Intelligent Assistance Systems Research Articles

A GUI Based Application for Breast Cancer Diagnosis from Histopathology Images Using a Sequential Convolutional Neural Network Model

Breast cancer is the most common type of cancer among women globally. Automated breast cancer diagnosis improves healthcare by saving time and providing valuable assistance to pathologists. The focus of this research is the development of a deep learning framework named BCnet (Breast Cancer net), which aims to automatically diagnose breast cancer using histopathological images at various levels of magnification. We train the model from scratch using the BreakHis dataset, incorporating augmentation techniques. BCnet's performance is subsequently evaluated against current architectures that utilize the transfer learning approach. Furthermore, a Graphical User Interface (GUI) application was specifically designed for pathologists to use. BCnet was 95% accurate, while VGG-16, VGG-19, Xception, InceptionResnetV2, and Resnet152V2 were 78%, 79%, 77%, 79%, and 84% accurate, respectively. BCnet hence functions as an intelligent assistance system for pathologists and a potent tool for timely and precise identification, with the capacity to significantly influence patient outcomes.

New intelligent flower arrangement system

Currently, the art of flower arrangement plays a significant role in both personal life and collective work. However, for non-professionals, creating flower arrangements with high aesthetic value can be challenging. With the rapid advancement of artificial intelligence technology, its application in the field of flower arrangement offers new possibilities for addressing this issue. To achieve a balance between aesthetic appeal and efficiency in intelligent flower arranging, this paper proposes an innovative intelligent flower arrangement assistance system. This system encompasses core functions such as intelligent recognition of floral materials, matching suitable options from a database, realizing flower arrangement designs, and constructing the database. Through a series of experiments, the system has demonstrated a good balance between aesthetic appeal and efficiency in flower arrangement design, with results indicating the effectiveness and feasibility of the proposed method.

Development and Application of Drilling Intelligent Assistant Decision System

Abstract In order to systematically harness the substantial volume of data generated during the drilling process and achieve intelligent analysis and auxiliary decision-making for real-time risk monitoring and warnings during drilling, an intelligent drilling auxiliary decision-making system has been developed. This system integrates various technologies, including physical models, intelligent algorithms, and trend analysis. By analyzing the characteristics of logging data under different operating conditions, an intelligent algorithm capable of automatically distinguishing six types of drilling operating conditions has been established. Taking into account the impact of rock cuttings on the stress on the pipe string and annular pressure loss during the drilling process, coupled frictional torque and cyclic pressure loss calculation models were developed under coupled conditions. Building upon this foundation and considering the trend of deviation between predicted and measured parameters, a monitoring algorithm for detecting stuck drilling, wellbore leakage, and overflow incidents was formulated. The development of the drilling intelligent decision-making system was completed based on a C/S three-layer architecture and a data center. This system has been successfully applied in more than 50 exploration wells of various types, including shale oil and gas horizontal wells and deep wells. The static analysis results demonstrated a compliance rate of 91.5% with on-site monitoring, playing a crucial role in drilling parameter optimization and risk monitoring. The practicality and feasibility of the system have been verified, providing essential support for efficient and safe drilling production.

Retraction Note: Simulation of optical imaging detection based on virtual reality assisted technology in intelligent assistance system for martial arts actions

Retraction Note: Simulation of optical imaging detection based on virtual reality assisted technology in intelligent assistance system for martial arts actions

Research on the Design of Medical Imaging Assisted Diagnosis System Based on Machine Learning

The design and application of medical imaging diagnostic assistance systems have become an indispensable facet of modern medicine. Technologies based on machine learning can handle vast and intricate medical imaging data, offering precise diagnostic support and significantly enhancing the efficiency and accuracy of clinical diagnoses. This study focuses on the development and implementation of an efficient medical imaging diagnostic assistance system utilizing machine learning algorithms such as Principal Component Analysis (PCA), Support Vector Machines (SVM), and Multi-Layer Perceptrons (MLP). Through the architectural design of intelligent diagnostic assistance systems, data preprocessing, and feature vectorization, combined with the application of classification algorithms, a robust diagnostic support system capable of addressing diverse medical scenarios has been constructed. Performance evaluation experiments indicate that this system demonstrates high accuracy and robustness in processing medical imaging data, holding potential for clinical implementation. The findings of this study provide new insights for the advancement of intelligent diagnostic systems in medical imaging and lay a solid foundation for the development of future clinical diagnostic tools.

Exemplary Practices in Kidney Care: Sharing the Experience of " Disease Care Certification"

<p>2019年8月，本院腎臟團隊首次參與醫策會「腎臟病照護品質認證」，在委員查核下通過認證。近三年來，本院透過外部審核和交流，不斷增強自身實力，凝聚成員共識，進行腎臟照護標準化。以「病人為中心」執行醫療服務，促進跨領域合作，拓展研究領域。本團隊亦致力於品質改進和減少異常發生，建立新透析中心積極應對COVID-19及其他嶄新挑戰。在數位轉型過程中，本院更引進電子病歷智慧輔助系統，利用One System整合平台提升照護效率。同時，針對末期腎病(Pre-ESRD)5期病人，提前啟動醫病共享決策(SDM)，成立「愛腎家族」群組，強調醫療團隊成員和病人之間的合作。在高品質連續性照護基礎上，為急性腎損傷、慢性腎臟病、和需要透析的病人建立全面性的照護機制。</p> <p> </p><p>In August 2019, our hospital’s renal team participated for the first time in the Disease-Specific Care Kidney Disease certification program and achieved certification from the Joint Commission of Taiwan. Over the preceding three years, this team has consistently strengthened itself through external audits and exchanges, fostering consensus among its members and achieving standardization in renal care. Focusing on patient-centered care, the team’s main goals are to enhance core medical services, promote cross-disciplinary integration, and expand research areas. Our team is also dedicated to improving quality of care and mitigate anomalies. Furthermore, a new dialysis center was established to proactively respond to COVID-19 and other emerging challenges. During the digital transformation process, our hospital introduced an intelligent electronic medical record assistance system and utilized the One System integration platform to enhance care efficiency. For patients with stage 5 pre-end-stage renal disease, we proposed early shared decision-making standards, established a “Love Kidney Family” group, and emphasized the importance of collaboration between healthcare providers and patients. These initiatives were aimed at developing a foundation for offering comprehensive, high-quality care for patients with acute kidney injury, chronic kidney disease, and those requiring dialysis.</p> <p> </p>

A knowledge graph based intelligent auxiliary diagnosis and treatment system for primary tinnitus using traditional Chinese medicine

Primary tinnitus is a disabling disease with an unknown pathogenesis and a high incidence rate in China. Its diagnosis and treatment are complex and difficult to control. Although many treatments are available for primary tinnitus, their efficacy is often unsatisfactory. This paper proposes a new diagnosis and treatment method using knowledge graphs, and an intelligent assistant decision system is developed. To support diagnosis, a knowledge graph is created as a decision support tool using traditional Chinese medicine (TCM). Based on the knowledge graph, a model for the syndrome differentiation of tinnitus in TCM is built. At tinnitus treatment, an intelligent recommandation model for pentatonic music using knowledge graph based heterogeneous label propagation is then used to provide patients with personalized treatment plans. According to evaluation results, the proposed method achieves an accuracy of 87.1 % in tinnitus diagnosis. Compared with the control group, the recommended pentatonic music had a more obvious effect, and the efficacy of the five types of tinnitus was increased by 33.34 %, 33.33 %, 20 %, 26.67 %, 33.34 %, respectively. The system developed in this paper will help clinicians improve the diagnosis and treatment of tinnitus while reducing unnecessary medical expenses and offering significant social and economic benefits.

The clinical application and experience of acupuncture in the treatment of asthma assisted by intelligent technology

Asthma, a prevalent chronic respiratory disease, impacts patients' quality of life. Traditional acupuncture therapy is effective but lacks personalization. This paper introduces AI technology into acupuncture treatment, proposing an intelligent assistance system. The system uses patients' physiological and imaging data to construct an intelligent diagnostic model, accurately identifying conditions and symptom types. Through reinforcement learning optimization algorithms, personalized acupuncture plans are tailored for each patient. Clinical trials show that AI-assisted acupuncture significantly improves symptom scores, lung function, and reduces attack frequency, enhancing clinical efficacy and serving as a valuable tool for traditional Chinese medicine acupuncture training.

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

The intelligent judicial trial assistance system is an intelligent auxiliary judicial trial tool based on generative artificial intelligence and machine learning technology. It can achieve auxiliary functions such as case upload, trial result prediction, and legal document generation through automated, digital, and informational means. It utilizes technologies such as Recurrent Neural Networks (RNN) and Long Short-Term Memory networks (LSTM) to train generative models for generating trial recommendations and assisting in decision-making. Through in-depth research on the working principles and optimization paths of the intelligent judicial trial assistance system, the best applicable path for the system in judicial trial practice is explored. By establishing optimized models, risks and biases are minimized to the greatest extent.

Intelligent assistance system for people with visual impairments

Subject of the Research: The creation of an intelligent assistance system for people with visual impairments. Nowadays, the task of developing effective intelligent assistance systems that allow people with vision problems to achieve maximum independence is important and relevant, as existing systems have a number of drawbacks, such as limited autonomy, limited integration with other devices and systems, limited analysis of dynamic obstacles, and limited user feedback capabilities, which in most cases are restricted to voice guidance. Objective: The objective of this work is to create a generalized functional model of an intelligent assistance system for people with visual impairments, which has enhanced autonomy, integration with other devices and systems, the ability to analyze dynamic objects and predict their movement trajectory, and provide diverse feedback to the user. Tasks: To achieve the set objective, the following tasks were accomplished: a generalized functional model of the proposed intelligent assistance system for people with visual impairments was created; the functional dependencies of the components of the developed model were substantiated; a review of the basic modules of the proposed system model was conducted. Methods: The methods used include functional modeling methods and system analysis methods. Results: The following results were obtained: a functional model of an intelligent assistance system for people with visual impairments was proposed. This system surpasses existing analogs in a number of functional capabilities: detection of static and dynamic obstacles with prediction of dynamic obstacles’ movement trajectory, the ability to operate in various conditions (indoors, outdoors, in light or dark, in different weather conditions), support for integration with other systems and devices, and a high level of autonomy. Conclusions: The developed system model has enhanced autonomy, integration with other devices and systems, the ability to analyze dynamic objects and predict their movement trajectory, and provides diverse feedback to the user.

Impact analysis of Advanced Driver Assistance Systems (ADAS) regarding road safety – computing reduction potentials

In the present study road safety impact analysis for certain advanced driver assistance systems (ADAS) was conducted. Based on a literature review, expert interviews and current adaptations in legislation, the most promising nine ADAS were selected. The impact was analysed based on statistical crash data from Austria. Factors such as infrastructure and weather conditions, market penetration, expected functionality of sensors, user acceptance and risk homeostasis were considered. A software tool was developed to calculate the crash reduction potential of the selected ADAS for the scenarios 2025, 2030 and 2040.The results show that the ADAS related to warning/braking have the greatest future reduction potential and could lead to a reduction of up to 8,700 crashes and 70 fatalities in Austria in 2040. In addition, the Intelligent Speed Assistance system would lead to an overall crash reduction of 8% compared to current crash numbers in Austria in 2040. The Turning Assistant for heavy goods vehicles shows the lowest reduction in crashes and casualties, but due to the highest severity per crash (93 fatalities per 1,000 crashes), it nevertheless provides an important contribution to the reduction of fatalities in road traffic.However, to benefit from the ADAS safety potential, it is highly relevant that these systems are used in a correct manner. In the future, it will be necessary to provide users with more information on the correct use, benefits and limitations of the respective ADAS and to integrate the use of these systems into driver education procedures and tests.

Humanzentrierter Gestaltungsprozess eines intelligenten Assistenzsystems in der manuellen Montage

Abstract This article presents a human-centered approach to developing an intelligent assembly assistance system, based on ISO 9241-210. Requirements were derived using user-centered methods, and a prototype was developed. A study evaluated the impact of error communication on users, highlighting challenges and potentials. The article demonstrates that the involvement of employees is crucial for the successful development of such systems.

AI-powered smart assistants: Your key to self-learning success

This research aims to develop a prototype of a smart assistant system for independent learning activities based on Artificial Intelligence. Web-based instructional design (WBID) approach is used in research to develop intelligent assistant systems. Artificial Intelligence-Learning Analytics helps students make important decisions and creates an effective and efficient learning environment while still building learning independence. The method used is a research and development (R&D) approach. The findings of this research are an intelligent assistant system in independent learning activities assisted by artificial intelligence in the form of the AILA ChatBot application. In the end, this research concludes that the ChatBOT application developed in this research is used to provide recommendations for using AI applications to students, which are then implemented and integrated into SPOC/MOOC.

Enhanced Design of a Tai Chi Teaching Assistance System Integrating DTW Algorithm and SVM

Physical education using technology has enabled traditional practices like Tai Chi, a martial art known for its multiple health benefits and meditative aspects, to set coordinated goals. This research presents an intelligent Tai Chi Teaching Assistance System supported by the integration of the Dynamic Time Warping algorithm and Support Vector Machine, in which can practitioners providing real-time feedback to improve Tai Chi learning and quality. In the system, the DTWA Dynamic Time Warping Algorithm was used to accurately compare a practitioner’s complex body movements with the Tai Chi standard movements dataset, taking into account execution speed deviations and others. Meanwhile, the SVM was employed to classify the movement as to quality and correctness, thereby being able to provide precise, individual feedback. This hybrid approach ensures a high-motion recognition accuracy rate while also adhering to nuanced Tai Chi requirements. The system was evaluated through detailed testing with various levels of Tai Chi experience. Evaluation showed that the students’ performance and understanding of most Taijiquan movements and related physical exercises improved significantly. It indicates the system has a practical application value for also beginners and intermediate and last expert, respectively. It also shows the effectiveness of combining DTW and SVM to support learners ‘body movement trajectory in a physical learning environment, opening them up to additional technology-assisted physical training applications. This provides implications for a more promising generation of future physical education involving the incorporation of complex AI technology.

Intelligent speech elderly rehabilitation learning assistance system based on deep learning and sensor networks

Recently, deep learning has been proved to significantly improve the quality of speech recognition. Convolutional neural networks are often used in speech recognition tasks because of their special network structure and powerful learning function. In order to solve the problem that the traditional convolutional neural network can not reflect the one-dimensional basic attributes of speech signal, this paper proposes to set the number of frames for one dimension of convolution kernel, and use one-dimensional model and two-dimensional convolutional network model for speech recognition. By moving the convolution kernel of time axis and frequency band, it can adapt to the time change of speech signal and maintain the correlation between frequency bands to a great extent. At the same time, this paper also discusses the speech signal preprocessing, feature parameter extraction and regularization algorithm. Due to the lack of hospital resources, the lag of information technology, the poor ability of accompanying and other reasons, the current accompanying service for elderly rehabilitation can not meet the needs of elderly patients. The rapid development and wide use of information technology provide opportunities for the optimization of health services for the elderly. In view of the difficulty of word memory and interpretation in current rehabilitation courses, a VR intelligent teaching system based on intelligent voice technology is developed. The application results show that the system can effectively improve the ability of language expression and word writing. At present, the system of intelligent speech function has not been completed, and lacks speech synthesis function. The next research will focus on the use of speech synthesis technology, in order to realize the man-machine dialogue between people and the system, and show a more real training situation.

Driver Drowsiness Detection System Based on Eye State Analysis

Driver Drowsiness Detection System Based on Eye State Analysis

Video Analysis of Physical Education Teaching and Design of Intelligent Assistant Teaching System: Based on Motion Recognition and Optimization Algorithm

Nowadays, science and technology has rapidly increasing due to the specific requirements of education. The main aim of this technique is to enhance the physical education activities and helps sports man to finest teaching facilities of these activities. Moreover, the body changes with sports activities based on Deep Learning (DL) frameworks and classifier technology are examined to promote the practical application. Therefore, this paper Deep Reinforcement Learning based Chimp optimization (DRLbCO) algorithm was developed to improve the teaching efficiency of physical education system through the intelligent computer aided assessment models. Also, this model improving the performance, enhancing coaching effectiveness, and gaining competitive advantages in sports. Consequently, the proposed system is based on DL based image detection model DRL, which can analyse and provides proper actions in teaching students and enhance the ability of motion analysis in sports video training quality. Moreover, the DL model is integrated with optimization algorithm to optimize the performance. This could involve reinforcement learning, and other classifiers such as Support Vector Machine (SVM). Based on this provides feedback to students and teachers during physical education classes. The developed model is significantly enhances the physical education teaching student action in virtual sports instruction. Overall intention of the video assessment system of the sports training performance in analyzed.

Design of intelligent assistive system for physical education: based on personalized training plan

Instead of viewing different components in solitude, teachers' opinion with respect and pedagogical knowledge demands a comprehension of the relationships connecting them. The instructor must be knowledgeable about the subject matter, comprehend the most effective delivery methods for the information, be acquainted with the traits of the children, and be aware of the academic setting to educate in that field effectively. Sport is still undergoing economic, cultural, and ethical change. On the other side, throughout the past century, science has been the sport's most pervasive transformation. Players can now jump higher, run faster, and, importantly—retain their health due to scientific understanding. Even though scholars, organizations, and governments have indeed encouraged physical education instructors to incorporate software in their classes, technology is often employed for routine duties, such as tracking enrollment and assessing, recording, and reporting children’s' progress. In order to estimate posture in physical training, this research proposes a continuous filter convolution neural network. The approach also assesses the learners' understanding, memory, and accomplishments and offers suggestions for enhancements and remedial actions. The framework and conventional teaching-learning methodologies are then compared characteristics per aspect for output criteria. Finally, the classification algorithm is contrasted with other deep learning algorithms, and it is found that the proposed ConFil_CNN achieves 98.5% accuracy, 96.7% of precision, 93.5% of recall, 95.2% of F1-score and 12.4ms of response time.

Synthesis of ISO 26262 and ISО 21448 methods to increase reliability of hazard analysis and risk assessment of intelligent driver assistance systems operation

Introduction (problem statement and relevance). Application of sensors to increase automation of vehicles is a growing global trend. However, as the number of these devices in vehicles increases, so does the number of risk factors caused by their application.The purpose of the study is to reduce the areas of unknown potentially dangerous situations of possible impact of these risks on a highly automated vehicle using a synthesis of the risk analysis methods.Methodology and research methods. This paper considers the system analysis methodologies set forth in GOST R ISO 26262, ISO 21448:2022.The scientific novelty is the interference of approaches to the analysis that are set forth in the standards.Practical significance. The results of application of the synthesized approach to risk analysis will form the basis of studies of the prospective intelligent driver assistance systems thus allowing the development of test programs for these systems within highly automated vehicles.

Natural Language Processing in Knowledge-Based Support for Operator Assistance

Manufacturing industry faces increasing complexity in the performance of assembly tasks due to escalating demand for complex products with a greater number of variations. Operators require robust assistance systems to enhance productivity, efficiency, and safety. However, existing support services often fall short when operators encounter unstructured open questions and incomplete sentences due to primarily relying on procedural digital work instructions. This draws attention to the need for practical application of natural language processing (NLP) techniques. This study addresses these challenges by introducing a domain-specific dataset tailored to assembly tasks, capturing unique language patterns and linguistic characteristics. We explore strategies to process declarative and imperative sentences, including incomplete ones, effectively. Thorough evaluation of three pre-trained NLP libraries—NLTK, SPACY, and Stanford—is performed to assess their effectiveness in handling assembly-related concepts and ability to address the domain’s distinctive challenges. Our findings demonstrate the efficient performance of these open-source NLP libraries in accurately handling assembly-related concepts. By providing valuable insights, our research contributes to developing intelligent operator assistance systems, bridging the gap between NLP techniques and the assembly domain within manufacturing industry.