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The purpose of this article is to present grassroots concepts and ideas about "the algorithm" in the religious context. The power and causality of algorithms are based on lines of computer code, making a society influenced by "black boxes" or "enigmatic technologies" (as they are incomprehensible to most people). On the other hand, the power of algorithms lies in the meanings that we attribute to them. The extent of the power, agency, and control that algorithms have over us depends on how much power, agency, and control we are willing to give to algorithms and artificial intelligence, which involves building the idea of their omnipotence. The key question is about the meanings and the ideas about algorithms that are circulating in society. This paper is focused on the analysis of "vernacular/folk" theories on algorithms, reconstructed based on posts made by the users of Polish Catholic forums. The qualitative analysis of online discourse makes it possible to point out several themes, i.e., according to the linguistic concept, "algorithm" is the source domain used in explanations of religious issues (God as the creator of the algorithm, the soul as the algorithm); algorithms and the effects of their work are combined with the individualization and personalization of religion; algorithms are perceived as ideological machines. [ABSTRACT FROM AUTHOR]

The algorithm not only amplifies every detail of human society but also has the same function as the famous nudge technique, i.e. choice architecture, which pushes people toward a certain direction while assuming it’s made by their own will. By this nudge-like function of the algorithm, I want to reevaluate the long-controversial issue of the concept of nudge: is this nudge technique harmless? And if it isn’t, can we still use this nudge technique even with good intention? I’ll start by introducing the concepts of nudge and sludge then talk about their main issues. Third, I’ll use three algorithmic examples to demonstrate the consequences of this nudge technique. Fourth, I will address the nature of the nudge technique and the meaning of intention in nudge. Fifth, I’ll push the discussion further for an important philosophical issue: the white lie. Finally, I’ll summarize my argument and conclude this paper. [ABSTRACT FROM AUTHOR]

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Copyright of Revista Derechos y Libertades is the property of Dykinson SL and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, future cars are attempting self-driving around the world. However, hacking, such as ECUs in automobiles, creates problems that are directly connected to human life. Therefore, this study wrote a paper that detects anomalies in such cars by field. As a related study, the study investigated the vulnerabilities of the automobile security committee and automobile security standards and investigated the detection of abnormalities in the hacking of geo-train cars using artificial intelligence's LSTM and blockchain consensus algorithm. In addition, in automobile security, an algorithm was studied to predict normal and abnormal values using LSTM-based anomaly detection techniques on the premise that automobile communication networks are largely divided into internal and external networks. In the methodology, LSTM's pure propagation malicious code detection technique was used, and it worked with an artificial intelligence consensus algorithm to increase security. In addition, Unity ML conducted an experiment by constructing a virtual environment using the Beta version. The LSTM blockchain consensus node network was composed of 50,000 processes to compare performance. For the first time, 100 Grouped Tx, 500 Channels were tested for performance. For the first time, the malicious code detection rate of the existing system was verified. Accelerator, Multichannel, Sharding, Raiden, Plasma, and Trubit values were verified, and values of approximately 15,000 to 50,000 were obtained. In this paper, we studied to become a paper of great significance on hacking that threatens human life with the development of self-driving cars in the future. [ABSTRACT FROM AUTHOR]

BACKGROUND: This narrative review aims to explore the current state and future perspective of artificial intelligence (AI) in respiratory care. The objective is to provide insights into the potential impact of AI in this field. METHODS: A comprehensive analysis of relevant literature and research studies was conducted to examine the applications of AI in respiratory care and identify areas of advancement. The analysis included studies on remote monitoring, early detection, smart ventilation systems, and collaborative decision-making. RESULTS: The obtained results highlight the transformative potential of AI in respiratory care. AI algorithms have shown promising capabilities in enabling tailored treatment plans based on patient-specific data. Remote monitoring using AI-powered devices allows for real-time feedback to health-care providers, enhancing patient care. AI algorithms have also demonstrated the ability to detect respiratory conditions at an early stage, leading to timely interventions and improved outcomes. Moreover, AI can optimize mechanical ventilation through continuous monitoring, enhancing patient comfort and reducing complications. Collaborative AI systems have the potential to augment the expertise of health-care professionals, leading to more accurate diagnoses and effective treatment strategies. CONCLUSION: By improving diagnosis, AI has the potential to revolutionize respiratory care, treatment planning, and patient monitoring. While challenges and ethical considerations remain, the transformative impact of AI in this domain cannot be overstated. By leveraging the advancements and insights from this narrative review, health-care professionals and researchers can continue to harness the power of AI to improve patient outcomes and enhance respiratory care practices. IMPROVEMENTS: Based on the findings, future research should focus on refining AI algorithms to enhance their accuracy, reliability, and interpretability. In addition, attention should be given to addressing ethical considerations, ensuring data privacy, and establishing regulatory frameworks to govern the responsible implementation of AI in respiratory care. [ABSTRACT FROM AUTHOR]

The text diagnoses two opposing tendencies in the research on algorithms: the first abstracts and unites heterogeneous developments under the term "algorithm"; the second emphasizes specifics such as data sets, material conditions, software libraries, interfaces, and so on, thus dissolving that which apparently algorithms do into more fine-grained analyses. The text proposes a research perspective that resolves this tension by conceiving of algorithms as a relation between the abstract and the concrete that allows to capture both in their interdependence. This approach is informed by two motives: first, the necessity to connect detailed analyses of specific information technologies with general political concerns; and second, the application of recent feminist critiques of epistemology to the analysis of algorithms. The ensuing relational perspective on algorithms is connected to the genealogy of algorithmic technology before being demonstrated regarding the mutually complementing relationships: algorithms-materiality, algorithms-data, algorithms-code, and algorithms-interfaces. [ABSTRACT FROM AUTHOR]

A nonlinear approach to identifying combinations of CpGs DNA methylation data, as biomarkers for Alzheimer (AD) disease, is presented in this paper. It will be shown that the presented algorithm can substantially reduce the amount of CpGs used while generating forecasts that are more accurate than using all the CpGs available. It is assumed that the process, in principle, can be non-linear; hence, a non-linear approach might be more appropriate. The proposed algorithm selects which CpGs to use as input data in a classification problem that tries to distinguish between patients suffering from AD and healthy control individuals. This type of classification problem is suitable for techniques, such as support vector machines. The algorithm was used both at a single dataset level, as well as using multiple datasets. Developing robust algorithms for multi-datasets is challenging, due to the impact that small differences in laboratory procedures have in the obtained data. The approach that was followed in the paper can be expanded to multiple datasets, allowing for a gradual more granular understanding of the underlying process. A 92% successful classification rate was obtained, using the proposed method, which is a higher value than the result obtained using all the CpGs available. This is likely due to the reduction in the dimensionality of the data obtained by the algorithm that, in turn, helps to reduce the risk of reaching a local minima. [ABSTRACT FROM AUTHOR]

Within the Architecture, engineering and construction (AEC) sector we see movements toward greater use of industrial robots, machine learning, algorithms, and other artificial intelligence (AI) tools. Yet, the AEC industry, despite being one of the largest fields on a global scale, is known for being the slowest to digitalize and innovate. Factors such as unrecognizing the value of digitalization by the decision-makers and making safety-related decisions under high levels of uncertainty, appear to be critical in preventing successful large-scale digitalization. This situation raises multiple questions from a risk science perspective. How, among other things, might the expansion of AI and more specifically AI algorithms usage in the AEC field affect uncertainties, and could AI be considered a tool for preventing crises? To obtain responses to these questions, we conducted 21 semistructured, in-depth interviews with AEC employees who are currently using AI and AI algorithms or will soon be doing so in their everyday work. Our findings show potential for wider use within the AEC field, subject to overcoming knowledge gaps. Moreover, although having the potential to reduce some uncertainties, the increased use of AI and AI algorithms appears to be introducing an entirely new set of uncertainties. As a result, although AI may effectively prevent certain crises and be regarded as crisis prevention tool, its inadequate implementation could potentially create new risks. [ABSTRACT FROM AUTHOR]

With growing evidence of deep learning-assisted smart process planning, there is an essential demand for comprehending whether cyber-physical production systems (CPPSs) are adequate in managing complexity and flexibility, configuring the smart factory. In this research, prior findings were cumulated indicating that the interoperability between Internet of Things-based real-time production logistics and cyber-physical process monitoring systems can decide upon the progression of operations advancing a system to the intended state in CPPSs. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March and August 2021, with search terms including "cyber-physical production systems", "cyber-physical manufacturing systems", "smart process manufacturing", "smart industrial manufacturing processes", "networked manufacturing systems", "industrial cyber-physical systems," "smart industrial production processes", and "sustainable Internet of Things-based manufacturing systems". As we analyzed research published between 2017 and 2021, only 489 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 164, chiefly empirical, sources. Subsequent analyses should develop on real-time sensor networks, so as to configure the importance of artificial intelligence-driven big data analytics by use of cyber-physical production networks. [ABSTRACT FROM AUTHOR]

Algorithmic discrimination has rapidly become a topic of intense public and academic interest. This article explores three issues raised by algorithmic discrimination: (1) the distinction between direct and indirect discrimination, (2) the notion of disadvantageous treatment, and (3) the moral badness of discriminatory automated decision-making. It argues that some conventional distinctions between direct and indirect discrimination appear not to apply to algorithmic discrimination, that algorithmic discrimination may often be discrimination between groups, as opposed to against groups, and that it is not necessarily the case that morally bad algorithmic discrimination gives us reason to not use automated decision-making. For each of the three issues, the article explores implications for algorithmic discrimination, suggests some alternative answers, and clarifies how we may want to think of discrimination more broadly in light of lessons drawn from the context of algorithmic discrimination. [ABSTRACT FROM AUTHOR]

In the digital age, the use of advanced technology is becoming a new paradigm in police work, criminal justice, and the penal system. Algorithms promise to predict delinquent behaviour, identify potentially dangerous persons, and support crime investigation. Algorithm-based applications are often deployed in this context, laying the groundwork for a 'smart criminal justice'. In this qualitative study based on 32 interviews with criminal justice and police officials, we explore the reasons why and extent to which such a smart criminal justice system has already been established in Switzerland, and the benefits perceived by users. Drawing upon this research, we address the spread, application, technical background, institutional implementation, and psychological aspects of the use of algorithms in the criminal justice system. We find that the Swiss criminal justice system is already significantly shaped by algorithms, a change motivated by political expectations and demands for efficiency. Until now, algorithms have only been used at a low level of automation and technical complexity and the levels of benefit perceived vary. This study also identifies the need for critical evaluation and research-based optimization of the implementation of advanced technology. Societal implications, as well as the legal foundations of the use of algorithms, are often insufficiently taken into account. By discussing the main challenges to and issues with algorithm use in this field, this work lays the foundation for further research and debate regarding how to guarantee that 'smart' criminal justice is actually carried out smartly. [ABSTRACT FROM AUTHOR]

This scoping study aims to provide an overview of the functions, utilities and limitations of applying "decision support algorithms" (DSAs) in different social work contexts. The study involves searching the Scopus and Web of Science databases, which results in the inclusion of 55 articles in the analysis. We find important differences in the functions, utilities and limitations of DSAs according to the choice of data, variables, technology and social work context. The reviewed literature indicates that social workers are rarely involved in developing DSAs. This seems to result in a limited innovation of DSAs that are capable of stimulating the reflection of social workers and thereby qualifying their decision-making process. This study calls for further research on how to enhance interaction between social workers at the street level and system builders and managers at the system level in developing DSAs designed for reflection. [ABSTRACT FROM AUTHOR]

Since the advent and usage of artificial intelligence approaches in architecture, a significant number of studies have focused on integrating technological solutions to architectural issues. Artificial intelligence applications in architectural design range from intelligent material design to architectural plan solutions. The ubiquity and distribution of research in this field, as well as the rising use of artificial intelligence techniques to solve design challenges, require an analytical classification of the essential literature review. This article presents a descriptive and analytical review of the work on artificial intelligence applications in architecture. A strong review has been made that identifies and addresses the gaps in artificial intelligence and architecture; and the literature review is transformed into statistical plots. The study's findings indicate a growing interest in artificial intelligence in the field of architecture. There is a need for novel research to be conducted in these areas using advanced technology and techniques. [ABSTRACT FROM AUTHOR]

The purpose of our bibliometric research was to capture and analyze the trends of two types of well-known classical artificial intelligence (AI) algorithms: neural networks (NNs) and genetic algorithms (GAs). Symmetry is a very popular international and interdisciplinary scientific journal that cover six major research subjects of mathematics, computer science, engineering science, physics, biology, and chemistry which are all related to our research on classical AI algorithms; therefore, we referred to the most innovative research articles of classical AI algorithms that have been published in Symmetry, which have also introduced new advanced applications for NNs and Gas. Furthermore, we used the keywords of "neural network algorithm" or "artificial neural network" to search the SSCI database from 2002 to 2021 and obtained 951 NN publications. For comparison purposes, we also analyzed GA trends by using the keywords "genetic algorithm" to search the SSCI database over the same period and we obtained 878 GA publications. All of the NN and GA publication results were categorized into eight groups for deep analyses so as to investigate their current trends and forecasts. Furthermore, we applied the Kolmogorov–Smirnov test (K–S test) to check whether our bibliometric research complied with Lotka's law. In summary, we found that the number of applications for both NNs and GAs are continuing to grow but the use of NNs is increasing more sharply than the use of GAs due to the boom in deep learning development. We hope that our research can serve as a roadmap for other NN and GA researchers to help them to save time and stay at the cutting edge of AI research trends. [ABSTRACT FROM AUTHOR]

Digitization in transplantation is not a new phenomenon. Algorithms are being used, for example, to allocate organs based on medical compatibility and priority criteria. However, digitization is accelerating as computer scientists and physicians increasingly develop and use machine learning (ML) models to obtain better predictions on the chances of a successful transplant. The objective of the article is to shed light on the potential threats to equitable access to organs allocated through algorithms, whether these are the consequence of political choices made upstream of digitization or of the algorithmic design, or are produced by self-learning algorithms. The article shows that achieving equitable access requires an overall vision of the algorithmic development process and that European legal norms only partially contribute to preventing harm and addressing equality in access to organs. [ABSTRACT FROM AUTHOR]

An algorithm refers to a series of stepwise instructions used by a machine to perform a mathematical operation. In 1955, the term artificial intelligence (AI) was coined to indicate that a machine could be programmed to duplicate human intelligence. Even though that goal has not yet been reached, the use of sophisticated machine learning algorithms has moved us closer to that goal. While algorithm‐enabled systems and devices will bring many benefits to occupational safety and health, this Commentary focuses on new sources of worker risk that algorithms present in the use of worker management systems, advanced sensor technologies, and robotic devices. A new "digital Taylorism" may erode worker autonomy, and lead to work intensification and psychosocial stress. The presence of large amounts of information on workers within algorithmic‐enabled systems presents security and privacy risks. Reliance on indiscriminate data mining may reproduce forms of discrimination and lead to inequalities in hiring, retention, and termination. Workers interfacing with robots may face work intensification and job displacement, while injury in the course of employment by a robotic device is also possible. Algorithm governance strategies are discussed such as risk management practices, national and international laws and regulations, and emerging legal accountability proposals. Determining if an algorithm is safe for workplace use is rapidly becoming a challenge for manufacturers, programmers, employers, workers, and occupational safety and health practitioners. To achieve the benefits that algorithm‐enabled systems and devices promise in the future of work, now is the time to study how to effectively manage their risks. [ABSTRACT FROM AUTHOR]

Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016 (Mittelstadt et al. Big Data Soc 3(2), 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative concerns, and to offer actionable guidance for the governance of the design, development and deployment of algorithms. [ABSTRACT FROM AUTHOR]

Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing. [ABSTRACT FROM AUTHOR]

This introduction to a special section on algorithmic thought provides a framework through which the articles in that collection can be contextualised and their individual contributions highlighted. Over the past decade, there has been a growing interest in artificial intelligence (AI). This special section reflects on this AI boom and its implications for studying what thinking is. Focusing on the algorithmic character of computing machines and the thinking that these machines might express, each of the special section's essays considers different dimensions of algorithmic thought, engaging with a diverse set of epistemological questions and issues. [ABSTRACT FROM AUTHOR]

With the development of technologies in various fields, more and more technologies have been applied to safety clothing, which has led to the rapid development of safety clothing. The improvement of living standards is accompanied by the change of consumption concepts. Consumers' requirements for clothing products have become more artistic, healthier, and more ecological, and they look forward to more and better safety clothing to meet their health needs. In this context, this article studies traffic safety outdoor clothing design based on big data (BD) and AI. This article introduces the design method of outdoor safety clothing for traffic based on BD and machine learning in artificial intelligence (AI) and did two experiments. To this end, this paper adopts a Deep Belief Network (DBN), which is trained layer by layer through Restricted Boltzmann Machine (RBM), and successfully solves the problems of lack of a large number of labeled samples and easy to fall into local optimum. The first experiment is to test the accuracy of various machine learning algorithms for clothing size measurement. The results obtained are as follows: the predicted value of the DBN neural network is the closest to the actual value, the average prediction accuracy of DBN for the cuff size is 90%, and the average prediction accuracy for the neck circumference is 91.5%. The second experiment is to investigate the functional needs and performance concerns of children and outdoor workers. The results of the experiment are as follows: for children, 79.9% of people want clothing to have a positioning function, which accounts for the highest proportion. For outdoor workers, the most important clothing function they need is eye-catching style, and 90.1% of those choose this option. In terms of clothing performance concerns, most people choose to care very much, and the second most people choose to care about comfort., Author(s): Ya Zhou (corresponding author) [1]; Yun Shi [1] 1. Introduction 1.1. Background With safety as the main purpose, emphasizing a healthy, comfortable, green, and environmentally friendly living environment is [...]

Jerome Workman, Jr. and Howard Mark In Part I (February 2023) of this two-part series on artificial intelligence (AI), and its subfield machine learning (ML), we presented the variety of [...]

In this article, I will argue in favor of both the ethical and epistemological utility of explanations in artificial intelligence (AI)-based medical technology. I will build on the notion of 'explicability' due to Floridi, which considers both the intelligibility and accountability of AI systems to be important for truly delivering AI-powered services that strengthen autonomy, beneficence, and fairness. I maintain that explicable algorithms do, in fact, strengthen these ethical principles in medicine, e.g., in terms of direct patient-physician contact, as well as on a longer-term epistemological level by facilitating scientific progress that is informed through practice. With this article, I will therefore attempt to counter arguments against demands for explicable AI in medicine that are based on a notion of 'whatever heals is right.' I will elucidate my elaboration on the positive aspects of explicable AI in medicine as well as by pointing out risks of non-explicable AI., Author(s): Christian Herzog [sup.1] Author Affiliations: (1) grid.4562.5, 0000 0001 0057 2672, Ethical Innovation Hub, University of Lübeck, , Ratzeburger Allee 160, 23562, Lübeck, Germany Introduction > The vision of [...]