Intelligent Technology Research Articles

Euler Kernel Mapping for Hyperspectral Image Clustering via Self-Paced Learning

Clustering, as a classical unsupervised artificial intelligence technology, is commonly employed for hyperspectral image clustering tasks. However, most existing clustering methods designed for remote sensing tasks aim to solve a non-convex objective function, which can be optimized iteratively, beginning with random initializations. Consequently, during the learning phase of the clustering model, it may easily fall into bad local optimal solutions and finally hurt the clustering performance. Additionally, prevailing approaches often exhibit limitations in capturing the intricate structures inherent in hyperspectral images and are very sensitive to noise and outliers that widely exist in remote sensing data. To address these issues, we proposed a novel Euler kernel mapping for hyperspectral image clustering via self-paced learning (EKM-SPL). EKM-SPL first employs self-paced learning to learn the clustering model in a meaningful order by progressing samples from easy to complex, which can help to remove bad local optimal solutions. Secondly, a probabilistic soft weighting scheme is employed to measure complexity across the data sample, which makes the optimization process more reasonable. Thirdly, in order to more accurately characterize the intricate structure of hyperspectral images, Euler kernel mapping is used to convert the original data into a reproduced kernel Hilbert space, where the nonlinearly inseparable clusters may become linearly separable. Moreover, we innovatively integrate the coordinate descent technique into the optimization algorithm to circumvent the computational inefficiencies and information loss typically associated with conventional kernel methods. Extensive experiments conducted on classic benchmark hyperspectral image datasets illustrate the effectiveness and superiority of our proposed model.

Landslide Displacement Prediction Stacking Deep Learning Algorithms: A Case Study of Shengjibao Landslide in the Three Gorges Reservoir Area of China

Computational models enable accurate, timely prediction of landslides based on the monitoring data on-site as the development of artificial intelligence technology. The most existing prediction methods focus on finding a single prediction algorithm with excellent performance or an integrated and efficient hyperparameter optimization algorithm with a highly accurate regression prediction algorithm. In order to break through the limitation of generalization of prediction models, this paper proposes an ensemble model that combines deep learning algorithms, with a stacking framework optimized with the sliding window method. Multiple deep learning algorithms are set as the first layer of the stacking framework, which is optimized with the sliding window method to avoid confusion in the time order of datasets based on time series analysis. The Shengjibao landslide in the Three Gorges Reservoir is used as a case study. First, the cumulative displacement is decomposed into a trend and a periodic term using a moving average method. A single-factor and a multi-factor superposition model based on multiple deep learning algorithms are used to predict the trend and periodic term of the displacement, respectively. Finally, the predicted values of the trend and periodic terms are added to obtain the total predicted landslide displacement. For monitoring point ZK2-3, the values of RMSE and MAPE of the total displacement prediction with the stacking model are 15.93 mm and 0.54%, and the values of RMSE and MAPE of the best-performing individual deep learning model are 20.00 mm and 0.64%. The results show that the stacking model outperforms other models by combining the advantages of each individual deep learning algorithm. This study provides a framework for integrating landslide displacement prediction models. It can serve as a reference for the geological disaster prediction and the establishment of an early warning system in the Three Gorges Reservoir Area.

Support of Migrant Reception, Integration, and Social Inclusion by Intelligent Technologies

Support of Migrant Reception, Integration, and Social Inclusion by Intelligent Technologies

Mitigating Bias Due to Race and Gender in Machine Learning Predictions of Traffic Stop Outcomes

Traffic stops represent a crucial point of interaction between citizens and law enforcement, with potential implications for bias and discrimination. This study performs a rigorously validated comparative machine learning model analysis, creating artificial intelligence (AI) technologies to predict the results of traffic stops using a dataset sourced from the Montgomery County Maryland Data Centre, focusing on variables such as driver demographics, violation types, and stop outcomes. We repeated our rigorous validation of AI for the creation of models that predict outcomes with and without race and with and without gender informing the model. Feature selection employed regularly selects for gender and race as a predictor variable. We also observed correlations between model performance and both race and gender. While these findings imply the existence of discrimination based on race and gender, our large-scale analysis (>600,000 samples) demonstrates the ability to produce top performing models that are gender and race agnostic, implying the potential to create technology that can help mitigate bias in traffic stops. The findings encourage the need for unbiased data and robust algorithms to address biases in law enforcement practices and enhance public trust in AI technologies deployed in this domain.

Requirements for the Concept of the Implementation of Artificial Intelligence as a Tool for Preventing Corruption in the Public Sector

Abstract Artificial intelligence (AI) technologies have enormous potential for preventing corruption in the public sector. However, at present, the use of AI in the public sector is not regulated. Consequently, the article aims to propose concrete steps and actions for the implementation of AI technology in the public sector with a view to using this technology as a new tool to identify and prevent situations of corruption. The author conducted a study of Latvian state and local government officials in different regions and positions throughout 2023. In the article, the author analyses the research data on officials’ experiences with AI, their expectations about the effectiveness of AI in preventing corruption, as well as their basic requirements for the concept of implementation of AI. The author of the article proposes to create a basis for the legal environment for the use of AI and recommends adopting the guidelines of AI on the following basic ethical standards: the priority of human well-being; prohibition of harm at the initiative of the AI system; human control; compliance of design with law; prevention of covert manipulation of human behaviour; built-in security. In the article, the author recommends creating a special procedure for testing and further implementation of solutions of AI, bypassing redundant administrative procedures. In addition, the author recommends stimulating anti-corruption institutions and developing approaches to issues of civil liability for mistakes made by AI technology.

Enhancing high-resolution reconstruction of flow fields using physics-informed diffusion model with probability flow sampling

The application of artificial intelligence (AI) technology in fluid dynamics is becoming increasingly prevalent, particularly in accelerating the solution of partial differential equations and predicting complex flow fields. Researchers have extensively explored deep learning algorithms for flow field super-resolution reconstruction. However, purely data-driven deep learning models in this domain face numerous challenges. These include susceptibility to variations in data distribution during model training and a lack of physical and mathematical interpretability in the predictions. These issues significantly impact the effectiveness of the models in practical applications, especially when input data exhibit irregular distributions and noise. In recent years, the rapid development of generative artificial intelligence and physics-informed deep learning algorithms has created significant opportunities for complex physical simulations. This paper proposes a novel approach that combines diffusion models with physical constraint information. By integrating physical equation constraints into the training process of diffusion models, this method achieves high-fidelity flow field reconstruction from low-resolution inputs. Thus, it not only leverages the advantages of diffusion models but also enhances the interpretability of the models. Experimental results demonstrate that, compared to traditional methods, our approach excels in generating high-resolution flow fields with enhanced detail and physical consistency. This advancement provides new insights into developing more accurate and generalized flow field reconstruction models.

Fabric‐Reinforced Functional Insoles with Superior Durability and Antifracture Properties for Energy Harvesting and AI‐Empowered Motion Monitoring

AbstractFunctional triboelectric insoles hold promise for advancing self‐powered wearable technologies. However, their durability is compromised by continuous compressive forces and friction, leading to surface abrasion and material fracturing. To address these challenges, an innovative fabric‐reinforced structure combined with a dual‐L backrest design is developed that enhances anti‐fracture capabilities and electric outputs while enabling AI‐empowered motion monitoring. Polydimethylsiloxane (PDMS) is used as the negative triboelectric material with a dual‐L backrest design, while insulated copper wire (icuW) serves as the positive triboelectric material with an annular structure design. These components are intricately nested to enable a multilayered friction pairing. The fabric‐reinforced structure demonstrates excellent compressive rebound resilience, withstanding forces of at least 1000 N. The functional insole, featuring a fabric‐reinforced dual‐L backrest structure (FRdL‐insole), efficiently harvests biomechanical energy with a peak power of 8214 µW and maintains highly consistent performance after 10 washing cycles and 60 000 durability tests. It can power portable electronic devices such as digital watches, calculators, hygrometers, and LEDs. Enhanced with machine learning algorithms, the FRdL‐insole processes sensor signals to monitor human movements, accurately identifying seven distinct motions. This positions the insole as a smart, real‐time, self‐powered tool for activity recognition, showcasing its potential in intelligent wearable technology.

Mapping of Artificial Intelligence and Robotics Technologies Applied to Offshore Wind Energy

Mapping of Artificial Intelligence and Robotics Technologies Applied to Offshore Wind Energy

Recent advances in fish cutting: From cutting schemes to automatic technologies and internet of things innovations.

Fish-cutting products are widely loved by consumers due to the unique nutrient composition and flavor in different cuts. However, fish-cutting faces the issue of labor shortage due to the harsh working environment, huge workload, and seasonal work. Hence, some automatic, efficient, and large-scale cutting technologies are needed to overcome these challenges. Accompanied by the development of Industry 4.0, the Internet of Things (IoT), artificial intelligence, big data, and blockchain technologies are progressively applied in the cutting process, which plays pivotal roles in digital production monitoring and product safety enhancement. This review focuses on the main fish-cutting schemes and delves into advanced automatic cutting techniques, showing the latest technological advancements and how they are revolutionizing fish cutting. Additionally, the production monitoring architecture based on IoT in the fish-cutting process is discussed. Fish cutting involves a variety of schemes tailored to the specific characteristics of each fish cut. The cutting process includes deheading and tail removal, filleting, boning, skinning, trimming, and bone inspection. By incorporating sensors, machine vision, deep learning, and advanced cutting tools, these technologies are transforming fish cutting from a manual to an automated process. This transformation has significant practical implications for the industry, offering improved efficiency, consistent product quality, and enhanced safety, ultimately providing a modernized manufacturing approach to fish-cutting automation within the context of Industry 4.0.

Multistream Adaptive Attention-Enhanced Graph Convolutional Networks for Youth Fencing Footwork Training.

The popularity of fencing and intense sports competition has burdened adolescents with excessive training, harming their immature bodies. Traditional training methods fail to provide timely movement corrections and personalized plans, leading to ineffective exercises. This paper aims to use artificial intelligence technology to reduce ineffective exercises and alleviate the training burden. We propose an action recognition algorithm based on the characteristics of adolescent athletes. This algorithm uses multimodal input data to comprehensively extract action information. Each modality is processed by the same network structure, utilizing attention mechanisms and adaptive graph structures. A multibranch feature fusion method is used to determine the final action category. We gathered the fencing footwork data set 2.0. Our model achieved 93.3% accuracy, with the highest precision at 95.8% and the highest F1-Score at 94.5% across all categories. It effectively recognized actions of adolescents with different heights and speeds, outperforming traditional methods. Our artificial intelligence-based training solution improves training efficiency and reduces the training burden on adolescents.

Concordance of ChatGPT With American Burn Association Guidelines on Acute Burns.

Burn injuries often require immediate assistance and specialized care for optimal management and outcomes. The emergence of accessible artificial intelligence technology has just recently started being applied to healthcare decision making and patient education. However, its role in clinical recommendations is still under scrutiny. This study aims to evaluate ChatGPT's outputs and the appropriateness of its responses to commonly asked questions regarding acute burn care when compared to the American Burn Association Guidelines. Twelve commonly asked questions were formulated by a fellowship-trained burn surgeon to address the American Burn Association's recommendations on burn injuries, management, and patient referral. These questions were prompted into ChatGPT, and each response was compared with the aforementioned guidelines, the gold standard for accurate and evidence-based burn care recommendations. Three burn surgeons independently evaluated the appropriateness and comprehensiveness of each ChatGPT response based on the guidelines according to the modified Global Quality Score scale. The average score for ChatGPT-generated responses was 4.56 ± 0.65, indicating the responses were exceptional quality with the most important topics covered and in high concordance with the guidelines. This initial comparison of ChatGPT-generated responses and the American Burn Association guidelines demonstrates that ChatGPT can accurately and comprehensibly describe appropriate treatment and management plans for acute burn injuries. We foresee that ChatGPT may play a role as a complementary tool in medical decision making and patient education, having a profound impact on clinical practice, research, and education.

Analysis of the Correlation of Topic Feature Changes Based on the LDA Model

Abstract. As is well known, with the continuous development of artificial intelligence technology and the increasing accessibility of data, various social platforms are committed to using intelligent recommendation algorithms to cater to user preferences. Some platforms even exaggerate facts and push sensational, valueless information to users, leading to the "Screaming Effect" and the "Echo Chamber Effect." The prolonged existence of these effects may result in "Information Cocoon," which is detrimental to the healthy development of individuals and society. To address this issue, different topics can have varying trajectories as online comments ferment, potentially reaching a neutral consensus or resulting in polarization. As a mainstream social platform in China, Weibo users serve as nodes for the dissemination of public opinion information, and the characteristics of information release, reception, forwarding, and commenting all influence the effectiveness of communication. First, we selected the topics "COVID-19" and "IG Electronic Sports Club" as our research subjects, identifying a range of topic popularity through Baidu Index, followed by data collection. Second, we used perplexity and coherence to determine the optimal number of topics for LDA, analyzing the changes in topic feature characteristics over different time periods. Through correlation analysis, we reached the following conclusions: Higher Weibo levels correlate with better information dissemination effectiveness; the quantity of information published on Weibo negatively impacts the forwarding and commenting behaviors; and as events progress, the enthusiasm for information dissemination on Weibo declines, with daily dissemination gradually decreasing.

Artificial intelligence applications in cataract and refractive surgeries.

This review highlights the recent advancements in the applications of artificial intelligence within the field of cataract and refractive surgeries. Given the rapid evolution of artificial intelligence technologies, it is essential to provide an updated overview of the significant strides and emerging trends in this field. Key themes include artificial intelligence-assisted diagnostics and intraoperative support, image analysis for anterior segment surgeries, development of artificial intelligence-based diagnostic scores and calculators for early disease detection and treatment planning, and integration of generative artificial intelligence for patient education and postoperative monitoring. The impact of artificial intelligence on cataract and refractive surgeries is becoming increasingly evident through improved diagnostic accuracy, enhanced patient education, and streamlined clinical workflows. These advancements hold significant implications for clinical practice, promising more personalized patient care and facilitating early disease detection and intervention. Equally, the review also highlights the fact that only some of this work reaches the clinical stage, successful integration of which may benefit from our focus.

The application and impact of artificial intelligence technology in graphic design: A critical interpretive synthesis

The application and impact of artificial intelligence technology in graphic design: A critical interpretive synthesis

Comparative studies "Effect of educational policy on artificial intelligence in the countries of America, Singapore and Iran

Artificial intelligence under the paradigm of the fourth industrial revolution has become one of the fateful trends of societies in such a way that university education methods, as one of the most important social constructions, have influenced the advancement of technology. Undoubtedly, educational policy has changed the speed, depth and extent of artificial intelligence technology Artificial intelligence is one of the most important emerging technologies, which today has caused huge changes in all areas, including educational policymaking, at a high speed. Among the important applications of artificial intelligence in educational policymaking, we can mention the prioritization of issues based on the real needs and demands of the society and the diagnosis of problems based on the existing conditions, which will lead to the design and formulation of macro-educational policies. The use of methods such as neural networks, natural language processing, and machine learning algorithms makes the process of policy making more intelligent and dynamic, and as a result, the role of policymakers in the application of this technology becomes more efficient. Today, new technologies such as artificial intelligence, sensor networks, blockchain are changing aspects of everyday life. The study of artificial intelligence began in the 1940s and in recent years it has overshadowed the government departments of a large number of countries, of which e-government is one of the examples. One of the topics that is analyzed in the discussion of educational policy is the effect of content on artificial intelligence. In other words, understanding the complexities of decision-making processes as well as the importance of the actors involved in such processes, has undoubtedly influenced artificial intelligence and is influenced by it. This article is designed as a review and library method and it examines the educational policies of Iran, America and Singapore in the field of artificial intelligence with an exploratory view.

I'm a chatbot, ask me anything: building Learning Development for the future

We explored whether artificial intelligence (AI) technologies can aid learning by offering more effective and dynamic ways for students to interact with learning material. Through a survey, we assessed how students perceive and use AI tools, their impressions of the importance of these tools to their future careers and their awareness of AI policies within their universities. Results show mixed responses in terms of students’ familiarity with the tools and what they believe the AI tools could and should not be used for. The results also indicated that students have a thirst to know more about AI tools and their applications. Based on these, we designed customised teaching sessions to teach students those skills that they highlighted in the survey. In addition, we designed an L4 assessment where students need to demonstrate their abilities to critique a ChatGPT output (assessment yet to be submitted by students). These teaching sessions could be rolled out at university level as they are not subject-specific. This conference session shared the design and delivery of the AI-specific teaching sessions and how these were perceived by students. In addition, it demonstrated the design of the L4 assessment with the AI element and how students performed on this. This innovative research on pedagogical methods supports a broader long-term ambition to better understand and improve our teaching, learning, and student engagement through the adoption of AI and the effective use of technology.

A multimodal educational robots driven via dynamic attention

IntroductionWith the development of artificial intelligence and robotics technology, the application of educational robots in teaching is becoming increasingly popular. However, effectively evaluating and optimizing multimodal educational robots remains a challenge.MethodsThis study introduces Res-ALBEF, a multimodal educational robot framework driven by dynamic attention. Res-ALBEF enhances the ALBEF (Align Before Fuse) method by incorporating residual connections to align visual and textual data more effectively before fusion. In addition, the model integrates a VGG19-based convolutional network for image feature extraction and utilizes a dynamic attention mechanism to dynamically focus on relevant parts of multimodal inputs. Our model was trained using a diverse dataset consisting of 50,000 multimodal educational instances, covering a variety of subjects and instructional content.Results and discussionThe evaluation on an independent validation set of 10,000 samples demonstrated significant performance improvements: the model achieved an overall accuracy of 97.38% in educational content recognition. These results highlight the model's ability to improve alignment and fusion of multimodal information, making it a robust solution for multimodal educational robots.

Artificial Intelligence: Communication, Technology, and Society (a Systematic Literature Review)

The development of artificial intelligence (AI) technology has transformed the way various social sectors operate, including healthcare, communications, and decision-making. The integration of AI into these domains not only opens up new opportunities for efficiency and innovation, but also presents significant challenges. Among these challenges, the emergence of algorithmic bias, inadequate data protection, technical limitations, and complex ethical dilemmas are of major concern. These issues affect the reliability and fairness of the use of AI technologies, potentially harming individuals and society as a whole. This study aims to investigate the barriers associated with the implementation of AI in society and provide theoretical and methodological solutions to address these challenges. This study uses a systematic literature review methodology to review 40 journals, with the aim of identifying key issues and suggesting solutions for the ethical and successful integration of AI. The findings show that the potential of AI is often hampered by biased algorithms, inadequate privacy laws, and a lack of transparency in decision-making processes. The study recommends the use of qualitative and quantitative methodologies, including experimental research and case studies, to investigate practical applications of AI. In addition, a multidisciplinary approach is suggested to address the technical and social dimensions of AI applications.

Centering equity, diversity, and inclusion in youth digital mental health: findings from a research, policy, and practice knowledge exchange workshop

BackgroundYouth mental health service organizations continue to rapidly broaden their use of virtual care and digital mental health interventions as well as leverage artificial intelligence and other technologies to inform care decisions. However, many of these digital services have failed to alleviate persistent mental health disparities among equity-seeking populations and in some instances have exacerbated them. Transdisciplinary and intersectional knowledge exchange is greatly needed to address structural barriers to digital mental health engagement, develop and evaluate interventions with historically underserved communities, and ultimately promote more accessible, useful, and equitable care.MethodsTo that end, the Digital, Inclusive, Virtual, and Equitable Research Training in Mental Health Platform (DIVERT), the Maritime Strategy for Patient Oriented Research (SPOR) SUPPORT (Support for People and Patient-Oriented Research and Trials) Unit and IWK Mental Health Program invited researchers, policymakers, interprofessional mental health practitioners, trainees, computer scientists, health system administrators, community leaders and youth advocates to participate in a knowledge exchange workshop. The workshop aimed to (a) highlight local research and innovation in youth-focused digital mental health services; (b) learn more about current policy and practice issues in inclusive digital mental health for youth in Canada, (c) participate in generating action recommendations to address challenges to inclusive, diverse and equitable digital mental health services, and (d) to synthesize cross-sector feedback to inform future training curriculum, policy, strategic planning and to stimulate new lines of patient-oriented research.ResultsEleven challenge themes emerged related to white-colonial normativity, lack of cultural humility, inaccessibility and affordability of participating in the digital world, lack of youth and community involvement, risks of too much digital time in youth's lives, and lack of scientific evidence derived from equity-deserving communities. Nine action recommendations focused on diversifying research and development funding, policy and standards, youth and community led promotion, long-term trust-building and collaboration, and needing to callout and advocate against unsafe digital services and processes.ConclusionKey policy, training and practice implications are discussed.

Deep learning based classification for alzheimer's disease detection using MRI images

Alzheimer's disease is a common type of dementia that can cause serious problems in cognitive functions and activities of daily living. Although there is no definitive cure for Alzheimer's disease today, early diagnosis is important to slow down the adverse conditions that may arise and to improve the quality of life. As a result of the development of artificial intelligence technologies and their consistent application in different fields, machine learning techniques have the potential to play an important role in the detection of Alzheimer's disease. In particular, deep learning-based methods, which have the ability to automatically extract patterns from complex patterns, are promising in this field. Recent studies show that the use of deep learning models for Alzheimer's detection on images is becoming widespread. In addition to contributing to the early diagnosis of the disease, these models also show potential in detecting different stages of the disease by analyzing the symptoms in magnetic resonance images. These developments enable the development of more effective treatment methods for patients. However, more studies are needed to evaluate the efficacy and safety of these technologies in clinical applications. In this study, classification studies were performed using MobileNetV2, InceptionV3, Xception, Vgg16 and Vgg19 models for the diagnosis of the disease on a publicly shared Alzheimer's dataset consisting of 6400 different samples and 4 different classes. An accuracy of 99.92% was calculated for the MobileNetV2 model. The performances of the models used in this study were compared with similar studies in the literature and their performances were reported in terms of different metrics. Among the five different models used, the highest accuracy value of 99.92% was obtained with MobileNetV2. It was concluded that the architectures used in the experimental studies produced generally better results than similar studies in the literature.