Artificial Intelligence Research Articles

A Quantum-Secure Cryptographic Algorithm Integrating Fractals and Prime Numbers

The present work introduces a new scheme of data cryptography in the context of emerging trends due to the challenge of defending critical network infrastructure against new exploit systems based on artificial intelligence or defending against quantum threats. In this paper, we will present an innovative cryptographic system composed of keys coming from fractals and prime numbers that are additionally manipulated through mathematical operations using matrices and quantum security. This technique ensures a high level of security, as demonstrated by the NIST p-values calculated on the key. This paper works upon the foundation on the previous work F&NIF (Fractal & Numerical Information Fusion), as we will discuss in the paper. In this work, we take this procedure and expand it with these new added features, using new fractal schemes and, in particular, implementing a novel quantum security procedure. This algorithm creates a security key applicable to cryptography that is resistant to quantum attacks since this procedure is quantum-crypto-agile.

Ultrasound in in vitro fertilization programs

Ultrasound examination is an integral part of in vitro fertilization programs. This work shows that the antral follicle count compared to the level of antimüllerian hormone is a preferred marker for assessing the ovarian reserve, response to ovarian stimulation and the results of in vitro fertilization programs, while counting the antral follicle count can be carried out on any day of the menstrual cycle without reducing the informativeness of the study of the ovarian reserve, which is convenient for both patients and doctors. 3D transvaginal ultrasound allows automated assessment of the number of antral follicles with high accuracy and efficiency in less time than 2D transvaginal ultrasound. Using 3D transvaginal ultrasound and artificial intelligence, a threshold value of dominant follicle volume of 0.5 cm3 or greater was determined as a marker for predicting the number of mature oocytes in in vitro fertilization programs. The measurement of endometrial thickness and volume in combination with the determination of vascularization index, flow index, vascularization-flow index and systole-diastolic ratio in the uterine artery at transvaginal ultrasound allows predicting the results of in vitro fertilization programs. Modern possibilities of transabdominal oocyte aspiration using vaginal probe are presented.

ANTHROPOLOGY OF THE FUTURE: HOW TECHNOLOGY WILL CHANGE OUR UNDERSTANDING OF HUMANITY IN 2025

As we venture into 2025, the intersection of anthropology and technology presents a fascinating frontier for understanding humanity. This article explores the transformative impact of emerging technologies on our perceptions and experiences of being human. Anthropology, traditionally focused on the study of human societies, cultures, and evolutionary development, is now increasingly intertwined with the rapid advancements in technology. (Lytvynova, L. V. 2004) The article delves into the evolution of anthropological studies in the context of technological innovation. Historically, anthropology has provided insights into the cultural and social dynamics of human societies. However, the advent of digital technologies, artificial intelligence, and biotechnology is reshaping these dynamics, prompting anthropologists to reconsider their frameworks and methodologies. The integration of big data analytics, for instance, allows for a more nuanced understanding of human behavior on a global scale, uncovering patterns and trends that were previously elusive. Next, the article examines how technologies such as virtual reality (VR) and augmented reality (AR) are revolutionizing anthropological research and education. These immersive technologies enable researchers to recreate and explore ancient civilizations, offering new perspectives on historical and cultural contexts. Moreover, VR and AR provide powerful tools for education, allowing students to engage with anthropological content in more interactive and impactful ways.

Bibliometric analysis of citizen science in global healthcare: Current trends, challenges, and future directions

With the development of information technologies such as big data, cloud computing, and artificial intelligence, the application scenarios of citizen science worldwide are no longer limited to natural science research. A large number of application cases have emerged in the field of medical health, which suggests that the development of citizen science may bring new impetus to medical health. This study employs CiteSpace for a bibliometric analysis to evaluate the current status, objectives, and methodologies of citizen science in global healthcare. An analysis of 248 articles from the Web of Science Core Collection reveals that citizen science is predominantly implemented in high-income countries, including the United States, the United Kingdom, Germany, and the Netherlands, with significant applications in public health, chronic disease management, infectious disease surveillance, and digital health. The COVID-19 pandemic has further catalyzed the integration of citizen science into health surveillance and public health responses. Conversely, in low- and middle-income countries (LMICs), the development and implementation of citizen science remain nascent, primarily concentrated in traditional domains such as environmental science, ecology, and biodiversity conservation, with limited penetration into the healthcare sector. Several structural barriers, including inadequate technological infrastructure, insufficient policy support, limited public engagement, and constrained resource allocation, have impeded the broader adoption of citizen science in these regions. To mitigate these challenges, this study proposes targeted strategies, these interventions are anticipated to facilitate the expansion of citizen sciences role in global health governance, ultimately contributing to improved public health outcomes and social welfare.

Development of an automated artificial intelligence-based system for urogenital schistosomiasis diagnosis using digital image analysis techniques and a robotized microscope.

Urogenital schistosomiasis is considered a Neglected Tropical Disease (NTD) by the World Health Organization (WHO). It is estimated to affect 150 million people worldwide, with a high relevance in resource-poor settings of the African continent. The gold-standard diagnosis is still direct observation of Schistosoma haematobium eggs in urine samples by optical microscopy. Novel diagnostic techniques based on digital image analysis by Artificial Intelligence (AI) tools are a suitable alternative for schistosomiasis diagnosis. Digital images of 24 urine sediment samples were acquired in non-endemic settings. S. haematobium eggs were manually labeled in digital images by laboratory professionals and used for training YOLOv5 and YOLOv8 models, which would achieve automatic detection and localization of the eggs. Urine sediment images were also employed to perform binary classification of images to detect erythrocytes/leukocytes with the MobileNetv3Large, EfficientNetv2, and NasNetLarge models. A robotized microscope system was employed to automatically move the slide through the X-Y axis and to auto-focus the sample. A total number of 1189 labels were annotated in 1017 digital images from urine sediment samples. YOLOv5x training demonstrated a 99.3% precision, 99.4% recall, 99.3% F-score, and 99.4% mAP0.5 for S. haematobium detection. NasNetLarge has an 85.6% accuracy for erythrocyte/leukocyte detection with the test dataset. Convolutional neural network training and comparison demonstrated that YOLOv5x for the detection of eggs and NasNetLarge for the binary image classification to detect erythrocytes/leukocytes were the best options for our digital image database. The development of low-cost novel diagnostic techniques based on the detection and identification of S. haematobium eggs in urine by AI tools would be a suitable alternative to conventional microscopy in non-endemic settings. This technical proof-of-principle study allows laying the basis for improving the system, and optimizing its implementation in the laboratories.

Enhancing the Rainfall Forecasting Accuracy of Ensemble Numerical Prediction Systems via Convolutional Neural Networks

Abstract Ensemble prediction systems are commonly used to demonstrate the potential uncertainty of weather forecasts. These systems also help provide weather predictions to prepare for disasters. Specifically, a type of prediction called a quantitative precipitation forecast (QPF) is calculated from the average of multiple forecasts. The probability-matched mean (PMM) method is used to improve these QPF predictions when they underestimate rainfall. However, the PMM method can exhibit limitations when dealing with certain types of rain patterns. To address this issue, this study focuses on improving short-term heavy rainfall predictions using an artificial intelligence (AI) algorithm that combines deep neural networks (DNNs), including convolutional neural networks (CNNs), with a space-based attention mechanism (convolutional block attention module [CBAM]). Four experiments were conducted to evaluate the new method, including those performed to optimize the timing of a 24-hour QPF model, adjust the training dataset, and refine the training algorithm. Two specific weather events were assessed: rainfall influenced by southwest winds after typhoons and afternoon thunderstorm rainfall. A comparison of the results obtained via the root mean square error (RMSE) and structural similarity index measure (SSIM) reveals that the accuracy and distribution of the QPF predictions significantly improved over those of the PMM method. Compared with the PMM method, our approach can reduce the RMSE from 77.51 to 19.52 in the Mei-yu case, an improvement of approximately 74.82%. The SSIM also increased from 0.33 to 0.56, indicating a 70.9% enhancement. Overall, the new approach successfully enhances rainfall prediction accuracy via AI techniques and has the potential to be applied in disaster preparedness operations.

Asteroid material classification based on multi-parameter constraints using artificial intelligence

Material types of asteroids provide key clues to their evolutionary history and contained resources. The Gaia mission has released extensive low-resolution spectral observation data of small Solar System bodies. However, methods for classifying asteroids based on low-resolution space-based spectra are still inadequate, and do not fully leverage the complementary features of spectra and multiple intrinsic attributes of asteroids to achieve precise material classification. Our goal is to propose a method with a higher generalization accuracy for asteroid material classification by integrating multi-source information, identifying optimal feature combinations for model inputs, and deepening the understanding of relationships among asteroid parameters. The effective asteroid photometric, physical, and orbital parameters were screened using the information gain ratio and Spearman's rank correlation coefficient. Then, artificial intelligence techniques were employed to combine asteroid spectra with the selected various parameters for six-class material classification. By comparing five machine learning models, we identified network structures with higher validation accuracy and stable generalization performance. Meanwhile, feature ablation experiments were conducted to determine the input parameter combinations suitable for different scenarios. Finally, based on the statistical results and model outputs, the constraint relationships among asteroid parameters were visualized and analyzed. The proposed AsterRF model achieved a validation accuracy of 92.2<!PCT!>, an improvement of approximately 7.8 percentage points compared to existing methods that use only spectra. V-type asteroids exhibited the highest classification accuracy, followed by A-type and D-type. X-type asteroids had the lowest precision and recall, and were easily confused with C-type. The model generally showed higher classification confidence for S-type asteroids. The top five attributes that the model focused on are the phase slope parameter (G), orbital type, albedo, H magnitude, and effective diameter. Additionally, the correlations between asteroid materials and other parameters were generally below 0.4. Incorporating optimal asteroid parameter combinations can significantly enhance classification accuracy based on spectra. A dual-channel network that processes spectra and parameter inputs separately, and employs a self-attention mechanism for feature fusion is effective in combining multi-source asteroid information. Both the statistical correlations and model performance-based importance rankings of parameters contribute to understanding the constraint relationships among asteroid attributes.

Mapping Research Themes and Trends in Ergonomics within Industry 5.0: A Bibliometric Analysis

Abstract: Industry 5.0, the next stage of development after Industry 4.0, centers around the seamless integration of individuals and technology inside industrial processes to foster innovation that prioritizes human needs and experiences. Industry 5.0 diverges from Industry 4.0 by placing greater emphasis on the collaborative partnership between humans and machines rather than solely focusing on automation and advanced technologies like IoT and AI. The study employs bibliometric analysis to delineate the subjects and research areas in ergonomics within the context of Industry 5.0. The study analyzed literature from the Scopus database to identify trends and gaps in implementing ergonomic principles across different industrial sectors. The study addresses two distinct research inquiries: identifying overarching research subjects and emerging research themes. The results indicate the existence of four primary research clusters: the interaction between humans and advanced technology, the safety of work and collaboration between humans and robots, the optimization of multiple purposes and sustainable development, and the application of augmented reality in industrial design. The report emphasizes the growing importance of human-robot collaboration and artificial intelligence while acknowledging a decline in emphasis on user experience and virtual reality. The findings thoroughly analyze the present condition and development of ergonomics research in Industry 5.0 while providing significant recommendations for future investigations and real-world implementations.

Atom-Vacancy-Defect-Derived Electric Hysteresis Loops and Stochastic Low-Frequency Noises in Few-Atom Layer MoS2.

Atom-vacancy-defects present in various materials yield numerous interesting physical phenomena, even obstructing high performance in some cases. On the other hand, their valuable applications to novel devices, such as nitrogen vacancy centers in diamond for quantum bits, have gathered significant attention. In particular, these tendencies become more substantial in two-dimensional (2D) (atomically) thin van der Waals layers. However, correlations with various kinds of atom defects are still under exploration. Herein, we find the stochastic behaviors of large hysteresis loops with strong photoresponse in the static electrical properties in few-atom layer semiconductors, molybdenum disulfide (MoS2). The temperature dependence and transmission electron microscopy reveal that they arise from pairs of two neighboring in-plane S-vacancy defects, which predominantly present only around the interface at the MoS2 flake/substrate, with activation energies ∼0.35 eV. The low-frequency (f) (LF) noise measurements clarify a high f shift in the two 1/f2-dependent regimes, implying stochastic behaviors of electric charges through the S-vacancy pairs with high-speed charge(spin) transitions across low kinetic energy barriers between narrow discrete states. The shallow energy sates are formed from the highly uniform S-vacancy pairs interacting with Mo atoms, which act like quantum dots. The observed stochastic operation holds promise for various application, particularly for probabilistic neuromorphic computation in artificial intelligence.

Detection of suicidality from medical text using privacy-preserving large language models.

Attempts to use artificial intelligence (AI) in psychiatric disorders show moderate success, highlighting the potential of incorporating information from clinical assessments to improve the models. This study focuses on using large language models (LLMs) to detect suicide risk from medical text in psychiatric care. To extract information about suicidality status from the admission notes in electronic health records (EHRs) using privacy-sensitive, locally hosted LLMs, specifically evaluating the efficacy of Llama-2 models. We compared the performance of several variants of the open source LLM Llama-2 in extracting suicidality status from 100 psychiatric reports against a ground truth defined by human experts, assessing accuracy, sensitivity, specificity and F1 score across different prompting strategies. A German fine-tuned Llama-2 model showed the highest accuracy (87.5%), sensitivity (83.0%) and specificity (91.8%) in identifying suicidality, with significant improvements in sensitivity and specificity across various prompt designs. The study demonstrates the capability of LLMs, particularly Llama-2, in accurately extracting information on suicidality from psychiatric records while preserving data privacy. This suggests their application in surveillance systems for psychiatric emergencies and improving the clinical management of suicidality by improving systematic quality control and research.

Flexible Thermoelectric BiSbTe/Carbon Paper/BiSbTe Sandwiches for Bimode Temperature‐Pressure Sensors

AbstractBimode temperature‐pressure sensors hold significant promise in personal health monitoring, wearables and robotic signal detection. Traditional bimode sensors typically combine two independent sensors, leading to fabrication complexity. This study develops a bimode temperature‐pressure sensor by using a facile electrodeposition method to create sandwiched BiSbTe/Carbon Paper/BiSbTe thin films and stacking them to a vertical structure. It demonstrates high sensitivity for temperature sensing, capable of detecting temperature difference as low as 1 K, and a rapid response time of 0.92 s due to a vertical structure. Utilizing its thermoelectric mechanism, the sensor achieves self‐powered sensing for finger touch and respiration states. Furthermore, its island‐like contact surface ensures high sensitivity with an extremely fast response time of 0.17 s, by rapidly changing contact resistance under pressure, allowing it to detect various human behaviors, including body movements and micro‐expressions. Beyond its sensing capabilities, the film excels in flexibility, electromagnetic interference shielding, and stability, presenting significant potential for integration into self‐powered electronic skin systems for health monitoring, wearables, artificial intelligence, and other electronic skin applications.

Artificial intelligence in diabetic retinopathy screening: Mind the workflow.

Artificial intelligence in diabetic retinopathy screening: Mind the workflow.

Intelligent Pressure Monitoring Method of BP Neural Network Optimized by Genetic Algorithm: A Case Study of X Well Area in Yinggehai Basin

While drilling formation pressure monitoring is an important basis for ensuring drilling safety and oil and gas discovery, the calculation of existing pressure monitoring methods is complicated and the accuracy is difficult to improve. Taking the actual well data of well area X in Yinggehai Basin as the object, correlation analysis was first carried out to select and standardize the data features, and relevant effective parameters were extracted. Two kinds of neural networks, back-propagation network BP and back-propagation network GA-BP optimized by genetic algorithm, were used to establish artificial intelligence monitoring models of formation pressure based on 10 kinds of measuring and logging data, respectively. The application effect of the model was evaluated based on the results of monitoring the pressure while drilling. The results show that the monitoring accuracy of the BP neural network model is 91.25%, and that of the GA-BP neural network model is 92.89%. The latter has a better monitoring effect on formation pore pressure. In formation pressure monitoring in areas with a high degree of well control, the introduction of artificial intelligence technology has the advantages of simplicity, speed and high precision, and can provide a reference for other areas of pressure monitoring while drilling.

Modified Metaheuristics Optimization for Cyberbullying Detection on Online Data Science Platform

Online harassment detection faces significant challenges due to its expansive reach and anonymity. Addressing this issue demands effective detection mechanisms capable of processing vast data streams and adapting to evolving online language. Leveraging advancements in artificial intelligence, we propose a novel approach grounded in natural language processing (NLP) and metaheuristic algorithms. Our methodology integrates term frequency-inverse document frequency (TF-IDF) encoding and the AdaBoost algorithm for classification. To tackle the NP-hard problem of hyperparameter selection, we introduce a modified crayfish optimization algorithm (COA), termed GI-COA. This paper represents a pioneering effort in utilizing metaheuristic algorithms for hyperparameter selection in harassment detection models. Through experimentation, we demonstrate the efficacy of our approach in fostering a safer online environment. The best performing optimize models demonstrate and accuracy exceeding 77%.

Scientific knowledge management in Brazil: an analysis of the Lattes Platform

The Lattes Platform (PL) is an essential resource for Brazil's scientific and technological landscape, centralizing researchers' curricular information and facilitating the management of scientific data. However, the lack of rigorous standardization in the data entered compromises the efficiency of retrieving and analyzing this information, limiting academic knowledge management. This study investigated the limitations of the PL and proposed a solution that integrates new technologies to optimize scientific data management. The research was conducted in two main stages: a systematic literature review on knowledge management and technologies applied to the PL, and a case study at the Federal Institute of Acre (IFAC). The review included the analysis of 79 articles and a survey of software records at the National Institute of Industrial Property (INPI). In the case study, semi-structured questionnaires were administered to IFAC staff to understand their needs and perceptions regarding the PL. The results reveal a significant demand for tools that automate data extraction and integrate the PL with other academic management systems. The analysis of software records at INPI highlighted solutions that can enhance the PL’s efficiency. The integration of Artificial Intelligence (AI), exemplified by the proposed LattesAI, emerges as a promising solution to overcome the platform's current limitations, automating processes and personalizing knowledge management. This innovation not only optimizes academic management but also strengthens Brazil's position in the global scientific landscape, promoting greater efficiency and collaboration in educational and research institutions. Future studies should focus on developing AI algorithms specifically for the PL, ensuring its continuous evolution in response to the demands of the academic environment.

AI-Driven Service Transformation : Revolutionizing Operational Excellence

Service industries are undergoing rapid transformation through the integration of artificial intelligence (AI) and automation. This article presents an AI-powered service delivery model that autonomously optimizes operations, improves customer satisfaction, and drives business growth. The article leverages AI algorithms to automate incident management, predict operational disruptions, and enhance customer experiences. Real-world implementations within retail, healthcare, and technology sectors demonstrate significant improvements in service efficiency, with operational costs reduced by 30% and customer satisfaction scores increasing by 30%. This article proposes AI-driven service enhancements as a game-changer in the delivery of complex, scalable, and reliable services.

AI-Powered Search Systems : Integrating Machine Learning with Search Technology for High-Scalability Applications

This article comprehensively analyzes integrating Artificial Intelligence (AI) and machine learning techniques into high-scalability search systems. We explore AI-powered search's theoretical foundations and practical implementations, focusing on advanced ranking algorithms, natural language processing for query understanding, and optimized distributed architectures. We demonstrate significant improvements in search relevance and efficiency through experiments conducted on a large-scale dataset comprising 100 million web pages and 1 million real-world queries. Our AI-powered system showed a 15% increase in Normalized Discounted Cumulative Gain (NDCG) for complex queries and a 12% improvement in Mean Reciprocal Rank (MRR) for navigational queries compared to traditional keyword-based approaches. We also address critical challenges in maintaining system scalability and performance, including data synchronization, real-time model updates, and resource management in distributed environments. The article further discusses emerging trends, such as graph neural networks and multimodal search capabilities, alongside ethical considerations and data privacy concerns. Our findings provide valuable insights for researchers and practitioners aiming to develop next-generation search platforms capable of handling the increasing complexity and volume of digital information while ensuring responsible AI integration.

An automated pheochromocytoma and paraganglioma lesion segmentation AI-model at whole-body 68Ga- DOTATATE PET/CT.

Somatostatin receptor (SSR) targeting radiotracer 68Ga-DOTATATE is used for Positron Emission Tomography (PET)/Computed Tomography (CT) imaging to assess patients with Pheochromocytoma and paraganglioma (PPGL), rare types of Neuroendocrine tumor (NET) which can metastasize thereby becoming difficult to quantify. The goal of this study is to develop an artificial intelligence (AI) model for automated lesion segmentation on whole-body 3D DOTATATE-PET/CT and to automate the tumor burden calculation. 132 68Ga-DOTATATE PET/CT scans from 38 patients with metastatic and inoperable PPGL, were split into 70, and 62 scans, from 20, and 18 patients for training, and test sets, respectively. The training set was further divided into patient-stratified 5 folds for cross-validation. 3D-full resolution nnUNet configuration was trained with 5-fold cross-validation. The model's detection performance was evaluated at both scan and lesion levels for the PPGL test set and two other clinical cohorts with NET (n = 9) and olfactory neuroblastoma (ONB, n = 5). Additionally, quantitative statistical analysis of PET parameters including SUVmax, total lesion uptake (TLU), and total tumor volume (TTV), was conducted. The nnUNet AI model achieved an average 5-fold validation dice similarity coefficient of 0.84 at the scan level. The model achieved dice similarity coefficients (DSC) of 0.88, 0.6, and 0.67 at the scan level, the sensitivity of 86%, 61.13%, and 61.64%, and a positive predictive value of 89%, 74%, and 86.54% at the lesion level for the PPGL test, NET and ONB cohorts, respectively. For PPGL cohorts, smaller lesions with low uptake were missed by the AI model (p < 0.001). Anatomical region-based failure analysis showed most of the false negative and false positive lesions within the liver for all the cohorts, mainly due to the high physiologic liver background activity and image noise on 68Ga- DOTATATE PET scans. The developed deep learning-based AI model showed reliable performance for automated segmentation of metastatic PPGL lesions on whole-body 68Ga-DOTATATE-PET/CT images, which may be beneficial for tumor burden estimation for objective evaluation during therapy follow-up. https://www. gov/study/NCT03206060 , https://www. gov/study/NCT04086485 , https://www. gov/study/NCT05012098 .

Using Explainable Artificial Intelligence to Interpret Remaining Useful Life Estimation with Gated Recurrent Unit

In engineering, prognostics can be defined as the estimation of the remaining useful life of a system given current and past health conditions. This field has drawn attention from research, industry, and government as this kind of technology can help improve efficiency and lower the costs of maintenance in a variety of technical applications. An approach to prognostics that has gained increasing attention is the use of data-driven methods. These methods typically use pattern recognition and machine learning to estimate the residual life of equipment based on historical data. Despite their promising results, a major disadvantage is that it is difficult to interpret this kind of methodologies, that is, to understand why a certain prediction of remaining useful life was made at a certain point in time. Nevertheless, the interpretability of these models could facilitate the use of data-driven prognostics in different domains such as aeronautics, manufacturing, and energy, areas where certification is critical. To help address this issue, we use Local Interpretable Model-agnostic Explanations (LIME) from the field of eXplainable Artificial Intelligence (XAI) to analyze the prognostics of a Gated Recurrent Unit (GRU) on the C-MAPSS data. We select the GRU as this is a deep learning model that a) has an explicit temporal dimension and b) has shown promising results in the field of prognostics and c) is of simplified nature compared to other recurrent networks. Our results suggest that it is possible to infer the feature importance for the GRU both globally (for the entire model) and locally (for a given RUL prediction) with LIME.

Assessing the Determinants of Generative AI Integration: A Study on Google's Gemini Adoption Among Thai University Graduate Students

The advent of Generative Artificial Intelligence (Gen AI) in educational tools like Google's Gemini represents a significant evolution. These technologies aim to revolutionize learning by creating human-like content in various formats, enhancing personalized and engaging educational experiences. This research investigates the factors influencing Thai graduate students' adoption of Google's Gemini, employing a quantitative approach with data from 826 respondents via closed-ended questionnaires through convenience sampling. The analysis included demographic, socio-economic, and media platform preference variables. Using binary logistic regression, the study evaluated how these factors affect graduate students' intentions to use Google’s Gemini in Thailand. Key determinants identified include age, education level, occupational status, and preferences for social media platforms like Facebook and YouTube. The study provides insights into the factors influencing Thai graduate students' use of Google's Gemini, useful for stakeholders in digital literacy and technology adoption. It stresses the importance of tailoring digital environments to learners' diverse needs, influenced by demographics and social media, and offers strategies for enhancing digital literacy and engagement. The research also sets the stage for further academic inquiry into educational technology, advocating for an interdisciplinary approach to better understand technology's educational impact.