Intelligent Technology Research Articles

Cybersecurity and defense in intelligent transportation systems

Intelligent Transportation Systems (ITS) have become increasingly crucial for enhancing transportation networks' efficiency, safety, and security. ITS includes advanced technologies such as electronic sensors, data transmission, and intelligent control technologies, providing better driver and rider services. However, these technologies pose significant security and privacy risks, in which attackers can easily disrupt transportation systems. This paper presents a survey of the challenges with ITS, the engineering requirements, attack challenges, and related work of securing ITS. This survey also discussed the elements of ITS, including advanced traffic management systems, connected and automated vehicles, intelligent infrastructure, and integrated data analytics, as well as the role of government policies, public-private partnerships, and stakeholder engagement. The aim is to look at present security challenges associated with ITS and provide better ways to the system. which is critical for creating smart cities and increasing the importance of individual and public transportation systems.

Automatic plant phenotyping analysis of Melon (Cucumis melo L.) germplasm resources using deep learning methods and computer vision

Cucumis melo L., commonly known as melon, is a crucial horticultural crop. The selection and breeding of superior melon germplasm resources play a pivotal role in enhancing its marketability. However, current methods for melon appearance phenotypic analysis rely primarily on expert judgment and intricate manual measurements, which are not only inefficient but also costly. Therefore, to expedite the breeding process of melon, we analyzed the images of 117 melon varieties from two annual years utilizing artificial intelligence (AI) technology. By integrating the semantic segmentation model Dual Attention Network (DANet), the object detection model RTMDet, the keypoint detection model RTMPose, and the Mobile-Friendly Segment Anything Model (MobileSAM), a deep learning algorithm framework was constructed, capable of efficiently and accurately segmenting melon fruit and pedicel. On this basis, a series of feature extraction algorithms were designed, successfully obtaining 11 phenotypic traits of melon. Linear fitting verification results of selected traits demonstrated a high correlation between the algorithm-predicted values and manually measured true values, thereby validating the feasibility and accuracy of the algorithm. Moreover, cluster analysis using all traits revealed a high consistency between the classification results and genotypes. Finally, a user-friendly software was developed to achieve rapid and automatic acquisition of melon phenotypes, providing an efficient and robust tool for melon breeding, as well as facilitating in-depth research into the correlation between melon genotypes and phenotypes.

The Role of AI Technology in Digital Media Technology Course Teaching

With the rapid development of Internet technology and the widespread use of smart terminals, we have entered the Internet era. The continuous innovation of artificial intelligence (AI) technology has enriched people's work and life and has changed how people receive information. Unlike traditional media such as newspapers, magazines, and television, digital media provides a more efficient way to access information. In the 21st-century knowledge economy, digital media has become a core industry. Under such circumstances, universities across the country have successively introduced related courses, with the Digital Media Technology program being one of the most representative. Due to the diverse fields and wide application range of digital media technology, its social functions have been fully demonstrated during its development. Therefore, integrating AI technology into the teaching of digital media technology courses can help adjust teaching goals, optimize teaching methods, and gradually enhance students' initiative. This paper aims to explore the specific application of AI technology in digital media technology course teaching to provide useful references for educators.

Business English Teaching with Intelligent Assistance—Research on Case-driven and Artificial Intelligence Application Improvement

This paper explores a new model of business English teaching in an intelligent-assisted environment. Through the application of case-driven and artificial intelligence technology, it aims to improve teaching effectiveness and learning efficiency. The article first analyzes the current status and problems of business English teaching, and then introduces in detail the specific application of case-driven teaching method and artificial intelligence technology in business English teaching. Finally, the effectiveness of this teaching model is verified through empirical research, and the prospects for future research are proposed. Resources.

A Practical Study on Artificial Intelligence (AI)-Assisted College English Translation Teaching

With the advent of the digital age, it has become an inevitable trend to integrate advanced artificial intelligence technology with English translation teaching in colleges and universities. This paper aims to explore the practical application of artificial intelligence (AI)-assisted English translation teaching in colleges and universities, analyze its advantages in improving teaching efficiency, promoting personalized learning, and providing intelligent tutoring, and put forward corresponding strategies and suggestions. Through specific cases, this paper demonstrates the actual application effect of AI in translation teaching and looks forward to the future development direction.

Thoughts on the Construction of Art Curriculum Using Generative AI

Curriculum construction is an important part of improving the quality of higher education. With the rapid development of artificial intelligence technology, generative AI is having a profound impact on the construction of first-class courses in art majors in colleges and universities. This study aims to explore how generative AI can enable the construction of first-class courses in art majors, and analyze its specific application and effect in curriculum construction through practical cases.

Colloidal Quantum Dots‐Based Photoelectrochemical‐Type Optoelectronic Synapse

AbstractCurrent artificial neuromorphic systems are mainly constructed using solid‐state optoelectronic synaptic devices, limiting their potential artificial intelligence applications in liquid medium. Here, colloidal CuInGaSe/ZnSe quantum dots (QDs) with environment‐benign feature and broad visible light absorption are prepared to fabricate a photoelectrochemical (PEC)‐type optoelectronic synaptic device operating in aqueous electrolyte, enabling unique biological synaptic behaviors including paired‐pulse facilitation (PPF), short‐term plasticity (STP), long‐term plasticity (LTP) and learning‐forgetting‐relearning process when simulated by optical pulses with various wavelength, pulse number, frequency, power density, and applied voltage. These results demonstrate the feasibility of building PEC‐type optoelectronic synapses using colloidal QDs, paving the way to realize available optoelectronic synaptic devices for prospective underwater artificial intelligence technology.

Artificial Intelligence in public health: A case study

Artificial Intelligence technology has become an innovative tool in global healthcare that helps to manage various problems of scale and complexity. Its use in public health is multifaceted and multivariate extending across early outbreak detection, risk modeling and prediction, efficient vaccine logistics and administration, reducing health disparities, aiding assist in diagnosis, everyday monitoring of chronic diseases, telemedicine and virtual chatbots especially for elderly care, mental health and oncology patients, and community engagement. Despite these huge generational advantages, multiple challenges exist in data quality, security and misuse, individual privacy, contextual application, accountability and oversight and remain primary concerns for universal adoption of artificial intelligence. There is a need for safeguards and a global regulatory framework to responsibly harvest the benefits of artificial intelligence for the advancement of public and community health.

Diagnostic Methods of Atherosclerotic Plaque and the Assessment of Its Prognostic Significance—A Narrative Review

Cardiovascular diseases (CVD) are a leading cause of death. The most notable cause of CVD is an atherosclerotic plaque. The aim of this review is to provide an overview of different diagnostic methods for atherosclerotic plaque relevant to the assessment of cardiovascular risk. The methods can be divided into invasive and non-invasive. This review focuses on non-invasive with attention paid to ultrasonography, contrast-enhanced ultrasonography, intravascular ultrasonography, and assessment of intima-media complex, coronary computed tomography angiography, and magnetic resonance. In the review, we discuss a number of Artificial Intelligence technologies that support plaque imaging.

Development and application of digital technology in craniomaxillofacial surgery

The treatment of craniomaxillofacial (CMF) defects and deformities is highly challenging due to the complex anatomical structures, significant individual variations and high demands for personalized reconstruction. In recent decades, the integration of cutting-edge technologies from fields such as computer science, mechanical engineering, and material science into medicine has greatly facilitated the transformation of CMF surgery from traditional, experience-based approaches to digitalized and intelligent methods. This review thoroughly analyzes the development and application areas of digital technology in CMF surgery including virtual surgical planning and facial prediction, three-dimensional printing, computer-assisted navigation and robotic surgery, virtual and mixed reality, as well as big data and artificial intelligence technologies. The aim is to offer comprehensive and in-depth reference information for the clinical practice in digital CMF surgery.

Development of an analytical model for assessing the risks of financial and economic activities of State (municipal) institutions based on the pairwise comparison method

Subject. The article considers developing an analytical model to assess the risks of financial and economic activities of State (municipal) institutions. Objectives. The aim is to devise a comprehensive analytical model for assessing the risks of financial and economic activities of State (municipal) institutions, taking into account their specific activities, and assess these risks. Methods. The study employs general and specific scientific methods of cognition (analysis and synthesis, induction and deduction, grouping, comparison, expert assessment, mathematical methods, like the method of analyzing hierarchies, etc.). Results. I designed an analytical (mathematical) model of integral indicator for risk assessment in State (municipal) institutions’ financial and economic activities, offered a scale used to assess such risks. The proposed mathematical model was tested in several State institutions, with the presentation of results. Conclusions. The development of software using intelligent technologies based on the analytical model of integral indicator will enable to more quickly and effectively assess the risks of financial and economic activities of a State (municipal) institution, and track the dynamics of risk changes.

Application of Intelligent Collaboration Technology in Skill Training and Job Docking of Data Tagger

With the rapid development of artificial intelligence and machine learning technology, the role of data tagger has become more and more important, responsible for providing high-quality annotated data for training algorithms, but traditional skills training and job docking methods cannot meet the needs of the rapid development of the industry. This paper studies the application of intelligent collaboration technology in improving the skill training efficiency of data taggers and promoting its effective docking with the post. By using the literature review method, in-depth interview method, and case analysis method, this paper analyzes the current challenges faced by data taggers, including skill gaps, insufficient training resources, and rapid changes in market demand. The results show that the application of intelligent collaboration technology can not only improve the skill training efficiency of data taggers but also promote effective docking between them and their positions, bringing a positive impact to the data tagging industry.

Selection of Artificial Intelligence Technology For Digitalization of Order Fulfillment on Drug Factory Using Multi Criteria Decision Making

Objective: This paper aims to identify the most suitable Artificial Intelligence (AI) technology for the order fulfillment process in pharmaceutical factory, along with the key criteria and sub-criteria for successful AI implementation. Theoretical Framework: The study integrates theories of digital transformation, process optimization, and decision-making. Method: The research assesses digital readiness to evaluate a company's preparedness for digital transformation. The as-is process is mapped using BPMN to identify potential AI applications. AI technologies are then ranked using BWM and PROMETHEE methods, followed by scenario analysis to determine the optimal technology. Results and Discussion: Demand Forecasting is the top AI technology for adoption in the pharmaceutical factory's order fulfillment process, reducing cycle time by 29.50% to 21.80 hours. Research Implications: The research contributes to the understanding of selecting the most effective AI technology for optimizing the order fulfillment process in pharmaceutical companies, providing valuable insights for improving efficiency. Originality/Value: The study proposes a perspective on applying AI to optimize the order fulfillment process in pharmaceutical factory, offering valuable insights for improving operational efficiency through digital transformation.

Advancements and Application of Artificial Intelligence Technologies in Education

In the field of education, information technology has been widely used in teaching and learning activities, bringing more efficient learning process for teachers as well as students. However, there are still some problems. The rapid development of Artificial Intelligence (AI) technology has brought a positive effect on the change of education technology. In this paper, the author did some investigation on the research of AI in education. The results of different researchers are divided into three parts in terms of application scenarios. The first part is student performance prediction. In this part, the author has categorized into machine learning and deep learning based on the kind of methods utilized. The second part is student data analysis, which can be functionally categorized into two directions, facial recognition and text analysis, based on the direction of the research. In the third part, the author summarizes some cases of AI-assisted teaching that are closer to actual teaching activities. These cases present solutions to the current deficiencies in the field of education, proving the efficiency of AI. Based on extensive research on many papers, the author suggests that AI currently faces difficult challenges in terms of interpretability, applicability, and safety. The application of AI in education can be improved in the future through technologies such as Open Learner Models (OLMs), expert system, and transfer learning. This paper provides an overview reference for research in this area.

Advantages of Big Data Analysis and AI Technology in Data Collecting And Processing of Preventive Maintenance

In recent years, the emergence of Cyber-Physical systems (CPS), big data, cloud computing, and industrial wireless networks have promoted the development of Industry 4.0. Intelligent manufacturing systems are the leaders of this change. In smart manufacturing, the emphasis will be placed on using technologies such as big data analytics, cloud computing, edge computing, and artificial intelligence (AI). The advantages of new technologies are significantly reflected in the comparison of preventive maintenance and traditional maintenance based on intelligent technology: preventive maintenance significantly reduces the cost of prevention and, at the same time, dramatically improves the accuracy and timeliness of maintenance, bringing inestimable value to intelligent manufacturing. This paper will analyze new technologies’ innovative advantages and technical prospects through big data analysis and specific application scenarios of AI technology in preventive maintenance. Firstly, in data acquisition, this paper examines the architecture of preventive maintenance systems based on big data. Then, it analyzes the composition of the data pipeline: in the realization of data collection and processing in the field of cloud computing and edge computing, and the aspect of data processing, it discusses the participation of AI technology in data understanding and processing and the unprecedented changes it brings.

Living in the Digital Age: AI Technology and the Changing Lifestyle of the Elderly

As technology advances, diverse artificial intelligence products are invented and put into use. While dramatically improving the quality of human life, digital technologies are also changing the lifestyles of different groups to varying degrees. Focusing on the elderly population, this study utilizes a combination of questionnaire, interview, and documentary research methods in hopes of collecting information in a broad and comprehensive manner. The study mainly focuses on how AI technologies in the digital age are changing the lifestyles of older people and the potential factors that lead to such changes. The study reveals that the digital age has a dual impact on the lives of older people. Thanks to the intelligence and simplicity of AI technology, older people’s lives have become much more efficient and convenient. However, the expensive price of AI devices and products, which also keeps some older people from enjoying the convenience of technological inventions, prevents them from integrating into the digital society. Among many lifestyles, changes in home-based lifestyles are the areas of greatest change in the lifestyles of the elderly in the digital age. These changes are mainly influenced by social, technological and personal factors. The present study expresses ample social concerns for the elderly population. It also conducive to helping older people fully integrate into a smart and convenient life and equally enjoy the dividends of the digital age.

AI-Powered Disinfection Force: Harnessing 5G for Intelligent Distributed Object Recognition and Sanitation Fleet

With the increasing prevalence of embedded cameras in devices such as phones, robots, and vehicles, there’s a growing need to enhance their perception capabilities through advanced deep learning techniques. These systems, however, often struggle under suboptimal conditions like environmental noise. The paper addresses these challenges by proposing a 5G-enabled solution that uses a fleet of unmanned ground vehicles (UGVs) for coordinated disinfection tasks in hospital environments. Utilizing high-bandwidth, low-latency 5G networks, these UGVs can share high-quality images for distributed object recognition tasks and execute precise disinfection routines using onboard UV lamps. The proposed system includes an optimal formation algorithm for UGVs to maintain effective positioning and coordination. By leveraging 5G connectivity, the fleet can efficiently exchange sensor data and perform real-time, innovative object recognition and disinfection, presenting a significant advancement in intelligent sanitation technology.

Artificial Intelligence Algorithms in Cardiovascular Medicine: An Attainable Promise to Improve Patient Outcomes or an Inaccessible Investment?

This opinion paper highlights the advancements in artificial intelligence (AI) technology for cardiovascular disease (CVD), presents best practices and transformative impacts, and addresses current concerns that must be resolved for broader adoption. With the evolution of digitization in data collection, large amounts of data have become available, surpassing the human capacity for processing and analysis, thus enabling the application of AI. These models can learn complex spatial and temporal patterns from large amounts of data, providing patient-specific outputs. These advantages have resulted, at the moment, in more than 900 AI-based devices being approved, today, by regulatory entities, for clinical use, with similar to improved performance and efficiency compared to traditional technologies. However, issues such as model generalization, bias, transparency, interpretability, accountability, and data privacy remain significant barriers for broad adoption of these technologies. AI shows great promise in enhancing CVD care through more accurate and efficient approaches. Yet, widespread adoption is hindered by unresolved concerns of interested stakeholders. Addressing these challenges is crucial for fully integrating AI into clinical practice and shaping the future of CVD prevention, diagnosis and treatment.

Rapid and accurate bacteria identification through deep-learning-based two-dimensional Raman spectroscopy

Surface-enhanced Raman spectroscopy (SERS) offers a distinctive vibrational fingerprint of the molecules and has led to widespread applications in medical diagnosis, biochemistry, and virology. With the rapid development of artificial intelligence (AI) technology, AI-enabled Raman spectroscopic techniques, as a promising avenue for biosensing applications, have significantly boosted bacteria identification. By converting spectra into images, the dataset is enriched with more detailed information, allowing AI to identify bacterial isolates with enhanced precision. However, previous studies usually suffer from a trade-off between high-resolution spectrograms for high-accuracy identification and short training time for data processing. Here, we present an efficient bacteria identification strategy that combines deep learning models with a spectrogram encoding algorithm based on wavelet packet transform and Gramian angular field techniques. In contrast to the direct analysis of raw Raman spectra, our approach utilizes wavelet packet transform techniques to compress the spectra by a factor of 1/15, while concurrently maintaining state-of-the-art accuracy by amplifying the subtle differences via Gramian angular field techniques. The results demonstrate that our approach can achieve a 99.64 % and a 90.55 % identification accuracy for two types of bacterial isolates and thirty types of bacterial isolates, respectively, while a 90 % reduction in training time compared to the conventional methods. To verify the model's stability, Gaussian noises were superimposed on the testing dataset, showing a specific generalization ability and superior performance. This algorithm has the potential for integration into on-site testing protocols and is readily updatable with new bacterial isolates. This study provides profound insights and contributes to the current understanding of spectroscopy, paving the way for accurate and rapid bacteria identification in diverse applications of environment monitoring, food safety, microbiology, and public health.

Ethical Reflections on the Application of Medical Robots in Healthcare

With the development and utilization of medical robots, more and more members of the public have begun to pay attention to the risks of practicing medical robots and have expressed concerns about their application. In order to deeply study the ethical risks of medical robots applied in health care, this study chooses the literature research method to reflect on the moral and ethical issues involved in medical robots. The study found that current medical robots have many application scenarios, such as: surgery, rehabilitation care, assisted work and telemedicine. These application scenarios of medical robots may bring ethical risks such as attribution of medical responsibility, doctor-patient relationship, and data algorithms. Intensively, the study points out that inadequate laws, immature values, and technological deficiencies are the most significant factors leading to the aforementioned risks. Thus, adequate attention should be paid to the medical career, the intelligent technology of robots should be improved, and the relevant laws and regulations should be actively improved. This study realistically reflects the current situation and risks of medical robot application, and through in-depth reflection, provides inspiration for robots to better serve the medical cause.