Progress In Artificial Intelligence Research Articles

Artificial Intelligence and Modern Information Technologies Applications in Islamic Sciences: A Survey

Considering the progress of Artificial Intelligence (AI) and the Information Technology (IT) we witness, during recent years, the spread of the application of these technologies in various fields. The research workflows, and in particular, the researches on Islamic sciences are not excepted from this issue. Several works have been carried out in order to exploit the AI and modern information technologies in the researches on Islamic sciences during recent years all over the Islamic regions and beyond them. It is very important to be aware of the latest developments in this field from different aspects like: 1) Benefiting from the advantages of modern technologies in the Islamic researches, 2) Reorganizing the educational plans in accordance with these developments, and 3) Introducing the new applications of AI in Islamic studies to the academics of computer sciences who may be interested in this field. In this paper, in the first step, a systematic review was conducted concerning more than four thousand international scientific articles related to applying AI and modern IT in Islamic studies, out of which 975 ones were chosen. At the same time, major institutions in this field were identified. In the next step the selected articles were classified in five thematic fields of 1) the Holy Qur’an, Tafsir and other related issues, 2) Hadith and Rijal Sciences, 3) Islamic Law and Jurisprudence, 4) the General Islamic Content in Social Media, 5) Other Subjects related to Islamic Sciences like Linguistics, History, Geography, etc. In the third step, the articles of each category were classified in a number of major subcategories that amount to 73 in total. Finally, in the last step, the distinctive articles in each field were introduced briefly.

Computer Vision and Image Processing in Structural Health Monitoring: Overview of Recent Applications

Structural deterioration is a primary long-term concern resulting from material wear and tear, events, solicitations, and disasters that can progressively compromise the integrity of a cement-based structure until it suddenly collapses, becoming a potential and latent danger to the public. For many years, manual visual inspection has been the only viable structural health monitoring (SHM) solution. Technological advances have led to the development of sensors and devices suitable for the early detection of changes in structures and materials using automated or semi-automated approaches. Recently, solutions based on computer vision, imaging, and video signal analysis have gained momentum in SHM due to increased processing and storage performance, the ability to easily monitor inaccessible areas (e.g., through drones and robots), and recent progress in artificial intelligence fueling automated recognition and classification processes. This paper summarizes the most recent studies (2018–2022) that have proposed solutions for the SHM of infrastructures based on optical devices, computer vision, and image processing approaches. The preliminary analysis revealed an initial subdivision into two macro-categories: studies that implemented vision systems and studies that accessed image datasets. Each study was then analyzed in more detail to present a qualitative description related to the target structures, type of monitoring, instrumentation and data source, methodological approach, and main results, thus providing a more comprehensive overview of the recent applications in SHM and facilitating comparisons between the studies.

MAPPING AGRICULTURAL WASTE FOR BIOGAS PRODUCTION USING A FULLY CONVOLUTIONAL NEURAL NETWORK AND REMOTE SENSING IMAGERY

The increasing generation of waste and depletion of natural resources has led to a growing need for innovative approaches for utilizing different types of waste as potential energy and material resources. Agricultural activities produce large amounts of Agricultural Waste (AW), which, if not adequately managed, can lead to environmental degradation. One potential solution for the effective utilization of AW is converting it into biogas. However, commercializing this process requires a comprehensive understanding of the types and quantities of AW generated. In this paper, the use of a Fully Convolutional Neural Network (FCN), which has rapidly advanced with the progress of Artificial Intelligence and become essential for tasks such as Semantic Segmentation, Object Detection, and Image Classification, is proposed to improve the prediction of AW for biogas production. Furthermore, this paper presents a Deep Learning-based image segmentation method to recognize vineyard fields, which are a significant source of AW, using remote satellite images. The proposed approach can significantly improve the identification of AW sources, and thus contribute to the efficient and sustainable utilization of AW for biogas production.

Application of Artificial Intelligence in Drug-Drug Interactions Prediction: A Review.

Drug-drug interactions (DDI) are a critical aspect of drug research that can have adverse effects on patients and can lead to serious consequences. Predicting these events accurately can significantly improve clinicians' ability to make better decisions and establish optimal treatment regimens. However, manually detecting these interactions is time-consuming and labor-intensive. Utilizing the advancements in Artificial Intelligence (AI) is essential for achieving accurate forecasts of DDIs. In this review, DDI prediction tasks are classified into three types according to the type of DDI prediction: undirected DDI prediction, DDI events prediction, and Asymmetric DDI prediction. The paper then reviews the progress of AI for each of these three prediction tasks in DDI and provides a summary of the data sets used as well as the representative methods used in these three prediction directions. In this review, we aim to provide a comprehensive overview of drug interaction prediction. The first section introduces commonly used databases and presents an overview of current research advancements and techniques across three domains of DDI. Additionally, we introduce classical machine learning techniques for predicting undirected drug interactions and provide a timeline for the progression of the predicted drug interaction events. At last, we debate the difficulties and prospects of AI approaches at predicting DDI, emphasizing their potential for improving clinical decision-making and patient outcomes.

Review Of Near Infrared Spectrum Detection Based on Deep Learning

Near infrared spectroscopy technology has the characteristics of accurate analysis of material composition and characteristics, quantitative testing, non-destructive testing, and so on. With the progress of artificial intelligence and the development of deep learning technology, near-infrared spectroscopy detection systems based on deep learning algorithms have emerged, which include various typical methods and have achieved good application results in food safety monitoring, pollutant detection, and drug analysis. This article provides a systematic review of domestic and foreign literature, introducing the principles, characteristics, and development history of deep learning and near-infrared spectroscopy technology. It discusses the research significance of near-infrared spectroscopy detection based on deep learning. It also reviews the latest progress in deep learning based near-infrared spectroscopy related technologies, elaborates on the advantages, disadvantages, and applicable fields of the methods, and makes prospects and predictions for the future development trends in this field.

Application of a Comprehensive Evaluation Framework to COVID-19 Studies: Systematic Review of Translational Aspects of Artificial Intelligence in Health Care.

Despite immense progress in artificial intelligence (AI) models, there has been limited deployment in health care environments. The gap between potential and actual AI applications is likely due to the lack of translatability between controlled research environments (where these models are developed) and clinical environments for which the AI tools are ultimately intended. We previously developed the Translational Evaluation of Healthcare AI (TEHAI) framework to assess the translational value of AI models and to support successful transition to health care environments. In this study, we applied the TEHAI framework to the COVID-19 literature in order to assess how well translational topics are covered. A systematic literature search for COVID-19 AI studies published between December 2019 and December 2020 resulted in 3830 records. A subset of 102 (2.7%) papers that passed the inclusion criteria was sampled for full review. The papers were assessed for translational value and descriptive data collected by 9 reviewers (each study was assessed by 2 reviewers). Evaluation scores and extracted data were compared by a third reviewer for resolution of discrepancies. The review process was conducted on the Covidence software platform. We observed a significant trend for studies to attain high scores for technical capability but low scores for the areas essential for clinical translatability. Specific questions regarding external model validation, safety, nonmaleficence, and service adoption received failed scores in most studies. Using TEHAI, we identified notable gaps in how well translational topics of AI models are covered in the COVID-19 clinical sphere. These gaps in areas crucial for clinical translatability could, and should, be considered already at the model development stage to increase translatability into real COVID-19 health care environments.

Ethical issues in the development of artificial intelligence: recognizing the risks

Purpose This study aims to analyse the ethical implications associated with the development of artificial intelligence (AI) technologies and to examine the potential ethical ramifications of AI technologies. Design/methodology/approach This study undertakes a thorough examination of existing academic literature pertaining to the ethical considerations surrounding AI. Additionally, it conducts in-depth interviews with individuals to explore the potential benefits and drawbacks of AI technology operating as autonomous ethical agents. A total of 20 semi-structured interviews were conducted, and the data were transcribed using grounded theory methodology. Findings The study asserts the importance of fostering an ethical environment in the progress of AI and suggests potential avenues for further investigation in the field of AI ethics. The study finds privacy and security, bias and fairness, trust and reliability, transparency and human–AI interactions as major ethical concerns. Research limitations/implications The implications of the study are far-reaching and span across various domains, including policy development, design of AI systems, establishment of trust, education and training, public awareness and further research. Notwithstanding the potential biases inherent in purposive sampling, the constantly evolving landscape of AI ethics and the challenge of extrapolating findings to all AI applications and contexts, limitations may still manifest. Originality/value The novelty of the study is attributed to its comprehensive methodology, which encompasses a wide range of stakeholder perspectives on the ethical implications of AI in the corporate sector. The ultimate goal is to promote the development of AI systems that exhibit responsibility, transparency and accountability.

Managerial Future of the Artificial Intelligence

The developed concept for Artificial Intelligence (AI) introduction to the management system is concerned with a range of ethical, social, environmental and legal issues. Management system as a form of organising chaos and complexity becomes the only platform to design business and to make it sustainable irrespective of location and personalities engaged. At the time of the world economy demand for social actors, activism in the necessary transition of management oriented to reach the Sustainable Development Goals (SDG) is a crucial factor to form the new management digitized system. Environmental, Social and Governance (ESG) Investing managed assets as a part of all corporate assets. ESG initiative was a proposal of the UN to promote principles for a sustainable economy. Companies with better ESG performance can increase shareholders’ value by managing risks related to emerging ESG issues, namely bring the corporation to have energy transition experience. Different approaches of millennials to managing enterprises show their higher interest than that of predecessors to introduce ESG standards and tasks to the day-to-day management. Millennials are more interested in social values than in the investment return. Even the future of investments is dependent on the basic idea that investors are not short-termist but tend to be loyal to a project about which they have more relevant information. So, they may support new AI-based management in the case it becomes an efficient platform to design a human-oriented enterprise. This study aims at showing what the relationship of the management structure and process should be in order to manage the AI progress. Understanding of management content and work with the AI representation is of strategic importance.

An end-to-end screen shooting resilient blind watermarking scheme for medical images

The rapid progress in artificial intelligence and network communication has led to the development of advanced regional medical systems, revolutionizing the way hospitals and patients connect. However, the security of these systems is threatened by the potential breach of medical data, especially sensitive medical images. Existing watermarking schemes have focused on optimizing either imperceptibility or robustness, creating a fundamental trade-off between these two aspects that poses a significant challenge in the field. To address this challenge and ensure the confidentiality and integrity of medical data, this paper proposes an end-to-end screen shooting resistant blind watermarking scheme for medical images. The proposed method offers a robust and reliable solution for the secure transmission and storage of medical images, particularly in the face of screen shooting attacks. To enhance both security and imperceptibility, the scheme employs a novel encryption technique based on BCH codes, resulting in a trinary sequence for watermark images. These watermarks are then embedded in the spatial domain, effectively protecting against unauthorized access while preserving the integrity of the original image. To further enhance robustness, the method introduces a neural network structure specifically designed to resilient screen shooting attacks. By simulating the screen shooting process and incorporating various distortions, this network combines the strengths of residual networks and generative adversarial networks. Moreover, the proposed method introduces a novel loss function that exploits the unique characteristics of the image transformation domain, optimizing both imperceptibility and robustness. In the subsequent experiment, the watermarked images achieved satisfactory results with the highest PSNR and SSIM values reaching 62.563 dB and 0.9999, respectively. The proposed scheme provides an effective solution to protect the privacy and copyright of medical images, effectively addressing a critical security concern prevalent in regional healthcare systems.

Studi Literatur: Artificial Intelligence Dalam Audit

The purpose of this study is to find out how the influence, role, and progress of Artificial Intelligence in Auditing. The approach in this study uses a type of research in the form of a literature study with data collection methods in the form of library data that has been selected, searched and analyzed. Based on the research results, it is known that Artificial Intelligence has quite a significant influence on the positive and negative sides. Artificial Intelligence also provides a useful role in auditing developments in the present and in the future, although in practice Artificial Intelligence still has many weaknesses because auditing still requires an auditor's assessment.

DIGITAL EDUCATION IN PRIMARY SCHOOLS IN THE REPUBLIC OF SERBIA

The goal of this research is to identify the main problems related to digital technology and skills in the elementary school education system of the Republic of Serbia, as well as opportunities for its improvement. The paper first analyzes the situation in the field of basic education, while the main part of the paper presents an analysis related to the innovative approach to the education process, with the application of information technology and comprehensive digitization. The strategy for the development of education and upbringing in Serbia until 2030 emphasizes the development of education aimed at the needs of modern society as a whole, conditioned by the progress of artificial intelligence, information and communication technology (ICT) and refers to the improvement of teaching content, standards of general cross-curricular competences, especially in cross-curricular multidisciplinary access to teaching in this field. Descriptive analysis was applied in the paper - application of usual scientific research instruments and relevant literature. The research results point to the conclusion that continuous measures are needed for more successful digital education, as well as the education of students in primary schools. 

Изкуственият интелект в здравния PR

What is the role of artificial intelligence (AI) in healthcare public relations? How is it reshaping the world of health PR? Is AI a threat to healthcare PR or becoming their indispensable assistant? The purpose of the scientific report is to answer these and other questions regarding the application of artificial intelligence in health public communications. The methods used for the purpose of this report are the analysis and synthesis of data from bibliographic sources, as well as the analysis of the results of conducting a scientific experiment in a digital environment with artificial intelligence of the latest generation. Results. The role of AI in health PR is increasingly tangible and fully imposed in the system of international health care – the chatbot of the WHO in the Viber communication platform is a universal bridge between the Organization and its audiences and provides multi-layered opportunities for awareness on all possible health issues. An identical result is also on Facebook‘s Messenger platform. AI is gradually changing the business environment in every field, including healthcare. AI in the face of ChatGPT is becoming both an assistant to health PRs and a major rival. Discussion. Recent breakthroughs in AI technologies could significantly and permanently transform health PR and how it is implemented. The PR profession is not a disappearing species, as it is associated with a deep thought process, creativity, contacts, and connections with „living” people. These activities are so far beyond the reach of artificial intelligence, yet the best professionals will stand out and remain. The results of the scientific experiment set a direction for striving for an increasingly accurate representation of health PR specialists in their professional field. Conclusion. ChatGPT can compose relevant texts, slogans, even messages that are highly effective when applied in the practice of health PR. In the foreseeable future, this technological innovation will be further refined. Chatbots as part of the idea of communication through AI is an area where the role of PRs is to carefully consider each message and the logical connection between the messages. The following recommendations emerge for future research on the topic: to monitor the technological progress of AI and to find new niches in health PR activities in which AI finds application.

ORKG-Leaderboards: a systematic workflow for mining leaderboards as a knowledge graph

AbstractThe purpose of this work is to describe theorkg-Leaderboard software designed to extractleaderboardsdefined astask–dataset–metrictuples automatically from large collections of empirical research papers in artificial intelligence (AI). The software can support both the main workflows of scholarly publishing, viz. as LaTeX files or as PDF files. Furthermore, the system is integrated with the open research knowledge graph (ORKG) platform, which fosters the machine-actionable publishing of scholarly findings. Thus, the systemsss output, when integrated within the ORKG’s supported Semantic Web infrastructure of representing machine-actionable ‘resources’ on the Web, enables: (1) broadly, the integration of empirical results of researchers across the world, thus enabling transparency in empirical research with the potential to also being complete contingent on the underlying data source(s) of publications; and (2) specifically, enables researchers to track the progress in AI with an overview of the state-of-the-art across the most common AI tasks and their corresponding datasets via dynamic ORKG frontend views leveraging tables and visualization charts over the machine-actionable data. Our best model achieves performances above 90% F1 on theleaderboardextraction task, thus provingorkg-Leaderboards a practically viable tool for real-world usage. Going forward, in a sense,orkg-Leaderboards transforms theleaderboardextraction task to an automated digitalization task, which has been, for a long time in the community, a crowdsourced endeavor.

Real-time Distributed Computing Model of Low-Voltage Flow Data in Digital Power Grid under Autonomous and Controllable Environments

Motivated by the progress in artificial intelligence and edge computing, this paper proposes a real-time distributed computing model for low-voltage flow data in digital power grids under autonomous and controllable environments. The model utilizes edge computing through wireless offloading to efficiently process and analyze data generated by low-voltage devices in the power grid. Firstly, we evaluate the performance of the system under consideration by measuring its outage probability, utilizing both the received signal-to-noise ratio (SNR) and communication and computing latency. Subsequently, we analyze the system’s outage probability by deriving an analytical expression. To this end, we utilize the Gauss-Chebyshev approximation to provide an approximate closed-form expression. The results of our experimental evaluation demonstrate the effectiveness of the proposed model in achieving real-time processing of low-voltage flow data in digital power grids. Our model provides an efficient and practical solution for the processing of low-voltage flow data, making it a valuable contribution to the field of digital power grids.

Expertise increases planning depth in human gameplay

A hallmark of human intelligence is the ability to plan multiple steps into the future1,2. Despite decades of research3-5, it is still debated whether skilled decision-makers plan more steps ahead than novices6-8. Traditionally, the study of expertise in planning has used board games such as chess, but the complexity of these games poses a barrier to quantitative estimates of planning depth. Conversely, common planning tasks in cognitive science often have a lower complexity9,10 and impose a ceiling for the depth to which any player can plan. Here we investigate expertise in a complex board game that offers ample opportunity for skilled players to plan deeply. We use model fitting methods to show that human behaviour can be captured using a computational cognitive model based on heuristic search. To validate this model, we predict human choices, response times and eye movements. We also perform a Turing test and a reconstruction experiment. Using the model, we find robust evidence for increased planning depth with expertise in both laboratory and large-scale mobile data. Experts memorize and reconstruct board features more accurately. Using complex tasks combined with precise behavioural modelling might expand our understanding of human planning and help to bridge the gap with progress in artificial intelligence.

Trust within human-machine collectives depends on the perceived consensus about cooperative norms

With the progress of artificial intelligence and the emergence of global online communities, humans and machines are increasingly participating in mixed collectives in which they can help or hinder each other. Human societies have had thousands of years to consolidate the social norms that promote cooperation; but mixed collectives often struggle to articulate the norms which hold when humans coexist with machines. In five studies involving 7917 individuals, we document the way people treat machines differently than humans in a stylized society of beneficiaries, helpers, punishers, and trustors. We show that a different amount of trust is gained by helpers and punishers when they follow norms over not doing so. We also demonstrate that the trust-gain of norm-followers is associated with trustors’ assessment about the consensual nature of cooperative norms over helping and punishing. Lastly, we establish that, under certain conditions, informing trustors about the norm-consensus over helping tends to decrease the differential treatment of both machines and people interacting with them. These results allow us to anticipate how humans may develop cooperative norms for human-machine collectives, specifically, by relying on already extant norms in human-only groups. We also demonstrate that this evolution may be accelerated by making people aware of their emerging consensus.

Non-Intrusive Load Monitoring (NILM), Interests and Applications

In developing effective energy management mechanisms, new concepts have been developed to provide new approaches. Non-intrusive load monitoring (NILM) is an approach that was originally developed to allow the occupants of a room to identify the contribution of each appliance to the total electricity consumption of the room through a single point measurement device. The aim is to provide customers with information that will enable them to act as `` ` consum'actors", i.e., people who undertake to change their electricity consumption habits for an objective cause. The progress of artificial intelligence in its various forms (machine learning, big data, internet of things) have greatly contributed to increase the interest of NILM among researchers in different fields. Indeed, some of them are adapting this concept to research areas such as water, transport, health, the environment and agriculture. In this context, applications in these fields have been developed to show the potential and benefits of using this approach. In addition to presenting non-intrusive load monitoring (NILM) in its general framework, this article presents the interests and applications of this approach in various fields.

Artificial Intelligence and Information Processing: A Systematic Literature Review

This study aims to understand the development trends and research structure of articles on artificial intelligence (AI) and information processing in the past 10 years. In particular, this study analyzed 13,294 papers published from 2012 to 2021 in the Web of Science, used the bibliometric analysis method to visualize the data of the papers, and drew a scientific knowledge map. By exploring the development of mainstream journals, author and country rankings, keyword evolution, and research field rankings in the past 10 years, this study uncovered key trends affecting AI progress and information processing that provide insights and serve as an important reference for future AI research and information processing. The results revealed a gradual increase in publications over the past decade, with explosive growth after 2020. The most prolific researchers in this field were Xu, Z.S.; Pedrycz, W.; Herrera-Viedma, E.; the major contributing countries were China, the USA, and Spain. In the AI and information processing research, keywords including “Deep learning”, “Machine learning”, and “Feature extraction” are components that play a crucial role. Additionally, the most representative research areas were “Engineering”, “Operations Research and Management Science”, and “Automation Control Systems”. Overall, this study used bibliometric analysis to provide an overview of the latest trends in artificial intelligence and information processing. Although AI and information processing have been applied to various research areas, many other sub-topics can be further applied. Based on the findings, this study presented research insights and proposed suggestions for future research directions on AI and information processing.

Artificial intelligence: a new journey in nephrology

The rapid development of artificial intelligence (AI) technology, especially the progress in deep learning methods and computing power of hardware, has greatly promoted the application of AI in the field of biomedicine and nephrology. AI can predict the occurrence of acute kidney injury, identify chronic kidney disease, and assist in the analysis of kidney disease pathology, prognosis prediction and decision-making. The application of AI in the field of nephrology depends on the coordinated development of nephrology and artificial intelligence, and requires close interdisciplinary cooperation between multiple disciplines. This article describes the application and research progress of AI in nephrology and provides insights for future directions.

Artificial neural network vision: Between myth and reality

Decades have passed since the world went crazy about the revolution of the machine. Through the years, pioneers in the field tried to imagine how such progress in artificial intelligence (AI) could influence our lives. Were we going to witness sentient machines among us? What would their capabilities, superpowers, and limitations be? Were there ghosts in the machines? Many of these questions confused the greatest minds of our era.