Artificial System Research Articles

THE INFLUENCE OF DATA SAMPLING ON SOLVING THE PROBLEM OF PATTERN RECOGNITION FOR DIAGNOSTICS OF INDUSTRIAL EQUIPMENT

With the sophisticated technology that modern industrial organizations are equipped with, state prediction and diagnostics are essential duties. The current research aims to develop a more accurate modified artificial intelligence system for industrial equipment diagnostics in the oil and gas industry. Researching faulty signals and processing methods utilized by equipment in the oil and gas industry, as well as assessing the advantages and disadvantages of different signal extraction strategies, are the first steps in the process. The second is the application of artificial intelligence to decision-making and equipment defect detection. This method widely used by the oil and gas sectors to lower equipment failure rates. The recommended diagnostic system helps organizations reduce the financial risks associated with equipment defects by increasing production dependability, enabling for maintenance planning, predicting probable failures, and expediting equipment repairs. The article is devoted to the study of the data sampling influence on the classifier’s predictive ability in diagnosing of the industrial equipment. Various types of data samples were considered, such as: simple random sample, cluster sample, systematic sample. According to the results of listed data samples were built classifiers based on particle swarm optimization and ensemble models (bagging and voting type). The best results were achieved using the systematic sampled dataset and an ensemble modeling strategy with voting, which combines forecasting based on a neural net, gradient boosted trees and naive Bayes models: accuracy 93.6%; classification error 8%; recall 94.32%; precision 93.87%. The resulting best strategy for diagnosing equipment based on data sampling and an ensemble model was used for implementation in FMEA (Failure Mode and Effects Analysis) technology in order to obtain an improved version, which is adapted for working with big data.

101 - Application of SMS-RESOLVE DWI technology combined with artificial intelligence diagnostic system in acute cerebral infarction

101 - Application of SMS-RESOLVE DWI technology combined with artificial intelligence diagnostic system in acute cerebral infarction

Bioinspired Artificial Intelligent Nociceptive Alarm System Based on Fibrous Biomemristors.

With the advancement of modern medical and brain-computer interface devices, flexible artificial nociceptors with tactile perception hold significant scientific importance and exhibit great potential in the fields of wearable electronic devices and biomimetic robots. Here, a bioinspired artificial intelligent nociceptive alarm system integrating sensing monitoring and transmission functions is constructed using a silk fibroin (SF) fibrous memristor. This memristor demonstrates high stability, low operating power, and the capability to simulate synaptic plasticity. As a result, an artificial pressure nociceptor based on the SF fibrous memristor can detect both fast and chronic pain and provide a timely alarm in the event of a fall or prolonged immobility of the carrier. Further, an array of artificial pressure nociceptors not only monitors the pressure distribution across various parts of the carrier but also provides direct feedback on the extent of long-term pressure to the carrier. This work holds significant implications for medical support in biological carriers or targeted maintenance of electronic carriers.

Lifelong Generative Adversarial Autoencoder.

Lifelong learning describes an ability that enables humans to continually acquire and learn new information without forgetting. This capability, common to humans and animals, has lately been identified as an essential function for an artificial intelligence system aiming to learn continuously from a stream of data during a certain period of time. However, modern neural networks suffer from degenerated performance when learning multiple domains sequentially and fail to recognize past learned tasks after being retrained. This corresponds to catastrophic forgetting and is ultimately induced by replacing the parameters associated with previously learned tasks with new values. One approach in lifelong learning is the generative replay mechanism (GRM) that trains a powerful generator as the generative replay network, implemented by a variational autoencoder (VAE) or a generative adversarial network (GAN). In this article, we study the forgetting behavior of GRM-based learning systems by developing a new theoretical framework in which the forgetting process is expressed as an increase in the model's risk during the training. Although many recent attempts have provided high-quality generative replay samples by using GANs, they are limited to mainly downstream tasks due to the lack of inference. Inspired by the theoretical analysis while aiming to address the drawbacks of existing approaches, we propose the lifelong generative adversarial autoencoder (LGAA). LGAA consists of a generative replay network and three inference models, each addressing the inference of a different type of latent variable. The experimental results show that LGAA learns novel visual concepts without forgetting and can be applied to a wide range of downstream tasks.

Locating Dipole Source Using Self-Propelled Robotic Fish With Artificial Lateral Line System

Locating Dipole Source Using Self-Propelled Robotic Fish With Artificial Lateral Line System

The Hydronephrosis Severity Index guides paediatric antenatal hydronephrosis management based on artificial intelligence applied to ultrasound images alone

Antenatal hydronephrosis (HN) impacts up to 5% of pregnancies and requires close, frequent follow-up monitoring to determine who may benefit from surgical intervention. To create an automated HN Severity Index (HSI) that helps guide clinical decision-making directly from renal ultrasound images. We applied a deep learning model to paediatric renal ultrasound images to predict the need for surgical intervention based on the HSI. The model was developed and studied at four large quaternary free-standing paediatric hospitals in North America. We evaluated the degree to which HSI corresponded with surgical intervention at each hospital using area under the receiver-operator curve, area under the precision-recall curve, sensitivity, and specificity. HSI predicted subsequent surgical intervention with > 90% AUROC, > 90% sensitivity, and > 70% specificity in a test set of 202 patients from the same institution. At three external institutions, HSI corresponded with AUROCs ≥ 90%, sensitivities ≥ 80%, and specificities > 50%. It is possible to automatically and reliably assess HN severity directly from a single ultrasound. The HSI stratifies low- and high-risk HN patients thus helping to triage low-risk patients while maintaining very high sensitivity to surgical cases. HN severity can be predicted from a single patient ultrasound using a novel image-based artificial intelligence system.

Artificial Intelligence-Based Adaptive Traffic Signal Control System: A Comprehensive Review

The exponential increase in vehicles, quick urbanization, and rising demand for transportation are straining the world’s road infrastructure today. To have a sustainable transportation system with dynamic traffic volume, an Adaptive Traffic Signal Control system (ATSC) should be contemplated to reduce urban traffic congestion and, thus, help reduce the carbon footprints/emissions of greenhouse gases. With dynamic cleave, the ATSC system can adapt the signal timing settings in real-time according to seasonal and short-term variations in traffic demand, enhancing the effectiveness of traffic operations on urban road networks. This paper provides a comprehensive study on the insights, technical lineaments, and status of various research work in ATSC. In this paper, the ATSC is categorized based on several road intersections (RIs), viz., single-intersection (SI) and multiple-intersection (MI) techniques, viz., Fuzzy Logic (FL), Metaheuristic (MH), Dynamic Programming (DP), Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), and hybrids used for developing Traffic Signal Control (TSC) systems. The findings from this review demonstrate that modern ATSC systems designed using various techniques offer substantial improvements in managing the dynamic density of the traffic flow. There is still a lot of scope to research by increasing the number of RIs while designing the ATSC system to suit real-life applications.

Development and application of an artificial intelligence-assisted endoscopy system for diagnosis of Helicobacter pylori infection: a multicenter randomized controlled study

BackgroundThe early diagnosis and treatment of Heliobacter pylori (H.pylori) gastrointestinal infection provide significant benefits to patients. We constructed a convolutional neural network (CNN) model based on an endoscopic system to diagnose H. pylori infection, and then examined the potential benefit of this model to endoscopists in their diagnosis of H. pylori infection.Materials and methodsA CNN neural network system for endoscopic diagnosis of H.pylori infection was established by collecting 7377 endoscopic images from 639 patients. The accuracy, sensitivity, and specificity were determined. Then, a randomized controlled study was used to compare the accuracy of diagnosis of H. pylori infection by endoscopists who were assisted or unassisted by this CNN model.ResultsThe deep CNN model for diagnosis of H. pylori infection had an accuracy of 89.6%, a sensitivity of 90.9%, and a specificity of 88.9%. Relative to the group of endoscopists unassisted by AI, the AI-assisted group had better accuracy (92.8% [194/209; 95%CI: 89.3%, 96.4%] vs. 75.6% [158/209; 95%CI: 69.7%, 81.5%]), sensitivity (91.8% [67/73; 95%CI: 85.3%, 98.2%] vs. 78.6% [44/56; 95%CI: 67.5%, 89.7%]), and specificity (93.4% [127/136; 95%CI: 89.2%, 97.6%] vs. 74.5% [114/153; 95%CI: 67.5%, 81.5%]). All of these differences were statistically significant (P < 0.05).ConclusionOur AI-assisted system for diagnosis of H. pylori infection has significant ability for diagnostic, and can improve the accuracy of endoscopists in gastroscopic diagnosis.Trial registrationThis study was approved by the Ethics Committee of Daping Hospital (10/07/2020) (No.89,2020) and was registered with the Chinese Clinical Trial Registration Center (02/09/2020) (www.chictr.org.cn; registration number: ChiCTR2000037801).

Effect of Different Basal Media and Organic Supplements on In Vitro Seedling Development of the Endangered Orchid Species Dendrobium moniliforme (L.) Swartz

The orchid Dendrobium moniliforme faces endangerment due to habitat loss and illegal harvesting, necessitating the development of an optimized artificial propagation system to aid conservation and reintroduction efforts. This study evaluated the effects of three plant growth media, namely Murashige and Skoog (MS), Hyponex, and Orchid Maintenance Medium (OMM) (P668), and various organic additives (apple homogenate, banana homogenate, and coconut water) on the in vitro seedling growth of D. moniliforme. The results reveal that, in early postgermination stages, seedlings achieve maximum growth in the Hyponex medium, with a fresh weight (92 mg) and root length (2.7 cm) approximately 20-fold greater than those in the MS medium and OMM. After 6 months, for seedlings grown in MS medium and OMM with banana (50 g·L−1), the mean fresh weights were 29 and 107 mg, respectively; however, the highest biomass was observed in seedlings grown in the Hyponex medium with coconut water (50 mL·L−1), exhibiting a mean fresh weight of 201 mg. This study highlights Hyponex medium with coconut water as the most effective combination for promoting D. moniliforme growth and identifies suitable organic supplements for the in vitro cultivation of seedlings from asymbiotic seed culture. This propagation system offers valuable technical support for the mass production and conservation of this epiphytic orchid.

Artificial Intelligence-Based Electric Vehicle Smart Charging System in Malaysia

The worldwide transition to electric vehicles (EVs) is gaining momentum, propelled by the imperative to reduce carbon emissions and foster sustainable transportation. In Malaysia, the government is facilitating this transformation through targeted initiatives aimed at promoting the use of electric vehicles (EVs) and developing the required infrastructure. This paper investigates the crucial role of artificial intelligence (AI) in developing intelligent electric vehicle (EV) charging infrastructure, specifically focusing on the context of Malaysia. The paper examines the current electric vehicle (EV) charging infrastructure in Malaysia, highlights advancements led by artificial intelligence (AI), and references both local and international case studies. Fluctuations in the Total Industry Volume (TIV) and Total Industry Production (TIP) reflect changes in market demand and production capabilities, with notable peaks in March 2023 and March 2024. The research reveals that AI technologies, such as machine learning and predictive analytics, can enhance charging efficiency, improve user experience, and support grid stability. A mathematical model for an AI-based smart charging system was developed, and the implemented system achieved 30% energy savings and a 20.38% reduction in costs compared to traditional methods. These findings underscore the system’s energy and cost efficiency. In addition, we outline the potential advantages and challenges associated with incorporating artificial intelligence (AI) into Malaysia’s electric vehicle (EV) charging infrastructure. Furthermore, we offer recommendations for researchers, industry stakeholders, and regulators. Malaysia can enhance the uptake of electric vehicles and make a positive impact on the environment by leveraging artificial intelligence (AI) to enhance its electric vehicle charging system (EVCS).

AI-Assisted Survival Prediction in Colorectal Cancer: A Clinical Decision Support Tool

Purpose: Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. Accurate survival prediction is crucial for advanced-stage patients to optimize treatment strategies and improve clinical outcomes. This study aimed to develop an artificial intelligence-assisted clinical decision support system (CDSS) for survival prediction in CRC patients using clinical and genomic data from the Cancer Genome Atlas Colon Adenocarcinoma Collection (TCGA-COAD) dataset. Methods: Machine learning algorithms, including C4.5 Decision Tree, Support Vector Machines (SVM), Random Forest, and Naive Bayes, were employed to create survival prediction models. Clinical parameters and genomic data from key pathways, such as glycolysis/gluconeogenesis and mTORC1, were integrated into the models. The models were evaluated based on accuracy and performance. Results: The Random Forest algorithm achieved the highest accuracy (82.3%) when only clinical parameters were used. When clinical data were combined with gene expression data, the model’s accuracy increased further. The resulting models were incorporated into a user-friendly web interface, SurvCOCA, for clinical use. Conclusions: This study demonstrates the potential of AI-based tools to improve prognosis predictions in CRC patients. Further research is needed, with larger datasets and additional machine learning algorithms, to enhance clinical decision-making and optimize treatment strategies.

Artificial olfactory memory system based on conductive metal-organic frameworks

The olfactory system can generate unique sensory memories of various odorous molecules, guiding emotional and cognitive decisions. However, most existing electronic noses remain constrained to momentary concentration, failing to trigger specific memories for different smells. Here, we report an artificial olfactory memory system utilizing conductive metal-organic frameworks (Ce-HHTP) that integrates sensing and memory and exhibits short- and long-term memory responses to alcohols and aldehydes. Experiments and theoretical calculations show that distinct memories are derived from the specific combinations of Ce-HHTP with O atoms in different guest. An unmanned aircraft equipped with this system realized the sensory memories in established areas. Moreover, the fusion of portable detection boxes and wearable flexible electrodes demonstrated the immense potential in off-site pollution monitoring and health management. This work represents an artificial olfactory memory system with two specific sensory memories under simultaneous conditions, laying the foundation for bionic design with qualities of human olfactory memory.

A cluster approach to matching the competences of data specialists with skills in demand on the labour market

This paper addresses the challenge of aligning the competences of data specialists with skills in demand on the labour market in the rapidly evolving field of data science. Using an open dataset of 3,744 IT job postings, the study applies K-means clustering to identify key skill groupings for data specialist positions. The optimal number of clusters is determined using the elbow method, resulting in four distinct clusters: Data Analyst &amp; Engineer, Data Platform Engineer, Data Science &amp; Engineering Specialist, and Cloud Data Engineer. The research methodology employs unsupervised learning techniques, specifically K-means clustering, to analyze the distribution of skills across job postings. The clusters are visualized using t-distributed Stochastic Neighbor Embedding (t-SNE), providing insights into the relationships between different skill sets. The study reveals that job titles do not always unambiguously define the required skills, emphasizing the importance of focusing on specific skill sets rather than job titles alone. To bridge the gap between specific subject competences academic programs and industry requirements, the paper proposes a novel approach for comparing the proportion of skills in job clusters with the proportion of professional competencies in academic programs. This method is demonstrated using the Information Systems and Technologies Master's program at Kherson State University as a case study. The chi-square test is applied to confirm the statistical similarity between the skill structure of the Data Science &amp; Engineering Specialist cluster and the competency structure of the academic program. The findings highlight the importance of continuous adaptation of profile of academic program to meet evolving industry needs. The proposed approach provides a data-driven framework for universities to align their programs with labor market demands, potentially improving graduate employability in the data science field. The study also underscores the need for personalized learning paths that can be tailored to individual career goals and skill gaps. Future research directions include the development of an artificial intelligence system to form individualized educational trajectories based on the skills required for specific job clusters. This could further enhance the alignment between education and industry needs, preparing students more effectively for the dynamic data science job market.

An Artificial Light-Harvesting System based on Supramolecular AIEgen Assembly.

Photosynthesis is a complex multi-step process in which light collection is the initial step of photosynthesis and plays an important role in the utilization of solar energy. In order to improve the utilization of sunlight, researchers have developed a variety of artificial light-harvesting systems to simulate photosynthesis in nature. Here, we report a supramolecular artificial light-harvesting system in aqueous solution. Since β-cyclodextrin (β-CD) has a hydrophobic cavity and a hydrophilic outer surface, we adopt β-cyclodextrin (β-CD) as the host molecule and use adamantane as the guest molecule. At the same time, we modified β-CD with the donor molecule naphthalimide and adamantane with the tetraphenylethylene molecule which has aggregation-induced emission (AIE) effects. By using fluorescent molecules with AIE, the self-quenching effect caused by aggregation in aqueous solution can be effectively avoided. Due to the host-guest interaction of β-CD and adamantane, nanoparticles with stable structure and uniform size can be spontaneously assembled in water. Because of the close distance and strong spectral overlap between naphthalimide and tetraphenylethylene, Förster resonance energy transfer (FRET) was realized, and artificial light-harvesting system was successfully constructed in aqueous solution. Therefore, this study provides a new strategy for constructing artificial light-harvesting system, and the artificial light-harvesting system shows broad application prospects in aqueous solutions.

Comparison of postural assessment and awareness in individuals receiving posture training using the digital AI posture assessment and correction system

Objectives. This study aimed to compare postural assessment and postural awareness using the artificial intelligence posture evaluation and correction system (APECS) in individuals receiving posture training. Methods. Participants’ physical characteristics were recorded. The participants’ posture was evaluated with APECS and the New York posture rating chart test (NYPR), and their body and postural awareness was evaluated with the body awareness questionnaire (BAQ), postural awareness form (PAF) and postural habits and awareness scale (PHAS). Results. In the study, results of the PAF (p = 0.000), BAQ (p = 0.013) and PHAS (p = 0.033) were found to be different between the groups. While the groups were similar in the PHAS sub-dimension of postural habit (p = 0.331), there was a significant difference between the groups in postural awareness (p = 0.04). NYPR results of the participants in the group receiving posture training were similar to those in the group not receiving posture training (p = 0.45). Conclusion. Postural deviations measured by digital posture assessment in individuals receiving posture training were significantly different in the group receiving posture training. Postural awareness and body awareness were better in individuals who received posture training than in individuals who did not receive posture training.

Exploring EFL learners’ perceived promise and limitations of using an artificial intelligence speech evaluation system for speaking practice

This study explores English as a Foreign Language (EFL) learners' perceptions of the promise and limitations of EAP Talk, an AI-based speech evaluation system, for speaking practice. Using a mixed-methods approach, data were collected from 366 EFL learners across five universities through questionnaires and semi-structured interviews. The findings reveal that EAP Talk significantly enhances speaking skills, including pronunciation, grammar accuracy, idea-organization, read-aloud, and presentation skills. Participants appreciated the convenience, motivational aspects, and institutional efficiency of EAP Talk, which allowed for flexible and autonomous learning. However, several limitations were identified. Issues with the accuracy of voice recognition and score feedback affected learners' confidence in the feedback. The relevance of practice materials to academic courses was another concern, as was the limited detail in the feedback provided by EAP Talk, which often lacked comprehensive explanations and corrective guidance. The absence of feedback on grammar accuracy and speech organization further limited the tool's effectiveness for developing comprehensive speaking proficiency. These findings suggest that while EAP Talk holds significant potential for enhancing language learning, ongoing improvements are necessary to address its limitations. This study contributes to the understanding of how AI tools can support EFL learners for speaking practice.

Use of deep learning to improve seismic data quality analysis

Seismic data are influenced by various types of noise, which are typically categorized into two primary classes: anthropogenic and environmental. However, the detection of instrumental noise or malfunctioning stations also plays a crucial role in ensuring the data quality and the efficiency of a seismic network. The visual inspection of seismic spectral diagrams (e.g. power spectral density) enables us to identify issues that could potentially compromise data quality, thereby affecting subsequent calculations such as Magnitude or Peak Ground Acceleration (PGA). However, this process is time-consuming and demands significant human expertise due to the complexity of the diagrams, compounded by the sheer number of stations requiring examination. Therefore, in this paper, we explore the feasibility of transferring human expertise into an artificial intelligence system to create an automated system capable of rapidly performing such detection. More specifically, in the first part of this paper, we use Probability Density Function (PDF) diagrams, enabling an initial assessment of station performance via visual inspection. We describe this plot type and provide examples that reveal whether a station is functioning correctly or if technical issues exist. A table containing the main evaluation criteria is provided. In the second part of this paper, we demonstrate that these plots can serve as input for a neural network, allowing the development of the aforementioned automated system. Through extensive testing under various conditions, we have observed that the trained network consistently achieves an accuracy rate exceeding 85% across all four conductedtests. In the latest and most significant test, the achieved accuracy is approximately 87%.

Tabula Rasa: A Case Study in Chronic Pain Management Using Individual-Centric AI.

There are numerous behavioural, social and environmental factors that influence the symptomatology of a chronic health condition. These factors and how they manifest are often very specific to the individual, which creates challenges for applying macro population health approaches and insights to guide treatment. An artificial intelligence system, referred to as a non-axiomatic reasoning system (NARS), is presented. Learning in NARS is incremental and ongoing. A practical application of NARS in chronic pain management is demonstrated, as NARS can establish associations with behavioural activities that might exacerbate pain levels and revise the strengths of these associations over time. The system has potential application in any condition requiring patient-centric adaption.

Опыт внедрения информационной системы с технологиями искусственного интеллекта в работу поликлиники для оптимизации организации периодических медицинских осмотров.

Organizing medical examinations for patients with minimal time losses is the most difficult task for an outpatient clinic when creating a New model of a medical organization providing primary health care. Since during medical examinations it is necessary to perform a whole range of works associated with certain sets of actions, the authors developed an information system with technologies of artificial intelligence, which was tested during periodic medical examinations. The purpose of the study is to develop and test an information system with technologies of artificial intelligence that allows balancing the capabilities of a medical organization and the needs of the patient when undergoing periodic medical examinations. Materials and methods. The tool for solving this problem was an artificial intelligence platform based on the principle of decision making by controlling the positions of attractors in the state space of the Hopfield neural network, which was built into the medical information system using HL7 FHIR. Results. The result of the work was the “Smart Clinic” project, which provides for the creation of an information system with technologies of artificial intelligence for routing patients during periodic medical examinations. The information system provided dynamic routing of patients in real time with the construction of the shortest trajectory and minimal movement across the floors of the building, based on current information about the workload of specialists and the duration of each stage of the examination. Conclusions. The use of an artificial intelligence information system in the outpatient clinic made it possible to reduce the average duration of a patient’s trajectory during a periodic medical examination by 45%, the average waiting time for an appointment – from 7 minutes to 3 minutes, and the total time for undergoing a preventive examination – from 200 to 150 minutes. This made it possible to increase the number of periodic medical examinations per year by 1.5 times, and the level of patient satisfaction reached 95%.

Artificial Intelligence Recommendation System Model for Military Cabin Space Environment Design

This paper proposes an artificial intelligence-based recommendation model for military cabin space environment design schemes to satisfy military cabin operators with a comfortable and efficient working environment and improve the safety and health of the operators. Each key design feature is used to extract the interactive relationship between the operator and the cabin design. Through the neural network, the operator's psychological comfort is interactively matched with the cabin space environment factors. As result, the interactive artificial intelligence cabin space environment design scheme recommendation system is recommended and established. The results showed that the method can optimize the environmental design of military cabins, alleviate some of the design complexities associated with military cabins, and improve the physical and mental health of operators. Additionally, by using an AI-based recommendation system for cabin space environment design, engineers can reduce the time and resources required to design military cabins, resulting in faster turnaround times and lower costs.