Development Of AI Research Articles

A Smart and Sustainable Framework to Combat Climate Change: A Comprehensive Approach of Artificial Intelligence and Human Psychology

The core of this paper, which explores the difficulties of resource problems and climate change, is the delicate interplay between AI, human psychology, and sustainable development. As global challenges escalate, the convergence of these domains presents a unique opportunity to devise effective strategies that facilitate environmentally conscious behaviors and resilient communities. This paper explores the psychological dimensions underlying resource dilemmas and climate change, emphasizing cognition, decision-making processes, social dynamics, and the role of AI in driving sustainable development. By amalgamating insights from psychology with cutting-edge technological advancements, society can aspire to navigate the path towards ecological equilibrium and a sustainable future

An Experimental Study on the Effect of AIGC on High School Students’ English Words Memory

Vocabulary memorizing is the foundation of English learning, and improving students’ vocabulary memorizing efficiency occupies an essential place in English learning. However, the current vocabulary teaching activities are stereotyped in design and superficial in practice and fail to achieve the teaching goal of improving students’ vocabulary learning ability. With the development of AI, its function for learning is obvious. Therefore, based on AIGC, this study designs an experiment with 60 students from two parallel classes in grade one of a high school in Pingjiang County as the research object. The author adopts the literature method, investigation method, and experimental method. After referring to lots of related literature at home and abroad and conducting the investigation and analysis of the current situation of English vocabulary learning, the design principles and strategies for this experiment are put forward. At the end of the practice, the English vocabulary test and questionnaire survey were used as research tools to collect experimental data, and the SPSS27.0 software was used for statistics and analysis. The results show that AIGC can stimulate students’ interest in vocabulary memorizing, promote memorizing efficiency, and enhance the ability to use the vocabulary.

Laying the foundations for using generative AI images in architectural research: do images convey the intended spaces and ambiances?

The advent of Generative Artificial Intelligence (GenAI) models, such as Stable Diffusion, Open AI's DALL-E2, and MidJourney, has opened the door to a huge range of new possibilities in architectural research and practice. Before architects and researchers can fully leverage these models, it is crucial to assess their proficiency in generating images that accurately depict real spaces and ambiances. Here, we assessed the proficiency of DALL-E2 in depicting intended spaces and intended ambiances, using real photographs from Google Images as a comparative benchmark. Images of eighteen distinct home spaces, each intended to evoke three different ambiances, were generated and presented to human observers. The images were evaluated in terms of their perceived realism, their accuracy in depicting the space and ambiance intended. Findings highlight GenAI's capability in depicting spaces and point to its potential in convincingly conveying ambiances. Beyond the substantive findings, this research highlights the importance of evaluating the rapidly emerging tools enabled by recent developments in AI.

Evidence for AI in healthcare

Abstract In healthcare, novel AI solutions are being created to address some of the biggest challenges in the prognosis, diagnosis, and treatment of disease, as well as clinician workflows and service improvement. The emergence of generative AI presents a compelling opportunity to revolutionize diagnostics, treatment planning, medical research, and patient engagement. The hypothesized uses of generative AI are broad, ranging from medical education, providing information to patients, generating synthetic patient data for the validation of AI tools, to the analysis of continuous data from wearables to detect early signs of disease. Adoption of digital solutions and AI in healthcare is slower than in other industries. The majority of clinicians don’t have direct experience with AI technologies, only a quarter have recommended a digital therapeutic, and less than a fifth have prescribed one. Safety, quality and confidence can be built through appropriate governance, testing, careful implementation and appropriate clinical use. There is a responsibility for AI developers, health systems and providers, as well as regulators, to create clear expectations for evidence and solutions. AI solutions require unique model evidence (evidence for the underlying algorithm) and solution evidence (evidence for the product in which the algorithm is embedded). Models will need validation first on internal and then on external datasets (internal and external validation). This will indicate how accurate and reliable the model is, but will be limited to the dataset that a client has access to. By examining ways to evaluate evidence for AI in healthcare, we can develop a better understanding of how best to use AI to its most appropriate advantages.

Przykłady wdrożeń i przyszłość AI w diagnostyce medycznej

AI is revolutionizing medical diagnostics around the world, innovating in a variety of contexts, from leading US hospitals to facilities in developing countries. Below we present examples of AI implementations in medical diagnostics from different regions, taking into account the effectiveness and results of these solutions and forecasts for the development of this technology. Regarding the future of artificial intelligence in medical diagnostics, the article considered potential innovations such as the development of deep learning algorithms and integration with 5G technologies and the Internet. Attention is paid to the possibilities of further personalization of healthcare and to the challenges related to the need to adapt legal regulations and data management. It also indicates the directions of future research that may contribute to the further development of AI in medical diagnostics and the improvement of the quality of healthcare not only in Poland, but around the world.

The Architecture of Immortality Through Neuroengineering

From mobile health and wearables to implantable medical devices and neuroprosthetics, the integration of machines into human biology and cognition is expanding. This paper explores the technological advancements that are pushing the human–machine boundaries further, raising profound questions about identity and existence in digital realms. The development of robots, androids, and AI–human hybrids promises to augment human capabilities beyond current limits. However, alongside these advancements, significant limitations arise: biological, technical, ethical, and legal. This paper further discusses the existential implications of these technological strides. It addresses the philosophical dimensions of mortality, forgiveness, and the significance of death in a world where technological immortality may be within reach. By addressing these questions, the paper seeks to provide a comprehensive analysis of the potential for these advancements to reshape our understanding of existence and the quest for immortality.

The Framework of GeoSOT-3D Grid Modeling for Spatial Artificial Intelligence

Abstract. With the development of society, spatial artificial intelligence (spatial AI) research is gradually able to play a greater role. However, spatial AI has problems such as data alignment, poor interpretability, and cross domain learning. Therefore, this paper proposes an innovative GeoSOT-3D grid modeling framework for spatial AI research, which enhances the application capabilities of spatial AI. Grid modeling will be able to run through the upstream and downstream of spatial AI research, providing encoding calculations and spatial neighborhood embedding matrices for spatial data. This paper also uses task examples to demonstrate how to effectively organize and index spatial data using GeoSOT-3D grids and conduct spatial AI research. The use of GeoSOT-3D grids for spatial AI analysis has enormous potential and broad application prospects, which will help promote the further development and application of spatial AI.

Managing distance e-learning knowledge in the digital age

ABSTRACT In parallel with the development of AI and IT, a variety of applications are being developed to solve various problems faced by people. One of these areas is called distance e-learning. Distance e-learning is a domain that interacts with many other domains. When distance e-learning is considered as a business process and its strategy is examined, it does suggest that the domains of ICT and knowledge management are of particular importance to it. This paper proposes a conceptual model for the integration of knowledge management, distance e-learning, ICT, and mighty AI. By using the proposed conceptual model, not only the challenges cited in the literature (facing e-learning) but also new ones that distance e-learning encounters can be successfully resolved. The development of the improved model is furthermore essential because of the fact that AI and ICT are constantly developing and they offer new solutions for all areas of life.

Perceptions of Yemeni EFL Students on the Use of AI Tools: Awareness, Frequency, and Preferred Applications

This study explores Yemeni EFL students' perceptions of using AI tools in their English language learning, focusing on awareness, frequency of use, and preferred applications. AI-powered tools like ChatGPT, Duolingo, and Grammarly offer personalized, interactive learning experiences that are especially valuable in regions with limited educational resources. The research utilized an online survey distributed via WhatsApp to gather data from Yemeni undergraduate students. Results indicate that while many students are familiar with AI tools and use them regularly for tasks such as vocabulary building and grammar checking, barriers like limited internet access and high AI tool costs remain significant. The study highlights the potential benefits of AI tools in accelerating learning, enhancing autonomy, and providing personalized feedback while addressing the need for improved infrastructure and support in under-resourced areas. The findings provide valuable insights for educators, policymakers, and AI developers to enhance the effectiveness and accessibility of AI tools in language learning, particularly in conflict-affected regions like Yemen.

Reshaping the Sound Landscape: The Application and Development of AI in the Sound Creation of Chinese Podcasts in the Era of Intelligent Media

With the rapid development of intelligent media technology, AI has greatly enriched the creation means and expression forms of Chinese podcasts and reshaped the diversity and personalization of speech synthesis, audio editing and content generation, through the diversity and personalization of sound landscape. Through the specific application cases of AI in the creation of Chinese podcast voice, this paper analyzes its positive effects in improving the efficiency of creation, optimizing user experience and promoting the development of the industry, and looks forward to the future development trend and challenges, aiming to provide valuable reference and inspiration for the development of the field of Chinese podcast

Prompting Considered Harmful

As systems graduate from labs to the open world, moving beyond prompting is central to ensuring that AI is useful, usable, and safe for end users as well as experts such as AI developers and researchers.

A Multimodal Analysis of Metaphors in Artificial Intelligence-Related Cartoons

This paper, adopting a multimodal perspective and Critical Metaphor Analysis (CMA) approaches, examines 50 cartoons about artificial intelligence published on American cartoon websites from 2023 to 2024. The study finds that AI cartoons mainly construct metaphors through a cross-modal mapping “text as target domain, image as source domain” structure. These cartoons are based on metonymy, presenting metaphorical scenes through the interaction of metonymy and metaphor. The abstract concept of “artificial intelligence” is often metaphorically represented as robots, thereby deepening readers’ understanding of the role of technological image in the socio-cultural context. By analyzing four major metaphorical scenes—devouring, competing, chasing, and hijacking—this paper elucidates the dual perceptions of the opportunities and challenges brought by technological advancements in AI as well as cautious attitude towards AI development.

Tech vs. Tradition: ChatGPT and Mindfulness in Enhancing Older Adults' Emotional Health.

To improve older adults' mental health, this study compared the effects of AI chatbots, such as ChatGPT 3.0, with traditional mindfulness therapies on loneliness and depression in older adults. Despite interest in AI as a complementary tool, empirical evidence on its impact remains limited. For an eight-week intervention, older adult participants from two nursing homes in Hangzhou were assigned to groups focused on mindfulness (group sessions) and chatting (one-on-one ChatGPT). Following the intervention, participants engaged in a researcher-led focus group discussion. After 8 weeks, tension had decreased significantly (p < 0.05) in the Mindfulness group of older adults, and there was no significant difference between the effects of ChatGPT and mindfulness on the emotional intervention of older adults. Findings indicated three themes, including (1) personal experience, reflecting older adults' use of AI technology and mindfulness; (2) attitudes and perspectives on the experiment's desirability and insufficiencies; and (3) needs and expectations for future AI and mindfulness developments, including usability and functional preferences. Similar to mindfulness practice, ChatGPT interactions helped older adults feel less depressed and might eventually reduce costs by replacing mindfulness. In the future, AI can be integrated with conventional techniques to improve interaction by giving AI a more human-like appearance.

Characteristics of the Reciprocal Movement of Radiographers’ Gaze Based on a Comparison of Entry-Level and Experienced Radiographers

With the development of AI, AI may substitute for physicians in radiological interpretations in the future. The purpose of this paper is to identify factors that could form the foundation for algorithms enabling AI to explore pathological findings. This study focuses on the "angles of gaze trajectory" and "average speeds of gaze movement," and conducts a comparative analysis between entry-level and experienced radiographers. Firstly, based on the results of angles of gaze movement, experienced radiographers exhibited frequent movements around the 180-degree mark compared to entry-level radiographer. Both entrylevel and experienced radiographers commonly displayed frequent movements around 0 degrees and near 350 degrees. Secondly, according to the results of average speeds of gaze movement, entry-level radiographers were found to have faster movement speeds compared to experienced radiographers.

Evaluating Explainable Artificial Intelligence (XAI) techniques in chest radiology imaging through a human-centered Lens.

The field of radiology imaging has experienced a remarkable increase in using of deep learning (DL) algorithms to support diagnostic and treatment decisions. This rise has led to the development of Explainable AI (XAI) system to improve the transparency and trust of complex DL methods. However, XAI systems face challenges in gaining acceptance within the healthcare sector, mainly due to technical hurdles in utilizing these systems in practice and the lack of human-centered evaluation/validation. In this study, we focus on visual XAI systems applied to DL-enabled diagnostic system in chest radiography. In particular, we conduct a user study to evaluate two prominent visual XAI techniques from the human perspective. To this end, we created two clinical scenarios for diagnosing pneumonia and COVID-19 using DL techniques applied to chest X-ray and CT scans. The achieved accuracy rates were 90% for pneumonia and 98% for COVID-19. Subsequently, we employed two well-known XAI methods, Grad-CAM (Gradient-weighted Class Activation Mapping) and LIME (Local Interpretable Model-agnostic Explanations), to generate visual explanations elucidating the AI decision-making process. The visual explainability results were shared through a user study, undergoing evaluation by medical professionals in terms of clinical relevance, coherency, and user trust. In general, participants expressed a positive perception of the use of XAI systems in chest radiography. However, there was a noticeable lack of awareness regarding their value and practical aspects. Regarding preferences, Grad-CAM showed superior performance over LIME in terms of coherency and trust, although concerns were raised about its clinical usability. Our findings highlight key user-driven explainability requirements, emphasizing the importance of multi-modal explainability and the necessity to increase awareness of XAI systems among medical practitioners. Inclusive design was also identified as a crucial need to ensure better alignment of these systems with user needs.

GPT-4 and Beyond : Advancements in AI Language Models

This comprehensive article explores the groundbreaking advancements and implications of GPT-4, the latest iteration in the Generative Pre-trained Transformer series. It delves into GPT-4's enhanced comprehension and contextual awareness, multilingual proficiency, expanded knowledge base, multimodal capabilities, and improved safety and ethical considerations. The article provides quantifiable improvements and practical implications across various domains, including natural language processing, machine translation, research assistance, education, and content creation. It also addresses the challenges and limitations of the model, such as biases and computational requirements, while discussing future directions in AI development. The piece concludes with projections for future AI capabilities, emphasizing more human-like reasoning, handling of complex tasks, and seamless integration with other systems, while also highlighting the ethical considerations and challenges that accompany these advancements.

Advancing Patient Safety: The Future of Artificial Intelligence in Mitigating Healthcare-Associated Infections: A Systematic Review.

Healthcare-associated infections are infections that patients acquire during hospitalization or while receiving healthcare in other facilities. They represent the most frequent negative outcome in healthcare, can be entirely prevented, and pose a burden in terms of financial and human costs. With the development of new AI and ML algorithms, hospitals could develop new and automated surveillance and prevention models for HAIs, leading to improved patient safety. The aim of this review is to systematically retrieve, collect, and summarize all available information on the application and impact of AI in HAI surveillance and/or prevention. We conducted a systematic review of the literature using PubMed and Scopus to find articles related to the implementation of artificial intelligence in the surveillance and/or prevention of HAIs. We identified a total of 218 articles, of which only 35 were included in the review. Most studies were conducted in the US (n = 10, 28.6%) and China (n = 5; 14.3%) and were published between 2021 and 2023 (26 articles, 74.3%) with an increasing trend over time. Most focused on the development of ML algorithms for the identification/prevention of surgical site infections (n = 18; 51%), followed by HAIs in general (n = 9; 26%), hospital-acquired urinary tract infections (n = 5; 9%), and healthcare-associated pneumonia (n = 3; 9%). Only one study focused on the proper use of personal protective equipment (PPE) and included healthcare workers as the study population. Overall, the trend indicates that several AI/ML models can effectively assist clinicians in everyday decisions, by identifying HAIs early or preventing them through personalized risk factors with good performance. However, only a few studies have reported an actual implementation of these models, which proved highly successful. In one case, manual workload was reduced by nearly 85%, while another study observed a decrease in the local hospital's HAI incidence from 1.31% to 0.58%. AI has significant potential to improve the prevention, diagnosis, and management of healthcare-associated infections, offering benefits such as increased accuracy, reduced workloads, and cost savings. Although some AI applications have already been tested and validated, adoption in healthcare is hindered by barriers such as high implementation costs, technological limitations, and resistance from healthcare workers. Overcoming these challenges could allow AI to be more widely and cost-effectively integrated, ultimately improving patient care and infection management.

An Artificial Intelligence-Based Platform for Medical Diagnosis

Medical diagnosis using artificial intelligence models uses methods of information collection via the Internet of Things (IoT) for the categorization of diseases. This may be attributed to a number different issues, such as inefficient auxiliary frameworks, high expenses associated with acquiring datasets, or difficulties encountered while constructing classifiers. The present paper analyses the recent developments in AI for medical diagnosis. The research uses a Deep Convolutional Neural Network (DCNN) to categorise medical conditions. In this paper, a model for categorising medical diagnosis has been introduced by making it easier for the network to adapt to new medical data. This article estimates the degree to which the classification falls into the right medical diagnosis category and separates the training sample into normal, critical, and suggestion samples using a dynamic threshold to solve the diagnostic issue. The recommended strategy categorises input to help the convolutional neural network learn more. This research modifies the convolutional neural network design to accommodate for input variety and data evolution temporal dynamics. This adjustment is made with the goal of achieving the five physiological data properties of bio signals (heart rate, blood pressure, EEG, ECG, and oxygen level). Because of the realised CNN optimisation algorithm model, the prediction effect has been increased, and the accuracy rate has been found to be 92.8% in medical diagnosis, which is a reasonably good performance in machine learning algorithms.

Does a lawyer need artificial intelligence?

According to the labyrinths of legal science, forensic science, criminal procedure, criminal law, judicial expertise, operational-search activity, and other legal sciences are generally considered applied in relation to natural and exact sciences. However, practice shows that legal professionals often successfully and productively utilize discoveries and developments from physicists and mathematicians, frequently developing and refining them to a level that surpasses the initial knowledge. Creativity is the foundation of human existence; creativity cannot be forbidden. Undoubtedly, lawyers, as the best representatives of legal professions, will contribute to the development of AI, where the main aspects are seen as systematic and consistent approaches.

Ethical AI Development for Sustainable Enterprises: A Review of Integrating Responsible AI with IoT and Enterprise Systems

Ethical AI Development for Sustainable Enterprises: A Review of Integrating Responsible AI with IoT and Enterprise Systems