Development Of Intelligence Research Articles

Ethics of Artificial Intelligence and Automation: Balancing Innovation and Responsibility

This paper examines the ethical challenges associated with the development and deployment of artificial intelligence (AI) and automation technologies. As AI continues to advance, it raises complex ethical issues regarding autonomy, accountability, fairness, transparency, privacy, and bias. The paper explores the key ethical concerns in AI and automation, focusing on how these technologies must be designed and governed to ensure they serve the public good without reinforcing societal inequalities. It emphasizes the importance of integrating ethical considerations into the design process from the outset, fostering a balanced approach to innovation and responsibility. The paper calls for a continued commitment to responsible AI development that promotes fairness, transparency, and accountability while mitigating the risks of harm.

Evolution and Impact of Artificial Intelligence on Advanced Defense Strategies in Cybersecurity

This article presents a detailed analysis of the use of AI in various aspects of cybersecurity, from training, threat detection and response, through identity and access management, to future directions and inherent challenges. It examines how this technology is transforming the field of digital security. The aim is not only to present the opportunities offered by AI, but also to draw attention to the need for a conscious and responsible approach to its implementation and exploitation.

The Effect of Perceived Father Involvement on Emotional Intelligence in Adolescents

The period of adolescence is often characterised by emotional agitation, creating a vulnerability to the influence of the surrounding environment. Adolescents frequently lack the capacity to recognise, regulate and control their emotions, which can result in social difficulties. This indicates that adolescents exhibit a deficiency in emotional intelligence. To fulfil their emotional intelligence, adolescence is influenced by a variety of factors, including the role of parents. Parents, as the primary figure in the care of adolescents, have a significant role in the development of adolescent emotional intelligence. The involvement of fathers in parenting is an important factor to consider when examining the relationship between fathers and their children in the context of emotional intelligence development. This study aims to determine the effect of perceived father involvement in parenting on adolescents' emotional intelligence. The research method was correlational research, conducted on a sample of 180 adolescents in Indonesia, aged between 13 and 18 years. A convenience sampling technique was used for the selection of participants. The father's involvement scale consists of 27 items with an alpha coefficient of 0.959, while the emotional intelligence scale consists of 12 items with a coefficient of 0.868. In this study, the hypothesis test is a simple linear regression analysis, using JASP 0.18.3 software. The findings of this study evidenced a significant influence between adolescents' perceptions of father involvement and their emotional intelligence. Furthermore, this study emphasises that adolescents with divorced parents have positive perceptions of father involvement and their emotional intelligence.

Robot Tracking Navigation Based on Visual SLAM

Visual Simulation Localization and Mapping technology is an important research direction in the fields of computer vision and robotics. The research background of this technology mainly stems from the insufficient accuracy of traditional positioning and map construction methods in the face of environmental changes. With the development of artificial intelligence, this technology has been involved in multiple disciplinary fields. Since 2015, researchers have begun to focus on the combination with deep learning to improve algorithm robustness, dynamic scene planning and other aspects. This article which introduces the existing tracking car system and visual SLAM (Simultaneous Localization and Mapping) system framework explores the robot tracking and navigation technology based on visual SLAM. Visual SLAM technology provides robots with an autonomous navigation solution that does not require external sensors through feature extraction, real-time positioning, mapping, and closed-loop detection. This article also discusses the advantages and disadvantages of visual SLAM navigation, and offers insights into the future development of VSLAM technology in robot tracking navigation.

Heparin in sepsis: current clinical findings and possible mechanisms.

Sepsis is a clinical syndrome resulting from the interaction between coagulation, inflammation, immunity and other systems. Coagulation activation is an initial factor for sepsis to develop into multiple organ dysfunction. Therefore, anticoagulant therapy may be beneficial for sepsis patients. Heparin possesses a variety of biological activities, so it has a broad prospect in sepsis. Previous studies suggested that patients with sepsis-induced disseminated intravascular coagulation and high disease severity might be suitable for anticoagulant therapy. With the development of artificial intelligence (AI), recent studies have shown that patients with severe coagulation activation represent the targeted patients for anticoagulant therapy in sepsis. However, it remains necessary to accurately define the relevant biomarkers indicative of this phenotype and validate their clinical utility by large randomized controlled trials (RCTs). Analyses of data from early small RCTs, subgroup analyses of large RCTs and meta-analyses have collectively suggested that anticoagulant therapy, particularly the use of heparin, may be an effective approach for managing sepsis patients. Concurrently, debate persists regarding the optimal selection of anticoagulants, proper timing, usage and dosage of administration that should be employed to assess treatment efficacy. The primary mechanisms of heparin are acting on heparan sulfate, histones, high mobility group box 1 and heparin-binding protein, which interfere with the regulation of inflammation, vascular permeability, coagulation, endothelial function and other biological activities. However, the underlying pathophysiological processes mediating the potential therapeutic effects of heparin in the context of sepsis remain incompletely understood and warrant additional rigorous investigation to establish the mechanism more conclusively.

The influence and development of artificial intelligence on automobile emergency system

In the automobile industry, the application of artificial intelligence is more and more extensive, among which the emergency hedging system of automobile is one of the important fields of artificial intelligence application. With the increasing growth of automobiles, traffic road safety has become a topic often talked about in People's Daily life. With the rapid development of artificial intelligence, many automobile companies begin to use artificial intelligence to help the car emergency hedging system. Artificial intelligence will greatly enhance the timeliness, accuracy and safety of the automobile emergency system. This paper aims to discuss the influence and development of artificial intelligence on automobile emergency avoidance system, and analyze its advantages and disadvantages and future development trend. Which has a very obvious help to the automatic driving technology, but also laid the development of automatic driving technology.

Tactical intent-driven autonomous air combat behavior generation method

With the rapid development and deep application of artificial intelligence, modern air combat is incrementally evolving towards intelligent combat. Although deep reinforcement learning algorithms have contributed to dramatic advances in in air combat, they still face challenges such as poor interpretability and weak transferability of adversarial strategies. In this regard, this paper proposes a tactical intent-driven method for autonomous air combat behaviour generation. Firstly, this paper explores the mapping relationship between optimal strategies and rewards, demonstrating the detrimental effects of the combination of sparse rewards and dense rewards on policy. Built around this, the decision-making process of pilot behavior is analyzed, and a reward mapping model from intent to behavior is established. Finally, to address the problems of poor stability and slow convergence speed of deep reinforcement learning algorithms in large-scale state-action spaces, the dueling-noisy-multi-step DQN algorithm is devised, which not only improves the accuracy of value function approximation but also enhances the efficiency of space exploration and network generalization. Through experiments, the conflicts between sparse rewards and dense rewards are demonstrated. The superior performance and stability of the proposed algorithm compared to other algorithms are captured by our empirical results. More intuitively, the strategies under different intents exhibit strong interpretability and flexibility, which can provide tactical support for intelligent decision-making in air combat.

How does anthropomorphism promote consumer responses to social chatbots: mind perception perspective

PurposeBenefiting from the development and innovation of artificial intelligence and affective computing technology, social chatbots that integrate cognitive analysis and affective social services have flooded into the consumer market. For cognition and emotion-oriented tasks, social chatbots do not always receive positive consumer responses. In addition, consumers have a contradictory attitude toward the anthropomorphism of chatbots. Therefore, from the perspective of mind perception and the two dimensions of social judgment, this research explores the mechanism of consumer responses to anthropomorphic interaction styles when social chatbots complete different service tasks.Design/methodology/approachThis paper utilizes three behavior experimental designs and survey methods to collect data and the ANOVA, t-test and bootstrap analysis methods to verify the assumed hypotheses.FindingsThe results indicate that when the service task type of a social chatbot is cognition-oriented, compared to a warm anthropomorphic interaction style, a competent anthropomorphic interaction style can improve consumer responses more effectively. During this process, agent-mind perception plays a mediating role. When the service task type of a social chatbot is emotion-oriented, compared with a competent anthropomorphic conversation style, a warm anthropomorphic conversation style can improve consumer responses. Experience-mind perception mediates this influencing relationship.Originality/valueThe research results theoretically enrich the relevant research on the anthropomorphism of social chatbots and expand the application of the theory of mind perception in the fields of artificial intelligence and interactive marketing. Our findings provide theoretical guidance for the anthropomorphic development and design of social chatbots and the practical management of service task scenarios.

Comparative study of Claude 3.5-Sonnet and human physicians in generating discharge summaries for patients with renal insufficiency: assessment of efficiency, accuracy, and quality.

The rapid development of artificial intelligence (AI) has shown great potential in medical document generation. This study aims to evaluate the performance of Claude 3.5-Sonnet, an advanced AI model, in generating discharge summaries for patients with renal insufficiency, compared to human physicians. A prospective, comparative study was conducted involving 100 patients (50 with acute kidney injury and 50 with chronic kidney disease) from the nephrology department of Ningbo Hangzhou Bay Hospital between January and June 2024. Discharge summaries were independently generated by Claude 3.5-Sonnet and human physicians. The main evaluation indicators included accuracy, generation time, and overall quality. Claude 3.5-Sonnet demonstrated comparable accuracy to human physicians in generating discharge summaries for both AKI (90 vs. 92 points, p > 0.05) and CKD patients (88 vs. 90 points, p > 0.05). The AI model significantly outperformed human physicians in terms of efficiency, requiring only about 30 s to generate a summary compared to over 15 min for physicians (p < 0.001). The overall quality scores showed no significant difference between AI-generated and physician-written summaries for both AKI (26 vs. 27 points, p > 0.05) and CKD patients (25 vs. 26 points, p > 0.05). Claude 3.5-Sonnet demonstrates high efficiency and reliability in generating discharge summaries for patients with renal insufficiency, with accuracy and quality comparable to those of human physicians. These findings suggest that AI has significant potential to improve the efficiency of medical documentation, though further research is needed to optimize its integration into clinical practice and address ethical and privacy concerns.

Using the generative Artificial Intelligence (AI) chatbots of Perplexity and ChatGPT as a teaching and learning tool for practice teachers and students within social work placement

Abstract: Artificial intelligence (AI) is the simulation of human intelligence processes by machines or computer systems. As large language generated pretrained transformers, when given a prompt the chatbot system rewards the user with a ‘human-like’ response from their massive dataset of information. Their velocity, scope and capabilities become immense. This article is an illustrative analysis focusing on the rise of AI and its applications within industry, health and in particular, social work placement. The suggestion is to encourage practice teachers and students within supervision to use the chatbots ChatGPT or Perplexity in order to enhance critical thinking. To further consider the areas such as language, power, intuition, confidentiality, ethics, values and beliefs in this brave new world. Limitations of chatbot use within supervision is explored and a balanced conclusion recognising the worth of social work as a profession but not denying the speed of artificial intelligence development is recognised. From a social work placement perspective this article considers how working from a new evidence base, the application of critically analysing digitally produced knowledge, can be a relevant teaching tool within placement supervision.

WAYS TO PROTECT PERSONAL DATA USİNG ARTİFİCİAL INTELLİGENCE METHODS

With the development of artificial intelligence (AI), protecting personal data has become crucial. AI systems can collect and analyze personal data, which can risk privacy and security. The main issues are privacy breaches, misuse of data, cybersecurity threats, and algorithmic bias. Addressing these problems requires legislation, transparency, technical security, and adherence to ethical guidelines. These measures can help ensure the protection of personal data. Keywords: protection, personal data, potential, artificial intelligence, machine learning algoritms

Artificial Intelligence in Banking : The Evidence from Poland

The widespread use of new technologies has reduced the precise boundary between physical and digital realities. Dynamic development of artificial intelligence (AI) systems has contributed to the digital transformation of the global economy. Given its extensive range of potential applications, AI has the potential to impact a multitude of socio-economic domains, including politics, security, health care, medicine, economy, trade, finance, taxes, and production. As the banking sector plays a crucial role in the global economy, the question arises as to whether and to what extent banking market entities are willing to use artificial intelligence solutions in their business processes. This study aims to determine the scope of AI use by key banks operating in the Polish banking market. Achieving this goal requires determining what categories of AI are used by key banks operating in Poland, analysing AI methods used by those banks, and the fields of AI implementation. The research scope covers the analysis of AI applications in the largest banks operating in Poland, which together cover almost half of the market share (PKO Bank Polski S.A., Bank Pekao S.A., Santander Bank Polska S.A., mBank S.A. and ING Bank Śląski S.A). To obtain an up-to-date overview of AI usage, the research period is 2019-2024. The empirical part of the research is supported by an analysis of the widespread use of artificial intelligence and its dynamic international development based on academic papers and industry reports.

Strategies for creation of data reserve and stress testing of medical AI products

With the fast development of artificial intelligence (AI) and its applications in medicine, it is often said that the time for intelligent medicine is arriving, if not already have arrived....

The use of 5.0 Technologies to foster the development of skills in accounting – The case of the Model of Simulator of Business Environment

This article aims to address technical, transversal and digital competences and their development in the context of accounting. The development of artificial intelligence and digitalization technologies has significantly changed the world. Higher education institutions are not yet prepared for this reality. They must find a way to equip their teachers with digital skills in the first instance, and then move on to make changes to the course curriculum to adapt the content to 5.0 technology. At Porto Accounting and Business School of Porto Polytechnic, these technologies have been implemented in a pivotal environment called the Model of Simulator of Business Environment. This model includes two curricular units that are part of the final year of the degree course in accounting and administration, Business Simulation Project I and II. This model is characterised as an active methodology in the teaching and learning process. Students are at the centre of this process and responsibility for their learning lies with them. Technical, transversal and digital accounting skills are the basis for building this model. This article presents the competences worked on to prepare students for the labour market. A set of activities proposed to the students in the working sessions is presented, with an explanation of the competences that are intended to be powered. The theoretical basis and the experience of more than 20 years working with this Model leads us to reflect on its future. This future includes the introduction of AI and tools that bring digitalisation in its true sense.

Artificial Intelligence Life Cycle: The Detection and Mitigation of Bias

The rapid expansion of Artificial Intelligence(AI) has outpaced the development of ethical guidelines and regulations, raising concerns about the potential for bias in AI systems. These biases in AI can manifest in real-world applications leading to unfair or discriminatory outcomes in areas like job hiring, loan approvals or criminal justice predictions. For example, a biased AI model used for loan prediction may deny loans to qualified applicants based on demographic factors such as race or gender. This paper investigates the presence and mitigation of bias in Machine Learning(ML) models trained on the Adult Census Income dataset, known to have limitations in gender and race. Through comprehensive data analysis, focusing on sensitive attributes like gender, race and relationship status, this research sheds light on complex relationships between societal biases and algorithmic outcomes and how societal biases can be rooted and amplified by ML algorithms. Utilising fairness metrics like demographic parity(DP) and equalised odds(EO), this paper quantifies the impact of bias on model predictions. The results demonstrated that biased datasets often lead to biased models even after applying pre-processing techniques. The effectiveness of mitigation techniques such as reweighting(Exponential Gradient(EG)) to reduce disparities was examined, resulting in a measurable reduction in bias disparities. However, these improvements came with trade-offs in accuracy and sometimes in other fairness metrics, identified the complex nature of bias mitigation and the need for precise consideration of ethical implications. The findings of this research highlight the critical importance of addressing bias at all stages of the AI life cycle, from data collection to model deployment. The limitation of this research, especially the use of EG, demonstrates the need for further development of bias mitigation techniques that can address complex relationships while maintaining accuracy. This paper concludes with recommendations for best practices in Artificial Intelligence development, emphasising the need for ongoing research and collaboration to mitigate bias by prioritising ethical considerations, transparency, explainability, and accountability to ensure fairness in AI systems.

Research Progress on Methods for Improving the Stability of Non-Destructive Testing of Agricultural Product Quality.

Non-destructive testing (NDT) technology is pivotal in the quality assessment of agricultural products. In contrast to traditional manual testing, which is fraught with subjectivity, inefficiency, and the potential for sample damage, NDT technology has gained widespread application due to its advantages of objectivity, speed, and accuracy, and it has injected significant momentum into the intelligent development of the food industry and agriculture. Over the years, technological advancements have led to the development of NDT systems predicated on machine vision, spectral analysis, and bionic sensors. However, during practical application, these systems can be compromised by external environmental factors, the test samples themselves, or by the degradation and noise interference inherent in the testing equipment, leading to instability in the detection process. This instability severely impacts the accuracy and efficiency of the testing. Consequently, refining the detection methods and enhancing system stability have emerged as key focal points for research endeavors. This manuscript presents an overview of various prevalent non-destructive testing methodologies, summarizes how sample properties, external environments, and instrumentation factors affect the stability of testing in practical applications, organizes and analyzes solutions to enhance the stability of non-destructive testing of agricultural product quality based on current research, and offers recommendations for future investigations into the non-destructive testing technology of agricultural products.

Limitations of using artificial intelligence services to analyze chest x-ray imaging

BACKGROUND: Chest X-ray examination is one of the first radiology areas that started applying artificial intelligence, and it is still used to the present. However, when interpreting X-ray scans using artificial intelligence, radiologists still experience several routine restrictions that should be considered in issuing a medical report and require the attention of artificial intelligence developers to further improve the algorithms and increase their efficiency. AIM: To identify restrictions of artificial intelligence services for analyzing chest X-ray images and assesses the clinical significance of these restrictions. MATERIALS AND METHODS: A retrospective analysis was performed for 155 cases of discrepancies between the conclusions of artificial intelligence services and medical reports when analyzing chest X-ray images. All cases included in the study were obtained from the Unified Radiological Information Service of the Unified Medical Information and Analytical System of Moscow. RESULTS: Of the 155 analyzed difference cases, 48 (31.0%) were false-positive and 78 (50.3%) were false-negative cases. The remaining 29 (18.7%) cases were removed from further studies because they were true positive (27) or true negative (2) in the expert review. Most (93.8%) of the 48 false-positive cases were due to the artificial intelligence service mistaking normal chest anatomy (97.8% of cases) or catheter shadow (2.2% of cases) for pneumothorax signs. Overlooked clinically significant pathologies accounted for 22.0% of false-negative scans. Nearly half of these cases (44.4%) were overlooked lung nodules. Lung calcifications (60.9%) were the most common clinically insignificant pathology. CONCLUSIONS: Artificial intelligence services demonstrate a tendency toward over diagnosis. All false-positive cases were associated with erroneous detection of clinically significant pathology: pneumothorax, lung nodules, and pulmonary consolidation. Among false-negative cases, the rate of overlooked clinically significant pathology was low, which accounted for less than one-fourth.

Development of an Open-Source Dataset of Flat-Mounted Images for the Murine Oxygen-Induced Retinopathy Model of Ischemic Retinopathy.

To describe an open-source dataset of flat-mounted retinal images and vessel segmentations from mice subject to the oxygen-induced retinopathy (OIR) model. Flat-mounted retinal images from mice killed at postnatal days 12 (P12), P17, and P25 used in prior OIR studies were compiled. Mice subjected to normoxic conditions were killed at P12, P17, and P25, and their retinas were flat-mounted for imaging. Major blood vessels from the OIR images were manually segmented by four graders (JSC, HKR, KBL, JM), with cross-validation performed to ensure similar grading. Overall, 1170 images were included in this dataset. Of these images, 111 were of normoxic mice retina, and 1048 were mice subject to OIR. The majority of images from OIR mice were obtained at P17. The 50 images obtained from an external dataset, OIRSeg, did not have age labels. All images were manually segmented and used in the training or testing of a previously published deep learning algorithm. This is the first open-source dataset of original and segmented flat-mounted retinal images. The dataset has potential applications for expanding the development of generalizable and larger-scale artificial intelligence and analyses for OIR. This dataset is published online and publicly available at dx.doi.org/10.6084/m9.figshare.23690973. This open access dataset serves as a source of raw data for future research involving big data and artificial intelligence research concerning oxygen-induced retinopathy.

Assessing Drone-Based Remote Sensing for Monitoring Water Temperature, Suspended Solids and CDOM in Inland Waters: A Global Systematic Review of Challenges and Opportunities

Monitoring water quality is crucial for understanding aquatic ecosystem health and changes in physical, chemical, and microbial water quality standards. Water quality critically influences industrial, agricultural, and domestic uses of water. Remote sensing techniques can monitor and measure water quality parameters accurately and quantitatively. Earth observation satellites equipped with optical and thermal sensors have proven effective in providing the temporal and spatial data required for monitoring the water quality of inland water bodies. However, using satellite-derived data are associated with coarse spatial resolution and thus are unsuitable for monitoring the water quality of small inland water bodies. With the development of unmanned aerial vehicles (UAVs) and artificial intelligence, there has been significant advancement in remotely sensed water quality retrieval of small water bodies, which provides water for crop irrigation. This article presents the application of remotely sensed data from UAVs to retrieve key water quality parameters such as surface water temperature, total suspended solids (TSS), and Chromophoric dissolved organic matter (CDOM) in inland water bodies. In particular, the review comprehensively analyses the potential advancements in utilising drone technology along with machine learning algorithms, platform type, sensor characteristics, statistical metrics, and validation techniques for monitoring these water quality parameters. The study discusses the strengths, challenges, and limitations of using UAVs in estimating water temperature, TSS, and CDOM in small water bodies. Finally, possible solutions and remarks for retrieving water quality parameters using UAVs are provided. The review is important for future development and research in water quality for agricultural production in small water bodies.

Is Artificial Intelligence (AI) currently able to provide evidence-based scientific responses on methods that can improve the outcomes of embryo transfers? No.

The rapid development of Artificial Intelligence (AI) has raised questions about its potential uses in different sectors of everyday life. Specifically in medicine, the question arose whether chatbots could be used as tools for clinical decision-making or patients' and physicians' education. To answer this question in the context of fertility, we conducted a test to determine whether current AI platforms can provide evidence-based responses regarding methods that can improve the outcomes of embryo transfers. We asked nine popular chatbots to write a 300-word scientific essay, outlining scientific methods that improve embryo transfer outcomes. We then gathered the responses and extracted the methods suggested by each chatbot. Out of a total of 43 recommendations, which could be grouped into 19 similar categories, only 3/19 (15.8%) were evidence-based practices, those being "ultrasound-guided embryo transfer" in 7/9 (77.8%) chatbots, "single embryo transfer" in 4/9 (44.4%) and "use of a soft catheter" in 2/9 (22.2%), whereas some controversial responses like "preimplantation genetic testing" appeared frequently (6/9 chatbots; 66.7%), along with other debatable recommendations like "endometrial receptivity assay", "assisted hatching" and "time-lapse incubator". Our results suggest that AI is not yet in a position to give evidence-based recommendations in the field of fertility, particularly concerning embryo transfer, since the vast majority of responses consisted of scientifically unsupported recommendations. As such, both patients and physicians should be wary of guiding care based on chatbot recommendations in infertility. Chatbot results might improve with time especially if trained from validated medical databases; however, this will have to be scientifically checked.