Development Of Intelligent System Research Articles

Development and application of intelligent judicial trial assistance system based on generative artificial intelligence and machine learning technology

Development and application of intelligent judicial trial assistance system based on generative artificial intelligence and machine learning technology

Edge computing and AI-driven intelligent traffic monitoring and optimization

Intelligent transportation system is a comprehensive system engineering, involving real-time data processing, security and privacy protection and other challenges. This paper discusses the key role of edge computing in intelligent transportation, especially its combination with SLAM technology. Edge computing enables faster data processing and response times by placing computing and data storage resources near the data source and end users, improving the efficiency and reliability of intelligent transportation systems. At the same time, edge computing can also enhance information security and privacy protection, and bring more possibilities for the future development of intelligent transportation systems. In the future, with the progress and development of technology, we can look forward to the deeper application of edge computing and SLAM technology in the field of intelligent transportation, bringing more innovation and development to smart city transportation.

An “intelligent-sensing and targeted release” antimicrobial pickering emulsion for banana preservation

Conventional Pickering emulsions can kill microorganisms by slow release of active substances to prevent foodborne contamination; however, their low bactericidal efficiency due to the absence of bacterial inducibility is a problem that needs to be addressed. Inspired by the fact that the growth of bacteria depends on the consumption of proteins and polysaccharides by microbial enzymes, the present study prepared a novel Pickering emulsion capable of intelligent sensing and targeted release using chitosan (CS)-soy protein isolate (SPI) colloidal particles as stabilizers and plant essential oils as active substances. The Pickering emulsion demonstrated optimal performance in achieving these goals when prepared with a CS:SPI ratio of 1:4, an oil fraction of 40%, at a homogeneous speed of 25 000 rpm, and utilizing cinnamon oil as the plant essential oil. This was evidenced by the emulsion having the smallest droplet size and the greatest stability, which can increase the chances of coming into contact with microorganisms and avoid premature demulsification. Antibacterial experiments demonstrated that the emulsion could control the bacteria-mediated release of essential oils, realizing intelligent sterilization. Experiments on banana preservation showed that the released essential oils effectively delayed banana spoilage. These findings contribute to the development of intelligent food preservation systems.

Development of a Collaborative Intelligent Individual Education Program System using a Prototyping Approach

Development of a Collaborative Intelligent Individual Education Program System using a Prototyping Approach

Analysis of the emotional coloring of text using machine and deep learning methods

The presented scientific article is a comprehensive study of machine learning and deep learning methods in the context of emotion recognition in text data. The main goal of the study is to conduct a comprehensive analysis and comparison of various machine learning and deep learning methods to classify emotions in text. During the work, special attention was paid to the analysis of traditional machine learning algorithms, such as multinomial naive Bayes (MNB), multilayer perceptron (MLP), and support vector machine (SVM), as well as the use of deep learning methods based on long short-term memory (LSTM). The experimental part of the study involves the analysis of different data sets covering a variety of text styles and contexts. The experimental results are analyzed in detail, identifying the advantages and limitations of each method. The article provides practical recommendations for choosing the optimal method depending on the specific tasks and context of the application. The data obtained is important for the development of intelligent systems that can effectively adapt to the emotional aspects of interaction with users. Overall, this work makes a significant contribution to the field of emotion recognition in text and provides a basis for further research in this area.

Unveiling Crash Casualties: A Neural Network Model for Crash Type Classification Using Traffic Datasets

Abstract: Traffic accidents, a significant public health burden with substantial economic costs, necessitate proactive measures for sustainable transportation. This study investigates the efficacy of neural networks in predicting crash types using traffic data. We propose a deep learning model that leverages the power of recurrent neural networks (RNNs) for sequential traffic pattern analysis and convolutional neural networks (CNNs) for extracting spatial features from traffic data. The model, trained on a labeled traffic dataset, learns to identify patterns and relationships between traffic characteristics (e.g., speed, volume, weather) and different crash types (e.g., rear-end collision, single-vehicle). The evaluation focuses on the model's accuracy in predicting crash types using metrics like precision, recall, and F1-score. This research contributes to the development of intelligent traffic management systems (ITMS) that can proactively identify and mitigate potential accidents, ultimately enhancing road safety and promoting a more sustainable transportation future.The implementation results indicate that the proposed method yields an accuracy of 82.93% approximately.

Traffic Sign Recognition Using Deep Learning

Abstract: Traffic sign recognition is a crucial component in advanced driver assistance systems and autonomous vehicles, enhancing road safety and overall transportation efficiency. Deep learning, specifically convolutional neural networks (CNNs), has emerged as a powerful tool for image based recognition tasks. The proposed deep learning-based traffic sign recognition system exhibits promising results, providing a foundation for the development of intelligent transportation systems. The trained model demonstrates remarkable accuracy in recognizing a wide range of traffic signs under different scenarios, including challenging lighting conditions and occlusions. The system's real-time performance is evaluated on both simulated and actual road scenarios, showcasing its feasibility for deployment in practical driving environments. The integration of such technology into vehicles holds great potential for enhancing road safety, reducing accidents, and contributing to the evolution of autonomous driving technology.

Bismuth-based ferroelectric memristive device induced by interface barrier for neuromorphic computing

The development of the Internet of Things (IoT) not only facilitates our lives but also dramatically grows data. Artificial synaptic devices appear to represent an emerging physical solution compared to the existing computational architectures. Memristive devices are of great interest with their high integration and low power consumption in the field of synaptic devices. In this work, we demonstrate short-term plasticity (STP) and long-term plasticity (LTP) by regulating synaptic weights with different stimulus pulses. Moreover, the application in neuromorphic computing is further exhibited by image recognition with an accuracy of 95.2 % under the modified National Institute of Standards and Technology database. Therefore, Nd-doped Bi4Ti3O12 memristors, as emerging artificial synaptic devices, are expected to achieve breakthroughs in artificial intelligence and promote the development of neuromorphic computing and intelligent systems.

Multi-Vehicle Collaborative Planning Technology under Automatic Driving

Autonomous vehicles hold the potential to significantly improve traffic efficiency and advance the development of intelligent transportation systems. With the progression of autonomous driving technology, collaborative planning among multiple vehicles in autonomous driving scenarios has emerged as a pivotal challenge in realizing intelligent transportation systems. Serving as the cornerstone of unmanned mission decision-making, collaborative motion planning algorithms have garnered increasing attention in both theoretical exploration and practical application. These methods often follow a similar paradigm: the system initially discerns the driving intentions of each vehicle, subsequently assesses the surrounding environment, engages in path-planning, and formulates specific behavioral decisions. The paper discusses trajectory prediction, game theory, following behavior, and lane merging issues within the paradigm mentioned above. After briefly introducing the background of multi-vehicle autonomous driving, it provides a detailed description of the technological prerequisites for implementing these techniques. It reviews the main algorithms in motion planning, their functionalities, and applications in road environments, as well as current and future challenges and unresolved issues.

Real-time segmentation of short videos under VR technology in dynamic scenes

Abstract This work addresses the challenges of scene segmentation and low segmentation accuracy in short videos by employing virtual reality (VR) technology alongside a 3D DenseNet model for real-time segmentation in dynamic scenes. First, this work extracted short videos by frame and removed redundant background information. Then, the volume rendering algorithm in VR technology was used to reconstruct short videos in dynamic scenes in 3D. It enriched the detailed information of short videos, and finally used the 3D DenseNet model for real-time segmentation of short videos in dynamic scenes, improving the accuracy of segmentation. The experiment compared the performance of High resolution network, Mask region based convolutional neural network, 3D U-Net, Efficient neural network models on the Densely annotation video segmentation dataset. The experimental results showed that the segmentation accuracy of the 3D DenseNet model has reached 99.03%, which was 15.11% higher than that of the ENet model. The precision rate reached 98.33%, and the average segmentation time reached 0.64 s, improving the segmentation accuracy and precision rate. It can adapt to various scene situations and has strong robustness. The significance of this research lies in its innovative approach in tackling these issues. By integrating VR technology with advanced deep learning models, we can achieve more precise segmentation of dynamic scenes in short videos, enabling real-time processing. This has significant practical implications for fields such as video editing, VR applications, and intelligent surveillance. Furthermore, the outcomes of this research contribute to advancing computer vision in video processing, providing valuable insights for the development of future intelligent video processing systems.

STBGRN: A Traffic Prediction Model Based on Spatiotemporal Bidirectional Gated Recurrent Units and Graph Convolutional Residual Networks

With the development of society and the advancement of urbanization, the development of intelligent transportation system has attracted much attention. Efficient and accurate traffic flow prediction is one of the core tasks in the research of intelligent transportation system. Most of the existing spatiotemporal traffic flow prediction models do not make full use of various periodic spatiotemporal dynamic characteristics of traffic flow, and it is difficult to effectively capture the complex spatiotemporal changes of traffic flow. To accurately predict the traffic flow of complex urban network, this paper proposes a traffic prediction model (STBGRN) based on spatiotemporal bidirectional gated cycle unit (ST-BiGRU) and graph convolution residual network (GCN-ResNet). The spatiotemporal information in traffic data is learned using the spatiotemporal bidirectional GRU coupled with the forward and backward information of the current time step, and the topology structure of the traffic network is captured by the graph convolution residual network, which is combined to complete the prediction task of the urban traffic network. In this paper, STBGRN was tested on two real data sets, SZ-taxi and Los-loop, and compared with other baseline methods, the RMSE index reached 3.857 and 5.461, and the MAE index reached 2.702 and 3.781, respectively. Accuracy index is 72.66% and 91.35%, R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}^{2}$$\\end{document} index is 0.858 and 0.845, var index is 0.858 and 0.849. The experimental results show that STBGRN model can obtain spatiotemporal dependence from complex traffic data, and can be used in spatiotemporal traffic data processing.

Comparison and Evaluation of Learning Capabilities of Deep Learning Methods for Predicting Ship Motions

The development of intelligent ship control systems in real-world conditions relies heavily on the accurate identification and prediction of ship seakeeping and maneuvering trajectories. In this study, we comprehensively evaluate a selection of deep learning methods to assess their learning capabilities in terms of idealizing ship motion behavior in realistic operational environments. To recover real conditions, we utilize historical Automatic Identification System (AIS) data and a time domain 6 Degree of Freedom (6- DoF) grounding dynamics model to generate ship motion sequences for a Ro-Ro passenger ship operating in the Gulf of Finland. Via a rigorous evaluation process, we validate the performance of these methods using extensive data streams. The analysis includes the identification and estimation of uncertainties between two ports. The paper demonstrates the proficiency of the selected deep learning methods in capturing ship maneuvering features, their potential use in the design of ship control and intelligent decision support systems.

A hybrid forecasting architecture for air passenger demand considering search engine data and spatial effect

As the global process of digitalization accelerates and breakthroughs in internet technology emerge, governments worldwide are advocating for data-driven decision-making, aiming to enhance public service efficiency and stimulate economic growth. Against this backdrop, this study focuses on utilizing search engine data (SED) to improve air passenger demand forecasting, responding to national policies aimed at enhancing data analysis capabilities and promoting the development of intelligent transportation systems; however, the existing research is confined to the exploration of the temporal dependency between SED and air passenger demand variables with ignoring the spatial dependency. In order to eliminate this blind spot and catch from various parts of tourist attention, this study proposes a novel SED-driven hybrid forecasting architecture inspired by the theory of spatial effect between adjacent tourist destinations. The architecture includes three main steps: (1) construction of spatial-temporal SED variables, based on two-stage data preprocessing method; (2) variable decomposition and reconstruction, based on TVF-EMD algorithm; (3) prediction of different components of air passenger demand, employed ARIMA model and IHGS-KELM based multi-model fusion strategy respectively, where the IHGS algorithm integrates the circle chaos initialization strategy and the nonlinear convergence factor strategy. To confirm the practical applicability of this hybrid architecture, five comparative experiments based on the actual dataset are designed. The principal results are concluded as follows: (1) spatial-temporal SED is conducive to a fairly accurate air passenger demand forecasting; (2) the multi-model fusion strategy can integrate the fortes of various types of prediction models to obtain better prediction accuracy; (3) the adaptive ensemble method based on IHGS-KELM can contribute to the upgradation of prediction performance of air passenger demand.

Precise positioning and prediction system for autonomous driving based on generative artificial intelligence

Self-driving systems collect vast amounts of data through a variety of sensors, including cameras, lidar, millimeter-wave radar, and more. This data needs to be processed in real time to identify obstacles such as roads, vehicles, pedestrians and make decisions accordingly. Therefore, this paper discusses the importance of accurate positioning and prediction system in automatic driving technology, and analyzes the performance of various positioning technologies in automatic driving applications.In addition, the paper explores the application potential of AI technology in autonomous driving and the prospect of combining advanced positioning and prediction systems with generative AI. Overall, this study highlights the importance of algorithm performance improvement and artificial intelligence technology in the development of autonomous driving technology, and provides new ideas and directions for the innovation and development of intelligent transportation systems in the future.

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.