Development Of Intelligent System Research Articles

Driving intelligent IoT monitoring and control through cloud computing and machine learning

At present, cloud computing and the Internet of Things are closely integrated. IoT devices gather data through sensors and transmit it to the cloud for storage, processing, and analysis. This synergy enables efficient data management and in-depth analysis, facilitating real-time monitoring and predictive maintenance. This article explores leveraging cloud computing and machine learning for intelligent IoT monitoring and control. Edge computing, a distributed architecture, decentralizes data processing from the cloud to reduce latency and improve efficiency. This combination enhances security and drives the development of intelligent systems.

Rapid Non-Destructive Detection Technology in the Field of Meat Tenderness: A Review.

Traditionally, tenderness has been assessed through shear force testing, which is inherently destructive, the accuracy is easily affected, and it results in considerable sample wastage. Although this technology has some drawbacks, it is still the most effective detection method currently available. In light of these drawbacks, non-destructive testing techniques have emerged as a preferred alternative, promising greater accuracy, efficiency, and convenience without compromising the integrity of the samples. This paper delves into applying five advanced non-destructive testing technologies in the realm of meat tenderness assessment. These include near-infrared spectroscopy, hyperspectral imaging, Raman spectroscopy, airflow optical fusion detection, and nuclear magnetic resonance detection. Each technology is scrutinized for its respective strengths and limitations, providing a comprehensive overview of their current utility and potential for future development. Moreover, the integration of these techniques with the latest advancements in artificial intelligence (AI) technology is explored. The fusion of AI with non-destructive testing offers a promising avenue for the development of more sophisticated, rapid, and intelligent systems for meat tenderness evaluation. This integration is anticipated to significantly enhance the efficiency and accuracy of the quality assessment in the meat industry, ensuring a higher standard of safety and nutritional value for consumers. The paper concludes with a set of technical recommendations to guide the future direction of non-destructive, AI-enhanced meat tenderness detection.

МЕТОД ДВОКАСКАДНОЇ ІМПЛАНТАЦІЇ ПРИХОВАНОЇ ІНФОРМАЦІЇ НА ОСНОВІ СТЕГАНОКОМПРЕСІЙНИХ ПЕРЕТВОРЕНЬ

In the article, the need for further development of the state is directly related to: solving issues of increasing defense capability and information security; development of information and intelligent systems. The necessity of ensuring the required level of completeness of information, compliance with the requirements of its relevance, achievement and maintenance of the appropriate level of integrity, accessibility and confidentiality is substantiated. The article emphasizes that for this purpose, complex systems of coding and information protection are being built. It is shown that recently, in order to further increase the level of security of information resources, methods of hidden embedding of information have been used. In this field of scientific and applied research, the direction of timely delivery of integral video information in a secure mode is outlined. For protection, the following can be used: meta-messages, which are formed on the basis of intelligent analysis of video frames; separate video segments of aerial photographs containing the most important information for decision-making. This article examines a class of methods of steganographic transformations, which are associated with embedding messages in digital containers, which are formed by a stream of video segments (VS). However, existing steganographic systems are based mainly on the use of the amount of psychovisual (PSV) redundancy available for reduction. Therefore, an increase in steganographic capacity leads to a loss of integrity and efficiency of delivery of video-container information. Steganocompression coding in the stegano-polyadic basis was created on the basis of taking into account the amount of redundancy permissible for reduction. In this case, in the process of compression of the components of the VS, the direct embedding of hidden information is organized. Concealment of information is carried out by technological stages, depending on the compression transformation of the transitional syntax of the quilted sequence. The key stage is the technology of two-stage implementation of the elements of the concealed message added to the stegano sequences to the syntax of the steganocompression representation of the BC components.

Optimization Design of High-Dimensional Parameters MIMO Antenna in Semantic-Based Mobile Applications

To ensure the quality of semantic communication, an optimization scheme for multiple-input and multiple-output (MIMO) antenna design using in semantic-based mobile is proposed and verified. The scheme is based on a modified algorithm MOEA/D-BH, which integrates the black-hole (BH) algorithm into multi-objective EA based on decomposition (MOEA/D). By introducing controllable absorption distance and neighborhood learning mechanism, MOEA/D-BH can deal with high-dimension parameters well in a multi-objective optimization problem. Thus, in a limited design space, a satisfied antenna can be optimized by proposed scheme efficiently. This can be a feasible candidate scheme for MIMO antenna design. A single-band and dual-band MIMO antenna, which are applied for semantic-based mobile system, are optimized by proposed scheme sharing the same model. Both the data of simulation and measurement show good electrical and MIMO performances in the working frequency band. Thus not only the performance of the semantic communication definitely improved, but also it provides useful exploration for the development of intelligent semantic mobile systems.

Machine learning models for abstract screening task - A systematic literature review application for health economics and outcome research

ObjectiveSystematic literature reviews (SLRs) are critical for life-science research. However, the manual selection and retrieval of relevant publications can be a time-consuming process. This study aims to (1) develop two disease-specific annotated corpora, one for human papillomavirus (HPV) associated diseases and the other for pneumococcal-associated pediatric diseases (PAPD), and (2) optimize machine- and deep-learning models to facilitate automation of the SLR abstract screening.MethodsThis study constructed two disease-specific SLR screening corpora for HPV and PAPD, which contained citation metadata and corresponding abstracts. Performance was evaluated using precision, recall, accuracy, and F1-score of multiple combinations of machine- and deep-learning algorithms and features such as keywords and MeSH terms.Results and conclusionsThe HPV corpus contained 1697 entries, with 538 relevant and 1159 irrelevant articles. The PAPD corpus included 2865 entries, with 711 relevant and 2154 irrelevant articles. Adding additional features beyond title and abstract improved the performance (measured in Accuracy) of machine learning models by 3% for HPV corpus and 2% for PAPD corpus. Transformer-based deep learning models that consistently outperformed conventional machine learning algorithms, highlighting the strength of domain-specific pre-trained language models for SLR abstract screening. This study provides a foundation for the development of more intelligent SLR systems.

A graph-based approach for traffic prediction using similarity and causal relations between nodes

Accurate traffic prediction is crucial for the development of intelligent transportation systems (ITS) and various smart applications. To achieve this, effectively capturing the complex interplay between spatial and temporal dependencies across traffic nodes is essential. However, existing research often focuses solely on fixed spatial dependencies between neighboring nodes, neglecting the potential influence of distant nodes. This paper addresses these limitations by exploring two key types of relationships among traffic nodes. First, we examine indirect relationships based on similarity, where nodes exhibit similar traffic patterns irrespective of their physical proximity. Second, we investigate direct causal relationships, where traffic conditions at one node are directly influenced by other nodes. Based on these findings, we propose a novel approach named TPSC (Traffic Prediction using nodes Similarity and Causal relations). TPSC incorporates a classifier that utilizes the K-Prototype algorithm to group nodes based on their traffic similarity and nearby points of interest (POIs). Separate models are then trained for each cluster. Our model leverages a spatial module that employs graph convolutional network representation learning alongside transfer entropy to capture causal relationships and dynamic spatial dependencies. Additionally, a temporal module is introduced to capture periodic temporal dependencies across three components: recent patterns, daily patterns, and weekly patterns. Extensive experiments are conducted to evaluate proposed model on four large-scale traffic datasets for multiple traffic characteristics including speed, flow, and travel time. The experimental results demonstrate that TPSC surpasses the performance of the compared baselines, highlighting its superior predictive capabilities in traffic prediction.

A road surface reconstruction dataset for autonomous driving

Recent developments in intelligent robot systems, especially autonomous vehicles, put forward higher requirements for safety and comfort. Road conditions are crucial factors affecting the comprehensive performance of ground vehicles. Nonetheless, existing environment perception datasets for autonomous driving lack attention to road surface areas. In this paper, we introduce the road surface reconstruction dataset, providing multi-modal, high-resolution, and high-precision data collected by real-vehicle platform in diverse driving conditions. It covers common road types containing approximately 16,000 pairs of stereo images, point clouds, and ground-truth depth/disparity maps, with accurate data processing pipelines to ensure its quality. Preliminary evaluations reveal the effectiveness of our dataset and the challenge of the task, underscoring substantial opportunities of it as a valuable resource for advancing computer vision techniques. The reconstructed road structure and texture contribute to the analysis and prediction of vehicle responses for motion planning and control systems.

Impact of risk perception and trust in autonomous vehicles on pedestrian crossing decision: Navigating the social-technological intersection with the ICLV model

In the rapidly evolving realm of transportation technology, the dynamic relationship between pedestrians and technological innovations has attained unprecedented importance. The complex social-technological intersection surrounding pedestrian road crossings has emerged as an attention for traffic safety. What distinguishes the contemporary urban environment is the rapid assimilation of intelligent transportation systems (ITS) into the transportation infrastructure, including technological elements such as autonomous vehicles, advanced surveillance systems, and smart infrastructure. To investigate how pedestrians perceive risks, trust technology, and make decisions in this era of technological progress, we designed a video-based questionnaire utilizing the stated preference (SP) methodology. We collected SP data from 589 Chinese pedestrians and employed an integrated choice and latent variable (ICLV) model to quantify the influence of risk perception and trust in autonomous vehicle (trust in AV), treated as latent variables, on their crossing decisions. Our findings indicate that the presence of autonomous vehicles significantly affects pedestrian crossing decisions. Specifically, an increase in the approaching vehicle speed and a decrease in the approaching vehicle distance increase the pedestrians’ tendency to choose not to cross the road, and the latent variables of risk perception and trust in AV strongly predict this phenomenon. The results of the scenario analysis show that, compared with overall pedestrians, middle-aged pedestrians and high-risk perception-level pedestrians are more conservative in their crossing decisions, but high levels of trust in AV improve pedestrians’ willingness to cross the street. Additionally, the pedestrian-related findings of this study at the social-technological intersection provide better understanding of the decision process and contribute to the planning and development of urban intelligent transportation systems.

Efficient electrocardiogram generation based on cardiac electric vector simulation model

This study introduces a novel Cardiac Electric Vector Simulation Model (CEVSM) to address the computational inefficiencies and low fidelity of traditional electrophysiological models in generating electrocardiograms (ECGs). Our approach leverages CEVSM to efficiently produce reliable ECG samples, facilitating data augmentation essential for the computer-aided diagnosis of myocardial infarction (MI). Significantly, experimental results show that our model dramatically reduces computation time compared to conventional models, with the self-adapting regression transformation matrix method (SRTM) providing clear advantages. SRTM not only achieves high fidelity in ECG simulations but also ensures exceptional consistency with the gold standard method, greatly enhancing MI localization accuracy by data augmentation. These advancements highlight the potential of our model to generate dependable ECG training samples, making it highly suitable for data augmentation and significantly advancing the development and validation of intelligent MI diagnostic systems. Furthermore, this study demonstrates the feasibility of applying life system simulations in the training of medical big models.

LINEAR ALGEBRA AS THE MATHEMATICAL FOUNDATION OF ARTIFICIAL INTELLIGENCE: CONCEPTS, APPLICATIONS, AND FUTURE PROSPECTS

Linear algebra, a fundamental tool in the field of mathematics, plays an essential part in the mathematical foundation of the groundbreaking arena of Artificial Intelligence (AI). This mathematics branch offers immense possibilities for capturing complex relationships within datasets through matrices and vectors. These mathematical tools facilitate the concise representation of large volumes of data, contributing to efficient data analysis between them, bringing immense value to AI algorithms and models. This research paper investigates the pivotal role of linear algebra as the fundamental mathematical framework underpinning artificial intelligence. This paper also explores the foundational concepts of linear algebra and its direct applications in various AI algorithms, demonstrating how matrices, vectors, and transformations provide the essential mathematical scaffolding for the development and optimization of intelligent systems.

인공지능과 인권

Human rights and artificial intelligence are two important concepts that interact with each other in today's rapidly developing digital age. How will artificial intelligence technology affect human rights that are spreading throughout people's lives? This question has become an increasing focus of attention. Artificial intelligence is the field that enables computer systems to simulate human-like thinking and decision-making capabilities. However, some of the problems that arise from artificial intelligence may have a direct impact on human rights. The interaction between human rights and artificial intelligence is a complex problem. While artificial intelligence technology has the potential to harm people and human rights, it also has great potential to be used to protect and promote human rights. Therefore, artificial intelligence technology must be developed and used while observing human rights through an approach supported by an ethical and legal framework. In this paper, after reviewing human rights issues such as artificial intelligence and freedom of expression, artificial intelligence and freedom of privacy, artificial intelligence and bias, hate, discrimination, artificial intelligence and defamation, artificial intelligence and copyright, we will discuss the issue of legal response to artificial intelligence. Regarding the relationship between the state and science and technology, the Constitution stipulates that “the state shall endeavor to develop the national economy through innovation of science and technology and the development of information and manpower (Article 127), and the rights of authors, inventors, and scientists are protected by law (Article 22).” It declares that the state will actively plan and form for the promotion of science and technology and lead in a certain direction. In relation to these constitutional provisions, the establishment and implementation of science and technology policies can be seen as an important duty given to the Republic of Korea as a democratic welfare state. It can be said that the main legislative duty is to achieve a balance between the value of promoting the development and utilization of artificial intelligence systems and the appropriate response to the risks that artificial intelligence can pose. To achieve a balance between artificial intelligence and human rights, it is necessary to comply with ethical values, use transparent and responsible systems, update legal regulations, and adopt an approach that guarantees stakeholder participation. By doing so, we will be able to build a future where human rights are protected while taking advantage of the potential benefits of artificial intelligence.

Future of Medical Equipment Technology

Advances in manufacturing and digital information technology have sparked the Industrial Revolution 4.0. UDIs and 6G telemedicine networks are reshaping the future of healthcare technology as part of the Fourth Industrial Revolution (4IR), which is now underway. With the help of UDIs, device recalls and post-market surveillance may be done more accurately and efficiently, and studies using UDI data can be conducted to help doctors make better clinical decisions. The development of intelligent healthcare systems is currently a hot topic, and wireless connectivity is a critical component. The goal of the 6G vision is to transform the globe into a secure and completely linked digital society through the use of communication technologies with intelligence. Stentrode, digital pacemaker, insulin pumps and coronary intervention with drug-eluting stents are some of the new methods that will improve human life. As technology continues to evolve, so will our methods of self-care. In the future, patients will continue to seek out companies that use technological innovation in medicine to empower them to take charge of their own health. The healthcare system is evolving to become more proactive, personalised, and easy for you, which makes it easier than ever to live longer and perform better.

Drowsiness Detection using CNN

Abstract: This research introduces a Driver Drowsiness Detection system employing Convolutional Neural Networks (CNN). The system analyses real-time facial features from in-vehicle cameras to determine a driver's alertness level. Trained on diverse datasets, the CNN model demonstrates high accuracy in identifying drowsiness signs, making it suitable for real-world deployment. This system contributes to road safety by providing timely alerts to prevent accidents caused by driver fatigue. As road safety remains a critical concern, the development of intelligent systems to mitigate driver-related risks has become imperative.

Revolutionizing Traffic Management: Advanced Machine Learning Techniques for Accurate Traffic Flow Prediction

In modern urban¬¬¬¬¬ transportation systems, efficient traffic flow prediction is of paramount importance to optimize traffic management, reduce congestion, and improve the overall commuting experience. This research report examines the application of state-of-the-art machine learning algorithms to accurately predict traffic flow patterns. By leveraging historical traffic data, weather conditions, time of day, and various other relevant features, our proposed model exhibits significant predictive capabilities. We study the effectiveness of various machine learning techniques, such as neural networks, decision trees, and ensemble methods, in capturing the complex dynamics of traffic flows. Through extensive experiments and validation using real datasets, we demonstrate the superiority of our approach compared to traditional methods. Ultimately, this research will contribute to the further development of intelligent transportation systems, paving the way for more efficient and sustainable urban mobility solutions

Research on the Influence of Teacher-Student Education Expectation Consistency on Students’ Physical Exercise: Based on Polynomial Regression and Response Surface Analysis

Students' physical exercise has important practical significance in enhancing students' physical health and promoting the health construction. Based on the expectation effect theory, this study explores the influence of teacher-student expectation consistency on students' physical exercise from a binary perspective. Using China Education Tracking Survey (CEPS) data, through polynomial regression and response surface analysis, it is found that: (1) There are four combinations of teacher-student educational expectation. Compared with the combination of "high-low", "low-high" and "low-low", when teacher-student educational expectation is consistent with "high-high", students' cognitive ability and students' physical exercise are higher; (2) Cognitive ability mediates the relationship between teacher-student educational expectation consistency and students' physical exercise; (3) The class atmosphere moderates the consistency of teacher-student education expectation and cognitive ability. This study employs computer technology to validate the impact of the teacher-student duality interaction on students' physical exercise through polynomial regression and response surface analysis. Through the design and development of intelligent auxiliary education system, teachers can gain better insight into students' educational expectations while promoting their engagement in physical exercise.

Research and Application of Large Model-Based Intelligent Customer Service System

With the rapid development of artificial intelligence technology, intelligent customer service systems have been widely used. This paper addresses the limitations of traditional intelligent customer service systems, such as limited language understanding ability, narrow knowledge coverage, and insufficient personalized service. It proposes an intelligent customer service system design scheme based on the RAG model. The scheme leverages the powerful language understanding and generation capabilities of large models, combined with dialogue management and knowledge base retrieval enhancement techniques, to build an efficient and intelligent customer service system. This paper introduces the overall architecture of the system, the design and implementation of each module, and comprehensively evaluates the system through experiments. The experimental results show that the system can provide accurate and fluent customer service, significantly improving customer satisfaction. The research in this paper provides new ideas and references for the development of intelligent customer service systems.

Application of Internet-of-Things Wireless Communication Technology in Agricultural Irrigation Management: A Review

The integration of Internet-of-Things technology with traditional agricultural irrigation is a crucial factor in the advancement of traditional agricultural irrigation towards smart irrigation. Despite the widespread use of conventional irrigation methods in many areas, they lead to the significant wastage of both human and water resources. Therefore, the development of energy-saving and efficient intelligent irrigation systems through the application of Internet-of-Things technology and wireless communication technology is the way forward. This paper summarizes the common wireless communication technologies in the agricultural Internet of Things: Fifth-generation, WiFi, ZigBee, LoRa, and NB-IoT. The research status of the above wireless communication technology in agricultural irrigation management is discussed, and the agricultural irrigation management example using the above wireless communication technology is also presented. The advantages and limitations of the application of the above wireless communication technology in agricultural irrigation management are sorted out. Finally, this paper analyzes the challenges of data security issues, data fusion problems, intelligent irrigation system costs, power and energy problems, and system equipment failures faced by the use of IoT wireless communication technology in agricultural irrigation management. This review aims to assist researchers and users in choosing the most suitable wireless communication technology for diverse applications.

Intelligent Power Management of Electric vehicle with onboard PV by ANN-based Model Predictive Control

This paper presents a novel configuration of Hybrid Electric Vehicle (HEV) with fuel cell as primary source and an on-board Photovoltaic (PV) array as secondary source. It incorporates an advanced Model Predictive Control (MPC) system that utilizes Artificial Neural Network (ANN) technology to control induction motor of electric vehicle. The system has three main parts: a fuel cell, an electrolyzer, and a PV array. Each part has a different job in supplying power to the vehicle. The PV array functions to supplement the fuel cell by providing power when ample irradiation is available. When the car isn't being used, the extra power from the solar panels goes to the electrolyzer. There, it turns into chemical energy, making hydrogen. This hydrogen is stored in the onboard hydrogen tank for future use. To fulfil the voltage needs of both the motor and energy sources, a quadratic bidirectional buck-boost converter (QBBC) is utilized. This converter efficiently manages the voltage output while ensuring compatibility with various energy sources. The system undergoes comprehensive analysis under different irradiance and speed conditions to evaluate its performance and efficiency. To make the solar power system more efficient and cost-effective, a Maximum Power Point Tracking (MPPT) algorithm is used. This algorithm helps get the maximum power from the PV system. An improved incremental conductance algorithm is used for this, making sure it accurately finds the best power point. The control strategy incorporates both outer voltage and inner current control, to effectively control the DC output voltage of the QBBC. The vehicle's drive system employs an indirect vector-controlled induction motor. To regulate the motor's speed effectively, an advanced ANN-MPC system is used, providing precise control and high efficiency. This paper introduces a novel approach for predictive torque control of AC machines, eliminating the need for weighting factors. An ANN estimator is used to accurately expect the motor speed, which improves the overall performance of the control system. By using MATLAB/SIMULINK to test how well the proposed Electric Vehicle (EV) setup works and make sure it's effective in real-life situations. Through this comprehensive approach, the paper contributes to advancing the development of efficient and intelligent electric vehicle systems..

Computer-aided mobility solutions: Machine learning innovations to secure smart urban transportation

This study introduces a groundbreaking hybrid deep learning Intrusion Detection System (IDS) tailored to improve the security of Electric Vehicle (EV) charging processes in the context of computer-aided mobility solutions for smart urban transportation. The proposed system ingeniously integrates XGBoost and Convolutional Neural Network (CNN) technologies, leveraging their combined strengths to fortify the resilience of EV charging infrastructure. In response to the demand for transparency in decision-making, our approach incorporates Explainable AI Techniques, specifically employing Shapley Additive exPlanations (SHAP) values. This not only enables the IDS to identify anomalous behavior but also provides clear insights into the features influencing the detection process. The transparency achieved is pivotal for fostering stakeholder trust, ensuring regulatory compliance, and facilitating well-informed decision-making within the dynamic landscape of smart urban transportation. A sophisticated mechanism is implemented to enable the model to autonomously adjust and learn from evolving charging patterns over time. This adaptive capability ensures the IDS remains effective in the face of changing charging behaviors, contributing to the resilience of the overall smart urban transportation system. This research not only propels the field of cybersecurity in smart urban transportation but also establishes a robust framework for the development of intelligent systems. These systems play a critical role in securing essential processes while providing interpretable insights for stakeholders in the realm of computer-aided mobility solutions.

Participation versus scale: Tensions in the practical demands on participatory AI

Ongoing calls from academic and civil society groups and regulatory demands for the central role of affected communities in development, evaluation, and deployment of artificial intelligence systems have created the conditions for an incipient “participatory turn” in AI. This turn encompasses a wide number of approaches — from legal requirements for consultation with civil society groups and community input in impact assessments, to methods for inclusive data labeling and co-design. However, more work remains in adapting the methods of participation to the scale of commercial AI. In this paper, we highlight the tensions between the localized engagement of community-based participatory methods, and the globalized operation of commercial AI systems. Namely, the scales of commercial AI and participatory methods tend to differ along the fault lines of (1) centralized to distributed development; (2) calculable to self-identified publics; and (3) instrumental to intrinsic perceptions of the value of public input. However, a close look at these differences in scale demonstrates that these tensions are not irresolvable but contingent. We note that beyond its reference to the size of any given system, scale serves as a measure of the infrastructural investments needed to extend a system across contexts. To scale for a more participatory AI, we argue that these same tensions become opportunities for intervention by offering case studies that illustrate how infrastructural investments have supported participation in AI design and governance. Just as scaling commercial AI has required significant investments, we argue that scaling participation accordingly will require the creation of infrastructure dedicated to the practical dimension of achieving the participatory tradition’s commitment to shifting power.