk management involves implementing dose optimization techniques, such as using low-dose imaging protocols and advanced technologies like iterative reconstruction and artificial intelligence (AI) algorithms, which enhance image quality while minimizing exposure. Additionally, ensuring the safety of healthcare staff is paramount. This requires adherence to strict safety protocols, including the use of personal protective equipment (PPE), regular monitoring of radiation levels, and continuous education on safe radiation practices. Institutional policies must also address the proper maintenance and calibration of imaging equipment to prevent unnecessary exposure due to equipment malfunction or improper use. Environmental safety is another critical component, necessitating the safe disposal of radioactive materials and the adoption of energy-efficient technologies to reduce the environmental footprint of medical imaging. Regulatory compliance with international safety standards, such as those set by the International Commission on Radiological Protection (ICRP) and national bodies, is essential for ensuring consistent safety practices across healthcare institutions. Future directions in radiation safety include the integration of digital health technologies to monitor and manage radiation exposure more effectively, and ongoing research into novel imaging techniques that could further reduce radiation risks. Collaboration between healthcare providers, researchers, and policymakers is crucial to advancing safety standards and improving risk management strategies in medical imaging. This review highlights the importance of a comprehensive approach to managing radiation risks in medical imaging, emphasizing the need for continuous innovation, education, and policy support to protect patients, healthcare workers, and the environment.

Combating terrorist financing in cryptocurrency platforms: The role of AI and machine learning

This study addresses the critical issue of terrorist financing through cryptocurrency platforms, a growing concern due to digital currencies' pseudonymous nature and global reach. The research explores the strategic role of Artificial Intelligence (AI) and Machine Learning (ML) in identifying, preventing, and disrupting the flow of illicit funds used to finance terrorism. Employing a mixed-methods approach, the study integrates qualitative case studies of documented instances of cryptocurrency-based terrorist financing with quantitative data analysis from significant cryptocurrency exchanges. Advanced AI and ML algorithms, including supervised learning models such as decision trees and neural networks, were applied to detect suspicious transactions indicative of terrorist activities. The findings reveal that AI and ML technologies significantly enhance the ability to identify patterns of terrorist financing within large and complex datasets, with models achieving precision and recall rates exceeding 90%. However, challenges remain, particularly regarding the quality and standardization of data across platforms, algorithmic biases, and the need for continuous updates to counter evolving tactics used by terrorist organizations. The study concludes that AI and ML present powerful tools for enhancing financial security. However, their successful implementation requires overcoming these challenges through collaborative efforts among stakeholders, including financial institutions, regulators, and technology providers. This research contributes to the growing field of economic crime prevention by offering a robust framework for integrating AI-driven solutions into the fight against terrorist financing on cryptocurrency platforms.

Advancements in artificial intelligence algorithms for the detection of dental caries: A narrative review

Dental caries is a common oral health problem that affects people all over the world. Diagnosing and treating it may be quite difficult. In order to ensure successful treatment and prevent the development of serious infections, early identification is essential. Visual examination and radiographic imaging are two common examples of traditional diagnostic techniques that often fail to reliably detect early-stage caries and differentiate them from non-carious diseases. New developments in artificial intelligence (also known as AI have improved the accuracy and efficacy of caries detection, which presents practical options.

Ultra-low cost and high-performance paper-based flexible pressure sensor for artificial intelligent E-skin

E-skin driven by artificial intelligence algorithms enables the innovation of future technologies such as IoT, intelligent manufacturing, health monitoring and human–machine interfaces for VR/AR. Although many flexible pressure sensors have been reported, the development of sensors that combine low-cost and high performance remains a huge challenge. Herein, we propose an all-paper-based pressure sensor with water-soluble graphite functional layer and carbon interdigital electrodes obtained by friction coating and screen printing, respectively. The sensor boasts a rectangular design measuring 1 cm × 2.15 cm, with a remarkably thin profile of only 71 μm and an ultra-light weight of 6 mg, and the cost of a single sensor is extremely low. Specifically, the sensor achieved a lower detection limit of 0.98 Pa and an upper detection limit of 2000 kPa, which represents a new benchmark among paper-based pressure sensors. It demonstrated high sensitivity across various pressure ranges: 319.94 kPa−1 (0–1 kPa), 97.63 kPa−1 (1–40 kPa), 18.75 kPa−1 (40–200 kPa), and 2.72 kPa−1 (200–2000 kPa). The sensor also exhibited a rapid response and recovery time of 8 ms and 9 ms, respectively, along with excellent durability (>20,000 fatigue tests, 10,000 cycles at 2 kPa and 10,000 cycles at 100 kPa) and high pressure stability. Furthermore, the eco-friendly sensor can identify eight surface textures with a recognition accuracy of 98.33% after training with a 2D convolutional neural network (2D CNN). Finally, a portable wireless wearable device with dimensions of 38 mm × 26 mm × 14 mm and a weight of only 11.265 g has been developed for pressure recognition, breathing, pulse, and heartbeat detection.

A Survey on Detecting Deep Fakes Using Advanced AI-Based Approaches

Today, artificial intelligence is used to clone human faces, which leads to a new technology known as “deepfakes.” Recently, machine learning (ML) approaches and the use of deep learning (DL) networks have captured researchers' competition to achieve the highest classification accuracy in building efficient models for digital content deepfake detection. Therefore, this review analyzes and compares existing deepfake detection methods based on advanced artificial intelligence algorithms. Thus, deepfake detection techniques were classified into three major categories based on the classifier model used (machine learning, deep learning, or hybrid) and then compared to show the aspects that influence the efficiency and accuracy of the algorithms. This research helps researchers develop efficient classification models for deepfake detection applications. Based on the survey information reviewed in this study, a discussion of open issues and future directions is presented. The most important challenges and research directions related to deepfake detection methods are discussed.

Post-operative breast imaging: a management dilemma. Can mammographic artificial intelligence help?

BackgroundImaging of the postoperative breast is a challenging issue for the interpreting physician with many variable findings that may require additional assessment through targeted ultrasound, more mammography views, or other investigations. Artificial intelligence (AI) is a fast-developing field with various applications in the breast imaging including the detection and classification of lesions, the prediction of therapy response, and the prediction of breast cancer risk. This study aimed to identify whether Artificial Intelligence improves the mammographic detection and diagnosis of breast post-operative changes and hence improves follow-up and diagnostic workflow and reduces the need for additional exposure to extra radiation or contrast material doses as in Contrast Enhanced Mammography, and the need for interventional procedures as biopsy.MethodsThis cross-sectional analytic study included 66 female patients following breast-conserving surgeries coming with breast complaints or for follow-up, with mammographically diagnosed changes.ResultsMammography had a sensitivity of 91.7%, a specificity of 94.4%, a positive predictive value (PPV) of 78.6%, a negative predictive value (NPV) of 98.1%, and an accuracy of 93.9%, while the AI method indices were sensitivity 91.7%, specificity 92.6%, (PPV) 73.3%, (NPV) 98%, and accuracy 92.4%. The calculated cut-off point for the quantitative AI (probability of malignancy “POM” score) was 51.5%. There was a statistically significantly higher average in the percentage of POM in malignant cases (76.5 ± 27.3%) compared to benign cases (27.1 ± 19.7%). However, the final indices for the combined use of mammography and (AI) were sensitivity 100%, specificity 88.9%, (PPV) 66.7%, (NPV) 100%, and accuracy 90.9%.ConclusionApplying the AI algorithm on mammograms showed positive impacts on the sensitivity of the post-operative breast assessment, with an excellent reduction of the mammographic missed cancers.

Precision HER2: a comprehensive AI system for accurate and consistent evaluation of HER2 expression in invasive breast Cancer

BackgroundWith the development of novel anti-HER2 targeted drugs, such as ADCs, it has become increasingly important to accurately interpret HER2 expression in breast cancer. Previous studies have demonstrated high intra-observer and inter-observer variabilities in evaluating HER2 staining by human eyes. There exists a strong requirement to develop artificial intelligence (AI) systems to achieve high-precision HER2 expression scoring for better clinical therapy.MethodsIn the present study, we collected breast cancer tissue samples and stained consecutive sections with anti-Calponin and anti-HER2 antibodies. High-quality digital images were selected from immunohistochemical slides and interpreted as HER2 3+, 2+, 1+, and 0. AI models were trained and assessed using annotated training and testing sets. The AI model was trained to automatically identify ductal carcinoma in situ (DCIS) by Calponin staining and myoepithelial annotation and filter out DCIS components in HER2-stained slides using image-overlapping techniques. Furthermore, we organized two-phase validation studies. In phase one, pathologists interpreted 112 HER2 whole-slide images (WSIs) without AI assistance, whereas in phase two, pathologists read the same slides using the AI system after a washing period of 2 weeks.ResultsOur AI model greatly improved the accuracy of reading (0.902 vs. 0.710). The number of HER2 1 + patients misdiagnosed as HER2 0 was significantly reduced (32/279 vs. 65/279), and they benefitted from ADC drugs. In addition, the AI algorithm improved the intra-group consistency of HER2 readings by pathologists with different years of experience (intra-class correlation coefficient [ICC]: 0.872–0.926 vs. 0.818–0.908), with the improvement most pronounced among junior pathologists (0.885 vs. 0.818).ConclusionsWe proposed a high-precision AI system to identify and filter out DCIS components and automatically evaluate HER2 expression in invasive breast cancer.

HUMAN RESOURCE MANAGEMENT AND ARTIFICIAL INTELLIGENCE: TRANSFORMATIVE EFFECTS AND FUTURE PROSPECTS

Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy. Ancient civilizations contained tales of intelligent robots, such as Greek and Chinese cultures. AI as a modern phenomenon which took shape in the mid-20th century with the advent of digital computers. The first breakthrough, published in 1950, was Alan Turing's paper, ‘Computing Machinery and Intelligence. AI can make provide detailed solution to users and experts. They can act independently, replacing the need for human intelligence or intervention (a classic example being a self-driving car). Many sectors like finance, healthcare, to human resource management (HRM), rely on AI technologies. AI's integration in HRM has brought about great change in conventional HR practices used to measure efficiency, accuracy, strategic decision-making, etc. AI technologies help to provide solution to automation of repetitive tasks, analysis of sprawling volumes of data and insights facilitating decision-making. Through this research paper we try to understand the AI's effects on HR processes, in terms of recruitment, onboarding, performance appraisal, training and development, employee engagement, workforce planning; understanding the benefits and cons of implementing AI in HRM; ethical considerations and potential biases in AI algorithms relevant to HR practices; analysis of emergent AI trends with respect to HRM. Trends in AI in HR tend towards a most promising future, presenting several innovations. Accordingly, HR professionals will be expected to acquire competency with regards to new roles and responsibilities to avail AI in enhancing strategic planning, ethical oversight, and data-driven decision-making.

Clinical Decision Support and Natural Language Processing in Medicine: Systematic Literature Review.

Ensuring access to accurate and verified information is essential for effective patient treatment and diagnosis. Although health workers rely on the internet for clinical data, there is a need for a more streamlined approach. This systematic review aims to assess the current state of artificial intelligence (AI) and natural language processing (NLP) techniques in health care to identify their potential use in electronic health records and automated information searches. A search was conducted in the PubMed, Embase, ScienceDirect, Scopus, and Web of Science online databases for articles published between January 2000 and April 2023. The only inclusion criteria were (1) original research articles and studies on the application of AI-based medical clinical decision support using NLP techniques and (2) publications in English. A Critical Appraisal Skills Programme tool was used to assess the quality of the studies. The search yielded 707 articles, from which 26 studies were included (24 original articles and 2 systematic reviews). Of the evaluated articles, 21 (81%) explained the use of NLP as a source of data collection, 18 (69%) used electronic health records as a data source, and a further 8 (31%) were based on clinical data. Only 5 (19%) of the articles showed the use of combined strategies for NLP to obtain clinical data. In total, 16 (62%) articles presented stand-alone data review algorithms. Other studies (n=9, 35%) showed that the clinical decision support system alternative was also a way of displaying the information obtained for immediate clinical use. The use of NLP engines can effectively improve clinical decision systems' accuracy, while biphasic tools combining AI algorithms and human criteria may optimize clinical diagnosis and treatment flows. PROSPERO CRD42022373386; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=373386.

Innovative Integration of Computer Network Technology in Modern Educational Systems

A novel educational system has been developed using the Radial Basis Function (RBF) algorithm to address the limitations of traditional classroom environments, which often rely on standardized material and fixed teaching methods. This research assesses recent advancements in interactive intelligent education systems by combining Artificial Intelligence (AI) with interactive teaching methods. A model based on cognitive functions is constructed using the RBF algorithm to personalize instructional approaches and foster a self-directed learning platform. The research methodology involves a comprehensive literature review, developing an Education Intelligent System (EIS) using AI-driven cognitive modelling, and the implementation of the RBF algorithm within a neural network architecture. The system's effectiveness is evaluated through empirical methods, including extensive data analysis and continuous refinement based on student performance feedback. Additionally, a network topology model is designed to enhance the system's adaptability for different roles within the educational framework. The results show significant improvements in instructional effectiveness, learner engagement, and personalized learning experiences. This research demonstrates that AI and interactive technologies can revolutionize conventional educational methods, enhance learner proficiency, and cultivate a more dynamic and engaging learning environment. Future work aims to improve the system's user interface and analyze larger datasets to refine the AI algorithms further.

AI-powered genomic mutation signature for predicting immune checkpoint inhibitor therapy outcomes in gastroesophageal cancer: a multi-cohort analysis

BackgroundImmune checkpoint inhibitors (ICIs) have significantly transformed the treatment of gastroesophageal cancer (GEC). However, the lack of reliable prognostic biomarkers hinders the ability to predict patient response to ICI therapy.MethodsIn this study, we engineered and validated a genomic mutation signature (GMS) utilizing an innovative artificial intelligence (AI) algorithm to forecast ICI therapy outcomes in GEC patients. We further explored immune profiles across subtypes through comprehensive multiomics analysis. Our investigation of drug sensitivity data from the Genomics of Drug Sensitivity in Cancer (GDSC) database led to the identification of trametinib as a potential therapeutic agent. We subsequently evaluated trametinib’s efficacy in AGS and MKN45 cell lines using Cell Counting Kit-8 (CCK8) assays and clonogenic experiments.ResultsWe developed a GMS by integrating 297 algorithms, enabling autonomous prognosis prediction for GEC patients. The GMS demonstrated consistent performance across three public cohorts, exhibiting high sensitivity and specificity for overall survival (OS) at 6, 12, and 18 months, as shown by Receiver Operator Characteristic Curve (ROC) analysis. Notably, the GMS surpassed traditional clinical and molecular features, including tumor mutational burden (TMB), programmed death-ligand 1 (PD-L1) expression, and microsatellite instability (MSI), in predictive accuracy. Low-risk samples exhibited elevated levels of cytolytic immune cells and heightened immunogenic potential compared to high-risk samples. Our investigation identified trametinib as a potential therapeutic agent. An inverse correlation was observed between GMS and trametinib IC50. Moreover, the high-risk-derived AGS cell line showed increased sensitivity to trametinib compared to the low-risk-derived MKN45 cell line.ConclusionThe GMS utilized in this study successfully demonstrated the ability to reliably predict the survival advantage for patients with GECs undergoing ICI therapy.

Using artificial intelligence algorithms in the investigation of mixed layer depth seasonal changes in the Barents Sea

The study aims at discovering features of seasonal changes in the mixed layer depth (MLD) of the Barents Sea in 1993–2020.Charts of the distribution of the MLD in the Barents Sea in 1993–2020 provided by the Copernicus Marine Service were used as the material of the study.Methods of the study: cluster analysis, machine learning, neuronal networks, the nearest neighbor method (kNN).Results. Classification of the data sets of the MLD distribution according to their seasonal features was carried out based on the modelling using AI algorithms and machine learning. It was concluded that winter is specified by two classes (increased/decreased values of the layer thickness). The third class includes spring and autumn when distributions of the MLD are close to one another, and the fourth class comprises summer (June-September) when the MLD grows very slowly.Practical relevance. The results will contribute to a better understanding of the hydrophysical processes of the Barents Sea and can further be used as series of independent variables to study the Barents Sea ecosystem and to estimate a stock and a catch forecast of commercial aquatic organisms.

Energy Efficiency Evaluation of Artificial Intelligence Algorithms

This article advances the discourse on sustainable and energy-efficient software by examining the performance and energy efficiency of intelligent algorithms within the framework of green and sustainable computing. Building on previous research, it explores the theoretical implications of Bremermann’s limit on efforts to enhance computer performance through more extensive methods. The study presents an empirical investigation into heuristic methods for search and optimisation, demonstrating the energy efficiency of various algorithms in both simple and complex tasks. It also identifies key factors influencing the energy consumption of algorithms and their potential impact on computational processes. Furthermore, the article discusses cognitive concepts and their interplay with computational intelligence, highlighting the role of cognition in the evolution of intelligent algorithms. The conclusion offers insights into the future directions of research in this area, emphasising the need for continued exploration of energy-efficient computing methodologies.

Development and evaluation of an emergency department serious game for undergraduate medical students

BackgroundSerious games are risk-free environments training various medical competencies, such as clinical reasoning, without endangering patients’ safety. Furthermore, serious games provide a context for training situations with unpredictable outcomes. Training these competencies is particularly important for healthcare professionals in emergency medicine.MethodsBased on these considerations, we designed, implemented, and evaluated a serious game in form of an emergency department, containing the features of a virtual patient generator, a chatbot for medical history taking with self-formulated questions, artificially generated faces based on an artificial intelligence algorithm, and feedback for students. The development process was based on an already existing framework resulting in an iterative procedure between development and evaluation. The serious game was evaluated using the System Usability Scale and the User Experience Questionnaire.ResultsThe System Usability Scale provided a substantial result for the usability. In terms of the user experience, four scales yielded positive results, whereas two scales yielded neutral results.ConclusionThe evaluation of both usability and user experience yielded overall positive results, while simultaneously identifying potential areas for improvement. Further studies will address the implementation of additional game design elements, and testing student learning outcome.