Field Of Intelligence Research Articles

Diversity and inclusion: A hidden additional benefit of Open Data.

The recent imperative by the National Institutes of Health to share scientific data publicly underscores a significant shift in academic research. Effective as of January 2023, it emphasizes that transparency in data collection and dedicated efforts towards data sharing are prerequisites for translational research, from the lab to the bedside. Given the role of data access in mitigating potential bias in clinical models, we hypothesize that researchers who leverage open-access datasets rather than privately-owned ones are more diverse. In this brief report, we proposed to test this hypothesis in the transdisciplinary and expanding field of artificial intelligence (AI) for critical care. Specifically, we compared the diversity among authors of publications leveraging open datasets, such as the commonly used MIMIC and eICU databases, with that among authors of publications relying exclusively on private datasets, unavailable to other research investigators (e.g., electronic health records from ICU patients accessible only to Mayo Clinic analysts). To measure the extent of author diversity, we characterized gender balance as well as the presence of researchers from low- and middle-income countries (LMIC) and minority-serving institutions (MSI) located in the United States (US). Our comparative analysis revealed a greater contribution of authors from LMICs and MSIs among researchers leveraging open critical care datasets (treatment group) than among those relying exclusively on private data resources (control group). The participation of women was similar between the two groups, albeit slightly larger in the former. Notably, although over 70% of all articles included at least one author inferred to be a woman, less than 25% had a woman as a first or last author. Importantly, we found that the proportion of authors from LMICs was substantially higher in the treatment than in the control group (10.1% vs. 6.2%, p<0.001), including as first and last authors. Moreover, we found that the proportion of US-based authors affiliated with a MSI was 1.5 times higher among articles in the treatment than in the control group, suggesting that open data resources attract a larger pool of participants from minority groups (8.6% vs. 5.6%, p<0.001). Thus, our study highlights the valuable contribution of the Open Data strategy to underrepresented groups, while also quantifying persisting gender gaps in academic and clinical research at the intersection of computer science and healthcare. In doing so, we hope our work points to the importance of extending open data practices in deliberate and systematic ways.

A High-Speed Silicon Ring Modulator with a Large Working Wavelength Range

With the advantages of high speed, small size, and easy integration, the silicon photonic resonant ring modulator has gradually become a critical device for emerging integrated optical platforms. Ring modulators are primarily used in optical communications, optical computing, artificial intelligence, and other fields. In this work, the proposed ring modulator can operate in both the O- and C-bands. The 3 dB electro-optical (EO) bandwidth of the ring modulator is 39 GHz and 34 GHz at −4 V in the O-band and C-band, respectively. The modulation efficiency of the device is 0.92 V·cm and 0.95 V·cm in the O-band and C-band, respectively. The eye diagram of an optical output signal from the device is tested using a 100 Gbit/s non-return-to-zero (NRZ) input signal with a 2.5 Vpp in both the O-band and C-band. The modulation speed can reach 140 Gb/s and 120 Gb/s in the O-band and C-band with four-level pulse amplitude modulation (PAM-4) formats at a voltage swing of 2.5 Vpp, respectively.

A New Reversible Data Hiding Method Using a Proportional Relation between Peak Signal-to-Noise Ratio and Embedding Capacity on Convolutional Neural Network

In the rapidly evolving fields of artificial intelligence and various industries, the secure processing and management of massive data have become paramount. This paper introduces an innovative reversible data hiding (RDH) method that leverages a Convolutional Neural Network (CNN)-based predictor to generate a predicted image from a given cover image. The secret data are ingeniously embedded within the differences in pixel values between the cover and predicted images. Our experimental analysis reveals a notable reduction in image distortion with increasing secret data size, showcasing the method’s potential for diverse applications. The unique aspect of our approach lies in the proportional relation between the Peak Signal-to-Noise Ratio (PSNR) and Embedding Capacity, highlighting its efficacy and efficiency in reversible data hiding.

A Bibliometrics-Based Review of Domestic Research in the Field of Artificial Intelligence

A Bibliometrics-Based Review of Domestic Research in the Field of Artificial Intelligence

How does artificial intelligence affect the business context? A bibliometric analysis

We conducted a descriptive bibliometric analysis to examine scientific production, identify the most influential publications, and identify the most and least researched topics in four specific knowledge domains. We used a quantitative, descriptive, and correlational research approach to scientific production to carry out the analysis, which involved extracting 7,937 articles from the Web of Science and distributing them into three search equations. Using SciMAT v1.1.04 software, we processed the data and conducted a descriptive analysis of scientific production, enabling the creation of maps highlighting scientists with the most and least researched topics. The analysis of published articles, author performance, most productive journals, and most cited articles provided a detailed view of the dominant trends and approaches in the fields of Artificial Intelligence and business. The analysis showed that there is a significant evolution in the scholarly output, with themes such as "Value Creation", "Artificial Intelligence", "Business Intelligence", "E-Commerce", "Decision Making" and "Management" emerging as central in different periods, indicating their continued importance. Additionally, we note the inclusion of emerging themes like 'Customer Experience', 'Chatbots', 'Internet of Things', and 'Machine Learning', which reflect the dynamics and evolution of research concerns over time. The results of the analysis have significant implications for business policy and strategy formulation. Understanding emerging trends can help organizations make informed decisions and proactively adapt to changes in the artificial intelligence and sustainability landscape.

Harnessing deep reinforcement learning algorithms for image categorization: A multi algorithm approach

Image categorization is an important task in the field of Artificial Intelligence. Deep reinforcement learning (DRL) algorithms are effective for image categorization, especially in the real-time scenarios. In this study, we explore how different DRL algorithms can be used for classifying images in the field of artificial intelligence across multiple benchmark datasets. Our primary goal is to illustrate the way DRL algorithms perform comparable to conventional machine learning(ML) and deep learning(DL) methods. This usage of diverse data allows to evaluate thoroughly how this DRL algorithms will adapt and learn under different situations similar to real-world scenarios with unpredictable data. Our findings suggest that deep reinforcement learning algorithms outperform other algorithms in environments with diverse and complex input. The DRL methods achieved greater accuracies when compared with most of the machine learning and deep learning approaches at 1 Million timesteps. Among the DRL models, Recurrent Proximal Policy Optimization(RPPO) with an accuracy of 97.57% for MNIST dataset, 89% for KMNIST dataset, 89% for EMNIST dataset and Deep Q Network(DQN) on Fashion_MNIST dataset with an accuracy of 87.40% outperformed most of the deep learning and machine learning models and some achieving almost similar to the deep learning and machine learning models. This study not only demonstrates DRL’s capability to handle real-time challenges but also underscores its importance as a valuable tool for computer vision tasks, such as image categorization.

The Important Role Of Artificial Intelligence Regulation In Protecting Public Interest

Artificial intelligence provides both good and evil. For this reason, artificial intelligence must be regulated to protect the public interest. The research results show that regulating artificial intelligence is not easy, it is very complicated, and there are many challenges, especially as the development of artificial intelligence technology is very rapid while the law is slow to anticipate it. By 2022, globally, there will be 37 regulations governing artificial intelligence. From the results of the comparison of various best practices and regulations from other countries leading in the field of artificial intelligence, such as the European Union, China, and the United States framework approach, it can be used as input for developing artificial intelligence regulations in Indonesia that guarantee the use of artificial intelligence responsibly, respecting values. humanity, and does not hinder the creation of an artificial intelligence development ecosystem.

Semantic-based neural network repair for AI models in dynamic environments

This paper addresses a critical challenge in the field of artificial intelligence (AI) and deep learning (DL) - the debugging and repair of AI models within dynamic environments. The rapid development of frameworks like TensorFlow or PyTorch can introduce version incompatibilities, potentially breaking existing code. To tackle this issue, it can be seen that a semantic-based neural network repair method that leverages an existing semantics called ExAIS is proposed, defined in the Prolog logic programming language. This method identifies architectural and parameter errors in AI models and generates debug messages to facilitate the repair process. Key contributions of this paper include introducing a novel semantic-based AI model repair method capable of suggesting multiple changes to rectify inefficiencies in AI models. We evaluate the effectiveness of our approach in a real-world context by collecting a set of real bugs from AI developers. The background section provides an overview of AI framework testing, machine learning, neural architecture search (NAS), and deep learning. The methodology section details the repair method based on ExAIS semantics, covering error identification and repair suggestions as the two main steps. The evaluation section showcases the repair effectiveness on automatically generated and manually written models, emphasizing the applicability and efficiency of the approach.

The eye of artificial intelligence - Convolutional Neural Networks

Inspired by the biological visual system, the convolutional neural network has been widely studied and invented in the field of artificial intelligence. As one of the important algorithms in artificial neural networks, convolutional neural networks have shown outstanding application potential in fields such as image recognition, computer vision, and natural language processing. This article will focus on exploring the powerful capabilities of convolutional neural networks in image processing. By delving into the implementation process of a convolutional neural network, readers will gain a deeper understanding of its working principles. In addition, this article will briefly introduce three classic models of convolutional neural networks, providing readers with more background knowledge. Next, this paper will analyze in detail two typical application cases of convolutional neural networks in the field of image processing: intelligent transportation systems and dental imaging technology. These cases demonstrate the successful application of convolutional neural networks in practical scenarios, pointing the way for their future development. In the future, convolutional neural networks will be more widely used in fields such as image and video processing as data scale increases and computing power improves. By using techniques such as model compression and hardware optimization, it is made more suitable for low-power and high-efficiency environments, and its interpretability and applicability are enhanced through data augmentation and model interpretation techniques.

Transforming Hypertension Diagnosis and Management in The Era of Artificial Intelligence: A 2023 National Heart, Lung, and Blood Institute (NHLBI) Workshop Report.

Hypertension is among the most important risk factors for cardiovascular disease, chronic kidney disease, and dementia. The artificial intelligence (AI) field is advancing quickly, and there has been little discussion on how AI could be leveraged for improving the diagnosis and management of hypertension. AI technologies, including machine learning tools, could alter the way we diagnose and manage hypertension, with potential impacts for improving individual and population health. The development of successful AI tools in public health and health care systems requires diverse types of expertise with collaborative relationships between clinicians, engineers, and data scientists. Unbiased data sources, management, and analyses remain a foundational challenge. From a diagnostic standpoint, machine learning tools may improve the measurement of blood pressure and be useful in the prediction of incident hypertension. To advance the management of hypertension, machine learning tools may be useful to find personalized treatments for patients using analytics to predict response to antihypertension medications and the risk for hypertension-related complications. However, there are real-world implementation challenges to using AI tools in hypertension. Herein, we summarize key findings from a diverse group of stakeholders who participated in a workshop held by the National Heart, Lung, and Blood Institute in March 2023. Workshop participants presented information on communication gaps between clinical medicine, data science, and engineering in health care; novel approaches to estimating BP, hypertension risk, and BP control; and real-world implementation challenges and issues.

Computational experiments with cellular-automata generated images reveal intrinsic limitations of convolutional neural networks on pattern recognition tasks

The extraordinary success of convolutional neural networks (CNNs) in various computer vision tasks has revitalized the field of artificial intelligence. The out-sized expectations created by this extraordinary success have, however, been tempered by a recognition of CNNs’ fragility. Importantly, the magnitude of the problem is unclear due to a lack of rigorous benchmark datasets. Here, we propose a solution to the benchmarking problem that reveals the extent of the vulnerabilities of CNNs and of the methods used to provide interpretability to their predictions. We employ cellular automata (CA) to generate images with rigorously controllable characteristics. CA allow for the definition of both extraordinarily simple and highly complex discrete functions and allow for the generation of boundless datasets of images without repeats. In this work, we systematically investigate the fragility and interpretability of the three popular CNN architectures using CA-generated datasets. We find a sharp transition from a learnable phase to an unlearnable phase as the latent space entropy of the discrete CA functions increases. Furthermore, we demonstrate that shortcut learning is an inherent trait of CNNs. Given a dataset with an easy-to-learn and strongly predictive pattern, CNN will consistently learn the shortcut even if the pattern occurs only on a small fraction of the image. Finally, we show that widely used attribution methods aiming to add interpretability to CNN outputs are strongly CNN-architecture specific and vary widely in their ability to identify input regions of high importance to the model. Our results provide significant insight into the limitations of both CNNs and the approaches developed to add interpretability to their predictions and raise concerns about the types of tasks that should be entrusted to them.

Improving the Efficiency and Effectiveness of Learning for Students of Tadris Social Studies Program at UIN Syarif Hidayatullah Jakarta with Artificial Intelligence

In this digital period, technological advancement is growing fleetly, including in the field of artificial intelligence( AI). AI has entered colorful aspects of life, including Education. In Education, AI offers colorful openings to ameliorate the effectiveness and effectiveness of pupil literacy. scholars are faced with colorful complex literacy tasks and demands. This frequently makes it delicate for them to manage their time and complete tasks meetly. Artificial intelligence( AI). comes as a result to help scholars complete their tasks more fluently, efficiently and effectively. This study aims to dissect the effectiveness and effectiveness of pupil literacy using artificial intelligence, and to identify the benefits and challenges of using AI in education. This study uses a quantitative descriptive system with primary and secondary data sources, with data collection procedures and interviews, and giving doubters to 104 Repliers using a Likert- Scale, Data Analysis with Data Presentation, Data Reduction and conclusions. The exploration was conducted at Syarif Hidayatullah State Islamic University, Jakarta, as Student- Repliers in Semester IV in the Social Studies Education- Programs. The results of this study indicate, the use of artificial intelligence( AI) can increase the effectiveness and effectiveness of pupil literacy in the social study- program, as substantiated by the results of doubters from repliers, that55.8 of the repliers are effective in the use of artificial intelligence( AI) and can increase effectiveness by51.9 and40.4 of the use of AI is applicable in perfecting understanding of lecture material, and52.9 of the use of AI is suitable to overcome time constraints in completing lecture assignments. This exploration is anticipated to make a precious donation to the development of education in the digital period and to help scholars to learn more effectively and efficiently. give recommendations for the wise and responsible use of artificial intelligence( AI) in education. Flash back AI is only an adjunct for druggies, not a cover for doing anything including lecture assignments

Artificial intelligence in e-health

Artificial intelligence (AI) technologies have had an impact on the healthcare industry for some time. The ongoing advancement of technology based on sophisticated machine learning and methods that can identify intricate patterns in data undoubtedly benefits this. A sophisticated model that can automate diagnosis could be created utilizing pooled healthcare data thanks to the quickly developing field of artificial intelligence. Additionally, customizing therapies and directing resources with maximum effectiveness in a timely and dynamic manner facilitates a more precise approach to medicine. Regrettably, a number of significant problems prevent AI's unambiguous affirmation. These range from the dearth of clinical studies that can show its dependability and superior effectiveness compared to conventional systems to the difficulties associated with allocating blame in the event of medical errors.

Enhancing Talent Recruitment in Business Intelligence Systems: A Comparative Analysis of Machine Learning Models

In the competitive field of business intelligence, optimizing talent recruitment through data-driven methodologies is crucial for better decision-making. This study compares the effectiveness of various machine learning models to improve recruitment accuracy and efficiency. Using the recruitment data from a major Yemeni organization (2019–2022), we evaluated models including K-Nearest Neighbors, Logistic Regression, Support Vector Machine, Naive Bayes, Decision Trees, Random Forest, Gradient Boosting Classifier, AdaBoost Classifier, and Neural Networks. Hyperparameter tuning and cross-validation were used for optimization. The Random Forest model achieved the highest accuracy (92.8%), followed by Neural Networks (92.6%) and Gradient Boosting Classifier (92.5%). These results suggest that advanced machine learning models, particularly Random Forest and Neural Networks, can significantly enhance the recruitment processes in business intelligence systems. This study provides valuable insights for recruiters, advocating for the integration of sophisticated machine learning techniques in talent acquisition strategies.

The Application of Artificial Intelligence (AI) in Library and Information Centre

Artificial intelligence has taken over many industries and is seen as a continuation of human intelligence. Artificial intelligence applications in libraries have revolutionized the information industry. Additionally, it has given modern libraries' development fresh life. It is believed that integrating artificial intelligence into library operations will open up new internet resources for libraries. Virtual reality, which engages users with libraries and improves information literacy abilities, is one of the valid innovations that librarians are constantly utilizing to engage and expand services for their patrons. It wouldn't be incorrect to argue that the development of the computer accelerated the process of digitization, much as the discovery of the wheel ushered in the mechanical age of human existence. Humans are the only animals with the innate ability to think for themselves. With the power of independent thought, humans have created a great deal of innovative technologies. One example of them is the development of the computer. The most significant development in computer technology that humans have made with the use of their intelligence is artificial intelligence. The goal of the computer science field of artificial intelligence is to build computers with human-like intelligence. Almost everywhere that computers are used, artificial intelligence is now being deployed. The need for this is growing daily, namely in the areas of science, health, automobiles, engineering, climates, business, pharmaceuticals, and academic libraries. AI must be used in libraries for both technical and library services purposes. The application of AI will expedite and improve the quality of work done in libraries, allowing them to offer a greater number of services with fewer staff members. KEYWORDS: Artificial Intelligence, Big Data, Internet of Things, Smart Library.

Construction of “island-bridge” microstructured conductive coating for enhanced impedance response of organohydrogel strain sensor

Flexible conductive hydrogels (CHs) have received widespread attention in the field of wearable strain sensor, electronic skin, and artificial intelligence owing to their excellent stretchability, biocompatibility, and real-time sensing performance. However, it still remains a critical challenge to design and construct the satisfactory wearable strain sensor integrated with high sensitivity, wide sensing range, and environmental stability, especially at extreme temperatures. Herein, ionic conductive polyacrylamide/sodium alginate nanocomposite organohydrogel anchored with electronic conductive “island-bridge” microstructured carbon nanotubes (CNTs) conformal coating was designed and fabricated for high-performance strain sensor, of which the existence of “island-bridge” microstructured electronic conductive layer and its special structure evolution upon external stretching can significantly enhance the alternating current impedance response behavior of the sensor, featuring with a high sensitivity of 76.54 at 600 % strain, wide sensing range (0–600 % strain), fast responsiveness (110 ms), extremely low detection limit (0.1 %), and favorable stability and reproducibility of over 3200 cycles. Meanwhile, the resultant organohydrogel strain sensor can precisely detect and differentiate complicated human activities even at the environment temperatures of −30 °C and 50 °C, opening a promising avenue for next-generation smart wearable electronics.

Detecting images generated by diffusers.

In recent years, the field of artificial intelligence has witnessed a remarkable surge in the generation of synthetic images, driven by advancements in deep learning techniques. These synthetic images, often created through complex algorithms, closely mimic real photographs, blurring the lines between reality and artificiality. This proliferation of synthetic visuals presents a pressing challenge: how to accurately and reliably distinguish between genuine and generated images. This article, in particular, explores the task of detecting images generated by text-to-image diffusion models, highlighting the challenges and peculiarities of this field. To evaluate this, we consider images generated from captions in the MSCOCO and Wikimedia datasets using two state-of-the-art models: Stable Diffusion and GLIDE. Our experiments show that it is possible to detect the generated images using simple multi-layer perceptrons (MLPs), starting from features extracted by CLIP or RoBERTa, or using traditional convolutional neural networks (CNNs). These latter models achieve remarkable performances in particular when pretrained on large datasets. We also observe that models trained on images generated by Stable Diffusion can occasionally detect images generated by GLIDE, but only on the MSCOCO dataset. However, the reverse is not true. Lastly, we find that incorporating the associated textual information with the images in some cases can lead to a better generalization capability, especially if textual features are closely related to visual ones. We also discovered that the type of subject depicted in the image can significantly impact performance. This work provides insights into the feasibility of detecting generated images and has implications for security and privacy concerns in real-world applications. The code to reproduce our results is available at: https://github.com/davide-coccomini/Detecting-Images-Generated-by-Diffusers.

The State of Moral Intelligence of Medical Students of Kermanshah University of Medical Sciences in 2022

Background: Recently, new dimensions have been recognized in the field of intelligence. One of the most important of these is moral intelligence, which has not been extensively addressed in Iran. Objectives: Moral intelligence represents the mental capacity of individuals to relate universal human principles to their values, goals, and actions. It guides all other types of human intelligence towards performing valuable work. Methods: The current descriptive cross-sectional study involved a statistical population of medical students, with a sample size of 248 people selected through random and available sampling methods. To collect data, the standard moral intelligence questionnaire by Lennik, Keel, and Jordan was used. The data collected was analyzed using SPSS-21 software, employing both descriptive and inferential statistics, including Spearman's correlation coefficient. Results: The total score of students' moral intelligence was 76.29 ± 9.80, which is evaluated as good. The area of honesty had the highest score (81.2), while the area of empathy had the lowest score (70.3). Among the indexes, keeping promises scored the highest (93.6), and admitting mistakes and omissions scored the lowest (70.5). Scores were higher among women and married individuals, and moral intelligence scores increased with advancing academic semesters. However, there were no significant differences overall, except that the empathy field had a significant relationship with age (P = 0.031), and gender had a significant relationship with the overall score of moral intelligence (P = 0.041). Conclusions: According to the total moral intelligence score of the students, which was 76.29 ± 9.80, it was determined that the moral intelligence status among the medical students of Kermanshah Medical School is in good condition.

Artificial Intelligence Applications in Administrative Law "An analytical study"

This study aims at determining the role of artificial intelligence systems in the development and improving of the administrative legal base, and to identify the theoretical framework in the field of using the artificial intelligence in the administrative life and its impacts. Moreover, the study provides recommendations to the competent authorities in the field of artificial intelligence for its uses in administrative work in a manner that guarantees the various dimensions of the administrative development, Especially the technological dimension. Among the outcomes reached through this study is that the artificial intelligence is a modern technology that has been able to enter all fields, especially utility and service ones, and it is indispensable to exploit such intelligence in describing service quality when needs are satisfied. Finally, one of the most important recommendations of the study is to find a legal regulation for the artificial intelligence, in addition to strengthening the technological infrastructure to accommodate the intervention of this technology in the field of the administrative law; as flexibility and scalability are of its most important characteristics.

Ultra-hydrophobic Eucommia gum coating with temperature-responsive and shape-memory performance

The intelligent and multifunctional composites offer important applications in many fields. However, the fabrication of intelligent and multifunctional composites still faces limitations due to complicated processes, high costs, and restricted area coverage. Here, we present a new approach for creating a micro/nano-hybrid hierarchical Eucommia ulmoides gum (EUG) coating with outstanding superhydrophobic, temperature-responsive and shape-memory characteristics. The intelligent EUG coating prepared in this study can regulate the change and recovery of the microstructure of the coating surface by altering temperature and external force, thereby achieving control of the surface properties and memory performance of the coating. These EUG hydrophobic shape-memory coatings are obtained through the preparation of superhydrophobic sepiolite particles, spin coating, and chemical cross-linking processes. In addition to its performance advantages, the outstanding advantage of these composites is the simple and easy preparation process, regardless of the substrate surface topography. These novel superhydrophobic and temperature-responsive shape-memory EUG composites can achieve contact angle and thickness regulation. These superhydrophobic and temperature-responsive shape-memory EUG composites are highly expected to be applied in medical devices and intelligent textiles fields.